Epidemiological Transition

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Epidemics 10 (2015) 40–44

Contents lists available at ScienceDirect

Epidemics

j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / e p i d e m i c s

ive challenges in evolution and infectious diseases

.J.E. Metcalf a,b,∗, R.B. Birger a, S. Funk c, R.D. Kouyos d, J.O. Lloyd-Smith b,e, V.A.A. Jansen f

Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA Division of Epidemiology and Population Studies, Fogarty International Center, National Institutes of Health, Bethesda, MD, USA Centre for the Mathematical Modelling of Infectious Diseases, London School of Hygiene & Tropical Medicine, London, UK Division of Infectious Diseases and Hospital Epidemiology, University Hospital Zurich, Zurich, Switzerland Department of Ecology and Evolutionary Biology, UCLA, Los Angeles, USA School of Biological Sciences, Royal Holloway University of London, Egham, UK

r t i c l e i n f o

rticle history: eceived 14 March 2014 eceived in revised form 9 December 2014 ccepted 10 December 2014 vailable online 18 December 2014

a b s t r a c t

Evolution is a key aspect of the biology of many pathogens, driving processes ranging from immune escape to changes in virulence. Because evolution is inherently subject to feedbacks, and because pathogen evo- lution plays out at scales ranging from within-host to between-host and beyond, evolutionary questions provide special challenges to the modelling community. In this article, we provide an overview of five challenges in modelling the evolution of pathogens and their hosts, and point to areas for development,

eywords: itness enetic systems iversity 0

focussing in particular on the issue of linking theory and data. © 2014 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND

license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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Evolution is the change in gene frequencies resulting from election (where genes with greater reproductive contributions to uture generations spread within populations), mutation, recom- ination or re-assortment (where genetic material is exchanged etween chromosomes), or drift. Evolution plays an important ole in the dynamics of many infectious diseases. Vaccine escape n influenza, drug-resistance in HIV, and virulence evolution n Marek’s disease are all examples of evolutionary processes. eveloping models that accurately describe pathogen evolution

s inherently challenging because of the complexity of pathogen ife cycles and the difficulty in characterizing the (dynamic) fitness andscapes driving pathogen evolution. Ultimately, the pathogen’s enotype, together with the characteristics of the host, determines oth how disease is caused and how much of the pathogen is emit- ed by the host. Once emitted, pathogens must infect new hosts. ow much transmission is realized also depends on the physical

nvironment, the host’s behaviour and population structure, as ell as the distribution of the disease in the population. To under-

tand pathogen evolution we need to integrate from the genotype,

∗ Corresponding author at: Department of Ecology and Evolutionary Biology, rinceton University, Princeton NJ, USA. Tel.: +001 609 258 6228.

E-mail address: [email protected] (C.J.E. Metcalf).

ttp://dx.doi.org/10.1016/j.epidem.2014.12.003 755-4365/© 2014 The Authors. Published by Elsevier B.V. This is an open access article un

and span these levels, encompassing stochastic processes such as transmission bottlenecks (see Gog et al., 2015). This requires the integration of knowledge from various fields: molecular biology, microbiology, medicine and epidemiology to name a few (Fig. 1).

Here, we outline five challenges of modelling evolution that reflect this interaction across scales. We start by detailing the most basic and general challenge of all, that of characterizing fitness. Next, we address challenges for modelling how pathogens shape each other’s evolution (coinfection) and the related topic of how pathogens shape host immune diversity; and the classic evolu- tionary problem of what forces allow maintenance of pathogen diversity (coexistence). Finally, we discuss how modelling can help us understand how mechanisms of pathogen replication influence the generation of genetic variation, upon which selection acts.

1. Defining and measuring fitness for pathogens across scales

If we know how fitness changes with changes in the genes in the pathogen, and how it does so across scales (Fig. 1), we can make informed statements about selection and adaptation. Fitness

is generally defined as the reproductive contribution of an individ- ual to the next generation, in a particular environment. Pathogens will experience different such environments over the course of an infection: for instance, they will have to overcome the host’s

der the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

C.J.E. Metcalf et al. / Epidemics 10 (2015) 40–44 41

Fig. 1. The scales of infectious disease dynamics and evolution. Diverse research fields address overlapping levels of this hierarchy. Genetics, cell biology, microbiology occur at the smallest scales, but have projections to larger scales; e.g. genetics also contributes to interpreting population level outcomes. Pharmacology, immunology and physiology o es and e nt bot e

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ccur primarily at the whole organism level, but comprise processes at smaller scal pidemiology arguably are relevant at each scale, with community ecology releva pidemiological principles describing dynamics in populations from cells to hosts.

efences, colonize the host, withstand attacks of the immune sys- em, and accomplish transmission and infection. The components f fitness can vary over such a cycle (and indeed the cycle often nvolves numerous pathogen generations), and to calculate fitness, n appropriate average has to be taken over this path, integrating nformation across various scales.

Although defining fitness of pathogens is straightforward in rinciple, linking this definition to attainable data in order to uantify fitness is not. Researchers have typically broken the evo-

utionary cycle apart to focus on particular levels of selection – for xample, distinguishing within-host fitness (describing the growth f the pathogen population within an infected individual) and etween-host fitness (describing transmission of infection to new ost individuals). This has the benefit of corresponding to clear iological differences, as well as quantities that can be measured although the path to linking processes across scales to fitness is not bvious). However, even with the process broken down into more anageable parts, there are still considerable barriers to defining

cale-specific fitness components (see Challenge 2 in Gog et al., 015) for more complexities related to within-host fitness), and here is no general relationship between fitness at the within-host cale and the number of new hosts infected (Park et al., 2013).

The challenges inherent to even the (apparently contained) roblem of measurement of the reproduction of individual athogens within-host have led to the development of a range f in vitro systems designed to quantify variation in rates of athogen replication in different contexts. Inevitably, these esti- ated pathogen replication rates reflect only one aspect of fitness

t an in vivo scale. Key modelling challenges include providing urther innovations in linking in vitro data-streams to in vivo mea- urements of aspects of fitness, such as viral titre kinetics or the utcome of competition assays (Huijben et al., 2013) (see also hallenge 7, Frost et al., 2015), and accounting for the fact that he genotype to phenotype to fitness map is likely to be context- ependent, and the within-host fitness landscape may change (see lso Challenge 3, Gog et al. (2015); and Challenge 6, Frost et al. 2015), on the challenges of developing genotype to phenotype

aps). In particular, the fitness of a genotype will often depend n the frequency of all other genotypes, as a result of immune sys-

em activity. Machine-learning and modelling approaches can be sed to bridge the in vitro and in vivo levels (Kouyos et al., 2011), ut given the nature of the underlying in vitro data such approaches urrently neglect crucial within-host fitness determinants such as

often have impacts at larger scales (e.g. herd immunity). Community ecology and h for understanding multiple host species but also gut microbiota dynamics, and

the immune system. This poses the challenge of finding novel ways to parameterize the activity of the host’s immune system from data (e.g., Metcalf et al., 2011) and incorporate it into the mod- els for pathogen fitness, and the associated (and shifting) fitness landscapes.

Beyond the individual host, other instances of population struc- ture (e.g., age groups or host species) may influence pathogen evolution. If these host classes additionally compete or otherwise interact, this will affect the pathogen’s evolution, and any evo- lutionary outcomes are likely to depend on the details of this interaction. The next generation matrix approach is useful for these types of systems (Diekmann et al., 2010), as are approaches that renormalize the system to describe group-level reproduction (Ball et al., 1997), but few general mathematical principles are known, and furthermore, parameterizing such models given available data remains challenging (Funk et al., 2013) (see also Buhnerkempe et al., 2015).

Bringing together all these various threads to estimate an all- encompassing fitness value for any particular pathogen genotype is a major challenge, and would be even if all the data were avail- able. Even though conceptual and mathematical frameworks for dealing with such multi-scale processes have been developed (Park et al., 2013; Lion et al., 2011), such calculations can be extremely cumbersome, and their interpretation complex, particularly when evolution operates at different scales, as is the case for pathogens.

2. Developing models to capture the impact of co-infection on the evolutionary process

For many pathogens, infection by multiple strains or other pathogens may have little or no epidemiological impact – the key distinction is simply whether a host is infected, or not (as for measles, for instance). However, there are pathogens for which coinfection alters pathogen dynamics, and this can have two major impacts on evolutionary processes. First, coinfection can lead to genetic exchange between co-infecting pathogens (especially for viruses and bacteria) that may be essential to immune escape, or host jumps. Such exchanges may occur both among pathogens of the same species (e.g. homologous recombination) or among

pathogens of different species (transformation, transduction, con- jugation) (see Challenge 5, Frost et al. (2015) for more details). Second, coinfection may be associated with within-host compe- tition (e.g., mouse malaria parasites may compete for red blood

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ells), or facilitation (e.g., helminth mediated immune-suppression ight increase microparasite within-host growth rates; Viney and raham, 2013), both of which can alter fitness, and thus evolution- ry outcomes. The resulting dynamics are analogous to individuals ompeting within metapopulations, and can be formulated the- retically using kin selection or multilevel selection formalisms Lion et al., 2011), and these models can be extended to encom- ass within-patch dynamics (Jansen and Vitalis, 2007). However, uch methods assume known costs and benefits to the interaction etween competing coinfecting pathogens. This implies develop-

ng adequate within-host models, a tremendous challenge given parse data on the complex nature of pathogen–pathogen interac- ions, as mediated by immune response, resource competition and reatment (see also Gog et al., 2015). Where sufficient elements of he biology are known, but the specifics of coinfection interactions emain unclear, models can be deployed to explore the outcomes of ifferent interactions in terms of measurable quantities to identify ey mechanisms (Mideo et al., 2011). These frameworks can then e used to prioritize experimental directions that inform expected volutionary trajectories. A key area for research of this kind is n how drug resistance spreads in the face of different treatment egimes in the context of co-infection (Huijben et al., 2013; Kouyos t al., 2014).

Theoretical challenges also remain: for some co-infecting athogens, the order of infection can affect prognosis, and whether

nfections are sequential, co-transmitted, or super-infecting can hange dynamics (see also Challenge 5, Gog et al., 2015). While ncorporating such effects into models can be straightforward, ealistically describing interdependent processes such as immuno- uppression or cross-reactive immunity can be complicated. odelling evolution under coinfection presents a special theoret-

cal challenge for bacterial communities, because of the genetic xchange between species through mobile genetic elements such s plasmids and phages (see also below, Challenge 5). The develop- ent of models that can capture community assembly, invasibility,

ompetition, and immune interactions within the bacterial micro- iome may be an especially rich area for modelling, in particular ith reference to exploiting the potential of meta-genomic and eta-transcriptomic data. Very few models have considered, e.g.,

he dynamics of traits within a community of pathogens that are xchanging genetic material (see Morton et al., 2014; San Millan t al., 2013).

. Modelling how pathogen characteristics shape the volution of host immune diversity

Host–parasite interactions reflect an inherently co-evolutionary rocess. Despite this, the research focus to date has been some- hat one-sided. Models of selection pressures on the parasite (e.g.,

volution of virulence, etc.) are widespread (Alizon et al., 2009). esearch into the host’s potential for coevolution (Little et al., 2010)

n terms of diversity of immune responses also has a relatively long istory (e.g., Borghans et al., 2004). However, key features of host iology such as immunopathology (Day et al., 2007), host variability

n tolerance (Best et al., 2008) or in susceptibility (Boots et al., 2012), nd structure across host immune recognition loci (Penman et al., 013) are just starting to be considered. This bias reflects in part he difference of time-scales in operation – parasites’ generation imes are generally so much shorter than hosts’ that a focus on par- site evolution alone seems justified. A key challenge is identifying hen, or for what host traits, this assumption is no longer valid;

nd subsequently, identifying what parasite community features elect for particular host responses. For example, what pathogen haracteristics might select for a “dangerous” immune response, .e., over-investment and risk of immune pathology?

ics 10 (2015) 40–44

There is also considerable opportunity to develop models that explore the mechanistic basis for results obtained in comparative immunology studies; e.g., correlations between promiscuity and white blood cell counts across primates (Nunn et al., 2000). Macro- evolutionary forces operating at broad scales and long timescales that act to shape host features will affect within-host dynamics, in particular those linked to aspects such as immunopathology. The role of feedbacks in this process is another very interesting ques- tion for which modelling might provide insights. For example, if changes in host ecology shift host longevity, this is likely to alter selection on pathogen virulence (assuming some form of virulence- transmission trade-off), which might then feed back onto altering selection on host longevity.

4. Understanding maintenance of pathogen diversity

A range of mechanisms can maintain pathogen diversity (Grenfell et al., 2004). At the most basic level, neutral processes can maintain diversity, via a balance of mutation (creating diversity) and genetic drift (destroying diversity). Selective mechanisms will also play a role, with life-history trade-offs (e.g. between within- host replication and transmissibility) allowing stable coexistence; and temporal and spatial fluctuations in selection allowing tem- poral or spatial niche separation and coexistence. For example, antibiotic consumption varies across individuals, age-classes, insti- tutions (hospital versus community), regions and countries (Cars et al., 2001), which creates a mosaic of selective pressures that may allow coexistence of resistant and sensitive strains. Similarly, selec- tion for antigenic escape varies as a function of differences in host genetics or exposure history (previous infections and vaccinations).

A key challenge is determining the relative contribution of neu- tral and selective mechanisms. For example, the diversity of HIV-1 has been regarded primarily as a result of the demography and geography of viral spread (Grenfell et al., 2004). However, several studies have also found adaptive substitutions in HIV-1 in particular populations of humans (Kawashima et al., 2009) and it is currently unclear whether selective or neutral processes dominate in shaping the diversity of HIV-1. Similarly, Cobey and Lipsitch (2012) argued that the diversity of Streptococcus pneumoniae can be explained by the interplay of niche and neutral effects. Building on these types of analyses to encompass a broader array of pathogens is a key direction for future research.

Another complication is that selective pressure may vary across scales (e.g., from within-host to between-host, see Challenge 1), but which scale is key to maintenance of diversity remains poorly resolved for most pathogens. For example, many bacterial species (such as Staphylococcus aureus) colonize different anatomical sites, which may impose different selective requirements (Klein et al., 2009). Assessing the impact of this within-patient heterogene- ity at the population level requires understanding the cross-scale dynamics of these pathogens, and models have a key role to play in linking the relevant mechanisms. Similarly, it has been argued for HIV that selection at the within-host and between host-level act in opposite directions (Lythgoe et al., 2013). Such a trade-off can help to maintain diversity, but the actual quantitative contribution of this mechanism remains to be determined.

Mathematical models have made a range of important predic- tions about the determinants of pathogen diversity. Much of the current concern about the wide spread use of antibiotics, and sub- sequent emergence or spread of resistance are at least implicitly based on evolutionary models. Likewise, the evolutionary conse-

quences of imperfect vaccines were, at first, theoretical prediction (Gandon et al., 2001). More specific questions have also been tackled – such as how the strain distribution of S. pneumoniae responds to vaccination (Cobey and Lipsitch, 2012; Bottomley et al.,

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013), or the potential for coexistence of drug resistant and drug- ensitive strains in specific settings (Kouyos et al., 2013). The hallenging goal of making more detailed predictions relating to ow much and when evolution will take place will require integrat-

ng the increasingly available data on selective forces (antibiotic onsumption, host genetic variation, exposure to pathogens) and ost demography into epidemiological models. Quantitative pre- ictive models will also require much better estimates of pathogen tness (see Challenge 1, especially with the application of in vitro ystems to in vivo inference) and mutational pathways, with par- icular focus on compensatory mutations, fitness at different levels within-host vs. transmission, see Challenge 1), and the interplay f selective and stochastic effects.

Finally, for some pathogens it is unclear why diversity is not arger than observed (HIV at the within-host scale, influenza at the opulation scale (Grenfell et al., 2004)). Modelling studies can play n important role in proposing mechanisms to explain this pattern Koelle et al., 2006), which can spur targeted empirical work to test hese hypotheses.

. Developing better models for the impact of genetic ystems on pathogen evolution

The operation of inheritance in bacteria is highly complex. nrelated individuals may exchange DNA (‘horizontal gene trans-

er’) and many individuals carry plasmids (DNA that is physically eparate of the organisms’ genomic DNA, and can be replicated ndependently from it) that can often carry key genes such as those inked to antibiotic resistance. The environment that a gene will ave experienced in its evolutionary past will depend on the route hrough which it is passed on: a gene on a plasmid will have been n different organisms and environments than a gene on a chromo- ome. Therefore the fitness, and selective forces on a gene depend n the details of the genetics. For many pathogens (viruses, as well s bacteria, etc.) related complexities emerge from the process of ecombination (see Frost et al. (2015) for more details). A challenge ere is to develop models not just of individual pathogens, but lso of individual genes, which take the genetic architecture into ccount.

Even for the comparatively straightforward process of mutation, he basic biology of pathogen replication can influence evolutionary ynamics strongly. The molecular- and cellular-scale mechanisms y which pathogens replicate their genomes and create new infec- ious particles are subjects of intensive study in microbiology and irology, but have been largely ignored in dynamic models of athogen evolution. Recent theoretical models have shown that he within-host emergence of new viral strains is strongly affected y whether offspring virions are released via budding or bursting Pearson et al., 2011), by different mechanisms of genome repli- ation (Loverdo et al., 2012), and by how genomes and proteins re mixed in virion assembly (Loverdo and Lloyd-Smith, 2013b). ne important challenge is to test and expand this developing ody of theory by comparison with experimental data, particu-

arly given new opportunities arising from deep sequencing data. urrent models have only scratched the surface of microbiolog-

cal knowledge, so there is scope to include many more details one key task for modellers will be to determine if, when, and ow these details impact evolutionary processes at higher scales, nd to assess whether the cost in additional model complexity is orth bearing. For instance, recent progress on modelling viral

eplication mechanisms (e.g. Sardanyés et al., 2012) opens inter-

sting opportunities for cross-scale modelling to explore potential mpacts on evolutionary dynamics. Do certain replication mecha- isms confer greater phenotypic robustness to mutations, or higher ropensity for adaptive evolution? Alternatively, could the details

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of pathogen replication present unrecognized barriers to adapta- tion such as the delayed expression of beneficial phenotypes (e.g. Loverdo and Lloyd-Smith, 2013b)? Conversely, better models of genome replication and viral packaging will advance the important goal of achieving better estimates of viral mutation rates (Thébaud et al., 2010).

A better understanding of the genetic systems of pathogens will be crucial for our understanding of how pathogen populations can move on fitness landscapes (see Challenge 1). For example fitness valleys can represent a barrier to adaptation at low mutation rates (Levin et al., 2000) but not at high mutation rates. Similarly the probability of evolutionary rescue of pathogen populations (e.g. in the context of antimicrobial therapy or vaccination) may depend crucially on mutation rates (Kirkpatrick and Peischl, 2013; Loverdo and Lloyd-Smith, 2013a).

Conclusion

Many of the challenges in modelling pathogen evolution that we introduced here revolve around questions of quantifying fitness. We focused particularly on biological complexities and uncertain- ties, the impact of coinfection, and evolutionary mechanisms that create and shape diversity in hosts and pathogens. However, the effects of evolution on pathogen dynamics are vast, and potential modelling challenges reflect this. Other papers within this Special Issue tackle questions arising in the context of emergence of novel pathogens (Lloyd-Smith et al., 2015), vaccine escape (Metcalf et al., 2015), or in extending the use of phylodynamics (Frost et al., 2015). Progress in tackling these challenges has the potential to contribute to a broad array of highly applied questions, including management of drug resistance, improvement of clinical care, and reconciling individual and population goals for public health in the context of pathogen evolution.

Acknowledgments

This work emerged from discussion at the Isaac Newton Insti- tute, Cambridge; and was funded by the Bill and Melinda Gates Foundation (CJEM), the National Science Foundation (EF-0928690; JLS), the Medical Research Council (SF), and the RAPIDD program of the Science & Technology Directorate, Department of Homeland Security and the Fogarty International Center, National Institutes of Health (CJEM, JLS).

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Maximum grade 40

 

Weighting 40%

· Assessment of essays

All essays returned to you will have a marking matrix attached with comments. These are meant to be constructive and are made to point out errors and areas where improvements could be made. The comments will explain why you got the mark you did. They are, therefore, usually ‘critical’. You should consider these comments carefully, and try to understand why they were made. If you do not see the point, or want further comment, please take this matter up with whoever marked your essay, preferably via the course coordinator A/Prof Lara Lamb.

The following points will be noted particularly in marking essays:

1.   Relevance to the topic set.

2.   Organisation and effectiveness of argument, and proper use of anthropological concepts and principles as outlined during the course of your reading.

3.   Evidence of reading outside the set texts and accuracy of facts presented in the essay.

4.   Originality – careful and critical thought about the topic, and use of illustrative material from independent reading and also, to some extent, from observation and experience.

5.   Accuracy and clarity of written English, including grammar, spelling, and punctuation. Overall legibility and general setting out will be noted, especially of essay structure and referencing.

How to write an essay/presentation

Do not go over the word limit. This is set specifically to help you develop a sharp and concise style. Going under the word limit is preferable to ‘padding out’ your answer with vagaries or ‘waffle’ to reach the word limit.

Do not use value judgements of subjective terminology such as: primitive, backward, surprisingly advanced, superior or developed. You must be objective and indicate clearly what you mean by your terms.

Writing an essay is a gradual process; the final version of an essay should have been developed over several drafts, prepared as you explore the topic and compile notes from reading material.

You will usually need to do some reading before you can grasp the significance of the set topic. Begin with the suggested references in your book of reading and, as you read, keep a copy of the actual wording of the topic/question in view. Initial reading will enable you to:

1.   Recognise the implications underlying the actual wording of the topic.

2.   Understand key ideas and terms.

3.   Identify all parts of the set question.

After some preliminary reading, when you feel you are beginning to grasp the topic, draft an outline plan for your essay. This will involve drawing up headings for each major section of your essay, writing a statement, in your own words, which expressed the key idea or main point of each section and noting relevant references to substantiate the points made. Take care to acknowledge debate and deal with controversy when it is evident in the literature; Alternative points of view must be taken into account; do not simply select literature which supports an argument you favour, or a point you believe is true. It is expected that the points you make will be supported by well reasoned anthropological argument – fully and correctly referenced.

Once you have drawn up a tentative outline plan, proceed with more reading and comprehensive note taking. Read widely and critically. Continue to develop your plan gradually by compiling evidence, examples and quotations from the literature and review your plan from time to time in the light of any new literature. Remember that this plan should be flexible and you should be prepared to change it as you read and write more. It is often useful to write separate points on separate pages or cards so that you can easily re-organise your thoughts. When you feel you have ‘covered’ the topic in your developing plan, write your introduction and conclusion, and examine carefully, the scope and structure of your plan. Ask yourself:

1.   Have I compiled all the material necessary to answer the set question/address the set topic?

Have I dealt with the whole topic?

Have I answered questions that are not asked? Or included material that is not relevant to the question?

2.   Is there a clear thread running through the plan linking each of the parts logically together?

3.   Does the conclusion clearly follow from the main points of argument?

Then read through all your notes to refresh your memory and write your first full draft. Don’t worry too much about the prose at this stage, just let it flow from your developed plan. Write in your own words; take care to reference correctly and use quotations appropriately. Do not plagiarise.

When you have completed your first full draft, re-examine the scope and structure of your essay and expand or prune if your draft is too short, too long or not well balanced. Evaluate the effectiveness of your introduction and conclusion and check that they point to and address the main issues of the set topic; ensure that you have included references where necessary and check their accuracy.

Ask yourself; is my argument convincing?

At this stage, it is very helpful if you can read your essay aloud to another person. Take note of any comments they have and make any necessary adjustments.

Write your final draft and take particular care with spelling, punctuation, grammar and legibility, and the presentation of references. When complete, ask someone else to read your essay. If you are satisfied, produce your final copy; proof-read it carefully. Make a copy; attach cover sheet and submit it by the due date.

Referencing

All written work must be referenced using the Harvard system.

Please refer to the USQ Library web site for referencing guides in the Harvard style. Go to <http://www.usq.edu.au/library/> and click on ‘Referencing Guides’. This provides details on the referencing of print and electronic publications.

Extensions

If you require extra time to complete the essay, you must contact the course examiner as soon as possible to apply for an extension. Failure to do so will result in a penalty of 5% of the available mark, per day. 

Choosing Internet sources  We can not stress enough though, how important it is that you are careful in choosing your sources. For academic purposes, there is a LOT of unsuitable material out there, and we expect you to be able to be discerning in this matter. For instance, Wikipedia and other online encyclopaedias are not considered appropriate resources at a tertiary level of study. A general rule of thumb is to use online journals that are contained in the library’s electronic data base (such as EBSCOhost). Internet material from academic institutions such as university and museum websites is also usually acceptable, but must be cited appropriately. In following these rules of thumb, you can generally be sure of the accuracy (and motives) of your sources. If you have any queries regarding the use of internet material, contact your lecturer for further guidance.

ANT2002 Major Essay Marking Sheet

	HD	A	B	C	F
1. Presentation
a. grammar, spelling	Essay observes all conventions of spelling, punctuation, grammar.	Essay observes almost all conventions of spelling, punctuation, grammar.	Essay observes most conventions of spelling, punctuation, grammar.	Essay is marred by some errors in spelling, punctuation, grammar.	Essay is compromised by many errors in spelling, punctuation, grammar.
b. referencing	Quoting and referencing are technically correct, consistent, and complete.	Quoting and referencing are complete, with minimal technical errors or inconsistencies.	Quoting and referencing are complete, although there may be some technical errors or inconsistencies.	There may be omissions or inconsistencies in quoting and referencing.	Quoting and referencing are incomplete or inconsistent.
c. writing style	Writing communicates concisely and effectively to intended audience.	Writing communicates very clearly to intended audience.	Writing communicates clearly to intended audience.	Writing communicates adequately to intended audience.	Little attention given to format and written structure.
2. Originality and critical analysis	You have made a thoroughly original and relevant contribution to the topic.	You have made a original and relevant contribution to the topic.	You have summarized points from sources, with some original contributions of your own.	You have summarized points from sources, with little original or critical content	You do not demonstrate the connection your points have to the topic.
3. Results	You have synthesised ideas and evidence to produce excellent arguments and justified conclusions.	You have produced reasoned arguments and draw on evidence to support conclusions.	You have produced well explained arguments that connect ideas but do not always connect with the evidence.	Selection and sequencing of ideas is not always logical or connected.	Difficult for reader to obtain meaning from explanations. Ideas are unconnected. No conclusions drawn.
4. Understanding	Demonstrates sophisticated understanding of theoretical concepts from readings and discussion.	Demonstrates thorough understanding of theoretical concepts from readings and discussion.	Demonstrates clear understanding of theoretical concepts from readings and discussion.	Demonstrates some understanding of theoretical concepts from readings and discussion.	Demonstrates limited understanding of theoretical concepts from readings and discussion.
5. Breadth	You have successfully drawn on an extensive range of sources and related the sources appropriately to your argument.	You have successfully drawn on a good range of sources and related the sources appropriately to your argument.	You have drawn on a range of relevant sources in your argument.	You have drawn on a somewhat limited range of sources in your argument.	You have drawn on insufficient sources in your argument.

TOTAL MARK…………………………/100 GENERAL COMMENTS:

A list of Anthropology journals

Resources - Some Anthropological Journals

American Anthropologist

Annual Review of Anthropology

Annual Review of Sociology

Anthropological Forum

Anthropological Quarterly

Anthropology Today

Australian Journal of Anthropology

Australian Journal of Social Issues

Canadian Review of Sociology and Anthropology

Cross-Cultural Research

Cultural Studies

Culture, Medicine and Psychiatry

Current anthropology

Ethnology

European Journal of Cultural Studies

Gender and Society

History and Theory

Human Organisation

Journal of Anthropological Research

Journal of Contemporary Ethnography

Journal of Intercultural Studies

Journal of Material Culture

Journal of the Royal Anthropological Institute

Mankind Quarterly

Medical Anthropology

Medical Anthropology Quarterly

Oceania

Qualitative Health Research

Qualitative Inquiry

Social Forces

Theory, Culture and Society

Urban Life

Urban Studies

Essay Que

stion:

Discuss the concept of an epidemiological transition. Explain the natures of those associated

with the Neolithic, urbanisation/civilisation, colonisation/migration/ conquest, and

modernisation

.

MAJOR ESSAY (2500

-

3000 WDS)

Assessment

·

Item

MAJOR ESSAY (2500

-

3000 WDS)

—

TWMBA ONLINE ONL

Due by

11 May 2020

Maximum grade

40

Weighting

40%

·

Assessment of essays

All essays returned to you will have a marking matrix attached with comments. These are

meant to be constructive and

are made to point out errors and areas where

improvements could be made. The comments will explain why you got the mark you did.

They are, therefore, usually ‘critical’. You should consider these comments carefully, and

try to understand why they were mad

e. If you do not see the point, or want further

comment, please take this matter up with whoever marked your essay, preferably via the

course coordinator A/Prof Lara Lamb.

The following points will be noted particularly in marking essays:

1.

Relevance to

the topic set.

2.

Organisation and effectiveness of argument, and proper use of anthropological

concepts and principles as outlined during the course of your reading.

3.

Evidence of reading outside the set texts and accuracy of facts presented in the

essay.

4.

Orig

inality

–

careful and critical thought about the topic, and use of illustrative material

from independent reading and also, to some extent, from observation and experience.

5.

Accuracy and clarity of written English, including grammar, spelling, and punc

tuation.

Overall legibility and general setting out will be noted, especially of essay structure and

referencing.

How to write an essay/presentation

Do not go over the word limit. This is set specifically to help you develop a sharp and

concise style. Goin

g under the word limit is preferable to ‘padding out’ your answer with

vagaries or ‘waffle’ to reach the word limit.

Essay Question:

Discuss the concept of an epidemiological transition. Explain the natures of those associated

with the Neolithic, urbanisation/civilisation, colonisation/migration/ conquest, and

modernisation.

MAJOR ESSAY (2500-3000 WDS)Assessment

 Item MAJOR ESSAY (2500-3000 WDS) — TWMBA ONLINE ONL

Due by 11 May 2020

Maximum grade 40

Weighting 40%

 Assessment of essays

All essays returned to you will have a marking matrix attached with comments. These are

meant to be constructive and are made to point out errors and areas where

improvements could be made. The comments will explain why you got the mark you did.

They are, therefore, usually ‘critical’. You should consider these comments carefully, and

try to understand why they were made. If you do not see the point, or want further

comment, please take this matter up with whoever marked your essay, preferably via the

course coordinator A/Prof Lara Lamb.

The following points will be noted particularly in marking essays:

1. Relevance to the topic set.

2. Organisation and effectiveness of argument, and proper use of anthropological

concepts and principles as outlined during the course of your reading.

3. Evidence of reading outside the set texts and accuracy of facts presented in the

essay.

4. Originality – careful and critical thought about the topic, and use of illustrative material

from independent reading and also, to some extent, from observation and experience.

5. Accuracy and clarity of written English, including grammar, spelling, and punctuation.

Overall legibility and general setting out will be noted, especially of essay structure and

referencing.

How to write an essay/presentation

Do not go over the word limit. This is set specifically to help you develop a sharp and

concise style. Going under the word limit is preferable to ‘padding out’ your answer with

vagaries or ‘waffle’ to reach the word limit.

Emerging_and_re-emerging_infectious_dise.pdf

Annu. Rev. Anthropol. 1998. 27:247–71 Copyright 1998 by Annual Reviews. All rights reserved

EMERGING AND RE-EMERGING

INFECTIOUS DISEASES: The Third

Epidemiologic Transition

Ronald Barrett, Christopher W. Kuzawa, Thomas McDade, and

George J. Armelagos Department of Anthropology, Emory University, Atlanta, Georgia 30322; e-mail: [email protected]; [email protected]; [email protected]; [email protected]

KEY WORDS: health transition, history of disease, political ecology, paleopathology,

medical anthropology

ABSTRACT

We use an expanded framework of multiple epidemiologic transitions to re- view the issues of re/emerging infection. The first epidemiologic transition was associated with a rise in infectious diseases that accompanied the Neo- lithic Revolution. The second epidemiologic transition involved the shift from infectious to chronic disease mortality associated with industrializa- tion. The recent resurgence of infectious disease mortality marks a third epi- demiologic transition characterized by newly emerging, re-emerging, and antibiotic resistant pathogens in the context of an accelerated globalization of human disease ecologies. These transitions illustrate recurring sociohis- torical and ecological themes in human–disease relationships from the Pa- leolithic Age to the present day.

INTRODUCTION

The problem of emerging infectious disease has recently captured the public’s imagination and the attention of the scientific community. Popular books (e.g. Preston 1994) and movies (e.g. Outbreak, released in 1995) tell grisly tales of hapless victims bleeding from all orifices, prey to mutating microbes that chal-

0084-6570/98/1015-0247$08.00

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lenge the supremacy of Western biomedical progress. A number of books aimed at an educated general audience chronicle the scientific research effort to understand these deadly pathogens (Garrett 1994; Rhodes 1997; Ryan 1993; Ryan 1997). Recent academic conferences (Lederberg et al 1992; Morse 1994) have brought together researchers in microbiology, public health, and bio- medicine to survey the seriousness of the problem; they report an ominous re- surgence of morbidity and mortality from new and old infectious diseases. These reports warn of the eroding efficacy of antimicrobial therapies in the face of growing multidrug resistance (Lewis 1994; Swartz 1994; Vareldzis et al 1994). They note the first rise in infectious disease deaths in affluent post- industrial nations since the Industrial Revolution: In the US, age-adjusted mor- tality from infectious disease has increased by 40% from 1980 to 1992 (Pinner et al 1996). For its part, the US Centers for Disease Control and Prevention (CDC) has compiled a list of 29 pathogens that have emerged since 1973 (Satcher 1995), and has initiated an online journal—Emerging Infectious Dis- eases—to address this growing problem.1

The current spate of attention belies the fact that emerging infections are not a recent phenomenon but have always played a major role throughout human history (Armelagos & McArdle 1975; Boyden 1970; Cockburn 1971; Fenner 1970; Lambrecht 1985; Polgar 1964). We seek to contextualize these recent emerging infectious disease trends within an evolutionary and historical per- spective, using an expanded framework of epidemiologic transition theory. By tracing the emergence of disease in the Paleolithic Age, the Neolithic Age, the Industrial Revolution, and contemporary global society, we argue for the exis- tence of three distinct epidemiologic transitions, each defined by a unique pat- tern of disease that is intimately related to modes of subsistence and social structure. We suggest that current trends—the re/emergence of infectious dis- ease in the industrialized world and an increasingly globalized disease ecology (Colwell 1996; Elliot 1993; Gubler 1996; Patz et al 1996)—herald the arrival of a qualitatively distinct third epidemiologic transition in human health.

Recognizing the complexity of the diverse sociocultural processes involved in the re/emergence of infectious disease, many researchers in biology, medi- cine, and public health are calling for input from the social and behavioral sci- ences (Sommerfeld 1995). With its integrative approach to complex biocul- tural issues, anthropology is well positioned to make significant theoretical and practical contributions.

In the sections that follow, we provide a brief overview of epidemiologic transition theory and propose an expanded framework to consider the recur- ring social, political, and ecological factors implicated in emerging disease

248 BARRETT ET AL

1 1Full text articles from CDC’s Emerging Infectious Diseases and Morbidity and Mortality Weekly Report can be accessed electronically using the CDC’s Web site at http://www.cdc.gov.

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patterns from the late Paleolithic era to the Industrial Revolution. We apply this broader framework to explain the most recent pattern of emerging disease as part of a third, qualitatively distinct, epidemiologic transition.

AN OVERVIEW OF EPIDEMIOLOGIC TRANSITIONS

The concept of the epidemiologic transition was first formulated by Omran as a model for integrating epidemiology with demographic changes in human populations (Omran 1971). Omran stated that this model “focuses on the com- plex change in patterns of health and disease and on the interactions between these patterns and the demographic, economic, and sociological determinants and consequences.” Omran described the epidemiologic transition as occur- ring in three successive stages, or “ages”: 1. of pestilence and famine; 2. of re- ceding pandemics; and 3. of degenerative and man-made diseases. The third age described the shift in age-specific disease mortality from infectious dis- eases to chronic degenerative diseases in England and Wales following the In- dustrial Revolution. Classically associated with the concept of the epidemiol- ogic transition as a whole, this particular sequence of events represented an im- portant tradeoff between mortality and morbidity as a result of the interaction between epidemiological and demographic processes. On one hand, decreased child and maternal mortality resulting from declining infectious diseases re- sulted in an overall increase in population size. On the other hand, a subse- quent increase in life expectancy entailed an aging population with increasing mortality because of chronic degenerative diseases associated with the latter years of life.

Important criticisms have been made concerning this initial framing of the epidemiologic transition. Akin to assumptions of unilinear evolutionary prog- ress in early models of cultural evolution, this framework implies that each stage of the transition is more advanced and desirable than previous stages. Be- cause epidemiologic transition theory focuses solely upon trends in mortality, debates surrounding the ramifications of increased longevity for quality of life and well-being are not addressed by the model. It has been argued that the in- crease in life expectancy associated with the shift from acute infectious to chronic disease may be gained at the expense of increased total suffering and ill-health (Johansson 1992; Riley 1992; Riley & Alter 1989). However, others contend that populations undergoing the epidemiologic transition may eventu- ally experience a delay in the age of onset of chronic disabilities and disease (Fries 1980; Olshansky & Ault 1986).

Second, although this framework emphasizes socioeconomic and ecologi- cal factors as chief determinants in disease mortality transition, the use of whole nations as units of analysis has been criticized for burying the differen- tial experience of these events according to race, gender, and class within

RE/EMERGING INFECTIOUS DISEASES 249

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population statistics (Gaylin & Kates 1997). A parallel criticism has been made of “emerging infectious diseases,” a classification which may not signify the emergence of new pathogens as much as a re-emerging awareness among affluent societies of old problems that never went away (Farmer 1996). These critiques underscore the need to expand this model to account for the heteroge- neity of disease experience within populations undergoing epidemiologic tran- sitions.

While Omran accounted for accelerated, delayed, and transitional variants of his “classical” model of epidemiologic transition in Europe and North America (Omran 1971, 1983), more recent modifications have improved its applicability to a broader array of contexts and issues. Bobadilla and col- leagues adapted the model to fit observations in “middle income” nations such as Mexico, where trends in chronic disease have increased despite a persis- tence of infectious disease morbidity and mortality, resulting in what they de- scribe as an overlap of eras (Bobadilla et al 1993). Popkin suggests that some chronic conditions have entered a refractory stage in populations such as in the United States, where individuals have changed their diet and lifestyle in an ef- fort to prolong a healthy lifespan (Popkin 1994). This is akin to an additional stage of the epidemiologic transition proposed to explain the delayed onset of the symptoms and ill-health associated with chronic conditions in some indus- trial nations (Olshansky & Ault 1986).

Even with these modifications, however, the epidemiologic transition is re- stricted to a particular set of historical circumstances in the recent shift from infectious to chronic disease mortality. Yet, by further expanding this frame- work to include multiple transitions from the Paleolithic Age to the present day, we are able to illustrate how recurring sociohistorical and ecological themes have had an important influence on shifting disease patterns through- out modern human evolution. In this manner, we have reset the baseline for three distinct epidemiologic transitions to the conditions that existed just prior to the widespread changes that occurred with the adoption of agriculture in hu- man populations.

EPIDEMIOLOGIC TRANSITIONS: FROM THE LATE PALEOLITHIC AGE TO THE INDUSTRIAL REVOLUTION

Paleolithic Age Baseline

During much of our evolutionary history, hominid ancestors of modern hu-

mans roamed the African savanna as small, nomadic bands of foragers. Early

hominid populations likely were too small and dispersed to support many of

the acute communicable pathogens common in densely populated sedentary

communities (Burnet 1962), especially those for which human populations are

the only disease pool (Cockburn 1971; Polgar 1964). Acute upper respiratory

250 BARRETT ET AL

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infections decline soon after being introduced to isolated communities, sug-

gesting that they would have been absent from the dispersed populations of the

Paleolithic era (Popkin 1994). Similarly, pathogens such as smallpox, measles,

and mumps were unlikely to afflict early hominid groups (Cockburn 1967a). Hominid social organization and demographics would have presented less

of a barrier to the transmission and perpetuation of pathogens with long peri- ods of latency or low virulence. Viruses such as chickenpox and herpes sim- plex may survive in isolated family units, suggesting that they could have been sustained in early dispersed and nomadic population. The current distribution of parasite species common to human and nonhuman primates provides evi- dence for longstanding hominid-parasite relationships that predate the diver- gence of the hominid lineage (Cockburn 1967b; Kliks 1983). Sprent (1969b) coined the apt term “heirloom species” to describe such parasites, which he distinguished from the “souvenir” parasites contracted through chance en- counters with infected nonhuman hosts or vectors.

Long-term coevolutionary relationships between hominids and a heirloom parasite imply a good match between the parasite’s mode of transmission, virulence, and lifecycle, and the lifestyle and demographics of early foraging bands (Sprent 1962, 1969a). As one example, the gregarious behavior, nesting habits, and frequency of hand-to-mouth contact typical of hominoid primates likely favored the persistence of the pinworm Enterobius vermicularis in hominid evolution, which continues to inflict contemporary human popula- tions (Kliks 1983). Similarly, ectoparasites such as head and body lice (Pedi- culus humanus) and enteric pathogens such as Salmonella would likely have infested early hominids (Cockburn 1971; Polgar 1964).

Hominids would have contracted novel, or souvenir, parasites in their daily rounds of collecting, preparing, and eating raw plants, insects, meat, and fish (Audy 1958; Bennett & Begon 1997). The distribution and characteristics of these pathogens would have placed constraints on the ecosystems open to hominid exploitation. Lambrecht contends that the trypanosomiasis parasite carried by the tsetse fly opened ecological niches for hominid exploitation by eliminating trypanosome-susceptible fauna (Lambrecht 1980). Because mod- ern humans are trypanosome-susceptible and thus have not developed genetic resistance to the disease, Lambrecht argues that early hominids must have adapted culturally and behaviorally to tsetse by residing in fly-free areas, and perhaps through the advent and use of fire. Similarly, Kliks argues that particu- larly problematic and ubiquitous helminths, such as those associated with schistosomiasis and onchocerciasis, may have limited access to productive niches, much as they do throughout large tracts of Africa today (Kliks 1983).

The distinction between the heirloom and souvenir parasites afflicting early

hominid bands underscores the antiquity of disease “emergence” in human

populations, which is as old as the hominid lineage itself (Sprent 1969a,b).

RE/EMERGING INFECTIOUS DISEASES 251

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Then as today, the environment provided the pool of potential emerging infec-

tions or parasites, and the social, demographic, and behavioral characteristics

of hominid adaptation provided the opportunity for disease emergence. The

rate of emergence may have increased as tool use allowed exploitation of novel

ecological niches (Kliks 1983), and as ecological zones shifted with climate

change during glacial and interglacial periods (Lambrecht 1980). The eventual

movement of hominid populations out of Africa into Europe, Asia, and beyond

would have exposed migrating bands to novel ecologies and parasites, increas-

ing the rate of emergence at least temporarily in such groups. However, it is

likely that disease ecologies in these new habitats would have remained quali-

tatively similar, owing to the continuation of a nomadic foraging adaptation

and low population densities.

The First Epidemiologic Transition

Beginning about 10,000 years ago, a major shift occurred in most human popu- lations, from a nomadic hunting and gathering lifestyle to sedentism and pri- mary food production. This shift involved major changes in human social or- ganization, diet, demographics, and behavior that created conditions favorable for zoonotic infections to make the transition to human hosts, and for pre- existing human pathogens to evolve to more virulent forms. We describe the subsequent increase in infectious disease mortality that arose in the context of these changes as the first epidemiologic transition.

The shift to permanent settlements created larger aggregates of potential

human hosts while increasing the frequency of interpersonal contact within

and between communities, likely fostering the spread and evolution of more

acute infections (Ewald 1994). In addition, accumulation of human waste

would have created optimal conditions for dispersal of macroparasites and

gastrointestinal infections. Skeletal remains from archaeological sequences

spanning this cultural transition generally show an increase in the prevalence

of infectious lesions as populations shifted from foraging to sedentism and

food production (Cohen & Armelagos 1984), adding empirical support to

these expectations. The appearance of domesticated animals such as goats, sheep, cattle, pigs,

and fowl provided a novel reservoir for zoonoses (Cockburn 1971). Tubercu-

losis, anthrax, Q fever, and brucellosis could have been readily transmitted

through the products of domesticated animals such as milk, hair, and skin, as

well as increased ambient dust (Polgar 1964). In these contexts, it should not

be surprising that many contemporary human infections have their origins in

the zoonoses of domesticated animals (Bennett & Begon 1997). Agricultural practices increased contact with nonvector parasites such as

schistosomal cercariae, contracted in irrigation work, and intestinal flukes,

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which were acquired through use of feces as fertilizer (Cockburn 1971). With

the advent of food storage, the threat of contamination and wide-scale out-

breaks of food poisoning increased (Brothwell 1972). Breaking the sod during

cultivation may expose workers to insect bites and diseases such as scrub ty-

phus (Audy 1961). Other vectors developed dependent relationships with hu-

man habitats, as in the case of the yellow and dengue fever-carrying mosquito,

Aedes aegypti, which breeds preferentially in artificial containers (Thompson

& O’Leary 1997; Whiteford 1997). Reliance upon staple crops and a decline in dietary diversity may have pre-

disposed Neolithic populations to nutritional problems similar to those experi-

enced by subsistence-level agrarian communities in developing nations today

(Harrison & Waterlow 1990). Most staple crops are efficient producers of

calories capable of supporting more dense populations yet often lack critical

micro- or macronutrients. Nutrient deficiencies are thus common in agrarian

societies and are often exacerbated during periods of seasonal hunger or peri-

odic droughts (Chambers et al 1981). Skeletal evidence suggests that such nu-

tritional problems were typical in early agrarian communities and increased

with agricultural intensification in some areas (Cohen & Armelagos 1984),

and may have contributed to a more vulnerable host population. Skeletal analyses demonstrate that women, children, and—with develop-

ment of stratified societies—the lower classes suffered disproportionately

from the first epidemiologic transition. Female remains among Neolithic

populations indicate higher frequencies of bone loss and nutritional anemia

(Martin & Armelagos 1979). Comparisons between agricultural populations

and their foraging predecessors show greater mortality, dental defects, and im-

paired bone growth among infants and young children for populations in tran-

sition (Cohen & Armelagos 1984). Artifacts indicating social status differ-

ences correlate positively with nutrition and bone-growth among Lower Illi-

nois Valley males during the Middle Woodland Period, emphasizing the role

of early social stratification in the differential experience of disease (Buikstra

1984). Related issues of political organization also had health implications, as

in the case of Nubian populations during the Neolithic period, in which life ex-

pectancies were inversely related to the degree of political centralization (Van-

Gerven et al 1990). The severity of disease outbreaks during the first epidemiologic transition

intensified as regional populations increased and aggregated into urban cen-

ters. The crowded, unsanitary living conditions and poor nutrition characteris-

tic of life in these early cities fostered rapid and devastating regional epidemics

(Flinn 1974; McNeill 1976; McNeill 1978). The establishment of large cities

increased problems of supplying clean water and removing human waste,

while facilitating spread of more virulent pathogens in enclosed and densely

crowded habitations (McNeill 1976; Risse 1988). Cholera contaminated water

RE/EMERGING INFECTIOUS DISEASES 253

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supplies, epidemics of vector-borne disease such as plague and typhus devas-

tated populations, and outbreaks of measles, mumps, smallpox, and other viral

infections were increasingly common (Knapp 1989). Unlike the infectious dis-

ease mortality common in early Neolithic populations, adults were frequently

the target of epidemic outbreaks, paralyzing societies economically in their

wake. As a dramatic example, tuberculosis routinely killed one third of all

adults in many European communities, and by the end of the nineteenth cen-

tury had claimed an estimated 350 million lives (Knapp 1989). Similarly, the

Black Death of the 1300s is estimated to have eliminated at least a quarter of

the European population in a decade (Laird 1989). McNeill (1976) also discusses two important historical trends that initiated

the global spread of pathogens across previously intractable geographic

boundaries. First, increasing migration and trade between state-level societies

in Eurasia led to the convergence of regional infectious disease pools begin-

ning in the fifth century CE. Second, expansion of these networks into the New

World through exploration and conquest brought European populations with

acquired immunity to childhood infections into contact with Native Americans

with no history of exposure to these pathogens (Black 1990). This contact re-

sulted in massive pandemics of smallpox and typhoid that killed millions of

people and facilitated the colonial domination of two continents (McNeill

1976, Dobyns 1993). It also probably resulted in the introduction of trepone-

mal infections to Europe (Baker & Armelagos 1988), where sexual promiscu-

ity in crowded urban centers may have favored a venereal mode of transmis-

sion in the form of syphilis (Hudson 1965). These historical events illustrate

how the globalization of state-level societies has provided opportunities for

pathogens to cross considerable social and geographic boundaries.

The Second Epidemiologic Transition

The second epidemiologic transition roughly coincided with the Industrial Revolution in mid-nineteenth century Europe and North America. It is distin- guished by a marked decline in infectious disease mortality within developed countries. This decline is the major focus of the second proposition in Omran’s model of epidemiologic transition: “a long-term shift in mortality and disease patterns whereby pandemics of infection are gradually displaced by degenera- tive and manmade disease as the chief form of morbidity and primary cause of death”(Omran 1971:516).

The decline of infectious diseases in the nineteenth and twentieth centuries

has often been cited as an objective landmark in the progress of modern civili-

zation—a product of developments in medical science and technology in the

industrialized world that would eventually diffuse to less-developed societies.

Garrett shows how early successes in the eradication of polio and smallpox in-

fluenced western medical establishments in their confident forecast for the

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eminent demise of infectious diseases before the end of this century (Garrett

1994). However, these projections did not consider that the larger secular trend

of declining infectious disease mortality was already well under way before

the advent and application of antimicrobial technologies (McKeown 1976). Based largely upon data from Scandinavia, Germany, France, Italy, and

England, Schofield & Reher roughly estimated the decline in European infec-

tious disease mortality to have occurred in three major phases beginning in the

late seventeenth century (Schofield & Reher 1991). The first phase, lasting

from the late seventeenth century to the beginning of the nineteenth century, is

characterized by a flattening of crisis mortality peaks owing to sporadic epi-

demics of diseases such as plague, smallpox, and typhus. Beginning in the

mid-nineteenth century, the second phase was characterized by an overall

secular decline in mortality that, although subject to significant regional varia-

tion, contributed to an increased life expectancy by more than three decades,

resulting in a major overall population increase despite concurrent fertility de-

clines. The third phase began with the advent of antimicrobial therapies in the

1940s, representing a more modest decline in infectious disease mortality in

more affluent nations that continued until the early 1980s. McKeown argued for the primacy of nutritional factors in declining Euro-

pean mortality (McKeown 1976). However, McKeown has been criticized for

weighing nutritional inferences beyond the resolution of available data

(Schofield & Reher 1991; Johansson 1992). While evidence suggests that the

creation of an international grain market may have spurred improved agricul-

tural yields and distribution networks, the relative importance of other factors

such as pasteurization, public hygiene, and home-based primary health care

deserve further evaluation (Kunitz 1991; Woods 1991). Moreover, there is lit-

tle disagreement that certain biomedical innovations such as the worldwide

vaccination campaigns against smallpox played a significant role in mortality

decline. The decrease in infectious disease in industrialized nations and the subse-

quent reduction in infant mortality has had unforeseen consequences for hu-

man health. Namely, the subsequent extension of life expectancy has also

brought increased morbidity from chronic diseases (Riley & Alter 1989).

These so-called “diseases of civilization” include cancer, diabetes, coronary

artery disease, and the chronic obstructive pulmonary diseases (Kaplan & Keil

1993). Other health tradeoffs of the second transition concern the role of indus-

trial technology in the creation of artificial environments that have influenced

the appearance of chronic diseases. Particularly in urban environments, in-

creasing water and air pollution subsequent to industrialization has been

linked to significantly higher rates of cancer (Anwar 1994; Dietz et al 1995),

allergies (Barnes et al 1998), birth defects (Palmer 1994), and impeded mental

development (Perrera 1993). These issues are compounded by the psychoso-

RE/EMERGING INFECTIOUS DISEASES 255

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matic effects of urbanization, which is correlated with increased levels and in- cidences of hypertension (Grossman & Rosenthal 1993), as well as depression and anxiety (Harpham 1994).

As in the cases of the Paleolithic-era baseline and the first epidemiologic transition, social inequalities account for many of the differences in the way the second transition has been experienced within and between populations. Within more industrialized societies, socioeconomic, ethnic, and gender dif- ferences are strongly associated with differences in morbidity and mortality for both chronic and infectious diseases (Arriaga 1989; Blair 1993; Dressler 1993). Buried within national statistics, and temporarily masked by antibiot- ics, the conditions selected for the first transition persisted among the poorest people of the richest nations in the second.

Following the Second World War, the second epidemiologic transition made a more modest appearance in many less-developed nations and was marked by improvements in child survival and life expectancy at birth (World Bank 1993). Unlike the epidemiologic transitions experienced in the United States and Europe, which largely proceeded the advent of modern biomedical innovation, biomedical fixes such as oral rehydration therapy, immunizations, and antibiotics played a pivotal role in the initial successes in mortality reduc- tion in these societies (Gwatkin 1980; Hill & Pebley 1989; Ruzicka & Kane 1990). While variability of these declines between countries and their possible deceleration since the 1960s has been a source of controversy (Gwatkin 1980; United Nations 1982), there is little doubt that the second transition has fallen short of optimistic projections for the developing world (Gobalet 1989). Rapid urbanization combined with marked social inequalities and a continued lack of public health infrastructure have led to communicable diseases among the ur- ban poor, with chronic degenerative diseases among the affluent and slowly emerging middle classes (Muktatkar 1995). In middle-income countries such as Mexico and Brazil, socioeconomic status now relates inversely to important chronic disease risk markers like obesity and hypertension (Popkin 1994), akin to similar associations in the United States, the United Kingdom, and other af- fluent nations (Kaplan & Keil 1993).

THE THIRD EPIDEMIOLOGIC TRANSITION

The current phenomenon of emerging infectious diseases indicates a third epi-

demiologic transition characterized by three major trends. First, an unprece-

dented number of new diseases have been detected over the last 25 years that

are becoming significant contributors to adult mortality. Second, there is an in-

creased incidence and prevalence of preexisting infectious diseases that were

previously thought to have been under better control. Third, many of these re-

emerging pathogens are generating antimicrobial-resistant strains at a faster

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rate than safe new drugs can be developed. These three trends are occurring

within the broader context of an increasing globalization, involving not only

international trade, migration, and information networks, but also the conver-

gence of human disease ecologies.

Recently Emerging Infections

The Centers for Disease Control and Prevention (CDC) has compiled a list of

29 newly emerging pathogens since 1973 (Satcher 1995). It is possible that the

overall size of this list is more a function of increased detection than the actual

emergence of new pathogens in human populations. Such is the case of the Le-

gionella bacterium responsible for the high-mortality pneumonia known as

Legionnaire’s Disease. Following its initial detection during a 1976 outbreak

in a convention of American World War II veterans (Fraser et al 1977), envi-

ronmental and retrospective patient cultures subsequently indicated that Le-

gionellae had long been responsible for 2000 to 6000 deaths previously diag-

nosed as pneumonias of unknown etiology (McDade et al 1977), many of

which were attributed to the exposure of susceptible elderly hosts to contami-

nated large-scale air conditioning units (Miller 1979; Morris et al 1979; Sara-

volatz et al 1979). Despite possible increases in detection rates, it cannot be denied that at least

some of these new diseases are making unprecedented contributions to adult mortality. The most dramatic example of this is the Human Immunodeficiency Virus (HIV). Although retrospective studies have detected cases in Europe and Africa going back as far as 1959 (Huminer et al 1987; Nahmias et al 1986), HIV has more recently become the second leading cause of death among adult males aged 25–40 years of age in the United States, and the chief contributor to a 40% increase in infectious disease mortality over the past 15 years (Pinner et al 1996). With the exception of the flu pandemic of 1918, this trend marks the first of such increases in affluent societies since the Industrial Revolution.

Phylogenetic analyses of HIV and related retroviruses indicate a recent

evolution from a simian virus of Central African origin (Essex & Kanki 1988).

Yet biological evolution alone does not account for the rampant spread of this

disease, nor its unequal distribution within and between populations (Ewald

1994; Feldman 1990; MacQueen 1994). Throughout Asia, Africa, and the

Americas, high HIV and sexually transmitted disease (STD) prevalence rates

have been indices of deeper sociohistorical issues such as neocolonialism

(Alubo 1990), the disintegration of poor families because of seasonal labor mi-

grations (Hunt 1995), sexual decision-making strategies (Bolton 1992; Wad-

dell 1996), and the gendered experience of poverty (Connors 1996; Daily et al

1996; Farmer et al 1993; MacQueen et al 1996; McCoy et al 1996). Yet, nei-

ther is this simply a case of the poor transmitting their problems to the affluent.

For example, contrary to the myth of Haitian origin following the initial dis-

RE/EMERGING INFECTIOUS DISEASES 257

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covery of AIDS, evidence suggests an earlier transmission to urban Haiti by

more affluent Westerners engaging in sex tourism (Farmer 1992). The social history of AIDS provides a prototype for similar issues sur-

rounding the transmission of other infectious diseases. Outbreaks of Ebola

hemorrhagic fever have received much attention in the popular press, which

has mainly focused on the gory aspects of its clinical manifestations, high mor-

tality rates, and fears of airborne transmission accentuated with images of “vi-

rus hunters” running around in spacesuits (Preston 1994). Contrary to these

dramatized accounts, however, the instances of possible airborne transmission

was restricted to very close contact between unprotected healthcare workers

and patients in the late stages of this disease (Garrett 1994). The Ebola out-

breaks along Kinshasa Highway of Central and Eastern Africa in the 1970s

mainly involved transmission via the commercial sex trade and the reuse of

dirty syringes by untrained Western missionaries and underequipped health-

care workers (Garrett 1994). Regarding fears of transmission across national

borders, the appearance of the closely related filoviruses detected in Reston,

Virginia, and Marburg, Germany, were caused by the importation of primates

for drug research, which ironically included the development of vaccines for

other viruses (Bonin 1971; Morse 1993, 1995). Ebola and Marburg are but two examples of a much larger set of recently

discovered hemorrhagic diseases. Recent outbreaks of these diseases in the

New World have been linked to climatic fluctuations and ecological disrup-

tion. In 1993, a sudden outbreak of a virulent hemorrhagic fever in the Four

Corners region of the American Southwest was quickly identified as a novel

strain of hantavirus spread through the excreta of the deer mouse, Peromyscus

maniculatus, but not before infecting 98 individuals in 21 states and claiming

51 lives (Weigler 1995). The 1993 outbreak was associated with abnormal

weather patterns (Epstein 1995), and oral histories of local American Indian

healers describe three clusters of similar outbreaks that coincide with identifi-

able ecological markers (Chapman & Khabbaz 1994), supporting the idea that

this disease has long coexisted with and periodically afflicted human popula-

tions across the United States without detection by the medical community

(Weigler 1995). The initial outbreaks of Argentinian hemorrhagic fever, or Ju-

nin, were traced to ecological disruption associated with the spread of maize

agriculture and increasing rodent vector habitats (Benenson 1995). First identified in the mid 1970s, tick-borne Lyme disease has since sur-

faced in all 50 states as well as overseas (Jaenson 1991), and has rapidly be-

come the most often reported anthropod-born disease in the United States (Ol-

iver 1996). Regrowth of Eastern forests felled in the eighteenth and nineteenth

centuries to make way for agricultural fields has greatly expanded the habitat

of deer, mice, and their Ixodes tick parasites, which carry the disease-causing

Borrelia spirochete (Walker et al 1996). Residential housing has expanded

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into forested areas, bringing populations into contact with the ticks and their

wild-animal reservoirs. As exemplified by diseases as distinct as HIV, Ebola

virus, and Lyme disease, pathogens are often provided the opportunity to jump

the “species barrier” (Lappe 1994) by a combination of ecological disruption

or change, and increased contact between humans and wild reservoir species.

The size and mobility of human populations increases the potential for the

pathogen to escape its geographic barrier (Armelagos 1998).

RE-EMERGING INFECTIONS Ecological disruption has also been cited as a ma- jor factor in re-emerging infectious diseases as well. Warmer climates have led to increased coastal blooms of algae, creating favorable environments for the proliferation of Vibrio cholerae, and inland changes in temperature and hu- midity are increasing the reproduction of malaria vectors (Martens et al 1995; Patz et al 1996). In addition, climactic fluctuations such as El Niño are thought to have significant effects on pathogen and disease vector environments (Bouma & Dye 1997; Colwell 1996).

While acts of nature may account for changing disease patterns, most of

these ecological changes have anthropogenic origins (Brown 1996; Coluzzi

1994; de Zulueta 1994). In the last 15 years, dengue fever has shown a dra-

matic resurgence in Asia and Latin America, where poorly developed urban

environments have led to the proliferation of the Aedes egypti mosquito vec-

tors in open water pools (Chinery 1995; Whiteford 1997), contributing as well

to sporadic outbreaks in the Southwestern United States (Gubler & Clark

1995). The practice of combined swine-duck agriculture in Southern China as

well as commercial swine and turkey farming in the United States is thought to

contribute to the genetic adaptability of flu viruses (Shortridge 1992; Shu et al

1994; Wright et al 1992). Bradley critically reviewed the practice of “third-

world dumping” by multinational corporations, in which industrial production

facilities are “outsourced” into developing countries with cheap labor pools

and greatly relaxed environmental regulations, resulting in localized climate

changes (Bradley 1993a,b). Increases in mosquito populations have com-

pounded the problem of malaria and dengue in places where poor living condi-

tions and the unequal distribution of health resources have already contributed

to higher levels of preventable mortality (Brown et al 1996; Gubler & Clark

1995). Among the re-emerging infectious diseases, tuberculosis (TB) is the great-

est contributor to human mortality, and it is estimated that nearly a third of the

world’s population has been latently infected with the mycobacterium (Malin

et al 1995). After more than a century of steady decline, the incidence of re-

ported TB cases in the United States increased by more than 20% from 1985 to

1992. This trend is particularly unsettling given that the previous decline of TB

was the single largest contributor to North American and European declines in

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infectious disease mortality during the middle stages of the second epidemiol- ogic transition (Caselli 1991; Puranen 1991).

The resurgence of tuberculosis in affluent nations was preceded by de- creased public health expenditures, becoming a forgotten disease in the con- text of overly optimistic predictions for its continued decline (Ryan 1993). Yet TB has remained the leading cause of infectious disease mortality in develop- ing countries, where 95% of all cases occur (Raviglione et al 1995). Notori- ously endemic to populations living under conditions of malnutrition, poor sanitation, and inadequate housing, tuberculosis has long been considered to be the classic disease of poverty (Darbyshire 1996). While HIV comorbidity is implicated in the most recent first world resurgence of TB, especially among young adults, higher rates of both diseases among the urban homeless indicate that socioeconomic issues play much the same etiological role in the re/emer- gence of infectious diseases today as they have in centuries past (Barclay et al 1995; Barnes et al 1996; Farmer 1997; Zolopa 1994).

ANTIMICROBIAL RESISTANCE The history of antimicrobial resistance is al- most as long—or rather, as short—as the widespread use of the drugs them- selves. The first recorded instance of drug resistance occurred in 1917 during the initial trials of Optochine in the treatment of pneumococcal pneumonia (Moellering 1995; Moore 1917). Three years after the 1941 introduction of penicillin for clinical use against gram-positive “staph” infections,2 new strains of Staphylococcus aureus began to emerge with penicillin-destroying beta lactamase enzymes (Neu 1992). The lessons of emerging resistance were well known even before the DDT fumigation campaigns to eradicate malaria- carrying Anopheles mosquitoes, in which warnings of impending insecticide susceptibility accompanied strong recommendations for a single major inter- national campaign (Brown 1996; Olliaro et al 1996; Roberts & Andre 1994). These unheeded warnings would prove correct, not only for the vectors, but for the quinine and chloroquine-resistant plasmodium parasite itself (de Zulueta 1994; Longworth 1995; Roberts & Andre 1994).

At present, more than 95% of S. aureus strains are resistant to most forms of

penicillin, and strains resistant to methycilline (MRSA) have become endemic

to US nursing homes and acute-care settings around the world (Jacoby 1996).

Last year, the first strains of S. aureus possessing intermediate resistance to

vancomycin were identified in Japan and the United States (Centers for Dis-

ease Control 1997), joining the ranks of already emerging Enteroccoci with

full resistance to this antibiotic (Nicoletti & Stefani 1995; Rice & Shlaes 1995;

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Swartz 1994). In many cases, vancomycin represents the last in the line of

“magic bullet” defenses against these kinds of pathogens (Gruneberg & Wil-

son 1994; Nicoletti & Stefani 1995; Rice & Shlaes 1995). As such, the emer-

gence of vancomycin-resistant pathogens hails the beginning of what has been

called “The Post-Antimicrobial Era” (Cohen 1992). In many ways, biological evolution provides the ultimate critique of bio-

medicine by demonstrating the inevitability of genetic adaptations of microor-

ganisms to the selective conditions posed by human technology and behaviors

(Lederberg 1997). Beyond this, however, predictions of specific resistance

patterns have been problematic. Streptococcus pneumoniae provides a good

example of this problem. Long since ranked among the pneumonias known as

“the old man’s friend” in affluent nations (Garrett 1994), S. pneumoniae has

also been the microbial source of more than 1,000,000 annual deaths of chil-

dren under five years of age (Obaro et al 1996). In the last five years, drug-re-

sistant strains of this bacteria have emerged worldwide (Gerber 1995; Gold-

stein & Garau 1994; Jernigan et al 1996), with reported frequencies as high as

50% among clinical isolates (Obaro et al 1996). Yet there is no theoretical ex-

planation for why it took more than 40 years for this organism to develop anti-

biotic resistance, while other drug-resistant species emerged in less than a dec-

ade (Bartlett & Froggatt 1995). Bartlett & Froggatt outline three general themes in the emergence of anti-

microbial resistance: 1. that high-grade resistant organisms are typically fore-

shadowed by low-grade resistant intermediates; 2. that resistant strains are

typically resistant to more than one antibiotic; and not surprisingly, 3. that re-

sistance develops under conditions of extensive antibiotic use (Bartlett &

Froggatt 1995). The overuse of antibiotics by both trained and untrained health

providers throughout the world is a major factor in the evolution of

antimicrobial-resistant pathogens (Kollef 1994; Kunin 1993; Kunin et al

1987). Besides the practices of health providers, the patients themselves have cre-

ated selective conditions for antimicrobial resistance by early termination of

prescribed courses of antibiotics, providing additional generation time for

partly reduced organism populations within the host (Appelbaum 1994). This

is especially problematic for diseases such as tuberculosis, which requires up

to a year of medication adherence in the absence of detectable symptoms to

completely eliminate the mycobacterium (Barnes & Barrows 1993). Acquired

resistance owing to incomplete adherence to TB regimens is partly responsible

for the emergence of multi-drug–resistant tuberculosis (MDRTB) (Jacobs

1994; Nunn & Felten 1994)—a situation compounded by issues of access and

conflicting explanatory models between patients and healthcare providers

(Dedeoglu 1990; Menegoni 1996; Rubel & Garro 1992; Sumartojo 1993; Vec-

chiato 1997).

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Host susceptibility is another major factor in the evolution of antimicrobial-

resistant pathogens (Morris & Potter 1997). The large majority of MDRTB

outbreaks in the United States occurred in the context of comorbidity among

HIV-infected patients (Crawford 1994; Zolopa 1994). Multi-drug resistant no-

socomial infections are predominantly found among elderly and immunocom-

promised patients in long-term and acute-care hospital settings (Hayden &

Hay 1992; Koll & Brown 1993; Kollef 1994; Rho & Yoshikawa 1995; Schen-

tag 1995; Toltzis & Blumer 1995). The emergence of the eighth cholera pan-

demic, involving the drug-resistant 0139 Bengal strain, has been found among

populations of refugees and the poorest inhabitants of the fourth world already

susceptible to the effects of unsanitary water sources (Martin et al 1994; Sid-

dique et al 1995; Islam et al 1995; Toole 1995; Weber et al 1994). The overuse of antiobiotics in industrial animal husbandry also contributes

to the rise of multi-drug resistant strains of food-borne pathogens (Tauxe

1997). Nontyphoid strains of Salmonella have been on the rise in the United

States since the Second World War, where it is currently the most common

food-borne infection. Overuse of antibiotics in chickens has contributed to the

emergence of Salmonella strains resistant to all known drug therapies. These

were recently identified in British travelers returning from the Indian subconti-

nent (Rowe et al 1997). In Europe, the emergence of strains of Campylobacter

resistant to enrofloxacin increased in parallel to the use of this antibiotic

among poultry (Endtz et al 1991). Similarly, the use of avoparicin as a growth-

promoter in European livestock is believed to have created selective condi-

tions for the emergence of vancomycin-resistant enterococci (VRE), which are

transmitted to human hosts through fecal-contaminated animal products

(McDonald et al 1997). While antibiotics have played a relatively minor role in the latter stage of

the second epidemiologic transition, the erosion of these human cultural adap-

tations in the face of more rapid genetic adaptations of microorganisms forces

us to confront major issues without the aid of technological crutches. We will

discover to what degree these magic bullets may have subsequently obscured

the relative efficacy of primary prevention in both affluent and underdevel-

oped societies.

INFLUENZA AND THE GLOBALIZATION OF HUMAN DISEASE ECOLOGIES Had

the historical precedents of influenza been given closer consideration, pre-

vious projections for the continued decline in infectious diseases might not

have been so optimistic. With an estimated worldwide mortality of over

20,000,000, the Spanish influenza pandemic of 1918–1919 killed more human

beings than any previous war or epidemic in recorded history (Crosby 1989).

This was followed by the less-virulent pandemics of 1957, 1968, and 1977

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(Wiselka 1994), each bringing the millenialist promise of another major out-

break at some unknown year to come (Glezen 1996; Webster et al 1993). Noting the rapidity with which the Spanish Flu spread throughout the world

in the days of steamships and isolationism, Garrett grimly suggested how such an outbreak could spread in the present age of international economics and jet travel (Garrett 1994), a timely subject given the recent appearance of a poten- tially lethal influenza strain in Hong Kong poultry markets with H5 antigens, to which humans have no known history of previous exposure (Cohen 1997; Shortridge 1995). With revolutionary changes in transportation technology (Reid & Cossar 1993; Wilson 1996), worldwide urbanization (Muktatkar 1995; Phillips 1993), and the increasing permeability of geopolitical bounda- ries (Farmer 1996), human populations are rapidly converging into a single global disease ecology (McNeill 1976).

McNeill (1976) cites the early effects of transnationalism on the transmis- sion of infectious diseases with the establishment of extensive Eurasian trade networks in the fifth century CE. Intercontinental shipping routes provided for the transport of pathogens as well as trade goods and organized violence. The European conquests of the new World presented a dramatic example of this trend, in which adult carriers of childhood diseases endemic to post-first tran- sition populations suddenly infected unexposed Native American populations, resulting in massive pandemics of smallpox and typhus. Neither was this a one-way trade, as returning sailors brought syphilis and tobacco back to the European continent with them.

The current trend of accelerated globalization challenges us to consider the health implications not just of converging microbial ecologies, but also of the international flow of ideologies, behavior patterns, and commodities that un- derlie human disease patterns. This broader picture of globalization, involving the international exchange of memes (units of cultural information) as well as microbes, entails a convergence of both chronic and infectious disease pat- terns. This is evidenced in the many developing societies that are suffering what has been called the “worst of both worlds”—the postwar rise in chronic degenerative diseases among the poor without significant declines in infec- tious disease mortality (Bradley 1993a), while these infections re-emerge in post-second transition societies (Armelagos et al 1996).

CONCLUSION

Buoyed by early successes in the control of scourges such as polio and small-

pox in the 1950s and 1960s, the Western medical establishment claimed that it

was time to close the book on infectious diseases and focus research attention

on the growing problem of chronic degenerative disease (Garrett 1993). Un-

RE/EMERGING INFECTIOUS DISEASES 263

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fortunately, the book on infectious disease remains very much open, and new

chapters continue to be added at an alarming pace. We address this issue from

an evolutionary perspective, using the concept of epidemiologic transition the-

ory as an organizing framework. Our discussion of epidemiologic transitions

during the course of human evolution reveals that disease “emergence” is not

new but has been a dynamic feature of the interrelationships between humans

and their sociocultural and ecological environments since the Paleolithic peri-

od. The initial formulations of the epidemiologic transition provided a useful

interdisciplinary framework for macrolevel analyses of demographic changes associated with major declines in infectious disease mortality in Europe and North America in the wake of the Industrial Revolution (Omran 1971). De- spite later modifications, however, interpretations of this framework still re- mained largely restricted to a single set of events at a particular period of hu- man history (Omran 1983). The subsequent particularism of this transition fueled notions of unilinear progress, resulting in falsely optimistic projections for the continued decline and eventual elimination of infectious disease in hu- man populations (Garrett 1994). Our expanded framework of multiple epide- miological transitions avoids these pitfalls by providing a broader historical and evolutionary perspective that highlights common themes that pervade changing human-disease relationships throughout modern human evolution.

In our review of epidemiologic transitions, we have highlighted the socioe- cological, technological, and political factors involved in human disease dy- namics. The US Institute of Medicine has identified six principal factors con- tributing to the current problem of re/emerging infectious diseases: 1. ecologi- cal changes; 2. human demographics and behavior; 3. international travel and commerce; 4. technology and industry; 5. microbial adaptation and change; and 6. breakdown in public health measures (Lederberg et al 1992; Morse 1995). The degree to which these factors are fundamentally anthropogenic cannot be overstated, nor can the influence of socioeconomic inequalities across these factors.

Recognizing the complexity of these sociobehavioral dynamics, many re- searchers in biology, medicine, and public health are calling for greater in- volvement of social and behavioral scientists in addressing infectious disease issues (Morse 1995; Satcher 1995; Sommerfeld 1995). By taking a holistic ap- proach to these important human issues, anthropologists are well positioned to make significant theoretical and practical contributions within interdiscipli- nary research settings. For example, 40 years ago, Livingstone described the emergence of malaria following the introduction of agriculture in sub-Saharan Africa in what has become a classic example of the ability of humans to shape their physical environments—with unforeseen health consequences (Living- stone 1958).

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Anthropologists have explored the health implications of (a) sexual behav- iors (Lindenbaum 1991; MacQueen et al 1996; Waddell 1996); (b) funerary practices (Lindenbaum 1990); (c) ethnic conflict and genocide (Tambiah 1989); and (d) population displacement (Bisharat 1995; Malkki 1995; Toole 1995). Recent work in transnationalism identifies the political, economic, and social trends that are increasingly integrating the world’s diverse populations (Kearney 1995). The emerging paradigm of evolutionary medicine demon- strates the applicability of evolutionary principles to contemporary health is- sues (Armelagos 1997), and emphasizes the ability of humans to shape their environment through pathogen selection (Lederberg 1997). Finally, anthro- pologists have critiqued the political-economic constraints that limit access to health care and basic public-health needs (Farmer 1996; Inhorn & Brown 1990; Risse 1988). Given this range of issues impacting human-disease rela- tionships, even anthropologists not directly concerned with infection can make significant contributions to an improved understanding of disease emergence.

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RE/EMERGING INFECTIOUS DISEASES 265

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Epidemilogical Triangle.JPG

GHA-7-23303.pdf

SPECIAL ISSUE: EPIDEMIOLOGICAL TRANSITIONS � BEYOND OMRAN’S THEORY

The evolution of disease: anthropological perspectives on epidemiologic transitions

Molly Kathleen Zuckerman1*, Kristin Nicole Harper2, Ronald Barrett3 and George John Armelagos4

1Department of Anthropology and Middle Eastern Cultures, Cobb Institute of Archaeology, Mississippi State University, Mississippi State, MS, USA; 2Department of Environmental Health Sciences, Columbia University Medical Center, New York, NY, USA; 3Department of Anthropology, Macalester College, Saint Paul, MN, USA; 4Department of Anthropology, Emory University, Atlanta, GA, USA

Background: The model of epidemiologic transitions has served as a guiding framework for understanding relationships between patterns of human health and disease and economic development for the past several

decades. However, epidemiologic transition theory is infrequently employed in epidemiology.

Objective: Moving beyond Omran’s original formulation, we discuss critiques and modifications of the theory of epidemiologic transitions and highlight some of the ways in which incorporating epidemiologic transition

theory can benefit theory and practice in epidemiology.

Design: We focus on two broad contemporary trends in human health that epidemiologic transition theory is useful for conceptualizing: the increased incidence of chronic inflammatory diseases (CIDs), such as allergic

and autoimmune diseases, and the emergence and reemergence of infectious disease.

Results: Situating these trends within epidemiologic transition theory, we explain the rise in CIDs with the hygiene hypothesis and the rise in emerging and reemerging infections with the concept of a third

epidemiologic transition.

Conclusions: Contextualizing these trends within epidemiologic transition theory reveals implications for clinical practice, global health policies, and future research within epidemiology.

Keywords: epidemiologic transitions; Omran; epidemiology; hygiene hypothesis; infectious disease

Responsible Editors: Nawi Ng, Umeå University, Sweden; Barthélémy Kuate Defo, University of Montreal, Canada.

*Correspondence to: Molly Kathleen Zuckerman, Department of Anthropology and Middle Eastern

Cultures, Cobb Institute of Archaeology, Mississippi State University, P.O. Box AR, Mississippi State, MS

39762, USA, Email: [email protected]

This paper is part of the Special Issue: Epidemiological Transitions � Beyond Omran’s Theory. More papers from this issue can be found at http://www.globalhealthaction.net

Received: 8 November 2013; Revised: 12 January 2014; Accepted: 21 January 2014; Published: 15 May 2014

F or the past several decades, the model of epidemio-

logic transitions has served as a guiding framework

for understanding relationships between patterns

of human health and disease and economic development

(1). As originally proposed by Omran (2), an epide-

miologic transition is a trend wherein a high burden of

mortality from infectious disease*primarily epidemic ‘childhood’ diseases such as pertussis and measles*is replaced by one of chronic and non-communicable di-

seases (NCDs), such as cardiovascular disease, cancer, and

diabetes. Omran’s ‘classic’ model was originally formula-

ted to capture the changes in cause-specific mortality that

followed the Industrial Revolution in the United States

and in Western Europe. However, in a modified form,

the transition is ongoing in many developing low- and

middle-income countries (LMICs), which carry a high bur-

den of mortality from infectious diseases and NCDs (3).

Recently, scholars have placed the classic transition

within an expanded evolutionary framework. This recog-

nizes a ‘first’ transition coincident with the Neolithic

period and the agricultural revolution and a ‘third’ transi-

tion of emerging and reemerging infectious diseases

occurring in the modern era (1). In this expanded frame-

work, Omran’s classic transition becomes the ‘second

epidemiologic transition’, as it is referred to here.

Epidemiologic transition theory provides a model for

the dynamics among economic, social, demographic,

and ecological factors and the evolution and spread of

disease (4), explaining major trends in the human disease-

scape and granting insight into ultimate causes of a given

trend and therefore potential solutions (5). Consequen-

tly, it has become paradigmatic in public health policy

(3, 6�9), demography (10, 11), biological and medical anthropology (12�14), and economics (15). However, it

Global Health Action �

Global Health Action 2014. # 2014 Molly Kathleen Zuckerman et al. This is an Open Access article distributed under the terms of the Creative Commons CC-BY 4.0 License (http://creativecommons.org/licenses/by/4.0/), allowing third parties to copy and redistribute the material in any medium or format and to remix, transform, and build upon the material for any purpose, even commercially, provided the original work is properly cited and states its license.

1

Citation: Glob Health Action 2014, 7: 23303 - http://dx.doi.org/10.3402/gha.v7.23303 (page number not for citation purpose)

http://www.globalhealthaction.net/index.php/gha/issue/view/1602#Special%20Issue:%20Epidemiological%20Transitions%20-%20Beyond%20Omran's%20Theory

http://www.globalhealthaction.net/index.php/gha/issue/view/1602#Special%20Issue:%20Epidemiological%20Transitions%20-%20Beyond%20Omran's%20Theory

http://www.globalhealthaction.net/index.php/gha/issue/view/1602#Special%20Issue:%20Epidemiological%20Transitions%20-%20Beyond%20Omran's%20Theory

http://www.globalhealthaction.net/index.php/gha/issue/view/1602#Special%20Issue:%20Epidemiological%20Transitions%20-%20Beyond%20Omran's%20Theory

http://www.globalhealthaction.net/index.php/gha/issue/view/1602#Special%20Issue:%20Epidemiological%20Transitions%20-%20Beyond%20Omran's%20Theory

http://www.globalhealthaction.net/index.php/gha/issue/view/1602#Special%20Issue:%20Epidemiological%20Transitions%20-%20Beyond%20Omran's%20Theory

http://www.globalhealthaction.net/index.php/gha/issue/view/1602#Special%20Issue:%20Epidemiological%20Transitions%20-%20Beyond%20Omran's%20Theory

http://www.globalhealthaction.net/index.php/gha/issue/view/1602#Special%20Issue:%20Epidemiological%20Transitions%20-%20Beyond%20Omran's%20Theory

http://www.globalhealthaction.net/index.php/gha/article/view/23303

http://dx.doi.org/10.3402/gha.v7.23303

has had much less impact in epidemiology (5, 16, 17).

This is because epidemiologists are generally concer-

ned with the study of one or a few specific diseases within

restricted time frames. Identifying a causal pathogen or

characterizing a novel condition necessitates attention

to its specific properties, such as statistical risk factors or

disease ecology. This epidemiological approach translates

into conceptualizing diseases, including emerging dis-

eases, as singular entities attributable to more immediate

and proximate causes, not as components of broader

health trends attributable to longer-term and ultimate

causes (5). However, as we discuss here, Omran’s classic

theory and its modifications can help to broaden the

epidemiologist’s understanding of the complex, multiple

dimensions of health and disease over time, revealing

potential proximate as well as ultimate causes, prevention

strategies, and predictions of future epidemiologic trends

and, in doing so, contribute to improving population

health (5, 16).

Moving beyond Omran’s original formulation, this

paper critiques and modifies the theory of epidemiologic

transitions, highlighting some of the ways that an under-

standing of epidemiologic transitions can benefit theory

and practice in epidemiology. We also focus on two broad

contemporary trends in human health that are relevant to

epidemiologic transition theory: the increased incidence

of chronic inflammatory diseases (CIDs), such as allergic

and autoimmune diseases, and the emergence and re-

emergence of infectious diseases. We situate these trends

within an expanded epidemiologic transition theory,

employing the hygiene hypothesis to explain the rise in

CIDs and the concept of a third epidemiologic transition

to explain the rise in emerging and reemerging infections.

We further discuss the implications that this approach

generates for preventive medicine, global health policies,

and future research in epidemiology.

Present investigation

The theory of epidemiologic transitions

Epidemiologic transition theory models the changes in

cause-specific mortality that accompanied the industria-

lization-associated demographic transition, the declines

in mortality and fertility, and the resulting population

growth (18) (see Fig. 1). Demographic transition theory

is a simplified, descriptive, multistage model of this tran-

sition (14). Stage 1 is largely preindustrial and features

high mortality and fertility, variable but generally low

life expectancy, and slow but stable population growth

interrupted by large periodic fluctuations in mortality

(crisis mortality). Stage 2 involves declining ‘normal’

mortality but persistently high fertility. Stage 3 features

continued declines in normal mortality as well as fertility,

increased life expectancy, and more sustained population

growth. Declining normal mortality began in the mid-

nineteenth century in western and northern Europe and

the United States and around 1920 in LMICs, such

as Chile. Crisis mortality continued into the twentieth

century, perhaps ending only with the 1918�1919 Spanish influenza outbreak (14). Stage 4 features low, still de-

clining mortality, as observed in developed countries after

World War II (19). Mortality declines continue, especially

among the elderly, but slowly, likely due to already low

mortality among infants, children, and young adults (14).

As originally formulated (2), the epidemiologic transi-

tion theory builds on demographic transition theory with a

consideration of changes in cause specific mortality across

four stages. Stage 1, the ‘Age of Pestilence and Famine’, is

preindustrial and is characterized by high frequencies

of epidemic infectious disease and crisis mortality. Stages

2 and 3, the ‘Age of Receding Pandemics’, involve a change

from epidemic to endemic infectious disease and decreas-

ing crisis mortality. Stage 4, the ‘Age of Degenerative and

Man-Made [sic] Diseases’, ushers in a high burden of

NCDs, the result of age-related degenerative processes and

anthropogenic factors including environmental hazards

and nutritional and behavioral patterns associated with

industrialization and urban living. The neat dichotomy

between infections and NCDs is blurred by the chronic

course of some infections, such as tuberculosis, and

increasing recognition of the role of infection and inflam-

matory processes in many chronic conditions, such as

cervical cancer (20) and coronary heart disease (21).

However, rather than precluding the use of transition

theory, this complication highlights the need to consider

the historical and evolutionary relationships between

humans and pathogens to understand current patterns of

human health.

Modifications and critiques of epidemiologic

transition theory

Multiple modifications have been made to Omran’s

original theory. Omran (2), attempting to accommodate

variations from the theory, initially characterized three

models for the progress of the transition. The ‘classical

model’, featuring slow progression from high to low

mortality and fertility, occurred in Western Europe, and

was driven by economic development and advances in

sanitation, public health, and medical knowledge. The

‘accelerated model’, seen in Japan, features more rapid

progress, purportedly driven by the same factors. The

‘contemporary model’ applies to LMICs, wherein mor-

tality has declined but fertility remains high and NCDs

do not yet constitute the primary epidemiologic burden.

Beyond the expansions suggested by Barrett and collea-

gues (1), Olshansky and colleagues have added a fourth

stage, the ‘Age of Delayed Degenerative Diseases’, which

recognizes a shift in NCDs to the elderly even as life

Molly Kathleen Zuckerman et al.

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expectancy increases, and a fifth, encompassing morbid-

ity and mortality from HIV/AIDS and other emerging

infectious diseases (22, 23). Graziano (24) has proposed

an alternative fifth stage, the ‘Age of Obesity and In-

activity’, in which increasing levels of weight gain and

obesity alter the pattern of NCDs among the elderly

(represented by Olshansky and Ault’s fourth stage).

From anthropological and epidemiological perspec-

tives, the original epidemiologic transition theory has

several shortcomings. The proposed explanations are

largely speculative, lacking reliable data or relying on

small sample sizes that are not likely to be generalizable

(16). Many have also argued that the theory insufficiently

addresses social factors, such as poverty (12); the differ-

ential progress of the transition within and across various

demographic subgroups*such as in relation to sex, gender, race, and location (12, 25); and the profound

contemporary impact of emerging and reemerging in-

fectious diseases, such as HIV/AIDS and multidrug

resistant tuberculosis (MDR TB)(12, 25, 26). It also

fails to differentiate adequately between the risk of dying

from any given cause or set of causes and the proportion

of overall mortality due to various causes (14, 25).

Additionally, by focusing on mortality, the theory largely

neglects issues of morbidity, disability, and quality of life.

This approach is sharply divergent from the increasing

use of more holistic definitions of health and the use of

broader measures of life expectancy, such as disability

adjusted life years (DALYs), by health researchers

and organizations (16). Saliently, Caldwell (6: 160) and

others have also critiqued Omran’s models for failing to

recognize ‘the global nature and historical sequence

of the mortality transition as it spread’, positing instead

that each society exhibits its own particular model.

Despite these issues, however, the theory remains pro-

foundly useful for investigating variation in patterns over

time and among locations, for conceptualizing historical

patterns, and for predicting future trends (17).

Epidemiologic transition theory and

epidemiology

Although epidemiology largely maintains its focus on

single disease conditions and proximate causes, the rise

of the socioecological model in epidemiology represents

an opportunity for integrating epidemiologic transi-

tion theory into epidemiologic theory and practice. This

model replaces the earlier ‘epidemiologic triad’ and

‘causal web’ models of disease risk, which paid inade-

quate attention to the multidimensional quality of factors

that affect health and disease (27), particularly the cul-

tural, political, and economic factors that act as ultimate

causes of disease (28, 29). Instead, the socioecological

model recognizes that a broad array of systems and

interrelated determinants of health exist, acting either

synergistically or antagonistically (16). The model verti-

cally expands the domain of epidemiological studies

‘upward’ to incorporate biological, behavioral, mental,

social, and environmental systems and factors (such as

policy and economic environments) as well as ‘down-

ward’ to the molecular and genetic levels. Additionally,

it extends the domain horizontally to consider trends

over time, ranging from the developmental issues ad-

dressed by life course epidemiology to evolving associa-

tions among the various levels (16: 6).

Fig. 1. The demographic transition model (Source: K. Montgomery).

Evolution of disease

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Epidemiologic transition theory provides the broad,

longitudinal, and historical perspectives that the early

models lacked (16). In epidemiology, the increasing

adoption of and reliance upon socioecological models

has also opened up a spectrum of possibilities for in-

vestigations of disease risk that include social, cultural,

policy, and economic factors, as well as how these factors

have changed over time (5, 16). The unique capacity

of epidemiologic transition theory to identify ultimate

causes in disease risk means that transition theory models

can be usefully incorporated into socioecological models

in epidemiology. As Fleischer and McKeown (16: 10)

discuss, such incorporation would be particularly useful

for social epidemiologists because it would shed addi-

tional light on the ways in which ‘upstream’, global

determinants differentially impact the health-states of

vulnerable, disadvantaged populations. Although the

focus on singular disease conditions, their risk factors,

and proximate causes is useful in some investigations,

epidemiologists are gradually recognizing that addressing

patterns of health-states might yield greater insights into

more upstream, shared determinants and therefore have a

profound impact upon population health (16).

Contemporary trends in health addressed

through epidemiologic transition theory

CIDs and emerging and reemerging infectious disease

Epidemiological transition theory can be usefully applied

to a variety of broad trends in patterns of health and

disease in contemporary human populations. Here we

focus on two specific trends: the increased incidence of

CIDs and the emergence and reemergence of infectious

diseases. We use these examples to demonstrate how the

interpretive lens and attention to ultimate causes gener-

ated by epidemiologic transition theory can be used to

inform clinical practice, global health policies, and future

research within epidemiology.

The hygiene hypothesis and the rise of CIDs Since the 1950s, numerous researchers have noted in-

creased rates of CIDs, namely allergic and autoimmune

diseases, in many developed nations (30). These include

allergic diseases, such as asthma and atopic dermatitis,

and autoimmune diseases, such as multiple sclerosis (MS)

and inflammatory bowel disease. For instance, the pre-

valence of atopic dermatitis has doubled or tripled in

developed countries over the past three decades, affecting

15 to 30% of children and 2 to 10% of adults (31). Part

of these increases may be attributable to detection

bias via improved diagnoses and access to medical

resources in many locations, but this does not entirely

explain the rapid rise in the incidence of these conditions,

particularly those such as MS that are easily diagnosed

(32).

Many epidemiologists have noticed that the increase in

CIDs is concomitant with a decrease in epidemic infec-

tions that occurred during the second epidemiologic tran-

sition. However, rather than highly virulent pathogens,

such as smallpox or measles (33), the phenomenon has

been attributed to the diminished exposure to environ-

mental microorganisms (specifically helminthic parasites,

chronic viruses and nonlethal bacterial infections, envi-

ronmental saprophytes) and to declines in the mass and

diversity of gut microbiota, which also occurred as part

of the second transition (34). Situated within epidemio-

logic transition theory, these observations have given rise

to the ‘hygiene hypothesis’, which claims that in devel-

oped nations, diminished childhood exposure during

childhood or even diminished prenatal (maternal) ex-

posure (35) to these microorganisms has resulted in

immunoregulatory failure, manifesting as an increased

incidence of CIDs. According to this theory, prior to

industrialization, humans lived in a state of ‘evolved

dependence’ with these microbes*our ‘old friends’*that was critical for successful immunological functioning,

specifically the development of an immunomodulatory

response that maintains tolerance of self-antigens and

abrogates autoimmune diseases (34). In effect, the life-

style changes*sanitary improvements, pasteurization, use of antibiotics, and improved hygiene*that contri- buted to the second transition may have produced a

substantial trade-off in health and quality of life, with

developed nations exchanging a high burden of infectious

disease for a higher burden of CIDs (36).

Using the hygiene hypothesis to identify potential ulti-

mate causes of CIDs has several direct implications for

epidemiology. Although the identified causes are broadly

environmental and evolutionary, they directly translate

into proximate causes and therefore specific risk factors

and targets for preventive medicine. Informed by the

hygiene hypothesis, researchers have identified causal

relationships among lifestyle changes, infectious burden,

and the incidence of allergic and autoimmune diseases.

They continue to investigate these dynamics using animal

models of autoimmune and allergic diseases and, to a

lesser extent, clinical intervention studies (32). For ex-

ample, the incidence of spontaneous type 1 diabetes

(T1D) is directly correlated with the sanitary conditions

of animal facilities for non-obese diabetic (NOD) mice,

with a low infectious burden translating into a high

T1D incidence (37). Another study found that intranasal

exposure of pregnant mice to cowshed derived, non-

pathogenic bacterium, Acinetobacter lwoffii F78, pro-

tected against the development of experimental asthma

in their progeny (35). Other researchers have found

that in humans, intentional infection with the swine-

derived helminth, Trichuris suis, ameliorated symptoms

in patients with active Crohn’s disease as well as ulcera-

tive colitis (38, 39). Although research findings are not

yet determinative*for instance, helminth eradication has been found to increase atopic skin sensitization (40) while

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improving asthma symptoms in the same population

(41)*studies such as these suggest that approaching the broad trend of increased incidence of CIDs through an

evolutionary, historical lens generates findings that can

be directly applied to improving population health.

Research focused on determining which types of micro-

bial exposures exert a protective effect on developing

allergic sensitization (and when during the life course

they occur) can translate into improved identification of

risk factors, targeted strategies for disease prevention,

and foci for preventive medicine.

A practical outcome of this approach can be found

in the recent proposed ruling by the Food and Drug

Administration (FDA) that manufacturers of antibacter-

ial soap that contains the chemicals triclosan and

triclocarban demonstrate that they are safer and more

effective than soap and water. This ruling is the product

of years of mounting fears that the chemicals in the

soaps, the use of which has greatly proliferated in

the past few decades, may disrupt normal development

of the reproductive system and metabolism and promote

drug-resistant infections, among other issues (42). This

action reflects increasing public health awareness that

interfering with long-standing balances between humans

and nonpathogenic microbes in their environments can

have pervasive and substantial health effects.

Addressing the ancient determinants of emerging

infections

One of the chief advantages of an expanded framework

of epidemiologic transitions, such as that described by

Barrett and colleagues (1), is that it allows us to backtrace

the ultimate determinants of current infections over long

stretches of time back into ancient history and pre-

history. Looking back to the Neolithic period, changes

in subsistence, settlement, and social organization asso-

ciated with the agricultural revolution created condi-

tions that selected for the emergence of acute, epidemic,

‘crowd’ infectious diseases, such as smallpox, measles, and

pertussis, which were among the chief causes of human

morbidity and mortality up until the second epidemiologic

transition (1). Agriculture allowed for the production of

high calorie foods at the cost of dietary diversity (43), and

agricultural communities that rely heavily on a few crops

often experience compromised nutrition that can predis-

pose them to infection (44, 45). In addition, domesticated

animals served as the source for many novel human

pathogens, such as measles and smallpox, as did the

expansion of farmers into new terrain suited to the

transmission of diseases such as malaria and yellow fever

(46). The transition to large and densely settled societies

also allowed for the ongoing transmission of more virulent

and shorter-lived infections, such as crowd diseases, within

and between human populations. Furthermore, the crea-

tion of social hierarchies led to the unequal distribution of

basic resources for healthy living, thereby creating reser-

voirs for new and recurring infections in impoverished

communities (12, 47).

Today, we see the same themes of subsistence, settle-

ment, and social organization in the emergence and

reemergence of infectious diseases. The hyperurbaniza-

tion of domesticated animals, combined with the use of

antibiotic growth factors, is contributing to increases in

selective conditions for the entry of zoonotic pathogens

from animal to human populations (48). Human settle-

ments have consolidated to the point that the majority of

the global population now lives in urban environments

with ample opportunities for ongoing disease transmis-

sion (49). Additionally, globalization has linked the

health problems of impoverished communities with other

populations throughout the globe, in developed nations

and LMICs, such that humans can now be said to live

within a single infectious disease ecology. The scale and

speed of human activities have greatly increased since the

Neolithic, but the activities themselves are qualitatively

the same, despite the novelty of some pathogens.

This longer term, evolutionary perspective has prac-

tical applications for the prevention and control of new

and drug-resistant infections. Recognizing that the agri-

cultural revolution brought new infections into human

populations through increased exposure to animals,

researchers have examined the entry of novel contempor-

ary pathogens through human settlement in uninha-

bited wilderness areas and subsistence practices such as

bushmeat hunting. Research has shown that bushmeat

handling and consumption of nonhuman primates in

particular has provided a highly effective pathway for

the transmission of zoonotic pathogens into human popu-

lations, both in the past and today (50); several infections

in humans, such as HIV/AIDS (via simian immunodefi-

ciency viruses [SIVs]) (51) and a wide variety of human T-

lymphotropic viruses (HTLVs), which are associated with

leukemia, lymphoma, and HTLV-associated myelopathy

(52), have been linked to pathogens present in nonhuman

primates. Understanding these relationships and the

pathological consequences of interspecies interactions

has substantial implications for public health. As in

Neolithic times, the wide array of subsistence practices

that increase exposure between humans and animals

increases the probability that nonhuman pathogens will,

through a series of evolutionary steps, evolve the ability

to infect humans, and later, evolve the capacity for

sustained transmission within and between human po-

pulations. Recognizing these dynamics helps to inform

international economic and environmental policies and

technological innovations aimed at reducing these ex-

posures (53).

Similarly, researchers can extend lessons from the

intensification of agriculture to preventing, or at least

reducing, the evolution of drug resistant infections.

Evolution of disease

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The FDA announced in December 2013 that it was

phasing out the use of antibiotics as growth promoters in

cows, pigs, and chickens (54). Although this policy is

voluntary for drug manufacturers and subject to loop-

holes, it represents one of the first attempts by the

U.S. government to address one of the ultimate causes

driving the third epidemiologic transition, specifically

the reemergence of previously controlled infectious dis-

eases. The European Union has long since recognized

this issue, having passed regulations against the use of

human antibiotics, and their analogs, as growth factors

in livestock (55). This policy change and the research

underlying it reflect an increased understanding in the

public health community that drug resistance not only

poses a large and growing threat to human health but

that this trend fits into a larger evolutionary pattern

of selective relationships between humans, animals,

and pathogens. Ten thousand years ago, domestication

of animals gave human populations zoonotic infections;

in the present, our interactions with domesticated ani-

mals are conferring drug resistant infections upon us.

Conclusion: global health and the third

epidemiologic transition

The current trends of novel, virulent, and drug resistant

infectious diseases represent a third epidemiologic transi-

tion in human disease. To some extent, this can be seen

as a convergence of disease patterns associated with the

two previous epidemiologic transitions. As with the first

transition, physical and social environments are once

again selecting for the emergence and transmission of

acute epidemic infections in human populations. As with

the second transition, NCDs continue to rise in popula-

tions rich and poor, in developed nations and LMICs

alike. In a combination of these two trends, acute and

chronic diseases are leading to the emergence of global

syndemics, wherein the interaction between two or more

health conditions*such as among diabetes, cardiopul- monary disease, and severe acute respiratory syndrome

(SARS)*has a multiplicative rather than additive impact on human well-being (56).

An expanded theory of epidemiologic transitions not

only deepens understanding of these global health trends,

it also informs policies and programs for prevention,

detection of novel conditions and pathogens, identifica-

tion of risk factors, and control of diseases. Shifting from

primarily vertically organized programs aimed at single

diseases and proximate causes, epidemiologists and other

health researchers can develop complementary horizon-

tally based programs aimed at combinations of infectious

and chronic diseases and the upstream determinants that

they have in common. The U.S. Centers for Disease

Control and Prevention (CDC) has begun this process

with the merging of previously independent programs and

divisions within its organization (57). Similarly, observers

of polio eradication programs, for example, have increas-

ingly begun to recognize that treatment and vaccination

cannot be successfully implemented without addressing

broader health determinants, such as those responsible

for the first and second epidemiologic transitions (58).

Applying these lessons to current global health challenges

may not eradicate all human diseases, but it could

generate and marshal support for novel, broadscale

interventions that target the ultimate causes for many

important health conditions. At that point, we hope to

write optimistically about the positive lessons evident a

fourth epidemiologic transition.

Main findings

. Integrating epidemiological transition theory into theory and practice in epidemiology can

broaden epidemiologists’ understanding of the

complex, multiple dimensions of health and dis-

ease over time. This can reveal potential prox-

imate and ultimate causes, prevention strategies,

and predictions of future epidemiologic trends,

therefore contributing to improvements in popu-

lation health.

. Contextualizing disease trends, such as the in- creased incidence of chronic inflammatory dis-

eases (CIDs), including allergy and autoimmune

diseases, and the emergence and re-emergence of

infectious disease, within epidemiological transi-

tion theory, and attending to historical and

evolutionary relationships between humans and

pathogens, has direct implications for clinical

practice, global health policies, and future re-

search within epidemiology.

Key messages for action

. Situating the increased incidence of CIDs within the hygiene hypothesis, a theory closely asso-

ciated with epidemiologic transition theory

that ties the trend to immunoregulatory failure

brought on by decreased early life exposure to

non-pathogenic environmental microbes, can be

used to identify specific risk factors, targeted

strategies for disease prevention, and foci for

preventive medicine in global efforts to reduce

CIDs.

. Addressing the emergence and re-emergence of infectious disease, such as HIV/AIDs and

MDR-TB, within long term evolutionary per-

spectives can reveal the ancient and prehistoric

ultimate determinants of current infections,

generating practical applications for the preven-

tion and control of new and drug-resistant

infections, such as attendance to the role of

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Conflict of interest and funding

The authors have not received any funding or benefits from

industry or elsewhere to conduct this study.

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http://www.globalhealthaction.net/index.php/gha/article/view/23303

http://dx.doi.org/10.3402/gha.v7.23303

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Infectious Diseases in Ancient Populations.pdf

Infectious Diseases in Ancient Populations Author(s): T. Aidan Cockburn Source: Current Anthropology, Vol. 12, No. 1 (Feb., 1971), pp. 45-62 Published by: The University of Chicago Press on behalf of Wenner-Gren Foundation for Anthropological Research Stable URL: https://www.jstor.org/stable/2740635 Accessed: 05-04-2020 07:08 UTC

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Infectious Diseases in Ancient

Populations

by T. Aidan Cockburn

INFECTIOUS DISEASES RESULT from the interplay of three main factors: the host, the parasite, and the environment. The matter is highly complex, since each of these factors can vary in many ways and many differing diseases can result. In this review, attention will be concentrated on two of these factors: the prehuman and human hosts and their environments during the periods of man's evolution from the earliest days to the present. A number of distinct eras can be discerned: those of the primate precursors of man, early man, agricultural man, indus- trial man, and the man of one world. The first three of these eras will be discussed here.

THE PRECURSORS OF MAN

The Primates are assumed to have descended from an insectivorous mammal somewhat resembling the modern

tree shrew. This animal is presumed to have had certain parasites and infections, and it is further presumed that some of these parasites and infections still exist, perhaps in somewhat modified forms, in its descendants of today. However, its descendants are now scattered over the world and live in many varying ecologic niches (Cockburn 1963, 1967; Cameron 1956; Ruch 1959; Dunn 1966; Fiennes 1967). Some of the ancestral parasites and infections probably failed to survive in certain host genera or species, owing to the differing conditions under which their hosts lived. On the other hand, some host lines would acquire new parasites and infections after branching off from the main phylogenetic tree, and these would continue to exist only in their descendants. This process is depicted in Figure 1.

OLD WORLD NEW WORLD OTHER PRIMATES MONKEYS APES MAN MONKEYS

2~~~~~~~~~

E~~~~~~

S~~~

Q tExtinct ancestral primates A, B, C, D, E with

Extinct ancestral parasites a, b, c, d, e

Existing primates

withi Existing related parasites bl, c', d', el, e2

FIG. 1. Evolution of Primates and their parasites (from Cockburn 1967).

In a previous work (Cockburn 1963), I have suggested that this is the explanation of the distribution of many parasites and infections common to man and other primates. For example, no fewer than 13 of the intestinal protozoa of man are found also in apes and monkeys, according to the findings of Dobell (1926), Kessel (1928), and Hegner and Chu (1928, 1930). This is difficult to explain on any Lrounds except that these intestinal

T. AIDAN COCKBURN, now Medical-Dental Director of the Mayor's Committee for Human Resources Development in Detroit, Mich., U.S.A., was born in England in 1912 and became a citizen of the United States in 1954. He was educated at Durham (M.B., B.S., 1935; M{.D., 1937) and London (D.P.H., 1940). He has served as Assistant Superintendent of the London Zoo (1946-48); as Special Consultant in Epidemiology for the U.S. Public Health Service and as Chief of the Encephalitis Investigations Unit (1948-54); as District Health Officer in Pittsfield, Mass. (1954- 55); as a WHO advisor to the government of Ceylon (1956-57) and as an I.C.A. advisor in health to the government of East Pakistan and Executive Vice-Chairman of the Epidemic Control Committee, which vaccinated 30,000,000 people in six months (1958-60); as Visiting Fellow at Johns Hopkins University, Baltimore, Md. (1960-61); and as Assistant Health Commissioner in Cincinnati, Ohio, in charge of clinics for the poor and school health services (1961-66). He has published some 85 papers dealing with various aspects of public health, infectious diseases, and zoology. He is the author of The Evolution and Eradication of Injectious Diseases (Baltimore: Johns Hopkins University Press, 1963) and the editor of Infectious Diseases: Their Evolution and Eradication (Springfield, Ill.: Charles C Thomas, 1967).

The present paper, submitted for publication 17 i 68, is a summary of part of a synthesis contained in the latter book. Portions of that book are reprinted here with the permission of the publisher. The original synthesis covered the time span from the origin of life to the present; this summary is confined to the period from the appearance of man's immediate precursors up to historic times. It was sent for CA* treatment to 50 scholars, of whom the following responded with comments: Kenneth A. Bennett, D. K. Bhattacharya, Brunetto Chiarelli, Marie Striegal Clabeaux, W. C. Osman Hill, F. P. Lisowski, Ralph S. Riffenburgh, Calvin Wells. Srboliub Zivanovic.

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protozoa were passed down to all these primates from a common ancestor.

Similarly, of 34 genera of parasitic helminths in the Hominoidea, man is known to be host to 20, Pan to 26, Pongo to 13, and Hylobates to 14. Seven genera, Trichuris, Strongyloides, Oesophagostomum, Ascaris, Dipetalonema, Diro- filaria, and Bertiella, have been reported from all four primate genera (Dunn 1966). The distribution of hel- minths is apparently related to the phylogenetic relation- ships of the primate hosts.

Other parasites include lice of the genus Pediculus, malaria parasites, the scabies mite, Acarus, herpes virus, infectious hepatitis virus, and numerous other viruses now being discovered. The monkey equivalent (Herpes simiae) of the human herpes virus is now recognized as a common infection (Wood and Shimada 1954, Keble et al. 1958, Hull and Nash 1960). Infectious hepatitis virus has not yet been isolated with certainty, but the numerous instances of transmission of the disease from chimpanzees to their keepers leave little doubt that the infection is a natural one among chimpanzees (Hillis 1961, Com- municable Disease Center 1965). Reviews of the literature on parasites of primates have been made by Ruch (1959), Dunn (1966), and Fiennes (1967). A summary of the viral infections is given by Andrewes (1964). Reviews of malaria parasites in monkeys and apes have been given recently by Coatney (1968), Eyles (1963), Bruce-Chwatt (1965), Bray (1963), and Garnham (1963).

Syphilis and other treponematoses (cf. Hudson 1963, 1965)-among them yaws, pinta, bejel, irkinja, etc.- may also have come down from a nonhuman primate ancestor (see Cockburn 1959, 1961a, 1963, 1967). The possibility that apes and monkeys share these infections has been argued for more than 50 years. Trappers have reported that apes in nature have a yaws-like disease (Raven 1950). A yaws-type lesion called groundou is not uncommon in monkeys (Marchoux and Mesnil 1911, Mouquet 1929, 1930, Bouffard 1909, Secques 1929), although some authorities, such as Ruch (1959), have denied that the condition has anything to do with yaws. Fribourg-Blanc and his colleagues (1963; also personal communication, 1967) tested large samples of sera from many species of ape and monkey and found many of the African, but not the Asian, ones to be serologically positive. This finding was confirmed by the U.S. Public Health Service Communicable Disease Center (personal communication), which tested 220 chimpanzees and found 10% of them positive. The French workers went on to recover treponemes from the popliteal glands of baboons. This is strong evidence in support of the theory that the treponemal infections have existed in man and his ancestors for many millions of years, possibly as far back as the Miocene. Although syphilis itself was "discovered" only when Columbus reached the New World, other diseases of the treponemal group would have existed before then on both sides of the Atlantic.

It seems generally accepted that our original ancestors were tree-living creatures. The primates of South America maintained a strictly arboreal life. On the other hand, many Old World primates, including the ancestors of man, learned to live either partially or entirely on the ground.

This basic difference in ecology must have had a marked effect on the infections to which the primates

were exposed. A primate whose living is basically arboreal is exposed to arthropod vectors different from those on the ground. The most obvious of these is the type of mosquito which feeds and breeds in a canopy, unlike those feeding and breeding at ground level. Many more bloodsucking arthropods are found on the ground than in the treetops, the ticks and mites being notable

examples. Furthermore, a ground-living primate is very

much more exposed to parasitic infections from the feces of other animals. The water drunk by the monkey in a hole halfway up a tree is much more likely to be uncon- taminated than that of the streams and lakes used by terrestrial primates. Wherever there are water snails in

tropical Africa, there is always the hazard of Schistosoma infections; tree-living primates would not be faced with this hazard to anything approaching the same extent

(Paoli 1965, Nelson 1960, Strong et al. 1961). Incident- ally, the classic studies ofJane Goodall among the chim- panzees of East Africa have shown that these primates prefer to drink running water and avoid standing pools. This behavior could be important in reducing exposure to schistosomes. Mobility or lack of it can have substantial effects on the parasites and infections maintained in a "herd." Washburn (1965:89) has said that if we look at the behavior of all the nonhuman primates,

we find that these creatures are incredibly restricted in the area that they occupy. Only the gorilla and the baboon have ranges as great as fifteen square miles; while in the majority of the nonhuman primates, an animal spends virtually its entire life within two or three square miles-a tiny area.

Just when early man or his immediate ancestors adopted the roving way of life we do not know. However, it seems that by the time early man appeared in the Olduvai Gorge some 2,000,000 years ago, he was already mobile.

A static group of animals becomes infected over and over again with the same lines of parasites. Because the ground or trees on which they live are soon contaminated with their feces, there is an almost direct route from one intestine to another. There is little contact with other groups of animals, so that parasites are apt to develop high degrees of adaptation to their hosts. The mosquitoes and other arthropods that feed on these animals can breed nearby in the certainty of finding food with very little effort. This tends to produce strains of arthropods with preference for the particular host's blood and, in turn, infection and reinfection of the host by the same strains of parasites.

In a mobile animal like man, these factors disappear, and highly specific parasites have very little chance of surviving unless they can travel along with the man. On the other hand, a mobile band of primates would be exposed to numerous new infections in areas into which it migrated, and some of these might establish themselves as permanent infections. Perhaps the schistosomal infections of the Far East arose in this fashion. Primates leaving Africa would free themselves automatically from the schistosomes of that continent, since the snails needed for continued transmission would be missing in their new homes. On arriving in the Far East, however, they would encounter a new form already established in many kinds of animals and would come to be included in

its ecology. When man first evolved, he must have been a

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Cockburn: INFECTIOUS DISEASES IN ANCIENT POPULATIONS comparatively rare creature, living in small bands of not more than 200-300 persons. His total population in the world was probably no greater than that of the chimpan- zees today. It is obvious, therefore, that those specifically human infections which can live only by rapid trans- mission from one host to another and which do not form "carrier" associations could not have survived. This matter is discussed in more detail later; it will suffice to state here that it is improbable that infections like measles, smallpox, and mumps were present in those early days. However, one school of thought is of the opinion that very few viruses specific to man could have existed. This idea has been put forward most strongly by Burnet (1946: 30-31), who states:

It is generally considered that in the early stages of human evolution primitive man and his subhuman progenitors existed in small wandering groups of at most a few families, and that these groups only rarely came into contact one with the other. Under such circumstances it would be virtually impossible for a pathogen to evolve as a specifically human parasite unless, as is the case with herpes simplex, the period over which a person remained capable of transferring infection was of the order of a generation....

To return to the question of the specifically human virus disease: we have given reasons for believing that in the early phase of human existence, from the beginning of the Pleistocene up to about 10,000 years ago, infectious disease due to micro- organisms specifically adapted to the human species was almost nonexistent. The herpes virus could have persisted with very much its present type of activity, but the viruses producing brief infection with subsequent immunity-measles, mumps, and the like-could obviously not have survived in anything like their present form.

Much has happened in the field of primate virology since Burnet expressed his views. The adoption of tissue culture for the laboratory growth of viruses led in the 1950's to the development of vaccines against diseases like poliomyelitis. This in turn resulted in large-scale commercial production of these vaccines, often using substantial amounts of monkey and ape tissues. These procedures activated latent viruses in the tissues. It soon became apparent that monkeys and apes are hosts to a great many viruses, the existence of which had previously gone unsuspected.

For many types of naturally occurring virus infections in humans, there are equivalent ones to be found in nonhuman primates. Except for those transmitted by arthropods, the differences between monkey viruses and human ones are slight; indeed, when a human infection like polio virus is found in a primate colony, the first conclusion is that it was acquired from some human carrier. Our knowledge of infections in the wild is very incomplete; reports of isolations from animals in captivity must be handled with the greatest of caution (Andrewes 1964, Guilloud 1965, Hahon 1961, Hull et al. 1958, Strode 1951, Arbovirus studies in Bush-Bush Forest, Trinidad 1968, Bhatt et al. 1966).

Since monkeys and apes have many apparently specific viral infections today in spite of their compara- tively small numbers, the arguments of Burnet and his fellow thinkers become invalid. Therefore, emerging and early man may also have had many specific viral

infections, perhaps as many as the monkeys and apes of today.

EARLY MAN

Two million years ago, man was a terrestrial creature probably no more numerous than the chimpanzees of today. He lived in small bands and ate whatever he could gather, kill, or find as carrion. Some of his infections were those handed down from his nonhuman primate ancestor and were probably much the same as those of the apes of his time living in the same environment. These infections have been outlined above. Among the other infections to which he was exposed would be parasites acquired by eating raw many kinds of insects, fish, birds, and mammals. He would also have been vulnerable to what are today called the zoonoses, infections of other animals transmitted to man by ticks, mites, mosquitoes, and other biting arthropods. Two zoonoses that probably occurred are anthrax and botulism.

Anthrax might have occurred in early man under conditions similar to those I witnessed when, in 1943, I was asked by the Colonial Government of the Gold Coast (Ghana) to help in an outbreak of disease in a village in the Northern Territories. On arrival, I found the village quarantined by Africans from the neighboring villages armed with spears. No one was being allowed to leave. The disease was anthrax. The villagers had killed and eaten a cow with a sore on its leg, having decided that the lesion was a snake bite and the cow therefore fit to eat. Thirty-seven people were very ill, and all who had eaten the flesh died from the disastrous intestinal form of the disease.

Endemic Type E botulism is not uncommon among Eskimos and Indians today and presumably occurred in similar fashion in times past. Dolman (1964) lists 18 outbreaks, evenly divided between Alaska and Labrador, between the years 1945 and 1962, in which 52 cases and 28 deaths were caused by eating marine animals.

The chief vehicle in the Alaskan outbreaks was muktuk, an Eskimo delicacy prepared by cutting the skin and underlying blubber or flippers of a beluga into chunks or strips and hanging them on a rack or over a pole outdoors for some days to dry. These pieces are then cured several weeks, or even months, in the comparative warmth of a hut. Mouthful-sized pieces of the matured muktuk are sliced off as needed. Two or three such pieces have been known to kill a hardy Eskimo male. Deaths from botulism have also been attributed to the utjak of the Eskimos of Labrador, prepared by letting seal flippers stand in oil, usually near a stove, for several days, until the skin falls loose and they are ripe for consumption.

Dolman also lists numerous outbreaks from another kind of botulogenic vehicle, variously called "salmon-egg cheese" and "stink eggs," traditionally popular among the salmon-eating Indians of the Northwest Pacific Coast. This concoction, usually eaten raw, has been responsible since 1940 for 14 authenticated outbreaks of botulism involving 34 persons, of whom 19 died. During

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the 23 years since the condition was first recognized there have no doubt been many other unreported outbreaks. Previously an indeterminate number of botulinic deaths and serious illnesses were presumably attributed to accidental or homicidal poisoning.

Tuberculosis in the early days of man was most probably a zoonotic infection. Man may have had a

specific mycobacterial infection of his own, but this most likely was leprosy and was associated with his ancestors

over vast periods of time. (If this reasoning is correct, then the apes of today probably also have a form of

leprosy.) There are several forms of tuberculosis. The

human variety of Mycobacterium is associated chiefly with

pulmonary disease in urban populations long exposed to it and with an acute progressive glandular type in non- urban peoples such as Africans and Eskimos. The earliest evidence of human pulmonary tuberculosis comes from the writings of Hippocrates. The disease probably arose

after the invention of agriculture (Cockburn 1963). The bovine Mycobacterium is responsible for most of the bone lesions in man. The skeletons, pictures, and clay pots giving evidences of spine disease and hunchbacks in ancient times must reflect infections acquired from cattle via infected milk or flesh. Tuberculosis of avian origin can cause human tuberculosis much like that of the specific human type, but does not spread rapidly from person to person. It is not uncommon in the southern states of the U.S.A. today. Fish tuberculosis can cause skin lesions in man. Several outbreaks associated with swimming pools have been reported.

Modern theory states that Homo erectus wandered from his place of origin in Africa to most parts of the Eurasian land mass suitable for occupation. Climatic and geo- graphical conditions in his times were much different from those of today; for much of the time, large sections of the northern parts of the territory were covered with sheets of ice. On leaving Africa, man must have taken with him all those parasites that were transmitted directly person to person, leaving behind those that required vectors found only in Africa. These vectors would include those of filaria, schistosomiasis, trypano- somiasis, many arboviruses, mite- and tick-borne rick- ettsias and spirochaetes, and malaria. (Of course, we cannot say how the distribution of these vectors and intermediate hosts differed in those times from today.) Temperature would have been an important factor here. All vector-borne infections require certain temperatures for the extrinsic reproduction of the parasite. In the northern limits of the newly occupied territories it would have been too cold to permit the establishment of many infections.

On the other hand, H. erectus would have encountered many new infections in his new locations. The animals already established there would have had numerous forms of viruses, intestinal pathogens, and helminths that would be readily transmitted to him in a variety of ways.

The first men to arrive in Australia would have encountered a completely new fauna, the marsupials. The infections of these marsupials would have differed considerably from those of the animals of the Asian area, although unfortunately there is little data on this point. As a result, the zoonotic infections acquired through contact with the marsupials would have been kinds man had not nrevioiislv exnerienced.

It would be the same story in the Americas, with the first migrants moving down from the north into a strange land filled with unusual animals. At that time, prehistoric forms of elephants and bison still roamed the prairies, and increasing evidence is being found to show that man hunted them. As man moved south, conditions changed. In the southern continent of the Americas he found an extremely wide range of habitats, with conditions ranging from the almost impassable rain forest of the Amazon to the high mountains of the Andes, the dry coastal plains of Chile, the wide pampas of Argentina and the cold and bleak coasts of Patagonia. In each of these, the infections of the animals would differ, and so of course would those acquired by man. The biting insects of the rain forest would transmit new forms of arboviruses, protozoa, and filaria; in drier regions man would acquire new kinds of leishmaniasis and, perhaps most serious of all, the American form of trypanosomiasis carried by Triatomata.

AGRICULTURAL MAN

Agriculture probably had a more significant effect on man than any other factor from his first appearance to the present time of scientific revolution. For the first time, not only was there plenty of food for all, buit it was of a kind that could be stored for periods of shortage. Man lost his mobility and became tied to his land, and many animals moved into his ecological niche to be supported by him, willingly or otherwise. Population increased and spread, bringing long-separated human groups into contact. All of these changes, as well as the agricultural practices themselves, tended to increase certain infectious hazards.

Loss OF MOBILITY

Man's loss of mobility meant that various parasites could now establish themselves under conditions which per- mitted constant reinfection of the host. The hookworms and ascarids could maintain themselves in situations in which the host excreted the eggs and through reinfection became the unwilling host of the larvae that hatched from them. The stage was now set for the massive infections seen only too commonly in children in under- developed countries today. The feces of the family are either dropped indiscriminately around the home or in fixed places in the fields to which the members go daily. Either practice makes the continued transmission of the worms inevitable.

ADDITION OF ANIMALS TO ECOLOGICAL NICHE

The anchoring of man to his fields led many animals to move in beside him-not only those which he domesti- cated, such as the cow, pig, sheep, cat, dog, and goat, but many that were unwanted, such as the rat and mouse, English sparrow, tick, flea, and mosquito. So long as man was a wanderer, it would be rare for a mosquito to develop a preference for human blood; but now it was possible for mosquitoes to breed near man's home or even inside it and be close at hand for a meal when this was required. In this fashion special strains of Aedes

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Cockburn: INFECTIOUS DISEASES IN ANCIENT POPULATIONS aegypti and Anopheles gambiae would be selected, and they would be excellent transmitters of infections such as yellow fever, dengue, and malaria. Later, man tamed a number of birds, such as the pigeon, jungle fowl, and duck and goose, and they also joined the community centered on man's habitation.

Each of these animals would be infected with its own collection of pathogens. The community living in which they participated would ensure that these pathogens would be spread readily among all its members, and that man would receive samples of them all. Among them would be all the intestinal bacteria, protozoa, helminths, viruses, and, as well, most of the parasites from other tracts of the body. Many of these would be unable to invade him successfully, but occasionally one would succeed. If this one could be transmitted from man to man, then the stage would be set for the evolution of a new specifically human pathogen.

AGRICULTURAL PRACTICES

Agricultural practices by themselves do not create new infections, but some can accentuate those already present or convert what was previously an occasional event into a major hazard to health. The subject is a vast one that cannot be covered in this review. It includes the influences of draining swamps on malaria, of the slave trade in transmitting parasites like those of schistosomiasis and filaria from one region to another, of the slash-and-burn type of farming on malaria, of the cutting of under- growth on trypanosomiasis (which it helps to suppress), and of many other interesting practices. Here, only two main agricultural practices will be discussed: the use of feces and urine as fertilizer, and various techniques of irrigation.

Spreading feces among crops can be a hazardous business. The possibilities of contamination with patho- genic organisms by those handling the material is obvious, but even more serious are the chances of transmission by the food plants fertilized in this way. There are many instances of typhoid epidemics originat- ing in this fashion, and probably many helminths and cyst-forming bacteria are similarly spread. The eggs of Ascaris (roundworm) and the cysts of Entamoeba histolytica (amoebic dysentery) are especially suited for this form of transmission. People walking barefooted on ground so fertilized are particularly vulnerable to massive infection with hookworm. (The South Korean Government has just announced that it is taking action on the use of feces for fertilizer. About 75% of all feces in the country is used for this purpose; 90% of the population have intestinal parasites, the commonest being roundworms and whip- worms in that order.)

The Chinese have carried the use of excreta to the extreme, employing it not only on the ground but also to support the growth of plants and fish in artificial ponds or canals. Since numerous snails are present in such places, this practice has caused the population to be heavily infected with a number of flukes. These flukes were present in the areas before the immigration of man, the hosts being various carnivorous and herbivorous animals, and the intermediate transmitters snails and fish, crustaceans, or plants. On arrival in the Orient, man became involved incidentally by eating raw the infected

fish or crab or, in the case of the intestinal fluke Fascio- lopsis buski, certain bulbs or fruits of edible water plants. The development of fish and water-plant cultivation, using feces as nutrient, converted what was an incidental infection into a major one. In all instances, the eggs of the flukes are excreted in the feces; when these are added to the water, miracidia hatch out and invade the snails present; after an interval, cercariae leave the snails and then encyst in various ways according to the species of fluke. Fasciolopsis buski encyst on almost any aquatic plant, although caltrops, water hyacinths, water chest- nuts, and bamboos usually serve as "transfer hosts." As many as 1,000 metacercariae have been found on a single water chestnut; if kept moist, most will live for a year. Since it is common practice to peel off the outer skin of water chestnuts with the teeth, infection takes place readily. The mature flukes develop in the intestine within a month.

Clonorchis sinensis, the liver fluke, has much the same cycle except that the cercariae penetrate into fish and the cysts they form are swallowed when the fish is eaten raw or undercooked. Drying, salting, or pickling the fish does not necessarily kill the parasites. Paragonimus, the lung fluke, differs from the others merely by using various freshwater crustaceans for the third stage in its life cycle. Infection occurs when these are eaten raw or partially cooked. The fluke develops in the human lung and the eggs are passed out in the sputum, which is often swallowed so that the eggs appear in the feces.

The numbers of persons in the Orient with these flukes are enormous (some populations are 50% infected); much of this must be blamed on using feces in the water farming of fish and water plants.

Irrigation must have been invented very early in the history of agriculture. As far back as recorded history goes, the civilizations of the Chinese, Egyptians, Sumeri- ans, and Incas were based on the production of crops supported by irrigation. Unfortunately, the artificial use of water in this fashion tends to accentuate infections in the same way as does the use of feces, and here again, a fluke, as well as certain insects, is involved.

The liver fluke Schistosoma has a cycle of transmission involving snails, just as have the others mentioned above, but because the cercariae are capable of penetrating the unbroken human skin man is infected merely by wading or swimming in water containing them. The eggs are excreted in either the feces or the urine. The snails can live in vast numbers in irrigation canals and rivers; one of the world's greatest health problems today is the spread of these snails to new areas being opened up by the vast dam-building and irrigation projects of Asia and Africa. After malaria and tuberculosis, schistosomiasis is probably the greatest cause of morbidity in much of Asia and Africa and will become even more prominent after malaria disappears and as irrigation extends.

Insects readily take advantage of certain aspects of irrigation practices, especially where leaky canals and uneven fields form small ponds of water, or where large lakes are formed behind dams. Anopheles mosquitoes, which transmit malaria, can breed in either the small pools or the lakes, according to the species. Because of this, malaria became a maior problem in the artificial

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lakes of the Tennessee Valley Authority, and may have been a factor in the breakdown of the old civilization in Ceylon. Culex mosquitoes transmit filaria and viruses; they are often directly linked with irrigation. For ex- ample, Culex tarsalis in irrigated areas of the Missouri River Valley Authority has caused several epidemics of encephalitis.

INCREASE IN POPULATION

In previous works (Cockburn 1963, 1967) I have theo- rized that many infections require minimum host populations for permanent maintenance; if the sizes fall below the threshold levels, the infections die out. There are many diseases in which the infectious stages are brief and which exist entirely by rapid transmission of the agents from one host to another. Obviously, this can only happen if large numbers of susceptibles are present to support such a chain of transmission. These have been referred to as the acute community infections; they include rubella, cholera, smallpox, mumps, measles, and chickenpox. Such infections are specific to man and have no animal host other than man.

The effect of the introduction of an acute infection, measles, into a small isolated community was first studied in 1846 by Panum, who collected much valuable data about disease in a small "herd" on the Faroe Islands off the northern coast of Scotland (Panum 1940). In 1846, when the population was 7,782, measles was imported and attacked nearly everyone. A previous epidemic in 1781 had also infected almost the whole population, but the disease had disappeared completely afterward, presumably owing to a lack of susceptible persons. This also happened in 1846, for, after some 95% of the population had been attacked, the disease dis- appeared again.

Even pathogens that can live in their hosts like commensals for months find it difficult to survive if the population is too small. Bodian (1955) has said that poliomyelitis dies out in small communities after a certain time, even though carriers can excrete the virus for many months.

In a very small population with few susceptible persons, the survival of a pathogen depends on its ability to exist until new hosts appear. Natural selection will, therefore, favor those pathogens that can live in a kind of commensal relationship with their hosts and those that can continue to live away from their hosts. In a small population there would be no infections like measles, which spreads rapidly and immunizes a majority of the population in one epidenlic, but many like typhoid, amoebic dysentery, pinta, trachoma, or leprosy, in which the host remains infective for long periods of time, and many like malaria, filaria and schistosomiasis, where the infection not only persists in the host for a long time but also has an outside vector or intermediate host to serve as an additional reservoir.

There is little in the way of precise and well-docu- mented data on the infections of small groups of people living in isolation under the conditions of a hunting economy. It is difficult now to find such groups that have not already been infected through contact with larger civilizations. About three decades ago, what was known about the infections of the Australian Aborigines was

written up by Cleland (1928) and Basedow (1932); more recently, Mann (1957) has published work on the eye infections of these people. As would be expected, most of the infections reported are of a chronic nature, such as trachoma, malaria, irkinja (a form of yaws), and roundworms. However, the influence of colonists and traders is obvious. An outbreak of smallpox, for instance, was reported within a year of the first British settlement. It was said to have crossed the continent from the north and possibly was started by Malay fishermen. Even trachoma may have been introduced by the white traders, as maintained by Mann (1957), although others think that Dampier, the first British explorer to reach Australia, was indeed describing the disease when he said that the natives had to throw their heads back to see straight ahead. Tuberculosis is apparently found pre- dominantly only among those natives living in close association with white men.

In Africa, Jeiliffe and his colleagues (1962) have studied the infections of the Hadza, a hunting people of northern Tanganyika, about 800 in number, who live an isolated life in the tsetse area of the savannah. The Hadza are very mobile, especially in wet weather. They eat almost anything they can get, including baboon, vulture, and hyena, but not tortoises. The food is usually barbe- cued. An examination of 62 children showed them to be well nourished and with good teeth. Malaria parasites were present in 27%. In the stools, four children had Taenia, probably from the wart hogs they ate, and three had Giardia. Conjunctivitis was found in 30%; many had ringworm. There was no roundworm or hookworm, presumably because constant moving prevented trans- mission. In other words, the only infection found was that which could survive in a small population always on the move. Other infections, such as measles, rubella, and chickenpox, come to the Hadza only as introduced infections from populations large enough to support them on a permanent basis.

Neel and his associates (1964, 1968) have reported on the infections of the Xavante Indians of Brazil. Tests for various zoonoses were positive. No evidence was found of tuberculosis or treponemal infections, but there were high percentages of positives for measles, poliomyelitis, whooping cough, and malaria. Unfortunately, these tribes are not completely isolated from the outside world. They had trade articles; these articles could reach them, so could infectious diseases. The only form of trade that cuts transmission to a minimum is that of "silent barter," where the persons exchanging goods do not come into personal contact with each other. Even in that instance, smallpox can be passed on through infected blankets, as some tribes of North American Indians found to their sorrow.

The matter of population size leads us to the interesting question of the threshold sizes needed to support the acute community infections. If some 7,000 people on the Faroes were not enough to support measles indefinitely, would it have taken ten times, 100 times, or even 1,000 times that number of people?

In Greenland today, the population is about 30,000 people; until recently it was very isolated. Measles was unknown until 1951, when a sailor brought the infection from Denmark. As a result, practically every susceptible person in the area contracted the disease, and then it

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Cockburn: INFECTIOUS DISEASES IN ANCIENT POPULATIONS disappeared. The same pattern was repeated in 1955, in 1959, and in 1962. The spread of the infection was greatly speeded by the introduction of air travel (Bech 1962, 1965). Presumably the population was too small to support the disease indefinitely.

It is obvious that in large urban populations, such as Greater New York City and Greater London, with their many millions of people, measles can continue to exist throughout the year without breaking the chain of transmission. The threshold size for measles must, therefore, be somewhere between the populations of the Faroe Islands and Greenland and those of Greater New York City and London.

The population of the city of Cincinnati is 503,000 people. If the suburbs and neighboring towns are included, there are a little over a million people living in Greater Cincinnati. In 1961, the City Health Depart- ment was invited to take part in a program to test measles vaccine organized by the United States Public Health Service Communicable Disease Center, and for the next two years a careful watch was kept on the measles situation in the city (Guinee et al. 1963). More than 1,000 children took part in an experiment in which one-half were given vaccine and one-half were given placebos. The surveillance programs showed that measles could not be detected for two approximately four-month periods in the latter parts of the years 1962 and 1964. During those periods, cases reported to the Health Department by physicians proved either not to be measles or to have been acquired outside the city. As far as could be determined, the findings support the concept that the chain of transmission during the four- month periods of 1962 and 1964 had, in fact, failed. It may be that in an American city like Cincinnati a population of about 1,000,000 is near the threshold required to support measles as an endemic infection (Cockburn 1967).

Populations of the size needed to support the acute community infections did not exist on earth until the agricultural revolution had progressed to a substantial degree. These infections could not have evolved earlier than 10,000 years ago. Even for some time after this, no single community had a population of a million people. In Iran, India, and China, however, there were civiliza- tions with cities of 100,000-200,000 people, and if a half-dozen of these cities were linked closely enough by trade to permit the interchange of infections, the stage would be set for the acute community infections. that these infections did originate in the Old World seems quite clear. When intercontinental travel opened up the world to the interchange of infections, the natives of the newly discovered areas reacted very severely to the acute community infections of the newcomers, thus demon- strating that they had not experienced them before (Hirsch 1883, Cummins 1939, Stearn and Stearn 1945, Ashburn 1947).

In several instances the sources of the organisms causing these new diseases probably were the animals living in close relationship with man. For example, smallpox virus is very similar to a range of viruses found in domestic animals, the closest being cowpox virus (Hahon 1961); measles virus belongs to a group contain- ing dog distemper and the cattle rindepest viruses; influenza virus is very closely related to viruses found in

domestic animals, particularly that of the hog (Andrewes 1964, Meenan et al. 1962, Kilbourne 1968).

DIFFERENTIAL RESISTANCE TO INFECTION

There are three main types of resistance to infection: active immunity, in which the body reacts specifically against the invader; passive immunity, in which anti- bodies are passed from the mother to the offspring via the placenta, milk, or egg; and genetically inherited nonspecific resistance. The existence of this latter kind of immunity, which will be discussed here, cannot be doubted although its mode of action is usually obscure.

Infection with a pathogen reduces the survival capacity of the host and, all other factors being equal, the host with the most resistance is the one most likely to survive. If this resistance is inherited, then natural selection can be expected to produce a population more and more resistant to the prevalent pathogens, and in time a benign host-pathogen relationship will be established. The earliest expression of this idea I can find is contained in a paper read by W. C. Wells (cited in Darwin 1906) before the Royal Society in 1813, entitled "An Account of a White Female, Part of Whose Skin Resembles That of a Negro." In this paper Wells distinctly recognized the principle of natural selection. He stated that of the accidental varieties of man which occurred among the first few and scattered inhabitants of the middle regions of Africa, one would have been better fitted than the others to withstand the diseases of the country. This race would consequently have multiplied; the others would have decreased, not only because of their inability to sustain the attacks of the disease, but also because of their incapacity to contend with their more vigorous neighbors (cf. Darwin 1858).

The existence of genetically controlled nonspecific resistance has been debated for more than 30 years, chiefly by two teams of workers, one in the United States led by Webster and another in England under Topley and Greenwood. Webster (1932) showed that mice varied in their resistance to infection with Salmonella species and that selective breeding would produce, from a strain of mice 40% susceptible to infection, strains that were either 10% or 90% susceptible. In 1935, Greenwood and his colleagues summarized their results by saying that the available experimental data appeared to have established quite clearly the existence of significant differences in resistance among strains of mice or rats. Even in the most favorable records, however, there was no instance in which animals of the selected strain were uniformly resistant to bacterial infection, even when the test was resistance to infection by the bacteria concerned in a dose that failed to kill 100% of the unselected controls.

This deficiency was made up by Sabin in 1952, when he found a strain of mice 100% resistant to a dosage of yellow fever virus that was 100% lethal to standard test animals. By cross-mating on Mendelian lines, he was able to show that this resistance was inherited according to Mendel's laws.

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These reports have been paralleled by similar findings for mouse typhoid (Gowan 1948, 1951), the rat cysti- cercus disease due to the cat tapeworm, Taenia taeniae- formis (Curtis et al. 1933), and, at the cellular level, virus infections (Morgan 1960).

The natural selection pressures from infectious diseases can be very intense. Perhaps the best-known instance is the relationship between malaria and the abnormal hemoglobin that causes sickle cell anemia. The latter condition can be lethal, but the abnormal hemoglobin has the big advantage of protecting against the much more serious malaria. Natural selection in malarious areas has apparently favored persons with the sickle cell trait (Allison 1954, Livingstone 1967). Yellow fever is frequently lethal to Europeans, but the African living in an endemic area is naturally immune (Strode 1951). Conversely, the European is little affected by measles, but populations not previously exposed react disastrously when the virus reaches them. The introduction of measles to America by the Spaniards caused many catastrophes among the Indians.

The intensity of the pressures can be illustrated by the example of smallpox. Until the end of the 18th century, practically everyone in Europe was infected sooner or later. In London about 1750, where half those born were dead before the age of three, there were nearly 20,000 cases of smallpox a year. Haygarth (1793) quotes French estimates that one in ten of all children born died of smallpox and cites Baron Dimsdale, Dr. Percival, and other authorities as stating that the proportion of births

to deaths from smallpox in London was 61 to one, in Manchester 61 to one, in Liverpool 5 1 to one, and in Chester 6 to one.

The contrast between the reactions to smallpox of Europeans and American Indians became evident early in the colonization of the New World. Diaz (1956) wrote:

Let us return now to Narvaez and a Negro he brought with him who was full of smallpox, and a very black dose it was for New Spain, for it was because of him that the whole country was stricken, with a great many deaths. According to what the Indians said, they had never had such a disease, and as they did not understand it, they washed themselves very often, and because of that, a great number of them died, so that black as was the luck of Narvaez, still blacker was the death of so many people who were not Christians.

The Spaniards were not affected, for they already had had the disease in childhood. So common was it in Spain to suffer from smallpox as a child that Ruy Diaz de Isla remarks that he knew a man that did not have it until his 20th year.

Perhaps more significant was the experience in Massachusetts. When smallpox broke out in the Plymouth Plantation, many were ill but only 20 persons died, including both young and aged. Yet when the Indians of Connecticut were attacked by the disease, nearly all died. Although the English nursed the sick Indians, none caught the disease (Bradford 1928).

That infectious disease can cause severe damage in a population not previously exposed to it has often been documented. This has led many workers to wonder if some of the ancient civilizations might not have been destroyed in this way. Did malaria lead to the downfall

of the Singhalese cultures of 2,000 years ago? Could some infection have been the factor that caused the abandonment of the cities of central Mexico, or the dwellings of Mesa Verde?

Undoubtedly, some local groups or tribes have been brought to near extinction by some pathogens. In addition to the instances involving smallpox just mentioned, one thinks of the reports that whole tribes of Eskimos died in Alaska during the influenza pandemic of 1918-19 and the estimate that worldwide the disease killed 20,000,000 people in that period. Again, sleeping sickness depopulated wide areas of Africa; malaria was so severe in such localities as the Pontine marshes near Rome and parts of northern Greece that only a handful of people could live there; and the Black Death is judged to have killed a quarter of the population of Europe.

One must conclude, nevertheless, that so far there is no good evidence that any single significant culture was ever wiped out by an infection. In theorizing on any particular culture in this fashion, one must ask what specific organism could have produced the disaster. As far as America is concerned, it is my opinion that the acute community infections, such as measles and smallpox, and possibly malaria and yellow fever as well, were intro- duced after the Spanish invasion. I cannot think of any infection present before that time in the Americas that could have caused morbidity or mortality of such a scale as to result in the breakdown of a civilization. It is a different story in other parts of the world; but even the Black Death or the epidemic in Athens described by Thucydides (MacArthur 1958) did not destroy the civilizations in question. The matter remains an interest- ing speculation, but nothing more.

STUDIES OF THE EVIDENCE

The importance of infectious disease in the control of populations demands that more attention be given to the study of the available evidence of infections in early man. This evidence is found primarily in fossil specimens, but ancient writings (Hoeppli 1959, Guerra 1964), paintings, sculptures, poetry, pottery, and folklore are also valuable sources. Analogies can often be made between primitive societies existing today and those of the past; such analogies lead to a better understanding of the societies of early man. Cultural patterns, civilizations, trade routes, occupations, and population sizes are all of great importance. Geographic alterations, climatic fluctuation, and changing distributions of insect vectors must all be considered.

Paleopathology came of age in Egypt in the first decade of the 20th century. At that time, a new dam at Aswan threatened a number of sites of great importance in archaeology, so several investigators attempted to study the materials available before they were lost to science. Over 8,000 mummies were examined and much patho- logy found (Ruffer 1921, Ruffer and Smith 1910, Ruffer and Ferguson 191 1, Smith and Wood Jones 1910, Morse, Brothwell, and Ucko 1964). It was found possible to reconstitute, to a surprising extent, the preserved soft tissue of the mummies and to demonstrate bacteria in sections of skin and various organs. such as the lungs and kidneys. The finds included a mummy with the calcified

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Cockburn: INFECTIOUS DISEASES IN ANCIENT POPULATIONS eggs of Schistosoma haematobium in the kidney, one with a psoas abscess, another with poliomyelitis, and yet another with possible smallpox lesions.

Since then, a great deal of work has been done in the field of the study of ancient human remains for evidence of disease; for reviews of the literature, see Moodie (1923), Pales (1930), Sigerist (1951), Brothwell (1961), Goldstein (1963), Ackerknecht (1965), Jarcho (1966), and Brothwell and Sandison (1967). There are still, however, sharp limitations to investigations of human relicts. Usually, only diseases that produce pathologies in bones can be recognized. This limits diagnoses of infec- tious diseases to those that produce abscesses in bones or deformities such as those resulting from leprosy, tuber- culosis, and syphilis.

The soft tissues are of most importance in infectious diseases, and all relicts with any tissues still attached demand special attention. The largest groups are still, of course, the Egyptian mummies; these deserve a fresh study using methods that were not available to Ruffer and his colleagues. Their pioneering attempts to reconstitute the soft tissues need reexamining, and better techniques should be devised. The reports that bacteria could be seen in sections should be followed up and attempts made to identify the bacteria. If chickenpox, warts, or molluscum contagiosum existed in those days the scars might be detectable on the skin. Guinea worm was almost certainly present (was it the fiery serpent that afflicted the Israelites in the desert?) and would leave a scar on the ankle. The eggs of lice might still be present on the hairs of the head and pubis. Containers in which intestines were preserved should be examined for parasite eggs or cysts.

A naturally mummified body of 4,000 years ago found in a cave in South America has provided the earliest record of head lice infestation (Ewing 1926); some of the hair was still attached, and on it were found head louse eggs. An even more valuable find was the frozen body of an Inca child found in a small stone building on a mountain near Santiago, Chile at an elevation of 17,658 ft. It was a remarkably well-preserved specimen, estimated as that of a boy eight-to-nine-years old who had died about 450 years ago. Contents of the rectum showed numerous eggs of Trichuris trichiura and some unidentifiable cysts of Entamoeba (Pizzi and Schenone 1955).

A very exciting find has been made in Russia (Arta- monov 1965). In the Altai mountains of that country, many tombs of the Scythians, dating back to 400 B.C., have been excavated, revealing the frozen bodies of the people and their horses. Apparently, the Scythians dug deep holes in the ground for burials and then roofed the tombs with tree trunks. Since the ground was very cold, the water of condensation which fell on the bodies froze, encasing everything in the tomb in ice. Some of the bodies are in an excellent state of preservation and appear as though buried just yesterday. According to Artamonov (personal communication), no medical inspections of the bodies have yet been made, but obviously all kinds of skin lesions should be discernible, and possibly parasites and their eggs may still exist in the parts of alimentary tracts that remain.

The single most promising field at the moment is the study of ancient feces. Very large quantities are available

already, and if special attention were given in the field much more would be forthcoming. The few studies of ancient feces already reported make it quite clear that parasite eggs in good condition can survive in recog- nizable forms for long periods of time.

Biddle (1967:58) gives this account of British studies:

The best work so far comes from Winchester, England. The first report of parasite eggs from archaeological excavations in Britain is that of Taylor (1955) who identified large numbers of eggs of the nematodes Ascaris lumbricoides and Trichuris trichiura and of the fluke Dicrocoelium dendriticum (Rud.) in soil from a medieval timber-lined cess-pit on a site at Middle Brook Street, Winchester. In 1964 Dr. H. H. Williams and Mr. A. W. Pike of the Commonwealth Bureau of Helminthology, alerted by Taylor's publication, took further samples from a second pit in Lower Brook Street (Biddle 1965, pp. 245-46). Pike has since confirmed the presence of very large numbers of helminth eggs of the nematodes Ascaris sp., Trichuris sp. and of the fluke Dicrocoelium dendriticum at average concentrations of 450, 2,300, and 216 per gramme respectively (Biddle and Pike 1966). All the eggs were in an excellent state of preservation and were easily recognizable, even the remains of the embryo within the egg being visible.

The possibility of eggs surviving in recognizable forms for perhaps thousands of years is suggested by a study on fecal material of about 3000 B.C. from Peru. The speci- mens consisted of coprolites that had been rapidly desiccated in the very dry atmosphere and material collected from the abdomen of a skeleton. The specimens were soaked in a 0.5% aqueous solution of sodium triphosphate for 72 hours, after which the mixture was sedimented and the precipitate examined for food particles and parasite eggs. The procedure was so successful that remains of plant and sea foods could be readily recognized and sometimes even the smell was recreated. The eggs of a species of Diphyllobothrium, a fish tapeworm common in carnivores in South America today, were identified (Callen and Cameron 1960).

Samuels (1965) examined feces from the Wetherill Mesa cliff dwellings. In one specimen he discovered eggs of the pinworm Enterobius vermicularis in which the larvae inside were still clearly visible. Numerous microorganisms were seen but not identified.

The statistical analysis of adequate samples of relicts could be a powerful tool in studies of diseases of ancient populations. As long ago as 1910, Smith and Wood Jones showed that the distribution of fractures in Nubian skeletons differed from that of a modern population. More recently, Brothwell (1961) has shown a probable difference in the susceptibility to osteoarthritis in modern and ancient British populations. The time has come when it is no longer enough for a fieldworker to select a few bones with obvious pathologies and ignore the remainder. Fortunately, several workers have undertaken the tedious procedure of examining all the material of a find with significant results. According to Roney (1959), Hooton's (1930) work on the Indians of Pecos Pueblo is an early example of the statistical study of evidences of disease in ancient populations. Roney also cites the work of Krogman (1940), Angel (1946), Goldstein (1957), Vallois (1937), and Todd (1927). More recent works

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along these lines are those of Angel (1967), Roney (1959) on a California archaeological site, Warwick (1964) at a Roman cemetery in York, England, and Anderson (1963) in an Iroquois ossuary. In all of these studies, there is emphasis upon studying a population sample, upon relating disease to age, sex, and race, upon observing

temporal trends in disease, and upon relating disease to cultural factors. This approach is, therefore, epidemio- logical, concerning itself with host and environmental factors in diseases occurring in a population sample.

SUMMARY

1. Most of man's specific infections are descended from those acquired from his prehuman ancestors. The apes and monkeys of today received infections from the same source.

2. Every change in the environment or culture is reflected in the patterns of the infectious diseases of the population. In particular, the invention of agriculture and the domestication of animals had a profound effect on the evolution of new infections and increased hazards from some previously existing ones.

3. Natural selection results in a population increasingly resistant to a pathogen to which it is exposed; and a population exposed to a pathogen to which it has not developed resistance may be devastated by it.

4. Since infectious disease is one of the most important factors controlling populations, more attention should be given to the study of the available evidence of infections of early man. The soft tissues that are found in a state of preservation are of particular importance, and special care should be taken of them. Feces are also an important and promising source of information. A statistical ap- proach to the evidence of disease in ancient populations can be a powerful research tool.

Comments

by KENNETH A. BENNETTr*

Eugene, Ore., U.S.A. 7 II 70

A close examination of Cockburn's "References Cited" section indicates that roughly half of the entries were published within the last decade. This could mean a number of different things, one of which undoubtedly reflects the current popularity of paleopathological studies. The lack of material evidence, however, or of any kind of rigorous methodology has not, so far as I have been able to determine, deterred either physical anthropologists or physicians from publishing their conclusions. Often these conclusions are derived by guess- work and as such are neither facts nor even working hypotheses. In this sense, it seems to me of doubtful value to speculate on the existence of anthrax or botulism in early man when we have absolutely no evidence and little hope of ever getting any. As an aside, it may be indicative of some degree of confusion in paleopathological studies to point out that Sterne and Van Heyningen (1958: 359) begin their discussion of botulism by stating "Botulism is not an infectious disease...."

It is difficult to either agree or disagree with the majority of Cockburn's con- clusions, as for the most part they have little basis in fact. It may be, for example, that specific human infections have descended from nonhuman primate an- cestors, but the mere observation that we presently share a number of intestinal protozoa with apes and monkeys is hardly the evidential support needed for the first statement in the "Summary." It may also be that man "adopted the

roving way of life" sometime prior to the advent of agriculture, but I suspect that the basis for this argument has all too often rested on the false assumption that mobility is synonymous with adap- tive radiation. As long as there is no evidence to influence us one way or the other, it appears to me that one could argue equally as well that this very mobility, even in groups numbering 200 to 300, could have been responsible for the maintenance of some acute com- munity infections.

My main criticism of this paper, if indeed it can be termed a criticism, is that not enough attention has been given to the potential importance of the phenomenon that Pimentel (1961) calls "genetic feedback." Cockburn does state in the "Summary" that disease is an important controlling factor in human populations, but there is only a vague comment or two on how it functions as one of these factors. To understand disease and its effects on the density of early human populations, we must know as much about the pathogen as we do about the deleterious results it may induce.

One of the better examples of a parasite-host system that has been investigated in depth and illustrates the principles of genetic feedback is the well- known relationship between the myxoma- tosis virus and the wild European rabbit in Australia. The virus, introduced by man into the wild rabbit population in an attempt to control their numbers, caused in the first epizootic a mortality rate of 97 to 99% (Fenner 1953). Not all cases were fatal, however, and in succeeding epizootics the mortality rate underwent a sharp decline (85 to 95% in the second, and 40 to 60% in the third). Part of the explanation for this

decrease may be found in the fact that natural selection favors those rabbits with increased resistance, but an equally important reason involves selection for comparatively nonvirulent strains of the myxomatosis virus. For obvious reasons, the selective premium will not be on the parasite or pathogen that quickly kills his host, but instead on the one that can live within his host without altering appreciably the reproductive potential of the latter. This results in a situation whereby the density of both populations may be controlled by an alternate functioning of the feedback of selection and genetic change (Pimentel 1968).

It has been demonstrated that a number of human diseases (e.g., small- pox and bubonic plague) have, through repeated infections in different genera- tions, lost some of their virulence in certain populations. To claim now that this has been due to genetic feedback would be unfortunately premature, but research in this area promises to shed additional light on the effects of disease in the human ecosystem.

by D. K. BHATTACHARYA*

Delhi, India. 31 I 70

Cockburn's essay was entirely instructive for me, especially because in India studies of this kind have so far not been ventured. The promising results already obtained through coprolite analysis elsewhere make it apparent how much significant material has been lost from Indian sites. The suddenness of the decline of the Indus Valley complex has led many to believe that some such disaster as an earthquake, flood, mass massacre, or famine may have been the reason for that decline, but we have no

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Cockburn: INFECTIOUS DISEASES IN ANCIENT POPULATIONS conclusive proof for any of these possible causes. An analysis of the coprolites from these sites could definitely show whether an epidemic was an added factor, perhaps acting in combination with others. After all, with the amount of trade evidenced for this civilization, the importation of a deadly parasite to which the community was not resistant is not difficult to visualize. The Neo-

Chalcolithic ash mounds in Andhra and other burials found in Adittanalur, T. Narsipur, Maski, Chandoli, Nevasa, Tekkalkota, and Langhraj also had a strong potentiality for yielding palaeo- pathological information for this sub- continent. Unfortunately, nothing towards this end has so far been attemp- ted. The need for such study becomes the more evident if we look into the finds of parasitic helminths and protozoa in contemporary hunting and gathering people (Dunn 1968). That there is an increase in the complexity of the eco- system and a simultaneous increase in the number of species of parasites found in a population in the tropical high- rainfall area has already been demon- strated. This discovery makes it seem probable that various kinds of infections will be found among the early Holocene populations of India (Pleistocene man being as yet unknown in this country).

To Cockburn's theorization as to the possible descent of many specific infec- tions of man from his prehuman ancestors, there should be, I think, no particular objection. Here only a note of warning needs to be added. The evolution of man definitelydoes not occurin a simple unilinear succession, as would appear from the author's Figure 1. Further, up to this time it has not been conclusively demonstrated that such an evolution took place at one and only one place in the world, though there is a strong suggestion that Africa may have had that honour. A consideration of all the possibilities will land us in a situation of extreme complexity and diversity of ecosystems. Further, these numerous stages of subhuman types developed different social habits regarding migra- tion and habitat. In other words, the ancestral parasitic type e need not give rise to the same parasitic type e' in both present-day man and apes, as the author himself points out in referring to the development of different strains of arthropods with a preference for the same kind of host. Thus, it would be more apt to assume that parasite e gives rise to parasites e' in present-day apes and

f in present-day man, where f is more similar to e' than to e. This slight change can take care of the multiple significant factors which took part in the evolution of the human form, with fission and fusion at various periods of its develop- mental past.

by BRUNETTO CHIARELLI*

Torino, Italy. 30 i 70

Cockburn's paper is the most complete one I know of on this subject. Of particular interest is the author's appli- cation of the comparative approach to the problem of infectious diseases in ancient populations, including the infectability of different species of primates. This point of view, occasionally found in recent papers, provides the field with a new and broader perspective. The problems of the infectability of existing

small human populations and the relationship between the population and the epidemiological environment are very interesting and clearly exhibited. Therefore, I consider this paper an important stepping stone in the study of the evolution of human populations, and a useful document to be considered in every attempt at prehistoric recon- struction.

I should like to add a few remarks and suggest that some arguments be clarified and further developed.

Among the numerous and proper references related to the parasitism of primates and man, I would suggest the inclusion of the papers of Kuhn (1968).

In the text that refers to Figure 1, I would like to see the differences between Hylobates and Pongo pointed out. Such differences with regard to parasitism seem to be yet another contribution to the recent evidence tending neatly to separate the Hyloba- tidae from the Hominoidea (Von Koenigswald 1967, 1968; Ankel 1965; Chiarelli 1968).

With reference to hereditary resistance to infections, I think that it would have been useful and important to stress the recent knowledge on the resistance to malaria due not only to the haemoglo- binopathies, but also to variations of the red cell enzymes. Such variations are typical not only of man, but also of various species of primates that lived in the same environment as man. Of special interest is the work of Crawford, Morrow, and Motulsky (1967), who have found that the chimpanzee and the gorilla, both parasitized by Plasmodium falciparum riechenowii, also maintain a polymorphism at the G6PD locus, indistinguishable from the sex-linked A and B electrophoretic phenotypes in African populations. Inhabiting similar environments (equatorial Africa) and faced with the same disease vector (Plasmodiumfalciparum), man and the two anthropoid apes may have solved this high-mortality problem similarly. Although there is no G6PD deficiency in any primate species tested to date

(Crawford et al. 1967), this polymorphism may be maintained through selection

operating upon the differential enzyme levels, with type A being 15-20% less

active than type B (Pik et al. 1960). For further palaeopathological and

palaeoepidemiological data on the

ancient Egyptian populations, I suggest that Cockburn get in contact with Dr. Merton Satinoff (Gibson Laboratories,

Radcliffe Infirmary, Oxford, England) and Dr. Emma Rabino Massa (Isituto di Antropologia, via Accademia Albertina 17, Torino, Italy), who are doing research in this field.

by MARIE STRIEGEL CLABEAUX*

Bronx, N.Y., U.S.A. 9 ii 70

Cockburn presents a cogent explanation of the origins and development of infectious diseases in human populations. Their history is reconstructed in a manner most logical and consistent with the available information on both human evolution and human and nonhuman primate disease. Such an undertaking must necessarily be limited, since it is not possible to determine the nature of all those pathologic conditions which have affected man. One could question the utilization of material derived from living populations to extrapolate the presence of the same diseases in the remote past, since pathogens and the manifestations of their activities in the body do change in time. However, since there are diseases common to all of the higher primates (indicative, as the author notes, of transmission from a common ancestor) and since at this time there are virtually no other avenues of approach to the problem, this must be considered a valid methodology.

The field of paleopathology could play a more vital role in the endeavor to trace the history of disease if its practi- tioners were more population-oriented. The description of a few spectacularly pathological specimens in a collection that may include hundreds of individuals is of little use unless that description is accompanied by complete demographic data. The simple tabulation of deaths by age, sex, and, where possible, relative time of occurrence may provide clues to events such as epidemics, severe childhood diseases, etc. and may also aid in interpreting cultural material. The contribution that epidemiologic paleopathology can make cannot be too strongly emphasized.

Those who work with skeletal popula- tions should pay special heed to Cock- bum's concluding remarks. The statisti- cally oriented epidemiologic approach which combines a total health profile

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(insofar as it can be reconstructed) with data on the cultural and physical environment should be the aim of the worker in this field. For the anthro- pological paleopathologist, the relation- ship between culture and disease is one of the most important aspects of the study of disease in ancient human populations. Much useful data is ignored in the majority of paleopathologic studies.

I hope that this situation will soon be corrected. Cockburn's work in relating cultural practices and diseases in modern populations should serve as an example of howilluminatingsuch a treatment can be.

by W. C. OSMAN HILL*

Dublin, Ireland. 12 i 70

Cockburn's title is somewhat misleading. Though ancient populations are com- prehensively considered, a large part of the paper, especially that dealing with "agricultural man," concerns itself with recent practices (e.g., in China). While these may have a distinct bearing upon the synthesis of the evolution of infectious diseases, some indication of this in the title would have been informative.

Epidemiologically, the division into precursors of man, early man, agricul- tural man, and industrial man is amply justified; these divisions mark the principal cultural phases of man's progress as a corollary to his environ- mental relationships (including, more specifically, host-parasite relationships).

"Agricultural man" is but another

expression of Childe's Neolithic revo- lution, which indubitably created a fundamental alteration between man and environment. Its effect is rightly emphasized by Cockburn. Combined with loss of mobility was the problem of increasing concentration of population and consequent greater ease of parasite transmission. Maybe it was at this stage that the virus of the common cold was evolved, though Cockburn makes no allusion to this all too specific human pathogen. No other primate suffers naturally from this disease, though immunologic resemblance among the Hominoidea renders the condition artificially transmissible to the Pongidae, whereas monkeys are immune.

Under the heading "Addition of Animals to Ecological Niche" (p. 48), the author is rather vague on the order in which animals were domesticated. No mention is made of tetanus-surely a hazard introduced when man tamed the horse and came to use its manure agriculturally. This was a relatively late event.

Although the author devotes a page and a half to the problem of increase in population, nowhere does he specifically deal with that important phase of cultural history, the establishment of

cities and city-states, which begar in Mesopotamia in the 7th millenniurm B.C. The Sumerians, for example, lefl evidence of their medical knowledge, and so, later, did the Egyptians.

Rivalry between city-states and raids

by their uncivilized neighbours inevitably led to mass population movements- hordes of refugees from war zones-with the consequent introduction and dis- semination of pathogens hitherto foreign to a given area. In later times, with major local wars, e.g., during and after the Peloponnesian war, agriculture was neglected and deforestation occurred, thereby hastening soil erosion and breakdown of drainage. This opened, during the 1st millennium B.C., the opportunity for the spread of disease, particularly malaria, in the Greek coastal lowlands (Darlington 1969:210). Later growth in size of cities, with subsequent environmental pollution and the evolution of slum conditions, are a logical sequel, leading finally to the conurbations of the 20th century with their built-in reserves of pathogens.

Passing now to the Bronze Age and Early Iron Age, with the emergence of seafaring peoples like the Phoenicians, still greater dissemination must have resulted, especially in the distribution of vectors such as Rattus rattus and its ectoparasites. Cockburn has little or nothing to say of them, yet the Phoeni- cians travelled very widely-as far as West Africa, for example (Hanno's voyage), whence tropical diseases were probably introduced to the Mediter- ranean world, and perhaps even as far as the New World (Boland 1963).

Another serious omission germane to the subject of the title concerns the epidemiological knowledge of the Hebrews gained from the record of their sojourn in Egypt and subsequent ex- periences during the migration to Palestine. There is a wealth of evidence here on the relation of man and his parasites to the environment. What, in fact, were the "plagues of Egypt"? Some documentation of the hygienic rules elaborated by Moses seems called for, as they include regulations both as regards personal cleanliness and dietetics. Examples are the segregation for "un- cleanness of issues" in the prophylaxis of venereal disease (Leviticus 15; Darling- ton 1969) and the prohibition of the flesh of the pig, probably on account of the prevalence of trichinosis infesta- tion. Nevertheless, Moses' zoological knowledge was not always accurate, witness the classification of the leporids as ruminants-or was this an inspired guess at the phenomenon of refection?

The later sufferings of Job also require explanation. Were the nocturnal achings of his bones the result of a treponematosis or due to gonococcal arthritis ?

Cockburn's emphasis on the evidence of palaeocoprology is commendable and should be eagerly followed up by archaeologists.

by F. P. LIsowSKI*

Hong Kong. 30 i 70

The article presented by Cockburn is

another contribution to an important aspect of the study of ancient populations that is now being re-examined in the light of new knowledge and better methods of laboratory investigation (cf. Wells 1964, Jarcho 1966, and Brothwell and Sandison 1967, to cite just a few titles). The only "criticism" I have of it is that I wish it were longer and more

detailed. What we have is something that stimulates the palate for more. Cockburn's background is ideal for such a compilation, and one hopes he will furnish us with further studies.

I like the way in which he subdivides his subject into three phases-the pre- cursors of man, early man, and agri- cultural man-and then argues his various points both by reviewing the literature and from personal observation in the field. Apart from examining ancient remains by modern methods, it is important to study some of the populations in the developing parts of the world that have had minimal contact with the life and medical care that obtains in the developed part. It is by studying these that we may arrive at an approximate answer as to what happened in the past and possibly how ancient man dealt with his ills. Thus I should like to add a few points, by way of comment, as a result of a lengthy stay in Ethiopia a few years ago.

During that time there occurred quite a serious outbreak of yellow fever in the southern part of the country, an area that succumbs from time to time to these attacks. It is considered that the reservoir for this particular virus is the nonhuman primate popula- tion in that region (P. Neri, personal communication, 1968). This is very much in line with what Cockburn says in his section about the precursors of man and the transmission of certain infectious diseases.

Similar to early historic sedentary man, the Ethiopian population of today is subject to a large variety of infectious. diseases. This fact was quite clear from my examination of 12 human cadavers at the Institute of Medical Sciences in Addis Ababa, as well as from some 14 postmortem examinations in the field; in all these specimens, most of the lymph nodes were enlarged, and particularly so in the abdomen. Furthermore, in all

of the adult specimens of 25 to 50 years of age the thymus gland was not only present but enlarged (on the average

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Cockburn: INFECTIOUS DISEASES IN ANCIENT POPULATIONS 50 mm. to 70 mm. long and about 15 mm. in diameter). Histological sections in a number of cases supported the diagnosis of active thymus tissue. It had at first been thought that these were enlarged lymph nodes. High gamma globulin titres (K. Weithaler, personal communication, 1967) in all the people examined are additional evidence of the incidence of infection among Ethiopians. And if further proof

were needed, the high mortality rate speaks for itself.

Because of unsanitary conditions, infestations with intestinal parasites are the rule. An examination of the gastrointestinal tract in 12 cadavers revealed an average of three tapeworms, seven roundworms, and some 250 whipworms in each. It is not uncommon for small children to have over a hundred roundworms. In addition, the habit of eating raw meat, especially in the rural areas-surely a practice our ancestors engaged in-must contribute its share of infective agents.

The practice of removing the canine teeth of young children with the aid of a rusty nail, in the belief that this intervention cures gastroenteritis, is still widespread. These mutilations can have quite serious consequences. In how many skulls have these gaps been misdiagnosed as due to ritual practice?

Even an examination of cremated human remains from archaeological sites will show that caries and osteo- arthritis were not so rare in times past as is often supposed (Lisowski 1968).

Chronic malnutrition-and, in par- ticular, the lack of protein-in the developing part of the world is a fact and can reveal a great deal about man's condition in early history. His poor nutritional status must obviously have contributed to a high rate of infection.

Thus the old tale that ancient man was a healthy specimen is far from the truth.

Cockburn also wisely points out the fact of differential resistance to infection and in this connection adds the impor- tant rider "that so far there is no good evidence that any single significant culture was ever wiped out by an infection"-a statement all historians ought to heed. In his final section Cockburn briefly deals with some of the more recent work and the directions that modern investigations are taking. This is altogether a stimulating article.

by RALPH S. RIFFENBURGH*

Pasadena, Calif., U.S.A. 7 ii 70

In any subject which can be studied only indirectly, the evidence is open to varying interpretations. It is also true that the subject of disease in ancient

populations is multifaceted and could be discussed from many viewpoints. Cockbum has chosen well in the aspects covered and has exercised care in his conclusions, though not everyone will agree with all of his interpretations.

How far one can generalize from the primates to early man is certainly debatable. With the differences in mobility and in dietary habits, it may be that food-related or food-carried infections of early man were more closely related to certain of the carni- vores than to the primates (Schaller and Lowther 1969).

The effects of irrigation and formation of artificial bodies of water on human disease cannot be overemphasized. Van der Schalie (1960) states that

schistosomiasis has now replaced malaria as a chief hindrance to progress in many underdeveloped regions of the world.

Because of the physical debility this disease produces in its victims all the heavy labor of Egypt is supplied from Upper Egypt, where the incidence of schistosomiasis is low. The Aswan Dam will open Upper Egypt to schistoso- miasis and may well cancel out the benefits from the dam construction. Prior to the relocation of Tonga villages in connection with the Kariba Dam project in Central Africa, village mosquito populations could be controlled through spraying. This is no longer possible for villages and fish camps sited close to a lake of 2,000 sq. mi. surface area. With this large, permanent breeding area, the villages are now heavily infested with mosquitoes (Thayer Scudder, personal communication, 1970). Though no study for malaria has been done under the new conditions, an increased incidence is likely. The water projects are also likely to alter disease patterns by increasing local population density.

Cockburn discusses minimum popu- lation size for maintaining infections. This should be open to statistical study. Theoretical conclusions based on studies of duration of infectability could be checked both historically, from com- pilations of previous epidemics, and by using partially immunized groups as natural laboratories.

The possible findings from ancient feces draw attention to a chronic problem. It is difficult to communicate to fieldworkers (and others) the potentialities for infor- mation in materials handled by new techniques. The importance, when exca- vating, of saving samples of materials outside the worker's owni interest must be more widely disseminated. There is also delayed transmission of methods from other fields which might be appli-

cable. For example, Courville's excellent detailed studies of cranial injuries in early populations in Oceania and America are little known in anthropology, having been published in relatively obscure medical journals (Courville 1948, 1951, 1952, 1956; Courville and Abbott 1942).

by CALVIN WELLS*

Norfolk, England. 26 I 70

What diseases infected ancient human and subhuman populations? Cockburn's latest attempt to infer a few answers attracts by its persuasive shrewdness, no less than it woos by clear presentation. He reviews, most usefully, current reasonable assumptions. I concur with his general opinions and wish to comment on only a few details.

It is almost certainly true that

some of the ancestral parasites and infections . . . failed to survive in certain host genera and species.

Unfortunately palaeopathological re- mains, whilst offering some suggestive hints, seem nowhere to give conclusive proof that this happened. We probably need further developments in palaeo- serology before final proof is forthcoming.

Gockburn suggests that, on quitting Africa, man took with him those para- sites which were transmitted from person to person, leaving behind those which needed vectors found only in Africa. Although probably true, this principle was perhaps modified by the ability of parasites to adapt to other vectors when their hosts infiltrated a new environment. No doubt most ancient migrations proceeded slowly, during which time many generations of parasites churned through the mill of natural selection. The more tolerant species or strains probably changed vectors in midstream with ease, if not with eagerness.

Parasites evolve in conjunction with their hosts and vectors, but some will be more versatile than others. Would anyone care to predict what extension of behaviour might be shown, under evolutionary pressures, by the organisms of Weil's disease, brucellosis, warts, or botulism? What is the truth about sudor anglicus ?

Cockburn refers to goundou in primates. He is right to be noncommittal about this interesting lesion: it is probably premature to ascribe it to yaws or any treponemal infection.

Reading Gockburn's paper, I found myself asking, "When is a disease not a disease?" Is poliomyelitis always patho- logical in humans ? Or is it rather a blessed symbiosis which only at times

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flares beyond the bounds of normality? Is it or is it not normal to accommodate Bacillus coli in our tripes ?

Even diseases which are clinically well defined-tuberculosis, leprosy, actinomycosis-are enigmatic enough in ancient skeletons, despite obtrusive bone lesions. How much more elusive are soft-tissue, nonspecific bacterial invasions, to say nothing of viral infections! Until palaeoserology takes a long haul forward we shall probably continue to fumble amid diagnostic uncertainty with most such lesions.

Cockburn rightly emphasizes the significance of helminth eggs, Entamoeba cysts, and the like. If these are objectively identified, presumption of infestation may be made, independently of the querulous ambiguity of surviving shreds of tissue. Natural sciences pass through various stages, more or less fruitful, which, for historical convenience, may be cliche-labelled by posterity: "the humoral period," "the phlogiston era," "the age of Newtonian physics," "the age of antiseptic surgery." Perhaps palaeopathology will reap its richest harvest during the next quarter-century if it aims to enter "the faeces phase." It is easy to see this as a neglected path of enquiry, with abundant rewards for a

modest outlay of effort. If so, Cockburn will have helped to sign-post this elementary track.

by SRBOLTUB 2WVANOVI6*

London, England. 22 I 70

The study of infectious diseases in ancient populations is a very interesting and promising subject, but sufficient data are still not available to provide a basis for a theory that would explain the origins of particular diseases. The evidence given in this article does nIot sufficiently support the conclusion that "most of man's specific infections are descended from those acquired from his prehuman ancestors" nor that different species of primates acquired the infections from the same source. Not much is known about the evolution of different parasites, but we do know that it is not very difficult to induce mutations in primitive organisms. Similarities between certain parasites in different species may merely mean that they have adapted to similar environments or similar condi- tions. Thus the fact that the same intes- tinal protozoa are found in various recent apes and monkeys does not imply that all these protozoa were passed down to all these primates from a

common ancestor. It may mean, instead, that the protozoa found similar con- ditions of life in these primates and so survived in all of them. That is, it is neither proof that the protozoa found in various recent primate species have a common ancestor, nor that the various primates have a common ancestor, but that a similar symbiosis between the protozoa and various primates exists, or existed in the past. Different primates may be in- fected in different ways by the same para- site, and not necessarilyin the same stage of development. Similar serological reactions in various species of monkeys are simply due to infection by similar, or the same, microorganisms. Serological differences between Asian and African monkeys or recovery of treponemas in baboons cannot be a strong evidence in support of the theory that some treponemal infections have existed in man and his ancestors for many millions of years.

I agree with the author that there are numerous possibilities for studying infec- tious diseases and epidemiology in ancient populations, but I feel that it is too early yet to draw any conclusions. Much more study is needed before the whole problem becomes clear. The author is to be commended for drawing attention to it and for making an attempt to explain it.

Reply by T. AIDAN COCKBURN

The remarks of the distinguished com- mentators are much appreciated, as are the references they give to additional papers not listed in my article.- Almost all ask for more information on matters of special interest to them. The article is already lengthy, however, and if all were to be satisfied, the result would be another book. Much additional infor- mation, including some on the questions, raised by Bennett, of genetic feedback and myxomatosis virus in rabbits, is given in my two existing books (1963, 1967).

Some unkind critic once defined prehistory as

the study of the unverifiable to prove the unwarrantable about what never happened anyway.

This appears to be in line with the stance taken by Bennett in his comments. It is such a widespread viewpoint that the first chapter of my first book was devoted to this subject and titled "Speculation in Research." A paragraph

from this chapter (Gockburn 1963:8) dealing with the propriety of using analogy to theorize on past happenings is as follows:

The most reliable data come from our know- ledge of infectious diseases today: by analogy, we can attempt to portray the diseases and infections of animals of past eras. There is much difference of opinion on the reliability of this method. One of its leading proponents was Wood Jones (1929) who thought that it was at least as good as the evidence produced by the geologic record. He recalled that the workers who described the evolution of the horse, using fossils to illustrate the various steps, did it correctly, but by error used fossil horses that were not in the correct line of descent. The task could have been done just as well by using analogies with existing animals. Le Gros Clark (1949) supports this, saying, "It is particularly noteworthy how closely some of the fossil types conform to inter- mediate stages that had been postulated on the evidence of comparative anatomy. Discoveries of such fossil relics, indeed, provide a remarkable vindication of the well- established methods of morphology... ." On the other hand, Hooton (1946) replies that this is not true. For example, early man walked upright but had a small brain, and since no such creature exists at the present time, no amount of reasoning by analogy could have inferred his existence. In the study of infections, such reasonings are largely academic, since all we have is the method of analogy, although some day it

may be possible to demonstrate ancient infections with some certainty.

To Bhattacharya, I would state that my four years' experience in India (see Cockburn 1960, 1961 b), including studies in the Indus Valley, impressed me with the importance and antiquity of smallpox in that subcontinent. Small- pox probably evolved either in India or China and must have been devastating when it first appeared. Its effects on the Indus Valley complex could well have been severe.

To Chiarelli, I would reply that I deliberately played down the subject of malaria, haemoglobins, and red cell enzymes because it is so well known. Almost every author gives it prominence, so I chose to use other illustrations.

Clabeaux stresses the importance of being population-oriented, and in this I am in full agreement with her. Incidentally, any study of an ossuary or cemetery shows that a large fraction of the skeletons are of small children. What were the main reasons for the deaths of these children? Hunger, violence, or infectious disease? I would suggest that the last was just as important as the first and much more so than the second.

Wells raises the question of reaction of certain pathogens to unusual evolu- tionary pressures. Of course, many

1 I would appreciate receiving reprints of articles or other information on the subjects discussed in this review, especially work in foreign languages (with translation attached).

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Cockburn: INFECTIOUS DISEASES IN ANCIENT POPULATIONS organisms can spread readily by means other than those usually utilized. Flea- transmitted plague bacilli and mosquito- borne Venezuelan encephalitis virus are notorious for their abilities to spread by air: tularemia organisms normally carried by biting insects to mammals can cause great water-borne epidemics if the mammals happen to be muskrats.

A change in environment might well lead to utilization of such potentialities and produce unusual forms of infectious diseases. On the other hand, some patho- gens seem to have disappeared or become relatively harmless. Wells mentions the sudor anglicus or English sweat of the 13th and 14th centuries. Was this influenza, or some other disease that has since disappeared? In the 19th century, scarlet fever was the leading cause of death in children; now it is of only minor importance. No one knows why.

Hill has misunderstood the purpose of my giving data about current practices. The sole reason for this was to draw conclusions as to past infections by analogies with present ones.

Lisowski'sdesireformore is appreciated. Perhaps some day a monograph on this subject will be attempted and his assistance requested. So much of the information is scattered in the journals of so many differing disciplines, however, that it would be a monumental task, one requiring the collaboration of scores of scientists. Here is a challenge for some university to organize the project.

Riffenburgh comments rightly on the growing threat from schistosomiasis. I agree that a statistical study on minimum population sizes for infections is urgently required. Perhaps he can stimulate some department to undertake the task.

Divanovic is skeptical of my view that the intestinal protozoa of man are derived from those of man's ancestors and suggests that the similarities may be the result of convergent evolution. There are some difficulties with his hypothesis. For example, what were the differing organisms that evolved simul- taneously in many primates' intestines until they all came to resemble one another? Where did they come from? Did the ancestral primate have any intestinal protozoa, and if so, what happened to them? Did the evolving primates start off without protozoa and pick up their parasite loads at later dates? Are the intestines of all primates so much alike-regardless of whether the food consists of flowers, leaves, seeds, or flesh-that diverse protozoa must converge into common forms? I find these questions too difficult to answer and prefer the simpler solution that all intestinal protozoa were passed down from the ancestor common to all the primates.

This review was written in early 1968. Since that time, much significant work has been reported. Fribourg-Blanc and Mollaret (1968) have succeeded in isolating in hamsters a strain of patho- genic treponeme from a West African baboon. The appearance of the lesions in the hamster is consistent with the concept that the treponeme is the same as that causing yaws in humans. These workers have now examined 2,000 sera from primates of different species in different parts of the world. Positive sera have been found in animals in parts of Central Africa. The areas of human yaws and nonhuman primate trepone- matosis coincide.

Kuhn, Brown, and Falcone (1968) have tested 250 chimpanzees serologi- cally and found 19% positive to one or another of three tests for syphilis. A wide range of other primate species was studied and antibodies found in a significant percentage of them.

Fresh evidence has been reported by Fenwick (1969) of the maintenance of the liver fluke Schistosoma mansoni in baboons in Tanzania. All seven baboons examined were found to have adult worms. Of the feces samples examined microscopically, 20% were found infec- ted. When a concentration technique

was used on 100 samples, 47 % were shown to be infected.

Pinworm (Enterobius vermicularis) eggs have been identified in human coprolites 10,000 years old from caves in Utah, U.S.A. In addition, eight specimens of eggs of the thorny-headed worm, Acan- thocephala, were found in feces from the same populations. This parasite is one found in wild animals and would infest man accidentally (Fry and Moore 1970).

Armelagos (1969) reports finding head lice (Pediculus humanus capitis) in the hair of 40% of well-preserved bodies in Nubia. The dates of the finds are not given, but the bodies examined covered a time range of 8,000 years.

Earlier in this review the work of Neel et al. on the Xavante Indians of Brazil was mentioned. These very isolated Indians were found to have measles and other acute community infections. A year ago, this was difficult to explain. Now, however, Hospital Tribune (December 15, 1969) recounts that hundreds of Brazilian landowners and government officials have been accused of carrying out biological warfare against the tribes. Those to be tried number 134, and 200 have been dismissed from government service. The charge against them is that they sent gifts of infected clothing and persons with smallpox, tuberculosis, influenza, and measles to the communi- ties, with the intent of spreading disease.

Other investigators studying primitive peoples must take note of this incident.

Neel and his colleagues have two papers in press (Neel 1970, Neel et al. 1970) on disease in another isolated Indian tribe in Brazil, -the Yanomamo, giving detailed accounts of diseases encountered and a description of a measles epidemic, and providing excellent lists of references. They raise the question of the cause of the high mortality seen in isolated or primitive groups when they are exposed to such diseases as measles and smallpox. Is this high mortality due to a basic susceptibility of genetic origin, or to cultural factors, or simply to the collapse of a precarious economy when all become ill at one time? If it is the latter two, then death occurs from starvation, thirst, or lack of care. Neel, a geneticist, is inclined to place the responsibility on the collapse of the economy, while not denying the existence of inherited resistance or susceptibility. My conver- sations with scientists in the past two years give me the impression that this is a majority opinion. Several have challenged me to produce evidence of differential resistance of a genetic nature in human populations. I shall therefore discuss the matter at some length.

Obviously, in any particular epidemic a number of factors are involved, and it is impossible to disentangle them in order to determine their relative impor- tance. However, the phenomenon of high mortality applies with such regu- larity to so many infections in so many diverse cultures that it is my opinion there must be a common factor. I admit readily to a bias in favor of differential susceptibility that has been with me since my earliest days as a physician. This bias arose from my experience with measles.

In 1935, I was in private practice in the coal-mining town of Bedlington, England when the triennial measles epidemic struck. Walking or cycling, I would visit the homes of sick children, and in one day would see 20-30 new measles cases. Those were the Depression years, and the children's diets were decidedly subnormal. It was also the days before antibiotics, so that treatment was mostly symptomatic and ineffectual. Yet out of more than 500 sick children under my care, not a single one died. There were plenty of complications, such as pneumonias and running ears, but no deaths.

In 1942-44, I was in the British Army in West Africa helping form the 81st and 82nd West African Divisions. About 50 cases of measles occurred among the families of the African soldiers, but this

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was quite a different story. They received the same care as the children in Bedling- ton, and their food was not too widely different. Of course, they had lots of parasites. However, they were far sicker and more prostrate than the English children, and several died. The only way I could account for the difference was in the body's reactions to the virus. (For contrary opinions on the subject of measles in Africans, see Seminar on the epidemiology and prevention of measles and rubella 1965.) Today measles in the unvaccinated is a prime killer in West Africa.

Some of the factors in genetically controlled susceptibility are given in an editorial in the British Medical Journal (see Genetics and infection 1969). Listed there are the following: agamma- globinemia in children; selective IgA deficiency in children; an inherited deficiency of DPNH oxidase activity that is possibly sex-linked in some families but autosomal in others; a defective granulopoeisis with intramedullary de- struction of granulocytes which is inherited as an autosomal recessive (the Chediak-Higaski syndrome), the basic defect being possibly in the structure of the membranes surrounding cyto- plasmic organelles; an absence of the enzyme myeloperoxidase in polymor- phonuclear leucocytes and monocytes, but not in eosinophils. This last type has been described in only one patient with disseminated candidiasis. His leucocytes showed deficient capacity to kill ingested Candida albicans and some bacteria.

The question of the relationship of the blood groups to susceptibility to infection is hotly debated at the present. Having no special knowledge of the subject, I shall not refer further to it.

Apart from the well-known story of malaria and sickle cell anemia, the best example seems to me to be the resistance of Africans, and the great susceptibility of Europeans, to yellow fever (see earlier in this review).

Many groups of people practice discrimination in breeding, restricting marriage to limited populations for religious or social reasons. Obvious examples of this are the Catholics, Jews, and Parsees. Selection on the basis of infectious disease only is much rarer, but where it occurs it is of interest in the study of inherited resistance to infection. I shall give two examples here:

1. Leprosy, among the people of Gojjam in the Ethiopian Highlands, is believed to be hereditary rather than

infectious. Therefore, strict genetic iso- lation, but no physical isolation, is practiced. Full genealogies are known for seven generations back, and marriage is forbidden between anyone who has leprosy in his genealogy and anyone who has not. In contrast, persons known to have leprosy may act as domestic servants to nonleprous families and mix socially with them in other ways, with no other discrimination being practiced or fear of infection being felt.

It appears that about 25% of the population is in the leprosy-genealogy group, whose members often, of course, have no surviving sufferers from the disease among their close relatives. The prevalence of the disease is very high, close to 5 % in the population at the center of this area. If there is a genetic component in the etiology of leprosy, this practice of genetic segre- gation is likely to have concentrated it effectively in the minority group. People say that they know of nobody born into the majority group who later contracted the disease (Schofield 1970).

2. Tinea imbricata (Tokelau ring- worm) affects about 150,000 persons in the lowlands of eastern New Guinea and the neighboring islands. Sufferers expe- rience continual itching and a severe social disability. People with the chronic disease are called pukpuk, pidgin for "crocodile"; the uninfected are called "clean skins." Babies are usually in- fected with Trichophyton concentricum by their mothers or nurses within the first few months of life. Appearance of the disease after the second year is uncommon. The disease restricts the choice of a marriage partner and is an important contributing cause of bachelor- hood among men. According to Schofield (Schofield, Parkinson, and Jeffrey 1963 :225):

Unequivocal evidence for or against a genetic factor has not been found. However the findings, that the great majority of children have become infected in the first 2 years of life and that children of infected mothers become significantly less often infected if their fathers are "clean skins" than if their fathers, too, have the disease, are suggestive. Children up to 2 years of age are in such intimate contact with their mothers that this contact risk for the children in both the Groups I and II must have been maximal. It is difficult to envisage any relative environmental advantage which "clean skin" fathers could confer upon these children at this age; therefore it is quite possible that this advantage may be a genetic one. Comparison of reinfection

rates with primary infection rates among adults in the same environment indicates that some people are permanently more prone to the disease than others.

Kuru is an infectious disease that probably has a genetic component. This fatal organic disease of the central nervous system occurs in a relatively small area of the Central Highlands of New Guinea. It affects people of the Fore linguistic group, of whom there are some 10,000 to 15,000 individuals. Between 100 and 200 of these people die of the disease each year. To the Fore people, kuru is the most important disease that occurs, and it is a dominant factor in their culture (Zigas and Gajdusek 1957). Recently the agent of kuru has been identified as a virus. There is an asymptomatic incubation period of 14 to 38 months (Gibbs and Gajdusek 1970). The most likely mech- anism of transmission is cannibalism. Women and small children eat the brain, and this is known to be heavily infected. Only women and small children acquire the disease. Since cannibalism was stopped in the late '50's, the disease has decreased sharply. Alpers writes

(1970: 137): The argument for genetic susceptibility rests not on the familial incidence (for this could equally point to an environment factor), nor on the genetic analysis of pedigrees (so we are not necessarily postulating a single gene effect), but on the fact that the disease remains confined to the Fore-speaking people and those who intermarry with them. The surrounding people have essentially the same culture as the Fore, including, until admini- strative contact, the practice of cannibalism, and the same recent history. The one thing that distinguishes kuru-affected from kuru- free clans and hamlets is intermarriage with the Fore.

The field of infectious diseases is so vast that it cannot be covered in toto in a review of this nature. Some of the major areas to which only passing references have been made include the natures and evolutions of the pathogens themselves, the roles of the intermediate hosts and arthropod vectors, the mortali- ties produced by infections in the varying peoples, and the influences of these mortalities on the destinies of the populations. One thing is quite clear, that the history of mankind has been shaped to a considerable degree by infectious disease. It is hoped that this paper will interest anthropologists into taking a closer look at this important but neglected aspect of the study of man.

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• Contents
• p. 45
• p. 46
• p. 47
• p. 48
• p. 49
• p. 50
• p. 51
• p. 52
• p. 53
• p. 54
• p. 55
• p. 56
• p. 57
• p. 58
• p. 59
• p. 60
• p. 61
• p. 62
• Issue Table of Contents
• Current Anthropology, Vol. 12, No. 1 (Feb., 1971) pp. 1-144
• Front Matter [pp. ]
• Our Readers Write [pp. 1-2+143-144]
• Materials for a History of Studies of Crisis Cults: A Bibliographic Essay [pp. 3-44]
• Infectious Diseases in Ancient Populations [pp. 45-62]
• Some Principles of Sociocultural Integration [pp. 63-71]
• Research Report
• 奡湯浡洃뼃ࠠ䡡汬畣楮潧敮猺⁁湴桲潰潬潧楣慬Ⱐ䉯瑡湩捡氬⁡湤⁃桥浩捡氠䙩湤楮杳ਜ਼灰⸠㜲⬷㌭㜴�
• Education for Mankind: A Struggle for Meaning: Report on an International Conference [pp. 75-81]
• Discussion and Criticism
• On Race and Violence [pp. 82]
• On the Social Responsibilities Symposium [pp. 83-87]
• On Farb's Book on the American Indians [pp. 88-94]
• On the Geography of the "Acheulian Culture Tradition" [pp. 95-97]
• Urgent Anthropology
• Further Comment on Urgent Social Research in India [pp. 97-98]
• Prizes [pp. 98]
• Terminology [pp. 99-105]
• Wanted [pp. 105]
• For Sale [pp. 105]
• The Plight of the Old Order Amish [pp. 106-107]
• Conferences [pp. 107-109]
• Serial Publications [pp. 109-110]
• Institutions [pp. 110-111]
• Current Publications [pp. 112-144]
• Back Matter [pp. ]

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Introduction The transition from hunting and gathering to agriculture has long

been regarded as one of the most important development in human history. At its roots was the shift from the reliance on wild plants and animals to domesticated plants and livestock. Domestication is the process by which humans are able to control the reproduction of plants and animals species and thus select for various desirable characteristics. Domestication first occurred in the Levant, around 10,000 BC and it marks the beginning of the archaeological period known as the “Neolithic”. From the Near East, farming spread across Europe between 8,500 and 4,000 years ago. However, the adoption of domestic plants and animals is only a single symptom of a major societal and economic transformation. Indeed, people changed their views of many things during the Neolithic period, including the returns expected from their quest of food, acceptable levels of risk, their ability to change their environment, residential stability and property rights, definitions of kinship and residential groupings and the benefits of having more children. Most of these changes find their roots in the Mesolithic period, when solely hunter-gatherers (HG) were living, but they came together during the Neolithic to produce a dramatic change in society.

Because the transition to agriculture encompasses a wide range of causes and consequences that are themselves multidimensional (economic, social, ecological, institutional, technical) its study has led to discussions and to some major debates and controversies among scholars. It is the purpose of this paper to present and critically evaluate these major debates. The first one is about the transition process itself; it has long been considered that, compared to the hunting- gathering lifestyle, the shift to agriculture was associated with many advantages and therefore was obvious. However, according to recent studies, the presumed superiority of the farming lifestyle – in the early ages of agricultural development - over foraging has been seriously questioned.1−3 The second one is also related to the transition process. It has long been assumed, following the seminal work of Childe (1936)

and the terminology he used the so-called “Neolithic revolution” - that the transition was rapid and radical. During the last decades this view has been challenged by various archaeological records and studies. The transition revealed indeed as a gradual and long-term process, with a mixed-economy (based on foraging and farming) during millennia and even temporary reversion to hunting and gathering lifestyle.4,5 The third debate is about the main theories explaining the transition to agriculture. Chronologically, those related to push factors such as climate change or population pressure were favored in the literature.5 However in the recent decades, theories related to pull factors such as social competition and feasting have been considered separately or side by side with the previous ones. The most recent of these pull explanations, based on the human management of the environment (more specifically on Niche Construction Theory) and the role of property rights has an increasing audience.6,7 When the Neolithic transition is considered as a special event on a much larger scale of time its study becomes intrinsically embedded in a fourth debate, the one about the origins of economic development. Two main views are present in the literature associated with this debate: one is focusing on the role of natural resource endowments, geographic and Biogeographic conditions; the other emphasizes the importance of institutions. The last debate is about the diffusion of agriculture from its original center to areas occupied by indigenous hunter-gatherers. The migrationist approach was initially dominant in the literature8,9 then it has been challenged by the cultural diffusion. More recently, both approaches have been combined in the integrationist approach which seems a more convincing theory since it fits better with archaeological records, at least for South-East, Central and Northern Europe.10,11

The paper is organized as follows. In section 2, it is shown that the presumed superiority of agriculture in its early ages was far from obvious and that hunting-gathering societies were highly resilient to external shocks. Section 3, explains that the transition to agriculture based on domestication and multiple technical innovations was therefore a gradual process over the long-term rather than a true

J His Arch & Anthropol Sci. 2017;1(2):53‒61 53 © 2017 Svizzero. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and build upon your work non-commercially.

Persistent controversies about the neolithic revolution

Volume 1 Issue 2 - 2017

Serge Svizzero Faculté de Droit et d’Economie, Université de La Réunion, France

Correspondence: Serge Svizzero, Faculté de Droit et d’Economie, Université de La Réunion, France, Tel +262 262 13 82 58, Email [email protected]

Received: February 28, 2017 | Published: May 16, 2017

Abstract

The Neolithic Revolution describes the transition from hunting and gathering to farming and then to the onset of agrarian societies. This process, which relied mainly on the domestication of wild plants and animals, occurred independently in at least seven parts of the world from 10,000 BC. It is widely agreed that the shift from a total reliance on wild resources to the use of domesticated foods led to a number of fundamental and far-reaching changes in human society. However, even eight decades after Childe’s (1936) seminal publication, the Neolithic revolution continues to lead to major debates and controversies among scholars. It is the purpose of this paper to present and critically evaluate these major debates. The latter are related to the presumed superiority of farming over foraging and to the speed of the transition process. They also concerned the origins of agriculture, the respective role of nature and culture in explaining the economic development, and the mechanisms bringing about the spread of agriculture.

Keywords: hunter-gatherer, foraging, farming, neolithic transition, biogeographic conditions, institutions, agriculture diffusion, european neolithization process

Journal of Historical Archaeology & Anthropological Sciences

Review Article Open Access

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Citation: Svizzero S. Persistent controversies about the neolithic revolution. J His Arch & Anthropol Sci. 2017;1(2):53‒61. DOI: 10.15406/jhaas.2017.01.00013

(Neolithic) revolution. The main models about the origins of the Neolithic transition are detailed in Section 4, be they related either to push factors, or to pull factors, or to an admixture of both. In section 5 are presented and then compared the two main views about the origins of economic development, one focusing on natural endowments and the other on the role of institutions. The mechanisms bringing about the spread of agriculture are analyzed in section 6, with special attention for the diffusion of agriculture to Europe from the Near-East. Section 7 concludes.

The presumed superiority of agriculture in its early ages

It is often believed that the initial effect of the shift from hunting- gathering to agriculture was an immediate increase of the amount of food production. Societies that adopted agriculture were able to produce far more food in a given territory than those that relied on foraging. This increase in productivity could be used either to expand the economic surplus or expand population, with both usually occurring. However, recent studies have deeply challenged this vision demonstrating that compared to foraging, agriculture in its early ages was an activity with low return and that farmers were incurring high risks.

The low attractiveness of the farming way of life

In Mesolithic Europe, for example and as illustrated by the Ertebølle1 culture, HG were not mobile and nor were they organizationally simple. On the contrary, they tended towards socio- economic complexity, including sedentism. Similarly Neolithic European farmers as illustrated by the LBK2 culture were not super- productive and sedentary. On the contrary, they were often mobile and had a mixed-economy, i.e. an economy combining hunting-gathering and farming. The cultural diffusion of the Neolithic revolution i.e. the deliberate choice of HG to switch to agriculture, finds therefore little support. Moreover, it was often believed that farmers were affluent and HG was poor. From the 1960s, the latter part of this vision was challenged by the results of ethnological studies12 of HG societies. Indeed, it appeared that some modern HG societies (mainly! Kung and Hadza, both located in Africa) were very different from the usual description of HG societies. Indeed, these societies did not experience scarcity of food and individuals had to do little work to satisfy their limited ends. Therefore, they were labeled as the “original affluent society”.13 Thus, the former part of the vision mentioned above has also been challenged. The first agriculturalists are now believed to have put in more rather than less labor to attain subsistence. As pointed out by14 “Traditional scholarship has regarded farming as highly desirable. Scholars of human history long assumed that once humans recognized the impressive gains from cultivation and domestication, they would immediately take up farming. However, more recent studies have indicated that early farming was indeed back breaking, time consuming and labour-intensive”.1 Also asked “Why farm? Why give up the 20‐hour work week and the fun of hunting in order to toil in the sun? Why work harder, for food less nutritious and 1The Ertebølle culture is the name given to the Late Mesolithic/Early Neolithic communities of Northern Europe – South Scandinavia, dated between 5400- 3900 BC, consisting of fisher-hunter-gatherers who adopted pottery but not agriculture from their neighbors. 2The Linearbandkeramik Culture (also called Bandkeramik or Linear Pottery Ceramic Culture or simply abbreviated LBK) is the first true farming communities in Central Europe, dated between about 5400 and 4900 BC.

a supply more capricious? Why invite famine, plague, pestilence and crowded living conditions?”

In other words, early agriculturists had to work more hours than foragers did. They were also more prone to lethal disease and malnutrition,15 as a result of the shift towards dependence on one or a few domesticated plants, with a diet based predominantly on complex carbohydrates. Increasing sedentism and living in close proximity to domestic animals led to poor sanitation and an increase prevalence of zoonotic disease. They also had to endure less egalitarian social structures than hunter-gatherer societies. Since there are almost no indications of increased standards of living immediately after the agricultural transition, why complex HG should have decided to give up their way of life in order to adopt agriculture?

The low attractiveness of agriculture is also confirmed by some cases of reversion from agriculture to hunting and gathering, depending on opportunity costs. Some examples of reversion are well documented in Northern America4 as well as in other regions.16 Indeed in North America the (re)-introduction of horses by conquistadors caused some north-American native Indians tribes3 to revert to hunting as a permanent way of life. Another example of reversion concerns the Levant and is about the well-known Natufians. Indeed, it appeared that the late Natufians reverted to a higher degree of mobility after having adopted a settled life. Decreases in site size, the decline of architecture, as well as changes in the burial record have been seen as indicators of increased mobility. It is suggested that the reason for higher mobility during the late Natufian was the climatic deterioration which occurred with the onset of the Younger Dryas, which depleted available resources. This in turn, resulted in a dispersal of populations across the region to maximize their returns from different areas and alleviate risk.

Adaptation and resilience of hunter-gatherer societies

Traditional climate forcing models17 intended to explain the origins of agriculture in the Near-East proposed that the shift to wild cereal cultivation was a solution to the failure of foraging systems driven by the terminal Pleistocene Younger Dryas climatic deterioration. In doing so, they assumed that the Neolithic revolution was a response to the earliest well-documented example of social collapse i.e. to the failure of foraging economies in the wake of abrupt climatic change. However, this view has been challenged18 in the case of HG societies living in the Levant - by assuming that climatic fluctuations leading to major restructuring of vegetation only resulted in a shift in resource focus of HG rather than forcing a collapse of foraging economies. Indeed the Levant is within the Mediterranean climatic zone where vegetation zones are complex: woodlands dominated the west and the north while grasses and other steppic plants are present in the east and the south. In this context, the vegetation responds to climatic changes by shifts in boundaries and shrinking or expanding within their respective zones. In other words, HG subsistence systems in the Levant were highly adaptable and resilient and robust in terms of diversity of options and the mobility of HG. HG societies had a broad range of economic strategies that enhanced their resilience. In difficult times, they may have had to extend what they foraged to include low ranked resources.19 This foragers’ adaptation has been labeled by Flannery KV20 “the broad-spectrum revolution”. In the late Pleistocene or early Holocene, low-level pre-domestication cultivation may have occurred and would have been one of many options available to foragers. It 3Cheyenne, Arapaho and Pawnee.

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Citation: Svizzero S. Persistent controversies about the neolithic revolution. J His Arch & Anthropol Sci. 2017;1(2):53‒61. DOI: 10.15406/jhaas.2017.01.00013

is only until well in the Holocene that cultivation had a significant impact on foraging economies. Moreover, long-term social memory of accumulated experiences was crucial in these HG societies for preparing and responding to economic challenges.

The transition to agriculture: a complex and long-term process

The “Neolithic Revolution”, as coined by Childe VG,21 was one of the major events in the history of humanity. Indeed, the shift from foraging to farming has been one of the most important events in the evolution of human societies. From Childe’s seminal paper, archaeological records and studies have contributed to qualify his initial view: the “Neolithic revolution” has been replaced by a long and gradual process called the “Neolithic transition”. Yet Childe was using the term Neolithic or Agricultural Revolution not in relation to speed but in relation to the revolutionary character of a change that no matter how rapid or how slow, transformed hunters and gatherers into shepherds and farmers. However, many of his successors have first interpreted the word “revolution”22 as meaning rapid and radical. Indeed many believed that the shift from foraging to farming was rapid, irreversible and featured by the one shot adoption of the so-called “Neolithic package” including agriculture, husbandry, sedentism, stone axes and pottery. Since then all these interpretations have been challenged.

From taming to domestication Although often characterized as rapid and the result of explicit

human intention, domestication is a complex process along a continuum of human, plant, animal relationships that often took place over a long period and was driven by a mix of ecological, biological and human cultural factors.23 The relationship between humans and the nature involves two polar cases: a behavior in which human acts as a prey against the nature and on the other hand the domestication24 of plants and animals. Between these two polar cases, there exist a wide range of relationships including taming. The latter encompasses commensalism/mutualism to low-level management, while directed control over reproduction is associated with domestication. Taming clearly differs from domestication by contrast with the latter, it does not imply morphological or biological modification of species. Of course, some plants as well as some animals were tamed25 by hunter- gatherers before the Neolithic revolution.

For plants, a wide range of “technologies” may be considered as taming or wild resources management. They include fire-stick agriculture4 to foster the growth of edible plants and to eliminate the others and also to attract game in the resulting meadows tending tubers, soil aeration, watering fields, semi-sowing or voluntary incomplete harvest of seeds.5 Until recently, all these proto-agricultural technologies were still used in many hunter-gatherer societies. The dog was probably the first animal to be domesticated, before the Neolithic period, even if it was not to provide food resources but mainly for helping humans in their hunting activities. Many other animals have been tamed: sheep, goat, cattle, pig and later horse, camel, llama (…). The reindeer6 is also a good example. During the Paleolithic period, it provided 80% of human diet. With the global warming 4For instance, in Australia, Aborigines used this technology since at least 9000 BC. 5In South California, once the seeds were harvested, the Kumeteyaay were burning the fields and thereafter they were sowing some of the seeds they had harvested. 6Rangifer tarandus.

of the Holocene era, herds of reindeer migrated north to the arctic and subarctic regions where they are still living nowadays. In these regions, they have been tamed, providing meat, milk, hide and being also used for traction. However, they have never been domesticated they may return to the wild easily and even they may interbreed with those still living in the wild. The taming of plants and animals also fostered the geographical dispersion of these species.26 For instance, the wild pig living in many European Islands7 was introduced there by human during the Mesolithic period. All these taming activities of plants and animals developed by hunter-gatherers are corresponding to a proto-agricultural process.27 In some places the so-called ‘nuclear zones’28 some of these taming activities have led to domestication, i.e. they have contributed to the Neolithic transition. It should however be noted that the process from taming to domestication was very long, as illustrated by Larson G et al.29 “In wheat, barley and rice, it took 2,000-4,000 years to fix the no shattering spikelet phenotype, a key indicator of cereal domestication”. The evidence for a slow pace of domestication implies a cultural period in agricultural origins called pre-domestication cultivation. This period lasted for many centuries and has been inferred from evidence in the Near-East and China. Moreover, the length of this domestication process may be explained through the distinction8 between conscious and unconscious selection. Indeed, during the domestication process, conscious selection means that humans directly select for desirable traits.9 In contrast, the nonshattering seeds in cereals a trait which took 2000-4000 years to be obtained are thought to have arisen as a by-product of stalk-harvesting by sickles rather than by harvesting with the swinging basket. This case illustrates what is unconscious selection i.e. when traits evolve as a by-product of growth and natural selection in field environments or from selection of other traits.

The required stream of innovations The domestication of plants and animals is a necessary but not a

sufficient condition for the transition from foraging to an economy fully-based on agriculture to occur. Indeed, domestication can be seen as an innovation but many other innovations are required for the whole human population to be fed from agropastoralism activities. These additional innovations are respectively related to the production of food resources, their processing, storage and consumption. Even if we consider agriculture in its first stage, specific tools and techniques are required, for instance a digging stick to sow grains, an irrigation system, even if it is very basic or a sickle to harvest cereals. Once they have been harvested, domestic cereals require human activity, in the form of threshing and winnowing, to separate and disperse seeds.10 Once the seeds were obtained, they had to be stored in order to reduce the seasonal food risks. This requires some storage systems30 such as small clay bins, larger storage pits or silos and granaries to prevent the seeds from rain, moisture, insects and rodents. Clay- pot and therefore the development of pottery, was necessary for the transportation and the conservation of grains and flour. Some plant processing instillations and tools were also necessary, such as mortars and pestles, to transform grains in flour. Even though the innovations listed above seem us to be very basic they were all necessary for a complete transition to agriculture. Therefore, the complete transition

7E.g. Ireland, the islands of the Baltic sea (Gotland, Bronholm and Saaremaa), Cyprus, Corsica and Sardinia. 8Darwin was the first to make explicitly this distinction. 9E.g. some Asian cultures had consciously selected glutinous grains of rice for their cuisine-prized trait. 10Wilds grasses generally disperse seeds by the presence of an abscission scar; however the latter is often lost in the process of domestication.

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Citation: Svizzero S. Persistent controversies about the neolithic revolution. J His Arch & Anthropol Sci. 2017;1(2):53‒61. DOI: 10.15406/jhaas.2017.01.00013

to agriculture was a slow process. It seems that early agriculturalists for a long time were also involved in hunting and gathering.31 In the beginning, it is likely that little or no economic surplus was yielded by agriculture. It probably was in the nature of an income support measure, rather than a major addition to income. A major step forward in Mesopotamia was the development of irrigation around 5900 BC and around 4500 BC the plough (called the ard) pulled by draught animals (donkeys or oxen) was introduced and the wheel was invented and used both for transport and making pottery. Heat-tolerant strains of wheat and barley were also selected. It was only with these additional and more advanced innovations that Mesopotamian farmers were able to produce food surplus.

Explaining the transition to agriculture: push factors, pull factors or both?

For more than one century, many explanations14,5 of the Neolithic transition have been given by archaeologists, anthropologists and pre-historians and more recently even by economists. Although it is widely agreed that this episode was crucial in human history, there is no unique explanation or theory of the Neolithic transition which therefore continues to attract discussions and intense debates. This transition to agriculture is viewed as the result of a few single agents that operate in the Near-East at the onset of the Holocene. Climate change, human population pressure and culturally driven alternatives, such as competitive feasting, are among numerous alternative explanations proposed in the literature.

Climate and environmental changes Often, the transition to food production is explained by human

adaptation to external shocks. Many external shocks are possible (e.g. wild animal extinction due to disease) but the most popular one currently is climate change and the induced transformations of ecosystems. This explanation is probably the most popular because past prevailing climate and ecosystems are nowadays perfectly known and measured by means of various modern techniques. Others features of the past such as the population size the degree of competition among neighboring tribes (…) are at best hypothesized.

One of the first and probably the most famous explanation of the Neolithic revolution based on climate change was proposed by Pumpelly R32 and popularized by Childe VG21 and is named the “Oases theory”. In this theory, bands of HG were initially living in an environment able to satisfy their basic needs. However, a major climate change occurred; the transition from the Pleistocene to the Holocene, around 15 to 12.000 years BC, was characterized by a global warming. With the end of the last ice age, some areas like the Sahara, which was initially a savannah where bands of HG were living, became an arid desert unsuitable for HG to live in. HG was therefore forced to migrate to the Near-East in places where life was still possible i.e. in oases and on the banks of large rivers.11 To survive in these places, they adapted their way of living and thus some of them the Natufians invented agriculture. The transition to agriculture results therefore from a logical sequence having some similarities with biological evolution theory. There is an exogenous shock climate change and then adaption and a process of natural selection that leads to agriculture i.e. to the emergence of a new human society, more developed than the previous ones. 11Such as the Nile, Euphrates and Tigris rivers.

Even though this theory is quite seductive, it does not explain why agriculture was not invented before this time. Indeed, many major climate changes have occurred since the appearance of Homo sapiens. Another shortcoming of this theory is that in the Near-East there is no evidence28 of major climate change for the period considered by Childe. Given this criticism, it has been argued recently33 that while the role of climate change in the evolution of human societies remained important, its contribution should be more qualified. Regions characterized by either too high or too low intertemporal climatic volatility are evolving more slowly, i.e. are experiencing a late onset of farming. Indeed, under static climatic conditions, HG is not forced to take advantage of the productive potential of their respective habitats and remain indefinitely in a hunter-gatherer regime as is assumed in the case of hunting and gathering “affluent societies”. In addition occurrences of extreme environmental stress e.g. a return to semi-glacial or arid conditions - by eliminating the potential for farming, erode any accumulated human capital useful for agriculture, further delaying its adoption. It is therefore suggested that it is rather intermediate levels of intertemporal climatic volatility which fostered the transition from foraging to sedentary agriculture.

Population Pressure Building on the ideas of Boserup E,34 who proposed that a growing

population provided the impetus for the development of intensive agriculture, some archaeologists35 have long argued that hunter- gatherer economies continually evolved to accommodate exogenously growing populations, with the ever-expanding need for increased food supplies eventually leading to the adoption of farming. All approaches highlighting the role of population pressure in explaining the evolution of human societies are closely related to biological evolution theory. This affiliation is obvious in many publications.36 In order to illustrate it, we may consider two stages in the economic development of any human society. The first one is the economy of subsistence. People are nomads; they get their food from hunting and gathering. Their main (unique) objective is to get enough food resources to satisfy their basic needs survival and reproduction and of course, to minimize their effort in doing so. They do not try to maximize their food procurement because their basic needs are satisfied and excess food resources would be wasted anyway (storage is not consistent with their nomadic way of life). If there is no population pressure, nothing is changing, i.e. this society may remain at this stage of economic development forever. However, as highlighted by T. Malthus, human population is growing faster than food resources provided by agricultural production and obviously, faster than food resources provided by foraging. So, once population pressure is introduced, so is the evolution of human society. An infinite motion starts, leading from foraging to farming society, then to the development of cities and the emergence of states. The underlying mechanism is the following: when population grows, the demand of food resources increases. To satisfy this additional demand, more food is gathered, new food resources are gathered while they were not before and people are trying to improve their labor productivity. All these changes in the food procurement strategy necessitate more cooperation among HG, more collective works, like groups contribution to larger-scale technologies or the formation of alliances to defend resources. Therefore, these changes imply the emergence of a class of non-food producers including chiefs, soldiers, traders, priests, craftsman (…). Although this class contributes, directly or not, to enhance the level of food resources, it also demands

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Citation: Svizzero S. Persistent controversies about the neolithic revolution. J His Arch & Anthropol Sci. 2017;1(2):53‒61. DOI: 10.15406/jhaas.2017.01.00013

more food resources for their own consumption. Consequently the economy shifts to a second stage of development. In this economy, people, either HG or farmers are now maximizing their procurement/ production of food resources since a significant food surplus is henceforward required to satisfy the needs of people belonging to the class of non-food producers.

Population pressure critics argue that because many societies possess methods for controlling fertility via delayed marriage, prolonged lactation, induced abortion or infanticide, the population level needs never reach any Malthusian limits, exceed carrying capacity or feel any of the supposed effects of an imbalance of persons to resources. Some authors37 maintain that population pressure alone could not have played a critical role since there is no archaeological evidence of food crises prior to the development of agriculture.

Unconstrained or voluntary adaptation The various theories of the Neolithic transition illustrated above

and based on push factors, related either on climate change21 or on population pressure,36 are sharing a common thread: the transition to agriculture occurs when there is an excess demand for food resources. The latter can result from the negative impact of climate change on environment. It may also appear when population growth exceeds the carrying capacity of environment. We therefore see that despite the diverse contributions of the economic literature in explaining the Neolithic Revolution, population pressure, in most cases, is the ultimate driving force behind the transition to agriculture. However this force is considered as a constraint. Indeed, people must adapt their strategy to get food in order to satisfy the excess demand, otherwise they die (or at least some of them will die). In that case adaptation is considered as it is in biology, as a selection process, i.e. it is not decided by human societies. We claim that even if there are facing some constraints, like the ones related to the environment, human societies largely decide their evolution. In other words, adaptation is largely endogenous in the social evolution. Therefore, the Neolithic transition can be the result of voluntary human adaptation, i.e. of adaptation decided without constraint. In order to illustrate our point of view we recall that, as it is usual in the biological evolutionist approach, evolution is assumed to transform most of the time - simple systems to complex domains and climate change is the perfect candidate for that purpose. As Childe assumed, the rise of agriculture could be humanity’s response to a climate change resulting in a worse environment (altering the availability of food for humans). In that case, the resulting ecosystems are worse than before, with greater scarcity of food resources, for example as a result of a drought. In order to survive, i.e. to avoid starvation and death, HG must find new ways to get food and this may have led to the start of agriculture.21 However, the rise of agriculture could be humanity’s response to a climate change resulting in a better environment. In that case, the resulting ecosystems support more abundant and diverse plants and animals. As a result, food procurement is easier for HG who therefore has more time for leisure and for experimenting with cultivation and the domestication of plants and animals. They may settle and have more children.12 These simple alternatives show that the agriculture onset can be the result of various external shocks (positive or negative) even when these shocks all arise from climate changes. More fundamentally, these alternatives demonstrate that in social evolution, opposite causes a negative or a positive shock may have the same consequence, i.e. may lead to the same evolution of human societies.

12This case can be illustrated by the way of life of complex HG (e.g. Natufians).

Social competition and feasting Another case may also lead to agriculture from conscious

adaptation, i.e. the excess demand for food resources can exist even if there is no population pressure. Indeed it is well known from Engel’s laws about consumption that when the income increases, consumption shifts from primary to luxury goods. Such transformation may have occurred during the early Holocene. During that period, postglacial environmental transformations38 have led to the diversification of food resources, i.e. to the so-called “Broad-spectrum revolution”.20 With more abundant and diverse food resources provided by the nature, HG may have chosen to consume more “luxury or prestige” goods, are these food resources or non food resources.13 However, the production of these prestigious goods required more labor and therefore led to an excess demand for (primary) food resources. In others words, social competition for prestige in HG societies occurred endogenously, without constraint and it led, by means of conscious adaptation, to the rise of agriculture.14,39−40

However this theory considers that farming was highly desirable from the earlier stages of agriculture development. In addition to the previous one, there are several others major problems with this theory about the Neolithic transition. One is that without explaining the underlying causes of competitive feasting, it fails to explain the development of agriculture and simply describes the process. Another problem comes from the fact that the surpluses needed for competitive feasting only became available as an outcome of food production not before.

Nature and culture as factors leading to the establishment and sustainability of agriculture

There is a debate among economists about whether economic development depends more on nature or on culture. This has led to the existence of two views or school of thoughts: for the first one, natural resource endowments (Biogeographic and geographic conditions) are the prime determinant of economic development while institutions are central to the second one. Of course each of these two views provides a different explanation of the Neolithic transition, i.e. of the establishment and sustainability of agriculture.

The Role of Natural Resource Endowments After,41 the various levels of economic development among

societies were widely explained by differences in geographic and biogeographic conditions. Geographic conditions42 include climate, latitude, soil, rain, orientation of continental axis (…); biogeographic conditions consist of edible plants and animals suitable for domestication and cultivation. They mainly refer to respectively large- seeded grasses and large mammals. It should be noted that geographic and biogeographic conditions do not have separate influence; they have a combined influence on plants and animals. Indeed, every plant or animal has certain habitat and environmental preferences. As such, they can only be cultivated and bred within their tolerance limits.43,15 Environmental factors such as temperatures, precipitation, solar radiation during the growth season, the length of the vegetation period (...) had overall influence on the crops cultivated and the animals bred. 13E.g. prestigious polished axes, furs of scarce animals, jewelry (made from amber or spondylus shell…). 14Many contributions in the literature are emphasizing the role of social competition or feasting to explain the Neolithic transition 15This phenomenon is called the minimum limiting factor

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Citation: Svizzero S. Persistent controversies about the neolithic revolution. J His Arch & Anthropol Sci. 2017;1(2):53‒61. DOI: 10.15406/jhaas.2017.01.00013

Many subsequent works following Diamond’s publication have tried to verify the importance of these conditions as factors influencing the occurrence of the Neolithic transition and in promoting the further economic development of the regions concerned. Some of the necessary conditions for agriculture to emerge are more easily identified when the diffusion of agriculture is studied rather than its origins. Indeed, in some areas the diffusion of agriculture has been hindered by geographical conditions (hills, mountains, rivers, seas). In some others areas, it has even been stopped by disease - in sub- Saharan Africa, cattle herding was not possible due to the presence of tsetse fly or by ecological barriers such as the one that existed in the Carpathian Basin44 where plants and animals reached in this place their tolerance limits and this stopped the diffusion of agriculture from the Balkans.

A central topic in these subsequent works45−48 following Diamond’s publication is about the influence of the timing of the transition to agriculture on further economic development. Implicitly or not, these works consider that institutions only have second-order effects on the economic development.

The role of institutions Following the definition given by North DC,49 institutions

are “a set of rules, compliance, procedures and moral and ethical behavioral norms designed to constrain the behavior of individuals.” In a later essay,50 he added: “If institutions are the rules of the game, organizations and their entrepreneurs are the players. Organizations are made up of groups of individuals bound together by some common purpose to achieve certain objectives. Organizations include political bodies, economic bodies, social bodies and educational bodies”. On the basis of the previous definition, some authors51 argue that the major impact of the environment on economic development runs through its long-lasting impact on institutions.16 In other words, tropics, germs and crops do not affect country incomes directly other than through institutions. Among the various forms of the latter, the implementation of private property rights is considered52 to be one of the main necessary conditions for the Neolithic revolution to occur. To account for the difference of economic development among countries, various types of institutions have been defined:53 inclusive ones favored economic growth whereas extractive ones lead - after a while - to crisis, economic and social collapses. The latter are called “extractive” because such institutions are designed to extract incomes and wealth from one subset of society (the commoners) to benefit a different subset (the elite).

Mutual Causation Between Both Factors Even though natural endowments were important in enabling

agriculture to become established one should not conclude that geographic or biogeographic determinism existed. Indeed, some resources were crucial at one point of time and of less importance later, due to innovation17 or because they became more abundant through trade. Similarly, when we talk about necessary eco- geographic conditions, we immediately think of edible plants and animals suitable for domestication. However, a critical resource may not necessarily be a food resource. For instance, during the Neolithic period agriculture was highly dependent on stone tools, especially on stone axes used for forest clearance. Although they were not a staple food, stone tools were therefore a critical resource for the agricultural 16Including technologies. 17Some stones (e.g. flint or obsidian) were valuable during the Neolithic period and used to make tools and weapons. However, with the introduction of metalworking, they became less valuable.

system indeed some of these stones (especially obsidian) were traded on several hundred kilometers from their origin area54 which confirms that they were highly valuable.

Therefore, resource endowments were important in enabling agriculture to become established while they were not unimportant for its sustainability; institutions assumed increasing importance after agriculture was established and were also important for continuing development. In other words, both factors were important but their relative importance varied along the development path of the agricultural system. For instance, human capital accumulation and intergenerational transmission of knowledge were also necessary conditions;4 consequently a symbol system18 was required for that purpose.

According to a recent publication,55 this combined influence of both factors could be explained through the following mechanism. If we consider any center (e.g. Eurasia) where initially agriculture emerged, we must distinguish between the core and the periphery of this region. In the core (e.g. the Near-East), economic development was important at the beginning but has slowed afterwards. This is because the institutions implemented in the core were extractive. In the periphery (e.g. Northern Europe and Scandinavia), agriculture has been adopted by diffusion and the resulting economic development (including the traits defining “civilization”) occurred later. Despite their later start, these countries are nowadays more developed compared to the Near-Eastern countries - because their institutions were inclusive from the beginning. Therefore, this third view assumes a degree of mutual causation between natural endowments and institutions. In other words, particular types of economic growth facilitated the development of particular institutions and social structures.

Mechanisms bringing about the global spread of agriculture

Current evidence suggests that the Neolithic materialistic culture was introduced to Europe via western Anatolia; this is the so-called neolithization process.56 All Neolithic sites in Europe contain the plants and animals initially domesticated in Southwest Asia: einkorn, emmer, barley, lentils, pigs, goats, sheep and cattle. Genetic data suggest that no independent domestication of animals took place in Neolithic Europe and that all domesticated animals were originally domesticated in Southwest Asia. It is therefore widely accepted that the onset of agriculture in the Near-East triggered a cultural change that diffused farming and associated technologies across Europe starting about 10,000 years ago. The information provided by archaeological remains and the trajectory of straight and short line paths suggest the estimated speed of agricultural spread was approximately 1 kilometer per year.8 Of course there were very significant regional variations in the rate of spread, e.g. unfavorable ecological and geographical factors caused a retardation of its spread to some part of Europe.

Despite these evidences, the Neolithic diffusion or the neolithization process of Europe has always been a controversial issue,57,19 not really solvable with known archaeological methods. Did people from the core invention area move en masse20 to other places bringing their innovations with them? Or did people from other places learn about 18It could be the spoken language and, for the elite, also the written language. 19It should be noted that until recently the same debate was present about the transition to agriculture in Japan. However, it is now widely agreed that the introduction of agriculture and the simultaneous replacement of the Jomon culture by the Yayoi were the result of a major incursion from mainland Asia. 20After Ammerman, A.J. and L. L. Cavalli-Sforza (1971), this massive folk migration is often called the “wave of advance”.

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innovations from trade or other relationships such as intermarriage? In other words, a major debate58 in the study of earliest European farmers is whether they were colonists who settled in the major river valleys of North-Central Europe or whether they were local hunter-gatherers who adopted domestic plants and animals coming from the Near-East?

The migrationist approach or the struggle for life

Among these two alternative demographic scenarios proposed to account for the Neolithic transition, the first one was21 and still is59 the most popular in the academic literature. This scenario is called the demic diffusion model22 or, more usually, the migrationist approach. In the demic diffusion model, the spread of technologies involved a massive movement of people. The demic diffusion is a kind of replacement model. It posits that there was a significant migration of farmers from the Fertile Crescent into Europe. Given their technological advantages these migrants would have displaced or absorbed the less numerous hunter-gathering populace. If the demic diffusion model is the most popular explanation of the neolithization process, it is because it is based on a mechanism similar to competition among species - or natural selection present in biological evolutionary theory.

We know that the Neolithic revolution is featured by a transition from foraging to farming and that both economic systems - food procurement and food production - have advantages and drawbacks. However, agriculture has for a long time ago essentially taken over the world and hunting and gathering is now found only in very marginal and supposedly “backwards” area. Such situation is implicitly explained by the existence of a tradeoff between having more leisure and better nutrition versus simply being able to feed more mouths. Any given person may well choose to have a more varied and interesting diet and more free time than to be able to feed more people but otherwise be more miserable. If the latter option wins out in the end, most explanations in the academic literature are based on a vision which, implicitly or not, is an evolutionary process. As for the development of any given species (a plant or an animal), the development of human societies is assumed to be greatly determined by what strategies produce the most offspring. In any biological evolutionary competition, the strategy that produces the most children generation after generation will eventually win over strategies that allow the production of fewer children no matter how happy or unhappy those children are. So agricultural societies simply fed more people, allowed for larger families and so could push out, absorb or slaughter the hunter-gathering societies in the long run. Therefore, demographic pressure is generally considered to be the prime mover of the Neolithic expansion. As pointed out by Diamond J,41 “A final factor in the transition became decisive at geographic boundaries between hunter-gatherers and food producers. The much denser populations of food producers enabled them to displace or kill hunter- gatherers by their sheer numbers, not to mention the other advantages associated with food production (including technology, germs, and professional soldiers)”.

Neolithization by cultural diffusion Nevertheless, the migrationist approach has two shortcomings:

it minimizes the role of cultural diffusion and overemphasizes the role of competition between HG and farmers. In doing so, it rejects 21This demographic scenario was already present in V. G. Childe (1936). 22This model has been first introduced by Ammerman A. J. and L. L. Cavalli- Sforza (1984).

the possibility that HG could have decided, without constraint, to adopt agriculture. However, the main rationale - which is the most often cited in the literature to explain the immigration of Neolithic farmers from the Near-East to Europe - i.e. the rapid population growth brought about the emergence and development of farming - can be challenged. Indeed, as pointed out by many authors, such as,10 “Archaeologically, there is no evidence for sustained and wide- ranging immigration that would support either the demic diffusion hypothesis or a major continent-wide migration”. Moreover, the presumed competition between HG and farmers, which is implicit in the migrationist approach, does not find support in ecological evidence. Indeed, before the late Neolithic, there is no indication of extensive agriculture such as woodland clearances and environmental degradation i.e. no indication of competition between two economic systems that would have provided a rationale for relocation of HG societies. On the one hand, farmers settled exclusively in specific areas that were suitable for agriculture i.e. in fertile loess area and close to lakes or rivers, the latter being necessary for fields’ irrigation. It should be noted that loess land was the best land for farmers while, with its dense lime stands, it was poor in game and yielded hardly any vegetable produce, i.e. was not consistent with HG economy. On the other hand, HG populations were much more attracted by coastal and lacustrine regions and along major rivers. Since there was no competition between these differing economic strategies, one can conclude that, “The arrival of these colonists does not resulted in any kind of direct violent conflict”.60

Despite the previous conclusions, the total neolithization of Europe by cultural diffusion is not obvious. As stated previously (see section 2), in its early stages, the superiority of agriculture over foraging was uncertain, especially for complex HG societies. This has led to a third explanation of the spread of agriculture, mixing migration and imitation.

The Integrationist Approach Most recent studies,59 as implied by archaeological data, show that

cultural diffusion explains between 30 to 40% of the spread rate of the Neolithic transition in Europe. Thus, cultural diffusion cannot be neglected, but demic diffusion was the most important mechanism in this major historical process at the continental scale.23 Mixed models of diffusion combining migrationist diffusion and cultural diffusion are constitutive of the integrationist approach.10 In this approach, the diffusion of agriculture results from various combinations of three mechanisms. Firstly, it relies on leapfrog colonization rather than on massive folk migration or demic diffusion. This denotes a selective colonization of an area by small groups, who target optimal areas for cultivation (usually, loess land). These groups are thus forming an enclave settlement among native inhabitants or HG.31 Secondly it considers frontier mobility, i.e. small-scale movement of population within contact zones between HG and farmers, occurring along the established social networks, such as trading partnerships, kinship lines and marriages alliances. Thirdly contact exists through trade within the framework of regional or extra-regional trading networks.61 These networks served as channels of communication through which innovations24 spread from farmers’ communities to HG. Archaeologically, ethnographically and ecologically, the migrationist 23By contrast to South-East and Central Europe where both diffusions – demic and cultural – were combined, the spread of farming in coastal west Mediterranean Europe now seems to have involved the rapid transport by sea of a complete package of Neolithic domesticates around 5400 BC by colonizing pioneer farmers. 24Such as cultivation of plants, domestication of animals, pottery (…).

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Citation: Svizzero S. Persistent controversies about the neolithic revolution. J His Arch & Anthropol Sci. 2017;1(2):53‒61. DOI: 10.15406/jhaas.2017.01.00013

approach as well as the cultural one finds little evidence to explain the agricultural diffusion in Europe. Therefore, one can conclude to the superiority of the integrationist approach, as10 did: “…I would argue that the agricultural transition in Europe was, in the main, accomplished by the local Hunter-gatherer communities, with varying degrees of gene flow between hunter-gatherer communities and the settlements of Neolithic farmers. Enduring contact and exchange between the foraging and the farming communities led to the development of agricultural zones, manifested in the archaeological record by enduring cultural boundaries, for example…the Mesolithic/ TRB cultures of north temperate Europe”.

Conclusion In the early twentieth century, archaeologists were steadily

accumulating data about past societies using a conceptual framework based on tools and technology.21 Most important contribution was to re-conceptualize the archaeological data in social terms and to identify a major social transformation the Neolithic Revolution that brought about new way of life and new form of society. Since Childe’s publication, it is widely agreed that the transition from hunting and gathering to farming was one of the most important development in human history. Despite this universal agreement, many debates and controversies among scholars remain about the Neolithic revolution, its causes, features and consequences. These vivid controversies overwhelmed traditional debates between different schools of thoughts and between different scientific fields. They result from the fact that the transition to agriculture encompasses a wide range of causes and consequences that are themselves multidimensional - economic, social, anthropological, ecological, biological, institutional and technical. Future researches on the Neolithic revolution should require, maybe more than on others topics, more interdisciplinary approaches.62−65

Acknowledgements None.

Conflict of interest Author declares there is no conflict of interest in publishing the

article.

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• Title
• Abstract
• Keywords
• Introduction
• The presumed superiority of agriculture in its early ages
• The low attractiveness of the farming way of life
• Adaptation and resilience of hunter-gatherer societies
• The transition to agriculture: a complex and long-term process
• From taming to domestication
• The required stream of innovations
• Explaining the transition to agriculture: push factors, pull factors or both?
• Climate and environmental changes
• Population Pressure
• Unconstrained or voluntary adaptation
• Social competition and feasting
• Nature and culture as factors leading to the establishment and sustainability of agriculture
• The Role of Natural Resource Endowments
• The role of institutions
• Mutual Causation Between Both Factors
• Mechanisms bringing about the global spread of agriculture
• The migrationist approach or the struggle for life
• Neolithization by cultural diffusion
• The Integrationist Approach
• Conclusion
• Acknowledgements
• Conflict of interest
• References

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Climate-change--human-health--and-epidemiological-tran_2015_Preventive-Medic.pdf

Preventive Medicine 70 (2015) 69–75

Contents lists available at ScienceDirect

Preventive Medicine

journal homepage: www.elsevier.com/locate/ypmed

Review

Climate change, human health, and epidemiological transition

Bruce Barrett 1, Joel W. Charles, Jonathan L. Temte University of Wisconsin School of Medicine and Public Health, Department of Family Medicine, University of Wisconsin—Madison, 1100 Delaplaine Street, Madison, WI 53715, United States.

E-mail address: [email protected] (B. Ba 1 Fax: +1 608 263 5813.

http://dx.doi.org/10.1016/j.ypmed.2014.11.013 0091-7435/© 2014 Elsevier Inc. All rights reserved.

a b s t r a c t

a r t i c l e i n f o

Available online 28 November 2014

Keywords: Climate change Environment Epidemiology Global warming Population health

The health of populations depends on the availability of clean air, water, food, and sanitation, exposure to pathogens, toxins and environmental hazards, and numerous genetic, behavioral and social factors. For many thousands of years, human life expectancy was low, and population growth was slow. The development of technology-based civilizations facilitated what Abdel Omran called “epidemiological transition,” with increasing life expectancy and rapid population growth. To a large extent, the spectacular growth of human populations during the past two centuries was made possible by the energy extracted from fossil fuels. We have now learned, however, that greenhouse gases from fossil fuel combustion are warming the planet's surface, causing changes in oceanic and atmospheric systems, and disrupting weather and hydrological patterns. Climate change poses un- precedented threats to human health by impacts on food and water security, heat waves and droughts, violent storms, infectious disease, and rising sea levels. Whether or not humanity can reduce greenhouse gas emissions quickly enough to slow climate change to a rate that will allow societies to successfully adapt is not yet known. This essay reviews the current state of relevant knowledge, and points in a few directions that those interested in human health may wish to consider.

© 2014 Elsevier Inc. All rights reserved.

Contents

Conflict of interest statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73

In his seminal 1971 paper “The epidemiological transition,” Abdel Omran put forth a conceptual framework linking the many facets of ep- idemiology with the forces behind population dynamics, emphasizing the changing nature of structural influence (Omran, 1971). At that point in human history, clear and convincing evidence had emerged documenting a “transition in which degenerative and man-made dis- eases displace pandemics of infection as the primary causes of morbid- ity and mortality.” Around the same time, scientists were beginning to report that human emissions of greenhouse gases were causing global warming and climate change (Benton, 1970; Gast, 1971; Manabe and Wetherald, 1975).

Omran divided human history into three major epidemiological epochs. The first, or “Age of Pestilence and Famine,” lasted for thousands of years and was characterized by cyclic patterns of localized population growth ended by major die-offs, often precipitated by war, civilization

rrett).

collapse, and/or epidemic infection. In the second “Age of Receding Pan- demics” Omran wrote that “mortality declines progressively; and the rate of decline accelerates as epidemic peaks become less frequent or disappear.” In Omran's third epoch, the “Age of Degenerative and Man-Made Diseases,” “mortality continues to decline and eventually approaches stability at a relatively low level. The average life expectancy at birth rises gradually until it exceeds 50 years. It is during this stage that fertility becomes the crucial factor in population growth.”

For more than a century, knowledge regarding “degenerative and man-made diseases” has increased at a dizzying pace. The era of using science to identify and respond to human-engendered disease arguably began when John Snow in 1854 traced the source of a London cholera epidemic and then removed the Broad Street pump handle to limit fur- ther contagion (Koch and Denike, 2009; Paneth, 2004; Tulchinsky, 2010). Not long afterwards, work by Louis Pasteur (Bordenave, 2003) and others led to a remarkably broad and detailed understanding of infectious disease, much of which was caused or exacerbated by popu- lation growth, urbanization, and crowding. These discoveries provided rationale for large scale potable water, sanitation and public health

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70 B. Barrett et al. / Preventive Medicine 70 (2015) 69–75

systems, which in turn facilitated even more rapid population growth in the world's emerging urban centers (Szreter, 2003).

More recently, the cardiovascular disease epidemic has been investi- gated and addressed at multiple levels, with several proven strategies to normalize blood pressure, prevent clots, and reduce cholesterol. For several cancers, useful screening and/or effective treatments are now available. Environmental, occupational and behavioral strategies have been even more effective. Science demonstrating tobacco's ill effects was followed by widespread and effective smoking cessation cam- paigns. Seat belts, safer automobiles and better roadways have greatly reduced motor vehicle casualties. Water and air pollution have im- proved in many locales, with measurable improvements in cardio- respiratory morbidity and mortality. Mostly missing from the medicine and public health discourse, however, has been the realization that massive-scale human activity is radically altering the atmosphere and surface of the planet, and that the basic functionality of our life-sustaining ecosystem can no longer be taken for granted.

It took several million years for anthropoid apes to evolve to ana- tomically modern human form, and then another 400 centuries before our Homo sapiens sapiens ancestors began to show their prowess. Fol- lowing the advent of agriculture around 10,000 years ago, populations began to increase substantively, spreading out across the globe, forming cities, kingdoms, and civilizations. By 1800, there were approximately a billion (1,000,000,000) people on the planet. This doubled to around 2 billion by 1922, 4 billion by 1974, and 7 billion today. New technolo- gies and systems of production led to rapid and widespread develop- ments in agriculture, transportation and sanitation, with ever-increasing numbers of people living longer, more productive, and more consumptive lives.

The past two centuries of explosive population growth were facili- tated in large part by the burning of fossil fuels. Mechanization of agri- culture, combined with increasing agrochemical inputs, not only fertilizers, but also pesticides, allowed huge increases in crop productiv- ity, which in turn fueled population growth. Exploitation of coal, oil, and natural gas yielded vast and rapid systems of transport, electrical power, and a globalized economy of relatively inexpensive and widely available products, services, and information exchange. This modern era of explo- sive growth, however, cannot continue unabated, given the finite nature of the resources and the ecological threats that unrestrained consump- tion poses. Having survived (so far) the specter of nuclear war, human- ity is now facing the fundamental contradiction of continued growth trajectories in the face of resource and ecosystem limitations. If we successfully respond to these challenges and transition to a sustainable future, humanity may enter a new age, characterized by much more prudent use of energy, among other things. These ideas are not entirely new. In 1798, Reverend Thomas Robert Malthus noted that finite resources, such as arable land, would eventually be overcome by sustained population growth: “The power of population is indefinitely greater than the power in the earth to produce subsistence for man (Malthus, 1798).”

Similar ideas have been put forth many times since, most notably in 1968 by Paul and Ann Ehrlich in The Population Bomb (Ehrlich, 1968) and then in 1972 in The Limits to Growth (Meadows et al., 1972) by Donella Meadows and colleagues from The Club of Rome, who showed with then state-of-art computer modeling that finite resources are in- compatible with unlimited economic and population growth. Similar notions were initially explored in the ecological literature by writers such as Pianka (1970) and MacArthur and Wilson (1967) who showed that reproduction rates and longevity dynamics combined with envi- ronmental constraints, such as availability of food and water, lead to “boom and bust” cycles, and, occasionally, to species extinction.

What is relatively new to this discourse, however, is the realization that human-emitted greenhouse gases are warming the planet, melting the ice caps, raising the oceans, and increasing the frequency of droughts, floods and extreme weather events. There is no longer any reasonable doubt that global warming is occurring, and that this is due primarily to

human activities (IPCC Working Group 1, 2013; IPCC Working Group 2, 2014; National Academy of Sciences, 2014; National Climate Assessment and Development Advisory Committee, 2014). There is also very little doubt that ensuing changes in climatic patterns will lead to myriad ad- verse outcomes, including heat waves, droughts, and increased frequency and violence of major weather events (Honda et al., 2014; Kravchenko et al., 2013; Lane et al., 2013; Stanke et al., 2013). These will in turn accelerate the already monumental and tragic loss of biodiver- sity, (Cardinale et al., 2012; Hooper et al., 2012; Mayhew et al., 2008; Pimm et al., 2014; World Resources Institute, 2005) and will promote the spread of infectious diseases such as malaria and gastrointestinal in- fections (De Luca and Giraldi, 2011; Murray et al., 2013; Patz and Reisen, 2001; Ramasamy and Surendran, 2011). The billion or so people living on low-lying islands and coastlines will need to immigrate, adapt, or perish (McMichael et al., 2012a). This will place pressure not only on those most directly threatened, but on political and economic systems in neighboring countries, and indeed, on all societies. Health outcomes, psychosocial stresses and behavioral responses cannot be predicted with confidence, but the broad outlines are extremely concerning (Patz et al., 2014).

Since “The epidemiological transition” was first published in 1971, the scientific understanding of anthropogenic global warming has ma- tured. While some details are still emerging, the broad outlines are in- controvertible. The burning of fossil fuels has released hundreds of gigatons of greenhouse gases, most notably carbon dioxide (CO2), which has increased in atmospheric concentration from pre-industrial levels of 280 parts per million (ppm) to more than 400 ppm today (IPCC Working Group 1, 2014). This has already contributed to a mean surface temperature increase of 0.9 °C, and an average ocean surface rise of more than 20 cm (Intergovernmental Panel on Climate Change, 2007). Current projections suggest that average global temperatures will rise to 2 to 6 °C above the levels in which humanity evolved (IPCC Working Group 1, 2014). Even the most conservative projections con- clude that this will constitute the most rapid change of atmospheric composition and global temperature ever occurring in our planet's 4.5 billion year history (National Research Council, 2013a). It will also accel- erate what is already by far the worst wave of plant and animal extinc- tions our planet has experienced, with approximately 3 species disappearing each hour and a third of all vertebrates disappearing in less than 50 years, an extinction rate of approximately 1000 times evolutionary background averages (Cardinale et al., 2012; Hooper et al., 2012; Mayhew et al., 2008; Pimm et al., 2014; World Resources Institute, 2005).

Following the advice of numerous scientific bodies, the 2009 Copen- hagen Accord called for policies that would limit average planetary warming to no more than 2.0 °C (Ramanathan and Xu, 2010). Current best estimates conclude that no more than an additional 400 to 800 gigatons of CO2 can be added to the atmosphere if we are to have a rea- sonable chance of staying within this limit (IPCC Working Group 1, 2014; Meinshausen et al., 2009). Burning all proven coal, oil and gas re- serves, however, would produce around 2800 gigatons, likely leading to temperature rises of 3 to 6 °C (Meinshausen et al., 2009). Warming of this magnitude could trigger positive feedback loops pushing atmo- spheric and oceanic systems past a series of crucial tipping points (Hansen et al., 2013; National Research Council, 2013a). As the ice cover melts, less of the sun's energy is reflected, increasing heating effects. Melting of the northern tundra would release vast quantities of methane and other greenhouse gases, further accelerating the pro- cess. Conservative models project that water from melting of the Green- land and Antartic ice sheets will combine with thermal expansion to yield sea level rises of 200-600 cm by the year 2100 (IPCC Working Group 1, 2014). Adding in tipping points and positive feedback loops, we may be looking at ocean surface rises of 10 m or more by the middle of next century (Hansen et al., 2013; Joughin et al., 2014; Rahmstorf, 2010; Rignot et al., 2014). Thirteen of the world's 20 largest cities are located on a coastline. More than 2 billion people live within 60 miles

71B. Barrett et al. / Preventive Medicine 70 (2015) 69–75

of the sea. The scale and complexity of relocation and adaptation efforts needed for sustainable survival in such a scenario are difficult to fore- cast. Current best estimates project the number of environmental refu- gees in the tens to hundreds of millions within the next few decades (McMichael et al., 2012a). In the longer term, global warming victims and refugees could number in the billions, as seas would rise by more than 200 ft if all the ice melts (Folger, 2013).

The broad outlines of the effect of global warming on human health are only beginning to emerge (Costello et al., 2011; Ebi, 2011; McMichael et al., 2012b; Patz et al., 2007, 2014; Rapley, 2012). One au- thoritative study published in the Lancet in 2009 (Costello et al., 2009) described six categories related to projected health effects: 1) changing patterns of disease, 2) food, 3) water and sanitation, 4) shelter and human settlements, 5) extreme weather events, and 6) migration. While useful, these categories are overlapping and interactive. Changing climatic patterns are causing both droughts (Battisti and Naylor, 2009; Stanke et al., 2013) and floods, (Lane et al., 2013; Woodward et al., 2013) directly impacting potable water availability (Bates et al., 2008) and agricultural productivity (Lobell and Gourdji, 2012). Extreme weather events are dangerous and costly, and may threaten water and sewage systems, (Lane et al., 2013; Schmeltz et al., 2013) reduce agri- cultural output, (Battisti and Naylor, 2009; Friel et al., 2009; Lobell et al., 2008) and damage housing and economic infrastructure (Lane et al., 2013; Sauerborn and Ebi, 2012). Malnutrition and poor sanitation lead to more frequent and virulent outbreaks of infectious disease. Eco- system changes can increase prevalence of vectors and reservoir hosts, which when combined with decreased human host resistance, can lead to deadly epidemics of cholera, dengue, cryptosporidia, West Nile, hantavirus, Lyme disease, or malaria (Bai et al., 2013; De Luca and Giraldi, 2011; McMichael et al., 2006; Patz et al., 2014; Ramasamy and Surendran, 2011). All of these climate-related effects would increase personal and social vulnerability, promoting conflict and/or migration (Bronen and Chapin, 2013; Burke et al., 2010; McMichael et al., 2012a; National Research Council, 2013b).

According to Fritze, mental health impacts of climate change will emerge in three distinct ways (Fritze et al., 2008). First, direct effects will occur through personal experience of extreme weather events, as has been observed among victims of various extreme weather events (Amstadter et al., 2009; Bronen and Chapin, 2013; Hart et al., 2011; Larrance et al., 2007; Schmeltz et al., 2013). Second, disruptions in so- cial, economic and environmental determinates of health will exacer- bate a variety of mental health conditions (Blashki et al., 2007). Third, individuals may experience emotional distress and anxiety about the fu- ture with the realization and understanding of the consequences of, and threats posed by, global warming (Fritze et al., 2008).

While a few geographical areas may see increased agricultural pro- ductivity, most experts agree that the overall trend will be towards worsened food insecurity (Lobell and Gourdji, 2012). Shorter winters and higher CO2 concentrations stimulate photosynthesis, potentially in- creasing crop yields by as much as 1.8% per decade (Lobell et al., 2011). At the same time, however, warmer temperatures increase plants' need for water, leading to drought and heat-related crop failure. Best current models put the plausible net change in global crop yield between 2% gain and −3% loss per decade, compared to today's productivity (Lobell and Gourdji, 2012). The bigger picture, however, includes not only average global production, but localized effects of droughts, floods, and insect damage. Increasing costs involved with transportation, irri- gation, fertilizers and pesticides will reduce local- and regional-level re- silience to climatic threats, creating life-threatening crises for the world's more vulnerable populations. The annual number of food emer- gencies, as defined by the United Nations World Food Program, has risen from 15 per year in the 1980s to 30 per year today (Schneider and Garrett, 2009). Food insecurity already threatens 1 in 7 of earth's population, with more than a billion people acutely or chronically mal- nourished on any given day. One study published in Science projected severe temperature increases in the tropics and sub-tropics, where

half of humanity lives, and warned that “human consequences of global climate change could be enormous” (Battisti and Naylor, 2009).

Systems providing clean drinking water and sanitary sewage dispos- al are among the most important achievements of the industrial revolu- tion, and a major driver of the second phase of Omran's epidemiological transition. Nevertheless, close to a billion people lack access to potable water, and more than 2 billion live without sanitary sewage systems (United Nations and World Health Organization, 2014). While this is clearly an area amenable to improvement regardless of warming- induced climate change, environmental threats to water security are sure to worsen. Some water-stressed regions will become hotter and more arid, decreasing food production, while others will suffer more frequent and consequential flooding, impacting water and sanitation as well as agriculture. Perhaps the most worrisome long term water- related threat comes from loss of mountain glaciers, which feed rivers providing water for more than a sixth of the world's population (Beniston and Stoffel, 2014; Laghari, 2013). Even if average annual rain and snowfall levels remain adequate, seasonal variations will in- crease, leading to more and worse downpours and flooding, and longer and more threatening dry seasons.

There is continuing uncertainty regarding projected increases in the frequency and severity of extreme weather events. Heat waves, heavy precipitation events, floods, droughts, and windstorms at sea and on land have all increased over the past few decades (Coumou and Rahmstorf, 2012; Diffenbaugh et al., 2013; Lane et al., 2013). However, the inherent variability in the measurement of these events has meant that some of these increases have not yet reached statistical signifi- cance. For instance, strong trends in worsening storms have been ob- served in the North Atlantic and the Indian Oceans (Emanuel, 2005). Heat waves are especially life-threatening, with the 2003 European heat wave responsible for the loss of more than 60,000 lives (Analitis et al., 2013; Filleul et al., 2006; Orru et al., 2013). Heat- and drought- related events will threaten some populations, but others will be faced with floods and freezing, as climate change disrupts moderating influ- ences such as temperate weather patterns and ocean currents.

Substantive questions emerge related to responsibility, ethics and, equity. Clearly, the negative health consequences of climate change will disproportionally harm the world's poor and disadvantaged popu- lations, who have in general contributed the least towards greenhouse gas emissions (Anstey, 2013; Bernstein and Rice, 2013; Bowen and Friel, 2012; Donohoe, 2003; Singh, 2012). For example, expected mortality impacts of environmental change have been projected to be as much as 500 times higher in African than European populations (McMichael et al., 2008). Disadvantaged people suffer first and worst when subjected to new threats, as they already have multiple risks and little margin of safety.

Appropriate response to the threats that climate change presents varies dramatically by locale (Patz and Olson, 2006). Poor, low-lying and under-developed regions are under the greatest threat, but also have the greatest potential cost-effective gain from appropriate action (Bowen and Friel, 2012). Improvements in built infrastructure targeting potable water, sewage, and storm run-off are of high priority, as are in- vestments in sustainable agriculture, housing, transportation and edu- cation. Emergency response systems able to alert people and provide safety during extreme weather events are needed, as are new or better sea walls in many coastal locations. Basic health services, including vaccination, maternity and infant care, rehydration, injury prevention and treatment, and infectious disease prevention and treatment will strengthen resilience and facilitate gradual long-term health gains. Long term sustainability in the developing world is crucially dependent on population stabilization (Carlowe, 2009).

The main challenge of the developed world is to vastly reduce green- house gas emissions from fossil fuel combustion, and to maintain a pro- ductive economy while doing so (National Climate Assessment and Development Advisory Committee, 2014). Climate science tells us that in order to maximize the likelihood of a sustainable future in a livable

72 B. Barrett et al. / Preventive Medicine 70 (2015) 69–75

world, only a small proportion of earth's remaining fossil fuel reserves can be burned for energy. In this light, seeking new coal or oil deposits, or exploiting existing tar sand deposits, does not appear to be compati- ble with a long-term livable future. Given the high levels of energy con- sumption that people in the developed world have come to expect, the challenges of rapidly moving to a net-zero carbon economy are formida- ble, indeed. But this is what it seems that we must do, if we are to leave our descendants with both civilization and planetary ecosystem intact.

Several authors have begun to investigate the potential health ben- efits of transition to a low-carbon economy (Anon, 2000; Bouzid et al., 2013; Cheng and Berry, 2013; Friel et al., 2011; Haines, 2012; Holmner et al., 2012; McMichael, 2013; Patz and Hahn, 2012). For in- stance, dramatic reductions in the use of animal-based foods and energy-intensive transportation (eg. personal vehicles, jet travel, eleva- tors, etc.) could lead to substantive reductions in obesity, diabetes and heart disease (Friel et al., 2009; Haines and Dora, 2012). Improvements in urban transportation systems could both reduce greenhouse gases and improve physical fitness, leading to better health (Schmeltz et al., 2013; Woodcock et al., 2009). Reducing the burning of fossil fuels, espe- cially coal, will lower rates of cardiac and respiratory disease and death attributable to air pollution (De et al., 2013; Li et al., 2013; Thurston, 2013). Better insulation and more efficient heating and cooling systems will reduce household energy costs, and savings could be shifted to- wards health-enhancing activities and products, such as more fruits and vegetables, bicycles and other exercise equipment, and healthy rec- reational activities (Wilkinson et al., 2009).

Transportation of goods and people requires massive energy expen- diture, nearly all of which comes from fossil fuels. Shifting towards more localized economies could substantively reduce humanity's carbon foot- print, enhancing both community and personal health. Efforts at reduc- ing carbon footprints should take into account efficiencies. For example, transportation of efficiently produced agricultural products by sea or rail might well leave less of an imprint than trying to produce them locally. Nevertheless, air transport of goods and people will need to be radically reduced if we are to transition to a sustainable carbon neutral economy.

Transmission of electrical energy from source to consumer involves large scale inefficiencies, often involving substantive energy losses. Locally-produced energy based on wind, solar, hydroelectric, geother- mal and tidal sources would increase transmission efficiency, provide good jobs, and empower communities to take responsibility for their energy use. Engaging local political, financial, education and health sec- tors in planning and carrying out energy system transformation could lead to feasible, efficient, and sustainable solutions that national or state level planning might overlook. While it is undoubtedly true that the scale of necessary change will require substantive upfront input, most economic models suggest that investing in preventive efforts now will cost far less than paying for the more expensive consequences later (Cooke, 2013; Haines, 2012; Knowlton et al., 2011; Nemet et al., 2010; Pindyck, in press; Ramanathan and Xu, 2010; Stern, 2013; Stern and Taylor, 2007). It is also true that the projected health co-benefits of reducing fossil fuel use are substantive, and will begin to accrue al- most as soon as mitigation efforts are initiated (Cheng and Berry, 2013; Nemet et al., 2010; Roberts, 2009; Thurston, 2013; West et al., 2013).

A few authors have begun to envision the changes that the health care system will need to undertake in response to climate change (Blashki et al., 2007; Ebi, 2011; Parker, 2011; Podein and Hernke, 2010; Richardson et al., 2009; Rosenblatt, 2005; Walker et al., 2011). Several of these have to do with infrastructure and technology. Health care buildings, especially hospitals, are among the most energy- intensive of facilities. Medical interventions, including pharmaceuticals and surgical procedures, depend on highly technical and fossil fuel based energy-intensive infrastructures. Reliance on unnecessary labora- tory testing, combined with costly, risky, and marginally effective phar- maceutical intervention, has reached epidemic proportions (Berwick

and Hackbarth, 2012; Brody and Light, 2011; Grady and Redberg, 2010; Lenzer, 2012). Overdiagnosis (Gotzsche et al., 2009; Hoffman and Cooper, 2012; Welch et al., 2011) and overtreatment (Abramson, 2004; Brownlee, 2007; Whitaker, 2010) for those that have health in- surance may lead to as much harm as lack of access for those that do not (Himmelstein et al., 2001; Lasser et al., 2006). As Ivan Illich warned in 1976 (Illich, 1976), we physicians and our associated biotechnical workforce may have – to some extent – created a “medical nemesis” during the “expropriation of health.” By reducing energy-intensive and low-yield interventions, and by transitioning towards practices aimed at psychosocial as well as physical health, we can support a tran- sition towards a sustainable future. This will involve adopting new paradigms as well as practical solutions, and will require new ways of training health care personnel to deal with new and evolving threats to human health.

Gradually shifting towards more appropriate and effective methods – such as health-enhancing behaviors, nutritional guidance, social sup- port and environmental safety – could integrate well with locally- based and health-promoting agriculture, water, sewage and energy sys- tems. Today, most Americans drive automobiles to get to their doctors. In the future, it might make more sense to use email and telephone for most communications, to have neighborhood-based clinicians available for direct interaction and physical exam when useful, and to reserve analysis of biological specimens and diagnostic imaging for cases where those modalities are proven to be cost-effective (a small fraction of current use, we believe). Delivery of effective public health interven- tions such as childhood immunization and annual influenza vaccination could be handled by nurses in schools, workplaces, or neighborhood community centers. Health education and promotion, (Patrick et al., 2012) including exercise and nutrition classes, could be led by public community health workers in easily accessed locations. Psychological counseling, social work services and dentistry could also be located at the community or neighborhood level, so that active transport (walk- ing, biking) could replace energy-intensive and polluting transportation as the predominant mode of accessing health care. To some extent, these sorts of things are already available in places such as Denmark, The Netherlands, and Cuba, where positive examples can be learned from, as high cost high impact systems in America and elsewhere seek to reduce their carbon footprints.

Health care systems are composed of numerous individuals, all of whom have the opportunity and perhaps moral obligation to act. Given the gravity of the situation, health professionals have a duty to ed- ucate their patients and the public, and use their societal positions to call for policies discussed above. First, interested health professionals should lobby their local medical schools and residency programs to adopt curricula in climate change health impacts, and health-related mitigation and adaptation. Second, doctors, nurses and allied health professionals should actively participate in groups like the European Health and Environment Alliance, the US Climate Health Alliance, and Health Care Without Harm (both US and EU), where they can learn how to educate and motivate institutional leadership and elected offi- cials towards better policies and practices. Third, health professionals should mobilize the power of their provider groups and representative organizations such as the American Medical Association and various European medical societies to put lobbying power behind the climate policy statements that many of these groups have already made. Fourth, and finally, to reach the public's ears, health professionals must work with well-connected environment and climate advocacy groups, such as the Sierra Club, 350.org, and the World Wide Fund for Nature.

For some readers, especially those not yet convinced of the basic findings of climate science, these ideas and proposals may seem difficult to accept, or even to discuss or consider. Nevertheless, the evidence is robust and the basic science is sound, with tremendous consensus among those who have studied it (Cook et al., 2013; IPCC Working Group 2, 2014; Patz et al., 2014). For those that do not accept the scien- tific consensus, perhaps the health co-benefits expected to come from

73B. Barrett et al. / Preventive Medicine 70 (2015) 69–75

transitioning towards a carbon-neutral economy will be enough to gain acceptance, if not active support. For those whose minds are open to sci- entific findings, and are able to understand and accept the enormity of the climate-related challenges facing humanity, this essay may make more sense, and will hopefully stimulate thought and conversation to move us forward. As Costello reminds us, neither attitudes of climate change de- nial nor catastrophic fatalism should be used to guide societal-level deci- sion making (Costello et al., 2011). Instead, we need to take a hard look at what the best science is telling us, and then work towards pragmatic ap- proaches to maximize benefits and minimize harms.

Which brings us back to Omran's “epidemiological transition,” a foundational theory these past few decades (Defo, 2014; Feinleib, 2008; Riley and Alter, 1989; Szreter, 2004). Taking a broad view of human history, it is clear that the forces shaping such basic parameters as fertility, mortality and life-expectancy are complex, changing, and influenced by a number of factors, including structural influences resulting from large-scale human activities. Human health – mental and physical, symptomatic and functional – is influenced by complex underlying phenomena inextricably linked to the world in which we live. Over several centuries, unprecedented technological change has fa- cilitated revolutions in agriculture, potable water and sewage, public health and medical care that have allowed tremendous increases in life-expectancy and population growth. Until recently, widespread be- lief in the power of technology and economic productivity to improve human lives has yielded a false promise that our collective future was assured. Until recently, the physical and biological nature of that world was understood to be relatively constant, with the assumption that our actions could not markedly influence the habitability of the planet as a whole. But now we know better. Widespread burning of fos- sil fuels, combined with the destruction of forests, has already influ- enced earth's temperature, sea level, and climate, and is threatening to wreak havoc on an unprecedented scale. Our ability to keep average global warming to the 2 °C considered safe by our planet's most knowl- edgeable scientists may be the major determinant of whether Omran's “Age of Degenerative and Man-Made Diseases” will continue, or wheth- er we will be entering an age of civilization collapse and population de- cline, as resource-depleted and socially-stressed societies fail to adapt to a heat-stressed planet with new coastlines, unpredictable water re- sources and agricultural capacity, and a small fraction of the biodiversity that a billion years of evolution has provided.

Conflict of interest statement

The authors declare that there are no conflicts of interests.

Acknowledgments

We would like to thank our colleagues in the University of Wiscon- sin, School of Medicine and Public Health, and our home Department of Family Medicine, and especially Mary Checovich for copy-editing, for- matting, and diligently assisting with the submission and review pro- cess. We would also like to acknowledge the thousands of scientists across the globe who have worked tirelessly to understand the nature and impact of anthropogenic global warming and climate change. Final- ly, we would like to dedicate this paper to Jeff Patterson DO, physician, teacher, humanitarian, and Physicians for Social Responsibility leader and activist, who died unexpectedly earlier this year. Bruce Barrett is supported by a Midcareer Investigator Award in Patient-Oriented Re- search (K24AT006543) from the National Centers of Complementary and Alternative Medicine at the National Institutes of Health.

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• Climate change, human health, and epidemiological transition
• Conflict of interest statement
• Acknowledgments
• References

Changing-health-in-China--re-evaluating-the-epidemiological-_2004_Health-Pol.pdf

Health Policy 67 (2004) 329–343

Changing health in China: re-evaluating the epidemiological transition model

Ian G. Cook∗, Trevor J.B. Dummer1

School of Social Science, Liverpool John Moores University, Henry Cotton Campus, 21–25 Webster Street, Liverpool L3 2ET, UK

Received 29 January 2003; accepted 15 July 2003

Abstract

This paper reviews the changing health situation in China, which has shown remarkable improvement in the 50 years since the founding of the People’s Republic of China (PRC) in 1949. At first sight this improving health situation follows the classical epidemiological transition model. Just three decades ago health in China was characterised by high rates of infectious disease and early mortality (diseases of poverty) in a mainly peasant society. More recently infectious disease rates have decreased, with cor- responding and extended morbidity and mortality associated with an aging population in a rapidly urbanising society. This process has given rise to new health problems, including chronic and degenerative diseases (diseases of affluence). Nonetheless, while there is some validity in the application of the epidemiological transition concept, further analysis demonstrates that China faces a new epidemiological phase, characterised by increasing life expectancy and diseases of affluence coupled with the emergence and re-emergence of infectious diseases. We demonstrate that China’s state policy plays a major role in defining the parameters of health in a Chinese context. We conclude that, today, China is faced with a new set of health issues, including the impact of smok- ing, hypertension, the health effects of environmental pollution and the rise of HIV/AIDS; however, state policy remains vital to the health of China’s vast population. The challenge for policy is to maintain health reform whilst tackling the problems associated with rapid urbanisation, widening social and spatial inequalities and the emergence of HIV/AIDS and other infectious diseases. © 2003 Elsevier Ireland Ltd. All rights reserved.

Keywords: People’s Republic of China; Epidemiological transition; Health transition; Diseases of poverty; Diseases of affluence; State policy

1. Introduction

On 1st October 1949 the People’s Republic of China (PRC) was founded. The new government was faced by a complex and difficult situation, including

∗ Corresponding author. Tel.:+44-151-231-4071; fax: +44-151-231-4359.

E-mail addresses: [email protected] (I.G. Cook), [email protected] (T.J.B. Dummer).

1 Tel.: +44-151-231-4063.

an economy and population shattered by years of war, both with Japan and between the Communist Party and the Guomindang. In health terms, floods and famine had taken a huge toll from the nineteenth cen- tury onwards, exacerbated by increasingly high levels of poverty. Such conditions enabled health threats, including malaria, typhus and other infectious and parasitic diseases, to flourish. The Chinese Commu- nist Party (CCP) sought to overcome these increasing health problems via a range of measures linked to wider socio-economic development policy. Of these,

0168-8510/$ – see front matter © 2003 Elsevier Ireland Ltd. All rights reserved. doi:10.1016/j.healthpol.2003.07.005

330 I.G. Cook, T.J.B. Dummer / Health Policy 67 (2004) 329–343

the ‘barefoot doctors’ campaign associated with the rise of the communes in the late-1950s is probably the best known, but related policies included the utili- sation of traditional Chinese medical practices as well as Westernised approaches, investment in the public health sector, and improved sanitation practices as part of the drive to improve the living conditions of the peasantry. These policies were markedly success- ful. By the late-1990s, China’s health situation had improved to such a level that life expectancy had dou- bled to 70 and infant mortality—a key indicator of a nation’s development—declined from 300 per 1000 births in 1950 to 31 per 1000 births in 1999[1,2].

The concept of the epidemiological transition pro- vides a useful model to describe and analyse the changing health situation in rapidly developing coun- tries [3]. The concept provides a framework to inter- link changing mortality and morbidity patterns with population change and developments associated with modernising societies[4,5]. The classical epidemi- ological transition model moves through a series of three stages linked to a country’s development: the age of pestilence and famine; the age of receding pan- demics; the age of degenerative and human induced diseases[4]. Initially, within undeveloped societies, mortality is associated with famine and infectious dis- ease epidemics (diseases of poverty), but as society de- velops there is a decline in infectious diseases coupled with increasing mortality and morbidity caused by lifestyle and degenerative (chronic) diseases that are associated with increasing life expectancy (diseases of affluence). The term “health transition” is increasingly used in place of the epidemiological transition be- cause it is considered more wide-ranging, encompass- ing cultural, social and environmental determinates of health, focusing on more than just improvements in medical and public health care[5]. Developments to the classical epidemiological (health) transition model have been proposed where some improvements in health are apparent as a result of medical devel- opments and socio-economic changes, but uneven development results in many continuing and emerging health problems[5,6]—such refinements are applica- ble to countries such as China. Indeed Islam and Tahir [7] argue that many developing nations face health problems in a new health transition phase, health problems that are associated with rapid unplanned urbanisation, emerging and re-emerging infectious

diseases and increasing obesity linked to the nutri- tional transition[8]. Further Heuveline et al.[9] show that whilst the global burden of infectious diseases has fallen, in line with global development, health problems facing the world’s poorest people are still dominated by infectious diseases—importantly, China accounts for 20% of the world’s poor. Thus, such a complexity of problems presents fresh challenges for health planning and policy development in China.

This paper reviews China’s changing health situa- tion since before the founding of the PRC in 1949 to the present day, via application of the epidemiological (health) transition model, in order to consider whether the health improvements are merely an inevitable consequence of the PRC’s development trajectory, or whether they more fundamentally reflect state policy, and the conflicting health priorities with which the Chinese state has grappled. We consider the applica- bility of the concept of the epidemiological (health) transition. We begin by summarising the health situ- ation in the years before the PRC was founded.

2. Pre-revolution health in China

The pre-revolutionary health situation in China was poor. Worth [10] summarises common diseases, in- cluding:

• enteric infections, such as typhoid fever, bacillary dysentery and cholera;

• hookworm disease; • childhood measles (often fatal), smallpox epi-

demics, diphtheria and tuberculosis, malaria, kala- azar, schistosomatosis, tetanus of the umbilical cord, venereal diseases;

• more widely, high death rates caused by impact of floods, drought, war or epidemic which we would refer to as ‘systemic breakdown’.

These epidemics and threats had a variable spatial impact. The Huang He (Yellow River) was known lo- cally as ‘China’s Sorrow’, and the traditional heartland of north China was the locus of floods or famine ev- ery 2–3 years. There were 1500 recorded instances of the Huang He bursting its banks in a recorded history of more than 3000 years, while 1056 droughts were recorded from 206b.c. to 1949 [1]. At their worst, such disasters caused immense loss of life, while at

I.G. Cook, T.J.B. Dummer / Health Policy 67 (2004) 329–343 331

Table 1 Health in pre-revolution China

Causes of death and health problems in pre-revolution China Source

Annual death rate from tuberculosis in the 1930s which ranged from 208 per 100,000 in a rural northern setting through to 500 per 100,000 in an urban, southern location. In 1948, 18.2% of students at one university in Beijing were found to have active tuberculosis.

[10]

4–5% of villagers died of kala-azar in the plains between the Huang He and the Yangtze, especially children and young adults (early-1930s).

[10]

10 million cases of schistosomiasis and 50 million cases of hookworm (1930s). In parts of Zhejiang province, half of deaths were attributed to schistosomiasis or complications (1936).

[10,11]

900,000 deaths caused by the deliberate flooding of the Huang He by the nationalist Guomindang government to delay invading Japanese forces in 1938. An estimated 10 million people lost their homes, with 50 million directly affected, when the Yangtze flooded in 1931.

[1]

9–13 million deaths in 1876–1879 from a catastrophic drought across the four provinces of Shaanxi, Shanxi, Henan and Hebei. 500,000 deaths and nearly 20 million declared destitute in 1919 following a drought which hit Hebei especially, plus Shandong, Shanxi, Shaanxi and Henan provinces.

[1]

best they would have a marked impact on the health profile of the peasantry especially.Table 1summarises the major causes of death and health problems in pre-revolution China.

In the face of such enormous problems, and in part prompted by the rise of communism, the nationalist Guomindang government sought to establish a health care system. Prior to this the health care system com- bined traditional medicine with some Western-based approaches. Their law, however, did not run over the whole country, half or more was controlled by warlords, and the Japanese, whose full invasion from their base in Manchuria (Manzhouguo) began in 1937. The founder of the Guomindang, Dr. Sun Yatsen, was himself a western-trained medical doctor, but one un- fortunate by-product of a clash between modern west- ern techniques and training compared with traditional medicine practitioners was the marginalisation and exclusion of the latter. These traditionalists—many of whom were scholarly and employed acupunc- ture, moxibustion and traditional herbal remedies— numbered up to 500,000 across the country. But such resources as were available were directed towards the 12,000–20,000 (estimates vary according to source) Western-trained doctors who were too few in number to have much of an impact, especially in more remote rural areas[10,11]. With regard to hospitals, Hillier and Zheng[11] provide a figure of 430 for the whole of China prior to 1949, although they note that each of the 2000 counties had a small health clinic. In sum, China’s health care provision was wholly inadequate, with considerable scope for improvement.

3. State policies under Mao

The first leader of the PRC, Mao Zedong, who died in 1976, developed the revolutionary doctrine termed Maoism, a variant of Marxism applied to the specific conditions of the peasant society which was China. Re- fined in the long struggle with both the Guomindang and the Japanese, revolutionary theory was fused with practice in the ‘base areas’ dominated by the CCP. In the new People’s Republic, however, there were two divergent strands within health care, with a marked contrast between those trained in the Soviet Union along more conventional Western lines, and with the ex-guerrillas who had worked closely with the peas- antry to improve basic conditions, especially in sanita- tion practices[11]. The new Ministry of Health (MoH) was dominated by the Soviet-trained officials but fur- ther down the pyramidal structure, which reached the township and county hospitals, came the party cadres and others who were motivated more by ideologi- cal concerns for revolutionary change rather than for health care per se. Thus, conflicts between these two groups arose, particularly due to contrasts between the top–down, centralised, Soviet-style approach and the Maoist, decentralised, bottom-up approach.

Contrasting outcomes of these policies include: mass campaigns in the 1950s against the parasitic and infectious diseases noted above, involving environ- mental clean-ups, inoculation programmes at the local level, the introduction of free health care within the communes in the late-1950s (abandoned later), along with the introduction of ‘barefoot doctors’ who we

332 I.G. Cook, T.J.B. Dummer / Health Policy 67 (2004) 329–343

would now term medical auxiliaries or paramedics. These people would receive basic training to deal with sanitation matters and hygiene, traditional herbal remedies and medical practices including acupunc- ture and would work at the commune level[12]. The famines and high death rates which followed the Great Leap Forward (GLF) led to a backlash against Mao’s policies, which were widely perceived as try- ing to do too much too soon. Thus, the Ministry of Public Health once again took over the running of health care until the Mao-inspired Great Proletarian Cultural Revolution (GPCR), which led to attacks on these and other officials and bureaucrats in other ministries in another attempt to revolutionise the di- rection of China’s development trajectory. The move back towards the local level led to the “heyday” of the barefoot doctors, and by 1972 there were 2,000,000 of these across China funded as part of a Cooperative Medical System which also provided medicines, a portion of hospital costs and clinic equipment[11]. But Mao’s death and the rise to power of Deng Xi- aoping heralded a markedly different direction for health care in China, and it is to this that we now turn.

4. The Dengist route to market socialism and public health provision

Deng Xiaoping ‘always had a clear vision about China’s progress—economic development and ma- terial progress had to take precedence over political mobilisation’ [13]. Deng until his death in 1997 led China towards market socialism, a policy which has been broadened and deepened by his successor Jiang Zemin, now retired. The CCP would remain paramount but would engage fully with the interna- tional community via the Open Door Policy, and would follow the ‘Four Modernisations’ to transform China. China today is now a markedly changed society, with an estimated 200 million middle class, mainly urban, located especially in the eastern provinces along the coast. Peasant society too has been transformed; be- ginning with the abolition of the communes in the 1980s and the introduction of the ‘Household Re- sponsibility System’, which permitted peasants to sell surplus produce in open markets. These agricultural reforms and the concomitant breakdown in collective endeavours heralded the demise of the Cooperative

Medical System and the introduction of a more pri- vatised health care system. Barefoot doctors faced pressures to acquire more skills via training and exam- inations, and although many chose to become ‘rural doctors’ instead, others opted for alternative employ- ment. The proportion of government expenditure on preventive public health institutions (PHIs) has de- creased, in line with the wider drive towards greater efficiency across the state-run sector, and greater funding is directed instead towards hospitals[14].

Table 2shows official data for changes in the num- ber of health institutions in China since the founding of the PRC in 1949. The data is not presented for 1960 due to upheavals following the GLF and the widely accepted view (both in China and outside) that statis- tics presented by the Chinese government at that time cannot be trusted[1]. We consider the reliability of the China Statistical Yearbooks data in more detail later. Interesting patterns can be seen in this table. The total number of health institutions multiplied by approxi- mately 90 times in this 50-year period, with a dip how- ever in the late-1960s to the early-1970s, which mainly reflects a marked decline in the number of clinics in the country, from 170,430 in 1965 to 79,600 in 1970. This was the period of the GPCR, when China was torn apart in the drive by Mao to reassert his power and revolutionise the masses. He had attacked the Ministry of Public Health in a speech in 1965 in which he ac- cused it of urban bias and only working for 15% of the total population of the country[11]. The ministry was closed down, and small mobile health teams and the barefoot doctors were sent out to rural areas. The ta- ble fails to reflect this small-scale dissemination to the local level, with the clinic level of provision being the one that deteriorated during this controversial period. Note that the number of sanitation and anti-epidemic agencies also shrank from 1965 to 1970, before a rapid recovery of provision took place as ideological attacks were reined in during the years after 1971 when it was realised that too much damage was being done to key facilities and provisions such as these.

Table 2also shows how, for nearly a decade, hospital provision was exclusively at or above the county level, from 1949. The GLF period did bring a greater decon- centration of these facilities, evidenced by the 1962 data. Since then, although large hospitals have contin- ued to grow in number, by a factor of 3, smaller hos- pitals have also grown in number, albeit by a smaller

I.G. Cook, T.J.B. Dummer / Health Policy 67 (2004) 329–343 333

Table 2 Number of health institutions, 1949–2001

Year Total Hospitals Hospitals at and above county level

Clinics Sanitation and anti- epidemic agencies

Maternity and child care centres

1949 3670 2600 2600 769 0 9 1952 38987 3540 3540 29050 147 2379 1957 122954 4179 4179 102262 1626 4599 1962 217985 34379 5300 172708 2208 2636 1965 224266 42711 5445 170430 2499 2795 1970 149823 64822 6030 79600 1714 1058 1975 151733 62425 7757 80739 2912 2025 1980 180553 65450 9478 102474 3105 2610 1985 200866 59614 11497 126604 3410 2724 1990 208734 62454 13489 129332 3618 2820 1995 190057 67807 14771 104406 3629 2832 2000 324771 66509 15446 240934 4065 2598 2001 330348 65424 15431 248061 4253 2548

Source: China Statistical Yearbook 2002[15].

factor of around 2. In terms of health personnel, to- tal numbers, as shown inTable 3, grew 10 times from 1949 to 2001. It is with reference to doctors of West- ern medicine, paramedics of Western medicine and nurses that the growth has been greatest. There are some paradoxical trends, however, again during the GPCR in particular. Thus, the attack on those with ‘bourgeois tendencies’ would seem to have been con- centrated more on the doctors of traditional medicine (associated with feudalism and superstition by left- ist CCP leaders) rather than those trained in western methods (associated in contrast with modern ratio- nal methods and techniques). As the table portrays,

Table 3 Number of persons engaged in health institutions, 1949–2001 (thousands, except final column)

Year Total health personnel

Doctors of traditional Chinese medicine

Doctors of western medicine

Paramedics of western medicine

Senior and junior nurses

Doctors per 1000 population

1949 541 276 38 49 33 0.67 1952 819 306 52 67 61 0.74 1957 1254 337 74 136 128 0.85 1962 1685 344 120 224 200 1.02 1965 1872 321 189 253 235 1.05 1970 1793 225 221 256 295 0.85 1975 2594 229 293 356 380 0.95 1980 3535 262 447 444 466 1.17 1985 4313 336 602 473 637 1.33 1990 4906 369 1058 331 975 1.54 1995 5373 359 1186 365 1126 1.58 2000 5591 337 1330 395 1267 1.67 2001 5584 334 1364 387 1287 1.69

Source: China Statistical Yearbook 2002[15].

numbers of the former declined from 321000 in 1965 to 225,000 in 1970 and rose only a little to 229,000 in 1975. Numbers have recovered since, but there was an- other decline in the 1990s. This was due to the forces of economic modernisation being unleashed in China, rather than the political emphasis of the GPCR, and the drive towards all things Western. Note that by 2000, the numbers of those engaged in traditional Chinese medicine was the same as it had been in 1957, over 40 years previously. In contrast, the doctors of western medicine have grown steadily since the founding of the PRC, with particular growth during the 1980s as Deng unleashed his modernisation project. Paramedic

334 I.G. Cook, T.J.B. Dummer / Health Policy 67 (2004) 329–343

Table 4 Number of beds in health institutions, 1949–2001 (thousands, except final column)

Year Total health institutions

Total hospitals

Hospitals at and above county level

Hospital beds per 1000 population

1949 85 80 80 0.15 1952 231 160 160 0.28 1957 462 295 295 0.46 1962 933 690 577 1.03 1965 1033 766 621 1.06 1970 1262 1105 712 1.33 1975 1764 1598 948 1.73 1980 2184 1982 1192 2.01 1985 2487 2229 1487 2.11 1990 2925 2624 1847 2.30 1995 3141 2836 2053 2.34 2000 3177 2948 2155 2.38 2001 3201 2976 2176 2.39

Source: China Statistical Yearbook 2002[15].

numbers declined after the first flush of modernisa- tion, however, as policies and resources were directed instead to better trained doctors supported by nurses, numbers of which have expanded by a factor of 400 since 1949.

As for number of doctors per 1000 people in China, the final column ofTable 3shows that, indeed, it was during the years of the GPCR that ratios dropped, from 1.05 in 1965 to 0.85 in 1970 and some small recov- ery to 0.95 in 1975. The proportion of hospital beds (Table 4) did not decline in that period, however, and this was where the non-traditional doctors were more likely to be located. Hospital bed provision at or above county level, has continued to increase steadily over the decades, but it was not until the GLF that there were beds made available at a more local level, with 113,000 by 1962. Since the reform era led by Deng, bed numbers of this localised level has remained con- stant at around 800,000 from 1980. It is the growth at the larger scale that has been more pronounced. This is laudable in terms of such parameters as economies of scale, expertise and variety of provision, but there remains the problem that the increase in resources to such curative activities has diminished the budget for preventative medicine[14], especially in a time when market imperatives have been introduced, with charges being levied on immunisation services to the detri- ment of take-up of these and a resultant increase in communicable diseases such as measles, polio, TB or schistosomiasis.

Patterns of disease, health and death changed markedly during the Deng era as China’s social and economic development trajectory moved it through the late stages of the epidemiological transition. Table 5shows incidence of major infectious diseases for the period 1994–2001. There was an overall de- cline in incidence of infectious diseases, with marked falls in cholera, dysentery, hepatitis and typhoid— these are the traditional diseases of poverty and

Table 5 Incidence (per 100,000 population) of infectious diseases, 1994–2001

Infectious disease cause 2001 1999 1994

Total causes 188.62 197.63 203.68 Cholera 0.22 0.37 3.06 Viral hepatitis 65.15 68.93 76.83 Dysentery 39.52 45.91 76.83 Typhoid and paratyphoid fever 4.84 3.87 8.77 AIDS 0.03 0.00 0.00 Gonorrhea 14.62 20.63 11.05 Syphilis 4.56 4.16 0.19 Measles 7.24 4.67 7.54 Pertussis 0.52 0.49 0.69 Epidemic encephalitis 0.18 0.23 0.56 Scarlet fever 0.91 1.15 2.13 Hemorrhagic fever 2.73 3.74 5.73 Encephalitis B 0.76 0.65 1.70 Malaria 2.00 2.22 5.6 Newborn baby tetanus 16.18 16.55 – Pulmonary tuberculosis 44.06 39.03 –

Source: China Statistical Yearbook 2002, 2000, 1995[15,18,19].

I.G. Cook, T.J.B. Dummer / Health Policy 67 (2004) 329–343 335

Table 6 Death rates from major causes, urban and rural areas, 1989–2001 (as percentage of total deaths)

Cause of death Urban Rural 1999 1995 1989

Urban Rural Urban Rural Urban Rural

Malignant tumour 24.9 17.7 23.9 18.4 21.8 16.5 21.42 15.33 Cerebrovascular disease 20.44 19.0 21.6 18.4 22.1 16.35 20.94 15.53 Heart disease 17.6 13.1 16.8 12.4 14.9 10.4 15.96 12.07 Respiratory disease 13.4 22.5 13.9 22.0 16.1 25.3 15.76 24.95 Trauma and toxicosis 5.9 10.7 6.3 11.0 6.7 12.0 7.43 11.58 Digestive diseases 3.1 4.1 3.0 4.0 3.5 4.8 3.93 5.15 Urinary disease 1.6 1.5 1.5 1.5 1.5 1.3 1.55 1.31 Mental disease 1.0 – 1.1 – – 1.8 – – Neuropathy 1.0 – 0.9 – 0.9 – – – Internal system, nutrition, metabolite and immunity 3.2 1.1 2.9 1.1 2.2 – – –

Source: China Statistical Yearbook 2002, 2000, 1995, 1990[15,18,19,20].

under-development. By contrast, there was a rise in some infectious diseases, including sexually trans- mitted diseases (STDs)—partly diseases of lifestyle. Coinciding with the overall fall in infectious diseases was an increase in deaths from chronic diseases, in- cluding heart problems and malignant tumours (see Table 6)—the predominant diseases of an aging and more affluent population. Interestingly,Table 6 also highlights disparities in causes of death between ur- ban and rural areas, with rural areas lagging behind urban areas with regard to the move from infec- tious disease to chronic diseases. However, within China as a whole, the burden of disease has shifted from childhood disorders, high infant mortality and infectious diseases to chronic (non-communicable) diseases, accidents and injuries[16]. For example, in 1990 75% of the burden of disease was attributable to non-communicable diseases and injuries, a percent- age approaching that for Western developed countries (92%) and much greater than that for developing na-

Table 7 International comparisons of healthy life expectancy (HALE) at birth (years)

Country Population life expectancy at birth

Male life expectancy at birth

Female life expectancy at birth

Male expected lost healthy years at birth

Female expected lost healthy years at birth

China 62.1 60.9 63.3 8.0 9.7 India 52.0 52.2 51.7 7.6 11.0 Indonesia 57.4 56.5 58.4 6.9 9.1 Russia 55.5 50.3 60.6 9.1 11.4 UK 69.9 68.3 71.4 6.5 8.5 USA 67.2 65.7 68.8 8.2 10.7

Source: WHO, 1992[21].

tions as a whole (52%)[17]. In addition, under Deng, infant mortality continued to decline, although some- what less rapidly than the huge decreases experienced between 1960 and 1980, where infant mortality fell from 300 to 50 per 1000 births[2]. However, dif- ferences in infant mortality between rural and urban areas have widened—the rural/urban infant mortality ratio increased from 1.67 in 1981 to 1.75 in 1990, and there was considerable variation in rates within rural areas[2].

The shifts in population health experience outlined above, although variable between urban and rural ar- eas, are indicative of a rapidly modernising and de- veloping country that is utilising advances in public health care and medicine.

In terms of evidence for the success or failure of health provision in China, the WHO’s healthy life ex- pectancy (HALE) index is a useful and reliable data source to complement the official China data[21], providing an estimate of the years of good health for

336 I.G. Cook, T.J.B. Dummer / Health Policy 67 (2004) 329–343

the population at birth, and separately for males and females. The WHO index also calculates years of lost healthy years.Table 7provides the figures for China, contrasting with India and Indonesia, two similarly large developing countries, plus Russia, the US and the UK for comparisons with developed nations. The data shows that overall population healthy life ex- pectancy is 62.1 years (the table omits uncertainty bands), 10 years more than India and only 5.1 years less than for the USA. The gap between expected fe- male life expectancy for China and India is even more marked, although there is some way to go relative to the UK for example. The levels are also much better than for the Russian Federation and 5 years better than for Indonesia, indicating a high rate of relative progress. However, the expectation of lost healthy years at birth for both males and females is relatively high. For males, the figure of 8.0 years is only just better than for India, for example, and for females the figure of 9.7 years is actually worse than for Indone- sia, at 9.1 years, although it is better than for the US. These last two columns of the table provide an indi- cation of some of new or resurgent health problems which China is facing. These will be considered next.

5. Contemporary health problems

5.1. Respiratory

The drive for rapid economic growth in the last two decades has brought impressive results. China is now widely regarded as an economic success story, and had the capacity to withstand the worst effects of the Asian Financial Crisis in 1998. But the drive for growth has been at a heavy environmental price as a number of authors have made clear[1,22–24]. This has had a se- vere knock-on effect on China’s health situation. For example, air pollution has become a major problem, and respiratory diseases have increased[1]. Coal is the major fuel, and much of it has a high sulphur content, therefore in many northern cities, as well as the cen- tral city of Chongqing which is surrounded by moun- tains, acid rain poses a severe threat in winter, and it is reckoned that 800,000 tons of SO2 falls annually on that city, implicated as causing high rates of asthma and lung cancer. By the late-1990s, six of China’s ma- jor cities—Beijing, Chongqing, Guangzhou, Shang-

hai, Shenyang and Xian—were ranked as among the most polluted in the world, a situation which is in- creasingly admitted by Chinese politicians. In Beijing and other cities new measures are being put in place to tackle these problems, including a switch to low sulphur coals, liquid petroleum gas, a complete ban- ning of the ‘miandi’ (small diesel powered minibuses) which cause a high proportion of traffic pollution, and greater restrictions on pollution from industrial sites such as the Shougang steel works, which is to be relo- cated to a new site two hundred miles away. Nonethe- less, the sheer scale of the problem makes it difficult to tackle effectively; city planning, which encourages a spread of urban activities outwards, often contradicts improvements by generating higher traffic levels[25], and by 1999 in China, respiratory diseases were re- sponsible for nearly 14% of deaths in urban areas[16].

But it is not just in the cities that there is a major problem. In rural areas respiratory diseases caused 22% of deaths and is the leading cause of death, whereas it is fourth in the urban rankings[16]. This is because 800 million Chinese use coal in their homes, and it contains a wide range of toxins such as arsenic, lead, mercury and fluorine[26]. In Guizhou province in the south west green peppers are a delicacy, they are dried over coal-fired stoves which impart up to 500 ppm of arsenic to them[27], and at least 3000 cases of chronic arsenic poisoning have been con- firmed in that locale. The lack of controls in rural areas means that it will be very difficult to address these problems effectively.

5.2. Resurgent diseases

As noted previously, there has been a resurgence in some of the infectious diseases of the pre-PRC era. Liu and Mills [14] show, for instance that rates of immu- nisation dropped after price charges were introduced, such as a 36% drop in Shandong Province, 1979–mid 1980s. Schistosomiasis prevalence increased from a rate of 540 per 10,000 in 1980 to 788 per 10,000 in 1990. A 2001 study estimated that 865,000 people and 100,250 bovines were infected in the provinces where the disease is endemic[28]. Leprosy rates and TB, too, have increased. Chen et al.[29] show that there are still more than 10% of counties or cities where the criterion for leprosy elimination of less than 1 per 100,000 has not yet been reached. There were an es-

I.G. Cook, T.J.B. Dummer / Health Policy 67 (2004) 329–343 337

timated 1–1.5 million additional TB cases remaining infectious during the 1980s, primarily because treat- ment was not free[14]. Apart from the cost aspect, a major reason for the continuation of many infectious diseases is water pollution. Beach[30] reports that 700 million Chinese are estimated to drink contaminated water, and that over 30 billion tons of urban sewage are discharged into water courses, lakes and seas an- nually, with 2.7–10% remaining untreated. Many of China’s wastewater treatment plants serve at least 1.5 million people per day, compared to 7–8000 in the UK for example[30]. Industrial wastewater also has to be dealt with, with at least 3.8 billion tons of industrial wastewater untreated by 1997[1]. Although the PRC government is acting to reduce the threat of wastewa- ter pollution from both domestic and industrial sources clearly more must be done, although economic issues often outweigh environmental concerns[30]. Paradox- ically, however, it is economic pressures that is largely forcing government action due to worrying economic projections concerning lack of industrial water supply and the impact of poor health on the labour force. But China is spending huge sums on other projects, such as the Sanxia Dam or the north–south water diversion project [1]. The Sanxia dam, in particular, is impli- cated in threatening to worsen rates of schistosomiasis [31,32], with high rates around reservoirs being a com- mon feature around the globe and local endemic sites being only 40 km downstream or 500 km upstream. Malaria, too, could well increase in such conditions.

5.3. Loss of life via ‘natural’ disasters

With the marked exception of the years following the GLF, when millions died as a result of famine, plus the loss of life of 250,000 in the Tangshan earth- quake of 1976, to date the high death tolls of the past have generally been avoided in recent decades. Nonetheless, threats still remain. For example, Tang- shan ‘(Un)fortunately and dangerously,. . . , with its metropolitan population of 1 million, has already been rebuilt on the same site’[33]. The Sanxia Dam noted above might also be susceptible to earthquake, and the potential loss of life would run into the millions due to the resultant floods downstream as well as the huge backwash upstream. Floods across China remain a perennial threat, with loss of life along the Yangtze River in 1998 and 1999, for example possibly run-

ning into the thousands according to foreign sources, far less according to the Chinese authorities. Defor- estation, corruption, and human action were all im- plicated in causing such floods, but climatic change and ‘exceptional factors’ became the officially recog- nised culprits in an official report following the 1998 floods along the Yangtze[1], a report questioned by the latter authors. The PRC government has, in gen- eral, done well to largely avoid the perils of the past, but this does not mean that these perils have disap- peared, rather they are latent and can relatively easily be triggered via human action or government inaction.

5.4. Threats via pesticide use

Pesticide use has more than tripled in recent decades, as the communal labour of the Maoist period has been replaced by the individual or family labour of today. In Zhejiang Province, researchers[34] found that the rate of pesticide use in rice production is more than double the national average, and similar to Japan or South Korea rather than other countries in the region. Nor was this use wholly efficient, because diminishing returns set in, and farmers were estimated to be overusing pesticides by more than 40%, reflect- ing gross overestimates of the threat of loss due to pests. Health impacts reported by the sample included eye effects, headaches, skin problems, liver problems and neurological effects. Of 100 farmers examined, 22 had impaired liver function, while 23 had abnor- mal levels of key chemicals in their kidneys. At the wider scale, 300–500 deaths per annum are estimated as being due to pesticides, use of which will increase as economic modernisation continues apace[1].

5.5. Impact of changing lifestyles and hypertension

The rapid change in lifestyle in China, consequent on the fast pace of economic modernisation, espe- cially in the burgeoning cities, is bringing with it a wide range of stresses. There is evidence for a vast increase in tobacco consumption[35,36] for example, with researchers predicting that 3 million young men could die per annum of smoking-related diseases dur- ing the 21st century, and the WHO forecasting 2 mil- lion deaths per annum by 2025[37]. As in some other countries, such as Japan, the situation is not helped via the role of the State in tobacco production, and

338 I.G. Cook, T.J.B. Dummer / Health Policy 67 (2004) 329–343

a tie-up was announced in 2002 between the major State Owned Enterprise, China National Tobacco Cor- poration, and the western tobacco giant, Gallaher[38]. There is hence a strong vested interest, due to the in- come it produces, for the state in playing down the impact of smoking on ill health, with one survey re- ported by Bradbury stating that 60% of Chinese adults do not know that smoking causes lung cancer, while 96% do not know that it causes heart disease[35,36]. Currently two-thirds of China’s young men become smokers before they are 25 years old[39]. Unless these rates show a marked decrease, perhaps highly unlikely due to the strength of the tobacco industry, up to one-third of China’s men could die eventually of smoking-related conditions[39]. The respiratory dis- eases noted above have a strong smoking-related di- mension, therefore, and are not just the result of coal burning and related factors.

Nutrition intakes have also shown a marked shift as Western food consumption patterns are adopted and China undergoes the nutrition transition[8]. The first ‘fat camp’ was opened in Beijing in 1994, reflecting a growing problem of obesity as Western ‘junk food’ becomes increasingly popular in the cities, where there are a plethora of McDonalds, KFC, New York Pizza and other Chinese variations on the Western theme. A paradox of the Single Child Family Pro- gramme introduced in the 1980s has been the emer- gence of a privileged group of single children, often boys, who are so doted upon and pampered by their parents and grandparents that they are known as the ‘little emperors’. Many of these children are over- weight, with obesity in young children increasing by over 50% from 1989 to 1997, from 4.2 to 6.4% in the 2–6 years old age range, and in cities from 1.5 to 12.6%. Overweight people increased from 14.6 to 28.9% in that time[40]. Increasing quantities of meat are being consumed, with corresponding higher levels of fat intake—by 1997 60% of urban adults consume over 30% of their energy from fat[41].

Health outcomes of increased smoking prevalence, high fat diets, sedentary lifestyles, increased alcohol consumption and high salt intakes include a large pro- portion of malnourished or obese people compared to other countries in Asia or Latin America[42], and an increase in diabetes and hypertension[43–45]. Hyper- tensive patients increased from 30 million in 1960 to 94 million in 1991, and a recent study indicated that

130 million adults—nearly 10% of the population— have blood pressure levels above the normal, con- tributing in turn to high rates of cardiovascular dis- eases. What is particularly disturbing is that the in- creasing rate of hypertension among young people is much higher than among the elderly[45].

Among young people an increase in mental health problems has been reported[46] while the suicide rate is also giving cause for concern[47]. The latter study adjusted official Ministry of Health (MoH) data for estimated unreported deaths and projected these to the corresponding population to arrive at a mean annual suicide rate of 23 per 100,000 and a total of 287,000 suicide deaths per year. Thus, overall, suicide is the leading cause of death in those aged 15–34 years. For rural and urban women aged 15–34 suicide was the leading cause of death, for rural and urban men it was the second leading cause of death (after motor vehicle accidents)[47,48]. There is dispute over the precise scale of suicides, and Phillips et al. contrast their find- ings with the 2001 WHO study which reports a rate of 14.0 per 100,000 and a 17.2% drop in suicide rates from 1988–1990 to 1996–1998, findings inconsistent with the Western Pacific Region as a whole, and also with a rate previously reported by the WHO of 32.9 per 100,000 for China[47].

5.6. HIV/AIDS

Another major health issue, which is partly lifestyle related, is the growing incidence of HIV/AIDS. China during the Maoist years was an austere place in which extra-marital sex, prostitution and drug use was min- imal. Since the Open Door Policy was introduced prostitution has shown a marked increase, especially in the cities, and drug-taking has also increased. As highlighted in Table 6, sexually transmitted diseases (STDs) are on the resurgence, having been virtually eradicated by the 1960s[49], and by 1998 were the third most-common group of infectious diseases in China, after dysentery and hepatitis[50]. Officially, in 2001, Gonorrhea had an incidence rate of 14.62 per 100,000 (compared to 65.15 for viral hepatitis and 39.52 for dysentery), while the Syphilis rate was 4.56 per 100,000[15]. The AIDS rate was reported as just 0.03.

While the Chinese authorities have been admitting for some time that China had HIV and AIDS cases,

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until recently they have played down the number of cases. It was not until 2001 that international experts noted that the authorities were speaking more openly and frankly on this topic, fuelled in part by a scandal of contaminated blood in Henan province[51,52]. By 2002, the official English-language China Daily re- ported a warning by health officials that around a mil- lion people were infected by HIV, a figure that could rise by a factor of 10 by 2010[53]. International es- timates go up to double that figure[54]. Qi Xiaoqiu, head of the Department of Disease Control of MoH warned that AIDS was ‘at a very dangerous stage in China on the verge of becoming an epidemic’. Such concerns were prompted by factors such as a UN es- timate of 1.5 million HIV cases, and an increase of 58% in HIV and AIDS during 2001, although the rate dropped to 17% to June 2002. AIDS activists are appearing in China, including one Shanghai resident who, under the pseudonym of Jiaming, began a web- site to publish his diary, following his infection from a prostitute[52]. The website received 2 million hits showing the huge potential interest in this issue in China. Despite the improved government attitude, an- other activist, Wan Yanhai, was arrested in September 2002 for ‘allegedly revealing state secrets’[53]. Fur- ther, there is gross under-reporting of AIDS cases, in part because in villages people are worried about stig- matisation and being unable to sell their vegetables or find jobs in the towns and cities[53]. Clearly, as with tobacco, there is much to be done to dispel ig- norance about this disease, and to assist prevention as well as treatment. Drug addiction in particular, via in- fected needles, has been suggested as the main cause of the disease in China, as in a number of other coun- tries [55], and the whole issue of drug use requires a more sophisticated policy than the execution of drug dealers currently applied in the PRC.

5.7. Severe acute respiratory syndrome (SARS)

The first cases of severe acute respiratory syndrome (SARS) emerged in Hanoi, Vietnam, in late-February 2003 and quickly began to be reported world wide. In late-February China also began reporting SARS, with 305 cases and five deaths[56]. However, it became clear at the end of late April 2003 that Chinese offi- cials had known about atypical cases of pneumonia in southern Guangdong province in November 2002[56]

and was effectively under-reporting and hiding SARS cases. In April, the deputy health minister acknowl- edged that the Chinese Ministry of Health was not ad- equately prepared to cope with the SARS emergency, a view also highlighted by the WHO, who noted, in general, the lack of investment in health care systems [57]. By April 2003, China (excluding Hong Kong) had reported a total of 1807 SARS cases and 79 deaths. However, it is likely that this figure still represents an underestimation of the scale of the problem, with many provinces still not reporting cases[56].

Although it is too early for a full assessment of this ongoing epidemic, what has already become clear from China’s handling of SARS is that a more open and inclusive approach to public health emergencies is required from government officials. Because of the scale of SARS the WHO suggests that it is a problem beyond the control of a single government[56]. Hence, in addition to more openness, partnership is required, with China adopting an open approach to reporting and treating cases, as well as utilising fully the resources of the international community. Although recent gov- ernment statements suggest this is indeed the situation now, only time will tell how this new policy fares.

5.8. Widening inequalities of access to health care

A by-product of rapid economic development is growing disparities at the regional level between China’s ‘gold coast’ and the interior, and also between urban and rural areas, as well as within rural and urban areas[13,58]. The knock-on effect on health is that a ‘medical poverty trap’ is emerging, as in other low-income countries[59], with poor people in poor areas facing particular difficulties, and widening inequity in access to health care is a major social and political issue in China[16,60]. Further, rural areas are suffering from a deteriorating health infrastructure, a lack of personnel, and an increase in the prevalence of some diseases[61]. We have already seen above, the inequities in respiratory diseases, arsenic poison- ing and suicide rates between rural and urban areas. The view of China as a dual society (urban–rural) is supported by Attane[62], who argues that peas- ant families have always been the poor relations of Chinese society, and by Liu et al.[2] who illustrate widening inequalities in health and health care in the 1980s and 1990s. There are also widening gaps

340 I.G. Cook, T.J.B. Dummer / Health Policy 67 (2004) 329–343

in health care within urban areas, as Gao et al.[63] demonstrate. In these studies, a common factor is the inability to pay for health care, coupled with a lack of an insurance system or alternative means of support for health provision. One response by the authorities is to markedly expand Community Health Services in cities to make medical services both more accessible and more affordable[64]. Liu et al. [65] note the promising signs from an evaluation of urban health insurance reforms which were introduced in 1998. The drive to improve the situation in rural areas will take much longer, however, if indeed it can be ade- quately tackled at all. Widening inequalities in access to healthcare inevitably lead to widening inequalities in health outcome. As noted, infant mortality has fallen dramatically in China, although major varia- tions exist between urban and rural areas and between the affluent and the poor. In 1987, the infant mortality rate was a huge 96.2 deaths per 1000 births in selected rural areas, whereas in cities the rate averaged 20 per 1000 births[60]—such inequalities persist: Hesketh and Zhu[66] estimate a 10-fold difference in infant mortality between urban and rural poor areas.

6. Implications for theory and policy

At the simplest level, it appears that the current health situation in China largely reflects the epidemi- ological transition model, in which economic devel- opment leads to a change from insanitary conditions, high fertility, high infant mortality and a high rate of infectious diseases, towards improved sanitation and water supply, low fertility, low infant mortality and a move away from the ‘diseases of poverty’ towards ‘diseases of affluence’, such as lung cancer and car- diovascular disease. However, while we have demon- strated that this is broadly the case, we also contend that in China state policy, over and above economic development, has played a major role in creating an improved environment for health. For example, the Single Child Family Policy (SCFP) introduced in 1978 and amended in the 1980s[67] has, despite the con- troversies over its human rights implications, meant that 250–300 million fewer Chinese have been born and this has undoubtedly led to many benefits for the country as a whole, providing more resources for the children who were born. However, we noted above the

situation of the ‘little emperors’, and apart from the new physical and mental health risks to this sector of the population, there is a further knock-on effect on the situation of the elderly in China, with the younger gen- eration proving to be far less willing to look after the elderly than previous generations in China. Powell and Cook [68,69] have considered the empirical and the- oretical implications of this for China’s ‘superaging’ population.

Also, above, we have shown that the state’s Dengist policies have led towards a ‘privatisation’ of health care in China, as market forces have been introduced from outside the country. Cornia[70] attributes much of China’s recent successes in health—similarly to such countries as Costa Rica and the East Asian tigers of Singapore, Hong Kong, South Korea and Taiwan— to growing access to global markets, savings and tech- nology. The down side of this, however, is lack of ac- cess of the poor, whether in urban or rural areas in China, to such benefits. What we are seeing in China today, therefore, is an increasingly dualistic structure of health, in which there is an upper tier of reasonably prosperous people who have the financial resources to access health care and support systems, contrasting with the lower tier who are dependent on state-funded systems, but these are being increasingly reduced by the state itself in favour of market solutions. Spatially, the latter are concentrated into the poorest parts of cities, often on their periphery and, more especially, within rural areas, especially those which are more re- mote from the centres of modernisation. Socially, these are the poor, and within this broad group, women, the elderly, disabled and minorities who, as elsewhere, bear the greatest burdens of inequality, and impor- tantly, it is the poor who bear the greatest burden of infectious diseases[9].

It is clear that the health of the Chinese popula- tion has improved dramatically over the past 50 years, and China’s socio-economic and public health devel- opment trajectory certainly follows the broad pattern of the traditional epidemiological (health) transition model [60]. However, health problems in China are not characterised only by problems of an aging popu- lation and increasing chronic diseases of affluence and development. Increasing urban/rural inequities in ac- cess to health care are developing[2], with important consequences for public health. Alongside this, rapid (and unplanned) urbanisation has major implications

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for the health of the urban population[7]. Infectious diseases still account for a large proportion of deaths of China poorest people[9]. Certain infectious diseases, such as TB, are re-emerging, whilst new infectious disease, including HIV/AIDS, have considerable im- plications for China’s population. As noted, the grow- ing smoking epidemic—with a concomitant increase in premature deaths from lung cancer, heart disease and stroke—has the potential to single-handedly wipe out China’s impressive success in increasing life ex- pectancy.

We would argue that the health situation in China reflects a new, late stage, epidemiological transition phase, where the transition from diseases of poverty to diseases of affluence has not reflected smoothly the economic and development transition[5,7]. Conse- quently, China faces health issues related to an aging and increasingly affluent population, combined with problems caused by rapid urbanisation, emerging and re-emerging infectious diseases and widening inequal- ities in health and health care. These features of the late stage transition model present considerable chal- lenges for policy, especially as both health care and health experience in China reflect very clear social and also spatial inequalities. Thus, health policy must be designed to target those most at risk: this includes not only the poor in general, but, specifically, the el- derly, women (particularly rural women) and the vast population living in rural and remote areas that are poorly served by primary and public health care facil- ities. Indeed, whilst rural residents account for 70% of the Chinese population, they consume just 30% of the countries medical resources[71]. It is clear from re- cent Government rhetoric and proposed policy targets that officials acknowledge this widespread disparity in health and health care between urban and rural areas [72]. Such acknowledgements are indeed laudable, but rhetoric must be translated into real policy, backed up by financial support and changes in the delivery and provision of health care in rural areas.

In addition, the current SARS epidemic shows that public health officials, policy makers and politicians in China need to adopt a more open approach to health and health care, including greater engagement with the WHO to ensure that epidemics of infectious diseases are dealt with swiftly and adequately. We argue that health policy in China needs to be made in partnership with external agencies (such as the WHO) and across

government sectors, ensuring that China’s health situ- ation improves for its entire people and that the public health infrastructure can respond effectively to crises such as SARS. In this way, China will benefit from outside support and expertise, whilst also contribut- ing experiences that will benefit similarly large and rapidly developing countries.

Much of this study has relied on the official China Statistical Yearbook, although we have complemented these data with other sources where available— including WHO data and other research studies. The validity of some of the official data sources has been questioned[1], and indeed we did not use data avail- able from 1960 because of acknowledged data defi- ciencies. However, more recently (and certainly since 1980) much more reliable data has become avail- able [73] and much of the official data is internally consistent, allowing trends over time to be analysed. Clearly, there are still issues surrounding the open- ness with which China treats certain health data and we note this in the case of the (under-)reporting of both HIV/AIDS and SARS cases.

In summary, we argue that health and social care policies need to focus, more closely, on disadvantaged groups (such as the rural poor), and their exclusion from health provision, seeking to ensure that the state responds effectively to the marginalised in Chinese society, not just to the needs of China’s ever-expanding and vocal middle class.

Acknowledgements

We thank Geoff Murray for providing useful infor- mation and drawing our attention to recent Chinese sources.

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• Changing health in China: re-evaluating the epidemiological transition model
• Introduction
• Pre-revolution health in China
• State policies under Mao
• The Dengist route to market socialism and public health provision
• Contemporary health problems
• Respiratory
• Resurgent diseases
• Loss of life via 'natural' disasters
• Threats via pesticide use
• Impact of changing lifestyles and hypertension
• HIV/AIDS
• Severe acute respiratory syndrome (SARS)
• Widening inequalities of access to health care
• Implications for theory and policy
• Acknowledgements
• References

Refocusing-the-lens--Epidemiologic-transition-theory--mor_1997_Social-Scienc.pdf

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Soc. Sci. Med. Vol. 44, No. 5, pp. 609-621, 1997 Copyright © 1997 Elsevier Science Ltd

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R E F O C U S I N G THE LENS: E P I D E M I O L O G I C T R A N S I T I O N T H E O R Y , M O R T A L I T Y D I F F E R E N T I A L S , A N D THE A I D S

P A N D E M I C

D A N I E L S. G A Y L I N a n d J E N N I F E R K A T E S

The Lewin Group, 9302 Lee Highway, Suite 500, Fairfax, VA 22031, U.S.A.

Abstract--The epidemiologic transition theory presented first by Omran [Omram, A. R. (1971) The epi- demiologic transition: a theory of the epidemiology of population change, Mildbank Quarterly 49(4), 509-538] was designed to explain global trends in the dynamic relationship between epidemiological phenomena and demographic change. This paper argues that universalizing this theory only partially serves to explain mortality declines over the last century and eclipses key epidemiologic differences between population subgroups based on socioeconomic status, race, and sex. This paper examines mor- bidity and mortality differentials between population subgroups and demonstrates important inconsis- tencies with the optimistic trends implied by the epidemiologic transition theory, an argument further developed using the HIV/AIDS pandemic as a case study. The paper argues that these differences should be brought from margins to center to present a more complex and comprehensive picture of how population subgroups experience epidemiologic transitions differently. Copyright © 1997 Elsevier Science Ltd

Key words---epidemiologic transition, mortalit) differentials, AIDS, infections

INTRODUCTION

...[l]f we are to understand our contemporary reaction lo a traditional stimulus, we must distinguish between the unique and the seemingly universal, between this epidemic at this time and this place and the way in which commu- nities have responded to episodic outbreaks of fulminal:ing infectious diseases in the past. We have become accus- tomed in the last half century to thinking of ourselves,; as no longer subject to the incursions of such ills; death fiom acute infectious diseases has seemed--like famine--lim:ted to the developing world. Life-threatening infectious ills had become almost by definition, amenable to therapeatic or prophylactic intervention (Rosenberg, 1989).

Extensive worldwide spread of HIV started in the mic to late 1970s. In less than two decades--during the first of which it was unknown and unsuspected--HIV became the first modern pandemic (Mann et al., 1992).

D u r i n g the p a s t century, the world has experi- enced d r a m a t i c m o r t a l i t y declines. D e m o g r a p h e r s , a n t h r o p o l o g i s t s , a n d sociologists have a t t r i b u t e d global m o r t a l i t y declines to the n e a r disappearalace o f infectious diseases a n d their related epidemics. I n 1971, O m r a n p u b l i s h e d the " T h e epidemiologic t r a n s i t i o n : a t h e o r y o f the e p i d e m i o l o g y o f p o p u - l a t i o n c h a n g e " in a n a t t e m p t to c a p t u r e the c h a n - ging n a t u r e o f epidemics a n d their d e m o g r a p h i c effects. T h e m a j o r c o n t r i b u t i o n o f this theory is its c o n n e c t i o n between m o d e r n i z a t i o n , socioeconomic progress, a n d m o r t a l i t y decline via c h a n g i n g epi,de- miologic c o n d i t i o n s , p r i m a r i l y the decline in infec- tious disease. In a d d i t i o n , the epidemiologic t r a n s i t i o n t h e o r y examines m o r t a l i t y differentials between s u b g r o u p s o f the p o p u l a t i o n - - p a r t i c u l a r l y

between U.S. blacks a n d whites a n d between m e n a n d w o m e n - - a n d argues that these differentials converge over time (actually, the theory favors w o m e n slightly over m e n in terms o f m o r t a l i t y gains). Thus, the epidemiologic t r a n s i t i o n theory has been generalized as a b r o a d e x p l a n a t o r y a p p r o a c h , o n e which is utilized to describe a general global epidemiologic experience.

In this paper, we argue t h a t universalizing the epidemiologic t r a n s i t i o n theory eclipses key epide- miologic differences between p o p u l a t i o n subgroups, a n d that these differences s h o u l d be b r o u g h t from the m a r g i n s to center to create a m o r e complete a n d accurate representation o f p o p u l a t i o n m o r b i d - ity a n d mortality. W h e n this is done, the picture presented by the epidemiologic t r a n s i t i o n theory significantly e x p a n d s to reflect n u m e r o u s epidemio- logic t r a n s i t i o n s intricately c o n n e c t e d to socioeco- nomic, sex, a n d racial differences. F u r t h e r m o r e , we argue that m o r t a l i t y trends since the p u b l i s h i n g o f the last u p d a t e o f the epidemiologic t r a n s i t i o n the- ory ( O m r a n , 1983) reflect a t r o u b l i n g rise in infec- tious d i s e a s e s - - p a r t i c u l a r l y tuberculosis a n d A I D S - - a n d a n increasing d i s p r o p o r t i o n a t e i m p a c t o f these epidemics o n p a r t i c u l a r p o p u l a t i o n sub- groups. We believe that such a n e x a m i n a t i o n a n d r e t h i n k i n g o f the theory n o t o n l y provides a needed critique, b u t it enhances the theory's ability to explain the d y n a m i c r e l a t i o n s h i p between p o p u - l a t i o n change a n d epidemiology as this relationship is experienced by different s u b g r o u p s o f the p o p u - lation. T h u s , r a t h e r t h a n reject the theory outright,

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610 Daniel S. Gaylin and Jennifer Kates

as some might argue, we believe that revisiting the epidemiologic transition theory in light o f recent developments contributes to ongoing dialogue, thereby improving our ability to characterize demo- graphic trends.

Our paper will focus largely on the U.S., but will also draw on relevant international research and data. In the first section o f the paper, we will describe the epidemiologic transition theory as it relates to the demographic transition. This section will explore recent expansions of the theory which attempt to bring social, cultural, and behavioral factors to the fore. In the next section, we will offer a critique o f the epidemiologic transition theory which discusses its limitations as a universalizing concept. This section will focus on the need to ~'particularize" epidemiological investigations and thus account for mortality and morbidity differen- tials between population subgroups. The final sec- tion o f our paper will focus on the H I V / A I D S pandemic as both a case study and microcosm o f the complexity o f the epidemiologic transition. The A I D S pandemic will be used to demonstrate the continued presence and threat o f infectious disease. In addition, the d i s p r o p o r t i o n a t e impact o f A I D S , due to sociak cultural, economic, and behavioral factors, on particular population subgroups threa- tens to exacerbate mortality differentials (and other demographic trends) and thus seriously questions the universalizing tendency o f the epidemiologic transition theory. Finally, we will attempt to demonstrate how the modern state o f epidemic affairs, particularly A I D S and its related epidemics, transforms a current view o f the epidemiological transition and more accurately accounts for the diversity of that transition for different subgroups o f the population.

E P I D E M 1 O L O G I C T R A N S I T I O N T H E O R Y

One o f the central tenets o f demographic theory is the concept o f the demographic transition. The demographic transition refers to declining mortality and fertility that occurs in societies as they develop. Recognizing that this concept is broad, demogra- phers have attempted to define it more precisely. One enhancement has been the development o f the theory o f epidemiologic transition. O m r a n first described this theory in 1971 (Omran, 1971) and later applied it to examine disease and mortality trends in the U.S. (Omran, 1977).

F o r purposes of explication, we shall first define epidemiologic transition theory in the context o f Western (European) experience. The theory "focuses on the shifting web o f health and disease patterns in population groups and their links with several demographic social, economic, ecologic, and biologic changes" (Omran, 1977). Olshansky and Ault (1986) provide a nice overview o f the basic

concepts underlying the epidemiologic transition theory:

As nations modernize they tend to improve their social, economic, and health conditions. Life conditions that were previously conducive to the spread of infectious and para- sitic disease are rapidly replaced by more sanitary living conditions, improved medical technology, and better life- styles. As the risk of dying from infectious diseases is reduced for a population, those saved from dying from such diseases survive into middle and older ages where they face the risk of dying from [chronic] diseases.

More specifically, epidemiologic transition theory embodies three primary stages through which mor- tality and disease patterns shift. The first stage is termed the "age o f pestilence and famine". In the context of European experience, this first stage is "an extension o f the pattern in the middle ages...and continued in Europe through most o f the 18th century" (Omran, 1977). The first stage is marked by high mortality and fertility, and high prevalence o f infectious diseases from which the high mortality derives. In this first stage, " m o r t a l i t y is high and fluctuating, thus precluding sustained population growth".

Gradually, the first stage gives way to the second stage, which is termed the "age o f receding pan- demics". During this time, the prevalence o f infec- tious diseases begins to decline, and with it, mortality declines also. Furthermore, according to Omran, the rate o f mortality decline "accelerates as epidemic peaks become less frequent or disappear" and societies begin to experience exponentially increasing populations. In the European context, this occurred during the 19th and early 20th centu- ries.

The third stage in the epidemiologic transition theory is the "age o f degenerative and man-made diseases". In this stage, infectious diseases become increasingly rare, and they are replaced by chronic, degenerative, and stress-related diseases as the pri- mary causes o f death. According to Omran, in the third stage, mortality decline decelerates and stabil- izes, life expectancy is high, and "fertility becomes the crucial factor in population growth".

Revisiting O m r a n ' s theory in 1986, Olshansky and Ault (1986) postulated that there is also a fourth stage o f the epidemiologic transition. A t the time O m r a n developed the theory, it was believed that declines in mortality from degenerative diseases would proceed at a very slow pace. However, life expectancies continued to improve in those countries that were experiencing this "final" stage in the transition. Thus, Olshansky and Ault, noting that what was occurring was a " p o s t p o n e m e n t o f the ages at which degenerative diseases tend to kill", suggested that the fourth stage should be characterized as the "age o f delayed degenerative diseases".

The model outlined above is the classic or Western model. F o r societies following this model,

Refocusing the lens 611

the transition is mostly complete, having occur:red over the past 200 hundred years. Death rates have declined from roughly 30 per 1000 population to 10 per 1000, while fertility has declined from 35 births per 1000 population to 20 per 1000. These con:~ti- tute changes from high rates to low rates. Omran characterizes these declines as "gradual" and occur- ring "in response to social, economic, and environ- mental improvements" that accomp~ny modernization (Omran, 1977). Initial mortality declines are thus more attributable to environm,~n- tal improvements (sanitation, etc.), while later declines are more attributable to improvements in medical technology and health care system develop- ment (aspects of the final steps in the completion of the transition through to the third stage).

Omran articulates two basic variants of the clas- sic epidemiologic transition. One variant is the "accelerated model" in which the mortality tran- sition was also socially determined, but was enhanced more powerfully by medical technology. According to Omran, this model applies to Japan, Eastern Europe, and the former U.S.S.R. Omran terms the second variant the delayed model, which applies to most of the Third World countries. In this model, mortality decline has only recently started to decline, although the rate of decline has been dramatic. The primary reason is large-scale importation of medical technologies and public health measures. Under this model, fertility declines have not equalled mortality declines, largely due to the rapid pace with which the mortality decline ensued. Omran notes that countries which have engaged in active population control programs rep- resent a subcategory of this model.

A main corollary to the epidemiologic transition theory is that demographic changes both shape and are shaped by epidemiologic changes. Thus, since infectious diseases tend to impact the young mast heavily, as a population shifts along the epidemiolo- gic transition between the first and second stages, survival gains will be highest among children and women.* Increased numbers of surviving children produce a "wave of children and youths moving up through the population pyramid", while increa,;ed survival for women relative to men implies a shift in the population sex ratio. Then, as the population moves into the third (and fourth) stage, increases in life expectancy translate into an aging population. Omran also notes some important social impli- cations which accompany the epidemiologic tran- sition:

[D]eclines [in morbidity and mortality] can increase the efficiency and productivity of the labor force: healthier

*As Omran notes, some of the reasons for increases in women's survival accompanying the epidemiologic transition are easily identified (e.g. better nutrition ~Lnd obstetrical care); however, other reasons for differential survival by sex are less well understood.

adults can work better and more surviving children means more potential workers.....Improved childhood survival presumably removes the complex social, emotional and economic rationales for high birth rates (Omran, 1977).

Omran demonstrates the applicability of his the- ory by examining morbidity and mortality trends in the United States during the 19th and 20th centu- ries. Specifically, he examines overall U.S. data from 1890 through 1970, supplemented with data from Massachusetts and New York City from 1800 through 1970. He points to declining death rates, particularly among infants and children, shifts in leading causes of death from infectious to chronic diseases, and decreasing fertility. Furthermore, he discusses declines in maternal mortality due largely to better nutrition, better overall medical care, and particularly, better obstetrical care, especially during labor and delivery. Finally, he notes that overall mortality declines during the late 19th cen- tury were attributable to improved sanitation, pub- lic health measures (e.g. immunization, isolation, and quarantine), and that later 20th century gains were realized from increasing organization and quality of the medical care sector.

Omran also spends some time developing the above descriptions of aspects of mortality and mor- bidity change in the U.S. that occurred during the country's epidemiologic transition. Importantly, he discusses differential mortality patterns by sub- groups. Thus, he notes that the transition favors the young over the old (because the declines occur as a result of reductions in infectious diseases, which exact their greatest toll on infants and chil- dren). Furthermore, according to Omran, the tran- sition favors females over males:

the female [of the 19th century and before] usually filled the roles of wife, mother, nurse, cook, and maid...She was overworked undernourished and frequently exposed to infection from several members of the family. With strains of repeated pregnancies, lengthy breastfeeding and pro- longed childrearing, she was vulnerable to many illnesses, especially tuberculosis, anemia, and disease related to pregnancy and labor. When the infectious disease subsided and women began to have fewer pregnancies, female mor- tality decreased correspondingly (Omran, 1977).

Omran also notes that the transition favors whites over non-whites. He discusses that whites in U.S. society have always had advantages in terms of socioeconomic status, education, nutrition, and access to health care. Therefore, whites have corre- spondingly lower mortality and morbidity. Thus, for whites the epidemiologic transition "started ear- lier and moved faster. In particular the shift from communicable to degenerative disease has been slower among nonwhites....[who} still have higher death rates from infectious diseases..."

After noting differential mortality patterns in the U.S. by various subgroups and discussing some weaknesses with early U.S. mortality and morbidity data, Omran recapitulates epidemiologic transition theory, and then proceeds to return to the general

612 Daniel S. Gaylin and Jennifer Kates

conclusions that the theory predicts. Thus, empha- sizing the overall changes in mortality, morbidity, and fertility that occurred in the U.S. from 1800 to 1970, Omran concludes his paper: "data from the US...leave no doubt that the transition in the US in the last two centuries belongs to the Classical or Western Model of the epidemiologic transition".

THE EPIDEMIOLOGIC TRANSITION REVISITED: THE HEALTH TRANSITION

As noted above, Omran briefly discusses sub- group differences in mortality, but these are second- ary points to his theory. His primary emphasis is on the changes in mortality brought about by the shift from mortality patterns dominated by infec- tious diseases to patterns dominated by degenera- tive diseases. Differential mortality, then, becomes more a function of which groups are more prone to dying and sickness from infectious diseases, and thus stand to benefit most as the society undergoes the epidemiologic transition and infectious diseases are virtually eliminated. By the time Omran first published his theory, it was already the subject of some debate, and this debate has continued into the present. Recently, a prominent group of scientists convened an international conference to re-examine epidemiologic transition theory. The conference's proceedings are titled What We Know About the Health Transition." The Cultural, Social, and Behavioral Determinants o f Health (Caldwell et al., 1990). Two important emphases of the conference are evident from this title. First, the concept of the epidemiologic transition has been broadened to that of a "health transition". John Caldwell (1990), in his introduction, notes:

A broader term than mortality transition has been the epi- demiological transition because it embraces changes in levels of sickness as well as mortality. For our purposes, neither term is sufficient because both are purely outcome measures. We want a term that includes the social and behavioral changes which parallels the epidemiological transition ...... We employ the term health transition to include both epidemiological and related social changes.

Thus, the health transition theory does not rele- gate social changes to a secondary position, but includes them as a central focus, along with the basic outcome measures of mortality and morbidity which had been the focus of epidemiologic tran- sition theory. Herein lies the second point evident from the title of the conference: scientists are going beyond the broad generalizations of the epidemiolo- gic transition theory to focus in greater depth on other important factors (cultural, social, and beha- vioral) which influence health outcomes.

Lado Ruzicka and Penny Kane (Ruzicka and Kane, 1990) provide an overview of morbidity and mortality trends in the health transition. Their p a p e r ' s d i s c u s s i o n o f " t h e inequality of death" is of crucial importance for this analysis. They provide a

thorough analysis of a fundamental truism in demo- g r a p h y - t h a t mortality patterns vary widely by race, sex, economic indicators, and class. Furthermore, the authors point out that there is substantial "heterogeneity within social class and, for that matter, within any other sociocultural, demographic or economic category" in terms of mortality risks. They also note an overconfidence embodied by the epidemiologic transition theory that societies will gradually progress to the point where they have virtually eliminated infectious dis- eases as a major health threat. In fact, they state, "the only disease that was eradicated and has not reappeared in more than a decade is small pox" and they conclude "[t]he emergence of AIDS...is a sobering warning that new disease may still emerge".

Stephen Kunitz's (Kunitz, 1990) discussion of the importance of sociocultural determinants of mor- tality differentials among subgroups is perhaps the most eloquent and comprehensive within this body of literature. Kunitz notes the value of the general- izations inherent in epidemiologic transition theory, but is quick to point out their problems:

The generalizations implied in these typologies are of course useful. It is possible to commit the historicist fal- lacy, however, and assume that development stages are everywhere the same and follow one another in some inevitable progression...while it is true that stages of devel- opment may be usefully regarded as ideal types--that is to say as models or heuristic devices---such constructs may be misleading because they may cause us to generalize inappropriately and to reify what began simply as an abstraction...(Kunitz, 1990).

Thus, Kunitz's main issue is that overgeneraliza- tion of the theory is fraught with pitfalls. Kunitz warns that identifying and understanding important social and cultural differences among subgroups are essential components to making good health policy.

Kunitz notes several areas in which standard thinking about health improvements is useless when faced with powerful sociocultural forces that work against standard solutions. He discusses the com- mon belief among health professionals that specific medical interventions can be mechanistically applied to cure certain diseases. Kunitz points out that this thinking works in the case of a disease condition which can be attributed to a single primary cause, but falls short in the case of a disease condition which is best explained by "multiple weakly suffi- cient causes, or risk factors". Indeed, such expla- nations are fundamentally important for understanding mortality differentials among sub- groups in the U.S.

Two main points regarding limitations of epide- miologic transition theory emerge from the above discussion. First, large differentials in mortality trends among various population subgroups may undermine the generalizability of epidemiologic transition theory. Second, the epidemiologic tran-

Relbcusing the lens 613

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sition implies a level of control of infectious dis- eases that has not been achieved among certain sub- groups, and in some cases, entire populations. In broadening the discussion from epidemiologic tran- sition to health transition, scientists have begun to address these issues. Nevertheless, even the health transition discussion has only somewhat acknowl- edged these limitations (with Kunitz's writings representing a notable exception). In the following sections of this paper, we will highlight these two main limitations, offer general examples for a num- ber of diseases and populations, and, finally, pre- sent a more detailed and comprehensive critique with our case study of HIV/AIDS in the U.S.

E X P A N D I N G T H E D I S C U S S I O N : L I M I T A T I O N S O F E P I D E M I O L O G I C T R A N S I T I O N T H E O R Y

As noted above, the two primary areas in which epidemiologic transition theory falls short of the

*It should be noted that extensive literature searches found no articles which examined long-term trends in differential survival by subgroups, by year, and by cause o f death. Omran's piece and other articles in the literature (e.g.U.S. Department of Health and Human Resources, 1990) note that black declines in infectious diseases h a v e been l e s s than those of whites. Nevertheless, our search did not discover adequate data to effectively illustrate this trend. This lack of data and dearth of literature further emphasize the point that to date, subgroup mortality differentials, particularly in the context of the epidemiologic tran- sition, have received inadequate attention.

mark are in its failure to particularize to important population subgroups, and in its suggestion that infectious diseases are gradually eliminated as a major health threat. We will first explore subgroup differences, focusing predominantly on the U.S., but also drawing on some international examples.

Blacks have consistently had higher mortality rates than whites throughout U.S. history. Moreover, as noted above, blacks have had, and continue to have, higher death rates from infectious diseases.* For example, age-adjusted influenza and pneumonia death rates for black males in 1986 were 2.72 deaths per 1000 p o p u l a t i o n - - w h i t e males were experiencing this same rate in 1950. The 1986 rate for white males is 1.75 deaths per 1000 population, or 35% lower than the rate for black males. One claim made by the health transition panelists is that while "total mortality differentials in the most advanced countries still persists...the onset of the health transition is marked by convergence [of mor- tality differentials], not divergence" (Palloni, 1990). In terms of blacks in the U.S., this claim appears to be incorrect. Figure 1 presents total age-adjusted death rates by race in the U.S. for the years 1900 through 1990. The death rates are shown on a log- arithmic scale so that the differential can be more clearly seen. Convergence does not occur, as can be seen by the dashed line which indicates excess black mortality as a percent of white m o r t a l i t y - - t h o u g h mortality rates for both races declined during this period, race differentials do not show improvement: in 1900 black mortality rates were 58% higher than

614 Daniel S. Gaylin and Jennifer Kates

white mortality rates; in 1990 the differential was 62%. Over the course of the 20th century, the differential exhibited marked fluctuation. It is worth noting that the publication of Omran's theory (Omran, 1971) coincided with the minimum black white mortality differential (the trough in the dashed line if Fig. 1). Since that time, the differen- tial has been increasing.

Geographic areas often exhibit racial mortality differentials in their starkest light. Massey and Denton (1989) show that U.S. inner city areas dis- play marked "hypersegregation". In these areas blacks are severely isolated from mainstream society across multiple social dimensions and indicators of this isolation are readily found, notably differences in fertility, language, employment, and family pat- terns. Massey and Denton do not explicitly point to health differentials in the inner city, but their analy- sis ties directly to recent work on this very topic. Two Harlem physicians analyzed excess mortality in Harlem and demonstrated that males in Harlem are less likely to reach age 65 than males in Bangladesh, and that the mortality rates in Harlem are more than twice those of U.S. whites. The researchers conclude that "Harlem and probably other inner city areas with largely black populations have extremely high mortality rates that justify special consideration analogous to that given to natural disaster areas" (McCord and Freeman, 1990). Even more relevant to the present analysis, a working paper from the Center for Population and Family Health at Columbia University describes the apparent reversal of the epidemiologic transition in Harlem:

...by 1990, AIDS, homicide, pneumonia, influenza, and other infections joined the ranks of major killers in this community. Thus, [a] major source of death in Harlem [is] early transitional infectious and respiratory diseases (Findley and Ford, 1993).

A recent article published by National Center for Health Statistics researchers in the S t a t i s t i c a l Bulletin (Queen et al., 1994) discusses socioeco- nomic disparities in mortality and mortality trends using data from the 1 9 8 6 National Mortality Followback Survey and the 1986 Health Interview Survey. In addition to noting the race differences discussed above ("the gap between the death rates of whites and blacks has widened even as the over- all US death rate has declined"), the paper also concludes that disparities in mortality rates on the basis of income and education have increased over the years 1960 to 1986. For example, age-adjusted mortality rates among white males with high edu- cational attainment decreased by 50% during this time period (from 5.7 to 2.8 deaths per 1000 popu-

*Readers interested in the methodological techniques used to accomplish these comparisons are referred to the original text.

lation), while for white males with low educational attainment, the decrease was only 15% (from 9 to 7.6 deaths per 1000 population).

In a more complex, regression-based analysis of the same subject, Paul Menchik (1993) suggests that income differences are of primary importance in determining mortality differences between older black and white men over the period 1966-1981. The article concludes,

differential mortality by economic status is strongly pre- sent in the United States today, and...this relationship is monotonic...differences in mortality between ethnic groups are. in large part, a consequence of poverty...as opposed to genotype (Menchik, 1993).

Numerous international evidence lends further credence to the thesis of this paper that the epide- miologic transition is much less uniform and con- sistent a process than widely held notions would suggest. Ruzicka and Kane's article (Ruzicka and Kane, 1990), in addition to showing point-in-time mortality differentials by income and race, contains tabulations of standardized mortality by social class in England for the year 1971. These data show vir- tually monotonic decreases in mortality as social class increases. The paper also presents standar- dized mortality ratios (SMRs) for 11 distinct inter- national regions (including the U.S.) by social class (six classes) across the years 1971-1975. These SMRs allow for the comparison of mortality differ- entials between regions but within a social class.* The differences between regions for a given social class are striking, even when considering regions which are in the same general stages of economic development. For example, in India and Pakistan the standardized mortality ratio in the lowest social class was 122, while for the West Indies it was 267. The analysis also shows that social class mortality differentials between countries were attenuated at higher social classes i.e. individuals of higher class do similarly well in any country.

Related international comparisons by John Caldwell suggest that "some countries reach health levels far above those that would be dictated by their economies and others fall far below" (Caldwell, 1986). The superior health achievers (e.g. Sri Lanka, Costa Rica) are characterized by high levels of female autonomy, emphasis on education, open political systems, and civilian societies without rigid class structures, while the lower health countries (e.g. Libya, Saudi Arabia) show a lack of these characteristics. While Caldwell's approach is an oversimplification of a complex interaction of social phenomena, it provocatively encapsulates the essence of this interaction.

The above examples demonstrate that mortality experiences vary widely by different subgroups, both within the U.S. and across countries. The other general area to explore is the degree to which infectious diseases are truly being eliminated as so- cieties develop. Again, substantial evidence suggests

Refocusing the lens 615

that the epidemiologic transition t h e o r y ' s asser:ion that development leads to an eventual triumph over infectious diseases is overstated. Pinner e t al. (1996) recently reviewed current trends in infectious disease m o r t a l i t y in the United States by studying ICD-9 codes and underlying cause o f death for diseases such as tuberculosis, HIV, meningitis, and respirat- ory and G I infections. They conclude, "[d]espite historical predictions that infectious diseases would wane in the United States...infectious diseases mor- tality in the United States has been increasing in recent years". A recent article in the lay press reviewed (admittedly in a somewhat alarmist fashion) the upsurge in the incidence o f epidemics in the U.S. (Lemonick, 1994). The article n,ates localized but non-trivial epidemics o f whooping cough, lyme disease, malaria, streptococcus-A infec- tions, hepatitis, and measles. It also points to the worldwide resurgence o f cholera, particularly wide- spread o u t b r e a k s in Latin America.

In a d d i t i o n to A I D S (which will be discu~;sed at length below), the accompanying resurgence o f antibiotic-resistant tuberculosis represents the most concerning new epidemic. In fact, the resurgence o f TB is largely due to the interaction between HIV infection and TB: "This relationship is synergistic...HIV multiplies the problems o f tuaer- culosis for individuals and entire communities; tuberculosis complicates the management and course o f HIV infection" (Mann e t a l . , 1992). The Centers for Disease Control and Preven:ion (CDC) recently noted that in the U.S. tubercalo- sis increased by 26.5% from 1986 to 1989 among young black and Hispanic adults as comparecl to less than 0.5% for non-Hispanic whites (Centers for Disease Control, 1991). I m p o r t a n t here, once again, is the concept o f subgroup differences. N o t only are infectious diseases still problematic, but they exact a heavier toll on disadvantaged groups. The recent measles epidemic in immigrant neigh- b o r h o o d s in Los Angeles also demonstrates this point. In terms o f the b r o a d categories o f the epi- demiologic transition, a recent study examining gender differences in the U.S. concluded, "infectious disease m o r t a l i t y declines more in males, while degenerative disease mort~tlity declines more in females" (Gage, 1994). Finally, Rudolfo Bulatao (1993), in a 1 9 9 3 N a t i o n a l Research Council conference on the epidemiologic transition in developing countries, demonstrated that while developing countries exhibit declining trends in infectious diseases that are consistent with the epidemiologic transition, alternative pro- jections o f these trends through the year 2015 give widely varying estimates o f where various countries will be in terms o f distribution o f causes o f death.

Several prominent works have recently been published on the subject o f emerging and per:~ist- ent infectious diseases. Each b o o k contains sir~ilar

conclusions: "diseases long thought to have been defeated could return" (Garrett, 1994), and " m a n y o f these diseases may be prevented...con- tained...and, in a very few cases e r a d i c a t e d - - b u t the majority are likely to persevere. We can also be confident that new diseases will emerge..." (Lederberg e t a l . , 1992). The research presented in these b o o k s highlights the interplay o f opportunis- tic infectious diseases and major human events: "social and economic change, changes in h u m a n behavior, and catastrophic events such as war and famine...may fan a m i n o r outbreak into a widespread epidemic" (Morse, 1993; Krause, 1981). Moreover, the b o o k s highlight the concern that human manipulation o f the environment can create higher mortality risks from infectious dis- eases: "changes at the micro level o f the environ- ment o f any nation can affect life at the global, macro level" (Garrett, 1994). A n excellent example o f these points is the emergence o f extre- mely robust, insecticide-resistant mosquitoes as a result o f failed attempts to eliminate malaria using D D T spraying. A n o t h e r example is numer- ous cholera and dysentery epidemics within Rwandan refugee camps.

While the re-emergence o f infectious diseases in Harlem (discussed above) offers a localized picture o f a reversal o f the epidemiologic transition, the deteriorating health conditions in Russia present a much b r o a d e r case o f the same phenomenon. Newly released statistics show that deaths in 1994 from infectious disease are up 17.9% from 1993, and that the incidence o f several diseases (typhus, diphtheria, and measles) is up over 300% (Feshbach, 1995). The ephemeral nature o f health trends in developed countries seriously undermines widely held notions o f epidemiologic transition as a stable march o f progress.

The above examples are not intended to provide exhaustive p r o o f o f the inadequacy o f epidemiologic transition theory, nor to suggest that the theory is useless as a tool for understanding the relationship between m o r t a l i t y change and development. Rather, the examples serve to briefly illustrate some o f the problems inherent in epidemiologic transition and health transition theory in order to constructively expand the current discussion so that it addresses these theoretical limitations. Generalizations o f the theory mask i m p o r t a n t subgroup differences. These differences imply that optimism regarding death rates is unjustifiable in the case o f certain sub- groups, who risk being lost in the b r o a d e r trends toward improved health shown by the p o p u l a t i o n at large. Moreover, evidence abounds which seriously questions the notion that infectious dis- eases have been mostly conquered by modern science. In order to develop this point more fully, we will examine one case in d e p t h - - t h e H I V / A I D S pandemic.

616 Daniel S. Gaylin and Jennifer Kates

T H E H I V / A I D S P A N D E M I C A N D T H E E P I D E M I O L O G 1 C T R A N S I T I O N

...[T]he AIDS pandemic has outgrown--both practically and intellectually the capacities of the global public health system. It has also outgrown many of the conven- tional wisdoms about its importance and implications (Hamilton, 1994).

As o f J u n e 1994, o v e r 400,000 cases o f A c q u i r e d I m m u n e Deficiency S y n d r o m e ( A I D S ) had been r e p o r t e d to the C D C (Centers for Disease C o n t r o l , 1994). T h e C D C estimates that there are o v e r 1 million H I V infected persons in the U n i t e d States and o v e r 13 million H I V infected persons w o r l d - wide. Projections for the year 2000 range between 38 and 110 million H I V infected persons ( M a n n et al., 1992).*

A I D S , as a s y n d r o m e a n d collection o f diseases a n d conditions, has t a k e n p a r t i c u l a r g e o g r a p h i c a n d population-specific shapes, resulting in a series o f c o m p l i c a t e d a n d diverse epidemics t h r o u g h o u t the world. F u r t h e r m o r e , A I D S is m o r e than a medical p h e n o m e n o n : the biological reality o f H I V has intersected with the social realities o f poverty, p o o r health status, homelessness, substance use, a n d dis- c r i m i n a t i o n to create d i s p r o p o r t i o n a t e impacts on p a r t i c u l a r p o p u l a t i o n s u b g r o u p s (Panos Institute, 1992; H a m i l t o n , 1994):

AIDS is socially constructed (as society perceives and frames the phenomenon, blames victims, and laboriously negotiates its response) yet at the same time fits nicely into a one-dimensionally reductionist and biologically based model of disease (Rosenberg, 1989).

A I D S represents b o t h a u n i q u e challenge to an era which heralds the end o f infectious diseases and epidemics and a r e m i n d e r that history has d e m o n - strated the persistent ability o f disease to d e v a s t a t e p o p u l a t i o n s , particularly the m o s t vulnerable. As such, A I D S promises to b o t h c o n t r i b u t e to and challenge any t h e o r y o f e p i d e m i o l o g i c a l transition. F u r t h e r m o r e , as an epidemic that m a y have con- siderable global e c o n o m i c and political conse- quences, it w a r r a n t s a t t e n t i o n f r o m any t h e o r y which seeks to explain societal transitions o f m o r - tality and morbidity. In this section, we will outline s o m e o f the distinct characteristics o f the A I D S epi-

*HIV projections vary largely due to different modeling techniques and assumptions. Mann et al. (1992), using the Delphi method, produce quite high estimates. The World Health Organization, on the other hand, uses much more conservative assumptions and low-end esti- mates. In general, however, there is no agreed upon projection technique, and the nature of the HIV dis- ease itself, such as the long incubation period (up to 10 years) and the lack of individual knowledge of serosta- tus complicate measurement of incidence and preva- lence.

t i t is important to note that even if a cure for AIDS were discovered, global problems of access, poor health infrastructures, and cost would seriously impede its ability to end the AIDS epidemic.

demic, while recognizing its historical c o n n e c t i o n s to past epidemics, a n d a t t e m p t to d e m o n s t r a t e h o w a c o n s i d e r a t i o n o f the A I D S p a n d e m i c e n h a n c e s a n d challenges the t h e o r y o f e p i d e m i o l o g i c tran- sition in the U n i t e d States.

It is i m p o r t a n t for any analysis o f the A I D S epi- d e m i c to e x a m i n e its distinct qualities as a m o d e r n d a y epidemic (Panos Institute, 1992; H a m i l t o n , 1994).

(1) A I D S is the first m a j o r epidemic to o c c u r in the past 50 years, after the a p p a r e n t c o n q u e r i n g o f infectious diseases;

(2) A I D S has a case-fatality rate a p p r o a c h i n g 100%, with no cure currently a v a i l a b l e ; t

(3) A I D S is p r i m a r i l y t r a n s m i t t e d t h r o u g h sexual contact, a l t h o u g h there is an increasing percen- tage o f cases due to injection d r u g use;

(4) A I D S has an e x t r a o r d i n a r y capacity for g r o w t h , spreading rapidly into g e o g r a p h i c areas and c o m m u n i t i e s previously u n e x p o s e d to the epidemic;

(5) within each affected c o m m u n i t y , the epidemic has e v o l v e d and b e c o m e m o r e c o m p l e x o v e r time;

(6) H I V has a long i n c u b a t i o n p e r i o d - - a n individ- ual m a y r e m a i n a s y m p t o m a t i c for up to 10 years;

(7) A I D S has p r o m p t e d a n d / o r e x a c e r b a t e d the spread o f n u m e r o u s o t h e r epidemics and dis- eases such as tuberculosis, o t h e r S T D s , m a l a r i a a n d certain cancers; and

(8) A I D S affects m o s t individuals in their econ- omically m o s t p r o d u c t i v e and c h i l d b e a r i n g years.

A t the same time, it must be r e m e m b e r e d that A I D S is n o t wholly different f r o m past epidemics, such as the b u b o n i c plague and cholera. G a r r e t t offers a r e m i n d e r that " H I V is but o n e o f a long series o f m i c r o b e s that have recently surfaced, and will be followed by m o r e " ( G a r r e t t , 1992). As dis- cussed previously, the e m e r g e n c e o f full-fledged epi- demics f r o m incidental m i c r o b i a l infections is due to a series o f factors that include g l o b a l i z a t i o n and e c o n o m i c interdependence, i n t e r n a t i o n a l travel, and i n t e r a c t i o n with s o c i o e c o n o m i c c o n d i t i o n s such as p o v e r t y and homelessness. T h e history o f epidemics, o f which A I D S is a part, is a clear indication that infectious diseases are a c o n t i n u a l c o m p o n e n t o f p o p u l a t i o n change.

The demographic impact o f A I D S

T h e distinct characteristics o f the A I D S p a n d e m i c c o m b i n e to p r o d u c e significant d e m o g r a p h i c conse- quences in both d e v e l o p e d and d e v e l o p i n g countries. T h e effects o f A I D S can be seen at the m i c r o (individual a n d family) a n d m a c r o (societal/ global) levels. T w o o f the most n o t a b l e c o m p o n e n t s o f the d e m o g r a p h i c effect o f A I D S are its a p p a r e n t 100% case-fatality rate a n d its i m p a c t o n individ- uals in their m o s t e c o n o m i c a l l y p r o d u c t i v e a n d

Refocusing the lens 617

reproductive years. At the family level, adults with AIDS require extensive household resources, par- ticularly for health expenditures, at the same time that their ability to work increasingly deteriorates, and their contribution to household income decreases. The net effect of reduced household income impacts all members of the household, an impact which is greatly exacerbated when a parent or household adult dies from AIDS (Armstrong, 1992). Furthermore, "decisions made at the house- hold level to reallocate resources...may, when aggre- gated play out at the sectoral and macroeconomic levels" (Hamilton, 1994). These demographic effects can most clearly be seen in the developing world and in populations of the developed world where AIDS is increasingly spread through heterosexual contact.

The demographic impact of AIDS on population growth largely depends on particular population characteristics and population diversity. For instance, high infection rates for women in tlheir reproductive years could more seriously affect population growth and structure than similar rates for women past their reproductive years or for men. Surveillance studies and AIDS projections indicate that women face increasing rates of HIV infect:~on, prompting the World Bank to estimate that AIDS may have a sizeable effect on population growth, one that will be increasingly evident over time:

The reason is the multiplier effect of mortality of adulls of reproductive age: the deficit of people [initially] is alraost exclusively due to AIDS mortality, which is only a s~aaall proportion of all deaths. Two or three decades from now, the reduced number of people will be due both to AIDS mortality and to births that did not occur because of the additional mortality among women of reproductive age (Armstrong, 1992).

Still another mortality effect is caused by the increasing number of infants born with HIV infec- tion; perinatal transmission rates are estimated to be between 7 and 42%, with higher rates in the developing world ( M a n n e t al., 1992).

AIDS has its most transparent demographic effects on mortality and morbidity. Recent projec- tions using New York City data for males and females indicate increases in age-specific mortality rates due to HIV-related mortality (Kates and Weinstein, in press). In addition, AIDS significantly affects the morbidity of a population since it encompasses numerous diseases and conditions which result in increasing disability. Projection,,; of life expectancy at birth are also affected by AIDS: first, AIDS may reverse previous trends of steadily increasing life expectancy, and second, in areas with hard-hit female populations, AIDS may reduce the gap in life expectancy which currently favors women over men. The S t a t i s t i c a l B u l l e t i n recently reported that preliminary life tables prepared by the Metropolitan Life Insurance Company indicate the first stagnation in improvements in U.S. life expect-

ancy since 1980. Researchers attribute this to the relatively large increase in overall mortality, particu- larly due to AIDS and related diseases (Kranczer, 1995). In addition to potential declines in life ex- pectancy due to AIDS, positive trends in infant and child mortality may also diminish, particularly in areas where women have a high rate of HIV infec- tion.

Another significant aspect of the AIDS pandemic is its interaction with other diseases and its capacity to create and/or exacerbate other epidemics:

AIDS is a unique disease; no other known infectious dis- ease causes its damage through a direct attack on the human immune system. Because the immune system is the final mediator of human host-infectious agent inter- actions, it was anticipated early on that HIV infection would complicate the course of other important human diseases (Mann et al., 1992).

HIV infection has been associated with increased occurrence of cancers such as Kaposi's sarcoma, non-Hodgkin's lymphoma and, for women, cervical cancer. HIV has also been associated with increased incidence of other sexually transmitted diseases, a relationship which is both highly dynamic and synergistic (Berezin, 1992). In particular, there has been a correlation between HIV infection, gonor- rhea, syphilis, chlamydia, h u m a n papillomavirus, and hepatitis B. In addition, it is important to note morbidity differentials during the course of disease progression. For instance, as women with HIV infection become increasingly immunosuppressed, they are more likely to develop gynecological com- plications than HIV negative women.

As discussed above, one of HIV's most serious effects, in terms of its demographic impact, has been its interaction with tuberculosis. This relation- ship is bi-directional--HIV further complicates TB treatment, while TB hampers care delivery of HIV infected persons. This synergism is particularly dangerous in developing countries where 95% of people with dual TB and HIV live, and in hard-hit urban centers of the developed world, where, until the advent of AIDS, TB was considered a "stable, endemic health problem". Estimates of the inter- action demonstrate that in 1991, of the 1.72 billion TB infections and the 11.8 million HIV infections, 4.6 million individuals were TB and HIV infected (Mann e t al., 1992).

In the U.S., the new demographics of TB show heavy concentrations of the disease in the most populous states. Like HIV, TB has historically been linked to environmental factors, such as poverty, crowding, and access to health care. The interaction of the two epidemics has been the most severe in areas with high rates of HIV infection, such as New York, California, Florida, Texas and New Jersey. These states rank as the top five in reported AIDS cases and, together, represent more than half of new U.S. TB cases. In urban areas of the U.S., the increase in TB cases is concentrated among young

618 Daniel S. Gaylin and Jennifer Kates

adults, particularly among ethnic and racial min- orities, homeless people, and injection drug users. Studies show that individuals with HIV disease are at increased risk of developing active TB disease, that people with AIDS have a higher prevalence of TB, and that TB is often one of the first opportu- nistic infections associated with HIV, which in part prompted the CDC to expand the surveillance defi- nition of AIDS to include TB as an AIDS-defining condition. Finally, recent evidence suggests that an individual with HIV may be more likely to become infected with TB after contact with a contagious in- dividual (Office of Technology Assessment, 1993).

The epidemiology o f A I D S in the United States

Of the over 400,000 reported number of AIDS cases for adults/adolescents and children in the United States, 85% are concentrated in metropoli- tan areas with populations of 500,000 or more. The majority of reported adult cases are among men (87%), with the predominant mode of transmission being homosexual/bisexual contact (61%). Injection drug use accounts for 21% of reported cases among men. Among women, who comprise 13% of cumu- lative reported adult AIDS cases, injection drug use and heterosexual transmission account for the ma- jority of cases (48% and 36%, respectively). Blacks are disproportionately represented among reported adult cases for both men (29%) and women (54%) compared to a U.S. black population of 13%. This is also true for pediatric cases, of which 56% of reported cases are among blacks, reflecting the fact that 89% of all pediatric cases are due to mother with or at risk for HIV infection, and black women are also disproportionately represented among female AIDS cases (Centers for Disease Control, 1994).

Between 1987 and 1991, the age-adjusted death rate for HIV more than doubled, from 5.5 to 11.3 deaths per 100,000 population, moving HIV infec- tion from 15th to eighth in the ranking of leading causes of death in 1992 (National Center for Health Statistics, 1994). In 1993, HIV became the leading cause of death for 25 44-year-olds in the United States (Centers for Disease Control, 1995b).

Changing trends of HIV infection and HIV mor- tality in the U.S. provide important epidemiological information about subgroup mortality differentials. In particular, mortality data indicate increasingly wide disparities based on sex and race, particularly where the two interact. The CDC reports that "racial and ethnic minority populations have been disproportionately affected by HIV infection and AIDS since the beginning of the epidemic in the United States" (Centers for Disease Control, 1993a). Through June 1994, half of reported AIDS cases were among blacks and Hispanics, although these two groups represent only one fifth of the total U.S. population (Centers for Disease Control, 1993a, 1994).

In 1991, the HIV infection age-adjusted death rate for black men (52.9 deaths per 100,000) was more than three times that for white men (16.7). The rate for black women (12.0) was more than nine times that for white women (1.3). In addition, HIV ranked much higher in cause of death rankings for both blacks and Hispanics as compared to whites (National Center for Health Statistics, 1994).

Women constitute the fastest growing group of newly diagnosed people with AIDS. A n n u a l reported AIDS cases among U.S. adult/adolescent women increased 9% from 1991 to 1992, as com- pared to 2.5% for men (Centers for Disease Control, 1993b). The incidence of AIDS among women increased 20-fold between 1981 and 1990 in the U.S. (Melnick et al., 1994). The most recent update from the CDC reports that incidence of HIV is increasing more rapidly in women than men; women comprise 18% of new AIDS cases compared to 13% of cumulative cases (Centers for Disease Control, 1995a). In 1994 the male-female AIDS case ratio was 4.9:1 compared to 14.1:1 in 1984 (Centers for Disease Control, 1993b, 1994). Although black and Hispanic women constitute 21% of all U.S. women, they account for 74% of women diagnosed with AIDS in the U.S. and 77% of cases reported in 1994. Case rates for black and Hispanic women were, respectively, 16 and 7 times greater than for white women (Centers for Disease Control, 1995a). Finally, in some hard-hit urban areas, such as New York City, AIDS has been the leading cause of death among women age 25-44 since 1988 (New York City Office of Vital Statistics and Epidemiology, 1988-1990).

The most recent study of HIV infection in women (Melnick et al., 1994) found that the risk of death differed for men and women with AIDS. Women had higher death rates than men even though disease progression rates did not differ sig- nificantly. More women than men experienced death as a first event, an effect that was exacerbated by race and/or injection drug use. In addition, the study found that although overall disease pro- gression rates were similar for women and men, the incidence of certain opportunistic diseases varied by sex. Women, for instance, were significantly more likely to develop bacterial pneumonia. The research- ers conclude that because "death was the first dis- ease progression event for more women than men...observed survival differences may reflect a differential access to or utilization of health care resources by gender".

A I D S and the epidemiologic transition theory

Omran's theory of the epidemiologic transition was developed for its explanatory and descriptive applicability to the relationship between changing mortality patterns and the demographic transition; specifically, the decline of mortality rates in both developed and developing countries due to modern-

Refi3cusing the lens 619

ization, a decline in infectious diseases, and a sub- sequent improvement in social, economic and health conditions.

Consistent with our earlier, more general discus- sion, the introduction o f A I D S into the global health economy questions two components o f the epidemiologic transition theory. First, A I D S chal- lenges the theory's assumption o f the disappearance o f infectious diseases and their accompanying mor- tality concentration at younger ages. W i t h the decline in infectious diseases, the age structure,; o f most countries shifted to reflect an older age distri- bution, one which was much more susceptible to degenerative diseases. Yet A I D S is an infectious dis- ease which largely affects the younger age groups (individuals in their most economically productive and childbearing years). A I D S m a y have consider- able demographic consequences not only on some developing countries, but on m a n y hard-hit urban areas o f developed countries, areas which are afflicted with poverty and inadequate health ser- vices. F u r t h e r m o r e , evidence has shown that A I D S has exacerbated, and perhaps even caused in some areas, a resurgence in other epidemics, such as TB. Given the current d i s p r o p o r t i o n a t e impact o f HIV infection on minorities, women, the poor, and young adults, and global A I D S projections which indicate significant impacts on past demographic trends, the A I D S pandemic highlights the need for a reformulation o f the epidemiologic transition dis- cussions.

The second limitation o f the epidemiologic tran- sition theory is its failure to adequately address m o r t a l i t y differentials between subgroups. A l t h o a g h O m r a n recognized that the decline in m o r t a l i t y rates was not the same for all populations within the United S t a t e s - - n a m e l y that minorities lagged behind whites in m o r t a l i t y d e c l i n e - - h i s theory nevertheless a t t e m p t e d to b r o a d l y generalize the epidemiologic transition. Yet, as Kunitz (1990) reminds us, such generalization

masks astonishing diversity and fails to include many explanatory variables that are of significance if we are ade- quately to understand the distribution and change of health and diseases in populations, particularly their cul- tural, social, and behavioral determinants.

As we have noted above, K u n i t z ' s work demon- strates the importance o f differences in m o r t a l i t y declines a m o n g cultures: " O n e must finely analyze the particularistic features o f nations, cultures, regions, and localities". A I D S , as b o t h a case study o f a modern epidemic and a microcosm o f the "inequality o f d e a t h " (Ruzicka and Kane, 1989) seriously questions the comprehensive applicability o f the epidemiologic transition theory to all popu- lation subgroups.

Epidemiologic information concerning A I D S pre- sented above demonstrates the increasingly disl:,ro- p o r t i o n a t e impact o f A I D S on particular subgroups o f the U.S. population. This demographic iml:,act

has serious repercussions both for past trends in m o r t a l i t y decline and future projections o f popu- lation growth, death rates, life expectancy and ferti- lity. In fact, the effects o f A I D S on particular subgroups o f the U.S. p o p u l a t i o n recall earlier stages o f the epidemiologic transition theory. Just as the "age o f pestilence and famine" (the first stage) is characterized by high mortality, high ferti- lity, and the prevalence o f infectious diseases, so is the current epidemiologic situation o f U.S. blacks in urban areas hard-hit by A I D S and TB. These epidemics have increased black m o r t a l i t y at younger ages, an effect exacerbated by d i s p r o p o r t i o n a t e pov- erty. Increasing death rates and mortality differen- tials for blacks due to A I D S threaten past trends in increasing life expectancy and an aging p o p u l a t i o n (the third and fourth stages o f the epidemiologic transition). Furthermore, previously lower mortality rates among women are diminishing as a "shift has occurred in the worldwide pattern o f A I D S inci- dence from a disease primarily among men to a pat- tern o f gender equity" (Melnick e t al., 1994). The larger implications o f this trend are substantial: " t h e HIV infection/AIDS problem in women and children will doubtless become one o f the m a j o r challenges to public health, health care, and social support systems worldwide" (Chin, 1990). The double effects o f race and gender further demon- strate our argument. F o r instance, as the leading cause o f death for black women age 25-44 in New Y o r k since 1988, A I D S will have a serious effect on the mortality differential between white and black women (Centers for Disease Control, 1993b). In ad- dition, A I D S will have a m o r t a l i t y multiplier effect due to births that did not occur due to A I D S mor- tality among black women o f reproductive age and to the increasing number o f children born with HIV. Furthermore, black women tend to be dispro- portionately more impoverished as c o m p a r e d to white women, therefore indicating an increasing d e m a n d for (less accessible) public health services, a severe drain on household resources and loss o f income.

The effects o f A I D S are likely to exacerbate the mortality differentials between whites and racial and ethnic minorities and negatively effect female life expectancy gains, as A I D S has moved up the rank order for cause o f death in the U.S. This epi- demiologic reality seriously questions the epidemio- logic transition theory's assumption that m o r t a l i t y due to infectious disease continues to decline and that this can be applied broadly to whole popu- lations. The d i s p r o p o r t i o n a t e impact o f A I D S , its effects on particular populations due to social, cul- tural and economic differences and biological reali- ties will have b o t h micro and macro level demographic effects that challenge the universal and optimistic picture presented by the epidemiologic transition theory. As Kunitz (1990) states,

620 Daniel S. Gaylin and Jennifer Kates

[M]any health problems in both rich and poor countries are still best explained by weakly sufficient causes, or risk factors. Understanding their incidence, prevalence, and distribution, as well as their treatment and prevention, may require intimate understanding of particular people and settings.

T h e c h a l l e n g e b e f o r e us, t h e n , is to seek t o d e v e l o p s u c h a n u n d e r s t a n d i n g in o r d e r to effec- tively i n t e r v e n e a n d resolve h e a l t h p r o b l e m s s u c h as

H I V / A I D S .

Affairs, and Office of Population Research. The opinions expressed are those of the authors and do not necessarily represent the opinions or policies of The Lewin Group or Princeton University. The authors thank Thomas Espenshade of the Office of Population Research, Princeton University, and Maxine Weinstein of Georgetown University for their valuable comments on previous drafts.

R E F E R E N C E S

CONCLUSION

In t h i s p a p e r , we h a v e a t t e m p t e d to d e m o n s t r a t e h o w t h e e p i d e m i o l o g i c t r a n s i t i o n t h e o r y , d e s i g n e d t o e x p l a i n g l o b a l t r e n d s in t h e d y n a m i c r e l a t i o n s h i p b e t w e e n e p i d e m i o l o g y a n d d e m o g r a p h i c c h a n g e , o n l y p a r t i a l l y serves to e x p l a i n m o r t a l i t y d e c l i n e s o v e r t h e l a s t c e n t u r y . I n s t e a d , d r a w i n g o n K u n i t z (1990), we a r g u e f o r t h e n e e d to " p a r t i c u l a r i z e " t h e focus o f e p i d e m i o l o g i c t r a n s i t i o n o n p o p u l a t i o n s u b g r o u p s . T h i s a p p r o a c h p r o v i d e s a fuller a n d m o r e a c c u r a t e a c c o u n t o f t h e e p i d e m i o l o g i c t r a n - sition, t h e r e b y e n h a n c i n g t h e e x i s t i n g t h e o r e t i c a l f r a m e w o r k .

Specifically, we h a v e s h o w n t h a t p a r t i c u l a r i z e d a t t e n t i o n t o p o p u l a t i o n s u b g r o u p s w i t h i n t h e U.S. d e m o n s t r a t e s t h a t all s u b g r o u p s h a v e n o t experi- e n c e d t h e e p i d e m i o l o g i c t r a n s i t i o n in t h e s a m e way. I n fact, c o n t r a r y t o t h e e p i d e m i o l o g i c t r a n s i t i o n t h e - o r y ' s c l a i m t h a t m o r t a l i t y d i f f e r e n t i a l s b e t w e e n U.S. b l a c k s a n d w h i t e s c o n v e r g e o v e r time, we h a v e f o u n d t h a t t h e s e d i f f e r e n t i a l s a c t u a l l y f l u c t u a t e a n d h a v e m o s t r e c e n t l y b e e n d i v e r g i n g . F u r t h e r m o r e , we a r g u e t h a t t h e t h e o r y h a s o v e r s t a t e d t h e d e c l i n e in i n f e c t i o u s diseases as c a u s e o f d e a t h . W e f o c u s o n t h e A I D S p a n d e m i c as a case s t u d y w h i c h b o t h s u p p o r t s t h i s c l a i m a n d d e m o n s t r a t e s h o w m o r t a l i t y d i f f e r e n t i a l s b e t w e e n p o p u l a t i o n s u b g r o u p s a r e a c t u - ally i n c r e a s i n g w i t h s e r i o u s d e m o g r a p h i c effects. T h e A I D S p a n d e m i c also c h a l l e n g e s t h e t h e o r y ' s s t a t e m e n t t h a t w o m e n are f a v o r e d o v e r m e n in the e p i d e m i o l o g i c t r a n s i t i o n , as A I D S s h o w s h o w w o m e n , d u e t o e n v i r o n m e n t a l a n d b e h a v i o r a l fac- tors, a r e i n c r e a s i n g l y i m p a c t e d b y the e p i d e m i c .

W e h o p e t h a t t h e a r g u m e n t p r e s e n t e d by this p a p e r will serve t o e n h a n c e t h e t h e o r y o f the epide- m i o l o g i c t r a n s i t i o n b y a r g u i n g for a p a r t i c u l a r i z e d lens. T h i s t e c h n i q u e r e s u l t s in a m o r e c o m p l e x a n d c o m p r e h e n s i v e p i c t u r e o f m u l t i p l e e p i d e m i o l o g i c t r a n s i t i o n s , a n d d e m o n s t r a t e s h o w p o p u l a t i o n s u b - g r o u p s e x p e r i e n c e t h e s e t r a n s i t i o n s differently. F i n a l l y , we h o p e t h a t s u c h a n a p p r o a c h will be used to g u i d e p u b l i c p o l i c y e n d e a v o r s t h a t seek to a d d r e s s e p i d e m i c s s u c h as A I D S a n d t h e i n e q u a l i t y o f d e a t h t h a t still p r e v a i l s in t h e U.S. a n d t h r o u g h - o u t t h e w o r l d .

Acknowledgements--This work commenced while the authors were graduate students at Princeton University's Woodrow Wilson School of Public and International

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Global--regional--and-national-disability-adjusted-life-years--DALY_2015_The.pdf

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Global, regional, and national disability-adjusted life years (DALYs) for 306 diseases and injuries and healthy life expectancy (HALE) for 188 countries, 1990–2013: quantifying the epidemiological transition GBD 2013 DALYs and HALE Collaborators*

Summary Background The Global Burden of Disease Study 2013 (GBD 2013) aims to bring together all available epidemiological data using a coherent measurement framework, standardised estimation methods, and transparent data sources to enable comparisons of health loss over time and across causes, age–sex groups, and countries. The GBD can be used to generate summary measures such as disability-adjusted life-years (DALYs) and healthy life expectancy (HALE) that make possible comparative assessments of broad epidemiological patterns across countries and time. These summary measures can also be used to quantify the component of variation in epidemiology that is related to sociodemographic development.

Methods We used the published GBD 2013 data for age-specifi c mortality, years of life lost due to premature mortality (YLLs), and years lived with disability (YLDs) to calculate DALYs and HALE for 1990, 1995, 2000, 2005, 2010, and 2013 for 188 countries. We calculated HALE using the Sullivan method; 95% uncertainty intervals (UIs) represent uncertainty in age-specifi c death rates and YLDs per person for each country, age, sex, and year. We estimated DALYs for 306 causes for each country as the sum of YLLs and YLDs; 95% UIs represent uncertainty in YLL and YLD rates. We quantifi ed patterns of the epidemiological transition with a composite indicator of sociodemographic status, which we constructed from income per person, average years of schooling after age 15 years, and the total fertility rate and mean age of the population. We applied hierarchical regression to DALY rates by cause across countries to decompose variance related to the sociodemographic status variable, country, and time.

Findings Worldwide, from 1990 to 2013, life expectancy at birth rose by 6·2 years (95% UI 5·6–6·6), from 65·3 years (65·0–65·6) in 1990 to 71·5 years (71·0–71·9) in 2013, HALE at birth rose by 5·4 years (4·9–5·8), from 56·9 years (54·5–59·1) to 62·3 years (59·7–64·8), total DALYs fell by 3·6% (0·3–7·4), and age-standardised DALY rates per 100 000 people fell by 26·7% (24·6–29·1). For communicable, maternal, neonatal, and nutritional disorders, global DALY numbers, crude rates, and age-standardised rates have all declined between 1990 and 2013, whereas for non- communicable diseases, global DALYs have been increasing, DALY rates have remained nearly constant, and age- standardised DALY rates declined during the same period. From 2005 to 2013, the number of DALYs increased for most specifi c non-communicable diseases, including cardiovascular diseases and neoplasms, in addition to dengue, food-borne trematodes, and leishmaniasis; DALYs decreased for nearly all other causes. By 2013, the fi ve leading causes of DALYs were ischaemic heart disease, lower respiratory infections, cerebrovascular disease, low back and neck pain, and road injuries. Sociodemographic status explained more than 50% of the variance between countries and over time for diarrhoea, lower respiratory infections, and other common infectious diseases; maternal disorders; neonatal disorders; nutritional defi ciencies; other communicable, maternal, neonatal, and nutritional diseases; musculoskeletal disorders; and other non-communicable diseases. However, sociodemographic status explained less than 10% of the variance in DALY rates for cardiovascular diseases; chronic respiratory diseases; cirrhosis; diabetes, urogenital, blood, and endocrine diseases; unintentional injuries; and self-harm and interpersonal violence. Predictably, increased sociodemographic status was associated with a shift in burden from YLLs to YLDs, driven by declines in YLLs and increases in YLDs from musculoskeletal disorders, neurological disorders, and mental and substance use disorders. In most country-specifi c estimates, the increase in life expectancy was greater than that in HALE. Leading causes of DALYs are highly variable across countries.

Interpretation Global health is improving. Population growth and ageing have driven up numbers of DALYs, but crude rates have remained relatively constant, showing that progress in health does not mean fewer demands on health systems. The notion of an epidemiological transition—in which increasing sociodemographic status brings structured change in disease burden—is useful, but there is tremendous variation in burden of disease that is not associated with sociodemographic status. This further underscores the need for country-specifi c assessments of DALYs and HALE to appropriately inform health policy decisions and attendant actions.

Funding Bill & Melinda Gates Foundation.

Lancet 2015; 386: 2145–91

Published Online August 27, 2015 http://dx.doi.org/10.1016/ S0140-6736(15)61340-X

See Editorial page 2118

See Comment page 2121

*Collaborators listed at the end of the Article

Correspondence to: Prof Christopher J L Murray, Institute for Health Metrics and Evaluation, 2301 5th Avenue, Suite 600, Seattle, WA 98121, USA [email protected]

http://crossmark.crossref.org/dialog/?doi=10.1016/S0140-6736(15)61340-X&domain=pdf

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Introduction The Global Burden of Disease study 2013 (GBD 2013) seeks to bring together all available epidemiological data using a coherent measurement framework, standardised estimation methods, and transparent data sources to allow comparisons of health loss to be made over time and across causes, age–sex groups, and geographies. The GBD 2013 data for disease and injury incidence and prevalence, years lived with disability (YLDs), causes of death, and years of life lost because of premature mortality (YLLs) for 188 countries provide an opportunity to assess the eff ect of recent changes in population health by examining summary measures of health loss attributed to specifi c causes, expressed in DALYs, and summary measures of average population health, expressed as HALE.1,2 These measures are crucial to track health progress, strengthen policy decisions, assess programme eff ects and results, and inform health service and research priorities. Such holistic measures of population health, encompassing both disability and mortality levels and patterns in populations, are also attracting interest as part of the discussion around the Sustainable Development Goals .3–5

A hallmark of the GBD approach is an emphasis on making national data easier to compare by taking into account the extensive variation that exists in national medical certifi cation and cause of death coding practices and widely varying case defi nitions and measurement methods used to track the incidence and prevalence of diseases and injuries.1,2 The GBD not only provides

detailed metrics for specifi c causes, but also generates summary measures, such as DALYs and HALE, which enable comparative assessments of broad epidemiological patterns across countries and diff erent time periods. HALE is a useful summary of overall health for a country and DALYs allow assessment of both premature mortality and non-fatal outcomes by cause. These broad summary measures allow quantifi cation of general trends, such as the epidemiological transition, while also making clear how countries and regions deviate from general patterns.6–9 The unfolding of the HIV epidemic and the rise of adult mortality, especially among men in Eastern Europe and Central Asia, have called into question the notion of a universal pattern of epidemiological change that occurs with sociodemographic development.2,10–13 However, the general notion of a shift from communicable to non-communicable causes of disease burden and injuries remains a powerful framework for global and regional health policy debates.9,14–18 The GBD provides an opportunity to quantify these patterns and explore the extent to which epidemiological change is driven by sociodemographic change, reduction of health risks, improvement of health management, or other local factors.

GBD 2013 results for deaths, YLLs, incidence, prevalence, and YLDs by cause for 1990 to 2013 for 188 countries have already been published.1,2 In this study we use these GBD 2013 results to calculate DALYs and HALE. These summary metrics are used to characterise broad patterns of lost healthy life and cross-country

Research in context

Evidence before this study In 2012, results from the fi rst complete revision of the Global Burden of Disease (GBD) since the fi rst assessment in 1993 became available. This eff ort was called the GBD 2010 study and reported on disability-adjusted life-years (DALYs) and health-adjusted life expectancy (HALE) by country for 1990 and 2010 based on analyses of an extensive data collection eff ort to collate all available information on causes of death and disease occurrence in 187 countries. In response to the need for up-to-date information about the health of populations to inform health policy decision making, a decision was made to produce annual updates. The GBD 2013 is the fi rst of these annual updates. In previous papers on the GBD 2013 study, we have documented the new data and new methods used to assess mortality and morbidity by country and over time.

Added value of this study Here, we present the results for the aggregation of mortality and morbidity in terms of DALYs and HALE by country and for the time period 1990 to 2013. We examined to what extent the changes in DALYs since 1990 by disease and country can be explained by a composite indicator of sociodemographic status, constructed from income per person, years of schooling after

age 15 years, median age of the population and total fertility rate. These GBD 2013 results for the period 1990 to 2013 for DALYs and HALE supersede all previously published GBD fi ndings on DALYs and HALE.

Implications of all the available evidence Numbers of DALYs and crude and age-standardised DALY rates for communicable diseases, maternal, neonatal, and nutritional disorders have decreased since 1990. For non-communicable diseases, the number of DALYs have increased, crude rates have remained stable, and age-standardised rates have decreased. Global health is improving but population increase and ageing are keeping the crude rates of DALYs constant, showing that progress in health does not mean fewer demands on health systems. The epidemiological transition, as quantifi ed using our sociodemographic status indicator, accounts for much of the variation between countries and over time for most communicable, maternal, and neonatal causes but not for many non-communicable causes such as cardiovascular disease. The large variation in burden that is not associated with sociodemographic status emphasises the need for ongoing detailed assessments of DALYs and HALE at the country level to inform health policies.

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variations within these patterns. The GBD 2013 provides a complete re-analysis of each country’s data from 1990 to 2013 and thus supersedes all previously published GBD analyses of DALYs and HALE.

Methods Study design GBD 2013 uses a hierarchy of causes that organises 306 diseases and injuries into four levels of classifi cation, the rationale for which has been described previously.2,19 The fi rst level distinguishes three broad categories: fi rst, communicable, maternal, neonatal, and nutritional disorders; second, non- communicable diseases; and third, injuries. Level 2 has 21 mutually exclusive and collectively exhaustive categories, level 3 has 163 categories, and level 4 has 254 categories. The full cause list, including International Classifi cation of Diseases tenth edition (ICD-10) codes, has been reported previously.1,2 Mortality rates and causes of death for each country– age–sex–year group have been estimated in accordance with some general principles: identifi cation of all available data sources, evaluation of the quality and correction for known bias in each data source, consistent statistical estimation including uncertainty analysis, and cross-validation analysis to assess model performance. Details of data sources and estimation methods used to deal with missing data and multiple measurements for the same country–age–sex–year group have been described previously.2 Disease and injury incidence and prevalence and computation of YLDs have been estimated in line with similar principles of identifi cation and assessment of the quality of all available sources for 2337 sequelae of the 301 diseases and injuries.1 The discrepancy between the 306 diseases and injuries for which DALYs are calculated and the 301 diseases and injuries for which YLDs are calculated is attributable to fi ve diseases that cause death but do not cause disability: sudden infant death syndrome, indirect maternal deaths, late maternal deaths, maternal deaths aggravated by HIV/AIDS, and aortic aneurysm. Various statistical estimation methods were used depending on the details of specifi c diseases, the most common approach being the application of a Bayesian metaregression model, DisMod-MR 2.0.20 We used alternative methods when the basic susceptible, with disease, and dead states in DisMod-MR 2.0 were insuffi cient to capture the natural history of a sequela. We aggregated sequelae prevalence into YLDs fi rst by estimating the distribution of comorbidities through microsimulation, and second by using disability weights derived from population-based surveys of the general public to assign disability weights to each sequela and combination of sequelae—details of both steps have been described previously.1,21

We used the GBD 2013 results for YLLs2 and YLDs1 to calculate DALYs. To calculate HALE, we used YLDs per

Figure 1: Survivorship curve stratifi ed by disability weight in 2013 Health survivorship function showing the fraction of a birth cohort alive at each age exposed to 2013 death rates, with the fraction of time spent at each age by the birth cohort decomposed by level of disability weight. Countries are grouped by socidemographic status into quintiles, including the lowest quintile (A), the three middle quintiles (B), and the highest quintile (C). DW=disability weight.

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person and life tables.1,2 We applied decomposition of variance using hierarchical regression to DALY rates by cause.

Years lived with disability For each year for which YLDs have been estimated (1990, 1995, 2000, 2005, 2010, and 2013), we computed DALYs by adding YLLs and YLDs for each age–sex–country group. We assumed that uncertainty in YLLs is independent of uncertainty in YLDs. We did this by summing the fi rst

draw of the 1000 draws for YLLs and YLDs and then repeating for each subsequent draw. We calculated 95% uncertainty intervals (UIs) using the 25th and 975th ordered draw of the DALY uncertainty distribution.

Healthy life expectancy We calculated HALE in accordance with the methods outlined by Salomon and colleagues.8 In brief, we used Sullivan’s method22 to incorporate information about average levels of health experienced at diff erent ages into

Figure 2: Total DALYs, crude DALY rates, and age-standardised DALY rates from 1990 to 2013 Changes in global DALYs caused by communicable, maternal, neonatal, and nutritional disorders, non- communicable diseases, and injuries shown in terms of numbers of DALYs (A), DALY rates per 100 000 people (B), and age-standardised DALY rates per 100 000 people (C). The diff erence in trends between A and B is caused by population growth and the diff erence between B and C because of changes in the percentage distribution of the population by age. Shaded areas show 95% uncertainty intervals. DALY=disability-adjusted life-years.

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Figure 3: Change in DALYs for GBD level 3 causes Increasing and decreasing global DALYs for GBD level 3 causes from 1990 to 2005 (A) and 2005 to 2013 (B). Within each tree map, the size of the rectangle for each

cause is proportional to the magnitude of the decrease or increase in DALYs for each cause. Dark shading show statistically signifi cant changes and light shading shows changes that are not signifi cant. Unmarked boxes represent causes for which the decrease or increase was less than 1 000 000 DALYs. Table 1 and the appendix

(p 4) contains numerical values for each cause. DALY=disability-adjusted life-years. GBD=Global Burden of Disease. Diarrhoea=diarrhoeal diseases. LRI=lower respiratory infections. NN enceph=neonatal encephalopathy due to birth asphyxia and trauma. NN haemol=haemolytic disease and other neonatal jaundice. Other NN=other neonatal disorders. NN preterm=preterm birth complications. Nematode=intestinal nematode infections. Iron=iron-defi ciency anaemia. PEM=protein-

energy malnutrition. STD=sexually transmitted diseases excluding HIV. TB=tuberculosis. Whooping=whooping cough. NN sepsis=neonatal sepsis and other neonatal infections. Congenital=congenital anomalies. RHD=rheumatic heart disease. Oral=oral disorders. Sense=sense organ diseases. Cirr alc=cirrhosis due to alcohol use.

Cirr hep C=cirrhosis due to hepatitis C. CKD=chronic kidney disease. CMP=cardiomyopathy and myocarditis. HTN HD=hypertensive heart disease. IHD=ischaemic heart disease. Stroke=cerebrovascular disease. Diabetes=diabetes mellitus. Alcohol=alcohol use disorders. Anxiety=anxiety disorders. Bipolar=bipolar disorder. Drugs=drug

use disorders. Depression=depressive disorders. Other MSK=other musculoskeletal disorders. Back and neck=low back and neck pain. Breast C=breast cancer. Colorectal C=colon and rectum cancer. Liver C=liver cancer. Lung C=tracheal, bronchus, and lung cancer. Alzheimer’s=Alzheimer disease and other dementias.

COPD=chronic obstructive pulmonary disease. Skin=skin and subcutaneous diseases. Fire=fi re, heat, and hot substances. Mech=exposure to mechanical forces. War=collective violence and legal intervention. Violence=interpersonal violence. Road inj=road injuries. Haemog=haemoglobinopathies and haemolytic anaemias.

ILD=interstitial lung disease and pulmonary sarcoidosis. Disaster=exposure to forces of nature.

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www.thelancet.com Vol 386 November 28, 2015 2149

an abridged life table to produce estimates of life expectancy that are adjusted for reductions in functional health status relating to prevalent health conditions. Eff ectively, the cumulative years lived in an age group in the abridged life table (the life expectancy column) for each country–age–sex group is multiplied by the YLDs per person for that country–age–sex group. Calculation of HALE relies on three inputs from GBD 2013: life

tables by sex, country, and year; estimates of the prevalence of 2337 sequelae by age, sex, country and year; and disability weights for 235 unique health states that collectively cover the range of functional health losses and symptoms associated with the 2337 sequelae. Wang and colleagues2 have described data sources and methods to estimate mortality and life tables, and Vos and colleagues1 have described these for the measurement of

2 1

3

4

6 5

7

9 10 8

Whoop- ing

Nema- todeNN

haemol

Iron

Hepatitis STD

RHDFire

Mech

War

Tuber- culosis

Diarrhoea LRI

Measles Meningitis

Tetanus

NN enceph

NN preterm Other NN

PEM

CongenitalDrowning

Increasing

11

14

15 13

12

16

17

18 19

20 21

22

23

25

26

24

27

28

NN sepsis

Breast C

Colo- rectal C

Liver C

Lung C

Oral Skin

CMP HTN HD

COPD

Cirr alc Cirr hep C

Alzheimer’s

Migraine

Alcohol Anxiety Bi- polar

Drugs

Schizo- phrenia

Osteo- arthritis

Other MSK

Falls

Self- harm

Vio- lence

HIV/AIDS Malaria

Sense

IHD

Stroke

Depression

CKD Diabetes

Back and neck

Road Inj

A Decreasing

IncreasingB Decreasing

Communicable Non-communicable Injuries

1 Intestinal infectious diseases 2 Encephalitis 3 Other neglected tropical diseases 4 Other nutritional deficiencies 5 Pneumoconiosis 6 Other chronic respiratory diseases 7 Peptic ulcer disease 8 Poisonings 9 Animal contact 10 Foreign body 11 Oesophageal cancer 12 Prostate cancer 13 Pancreatic cancer 14 Non-Hodgkin lymphoma 15 Other neoplasms 16 Other cardiovascular and circulatory diseases 17 Interstitial lung disease and pulmonary sarcoidosis 18 Cirrhosis due to hepatitis B 19 Cirrhosis due to other causes 20 Epilepsy 21 Medication overuse headache 22 Autistic spectrum disorders 23 Other mental and substance use disorders 24 Urinary diseases and male infertility 25 Gynaecological diseases 26 Haemoglobinopathies and haemolytic anaemias 27 Rheumatoid arthritis 28 Exposure to forces of nature

1 Intestinal infectious diseases 2 Whooping cough 3 Maternal haemorrhage 4 Other infectious diseases 5 Stomach cancer 6 Alcohol use disorders 7 Fire, heat, and hot substances 8 Poisonings 9 Exposure to mechanical forces 10 Interpersonal violence 11 Oesophageal cancer 12 Liver cancer 13 Breast cancer 14 Prostate cancer 15 Pancreatic cancer 16 Non-Hodgkin lymphoma 17 Other neoplasms 18 Other cardiovascular and circulatory diseases 19 Cirrhosis due to hepatitis C 20 Medication overuse headache 21 Schizophrenia 22 Drug use disorders 23 Bipolar disorder 24 Other mental and substance use disorders 25 Urinary diseases and male infertility 26 Gynaecological diseases 27 Diabetes, urogenital, blood, and endocrine diseases

1

2

3

4

5

6

7 9

8 10

Meningitis Tetanus

Other NN

Iron

PEM

STD

Road inj DrowningSelf harm Disaster War

HIV/AIDS Tuber- culosis

Diarrhoea

LRI

Measles

Malaria

NN enceph

NN preterm

13

11

12

16

17

15

14

18

19

20

23

22

24

21

27

26

25

Colorectal C

Lung C

Oral Skin

HTN HD

ILD

Alzheimer

Migraine

Anxiety

CKD

Haemog

Osteo- arthritis

Other MSK

Sense

IHD Stroke

COPD

Depression

Diabetes

Back and neck

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2150 www.thelancet.com Vol 386 November 28, 2015

prevalence of sequelae and disability weights.1 We combined information about prevalence and disability weights into measures of the overall rate of YLDs per person in each age–sex–country group. We make the strong assumption that uncertainty in YLDs per person is independent of uncertainty in age-specifi c death rates to calculate uncertainty distributions for HALE.

Decomposition of variance and epidemiological transition The epidemiological transition is an extension of the notion of the demographic transition. In demographic transition, a characteristic evolution occurs in populations over time towards reduced fertility rates, reduced mortality rates, and an older age distribution of the population. The widely used concept of the epidemiological transition adds the idea that, in addition to these changes, a characteristic change occurs in the contributing causes of death. The epidemiological transition has been broadened to encompass the more general health transition, including both morbidity and mortality. A single variable to characterise countries over time in terms of their demographic and epidemiological status would be crucial to describe the epidemiological transition. Some studies examine associations with income per person, whereas others use variables such as mean age of the population.23,24 We aimed to construct a single composite variable to represent both demographic status and socioeconomic development to explore the patterns of the epidemiological transition. To construct

this sociodemographic status variable, we assessed variables indicative of socioeconomic status and demographic change that were available for all 188 countries from 1990 to 2013. We did not include measures of income inequality, such as the Gini coeffi cient, because these were not available for all countries for each year. We used principal components analysis (PCA) of the log transformation of income per person (in constant international dollars), average years of schooling of the population after age 15 years, the log of the total fertility rate, and the log of the mean age of the population. The relationship between the PCA variables and DALY rates were highly non-linear, but became linear with respect to the log DALY rates after log transformation of three of the four sociodemographic status component variables. Before using PCA, we normalised each variable to have a mean of zero and a standard deviation of 1·0. Only the fi rst component of the PCA had an eigenvalue greater than 1·0 and the weights were 0·471 for income per person, −0·517 for total fertility rate, 0·495 for education per person, and 0·516 for mean age.25 As expected, the sign on the total fertility rate was negative, whereas the sign on the other three components was positive. We also tested all possible combinations of the four variables using the same PCA approach to confi rm that the principal component of all four was the most predictive of variation in DALY rates by cause. We used the predicted value of the fi rst component for each country–year in the subsequent ANOVA and predictions

Figure 4: 25 most common GBD level 3 causes of global DALYs for both sexes combined, 1990, 2005, and 2013, with age-standardised median percentage change Ranks are based on the number of DALYs. 95% UIs for mean rank are from 1000 draws of DALYs. Communicable, maternal, neonatal, and nutritional disorders causes are shown in red, non-communicable causes in blue, and injuries in green. DALY=disability-adjusted life-years. GBD=Global Burden of Disease. UI=uncertainty interval. COPD=chronic obstructive pulmonary disease.

28 Chronic kidney disease 26 Alzheimer’s disease 27 Migraine

26 Protein–energy malnutrition 28 Other neonatal30 Neonatal sepsis

31 Meningitis 42 Measles

42 HIV/AIDS

Mean rank (95% UI)

Mean rank (95% UI)

Mean rank (95% UI)2005 leading causes

Age-standardised % change 1990–2005 2013 leading causes

Age-standardised % change 2005–2013

· · · ·

· ·

· ·

1·1 (1–2) 2·0 (1–3) 3·0 (3–3) 4·0 (4–4) 5·2 (5–7)

6· · · · · ·

7·1 (5–10) 7·5 (6–8)

2 Ischaemic heart disease 3 Diarrhoeal diseases 4 Cerebrovascular disease 5 Neonatal preterm birth 6 COPD 7 Low back and neck pain 8 Tuberculosis

· · ·

4

5 Low back and neck pain 6 Malaria 7 HIV/AIDS 8 COPD 9 Neonatal preterm birth 10 Road injuries 11 Tuberculosis 12 Neonatal encephalopathy 13 Sense organ diseases 14 Depressive disorders 15 Diabetes 16 Congenital anomalies 17 Iron-deficiency anaemia 18 Lung cancer 19 Self-harm 20 Skin diseases 21 Chronic kidney disease

12·1 (11–15) –35% (–42 to –30%) 12·5 (11–16) –10% (–22 to 2%) 13·8 (10–17) –5% (–6 to –4%) 14·1 (11–18) 3% (0 to 6%) 14·4 (13–16) 18% (15 to 22%) 14·6 (11–18) –14% (–22 to –5%) 17·1 (15–20) –21% (–25 to –18%) 18·9 (17–20) –11% (–14 to –8%) 18·9 (16–22) –8% (–18 to –1%) 19·9 (16–27) 0% (–2 to 1%) 21·7 (20–25) 13% (5 to 16%)

·

3

11·1 (9–15) 10 Neonatal encephalopathy 11·4 (10–14) 11 Malaria 12·5 (9–16) 12 Congenital anomalies 13·1 (9–19) 13 Sense organ diseases 13·9 (11–18) 14 Iron-deficiency anaemia 14·8 (10–22) 15 Depressive disorders 17·1 (15–20) 16 Diabetes 17·6 (12–24) 17 Other neonatal 18·3 (15–21) 18 Lung cancer 19·7 (16–24) 19 Self-harm 20·4 (11–35) 20 Measles 21·3 (16–29) 21 Drowning 22·4 (15–31) 22 Skin diseases 22·6 (17–28) 23 Protein–energy malnutrition 23·5 (19–27) 24 Meningitis

·

5 COPD 6 Diarrhoeal diseases 7 Road injuries 8 Neonatal preterm birth 9 HIV/AIDS 10 Malaria 11 Depressive disorders 12 Diabetes 13 Sense organ diseases 14 Neonatal encephalopathy 15 Congenital anomalies 16 Tuberculosis 17 Iron-deficiency anemia 18 Skin diseases 19 Lung cancer 20 Chronic kidney disease 21 Self-harm 22 Falls 23 Neonatal sepsis 24 Alzheimer’s disease

10·6 (6–15) –36% (–45 to –22%) 11·4 (5–17) 1% (–2 to 4%) 11·8 (9–14) 1% (–2 to 4%) 12·1 (5–16) –3% (–5 to –2%) 13·5 (10–17) –18% (–27 to –7%) 13·6 (10–17) –5% (–16 to 2%) 15·7 (13–18) –27% (–32 to –21%) 17·5 (14–21) –12% (–14 to –10%) 18·5 (13–24) 0% (–2 to 2%) 18·5 (17–21) –7% (–12 to –3%) 20·4 (18–23) 0% (–4 to 4%) 21·0 (18–25) –17% (–25 to –8%) 24·0 (22–28) –13% (–18 to –9%) 24·2 (17–36) –6% (–21 to 13%) 25·2 (23–28) –2% (–6 to 1%)

23 Protein–energy malnutrition 24·4 (20–32) –21% (–30 to –12%) 24 Neonatal sepsis 24·9 (18–39) 11% (–9 to 39%) 25 Other neonatal 25·3 (20–32) –38% (–49 to –24%)

28 Drowning

1 Lower respiratory infections

6·4 (5–8)

9·2 (8–10) 9 Road injuries

24·3 (21–27) 25 Falls

1 Ischaemic heart disease 1·0 (1–1) –13% (–16 to –10%) 2 Cerebrovascular disease 2·3 (2–3) –14% (–17 to –11%) 3 Lower respiratory infections 2·7 (2–3) 4 Diarrhoeal diseases 4·8 (4–6) –46% (–49 to –42%)

–1% (–2 to 1%)

–39% (–42 to –35%)

6 6·3 (4–9) 35% (14 to 60%) 6 6·5 (4–9) 360% (280 to 438%)

5·4 (3–10)

8 8·1 (6–10) –20% (–25 to –15%) 8 8·3 (5–11) –33% (–39 to –24%)

9·8 (8–11) –2% (–10 to 2%)

22 Falls 22·7 (21–25) –7% (–18 to –4%)

1 Ischaemic heart disease 1·0 (1–1) –11% (–15 to –6%) 2 Cerebrovascular disease 2·2 (2–3) –14% (–17 to –10%) 3 Lower respiratory infections 3·4 (3–4) –22% (–28 to –15%) 4 Low back and neck pain 3·5 (2–5) 0% (–1 to 3%)

5·2 (5–7) –11% (–15 to –6%) 7·2 (5–11) –32% (–38 to –26%) 7·3 (5–10) –14% (–18 to –9%) 9·0 (5–14) –24% (–32 to –17%) 9·3 (6–12) –32% (–35 to –27%)

1990 leading causes

25 Migraine 26·0 (18–39) 1% (–2 to 3%)

Communicable, maternal, neonatal, and nutritionalKey Non–communicable Injuries

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www.thelancet.com Vol 386 November 28, 2015 2151

All ages DALYs (thousands)* Age-standardised DALYs (per 100 000)*

2005 2013 Percentage change 2005 2013 Percentage change

All causes 2 513 239·2 (2 331 974·5 to 2 717 184·5)

2 449 810·0 (2 234 094·6 to 2 675 167·6)

–2·5 (–4·7 to –0·3)*

41 072·6 (38 101·1 to 44 409·9)

35 523·9 (32 416·1 to 38 774·8)

–13·5 (–15·3 to –11·6)*

Communicable, maternal, neonatal, and nutritional diseases

943 358·6 (903 197·4 to 985 632·0)

769 288·8 (725 481·2 to 814 936·0)

–18·5 (–21·2 to –15·8)*

14 031·9 (13 434·2 to 14 662·0)

10 606·9 (10 004·2 to 11 234·3)

–24·4 (–26·9 to –21·9)*

HIV/AIDS and tuberculosis 150 304·0 (141 828·2 to 159 539·6)

119 179·6 (112 497·7 to 127 584·9)

–20·8 (–24·4 to –16·5)*

2344·3 (2214·0 to 2489·5)

1656·0 (1563·5 to 1774·5)

–29·5 (–32·6 to –25·6)*

Tuberculosis 59 600·6 (53 405·7 to 64 094·8)

49 816·2 (44 744·3 to 54 313·4)

–16·4 (–22·9 to –9·4)*

964·1 (866·4 to 1035·6)

706·9 (635·9 to 771·7)

–26·7 (–32·3 to –20·5)*

HIV/AIDS 90 703·4 (83 401·7 to 99 132·8)

69 363·4 (64 972·5 to 76 330·2)

–23·9 (–28·1 to –18·5)*

1380·2 (1269·6 to 1507·9)

949·1 (890·0 to 1045·1)

–31·5 (–35·3 to –26·7)*

HIV/AIDS resulting in mycobacterial infection

6573·9 (5371·5 to 8050·8)

4303·1 (3496·7 to 5323·6)

–34·8 (–38·8 to –29·4)*

100·3 (82·0 to 122·8)

58·9 (47·9 to 72·9)

–41·5 (–45·1 to –36·7)*

HIV/AIDS resulting in other diseases

84 129·5 (77 516·6 t o 92 482·2)

65 060·3 (60 939·8 t o 71 903·6)

–23·0 (–27·3 to –17·6)*

1279·9 (1179·5 t o 1404·1)

890·2 (834·3 t o 984·9)

–30·7 (–34·6 to –25·9)*

Diarrhoea, lower respiratory, and other common infectious diseases

316 908·8 (298 964·3 to 335 711·2)

249 855·1 (231 222·1 to 269 625·3)

–21·1 (–26·0 to –16·4)*

4762·3 (4499·7 to 5038·0)

3488·6 (3231·6 to 3760·2)

–26·7 (–31·2 to –22·4)*

Diarrhoeal diseases 99 453·9 (90 724·0 to 108 320·9)

72 796·6 (65 452·9 to 80 756·7)

–26·9 (–33·5 to –19·7)*

1497·5 (1370·2 t o 1625·7)

1015·5 (915·5 t o 1126·3)

–32·3 (–38·4 to –25·8)*

Intestinal infectious diseases 17 538·4 (9936·1 t o 28 497·3)

15 376·5 (8627·8 t o 24 958·9)

–12·6 (–22·2 to 1·0)

252·9 (143·5 t o 410·7)

210·7 (118·4 t o 341·5)

–17·0 (–26·2 to –4·2)*

Typhoid fever 12 863·1 (7058·4 t o 21 368·4)

11 127·8 (6013·6 t o 18 314·8)

–13·7 (–24·6 to 1·0)

185·4 (101·8 t o 307·6)

152·5 (82·6 t o 250·5)

–17·9 (–28·3 to –4·0)*

Paratyphoid fever 4150·1 (2271·2 t o 6940·7)

3820·5 (2084·1 t o 6493·0)

–8·0 (–25·2 to 12·5)

59·8 (32·8 t o 100·1)

52·3 (28·6 t o 89·0)

–12·5 (–28·8 to 6·7)

Other intestinal infectious diseases

525·2 (460·0 t o 598·7)

428·3 (373·2 t o 486·9)

–18·4 (–25·3 to –11·4)*

7·7 (6·8 to 8·8)

5·9 (5·1 to 6·7)

–24·2 (–30·6 to –17·7)*

Lower respiratory infections 133 899·9 (124 847·7 t o 142 498·1)

113 363·1 (103 083·5 t o 122 202·1)

–15·3 (–22·1 to –8·2)*

2043·4 (1912·2 t o 2169·8)

1599·1 (1451·9 t o 1721·3)

–21·7 (–27·8 to –15·5)*

Upper respiratory infections 2743·4 (1630·3 t o 4451·0)

3031·0 (1757·4 t o 4934·2)

10·4 (7·3 to 13·4)*

41·3 (24·5 t o 66·8)

41·9 (24·3 t o 68·2)

1·6 (–1·3 to 4·3)

Otitis media 1751·5 (1102·5 t o 2735·2)

1806·5 (1129·8 t o 2812·2)

3·1 (0·5 to 5·7)*

26·2 (16·5 t o 40·8)

25·0 (15·6 t o 38·9)

–4·5 (–7·0 to –2·0)*

Meningitis 24 317·0 (21 010·6 t o 27 891·4)

21 014·9 (17 519·8 t o 24 328·1)

–13·7 (–20·8 to –5·5)*

358·3 (310·2 t o 410·1)

288·2 (240·6 to 333·3)

–19·7 (–26·3 to –12·1)*

Pneumococcal 6343·8 (5493·7 t o 7161·7)

5509·2 (4678·8 t o 6363·5)

–13·3 (–20·8 to –3·1)*

93·9 (81·5 t o 105·7)

75·7 (64·3 t o 87·4)

–19·5 (–26·5 to –10·4)*

Haemophilus infl uenzae type B 6300·1 (5188·0 t o 7431·5)

5177·1 (4196·8 t o 6211·1)

–17·8 (–27·9 to –7·7)*

91·6 (75·4 t o 108·1)

70·7 (57·3 t o 84·7)

–22·9 (–32·2 to –13·3)*

Meningococcal 4733·3 (4055·1 t o 5610·2)

4314·7 (3583·0 t o 5116·6)

–8·5 (–18·7 to 1·3)

69·9 (59·8 t o 82·5)

59·2 (49·2 to 70·1)

–15·0 (–24·4 to –5·8)*

Other meningitis 6939·8 (6097·9 t o 8066·0)

6014·0 (5084·4 t o 6954·2)

–13·2 (–22·2 to –2·9)*

102·9 (90·6 t o 119·5)

82·6 (70·0 t o 95·4)

–19·7 (–28·0 to –10·2)*

Encephalitis 5087·0 (4236·1 t o 6021·8)

4804·2 (4022·4 t o 5926·9)

–5·3 (–19·2 to 9·0)

75·4 (62·9 t o 89·4)

66·3 (55·6 to 81·9)

–11·9 (–24·6 to 1·3)

Diphtheria 316·5 (153·3 t o 671·8)

253·6 (126·5 t o 536·1)

–17·8 (–74·5 to 124·7)

4·6 (2·2 to 9·8)

3·5 (1·7 to 7·3)

–22·8 (–75·9 to 111·9)

Whooping cough 6478·7 (2580·1 t o 12 839·5)

5250·9 (2029·0 t o 11 658·9)

–22·4 (–71·4 to 125·5)

93·9 (37·4 t o 186·1)

71·5 (27·6 t o 158·8)

–27·0 (–73·1 to 112·1)

Tetanus 7223·6 (4402·4 t o 8704·6)

3654·7 (2312·7 t o 4911·4)

–49·8 (–58·0 to –36·8)*

105·1 (64·5 t o 126·3)

50·2 (32·0 t o 67·5)

–52·6 (–60·2 to –40·5)*

Measles 17 635·2 (9981·3 t o 28 573·5)

8015·1 (4077·1 t o 14 458·0)

–55·9 (–74·0 to –17·9)*

256·3 (145·0 t o 414·8)

109·7 (55·8 t o 197·8)

–58·5 (–75·5 to –22·7)*

Varicella and herpes zoster 463·7 (371·2 t o 580·9)

487·9 (384·7 t o 622·7)

5·2 (–12·2 to 26·9)

7·4 (5·9 to 9·2)

7·0 (5·5 to 8·9)

–6·1 (–20·7 to 12·4)

(Table 1 continues on next page)

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2152 www.thelancet.com Vol 386 November 28, 2015

All ages DALYs (thousands)* Age-standardised DALYs (per 100 000)*

2005 2013 Percentage change 2005 2013 Percentage change

(Continued from previous page)

Neglected tropical diseases and malaria

121 587·6 (108 043·8 t o 136 597·5)

90 676·8 (75 748·9 t o 107 737·6)

–25·8 (–34·3 to –14·6)*

1798·1 (1596·1 t o 2023·0)

1248·4 (1043·0 t o 1483·2)

–30·9 (–38·7 to –20·6)*

Malaria 94 497·4 (83 484·0 t o 106 879·0)

65 493·1 (53 064·9 t o 79 960·7)

–31·3 (–41·6 to –16·9)*

1385·1 (1224·9 t o 1565·9)

897·6 (728·1 t o 1094·8)

–35·7 (–45·3 to –22·3)*

Chagas disease 326·6 (172·5 t o 850·4)

338·5 (183·8 t o 846·4)

4·6 (–18·4 to 27·8)

5·9 (3·1 to 15·9)

5·2 (2·8 to 12·8)

–12·3 (–32·6 to 7·4)

Leishmaniasis 3939·2 (3292·5 t o 4619·1)

4283·1 (3527·8 t o 5090·9)

8·8 (–6·4 to 25·3)

57·3 (47·9 t o 67·1)

58·6 (48·2 t o 69·7)

2·5 (–11·7 to 18·0)

Visceral 3908·5 (3272·6 t o 4591·9)

4241·5 (3488·2 t o 5044·7)

8·7 (–6·7 to 25·2)

56·8 (47·5 t o 66·7)

58·0 (47·7 t o 69·0)

2·4 (–12·0 to 18·0)

Cutaneous and mucocutaneous 30·6 (14·0 t o 58·4)

41·7 (19·0 t o 80·1)

35·9 (23·7 to 49·0)*

0·5 (0·2 to 0·9)

0·6 (0·3 to 1·1)

23·0 (12·3 to 34·8)*

African trypanosomiasis 854·4 (454·4 t o 1366·7)

390·1 (211·4 t o 615·3)

–54·3 (–58·7 to –49·1)*

12·6 (6·7 to 20·2)

5·3 (2·9 to 8·3)

–58·2 (–62·2 to –53·5)*

Schistosomiasis 3511·3 (1999·8 t o 6207·9)

3062·8 (1690·1 t o 5662·0)

–13·9 (–18·5 to –1·4)*

52·3 (29·9 t o 92·1)

42·1 (23·3 t o 77·8)

–20·5 (–24·7 to –8·8)*

Cysticercosis 409·7 (291·1 t o 530·4)

341·2 (244·4 t o 442·0)

–16·4 (–31·7 to 1·3)

6·4 (4·6 to 8·3)

4·7 (3·4 to 6·1)

–26·0 (–39·2 to –11·0)*

Cystic echinococcosis 211·5 (185·0 t o 243·3)

181·7 (155·7 t o 211·7)

–14·1 (–17·3 to –11·1)*

3·3 (2·9 to 3·8)

2·6 (2·2 to 3·0)

–22·3 (–25·0 to –19·7)*

Lymphatic fi lariasis 2406·4 (1241·2 t o 4094·3)

2022·1 (1096·3 t o 3294·4)

–14·3 (–31·4 to –5·3)*

39·6 (20·4 t o 67·1)

28·9 (15·7 t o 47·1)

–25·7 (–40·1 to –18·0)*

Onchocerciasis 1445·3 (792·4 t o 2241·9)

1179·8 (556·6 t o 1992·7)

–19·4 (–33·0 to –5·0)*

22·6 (12·6 t o 34·4)

16·6 (7·9 to 27·6)

–27·5 (–40·0 to –14·4)*

Trachoma 208·9 (141·3 t o 286·9)

171·2 (115·3 t o 241·7)

–18·1 (–27·5 to –8·4)*

4·2 (2·9 to 5·8)

2·8 (1·9 to 4·0)

–33·4 (–41·0 to –25·6)*

Dengue 957·9 (627·9 t o 1395·8)

1142·7 (727·6 t o 1978·2)

17·0 (–7·9 to 53·1)

14·1 (9·3 to 20·6)

15·8 (10·1 t o 27·4)

9·8 (–13·3 to 43·0)

Yellow fever 30·2 (25·1 t o 36·8)

30·7 (25·3 t o 37·1)

1·8 (–18·2 to 25·3)

0·4 (0·4 to 0·5)

0·4 (0·3 to 0·5)

–4·2 (–22·9 to 17·5)

Rabies 1449·7 (1124·4 t o 1833·1)

1242·9 (914·6 t o 1526·7)

–14·6 (–27·6 to 0·9)

21·8 (16·9 t o 27·5)

17·3 (12·7 t o 21·2)

–20·9 (–32·9 to –6·5)*

Intestinal nematode infections 4641·3 (2899·4 t o 7110·5)

4029·4 (2516·8 t o 6137·0)

–13·1 (–18·3 to –7·8)*

69·4 (43·3 t o 106·4)

55·7 (34·8 t o 84·9)

–19·6 (–24·4 to –14·7)*

Ascariasis 1796·2 (1150·3 t o 2720·3)

1271·7 (843·1 t o 1916·7)

–29·0 (–35·9 to –21·3)*

26·8 (17·2 t o 40·7)

17·6 (11·6 t o 26·5)

–34·3 (–40·7 to –27·2)*

Trichuriasis 652·0 (357·4 t o 1063·6)

576·0 (310·1 t o 972·6)

–12·3 (–26·3 to 8·8)

9·8 (5·4 to 16·0)

8·0 (4·3 to 13·5)

–19·1 (–32·1 to 0·4)

Hookworm disease 2193·2 (1335·6 t o 3401·2)

2181·7 (1338·6 t o 3354·5)

–0·5 (–6·9 to 6·6)

32·8 (20·0 t o 50·8)

30·2 (18·5 t o 46·4)

–7·8 (–13·9 to –1·3)*

Food-borne trematodiases 3161·5 (1039·8 t o 6574·9)

3634·8 (1160·2 t o 7692·4)

14·6 (8·6 to 23·2)*

51·3 (16·8 t o 106·9)

51·3 (16·3 t o 108·6)

–0·3 (–5·4 to 6·5)

Other neglected tropical diseases 3536·4 (2652·7 t o 4638·1)

3132·7 (2328·1 t o 4208·7)

–11·8 (–18·0 to –3·1)*

51·8 (38·9 t o 67·8)

43·5 (32·3 t o 58·4)

–16·3 (–22·2 to –8·3)*

Maternal disorders 21 717·2 (19 935·4 t o 23 449·9)

18 027·8 (16 051·8 t o 19 989·5)

–17·0 (–25·6 to –7·9)*

312·7 (287·2 t o 337·5)

239·2 (213·3 t o 264·9)

–23·5 (–31·4 to –15·3)*

Maternal haemorrhage 3551·9 (3154·8 t o 3980·3)

2561·7 (2219·9 t o 2926·6)

–28·2 (–38·0 to –16·3)*

51·3 (45·6 to 57·5)

34·0 (29·5 to 38·9)

–34·0 (–42·7 to –23·0)*

Maternal sepsis and other maternal infections

1781·7 (1580·7 t o 2007·9)

1369·6 (1156·9 t o 1624·0)

–23·5 (–36·2 to –7·9)*

25·6 (22·7 t o 28·8)

18·2 (15·3 t o 21·5)

–29·4 (–41·0 to –15·1)*

Maternal hypertensive disorders 2281·0 (2038·0 t o 2547·4)

1753·2 (1523·0 t o 1996·7)

–23·4 (–32·7 to –10·8)*

32·6 (29·2 t o 36·4)

23·2 (20·2 t o 26·4)

–29·1 (–37·5 to –17·3)*

Obstructed labour 2312·0 (1963·9 t o 2679·8)

2023·4 (1686·8 t o 2414·0)

–12·5 (–20·5 to –3·8)*

33·6 (28·5 t o 39·0)

27·1 (22·5 t o 32·3)

–19·5 (–26·9 to –11·6)*

Complications of abortion 2886·2 (2603·8 t o 3192·2)

2476·5 (2169·9 t o 2841·6)

–14·6 (–24·6 to –0·5)*

41·6 (37·6 t o 46·0)

32·8 (28·7 t o 37·6)

–21·6 (–30·8 to –8·8)*

(Table 1 continues on next page)

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All ages DALYs (thousands)* Age-standardised DALYs (per 100 000)*

2005 2013 Percentage change 2005 2013 Percentage change

(Continued from previous page)

Indirect maternal deaths 2391·2 (2110·7 t o 2718·2)

1790·8 (1534·9 t o 2074·7)

–25·3 (–37·7 to –9·9)*

34·3 (30·3 t o 38·9)

23·7 (20·3 t o 27·4)

–31·1 (–42·4 to –17·2)*

Late maternal deaths 2525·7 (2151·7 t o 2947·8)

2481·8 (2030·4 t o 2992·2)

–1·3 (–23·0 to 23·2)

36·2 (30·8 t o 42·2)

32·9 (27·0 t o 39·7)

–8·6 (–28·6 to 13·7)

Maternal deaths aggravated by HIV/AIDS

184·7 (113·8 t o 248·6)

117·2 (72·6 t o 162·7)

–36·6 (–43·9 to –27·7)*

2·7 (1·7 to 3·6)

1·5 (1·0 to 2·1)

–42·5 (–49·2 to –34·3)*

Other maternal disorders 3761·6 (3379·7 t o 4197·0)

3420·6 (2936·5 t o 3954·0)

–9·4 (–21·3 to 7·9)

54·2 (48·7 t o 60·4)

45·4 (39·0 t o 52·4)

–16·6 (–27·5 to –0·5)*

Neonatal disorders 221 687·5 (213 295·0 t o 230 283·3)

189 601·0 (179 024·1 t o 200 044·0)

–14·5 (–18·1 to –10·7)*

3155·7 (3035·7 t o 3278·5)

2560·0 (2416·8 t o 2700·5)

–18·9 (–22·3 to –15·3)*

Preterm birth complications 88 971·6 (75 761·9 t o 108 464·4)

70 843·1 (57 523·4 t o 85 348·6)

–20·3 (–28·6 to –12·2)*

1267·2 (1079·1 t o 1543·9)

957·0 (777·2 t o 1152·8)

–24·4 (–32·3 to –16·7)*

Neonatal encephalopathy (birth asphyxia and trauma)

66 760·9 (53 355·4 t o 77 432·1)

58 012·7 (46 947·7 t o 68 198·2)

–13·2 (–22·7 to –1·8)*

948·9 (758·6 t o 1100·2)

782·6 (633·4 t o 919·7)

–17·6 (–26·6 to –6·8)*

Sepsis and other neonatal infections

32 000·2 (19 472·8 t o 44 418·3)

31 631·8 (20 147·7 t o 44 128·5)

–1·0 (–16·8 to 19·3)

455·0 (277·0 t o 631·4)

426·7 (271·9 t o 595·3)

–6·1 (–21·1 to 13·2)

Haemolytic disease and other neonatal jaundice

3639·6 (2720·1 t o 4907·6)

3299·4 (2496·2 t o 4382·9)

–9·0 (–29·7 to 14·7)

52·5 (39·3 t o 70·7)

44·9 (33·9 t o 59·6)

–14·2 (–33·6 to 8·0)

Other neonatal disorders 30 315·2 (24 647·8 t o 38 101·8)

25 814·0 (20 483·9 t o 32 840·8)

–14·7 (–27·3 to 0·7)

432·1 (351·0 t o 542·8)

348·8 (276·7 t o 443·7)

–19·1 (–31·1 to –4·6)*

Nutritional defi ciencies 79 695·9 (63 911·4 t o 99 518·1)

74 834·4 (59 402·0 t o 94 084·1)

–6·2 (–10·9 to –1·4)*

1192·4 (958·9 t o 1486·1)

1040·7 (828·2 t o 1306·8)

–12·8 (–17·1 to –8·5)*

Protein-energy malnutrition 29 772·1 (23 294·2 t o 35 802·7)

27 709·9 (21 411·5 t o 33 507·3)

–7·1 (–17·5 to 4·5)

447·4 (348·0 t o 536·2)

386·1 (298·4 t o 465·7)

–13·7 (–23·1 to –3·6)*

Iodine defi ciency 2155·1 (1364·0 t o 3259·2)

2189·6 (1406·6 t o 3401·4)

1·5 (–5·9 to 9·8)

32·6 (20·7 t o 49·4)

30·1 (19·3 t o 46·7)

–7·9 (–14·7 to –0·3)*

Vitamin A defi ciency 177·2 (113·8 t o 261·1)

153·7 (99·0 t o 224·9)

–13·2 (–19·5 to –6·8)*

2·6 (1·7 to 3·9)

2·1 (1·4 to 3·1)

–19·1 (–24·8 to –13·2)*

Iron-defi ciency anaemia 46 359·5 (33 059·4 t o 64 257·3)

43 747·6 (30 848·7 t o 61 398·4)

–5·6 (–8·2 to –3·6)*

690·1 (494·3 t o 954·8)

607·6 (428·9 t o 852·3)

–12·0 (–14·4 to –10·0)*

Other nutritional defi ciencies 1232·0 (847·8 t o 2025·2)

1033·5 (715·8 t o 1747·2)

–16·1 (–29·0 to –2·5)*

19·6 (13·4 t o 31·8)

14·8 (10·3 t o 25·1)

–24·3 (–35·1 to –12·3)*

Other communicable, maternal, neonatal, and nutritional diseases

31 457·6 (24 584·5 t o 39 680·1)

27 114·0 (21 684·1 t o 33 977·7)

–13·5 (–23·8 to –3·7)*

466·5 (367·2 t o 585·0)

373·8 (300·0 t o 466·4)

–19·7 (–29·1 to –10·7)*

Sexually transmitted diseases excluding HIV

15 145·4 (9593·7 t o 22 186·1)

12 857·2 (8079·7 t o 19 013·3)

–14·9 (–30·4 to –0·2)*

218·6 (139·2 t o 319·6)

174·6 (109·8 t o 258·0)

–19·9 (–34·5 to –6·2)*

Syphilis 13 710·1 (8228·6 t o 20 649·7)

11 324·5 (6634·9 t o 17 484·8)

–17·1 (–33·7 to –1·2)*

197·1 (118·6 t o 296·5)

153·8 (90·3 t o 237·3)

–21·8 (–37·3 to –6·7)*

Chlamydial infection 645·9 (424·5 t o 990·0)

692·4 (454·5 t o 1065·5)

7·2 (2·2 to 12·7)*

9·4 (6·2 to 14·4)

9·3 (6·1 to 14·2)

–1·6 (–6·4 to 3·4)

Gonococcal infection 293·9 (219·3 t o 401·1)

313·9 (229·4 t o 438·1)

6·8 (–3·2 to 16·8)

4·3 (3·3 to 5·8)

4·2 (3·1 to 5·9)

–2·3 (–11·5 to 7·1)

Trichomoniasis 105·1 (41·3 t o 221·3)

113·9 (45·1 t o 242·9)

8·2 (–1·8 to 20·0)

1·5 (0·6 to 3·2)

1·5 (0·6 to 3·2)

–0·8 (–9·9 to 10·0)

Genital herpes 279·9 (89·6 t o 671·1)

311·6 (98·3 t o 748·5)

11·2 (8·5 to 13·6)*

4·5 (1·4 to 10·9)

4·4 (1·4 to 10·5)

–3·0 (–4·9 to –1·0)*

Other sexually transmitted diseases

110·5 (93·5 t o 133·7)

101·0 (86·1 t o 121·0)

–8·8 (–16·9 to 2·4)

1·7 (1·4 to 2·0)

1·4 (1·2 to 1·7)

–17·7 (–25·2 to –7·6)*

Hepatitis 7094·0 (6392·5 t o 8180·0)

6556·8 (5774·7 t o 8208·0)

–8·2 (–17·4 to 5·3)

108·7 (98·6 t o 124·5)

91·2 (80·5 t o 113·7)

–16·6 (–24·8 to –4·2)*

Hepatitis A 1456·7 (673·5 t o 2476·5)

1214·6 (553·8 t o 2108·4)

–17·2 (–34·1 to 6·1)

21·3 (9·8 to 36·1)

16·6 (7·6 to 28·8)

–22·4 (–38·2 to –0·7)*

Hepatitis B 2860·3 (2022·3 t o 3868·0)

2587·3 (1839·1 t o 3512·8)

–10·1 (–21·5 to 4·9)

46·3 (33·3 t o 61·3)

37·1 (26·6 t o 49·8)

–20·4 (–30·0 to –8·0)*

Hepatitis C 126·5 (37·9 t o 269·5)

138·0 (41·4 t o 310·4)

8·1 (–8·7 to 34·5)

2·0 (0·6 to 4·3)

2·0 (0·6 to 4·4)

–5·0 (–19·3 to 17·0)

(Table 1 continues on next page)

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2154 www.thelancet.com Vol 386 November 28, 2015

All ages DALYs (thousands)* Age-standardised DALYs (per 100 000)*

2005 2013 Percentage change 2005 2013 Percentage change

(Continued from previous page)

Hepatitis E 2650·5 (1991·7 t o 3421·7)

2616·9 (1962·3 t o 3508·9)

–1·8 (–12·8 to 13·4)

39·1 (29·2 t o 50·8)

35·6 (26·6 t o 47·8)

–9·5 (–19·6 to 4·5)

Leprosy 36·6 (24·2 t o 51·9)

39·7 (26·6 t o 56·0)

8·6 (2·3 to 15·4)*

0·7 (0·4 to 0·9)

0·6 (0·4 to 0·9)

–8·9 (–13·9 to –3·5)*

Other infectious diseases 9181·6 (5667·4 t o 12 740·2)

7660·3 (5301·6 t o 10 204·5)

–14·5 (–33·4 to 2·7)

138·6 (87·1 t o 190·1)

107·4 (74·9 t o 142·3)

–20·9 (–37·7 to –5·1)*

Non-communicable diseases 1 302 199·4 (1 155 437·8 t o 1 460 687·4)

1 432 938·8 (1 256 004·9 t o 1 614 026·7)

10·0 (7·7 to 12·8)*

22 873·8 (20 458·5 t o 25 487·7)

21 452·8 (18 880·3 t o 24 078·4)

–6·3 (–8·3 to –3·8)*

Neoplasms 180 409·6 (175 482·3 t o 185 592·2)

197 093·5 (189 237·0 t o 206 258·5)

9·3 (4·9 to 13·9)*

3289·8 (3196·1 t o 3384·1)

3001·7 (2881·6 t o 3136·4)

–8·7 (–12·3 to –5·0)*

Oesophageal cancer 8905·2 (7787·5 t o 10 237·4)

9843·1 (8655·5 t o 11 620·1)

10·3 (1·9 to 20·3)*

168·8 (148·2 t o 193·7)

152·3 (134·0 t o 180·1)

–9·9 (–16·7 to –1·7)*

Stomach cancer 19 059·1 (18 331·4 t o 19 926·9)

17 906·5 (16 863·7 t o 19 067·8)

–6·0 (–11·5 to –0·6)*

357·4 (343·9 t o 373·8)

277·7 (261·5 t o 295·9)

–22·2 (–26·8 to –17·9)*

Liver cancer 19 175·3 (18 331·6 t o 20 085·5)

20 888·7 (19 321·9 t o 22 518·1)

9·2 (–0·2 to 17·7)

344·0 (329·1 t o 360·4)

313·0 (289·9 t o 336·5)

–8·9 (–16·3 to –1·9)*

Liver cancer due to hepatitis B 8198·9 (7558·5 t o 8758·0)

8590·9 (7761·8 t o 9462·3)

4·8 (–6·5 to 16·2)

143·6 (132·7 t o 153·2)

126·2 (114·1 t o 138·7)

–12·1 (–21·3 to –2·7)*

Liver cancer due to hepatitis C 5902·3 (5484·4 t o 6445·1)

7967·1 (7271·6 t o 8807·4)

35·1 (21·9 to 47·7)*

109·0 (101·5 t o 118·8)

121·4 (111·1 t o 133·6)

11·5 (0·7 to 21·5)*

Liver cancer due to alcohol use 2450·5 (2239·5 t o 2675·5)

1980·4 (1813·1 t o 2189·7)

–19·1 (–27·9 to –9·7)*

46·1 (42·2 t o 50·2)

30·7 (28·2 t o 33·9)

–33·2 (–40·2 to –25·7)*

Liver cancer due to other causes 2623·7 (2366·6 t o 2881·3)

2350·2 (2098·1 t o 2595·8)

–9·4 (–24·9 to 2·8)

45·3 (40·9 t o 49·7)

34·6 (31·0 t o 38·3)

–22·8 (–35·9 to –12·4)*

Larynx cancer 2075·5 (1812·6 t o 2544·4)

2136·7 (1815·5 t o 2620·1)

3·0 (–3·8 to 10·0)

38·6 (33·6 t o 47·1)

32·6 (27·8 t o 39·9)

–15·5 (–20·9 to –9·9)*

Tracheal, bronchus, and lung cancer

30 791·6 (29 492·6 t o 31 587·1)

34 732·9 (33 042·6 t o 36 328·1)

12·9 (6·6 to 19·1)*

586·7 (562·2 t o 601·6)

542·8 (516·4 t o 567·1)

–7·4 (–12·4 to –2·5)*

Breast cancer 11 762·5 (10 713·2 t o 13 178·0)

13 258·7 (12 105·4 t o 14 558·1)

13·0 (4·5 to 19·8)*

209·8 (190·6 t o 234·6)

196·4 (178·1 t o 215·5)

–6·1 (–13·0 to –0·7)*

Cervical cancer 6775·6 (5813·9 t o 7591·5)

6914·7 (5774·5 t o 7589·1)

2·1 (–5·7 to 9·5)

118·0 (101·3 t o 131·9)

100·9 (84·4 t o 110·5)

–14·4 (–20·7 to –8·2)*

Uterine cancer 1526·9 (1184·9 t o 1824·8)

1660·9 (1276·3 t o 1961·6)

8·1 (–1·1 to 21·3)

28·5 (22·3 t o 34·0)

25·4 (19·6 t o 30·0)

–11·3 (–18·6 to –0·7)*

Prostate cancer 3812·1 (3236·1 t o 4802·3)

4768·8 (4067·0 t o 6034·1)

25·0 (19·3 to 31·6)*

80·2 (68·3 t o 100·9)

81·3 (69·2 t o 103·0)

1·3 (–3·3 to 6·7)

Colon and rectum cancer 13 747·9 (13 378·9 t o 14 138·6)

15 794·1 (15 165·3 t o 16 421·4)

14·9 (10·8 to 19·1)*

261·6 (254·4 t o 268·9)

246·7 (237·0 t o 256·2)

–5·7 (–9·0 to –2·4)*

Lip and oral cavity cancer 2963·9 (2620·9 t o 3442·8)

3589·3 (3031·8 t o 4109·0)

21·1 (9·8 to 32·1)*

53·8 (47·7 t o 62·6)

53·8 (45·5 t o 61·7)

0·0 (–9·1 to 8·7)

Nasopharynx cancer 2034·5 (1831·3 t o 2318·1)

1933·7 (1723·7 t o 2211·8)

–5·0 (–13·1 to 4·1)

34·5 (31·2 t o 39·3)

27·9 (24·9 t o 31·9)

–19·3 (–26·1 to –11·7)*

Other pharynx cancer 1732·8 (1545·7 t o 1880·0)

2137·7 (1832·0 t o 2368·2)

23·3 (10·9 to 36·4)*

31·5 (28·2 t o 34·2)

31·8 (27·3 t o 35·2)

1·2 (–9·1 to 11·9)

Gallbladder and biliary tract cancer

2550·4 (2310·8 t o 2841·4)

2701·1 (2338·8 t o 2977·8)

6·3 (–2·9 to 14·1)

49·0 (44·4 t o 54·8)

42·4 (36·9 t o 47·0)

–13·0 (–20·7 to –6·7)*

Pancreatic cancer 5704·7 (5557·7 t o 5841·8)

7029·1 (6775·5 t o 7276·7)

23·2 (19·2 to 27·5)*

109·5 (106·6 t o 112·1)

110·2 (106·3 t o 114·1)

0·6 (–2·7 to 4·0)

Malignant skin melanoma 1394·8 (1102·7 t o 1877·5)

1555·5 (1227·7 t o 2089·3)

12·0 (3·5 to 18·7)*

24·8 (19·3 t o 33·5)

23·2 (18·1 t o 31·1)

–6·1 (–13·0 to –0·8)*

Non-melanoma skin cancer 724·2 (602·1 t o 903·6)

816·5 (682·2 t o 1039·9)

12·4 (6·7 to 20·1)*

13·7 (11·4 t o 16·9)

12·9 (10·8 t o 16·3)

–6·2 (–10·7 to 0·0)

Ovarian cancer 3541·7 (3324·8 t o 3725·9)

4056·5 (3794·9 t o 4400·2)

14·5 (7·0 to 23·1)*

64·1 (60·2 t o 67·3)

60·6 (56·6 t o 65·5)

–5·5 (–11·4 to 1·3)

(Table 1 continues on next page)

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All ages DALYs (thousands)* Age-standardised DALYs (per 100 000)*

2005 2013 Percentage change 2005 2013 Percentage change

(Continued from previous page)

Testicular cancer 354·9 (284·9 t o 439·2)

378·7 (284·3 t o 470·6)

7·2 (–3·8 to 16·4)

5·5 (4·4 to 6·8)

5·2 (3·9 to 6·5)

–4·4 (–14·2 to 3·6)

Kidney cancer 2810·2 (2675·3 t o 2923·1)

3150·3 (2988·6 t o 3320·7)

12·2 (5·8 to 18·1)*

51·8 (49·2 t o 54·0)

48·3 (45·8 t o 50·8)

–6·7 (–12·0 to –2·2)*

Bladder cancer 2987·4 (2743·2 t o 3270·2)

3139·9 (2868·8 t o 3479·6)

4·9 (0·8 to 10·3)*

59·0 (54·0 t o 64·4)

50·6 (46·3 t o 56·0)

–14·4 (–17·6 to –10·0)*

Brain and nervous system cancer 6163·4 (5120·0 t o 7002·9)

6692·2 (5592·3 t o 7765·2)

8·4 (2·3 to 15·4)*

102·6 (85·2 t o 115·8)

96·8 (80·9 t o 112·1)

–5·7 (–10·8 to 0·4)

Thyroid cancer 764·5 (673·5 t o 870·8)

851·9 (739·8 t o 983·2)

12·3 (2·4 to 19·2)*

14·0 (12·4 t o 16·0)

13·0 (11·3 t o 14·9)

–6·7 (–14·4 to –1·1)*

Mesothelioma 504·0 (444·0 t o 581·2)

763·5 (686·2 t o 864·4)

51·8 (40·0 to 63·4)*

9·5 (8·3 to 11·0)

11·8 (10·5 t o 13·4)

24·4 (14·7 to 33·8)*

Hodgkin’s lymphoma 1126·5 (1027·5 t o 1378·4)

989·6 (867·9 t o 1304·0)

–13·5 (–21·9 to 4·2)

17·7 (16·2 t o 21·8)

14·0 (12·3 t o 18·4)

–22·3 (–29·4 to –7·0)*

Non-Hodgkin lymphoma 5627·7 (4867·0 t o 6301·8)

6412·8 (5495·7 t o 7202·7)

14·6 (5·7 to 21·1)*

96·9 (83·1 t o 107·1)

94·9 (81·2 t o 106·2)

–1·4 (–8·9 to 4·0)

Multiple myeloma 1384·2 (1174·0 t o 1635·6)

1661·5 (1397·5 t o 1964·8)

20·3 (13·4 to 25·0)*

26·6 (22·5 t o 31·5)

26·1 (21·8 t o 31·0)

–1·7 (–7·3 to 2·2)

Leukaemia 9384·9 (9081·5 t o 9744·4)

9301·0 (8869·0 t o 9752·8)

–0·7 (–6·0 to 3·6)

150·0 (145·2 t o 155·3)

133·7 (127·6 t o 140·1)

–10·8 (–15·2 to –7·0)*

Other neoplasms 11 023·4 (10 179·5 t o 12 208·5)

12 027·7 (10 693·3 t o 13 370·7)

10·3 (–3·1 to 17·1)

182·0 (167·7 t o 200·3)

175·3 (156·1 t o 194·1)

–2·7 (–13·8 to 2·9)

Cardiovascular diseases 308 887·0 (294 356·7 t o 324 066·8)

329 705·6 (311 188·8 t o 348 206·2)

6·7 (2·6 to 11·7)*

5907·1 (5641·6 t o 6181·0)

5206·3 (4924·1 t o 5485·9)

–11·9 (–15·1 to –8·0)*

Rheumatic heart disease 10 103·6 (8576·3 t o 12 982·8)

9517·7 (7867·8 t o 11 950·8)

–5·9 (–13·9 to 3·4)

170·0 (144·4 t o 219·1)

138·9 (115·1 t o 174·2)

–18·4 (–25·5 to –10·3)*

Ischaemic heart disease 138 547·2 (127 675·5 t o 149 798·3)

150 238·6 (135 388·5 t o 162 458·7)

8·4 (2·9 to 15·0)*

2670·7 (2461·1 t o 2880·1)

2375·9 (2142·4 t o 2565·1)

–11·1 (–15·2 to –6·0)*

Cerebrovascular disease 107 737·1 (99 331·7 t o 116 802·3)

112 878·9 (104 002·3 t o 124 567·7)

4·7 (0·2 to 9·6)*

2096·8 (1934·1 t o 2266·5)

1806·9 (1667·4 t o 1991·7)

–13·9 (–17·5 to –9·9)*

Ischaemic stroke 44 730·9 (38 134·8 t o 49 037·1)

47 424·7 (40 537·5 t o 52 211·8)

6·0 (0·8 to 11·0)*

920·4 (787·2 t o 1007·9)

791·3 (678·0 t o 868·8)

–14·0 (–18·2 to –10·0)*

Haemorrhagic stroke 63 006·2 (57 306·5 t o 70 880·3)

65 454·2 (59 497·4 t o 74 654·7)

3·8 (–1·6 to 10·1)

1176·4 (1068·3 t o 1325·3)

1015·6 (923·2 t o 1163·2)

–13·8 (–18·2 to –8·5)*

Hypertensive heart disease 16 427·8 (13 746·5 t o 19 904·3)

19 248·1 (15 498·3 t o 22 588·0)

17·7 (6·2 to 27·9)*

320·7 (269·1 t o 388·9)

308·0 (248·4 t o 360·3)

–3·6 (–12·9 to 4·7)

Cardiomyopathy and myocarditis 12 876·8 (10 178·6 t o 14 361·3)

12 472·7 (10 209·8 t o 14 036·3)

–3·5 (–8·0 to 5·8)

220·3 (176·5 t o 244·8)

184·3 (151·9 t o 207·8)

–16·7 (–20·3 to –9·5)*

Atrial fi brillation and fl utter 1477·2 (1238·5 t o 1748·3)

1888·7 (1590·0 t o 2224·9)

28·1 (20·9 to 34·6)*

32·1 (27·0 t o 37·9)

32·6 (27·5 t o 38·2)

1·6 (–4·1 to 6·9)

Aortic aneurysm 2404·4 (1973·3 t o 2797·7)

2652·7 (2217·4 t o 3109·6)

10·2 (5·5 to 16·3)*

46·5 (38·3 t o 54·3)

42·2 (35·2 t o 49·4)

–9·4 (–13·1 to –4·7)*

Peripheral vascular disease 510·7 (438·1 t o 599·2)

596·1 (515·2 t o 705·5)

16·8 (10·6 to 23·2)*

11·0 (9·4 to 12·9)

10·2 (8·8 to 12·1)

–7·1 (–12·1 to –2·1)*

Endocarditis 1769·6 (1301·1 t o 2161·5)

1913·5 (1420·8 t o 2342·1)

7·9 (0·2 to 17·9)*

29·8 (22·1 t o 36·3)

28·0 (20·8 t o 34·4)

–6·4 (–12·7 to 2·4)

Other cardiovascular and circulatory diseases

17 032·8 (14 236·3 t o 21 254·9)

18 298·8 (15 153·9 t o 22 824·3)

7·3 (–6·6 to 23·5)

309·3 (259·6 t o 383·7)

279·3 (231·5 t o 346·9)

–9·7 (–21·1 to 3·6)

Chronic respiratory diseases 104 250·7 (92 540·7 t o 118 201·1)

112 710·7 (98 871·9 t o 128 147·8)

8·1 (2·7 to 13·8)*

1935·3 (1734·0 t o 2180·3)

1754·3 (1550·4 t o 1981·3)

–9·3 (–13·9 to –4·6)*

Chronic obstructive pulmonary disease

66 478·5 (58 577·5 t o 75 309·8)

71 900·7 (61 998·5 t o 82 621·4)

8·2 (3·0 to 13·6)*

1276·2 (1136·1 t o 1435·7)

1137·9 (990·5 t o 1299·5)

–10·8 (–15·1 to –6·3)*

Pneumoconiosis 4770·7 (3830·8 t o 6066·8)

5468·0 (4285·5 t o 6974·4)

14·4 (–1·8 to 33·7)

90·3 (72·5 t o 114·6)

85·5 (67·0 t o 108·8)

–5·6 (–18·9 to 10·3)

Silicosis 926·8 (635·0 t o 1323·0)

983·6 (682·7 t o 1386·8)

6·0 (–8·0 to 22·3)

17·5 (12·0 t o 24·9)

15·4 (10·8 t o 21·5)

–12·2 (–23·6 to 1·2)

(Table 1 continues on next page)

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All ages DALYs (thousands)* Age-standardised DALYs (per 100 000)*

2005 2013 Percentage change 2005 2013 Percentage change

(Continued from previous page)

Asbestosis 467·4 (340·7 t o 629·7)

554·3 (403·8 t o 754·9)

18·2 (1·8 to 40·0)*

8·7 (6·3 to 11·7)

8·5 (6·2 to 11·6)

–2·2 (–15·9 to 15·7)

Coal workers’ pneumoconiosis 531·1 (393·2 t o 749·6)

600·2 (447·6 t o 838·6)

13·0 (–2·5 to 31·4)

9·8 (7·3 to 14·0)

9·2 (6·9 to 12·8)

–6·8 (–19·3 to 8·2)

Other pneumoconiosis 2845·4 (2258·8 t o 3640·8)

3329·9 (2525·9 t o 4296·4)

16·7 (–3·9 to 40·9)

54·3 (43·2 t o 69·6)

52·4 (39·8 t o 67·9)

–3·7 (–20·4 to 16·5)

Asthma 22 240·4 (17 995·5 t o 27 896·9)

22 182·7 (17 852·3 t o 28 053·6)

–0·5 (–6·7 to 6·9)

373·1 (304·1 t o 471·9)

326·4 (263·3 t o 414·3)

–12·7 (–19·0 to –6·1)*

Interstitial lung disease and pulmonary sarcoidosis

5929·3 (4466·5 t o 7311·7)

8178·0 (6359·8 t o 10 399·7)

38·8 (18·3 to 56·6)*

116·0 (88·1 t o 142·5)

131·3 (102·3 t o 166·8)

13·8 (–2·7 to 28·6)

Other chronic respiratory diseases 4831·8 (3897·3 t o 5955·2)

4981·3 (4025·8 t o 6185·0)

2·9 (–5·0 to 11·6)

79·6 (63·8 t o 99·0)

73·2 (59·3 t o 90·8)

–8·3 (–15·0 to –0·6)*

Cirrhosis 35 528·4 (34 221·3 t o 36 967·4)

36 858·1 (35 053·9 t o 39 022·5)

3·6 (–1·5 to 9·7)

606·6 (585·5 t o 629·5)

535·9 (510·2 t o 567·0)

–11·8 (–16·1 to –6·8)*

Cirrhosis due to hepatitis B 9321·9 (8709·4 t o 9936·2)

9399·4 (8557·4 t o 10 303·7)

0·7 (–8·4 to 12·2)

159·9 (149·6 t o 170·4)

136·9 (124·9 t o 149·7)

–14·5 (–22·0 to –4·7)*

Cirrhosis due to hepatitis C 8937·8 (8404·8 t o 9505·2)

9939·9 (9200·4 t o 10 788·7)

11·3 (1·7 to 22·5)*

156·3 (147·2 t o 165·7)

146·2 (135·9 t o 158·6)

–6·5 (–14·2 to 2·6)

Cirrhosis due to alcohol use 11 182·1 (10 401·0 t o 11 948·2)

10 886·3 (9929·1 t o 11 927·3)

–2·8 (–12·5 to 8·9)

195·0 (181·9 t o 207·7)

159·7 (146·2 t o 174·5)

–18·3 (–26·0 to –8·6)*

Cirrhosis due to other causes 6086·6 (5445·6 t o 6855·8)

6632·4 (5969·0 t o 7450·4)

9·0 (–6·4 to 26·6)

95·3 (85·3 t o 107·3)

93·1 (83·9 t o 104·3)

–2·4 (–16·2 to 13·9)

Digestive diseases 37 037·5 (33 945·1 t o 40 627·4)

37 341·2 (33 670·4 t o 41 452·4)

0·7 (–5·3 to 7·7)

643·0 (589·7 t o 704·5)

557·3 (502·6 t o 617·9)

–13·5 (–18·2 to –7·7)*

Peptic ulcer disease 9090·0 (7900·7 t o 10 341·3)

8457·8 (6967·0 t o 9805·3)

–7·2 (–15·3 to 2·8)

163·6 (142·5 t o 185·8)

128·5 (106·4 t o 148·5)

–21·7 (–27·9 to –13·7)*

Gastritis and duodenitis 3900·1 (2949·8 t o 5033·5)

3860·1 (2931·3 t o 4985·7)

–1·0 (–6·7 to 4·7)

67·1 (51·1 t o 86·6)

58·1 (44·1 t o 75·2)

–13·5 (–18·2 to –8·7)*

Appendicitis 3082·0 (2452·4 t o 3652·8)

2760·7 (2084·3 t o 3383·6)

–10·9 (–23·0 to 4·8)

48·7 (38·6 t o 57·5)

39·1 (29·4 t o 47·8)

–20·0 (–30·4 to –6·4)*

Paralytic ileus and intestinal obstruction

5468·0 (4182·4 t o 7560·0)

6071·7 (4684·6 t o 8303·2)

10·9 (1·4 to 22·2)*

93·8 (71·4 t o 129·9)

90·0 (69·3 t o 123·2)

–4·2 (–12·2 to 5·1)

Inguinal, femoral, and abdominal hernia

982·8 (773·4 t o 1426·7)

954·8 (742·2 t o 1381·4)

–2·0 (–16·9 to 9·4)

17·8 (14·1 t o 25·4)

14·7 (11·5 t o 21·2)

–16·5 (–28·2 to –6·2)*

Infl ammatory bowel disease 3545·7 (2854·3 t o 4368·0)

3729·1 (2964·8 t o 4665·3)

5·2 (0·2 to 10·0)*

59·1 (47·4 t o 72·8)

54·1 (43·1 t o 67·6)

–8·6 (–12·6 to –4·7)*

Vascular intestinal disorders 1158·5 (729·7 t o 1739·6)

1241·5 (800·0 t o 1839·5)

7·2 (–0·5 to 14·9)

22·6 (14·3 t o 33·4)

20·0 (13·0 t o 29·4)

–11·6 (–17·8 to –5·5)*

Gallbladder and biliary diseases 2420·2 (2141·2 t o 2789·6)

2559·7 (2191·8 t o 2924·8)

6·0 (–0·8 to 11·1)

44·6 (39·6 t o 51·3)

39·7 (34·0 t o 45·5)

–10·8 (–16·5 to –6·8)*

Pancreatitis 3925·9 (2838·2 t o 4834·3)

4198·8 (3062·6 t o 5140·9)

7·0 (–2·3 to 17·5)

66·0 (47·7 t o 81·2)

60·8 (44·4 t o 74·4)

–7·8 (–15·5 to 0·9)

Other digestive diseases 3464·3 (2944·0 t o 4039·4)

3506·9 (2924·0 t o 4110·0)

1·2 (–3·9 to 7·0)

59·8 (51·0 t o 69·8)

52·4 (43·8 t o 61·3)

–12·5 (–16·6 to –7·7)*

Neurological disorders 72 438·0 (56 404·6 t o 91 027·3)

84 048·0 (65 694·2 t o 105 692·5)

16·1 (13·4 to 18·4)*

1267·8 (1007·2 t o 1568·2)

1264·4 (1000·7 t o 1571·9)

–0·2 (–2·5 to 1·7)

Alzheimer’s disease and other dementias

17 737·9 (16 089·2 t o 19 551·9)

22 238·9 (19 993·3 t o 24 542·5)

25·3 (21·2 to 29·7)*

404·3 (366·7 t o 445·2)

394·6 (354·9 t o 435·5)

–2·4 (–5·6 to 1·1)

Parkinson’s disease 1489·6 (1240·4 t o 1727·5)

1829·0 (1502·7 t o 2135·0)

22·9 (18·0 to 26·8)*

31·5 (26·2 t o 36·4)

31·2 (25·7 t o 36·3)

–0·8 (–4·8 to 2·6)

Epilepsy 13 039·4 (10 714·7 t o 15 492·8)

13 372·1 (10 920·9 t o 15 979·4)

2·4 (–4·5 to 10·0)

196·8 (161·5 t o 233·9)

185·2 (151·2 t o 221·3)

–6·0 (–12·3 to 0·9)

Multiple sclerosis 1150·9 (906·8 t o 1361·7)

1342·8 (1068·4 t o 1625·8)

16·6 (9·3 to 25·1)*

19·3 (15·2 t o 22·9)

19·2 (15·3 t o 23·1)

–0·8 (–7·2 to 6·4)

Migraine 25 780·9 (15 613·2 t o 37 987·5)

28 898·1 (17 585·8 t o 42 420·1)

12·1 (8·8 to 15·3)*

395·8 (240·2 t o 582·7)

398·4 (242·4 t o 584·9)

0·6 (–2·2 to 3·5)

(Table 1 continues on next page)

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All ages DALYs (thousands)* Age-standardised DALYs (per 100 000)*

2005 2013 Percentage change 2005 2013 Percentage change

(Continued from previous page)

Tension-type headache 2031·8 (985·5 t o 3558·8)

2363·2 (1151·9 t o 4155·0)

16·3 (13·7 to 19·0)*

31·6 (15·3 t o 55·2)

32·9 (16·0 t o 57·8)

4·2 (1·9 to 6·6)*

Medication overuse headache 7577·4 (4426·0 t o 11 589·5)

9845·7 (5777·9 t o 15 100·3)

30·0 (22·7 to 37·1)*

121·8 (71·3 t o 186·3)

138·5 (81·3 t o 212·1)

13·7 (7·3 to 19·8)*

Other neurological disorders 3630·1 (3079·2 t o 4245·0)

4158·2 (3465·2 t o 4719·9)

15·0 (7·2 to 20·4)*

66·7 (56·2 t o 78·3)

64·4 (53·8 t o 73·5)

–3·0 (–9·2 to 1·5)

Mental and substance use disorders

157 899·7 (117 039·4 t o 202 585·3)

173 177·4 (127 426·5 t o 221 734·1)

9·7 (7·8 to 11·0)*

2443·3 (1812·5 t o 3131·3)

2399·8 (1765·2 t o 3064·4)

–1·8 (–3·4 to –0·7)*

Schizophrenia 13 972·2 (10 445·2 t o 16 601·6)

15 687·2 (11 647·4 t o 18 704·1)

12·3 (10·8 to 13·6)*

221·6 (165·7 t o 263·0)

217·2 (161·4 t o 258·7)

–1·9 (–3·2 to –0·8)*

Alcohol use disorders 13 856·3 (10 676·5 t o 17 518·8)

12 772·1 (9872·5 t o 16 401·5)

–8·0 (–11·5 to –3·5)*

217·5 (168·0 t o 274·0)

175·7 (136·1 t o 224·8)

–19·3 (–22·7 to –15·0)*

Drug use disorders 16 573·6 (12 990·6 t o 20 087·4)

17 953·0 (14 163·9 t o 21 969·4)

8·3 (4·5 to 11·8)

247·3 (194·2 t o 299·1)

242·2 (191·3 t o 296·2)

–2·1 (–5·5 to 1·0)

Opioid use disorders 8577·2 (6762·8 t o 10 512·9)

8136·2 (6171·1 t o 10 485·5)

–5·4 (–11·2 to 0·9)

130·1 (102·8 t o 159·0)

110·3 (83·7 t o 142·2)

–15·4 (–20·6 to –9·6)*

Cocaine use disorders 1056·2 (739·6 t o 1439·9)

1200·4 (851·2 t o 1619·0)

13·8 (9·3 to 18·5)*

15·6 (11·0 t o 21·3)

16·1 (11·5 t o 21·7)

3·3 (–0·8 to 7·5)

Amphetamine use disorders 1937·0 (1244·8 t o 2768·9)

2117·2 (1388·2 t o 2987·5)

9·3 (4·9 to 14·3)*

27·9 (18·0 t o 39·9)

28·2 (18·5 t o 39·8)

1·1 (–2·9 to 5·5)

Cannabis use disorders 383·5 (254·8 t o 557·0)

395·6 (261·2 t o 576·2)

3·2 (0·1 to 6·4)*

5·5 (3·6 to 7·9)

5·3 (3·5 to 7·7)

–3·0 (–5·8 to –0·1)*

Other drug use disorders 4619·7 (3665·7 t o 5670·2)

6103·5 (5006·4 t o 7312·4)

32·3 (23·5 to 41·6)*

68·1 (54·2 t o 83·3)

82·2 (67·5 t o 98·3)

20·8 (12·9 to 29·1)*

Depressive disorders 54 086·1 (36 401·9 t o 75 052·8)

61 632·8 (41 353·8 t o 85 621·4)

14·0 (10·4 to 17·1)

856·4 (580·2 t o 1186·5)

864·4 (580·0 t o 1202·1)

1·1 (–2·5 to 3·6)

Major depressive disorder 45 539·4 (29 829·4 t o 64 133·2)

51 783·9 (33 888·2 t o 73 665·8)

13·8 (9·4 to 17·5)*

717·2 (471·7 t o 1011·6)

724·9 (475·7 t o 1030·7)

1·3 (–2·8 to 4·4)

Dysthymia 8546·7 (5687·3 t o 12 278·3)

9848·9 (6586·6 t o 14 166·0)

15·2 (14·0 to 16·3)*

139·3 (93·5 t o 200·2)

139·5 (93·7 t o 200·9)

0·2 (–0·6 to 0·9)

Bipolar disorder 8715·9 (5487·1 t o 13 043·4)

9911·1 (6260·6 t o 14 791·0)

13·7 (12·1 to 15·8)*

135·4 (85·0 t o 201·5)

136·6 (86·3 t o 202·5)

0·8 (–0·4 to 2·5)

Anxiety disorders 21 949·1 (14 287·0 t o 31 597·3)

24 355·8 (16 148·6 t o 35 139·0)

11·0 (8·5 to 13·6)*

337·7 (221·6 t o 481·8)

337·7 (224·4 t o 486·3)

0·0 (–2·0 to 1·9)

Eating disorders 1742·9 (1135·8 t o 2601·1)

1853·7 (1189·9 t o 2753·8)

6·3 (4·2 to 8·5)*

24·6 (16·0 t o 36·8)

24·6 (15·9 t o 36·6)

0·0 (–2·0 to 1·8)

Anorexia nervosa 448·5 (302·6 t o 644·7)

474·0 (318·2 t o 682·3)

5·7 (1·9 to 9·4)*

6·3 (4·3 to 9·1)

6·3 (4·3 to 9·1)

–0·2 (–3·8 to 3·2)

Bulimia nervosa 1294·4 (797·2 t o 1995·9)

1379·7 (850·7 t o 2136·6)

6·5 (4·1 to 9·1)*

18·3 (11·3 t o 28·1)

18·3 (11·3 t o 28·3)

0·0 (–2·1 to 2·4)

Autistic spectrum disorders 7721·8 (5369·6 t o 10 463·8)

8449·0 (5888·1 t o 11 458·7)

9·4 (8·6 to 10·3)*

116·6 (81·1 t o 158·0)

117·1 (81·6 t o 158·7)

0·4 (–0·4 to 1·1)

Autism 4884·2 (3285·3 t o 6671·6)

5345·0 (3583·6 t o 7309·9)

9·4 (8·3 to 10·6)*

73·8 (49·6 t o 100·6)

74·1 (49·7 t o 101·3)

0·4 (–0·6 to 1·4)

Asperger’s syndrome 2837·6 (1981·9 t o 3949·6)

3104·0 (2169·6 t o 4325·0)

9·4 (8·5 to 10·3)*

42·9 (30·0 t o 59·6)

43·0 (30·1 t o 59·9)

0·3 (–0·5 to 1·1)

Attention-defi cit hyperactivity disorder

478·9 (287·4 t o 740·8)

479·9 (287·4 t o 745·8)

0·2 (–1·5 to 1·9)

6·6 (4·0 to 10·2)

6·6 (3·9 to 10·2)

0·0 (–1·7 to 1·7)

Conduct disorder 6192·7 (3889·5 t o 8986·2)

6159·0 (3868·2 t o 8911·6)

–0·5 (–1·7 to 0·6)

84·0 (52·7 t o 122·0)

85·3 (53·6 t o 123·4)

1·5 (0·3 to 2·6)*

Idiopathic intellectual disability 4575·2 (3011·1 t o 6496·0)

4666·7 (3084·8 t o 6640·0)

2·1 (–4·1 to 8·6)

68·0 (44·7 t o 96·6)

64·3 (42·5 t o 91·5)

–5·3 (–10·9 to 0·7)

Other mental and substance use disorders

8035·0 (5442·0 t o 10 785·3)

9257·2 (6277·9 t o 12 411·5)

15·2 (14·2 to 16·2)*

127·6 (86·5 t o 170·7)

128·1 (86·9 t o 171·6)

0·3 (–0·4 to 1·1)

(Table 1 continues on next page)

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2158 www.thelancet.com Vol 386 November 28, 2015

All ages DALYs (thousands)* Age-standardised DALYs (per 100 000)*

2005 2013 Percentage change 2005 2013 Percentage change

(Continued from previous page)

Diabetes, urogenital, blood, and endocrine diseases

120 976·5 (101 712·4 t o 143 463·1)

141 620·9 (118 713·4 t o 168 158·3)

17·0 (13·1 to 21·5)*

2069·2 (1759·6 t o 2429·2)

2089·4 (1759·1 t o 2468·6)

0·8 (–2·1 to 5·2)

Diabetes mellitus 46 039·4 (38 599·7 t o 54 434·2)

55 832·6 (46 374·6 t o 66 808·6)

21·2 (17·6 to 25·3)*

837·8 (706·6 t o 981·8)

846·2 (704·8 t o 1007·4)

0·9 (–2·1 to 4·4)

Acute glomerulonephritis 805·7 (535·0 t o 1067·9)

715·4 (519·0 t o 923·1)

–11·2 (–22·1 to 4·4)

12·6 (8·5 to 16·6)

10·2 (7·4 to 13·1)

–19·4 (–28·8 to –6·2)*

Chronic kidney disease 28 349·4 (24 290·4 t o 31 773·7)

33 187·2 (28 461·0 t o 37 316·0)

17·1 (12·5 to 22·0)*

497·7 (425·7 t o 555·5)

497·3 (427·5 t o 557·2)

–0·1 (–4·0 to 4·1)

Chronic kidney disease due to diabetes mellitus

4493·8 (3673·0 t o 5204·8)

5939·3 (5014·8 t o 6940·1)

31·8 (25·4 to 43·0)*

82·9 (67·7 t o 95·9)

90·9 (77·1 t o 105·9)

9·4 (4·4 to 18·6)*

Chronic kidney disease due to hypertension

6482·8 (5143·8 t o 7544·1)

7986·4 (6335·9 t o 9233·9)

23·1 (17·4 to 29·7)*

116·3 (92·2 t o 134·8)

121·1 (95·7 t o 139·8)

4·1 (–0·9 to 9·6)

Chronic kidney disease due to glomerulonephritis

6585·6 (5631·4 t o 7535·5)

6126·2 (5138·3 t o 7170·7)

–7·2 (–12·0 to –1·1)*

108·1 (92·5 t o 123·7)

88·2 (74·0 t o 103·2)

–18·6 (–22·9 to –13·2)*

Chronic kidney disease due to other causes

10 787·2 (8898·8 t o 12 207·3)

13 135·4 (10 821·2 t o 14 992·9)

21·7 (14·7 to 28·9)*

190·4 (156·3 t o 215·6)

197·2 (162·6 t o 225·1)

3·5 (–2·4 to 9·7)

Urinary diseases and male infertility

8848·5 (7381·2 t o 10 580·7)

10 292·4 (8404·5 t o 12 529·3)

16·1 (12·2 to 21·0)*

163·6 (135·1 t o 197·1)

160·4 (130·6 t o 196·4)

–2·2 (–5·2 to 1·6)

Interstitial nephritis and urinary tract infections

3481·9 (2922·5 t o 3776·0)

3808·0 (3143·5 t o 4201·1)

9·0 (3·4 to 16·7)*

61·4 (51·6 t o 66·3)

57·6 (47·6 t o 63·3)

–6·5 (–11·3 to –0·2)*

Urolithiasis 923·5 (699·2 t o 1196·0)

1006·8 (748·8 t o 1326·3)

9·0 (2·9 to 14·6)*

16·5 (12·6 t o 21·4)

15·2 (11·3 t o 20·0)

–7·9 (–12·8 to –3·4)*

Benign prostatic hyperplasia 2759·5 (1817·4 t o 3851·3)

3552·9 (2316·5 t o 4993·7)

28·7 (25·2 to 32·1)*

56·9 (37·5 t o 79·4)

59·1 (38·6 t o 83·1)

3·8 (1·0 to 6·6)*

Male infertility due to other causes

221·8 (95·4 t o 456·2)

258·6 (111·8 t o 531·4)

16·4 (7·7 to 26·6)*

3·2 (1·4 to 6·7)

3·4 (1·5 to 7·1)

5·6 (–2·1 to 14·7)

Other urinary diseases 1461·7 (1070·5 t o 1708·8)

1666·0 (1158·6 t o 1976·5)

13·9 (5·7 to 23·1)*

25·5 (18·6 t o 29·9)

25·0 (17·4 t o 29·7)

–2·1 (–8·8 to 5·4)

Gynaecological diseases 8262·5 (5405·8 t o 12 229·0)

9237·3 (6081·1 t o 13 702·3)

11·7 (8·8 to 15·2)*

124·5 (81·5 t o 184·8)

124·5 (81·9 t o 184·7)

–0·1 (–2·6 to 3·0)

Uterine fi broids 2012·5 (1178·8 t o 3388·1)

2187·3 (1265·4 t o 3702·5)

8·4 (5·7 to 13·8)*

31·0 (18·2 t o 52·1)

29·6 (17·2 t o 50·2)

–4·5 (–6·9 to 0·2)

Polycystic ovarian syndrome 1085·3 (512·3 t o 2026·3)

1196·1 (567·0 t o 2231·8)

10·2 (6·4 to 14·0)*

16·1 (7·6 to 30·0)

16·0 (7·6 to 29·9)

–0·5 (–3·8 to 2·8)

Female infertility due to other causes

169·4 (68·0 t o 357·4)

191·9 (75·2 t o 399·1)

13·2 (2·6 to 25·3)*

2·5 (1·0 to 5·2)

2·6 (1·0 to 5·3)

4·0 (–5·6 to 14·9)

Endometriosis 1227·9 (824·6 t o 1673·5)

1371·5 (917·7 t o 1873·2)

11·8 (7·4 to 16·6)*

18·4 (12·3 t o 25·0)

18·4 (12·3 t o 25·1)

0·0 (–3·9 to 4·4)

Genital prolapse 960·4 (483·2 t o 1792·7)

1111·1 (550·4 t o 2058·2)

15·5 (12·4 to 19·2)*

15·5 (7·8 to 28·9)

15·5 (7·7 to 28·7)

–0·6 (–3·2 to 2·5)

Premenstrual syndrome 2136·2 (1333·1 t o 3184·3)

2548·6 (1581·3 t o 3777·0)

19·0 (9·9 to 30·9)*

31·1 (19·4 t o 46·3)

33·9 (21·0 t o 50·2)

8·7 (0·4 to 19·6)*

Other gynaecological diseases 670·9 (472·0 t o 922·5)

630·8 (443·0 t o 873·1)

–5·7 (–13·1 to –0·5)*

9·9 (7·0 to 13·6)

8·5 (6·0 to 11·7)

–14·3 (–21·0 to –9·5)*

Haemoglobinopathies and haemolytic anaemias

20 495·6 (12 009·2 t o 32 293·0)

23 368·9 (12 797·5 t o 39 245·7)

11·9 (0·7 to 33·0)*

302·4 (178·3 t o 473·5)

322·0 (177·3 t o 538·3)

4·4 (–5·9 to 24·2)

Thalassaemias 2096·3 (1466·2 t o 2677·9)

1814·1 (1257·4 t o 2332·0)

–15·4 (–23·1 to 4·8)

30·1 (21·0 t o 38·5)

24·7 (17·1 t o 31·7)

–19·9 (–27·1 to –0·6)*

Thalassaemia trait 3401·3 (2267·4 t o 4899·0)

3769·6 (2508·9 t o 5442·2)

10·8 (8·1 to 13·8)*

51·0 (34·1 t o 73·6)

52·4 (34·9 t o 75·7)

2·7 (0·1 to 5·5)*

Sickle cell disorders 11 142·6 (3743·2 t o 22 090·0)

13 650·5 (4382·8 t o 29 097·8)

20·8 (0·1 to 52·5)*

161·9 (54·6 t o 320·2)

186·5 (59·8 t o 397·2)

13·4 (–6·1 to 43·9)

Sickle cell trait 1251·0 (829·2 t o 1811·0)

1396·6 (929·4 t o 2004·9)

11·5 (6·2 to 19·0)*

18·4 (12·2 t o 26·7)

19·3 (12·9 t o 27·8)

4·8 (–0·1 to 11·8)

Glucose-6-phosphate dehydrogenase defi ciency

265·1 (176·5 t o 341·5)

269·1 (174·8 t o 371·8)

–0·7 (–11·3 to 22·7)

4·0 (2·6 to 5·1)

3·7 (2·4 to 5·1)

–8·2 (–17·9 to 12·8)

(Table 1 continues on next page)

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www.thelancet.com Vol 386 November 28, 2015 2159

All ages DALYs (thousands)* Age-standardised DALYs (per 100 000)*

2005 2013 Percentage change 2005 2013 Percentage change

(Continued from previous page)

Glucose-6-phosphate dehydrogenase defi ciency trait

44·6 (27·7 t o 66·0)

48·8 (30·1 t o 73·3)

9·2 (–12·1 to 34·0)

0·7 (0·4 to 1·0)

0·7 (0·4 to 1·0)

2·4 (–17·6 to 25·1)

Other haemoglobinopathies and haemolytic anaemias

2294·8 (1707·8 t o 2962·2)

2420·1 (1814·8 t o 3146·8)

5·1 (–1·0 to 13·6)

36·4 (27·2 t o 46·8)

34·7 (26·2 t o 45·1)

–4·9 (–10·2 to 2·2)

Endocrine, metabolic, blood, and immune disorders

8175·5 (7030·5 t o 9563·7)

8987·0 (7724·2 t o 10 348·0)

10·1 (1·9 to 17·8)*

130·6 (111·8 t o 152·6)

128·9 (110·9 t o 148·3)

–1·1 (–8·2 to 5·4)

Musculoskeletal disorders 126 874·2 (91 296·7 t o 167 000·8)

149 435·7 (106 888·5 t o 197 565·1)

17·7 (16·2 to 19·8)*

2162·3 (1559·6 t o 2834·5)

2178·0 (1561·5 t o 2875·0)

0·6 (–0·5 to 2·4)

Rheumatoid arthritis 4299·0 (3304·9 t o 5461·2)

4741·2 (3597·6 t o 5988·1)

10·2 (7·5 to 13·5)*

78·0 (60·2 t o 98·8)

72·5 (55·1 t o 91·3)

–7·2 (–9·5 to –4·4)*

Osteoarthritis 10 401·5 (7337·3 t o 14 133·8)

12 811·1 (9030·0 t o 17 281·2)

23·2 (21·6 to 24·7)*

201·3 (142·1 t o 272·9)

201·7 (142·3 t o 271·8)

0·2 (–1·1 to 1·5)

Low back and neck pain 91 729·2 (64 002·3 t o 123 315·7)

106 665·5 (74 116·9 t o 142 959·7)

16·2 (14·0 to 19·2)*

1525·7 (1066·3 t o 2048·4)

1532·8 (1065·6 t o 2052·0)

0·4 (–1·4 to 2·9)

Low back pain 61 611·0 (42 074·7 t o 84 850·7)

72 317·6 (49 051·0 t o 99 738·5)

17·2 (14·9 to 21·1)*

1032·4 (705·9 t o 1418·2)

1045·3 (710·2 t o 1440·6)

1·0 (–0·8 to 4·4)

Neck pain 30 118·2 (20 855·1 t o 41 090·2)

34 347·9 (23 792·0 t o 47 418·5)

14·1 (10·1 to 18·3)*

493·3 (342·4 t o 672·2)

487·5 (337·8 t o 672·2)

–1·1 (–4·5 to 2·5)

Gout 154·3 (106·8 t o 205·2)

185·5 (129·0 t o 249·2)

20·1 (16·5 to 24·5)*

2·9 (2·0 to 3·8)

2·8 (2·0 to 3·8)

–0·8 (–3·7 to 2·8)

Other musculoskeletal disorders 20 290·1 (14 431·4 t o 27 422·7)

25 032·4 (17 671·6 t o 34 085·8)

23·3 (21·2 to 25·5)*

354·4 (251·9 t o 481·1)

368·3 (260·1 t o 502·1)

3·9 (2·4 to 5·6)*

Other non-communicable diseases

157 897·8 (120 772·1 t o 204 769·6)

170 947·9 (130 922·9 t o 223 484·3)

8·7 (3·1 to 11·6)*

2549·4 (1934·9 t o 3320·6)

2465·7 (1884·2 t o 3234·3)

–3·0 (–7·3 to –0·6)*

Congenital anomalies 56 944·6 (49 141·0 t o 69 460·2)

57 173·2 (50 550·4 t o 66 265·6)

1·6 (–10·4 to 9·0)

827·1 (714·5 t o 1007·5)

779·9 (689·9 t o 903·0)

–4·6 (–15·8 to 2·4)

Neural tube defects 7161·8 (4444·2 t o 12 759·5)

6236·6 (3854·9 t o 10 918·2)

–12·7 (–28·6 to 4·8)

102·6 (63·7 t o 183·0)

84·4 (52·2 t o 147·9)

–17·4 (–32·5 to –0·9)*

Congenital heart anomalies 26 144·1 (22 321·2 t o 32 868·8)

26 219·2 (23 222·4 t o 30 340·6)

1·7 (–12·6 to 11·8)

377·4 (322·2 t o 473·6)

356·6 (316·0 t o 412·7)

–4·1 (–17·6 to 5·4)

Orofacial clefts 416·1 (257·1 t o 602·1)

352·3 (229·2 t o 515·7)

–15·6 (–31·8 to 8·8)

6·0 (3·7 to 8·6)

4·8 (3·1 to 7·0)

–20·3 (–35·5 to 2·6)

Down’s syndrome 3578·6 (2139·0 t o 5189·6)

3851·4 (2556·6 t o 5223·2)

8·7 (–8·6 to 27·8)

53·5 (32·8 t o 76·8)

53·2 (35·5 t o 71·8)

0·5 (–14·9 to 17·5)

Turner’s syndrome 3·8 (1·9 to 6·1)

4·3 (2·2 to 6·9)

13·3 (5·1 to 23·2)*

0·1 (0·0 to 0·1)

0·1 (0·0 to 0·1)

4·4 (–3·3 to 13·4)

Klinefelter’s syndrome 1·2 (0·6 to 2·2)

1·3 (0·6 to 2·4)

13·0 (5·2 to 21·5)*

0·0 (0·0 to 0·0)

0·0 (0·0 to 0·0)

2·1 (–4·9 to 9·6)

Chromosomal unbalanced rearrangements

2629·6 (1930·4 t o 3774·0)

2985·4 (2313·0 t o 3917·7)

16·2 (–2·6 to 25·0)

40·2 (29·9 t o 56·8)

41·7 (32·4 t o 54·6)

5·8 (–9·8 to 13·3)

Other congenital anomalies 17 009·4 (13 826·2 t o 25 442·3)

17 522·7 (14 425·9 t o 24 552·1)

4·1 (–9·1 to 14·4)

247·4 (201·2 t o 368·7)

239·1 (196·7 t o 334·7)

–2·4 (–14·5 to 7·1)

Skin and subcutaneous diseases 37 827·9 (25 158·8 t o 56 628·8)

41 597·6 (27 763·0 t o 62 743·1)

10·0 (7·8 to 12·0)*

582·5 (390·2 t o 865·7)

582·9 (390·1 t o 872·3)

0·1 (–1·8 to 1·8)

Dermatitis 8431·6 (5490·7 t o 12 137·7)

9278·4 (6029·0 t o 13 326·7)

10·0 (9·2 to 11·0)*

128·6 (83·5 t o 184·8)

128·7 (83·6 t o 184·9)

0·1 (–0·4 to 0·6)

Psoriasis 4187·6 (2896·0 t o 5899·7)

4726·7 (3254·7 t o 6621·9)

12·9 (11·4 to 14·4)*

67·2 (46·4 t o 94·6)

66·8 (46·0 t o 93·6)

–0·5 (–1·6 to 0·6)

Cellulitis 1083·0 (809·2 t o 1402·4)

1064·7 (814·0 t o 1397·5)

–2·3 (–11·5 to 11·1)

17·7 (13·1 t o 23·0)

15·5 (11·8 t o 20·2)

–13·2 (–21·1 to –1·7)*

Pyoderma 943·5 (698·5 t o 1195·4)

1141·6 (888·5 t o 1330·2)

21·4 (4·7 to 39·8)*

15·7 (11·7 t o 19·6)

16·6 (13·0 t o 19·3)

6·4 (–7·2 to 21·3)

Scabies 1624·7 (927·0 t o 2620·2)

1705·4 (967·2 t o 2711·6)

4·8 (–3·4 to 15·1)

24·1 (13·8 t o 38·9)

23·5 (13·3 t o 37·3)

–2·8 (–10·2 to 6·7)

Fungal skin diseases 3447·0 (1403·8 t o 7290·4)

3847·2 (1574·5 t o 8139·8)

11·6 (10·5 to 12·8)*

53·4 (21·8 t o 113·0)

54·0 (22·1 t o 114·2)

1·0 (0·5 to 1·4)*

(Table 1 continues on next page)

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2160 www.thelancet.com Vol 386 November 28, 2015

All ages DALYs (thousands)* Age-standardised DALYs (per 100 000)*

2005 2013 Percentage change 2005 2013 Percentage change

(Continued from previous page)

Viral skin diseases 3752·1 (2284·2 t o 5820·4)

3955·0 (2398·4 t o 6150·9)

5·4 (4·1 to 6·7)*

55·1 (33·4 t o 85·5)

54·7 (33·3 t o 85·0)

–0·6 (–1·5 to 0·3)

Acne vulgaris 6982·2 (3360·8 t o 12 916·9)

7180·8 (3451·6 t o 13 214·1)

2·5 (–3·7 to 11·1)

96·9 (46·7 t o 179·1)

96·7 (46·4 t o 177·8)

–0·5 (–6·5 to 7·7)

Alopecia areata 257·7 (163·9 t o 382·5)

292·4 (186·8 t o 435·2)

13·5 (10·3 to 16·8)*

4·2 (2·7 to 6·3)

4·2 (2·7 to 6·3)

–0·1 (–2·9 to 2·7)

Pruritus 9·2 (4·4 to 17·1)

10·8 (5·1 to 20·0)

17·4 (9·2 to 25·7)*

0·2 (0·1 to 0·3)

0·2 (0·1 to 0·3)

0·9 (–6·2 to 8·6)

Urticaria 3993·4 (2616·6 t o 5702·1)

4720·7 (3036·5 t o 6737·2)

19·0 (2·7 to 32·6)*

64·6 (42·5 t o 92·2)

67·0 (43·2 t o 95·5)

4·3 (–9·4 to 16·1)

Decubitus ulcer 546·8 (454·9 t o 647·3)

660·6 (553·9 t o 782·1)

20·9 (14·5 to 27·7)*

10·9 (9·1 to 12·8)

10·8 (9·1 to 12·7)

–0·8 (–5·8 to 4·6)

Other skin and subcutaneous diseases

2569·0 (1164·2 t o 5187·3)

3013·3 (1374·5 t o 6234·7)

17·2 (13·2 to 20·7)*

44·0 (19·4 t o 93·1)

44·2 (19·8 t o 93·3)

0·4 (–0·7 to 1·6)

Sense organ diseases 47 426·9 (31 917·8 t o 66 335·1)

54 428·1 (36 458·4 t o 76 075·4)

14·7 (13·2 to 16·2)*

868·4 (588·1 t o 1206·6)

839·3 (564·9 t o 1165·9)

–3·4 (–4·6 to –2·2)*

Glaucoma 701·8 (496·4 t o 963·7)

807·5 (571·6 t o 1102·8)

15·0 (10·0 to 20·2)*

13·3 (9·5 to 18·2)

12·7 (9·0 to 17·3)

–5·1 (–9·4 to –1·0)*

Cataract 2592·9 (1853·6 t o 3530·8)

2916·7 (2055·1 t o 3962·2)

12·5 (8·2 to 16·9)*

54·1 (38·6 t o 73·4)

49·0 (34·7 t o 66·4)

–9·4 (–12·8 to –5·9)*

Macular degeneration 516·8 (368·3 t o 697·4)

725·6 (509·4 t o 985·1)

40·4 (33·8 to 47·0)*

10·8 (7·7 to 14·5)

11·9 (8·4 to 16·2)

10·6 (5·1 to 16·0)*

Uncorrected refractive error 10 004·7 (6360·2 t o 15 412·8)

11 257·2 (7149·8 t o 17 452·3)

12·5 (10·7 to 14·3)*

176·9 (112·7 t o 272·8)

169·2 (107·6 t o 261·9)

–4·4 (–5·7 to –3·0)*

Age-related and other hearing loss

28 010·5 (18 942·1 t o 39 007·8)

32 579·7 (22 083·7 t o 45 846·1)

16·3 (13·9 to 18·8)*

521·4 (355·2 t o 721·5)

507·3 (346·5 t o 710·4)

–2·7 (–4·5 to –0·9)*

Other vision loss 1690·5 (1191·0 t o 2309·3)

1793·5 (1260·4 t o 2452·0)

6·0 (2·8 to 9·7)*

29·8 (21·1 t o 40·7)

27·1 (19·1 t o 36·9)

–9·4 (–11·8 to –6·5)*

Other sense organ diseases 3909·6 (2421·5 t o 5767·2)

4348·0 (2704·3 t o 6435·1)

11·2 (9·5 to 12·9)*

62·1 (38·5 t o 91·5)

62·2 (38·7 t o 91·9)

0·1 (–1·3 to 1·6)

Oral disorders 14 385·1 (8778·2 t o 22 332·8)

16 449·5 (10 022·3 t o 25 506·3)

14·3 (13·2 to 15·5)*

252·5 (156·1 t o 388·1)

245·9 (151·1 t o 378·7)

–2·7 (–3·9 to –1·5)*

Deciduous caries 173·7 (75·1 t o 335·6)

181·1 (79·0 t o 350·9)

4·2 (2·7 to 5·9)*

2·5 (1·1 to 4·9)

2·5 (1·1 to 4·9)

–0·2 (–1·6 to 1·4)

Permanent caries 2190·3 (1007·6 t o 4232·7)

2411·0 (1102·6 t o 4664·5)

10·1 (8·6 to 11·5)*

33·2 (15·3 t o 64·0)

33·4 (15·3 t o 64·5)

0·5 (–0·9 to 1·8)

Periodontal diseases 2748·2 (1103·3 t o 5617·2)

3286·0 (1318·3 t o 6750·3)

19·6 (17·2 to 22·0)*

47·2 (19·0 t o 96·4)

47·7 (19·1 t o 97·9)

1·0 (–1·0 to 3·0)

Edentulism and severe tooth loss

5953·6 (4032·0 t o 8148·1)

6855·6 (4647·2 t o 9420·4)

15·2 (13·1 to 17·2)*

117·6 (79·7 t o 160·8)

110·5 (75·0 t o 151·7)

–6·1 (–7·6 to –4·5)*

Other oral disorders 3319·4 (2096·9 t o 4915·4)

3715·7 (2347·5 t o 5558·6)

11·9 (10·2 to 13·8)*

52·0 (32·9 t o 77·0)

51·9 (32·8 t o 77·6)

–0·2 (–1·7 to 1·4)

Sudden infant death syndrome 1313·3 (860·2 t o 2147·1)

1299·5 (828·6 t o 1849·5)

0·6 (–27·3 to 25·2)

18·8 (12·3 t o 30·8)

17·6 (11·2 t o 25·0)

–5·1 (–31·4 to 18·1)

Injuries 267 681·2 (250 424·7 t o 283 221·0)

247 582·4 (231 253·2 t o 265 122·7)

–7·6 (–11·7 to –2·8)*

4166·9 (3895·0 t o 4427·3)

3464·2 (3234·7 t o 3720·4)

–17·0 (–20·6 to –12·3)*

Transport injuries 82 941·2 (75 570·2 t o 87 805·0)

78 952·9 (72 122·8 t o 85 115·6)

–4·9 (–9·8 to 0·7)

1270·5 (1154·4 t o 1349·4)

1092·2 (998·8 t o 1177·4)

–14·1 (–18·5 to –9·1)*

Road injuries 76 626·7 (70 107·3 t o 81 166·5)

73 251·1 (66 857·1 t o 78 671·0)

–4·4 (–9·5 to 1·0)

1171·6 (1069·3 t o 1242·2)

1012·5 (923·7 t o 1087·5)

–13·6 (–18·1 to –8·7)*

Pedestrian road injuries 26 400·1 (22 734·6 t o 30 607·2)

25 580·0 (21 221·0 t o 29 568·6)

–2·9 (–10·5 to 4·7)

408·7 (352·0 t o 471·8)

358·0 (297·3 t o 412·8)

–12·2 (–19·0 to –5·5)*

Cyclist road injuries 5010·9 (4305·2 t o 5689·1)

4701·5 (4005·2 t o 5424·2)

–6·4 (–12·6 to 0·7)

78·5 (67·6 t o 88·9)

66·2 (56·5 t o 76·3)

–15·9 (–21·4 to –9·4)*

Motorcyclist road injuries 15 234·5 (12 694·2 t o 17 550·9)

14 199·2 (11 743·8 t o 16 579·2)

–7·0 (–13·6 to 0·7)

227·8 (190·0 t o 262·2)

192·8 (159·4 t o 225·1)

–15·5 (–21·5 to –8·6)*

(Table 1 continues on next page)

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www.thelancet.com Vol 386 November 28, 2015 2161

of the epidemiological change with sociodemographic status.

We used hierarchical regression to decompose variance in log DALY rates into components related to the sociodemographic status, intercountry variation, year, and fraction explained by the interactions of the other variables. This approach estimates a simple model with uncorrelated random eff ects for year, country, and sociodemographic status.26 We divided sociodemographic status into vigintiles (20 equal interval bins) to allow for non-linear correlations between log DALY rates and sociodemographic status for a cause. We did tests with

up to 50 bins for the sociodemographic status variable with no change in qualitative results. We divided the variance of each random eff ect by total variance to decompose variance into diff erent factors. We did this variance decomposition analysis for GBD level 2 and level 3 causes. We use these regressions to predict the pattern of DALYs by cause (and through separate regressions for YLLs and YLDs) purely as a function of sociodemographic status, holding all other random eff ects (year and country) at zero. Because there could be lagged eff ects between sociodemographic status and DALY rate, we tested alternative models with

All ages DALYs (thousands)* Age-standardised DALYs (per 100 000)*

2005 2013 Percentage change 2005 2013 Percentage change

(Continued from previous page)

Motor vehicle road injuries 28 677·5 (25 364·5 t o 31 686·1)

27 692·3 (24 232·9 t o 30 737·9)

–3·4 (–8·0 to 1·4)

436·9 (386·5 t o 482·5)

380·6 (333·3 t o 422·3)

–12·9 (–16·9 to –8·7)*

Other road injuries 1303·7 (971·1 t o 1666·4)

1078·1 (779·9 t o 1337·0)

–17·6 (–28·2 to –2·3)*

19·7 (14·7 t o 25·2)

14·9 (10·8 t o 18·5)

–24·5 (–34·3 to –11·3)*

Other transport injuries 6314·5 (5315·6 t o 7082·6)

5701·8 (4908·3 t o 6443·6)

–10·1 (–15·9 to –0·8)*

98·8 (83·0 t o 111·4)

79·7 (68·6 t o 90·4)

–19·7 (–24·6 to –11·7)*

Unintentional injuries 112 792·0 (104 542·3 t o 121 686·5)

105 941·3 (96 996·1 t o 117 265·2)

–6·3 (–11·1 to 0·2)

1789·1 (1651·5 t o 1938·7)

1509·4 (1379·5 t o 1673·8)

–15·8 (–20·0 to –10·1)*

Falls 26 950·1 (22 901·1 t o 31 245·3)

27 491·4 (23 388·5 t o 31 888·5)

2·1 (–4·0 to 7·6)

480·3 (407·5 t o 561·2)

415·4 (352·5 t o 483·2)

–13·5 (–18·4 to –9·0)*

Drowning 25 529·1 (21 942·1 t o 29 975·8)

21 608·0 (18 192·8 t o 29 799·1)

–17·3 (–23·9 to 4·0)

376·2 (323·4 t o 441·8)

297·7 (250·8 t o 410·4)

–22·6 (–28·7 to –2·8)*

Fire, heat, and hot substances 13 280·4 (11 575·9 t o 15 303·6)

12 314·8 (10 493·2 t o 14 700·0)

–7·8 (–20·3 to 11·5)

202·5 (176·3 t o 232·5)

170·8 (145·7 t o 203·9)

–16·2 (–27·1 to 0·7)

Poisonings 5492·5 (4200·6 t o 6146·7)

4535·6 (3221·0 t o 5172·8)

–17·4 (–25·8 to –9·0)*

83·4 (64·0 t o 93·1)

62·7 (44·6 t o 71·5)

–24·8 (–32·6 to –17·2)*

Exposure to mechanical forces 14 798·4 (13 371·9 t o 16 946·5)

14 037·9 (12 407·2 t o 17 176·1)

–6·1 (–11·5 to 6·0)

226·7 (204·2 t o 259·5)

194·8 (172·2 t o 237·8)

–14·9 (–19·6 to –3·8)*

Unintentional fi rearm injuries 2843·7 (2559·5 t o 3173·4)

2502·6 (2193·8 t o 2959·6)

–12·2 (–23·2 to 1·8)

42·5 (38·3 t o 47·3)

34·2 (30·0 t o 40·2)

–19·8 (–29·5 to –7·2)*

Unintentional suff ocation 2365·8 (1972·6 t o 3969·2)

2586·9 (1941·6 t o 5623·3)

4·2 (–13·5 to 51·0)

34·5 (28·8 t o 57·8)

35·4 (26·6 t o 76·8)

–2·4 (–18·9 to 41·8)

Other exposure to mechanical forces

9588·9 (8377·2 t o 11 050·4)

8948·4 (7781·4 t o 10 308·1)

–6·7 (–12·1 to –1·3)*

149·6 (130·2 t o 173·0)

125·3 (108·6 t o 144·8)

–16·3 (–20·9 to –11·6)*

Adverse eff ects of medical treatment

5102·7 (3964·6 t o 5855·4)

5392·2 (4125·3 t o 6588·9)

5·5 (–7·8 to 22·5)

81·5 (63·3 t o 93·0)

76·9 (58·9 t o 93·8)

–5·8 (–17·1 to 8·4)

Animal contact 4358·7 (3411·5 t o 6835·1)

4281·1 (3418·6 t o 6930·8)

–2·7 (–15·2 to 14·8)

66·5 (52·4 t o 104·3)

59·7 (47·7 t o 96·8)

–11·0 (–22·2 to 5·0)

Venomous 3081·3 (2335·1 t o 5172·1)

3002·4 (2356·4 t o 5144·3)

–3·4 (–17·3 to 17·0)

46·6 (35·5 t o 78·5)

41·7 (32·7 t o 71·6)

–11·2 (–23·9 to 7·3)

Non-venomous 1277·4 (965·0 t o 1739·0)

1278·7 (1012·2 t o 1926·0)

–1·5 (–12·5 to 20·5)

19·9 (15·1 t o 27·0)

18·0 (14·3 t o 27·1)

–10·7 (–20·4 to 8·6)

Foreign body 7331·3 (5388·3 t o 10 125·9)

6988·8 (4964·4 t o 9369·6)

–4·9 (–16·4 to 8·1)

112·8 (82·5 t o 154·6)

98·5 (69·8 t o 131·8)

–12·9 (–22·9 to –1·4)*

Pulmonary aspiration and foreign body in airway

6999·5 (5046·8 t o 9797·1)

6633·1 (4586·4 t o 8996·4)

–5·5 (–17·1 to 8·1)

107·4 (77·4 t o 149·2)

93·4 (64·4 t o 126·4)

–13·2 (–23·5 to –1·2)*

Foreign body in eyes 56·4 (33·4 t o 85·5)

60·3 (35·0 t o 91·7)

6·9 (3·8 to 9·6)*

0·9 (0·5 to 1·4)

0·9 (0·5 to 1·3)

–5·5 (–9·0 to –2·8)*

Foreign body in other body part 275·3 (225·5 t o 344·5)

295·4 (241·7 t o 384·4)

7·5 (–1·7 to 15·7)

4·5 (3·7 to 5·5)

4·2 (3·5 to 5·5)

–5·4 (–13·0 to 2·2)

Other unintentional injuries 9948·8 (9059·1 t o 10 862·9)

9291·4 (8357·0 t o 10 332·8)

–6·8 (–12·5 to 0·4)

159·1 (144·0 t o 175·0)

132·8 (118·9 t o 148·0)

–16·7 (–21·7 to –10·5)*

(Table 1 continues on next page)

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2162 www.thelancet.com Vol 386 November 28, 2015

sociodemographic status lagged from 1 to 10 years. Use of sociodemographic status from the same year as the DALY rates explained, on average, the highest proportion of the variance in DALY rates.

Age standardisation We selected the revised GBD 2013 world population standard for the age standardisation of rates. Details of the age standard, and its development, have been reported previously.2

Role of the funding source The funder of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report. All authors had access to the data in the study and fi nal responsibility to submit the paper.

Results Global Global life expectancy at birth for both sexes combined increased from 65·3 years (95% UI 65·0–65·6) in 1990 to 71·5 years (71·0–71·9) in 2013, whereas during the same interval, HALE at birth for both sexes combined increased from 56·9 years (54·5–59·1) to 62·3 years (59·7–64·8). The survivorship curves shift up and to the right with increasing quintiles of country sociodemographic status (fi gure 1). In the three groups of countries, defi ned as the lowest, middle three, and highest quintiles of country sociodemographic status in 2013, individuals are distributed across ranges of disability weights, with the majority of the population in most age groups living in health states with disability weights in the range 0·0–0·1.

The fraction of individuals in the life table in full health (ie, living with a disability weight of zero) is 16·3% in the lowest sociodemographic status quintile of countries. Even in the most advantageous sociodemographic quintile of countries, the time lived in full health constitutes only a small fraction (17·5%) of the overall life course. At the other end of the spectrum, the expectation of years lived with disability weights greater than 0·5 is 3·59 years in the lowest quintile and 6·60 years in the highest quintile of countries.

Figure 2 shows the need to understand global epidemiological change in terms of numbers, rates, and age-standardised rates. The number of DALYs caused by communicable, maternal, neonatal, and nutritional disorders has declined steadily from 1·19 billion (95% UI 1·15 billion to 1·24 billion) in 1990 to 769·3 million (725·5 million to 814·9 million) in 2013, whereas DALYs for non-communicable diseases (NCDs) have increased steadily, rising from 1·08 billion (0·97 billion to 1·20 billion) to 1·43 billion (1·26 billion to 1·61 billion) during the same period (fi gure 2A). The year of crossover, during which global DALYs for NCDs exceeded those for global communicable, maternal, neonatal, and nutritional causes, was 1994. DALYs due to injuries have remained relatively constant, decreasing slightly from 269·6 million (251·6 million to 286·7 million) to 247·6 million (231·3 million to 265·1 million). Figure 2B shows crude DALY rates per 100 000 people for these three broad cause groups, thereby removing the eff ect of global population growth during the period. The DALY rate for NCDs has remained fairly constant, while substantial declines have occurred in

All ages DALYs (thousands)* Age-standardised DALYs (per 100 000)*

2005 2013 Percentage change 2005 2013 Percentage change

(Continued from previous page)

Self-harm and interpersonal violence

60 826·5 (51 784·1 t o 65 431·4)

56 574·6 (48 677·7 t o 63 256·5)

–7·1 (–13·5 to 1·2)

925·1 (787·0 t o 993·3)

773·4 (665·2 t o 864·1)

–16·5 (–22·3 to –9·0)*

Self-harm 37 921·9 (31 030·3 t o 40 888·4)

35 170·4 (29 194·0 t o 39 484·9)

–7·5 (–15·9 to 3·5)

584·8 (478·8 t o 629·8)

484·3 (403·8 t o 542·8)

–17·4 (–24·7 to –7·7)*

Interpersonal violence 22 904·6 (17 216·9 t o 27 308·2)

21 404·2 (16 041·0 t o 25 695·2)

–6·8 (–12·2 to 1·0)

340·3 (255·2 t o 405·6)

289·1 (216·7 t o 347·3)

–15·3 (–20·2 to –8·2)*

Assault by fi rearm 9378·3 (6442·6 t o 11 785·2)

9601·7 (6465·3 t o 12 129·8)

2·0 (–5·5 to 12·4)

137·6 (94·4 t o 173·8)

128·9 (86·8 t o 163·0)

–6·6 (–13·4 to 2·8)

Assault by sharp object 6242·7 (4123·9 t o 8202·9)

5907·0 (4065·6 t o 8254·7)

–6·1 (–16·0 to 10·5)

92·9 (61·4 t o 121·8)

79·7 (54·9 t o 111·1)

–14·8 (–23·8 to 0·2)

Assault by other means 7283·5 (5393·8 t o 9079·7)

5895·5 (4268·5 t o 7509·6)

–19·7 (–25·6 to –8·9)*

109·9 (81·3 t o 136·6)

80·5 (58·3 t o 102·3)

–27·3 (–32·6 to –17·7)*

Forces of nature, war, and legal intervention

11 121·5 (6687·7 t o 18 862·5)

6113·6 (3504·8 t o 11 068·7)

–45·0 (–55·3 to –35·0)*

182·2 (108·3 t o 312·3)

89·1 (50·7 t o 160·0)

–51·2 (–59·7 to –42·9)*

Exposure to forces of nature 4123·6 (2740·1 t o 7479·8)

1325·5 (818·9 t o 2516·9)

–69·6 (–75·1 to –51·1)*

64·3 (42·4 t o 116·3)

19·1 (11·8 t o 36·1)

–71·9 (–76·9 to –55·7)*

Collective violence and legal intervention

6997·9 (3408·9 t o 12 878·9)

4788·1 (2602·8 t o 8707·0)

–31·2 (–47·5 to –5·7)*

118·0 (57·1 t o 219·7)

70·1 (37·7 t o 128·1)

–40·4 (–53·2 to –19·7)*

Data are DALYs (95% UI) or % change (95% UI). UI=uncertainty interval. DALY=disability-adjusted life-years. *Percentage change is statistically signifi cant (p<0·05).

Table 1: Global all-age DALYs and age-standardised DALYs for 306 causes in 2005 and 2013 with percentage change

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www.thelancet.com Vol 386 November 28, 2015 2163

DALY rates for communicable, maternal, neonatal, and nutritional disorders (52·2%, 50·1–54·4) and injuries (32·0%, 27·0–35·9). The analysis of age-standardised DALY rates, shows that, after controlling for changes in population size and composition, NCD disease burden worldwide, has continued to decline, falling by 14·5% (11·6–17·3) between 1990 and 2013 (fi gure 3C). During the same period, worldwide age-standardised DALY rates fell by 42·4% (40·0–45·0) for communicable, maternal, neonatal, and nutritional disorders and 30·9% (26·1–34·7) for injuries.

We decomposed the changes in the number of DALYs into trends for two periods: 1990–2005 and 2005–2013 (fi gure 3).10 Between 1990 and 2005, the number of global DALYs changed only slightly, from 2·54 billion (95% UI 2·40 billion to 2·70 billion) to 2·51 billion (2·33 billion to 2·72 billion). Looking at disease-level details within this relative stagnation reveals important trends for specifi c diseases (fi gure 3A); the earlier period was characterised by decreases in the number of DALYs from diarrhoea, lower respiratory infections, measles, neonatal causes, tuberculosis, and tetanus, with smaller contributions from declines in congenital causes and some injuries. Conversely, large increases in disease burden were recorded for HIV/AIDS and malaria, with smaller increases for road injuries and a diverse range of NCDs, including ischaemic heart disease, diabetes, low back and neck pain, stroke, and depression, in addition to several types of

cancer. From 2005 to 2013, total DALYs worldwide decreased from 2·51 billion (2·33 billion to 2·72 billion) to 2·45 billion (2·23 billion to 2·68 billion; fi gure 3B). Decreases were recorded for diarrhoea, malaria, HIV/AIDS, lower respiratory infections, measles, tuberculosis, and neonatal causes, and nearly all injuries. Increases were noted for a wide range of NCDs, especially low back and neck pain, ischaemic heart disease, diabetes, chronic obstructive pulmonary disease (COPD), depression, stroke, and sense organ disorders. Although they were not large contributors to the number of DALYs, notable increases were seen for dengue, food-borne trematodes, and leishmaniasis. Separate analyses of changes in age- standardised DALY rates for the period 2005–13 (data not shown) suggest that most of the increases shown in fi gure 3 are caused by ageing of the population and population growth.

We assessed changes in the age-standardised DALY rates of the leading GBD level 3 causes for 1990–2005 and 2005–2013 (fi gure 4; level 4 of the GBD cause hierarchy is reported in the appendix p 2). Between 1990 and 2005 huge reductions occurred in measles, meningitis, iron- defi ciency anaemia, congenital anomalies, tuberculosis, drowning, protein-energy malnutrition, and some neonatal disorders, whereas disease burden from HIV/AIDS and malaria substantially increased (fi gure 4). From 2005 to 2013, age-standardised DALY rates for ischaemic heart disease, lower respiratory infections, and

Sociodemographic status

Year Country Unexplained

A.1. HIV/AIDS and tuberculosis 20·65% 1·13% 73·09% 5·13%

A.2. Diarrhoea, lower respiratory, and other common infectious diseases 79·14% 0·76% 18·19% 1·91%

A.3. Neglected tropical diseases and malaria 14·04% 0·08% 84·98% 0·91%

A.4. Maternal disorders 80·34% 0·17% 17·75% 1·74%

A.5. Neonatal disorders 86·90% 0·25% 11·29% 1·56%

A.6. Nutritional defi ciencies 80·48% 0·06% 17·12% 2·33%

A.7. Other communicable, maternal, neonatal, and nutritional diseases 56·61% 0·52% 40·94% 1·94%

B.1. Neoplasms 15·62% 0·28% 80·91% 3·19%

B.2. Cardiovascular diseases 3·01% 1·19% 88·69% 7·11%

B.3. Chronic respiratory diseases 6·41% 3·05% 82·74% 7·80%

B.4. Cirrhosis 1·18% 0·10% 90·78% 7·94%

B.5. Digestive diseases 17·49% 0·96% 76·95% 4·60%

B.6. Neurological disorders 45·38% 0·01% 53·01% 1·61%

B.7. Mental and substance use disorders 28·62% 0·37% 68·41% 2·60%

B.8. Diabetes, urogenital, blood, and endocrine diseases 8·77% 1·85% 83·05% 6·33%

B.9. Musculoskeletal disorders 65·71% 0·02% 33·30% 0·96%

B.10. Other non-communicable diseases 57·14% 1·05% 33·72% 8·08%

C.1. Transport injuries 21·39% 1·76% 63·26% 13·58%

C.2. Unintentional injuries 4·58% 6·27% 80·81% 8·33%

C.3. Self-harm and interpersonal violence 2·61% 0·36% 91·23% 5·80%

C.4. Forces of nature, war, and legal intervention 24·54% 1·18% 55·63% 18·65%

DALY=disability-adjusted life-years. GBD=Global Burden of Disease.

Table 2: Decomposition of variance in 2013 global DALY rates per 100 000 people for GBD level 2 causes using hierarchical regression

See Online for appendix

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2164 www.thelancet.com Vol 386 November 28, 2015

cerebrovascular disease have declined, although not suffi ciently for these conditions to be replaced as the leading causes of disease burden worldwide. The ranks of low back and neck pain, road injuries and COPD have all increased since 2005. Age-standardised rates decreased signifi cantly for 16 of the 25 leading causes of DALYs in 2013; for the remaining nine causes (Alzheimer’s disease, chronic kidney disease, congenital anomalies, depressive disorders, diabetes, low back and neck pain, migraine, neonatal sepsis, and skin diseases), age-standardised rates did not signifi cantly change.

Table 1 shows DALYs for each cause in 2005 and 2013 and changes in numbers and age-standardised rates of the DALYs (for the same information for 1990 to 2013 see appendix p 4).

Decomposition of epidemiological patterns We decomposed the variance of DALY rates for GBD level 2 causes into contributions from sociodemographic status, year, country, and unexplained sources (residual; table 2). Sociodemographic status explained more than 50% of the variance for diarrhoea, lower respiratory infections and other common infectious diseases; maternal disorders; neonatal disorders; nutritional defi ciencies; other communicable diseases; musculoskeletal disorders; and other NCDs. Furthermore, sociodemographic status explains between a fi fth and a half of the variance for HIV/AIDS and tuberculosis; neurological disorders; mental and

substance use disorders; transport injuries; and forces of nature, war, and legal intervention. Sociodemographic status explained little of the variance in the DALY rates for neglected tropical diseases and malaria, for which time-invariant country diff erences account for 84·98% of the variance. Notably, less than 10% of the variance in the burden of several level 2 causes could be related to sociodemographic status, including cardiovascular diseases; chronic respiratory diseases; cirrhosis; diabetes, urogenital, blood, and endocrine diseases; unintentional injuries; and self-harm and interpersonal violence. Year explained less than 7% of variance for all causes. By contrast, time invariant intercountry variation was an important component of the variance in DALY rates for all causes, ranging from a low of 11·29% for neonatal disorders to 91·23% for self-harm and interpersonal violence. Intercountry variation explains more than two- thirds of the total variance in DALY rates for HIV/AIDS and tuberculosis; neglected tropical diseases and malaria; neoplasms; cardiovascular diseases; chronic respiratory diseases; cirrhosis; digestive diseases; mental and substance use disorders; diabetes, urogenital, blood, and endocrine disorders; unintentional injuries; and self- harm and interpersonal violence. Notably, together, sociodemographic status and country account for more than 90% of the variance for 17 of 21 GBD level 2 causes; indeed, the lowest fraction of variance accounted for by these three factors is 80·2% for forces of nature, war, and legal intervention.

Figure 5: YLL and YLD cause composition of DALY rates by sociodemographic status vigintile The epidemiological transition based on predicted YLL and YLD rates per 100 000 people as a function of the level of sociodemographic status by vigintile and broken down into GBD level 2 causes. These predicted levels control for variation explained by year and country. YLL= years of life lost. YLD=years lived with disability. GBD=Global Burden of Disease.

80 000 70 000 60 000 50 000 40 000 30 000 20 000 10 000 0 10 000

20

19

18

17

16

15

14

13

12

11

10

9

8

7

6

5

4

3

2

1

(highest SDS)

(lowest SDS)

YLL YLD

Vi gi

nt ile

s

Rate per 100 000 people

Forces of nature, war, and legal intervention Self-harm and interpersonal violence Unintentional injuries Transport injuries Other non-communicable diseases Musculoskeletal disorders Diabetes, urogenital, blood, and endocrine diseases Mental and substance use disorders Neurological disorders Digestive diseases Cirrhosis Chronic respiratory diseases Cardiovascular diseases Neoplasms Other communicable, maternal, neonatal, and nutritional diseases Nutritional deficiencies Neonatal disorders Maternal disorders Neglected tropical diseases and malaria Diarrhoea, lower respiratory, and other common infectious diseases HIV/AIDS and tuberculosis

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1990 2005 2013

Male population Female population Male population Female population Male population Female population

Life expectancy (years)

HALE (years)

Life expectancy (years)

HALE (years)

Life expectancy (years)

HALE (years)

Life expectancy (years)

HALE (years)

Life expectancy (years)

HALE (years)

Life expectancy (years)

HALE (years)

Global 63·01 (62·59– 63·46)

55·40 (53·10– 57·42)

67·68 (67·20– 68·10)

58·51 (55·90– 60·87)

66·23 (65·90– 66·57)

58·27 (55·96– 60·39)

71·31 (70·95– 71·64)

61·54 (58·67– 64·08)

68·80 (68·16– 69·41)

60·59 (58·15– 62·89)

74·29 (73·79– 74·79)

64·13 (61·25– 66·84)

Developed 70·64 (70·56– 70·71)

62·12 (59·70– 64·26)

77·97 (77·90– 78·04)

67·18 (64·15– 69·86)

72·55 (72·51– 72·59)

63·56 (61·04– 65·82)

79·85 (79·82– 79·89)

68·48 (65·28– 71·34)

75·50 (75·27– 75·76)

66·00 (63·26– 68·39)

81·82 (81·62– 82·02)

70·03 (66·71– 73·04)

Developing 61·40 (60·86– 61·99)

54·04 (51·75– 56·06)

64·89 (64·25– 65·43)

56·26 (53·79– 58·54)

64·96 (64·54– 65·41)

57·30 (55·07– 59·35)

69·14 (68·67– 69·57)

59·88 (57·14– 62·28)

67·30 (66·50– 68·09)

59·47 (57·11– 61·77)

72·28 (71·67– 72·92)

62·65 (59·89– 65·27)

High income 72·63 (72·60– 72·67)

63·70 (61·19– 65·94)

79·35 (79·31– 79·39)

68·06 (64·92– 70·91)

76·40 (76·37– 76·44)

66·56 (63·82– 69·04)

82·15 (82·11– 82·18)

70·06 (66·73– 73·10)

77·83 (77·51– 78·15)

67·72 (64·81– 70·25)

83·22 (82·97– 83·49)

70·92 (67·54– 74·08)

Australasia 73·68 (73·57– 73·78)

64·00 (61·31– 66·46)

79·82 (79·70– 79·94)

67·80 (64·40– 70·85)

78·60 (78·48– 78·70)

67·68 (64·65– 70·43)

83·32 (83·23– 83·43)

70·30 (66·64– 73·65)

79·53 (79·02– 80·03)

68·39 (65·28– 71·11)

83·77 (83·32– 84·22)

70·60 (66·94– 73·89)

Australia 73·93 (73·80– 74·05)

64·14 (61·40– 66·61)

80·15 (80·01– 80·28)

67·93 (64·47– 71·03)

78·80 (78·67– 78·93)

67·79 (64·76– 70·55)

83·62 (83·52– 83·74)

70·35 (66·60– 73·75)

79·71 (79·13– 80·30)

68·43 (65·30– 71·22)

83·99 (83·48– 84·52)

70·63 (66·92– 73·92)

New Zealand 72·49 (72·26– 72·69)

63·27 (60·70– 65·60)

78·21 (77·98– 78·43)

67·18 (64·06– 69·92)

77·56 (77·36– 77·76)

67·12 (64·14– 69·78)

81·86 (81·66– 82·06)

70·02 (66·72– 73·04)

78·61 (77·82– 79·36)

68·19 (65·19– 70·88)

82·66 (82·02– 83·32)

70·48 (66·98– 73·58)

High-income Asia Pacifi c

74·20 (74·09– 74·30)

66·37 (64·12– 68·35)

80·79 (80·68– 80·90)

71·03 (68·18– 73·55)

77·97 (77·92– 78·02)

69·16 (66·57– 71·42)

84·87 (84·81– 84·93)

73·98 (70·83– 76·75)

79·43 (78·82– 80·08)

70·45 (67·82– 72·84)

85·91 (85·37– 86·50)

74·82 (71·64– 77·76)

Brunei 72·41 (71·88– 72·90)

65·11 (62·90– 67·06)

75·67 (75·09– 76·25)

67·12 (64·58– 69·36)

75·75 (74·97– 76·65)

67·79 (65·37– 69·99)

78·90 (78·23– 79·69)

69·67 (66·94– 72·16)

76·88 (74·71– 78·99)

68·80 (66·05– 71·51)

80·65 (78·76– 82·42)

70·97 (67·86– 73·94)

Japan 76·04 (75·98– 76·10)

68·09 (65·83– 70·11)

81·96 (81·86– 82·05)

72·24 (69·38– 74·77)

78·66 (78·60– 78·71)

69·89 (67·31– 72·12)

85·48 (85·41– 85·54)

74·77 (71·66– 77·46)

80·05 (79·26– 80·84)

71·11 (68·50– 73·57)

86·39 (85·74– 87·12)

75·56 (72·46– 78·42)

Singapore 72·60 (72·39– 72·79)

65·26 (63·12– 67·16)

77·61 (77·39– 77·83)

68·49 (65·76– 70·90)

77·99 (77·79– 78·20)

69·25 (66·67– 71·56)

82·28 (82·05– 82·51)

72·00 (69·01– 74·73)

79·71 (79·01– 80·39)

70·75 (68·01– 73·17)

84·03 (83·33– 84·76)

73·35 (70·28– 76·30)

South Korea 67·74 (67·42– 68·02)

60·48 (58·30– 62·38)

76·26 (76·00– 76·49)

66·48 (63·66– 69·02)

75·32 (75·19– 75·45)

66·59 (64·02– 68·81)

81·95 (81·80– 82·11)

70·79 (67·58– 73·68)

77·20 (76·37– 78·07)

68·26 (65·57– 70·64)

83·66 (82·95– 84·33)

72·05 (68·74– 75·07)

High-income North America

72·10 (72·02– 72·18)

62·91 (60·36– 65·24)

79·00 (78·94– 79·06)

67·13 (63·94– 70·09)

75·32 (75·23– 75·41)

65·08 (62·23– 67·67)

80·48 (80·39– 80·56)

67·94 (64·49– 71·10)

76·64 (75·90– 77·42)

66·17 (63·13– 68·98)

81·62 (80·89– 82·28)

68·85 (65·36– 72·17)

Canada 74·20 (74·10– 74·31)

65·13 (62·54– 67·48)

80·59 (80·48– 80·69)

68·74 (65·50– 71·70)

77·87 (77·74– 77·99)

67·84 (64·98– 70·45)

82·64 (82·53– 82·75)

70·31 (66·84– 73·46)

79·44 (78·85– 80·01)

69·11 (66·08– 71·82)

83·43 (82·85– 83·95)

71·04 (67·54– 74·25)

USA 71·87 (71·79– 71·96)

62·66 (60·13– 64·98)

78·84 (78·77– 78·90)

66·96 (63·75– 69·92)

75·04 (74·94– 75·14)

64·78 (61·92– 67·37)

80·25 (80·16– 80·34)

67·68 (64·23– 70·85)

76·33 (75·50– 77·18)

65·84 (62·83– 68·74)

81·42 (80·58– 82·16)

68·61 (65·10– 71·93)

Southern Latin America

69·14 (69·02– 69·26)

61·39 (59·16– 63·30)

76·39 (76·26– 76·51)

66·72 (64·02– 69·17)

72·67 (72·59– 72·76)

64·31 (61·92– 66·41)

79·54 (79·45– 79·62)

69·08 (66·14– 71·66)

73·38 (72·73– 73·99)

65·03 (62·64– 67·23)

80·20 (79·73– 80·71)

69·81 (66·80– 72·49)

Argentina 69·00 (68·84– 69·16)

61·27 (58·99– 63·19)

76·21 (76·02– 76·39)

66·60 (63·88– 69·09)

71·79 (71·68– 71·90)

63·59 (61·22– 65·66)

79·00 (78·88– 79·12)

68·72 (65·82– 71·31)

72·29 (71·60– 72·96)

64·17 (61·75– 66·39)

79·58 (79·03– 80·21)

69·44 (66·52– 72·10)

Chile 69·51 (69·36– 69·67)

61·72 (59·50– 63·68)

76·54 (76·39– 76·69)

66·79 (63·99– 69·32)

75·51 (75·38– 75·63)

66·51 (63·90– 68·78)

81·12 (80·98– 81·26)

70·19 (67·05– 72·90)

76·31 (75·33– 77·32)

67·32 (64·75– 69·69)

81·72 (80·79– 82·63)

70·77 (67·66– 73·58)

Uruguay 69·20 (68·98– 69·43)

61·49 (59·32– 63·40)

76·68 (76·40– 76·94)

67·00 (64·18– 69·48)

71·69 (71·33– 72·03)

63·79 (61·52– 65·79)

79·01 (78·66– 79·32)

68·69 (65·72– 71·30)

73·03 (71·67– 74·49)

64·78 (62·07– 67·23)

80·62 (79·28– 81·86)

70·03 (66·78– 72·90)

(Table 3 continues on next page)

Articles

2166 www.thelancet.com Vol 386 November 28, 2015

1990 2005 2013

Male population Female population Male population Female population Male population Female population

Life expectancy (years)

HALE (years)

Life expectancy (years)

HALE (years)

Life expectancy (years)

HALE (years)

Life expectancy (years)

HALE (years)

Life expectancy (years)

HALE (years)

Life expectancy (years)

HALE (years)

(Continued from previous page)

Western Europe 72·89 (72·86– 72·92)

63·57 (60·94– 65·90)

79·48 (79·44– 79·52)

67·84 (64·61– 70·79)

77·01 (76·98– 77·05)

66·88 (64·04– 69·39)

82·60 (82·56– 82·63)

70·20 (66·77– 73·31)

78·64 (78·43– 78·84)

68·17 (65·17– 70·76)

83·68 (83·50– 83·87)

71·07 (67·59– 74·21)

Andorra 77·26 (74·31– 79·57)

66·90 (63·21– 70·19)

83·69 (81·63– 85·87)

71·01 (67·26– 74·61)

79·61 (78·44– 80·81)

68·73 (65·72– 71·63)

86·39 (85·04– 87·63)

73·05 (69·23– 76·57)

80·88 (78·82– 83·59)

69·92 (66·35– 73·17)

86·62 (84·68– 88·75)

73·39 (69·46– 77·29)

Austria 72·18 (72·05– 72·31)

63·40 (60·77– 65·66)

78·81 (78·65– 78·99)

68·01 (64·96– 70·61)

76·69 (76·56– 76·82)

66·97 (64·22– 69·33)

82·35 (82·21– 82·52)

70·71 (67·31– 73·55)

78·30 (77·65– 78·98)

68·47 (65·67– 71·07)

83·10 (82·49– 83·72)

71·21 (67·86– 74·27)

Belgium 72·68 (72·54– 72·81)

63·02 (60·31– 65·48)

79·22 (79·06– 79·38)

67·60 (64·25– 70·55)

76·26 (76·13– 76·37)

65·74 (62·81– 68·36)

81·98 (81·84– 82·12)

69·51 (65·98– 72·67)

77·62 (76·62– 78·57)

67·05 (64·00– 69·94)

82·66 (81·75– 83·53)

70·29 (66·89– 73·64)

Cyprus 75·59 (75·07– 76·05)

65·10 (61·90– 67·84)

81·06 (80·65– 81·43)

69·34 (66·03– 72·36)

77·50 (77·11– 77·91)

67·34 (64·45– 69·97)

83·44 (83·14– 83·74)

71·11 (67·61– 74·16)

79·59 (78·51– 80·61)

69·16 (66·02– 71·97)

84·73 (83·83– 85·56)

72·22 (68·71– 75·60)

Denmark 72·31 (72·15– 72·47)

63·20 (60·51– 65·44)

77·81 (77·62– 78·02)

66·85 (63·78– 69·53)

75·85 (75·66– 76·02)

66·07 (63·26– 68·48)

80·39 (80·22– 80·56)

68·69 (65·44– 71·58)

77·82 (77·05– 78·54)

67·79 (64·88– 70·32)

82·02 (81·29– 82·77)

70·14 (66·78– 73·26)

Finland 70·94 (70·78– 71·10)

61·40 (58·70– 63·74)

79·00 (78·79– 79·18)

67·29 (63·96– 70·27)

75·30 (75·12– 75·47)

64·68 (61·60– 67·26)

82·11 (81·92– 82·31)

69·09 (65·56– 72·28)

77·37 (76·69– 78·13)

66·45 (63·30– 69·33)

83·79 (83·11– 84·47)

70·68 (66·93– 73·93)

France 73·04 (72·96– 73·13)

64·00 (61·40– 66·24)

81·21 (81·09– 81·32)

69·54 (66·36– 72·47)

76·84 (76·74– 76·94)

67·10 (64·29– 69·51)

83·88 (83·78– 83·97)

71·37 (67·86– 74·44)

78·38 (77·81– 78·98)

68·43 (65·51– 71·11)

84·91 (84·43– 85·40)

72·32 (68·87– 75·53)

Germany 71·96 (71·88– 72·04)

62·32 (59·63– 64·74)

78·52 (78·42– 78·60)

66·70 (63·42– 69·72)

76·59 (76·51– 76·67)

66·11 (63·13– 68·71)

82·07 (82·00– 82·13)

69·35 (65·83– 72·52)

78·18 (77·94– 78·42)

67·27 (64·18– 70·02)

83·14 (82·91– 83·37)

70·31 (66·67– 73·60)

Greece 74·53 (74·38– 74·68)

65·34 (62·70– 67·63)

79·44 (79·29– 79·59)

68·42 (65·15– 71·20)

76·40 (76·26– 76·55)

66·82 (64·07– 69·25)

81·47 (81·28– 81·68)

70·22 (66·93– 73·03)

77·41 (76·77– 78·07)

67·90 (65·13– 70·44)

82·24 (81·67– 82·75)

70·75 (67·46– 73·67)

Iceland 75·98 (75·51– 76·51)

65·94 (63·07– 68·59)

80·23 (79·63– 80·81)

68·47 (65·11– 71·38)

79·54 (79·01– 80·13)

68·66 (65·52– 71·55)

83·07 (82·52– 83·65)

70·50 (66·98– 73·68)

80·81 (79·45– 82·21)

69·72 (66·51– 72·60)

84·82 (83·61– 86·05)

72·00 (68·40– 75·30)

Ireland 72·13 (71·94– 72·33)

63·31 (60·81– 65·50)

77·64 (77·39– 77·89)

66·86 (63·75– 69·59)

76·58 (76·36– 76·79)

66·74 (63·94– 69·22)

81·26 (81·01– 81·50)

69·49 (66·14– 72·38)

78·20 (76·70– 79·55)

68·20 (65·13– 71·01)

82·67 (81·36– 83·81)

70·73 (67·38– 73·99)

Israel 74·72 (74·50– 74·93)

65·22 (62·56– 67·62)

78·08 (77·89– 78·28)

67·51 (64·53– 70·18)

77·86 (77·69– 78·02)

67·62 (64·73– 70·17)

82·03 (81·85– 82·21)

70·20 (66·83– 73·12)

80·25 (79·82– 80·68)

69·46 (66·46– 72·26)

84·02 (83·59– 84·42)

71·70 (68·22– 74·95)

Italy 73·60 (73·53– 73·67)

64·47 (61·92– 66·74)

80·23 (80·11– 80·35)

68·12 (64·71– 71·15)

78·32 (78·24– 78·38)

68·32 (65·48– 70·83)

83·76 (83·68– 83·84)

70·95 (67·41– 74·16)

79·45 (78·63– 80·20)

69·11 (66·24– 71·89)

84·60 (83·80– 85·28)

71·36 (67·70– 74·92)

Luxembourg 71·64 (71·19– 72·08)

62·52 (59·85– 64·89)

78·43 (77·94– 78·93)

67·06 (63·74– 70·02)

76·90 (76·53– 77·29)

66·59 (63·67– 69·23)

82·47 (82·03– 82·88)

69·81 (66·27– 73·04)

79·05 (78·15– 80·13)

68·14 (65·04– 71·19)

83·10 (81·79– 84·36)

70·48 (66·79– 73·79)

Malta 74·99 (74·46– 75·60)

65·03 (62·33– 67·50)

80·04 (79·54– 80·55)

67·90 (64·41– 70·91)

78·82 (78·35– 79·27)

67·88 (64·82– 70·72)

82·77 (82·30– 83·19)

69·93 (66·32– 73·25)

79·81 (78·49– 81·02)

68·77 (65·75– 71·73)

84·35 (83·19– 85·46)

71·16 (67·36– 74·72)

Netherlands 73·85 (73·74– 73·96)

64·56 (61·93– 66·91)

80·09 (79·97– 80·22)

68·33 (65·06– 71·32)

77·28 (77·15– 77·41)

67·24 (64·43– 69·80)

81·60 (81·46– 81·72)

69·55 (66·19– 72·56)

78·69 (78·07– 79·30)

68·35 (65·30– 71·00)

82·42 (81·79– 83·02)

70·17 (66·64– 73·28)

Norway 73·62 (73·43– 73·80)

64·23 (61·62– 66·54)

80·04 (79·83– 80·25)

68·57 (65·38– 71·53)

77·72 (77·55– 77·88)

67·43 (64·54– 70·01)

82·30 (82·09– 82·50)

70·38 (67·03– 73·42)

79·09 (78·66– 79·48)

68·56 (65·57– 71·25)

83·71 (83·32– 84·16)

71·66 (68·36– 74·79)

Portugal 70·65 (70·52– 70·80)

61·78 (59·26– 64·05)

77·84 (77·67– 77·98)

66·87 (63·72– 69·66)

75·03 (74·90– 75·15)

65·53 (62·79– 67·95)

81·71 (81·56– 81·85)

69·72 (66·30– 72·67)

76·65 (76·00– 77·31)

66·74 (63·86– 69·29)

82·95 (82·36– 83·62)

70·75 (67·29– 74·09)

(Table 3 continues on next page)

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Male population Female population Male population Female population Male population Female population

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Life expectancy (years)

HALE (years)

Life expectancy (years)

HALE (years)

Life expectancy (years)

HALE (years)

Life expectancy (years)

HALE (years)

Life expectancy (years)

HALE (years)

(Continued from previous page)

Spain 73·33 (73·22– 73·45)

63·89 (61·15– 66·19)

80·57 (80·44– 80·70)

68·88 (65·50– 71·82)

77·05 (76·97– 77·12)

66·90 (64·07– 69·48)

83·74 (83·63– 83·86)

71·49 (68·08– 74·58)

78·97 (78·41– 79·57)

68·40 (65·34– 71·23)

84·43 (83·98– 84·90)

71·83 (68·22– 75·18)

Sweden 74·78 (74·63– 74·91)

64·73 (61·96– 67·24)

80·39 (80·21– 80·55)

68·22 (64·89– 71·29)

78·35 (78·22– 78·49)

67·51 (64·50– 70·19)

82·54 (82·37– 82·70)

69·96 (66·57– 73·13)

79·64 (79·01– 80·28)

68·53 (65·53– 71·37)

83·87 (83·26– 84·54)

70·95 (67·58– 74·27)

Switzerland 74·07 (73·91– 74·22)

63·59 (60·69– 66·15)

80·94 (80·76– 81·12)

67·93 (64·53– 71·15)

78·63 (78·48– 78·78)

66·97 (63·79– 69·86)

83·64 (83·48– 83·81)

69·88 (66·12– 73·31)

80·46 (79·81– 81·09)

68·63 (65·34– 71·51)

84·77 (84·17– 85·35)

71·16 (67·44– 74·56)

UK 72·87 (72·79– 72·95)

63·76 (61·12– 66·08)

78·44 (78·35– 78·52)

67·26 (64·17– 70·08)

76·83 (76·71– 76·95)

66·73 (63·94– 69·28)

81·21 (81·11– 81·31)

69·26 (65·88– 72·21)

79·09 (78·57– 79·67)

68·48 (65·50– 71·16)

82·84 (82·25– 83·35)

70·56 (67·16– 73·66)

Central Europe, Eastern Europe, and central Asia

64·77 (64·58– 64·95)

57·48 (55·33– 59·31)

73·62 (73·44– 73·77)

64·25 (61·49– 66·62)

62·77 (62·65– 62·88)

55·90 (53·86– 57·62)

73·51 (73·41– 73·60)

64·05 (61·28– 66·45)

67·73 (67·47– 67·96)

60·24 (58·08– 62·23)

76·92 (76·69– 77·15)

66·89 (63·97– 69·48)

Central Asia 62·82 (62·48– 63·18)

56·08 (54·11– 57·81)

70·31 (69·93– 70·66)

61·67 (59·05– 63·90)

63·38 (62·85– 63·84)

56·64 (54·66– 58·45)

71·32 (70·88– 71·74)

62·56 (59·99– 64·86)

65·59 (64·52– 66·56)

58·68 (56·55– 60·70)

73·64 (72·74– 74·62)

64·59 (61·99– 67·18)

Armenia 66·32 (65·52– 66·98)

59·11 (56·97– 61·10)

73·60 (72·91– 74·40)

64·46 (61·72– 66·84)

68·28 (67·56– 69·09)

60·93 (58·76– 62·90)

76·06 (75·25– 76·66)

66·40 (63·52– 68·93)

70·22 (68·97– 71·45)

62·73 (60·21– 64·87)

77·55 (76·24– 78·50)

67·76 (64·80– 70·53)

Azerbaijan 62·31 (61·45– 63·17)

55·71 (53·62– 57·71)

70·03 (69·25– 70·76)

61·14 (58·56– 63·59)

66·22 (65·36– 67·15)

59·00 (56·67– 61·06)

72·87 (71·98– 73·73)

63·31 (60·52– 65·93)

68·75 (66·87– 71·05)

61·21 (58·58– 63·85)

75·82 (74·11– 77·11)

65·75 (62·73– 68·70)

Georgia 65·82 (64·82– 66·57)

58·78 (56·67– 60·77)

74·23 (73·45– 75·01)

65·22 (62·54– 67·64)

67·70 (66·85– 68·43)

60·36 (58·07– 62·34)

76·98 (76·19– 77·54)

67·23 (64·38– 69·77)

67·69 (66·45– 69·02)

60·52 (58·20– 62·73)

78·16 (76·86– 79·18)

68·28 (65·34– 71·05)

Kazakhstan 61·99 (61·16– 62·82)

55·43 (53·47– 57·27)

71·29 (70·56– 72·00)

62·53 (59·97– 64·93)

59·28 (58·44– 60·17)

53·37 (51·48– 55·11)

70·47 (69·77– 71·19)

62·03 (59·53– 64·34)

62·60 (60·59– 64·29)

56·29 (53·79– 58·55)

73·07 (71·44– 74·50)

64·24 (61·46– 66·82)

Kyrgyzstan 61·42 (60·60– 62·29)

54·90 (52·81– 56·80)

69·11 (68·30– 69·89)

60·73 (58·14– 63·05)

62·52 (61·66– 63·36)

56·01 (53·90– 57·86)

70·60 (69·98– 71·34)

62·17 (59·57– 64·44)

64·48 (62·94– 66·14)

57·92 (55·68– 60·12)

72·87 (71·45– 74·28)

64·10 (61·30– 66·70)

Mongolia 59·39 (58·19– 60·67)

53·08 (50·91– 55·02)

64·37 (63·27– 65·64)

57·01 (54·66– 59·31)

58·36 (57·25– 59·47)

52·57 (50·64– 54·27)

66·62 (65·71– 67·49)

59·24 (56·96– 61·31)

60·54 (58·40– 62·54)

54·58 (52·13– 56·86)

69·38 (67·53– 71·26)

61·61 (59·06– 64·22)

Tajikistan 62·39 (61·39– 63·34)

55·52 (53·35– 57·54)

67·50 (66·57– 68·38)

59·37 (56·85– 61·69)

66·40 (65·49– 67·51)

58·50 (55·99– 60·91)

71·32 (70·22– 72·22)

62·40 (59·58– 64·85)

68·09 (66·48– 70·16)

60·24 (57·54– 62·96)

73·19 (71·34– 74·70)

64·08 (61·21– 66·81)

Turkmenistan 59·89 (58·84– 60·89)

53·57 (51·46– 55·53)

66·63 (65·60– 67·59)

58·69 (56·17– 61·00)

60·89 (57·91– 63·95)

54·75 (51·92– 57·51)

68·84 (65·92– 71·36)

60·71 (57·48– 63·67)

63·47 (60·49– 66·53)

57·11 (54·02– 60·18)

71·73 (68·65– 73·93)

63·19 (59·90– 66·28)

Uzbekistan 64·58 (63·81– 65·34)

57·60 (55·50– 59·50)

70·64 (69·96– 71·43)

61·90 (59·14– 64·24)

65·38 (64·24– 66·43)

58·25 (56·04– 60·37)

70·95 (69·86– 72·09)

62·35 (59·76– 64·81)

66·64 (63·73– 69·56)

59·60 (56·48– 62·60)

72·95 (70·38– 75·91)

64·25 (61·01– 67·69)

Central Europe 67·25 (67·09– 67·41)

59·27 (56·95– 61·29)

74·90 (74·74– 75·04)

65·29 (62·56– 67·72)

70·64 (70·59– 70·69)

62·07 (59·52– 64·25)

78·10 (78·05– 78·16)

67·63 (64·59– 70·24)

73·14 (72·84– 73·45)

64·30 (61·76– 66·57)

80·09 (79·83– 80·31)

69·39 (66·32– 72·11)

Albania 71·02 (70·35– 71·64)

61·83 (59·17– 64·26)

76·24 (75·66– 76·77)

65·74 (62·76– 68·51)

71·53 (70·46– 72·58)

62·64 (59·98– 65·15)

77·62 (76·54– 78·60)

67·00 (63·88– 69·82)

72·68 (69·64– 75·97)

63·85 (60·53– 67·08)

79·32 (76·65– 81·80)

68·64 (64·96– 71·89)

Bosnia and Herzegovina

69·36 (69·14– 69·56)

61·24 (58·87– 63·32)

76·71 (76·39– 77·06)

66·88 (63·95– 69·43)

72·55 (72·33– 72·78)

63·09 (60·24– 65·71)

78·74 (78·51– 79·01)

68·01 (64·91– 70·72)

74·36 (73·42– 75·24)

65·23 (62·44– 67·69)

80·63 (79·73– 81·58)

70·05 (67·03– 72·93)

Bulgaria 68·31 (68·13– 68·49)

60·02 (57·61– 62·15)

74·78 (74·60– 74·96)

65·13 (62·34– 67·56)

69·06 (68·91– 69·21)

60·83 (58·37– 62·96)

76·20 (76·01– 76·37)

66·24 (63·32– 68·84)

71·23 (70·72– 71·70)

62·75 (60·29– 64·94)

77·81 (77·39– 78·25)

67·64 (64·72– 70·37)

(Table 3 continues on next page)

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2168 www.thelancet.com Vol 386 November 28, 2015

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Male population Female population Male population Female population Male population Female population

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Life expectancy (years)

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Life expectancy (years)

HALE (years)

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HALE (years)

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HALE (years)

Life expectancy (years)

HALE (years)

(Continued from previous page)

Croatia 68·71 (68·52– 68·90)

60·96 (58·66– 62·97)

76·69 (76·44– 76·96)

67·03 (64·21– 69·46)

72·43 (72·24– 72·61)

63·91 (61·43– 66·08)

79·99 (79·70– 80·29)

69·49 (66·43– 72·13)

74·18 (73·61– 74·67)

65·52 (62·97– 67·85)

81·29 (80·71– 81·86)

70·62 (67·48– 73·43)

Czech Republic 67·82 (67·69– 67·94)

59·61 (57·25– 61·68)

75·45 (75·29– 75·61)

65·50 (62·63– 68·02)

73·01 (72·89– 73·13)

63·57 (60·79– 65·96)

79·44 (79·29– 79·57)

68·35 (65·21– 71·15)

75·33 (74·98– 75·71)

65·62 (62·75– 68·06)

80·93 (80·56– 81·30)

69·79 (66·58– 72·65)

Hungary 65·27 (65·13– 65·42)

57·76 (55·64– 59·68)

73·90 (73·73– 74·06)

64·27 (61·50– 66·69)

68·76 (68·66– 68·88)

60·69 (58·36– 62·74)

77·26 (77·12– 77·40)

66·88 (63·87– 69·48)

72·18 (71·77– 72·58)

63·60 (61·08– 65·84)

79·26 (78·89– 79·62)

68·68 (65·67– 71·37)

Macedonia 70·40 (69·91– 70·79)

61·66 (59·04– 63·95)

75·46 (75·06– 75·90)

65·51 (62·57– 67·99)

72·34 (72·20– 72·49)

63·59 (61·01– 65·82)

77·95 (77·73– 78·19)

67·62 (64·62– 70·21)

73·91 (73·00– 74·90)

65·13 (62·37– 67·69)

79·42 (78·42– 80·38)

69·00 (65·93– 71·88)

Montenegro 70·21 (67·86– 72·65)

61·68 (58·69– 64·59)

77·34 (75·08– 79·25)

66·87 (63·62– 69·97)

71·08 (70·58– 71·55)

62·64 (60·24– 64·78)

76·75 (76·29– 77·25)

66·89 (64·01– 69·50)

72·59 (71·17– 74·26)

64·11 (61·38– 66·63)

78·61 (77·19– 80·17)

68·44 (65·45– 71·35)

Poland 66·43 (66·32– 66·54)

58·66 (56·34– 60·62)

75·40 (75·28– 75·51)

65·64 (62·78– 68·14)

70·69 (70·61– 70·77)

62·05 (59·48– 64·30)

79·29 (79·19– 79·39)

68·46 (65·30– 71·15)

72·64 (71·98– 73·25)

63·86 (61·35– 66·20)

81·02 (80·50– 81·58)

70·03 (66·77– 72·88)

Romania 66·64 (66·44– 66·85)

58·68 (56·32– 60·71)

73·11 (72·89– 73·32)

64·15 (61·55– 66·45)

69·09 (68·96– 69·21)

60·85 (58·43– 62·91)

76·16 (76·01– 76·29)

66·29 (63·39– 68·74)

72·77 (71·99– 73·46)

64·02 (61·45– 66·36)

79·17 (78·50– 79·82)

68·77 (65·76– 71·48)

Serbia 70·58 (68·51– 72·80)

61·93 (58·81– 64·79)

76·73 (74·70– 78·70)

66·93 (63·72– 69·80)

72·99 (72·85– 73·12)

64·43 (61·89– 66·61)

78·58 (78·42– 78·75)

68·07 (65·09– 70·70)

75·02 (74·44– 75·54)

66·24 (63·74– 68·54)

80·18 (79·68– 80·73)

69·73 (66·76– 72·56)

Slovakia 66·63 (66·46– 66·79)

58·83 (56·55– 60·78)

75·38 (75·21– 75·57)

65·80 (63·00– 68·23)

70·36 (70·20– 70·51)

61·88 (59·37– 64·05)

78·16 (77·98– 78·33)

67·92 (64·97– 70·45)

72·61 (71·77– 73·36)

63·87 (61·32– 66·26)

79·70 (79·00– 80·39)

69·25 (66·22– 72·06)

Slovenia 69·62 (69·39– 69·85)

60·87 (58·38– 63·06)

77·39 (77·10– 77·67)

66·96 (63·96– 69·60)

73·52 (73·29– 73·75)

63·98 (61·29– 66·35)

80·73 (80·38– 81·08)

69·37 (66·15– 72·17)

76·86 (75·83– 77·82)

66·87 (63·87– 69·51)

82·95 (82·02– 83·74)

71·35 (68·04– 74·39)

Eastern Europe 64·62 (64·25– 64·93)

57·52 (55·41– 59·37)

74·44 (74·15– 74·70)

64·94 (62·12– 67·36)

59·58 (59·47– 59·68)

53·36 (51·52– 54·97)

72·52 (72·42– 72·61)

63·25 (60·46– 65·61)

66·00 (65·69– 66·33)

59·04 (56·97– 60·93)

76·70 (76·42– 76·93)

66·66 (63·69– 69·20)

Belarus 65·77 (65·19– 66·30)

58·73 (56·65– 60·72)

75·48 (74·99– 75·87)

65·94 (63·06– 68·57)

62·93 (62·52– 63·31)

56·49 (54·53– 58·22)

74·89 (74·59– 75·19)

65·41 (62·65– 67·90)

64·32 (63·22– 65·64)

57·93 (55·72– 59·87)

76·18 (75·26– 77·16)

66·62 (63·60– 69·31)

Estonia 65·23 (65·01– 65·47)

58·18 (56·13– 59·98)

75·36 (75·06– 75·61)

65·53 (62·63– 68·09)

66·86 (66·62– 67·10)

59·62 (57·49– 61·50)

77·67 (77·44– 77·91)

67·34 (64·28– 69·99)

71·54 (70·98– 72·14)

63·63 (61·34– 65·78)

81·10 (80·56– 81·89)

69·94 (66·67– 72·87)

Latvia 64·79 (64·55– 65·02)

57·86 (55·87– 59·65)

75·03 (74·76– 75·30)

65·88 (63·26– 68·26)

65·42 (65·20– 65·63)

58·45 (56·39– 60·26)

76·21 (75·99– 76·44)

66·73 (63·95– 69·10)

70·17 (69·64– 70·64)

62·63 (60·44– 64·64)

79·83 (79·35– 80·36)

69·68 (66·60– 72·35)

Lithuania 66·46 (66·25– 66·66)

59·14 (56·96– 60·99)

76·35 (76·10– 76·57)

66·59 (63·63– 69·12)

65·56 (65·39– 65·74)

58·41 (56·28– 60·28)

77·47 (77·28– 77·69)

67·21 (64·18– 69·90)

69·67 (69·15– 70·23)

62·08 (59·81– 64·17)

80·01 (79·54– 80·50)

69·35 (66·13– 72·10)

Moldova 64·45 (64·01– 64·92)

57·65 (55·66– 59·58)

71·41 (70·90– 71·85)

62·59 (59·98– 64·98)

65·50 (65·01– 65·95)

58·62 (56·54– 60·49)

73·85 (73·45– 74·18)

64·34 (61·54– 66·82)

67·37 (66·79– 67·99)

60·40 (58·33– 62·29)

76·32 (75·70– 76·95)

66·44 (63·62– 69·07)

Russia 64·34 (63·80– 64·80)

57·27 (55·14– 59·17)

74·42 (73·96– 74·78)

64·85 (62·06– 67·32)

58·40 (58·32– 58·48)

52·34 (50·54– 53·90)

71·90 (71·81– 71·99)

62·67 (59·86– 65·03)

65·74 (65·40– 66·06)

58·86 (56·79– 60·72)

76·65 (76·35– 76·92)

66·56 (63·60– 69·17)

Ukraine 65·09 (64·60– 65·51)

57·88 (55·72– 59·76)

74·39 (74·05– 74·73)

65·05 (62·30– 67·52)

61·52 (61·12– 61·88)

54·97 (53·01– 56·76)

73·21 (72·91– 73·50)

63·98 (61·28– 66·38)

66·55 (65·81– 67·26)

59·31 (57·15– 61·36)

76·52 (75·98– 77·07)

66·64 (63·73– 69·31)

Latin America and Caribbean

66·80 (66·63– 66·99)

58·24 (55·84– 60·44)

73·11 (72·88– 73·23)

62·76 (59·85– 65·38)

70·69 (70·52– 70·83)

61·88 (59·32– 64·14)

76·85 (76·65– 76·93)

65·89 (62·77– 68·62)

71·85 (71·35– 72·32)

63·03 (60·41– 65·36)

78·02 (77·58– 78·37)

66·91 (63·70– 69·77)

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Andean Latin America

66·73 (66·20– 67·24)

54·62 (49·76– 58·16)

70·54 (69·95– 71·11)

58·94 (54·95– 62·30)

72·47 (71·90– 73·10)

62·32 (59·21– 65·07)

76·28 (75·73– 76·83)

65·06 (61·79– 68·02)

73·49 (72·06– 74·94)

64·04 (61·05– 66·76)

77·49 (76·02– 78·80)

66·51 (63·21– 69·68)

Bolivia 61·31 (60·09– 62·70)

54·36 (52·12– 56·53)

63·80 (62·05– 65·35)

55·54 (52·86– 58·13)

68·76 (67·20– 70·26)

60·77 (58·02– 63·32)

71·24 (69·73– 72·84)

61·68 (58·74– 64·44)

70·53 (67·12– 73·50)

62·39 (58·87– 65·86)

73·19 (70·12– 76·11)

63·46 (59·80– 67·19)

Ecuador 68·77 (68·10– 69·41)

60·68 (58·37– 62·82)

74·09 (73·48– 74·68)

63·91 (61·04– 66·59)

70·95 (69·90– 71·99)

62·62 (59·98– 65·05)

76·74 (75·82– 77·69)

66·21 (63·10– 69·16)

71·75 (69·49– 74·26)

63·45 (60·47– 66·36)

78·09 (75·58– 80·35)

67·38 (63·87– 70·76)

Peru 67·63 (66·77– 68·44)

51·82 (43·77– 57·25)

71·27 (70·49– 72·04)

57·85 (51·98– 62·28)

74·59 (73·75– 75·56)

62·69 (58·53– 66·09)

77·86 (77·20– 78·40)

65·71 (61·97– 69·09)

75·47 (73·45– 77·69)

64·95 (61·34– 68·17)

78·71 (76·69– 80·31)

67·21 (63·69– 70·50)

Caribbean 65·94 (65·48– 66·40)

58·29 (56·06– 60·37)

69·11 (68·61– 69·58)

60·11 (57·39– 62·48)

68·44 (67·99– 68·87)

60·49 (58·13– 62·61)

71·85 (71·33– 72·37)

62·19 (59·39– 64·66)

70·12 (69·06– 71·09)

61·93 (59·32– 64·28)

74·25 (73·15– 75·18)

64·12 (61·21– 66·80)

Antigua and Barbuda

69·48 (68·01– 70·85)

61·88 (59·39– 64·31)

75·16 (73·78– 76·47)

65·48 (62·64– 68·31)

72·07 (70·80– 73·08)

63·90 (61·33– 66·24)

76·45 (75·18– 77·86)

66·22 (63·11– 69·20)

72·45 (69·25– 75·27)

64·41 (61·01– 67·49)

77·84 (74·41– 80·89)

67·38 (63·64– 71·33)

Barbados 70·52 (69·05– 71·52)

62·40 (59·79– 64·73)

75·45 (74·63– 76·33)

65·55 (62·77– 68·30)

73·47 (72·37– 74·10)

64·72 (62·07– 67·06)

76·53 (75·62– 77·45)

66·13 (63·15– 68·83)

73·78 (71·67– 76·13)

65·20 (62·27– 68·17)

77·34 (74·82– 79·82)

66·83 (63·17– 70·59)

Belize 69·85 (68·59– 71·05)

61·70 (59·12– 64·27)

74·42 (73·27– 75·58)

64·42 (61·42– 67·34)

67·86 (67·20– 68·59)

60·34 (58·06– 62·47)

73·64 (73·00– 74·35)

63·80 (60·85– 66·43)

67·48 (64·62– 70·39)

60·28 (57·29– 63·24)

72·66 (69·93– 75·24)

63·18 (59·80– 66·44)

Cuba 72·80 (72·66– 72·95)

64·41 (61·88– 66·60)

76·62 (76·46– 76·78)

66·40 (63·37– 69·02)

75·33 (75·20– 75·46)

66·41 (63·75– 68·75)

78·85 (78·73– 78·98)

67·87 (64·64– 70·66)

76·29 (75·31– 77·19)

67·31 (64·61– 69·79)

80·43 (79·55– 81·35)

69·06 (65·61– 72·13)

Dominica 69·67 (67·59– 71·42)

61·12 (58·05– 64·06)

72·82 (71·22– 74·39)

63·38 (60·21– 66·37)

71·26 (69·90– 72·53)

62·90 (60·34– 65·46)

77·01 (75·42– 78·30)

66·38 (63·10– 69·42)

70·71 (67·52– 73·84)

62·83 (59·09– 66·27)

78·45 (75·51– 81·11)

67·51 (63·68– 71·15)

Dominican Republic

69·34 (68·52– 70·17)

61·25 (58·82– 63·41)

73·56 (72·77– 74·28)

63·91 (61·13– 66·47)

69·47 (68·34– 70·74)

61·63 (59·28– 63·87)

75·03 (73·96– 76·07)

65·11 (62·17– 67·78)

70·99 (67·80– 73·66)

63·05 (59·61– 66·12)

76·53 (73·66– 78·84)

66·44 (63·09– 69·78)

Grenada 67·94 (66·69– 69·15)

60·15 (57·44– 62·50)

72·25 (70·67– 73·61)

62·78 (59·67– 65·61)

67·27 (66·09– 68·24)

59·94 (57·73– 62·21)

73·06 (71·98– 74·00)

63·54 (60·66– 66·18)

69·00 (66·95– 71·22)

61·40 (58·74– 64·16)

73·96 (72·07– 75·68)

64·32 (61·26– 66·99)

Guyana 61·93 (60·90– 63·03)

55·17 (52·96– 57·32)

68·21 (67·31– 69·14)

59·38 (56·76– 61·77)

59·57 (58·42– 60·67)

53·39 (51·38– 55·36)

65·87 (64·73– 66·99)

57·60 (55·13– 59·90)

61·03 (57·29– 64·50)

54·77 (51·53– 58·14)

67·15 (63·61– 71·21)

58·74 (55·03– 62·34)

Haiti 54·00 (52·74– 55·32)

47·59 (45·49– 49·63)

55·42 (54·15– 56·73)

48·54 (46·32– 50·74)

59·86 (58·77– 61·06)

52·83 (50·44– 54·91)

60·76 (59·35– 62·21)

52·98 (50·38– 55·22)

63·35 (61·51– 65·34)

55·60 (52·53– 58·21)

65·31 (63·15– 67·52)

56·71 (53·79– 59·35)

Jamaica 73·68 (72·72– 74·67)

65·10 (62·38– 67·60)

75·08 (74·16– 76·03)

65·29 (62·28– 68·01)

73·98 (72·85– 75·01)

65·32 (62·80– 67·75)

75·43 (74·33– 76·45)

65·30 (62·37– 68·03)

74·29 (71·66– 77·14)

65·66 (62·62– 69·03)

76·72 (74·10– 79·62)

66·30 (62·58– 70·05)

Saint Lucia 68·20 (66·59– 69·36)

60·22 (57·37– 62·62)

72·27 (70·97– 73·50)

62·63 (59·42– 65·56)

70·43 (69·07– 71·42)

62·46 (59·97– 64·84)

76·20 (75·14– 77·26)

65·92 (62·80– 68·66)

72·11 (69·47– 74·71)

63·94 (60·67– 67·08)

76·89 (74·31– 79·65)

66·62 (63·14– 70·16)

Saint Vincent and the Grenadines

67·79 (65·18– 69·50)

60·24 (57·13– 62·74)

71·63 (69·57– 73·42)

62·51 (59·31– 65·33)

68·25 (67·15– 69·44)

60·69 (58·28– 63·06)

73·36 (72·37– 74·33)

63·68 (60·80– 66·27)

70·53 (67·79– 72·68)

62·63 (59·64– 65·50)

74·99 (72·73– 77·70)

64·98 (61·51– 68·17)

Suriname 66·27 (65·15– 67·16)

57·89 (54·59– 60·45)

71·32 (70·30– 72·27)

61·21 (58·07– 64·07)

65·62 (64·65– 66·46)

58·15 (55·67– 60·42)

71·56 (70·57– 72·48)

61·85 (58·98– 64·56)

66·99 (63·70– 70·28)

59·70 (56·42– 63·15)

73·33 (70·35– 76·36)

63·53 (60·05– 67·29)

The Bahamas 65·29 (63·90– 66·81)

58·25 (55·83– 60·45)

71·86 (70·42– 73·21)

62·64 (59·68– 65·42)

69·47 (68·27– 70·60)

61·58 (59·12– 63·88)

74·57 (73·76– 75·48)

64·55 (61·46– 67·26)

69·54 (66·41– 72·85)

61·82 (58·34– 65·51)

75·47 (72·78– 78·90)

65·27 (61·59– 69·26)

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2170 www.thelancet.com Vol 386 November 28, 2015

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HALE (years)

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HALE (years)

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Trinidad and Tobago

66·98 (66·19– 67·62)

59·86 (57·65– 61·95)

72·47 (71·67– 73·04)

63·45 (60·64– 65·84)

67·42 (66·79– 68·06)

60·18 (58·04– 62·22)

74·54 (73·88– 75·18)

64·73 (61·81– 67·36)

67·39 (65·08– 69·56)

60·29 (57·41– 62·96)

75·47 (73·48– 77·28)

65·45 (62·18– 68·64)

Central Latin America

68·19 (67·94– 68·43)

59·32 (56·86– 61·62)

74·69 (74·46– 74·90)

64·28 (61·23– 66·91)

71·58 (71·33– 71·80)

62·60 (60·02– 64·87)

77·81 (77·60– 78·03)

66·74 (63·50– 69·49)

72·07 (71·47– 72·57)

63·30 (60·82– 65·61)

78·58 (78·08– 79·01)

67·52 (64·36– 70·39)

Colombia 66·96 (66·36– 67·62)

58·42 (56·00– 60·68)

75·01 (74·50– 75·54)

64·38 (61·34– 67·27)

70·74 (69·99– 71·43)

61·98 (59·42– 64·30)

77·49 (76·94– 78·09)

66·31 (63·14– 69·24)

72·28 (69·99– 74·50)

63·24 (60·21– 66·28)

78·94 (77·10– 80·67)

67·56 (64·17– 70·99)

Costa Rica 74·59 (74·26– 74·86)

65·40 (62·71– 67·75)

78·72 (78·44– 79·01)

67·90 (64·73– 70·66)

76·61 (76·40– 76·82)

67·20 (64·50– 69·60)

81·16 (80·94– 81·39)

69·29 (65·86– 72·25)

77·58 (76·86– 78·34)

68·01 (65·07– 70·58)

82·11 (81·47– 82·84)

70·14 (66·72– 73·30)

El Salvador 65·26 (64·81– 65·68)

57·29 (54·96– 59·43)

74·35 (73·86– 74·77)

63·01 (59·45– 66·05)

68·68 (68·46– 68·93)

58·11 (54·46– 61·04)

77·85 (77·61– 78·10)

66·17 (62·76– 69·13)

69·29 (66·60– 71·68)

60·38 (57·02– 63·61)

78·10 (75·93– 80·12)

66·84 (63·32– 70·22)

Guatemala 62·56 (62·09– 63·14)

50·27 (44·91– 54·23)

67·16 (66·50– 67·70)

56·64 (52·73– 59·76)

65·85 (65·49– 66·22)

55·49 (52·06– 58·41)

73·06 (72·67– 73·38)

62·22 (58·97– 65·21)

69·34 (68·20– 70·46)

59·51 (56·56– 62·31)

75·21 (74·20– 76·29)

64·53 (61·42– 67·43)

Honduras 66·49 (65·24– 67·77)

57·85 (54·79– 60·34)

70·15 (68·65– 71·45)

60·52 (57·48– 63·32)

68·40 (65·16– 72·20)

59·98 (56·15– 63·88)

71·83 (68·06– 76·28)

61·86 (57·69– 66·16)

70·11 (66·66– 73·88)

61·70 (57·79– 65·17)

74·00 (70·36– 78·37)

63·93 (60·02– 68·15)

Mexico 68·88 (68·54– 69·23)

60·78 (58·34– 62·95)

75·34 (74·95– 75·66)

65·17 (62·17– 67·85)

72·57 (72·35– 72·77)

64·01 (61·52– 66·28)

78·48 (78·26– 78·70)

67·47 (64·24– 70·22)

72·21 (71·91– 72·53)

63·80 (61·31– 65·99)

78·72 (78·41– 78·99)

67·79 (64·64– 70·58)

Nicaragua 68·63 (67·90– 69·41)

41·79 (31·29– 50·92)

73·23 (72·50– 73·94)

56·90 (49·16– 63·35)

71·89 (71·30– 72·44)

54·16 (47·43– 59·86)

77·33 (76·85– 77·86)

64·33 (59·85– 68·32)

73·74 (72·53– 74·82)

60·76 (55·84– 64·58)

79·07 (78·00– 80·06)

67·36 (63·99– 70·50)

Panama 72·78 (72·14– 73·47)

63·84 (61·09– 66·22)

77·80 (77·19– 78·41)

67·06 (63·78– 69·90)

74·78 (74·21– 75·33)

65·50 (62·80– 68·04)

79·86 (79·31– 80·42)

68·43 (65·07– 71·44)

74·99 (73·10– 76·78)

65·80 (62·79– 68·78)

80·89 (79·43– 82·18)

69·38 (65·87– 72·44)

Venezuela 69·93 (69·76– 70·12)

61·55 (59·10– 63·76)

75·52 (75·33– 75·71)

65·64 (62·79– 68·19)

71·55 (71·42– 71·67)

63·02 (60·55– 65·19)

78·63 (78·49– 78·76)

67·69 (64·47– 70·53)

71·84 (70·56– 73·29)

63·31 (60·73– 65·93)

79·32 (78·08– 80·48)

68·21 (64·97– 71·11)

Tropical Latin America

65·64 (65·31– 65·99)

57·90 (55·61– 59·88)

73·04 (72·69– 73·40)

62·80 (59·90– 65·44)

69·89 (69·68– 70·09)

61·34 (58·84– 63·54)

77·10 (76·89– 77·28)

66·11 (62·98– 68·94)

71·62 (70·65– 72·62)

62·75 (59·96– 65·32)

78·37 (77·61– 79·20)

67·02 (63·70– 70·07)

Brazil 65·47 (65·13– 65·83)

57·76 (55·47– 59·74)

72·97 (72·60– 73·32)

62·73 (59·82– 65·38)

69·84 (69·62– 70·05)

61·29 (58·78– 63·49)

77·14 (76·92– 77·34)

66·13 (62·99– 68·97)

71·63 (70·63– 72·65)

62·75 (59·94– 65·33)

78·43 (77·64– 79·28)

67·06 (63·72– 70·13)

Paraguay 72·58 (72·00– 73·18)

63·75 (61·10– 66·06)

75·78 (75·16– 76·45)

65·37 (62·37– 68·12)

71·70 (71·14– 72·24)

63·12 (60·63– 65·33)

75·42 (74·54– 76·42)

65·35 (62·30– 68·14)

71·42 (69·03– 73·49)

63·07 (60·11– 65·98)

76·21 (74·30– 78·87)

66·02 (62·77– 69·52)

Southeast Asia, east Asia, and Oceania

65·45 (64·63– 66·51)

58·32 (56·00– 60·42)

69·81 (68·68– 70·74)

61·24 (58·71– 63·66)

70·01 (69·48– 70·59)

62·65 (60·44– 64·58)

75·50 (74·97– 75·98)

66·30 (63·57– 68·74)

72·04 (71·13– 73·29)

64·44 (62·14– 66·78)

78·26 (77·45– 79·15)

68·76 (65·95– 71·43)

East Asia 66·08 (65·05– 67·45)

59·22 (56·89– 61·40)

70·28 (68·77– 71·46)

61·94 (59·36– 64·44)

71·26 (70·70– 71·96)

64·00 (61·70– 65·99)

76·84 (76·27– 77·47)

67·56 (64·84– 70·09)

73·50 (72·43– 75·23)

65·85 (63·48– 68·42)

79·90 (78·87– 81·02)

70·19 (67·34– 73·00)

China 66·01 (64·94– 67·45)

59·16 (56·83– 61·37)

70·21 (68·67– 71·46)

61·88 (59·29– 64·40)

71·27 (70·68– 72·01)

64·01 (61·72– 66·00)

76·88 (76·29– 77·54)

67·59 (64·86– 70·10)

73·53 (72·44– 75·35)

65·89 (63·53– 68·46)

79·99 (78·92– 81·17)

70·28 (67·41– 73·11)

North Korea 66·04 (62·08– 70·34)

59·34 (55·65– 63·20)

69·39 (65·43– 73·16)

61·34 (57·45– 65·26)

67·48 (65·52– 69·52)

60·81 (58·22– 63·41)

72·30 (70·71– 73·80)

64·02 (61·32– 66·58)

68·30 (66·07– 71·15)

61·56 (59·00– 64·52)

73·83 (72·00– 76·53)

65·46 (62·51– 68·39)

(Table 3 continues on next page)

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Male population Female population Male population Female population Male population Female population

Life expectancy (years)

HALE (years)

Life expectancy (years)

HALE (years)

Life expectancy (years)

HALE (years)

Life expectancy (years)

HALE (years)

Life expectancy (years)

HALE (years)

Life expectancy (years)

HALE (years)

(Continued from previous page)

Taiwan (province of China)

71·31 (71·18– 71·45)

63·71 (61·38– 65·62)

76·74 (76·61– 76·87)

67·27 (64·42– 69·83)

74·50 (74·39– 74·63)

66·68 (64·37– 68·71)

80·72 (80·61– 80·85)

70·41 (67·46– 73·07)

76·42 (76·10– 76·75)

68·11 (65·59– 70·23)

82·36 (82·05– 82·66)

71·66 (68·51– 74·46)

Oceania 56·67 (52·29– 60·98)

51·09 (47·14– 54·96)

59·22 (54·36– 63·81)

52·77 (48·43– 56·95)

57·93 (53·18– 62·40)

52·32 (47·99– 56·32)

60·36 (55·18– 64·99)

53·93 (49·17– 58·17)

59·83 (54·91– 64·24)

54·09 (49·75– 58·22)

62·70 (57·29– 67·32)

55·93 (51·14– 60·21)

Federated States of Micronesia

59·95 (54·43– 65·55)

54·47 (49·56– 59·37)

64·81 (59·21– 70·08)

57·91 (52·81– 62·55)

63·33 (57·55– 68·41)

57·51 (52·28– 62·09)

68·05 (62·19– 73·11)

60·73 (55·53– 65·48)

64·41 (58·75– 69·51)

58·47 (53·21– 62·96)

69·49 (63·79– 74·55)

61·93 (56·72– 66·52)

Fiji 61·58 (58·49– 65·13)

55·47 (52·25– 58·70)

67·05 (64·20– 70·16)

59·54 (56·25– 62·68)

63·38 (61·16– 65·72)

57·09 (54·51– 59·57)

67·36 (65·40– 69·61)

59·76 (57·11– 62·53)

64·02 (60·96– 67·18)

57·84 (54·54– 60·92)

68·28 (64·85– 71·18)

60·63 (57·11– 63·76)

Kiribati 55·06 (52·12– 58·25)

49·46 (46·51– 52·44)

60·17 (57·20– 63·33)

53·28 (50·08– 56·34)

56·82 (52·98– 60·99)

51·08 (47·57– 54·96)

64·64 (60·50– 68·87)

57·10 (53·32– 60·83)

58·08 (53·42– 63·29)

52·31 (48·37– 56·65)

66·48 (62·29– 71·41)

58·63 (54·41– 62·91)

Marshall Islands

61·52 (59·56– 63·93)

55·56 (53·08– 58·04)

67·57 (65·38– 69·58)

59·82 (56·90– 62·49)

61·59 (58·45– 64·88)

55·78 (52·50– 59·14)

65·52 (62·43– 68·60)

58·32 (55·01– 61·61)

62·24 (57·49– 67·14)

56·44 (51·92– 60·58)

67·35 (63·30– 71·76)

59·91 (55·99– 64·10)

Papua New Guinea

54·87 (49·90– 60·47)

49·46 (45·01– 54·18)

56·69 (51·38– 62·11)

50·56 (45·86– 55·17)

56·33 (51·09– 61·64)

50·89 (46·02– 55·52)

58·32 (52·63– 63·67)

52·20 (46·96– 56·84)

58·59 (53·01– 63·81)

52·99 (48·13– 57·64)

61·08 (55·08– 66·49)

54·58 (49·39– 59·25)

Samoa 65·49 (61·83– 69·06)

59·20 (55·75– 62·75)

71·68 (68·46– 74·72)

63·83 (60·19– 67·33)

68·91 (66·70– 71·20)

62·13 (59·61– 64·74)

72·89 (70·54– 75·05)

64·82 (62·06– 67·67)

70·77 (68·26– 73·15)

63·67 (60·70– 66·62)

72·82 (70·20– 75·13)

64·75 (61·61– 67·68)

Solomon Islands

59·08 (53·34– 64·73)

53·79 (48·57– 58·71)

61·79 (55·62– 67·96)

55·47 (49·95– 60·64)

60·71 (54·84– 66·02)

55·33 (50·15– 60·01)

63·35 (57·36– 68·88)

56·84 (51·39– 61·58)

62·19 (56·61– 67·44)

56·65 (51·37– 61·22)

65·35 (59·63– 70·85)

58·46 (53·13– 63·23)

Tonga 67·00 (63·85– 70·56)

60·25 (57·05– 63·56)

70·38 (67·05– 73·09)

62·51 (59·13– 65·83)

66·70 (65·07– 68·22)

60·27 (57·92– 62·51)

72·40 (70·69– 74·04)

64·04 (61·06– 66·75)

67·37 (63·82– 71·10)

60·89 (57·41– 64·39)

73·23 (69·93– 76·78)

64·83 (61·30– 68·18)

Vanuatu 60·79 (55·27– 66·22)

55·08 (50·25– 59·88)

64·71 (58·84– 70·27)

58·05 (52·85– 62·88)

61·26 (55·67– 66·89)

55·80 (50·65– 60·38)

65·22 (59·24– 70·43)

58·55 (53·26– 63·25)

62·49 (57·16– 67·84)

56·76 (51·62– 61·35)

66·91 (61·17– 72·08)

59·85 (54·66– 64·43)

Southeast Asia 63·89 (63·01– 64·63)

55·91 (53·30– 58·25)

68·78 (67·77– 69·63)

59·53 (56·71– 62·20)

67·26 (66·37– 68·06)

59·62 (57·30– 61·77)

72·77 (71·89– 73·61)

63·80 (61·05– 66·32)

68·83 (67·78– 69·85)

61·37 (59·18– 63·53)

74·80 (73·70– 75·71)

65·84 (63·19– 68·32)

Cambodia 56·87 (55·77– 57·92)

39·74 (30·29– 46·94)

61·16 (59·88– 62·44)

47·15 (37·92– 52·90)

61·80 (60·74– 62·90)

48·76 (41·38– 53·92)

67·05 (66·05– 68·22)

55·17 (48·33– 59·46)

64·82 (62·86– 66·47)

54·62 (49·86– 58·25)

70·56 (68·95– 72·26)

60·23 (55·85– 64·00)

Indonesia 63·18 (62·52– 63·85)

56·20 (54·04– 58·16)

66·84 (66·03– 67·60)

59·01 (56·76– 61·25)

67·24 (66·63– 67·91)

60·17 (58·05– 62·15)

70·76 (69·90– 71·78)

62·79 (60·33– 65·03)

68·34 (67·17– 69·90)

61·33 (59·04– 63·65)

72·66 (71·50– 74·11)

64·51 (62·03– 67·01)

Laos 54·51 (49·52– 59·71)

47·82 (43·37– 52·22)

57·15 (52·12– 62·32)

50·26 (45·56– 54·90)

60·38 (54·85– 65·34)

53·28 (48·40– 57·79)

63·80 (57·96– 68·83)

56·17 (51·14– 60·87)

63·82 (58·29– 68·96)

56·48 (51·52– 60·92)

67·81 (62·47– 72·87)

59·74 (54·79– 64·40)

Malaysia 69·88 (69·76– 70·00)

62·40 (60·17– 64·35)

74·53 (74·35– 74·72)

65·99 (63·45– 68·23)

71·90 (71·81– 72·00)

63·96 (61·69– 66·01)

77·15 (77·02– 77·26)

68·10 (65·42– 70·38)

71·75 (70·67– 72·89)

64·09 (61·69– 66·23)

78·04 (77·19– 79·19)

68·89 (66·22– 71·62)

Maldives 66·02 (65·16– 66·81)

58·22 (55·73– 60·37)

65·42 (64·62– 66·13)

57·78 (55·33– 59·94)

75·44 (74·81– 75·96)

66·56 (63·80– 68·95)

78·11 (77·65– 78·53)

68·22 (65·18– 70·79)

77·62 (76·30– 78·84)

68·55 (65·54– 71·22)

81·21 (79·85– 82·41)

70·98 (67·96– 73·86)

Myanmar 56·43 (51·30– 61·44)

49·78 (45·00– 54·12)

59·74 (54·61– 65·47)

52·71 (48·14– 57·22)

60·82 (55·41– 65·96)

54·23 (49·31– 58·69)

66·18 (60·60– 71·60)

58·52 (53·89– 63·17)

64·21 (58·79– 69·78)

57·55 (52·69– 62·28)

69·98 (64·65– 75·33)

61·97 (57·05– 66·57)

Philippines 64·70 (64·00– 65·35)

54·96 (50·53– 57·93)

72·20 (71·57– 72·78)

62·28 (59·07– 65·06)

65·39 (64·65– 66·11)

57·25 (54·68– 59·54)

72·87 (72·28– 73·44)

63·40 (60·49– 65·96)

66·41 (63·92– 68·85)

58·76 (55·97– 61·56)

73·75 (71·51– 75·85)

64·59 (61·51– 67·55)

(Table 3 continues on next page)

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2172 www.thelancet.com Vol 386 November 28, 2015

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Male population Female population Male population Female population Male population Female population

Life expectancy (years)

HALE (years)

Life expectancy (years)

HALE (years)

Life expectancy (years)

HALE (years)

Life expectancy (years)

HALE (years)

Life expectancy (years)

HALE (years)

Life expectancy (years)

HALE (years)

(Continued from previous page)

Sri Lanka 67·35 (66·72– 67·76)

59·69 (57·48– 61·79)

75·39 (74·83– 75·75)

66·08 (63·27– 68·58)

70·53 (70·30– 70·75)

62·64 (60·29– 64·67)

78·13 (77·92– 78·35)

68·66 (65·78– 71·15)

72·09 (70·17– 73·90)

64·14 (61·39– 67·00)

80·23 (78·57– 81·72)

70·62 (67·56– 73·53)

Thailand 68·44 (67·58– 69·32)

61·38 (59·18– 63·43)

75·10 (74·38– 75·84)

66·07 (63·42– 68·44)

69·36 (68·44– 70·28)

62·39 (60·24– 64·41)

76·36 (75·73– 77·01)

67·50 (64·87– 69·89)

71·51 (68·75– 74·21)

64·30 (61·20– 67·29)

78·49 (76·79– 80·37)

69·20 (66·23– 72·12)

Timor-Leste 58·98 (57·32– 60·86)

51·40 (48·76– 53·96)

58·98 (57·01– 60·83)

52·45 (49·75– 54·87)

65·99 (64·83– 67·11)

57·95 (55·28– 60·19)

68·09 (66·71– 69·39)

60·50 (58·07– 62·71)

70·20 (68·10– 71·96)

61·65 (58·86– 64·34)

72·70 (70·86– 74·59)

64·57 (61·85– 67·33)

Vietnam 65·94 (64·06– 67·54)

55·60 (50·67– 59·15)

72·14 (70·50– 73·79)

58·75 (52·49– 63·47)

70·58 (68·15– 73·19)

61·37 (57·74– 64·78)

78·45 (76·84– 79·81)

66·82 (62·56– 70·54)

72·26 (69·13– 75·51)

63·77 (60·17– 67·14)

80·07 (77·85– 82·37)

69·33 (65·80– 72·92)

South Asia 57·60 (56·39– 58·88)

50·21 (47·81– 52·48)

58·53 (57·29– 59·84)

50·29 (47·74– 52·62)

62·00 (60·85– 63·16)

54·13 (51·67– 56·55)

64·81 (63·52– 65·96)

55·58 (52·65– 58·30)

64·36 (62·46– 66·29)

56·50 (53·78– 59·17)

68·34 (66·82– 70·02)

58·78 (55·72– 61·80)

Afghanistan 50·71 (46·90– 54·80)

42·96 (38·35– 46·87)

49·98 (46·73– 53·18)

43·16 (39·99– 46·36)

53·29 (49·17– 57·71)

45·91 (41·47– 50·15)

52·48 (48·79– 56·28)

45·71 (42·20– 49·29)

56·45 (52·17– 61·38)

49·07 (44·73– 53·64)

55·99 (52·21– 60·36)

48·78 (45·04– 52·69)

Bangladesh 58·12 (56·13– 60·26)

49·99 (47·10– 52·67)

58·98 (56·73– 61·32)

50·06 (47·02– 53·22)

66·57 (65·28– 67·87)

57·55 (54·82– 60·18)

69·39 (68·05– 70·55)

58·68 (55·47– 61·71)

68·29 (66·02– 71·23)

59·49 (56·29– 62·89)

70·92 (68·78– 73·75)

60·40 (56·86– 63·90)

Bhutan 59·02 (53·93– 64·25)

52·07 (47·45– 56·47)

59·72 (54·45– 64·96)

51·80 (47·15– 56·58)

65·50 (59·49– 71·17)

57·77 (52·76– 62·45)

68·10 (62·54– 73·04)

58·90 (54·01– 63·48)

68·03 (61·82– 73·67)

60·12 (54·88– 64·93)

71·36 (65·49– 76·31)

61·76 (56·54– 66·54)

India 57·25 (55·79– 58·77)

50·07 (47·64– 52·43)

58·19 (56·64– 59·77)

50·15 (47·48– 52·63)

61·76 (60·41– 63·16)

54·11 (51·57– 56·51)

64·74 (63·11– 66·12)

55·71 (52·80– 58·54)

64·16 (61·97– 66·70)

56·52 (53·59– 59·25)

68·48 (66·60– 70·62)

59·11 (56·08– 62·28)

Nepal 58·00 (56·21– 59·84)

50·49 (47·98– 52·99)

58·93 (56·88– 61·21)

51·15 (48·30– 53·99)

66·05 (64·66– 67·70)

57·38 (54·63– 60·07)

68·62 (66·70– 70·38)

59·38 (56·51– 62·28)

69·10 (66·94– 71·40)

60·36 (57·35– 63·44)

72·14 (69·93– 74·68)

62·53 (59·44– 65·69)

Pakistan 62·20 (60·46– 64·05)

54·08 (51·45– 56·53)

62·54 (60·56– 64·43)

53·60 (50·74– 56·42)

61·69 (59·82– 63·99)

53·96 (51·34– 56·60)

63·58 (61·63– 65·80)

54·61 (51·72– 57·57)

64·33 (61·38– 67·72)

56·46 (53·08– 59·58)

67·20 (63·95– 70·05)

57·89 (54·30– 61·48)

North Africa and Middle East

64·83 (64·16– 65·43)

56·86 (54·60– 59·03)

69·12 (68·60– 69·61)

59·39 (56·62– 62·00)

69·92 (69·25– 70·67)

61·45 (58·92– 63·71)

74·18 (73·61– 74·71)

63·62 (60·55– 66·47)

71·96 (71·14– 72·70)

63·28 (60·79– 65·75)

76·33 (75·62– 77·04)

65·44 (62·35– 68·45)

Algeria 69·03 (67·05– 71·17)

60·45 (57·54– 63·14)

71·94 (70·28– 73·44)

61·85 (58·46– 64·72)

73·13 (71·20– 75·37)

63·97 (60·92– 66·98)

75·83 (73·43– 77·50)

65·07 (61·62– 68·18)

75·13 (73·63– 76·83)

65·60 (62·53– 68·31)

77·42 (75·34– 78·51)

66·34 (63·08– 69·49)

Bahrain 70·70 (69·03– 72·34)

61·72 (58·98– 64·41)

72·41 (71·18– 73·62)

61·72 (58·45– 64·58)

73·92 (72·99– 75·49)

63·97 (61·01– 66·61)

76·88 (76·09– 77·92)

64·88 (61·46– 68·17)

78·26 (75·71– 80·67)

67·22 (63·53– 70·72)

79·87 (77·82– 82·01)

66·89 (62·95– 70·80)

Egypt 62·42 (61·41– 63·48)

55·08 (52·89– 57·21)

66·69 (65·52– 67·69)

57·19 (54·21– 59·88)

67·42 (66·60– 68·19)

59·39 (56·92– 61·59)

72·24 (71·58– 72·92)

61·68 (58·47– 64·53)

68·31 (66·78– 69·83)

60·37 (57·90– 62·73)

73·62 (72·17– 75·05)

62·93 (59·87– 65·93)

Iran 63·48 (60·90– 65·50)

55·60 (52·69– 58·44)

69·46 (67·74– 70·95)

59·14 (56·08– 62·29)

72·24 (71·15– 73·16)

63·38 (60·61– 65·80)

77·28 (76·49– 78·02)

65·40 (62·01– 68·62)

76·12 (73·81– 78·08)

66·75 (63·65– 69·75)

80·58 (79·10– 82·21)

68·23 (64·62– 71·75)

Iraq 68·59 (65·82– 71·61)

59·17 (55·47– 62·60)

69·86 (66·82– 72·60)

59·84 (56·29– 63·25)

64·79 (61·12– 69·62)

56·55 (52·09– 61·60)

68·52 (66·48– 71·65)

59·06 (55·74– 62·81)

70·57 (66·63– 74·38)

61·04 (57·09– 65·07)

72·02 (69·01– 75·48)

61·95 (58·58– 65·64)

Jordan 70·83 (67·92– 73·19)

61·50 (58·17– 64·63)

73·20 (71·64– 75·02)

62·35 (58·94– 65·48)

74·64 (73·33– 75·94)

64·13 (60·90– 67·00)

73·90 (72·84– 75·70)

62·77 (59·44– 65·98)

76·77 (74·45– 78·97)

66·07 (62·76– 69·47)

79·56 (78·23– 81·39)

66·89 (63·14– 70·49)

Kuwait 76·74 (76·32– 77·11)

65·32 (61·95– 68·16)

79·09 (78·78– 79·35)

67·07 (63·55– 70·18)

76·96 (76·69– 77·19)

66·42 (63·31– 69·08)

80·38 (80·16– 80·63)

68·41 (64·86– 71·47)

79·39 (78·72– 80·09)

68·54 (65·42– 71·51)

81·69 (81·10– 82·28)

69·58 (66·08– 72·68)

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Life expectancy (years)

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HALE (years)

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Lebanon 65·28 (58·68– 69·57)

52·87 (45·32– 58·65)

70·63 (66·68– 73·72)

57·89 (51·52– 62·99)

76·11 (73·88– 78·14)

64·20 (59·27– 68·10)

78·53 (77·05– 79·64)

66·15 (62·04– 69·80)

76·83 (73·57– 79·42)

66·64 (62·85– 70·09)

80·01 (78·36– 81·87)

68·54 (65·00– 71·93)

Libya 71·75 (68·94– 74·40)

62·27 (58·81– 65·44)

74·36 (71·94– 76·30)

63·54 (59·86– 67·04)

73·83 (72·31– 75·15)

64·09 (60·89– 66·91)

76·41 (74·64– 78·01)

65·10 (61·55– 68·27)

73·82 (71·42– 76·63)

64·42 (61·35– 67·89)

77·34 (75·17– 79·61)

65·79 (61·94– 69·46)

Morocco 66·56 (65·04– 67·91)

57·92 (55·10– 60·38)

69·97 (69·10– 70·87)

60·45 (57·66– 63·24)

70·81 (68·44– 72·77)

61·61 (58·42– 64·49)

74·05 (71·96– 75·73)

63·82 (60·57– 67·10)

72·60 (69·68– 75·11)

63·19 (59·67– 66·21)

75·95 (73·34– 78·10)

65·42 (61·76– 69·03)

Oman 69·98 (66·53– 73·97)

60·40 (56·62– 64·63)

71·59 (68·04– 75·01)

60·81 (56·87– 64·93)

73·04 (71·08– 75·15)

63·05 (59·86– 66·00)

76·10 (74·72– 78·09)

64·46 (60·96– 67·82)

73·83 (71·48– 77·29)

64·04 (60·78– 67·53)

78·13 (75·96– 81·26)

66·03 (62·16– 70·05)

Palestine 68·56 (65·44– 72·23)

59·57 (55·84– 63·22)

72·08 (68·47– 74·99)

61·63 (57·62– 65·27)

70·92 (68·99– 72·73)

61·40 (58·47– 64·34)

76·78 (75·27– 77·84)

64·87 (61·36– 68·10)

71·57 (68·06– 75·19)

62·36 (58·53– 66·07)

77·95 (74·69– 80·32)

65·97 (61·93– 69·68)

Qatar 76·75 (75·83– 77·79)

65·74 (62·55– 68·69)

77·41 (76·04– 78·53)

65·41 (61·97– 68·76)

78·14 (77·25– 78·95)

66·87 (63·50– 69·83)

80·22 (79·52– 80·87)

67·41 (63·63– 70·81)

81·20 (79·81– 82·60)

69·23 (65·65– 72·47)

83·08 (81·39– 84·87)

69·47 (65·55– 73·31)

Saudi Arabia 71·66 (67·89– 75·35)

61·87 (58·11– 65·55)

74·66 (72·03– 77·44)

63·45 (59·79– 67·15)

75·76 (74·51– 76·87)

65·07 (61·96– 68·01)

79·18 (78·46– 79·80)

66·66 (62·89– 69·97)

75·79 (73·26– 78·16)

65·24 (61·78– 68·48)

80·76 (79·40– 82·14)

67·29 (63·37– 71·01)

Sudan 60·25 (58·23– 62·34)

52·18 (49·46– 55·05)

62·30 (60·40– 64·27)

52·90 (49·67– 55·90)

64·68 (62·54– 67·14)

56·08 (53·21– 59·18)

68·03 (65·29– 70·68)

57·61 (53·81– 60·99)

67·01 (64·35– 69·73)

58·24 (55·07– 61·56)

70·81 (67·86– 73·77)

60·06 (56·27– 63·47)

Syria 67·88 (65·36– 70·59)

55·64 (49·16– 60·53)

71·73 (69·38– 74·44)

59·84 (54·99– 64·27)

73·92 (73·03– 74·90)

63·24 (59·80– 66·34)

77·94 (76·81– 78·64)

65·71 (61·93– 68·95)

69·42 (64·52– 72·67)

59·96 (55·72– 63·67)

75·90 (72·16– 78·35)

63·73 (59·66– 67·54)

Tunisia 69·86 (68·63– 71·18)

61·50 (58·86– 64·14)

74·12 (73·18– 75·11)

64·57 (61·65– 67·27)

73·73 (70·79– 76·71)

64·86 (61·41– 68·13)

78·42 (76·01– 80·38)

67·96 (64·53– 71·09)

74·53 (71·84– 77·89)

65·74 (62·32– 69·51)

79·78 (77·76– 81·94)

69·10 (65·50– 72·49)

Turkey 64·01 (62·86– 65·07)

56·73 (54·52– 58·98)

70·73 (69·79– 71·63)

60·59 (57·76– 63·30)

70·35 (68·57– 71·73)

62·11 (59·47– 64·62)

77·35 (76·64– 78·08)

65·71 (62·36– 68·91)

72·92 (70·79– 74·93)

64·26 (61·39– 67·25)

79·65 (78·46– 81·04)

67·63 (64·07– 70·84)

United Arab Emirates

71·67 (67·95– 75·06)

62·61 (59·10– 66·20)

73·52 (71·15– 76·82)

63·49 (59·84– 66·78)

74·12 (72·88– 76·48)

64·64 (61·73– 67·70)

77·46 (76·37– 78·93)

66·37 (63·17– 69·61)

75·14 (72·46– 78·80)

65·44 (62·19– 69·07)

79·25 (76·61– 82·18)

67·59 (63·88– 71·45)

Yemen 59·70 (54·52– 64·20)

52·33 (47·98– 56·38)

60·46 (55·14– 65·43)

52·46 (48·01– 56·87)

65·00 (59·21– 70·20)

56·96 (52·07– 61·77)

65·11 (59·17– 70·61)

56·38 (51·29– 61·49)

66·95 (61·03– 72·36)

58·79 (53·68– 63·45)

67·08 (60·97– 72·69)

58·13 (52·83– 63·18)

Sub-Saharan Africa 53·02 (52·49– 53·50)

45·62 (43·40– 47·56)

56·05 (55·59– 56·51)

47·82 (45·43– 50·04)

54·51 (54·06– 54·92)

47·02 (44·83– 48·99)

56·56 (56·08– 57·00)

48·30 (45·87– 50·47)

58·79 (58·07– 59·45)

51·02 (48·68– 53·12)

61·64 (60·92– 62·29)

52·83 (50·29– 55·17)

Central sub- Saharan Africa

50·72 (49·25– 52·29)

43·35 (40·79– 45·65)

54·04 (52·59– 55·61)

45·82 (43·12– 48·35)

52·77 (51·53– 54·04)

45·24 (42·84– 47·53)

55·98 (54·66– 57·17)

47·52 (44·77– 49·96)

56·24 (54·17– 58·30)

48·67 (45·89– 51·50)

59·92 (57·88– 61·93)

51·15 (48·09– 54·07)

Angola 48·22 (44·51– 52·35)

41·69 (37·99– 45·59)

52·50 (48·30– 57·48)

44·73 (40·64– 49·18)

54·56 (50·54– 57·60)

47·47 (43·45– 50·68)

57·50 (53·26– 61·04)

49·32 (45·14– 52·95)

58·79 (54·20– 62·20)

51·36 (47·22– 54·83)

62·05 (57·55– 65·92)

53·33 (49·10– 57·32)

Central African Republic

43·68 (41·23– 45·59)

37·90 (35·34– 40·21)

50·67 (49·11– 52·30)

43·35 (40·92– 45·75)

47·29 (45·41– 49·46)

41·16 (38·86– 43·51)

49·55 (47·40– 51·64)

42·60 (39·82– 45·34)

51·79 (48·56– 55·27)

45·29 (41·99– 48·51)

53·81 (50·46– 56·82)

46·46 (42·88– 49·78)

Congo 53·14 (51·55– 54·73)

46·73 (44·30– 48·87)

58·30 (56·91– 59·57)

50·23 (47·51– 52·82)

54·46 (52·98– 56·03)

47·80 (45·67– 49·91)

57·88 (56·30– 59·42)

49·77 (47·14– 52·27)

59·12 (57·13– 61·41)

52·11 (49·48– 54·85)

64·05 (62·27– 65·89)

55·03 (52·17– 57·77)

Democratic Republic of the Congo

52·29 (50·55– 54·30)

44·28 (41·47– 47·00)

54·37 (52·53– 56·23)

45·94 (43·15– 48·61)

52·66 (51·19– 54·22)

44·78 (42·27– 47·41)

55·82 (54·29– 57·32)

47·14 (44·35– 49·78)

55·70 (52·82– 58·53)

47·94 (44·71– 51·20)

59·35 (56·70– 62·08)

50·51 (47·18– 53·77)

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Equatorial Guinea

49·16 (45·45– 53·24)

43·25 (39·74– 47·01)

53·88 (49·46– 58·56)

46·60 (42·56– 50·82)

54·04 (49·26– 58·32)

47·33 (43·07– 51·26)

57·24 (52·24– 61·53)

49·33 (44·79– 53·52)

57·29 (52·57– 61·02)

50·34 (46·18– 53·91)

60·54 (55·38– 64·60)

52·29 (47·83– 56·34)

Gabon 57·20 (55·86– 58·70)

50·21 (47·81– 52·59)

63·85 (62·74– 65·04)

54·64 (51·72– 57·22)

52·59 (50·88– 54·27)

46·08 (43·76– 48·31)

62·31 (60·46– 64·13)

53·35 (50·25– 56·21)

54·37 (52·36– 56·50)

47·77 (45·22– 50·24)

65·18 (62·74– 68·05)

55·76 (52·25– 59·06)

Eastern sub- Saharan Africa

50·85 (50·12– 51·50)

43·83 (41·77– 45·82)

53·84 (53·25– 54·38)

46·17 (43·93– 48·15)

55·11 (54·61– 55·63)

47·54 (45·35– 49·51)

57·08 (56·53– 57·58)

48·92 (46·64– 50·99)

59·97 (59·23– 60·72)

52·05 (49·71– 54·19)

62·73 (61·97– 63·46)

53·91 (51·38– 56·22)

Burundi 48·67 (46·78– 50·53)

42·96 (40·59– 45·31)

49·38 (47·59– 51·19)

43·57 (41·42– 45·76)

53·06 (51·69– 54·47)

45·59 (42·57– 48·06)

54·57 (53·39– 55·82)

47·28 (44·80– 49·61)

58·51 (56·36– 60·40)

51·00 (48·05– 53·64)

61·08 (59·31– 63·03)

53·30 (50·48– 56·00)

Comoros 55·93 (51·67– 60·09)

48·81 (44·91– 52·52)

57·87 (54·17– 62·12)

50·59 (46·65– 54·25)

60·35 (56·07– 64·69)

52·96 (49·20– 57·09)

63·48 (59·71– 67·66)

55·33 (51·56– 59·01)

61·90 (57·89– 66·15)

54·50 (50·64– 58·69)

65·53 (61·76– 70·22)

57·17 (53·20– 61·34)

Djibouti 60·48 (55·96– 64·54)

52·86 (48·85– 56·87)

60·63 (56·40– 65·12)

52·76 (48·69– 57·09)

59·53 (55·00– 62·97)

52·26 (48·24– 55·57)

60·30 (55·89– 63·91)

52·37 (48·32– 56·00)

62·69 (57·82– 66·02)

55·04 (50·76– 58·46)

64·07 (59·41– 67·56)

55·71 (51·54– 59·23)

Eritrea 50·96 (49·45– 52·44)

44·33 (42·02– 46·45)

54·01 (52·63– 55·51)

46·80 (44·45– 49·20)

57·83 (55·68– 60·18)

49·42 (46·33– 52·65)

60·22 (58·19– 62·29)

51·60 (48·67– 54·58)

60·50 (57·99– 63·41)

52·08 (48·74– 55·45)

63·59 (61·16– 65·96)

54·72 (51·48– 57·93)

Ethiopia 45·51 (43·86– 47·20)

39·36 (37·14– 41·63)

48·89 (47·31– 50·34)

42·42 (40·13– 44·49)

55·98 (54·81– 57·13)

48·45 (46·08– 50·75)

57·68 (56·44– 58·83)

50·04 (47·63– 52·14)

61·43 (59·61– 63·21)

53·37 (50·60– 56·00)

63·70 (62·06– 65·43)

55·27 (52·46– 57·95)

Kenya 61·77 (60·74– 62·73)

53·77 (51·30– 56·02)

64·17 (63·21– 65·25)

55·60 (52·98– 58·04)

57·37 (56·19– 58·60)

49·92 (47·72– 51·97)

60·25 (58·75– 61·60)

51·93 (49·49– 54·35)

62·98 (61·28– 64·80)

55·01 (52·30– 57·76)

67·52 (65·36– 69·25)

58·21 (55·18– 61·16)

Madagascar 55·11 (53·62– 56·74)

47·25 (44·52– 49·79)

57·84 (56·29– 59·21)

49·50 (46·84– 51·97)

61·10 (59·00– 62·82)

52·48 (49·26– 55·26)

63·56 (61·91– 65·09)

54·47 (51·47– 57·10)

62·73 (59·02– 65·92)

54·29 (50·72– 57·42)

65·82 (62·70– 69·32)

56·60 (52·82– 60·46)

Malawi 48·69 (47·28– 50·16)

41·17 (38·70– 43·56)

50·36 (48·77– 51·84)

43·05 (40·72– 45·31)

48·13 (46·79– 49·85)

41·30 (38·99– 43·57)

48·64 (47·14– 50·42)

41·67 (39·35– 43·78)

55·66 (53·94– 57·46)

48·56 (46·18– 51·03)

58·88 (57·20– 60·76)

50·69 (48·03– 53·29)

Mauritius 65·57 (65·33– 65·81)

59·07 (57·14– 60·78)

73·70 (73·39– 74·04)

65·38 (62·77– 67·61)

68·71 (68·46– 68·94)

61·62 (59·51– 63·44)

75·40 (75·11– 75·68)

66·82 (64·24– 69·06)

69·64 (68·72– 70·58)

62·51 (60·45– 64·67)

77·19 (76·19– 78·25)

68·33 (65·38– 71·01)

Mozambique 49·53 (48·08– 51·00)

42·31 (39·68– 44·64)

53·77 (51·95– 55·29)

45·35 (42·42– 47·90)

51·58 (50·28– 52·85)

44·42 (42·14– 46·50)

56·25 (55·10– 57·58)

47·48 (44·72– 50·03)

53·98 (52·24– 55·69)

46·68 (44·28– 48·99)

58·43 (56·76– 60·25)

49·48 (46·45– 52·09)

Rwanda 47·90 (46·41– 49·60)

42·21 (40·07– 44·32)

51·03 (49·73– 52·31)

44·83 (42·85– 46·78)

55·38 (54·22– 56·60)

45·60 (41·35– 48·76)

59·21 (58·03– 60·28)

49·61 (45·97– 52·47)

62·94 (61·07– 64·62)

53·24 (49·70– 56·56)

67·53 (65·86– 69·30)

57·45 (53·88– 60·48)

Seychelles 65·06 (64·31– 65·80)

58·71 (56·87– 60·55)

74·24 (73·45– 75·12)

65·74 (63·20– 68·09)

68·24 (67·56– 68·93)

61·06 (58·86– 63·04)

75·65 (74·95– 76·27)

66·79 (64·20– 69·24)

69·92 (68·61– 71·00)

62·46 (59·99– 64·65)

76·89 (75·90– 78·69)

67·81 (64·80– 70·70)

Somalia 52·17 (47·32– 57·01)

45·01 (40·78– 49·18)

52·77 (47·96– 58·01)

45·85 (41·56– 49·99)

55·70 (50·46– 60·91)

48·65 (44·13– 53·14)

56·66 (51·45– 62·24)

49·26 (44·79– 53·93)

57·15 (51·39– 62·48)

49·94 (45·07– 54·75)

58·37 (53·02– 64·50)

50·78 (45·92– 55·71)

South Sudan 49·50 (45·59– 53·83)

42·20 (38·37– 46·03)

55·39 (51·53– 58·75)

46·30 (42·34– 50·07)

52·45 (50·05– 54·72)

45·05 (41·95– 47·85)

58·30 (56·13– 60·37)

49·16 (45·98– 52·06)

54·57 (51·92– 57·63)

47·29 (44·34– 50·40)

60·17 (57·64– 62·84)

51·17 (47·86– 54·44)

Tanzania 56·43 (55·13– 57·77)

48·75 (46·26– 51·02)

58·08 (56·71– 59·36)

49·86 (47·44– 52·30)

57·20 (56·00– 58·58)

49·70 (47·29– 51·90)

57·23 (55·88– 58·87)

49·25 (46·85– 51·70)

61·52 (59·93– 63·45)

53·73 (50·86– 56·36)

62·89 (60·88– 64·84)

54·20 (51·36– 57·02)

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1990 2005 2013

Male population Female population Male population Female population Male population Female population

Life expectancy (years)

HALE (years)

Life expectancy (years)

HALE (years)

Life expectancy (years)

HALE (years)

Life expectancy (years)

HALE (years)

Life expectancy (years)

HALE (years)

Life expectancy (years)

HALE (years)

(Continued from previous page)

Uganda 49·86 (48·59– 51·23)

42·64 (40·27– 44·93)

53·55 (52·35– 54·69)

43·93 (38·85– 47·24)

52·74 (51·57– 53·97)

45·66 (43·38– 47·92)

56·43 (55·23– 57·81)

47·11 (43·13– 50·29)

58·17 (56·12– 59·85)

50·74 (48·10– 53·27)

61·62 (59·96– 63·12)

52·10 (48·37– 55·08)

Zambia 50·92 (48·50– 52·81)

42·92 (40·14– 45·67)

51·83 (50·43– 53·08)

44·70 (42·26– 46·93)

49·04 (47·57– 50·48)

42·27 (39·97– 44·40)

47·01 (45·50– 48·46)

40·71 (38·67– 42·70)

58·02 (56·15– 59·97)

50·26 (47·39– 53·01)

55·61 (53·39– 57·63)

48·14 (45·52– 50·70)

Southern sub- Saharan Africa

60·57 (59·22– 61·81)

52·69 (50·25– 54·95)

68·04 (66·88– 69·03)

58·28 (55·36– 60·90)

50·23 (48·81– 51·55)

43·58 (41·41– 45·70)

53·38 (51·84– 54·69)

45·63 (43·29– 47·85)

56·46 (55·27– 57·83)

49·03 (46·80– 51·21)

61·66 (60·26– 63·17)

52·69 (49·87– 55·18)

Botswana 64·14 (60·49– 67·24)

55·81 (52·09– 59·23)

71·43 (68·02– 74·56)

60·76 (56·91– 64·42)

51·45 (49·04– 54·01)

45·04 (42·36– 47·69)

57·73 (52·98– 62·43)

49·10 (44·97– 53·31)

61·60 (58·41– 65·00)

53·56 (49·98– 57·02)

70·61 (63·80– 75·86)

59·67 (54·23– 64·64)

Lesotho 56·73 (55·23– 58·27)

50·18 (47·93– 52·40)

65·84 (64·33– 67·15)

56·55 (53·72– 59·21)

42·25 (40·93– 43·75)

37·14 (35·48– 39·00)

45·77 (43·83– 48·10)

39·43 (37·16– 42·00)

45·55 (43·86– 47·30)

40·06 (38·12– 42·15)

51·16 (48·33– 53·87)

44·02 (41·03– 47·13)

Namibia 59·02 (58·10– 60·05)

52·22 (50·08– 54·19)

65·68 (64·92– 66·52)

56·84 (54·12– 59·21)

50·04 (48·69– 51·49)

44·28 (42·38– 46·25)

53·80 (52·19– 55·49)

46·67 (44·44– 49·10)

56·22 (54·31– 58·13)

49·73 (47·32– 52·19)

65·35 (63·13– 67·14)

56·40 (53·40– 59·28)

South Africa 60·46 (58·65– 62·17)

52·97 (50·51– 55·42)

68·87 (67·31– 70·28)

59·19 (56·19– 61·89)

51·14 (49·06– 53·01)

44·51 (42·01– 46·82)

55·05 (53·08– 56·85)

47·18 (44·58– 49·65)

57·67 (55·98– 59·45)

50·09 (47·72– 52·58)

63·01 (61·30– 64·91)

53·85 (50·87– 56·75)

Swaziland 59·13 (57·42– 60·99)

51·78 (49·29– 54·18)

65·18 (63·83– 66·69)

55·95 (53·00– 58·62)

42·40 (40·74– 43·86)

36·93 (34·95– 38·75)

44·66 (42·70– 46·93)

38·26 (36·02– 40·73)

47·69 (46·06– 49·41)

41·66 (39·41– 43·77)

54·14 (51·86– 56·53)

46·36 (43·52– 49·25)

Zimbabwe 61·70 (60·68– 62·77)

51·60 (47·94– 54·54)

65·00 (63·81– 66·06)

54·77 (51·25– 57·64)

47·33 (45·57– 48·94)

40·24 (38·06– 42·48)

47·91 (45·72– 50·45)

40·34 (37·79– 42·95)

54·09 (52·15– 56·34)

46·64 (43·95– 49·25)

57·87 (55·11– 60·92)

49·33 (46·17– 52·56)

Western sub- Saharan Africa

54·24 (53·25– 55·08)

46·51 (44·11– 48·61)

56·07 (55·13– 56·96)

47·60 (45·04– 50·03)

56·27 (55·46– 57·04)

48·51 (46·20– 50·64)

57·65 (56·87– 58·39)

49·10 (46·50– 51·43)

59·58 (58·47– 60·69)

51·72 (49·34– 54·01)

61·44 (60·23– 62·40)

52·63 (49·89– 55·09)

Benin 54·91 (53·42– 56·36)

47·50 (45·03– 49·83)

59·15 (57·75– 60·48)

49·96 (47·05– 52·81)

58·96 (57·46– 60·34)

51·87 (49·45– 54·10)

63·84 (62·21– 65·33)

54·58 (51·56– 57·54)

62·39 (59·93– 64·60)

55·18 (52·41– 57·88)

67·44 (65·29– 69·58)

57·92 (54·66– 61·11)

Burkina Faso 51·16 (49·57– 52·72)

43·93 (41·41– 46·30)

54·11 (52·54– 55·56)

45·92 (43·19– 48·41)

55·76 (54·50– 56·95)

48·50 (45·89– 50·76)

57·62 (56·32– 58·93)

49·35 (46·73– 51·88)

60·81 (58·98– 62·51)

53·51 (50·86– 55·94)

63·08 (61·26– 65·00)

54·54 (51·69– 57·34)

Cameroon 57·43 (56·28– 58·49)

49·59 (47·09– 52·01)

60·01 (58·83– 61·15)

51·18 (48·49– 53·76)

53·87 (52·42– 55·31)

46·80 (44·32– 49·11)

56·49 (55·36– 57·82)

48·32 (45·71– 50·62)

56·99 (54·99– 58·81)

49·92 (47·19– 52·49)

60·53 (58·57– 62·40)

52·13 (49·32– 54·83)

Cape Verde 64·99 (64·35– 65·68)

57·50 (55·26– 59·61)

72·38 (71·69– 73·13)

62·17 (59·07– 64·91)

67·30 (63·49– 71·13)

59·51 (55·92– 63·14)

76·07 (72·62– 78·83)

65·27 (61·35– 68·81)

69·83 (66·03– 74·09)

61·86 (57·86– 65·61)

78·50 (74·70– 81·11)

67·43 (63·69– 70·93)

Chad 52·00 (50·38– 53·80)

43·88 (41·10– 46·60)

55·22 (53·64– 56·81)

46·55 (43·58– 49·27)

52·24 (50·13– 54·55)

44·70 (41·97– 47·22)

54·92 (53·09– 56·99)

46·78 (43·95– 49·65)

55·74 (53·11– 58·51)

48·11 (45·13– 50·99)

58·49 (55·53– 61·10)

50·08 (46·80– 53·22)

Côte d’Ivoire 53·95 (52·66– 55·22)

46·71 (44·41– 48·96)

58·88 (57·65– 60·21)

49·89 (47·04– 52·61)

52·11 (50·78– 53·57)

45·62 (43·51– 47·66)

55·65 (54·40– 57·09)

47·84 (45·46– 50·27)

57·07 (54·84– 58·89)

50·07 (47·52– 52·39)

61·03 (59·51– 62·65)

52·65 (49·90– 55·35)

Ghana 59·34 (57·79– 61·12)

51·91 (49·22– 54·27)

61·23 (59·50– 63·06)

52·54 (49·68– 55·26)

60·29 (59·09– 61·54)

53·29 (50·81– 55·57)

62·63 (61·03– 64·27)

54·06 (51·35– 56·71)

62·99 (61·10– 64·91)

56·00 (53·45– 58·53)

66·85 (64·77– 68·97)

57·90 (54·93– 61·00)

Guinea 52·52 (50·79– 54·41)

45·36 (42·82– 47·77)

52·22 (50·46– 53·86)

44·72 (41·94– 47·16)

56·94 (55·35– 58·85)

49·76 (47·27– 52·07)

57·71 (56·19– 59·25)

49·56 (46·83– 52·20)

59·66 (57·53– 61·86)

52·44 (49·66– 55·06)

60·67 (58·43– 63·06)

52·46 (49·24– 55·33)

Guinea-Bissau 50·25 (45·91– 54·83)

43·34 (39·05– 47·37)

52·34 (47·31– 57·34)

44·96 (40·35– 49·16)

50·23 (45·79– 54·07)

43·87 (39·99– 47·50)

51·52 (47·20– 55·68)

44·64 (40·94– 48·33)

52·21 (48·32– 55·56)

45·85 (42·12– 49·23)

53·48 (49·56– 57·13)

46·59 (42·91– 50·15)

(Table 3 continues on next page)

Articles

2176 www.thelancet.com Vol 386 November 28, 2015

Figure 5 shows the predicted global composition of YLLs and YLDs for level 2 causes at diff erent levels of sociodemographic status, controlling for year and country. YLLs from diarrhoea, lower respiratory infections, and other common infections, and neonatal disorders fall substantially as sociodemographic status increases. Other YLLs that fall noticeably with rising sociodemographic status include YLLs from maternal causes, nutritional defi ciencies, other non-communicable causes (including congenital causes), and unintentional injuries. YLLs from cardiovascular diseases at fi rst increase slightly with increasing sociodemographic status, but then decrease at the highest levels of country sociodemographic status. Some important causes of global YLLs are not strongly related to sociodemographic status because they are largely country-specifi c, such as neglected tropical diseases and malaria, neoplasms, and intentional injuries. By contrast, overall YLDs decline slightly at fi rst, but then increase substantially, showing the opposite trend to YLLs. The large increases in YLDs

are related to musculoskeletal disorders; mental and substance use disorders; diabetes, urogenital, blood, and endocrine diseases; and neurological disorders. As sociodemographic status rises, the steady decreases in YLLs and increases in YLDs cause the proportion of total DALYs attributable to YLDs to steadily rise from 9·9% at the lowest level of sociodemographic status to 49·1% in the highest vigintile. Above the tenth vigintile of sociodemographic status, the rise in YLDs and fall in YLLs nearly compensate for each other so that DALY rates have remained largely constant.

Country-specifi c results In 1990, life expectancy ranged from 46·9 years (95% UI 45·1–48·2) in the Central African Republic to 80·7 years (78·7–82·5) in Andorra, while HALE ranged from 40·4 years (38·2–42·5) in the Central African Republic to 70·2 years (67·7–72·5) in Japan. By 2013, life expectancy ranged from 48·3 years (46·5–50·1) in Lesotho to 83·9 years (82·3–85·5) in Andorra, and HALE

1990 2005 2013

Male population Female population Male population Female population Male population Female population

Life expectancy (years)

HALE (years)

Life expectancy (years)

HALE (years)

Life expectancy (years)

HALE (years)

Life expectancy (years)

HALE (years)

Life expectancy (years)

HALE (years)

Life expectancy (years)

HALE (years)

(Continued from previous page)

Liberia 50·23 (46·87– 52·55)

42·04 (38·82– 45·14)

52·56 (50·04– 54·61)

43·82 (40·63– 46·72)

59·29 (57·88– 60·77)

49·72 (46·47– 52·50)

59·28 (57·90– 60·67)

49·60 (46·57– 52·56)

62·71 (60·71– 64·91)

53·42 (50·19– 56·34)

63·57 (61·45– 65·72)

53·78 (50·53– 56·94)

Mali 48·75 (46·91– 50·59)

42·37 (39·87– 44·84)

48·86 (47·15– 50·61)

41·79 (39·31– 44·07)

54·90 (53·11– 56·63)

47·83 (45·29– 50·22)

53·72 (52·16– 55·42)

46·15 (43·54– 48·44)

57·58 (54·82– 60·19)

50·73 (47·71– 53·72)

56·97 (54·33– 59·91)

49·35 (46·16– 52·40)

Mauritania 59·87 (58·47– 61·30)

51·72 (49·04– 54·10)

60·89 (59·66– 62·12)

51·98 (49·19– 54·60)

61·87 (59·79– 63·98)

53·96 (51·12– 56·68)

63·45 (61·36– 65·84)

54·42 (51·17– 57·53)

64·02 (61·54– 66·77)

56·03 (52·87– 59·09)

66·13 (63·43– 68·94)

56·97 (53·47– 60·32)

Niger 45·96 (44·12– 47·99)

40·12 (37·69– 42·61)

48·03 (45·94– 49·79)

41·47 (38·75– 44·02)

56·46 (55·08– 57·80)

49·23 (46·79– 51·51)

58·23 (56·93– 59·59)

50·17 (47·60– 52·68)

60·45 (58·80– 62·23)

53·26 (50·77– 55·66)

62·89 (61·17– 64·61)

54·46 (51·72– 57·06)

Nigeria 54·94 (52·82– 56·68)

46·54 (43·60– 49·12)

55·84 (53·79– 57·57)

47·13 (44·22– 49·86)

56·79 (55·14– 58·34)

48·24 (45·62– 50·69)

56·97 (55·33– 58·37)

48·11 (45·29– 50·77)

59·78 (57·48– 62·01)

51·15 (48·34– 54·01)

60·42 (58·01– 62·31)

51·34 (48·27– 54·24)

São Tomé and Príncipe

63·38 (61·38– 65·51)

55·42 (52·46– 58·43)

65·31 (63·04– 67·11)

56·22 (53·15– 59·20)

64·99 (62·96– 67·19)

56·99 (53·86– 60·02)

67·42 (65·57– 69·34)

58·15 (55·01– 61·02)

66·88 (62·57– 70·65)

58·74 (53·87– 62·93)

69·95 (66·64– 73·27)

60·40 (56·40– 64·01)

Senegal 56·50 (55·16– 57·85)

49·73 (47·31– 51·86)

59·63 (58·22– 60·92)

51·28 (48·62– 53·67)

61·39 (59·91– 62·84)

53·94 (51·39– 56·25)

64·19 (62·65– 65·81)

55·16 (52·32– 58·06)

64·25 (62·09– 66·24)

56·59 (53·78– 59·29)

66·99 (64·90– 69·18)

57·71 (54·49– 60·73)

Sierra Leone 48·80 (46·71– 50·95)

42·31 (39·72– 44·98)

53·68 (51·90– 55·58)

45·82 (42·99– 48·50)

52·59 (50·86– 54·25)

45·43 (42·97– 47·84)

56·25 (54·78– 57·79)

48·03 (45·38– 50·59)

55·83 (53·47– 57·85)

49·02 (46·32– 51·50)

59·73 (57·45– 61·87)

51·66 (48·70– 54·56)

The Gambia 56·39 (51·14– 61·18)

49·29 (44·63– 53·70)

58·71 (53·52– 63·42)

50·61 (45·86– 55·02)

60·02 (54·45– 65·44)

52·82 (48·04– 57·42)

62·66 (56·88– 67·74)

54·09 (49·12– 58·83)

62·69 (56·39– 68·33)

55·34 (49·81– 60·37)

66·03 (59·56– 71·65)

57·17 (51·65– 62·47)

Togo 57·42 (55·98– 58·93)

50·07 (47·49– 52·52)

59·72 (58·22– 61·25)

51·29 (48·63– 53·88)

57·66 (55·78– 59·39)

50·34 (47·67– 52·89)

60·25 (58·57– 62·04)

51·52 (48·71– 54·35)

61·12 (58·52– 63·71)

53·81 (50·88– 56·67)

64·76 (62·61– 66·67)

55·64 (52·45– 58·65)

Data are years (95% uncertainty interval). HALE=healthy life expectancy.

Table 3: Life expectancy at birth and HALE at birth for 1990, 2005, and 2013 for both sexes and 188 countries

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www.thelancet.com Vol 386 November 28, 2015 2177

(Figure 6 continues on next page)

1 2 3 4 5 6 7 8 9 10 Global IHD LRI Stroke Back & neck Road inj Diarrhoea COPD NN preterm HIV/AIDS Malaria Developed IHD Back & neck Stroke Lung C Depression COPD Sense Diabetes Alzheimer’s Falls Developing IHD LRI Stroke Back & neck Diarrhoea NN preterm HIV/AIDS Road inj Malaria COPD High-income Back & neck IHD Stroke Lung C COPD Depression Diabetes Alzheimer’s Sense Other MSK Australasia Back & neck IHD Depression Other MSK COPD Alzheimer’s Lung C Stroke Diabetes Sense Australia Back & neck IHD Depression Other MSK COPD Alzheimer’s Lung C Stroke Diabetes Sense New Zealand Back & neck IHD COPD Depression Alzheimer's Lung C Stroke Diabetes Sense Other MSK High-income Asia Pacific Back & neck Stroke IHD Diabetes Lung C Self-harm Other MSK LRI Sense Depression Brunei Back & neck IHD Diabetes Stroke Depression Road inj Congenital Skin Iron Other MSK Japan Back & neck Stroke IHD LRI Lung C Other MSK Diabetes Sense Self-harm Depression Singapore IHD Depression Diabetes Back & neck LRI Stroke Sense Other MSK Lung C Skin South Korea Back & neck Stroke Diabetes Self-harm IHD Liver C Lung C Other MSK Stomach C Migraine High-income North America IHD Back & neck COPD Lung C Depression Diabetes Alzheimer's Other MSK Stroke Sense Canada Back & neck IHD Lung C Other MSK Alzheimer's Diabetes Sense Depression COPD Stroke USA IHD Back & neck COPD Lung C Depression Diabetes Alzheimer's Other MSK Stroke Sense Southern Latin America IHD Back & neck COPD Stroke Depression LRI Road inj Congenital Diabetes Skin Argentina IHD COPD Back & neck Stroke LRI Depression Road inj Congenital Diabetes Skin Chile Back & neck IHD Stroke Depression COPD Road inj Skin Congenital Anxiety CKD Uruguay IHD Stroke Back & neck COPD Alzheimer’s Lung C Depression Road inj Sense LRI Western Europe Back & neck IHD Stroke Lung C Alzheimer’s Sense Falls Depression COPD Diabetes Andorra Back & neck IHD Stroke Sense Lung C Depression Alzheimer’s Falls COPD Skin Austria IHD Back & neck Stroke Alzheimer's Lung C Sense Falls Depression Diabetes COPD Belgium Back & neck IHD Lung C COPD Alzheimer's Stroke Falls Road inj Sense Self-harm Cyprus Back & neck IHD Diabetes Depression Stroke Sense Alzheimer’s Skin Lung C CKD Denmark Back & neck IHD COPD Stroke Lung C Alzheimer’s Falls Depression Skin Colorect C Finland IHD Back & neck Falls Alzheimer’s Stroke Depression Diabetes Sense Lung C Self-harm France Back & neck IHD Lung C Falls Depression Sense Stroke Skin Alzheimer's Self-harm Germany Back & neck IHD Stroke Sense Alzheimer’s Lung C Diabetes COPD Falls CKD Greece IHD Back & neck Stroke Lung C COPD Alzheimer’s Sense Depression CKD Falls Iceland Back & neck IHD Skin COPD Sense Alzheimer’s Lung C Depression Diabetes Stroke Ireland IHD Back & neck Depression COPD Lung C Stroke Skin Sense Falls Diabetes Israel Back & neck IHD Depression Diabetes Alzheimer's CKD Skin Sense COPD Lung C Italy Back & neck IHD Alzheimer’s Sense Stroke Lung C Falls Depression Migraine COPD Luxembourg Back & neck IHD Stroke Lung C COPD Depression Migraine Sense Falls Diabetes Malta Back & neck IHD Diabetes Stroke Depression Sense Lung C Falls CKD COPD Netherlands Back & neck IHD Lung C COPD Skin Stroke Diabetes Sense Depression Alzheimer’s Norway Back & neck IHD Alzheimer’s Stroke Lung C Falls COPD Anxiety Depression Skin Portugal Back & neck Stroke IHD Diabetes Depression Alzheimer’s Sense Lung C CKD COPD Spain Back & neck IHD Diabetes Stroke COPD Alzheimer’s Depression Lung C Sense Falls Sweden Back & neck IHD Stroke Falls COPD Depression Sense Alzheimer’s Lung C Skin Switzerland Back & neck IHD Falls COPD Alzheimer’s Stroke Sense Lung C Depression Skin UK Back & neck IHD Stroke COPD Lung C Alzheimer’s Sense Depression Falls Skin England Back & neck IHD Stroke COPD Lung C Alzheimer’s Sense Depression Falls Skin Northern Ireland IHD Back & neck Depression Lung C Stroke COPD Alzheimer’s Falls Skin Sense Scotland Back & neck IHD Lung C Stroke COPD Alzheimer’s Sense Diabetes Falls Skin Wales Back & neck IHD Stroke COPD Lung C Alzheimer’s Depression Sense Falls Diabetes Central Europe, eastern Europe, and central Asia IHD Stroke Back & neck LRI Self-harm Depression Sense Lung C Road inj COPD Central Asia IHD LRI Stroke NN enceph Back & neck Congenital Road inj Depression Diabetes NN preterm Armenia IHD Stroke Diabetes Back & neck Lung C Depression Sense Road inj Congenital COPD Azerbaijan IHD LRI Stroke Back & neck Diabetes NN enceph Depression Congenital Road inj Sense Georgia IHD Stroke COPD Diabetes Back & neck Sense Depression Falls Alzheimer’s Road inj Kazakhstan IHD Stroke Self-harm Back & neck Road inj Congenital LRI COPD NN enceph Depression Kyrgyzstan IHD Stroke LRI NN enceph NN preterm Congenital Back & neck Road inj COPD Depression Mongolia IHD Stroke LRI Liver C NN enceph Congenital Self-harm Road inj NN preterm Back & neck Tajikistan LRI IHD NN preterm Diarrhoea NN enceph Congenital Stroke Back & neck Depression Drown Turkmenistan IHD LRI Stroke NN enceph Diarrhoea Back & neck Congenital NN preterm Depression Diabetes Uzbekistan IHD LRI Stroke NN enceph Back & neck Road inj Depression Diabetes Congenital HTN HD Central Europe IHD Stroke Back & neck Lung C Falls COPD Sense Diabetes Depression Alzheimer’s Albania IHD Stroke Back & neck Depression LRI Falls Lung C Sense COPD Diabetes Bosnia and Herzegovina IHD Stroke Back & neck Diabetes Lung C CMP COPD Sense Depression Falls Bulgaria IHD Stroke Back & neck COPD Diabetes HTN HD Falls Lung C Sense Alzheimer’s Croatia IHD Stroke Back & neck Lung C Sense COPD Diabetes Alzheimer’s Depression Falls Czech Republic IHD Back & neck Stroke Falls Lung C Sense Diabetes COPD Depression Alzheimer’s Hungary IHD Stroke Back & neck Lung C Falls COPD Sense Diabetes Colorect C Depression Macedonia Stroke IHD Back & neck Diabetes Lung C Depression Sense Falls COPD CKD Montenegro IHD Stroke Back & neck Lung C Diabetes Sense Falls Depression Self-harm CKD Poland IHD Stroke Back & neck Lung C Falls COPD Sense Depression Diabetes Self-harm Romania IHD Stroke Back & neck Falls Lung C Sense Depression COPD Diabetes Alzheimer’s Serbia IHD Stroke Back & neck CMP Lung C Diabetes Sense COPD Depression Alzheimer’s Slovakia IHD Back & neck Stroke Falls Lung C Sense Depression Diabetes Colorect C COPD Slovenia Back & neck IHD Falls Stroke Lung C Sense Depression COPD Alzheimer’s Diabetes Eastern Europe IHD Stroke Back & neck Self-harm CMP Depression Sense Alcohol Lung C Road inj Belarus IHD Stroke Back & neck Self-harm Road inj Lung C Depression Sense COPD Alcohol

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2178 www.thelancet.com Vol 386 November 28, 2015

(Figure 6 continues on next page)

LRI NN preterm IHD Stroke Diarrhoea Road inj Congenital NN enceph Iron TB IHD Road inj Stroke LRI Diabetes Back & neck COPD Skin Depression Nematode IHD Back & neck Congenital Stroke COPD Sense NN enceph Diabetes Skin NN preterm

Cambodia IHD LRI NN preterm War Stroke Congenital Road inj Iron TB OPD Indonesia Stroke IHD TB LRI Diabetes Road inj NN enceph Back & neck Diarrhoea NN preterm Laos Malaysia Maldives

IHD LRI Stroke Back & neck TB NN preterm Congenital Diabetes COPD Iron IHD Self-harm Diabetes COPD Back & neck Stroke Sense Road inj Asthma Depression IHD Road inj Stroke Diabetes LRI Sense COPD CKD Liver C HIV/AIDS LRI NN preterm Diarrhoea Congenital IHD Iron Stroke Other NN NN enceph Road inj Stroke Road inj Back & neck Sense LRI NN preterm Liver C Depression War COPD IHD LRI NN enceph NN preterm Diarrhoea COPD TB Stroke Back & neck Road inj LRI Diarrhoea NN preterm IHD Congenital Road inj Stroke Iron Meningitis Other NN Stroke Back & neck NN enceph IHD NN preterm LRI COPD Drown Iron Depression IHD LRI NN enceph Stroke Back & neck NN preterm COPD Road inj Depression Diabetes IHD COPD TB LRI NN preterm NN enceph Diarrhoea Stroke Road inj Back & neck

Belarus IHD Stroke Back & neck Self-harm Road inj Lung C Depression Sense COPD Alcohol Estonia IHD Stroke Back & neck Depression Lung C Sense Alzheimer's Alcohol Diabetes HTN HD Latvia IHD Stroke Back & neck Sense CMP Lung C Alzheimer's Self-harm Depression Diabetes Lithuania IHD Stroke Back & neck Self-harm Sense Depression Lung C Alzheimer’s COPD Road inj Moldova IHD Stroke Back & neck Depression Cirr HepC Sense LRI COPD Self-harm Lung C Russia IHD Stroke Back & neck CMP Self-harm Depression Alcohol Sense Road inj Lung C Ukraine IHD Stroke Back & neck Depression Self-harm Sense HIV/AIDS Lung C Road inj COPD Latin America and Caribbean Andean Latin America

IHD LRI Road inj IHD Back & neck Congenital Depression NN preterm Sense Stroke F Body

Violence Back & neck Road inj Diabetes Depression Stroke LRI Congenital Sense

Bolivia LRI F Body Road inj NN preterm IHD Congenital NN enceph Back & neck Depression Diarrhoea Ecuador LRI Road inj IHD Congenital Violence Back & neck Depression CKD Stroke Sense Peru LRI Back & neck IHD Depression Road inj Sense Congenital COPD NN preterm Stroke Caribbean IHD Stroke Diabetes LRI HIV/AIDS Road inj Depression Diarrhoea Back & neck Congenital Antigua and Barbuda Diabetes IHD Stroke Depression Back & neck Sense Road inj Iron CKD LRI Barbados Diabetes IHD Stroke Back & neck Depression Sense CKD LRI COPD Skin Belize Diabetes IHD Stroke Violence Road inj NN preterm Depression Congenital Iron HIV/AIDS Cuba IHD Stroke Diabetes Sense Depression Lung C Back & neck COPD LRI Road inj Dominica Diabetes IHD Stroke Depression Back & neck Road inj Sense LRI CKD NN preterm Dominican Republic IHD Road inj Stroke NN preterm Congenital Diabetes Depression Back & neck LRI Violence Grenada IHD Diabetes Stroke Road inj Depression Back & neck LRI Sense HIV/AIDS Violence Guyana IHD HIV/AIDS Stroke Diabetes Road inj Congenital LRI Self-harm Violence NN preterm Haiti HIV/AIDS LRI Diarrhoea Stroke PEM NN sepsis Iron IHD NN preterm Congenital Jamaica Diabetes Stroke IHD Violence Depression Back & neck NN preterm Sense CKD Congenital Saint Lucia Diabetes IHD Stroke Depression Back & neck Sense Violence Road inj LRI COPD Saint Vincent and the Grenadines IHD Diabetes Stroke Depression Back & neck NN preterm Road inj HIV/AIDS Violence Sense Suriname IHD Stroke Diabetes NN preterm Congenital Road inj LRI Depression HIV/AIDS Back & neck The Bahamas IHD Diabetes Stroke HIV/AIDS Depression Back & neck Violence Road inj CKD Sense Trinidad and Tobago Diabetes IHD Stroke Road inj Violence Depression Back & neck CKD Sense HIV/AIDS Central Latin America Violence IHD Diabetes Back & neck Road inj Depression CKD Congenital LRI Sense Colombia Violence IHD Depression Back & neck Road inj Congenital COPD Stroke Diabetes Sense Costa Rica Back & neck IHD Depression Road inj Sense Congenital CKD COPD Asthma Diabetes El Salvador Violence IHD Road inj Back & neck CKD Congenital Diabetes LRI Depression Iron Guatemala LRI Violence Diarrhoea NN preterm Back & neck IHD Iron Congenital Diabetes PEM Honduras IHD Congenital Violence Depression NN preterm COPD Back & neck Stroke Diarrhoea LRI Mexico Diabetes IHD CKD Back & neck Depression Road inj Congenital Violence Sense COPD Nicaragua Congenital CKD Back & neck War Depression LRI IHD NN preterm Diabetes Road inj Panama Back & neck IHD Congenital Depression Violence Road inj Diabetes Stroke Sense CKD Venezuela Violence IHD Road inj Back & neck Diabetes Depression Stroke Congenital Sense CKD Tropical Latin America IHD Back & neck Violence Stroke Road inj Diabetes Depression Anxiety COPD Sense Brazil IHD Back & neck Violence Stroke Road inj Diabetes Depression Anxiety COPD Sense Paraguay IHD Road inj Back & neck Stroke Congenital Diabetes NN preterm Depression LRI Violence Southeast Asia, east Asia, and Oceania Stroke IHD Back & neck Road inj COPD Diabetes Sense Lung C Depression LRI East Asia Stroke Back & neck IHD COPD Road inj Lung C Sense Depression Diabetes Liver C

Stroke Back & neck IHD COPD Road inj Lung C Depression Sense Diabetes Liver C Stroke IHD Back & neck COPD Lung C Road inj Liver C Stomach C Sense Congenital

China North Korea Taiwan (province of China) Back & neck Diabetes IHD Stroke Liver C Sense Road Inj Lung C Skin Self-harm Oceania LRI IHD Diabetes Diarrhoea Malaria Congenital NN preterm Stroke Road inj Other NN Federated States of Micronesia IHD Diabetes Stroke LRI Congenital Road inj Back & neck Skin COPD Asthma Fiji IHD Diabetes Stroke LRI Congenital NN preterm Back & neck CKD COPD Sense Kiribati Diabetes Stroke IHD LRI Other NN Congenital Road inj Diarrhoea Asthma Back & neck Marshall Islands IHD Diabetes LRI Stroke Congenital NN preterm Road inj Other NN Back & neck CKD Papua New Guinea LRI IHD Diarrhoea Diabetes Malaria NN preterm Congenital Other NN Road inj HIV/AIDS Samoa Diabetes IHD Stroke Back & neck LRI Congenital Skin Sense CKD Road inj

Solomon Islands IHD Diabetes Stroke LRI Diarrhoea Congenital NN preterm Back & neck Asthma TB

Tonga IHD Diabetes LRI Stroke NN preterm Back & neck Congenital Road inj Skin Other NN

Vanuatu IHD Diabetes LRI Stroke NN preterm Congenital Other NN Road inj Diarrhoea Back & neck Southeast Asia Stroke IHD LRI Road inj TB Diabetes Back & neck NN preterm Sense COPD

C

Myanmar Stroke LRI TB IHD Sense Malaria COPD NN preterm Diabetes Road inj

Philippines Sri Lanka Thailand Timor-Leste Vietnam South Asia Afghanistan Bangladesh Bhutan India

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(Figure 6 continues on next page)

IHD COPD TB LRI NN preterm NN enceph Diarrhoea Stroke Road inj Back & neck LRI IHD Back & neck NN enceph Diarrhoea Stroke COPD TB Self-harm NN preterm LRI NN enceph Diarrhoea IHD NN preterm NN sepsis Stroke Meningitis Road inj Congenital IHD Back & neck Congenital NN preterm Stroke Diabetes Road inj Depression LRI COPD NN preterm IHD Back & neck Congenital Road inj Stroke Diabetes Depression Sense Iron Diabetes Back & neck Drugs Depression IHD Road inj Skin Sense Iron Congenital IHD Stroke Back & neck Congenital COPD LRI Diabetes Cirr hep C Sense Other cardio IHD Back & neck Congenital NN preterm Depression Road inj Diabetes Sense Stroke Drugs NN preterm IHD Congenital Stroke Diabetes Back & neck LRI Iron CKD Road inj Congenital Back & neck NN preterm Diabetes IHD Depression Drugs Iron Skin LRI IHD Diabetes Depression Drugs Congenital Road inj Back & neck Skin Sense NN preterm IHD Diabetes Depression Back & neck Congenital Skin War COPD Stroke Drugs IHD Back & neck Diabetes Congenital Stroke Depression Road inj NN preterm COPD Iron Back & neck NN preterm IHD Diabetes Drugs Depression Stroke Road inj Congenital NN enceph Road inj Back & neck IHD Diabetes Depression Sense Congenital Drugs Skin Stroke Congenital IHD Depression Back & neck Diabetes Stroke NN preterm LRI Iron Road inj Drugs Back & neck Road inj Depression Diabetes Skin Congenital IHD NN preterm Sense Road inj Diabetes Back & neck IHD Depression Congenital Drugs NN preterm Skin Stroke NN preterm IHD Congenital Diarrhoea LRI Stroke Back & neck Road inj Iron Malaria War IHD Stroke Back & neck Depression Congenital Diabetes Iron Migraine COPD IHD Back & neck Road inj Stroke Depression Congenital Diabetes COPD Sense NN preterm IHD Back & neck COPD Diabetes Congenital Stroke Lung C Depression Road inj NN preterm Road inj Back & neck Drugs Depression IHD Diabetes Skin Congenital Sense COPD NN preterm IHD Diarrhoea Congenital LRI Stroke Iron Malaria Road inj Back & neck HIV/AIDS Malaria LRI Diarrhoea NN preterm PEM NN enceph Congenital TB Road inj LRI Diarrhoea Malaria PEM HIV/AIDS NN preterm Congenital TB NN enceph Meningitis LRI Diarrhoea HIV/AIDS Malaria Congenital PEM NN preterm TB NN enceph Road inj HIV/AIDS LRI Diarrhoea Malaria TB NN preterm PEM STD Meningitis NN enceph HIV/AIDS LRI Malaria Congenital Stroke Diarrhoea NN preterm NN enceph Measles TB Diarrhoea LRI Malaria PEM NN preterm HIV/AIDS Congenital TB NN enceph Iron HIV/AIDS LRI Malaria Congenital Road inj Diarrhoea Stroke NN preterm PEM NN enceph HIV/AIDS LRI Malaria Stroke Road inj Congenital TB Diarrhoea IHD NN enceph HIV/AIDS LRI Malaria Diarrhoea TB NN preterm NN enceph PEM NN sepsis Congenital Malaria LRI Diarrhoea TB HIV/AIDS NN preterm PEM NN enceph NN sepsis Congenital LRI Diarrhoea TB NN preterm Malaria NN enceph Stroke NN sepsis Road inj Congenital HIV/AIDS LRI Malaria Diarrhoea TB Stroke NN enceph Depression PEM Congenital Diarrhoea LRI TB HIV/AIDS Malaria Iron NN preterm PEM Depression Meningitis LRI Diarrhoea HIV/AIDS TB NN preterm NN enceph Malaria NN sepsis Congenital Meningitis HIV/AIDS LRI Diarrhoea TB NN preterm Malaria NN enceph Congenital PEM NN sepsis LRI Diarrhoea Stroke NN preterm PEM Malaria STD Iron Depression Congenital HIV/AIDS LRI Diarrhoea PEM Malaria TB NN preterm Congenital NN enceph Meningitis Diabetes IHD Stroke CKD Back & neck Sense COPD Road inj Depression LRI HIV/AIDS Malaria LRI Diarrhoea TB NN sepsis NN enceph NN preterm STD Road inj HIV/AIDS LRI Malaria Diarrhoea NN preterm War NN enceph Road inj TB NN sepsis IHD Stroke LRI Diabetes Back & neck HTN HD Sense COPD Road inj Depression Diarrhoea LRI Malaria TB PEM Meningitis NN preterm NN enceph Tetanus Other NN LRI Diarrhoea HIV/AIDS TB PEM Meningitis Malaria STD NN preterm NN enceph HIV/AIDS LRI Malaria Diarrhoea TB Congenital PEM NN enceph STD NN preterm HIV/AIDS Malaria LRI Diarrhoea NN preterm NN enceph TB NN sepsis PEM Road inj HIV/AIDS Malaria LRI Diarrhoea PEM TB NN enceph Congenital NN sepsis Meningitis HIV/AIDS LRI Diarrhoea TB Back & neck Violence NN preterm Stroke Diabetes Road inj HIV/AIDS TB LRI Diarrhoea Back & neck NN preterm Road inj Depression Other NN Self-harm HIV/AIDS TB Diarrhoea LRI NN preterm Violence Other NN NN enceph Road inj Self-harm HIV/AIDS TB LRI Diarrhoea Stroke Self-harm Road inj NN preterm Other NN Back & neck HIV/AIDS LRI TB Diarrhoea Back & neck Diabetes Violence Stroke COPD Road inj HIV/AIDS LRI TB Diarrhoea Road inj NN preterm Other NN Violence Self-harm Stroke HIV/AIDS LRI Diarrhoea TB NN preterm NN enceph Stroke PEM Malaria Road inj Malaria LRI HIV/AIDS Diarrhoea NN preterm NN enceph Haemog Road inj PEM NN sepsis Malaria LRI HIV/AIDS Diarrhoea NN preterm NN enceph Congenital Iron Road inj NN sepsis Malaria LRI Diarrhoea NN preterm Congenital Meningitis NN enceph Road inj HIV/AIDS NN sepsis HIV/AIDS Malaria LRI Diarrhoea Road inj NN preterm NN enceph Congenital PEM NN sepsis Stroke Back & neck Depression Congenital IHD LRI Iron COPD Sense Skin Diarrhoea LRI Malaria HIV/AIDS PEM NN preterm Meningitis NN enceph Iron Congenital LRI HIV/AIDS Malaria Diarrhoea NN preterm NN enceph Road inj NN sepsis Congenital PEM Malaria LRI HIV/AIDS NN sepsis NN preterm PEM NN enceph Stroke Road inj Iron Malaria LRI Diarrhoea HIV/AIDS NN preterm NN enceph PEM NN sepsis Meningitis Road inj Malaria HIV/AIDS LRI Diarrhoea NN preterm PEM NN enceph Meningitis Road inj Congenital Malaria LRI Diarrhoea HIV/AIDS NN preterm NN enceph PEM TB NN sepsis Congenital Malaria Diarrhoea LRI PEM NN preterm NN enceph Iron Meningitis NN sepsis Congenital LRI Malaria Diarrhoea NN enceph NN preterm Road inj Congenital Iron NN sepsis Stroke Malaria Diarrhoea LRI PEM NN preterm Meningitis Iron Congenital NN enceph NN sepsis Malaria LRI HIV/AIDS Haemog Road inj NN preterm NN enceph Diarrhoea PEM NN sepsis Malaria LRI Stroke NN preterm Congenital NN enceph Iron NN sepsis PEM Diarrhoea

India Nepal Pakistan North Africa and Middle East Algeria Bahrain Egypt Iran Iraq Jordan Kuwait Lebanon Libya Morocco Oman Palestine Qatar Saudi Arabia Sudan Syria Tunisia Turkey United Arab Emirates Yemen Sub-Saharan Africa Central sub-Saharan Africa Angola Central African Republic Congo Democratic Republic of the Congo Equatorial Guinea Gabon Eastern sub-Saharan Africa Burundi Comoros Djibouti Eritrea Ethiopia Kenya Madagascar Malawi Mauritius Mozambique Rwanda Seychelles Somalia South Sudan Tanzania Uganda Zambia Southern sub-Saharan Africa Botswana Lesotho Namibia South Africa Swaziland Zimbabwe Western sub-Saharan Africa Benin Burkina Faso Cameroon Cape Verde Chad Côte d’Ivoire Ghana Guinea Guinea-Bissau Liberia Mali Mauritania Niger Nigeria São Tomé and Príncipe

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ranged from 42·0 years (39·8–44·2) in Lesotho to 73·4 years (70·5–76·0) in Japan. Disaggregating by sex, in 1990, there were no countries in which men had attained a HALE of 70 years or more, and only in Japan and Andorra did women attain this. By 2013, HALE for men exceeded 70 years in only two countries (Japan and Singapore), whereas HALE for women exceeded 70 years in 40 countries (table 3). Of the 21 GBD regions, nine contained at least one country in which female HALE was at least 70 years in 2013. For most countries, changes in HALE were positive for both men and women between 1990 and 2013, and the diff erences were signifi cant. However, HALE in 2013 was not signifi cantly higher than it was in 1990 in 43 countries for men and in 32 countries for women. As life expectancy increases, the gap between life expectancy and HALE widens, increasing to more than 10 years at a life expectancy of 77 years for women and 78 years for men. The life expectancy minus HALE divided by life expectancy (the percentage of life expectancy lost because of poor health) ranged between 11·5% and 15·0%.

Figure 6 shows the ten most common causes of DALYs for each country in 2013. The leading causes of DALYs vary substantially across regions, representing both diff erent levels of sociodemographic status and distinct regional patterns. In high-income regions, low back and neck pain, ischaemic heart disease, cerebrovascular disease, and tracheal, bronchus, and lung cancer are often among the four most common causes, although major depression, COPD, and diabetes come into the top four slots in some countries. In central and eastern Europe, cardiovascular diseases rank consistently in the most common causes of DALYs. Self-harm and depression frequently rank higher in eastern Europe than in central Europe or elsewhere. In central Asia, representative of the mixed levels of sociodemographic status present in the region, leading causes include neonatal encephalopathy

and congenital causes. In central Latin America, violence, ischaemic heart disease, diabetes, low back and neck pain, and road injuries comprise the top fi ve causes. Other examples of distinct regional patterns include the high ranking of COPD in east Asia, the dominance of malaria in west Africa, and the dominance of HIV/AIDS in eastern and southern sub-Saharan Africa. Within some regions, such as north Africa and the Middle East, the leading causes varied substantially.

Discussion Global health is improving: life expectancy at birth rose by 6·2 years between 1990 and 2013, while HALE at birth increased by 5·4 years during the same interval; worldwide, age-standardised DALY rates fell by 27%. Global progress has accelerated since 2005 because of major reductions in HIV/AIDS and malaria, in addition to continued progress against other major communicable, maternal, neonatal, and nutritional disorders. Although the total volume of DALYs is down by only 3·6% over the 23 year period, this is largely explained by population growth and ageing driving up numbers of DALYs. Declines in age-standardised DALY rates are counterbalanced by population growth and ageing, so that, despite improvements in age–sex-specifi c health status, demands on health systems remain high. An example of these demands is the fact that the number of individuals in the world living in states of health characterised by a disability weight greater than 0·1 has increased by 43% from 1990 to 2013.

In 1971, Omran27 outlined the concept of the epidemiological transition to describe the changing pattern of causes of death that results from sociodemographic development. The notion of the epidemiological transition has been expanded to recognise the phase in transition that leads to double burden of disease9,28,29 and the countertransitions of the HIV/AIDS epidemic and the rise

Figure 6: Ten most common GBD level 3 causes of DALYs for 188 countries in 2013 The 15 most common global causes of DALYs are coloured. Alcohol=alcohol use disorders. Alzheimer=Alzheimer’s disease and other dementias. Anxiety=anxiety disorders. Back and neck=low back and neck pain. Cirr Hep C=cirrhosis due to hepatitis C. CKD=chronic kidney disease. CMP=cardiomyopathy and myocarditis. Colorect C=colon and rectum cancer. Congenital=congenital disorders. COPD=chronic obstructive pulmonary disease. Depression=depressive disorders. Diabetes=diabetes mellitus. Diarrhoea=diarrhoeal diseases. Drown=drowning. Drugs=drug use disorders. F Body=foreign body. Haemog=haemoglobinopathies and haemolytic anaemias. HTN HD=hypertensive heart disease. IHD=ischaemic heart disease. Iron=iron-defi ciency anaemia. Liver C=liver cancer. LRI=lower respiratory infections. Lung C=tracheal, bronchus, and lung cancer. Nematode=intestinal nematode infections. NN enceph=neonatal encephalopathy due to birth asphyxia and trauma. NN preterm=preterm birth complications. NN sepsis=neonatal sepsis and other neonatal infections. Other cardio=other cardiovascular and circulatory diseases. Other MSK=other musculoskeletal disorders. Other NN=other neonatal disorders. PEM=protein-energy malnutrition. Road inj=road injuries. Sense=sense organ diseases. Skin=skin and subcutaneous diseases. STD=sexually transmitted diseases excluding HIV. Stomach C=stomach cancer. Stroke=cerebrovascular disease. TB=tuberculosis. Violence=interpersonal violence. War=collective violence and legal intervention. DALY=disability-adjusted life-years. GBD=Global Burden of Disease.

Malaria LRI HIV/AIDS Haemog Road inj NN preterm NN enceph Diarrhoea PEM NN sepsis Malaria LRI Stroke NN preterm Congenital NN enceph Iron NN sepsis PEM Diarrhoea Malaria LRI Diarrhoea NN preterm NN enceph Iron NN sepsis HIV/AIDS Congenital Road inj Malaria LRI HIV/AIDS PEM NN preterm Diarrhoea NN enceph Congenital NN sepsis Haemog Malaria LRI Diarrhoea Congenital NN preterm HIV/AIDS NN sepsis NN enceph Road inj PEM Malaria LRI HIV/AIDS Diarrhoea NN preterm NN enceph Congenital PEM NN sepsis Haemog

Sense organ diseases Diabetes mellitus Congenital anomalies

Malaria Preterm birth complications Chronic obstructive pulmonary disease

Nigeria São Tomé and Príncipe Senegal Sierra Leone The Gambia Togo

Ischaemic heart disease IHD Road inj Depression Lower respiratory infections LRI Diarrhoea NN enceph Cerebrovascular disease Stroke COPD Congenital HIV/AIDS HIV/AIDS NN preterm Diabetes Low back and neck pain Back & neck Malaria Sense

Neonatal encephalopathy due to birth asphyxia and trauma Depressive disorders

Diarrhoeal diseases Road injuries

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of mortality in the former Soviet Union.2,10,11,13,30–32 Many studies have recognised regional and national variation in the epidemiological transition.9,14,33–35 Taking advantage of the database of the GBD 2013 country-level results from 1990 to 2013, we have quantifi ed the extent to which sociodemographic status accounts for changes in epidemiological patterns, as opposed to other factors changing over time or static variation between countries. Isolation of this component of the variation of DALY rates allows examination of the shifts in disease and injury patterns that would be expected purely as a function of changing sociodemographic status. As sociodemographic status rises, YLLs from diarrhoea, lower respiratory infections, neonatal causes, maternal mortality, and other infectious causes decline substantially, while at the same time, there has been a slower rise in YLDs from musculoskeletal disorders, mental and substance use disorders, neurological disorders, and diabetes, urogenital, blood, and endocrine diseases. DALY rates for neoplasms and cardiovascular disease are minimally related to sociodemographic status; instead local factors have a profound eff ect. However, with rising sociodemographic status, the proportion of DALYs due to these causes increases because of decreases in other causes of YLLs. Although, DALY rates for cardiovascular disease seem not to be related to sociodemographic status, large declines have been recorded for these causes in high-income countries in age-standardised rates over the past decades. The wide variation between some high sociodemographic status countries, such as Japan and Finland, shows how other factors, such as diet, physical activity, and other risks, vary substantially within the same level of sociodemographic status and also aff ect cardiovascular disease outcomes. Furthermore, our analysis of the epidemiological transition is based on crude population rates, so reductions in age-specifi c cardiovascular rates associated with rising sociodemographic status are countered by shifts towards an older population. Our analysis of the epidemiological transition shows decreases in DALY rates for cardiovascular disease at the very highest levels of sociodemographic status. Notably, the predictable rise in YLD rates for some causes (such as musculoskeletal disorders, diabetes mellitus, and mental and substance use disorders) driven by population ageing is not well recognised in the literature about the epidemiological transition.

Our decomposition of variance analysis shows that, for many NCDs, the main determinants of variation in DALY rates are country-specifi c eff ects, not the epidemiological transition. Global health can be understood in terms of a general theme in which change in epidemiological patterns is related to sociodemographic status, upon which country-specifi c patterns are overlaid. Little of the variation in DALY rates was attributable to the year, which contrasted with previous fi ndings showing that the association between life expectancy and income and education has shifted over time.24,36–38 Our

analysis only covers a 23 year period, which might be too short to fully capture the eff ects of changing sociodemographic status. Some of the country eff ects, such as those noted for neglected tropical diseases and malaria, might have been related to sociodemographic status in a longer run analysis. The substantial eff ect of country variation on the epidemiological transition pattern reinforces the importance of estimating the burden of disease for each country individually. The GBD results can be used productively in the future to characterise the deviation of individual countries from the general epidemiological transition. Our analysis of the association between crude DALY rates and country sociodemographic status does not provide insights into within-country disease associations across individual levels of socioeconomic status. For example, fi ndings from multiple studies have shown that individuals of lower socioeconomic status have increased rates of cardiovascular and circulatory diseases.39–47

HALE varies widely between countries and over time. As a single summary measure of population health, HALE is fairly simple to explain and provides an indicator that is aff ected by any changes in mortality rates or prevalence of disease or injury. HALE has been proposed as an indicator for the Sustainable Development Goals. As calculated through the GBD, HALE is an attractive measure that is sensitive to intervention and comparable over time and between populations. Although HALE needs input about the prevalence of multiple sequelae, the annual revisions of the GBD provide a widely available source for regular updates. By contrast, some other variants of health expectancies might be less appropriate for intertemporal or cross-country comparison. With measures that defi ne disability according to arbitrary thresholds of disability weight, such as disability-free life expectancy (DFLE),48 even slight changes in the disability weight threshold will profoundly aff ect conclusions about levels and changes in healthy life expectancy, as can be seen in fi gure 1. Moreover, these measures are non- standardised and hence not comparable: in some implementations of DFLE, the choice of the severity threshold to defi ne disability is left to individual respondents in the surveys. For example, the European Union49 has adopted a measure of healthy life expectancy based on survey responses to a single item on activity limitations. Such a measure is susceptible to variation in the meaning attached to categorical descriptions of limitation levels, both between individuals and over time, as seen in related survey items on health status.50–52 An example of the sensitivity of DFLE measured in Europe is the reported decline in DFLE in Italy after 2004, which was caused by a change in question phrasing; we noted an increase in HALE for Italy in this study.53 Although HALE and DFLE both use disability weights, the continuous scale of disability weights used in HALE makes it less sensitive to measurement error than are the dichotomous (zero or one) weights used in DFLE.

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Our fi ndings support those of Salomon and colleagues,8 which showed that HALE is increasing more slowly than life expectancy: ie, as life expectancy increases, the expectation of years lived with multiple sequelae increases as well. The diff erence between life expectancy and HALE has increased, whereas the ratio of this diff erence to life expectancy has remained fairly constant. Whether or not this change should be termed an expansion of morbidity is not the issue. Rather, we saw that while health loss because of YLDs from cardiovascular and circulatory diseases and neoplasms might be decreasing, the real drivers of the diff erence between HALE and life expectancy are musculoskeletal disorders, mental and substance use disorders, neurological disorders, and diabetes, along with vision loss and hearing loss. Prevalence for all of these conditions is strongly age-related, and age-standardised rates for them are not declining. Even though the age of onset for mental health and substance use disorders is not strongly age-related, the prevalence of these conditions tends to rise with age. Prevalence of musculoskeletal disorders, neurological disorders, diabetes, and hearing and vision loss rise even more profoundly with age. As individuals increasingly survive to 80 years and older, the amount of time spent with a combination of these disorders increases, even though age-standardised rates have not increased over time. According to our analysis, the available therapies have not led to signifi cant declines in age-standardised YLD rates. Very few, if any, of these disorders receive the attention they deserve in public policy discourse about health and health research priorities.

Global health progress has been driven by impressive progress in reducing age-standardised rates for a wide range of causes of death. Age-standardised YLD rates, however, are not declining. Many potential reasons exist to explain the general success for mortality and absence of success for disease prevalence. Research and development investments by funders such as the US National Institutes of Health (NIH) and the pharmaceutical industry have focused on cardiovascular diseases, neoplasms, and endocrine disorders.54–59 As we report, in the early phases of the epidemiological transition, the major driver of change in disease and injury patterns is progress in the reduction of YLLs. We believe that the historical focus of health research funding on causes of YLLs was probably appropriate. However, health progress now means that more research investment is needed for the disorders that debilitate, rather than kill. With each passing year, the shift towards YLDs as the leading causes of disease burden will be more evident. Action is needed now to develop preventive, curative, and ameliorative strategies for these conditions rather than waiting until this shift is even more obvious.

Controlling for sociodemographic status, substantial variation exists for DALY rates between countries. In our

decomposition of variance, the importance of intercountry variation fl uctuated by cause; for example, country level variation explains little variation for neonatal causes, but most variation for self-harm and interpersonal violence. These fi ndings raise the question of whether the division of countries into 21 regions in GBD based on geographic contiguity and the levels and patterns of adult and child mortality rates is the best way to explain the variance in DALY rates. With country- specifi c results now available, more sophisticated clustering methods could be used, with various constraints, to propose a more empirically calculated set of regions. Regions have two dimensions: analytical and presentational. The presentational dimension is easily addressed because results for any set of countries can easily be generated from the country-specifi c results. In fact, the GBD Compare data visualisation tool provides several alternative presentational groupings, such as WHO political regions or World Bank regions. However, regions have an analytical eff ect on the results if the super-region and region hierarchy have been used in the Bayesian modelling. In the GBD cause of death analysis, spatiotemporal Gaussian process regression models use the GBD hierarchy to borrow strength. In DisMod-MR 2.0, the regional hierarchy also aff ects estimation of the prior for each country analysis. More simply, where data are sparse or not available, the GBD regional groupings can have an important analytical eff ect. More research is needed on two fronts. First, to explore the extent to which alternative regional groupings would explain more of the variance in the DALY results (or any other GBD indicator). Second, whether analytical tools, such as cause of death ensemble modelling (CODEm)60 or DisMod-MR 2.0,1 could be modifi ed to easily allow for diff erent regional hierarchies for diff erent causes.

This study has all the limitations previously reported for the GBD 2013 analysis of YLLs and YLDs.1,2 Additionally, a key limitation is the assumption that uncertainty is independent for YLLs and YLDs. In fact, for diseases modelled with DisMod-MR 2.0, we estimated a correlation between the uncertainty in condition-specifi c mortality and the uncertainty in prevalence. However, DisMod-MR 2.0 estimates excess mortality related to a cause, not the mortality that would be assigned to a cause according to the ICD principles of underlying cause. In future iterations of the GBD, it might be useful to attempt to use the correlation structure produced from DisMod-MR 2.0 as a proxy for the correlation between the underlying cause and prevalence. By assuming independence, we might be underestimating overall uncertainty in DALYs. However, more careful examination of the uncertainty in YLDs reveals that most uncertainty stems from uncertainty in disability weights and not from uncertainty in prevalence. We have no reason to assume that the uncertainty associated with valuations of health states in population surveys would be correlated with either

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disease or death outcomes. Inclusion of the smaller component of the correlation of prevalence with mortality in the estimation of DALY uncertainty would probably not substantially alter the large uncertainty already recorded for DALYs. Another key limitation, is that our assessment of the burden of disease related to sociodemographic status was aff ected by the choice of variables that we have included in our sociodemographic status indicator (income per person, average years of schooling after age 15 years, total fertility rate, and mean age of the population). Alternative measures of sociodemographic status could be developed that might explain more of the variance seen for some GBD level 2 causes of DALYs. However, we experimented with alternative formulations, including the addition of urbanisation and all combinations of the four variables in our index, and established that the approach we used had the most explanatory power. Other variables that would be interesting to include in a composite country- level sociodemographic status measure, such as the Gini coeffi cient, were not available for all countries in all years. Notably, where both measures are available, the correlation between the sociodemographic status indicator and the UN Human Development Index during the 23 year time period was 0·95.

In the post-Millennium Development Goal (MDG) era, there is much interest in identifi cation of appropriate goals for population health and how these goals should be monitored.4 Increasingly, measurement frameworks to assess levels of health in populations have moved away from measures of survival to encompass more holistic measures of disability as well as mortality. Our fi ndings suggest that this broader focus is probably going to be increasingly relevant to guide countries’ public policy interventions. Large, impressive, and sustained gains are being made against the majority of leading causes of death in most countries, but as our fi ndings show, these gains are not being accompanied by commensurate declines in age-standardised rates of disability, especially from major musculoskeletal disorders, mental and substance use disorders, neurological disorders, and diabetes. Moreover, the failure of health information systems to reliably describe trends in these disorders not only severely hampers policy responses, but contributes greatly to their further neglect and insuffi cient awareness of the signifi cant part they now play in overall population health. Despite the important country variation in measures of population, now is the time for the global health community, donors and countries alike, to maximise the opportunities for health that have resulted from the data revolution, and ensure that priority is given to the development of scientifi cally valid, feasible, and informative data systems to measure progress in reducing disability. Improved data and monitoring can help decision makers to reduce DALYs from a wide range of causes by pursuing the most important opportunities for prevention, treatment, and rehabilitation.

For the United Nations Development Programme Human Development Index see http://hdr.undp.org/en/content/ human-development-index-hdi

GBD 2013 DALYs and HALE Collaborators Christopher J L Murray, Ryan M Barber, Kyle J Foreman, Ayse Abbasoglu Ozgoren*, Foad Abd-Allah*, Semaw F Abera*, Victor Aboyans*, Jerry P Abraham*, Ibrahim Abubakar*, Laith J Abu-Raddad*, Niveen M Abu-Rmeileh*, Tom Achoki*, Ilana N Ackerman*, Zanfi na Ademi*, Arsène K Adou*, José C Adsuar*, Ashkan Afshin*, Emilie E Agardh*, Sayed Saidul Alam*, Deena Alasfoor*, Mohammed I Albittar*, Miguel A Alegretti*, Zewdie A Alemu*, Rafael Alfonso-Cristancho*, Samia Alhabib*, Raghib Ali*, François Alla*, Peter Allebeck*, Mohammad A Almazroa*, Ubai Alsharif *, Elena Alvarez*, Nelson Alvis-Guzman*, Azmeraw T Amare*, Emmanuel A Ameh*, Heresh Amini*, Walid Ammar*, H Ross Anderson*, Benjamin O Anderson*, Carl Abelardo T Antonio*, Palwasha Anwari*, Johan Arnlöv*, Valentina S Arsic Arsenijevic*, Al Artaman*, Rana J Asghar*, Reza Assadi*, Lydia S Atkins*, Marco A Avila*, Baff our Awuah*, Victoria F Bachman*, Alaa Badawi*, Maria C Bahit*, Kalpana Balakrishnan*, Amitava Banerjee*, Suzanne L Barker-Collo*, Simon Barquera*, Lars Barregard*, Lope H Barrero*, Arindam Basu*, Sanjay Basu*, Mohammed O Basulaiman*, Justin Beardsley*, Neeraj Bedi*, Ettore Beghi*, Tolesa Bekele*, Michelle L Bell*, Corina Benjet*, Derrick A Bennett*, Isabela M Bensenor*, Habib Benzian*, Eduardo Bernabé*, Amelia Bertozzi-Villa*, Tariku J Beyene*, Neeraj Bhala*, Ashish Bhalla*, Zulfi qar A Bhutta*, Kelly Bienhoff *, Boris Bikbov*, Stan Biryukov*, Jed D Blore*, Christopher D Blosser*, Fiona M Blyth*, Megan A Bohensky*, Ian W Bolliger*, Berrak Bora Başara*, Natan M Bornstein*, Dipan Bose*, Soufi ane Boufous*, Rupert R A Bourne*, Lindsay N Boyers*, Michael Brainin*, Carol E Brayne*, Alexandra Brazinova*, Nicholas J K Breitborde*, Hermann Brenner*, Adam D Briggs*, Peter M Brooks*, Jonathan C Brown*, Traolach S Brugha*, Rachelle Buchbinder*, Geoff rey C Buckle*, Christine M Budke*, Anne Bulchis*, Andrew G Bulloch*, Ismael R Campos-Nonato*, Hélène Carabin*, Jonathan R Carapetis*, Rosario Cárdenas*, David O Carpenter*, Valeria Caso*, Carlos A Castañeda-Orjuela*, Ruben E Castro*, Ferrán Catalá-López*, Fiorella Cavalleri*, Alanur Çavlin*, Vineet K Chadha*, Jung-Chen Chang*, Fiona J Charlson*, Honglei Chen*, Wanqing Chen*, Peggy P Chiang*, Odgerel Chimed-Ochir*, Rajiv Chowdhury*, Hanne Christensen*, Costas A Christophi*, Massimo Cirillo*, Matthew M Coates*, Luc E Coff eng*, Megan S Coggeshall*, Valentina Colistro*, Samantha M Colquhoun*, Graham S Cooke*, Cyrus Cooper*, Leslie T Cooper*, Luis M Coppola*, Monica Cortinovis*, Michael H Criqui*, John A Crump*, Lucia Cuevas-Nasu*, Hadi Danawi*, Lalit Dandona*, Rakhi Dandona*, Emily Dansereau*, Paul I Dargan*, Gail Davey*, Adrian Davis*, Dragos V Davitoiu*, Anand Dayama*, Diego De Leo*, Louisa Degenhardt*, Borja Del Pozo-Cruz*, Robert P Dellavalle*, Kebede Deribe*, Sarah Derrett*, Don C Des Jarlais*, Muluken Dessalegn*, Samath D Dharmaratne*, Mukesh K Dherani*, Cesar Diaz-Torné*, Daniel Dicker*, Eric L Ding*, Klara Dokova*, E Ray Dorsey*, Tim R Driscoll*, Leilei Duan,* Herbert C Duber*, Beth E Ebel*, Karen M Edmond*, Yousef M Elshrek*, Matthias Endres*, Sergey P Ermakov*, Holly E Erskine*, Babak Eshrati*, Alireza Esteghamati*, Kara Estep*, Emerito Jose A Faraon*, Farshad Farzadfar*, Derek F Fay*, Valery L Feigin*, David T Felson*, Seyed-Mohammad Fereshtehnejad*, Jeff erson G Fernandes*, Alize J Ferrari*, Christina Fitzmaurice*, Abraham D Flaxman*, Thomas D Fleming*, Nataliya Foigt*, Mohammad H Forouzanfar*, F Gerry R Fowkes*, Urbano Fra.Paleo*, Richard C Franklin*, Thomas Fürst*, Belinda Gabbe*, Lynne Gaffi kin*, Fortuné G Gankpé*, Johanna M Geleijnse*, Bradford D Gessner*, Peter Gething*, Katherine B Gibney*, Maurice Giroud*, Giorgia Giussani*, Hector Gomez Dantes*, Philimon Gona*, Diego González-Medina*, Richard A Gosselin*, Carolyn C Gotay*, Atsushi Goto*, Hebe N Gouda*, Nicholas Graetz*, Harish C Gugnani*, Rahul Gupta*, Rajeev Gupta*, Reyna A Gutiérrez*, Juanita Haagsma*, Nima Hafezi-Nejad*, Holly Hagan*, Yara A Halasa*, Randah R Hamadeh*, Hannah Hamavid*, Mouhanad Hammami*, Jamie Hancock*, Graeme J Hankey*, Gillian M Hansen*, Yuantao Hao*, Hilda L Harb*,

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Josep Maria Haro*, Rasmus Havmoeller*, Simon I Hay*, Roderick J Hay*, Ileana B Heredia-Pi*, Kyle R Heuton*, Pouria Heydarpour*, Hideki Higashi*, Martha Hijar*, Hans W Hoek*, Howard J Hoff man*, H Dean Hosgood*, Mazeda Hossain*, Peter J Hotez*, Damian G Hoy*, Mohamed Hsairi*, Guoqing Hu*, Cheng Huang*, John J Huang*, Abdullatif Husseini*, Chantal Huynh*, Marissa L Iannarone*, Kim M Iburg*, Kaire Innos*, Manami Inoue*, Farhad Islami*, Kathryn H Jacobsen*, Deborah L Jarvis*, Simerjot K Jassal*, Sun Ha Jee*, Panniyammakal Jeemon*, Paul N Jensen*, Vivekanand Jha*, Guohong Jiang*, Ying Jiang*, Jost B Jonas*, Knud Juel*, Haidong Kan*, André Karch*, Corine K Karema*, Chante Karimkhani*, Ganesan Karthikeyan*, Nicholas J Kassebaum*, Anil Kaul*, Norito Kawakami*, Konstantin Kazanjan*, Andrew H Kemp*, Andre P Kengne*, Andre Keren*, Yousef S Khader*, Shams Eldin A Khalifa*, Ejaz A Khan*, Gulfaraz Khan*, Young-Ho Khang*, Christian Kieling*, Daniel Kim*, Sungroul Kim*, Yunjin Kim*, Yohannes Kinfu*, Jonas M Kinge*, Miia Kivipelto*, Luke D Knibbs*, Ann Kristin Knudsen*, Yoshihiro Kokubo*, Soewarta Kosen*, Sanjay Krishnaswami*, Barthelemy Kuate Defo*, Burcu Kucuk Bicer*, Ernst J Kuipers*, Chanda Kulkarni*, Veena S Kulkarni*, G Anil Kumar*, Hmwe H Kyu*, Taavi Lai*, Ratilal Lalloo*, Tea Lallukka*, Hilton Lam*, Qing Lan*, Van C Lansingh*, Anders Larsson*, Alicia E B Lawrynowicz*, Janet L Leasher*, James Leigh*, Ricky Leung*, Carly E Levitz*, Bin Li*, Yichong Li*, Yongmei Li*, Stephen S Lim*, Maggie Lind*, Steven E Lipshultz*, Shiwei Liu*, Yang Liu*, Belinda K Lloyd*, Katherine T Lofgren*, Giancarlo Logroscino*, Katharine J Looker*, Joannie Lortet-Tieulent*, Paulo A Lotufo*, Rafael Lozano*, Robyn M Lucas*, Raimundas Lunevicius*, Ronan A Lyons*, Stefan Ma*, Michael F Macintyre*, Mark T Mackay*, Marek Majdan*, Reza Malekzadeh*, Wagner Marcenes*, David J Margolis*, Christopher Margono*, Melvin B Marzan*, Joseph R Masci*, Mohammad T Mashal*, Richard Matzopoulos*, Bongani M Mayosi*, Tasara T Mazorodze*, Neil W Mcgill*, John J Mcgrath*, Martin Mckee*, Abigail Mclain*, Peter A Meaney*, Catalina Medina*, Man Mohan Mehndiratta*, Wubegzier Mekonnen*, Yohannes A Melaku*, Michele Meltzer*, Ziad A Memish*, George A Mensah*, Atte Meretoja*, Francis A Mhimbira*, Renata Micha*, Ted R Miller*, Edward J Mills*, Philip B Mitchell*, Charles N Mock*, Norlinah Mohamed Ibrahim*, Karzan A Mohammad*, Ali H Mokdad*, Glen L D Mola*, Lorenzo Monasta*, Julio C Montañez Hernandez*, Marcella Montico*, Thomas J Montine*, Meghan D Mooney*, Ami R Moore*, Maziar Moradi-Lakeh*, Andrew E Moran*, Rintaro Mori*, Joanna Moschandreas*, Wilkister N Moturi*, Madeline L Moyer*, Dariush Mozaff arian*, William T Msemburi*, Ulrich O Mueller*, Mitsuru Mukaigawara*, Erin C Mullany*, Michele E Murdoch*, Joseph Murray*, Kinnari S Murthy*, Mohsen Naghavi*, Aliya Naheed*, Kovin S Naidoo*, Luigi Naldi*, Devina Nand*, Vinay Nangia*, K M Venkat Narayan*, Chakib Nejjari*, Sudan P Neupane*, Charles R Newton*, Marie Ng*, Frida N Ngalesoni*, Grant Nguyen*, Muhammad I Nisar*, Sandra Nolte*, Ole F Norheim*, Rosana E Norman*, Bo Norrving*, Luke Nyakarahuka*, In-Hwan Oh*, Takayoshi Ohkubo*, Summer L Ohno*, Bolajoko O Olusanya*, John Nelson Opio*, Katrina Ortblad*, Alberto Ortiz*, Amanda W Pain*, Jeyaraj D Pandian*, Carlo Irwin A Panelo*, Christina Papachristou*, Eun-Kee Park*, Jae-Hyun Park*, Scott B Patten*, George C Patton*, Vinod K Paul*, Boris I Pavlin*, Neil Pearce*, David M Pereira*, Rogelio Perez-Padilla*, Fernando Perez-Ruiz*, Norberto Perico*, Aslam Pervaiz*, Konrad Pesudovs*, Carrie B Peterson*, Max Petzold*, Michael R Phillips*, Bryan K Phillips*, David E Phillips*, Frédéric B Piel*, Dietrich Plass*, Dan Poenaru*, Suzanne Polinder*, Daniel Pope*, Svetlana Popova*, Richie G Poulton*, Farshad Pourmalek*, Dorairaj Prabhakaran*, Noela M Prasad*, Rachel L Pullan*, Dima M Qato*, D Alex Quistberg*, Anwar Rafay*, Kazem Rahimi*, Sajjad U Rahman*, Murugesan Raju*, Saleem M Rana*, Homie Razavi*, K Srinath Reddy*, Amany Refaat*, Giuseppe Remuzzi*, Serge Resnikoff *, Antonio L Ribeiro*, Lee Richardson*, Jan Hendrik Richardus*, D Allen Roberts*, David Rojas-Rueda*, Luca Ronfani*, Gregory A Roth*, Dietrich Rothenbacher*, David H Rothstein*, Jane T Rowley*,

Nobhojit Roy*, George M Ruhago*, Mohammad Y Saeedi*, Sukanta Saha*, Mohammad Ali Sahraian*, Uchechukwu K A Sampson*, Juan R Sanabria*, Logan Sandar*, Itamar S Santos*, Maheswar Satpathy*, Monika Sawhney*, Peter Scarborough*, Ione J Schneider*, Ben Schöttker*, Austin E Schumacher*, David C Schwebel*, James G Scott*, Soraya Seedat*, Sadaf G Sepanlou*, Peter T Serina*, Edson E Servan-Mori*, Katya A Shackelford*, Amira Shaheen*, Saeid Shahraz*, Teresa Shamah Levy*, Siyi Shangguan*, Jun She*, Sara Sheikhbahaei*, Peilin Shi*, Kenji Shibuya*, Yukito Shinohara*, Rahman Shiri*, Kawkab Shishani*, Ivy Shiue*, Mark G Shrime*, Inga D Sigfusdottir*, Donald H Silberberg*, Edgar P Simard*, Shireen Sindi*, Abhishek Singh*, Jasvinder A Singh*, Lavanya Singh*, Vegard Skirbekk*, Erica Leigh Slepak*, Karen Sliwa*, Samir Soneji*, Kjetil Søreide*, Sergey Soshnikov*, Luciano A Sposato*, Chandrashekhar T Sreeramareddy*, Jeff rey D Stanaway*, Vasiliki Stathopoulou*, Dan J Stein*, Murray B Stein*, Caitlyn Steiner*, Timothy J Steiner*, Antony Stevens*, Andrea Stewart*, Lars J Stovner*, Konstantinos Stroumpoulis*, Bruno F Sunguya*, Soumya Swaminathan*, Mamta Swaroop*, Bryan L Sykes*, Karen M Tabb*, Ken Takahashi*, Nikhil Tandon*, David Tanne*, Marcel Tanner*, Mohammad Tavakkoli*, Hugh R Taylor*, Braden J Te Ao*, Fabrizio Tediosi*, Awoke M Temesgen*, Tara Templin*, Margreet Ten Have*, Eric Y Tenkorang*, Abdullah S Terkawi*, Blake Thomson*, Andrew L Thorne-Lyman*, Amanda G Thrift*, George D Thurston*, Taavi Tillmann*, Marcello Tonelli*, Fotis Topouzis*, Hideaki Toyoshima*, Jeff erson Traebert*, Bach X Tran*, Matias Trillini*, Thomas Truelsen*, Miltiadis Tsilimbaris*, Emin M Tuzcu*, Uche S Uchendu*, Kingsley N Ukwaja*, Eduardo A Undurraga*, Selen B Uzun*, Wim H Van Brakel*, Steven Van De Vijver*, Coen H van Gool*, Jim Van Os*, Tommi J Vasankari*, N Venketasubramanian*, Francesco S Violante*, Vasiliy V Vlassov*, Stein Emil Vollset*, Gregory R Wagner*, Joseph Wagner*, Stephen G Waller*, Xia Wan*, Haidong Wang*, Jianli Wang*, Linhong Wang*, Tati S Warouw*, Scott Weichenthal*, Elisabete Weiderpass*, Robert G Weintraub*, Wang Wenzhi*, Andrea Werdecker*, Ronny Westerman*, Harvey A Whiteford*, James D Wilkinson*, Thomas N Williams*, Charles D Wolfe*, Timothy M Wolock*, Anthony D Woolf *, Sarah Wulf *, Brittany Wurtz*, Gelin Xu*, Lijing L Yan*, Yuichiro Yano*, Pengpeng Ye*, Gökalp K Yentür*, Paul Yip*, Naohiro Yonemoto*, Seok-Jun Yoon*, Mustafa Z Younis*, Chuanhua Yu*, Maysaa E Zaki*, Yong Zhao*, Yingfeng Zheng*, David Zonies*, Xiaonong Zou*, Joshua A Salomon†, Alan D Lopez†, Theo Vos†. *Authors listed alphabetically. †Joint senior authors.

Affi liations Institute for Health Metrics and Evaluation (Prof C J L Murray D Phil, R M Barber BS, K J Foreman MPH, T Achoki MD, A Afshin MD, V F Bachman BA, A Bertozzi-Villa BA, K Bienhoff MA, S Biryukov BS, J D Blore PhD, I W Bolliger BA, J C Brown MAIS, A Bulchis MPH, M M Coates BS, L E Coff eng PhD, M S Coggeshall BA, Prof L Dandona PhD, E Dansereau BA, D Dicker BS, H C Duber MD, K Estep MPA, C Fitzmaurice MD, A D Flaxman PhD, T D Fleming BS, Prof M H Forouzanfar PhD, D González-Medina BA, N Graetz BS, J Haagsma PhD, H Hamavid BA, J Hancock MLS, G M Hansen MSW, Prof S I Hay DSc, K R Heuton BChe, H Higashi PhD, C Huynh BA, M L Iannarone MSc, N J Kassebaum MD, H H Kyu PhD, C E Levitz MPH, S S Lim PhD, M Lind BS, K T Lofgren MPH, R Lozano PhD, M F MacIntyre MEd, C Margono BS, A McLain MA, A H Mokdad PhD, M D Mooney BS, M Moradi-Lakeh PhD, M L Moyer BS, W T Msemburi MPhil, E C Mullany BA, Prof M Naghavi PhD, M Ng PhD, G Nguyen BA, S L Ohno BA, K Ortblad MPH, A W Pain MPH, B K Phillips BA, D E Phillips BS, L Richardson BS, D A Roberts BS, G A Roth MD, L Sandar BS, A E Schumacher BS, P T Serina MPH, K A Shackelford BA, L Singh BS, E Slepak MLIS, J D Stanaway PhD, C Steiner MPH, A Stevens PhD, A Stewart MPH, A M Temesgen PhD, T Templin BA, B Thomson BA, T Vos PhD, J Wagner BS, X Wan PhD, H Wang PhD, T M Wolock BA, S Wulf MPH, B Wurtz MPH), Harborview Injury Prevention and Research Center (B E Ebel MD), Seattle Children’s Hospital

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(N J Kassebaum MD) University of Washington, Seattle, WA, USA (R Alfonso-Cristancho PhD, Prof B O Anderson MD, C D Blosser MD, P N Jensen PhD, C N Mock PhD, T J Montine PhD, D Quistberg PhD); Department of Infectious Disease Epidemiology (T Fürst PhD), Imperial College London, London, UK (G S Cooke DPhil, K J Foreman MPH, Prof D L Jarvis MD, Prof T N Williams MD); Institute of Population Studies (A Abbasoglu Ozgoren MSc, A Çavlin PhD), Institute of Public Health (B Kucuk Bicer PhD), Hacettepe University, Ankara, Turkey; Faculty of Medicine, Cairo-University, Cairo, Egypt (Prof F Abd-Allah MD); School of Public Health, College of Health Sciences, Mekelle University, Mekelle, Ethiopia (S F Abera MSc); Kilte Awlaelo-Health and Demographic Surveillance Site, Mekelle, Ethiopia (S F Abera MSc); Dupuytren University Hospital, Limoges, France (Prof V Aboyans PhD); Family Medicine Residency Program at California Hospital, University of Southern California, Los Angeles, CA, USA (J P Abraham MD); Institute for Global Health (J P Abraham MD), Department of Nutrition (A L Thorne-Lyman ScD), School of Public Health (J P Abraham MD, E L Ding ScD, G R Wagner MD), Harvard University, Boston, MA, USA (I R Campos-Nonato PhD, J A Salomon PhD, M G Shrime MD); Department of Epidemiology and Public Health (H Benzian PhD), University College London, London, UK (Prof I Abubakar PhD); Weill Cornell Medical College Ar-Rayyan Qatar, Doha, Qatar (L J Abu-Raddad PhD); Institute of Community and Public Health, Birzeit University, Ramallah, Palestine (N M Abu-Rmeileh PhD); General Practice and Primary Health Care Academic Centre (P P Chiang PhD), Departments of Medicine and the Florey (A Meretoja PhD), University of Melbourne, Melbourne, VIC, Australia (I N Ackerman PhD, Z Ademi PhD, J D Blore PhD, M A Bohensky PhD, Prof P M Brooks MD, S M Colquhoun PhD, Prof A D Lopez PhD, Prof G C Patton MD, Prof H R Taylor AC, R G Weintraub MBBS); University of Basel, Basel, Switzerland (Z Ademi PhD, H Amini MSPH), Swiss Tropical and Public Health Institute (Prof M Tanner PhD, F Tediosi PhD), Basel University, Basel, Switzerland (Z Ademi PhD); Association Ivoirienne pour le Bien-Être Familial, Abidjan, Côte d’Ivoire (A K Adou MD); University of Extremadura, Cáceres, Spain (Prof J C Adsuar PhD, U-F Paleo PhD); Friedman School of Nutrition Science and Policy (A Afshin MD, R Micha PhD, D Mozaff arian MD), Tufts University, Boston, MA, USA (S Shangguan MD, P Shi PhD); Institution of Public Health Sciences, Stockholm, Sweden (E E Agardh PhD); International Centre for Diarrhoeal Disease Research (ICDDR), Bangladesh, Dhaka, Bangladesh (S Alam MSc, A Naheed PhD); Ministry of Health, Al Khuwair, Oman (D Alasfoor MSc); Independent, Damascus, Syria (M I Albittar BS); Departamento de Medicina Preventiva y Social (M A Alegretti MD), Universidad de la República—Facultad de Medicina, Montevideo, Uruguay (F Cavalleri BS); Debre Markos University, Addis Ababa, Ethiopia (Z A Alemu MPH); King Abdullah Bin Abdulaziz University Hospital, Riyadh, Saudi Arabia (S Alhabib PhD); Department of Zoology (P Gething PhD), University of Oxford, Oxford, UK (R Ali MSc, D A Bennett PhD, A D Briggs MSc, Prof S I Hay DSc, F B Piel PhD, K Rahimi DM, P Scarborough DPhil); Melbourne Health, Parkville, VIC, Australia (K B Gibney FRACP); School of Public Health, University of Lorraine, Nancy, France (Prof F Alla PhD); Department of Public Health Sciences (P Allebeck PhD, N Roy MD), Department of Neurobiology, Care Sciences and Society (NVS) (S Fereshtehnejad MD), Department of Medical Epidemiology and Biostatistics (E Weiderpass PhD), Aging Research Center (Prof M Kivipelto PhD), Karolinska Institute, Stockholm, Sweden (R Havmoeller PhD, S Sindi PhD); Ministry of Health, Riyadh, Saudi Arabia (M A AlMazroa MD, M O Basulaiman PhD, M Y Saeedi PhD); Charité University Medicine Berlin, Berlin, Germany (U Alsharif MPH, Prof M Endres MD); Government, Madrid, Spain (E Alvarez PhD); Universidad de Cartagena, Cartagena de Indias, Colombia (Prof N Alvis-Guzman PhD); Department of Epidemiology (A T Amare MPH), Department of Psychiatry (Prof H W Hoek MD), University Medical Center Groningen, University of Groningen, Groningen, Netherlands; College of Medicine and Health Sciences, Bahir Dar University, Bahir Dar, Ethiopia (A T Amare); Ahmadu Bello University, Zaria, Nigeria (Prof E A Ameh MBBS); Kurdistan Environmental Health Research Center, Kurdistan University of Medical Sciences, Sanandaj, Iran (H Amini MSPH); Ministry of Public Health, Beirut, Lebanon

(W Ammar PhD, H L Harb MPH); St George’s, University of London, London, UK (Prof H R Anderson MD); Department of Health Policy and Administration, College of Public Health (C T Antonio MD), University of the Philippines Manila, Manila, Philippines (C I A Panelo MD); Self employee, Kabul, Afghanistan (P Anwari MS); Department of Medical Sciences, Uppsala University, Uppsala, Sweden (Prof J Arnlöv PhD); Dalarna University, Falun, Sweden (Prof J Arnlöv PhD); School of Medicine, Institute of Microbiology and Immunology, University of Belgrade, Belgrade, Serbia (Prof V S Arsic Arsenijevic PhD); University Children’s Hospital Belgrade, Belgrade, Serbia (Prof V S Arsic Arsenijevic PhD); Consultant, Windsor, ON, Canada (A Artaman PhD); South Asian Public Health Forum, Islamabad, Pakistan (R J Asghar MD); Mashhad University of Medical Sciences, Mashhad, Iran (R Assadi PhD); Wellness, Human Services and Gender Relations, Ministry Of Health, Castries, Saint Lucia (L S Atkins MPH); National Institute of Public Health, Cuernavaca, Mexico (M A Avila BS, S Barquera PhD, I R Campos-Nonato PhD, L Cuevas-Nasu MsC, H Gomez Dantes MC, I Heredia-Pi PhD, R Lozano PhD, C Medina MS, J C Montañez Hernandez MSc, Prof E E Servan-Mori MSc, T Shamah Levy PhD); Komfo Anokye Teaching Hospital, Kumasi, Ghana (B Awuah MD); Public Health Agency of Canada, Toronto, ON, Canada (A Badawi PhD); INECO Neurociencias, Rosario, Argentina (M C Bahit MD); Sri Ramachandra University, Chennai, India (K Balakrishnan PhD); University of Birmingham, Birmingham, UK (A Banerjee DPhil); School of Psychology (S L Barker-Collo PhD), University of Auckland, Auckland, New Zealand (B del Pozo-Cruz PhD); Department of Occupational and Environmental Health, University of Gothenburg, Gothenburg, Sweden (Prof L Barregard MD); Department of Industrial Engineering, Pontifi cia Universidad Javeriana, Bogota, Colombia (L H Barrero ScD); School of Health Sciences, University of Canterbury, Christchurch, New Zealand (A Basu PhD); School of Medicine (L Gaffi kin DrPH), Stanford University, Stanford, CA, USA (S Basu PhD); Oxford University, Ho Chi Minh City, Vietnam (J Beardsley MBChB); College of Public Health and Tropical Medicine, Jazan, Saudi Arabia (N Bedi MD); IRCCS Istituto di Ricerche Farmacologiche Mario Negri, Milano, Italy (E Beghi MD, M Cortinovis Biotech D, G Giussani Biol D); Madawalabu University, Bale Goba, Ethiopia (T Bekele MPH); Yale University, New Haven, CT, USA (Prof M L Bell PhD, J J Huang MD); National Institute of Psychiatry Ramon de la Fuente Muñiz, Mexico City, Mexico (C Benjet PhD, R A Gutiérrez PhD); University of Sao Paulo, Sao Paulo, Brazil (I M Bensenor PhD, Prof P A Lotufo DrPH, Prof I S Santos PhD, Prof A H Kemp PhD); Department of Epidemiology and Health Promotion, College of Dentistry (H Benzian PhD), Nelson Institute of Environmental Medicine, School of Medicine (Prof G D Thurston ScD), New York University, New York, NY, USA (Prof H Hagan PhD); King’s College London, London, UK (E Bernabé PhD, Prof R J Hay MD, Prof C D Wolfe MD); School of Public Health (K Deribe MPH), Addis Ababa University, Addis Ababa, Ethiopia (T J Beyene DVM, W Mekonnen PhD); Queen Elizabeth Hospital Birmingham, Birmingham, UK (N Bhala DPhil); University of Otago Medical School, New Zealand (N Bhala DPhil); Postgraduate Institute of Medical Education and Research, Chandigarh, India (A Bhalla MD, Prof V Jha DM); Medical Center (Z A Bhutta PhD), Aga Khan University, Karachi, Pakistan (M I Nisar MSc); A.I.Evdokimov Moscow State University of Medicine and Dentistry, Moscow, Russia (B Bikbov MD); Academician V.I.Shumakov Federal Research Center of Transplantology and Artifi cial Organs, Moscow, Russia (B Bikbov MD); Sydney School of Public Health (T R Driscoll PhD), University of Sydney, Sydney, NSW, Australia (F M Blyth PhD, Prof A H Kemp PhD, J Leigh PhD); General Directorate of Health Research (B Bora Başara PhD, G K Yentür PhD), Ministry of Health, Ankara, Turkey (S B Uzun MD); Tel Aviv Saurasky Medical Center, Tel Aviv, Israel (Prof N M Bornstein MD); World Bank, Washington, DC, USA (D Bose PhD); Transport and Road Safety (TARS) Research (S Boufous PhD), BHVI (Prof S Resnikoff MD), University of New South Wales, Kensington, NSW, Australia (Prof L Degenhardt PhD, Prof P B Mitchell MD); Anglia Ruskin University, Cambridge, UK (Prof R R A Bourne FRCOphth); Georgetown University, Washington, DC, USA (L N Boyers BA); Danube-University Krems, Krems, Austria (Prof M Brainin PhD); Cambridge Institute of Public Health, Cambridge, UK (Prof C E Brayne MD); Faculty of Health Sciences and

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Social Work (A Brazinova PhD), Department of Public Health (M Majdan PhD), Trnava University, Trnava, Slovakia; The Ohio State University, Columbus, OH, USA (Prof N J K Breitborde PhD); University of Arizona, Tucson, AZ, USA (Prof N J K Breitborde PhD); Division of Clinical Epidemiology and Aging Research (B Schöttker MPH), German Cancer Research Center, Heidelberg, Germany (Prof H Brenner MD); University of Leicester, Leicester, UK (Prof T S Brugha MD); Monash Department of Clinical Epidemiology, Cabrini Institute, Melbourne, VIC, Australia (Prof R Buchbinder PhD); Department of Epidemiology and Preventive Medicine, School of Public Health and Preventive Medicine (Prof R Buchbinder PhD), Eastern Health Clinical School (B K Lloyd PhD), Monash University, Melbourne, VIC, Australia (Prof B Gabbe PhD, K B Gibney FRACP, Prof A G Thrift PhD); University of California San Francisco, San Francisco, CA, USA (G C Buckle MD, R A Gosselin MD); Texas A&M University, College Station, TX, USA (C M Budke PhD); University of Calgary, Calgary, AB, Canada (A G Bulloch PhD, Prof S B Patten PhD, Prof M Tonelli MD, J Wang PhD); University of Oklahoma Health Sciences Center, Oklahoma City, OK, USA (H Carabin PhD); Telethon Institute for Child Health Research, Subiaco, WA, Australia (Prof J R Carapetis PhD); Universidad Autonoma Metropolitana, Mexico City, Mexico (R Cárdenas ScD); University at Albany, Rensselaer, NY, USA (Prof D O Carpenter MD); University of Perugia, Perugia, Italy (V Caso MD); Colombian National Health Observatory, Bogota, Colombia (C A Castañeda-Orjuela MS); Epidemiology and Public Health Evaluation Group, Public Health Department, Universidades Nacional de Colombia, Bogota, Colombia (C A Castañeda-Orjuela MS); Universidad Diego Portales, Santiago, Chile (R E Castro PhD); Division of Pharmacoepidemiology and Pharmacovigilance, Spanish Medicines and Healthcare Products Agency (AEMPS), Ministry of Health, Madrid, Spain (F Catalá-López PhD); Department of Medicine, University of Valencia/CIBERSAM, Valencia, Spain (F Catalá-López PhD); National Tuberculosis Institute, Bengaluru, India (V K Chadha MD); College of Medicine, National Taiwan University, Taipei, Taiwan (Prof J Chang PhD); School of Population Health (D G Hoy PhD), School of Public Health (L D Knibbs PhD), Centre for Clinical Research (J G Scott PhD), University of Queensland, Brisbane, QLD, Australia (F J Charlson MIPH, H E Erskine B PsySc, A J Ferrari B PsySc, H N Gouda PhD, Prof J J McGrath MD, Prof H A Whiteford MD); Division of Intramural Research, National Institute of Environmental Health Sciences, National Institutes of Health, Department of Health and Human Services, Research Triangle Park, NC, USA (H Chen PhD); Department of Health Development (Y Jiang PhD), Institute of Industrial Ecological Sciences (Y Jiang PhD, Prof K Takahashi MD), Department of Environmental Epidemiology (Y Jiang PhD, O Chimed-Ochir MPH, Prof K Takahashi MD) University of Occupational and Environmental Health, Kitakyushu, Japan; University of Cambridge, Cambridge, UK (R Chowdhury PhD, J Murray PhD); Bispebjerg University Hospital, Copenhagen, Denmark (Prof H Christensen DMSCi); Cyprus University of Technology, Limassol, Cyprus (C A Christophi PhD); University of Salerno, Baronissi, Italy (Prof M Cirillo MD); Universidad de la República, Montevideo, Uruguay (V Colistro MSc); Ministerio de Salud Pública, Montevideo, Uruguay (V Colistro); Murdoch Childrens Research Institute, Melbourne, VIC, Australia (S M Colquhoun PhD); MRC Lifecourse Epidemiology Unit, University of Southampton, Southampton, UK (Prof C Cooper FMedSci); University of California, San Diego, La Jolla, CA, USA (M H Criqui MD, S K Jassal MD, M B Stein MD); Mayo Clinic, Rochester, MN, USA (L T Cooper MD); Hospital Municipal Ramon Santamarina, Tandil, Argentina (L M Coppola MD); Centre for International Health, Dunedin School of Medicine (Prof J A Crump MD), Department of Preventive and Social Medicine, Dunedin School of Medicine (S Derrett PhD), University of Otago, Dunedin, New Zealand (Prof R G Poulton PhD); School of Public Health, College of Health, Massey University, Palmerston North, New Zealand (S Derrett PhD); Walden University, Minneapolis, MN, USA (H Danawi PhD); Public Health Foundation of India, New Delhi, India (Prof L Dandona PhD, R Dandona PhD, G Kumar PhD, K S Murthy MPH, K Reddy DM [Card]); Guy’s and St. Thomas’ NHS Foundation Trust, London, UK (P I Dargan FRCP); Wellcome Trust Brighton & Sussex Centre for Global Health Research, Brighton, UK

(Prof G Davey MD); Public Health England, London, UK (A Davis PhD, D F Fay MSc); University of Medicine and Pharmacy Bucharest, Bucharest, Romania (D V Davitoiu PhD); Department of Surgery, Jacobi Medical Center, Atlanta, GA, USA (A Dayama MD); School of Dentistry and Oral Health (Prof R Lalloo PhD), Griffi th University, Brisbane, QLD, Australia (Prof D De Leo DSc); US Department of Veterans Aff airs, Eastern Colorado Healthcare System, Denver, CO, USA (R P Dellavalle MD); Brighton and Sussex Medical School, Brighton, UK (K Deribe MPH); Mount Sinai Beth Israel, New York, NY, USA (Prof D C Des Jarlais PhD); Icahn School of Medicine at Mount Sinai New York City, NY, USA (Prof D C Des Jarlais PhD); Africa Medical and Research Foundation in Ethiopia, Addis Ababa, Ethiopia (M Dessalegn MPH); University of Peradeniya, Peradeniya, Sri Lanka (S D Dharmaratne MD); School of Medicine (Prof R Lunevicius PhD), The University of Liverpool, Liverpool, UK (M K Dherani PhD, D Pope PhD); Hospital de la Santa Creu i Sant Pau, Barcelona, Spain (C Diaz-Torné MD); Department of Social Medicine, Faculty of Public Health, Medical University—Varna, Varna, Bulgaria (K Dokova PhD); University of Rochester Medical Center, Rochester, NY, USA (Prof E R Dorsey MD); University of Western Australia, Perth, WA, Australia (Prof K M Edmond PHD, Prof G J Hankey MD); Food Science Department, Faculty of Agriculture, University of Tripoli, Tripoli, Libya (Prof Y M Elshrek PhD); The Institute of Social and Economic Studies of Population, Russian Academy of Sciences, Moscow, Russia (Prof S P Ermakov DSc); Federal Research Institute for Health Organization and Informatics, Ministry of Health of Russian Federation, Moscow, Russia (Prof S P Ermakov DSc); Arak University of Medical Sciences and Health Aff airs, Arak, Iran (B Eshrati PhD); Non- Communicable Diseases Research Center, Endocrinology and Metabolic Research Institute (Prof A Esteghamati MD, F Farzadfar MD, N Hafezi-Nejad MD, S Sheikhbahaei MD), Multiple Sclerosis Research Center, Neuroscience Institute (P Heydarpour MD, M Sahraian MD), Digestive Diseases Research Institute (S G Sepanlou MD), Tehran University of Medical Sciences, Tehran, Iran; DHPA, UP College of Public Health, Manila, Philippines (E A Faraon MD); National Institute for Stroke and Applied Neurosciences (V L Feigin PhD), Auckland University of Technology, Auckland, New Zealand (B J Te Ao MPH); Boston University, Boston, MA, USA (Prof D T Felson MD); Institute of Education and Sciences, German Hospital Oswaldo Cruz, São Paulo, Brazil (Prof J G Fernandes PhD); Institute of Gerontology, Academy of Medical Science, Kiev, Ukraine (N Foigt PhD); University of Edinburgh, Edinburgh, UK (Prof F R Fowkes PhD); James Cook University, Townsville, QLD, Australia (R C Franklin PhD); Leras Afrique, Cotonou, Benin (F G Gankpé MD); CHU Hassan II, Fez, Morocco (F G Gankpé MD); Division of Human Nutrition, Wageningen University, Wageningen, Netherlands (J M Geleijnse PhD); Agence de Medecine Preventive, Paris, France (B D Gessner MD); University Hospital of Dijon, Dijon, France (Prof M Giroud MD); University of Massachusetts Boston, Boston, MA, USA (Prof P Gona PhD); School of Population and Public Health (F Pourmalek PhD), University of British Columbia, Vancouver, BC, Canada (C C Gotay PhD); Department of Public Health, Tokyo Women’s Medical University, Tokyo, Japan (A Goto MD); Saint James School of Medicine, Kralendijk, Netherlands Antilles (Prof H C Gugnani PhD); West Virginia Bureau for Public Health, Charleston, WV, USA (R Gupta MD); Fortis Escorts Hospital, Jaipur, India (R Gupta PhD); Brandeis University, Waltham, MA, USA (Y A Halasa MS MA, E A Undurraga PhD); Arabian Gulf University, Manama, Bahrain (Prof R R Hamadeh DPhil); Wayne County Department of Health and Human Services, Detroit, MI, USA (M Hammami MD); School of Public Health (Prof Y Hao PhD), Zhongshan Ophthalmic Center (Y Zheng MD), Sun Yae-Sen University, Guangzhou, China; Parc Sanitari Sant Joan de Déu—CIBERSAM, Sant Boi de Llobregat, Barcelona, Spain (J Haro MD); Universitat de Barcelona, Barcelona, Spain (J Haro MD); International Foundation for Dermatology, London, UK (Prof R J Hay MD); Fundacion Entornos AC, Cuernavaca, Mexico (M Hijar PhD); Department of Epidemiology (Prof H W Hoek MD), Columbia University, New York, NY, USA (A E Moran MD); Epidemiology and Statistics Program, National Institute on Deafness and Other Communication Disorders (H J Hoff man MA), Center for Translation Research and Implementation Science, National Heart, Lung, and Blood Institute

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(G A Mensah MD), National Heart Lung and Blood Institute (U K A Sampson MD), National Institutes of Health, Bethesda, MD, USA; Albert Einstein College of Medicine, Bronx, NY, USA (Prof H Hosgood PhD); London School of Hygiene & Tropical Medicine, London, UK (M Hossain MSc, Prof M McKee DSc, Prof N Pearce PhD, R L Pullan PhD, T Tillmann MBChB); Baylor College of Medicine, Houston, TX, USA (P J Hotez PhD); Public Health Division, Secretariat of the Pacifi c Community, Noumea, New Caledonia (D G Hoy PhD); National Institute of Public Health, Tunis, Tunisia (Prof M Hsairi MD); School of Public Health, Central South University, Changsha, China (G Hu PhD); George Washington University, Washington, DC, USA (Prof C Huang PhD); Qatar University, Doha, Qatar (A Husseini PhD); Aarhus University, Aarhus, Denmark (K M Iburg PhD); National Institute for Health Development, Tallinn, Estonia (K Innos PhD); Graduate School of Medicine (M Inoue PhD), School of Public Health (Prof N Kawakami MD), The University of Tokyo, Tokyo, Japan (K Shibuya MD); American Cancer Society, New York, NY, USA (F Islami PhD); George Mason University, Fairfax, VA, USA (K H Jacobsen PhD); Graduate School of Public Health, Yonsei University, Seoul, South Korea (Prof S Jee PhD); Centre for Chronic Disease Control, New Delhi, India (P Jeemon PhD, D Prabhakaran DM); Tianjin Centers for Disease Control and Prevention, Tianjin, China (G Jiang MD); Department of Ophthalmology, Medical Faculty Mannheim, Ruprecht-Karls-University Heidelberg, Mannheim, Indonesia (Prof J B Jonas MD); The National Institute of Public Health, Copenhagen, Denmark (Prof K Juel PhD); Zhongshan Hospital (J She MD), Fudan University, Shanghai, China (H Kan MD); Department of Epidemiology, Helmholtz Centre for Infection Research, Braunschweig, Germany (A Karch MD); Hannover-Braunschweig Site, German Center for Infection Research (DZIF), Braunschweig, Germany (A Karch MD); Malaria and Other Parasitic Diseases Division, Ministry of Health, Kigali City, Rwanda (C K Karema MSc); Case Western University Hospitals, Cleveland, OH, USA (C Karimkhani MD); All India Institute of Medical Sciences, New Delhi, India (Prof G Karthikeyan DM, V K Paul MD, Prof N Tandon PhD, M Satpathy PhD); Oklahoma State University, Tulsa, OK, USA (A Kaul MD); National Center for Disease Control and Public Health, Tbilisi, Georgia (K Kazanjan MS); South African Medical Research Council, Cape Town, South Africa (A P Kengne PhD, R Matzopoulos PhD, W T Msemburi MPhil, Prof D J Stein MD); Faculty of Health Sciences, Hatter Institute for Cardiovascular Research in Africa (Prof K Sliwa PhD), School of Public Health and Family Medicine (R Matzopoulos PhD), University of Cape Town, Cape Town, South Africa (A P Kengne PhD, Prof B M Mayosi DPhil, Prof D J Stein MD); Cardiology, Hadassah Ein Kerem University Hospital, Jerusalem, Israel (Prof A Keren MD); Jordan University of Science and Technology, Irbid, Jordan (Prof Y S Khader ScD); Supreme Council of Health, Doha, Qatar (S A Khalifa MSc); Health Services Academy, Islamabad, Pakistan (E A Khan MPH); Expanded Programme on Immunization, Islamabad, Pakistan (E A Khan MPH); UAE University, Al Ain, United Arab Emirates (G Khan PhD); Seoul National University College of Medicine, Seoul, South Korea (Prof Y Khang MD); Federal University of Rio Grande do Sul, Porto Alegre, Brazil (C Kieling PhD); Northeastern University, Boston, MA, USA (Prof D Kim DrPH); Soonchunhyang University, Seoul, South Korea (S Kim PhD); Southern University College, Johor, Malaysia (Y Kim PhD); University of Canberra, Canberra, ACT, Australia (Y Kinfu PhD); Department of Health Registries (A Knudsen PhD), Norwegian Institute of Public Health, Oslo, Norway (J M Kinge PhD, Prof V Skirbekk MD, Prof S Vollset MD); National Cerebral and Cardiovascular Center, Suita, Japan (Y Kokubo PhD); Center for Community Empowerment, Health Policy and Humanities, NIHRD, Jakarta, Indonesia (S Kosen MD); Oregon Health and Science University, Portland, OR, USA (S Krishnaswami MD, D Zonies MD); University of Montreal, Montreal, QC, Canada (Prof B Kuate Defo PhD); Erasmus MC, University Medical Center Rotterdam, Rotterdam, Netherlands (Prof E J Kuipers PhD, S Polinder MS, Prof J Richardus PhD); Rajrajeswari Medical College & Hospital, Bangalore, India (Prof C Kulkarni MD); Arkansas State University, Jonesboro, AR, USA (V S Kulkarni PhD); Fourth View Consulting, Tallinn, Estonia (T Lai PhD); Australian Research Centre for Population Oral Health, School of Dentistry, The University of Adelaide, Adelaide,

SA, Australia (Prof R Lalloo PhD); Disability Prevention Research Centre (T Lallukka PhD), Finnish Institute of Occupational Health, Helsinki, Finland (R Shiri PhD); Faculty of Medicine, University of Helsinki, Helsinki, Finland (T Lallukka PhD); Institute of Health Policy and Development Studies, National Institutes of Health, Manila, Philippines (Prof H Lam PhD); National Cancer Institute, Rockville, MD, USA (Q Lan PhD); Help Me See, Inc, New York, NY, USA (V C Lansingh PhD); Instituto Mexicano de Oftalmologia, Queretaro, Mexico (V C Lansingh PhD); Uppsala University, Uppsala, Sweden (Prof A Larsson PhD); Instituto Nacional de Epidemiología “Dr. Juan H. Jara”, Mar del Plata, Argentina (A E B Lawrynowicz MPH); Nova Southeastern University College of Optometry, Fort Lauderdale, FL, USA (J L Leasher OD); SUNY-Albany, Rensselaer, NY, USA (R Leung PhD); Jinan Central Hospital, Jinan, China (B Li PhD); National Center for Chronic and Noncommunible Disease Control and Prevention, China CDC, Beijing, China (Y Li MPH, S Liu PhD, L Duan MS, Prof L Wang MD, P Ye MPH); Anolinx, LLC, Salt Lake City, UT, USA (Y Li PhD); Wayne State University, Miami, FL, USA (Prof S E Lipshultz MD); Rollins School of Public Health (E P Simard PhD), Emory University, Atlanta, GA, USA (Prof Y Liu PhD, Prof K Narayan MD); Turning Point, Eastern Health, Melbourne, VIC, Australia (B K Lloyd PhD); University of Bari, Bari, Italy (Prof G Logroscino PhD); University of Bristol, Bristol, UK (K J Looker PhD); American Cancer Society, Atlanta, GA, USA (J Lortet-Tieulent MSc); The Australian National University, Canberra, ACT, Australia (Prof R M Lucas PhD); Aintree University Hospitals NHS Foundation Trust, Liverpool, UK (Prof R Lunevicius PhD); Swansea University, Swansea, UK (Prof R A Lyons MD); Ministry of Health Singapore, Singapore, Singapore (S Ma PhD); Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore (S Ma PhD); Murdoch Children’s Research Institute (R G Weintraub MBBS), Royal Children’s Hospital, Melbourne, VIC, Australia (M T Mackay MBBS); Digestive Disease Reseach Institute, Shariati Hospital, Tehran, Iran (Prof R Malekzadeh MD); Shiraz University of Medical Scinces, Shiraz, Iran (Prof R Malekzadeh MD); Queen Mary University of London, London, UK (Prof W Marcenes PhD); Pereleman School of Medicine (P A Meaney MD), University of Pennsylvania, Philadelphia, PA, USA (D J Margolis PhD, D H Silberberg MD); University of the East Ramon Magsaysay Memorial Medical Center, Quezon City, Philippines (M B Marzan MSc); Elmhurst Hospital Center, Mount Sinai Services, Elmhurst, NY, USA (J R Masci MD); Ministry of Public Health, Kabul, Afghanistan (M T Mashal PhD); AIDC EC, Port Elizabeth, South Africa (T T Mazorodze MA); Royal Prince Alfred Hospital, Camperdown, NSW, Australia (N W McGill MB BS); Children’s Hospital of Philadelphia, Philadelphia, PA, USA (P A Meaney MD); Janakpuri Superspecialty Hospital, New Delhi, India (Prof M Mehndiratta DM); Mekelle University, Mekelle, Ethiopia (Y A Melaku MPH); Thomas Jeff erson University, Philadelphia, PA, USA (M Meltzer MD); Saudi Ministry of Health, Riyadh, Saudi Arabia (Prof Z A Memish MD); Department of Neurology, Helsinki University Central Hospital, Helsinki, Finland (A Meretoja PhD); Ifakara Health Institute, Bagamoyo, Tanzania (F A Mhimbira MS); Pacifi c Institute for Research & Evaluation, Calverton, MD, USA (T R Miller PhD); Curtin University Centre for Population Health, Perth, WA, Australia (T R Miller PhD); University of Ottawa, Ottawa, ON, Canada (E J Mills PhD); Department of Medicine, Universiti Kebangsaan Malaysia Medical Center, Bandar Tun Razak, Malaysia (Prof N Mohamed Ibrahim MRCP); University of Salahaddin, Erbil, Iraq (K A Mohammad PhD); University of Papua New Guinea, Boroko, Papua New Guinea (Prof G L D Mola MD); Institute for Maternal and Child Health, IRCCS “Burlo Garofolo”, Trieste, Italy (L Monasta DSc, L Ronfani PhD, M Montico Msc); University of North Texas, Denton, TX, USA (Prof A R Moore PhD); Department of Community Medicine, Iran University of Medical Sciences, Tehran, Iran (M Moradi-Lakeh PhD); National Center for Child Health and Development, Setagaya, Japan (R Mori PhD); Department of Medicine (Prof M Tsilimbaris PhD), University of Crete, Crete, Greece (J Moschandreas PhD); Egerton University, Egerton, Kenya (W N Moturi PhD); Philipps-University Marburg, Marburg, Germany (Prof U O Mueller PhD); Okinawa Chubu Hospital, Okinawa, Japan (M Mukaigawara MD); West Herts Hospitals NHS Trust, Watford, UK

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(M E Murdoch FRCP); University of KwaZulu-Natal, Durban, South Africa (Prof K S Naidoo PhD); Azienda Ospedaliera papa Giovanni XXIII, Bergamo, Italy (Prof L Naldi MD); Ministry of Health, Suva, Fiji (D Nand MPH [Hons]); Suraj Eye Institute, Nagpur, India (Prof V Nangia MD); Faculty of Medicine, Fez, Morocco (Prof C Nejjari PhD); University of Oslo, Oslo, Norway (S P Neupane PhD); Kilifi , Kenya (Prof C R Newton MD); Ministry of Health and Social Welfare, Dar es Salaam, Tanzania (F N Ngalesoni MSc); Charité University Medicine Berlin, Berlin, Germany (S Nolte PhD, C Papachristou PhD); Deakin University, Melbourne, VIC, Australia (S Nolte PhD); Department of Global Public Health and Primary Care (Prof S Vollset MD), University of Bergen, Bergen, Norway (Prof O F Norheim PhD); Institute of Health and Biomedical Innovation, Queensland University of Technology, Brisbane, QLD, Australia (R E Norman PhD); Department of Clinical Sciences, Lund University, Lund, Sweden (Prof B Norrving PhD); Makerere University, Kampala, Uganda (L Nyakarahuka MPH); Kyung Hee University, Seoul, South Korea (Prof I Oh PhD); Teikyo University School of Medicine, Tokyo, Japan (Prof T Ohkubo MD); Center for Healthy Start Initiative, Ikoyi, Nigeria (B O Olusanya PhD); Lira District Local Government, Lira Municipal Council, Uganda (J N Opio MPH); IIS-Fundacion Jimenez Diaz, Madrid, Spain (Prof A Ortiz PhD); Christian Medical College Ludhiana, Ludhiana, India (J D Pandian DM); Kosin University College of Medicine, Busan, South Korea (E Park PhD); School of Medicine, Sungkyunkwan University, Suwon, South Korea (Prof J Park MPH); Independent Researcher, Boroko, Papua New Guinea (B I Pavlin MD); REQUIMTE/LAQV, Laboratório de Farmacognosia, Departamento de Química, Faculdade de Farmácia, Universidade do Porto, Porto, Portugal (Prof D M Pereira PhD); National Institute of Respiratory Diseases, Mexico City, Mexico (Prof R Perez-Padilla MD); Hopsital Universitario Cruces, OSI EE-Cruces, Barakaldo, Spain (F Perez-Ruiz PhD); Biocruces Health Research Institute, Baracaldo, Spain (F Perez-Ruiz PhD); Mario Negri Institute for Pharmacological Research, Ranica, Italy (N Perico MD, M Trillini MD); Postgraduate Medical Institute, Lahore, Pakistan (A Pervaiz MHA); Flinders University, Adelaide, SA, Australia (Prof K Pesudovs PhD); Aalborg University, Aalborg Esst, Denmark (C B Peterson PhD); Health Metrics Unit, Gothenburg, Sweden (Prof M Petzold PhD); The University of the Witwatersrand, Johannesburg, South Africa (Prof M Petzold PhD); Shanghai Jiao Tong University School of Medicine, Shanghai, China (Prof M R Phillips MD); Emory University, Atlanta, GA, USA (Prof M R Phillips MD); Section Exposure Assessment and Environmental Health Indicators, Federal Environment Agency, Berlin, Germany (D Plass Dr. PH); Montreal Children’s Hospital, McGill University, Montreal, QC, Canada (Prof D Poenaru MD); MyungSung Medical College, Addis Ababa, Ethiopia (Prof D Poenaru MD); Centre for Addiction and Mental Health (CAMH), Toronto, ON, Canada (S Popova PhD); The Fred Hollows Foundation, Sydney, NSW, Australia (N M Prasad DO); University of Illinois, Chicago, IL, USA (D M Qato PhD); Contech International Health Consultants, Lahore, Pakistan (A Rafay MS); Hamad Medical Corporation, Doha, Qatar (S U Rahman FCPS); University of Missouri, Columbia, MO, USA (M Raju PhD); Contech School of Public Health, Lahore, Pakistan (A Rafay MS, Prof S M Rana PhD); Contech Internal Health Consultants, Lahore, Pakistan (Prof S M Rana PhD); Center for Disease Analysis, Louisville, CO, USA (H Razavi PhD); Suez Canal University, Ismailia, Egypt (Prof A Refaat PhD); Centro Anna Maria Astori, IRCCS Mario Negri Institute for Pharmacological Research, Bergamo, Italy (Prof G Remuzzi MD); International Health and Development, Geneva, Switzerland (Prof S Resnikoff MD); Hospital das Clinica da Universidade Federal de Minas Gerais, Belo Horizonte, Brazil (Prof A L Ribeiro PhD); Centre for Research in Environmental Epidemiology (CREAL), Barcelona, Spain (D Rojas-Rueda PhD); Institute of Epidemiology and Medical Biometry, Ulm University, Ulm, Germany (Prof D Rothenbacher MD); Ann and Robert H Lurie Children’s Hospital of Chicago, Chicago, IL, USA (D H Rothstein MD); Independent researcher, London, UK (J T Rowley PhD); BARC Hospital, HBNI University, Mumbai, India (N Roy MD); Muhimbili University of Health and Allied Sciences, Dar es Salaam, Tanzania (G M Ruhago MA, B F Sunguya MSc); Queensland Centre for Mental Health Research, The Park Centre for Mental Health, Brisbane, QLD, Australia (S Saha PhD);

Case Western Reserve University, Cleveland, OH, USA (J R Sanabria MD); Cancer Treatment Centers of America—RFU Chicago Medical School, North Chicago, IL, USA (J R Sanabria MD); Marshall University, Huntington, WV, USA (M Sawhney PhD); Federal University of Santa Catarina, Florianópolis, Brazil (I J Schneider PhD); University of Alabama at Birmingham, Birmingham, AL, USA (D C Schwebel PhD, J A Singh MD); Stellenbosch University, Cape Town, South Africa (Prof S Seedat PhD); An-Najah University, Nablus, Palestine (A Shaheen PhD); Tufts Medical Center, Boston, MA, USA (Prof S Shahraz PhD); University of Pittsburgh Medical Center, Pittsburgh, PA, USA (S Shangguan); Tachikawa Hospital, Tokyo, Japan (Y Shinohara PhD); Finnish Institute of Occupational Health, Helsinki, Finland (R Shiri PhD); Washington State University, Spokane, WA, USA (K Shishani PhD); Northumbria University, Newcastle upon Tyne, UK (I Shiue PhD); Alzheimer’s Scotland Dementia Research Centre, University of Edinburgh, Edinburgh, UK (I Shiue PhD); Reykjavik University, Reykjavik, Iceland (I D Sigfusdottir PhD); International Institute for Population Sciences, Mumbai, India (A Singh PhD); Dartmouth College, Lebanon, NH, USA (S Soneji PhD); Stavanger University Hospital, Stavanger, Norway (K Søreide PhD); Federal Research Institute for Health Organization and Informatics, Ministry of Health of the Russian Federation, Moscow, Russia (S Soshnikov PhD); Department of Clinical Neurological Sciences, Western University, London, ON, Canada (L A Sposato MD); Faculty of Medicine and Health Sciences, Universiti Tunku Abdul Rahman, Kajang, Cheras, Malaysia (C T Sreeramareddy MPH); “Attikon” University Hospital, Athens, Greece (V Stathopoulou PhD); Department of Neuroscience (Prof L J Stovner PhD), Norwegian University of Science and Technology, Trondheim, Norway (Prof T J Steiner PhD); Norwegian Advisory Unit on Headache, St Olavs Hospital, Trondheim, Norway (Prof L J Stovner PhD); “Alexandra” General Hospital of Athens, Athens, Greece (K Stroumpoulis PhD); Centre Hospitalier Public du Cotentin, Cherbourg, France (K Stroumpoulis); National Institute for Research in Tuberculosis, Chennai, India (S Swaminathan MD); Northwestern University, Chicago, IL, USA (M Swaroop MD, Y Yano MD); University of California Irvine, Irvine, CA, USA (Prof B L Sykes PhD); University of Illinois at Urbana-Champaign, Champaign, IL, USA (K M Tabb PhD); Chaim Sheba Medical Center, Tel Hashomer, Israel (Prof D Tanne MD); Tel Aviv University, Tel Aviv, Israel (Prof D Tanne MD); Westchester Medical Center, Valhalla, NY, USA (M Tavakkoli MD); Netherlands Institute of Mental Health and Addiction, Utrecht, Netherlands (M Ten Have PhD); Memorial University, St John’s, NL, Canada (E Y Tenkorang PhD); Department of Anesthesiology, University of Virginia, Charlottesville, VA, USA (A S Terkawi MD); Outcomes Research Consortium, Cleveland, OH, USA (A S Terkawi); Department of Anesthesiology, King Fahad Medical City, Riyadh, Saudi Arabia (A S Terkawi); WorldFish, Penang, Malaysia (A L Thorne-Lyman ScD); Aristotle University of Thessaloniki, Thessaloniki, Greece (Prof F Topouzis PhD); Health Care Center of Anjo Kosei Hospital, Anjo City, Japan (H Toyoshima MD); University of Southern Santa Catarina, Palhoça, Brazil (Prof J Traebert PhD); Johns Hopkins University, Baltimore, MD, USA (B X Tran PhD); Hanoi Medical University, Hanoi, Vietnam (B X Tran PhD); Department of Neurology, Rigshospitalet, University of Copenhagen, Copenhagen, Denmark (T Truelsen DSc); Cleveland Clinic, Cleveland, OH, USA (Prof E M Tuzcu MD); Department of Veterans Aff airs, Washington, DC, USA (U S Uchendu MD); Department of Internal Medicine Federal Teaching Hospital, Abakaliki, Nigeria (K N Ukwaja MD); Netherlands Leprosy Relief, Amsterdam, Netherlands (W H van Brakel PhD); African Population and Health Research Center, Nariobi, Kenya (S van de Vijver MD); National Institute for Public Health and the Environment, Bilthoven, Netherlands (C H van Gool PhD); Maastricht University Medical Centre, Maastricht, Netherlands (Prof J van Os PhD); UKK Institute for Health Promotion Research, Tampere, Finland (Prof T J Vasankari PhD); Neuroscience Centre, Raffl es Hospital, Singapore, Singapore (N Venketasubramanian MD); University of Bologna, Bologna, Italy (Prof F S Violante MD); Higher School of Economics, Moscow, Russia (Prof V V Vlassov MD); National Institute for Occupational Safety and Health, Washington, DC, USA (G R Wagner MD); Uniformed Services University of Health Sciences, Bethesda, MD, USA (Prof S G Waller MD); Institute of Basic Medical

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Sciences (X Wan PhD), and Cancer Institute (W Chen PhD, Prof X Zou MD), Chinese Academy of Medical Sciences, Beijing, China; NIHRD, Ministry of Health Indonesia, Jakarta, Indonesia (T S Warouw PhD); Health Canada, Ottawa, ON Canada (S Weichenthal PhD); Beijing Neurosurgical Institute, Beijing, China (Prof W Wenzhi MD); Institute of Medical Sociology and Social Medicine, Marburg, Germany (A Werdecker DiplOecTroph.); Federal Institute for Population Research, Wiesbaden, Germany (R Westerman PhD); German National Cohort Consortium, Heidelberg, Germany (R Westerman PhD); Wayne State University, Detroit, MI, USA (Prof J D Wilkinson MD); Royal Cornwall Hospital, Truro, UK (Prof A D Woolf FRCP); Nanjing University School of Medicine, Nanjing, China (Prof G Xu PhD); Duke Kunshan University, Kunshan, China (Prof L L Yan PhD); Jichi Medical School, Tochigi, Japan (Y Yano MD); The University of Hong Kong, Hong Kong Special Administrative Region, China (Prof P Yip PhD); National Center of Neurology and Psychiatry, Kodaira, Japan (N Yonemoto MPH); Korea University, Seoul, South Korea (S Yoon PhD); Jackson State University, Jackson, MS, USA (Prof M Z Younis PhD); School of Public Health, Wuhan, China (Prof C Yu PhD); Global Health Institute, Wuhan University, Wuhan, China (Prof C Yu PhD); Mansoura Faculty of Medicine, Mansoura, Egypt (Prof M E Zaki MD); and Chongqing Medical University, Chongqing, China (Prof Y Zhao MSc)

Contributors TV, ADL, JAS, and CJLM prepared the fi rst draft. CJLM and TV fi nalised the draft based on comments from other authors and reviewer feedback. TV, ADL, JAS, and CJLM conceived of the study and provided overall guidance. RB and KF performed fi nal statistical analyses. All other authors provided data, developed models, reviewed results, provided guidance on methodology, and reviewed the manuscript.

Declaration of interests BDG works for AMP, which receives grant-specifi c support from Crucell, GlaxoSmithKline, Merck, Novartis, Pfi zer, and Sanofi Pasteur; however, none of this support is for work related to the present report. MGS received a speaking honorarium from Ethicon for work unrelated to this manuscript. MBS is a paid consultant to Janssen, Pfi zer, and Tonix Pharmaceuticals and is also paid for his editorial work on Up-to-Date and on the journal Biological Psychiatry. PJ is supported by a career development fellowship from the Wellcome Trust, Public Health Foundation of India and a Consortium of UK Universities. SIH is funded by a Wellcome Trust Grant. JAS has received grant support from Takeda and Savient Pharmaceuticals and consultant fees from Takeda, Regeneron, Allergan, and Savient. JAS is an executive member of OMERACT, an organisation that received arms-length funding from 36 pharmaceutical companies. KJL was funded by WHO to conduct the review of HSV-2 seroprevalence which informs this study. During the study, KJL received funding from Health Protection Scotland, the National Institute for Health Research (NIHR) Health Protection Research Unit (HPRU) in Evaluation of Interventions, and Sexual Health 24: the funding sources had no role in the writing of the manuscript or the decision to submit it for publication. FP-R is a consultant for AstraZeneca, Cimabay, Menarini, and Pfi zer, and has received investigation grants from the Spanish Health Ministry, Spanish Foundation for Rheumatology, and Cruces Hospital Rheumatology Association. PJH is principal investigator on vaccines in clinical trials against hookworm and schistosomiasis as well as several other neglected tropical disease vaccines in development. CKi receives research grants from Brazilian public funding agencies Conselho Nacional de Desenvolvimento Científi co e Tecnológico (CNPq), Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES), and Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul (FAPERGS). CKi has also received authorship royalties from publishers Artmed and Manole. KBG received the NHMRC-Gustav Nossal scholarship sponsored by CSL in 2012. This award is peer-reviewed through the standard NHMRC peer-review process; CSL played no part in selection of the awardee. HAW, AJF, FJC, and HEE are all affi liated with the Queensland Centre for Mental Health Research, which receives funding from the Queensland Department of Health. DJS has received research grants and consultancy honoraria in the past three years from AMBRF, Biocodex, Cipla, Lundbeck, National Responsible Gambling Foundation,

Novartis, Servier, and Sun. DJS is also supported by the Medical Research Council of South Africa. DM reports being on the scientifi c advisory board of Unilever North America and ad hoc honoraria or consulting from Bunge, Nutrition Impact, Amarin, AstraZeneca, and Life Sciences Research Organization. DAQ was supported by The Eunice Kennedy Shriver National Institute of Child Health and Human Development of the National Institutes of Health under award number 5T32HD057822. RAL receives funding through the Farr Institute of Health Informatics Research. The Farr Institute is supported by a consortium of ten UK research organisations: Arthritis Research UK, the British Heart Foundation, Cancer Research UK, the Economic and Social Research Council, the Engineering and Physical Sciences Research Council, the Medical Research Council, the National Institute of Health Research, the National Institute for Social Care and Health Research (Welsh Government), and the Chief Scientist Offi ce (Scottish Government Health Directorates). KeS is affi liated with Grant in Aid for Scientifi c Research from the Ministry of Education, Culture, Sports and Technology in Japan. JM received a fellowship from the Wellcome Trust. AKau acknowledges that she receives funding from Oklahoma Center for the Advancement of Science and Technology (OCAST) as co- principal investigator. HoC acknowledges that the study was in part supported by the intramural research program of the NIH, the National Institute of Environmental Health Sciences. SS received research support and funding from the NIH and NRF and an honorarium from Pharmaceutical companies and is an employee at the NRF. VC acknowledges the following confl icts of interest: Speaker Bureau Boehringer Ingelheim, BMS Pfi zer Advisory board: Boehringer Ingelheim Mindmaze. All other authors declare no competing interests.

Acknowledgments On behalf of the GBD 2010 Genitourinary Diseases Expert Group, GR, NoP, and BB would like to acknowledge that their activities within the GBD 2013 have been made on the behalf of the International Society of Nephrology (ISN). ATA received institutional support and grants from the Graduate School of Medical Sciences, University Medical Center Groningen (UMCG). JM has received support from the National Health and Medical Research Council. LLY is also supported by the National Natural Sciences Foundation of China. UOM would like to acknowledge his funding by the German National Cohort Consortium. RB was provided funding support by the Brien Holden Vision Institute. INA would like to acknowledge her funding support from the National Health and Medical Research Council Public Health (Australian) Early Career Fellowship. KD is supported by a Wellcome Trust Fellowship in Public Health and Tropical Medicine (grant number 099876). The funding sources had no role in the writing of the manuscript or the decision to submit it for publication. JS was supported by the National Natural Science Foundation for Young Scholars of China, (number 81200051); Research Fund for the Doctoral Program of Higher Education of China (number 20110071120060); Science Foundation for Young Scholars in Zhongshan Hospital (number 2012ZSQN04); and the Scientifi c Project for Fudan University (number 20520133474). RB is funded by an Australian National Health and Medical Research Council Senior Principal Research Fellow. TF is grateful to the Swiss National Science Foundation for an Early and an Advanced Postdoc Mobility fellowship (project numbers PBBSP3-146869 and P300P3-154634). SJY and IHO received funding for their work in a grant from the Korean Health Technology R&D Project (Ministry of Health & Welfare, Republic of Korea; grant number HI13C0729). ALT-L was supported by the CGIAR Research Program on Aquatic Agricultural Systems.

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• Global, regional, and national disability-adjusted life years (DALYs) for 306 diseases and injuries and healthy life expectancy (HALE) for 188 countries, 1990–2013: quantifying the epidemiological transition
• Introduction
• Methods
• Study design
• Years lived with disability
• Healthy life expectancy
• Decomposition of variance and epidemiological transition
• Age standardisation
• Role of the funding source
• Results
• Global
• Decomposition of epidemiological patterns
• Country-specific results
• Discussion
• Acknowledgments
• References

Late-stages-of-epidemiological-transition--health-status-in-_1999_Health---P.pdf

Late stages of epidemiological transition: health status in the developed world

Matthew Smallman-Raynor*, David Phillips

Health Research Group, School of Geography, University of Nottingham, University Park, Nottingham, NG7 2RD, UK

Received 30 December 1997; received in revised form 18 December 1998; accepted 1 January 1999

Abstract

Drawing on the example of twentieth century Europe, this paper examines themes in the spatial development of

the late stages of epidemiological transition in developed countries. A preliminary analysis of mortality trends for sample countries in four European regions (north, Scandinavia, south and east) suggests that, as the epidemiological transition progressed to its later stages during the period 1901±1975, spatial variability in the importance of classical infectious diseases increased. This trend was countered by a spatial convergence in the importance of disease

groupings that typify late transition. An apparently new epidemiological phase in late transition, linked to the emergence and re-emergence of infectious and parasitic diseases, is illustrated with reference to tuberculosis and the acquired immunode®ciency syndrome (AIDS). # 1999 Elsevier Science Ltd. All rights reserved.

Keywords: Epidemiological transition; Developed countries; Diseases of modernisation; Emergent diseases

1. Introduction

Over the past 15 years or so, the concept of epide-

miological transition (Omran, 1971), and the wider

concept of health transition (Caldwell and Santow,

1989; Caldwell et al., 1990), have provided an analyti-

cal framework for a number of geographical studies of

mortality, morbidity, health and development. In par-

ticular, the framework has been used widely in the

examination of health developments in rapidly chan-

ging lower- and middle-income countries, as witnessed

by studies of Hong Kong (Phillips, 1986, 1988), La

Re union (Lopez, 1989), Mauritius (Kalla, 1995), Nepal

(Hellen, 1983) and the islands of the Caribbean

(McGlashan, 1982) and the Paci®c (Lewis and

Rapaport, 1995).

But epidemiological change also remains relevant to

the higher-income countries of Europe, North America

and Australasia, where the transition is generally con-

sidered to be well advanced (Caselli, 1994). Today,

many developed countries are experiencing a shift in

the importance of some of the chronic and degenera-

tive diseases of `late' transition. The emergence (re-

emergence) of new (classical) infections, and their

manifestation as the leading causes of death in some

socio-economic groups, has placed infectious and para-

sitic diseases back on the public health agenda, whilst

medical advances are giving rise to improved survivor-

ship (if an impaired quality of life) in elderly popu-

lations. Indeed, such have been the recent

epidemiological changes in many developed countries

that some authors have mooted the extension of the

classical three-stage model of epidemiological tran-

sition to a fourth (Olshansky and Ault, 1986) Ð or

even a ®fth (Olshansky et al., 1997) Ð stage.

Against this background, the present paper draws

Health & Place 5 (1999) 209±222

1353-8292/99/$ - see front matter # 1999 Elsevier Science Ltd. All rights reserved. PII: S1353-8292(99)00010-6

www.elsevier.com/locate/healthplace

* Corresponding author. Tel: +44-115-951-5432; fax: +44-

115-951-5249.

on the example of twentieth century Europe to high- light some broad themes in the late stages of epidemio-

logical transition. The paper begins, in Section 2, with a brief review of the concept of epidemiological tran- sition; the original model is outlined, its major variants

and extensions are summarised, and its limitations are considered. Section 3 examines the historical develop- ment of epidemiological transition to its late stages in

Europe; regional trends in deaths from all causes and two `touchstone'' diseases (pulmonary tuberculosis and cardio-vascular disease) are examined for the period

1901±1975, and a tentative model of the spatial devel- opment of the transition is proposed. Finally, using the examples of tuberculosis and the acquired immuno- de®ciency syndrome (AIDS), Section 4 examines some

spatial aspects of emergent and re-emergent infections in late twentieth century Europe.

2. The general concept of epidemiological transition

The basic principles of epidemiological transition, and its relationships with demographic transition, are well known and have been outlined by authors such as

Omran (1971, 1977); Caldwell (1982); Phillips (1994). The idea of epidemiological transition is quite straight- forward and the `theory', as Omran (1971) calls it,

``focuses on the complex change in patterns of health and disease and on the interactions between these pat- terns and their demographic, economic and sociologic

determinants and consequences'' (Omran, 1971, p. 510). As a concept, epidemiological transition addresses the nature of the relative balance between

various causes of mortality in particular (and morbid- ity implicitly) and the ways in which changes occur whilst societies modernise. Today, the term health transition is preferred by

some as it is felt to be a broader concept, involve the cultural, social and behavioural determinants of health and implies a concern with health and survival rather

than death. It also implies continuing, socially in¯u- enced, change (see, for example: Caldwell and Santow, 1989; Caldwell et al., 1990). The health transition, in

its focus on the cultural, social and behavioural deter- minants of health, attempts to highlight factors other than medical interventions and income. It is generally accepted that the provision of modern medical services

reduces ill health and lowers mortality although its precise in¯uence is di�cult to quantify and disentangle from other sources of change. These include improving

material living standards, education, housing and wider public health interventions. However, what Caldwell and Santow (1991) refer to as `the health

transition factor' is also very in¯uential. Societies with similar levels of income and provision of health ser- vices can exhibit very di�erent levels of health and

mortality. This is also seen in di�erent cultures and families, or even between apparently similar house-

holds within the same societies. There is clearly a beha- vioural, attitudinal e�ect at work and also, highly probably, a genetic or inheritance e�ect.

2.1. Stages of epidemiological transition

As initially envisaged (Omran, 1971), epidemiologi- cal transition would involve a one-way movement through a sequence of three stages. These start with an

`Age of Pestilence and Famine' (stage 1), through an `Age of Receding Pandemics' (stage 2), to an `Age of Degenerative and Man-Made Diseases' (stage 3).

Further changes in the mortality pro®le have been as- sociated with a proposed fourth stage of the transition, in which the length of life expectancy increases (as the

major killer diseases of later transition are better trea- ted or detected) but in which health status may de- teriorate, as the causes of chronic but non-fatal morbidity are yet to be defeated (Verbrugge, 1984;

Riley, 1989; Riley and Alter, 1989; Phillips, 1991). An increasing incidence of mental disorders seems also to be characteristic of this fourth stage. Olshansky and

Ault (1986) have called this the `Age of Delayed Degenerative Diseases' which they see as a stage that will propel life expectancy into, and perhaps beyond,

the eighth decade. The major degenerative causes of death that prevailed during stage 3 of the transition remain as major killers, but with relatively rapid

improvements in survival concentrated among the older population. This stage of transition is already evident in a number of developed countries and in some middle- and higher-income newly industrialising

countries, particularly in Southeast and East Asia (Leete, 1985; Phillips, 1992, 1994; Leete and Alam, 1993).

Most recently, the existence of a ®fth stage of epide- miological transition has been posited (Olshansky et al., 1997, pp. 11±12). This stage, which we tentatively

refer to as the `Age of Emergent and Re-emergent Infections', is associated with the resurgence of infec- tious and parasitic diseases (both old and `new') as a serious public health concern in developed countries.

In particular, the emergence of AIDS as a leading cause of death among young adults in cities of North America and Europe, coupled with the re-emergence of

some classical infectious diseases (most notably, tuber- culosis), has prompted Olshansky and colleagues (Olshansky et al., 1997, p. 12) to suggest that ``the

unique attributes of this `new' trend in infectious dis- ease mortality qualify it as a distinct stage in our epi- demiologic history''.

M. Smallman-Raynor, D. Phillips / Health & Place 5 (1999) 209±222210

2.2. Progress of transition

The notion of `progress' through the stages of epide- miological transition is the subject of a substantial and ongoing debate (see, for example, Phillips and

Verhasselt, 1994). We ®rst outline the major variants of progression as described by Abdel Omran. We then review some of the major issues that have emerged

from the broader debate on progress through the tran- sition.

2.2.1. Transition variants

Three major variants of the model of epidemiologi- cal transition are recognised by Omran (1971):

1. The classical (Western) model. This model, which is

associated with an advanced stage of transition, typi®es the experience of Western Europe. Progress from stage 1 to stage 3 took some 200±300 years and was underpinned by a gamut of factors that

were associated with the modernisation complex. 2. The accelerated model. This model, which is also as-

sociated with an advanced stage of transition, typi-

®es the experience of Japan and, probably, the former USSR and certain countries of Eastern Europe and South-East Asia. Here, mortality and

fertility declined rapidly from the late nineteenth and early twentieth centuries, and the change to a modern epidemiological pro®le was swift. Indeed,

many of the developing countries in the middle- income group have life expectancies and mortality patterns similar to those of developed countries, even if these may ¯uctuate over the short term

(Vallin et al., 1996). 3. The delayed model. This model typi®es the experi-

ence of many countries of the poorer Third World.

Their transitions were delayed until the post-war period and have been largely attributable to Western technology. Insofar as fertility has not

always declined rapidly, and living conditions have not always improved substantially for all people in these countries, their transitions have lagged behind other regions of the world.

To these three variants, Omran (1983) has sub- sequently added the so-called `transitional variant of the delayed model'. This model, which he cites to

include some countries of South-East Asia and the Caribbean, represents a variant of the delayed model in that it encompasses health changes due to both

Western technology and socio-economic develop- ment.

2.2.2. Critical assessments

The general utility of the model of epidemiological transition has been widely debated by economic and medical historians, health and population geographers,

demographers and others; the core issues are reviewed

from a geographical perspective by Jones and Moon (1992). At one level, empirical evidence Ð both his- torical and contemporary Ð warns against the oper-

ation of a smooth and uninterrupted progression from stage 1 to stage 3 and beyond. In particular, it appears that changes in mortality may be reversible and that a

`counter-transition' can occur. The contemporary resurgence of one infectious disease (diphtheria) in the

Newly Independent States (NIS) of the former Soviet Union, and its relationship to the recent social uphea- vals in the region, could be considered illustrative of

such a phenomenon (Vitek and Wharton, 1998). In ad- dition, it may be that only certain sectors of the popu-

lation (for example, the very young, mothers, the poor or the in®rm) are a�ected by epidemiological change, and di�erent diseases (and, hence, stages of transition)

may coexist. This latter phenomenon, which Frenk et al. (1989) have referred to as `protracted' transition, is especially evident in the large cities of some developing

countries. Here, the richer sectors of the population may develop more or less `modern' health and disease

pro®les whilst some poorer sectors may experience the double jeopardy of infectious diseases and chronic/ degenerative ailments (Frenk et al., 1989, 1996;

Phillips, 1993). More generally, the model of epidemiological tran-

sition is compromised by the uncertain nature of the mechanisms that drive progress through the transition. Indeed, although Omran (1971) identi®ed a broad

series of factors (biophysiologic, socio-economic and psychologic/emotional) that were associated with stages 1±3, the exact role of these and other com-

ponents in epidemiological change remain a hotbed of controversy (see, for example, Cli� et al., 1998).

Inevitably, such uncertainty limits the analytical and predictive power of the model (however, see Phillips, 1994). The terminal point of the original model is also

ambiguous and ill-de®ned, whilst contemporary health developments raise the issue as to whether the tran- sition can ever be considered complete. In particular,

extensions of the model to include a stage 4 and a stage 5 re¯ect the fundamental dynamism of chronic

and infectious conditions and have prompted some observers to moot the existence of several epidemiolo- gical transitions (Mackenbach, 1994). Finally, some

commentators have challenged the utility of speci®c disease groups as markers of the stage of transition

(Turshen, 1989; Mackenbach, 1994), whilst the applica- bility of the model to trends in morbidity (as opposed to mortality) has been questioned by Riley and Alter

(1989). To set against these criticisms, the model of epide-

miological transition does provide a broad conceptual

framework for the examination of historical changes in mortality (Phillips, 1988, 1990; see also Jones and

M. Smallman-Raynor, D. Phillips / Health & Place 5 (1999) 209±222 211

Fig. 1. Pulmonary tuberculosis: trends in male mortality for four European regions, 1901±05 to 1971±75. The average value (heavy

line trace) and range (shaded envelope) of the standardised mortality ratio (SMR) per 1000 population in each quinquennium is

shown. (A) Northern Europe. (B) Scandinavia. (C) Southern Europe. (D) Eastern Europe.

M. Smallman-Raynor, D. Phillips / Health & Place 5 (1999) 209±222212

Fig. 2. Cardio-vascular diseases: trends in male mortality for four European regions, 1901±05 to 1971±75. The average value

(heavy line trace) and range (shaded envelope) of the standardised mortality ratio (SMR) per 10,000 population in each quinquen-

nium is shown. (A) Northern Europe. (B) Scandinavia. (C) Southern Europe. (D) Eastern Europe.

M. Smallman-Raynor, D. Phillips / Health & Place 5 (1999) 209±222 213

Moon, 1992). It is within this context that, in sub- sequent sections of the paper, we examine aspects of

the late stages of epidemiological transition in Europe.

3. An historical perspective on late transition: European

mortality trends, 1901±1975

The late stages of epidemiological transition mark the culmination of some three centuries of mortality

decline in Europe (Scho®eld et al., 1991; Kunitz, 1993; Caselli, 1994). Thus, in his classic book World Population: Past Growth and Present Trends, Carr-

Saunders (1936) traced the origins of the European mortality decline to north-Western Europe at the start of the eighteenth century, with the onset in other

regions Ð the south and east Ð lagging the north- west by several decades or more. The start of the twentieth century is generally

regarded as marking the onset of a modern epidemio-

logical pro®le in Europe. The development of this modern pro®le is succinctly summarised by Graziella Caselli (1994) in the following terms:

The twentieth century saw the start of a new phase in the evolution of the mortality rate, which admi- nistered a fresh stimulus to the falling trend,

enabling populations in the countries of Europe to reach unhoped-for life-expectancy levels. . .[I]t was during this phase that a complete transformation in

the age±sex structure of the mortality rate occurred. This transformation meant that the potential causes of death, applicable to societies in the past, were

being superseded by new risks directly linked with economic and industrial development (Caselli, 1994, p. 3).

To examine European mortality patterns during this evolving phase of late transition, we use the data assembled by Michael Alderson (1981) in his

International Mortality Statistics. The nature of the data is reviewed by Alderson (1981, pp. 3±109) and Cli� et al. (1993, 1998), where data quality issues are also considered. In brief, for each of 31 countries and

15 quinquennial periods from 1901±05 to 1971±75 in- clusive, the dataset includes age±sex Standardised Mortality Ratios (SMRs) for (i) each of 177 ICD-

coded causes of death and (ii) deaths from all causes. Here, we draw on the SMRs relating to a subset of 19 European countries (as de®ned by their political

boundaries in 1975) in four geographical areas:

1. Northern Europe (Belgium, England and Wales,

France, Netherlands, Scotland); 2. Scandinavia (Denmark, Finland, Iceland, Norway,

Sweden);

3. Southern Europe (Greece, Italy, Portugal, Spain);

4. Eastern Europe (Bulgaria, Czechoslovakia, Hungary,

Romania, Yugoslavia).

Unless stated otherwise, all subsequent analysis relates

to the four geographical areas so de®ned.A detailed

analysis of the disaggregated patterns of mortality in

the 19 countries over the entire observation period,

1901±1975, is a large undertaking. So, for the present

analysis, we have selected two `touchstone' diseases for

examination: (i) pulmonary tuberculosis, a bacterial

disease that, until its resurgence with the AIDS epi-

demic in the 1980s (Smallman-Raynor et al., 1992),

was considered to typify the declining mortality from

infectious disease in Europe; and (ii) cardio-vascular

disease, a chronic illness characteristic of the late

stages of epidemiological transition. In addition to

these two ICD-coded diseases, we also utilise SMRs

for deaths from all causes. For all three mortality cat-

egories (pulmonary tuberculosis, cardio-vascular dis-

ease and all causes), we restrict our analysis here to

male mortality.

For a given category of deaths, two fundamental

questions can be asked of the long-term mortality ex-

periences of European regions. First, to what extent

have the temporal trends in death rates varied between

the regions? Second, to what extent have the temporal

trends varied within the regions? We consider each

question in turn.

Table 1

Critical results of simple linear regression analysis to identify

long-term trends in male standardised mortality ratios

(SMRs), four European regions, 1901±1905 to 1971±1975; � statistically signi®cant at the p = 0.05 level in a two-tailed

test

Region b̂1 (t ) R 2 (F)

(A) Pulmonary tuberculosis

Northern Europe ÿ0.11 (ÿ10.03�) 0.89 (100.51�) Scandinavia ÿ0.14 (ÿ9.78�) 0.88 (95.74�) Southern Europe ÿ0.07 (ÿ6.29�) 0.75 (39.55�) Eastern Europe ÿ0.12 (ÿ13.97�) 0.95 (195.08�)

(B) Cardio-vascular disease

Northern Europe 0.01 (4.26 � ) 0.58 (18.12

� )

Scandinavia 0.03 (12.80 � ) 0.93 (163.93

� )

Southern Europe ÿ0.01 (ÿ5.70�) 0.71 (32.48�) Eastern Europe 0.01 (10.40

� ) 0.92 (108.25

� )

(C) All causes

Northern Europe ÿ0.03 (ÿ19.21�) 0.97 (368.85�) Scandinavia ÿ0.03 (ÿ14.25�) 0.94 (202.94�) Southern Europe ÿ0.04 (ÿ14.34�) 0.94 (205.60�) Eastern Europe ÿ0.04 (ÿ13.74�) 0.95 (188.80�)

M. Smallman-Raynor, D. Phillips / Health & Place 5 (1999) 209±222214

Fig. 3. Variation in the range of standardised mortality ratio (SMR) values for all causes and two diseases, Europe, 1901±1905 to

1971±1975. The coe�cient of variation (CV ) has been used as a measure of the relative dispersion in the SMR values; for each

series, the CV in the ®rst quinquennial period for which data are available (usually 1901±1905) has been standardised at 100 with

subsequent observations indexed to that standard. The results are shown for 20 countries (A) and by region for all causes (B), pul-

monary tuberculosis (C) and cardio-vascular disease (D).

M. Smallman-Raynor, D. Phillips / Health & Place 5 (1999) 209±222 215

3.1. Trends I: inter-regional comparisons

For each of the four regions, Fig. 1 illustrates the reported trend in male mortality due to pulmonary tuberculosis for the 15 quinquennial periods, 1901±

1905 to 1971±1975. The heavy line plots the average SMR (per 1000 population) across the constituent countries of each region, while the shaded area forms

an envelope that delimits the range of values from the highest to lowest SMR in each time period. Fig. 2 shows the equivalent information, but with SMRs

formed to a factor of 10,000, for cardio-vascular dis- ease. Finally, to compare the direction and relative strength of the observed trends in Figs. 1 and 2, a lin- ear regression model in time was ®tted to the log-trans-

formed series of the average SMR for each disease and region. The critical results of the regression analysis are summarised in Table 1; the slope coe�cient (b̂1) of

the ®tted regression model is indicated, along with the associated Student's t-statistic, the coe�cient of deter- mination (R

2 ) and the F-ratio. Statistically signi®cant

parameters at the p = 0.05 level in a two-tailed test are indicated by an asterisk.

3.1.1. Pulmonary tuberculosis

The most striking feature of Fig. 1 is the sharp decline in the average SMRs, albeit from very di�erent levels, in all regions during the twentieth century.

These declines are con®rmed by the statistically signi®- cant and negative b̂1 coe�cients in Table 1. However, it is also apparent from Fig. 1 that there were regional variations in the timing of the decline. Thus, while the

series of average SMRs for northern Europe (chart A) and Scandinavia (B) display a more or less consistent fall from the start of the observation period, the

decline in southern Europe (C) was delayed until the inter-war period. Unfortunately, evidence regarding the timing of onset of the decline in Eastern Europe

(D) is limited by the lack of disease records for the period prior to the First World War. However, a steep fall is registered thereafter.

3.1.2. Cardio-vascular disease Fig. 2 and Table 1 identify a contrasting pattern of

regional experiences. For three regions (northern Europe, Scandinavia and Eastern Europe), charts A, B

and D of Fig. 2 reveal some evidence of a rise in the average SMRs; this feature is con®rmed by the signi®- cant and positive b̂1 coe�cients in Table 1. However,

the magnitude of the slope coe�cients re¯ects some variation in the strength of the increase, being most pronounced in Scandinavia (b̂1=0.03). By contrast,

Southern Europe (chart C) displays an anomalous pat- tern of mortality decline (b̂1=ÿ0.01) which, as for tuberculosis (Fig. 1C), began in the inter-war period.

3.1.3. Deaths from all causes Although not plotted, the log-transformed SMRs

(per 10,000 population) for deaths from all causes were also examined for statistical evidence of trend. Again, the results of the regression analysis are summarised in

Table 1. In all instances the b̂1 coe�cients are statisti- cally signi®cant and negative, thereby con®rming the decline in overall mortality rates during the obser-

vation period. Moreover, the near-parity of the coe�- cients indicates a strong similarity in the strength of the overall mortality decline in the regions.

3.2. Trends II: intra-regional variability

In Figs. 1 and 2, the average SMR for each region

(depicted by the heavy line) is surrounded by a shaded envelope that delimits the range of national-level SMRs in each ®ve-year time period. Variability in the

SMR envelope can be assessed using a dimensionless measure of variability known as the coe�cient of vari- ation, CV. CV is de®ned as

CV � 100�s= �x� �1�

where, for a given set of countries, �x and s are the arithmetic mean and standard deviation respectively of the SMR values in each quinquennium. The coe�cient is large where there is high variation in relation to the

mean, and small when the variation is relatively low. For the entire set of 19 countries in each quinquen-

nium, Fig. 3A plots the coe�cients of variation, CV,

of male SMRs for (i) pulmonary tuberculosis and (ii) cardio-vascular disease. For reference, the coe�cients of variation are also shown for deaths from all causes.

Each series has been plotted in standardised format by setting the CV in the ®rst quinquennium (1901±1905) equal to 100, and indexing later values with respect to

the initial values. Fig. 3A shows that, across the 19 sample countries,

the series relating to deaths from all causes can be divided into two phases:

1. The period up to the Second World War, during which variability in SMRs increased to an index

value of 145 (1941±1945). This implies a divergence in the SMRs (and hence, mortality patterns) as judged across the 19 countries.

2. The period after the Second World War, during which the variability in SMRs approximately halved from the index value of 145 (1941±1945) to 70

(1971±1975). This implies a convergence in the SMRs across the 20 countries.

A rather di�erent pattern emerges in Fig. 3A for indi-

vidual causes of death. First, the steep rise in the series for tuberculosis implies that, despite the tendency for mortality rates to decline in all regions (Fig. 1), some

M. Smallman-Raynor, D. Phillips / Health & Place 5 (1999) 209±222216

countries made much more rapid progress to disease elimination than others. Second, the decline in the

series for cardio-vascular disease from 1911±1915 indi- cates a convergence of SMRs across Europe. The remaining charts of Fig. 3 plot, on a regional

basis, the coe�cients of variation for the SMRs relat- ing to all causes (chart B), pulmonary tuberculosis (C) and cardio-vascular disease (D). Again, each series has

been indexed to the CV in the ®rst quinquennium for which data are available (=100). The following fea- tures of Fig. 3 are evident:

1. For deaths from all causes (chart B), all four line traces display sharp peaks in intra-regional variabil-

ity, and these are most pronounced around the world wars. These ¯uctuations are superimposed on broad trends which de®ne a remarkable spectrum of

regional mortality patterns: increased intra-regional variability (southern Europe), implying a divergence of SMRs in the constituent countries; stability (northern Europe), implying an unchanging distri-

bution of SMRs; and decreased intra-regional varia- bility (Scandinavia), implying a convergence of SMRs. Finally, the pattern for Eastern Europe is

characterised by a steep rise in variability between 1936±1940 and 1951±1955, but with a sharp and consistent fall thereafter.

2. For tuberculosis (chart C), all four line traces indi- cate a growing intra-regional variability in SMRs over the observation period. These results are con- sistent with the ®ndings at the European level (Fig.

3A); despite the overall decline in mortality from tuberculosis in individual regions (Fig. 1), some countries have been more successful at disease elim-

ination than others. In this context, the rapid growth in intra-regional variation for Eastern Europe is spectacular.

3. With the exception of northern Europe, the line traces for cardio-vascular disease (chart D) depict sharp rises in intra-regional variability in the inter-

war/World War II period. These rises were coun- tered by falls, albeit of varying intensity, in the World War II/post-war period.

3.3. Conclusions

Taken together, the results in Figs. 1±3 and Table 1 underscore the complexity of mortality change as the modern epidemiological pro®le has developed in

Europe (Caselli, 1994). However, as judged by the evi- dence of our two `marker' diseases in Fig. 3A, a tenta- tive model of the spatial development of the

epidemiological transition in Europe also emerges. It may be suggested that, as the epidemiological transition has progressed to its late stages during the ®rst three-

quarters of the twentieth century, spatial variability in the importance of classical infectious diseases has

increased, and this has been countered by a spatial con- vergence in the importance of diseases that typify late transition. Whilst the increase in the spatial variability

of mortality from classical infectious diseases in Europe is con®rmed by studies of a number of other ailments (for example, diphtheria, measles, enteric

fever and whooping cough; see Cli� et al., 1998) further analyses, relating to a broad range of degenera- tive and chronic conditions, are required to establish

the veracity of the tentative model.

4. The resurgence of infectious and parasitic diseases in

Europe

Such were the spectacular falls in mortality from infectious diseases during the ®rst 75 years of this cen- tury that, in the developed world at least, plagues and

epidemics began to be viewed as essentially historical phenomena, scourges that had devastated past human populations, but which had been largely eliminated with advances in medical knowledge and the advent of

vaccines and antibiotics (Cairns, 1975). But the epide- miological developments of the past 20 years have thrown infectious diseases into a radically di�erent per-

spective, and the countries of the developed world may now be on the brink of a unique phase in their epide- miological history (Olshansky et al., 1997). This new

phase, representing a possible ®fth stage of epidemiolo- gical transition, is associated with the emergence and re-emergence of infectious diseases as a serious threat

to public health. Two institutional responses Ð the publication of the strategic policy document Preventing Emerging Infectious Diseases: A Strategy for the 21st Century by the US Centers for Disease Control and

Prevention (see CDC, 1998) and the recent establish- ment of the rapid-response Division of Emerging Viral and Bacterial Diseases Surveillance and Control by the

World Health Organization Ð are illustrative of the importance that now attaches to the problem. Undoubtedly the most prominent of the emerging

diseases is the acquired immunode®ciency syndrome (AIDS) and its causative agent, the human immunode- ®ciency virus (HIV). In the United States, for example, HIV is now the leading cause of death in young adult

males in most major cities (Selik et al., 1993). Likewise, in some cities of Europe, HIV has out- stripped accidents, overdose, suicide and murder as the

leading cause of death among groups of young, high- risk, adults (Galli et al., 1989; Gattari et al., 1991). But, coupled with this emergence of an apparently

`new' disease, the HIV/AIDS epidemic has also played a pivotal role in the re-emergence of such classical infections as tuberculosis.

M. Smallman-Raynor, D. Phillips / Health & Place 5 (1999) 209±222 217

4.1. Emergent and re-emergent diseases I: AIDS

Within months of the ®rst reports of AIDS in

North-America in 1981, British and Danish physicians

were describing a similar syndrome in European homo-

sexuals (du Bois et al., 1981; Thomsen et al., 1981). By

1982, the disease was being reported from France

(Rozenbaum et al., 1982) and Spain (Vilaseca et al.,

1982) and, a year later, AIDS had appeared through-

out Western Europe (World Health Organization

Collaborating Centre on AIDS, 1990a). In the mid-

1980s, sporadic reports of the disease started to ®lter

through from the countries of Eastern Europe (World

Health Organization Collaborating Centre on AIDS,

1990b) and, today, HIV infection and disease has been

recognised in every country of the European continent

(European Centre for the Epidemiological Monitoring

of AIDS, 1997b).

One of the most striking demographic features of

the HIV/AIDS epidemic is its tendency to selectively

target the younger adult populations. Thus, of the

185,808 AIDS cases identi®ed and reported in Europe

to the end of 1996, some 86% were aged 20±49 years

at the time of diagnosis (European Centre for the

Epidemiological Monitoring of AIDS, 1997a). Within

the adult age-bands, the majority of cases were largely

con®ned to certain well-de®ned transmission cat-

egories; evidence for the widespread dissemination of

the disease in the general population of European

countries is still limited. Thus, as Table 2 shows, some

three-quarters of adult AIDS cases were associated

with male homosexual/bisexual contact and/or intrave-

nous drug use (IVDU), with these two categories in

roughly equal proportions. In contrast, heterosexual

transmission accounted for less than 15% of cases, the

majority of which reported either (i) sexual contact

with members of high-risk groups (primarily male

bisexuals and IVDU) or (ii) origin in, or exposure to,

individuals from, regions where heterosexual trans- mission predominates (sub-Saharan Africa, the

Caribbean Basin and parts of Latin America). The HIV/AIDS epidemic has assumed diverse forms

in the various countries of Europe (Smallman-Raynor

et al., 1992). To illustrate the epidemiological mosaic, Fig. 4 maps the cumulative number of AIDS cases reported to the European Centre for the

Epidemiological Monitoring of AIDS from various European countries to 31st December 1996. An important feature of Fig. 4 is the geographical

polarisation of the epidemic, both between east and west and, within Western Europe, between some countries of the north and the south. The continental (east±west) divide is especially prominent. Thus, of the

186,000 or so AIDS cases identi®ed in Europe to the end of 1996, some 96% were reported from the countries of Western Europe. This di�erential develop-

ment of the epidemic may be attributed, in large measure, to the relative social isolation of Eastern Europe prior to the late 1980s. Thereafter, increased

interaction with the countries of Western Europe has been associated with a marked increase in the inci- dence and prevalence of HIV infection and disease

(Kiehl et al., 1992). Within Western Europe, the AIDS epidemic displays

a primary focus on just three countries: France, Italy and Spain (Fig. 4). Together, these countries had

reported over two-thirds of the entire European AIDS toll to the end of 1996. Italy and Spain are particularly noteworthy for the extent to which the AIDS epidemic

has centred on intravenous drug users, with this risk category accounting for some two-thirds of the 80,000 AIDS cases reported from these countries by the end

of 1996 (European Centre for the Epidemiological Monitoring of AIDS, 1997b). In these same countries, the social parameters of intravenous drug use have ensured a more extensive spread of the HIV/AIDS epi-

demic to females and, via perinatal transmission of HIV, to infants. Elsewhere, in France and many other countries of north-Western Europe, male homosexuals/

bisexuals remain the leading transmission category.

4.2. Emergent and re-emergent diseases II: tuberculosis mortality in the `AIDS era'

Full details of the synergistic relationship of AIDS

and tuberculosis are given elsewhere (Mann et al., 1992; Rieder, 1994; McKinney et al., 1998) but, for in- dividuals whose immune systems have been severely

compromised by HIV, tuberculosis is now ranked as one of the most common AIDS indicator diseases. Thus, of a sample 61,450 European AIDS cases diag-

nosed and reported in the three years to December 1996, AIDS-indicative tuberculosis was recorded in 10.7% (pulmonary tuberculosis) and 9.2% (extra-pul-

Table 2

Transmission categories of adult AIDS cases noti®ed in the

countries of Europe to 31st December 1996; source: European

Centre for the Epidemiological Monitoring of AIDS, 1997a

Category Cases a

Homosexual/bisexual male 64,101 (35.8)

Intravenous drug user (IVDU) 70,825 (39.6)

Homosexual/bisexual IVDU 2680 (1.5)

Heterosexual 26,061 (14.6)

Haemophiliac 3121 (1.7)

Transfusion recipient 3326 (1.9)

Other/undetermined 8711 (4.9)

Total 178,825 (100.0)

a Cases as a percentage proportion of the total shown in

parentheses.

M. Smallman-Raynor, D. Phillips / Health & Place 5 (1999) 209±222218

monary tuberculosis) of all patients (European Centre

for the Epidemiological Monitoring of AIDS, 1997a).

The evolution and spread of multi-drug resistant

(MDR) strains of Mycobacterium tuberculosis have

further complicated the epidemiology of the disease in

Europe, although a causal link to the HIV/AIDS epi-

demic is not generally suspected (McKinney et al.,

1998).

For a selection of European countries analysed in

Figs. 1±3, Table 3 gives the male standardised mor-

Fig. 4. Cumulative AIDS cases recorded in sample European countries to 31st December 1996. Source: data from European Centre

for the Epidemiological Monitoring of AIDS, 1997a.

M. Smallman-Raynor, D. Phillips / Health & Place 5 (1999) 209±222 219

tality ratio (SMR) per 100,000 for pulmonary tubercu-

losis in years around 1985 and 1995. All rates are

based on information published in the annual volumes

of the World Health Organization's World Health

Statistics Annual. To highlight changes in mortality,

the column to the right of Table 3 expresses the [(1995

SMR)ÿ(1985 SMR)=] change in the SMR as a per- centage proportion of the 1985 value.

When read in conjunction with Fig. 1, Table 3 indi-

cates that the HIV/AIDS epidemic has not yet served

to de¯ect the long-term decline in tuberculosis mor-

tality in many European countries. So, despite the

dominant tendency for noti®ed cases of tuberculosis to

stabilise or increase in the late 1980s and early 1990s

(Raviglione et al., 1993), the table reveals a substantial

and near-universal decline in mortality for the

countries of Northern and Southern Europe and

Scandinavia; with the exception of Portugal, national

mortality rates in these regions had fallen to below 2

per 100,000 by the mid-1990s. The results for Eastern Europe are rather more complex, although the wide

variety of national experiences are consistent with the patterns displayed in Fig. 3C.

5. Conclusion

This paper has presented an initial sifting of the changing epidemiological pro®les of European countries

during the twentieth century. That much of the analysis has focused on mortality patterns is one of necessity rather than preference; even for the most advanced economies of twentieth century Europe, internationally-

comparable morbidity statistics are unavailable for many diseases over extended time frames. The signi®- cance of this limitation becomes apparent when it is

recognised that, in late stage epidemiological transition in particular, there is little correlation between the causes of death and the actual morbidity that people ex-

perience in their lives (Phillips, 1994). Notwithstanding this limitation, the present paper

has underscored the complexity of mortality change in Europe during the ®rst three-quarters of the twentieth

century (Caselli, 1994). However, as judged by the evi- dence of two `marker' diseases, cardio-vascular disease and pulmonary tuberculosis, a simple model of the

spatial development of the epidemiological transition to its later stages in Europe can be tentatively pro- posed. It is suggested that, as the epidemiological tran-

sition has progressed during the twentieth century, spatial variability in the importance of classical infec- tious diseases has increased, and this has been coun-

tered by a spatial convergence in the importance of diseases that typify late transition. While the con- clusions regarding infectious diseases are supported by other studies (Cli� et al., 1998), further analyses relat-

ing to a broad range of degenerative and chronic con- ditions, are required to establish the veracity of this simple model.

For many European countries, epidemiological change has continued in the 1980s and 1990s. During the past decade or so, the countries of northern

Europe, Scandinavia and southern Europe have achieved substantial reductions in mortality from some degenerative diseases. Likewise, some countries of southern and eastern Europe have made considerable

advances in combating a range of infectious and para- sitic diseases. Nevertheless, the threat of AIDS, and the resurgence of the classic infectious diseases that are

following in its wake, has placed infectious and para- sitic diseases on the European health agenda once again. These diseases are now vying for the scarce

health resources that could, otherwise, be channelled into the continued treatment and control of the chronic and degenerative conditions of late transition.

Table 3

Male standardized mortality ratios (SMRs) per 100,000 for

pulmonary tuberculosis, 1985 and 1995; source: WHO World

Health Statistics Annual

Region/country Standardized mortality ratio (SMR)

per 100,000

1985 1995 percentage change a

Northern Europe

Belgium 2.0 1.2 ÿ40.0 France 2.1 1.5 ÿ28.6 Netherlands 0.2 0.2 0.0

England and Wales 1.1 0.9 ÿ18.2 Scotland 1.3 0.9 ÿ30.8 Scandinavia

Denmark 0.6 0.4 ÿ33.3 Finland 2.1 1.5 ÿ28.6 Norway 0.4 0.6 +50.0

Sweden 0.8 0.2 ÿ75.0 Southern Europe

Greece 3.4 1.3 ÿ61.8 Italy 2.1 1.2 ÿ42.9 Portugal 5.6 4.3 ÿ23.2 Spain 3.7 1.9 ÿ48.6 Eastern Europe

Bulgaria 4.3 5.9 +37.2

Czechoslovakia 1.9 1.3 ÿ31.6 Hungary 8.2 8.5 +3.7

Poland 8.3 4.5 ÿ45.8 Romania 6.1 19.7 +223.0

a Calculated as [(1995 SMR)ÿ (1985 SMR)=] change in the

SMR, expressed as a percentage proportion of the 1985 value.

Increases in the SMR are marked `+' and decreases `ÿ '.

M. Smallman-Raynor, D. Phillips / Health & Place 5 (1999) 209±222220

Acknowledgements

An early version of this paper was presented at 51. Deutscher Geographentag, Bonn, in October 1997; the authors express their thanks to Dr. Harald Leisch and

Dr. Thomas Kistemann for support.

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Epidemiological-transition-in-Venezuela--Relationships-betwe_2010_Journal-of.pdf

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pidemiological transition in Venezuela: Rela- ionships between infectious diarrheas, ischemic eart diseases and motor vehicles accidents mor- alities and the Human Development Index (HDI) n Venezuela, 2005—2007

KEYWORDS Epidemiologial transition; Infection; Diarrhea; Chronic diseases; Development

Socioeconomical approaches assessing quantita- ively relationships between nations’ development nd diseases epidemiology are still limited [1,2] articularly in Latin America. In the case of he United Nations Development Programme’s UNDP) Human Development Index (HDI), few stud- es have explored its relationship and/or impact n the epidemiology of communicable and non- ommunicable diseases [3—6]. The HDI is the ormalized measure of life expectancy (LEI), lit- racy, education (EI), standard of living, and gross omestic product per capita (GDP index) for coun- ries worldwide. This index is included in the Human evelopment Report (HDR), which is annually pub-

ished by the UNDP using data of the 2 years before he date of the report (e.g. HDR of 2009 used data f year 2007). It is a standard means of measur- ng well-being. It is used to determine and indicate hether a country is a developed, developing, or nderdeveloped and also to measure the impact of conomical, social and policies on quality of life 7,8].

This study describes relationships between the DI and its components and the mortality due o infectious diarrheas (ID), ischemic heart dis-

ases (IHD) and motor vehicles accidents (MVA) in ll the Bolivarian Republic of Venezuela federal ntities, which includes 23 states and a Capital istrict, during 2005—2007 as a reflect of the coun-

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876-0341/$ — see front matter © 2010 King Saud Bin Abdulaziz University for Health

oi:10.1016/j.jiph.2010.05.002

ry’s epidemiological transition. Socioeconomical ata was obtained from the National Institute of tatistics, including the HDI and its components for ach year studied; and the epidemiological data mean rates) from the Ministry of Health, both rom Venezuela. The general formula of the HDI is: ndex = (LEI + EI + GDP index)/3 [8]. For the analy- is, regression models were done using GraphPad rism®, at 95% confidence level.

The HDI varied in the states from 0.711 to .866 (an HDI ≥ 0.800 is high development, DI 0.500—0.799 is medium development and DI < 0.500 is low development) [8]. ID mortality anged from 1.55 to 49.62 deaths/100,000pop; IHD ortality from 14.77 to 97.78 deaths/100,000pop;

nd MVA mortality from 12.63 to 47.05 eaths/100,000pop. Linear regression models videnced that the relationship between different auses of mortality and HDI was negative for ID, ositive for IHD and neutral for MVA. Those states ith higher HDI and its components had lower ID ortality rates (r2 = 0.2341; p < 0.0001; Fig. 1A) and

igher IHD mortality rates (r2 = 0.1853; p = 0.0001; ig. 1B). In the case of MVA there was no significant ariation regarding the HDI and its components r2 = 0.01624; p = 0.2758; Fig. 1C).

These preliminary results reflect significant nfluences of a socioeconomical indicator of devel- pment, such as the HDI, on the ID and IHD mortality ates in Venezuela, with different patterns, com- atible with the epidemiological transition in he country. Better socioeconomical conditions, eflected in the HDI and its components, result n lower mortality rates from ID but higher rates rom IHD. However, in further studies, extended n time, components of the HDI should be also ncluded and presented in order to have a bet- er understanding of these relationships between ocioeconomical conditions and diseases epidemi- logy in the country. A developing country such

s Venezuela (HDI of 0.844, HDR 2009) [8] shows ixed patterns of disease with a significant revalence of chronic-non-transmissible conditions redominantly in north coastal urban regions, and

Sciences. Published by Elsevier Ltd. All rights reserved.

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Fig. 1 Relations between the Human Development Index (HDI) and the mortality rates in Venezuela, 2005—2007. (A) Linear regression between infectious diarrhea mortal- ity rates and HDI. (B) Linear regression between ischemic heart diseases mortality rates and HDI. (C) Linear regres- sion between motor vehicles accidents mortality rates and HDI, 2005—2007. Central red line represents the regression slope and the dotted blue lines the 95% con- fidence interval. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of the article.)

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Letter to the Editor

onversely, a significant prevalence of infectious- ransmissible diseases predominantly in southern ural areas [9,10].

onflicts of interest

he authors have no conflict of interest to disclose.

cknowledgements

his work was previously presented in part as a oster at the 14th International Congress on Infec- ious Diseases, Miami, FL, USA, March 2010. Poster o. 81.005.

Funding: Presentation of this work by A.J. odriguez-Morales at the 14th International ongress on Infectious Diseases was partially unded by the International Society for Infectious iseases (as an Invited Speaker for a conference itled Epidemiology of P. vivax burden in Latin merica and in the World) and by the Faculty f Medicine, Universidad Central de Venezuela economical support for professors presenting cientific works in meetings), Caracas, Venezuela.

Ethical approval: Not required.

eferences

[1] Onwujekwe O, Ojukwu J, Shu E, Uzochukwu B. Inequities in valuation of benefits, choice of drugs, and mode of payment for malaria treatment services provided by com- munity health workers in Nigeria. Am J Trop Med Hyg 2007;77:16—21.

[2] Somi MF, Butler JR, Vahid F, Njau JD, Kachur SP, Abdulla S. Economic burden of malaria in rural Tanzania: variations by socioeconomic status and season. Trop Med Int Health 2007;12:1139—47.

[3] Lee KS, Park SC, Khoshnood B, Hsieh HL, Mittendorf R. Human development index as a predictor of infant and maternal mortality rates. J Pediatr 1997;131:430—3.

[4] Hobdell MH, Lalloo R, Myburgh NG. The Human Devel- opment Index and Per Capita Gross National Product as predictors of dental caries prevalence in industri- alized and industrializing countries. Ann N Y Acad Sci 1999;896:329—31.

[5] Tapia Granados JA. Human development index and health in Spain. Med Clin 2007;129:557—8.

[6] Rodríguez-Morales AJ, Pascual-González Y, Benítez JA, López-Zambrano MA, Harter-Griep R, Vilca-Yengle LM, et al. Asociación entre la Incidencia de Leishmaniosis Cutánea y el Índice de Desarrollo Humano y sus Componentes en

Cuatro Estados Endémicos de Venezuela. Rev Peru Med Exp Salud Publica 2010;27:22—30.

[7] Davies A, Quinlivan G. A panel data analysis of the impact of trade on human development. J Socio-Econ 2006;35:868—76.

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etter to the Editor

[8] United Nations Development Programme. Human develop- ment report 2009: overcoming barriers: human mobility and development. New York, NY: Oxford University Press; 2009.

[9] Rodriguez-Morales AJ, Benitez JA, Arria M. Malaria mortality in Venezuela: focus on deaths due to Plas- modium vivax in children. J Trop Pediatr 2007;54: 94—101.

10] Risquez A, Marrero A, Naranjo N, Palacios Y, Rossomando MT, Rodriguez-Morales AJ. Diseases and injuries associated with travel among students, employees and teachers of the Central University of Venezuela during the national summer vacations. Travel Med Infect Dis 2010;8:41—6.

Alejandro Risquez

Luis Echezuria

Department of Preventive and Social Medicine, Razetti Medical School, Faculty of Medicine, Universidad Central de Venezuela, Caracas,

Venezuela

Available online at www.

97

Alfonso J. Rodriguez-Morales ∗,1 Department of Preventive and Social Medicine,

Razetti Medical School, Faculty of Medicine, Universidad Central de Venezuela, Caracas,

Venezuela Instituto Jose Witremundo Torrealba, Universidad

de Los Andes, Trujillo, Venezuela ∗ Corresponding author at: Department of

Preventive and Social Medicine, Razetti Medical School, Faculty of Medicine, Universidad Central de Venezuela, Ciudad Universitaria, Caracas, DC

1050, Venezuela. Tel.: +58 416 826 94 8. E-mail address: [email protected]

(A.J. Rodriguez-Morales) 1 Direction of Environmental Health, Ministry of

Health, Maracay, Venezuela (formerly).

4 May 2010

sciencedirect.com

mailto:[email protected]

• Epidemiological transition in Venezuela: Relationships between infectious diarrheas, ischemic heart diseases and motor vehicles accidents mortalities and the Human Development Index (HDI) in Venezuela, 2005-2007
• Conflicts of interest
• Acknowledgements
• References

PIN104---Epidemiologic-transition-of-hepatitis-a-in-six-countrie_2013_Value-.pdf

A98 V A L U E I N H E A L T H 1 6 ( 2 0 1 3 ) A 1 - A 2 9 8

diagnosed in 2000-2003 vs 2008-2010 (for HAART initiation: 62.5% vs 78.2%, P<0.01; for VS: 53.4% vs 70.7%, P<0.01, respectively), age <30 vs ≥40 years (57.6% vs 71.8%, P = 0.01 and 50.6% vs 66.3%, P<0.01), or non-Hispanic blacks (NHB) compared with non-Hispanic whites (NHW) (63.4% vs 67.1%, P = 0.01 and 56.2% vs 62.4%, P = 0.01). In multivariable models, patients were more likely to initiate HAART sooner if diagnosed after 2000-2003 (adjusted hazard ratios [95% confidence intervals] for 2004-2007 and 2008-2010: 1.2 [1.0-1.3] and 1.6 [1.3-1.9]) but were less likely to start if age <30 vs ≥40 years (0.8 [0.7-0.9]), NHB vs NHW (0.7 [0.6-0.9]) and female (0.8 [0.7-1.0]). Similar findings were observed for achieving VS after diagnosis with the exception of sex. In an analysis of outcomes after HAART initiation, further adjusted for CD4 count and plasma HIV RNA viral load (VL) at HAART initiation, NHB compared with NHW were less likely to achieve VS after HAART initiation (0.7 [0.6-0.9]), while age and gender were no longer significant explanatory factors. CONCLUSIONS: During 2000-2010, starting HAART and achieving VS ≤12 months became increasingly more common. Adjusting for CD4 and VL at start of HAART, only NHB had decreased likelihood of achieving VS after HAART initiation. PIN99 DISPARITIES IN INFLUENZA VACCINATIONS AMONG COMMUNITY PHARMACY USERS AND NON-USERS Munshi KD, Hong SH, Wang J The University of Tennessee Health Science Center, Memphis, TN, USA

OBJECTIVES: This study examined the influenza vaccination rates and racial and ethnic disparities in receiving influenza vaccinations within the past year among community pharmacy users and individuals who did not utilize community pharmacies. METHODS: The 2009 Medical Expenditure Panel Survey was analyzed. The sample consisted of respondents aged 50 years or older, as per the 2009 recommendations by the Advisory Committee on Immunization Practices. Bivariate analyses and multivariate logistic regression were conducted to examine the influenza vaccination rates and disparities in the likelihood of receiving influenza vaccinations within past year between non-Hispanic Whites (Whites), non-Hispanic Blacks (Blacks) and Hispanics. The influenza vaccination rates and the likelihood of receiving influenza vaccinations between community pharmacy users and non-users were also examined. RESULTS: The sample consisted of 71,135,249 (weighted) community pharmacy users and 20,565,253 (weighted) non-users. Bivariate analyses found that a greater proportion of Whites received influenza vaccinations compared to Blacks and Hispanics, among both the community pharmacy users (60.1% vs. 49.1% and 51.7%, respectively; P<0.0001) and non-users (41.0% vs. 24.3% and 26.0%, respectively; P<0.0001). Adjusted logistic regression analyses found significant racial disparities between Blacks and Whites; compared to Whites, Blacks were found to have a 19 percent lower likelihood among community pharmacy users (odds ratio [OR]: 0.81; 95% CI: 0.69-0.95), and a 34 percent lower likelihood among non- users (OR: 0.66; 95% CI: 0.46-0.94), respectively, of receiving influenza vaccinations. Sociodemographic characteristics and health status accounted for the disparities between Hispanics and Whites. Overall, community pharmacy users had higher influenza vaccination rates (P<0.0001) and were more likely to receive influenza vaccinations compared to non-users (P<0.05). CONCLUSIONS: Despite influenza vaccination rates being higher among community pharmacy users, there were racial disparities in receiving influenza vaccinations among both community pharmacy users and non-users. Increased emphasis on educational and awareness campaigns among pharmacists and pharmacy patrons is needed. PIN100 WOMEN'S EMPOWERMENT AND HIV PREVENTION IN RURAL MALAWI Gerritzen BC University of St. Gallen SIAW-HSG, St. Gallen, Switzerland

OBJECTIVES: Gender inequality has been identified as a key driver of the HIV epidemic by UN AIDS; understanding the gendered nature of the HIV epidemic is thus of high policy-relevance. Condom use and communication among sexual partners are important strategies for HIV prevention. Previous research on the impact of women’s empowerment on the uptake of HIV prevention has either focused on economic empowerment only or has left unresolved issues with respect to unobserved heterogeneity. METHODS: Using a panel data set of more than 1,200 married women in rural Malawi from 1998-2008, this paper shows that adequate HIV prevention strategies, i.e. condom use within marriage and HIV-related spousal communication, are more widely used as women's bargaining power increases. I focus on different dimensions of women’s empowerment, namely personal (e.g. education and economic empowerment) and interpersonal empowerment (e.g. social status and outside options). RESULTS: Among the proxies used for women's empowerment, own income, knowledge of other local languages and awareness of exit options from marriage are found to play a particularly important role in promoting adequate preventive behaviors. The main findings continue to hold after individual-specific fixed effects and time dummies are included in order to account for unobserved heterogeneity and time trends. CONCLUSIONS: The importance of women’s empowerment in fighting the spread of HIV is rarely disputed, yet many HIV/AIDS campaigns fail to tackle underlying gender inequalities. The results from my analysis suggest that greater emphasis should be placed on women’s empowerment in order to effectively combat the spread of the HIV/AIDS epidemic, particularly in developing countries. PIN101 COMPLIANCE WITH THE BIRTH DOSE OF HEPATITIS B VACCINE IN HIGH ENDEMIC AND HARD TO REACH AREAS IN COLOMBIAA

De la Hoz-Restrepo F, Choconta-Piraquive LA Universidad Nacional de Colombia, Bogota, Colombia OBJECTIVES: Estimate vaccine coverage with hepatitis B birth dose in children under 10 years old in high endemic areas of Colombia. Describe how children are vaccinated against Hepatitis B in high endemic areas that are hard to reach due to geographical barriers. Evaluate factors associated with adequate vaccination of newborns with the Hepatitis B birth dose. METHODS: A cross sectional study is being carried out in rural areas of the Colombian Amazon. Vaccination history was recorded for 953 children < 10yrs who had a vaccine card. Data were recollected in three areas of the Colombian Amazon: Leticia, Puerto Nariño and Tarapaca.. Children were considered to have a birth dose if they were vaccinated with the monovalent vaccine in the first seven days after delivery. Logistic models were used to estimate association of valid vaccination with several variables. RESULTS: A total of 79.9% of the children received a birth dose of hepatitis B, 38.4% received the vaccine in the first week after delivery; 30.9% were vaccinated in the first day and 7.6% in the next seven days. Bivariate analysis: Birth dose was associated (p<0.00) to children delivered at a health facility, health professional assisting the delivery, children living in households with at least one of the following (TV, radio, refrigerator or a boat), children born to women with a higher education level. Multivariate analysis: Only delivering at a health facility was associated with receiving of the birth dose (OR 26.9 CI 95% 17.8-40.7) CONCLUSIONS: Our study shows children in rural areas are inadequately vaccinated even though they live in a high risk area for hepatitis B infection and vertical transmission of the HVB is common. Because all children cannot be delivered at a hospital due to geographical barriers new strategies need to be studied to vaccinate newborns in the rural areas. PIN102 UNINSURED CHRONIC HEPATITIS C PATIENTS AND THEIR COST IMPLICATIONS UNDER THE AFFORDABLE CARE ACT Scaife J1, Kuti E2, Acampa L1, Alvrtsyan H1, Million R1, Miyasato G1, Wang Z1, Sander S2, Sanchez H1, Kokkotos FK1 1Trinity Partners, LLC, Waltham, MA, USA, 2Boehringer Ingelheim Pharmaceuticals, Inc, Ridgefield, CT, USA OBJECTIVES: To estimate the financial impact of currently uninsured individuals infected with chronic hepatitis C (CHC) who will enter the health care system in 2014 under the Affordable Care Act. METHODS: Uninsured CHC prevalence estimates among the US household population were obtained by aggregating data from three National Health and Nutrition Examination Surveys (NHANES) conducted between 2005 and 2010. Commercial insurance claims data from 2007 to 2011 were used to estimate the average annualized all-cause direct medical cost of a CHC patient. ICD-9 diagnosis codes were used to identify CHC patients. All medical service and prescription pharmacy costs were tracked longitudinally and subsequently adjusted for inflation and length of enrollment. RESULTS: The prevalence rate of CHC among the US household population was 0.89% during 2005-2010 (95% CI (0.69%, 1.09%)). A total of 40.12% of these individuals were uninsured (95% CI (32.02%, 48.21%)). Applying NHANES prevalence rates to US Census Bureau’s current population survey data, the estimated 2011 uninsured CHC population was 1.1MM. However, only 25% or 275,000 individuals may be diagnosed. The average annual medical cost for privately insured diagnosed CHC patients was approximately $24,000. Combining the uninsured CHC prevalence and cost estimates, the annual direct cost of these currently uninsured diagnosed CHC individuals is approximately $6.6B when they enter the health system. The overall incremental cost of currently uninsured CHC individuals may be even higher due to the cost incurred by the undiagnosed CHC population. CONCLUSIONS: A large proportion of CHC infected individuals are currently uninsured. When the ACA health insurance mandate takes effect in 2014, this population will gain access to health care coverage and could substantially increase the cost to the health care system. PIN103 TREATMENT BURDEN ASSOCIATED WITH PEGINTERFERON-BASED ANTIVIRAL THERAPY FOR PATIENTS WITH CHRONIC HEPATITIS C (CHC) IN JAPAN Kuwabara H Janssen Pharmaceutical KK, Tokyo, Japan OBJECTIVES: Several randomized studies confirmed the efficacy and safety of peginterferon-based antiviral therapy for patients with hepatitis C virus infection. However, various types of patients who were not included in those studies are possibly seeking antiviral therapy in daily practice. The study aim was to analyze those patients and their treatment situation in a real-world setting. METHODS: A large claims database (Medical Data Vision, Japan; 4/2010 - 6/2012) was retrospectively analyzed. The study patients were those who had evidence of CHC (ICD10: B182) and initiated either the combination therapy with peginterferon + ribavirin (PR) or the triple therapy with peginterferon + ribavirin + telaprevir (PR+T) from November, 2011. RESULTS: A total of 207 patients were identified, with 136 patients initiating PR (median age 56 years) and 71 patients initiating PR+T (median age 60 years). A quarter of patients were aged ≥65 in both therapy groups. 6% of patients given PR and 13% of patients given PR+T were previously diagnosed with hepatocellular carcinoma (HCC). Mean initial doses of peginterferon and ribavirin were similar between PR and PR+T however, mean adherence to the ribavirin dose in PR+T was 78% and lower than PR (95%). Patients requiring hospitalizations for treatment initiation were significantly different between PR and PR+T (71% vs. 93%, P<0.01); lengths of stay were 11.8 and 18.5 days in PR and PR+T, respectively (P<0.01), P-values were not changed after adjusting for patient age, gender, and previous diagnosis of HCC and cirrhosis. CONCLUSIONS: The study found that a quarter of patients were elderly and the majority of patients required hospitalization to start treatment with either PR or PR+T. More effective and safer treatment is desirable. Further

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research is needed to understand how differences in the characteristics of the patients treated with PR+T versus PR impact on treatment adherence and hospitalization patterns observed. PIN104 EPIDEMIOLOGIC TRANSITION OF HEPATITIS A IN SIX COUNTRIES AND IMPLICATIONS FOR VACCINATION POLICY: DATA FROM A SYSTEMATIC LITERATURE REVIEW Durden E1, Maiese BA2, Foley K2 1Truven Health, Austin, TX, USA, 2Truven Health Analytics, Cambridge, MA, USA OBJECTIVES: To evaluate hepatitis A (HAV) endemicity in six countries via systematic review of published literature. The countries represent varying seroprevalence (different stages of the HAV epidemiologic transition), stages of vaccine consideration, and face different circumstances that may affect vaccine adoption. METHODS: Articles published from 1990 to October 2011 were identified through a number of article search engines, including PubMed®. Search terms were “Hepatitis A” and [Country]. Reference lists of identified articles were reviewed for relevant articles published prior to 1990. A supplementary Internet search identified additional information not indexed in the reviewed search engines. Articles were excluded if they focused on the biological mechanisms of hepatitis A, were non-human studies, vaccine trial results, case studies or opinion pieces. RESULTS: A total of 797 articles were identified. After exclusions, the number of articles reviewed were: Chile, 33; India, 80; Mexico, 25; Russia, 38; South Korea, 75; and Taiwan, 65. India is still considered to have high HAV endemicity, while Chile and Mexico are intermediate, and Korea, Russia, and Taiwan are low. The timeframe of available data differed greatly by country and often region/city within a country, with some regions only having data as recent as five (in Mexico, Taiwan) or ten (in Chile, India, Russia) years ago. Data supporting the HAV epidemiologic transition varied by country, and it was often unclear at which point the country transitioned to a lower endemicity category, if at all. Hepatitis A incidence data were sparse for some countries, and recent outbreaks were reported in Korea and Taiwan. CONCLUSIONS: Data gaps, including determination of the HAV epidemiologic transition, exist to some extent in all the countries studied. Filling these data gaps to enhance knowledge of the burden of HAV will assist countries in decision-making regarding vaccine adoption. PIN105 THE IMPACT OF PRICING METHODOLOGIES ON COMMUNITY ACQUIRED PNEUMONIA (CAP) DRUGS IN BRAZIL Stevens CA1, Miller KL1, Rossi C2 1PAREXEL Consulting, Waltham, MA, USA, 2PAREXEL INTERNATIONAL, Uxbridge, UK OBJECTIVES: Pharmaceutical spending in Brazil represents approximately 11% of its total Gross Domestic Product (GDP). Sales of generics represented 17.2% of the pharmacy sector by value and 21.3% by volume in 2010, and are expected to grow at a higher rate than the overall pharmacy market. This study looks at how the increase in generics use, along with reference price controls, may impact access to newly approved Community Acquired Pneumonia (CAP) drugs. METHODS: An array of published data such as pricing process, current policies, sector-specific research articles contributed towards a framework to understand the key factors affecting access to CAPs drugs, were gathered. The data then informed a telephone survey of national and regional health care stakeholders (N=5). RESULTS: Findings show that in Brazil: 1) New CAP products are placed in 1 of 2 product categories based on comparisons to comparator agents; 2) Category I products are considered to be better than the comparator and can charge a premium price; 3) The price cannot exceed the lowest price of 9 reference countries, which are Australia, Canada, Spain, USA, France, Greece, Italy, New Zealand and Portugal; 4) Category II products do not demonstrate a benefit over the comparator and the price is based on a cost-minimization analysis; and 5) There are no national guidelines for treating CAP. CONCLUSIONS: Drugs used to treat CAP are compared to comparator agents based on clinical efficacy and overall patient benefit. This will determine placement in a product category that will drive pricing. Pharmaceutical companies developing new antibiotics to treat CAP must 1) consider launch sequence/timing in reference price countries; 2) assess the impact of price based on product category placement; 3) demonstrate cost–effectiveness of the drug; and 4) determine whether a new product will provide a benefit over comparator agents. PIN106 HIV LABORATORY TESTS USED AS A PROXY FOR MEDICAL VISITS FOR DEFINING ENGAGEMENT IN CARE Dean B1, Debes R2, Bozzette S3, Buchacz K4, Brooks JT4 1Cerner Research, Culver City, CA, USA, 2Cerner Corporation, North Kansas City, MO, USA, 3Cerner Research and the University of California, San Diego, Culver City, CA, USA, 4Centers for Disease Control and Prevention, Atlanta, GA, USA OBJECTIVES: Attendance at biannual medical visits has been proposed as a minimum U.S. standard for adequate engagement in human immunodeficiency virus (HIV) care. The U.S. National HIV Surveillance System collects dates of HIV laboratory tests but not medical visits, the current metric for determining if a patient is engaged in medical care. Using data from the HIV Outpatient Study (HOPS), we analyzed how reported laboratory data correlated with actual engagement in care. METHODS: The HOPS is an open prospective study of HIV- infected patients receiving outpatient care. The dataset included dates for laboratory measurements and medical encounters. We included patients with at least one HIV laboratory test and one medical visit during 2010-2011. An HIV laboratory test was associated with a medical visit if it occurred within 3 weeks of the visit. We assessed the predictive value of HIV laboratory tests as a proxy for adequate engagement in clinical care, defined as having had ≥2 HIV

laboratory tests within 1 year where the tests were performed ≥90 days apart. RESULTS: A total of 10,301 HIV laboratory tests were recorded from among 2,895 patients. Most (75%) laboratory tests were measured on the same day as a clinical visit; 90% were within ±3 weeks. The prevalence of adequate engagement in clinical care in HOPS based on medical visits was 89%. Using HIV laboratory tests to measure engagement had sensitivity of 85%, specificity of 87%, and positive and negative predictive values of 98% and 42%. Of the 21.4% of persons classified as not engaged in care by the proxy measure, 58% were actually engaged. CONCLUSIONS: Using ≥2 documented HIV laboratory tests measured ≥90 days apart reliably classified persons as engaged in care. However, more than half of persons not meeting the proxy definition were misclassified as not adequately engaged in care. PIN107 ANTIFUNGAL TREATMENT PATTERNS AND OUTCOMES IN PATIENTS WITH A BLOOD CULTURE POSITIVE FOR CANDIDA AND WITH SEPSIS OR CRITICAL ILLNESS Wade RL1, Chaudhari P2, Yu HT1, Nathanson BH3, Hays HD1, Yi J1, Horn D4 1Cerner Research, Culver City, CA, USA, 2Astellas Pharma US, Northbrook, IL, USA, 3OptiStatim LLC, Longmeadow, MA, USA, 4David Horn, LLC, Doylestown, PA, USA OBJECTIVES: Timely initiation of antifungal therapy in septic or critically ill (CrILL) patients with candidemia is crucial, and real-world treatment and outcomes data in such patients are limited. We examined treatment patterns and outcomes for septic/CrILL inpatients with candidemia. METHODS: This retrospective study of electronic health record data from 7/2005-3/2012 used Cerner’s Health Facts. Adult inpatients with ≥1 blood culture positive for any species of Candidaand sepsis/CrILL were studied. CrILL was defined as ≥1 organ system dysfunction plus intensive care unit exposure. Timing of initial antifungal therapy relative to the index blood culture draw (BCx), length-of-stay following first antifungal order (AF-LOS), mortality, and measures of resource utilization were explored using descriptive statistics. Chi-square was used for proportional, and t-test for continuous data comparisons. RESULTS: Of 1,288 candidemia patients with sepsis/CrILL, 266 initiated antifungal therapy prior to BCx, 150 within 24h of BCx, 590 after 24h, and 282 received no antifungal therapy. The mortality rates were 39.9%, 30.7%, 29.7%, and 51.1%, respectively. (P<0.001). Initial antifungal therapies in those treated ≤24h of BCx were: fluconazole in 54.0%, echinocandin in 39.3%, other azoles in 3.3%, and amphotericin B in 3.3%. In patients treated within 24h after BCx with fluconazole (n=81) or echinocandin (n=59), occurrence of bacteremia was high (71% vs. 75%); echinocandin patients had a higher mean number of organ system dysfunctions than fluconazole patients (1.8 vs. 1.5, P=0.04). In the ≤24h treated groups, AF-LOS (fluconazole 20.8, echinocandin 22.1 days; P=0.63) and mortality (fluconazole 25.9%, echinocandin 32.2%; P=0.42) was similar, as were total charges (approximately US$76,000). CONCLUSIONS: A large proportion of inpatients with candidemia and sepsis/CrILL failed to receive antifungal therapy within 24h of the first positive BCx, with an adverse mortality effect. Nonetheless, patients treated within 24h have high mortality and resource utilization. PIN108 TREATMENT PATTERNS IN PEDIATRIC ANTIBIOTIC FORMULATIONS: AN ANALYSIS OF THE RAMQ DATABASE Lachaine J1, Beauchemin C1, Lapierre ME1, Snow LA2 1University of Montreal, Montreal, QC, Canada, 2Abbott, St-Laurent, QC, Canada OBJECTIVES: In clinical practice, the taste of liquid pediatric antibiotics may contribute to treatment acceptance and compliance. The purpose of this study was to analyze treatment patterns and persistence with liquid pediatric antibiotics, in a real life setting, using the RAMQ database. METHODS: Selected patients were < than 20 years old and were covered by the Quebec provincial drug reimbursement program (RAMQ). They were prescribed a liquid pediatric antibiotic (branded or generic formulation) during the period from July 2008 to April 2011 at least once. The analyses evaluated patients and treatment characteristics and patterns in terms of short-term (30 days or less) and long- term (> than 30 days) repeated use. RESULTS: Data were available for a sample of 67,727 patients who used an antibiotic of interest. The average age of the study population was 4.7 years (SD=3.5) and 89.9% were < than 10 years old. The proportion of boys versus girls was similar (51.2% versus 48.8%, respectively). Amoxicillin trihydrate and macrolides, clarithromycin and azithromycin were most often used (49.3% and 33.0%, respectively). About 55.4% of children received more than one antibiotic during the study period. Among children who received a second antibiotic, about 22.5% required it within 30 days following treatment initiation. In the short-term, the need for a second antibiotic was more frequent when cephalosporin was the initial treatment. In the long-term, when the initial treatment was amoxicillin or a macrolide, subsequent antibiotics were more likely to be the same as the first antibiotic. CONCLUSIONS: Many children will require more than one antibiotic treatment during their childhood. Several factors may contribute to short-term acceptance and compliance of the initial antibiotic, one of which may be taste. The better the acceptance and compliance to the initial antibiotic, the more likely the same antibiotic will be used for subsequent treatments. NEUROLOGICAL DISORDERS – Clinical Outcomes Studies

PND1 COMPARATIVE RISKS OF SEVERE CUTANEOUS REACTIONS, ASEPTIC MENINGITIS, AND ORGAN DYSFUNCTION ASSOCIATED WITH ANTIEPILEPTIC DRUGS Chen P, Teigland C, Parente A, Jones B, Scoggins J, Mehta S, Yang X Inovalon Inc., Bowie, MD, USA

The-epidemiological-transition-of-first-ever-stroke-among-low-inco_2015_The-.pdf

Poster Abstracts

66 www.thelancet.com

Published Online October 30, 2015

Laboratory of Epidemiology, Tianjin Neurological Institute,

Tianjin, China (X Ning PhD, C Zhan BS, J Tu BS, L Bai BS,

J Wang PhD); Department of Neurology, Tianjin Medical

University General Hospital, Tianjin, China (X Ning,

L Yang MD, C Zhan, J Tu, L Bai, J Wang); Department of

Radiology, Feinberg School of Medicine, Northwestern

University, Chicago, IL, USA (Y Yang MD); Department of Neurology, Tianjin Huanhu

Hospital, Tianjin, China (Z An MD); Department of Neurology, Tianjin Haibin People’s Hospital, Tianjin,

China (B Li MD, H Gu MD)

Correspondence to: Dr Jinghua Wang, Department of

Epidemiology, Tianjin Neurological Institute,

Department of Neurology, Tianjin Medical University

General Hospital, Heping District, Tianjin 300052, China

[email protected]

The epidemiological transition of fi rst-ever stroke among low-income population in China: a population-based study from 1992 to 2014 Xianjia Ning, Yihe Yang, Li Yang, Zhongping An, Bin Li, Wenjuan Zhao, Hongfei Gu, Changqing Zhan, Jun Tu, Lingling Bai, Jinghua Wang

Abstract Background Stroke is the leading cause of death in rural areas and the third cause of death in urban areas in China, but the epidemiological transition of stroke during periods of rapid economic development is unknown in China, especially in rural areas. We aimed to investigate the secular trends in incidence, prevalence, and the 30-day case fatality of fi rst-ever stroke in rural China between 1992 and 2014.

Methods We assessed the secular trends in the epidemiological transition of stroke in Tianjin, China. The study population was from the Tianjin Brain Study, a population-based stroke surveillance study among low-income residents in a township in Tianjin, China, where stroke events and all deaths have been registered annually from 1992 to 2014. We used data from the Tianjin Brain Study to estimate case-fatality rates, the age-standardised incidence and prevalence of fi rst-ever stroke per 100 000 population with the world standardisation population. Trends in age- standardised incidence and prevalence of stroke were assessed from annual percentage of change by sex and subtypes using the regression model: log(rt)=a+bt, where log denotes the natural logarithm and t is the year, and 100b represents the estimated annual percentage of change.

Findings Between 1992 and 2014, the age-standardised incidence of fi rst-ever stroke per 100 000 population increased annually by 6·3% overall, by 5·5% in men, and by 7·8% in women (p<0∙0001); the case fatality decreased annually by 3·9% overall (p=0∙024) and by 6·0% in women (p=0∙015), but did not change in men (p=0∙072). Simultaneously, the age-standardised prevalence of fi rst-ever stroke per 100 000 population increased annually by 10·6% overall, by 9·9% in men, and by 11·5% in women (p<0∙0001). With respect to stroke subtypes, the age-standardised incidence of fi rst- ever stroke from intracerebral haemorrhage increased annually by 4·6% overall (p=0∙022), by 3·5% in men (p=0∙009), and by 4·8% in women (p=0∙016), whereas the incidence of ischaemic stroke increased annually by 7·2% overall, by 6·6% in men, and by 8·3% in women (p<0∙0001). The corresponding prevalence of intracerebral haemorrhage increased annually by 8·0% overall, 7·0% in men, and 9·9% in women, whereas the prevalence of ischaemic stroke increased annually by 11·4% overall, by 10·9% in men, and by 12·4% in women.

Interpretation The epidemiological transition of stroke was found among a low-income population in China. The incidence and prevalence of stroke from intracerebral haemorrhage and ischaemic stroke increased rapidly in both men and women. These fi ndings suggest that it is crucial to prevent stroke among low-income population in China to reduce the burden of disease worldwide.

Funding The Ministry of Science and Technology of the People’s Republic of China and National Key Project of Clinical Neurology, Tianjin Medical University General Hospital.

Contributors XN and JW obtained funding for this study and were involved in conception and design, data collection, data interpretation, drafting, and critical

review for this abstract. YY and JW were involved in data analysis, conception and design, data interpretation, manuscript drafting, and critical review

for this abstract. LY and ZA were involved in data collection, case diagnosis and confi rmation, and critical review for this abstract. BL, WZ, HG, and

CZ participated in data collection, and case diagnosis, and confi rmation. JT participated in data collection and data management for this abstract.

Declaration of interests We declare no competing interests.

The-Epidemiologic-Transition-Model--Accomplishments-a_2008_Annals-of-Epidemi.pdf

The Epidemiologic Transition Model: Accomplishments and Challenges

MANNING FEINLEIB, MD, DRPH, FACE

Changes in population size and structure are determined by basic processes that can be summarized by the demographic equation (Fig. 1). While demographers and those concerned with population growth tend to emphasize fertility trends, and immigration policy is very much on the current political agenda, epidemiologists tend to concentrate their efforts studying the factors associated with the third part of the equation.

Late in the nineteenth century, demographers, following Malthusian principles, developed several theories to de- scribe how populations change over long periods of time. Dudley Kirk (1) points out that, in 1929, Warren Thompson categorized populations on the basis of fertility and mortality (2), followed in 1934 by the first use of the term ‘‘transition’’ by Adolphe Landry (3). But it is Frank Notestein who is given credit for the first full statement of the demographic transition model in 1945 (4). This model was expanded by Abdel Omran in a seminal article in 1971 in which he re- named the model the epidemiologic transition model (5).

Whereas earlier demographers were concerned primarily with changes in fertility, Omran emphasized the mortality aspects. Omran renamed the four stages of the demogra- phers’ transition model for population evolution to empha- size some epidemiologic aspects (Fig. 2): Stage 1, which demographers called pre-modern, he labeled the stage of pestilence and famine characterized by high death rates and high birth rates with low population size. Stage 2dur- banizing and industrializingdhe described as the stage of re- ceding pandemics resulting from a gradual conquest of disease primarily through better sanitation and nutrition and resulting in a reduction of mortality, especially child mortality, and a concomitant gradual increase in population size. Stage 3dmature industrialdhe called the stage of de- clining births with a peaking of the size of the population. Stage 4dpostindustrialdwas characterized as the stage of degenerative and man-made disease with a balance between birth and death rates, both at low levels, and a leveling off of the population size.

It should be pointed out that the model is an idealized summary of the stages in a population’s development. It fits fairly well the demographic changes which occurred in Western Europe and the English-speaking countries during

Address correspondence to: Dr. Manning Feinleib, Johns Hopkins Bloomberg School of Public Health, Epidemiology, Bloomberg E6153, Baltimore, MD. Tel: (410) 614-0146. Fax: (410) 955-0863. E-mail: [email protected].

� 2008 Elsevier Inc. All rights reserved. 360 Park Avenue South, New York, NY 10010

the late eighteenth century through the mid-twentieth cen- tury, and there is evidence that the pattern is being followed by many developing countries, although the pace of the transitions varies greatly.

My aim in this short essay is not to discuss the details of the various stages of the epidemiologic transition model but to use it as a platform to mention some of the major accom- plishments of epidemiology in the past and to highlight the challenges and opportunities that lay ahead (Fig. 3).

I view epidemiology as the provider of the evidential basis for public health action and for many clinical practices. Thus the accomplishments of epidemiology in preventing and controlling disease and enhancing longevity are intri- cately dependent on the implementation of its findings at the individual patient level and through community public health programs.

During the pre-modern stage, epidemiology contributed little other than to emphasize the periodic increases in mor- tality resulting from epidemics, famine, and other hardships. But during stage 2, as epidemiologic and medical knowledge and public health applications increased, there was a gradual diminution of infectious diseases generally and some curtail- ment of epidemics. Infant mortality especially improved while the birth rate remained high so that populations grad- ually increased in size.

The third stage is characterized in this model by a reduc- tion in the birth rate. The reasons for this decline are com- plex, involving many societal, cultural, and economic factors (1), but some of the epidemiologic and public health influences were the improved survival of children and, more recently, the availability of effective birth control methods. This period also saw improvements in nutrition and greater concern with the health and safety of the labor force. With both mortality and birth rates at low levels the growth of the population leveled off and the concerns of epidemiologists and public health workers shifted to chronic diseases and their prevention. Many personal risk factors related to life- style and individual behaviors were identified. A greater awareness of environmental hazards grew and many regula- tory and educational policies were instituted to reduce envi- ronmental risks. Omran dubbed this stage the age of degenerative and man-made disease (5).

I would like to propose that we should now recognize a fifth stage in the epidemiologic model. This stage is char- acterized by birth rates below the population replacement level, an aging population with many more elderly depen- dent on a diminishing working population for their

1047-2797/08/$–see front matter doi:10.1016/j.annepidem.2008.08.004

mailto:[email protected]

The Demographic Equation

Family Values

Fecundity

Gender roles

Contraception

Economics

Politics

Persecution

Opportunities

Disease

Famine

War

Senescence

Suicide

Risky behavior

ΔP = Births ± Migration - Deaths

FIGURE 1. The demographic equation.

Feinleib AEP Vol. 18, No. 11 EPIDEMIOLOGIC TRANSITION November 2008: 865–867

866

economic and healthcare support, and, medically, greater dependence on advanced technological devices and proce- dures to diagnose and treat diseases. This newly proposed stage we may call that of aging and shrinking populations. Many developed nations are already well into this phase.

What can we look forward to in the twenty-first century? I think that there is still much that epidemiologists and pub- lic health workers in general can contribute at every stage of the epidemiologic transition model (see Fig. 3, lower panel).

In those societies that are at the pre-modern stage, where disease levels are high and famine is still a constant threat, epidemiologists can contribute to improving health by advocating for basic improvements in sanitation and wider immunization of children. They can also urge the develop- ment of basic epidemiologic toolsdvital registration and health statistics systems. Lacking accurate and complete data, it is difficult to assess the magnitude of health prob- lems, identify vulnerable subgroups, or measure the impact of public health programs.

Although great progress has been made in controlling in- fectious diseases in virtually all developed nations, emerging infections, exemplified by HIV/AIDS, are a major problem in urbanizing/industrializing areas. Combating these

Epidemiologic Transition M

0

5

10

15

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FIGURE 2. The epidemiol

diseases will engage epidemiologists for decades to come. There is also the opportunity for epidemiologists to guide planners and policy makers in developing countries in avoiding some of the hazards that urbanized communities have experienced. Better city planning, avoidance of over- crowding, provision of adequate transportation systems, safe work environments, and modern educational systems would go far to preventing future health problems as well as many other social ills.

In mature industrial societies epidemiologists will devote most of their efforts to preventing chronic diseases, educat- ing the public about healthy lifestyles, assessing early detec- tion and treatment programs, and improving the availability, accessibility, and utilization of health services. These activities will merge with evolving progress in the fields of genomics, immunology, and diagnostics to better define and detect illnesses. Epidemiologists will broaden their involvement in ‘‘social diseases,’’ including substance abuse and violence. They will also be major players in under- standing and ameliorating subgroup disparities in health and longevity.

As we enter the fifth stage of the epidemiologic transition model, epidemiologists will become increasingly involved with the health conditions that prevail at both extremes of the age distribution. Providing for the health needs of old- er patients will encumber greater proportions of communi- ties’ resources. Broader aspects of the well-being of older persons, especially the ‘‘oldest old’’, will necessitate expanded information about their physical, social, economic, and psychologic environments, not only their biologic, physiologic, and cognitive functioning. As the birth rates decrease, children will become increasingly precious. There will be ever-increasing devotion to the salubrious develop- ment of children in all aspects, including physical, mental, emotional, and societal.

odel

Population

5 1 - Pestilence and

famine

2 - Receding

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births

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Elder care –

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1

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Stage

FIGURE 3. Impact of epidemiology.

AEP Vol. 18, No. 11 Feinleib November 2008: 865–867 EPIDEMIOLOGIC TRANSITION

867

This has been a wide view of the accomplishments of epidemiology and some of the challenges we will encounter in coming decades. The American College of Epidemiology has played an important role in encour- aging and recognizing epidemiologists in their endeavors and will continue to provide leadership, guidance, and stimulation in the future. It has been a pleasure and an honor for me personally to have been a participant in these activities.

REFERENCES

1. Kirk D. Demographic transition theory. Population Studies. 1996;50:361– 387.

2. Thompson WS. Population. Am J Sociol. 1929;34:959–975.

3. Landry A. La revolution demographique. Paris; 1934.

4. Notestein F. Population: the long view. In: Schultz T, ed. Food for the world. Chicago: University of Chicago Press.; 1945. p. 36–57.

5. Omran AR. The epidemiologic transition: a theory of the epidemiology of population change. Milbank Mem Fund Q. 1971;49:509–538.

• The Epidemiologic Transition Model: Accomplishments and Challenges
• References

Life-expectancy-and-human-capital--Evidence-from-the-in_2013_Journal-of-Heal.pdf

L e

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Journal of Health Economics 32 (2013) 1142– 1152

Contents lists available at ScienceDirect

Journal of Health Economics

j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / e c o n b a s e

ife expectancy and human capital: Evidence from the international pidemiological transition

asper Worm Hansen ∗

arhus University, Department of Economics and Business, Fuglesangs Allé 4, 8210 Aarhus V, Denmark

r t i c l e i n f o

rticle history: eceived 26 March 2013 eceived in revised form 3 September 2013 ccepted 20 September 2013 vailable online 4 October 2013

EL classification: 15 24

a b s t r a c t

Exploiting preintervention variation in mortality from various infectious diseases, together with the time variation arising from medical breakthroughs in the late 1940s and the 1950s, this study examines how a large positive shock to life expectancy influenced the formation of human capital within countries during the second half of the 20th century. The results establish that the rise in life expectancy was behind a significant part of the increase in human capital over this period. According to the baseline estimate, for one additional year of life expectancy, years of schooling increase by 0.17 year. Moreover, the evidence suggests that declines in pneumonia mortality are the underlying cause of this finding, indicating that improved childhood health increases human capital investments.

11

eywords: ife expectancy ealth shock ohort schooling data

© 2013 Elsevier B.V. All rights reserved.

( s

i w c t fi v t m n s den of infectious diseases to cohorts of individuals in countries with a lower burden of infectious diseases before and after the medical advancements.

conomic development

. Introduction

What causes some countries in the world to remain under- eveloped and what can be done to help those countries escape conomic stagnation and poverty? In an attempt to answer such hallenging but relevant questions, one part of the literature has ocused on the relationship between the health (life expectancy) nd the wealth (GDP per capita) of countries. This literature is ypically motivated by a strong positive cross-country correla- ion between health and wealth – the so-called Preston curve Preston, 1975) – that is to say, healthier countries are also wealth- er countries.

The current paper continues this line of inquiry. A seemingly mportant mechanism between health and wealth is the human apital channel: healthier individuals, who expect longer lives, ave stronger incentives (and are more able) to acquire human

apital skills. While a range of micro studies confirm the relevance f the argument, the conclusions in recent macro-empirical studies re less clear: Acemoglu and Johnson (2007) and Lorentzen et al.

∗ Tel.: +45 87165264. E-mail address: [email protected]

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167-6296/$ – see front matter © 2013 Elsevier B.V. All rights reserved. ttp://dx.doi.org/10.1016/j.jhealeco.2013.09.011

2008).1 Motivated by this puzzle, the paper revisits the relation- hip between life expectancy and human capital.

To investigate the causal effect of life expectancy on human cap- tal, the analysis exploits a large positive shock to life expectancy,

hich was a part of the international epidemiological transition,2

aused by the breakthrough of antibiotics and new intervention echniques (e.g., malaria eradication). More specifically, the identi- cation strategy relies on the interaction between preintervention ariation in the mortality from different infectious diseases and ime variation occurring from the new medical technologies in the

id-20th century. Utilizing this interaction as a plausible exoge- ous variation in life expectancy, the strategy compares years of chooling for cohorts of individuals in countries with a higher bur-

1 Micro studies finding a positive effect of health on human capital are: Soares 2006), Bleakley and Lange (2009), Jayachandran and Lleras-Muney (2009), Bleakley 2009), Lucas (2010), Bhalotra and Venkataramani (2012), and Oster et al. (2013).

2 The international epidemiological transition refers to Omran’s (1971) theory f epidemiologic transition regarding the health progresses made in industrialized ountries since the 18th century due to, for example, the germ theory of disease. This tudy, however, focuses on the first stage in the transition, occurring in developing ountries since the mid-20th century (e.g., Vallin and Meslé, 2004).

dx.doi.org/10.1016/j.jhealeco.2013.09.011

http://www.sciencedirect.com/science/journal/01676296

http://www.elsevier.com/locate/econbase

http://crossmark.crossref.org/dialog/?doi=10.1016/j.jhealeco.2013.09.011&domain=pdf

mailto:[email protected]

dx.doi.org/10.1016/j.jhealeco.2013.09.011

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The analysis finds that countries with higher levels of preinter- ention infectious disease mortality experienced larger increases n schooling years, suggesting that life expectancy had a positive ffect on the accumulation of human capital.3 According to the aseline estimate then for every extra year of life expectancy, years f schooling increase by 0.17 year. In addition, the first-stage results eveal that the new medical technologies were accountable for n increase in life expectancy of about 6.3 years.4 The implica- ion is that the health shock increased years of schooling by 1.1 ears. The analysis also establishes that this conclusion is robust to he potential convergence dynamics of schooling, functional form pecifications, and so on.

The evidence suggests that the decline in pneumonia mortality as the main force contributing to the rise in human capital. As

he mortality from that disease is concentrated at earlier ages of ife, this instigates the argument that childhood health is a particu- ar important factor in the process of human capital accumulation. owever, it should be stressed that the baseline finding seems not

o be attributable to the large decline in infant mortality observed ver the 20th century.

By demonstrating that the effect of health on human capital is ositive, the paper delivers new insights to the macro-empirical

iterature. Deploying the same empirical strategy, Acemoglu nd Johnson (2006, 2007), henceforth AJ, find no relationship etween average years of schooling in the adult population nd life expectancy. The fact that their human-capital variable s only marginally affected by the entry of the new and bet- er educated cohorts of individuals could, however, account for his discrepancy.5 Moreover, while data availability on schooling estricts AJ to study the relationship in the 1960–2000 period, this aper exploits newer schooling data at the cohort level based on he 5-year age groups from Barro and Lee (2013), allowing a rigor- us empirical analysis with data on years of schooling throughout he 20th century.6

An assessment of other empirical evidence on the quantita- ive importance of health improvements on human capital at the ountry level is mixed. On the one hand, some studies have demon- trated positive correlations (e.g., Zhang and Zhang, 2005; Tamura, 006; Murphy et al., 2008), and the current paper contributes by stablishing a positive link running from health to schooling and ereby supports the conclusions made in that research. On the ther hand, the study by Lorentzen et al. (2008), which exploits eographical variables to identify the effect of adult mortality on conomic outcomes, finds that mortality has no effect on human apital. Relying on an alternative identification strategy, exploi- ing the panel data structure to eliminate country fixed effects, nd using cohort data on schooling, the analysis here reaches the pposite conclusion: the human capital channel is important in the

nderstanding of how health is related to economic development.

This work also relates to the research of Cervellati and Sunde 2011a,b). Their analysis reveals that the impact of life expectancy

3 This conclusion is also consistent with a recent micro-empirical study (Lucas, 010), which deploys a similar empirical strategy over the same time period. It finds he mid-20th century malaria eradication campaigns in Sri Lanka and Paraguay to ave increased educational attainment at the individual level. 4 In comparison, Jayachandran et al. (2010) find the introduction of sulfa drugs

in 1937) to have increased US life expectancy by 0.38–0.68 year. 5 As also argued in Weil (2013, p. 181), this finding has the potential to explain hy AJ found that the same health shock had no effect on GDP per capita, that is, ue to the timing of the economic benefits of the medical breakthroughs. 6 The finding of a positive relationship between life expectancy and human capital

lso contributes to the macro-theoretical literature, arguing the relation to be vital in nderstanding the process of economic development (e.g., Boucekkine et al., 2002; ervellati and Sunde, 2005, 2013b; Hazan and Zoabi, 2006; Soares, 2005; De la Croix nd Licandro, 2012).

s t p c h

3

s

t

c r

omics 32 (2013) 1142– 1152 1143

n GDP per capita is negative and statistical insignificant before he onset of the demographic transition, whereas after its onset he effect is positive and significant. In Cervellati and Sunde (2009), hey argue that the relationship between life expectancy and aver- ge years of schooling in the population follows a similar pattern. hen considering cohort based data on schooling instead, the evi-

ence presented here indicates that health has a positive effect on uman capital for all countries in the AJ sample.7

The paper is organized as follows. Section 2 provides a short verview of theories of the human capital channel. Section 3 escribes the data. Section 4 outlines the empirical strategy and resents the main findings. Section 5 reports the robustness anal- sis. Section 6 offers a concluding discussion.

. A theoretical perspective

The hypothesis under investigation is whether country level ealth improvements, as measured by changes in life expectancy t birth, have a positive effect on human capital.

Textbook models in economics provide a straightforward link etween life expectancy and human capital (i.e., the horizon effect r Ben-Porath mechanism; Ben-Porath, 1967): the benefits from chooling are reaped over a longer period when the working orizon increases, augmenting incentives to obtain human capi- al skills. One example of a paper which applies this mechanism n its theoretical setup is Boucekkine et al. (2002). On the other and, Hazan (2009) questions the historical relevance of the basic en-Porath mechanism for the U.S. and some European countries. evertheless, modifying the mechanism by the introduction of ncertain survival along with perfect annuity markets (Cervellati nd Sunde, 2013a) or by introducing imperfect credit markets Hansen and Lønstrup, 2012) has the potential to alter Hazan’s triking conclusion. In addition, although Hazan and Zoabi (2006) heoretically demonstrate that the horizon effect is not of much elevance in parental schooling investments, the authors find that ore general health improvements could trigger the quantity qual-

ty trade-off as children become more productive in school when heir health increases, which induces their parents make further uman capital investments in them.

Furthermore, through the accumulation of physical capital or hanges in population size, increasing life expectancy potentially auses general equilibrium effects, injecting back into the wage rate nd schooling outcomes.8

An alternative mechanism is proposed by Albanesi and Olivetti 2010). They present a model based on Barro and Becker’s (1989)

odel of fertility choice extended with maternal mortality where oth fertility and parental investments in human capital increase

n response to reductions in maternal mortality. Besides the few arguments presented here, there are possibly

everal other channels through which rising life expectancy affects he aggregate human capital stock. But it seems as if the theory redicts that life expectancy matters to the acquisition of human apital skills. However, whether the underlying mechanism is the orizon effect or a more general health effect is less clear.

. Data

This section describes the data used in the analysis. The mea- ure of human capital used as the outcome variable is the average

7 For a comprehensive overview of economic theories about the demographic ransition see Galor (2011a,b).

8 The direction of such second order effects on schooling is uncertain as it is not lear whether changes in life expectancy have positive or negative general equilib- ium effects.

1144 C.W. Hansen / Journal of Health Econ

Table 1 Descriptive statistics for base sample.

Variable # obs. Mean S.D. Min Max

Years of schooling 140 5.929 2.748 0.233 11.52 Life expectancy 140 57.01 13.73 26.91 76.01 Health shock15 140 0.224 0.297 0 1.126 Health shock3 140 0.193 0.259 0 0.869 Malaria × Ipost 140 0.023 0.093 0 0.853 Pneumonia × Ipost 140 0.092 0.141 0 0.621 Tuberculosis × Ipost 140 0.078 0.117 0 0.608

N 1 A

n t r i e ( i f I u 1 c e

L b p

h l p t ( c l c a B p a a

o o s A b

4

4

t

S

w t

c o L e s o

e c T v

p b r s s m p t

L

w i p l i c l c I

l p B

4

b t o s o b r w g y t t i p

f 1940–2000 in columns 2 and 4. For the base sample of 70 countries,

otes: The descriptive statistics are based on the two baseline two dates (1940 and 980) and the base sample of 70 countries. Data sources are: Barro and Lee (2013), cemoglu and Johnson (2007), and UN Demographic Yearbook.

umber of schooling years for a “school cohort”, Schoolct, defined as he cohort of individuals aged 5–10 in year t. The individuals who epresent a given school cohort (or age group) therefore started n the formal education system of country c around year t. For xample, as the baseline empirical model only considers two dates t1 = 1940 and t2 = 1980, 2000), Schoolc1940 is years of schooling for ndividuals in country c who entered into the educational system rom 1935–1940 as observed in the Barro–Lee dataset in 1950. n the same way, Schoolc1980 is years of schooling for individ- als in country c who entered into the educational system from 975–1980 observed in the Barro–Lee dataset in 1990. As indi- ated, these data come from Barro and Lee (2013), who provide ducational attainment by 5-year age groups over 1950–2010.

The explanatory variable is life expectancy at birth, ife expectancyct, obtained from the UN Demographic Year- ook. This variable is cross-sectional and reflects the mortality attern of all ages in a given year.

The empirical strategy relies on measuring the intensity of the ealth shock caused by the wave of medical innovations from the

ate 1940s to the mid-1950s. In order to capture this aspect, data on reintervention (i.e., 1940) mortality rates (deaths per 100s) of up o 15 infectious diseases are collected from Acemoglu and Johnson 2007). They include: malaria, pneumonia, tuberculosis, influenza, holera, smallpox, shigella, whooping cough, typhus, plague, yel- ow fever, scarlet fever, diphtheria, measles, and typhoid. The ommon factor of the diseases is that they became treatable as

result of the medical innovations around this period of time. ecause the baseline analysis is performed using only two dates, the ostintervention period is simply 1980 (or 2000). The robustness nalysis, however, also studies the mechanism using observations t 10-year intervals between the indicated dates.

Due to data availability, the base sample consists of 70 countries bserved at the dates t1 = 1940 and t2 = 1980, 2000.9 Further, in rder to compare the results with the findings in AJ, the hypothe- is is also investigated in a restricted sample of 47 countries (i.e., J sample). The summary statistics as well as data sources of the aseline variables are provided in Table 1.

. Empirical strategy and main results

.1. Empirical strategy

The baseline structural model is given by the following equa- ion:

choolct = � Life expectancyct + ıc + �t + �ct , (1) here c indexes countries and t indexes time periods, which in

he baseline model are 1940 and 1980 (or 2000). The human

9 Table A1 in Appendix provides a complete list of countries in the base sample.

a a

H A

omics 32 (2013) 1142– 1152

apital variable Schoolct denotes years of schooling for the cohort f individuals aged 5–10 in year t. The explanatory variable is ife expectancyct (at birth), ıc and �t are country and time fixed ffects, and �ct is the disturbance term. The estimated coefficient �̂ hould be interpreted as the reduced-form effect of life expectancy n years of schooling as also argued in Section 2.

While ıc and �t are easily removed from the error term, OLS stimates of � remain suggestive due to problems such as reverse ausality, time varying omitted variables, and measurement error. he analysis utilizes a large health shock as a plausible exogenous ariation in life expectancy at birth to overcome such issues.

By comparing the relative change in life expectancy in the ostintervention period relative to the preintervention period etween countries that were initially affected differently by a ange of infectious diseases, the identification strategy follows a tandard differences-in-differences setup. However, the current trategy measures the intensity of the shock by the sum of the ortality rates from 15 infectious diseases in the preintervention

eriod (i.e., 1940). In this IV-framework, the first-stage equation akes the following form:

ife expectancyct = ˇ D∑

d=1 Mortalitycd × Ipostt + ıc + �t + �̃ct , (2)

here Mortalitycd is the mortality from disease d (D ≤ 15) and Ipost s an indicator variable that equals one in the postintervention eriod. As the baseline empirical model investigates the effect of

ife expectancy on human capital at a long horizon (i.e., a panel ncluding only two dates: t1 = 1940 and t2 = 1980, 2000), the indi- ator is zero in 1940 and one in 1980 (or 2000). One advantage of the ong-horizon framework is that it lowers the uncertainty about the oding of the indicator variable, Ipostt . The variable

∑ Mortalitydc ×

post t is referred to as the “Health shockD”.

10

Finally, it can be noted that the proposed empirical strategy fol- ows the same logic as the empirical models in many important apers that study the same question at the individual level (e.g., leakley, 2007; Lucas, 2010; Bhalotra and Venkataramani, 2012)

.2. Main results

The analysis starts by studying the first-stage relationship etween life expectancy and the health-shock variable coded on he basis of 15 infectious diseases as proposed by Eq. (2). By means f first differencing ̌ is estimated for the period 1940–1980. Fig. 1 hows that the coefficient is estimated to 14.3 with a standard error f 2.8, which implies that the health shock increased life expectancy y about 6 years. In the same way, Fig. 2 next shows that the educed-form effect of Health shock15 on years of schooling is 2.5 ith a standard error of 0.9. Taken together, these findings sug-

est that the mid-20th century medical advances led to 1.1 more ears of schooling. Moreover, examining the figures shows that he relationships are not driven by a small number of unimpor- ant countries or outliers. Figs. 1 and 2 provide the first evidence n favor of the argument that life expectancy indeed influences the rocess of human capital accumulation. This is now tested formally.

Panel A of Table 2 reports OLS estimates using information or the period 1940–1980 in columns 1 and 3 and for the period

significant positive relationship between life expectancy at birth nd years of schooling is recovered. Specifically, the estimated coef-

10 When summing over the mortality from all 15 diseases the variation in ealth shock15 is identical to the variation in the predicted-mortality variable from J.

C.W. Hansen / Journal of Health Economics 32 (2013) 1142– 1152 1145

YUG

HTI JAM

TTO

CAN

DNKSWE USA

CHEBEL

NLD GER

KOR

ZAF

DOM

ARG

NZL

NOR

MAR

FIN

AUS

HUN

GBR

FRA

CZE

CHN

AUTIRL

POL

DZA

MYS

BGR URY

PRY IRN

EGY ESP

RUS

GRC

JPN

ROM

NIC

VEN

GUY

THA

BRA

COL PANHND LKA

MEX

MMR

PRT

CRI

BGD

TUNIRQ

CHLGTM

PAK

ITA

PER IDN

MUS

ECU

BOL

VNM

SLV

PHL

IND

-2 0

-1 0

0 1

0 2

0

e (

C h a

n g

e i n

L if e

e x p e

c ta

n c y , 1 9

4 0

-1 9

8 0 | X

)

-.4 -.2 0 .2 .4 .6 e( Change in Health Shock 15, 1940-1980 | X )

coef = 14.310719, (robust) se = 2.7977501, t = 5.12

Fig. 1. Life expectancy and the health shock. Notes: Partial correlation plot between t

fi e 0 fi r l s

i a fi b i t

YUG

HTIJAM

TTO

CAN

DNK

SWEUSA

CHE

BEL NLD

GER

KOR

ZAF

DOM ARG

NZLNOR

MAR

FIN

AUS

HUN

GBR

FRA

CZE

CHN

AUT

IRLPOL

DZA MYS

BGR

URY PRY

IRN

EGY

ESP RUS

GRC

JPN ROM

NIC

VEN GUY

THA

BRA COL

PAN

HND

LKA MEX

MMR

PRT

CRI

BGD

TUN

IRQ

CHL

GTM PAK

ITA

PERIDN

MUS

ECU

BOL

VNM

SLV

PHL IND

-6 -4

-2 0

2 4

e (

C h a

n g

e i n

y e a

rs o

f s c h

o o

li n g

, 1 9

4 0

-1 9 8

0 | X

)

-.4 -.2 0 .2 .4 .6 e( Change in Health shock 15, 1940-1980 | X )

coef = 2.4760298, (r obus t) se = . 8588 0709, t = 2.88

Fig. 2. Years of schooling and the health shock. Notes: Partial correlation plot b 1

n s

e M 9 i

p m s

T B

N i l f

he � life expectancy and the � health shock15 . Base sample over 1940–1980.

cient for the period 1940–1980 implies that one extra year of life xpectancy is associated with an increase in years of schooling by .11 years (column 1), whereas for the period 1940–2000 the coef- cient is estimated to 0.18 (column 2). Columns 3 and 4 show that estricting the sample to the 47 countries constituting the base- ine sample in AJ provides results similar to those inferred with the ample of 70 countries.

Panel B of Table 2 presents the corresponding 2SLS estimates. As ndicated by the relationship depicted in Fig. 1, using Health shock15 s an instrumental variable for life expectancy produces strong rst-stage estimates with F-statistics that are well above 10 (see the

ottom of Table 2). The effect of life expectancy on human capital

ncreases in magnitude and is statistical significant in all specifica- ions. For example, the baseline estimate for the period 1940–1980

v r c

able 2 aseline results.

Dependent variable

Years of schooling, cohort

Base sample AJ sample

1940–1980 1940–2000 1940–1980 19 (1) (2) (3) (4)

(Panel A) OLS estimates Life expectancy at birth 0.112*** 0.182*** 0.145*** 0.1

(0.0387) (0.0289) (0.0292) (0. # obs. 140 140 94 94 # of countries 70 70 47 47

(Panel B) 2SLS estimates Life expectancy at birth 0.173** 0.213*** 0.177*** 0.1

(0.0688) (0.0479) (0.0401) (0. # of obs. 140 140 94 94 # of countries 70 70 47 47

First stage results for life expectancy Health shock15 14.31*** 19.26*** 16.07*** 22

(2.787) (2.816) (2.802) (3. 1-Stage F-stat. 26.36 46.77 32.86 53

otes: Panel A (B) reports OLS (2SLS) estimates. All regressions include country and time n the age group 5–9 in year t (cohort). In columns 5–8 the dependent is average years of ead, i.e., 1950–1990 and 1950–2010. The explanatory variable is life expectancy at birth or life expectancy. Standard errors are clustered at the country level.

* p < 0.1. ** p < 0.05.

*** p < 0.01.

etween the � in Years of schooling and the � in Health shock15 . Base sample over 940–1980.

ow indicates that for every extra year of life expectancy, years of chooling increase by 0.17 years (column 1).

Compared to other (micro) studies, the magnitudes of the 2SLS stimates are reasonable. For instance, Jayachandran and Lleras- uney (2009) recover an estimate of 0.11, which falls within the

5 percent confidence interval bands of the estimated relationship n column 1 in panel B.

In order to make the main contribution of this paper appear erfectly clear, columns 5–8 of Table 2 reproduce the basic insight ade in Acemoglu and Johnson (2006): using average years of

chooling in the population aged 15 and over as the dependent

ariable (instead of the cohort variable) reveals a weak positive elationship between life expectancy and schooling. More pre- isely, while the 2SLS estimates are close to zero but still positive,

Years of schooling, 15–99

Base sample AJ sample

40–2000 1940–1980 1940–2000 1940–1980 1940–2000 (5) (6) (7) (8)

85*** 0.0150 0.0454*** 0.0237 0.0576***

0284) (0.0143) (0.0168) (0.0150) (0.0188) 140 140 94 94 70 70 47 47

93*** 0.0225 0.0427 0.00972 0.0334 0420) (0.0283) (0.0262) (0.0285) (0.0266)

140 140 94 94 70 70 47 47

.53*** 14.31*** 19.26*** 16.07*** 22.53***

072) (2.787) (2.816) (2.802) (3.072) .58 26.36 46.77 32.86 53.58

fixed effects. In columns 1–4 the dependent variable is average years of schooling schooling in the population aged 15 and over and it is measured with a one-period . Panel B uses the health-shock based on 15 diseases as the instrumental variable

1146 C.W. Hansen / Journal of Health Economics 32 (2013) 1142– 1152

Table 3 Flexible reduced-form results.

Dependent variable is years of schooling (cohort)

OLS estimates, 10-year panel

Base sample AJ sample

1940–1980 1940–2000 1940–1980 1940–2000 (1) (2) (3) (4)

∑ Mortality × 1950 0.300 0.300 −0.0173 −0.0173

(0.238) (0.238) (0.234) (0.234)∑ Mortality × 1960 0.357 0.357 0.109 0.109

(0.384) (0.384) (0.405) (0.405)∑ Mortality × 1970 0.914* 0.914* 0.792 0.792

(0.480) (0.480) (0.566) (0.566)∑ Mortality × 1980 2.476*** 2.476*** 2.840*** 2.840***

(0.846) (0.846) (0.697) (0.697)∑ Mortality × 1990 3.011*** 3.736***

(0.876) (1.001)∑ Mortality × 2000 4.110*** 4.342***

(0.862) (1.036) Joint significance >1940, p-value 0.036 0.000 0.000 0.000

# obs. 350 490 235 329 # of countries 70 70 47 47

Notes: The table reports OLS estimates. The estimates are based on 10-panel year panel models, start and end dates are indicated in the columns. All regressions include country and time fixed effects. The dependent variable is average years of schooling (cohort).

∑ Mortality is the sum of the mortality rates of 15 diseases in 1940, which is

i red at

t s

w t t w t a t f

S

w i o r t j ( E t ˛ s b

s s

o o t

t m n e 0 s t 2

n s j t fi e s m

5

5

a a c s

nteracted with year dummies. The omitted year is 1940. Standard errors are cluste * p < 0.1.

*** p < 0.01.

hey are not even close to crossing the conventional thresholds for tatistical significance (panel B).11

Table 2 demonstrated that the coefficient estimates are larger hen looking at the longer horizon from 1940–2000, suggesting

hat the long-run effect of the health shock is larger in magni- ude compared to the short-run effect. Because the effect should be eaker for cohorts that were already older at the time of the transi-

ion, this finding is consistent with what one would have expected priori. Nevertheless, studying a flexible model could provide fur- her insights into this matter.12 The estimation equation takes the ollowing form:

choolct = ˛j 1980∑

j=1950

15∑

d=1 Mortalitycd × Ijt + +ıc + �t + �ct , (3)

here ∑

Mortalitycd is the sum of the mortality rates of the 15 nfectious diseases in 1940, which is interacted with a full set f time dummies from 1950–1980 (or 1950–2000),

∑ I j t . The

emaining variables are defined above. The estimated ˛js give he effect of the health shock on schooling for each year (cohort)

∈ {1950, 1960, . . ., 1980 (or 2000)} relative to the comparison year cohort) 1940. Notice, while the empirical model summarized by qs. (1) and (2) used the health shock as an instrumental variable o estimate the effect of life expectancy on years of schooling, the js should be interpreted in a reduced-form sense. It should also be tressed that this model utilizes observations at 10 year intervals

etween the indicated dates.

Estimates of Eq. (3) are reported in Table 3. Columns 1 and 2 how the estimates for the base sample, while columns 3 and 4 how them for the AJ sample. A clear pattern emerges from the

11 The Barro–Lee dataset only provides average years of schooling in the population ver the period 1950–2010. This implies that columns 5–8 use this variable with a ne-period lead (i.e., 1950 and 1990 or 2010). The estimates in columns 5 and 7 are herefore directly comparable to the findings in Acemoglu and Johnson (2006). 12 I thank an anonymous reviewer for this suggestion.

b p t

r

a

the country level. p < 0.05.

able. The relationship between the sum of the preintervention ortality rates and schooling is monotonically increasing in mag-

itude from 1950–1980 (or 1950–2000) in all specifications. For xample, column 1 reports ˆ̨ 1950 = 0.30 with a standard error of .24, so that by 1950 there was no statistical effect of the health hock on human capital. By 1970, however, the effect becomes sta- istical significant and by 1980 the effect is even stronger ( ˆ̨ 1980 = .48 with a standard error of 0.85).

Although the estimated ˛js for the years 1950 and 1960 are ot individually significant, the p-values of the F-test for the joint ignificance of all the years (i.e., 1950–1980 and 1950–2000) are ointly statistically different from the baseline year 1940 (see bot- om of the table). This result also indicates that one is likely to nd a positive and statistical significant relationship between life xpectancy and schooling in a 10-year panel model, albeit the effect hould be smaller in magnitude compared to the long-horizon odel presented in Table 2.13

. Extensions and robustness

.1. Pre-existing trends in schooling

This subsection considers whether convergence in the outcome ccounts for the positive relationship between life expectancy nd human capital. If years of schooling were converging across ountries prior to the medical breakthroughs, the increase in chooling could have occurred even in the absence of the medical reakthroughs. Table 4 proposes two strategies to assess whether reexisting trends in schooling are an issue in the interpretation of

he baseline estimates for the period 1940–1980.14

The first strategy includes lagged years of schooling in the egressions. However, as seen from columns 1 and 2, the

13 The robustness analysis confirms these predictions; see Table 5. 14 The period 1940–2000 provides similar results, but they are not reported (avail- ble upon request).

C.W. Hansen / Journal of Health Economics 32 (2013) 1142– 1152 1147

Table 4 Robustness to preexisting trends in human capital.

Dependent variable is years of schooling (cohort)

Convergence Falsifications tests

1900–1940 Age group 60–64

Base sample AJ sample Base sample AJ sample Base sample AJ sample (1) (2) (3) (4) (5) (6)

(Panel A) OLS estimates Life expectancy at birth 0.116*** 0.143*** 0.0141 0.0158 −0.0591*** −0.0628***

(0.0359) (0.0288) (0.0208) (0.0208) (0.0177) (0.0213) Lagged schooling −0.313 0.0991

(0.192) (0.198) # obs. 140 94 140 94 140 94 # of countries 70 47 70 47 70 47

(Panel B) 2SLS estimates Life expectancy at birth 0.180*** 0.176*** 0.0206 0.00578 −0.02042 −0.01840

(0.0659) (0.0394) (0.0316) (0.0317) (0.0453) (0.0531) Lagged schooling −0.341* 0.0796

(0.201) (0.203) # of obs. 140 94 140 94 140 94 # of countries 70 47 70 47 70 47

First stage results for life expectancy Health shock15 14.31*** 16.07*** 19.26*** 22.53*** 19.26*** 22.53***

(2.787) (2.802) (2.816) (3.072) (2.816) (3.072) 1-Stage F-stat. 26.87 31.80 26.36 32.86 26.36 32.86

Notes: Panel A (B) reports OLS (2SLS) estimates. All regressions include country and time fixed effects. In columns 1 and 2 the dependent variable is average years of schooling (cohort), measured in 1940 and 1980. In columns 3 and 4 this variable is measured in 1900 and 1940. In columns 5 and 6 the dependent variable is average years of schooling for the group 60–64 (cohort), measured in1940 and 1980. The explanatory variable is life expectancy at birth. Panel B uses the health-shock based on 15 diseases as the instrumental variable for life expectancy. Lagged schooling is years of schooling in 1900. Standard errors are clustered at the country level.

c t i o s

o i p m t a y “ a s I f i b i e g t b i

e i G s

5

c d a t t e p

i a o o T l 1 t m i a b

* p < 0.1. ** p < 0.05.

*** p < 0.01.

oefficients remain rather stable in magnitude as well as in sta- istical significance. Specifically, the 2SLS estimate indicates that ncreasing life expectancy by one year leads to 0.18 extra years f schooling (column 1 in panel B). The effect of lagged years of chooling is in most specifications statistical insignificant.15

The second strategy includes two falsification tests. The logic f the first test is to check whether the 1940–1980 health mprovements have predictive power over human capital in the reintervention period from 1900–1940. Thus, if the baseline esti- ate is indeed capturing some underlying trend in human capital

his test would fail (i.e., the coefficients would come out positive nd significant). Columns 3 and 4 present evidence from regressing ears of schooling 1900–1940 on the health shock 1940–1980. The false” relationship between schooling and life expectancy is virtu- lly zero and statistical insignificant: the 2SLS estimate for the base ample is 0.02 with a standard error of 0.03 (column 3 in panel B). n the second falsification test the dependent variable is schooling or the age group 60–64 over the period 1940–1980. For example, ndividuals aged 60 and over in 1980 should not have been affected y the health shock as they already completed their educational

nvestments by the time of the shock.16 As realized from the 2SLS stimates reported in columns 5 and 6, schooling years for the age roup 60–64 are neutral to variations in life expectancy caused by he health shock. Hence, as both falsification tests are passed, the

aseline estimates are not likely to be driven by preexisting trends

n human capital.

15 Including a lagged dependent variable in a panel model raises the issue of Nick- ll Bias (i.e., lagged dependent variable bias). Thus, one should be careful when nterpreting the estimates in columns 1 and 2. However, applying the difference MM estimator (Arellano-Bond) in a 10-year panel model from 1940–2000 provides imilar results. 16 I thank an anonymous reviewer for suggesting this test.

5

i c

u

.2. Panel structure

This subsection examines the robustness of the findings to the hoice of panel structure. The baseline model is estimated in long ifferences, that is, in a panel including only two dates (t1 = 1940 nd t2 = 1980 or 2000). This specification was motivated by the fact hat decadal changes in life expectancy are not expected to have heir full impact on schooling. Nevertheless, Table 5 reports the stimates of life expectancy on schooling when using a 10-year anel model.

The structure of Table 5 follows the preceding tables. In all spec- fications, the point estimates for life expectancy remain positive nd statistical significant, suggesting that the estimated impacts f life expectancy on schooling are robust to the panel structure f the models. Consistent with the results of the flexible model in able 3, the effects are slightly lower than those from the base- ine long-differences model. The obvious explanation is that the 0-year panel model compares the cohorts from 1950–1980 to he preintervention cohort in 1940, whereas the long differences

odel compares only the 1980 cohort to the preintervention cohort n 1940. As the effect should be weaker for cohorts that were lready older at the time of the transition, the estimated coefficients ecome smaller in magnitude in the 10-year panel model.17

.3. Functional form specification

The baseline finding was derived in terms of a level-level spec- fication. Table 6 checks whether this assumption matters to the onclusion that health affects human capital positively.

17 Similar results are obtained with a 20-year panel model (results are available pon request).

1148 C.W. Hansen / Journal of Health Economics 32 (2013) 1142– 1152

Table 5 Robustness to panel structure.

Dependent variable is years of schooling (cohort)

10-Year panel

Base sample AJ sample

1940–1980 1940–2000 1940–1980 1940–2000 (1) (2) (3) (4)

(Panel A) OLS estimates Life expectancy at birth 0.0935*** 0.162*** 0.105*** 0.167***

(0.0260) (0.0263) (0.0214) (0.0259) # obs. 350 490 235 329 # of countries 70 70 47 47

(Panel B) 2SLS estimates Life expectancy at birth 0.108** 0.150*** 0.0888** 0.140***

(0.0522) (0.0502) (0.0407) (0.0383) # of obs. 350 490 235 329 # of countries 70 70 47 47

First stage results for life expectancy Health shock15 9.45*** 12.65*** 10.69*** −14.44***

(2.319) (2.450) (2.289) (2.471) 1-Stage F-stat. 16.62 26.64 21.78 34.13

Notes: Panel A (B) reports OLS (2SLS) estimates. The estimates are based on 10-year panel models, start and end dates are indicated in the columns. All regressions include country and time fixed effects. The dependent variable is average years of schooling (cohort). The explanatory variable is life expectancy at birth. Panel B uses the health-shock b rs are

1 m e i a e s w 4 A

i

i c s

5

b c

T R

N y f

ased on 15 diseases as the instrumental variable for life expectancy. Standard erro * p < 0.1.

** p < 0.05. *** p < 0.01.

Panel A provides OLS estimates using information for the period 940–1980, whereas panel B gives the corresponding 2SLS esti- ates for the same period. The results show that the effect of life

xpectancy on schooling remains robust to whether human cap- tal is measured in years of schooling or in log years of schooling nd to whether health is measured by life expectancy or by log life xpectancy. For example, in the level-log specification in the base ample of countries, the coefficient on log life expectancy is 6.2 ith a standard error of 2.5 (column 1 in panel B). From columns

–6 it is evident that the picture is the same when looking at the J sample.

Finally, it can be noted that the when human capital is measured n log years of schooling, the statistical significance of the estimates

m o t

able 6 obustness to functional form specifications.

Dependent variable is (log) years of schooling

Base sample

Level-log Log-level (1) (2)

(Panel A) OLS estimates Log life expectancy at birth 4.340**

(1.716) Life expectancy at birth 0.0381***

(0.0107) of obs. 140 140 # of countries 70 70

(Panel B) 2SLS estimates Log life expectancy at birth 6.231**

(2.471) Life expectancy at birth 0.0526***

(0.020) # of obs. 140 140 # of countries 70 70

otes: Panel A (B) reports OLS (2SLS) estimates. All regressions include country and time ears of schooling (cohort). The main explanatory variable is (log) life expectancy at birth or life expectancy. First-stage results are omitted. Standard errors are clustered at the co

* p < 0.1. ** p < 0.05.

*** p < 0.01.

clustered at the country level.

s increased. This could be because the health shock is picking up onvergence in schooling, which is much more pronounced in this pecification (Sunde and Vischer, 2011).

.4. Confounding variables

Table 7 establishes the robustness of the main results for the ase sample of countries over 1940–1980 with respect to possible onfounding factors.

The first three columns investigate the role of income improve- ents and economic institutions. Column 1 reveals the robustness

f the result controlling for log GDP per capita. Even though his control is potential endogenous due to reverse causality, the

, cohort

AJ sample

Log-level Level-log Log-level Log-level (3) (4) (5) (6)

1.857*** 5.236*** 1.990***

(0.510) (1.167) (0.235) 0.0435***

(0.00656) 140 94 94 94 70 47 47 47

1.894*** 6.383*** 2.411***

(0.706) (1.553) (0.256) 0.0667***

(0.00955) 140 94 94 94 70 47 47 47

fixed effects. The observation period is 1940–1980. The dependent variable is (log) . Panel B uses the health-shock based on 15 diseases as the instrumental variable

untry level.

C.W. Hansen / Journal of Health Economics 32 (2013) 1142– 1152 1149

Table 7 Robustness to confounders.

Dependent variable is years of schooling (cohort)

Base sample, 1940–1980

Income and institutions Child and adult health

(1) (2) (3) (4) (5) (6)

(Panel A) OLS estimates Life expectancy at birth 0.139*** 0.0862** 0.137*** 0.138*** 0.0876**

(0.0304) (0.0438) (0.0367) (0.0440) (0.0399) Log GDP/capita 0.00514 0.168

(0.349) (0.313) Institutions × 1980 −0.193* −0.101

(0.102) (0.104) Life expectancy at age 20 0.180*** 0.0183 0.113**

(0.0544) (0.0647) (0.0570) Infant mortality 0.476

(0.314) # of obs. 114 134 112 90 90 76 # of countries 57 67 56 45 45 38

(Panel B) 2SLS estimates Life expectancy at birth 0.176*** 0.153* 0.177*** 0.214* 0.260***

(0.0382) (0.0803) (0.0430) (0.113) (0.100) Log GDP/capita 0.0860 0.264

(0.359) (0.318) Institutions × 1980 −0.0843 −0.0409

(0.140) (0.0982) Life expectancy at age 20 0.297*** −0.0705 −0.139

(0.0910) (0.145) (0.169) Infant mortality 0.193

(0.364) # of obs. 114 134 112 90 90 76 # of countries 57 67 56 45 45 38

Notes: Panel A (B) reports OLS (2SLS) estimates. All regressions include country and time fixed effects. The dependent variable is average years of schooling (cohort) The explanatory variables are: life expectancy at birth, Log GDP per capita, average of the constraints on the executive from the Polity IV data over 1950–1970 (Institutions) interacted with a year dummy, life expectancy at age 20, and the log infant mortality rate. Panel B uses the health-shock based on 15 diseases as the instrumental variable for life expectancy at birth and life expectancy at age 20 in column 4. The first-stage results are not reported. The observation period is over 1940–1980 (40-year panel) for the base sample of countries. Standard errors are clustered at the country level.

c b m w f d o c r t v

o f i 4 h s a t m 0 i i

h p e t l i t F b o t c m a

5

This subsection turns to a closer analysis of the mechanism underlying the basic results. One important question is whether

* p < 0.1. ** p < 0.05.

*** p < 0.01.

oefficient on life expectancy remains stable in comparison to the aseline estimates from column 1 of Table 2. Column 2 includes a easure of the quality of the economic institutions. This variable as constructed as the average of the constraints on the executive

rom the Polity IV data set over 1950–1970 interacted with the year ummy for 1980. As seen, however, the effect of life expectancy n schooling remains largely stable, albeit the statistical signifi- ance reduces somewhat. The regressions in column 3 extend the obustness of columns 1 and 2 by adding both controls at the same ime. The estimates with controls for income and institutions are irtually identical to the baseline estimates.

Bleakley (2006) argues that much of the gain in life expectancy ver this period occurred at younger ages. Therefore, the results so ar could be understood with this in mind: health during childhood s a particularly important determinant of human capital. Columns –6 of Table 7 examine the relative importance of improved child ealth and adult health for schooling outcomes.18 The analysis tarts by replacing life expectancy at birth with life expectancy at ge 20 as the explanatory variable. While data availability reduces he sample to 45 countries, column 4 reports that the 2SLS esti-

ate on life expectancy at age 20 is 0.29 with a standard error of

.09 (panel B). This finding indicates that adult health is after all an

mportant factor in the understanding of human capital inequal- ties across countries. In this specification, however, childhood

18 I thank an anonymous reviewer for suggesting these extensions.

t 2 p

c l

ealth could be considered as a confounding factor. Column 5 in anel A therefore reports OLS estimates when controlling for life xpectancy at birth. The results show a statistical significant posi- ive effect of life expectancy at birth on schooling but no effect of ife expectancy at age 20 on schooling. The specification presented n panel B, where life expectancy at birth is instrumented with he health shock based on 15 diseases, provides similar results.19

inally, the regression in column 6 extends the result of column 5 y adding the infant mortality rate. It demonstrates that the effect f life expectancy at birth on human capital cannot be attributed o the declines in infant mortality over the second half of the 20th entury. Overall, the evidence in columns 4–6 instigates the argu- ent that childhood health is a particularly important factor in the

ccumulation of human capital.20

.5. The three big killers

19 Using the shocks to malaria, pneumonia, and tuberculosis separately (i.e., as hree instrumental variables) allows me also to instrument life expectancy at age 0. But the results from this exercise are similar to those presented in column 5 in anel B. 20 This is also in line with the empirical analysis in Hazan (2012), showing that hanges in human capital over 1950–1980 are possibly attributable to changes in ife expectancy 0–5.

1150 C.W. Hansen / Journal of Health Economics 32 (2013) 1142– 1152

Table 8 The health-shock variable.

Dependent variable is years of schooling, cohort

2SLS estimates

Base sample AJ sample

(1) (2) (3) (4) (5) (6)

Life expectancy at birth 0.173** 0.171** 0.176** 0.177*** 0.180*** 0.181***

(0.0688) (0.0696) (0.0686) (0.0401) (0.0409) (0.0377) # of obs. 140 140 140 94 94 94 # of countries 70 70 70 47 47 47

First stage results for life expectancy Health shock15 14.31*** 16.07***

(2.787) (2.802) Health shock3 15.75*** 18.01***

(3.1470) (3.240) Malaria × 1980 19.41*** 34.85

(5.977) (21.61) Pneumonia × 1980 21.09*** 19.37***

(5.033) (6.112) Tuberculosis × 1980 5.617 11.592

(6.538) (7.585) 1-Stage F-stat. 26.36 25.05 12.02 47.74 30.87 15.04 Hansen J-stat. 0.189 1.740 (p-Value) (0.910) (0.419)

Notes: The table reports 2SLS for life expectancy. All regressions include country and time fixed effects. The observation period is 1940–1980. The dependent variable is years of schooling (cohort). The main explanatory variable is life expectancy at birth. The instrumental variable Health shock15 is based on 15 diseases, whereas the instrumental v rrors

t i r d d i f m p o o

d r C t

c s v u h e o o i a s e

T T

N ( a

ariable Health shock3 is based on malaria, pneumonia, and tuberculosis. Standard e * p < 0.1.

** p < 0.05. *** p < 0.01.

he positive estimate on life expectancy at birth is due to health mprovements in childhood or in adult life (or both). While the esults in Table 7 suggest childhood health is the likely candi- ate, recognizing that the mortality from the different infectious iseases had different age distributions could provide additional

nsights into the underlying mechanism. To this end, the analysis ocuses its attention towards the three big killers: malaria, pneu-

onia, and tuberculosis. This is motivated by the fact that the neumonia and malaria mortality was concentrated on children f younger age, while tuberculosis was mainly a burden on adults f reproductive ages (e.g., Bleakley, 2006).

Table 8 presents 2SLS estimates and first-stage results using

ifferent types of health shocks. In particular, columns 1 and 4 eproduce the baseline 2SLS estimates based on Health shock15. olumns 2 and 5 utilize the sum of the mortality rates from the hree big killers as the instrumental variable, Health shock3, while

r 3

a

able 9 he shocks to malaria, pneumonia, and tuberculosis.

Dependent variable is years of schooling, cohort

Reduced-form OLS estimates

Base sample

(1) (2) (3) (

Malaria × 1980 3.300*** 3 (0.933) (

Pneumonia × 1980 3.585** 3 (1.527) (

Tuberculosis × 1980 1.472 0 (1.464) (

# of obs. 140 140 140 1 # of countries 70 70 70 7

otes: The table reports OLS estimates. All regressions include country and time fixed effect cohort). Malaria × 1980 is the malaria mortality rate in 1940 interacted with a preinterven re constructed in a similar way. Standard errors are clustered at the country level.

* p < 0.1. ** p < 0.05.

*** p < 0.01.

are clustered at the country level.

olumns 3 and 6 use information on each of the three big killers eparately. Thus, the latter specifications have three instrumental ariables and one endogenous variable (i.e., life expectancy). Firstly, sing Health shock3 as the instrumental variable for life expectancy as no effect on the estimate, which remains 0.17 with a standard rror of 0.07 for the base sample over 1940–1980 (column 2). Sec- ndly, column 3 demonstrates for the base sample of countries that nly the shocks to malaria and pneumonia have statistical signif- cant positive effects on life expectancy. However, when looking t the same first-stage regression for the 47 countries in the AJ ample, only the “pneumonia shock” is significantly related to life xpectancy (column 6). As becomes clear momentarily, this is a

esult of the first-stage estimate on the malaria-shock (in column ) being driven by one outlier country.

In addition, in the specifications with three instrumental vari- bles tests of the over-identifying restrictions are possible. The

AJ sample

4) (5) (6) (7) (8)

.704*** 12.32*** 1.776 1.048) (3.812) (3.645) .692** 5.081*** 4.679***

1.582) (1.152) (1.199) .590 2.442 1.459 1.339) (1.711) (1.461) 40 94 94 94 94 0 47 47 47 47

s. The observation period is 1940–1980. The dependent variable is years of schooling tion dummy variable (year 1980). The pneumonia and tuberculosis-shock variables

C.W. Hansen / Journal of Health Econ

Panel A: Δ in Years of sch ooling an d the Δ in shoc k to malaria. Base sample ove r 1940--1980.

YUG

ZAF

KOR

CAN

HTIJAM

TTO

DNK

CHE

SWE USA BEL

GER

NOR

NLD

MAR

AUS GBR

ARG FRA

EGY

CHN NZL

FINHUN

CZEAUT

POLIRL

IRN

DZA

ESP

BGR

URY

COL

RUS

JPN ROM

MEX

GRC

PRY PAN

MYS

IRQ

CHL

PRT

PAK

TUN

VEN

THA

LKA

PHL ITA

IDN

BRA

NIC BGD

ECUHND

MMR

CRI

PER

GTM

DOM

IND

VNM

SLV GUY

BOL

MUS

-6 -4

-2 0

2 4

e (

C h a

n g

e i n

y e a

rs o

f s c h

o o

li n g

, 1 9

4 0

-1 9 8

0 | X

)

0 .2 .4 .6 .8 e( Change in malaria shock , 19 40-1980 | X )

coef = 3.703698, (r obust) se = 1.0795 226, t = 3.43

Pan el B: Δ in Years of sch ooling an d the Δ in shoc k to pn eumonia. Bas e sample over 1940--1980.

Pan el C: Δ in Years of sch ooling an d the Δ in shoc k to tub erculosis. Bas e sample over 1940--1980.

PER

VEN ITA

NZL

FIN

YUG

DOM

NLD

SWE

PRY

CHE GER

GUY

MARBOL

POL CAN

URY

IRL

ARG

DZA

BEL

DNK

USA

MUS

ZAF

HUN

PRT BRA

BGD GBR

CZE

THA

RUS

NOR

AUT AUSJPN

HTIJAM

TTO

BGR

LKA

GRC

CHN

ROM

ESP

FRA

KORTUN

NIC

IRN

COL

EGY

PANSLV PHL

MYS

CHL

MMR PAK

IRQ

GTM

MEX

VNM

CRI

HNDECU

IND

IDN

-5 0

5 e

( C

h a

n g

e i n

y e a

rs o

f s c h

o o

li n g

, 1 9

4 0

-1 9 8

0 | X

)

-.2 0 .2 .4 e( Change in pneumonia shock, 1940-19 80| X )

coef = 3.691702, (r obust) se = 1.6292 939, t = 2.27

HND

MYS

KOR HTIJAM

TTO

MEX

CRI

YUG

DOM

IRN

EGY

ZAF

DNK

CANGUY

NIC

USA IND

NOR

FRA

BEL

CHE

SWE

AUS IDN

CHN MUS

ARG

GER

GBR

MMR

MAR

IRQ

NLD

COL

DZA

VNM

AUTCZE

HUN

ESP

GRC

BGR

GTM

BGD

PAK

ECU

IRLPOLNZL

FIN

ROM

CHL

URY

JPN

RUS

LKA

PRY PAN

BRA

THA

TUN

BOL

SLV PHL

PRT

VEN ITA

PER

-6 -4

-2 0

2 4

e (

C h a

n g

e i n

y e a

rs o

f s c h

o o

li n g

, 1 9

4 0

-1 9 8

0 | X

)

-.2 0 .2 .4 .6 e( Change in tuberc ulosis shock, 1940-1980 | X )

coef = . 58986707, (r obus t) se = 1.378 9371, t = . 43

Fig. 3. Partial correlation plots between years of schooling and the shocks to malaria, pneumonia, and tuberculosis. (Panel A) � in years of schooling and the � in shock t � s

b i v a m

t v

m t n 3 p t r o w T i fi n

p m a t fi

6

t l s

J c y m T t t i h o i

r e c t H m t a

is that the disease-specific mortality rates component in the health shock variable is endogenous to economic development. How- ever, the current type of empirical strategy has the flavor of a

21 It can be noted that Bleakley (2010) stresses that the international epidemio- logical transition (i.e., the convergence in life expectancy over the period) might be the variance that one would like to study the consequences of in the first place.

22 I have also checked the specifications as suggested by Bloom et al. (2013). While it is hard to infer any conclusive evidence from the IV analysis, since including initial life expectancy results in a weak first stage, the OLS estimate in the AJ sample over

o malaria. Base sample over 1940–1980. (Panel B) � in years of schooling and the in shock to pneumonia. Base sample over 1940–1980. (Panel C) � in years of

chooling and the � in shock to tuberculosis. Base sample over 1940–1980.

ottom of Table 8 reports Hansen-J statistics. The null hypothesis

s a joint hypothesis that the error term is unrelated to the shock ariables. In column 3 the Hansen-J takes on the value of 0.19 with n associated p-value of 0.9, implying that the validity of the instru- ents cannot be rejected. If one is willing to assume that just one of

1 b 1 s

omics 32 (2013) 1142– 1152 1151

he three shocks is truly exogenous, this test confirms the internal alidity of the proposed identification strategy.

Table 9 takes on a reduced-form approach and presents esti- ates from directly regressing years of schooling on each of the

hree shocks. When all the shock variables are entered simulta- eously, the effect of the malaria shock on years of schooling is .7 with a standard error of 1.0. The coefficient estimate on the neumonia shock is similar, whereas the estimate on the shock to uberculosis is small and statistical insignificant. The partial cor- elation plots are shown in Fig. 3. It is clear that the coefficient n the malaria shock is driven by Mauritius (MUS). Accordingly, ithout this observation in the sample the relationship disappears.

his is also the reason why the coefficient on Malaria × 1980 is nsignificant in the AJ sample. Fig. 3 further shows that the coef- cient estimates on Pneumonia × 1980 and Tuberculosis × 1980 are ot being influenced by a small number of countries.

Taken together, this subsection establishes that the shock to neumonia mortality is the main cause of the baseline result. Since ortality from this particular disease is concentrated at younger

ges, this could indicate that childhood health is an important fac- or in the formation of human capital, which is consistent with the ndings of Table 7.

. Concluding remarks

This article has provided evidence on the relevance and impor- ance of the human capital channel: healthier countries with onger-lived populations also spend more (effective) time on chooling and hereby acquire better human capital skills.

Using the same empirical strategy as proposed by Acemoglu and ohnson (2007) to identify the effect of life expectancy on human apital, the analysis finds that for one extra year of life expectancy, ears of schooling increase by 0.17 year, which implies that the edical advances of this period raised schooling by about 1.1 years.

hus, the bulk of evidence indicates that life expectancy has a posi- ive effect on human capital. Moreover, the mechanism underlying he result seems to be the mortality declines in pneumonia, point- ng towards early life health being an important determinant of uman capital. Nevertheless, this is far from conclusive evidence n the mechanism and future research should dig deeper into this ssue.

At this point, however, a discussion of the limitations of the esult is warranted. In particular, potential concerns about the mpirical approach put forward in other papers should be dis- ussed (Aghion et al., 2010; Bloom et al., 2013). One concern is hat the regressions should have included initial life expectancy.21

owever, as shown by Aghion et al. (2010), if the model is in fact isspecified then because of mean reversion in life expectancy

his would bias the estimate on life expectancy downwards, which ctually strengthens the results presented in the current paper.22

Another important concern, put forward in Bloom et al. (2013),

940–1980 is within the same range as the baseline, whereas over 1940–2000 it ecomes somewhat weaker but remains statistically significant. In addition, in the 0-year panel model, the OLS estimates become larger in magnitude in the all the pecifications proposed in this paper.

1152 C.W. Hansen / Journal of Health Econ

Table A1 Countries in the base sample.

Algeria Finland Morocco Thailand

Argentina France Myanmar Trinidad &Tobago Australia Germany Netherlands Tunisia Austria Greece New Zealand Turkey Bangladesh Guatemala Nicaragua UK Belgium Haiti Norway US Bolivia Honduras Pakistan Uruguay Brazil Hungary Panama Venezuela Bulgaria India Paraguay Vietnam Canada Indonesia Peru Yugoslavia Chile Iran Philippines China Iraq Poland Colombia Ireland Portugal Costa Rica Italy Romania Czech Republic Jamaica Russia Denmark Japan South Africa Dom. Republic Korea Spain

d a w 2 m m t

A

A h a W C S c

A

R

A

A

A

A

B

B

B

B

B

B

B

B

B

B

B

C

C

C

C

C

C

D

G G

H

H

H

H

J

J

L

L

M

O

O

P

S

S

S

T

V approach to health transition. Demographic Research 2 (2), 11–41.

Ecuador Malaysia Sri Lanka Egypt Mauritius Sweden El Salvador Mexico Switzerland

ifference-in-difference model and other studies following this pproach also use preintervention health conditions interacted ith the intervention date (e.g., Bleakley, 2007; Bleakley and Lange,

009; Lucas, 2010; Bhalotra and Venkataramani, 2012). Further- ore, the similarity in empirical strategy to various micro studies akes it easier to compare the macro and micro-estimates quali-

atively, which was the starting point of the current study.

cknowledgements

I would like to thank two anonymous referees, the editor ndrew Street, Matteo Cervellati, Davide de la Croix, Peter Sand- olt Jensen, Lars Lønstrup, Uwe Sunde, and seminar participants t Aarhus University, the Danish Public Choice Workshop, the 3rd orkshop on Growth, History, and Development (University of

openhagen), and the 26th Annual Conference of the European ociety for Population Economics (University of Bern) for useful omments and suggestions. All errors and omissions are my own.

ppendix A.

See Table A1.

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• Life expectancy and human capital: Evidence from the international epidemiological transition
• 1 Introduction
• 2 A theoretical perspective
• 3 Data
• 4 Empirical strategy and main results
• 4.1 Empirical strategy
• 4.2 Main results
• 5 Extensions and robustness
• 5.1 Pre-existing trends in schooling
• 5.2 Panel structure
• 5.3 Functional form specification
• 5.4 Confounding variables
• 5.5 The three big killers
• 6 Concluding remarks
• Acknowledgements
• References
• References

Epidemiological-transition-in-the-Americas--changes-and-inequa_2013_The-Lanc.pdf

Meeting Abstracts

www.thelancet.com 89

Epidemiological transition in the Americas: changes and inequalities Fatima M Marinho, Patricia Soliz, Vilma Gawryszewski, Andrea Gerger

Abstract Background In the last decades, the overall health situation has improved in the Americas; however, health inequality persists. Our aim is to describe the epidemiological transition in the region.

Methods This descriptive study uses mortality data reported by Pan American Health Organization/WHO countries since 1950. We analysed data from Brazil, Canada, Colombia, Costa Rica, Ecuador, Guatemala, Mexico, the USA, Uruguay, and Venezuela. Data were classifi ed in four groups: (1) communicable, maternal, perinatal, nutritional conditions; (2) non-communicable diseases (NCDs); (3) injuries; and (4) ill-defi ned causes of death. Data were codifi ed using the International Classifi cation of Disease (ICD) 6–10 and aggregated to guarantee the equivalence among ICD versions.

Findings The analysis reveals three diff erent stages of epidemiological transition. (1) Completed transition: high burden of NCDs and low infectious diseases since the 1950s (Canada, USA, Uruguay). (2) Recent transition: the increase in the NCD burden that occurred during the 1980s and 1990s (Brazil, Colombia, Costa Rica, Mexico, Venezuela). From 1990 to 2000 in Mexico, deaths decreased from 32% (n=134 167) to 15% (67 322) in group 1 and increased by 50% in group 2 (from 230 343 in 1990, to 309 568 in 2000). Likewise, in Brazil, group 1 decreased from 10% (n=80 992) to 5% (44 284), and NCDs increased from 58% (457 642) to 72% (598 162). (3) Delayed transition: low burden of NCDs and high proportion of infectious diseases (Ecuador, Guatemala). In 1990 in Guatemala, group 1 was responsible for 55% (n=49 067) of all deaths, group 2 for 25% (15 864), and injuries for 3% (6286). In 2006, 30% (n=20 892) of deaths were due to group 1, 46% (32 137) due to group 2, and injuries accounted for 18% (32 137).

Interpretation The Americas are going simultaneously through diff erent stages of the epidemiological transition. In developed countries, NCDs have been predominant since the 1950s, whereas developing countries with inequalities and less access to health care are still facing a delayed transition, presenting a high proportion of deaths due to group 1 and increases in NCDs and injuries. Countries need to redefi ne their priorities to tackle this triple burden of disease.

Funding None.

Contributors All authors conceived the report, discussed and agreed on the content, and approved the fi nal version. FMM wrote the fi rst draft. FMM, VG, and PS

collated and analysed data. AG reviewed the document and gave feedback.

Confl icts of interest We declare that we have no confl icts of interest.

Published Online June 17, 2013

Pan American Health Organization (PAHO)/World Health Organization (WHO), Health Surveillance and Disease Prevention and Control Area, Washington, DC, USA (F M Marinho PhD); and PAHO/WHO, Health Information and Analysis Project, Washington DC, USA (P Soliz MD, V Gawryszewski PhD, A Gerger MSc)

Correspondence to: Fatima Marinho, Pan American Health Organization/World Health Organization, Health Surveillance and Disease Prevention and Control (HSD), 525 23rd Street, NW, Washington, DC, USA [email protected]

Food--parasites--and-epidemiological-transiti_2013_International-Journal-of-.pdf

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International Journal of Paleopathology 3 (2013) 150– 157

Contents lists available at ScienceDirect

International Journal of Paleopathology

j o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / i j p p

eview

ood, parasites, and epidemiological transitions: A broad perspective

.J. Reinhard a, L.F. Ferreira b, F. Bouchet c, L. Sianto b, J.M.F. Dutra b, A. Iniguez b, D. Leles d, . Le Bailly e, M. Fugassa f, E. Pucu b, A. Araújo b,∗

University of Nebraska-Lincoln, 719 Hardin Hall, 3100 Holdrege Street, Lincoln, NE 68583-0987, USA Fundaç ão Oswaldo Cruz, Avenida Brasil 4365, Manguinhos, CEP 21040-900 Rio de Janeiro, RJ, Brazil Université de Reims, 51 rue Cognacq-Jay, 51096 Reims Cedex, France Universidade Federal Fluminense, Rua Professor Hernani Melo 101, São Domingos, CEP 24210-130 Niterói, RJ, Brazil Faculté des Sciences et Techniques de l’Université de Franche-Comté, France Universidad Nacional de Mar del Plata, Diag. Alberdi Juan Bautista 2695, 7600 Mar Del Plata, Buenos Aires, Argentina

r t i c l e i n f o

rticle history: eceived 8 February 2013 eceived in revised form 14 May 2013 ccepted 17 May 2013

eywords: aleoparasitology rchaeoparasitology

a b s t r a c t

Pathoecology provides unique frameworks for understanding disease transmission in ancient popula- tions. Analyses of Old and New World archaeological samples contribute empirically to our understanding of parasite infections. Combining archaeological and anthropological data, we gain insights about health, disease, and the way ancient people lived and interacted with each other and with their environments. Here we present Old and New World parasite evidence, emphasizing how such information reflects the different ways ancient populations exploited diverse environments and became infected with zoonotic parasites. It is clear that the most common intestinal helminths (worm endoparasites) were already

athoecology oprolites arasites ood remains ncient diseases

infecting ancient inhabitants of the New World prior to the European conquest, although not so intensely as in ancient Europe. The first paleoepidemiological transition from hunting–gathering to agriculture did not change the zoonotic infection pattern of people in the Americas. However, the same transition in Europe resulted in increased zoonotic parasitism with parasites from domestic animals. Therefore, there is a demonstrable difference in the impact of the first paleoepidemiologic transition in the Americas compared to Europe.

© 2013 Elsevier Inc. All rights reserved.

ontents

1. Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150 2. Parasite migration to the New World . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151 3. Diet and parasitism in the New World . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151 4. Diet and parasitism in the Old World . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153 5. Contrasts in paleoepidemiologic transitions, Old World and New . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154 6. Agriculture practices—indirect influence of subsistence on parasitism . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154 7. Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155

Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155

∗ Corresponding author. Tel.: +55 21 25982566; fax: +55 21 25982610. E-mail addresses: [email protected] (K.J. Reinhard),

[email protected] (L.F. Ferreira), [email protected] F. Bouchet), [email protected] (L. Sianto), [email protected] (A. Iniguez), [email protected] (D. Leles), [email protected] (M. Le Bailly), [email protected] (M. Fugassa), [email protected], [email protected]

A. Araújo).

879-9817/$ – see front matter © 2013 Elsevier Inc. All rights reserved. ttp://dx.doi.org/10.1016/j.ijpp.2013.05.003

1. Introduction

Pathoecology, as defined by Martinson et al. (2003), is the study of parasitism in context of culture and environment. In a paleopathological sense, the evolution of Homo was an entirely new adaptive process. Typically, parasitism is derived from the coevolution of vertebrate hosts with parasites in a specific envi-

ronment (Gandon et al., 2008). With humans, however, adaptive behavior strategies could be developed to accommodate this interaction. With the genus Homo, there evolved a capacity for sym- bolic thought that resulted in an enormous diversity of cultural

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daptation to an equally diverse array of environments on a global cale. Thus, cultural evolution produced a variety of behaviors that nabled humans to adapt all environments and to spread the planet n hundreds of ecological niches, each with its own array of para- ites. Therefore, throughout the early radiation of Homo, symbolic hought and flexible cultural evolution had adaptive value in coping ith the endemic parasites encountered in diverse environments

Donald, 1993). This is especially true of food-borne parasites which ere encountered as people adapted gastronomically to new and ifferent fauna. In the New World archaeological record, we can see hese processes at play.

The paleopathology of infections disease has been viewed as assing through distinct “paleoepidemiologic” transitions. Barrett t al. (1998) asserted that there are three pathoecologic phases and wo major paleopaleoepidemiologic transitions. They proposed a Paleolithic Age Baseline” of human infection and stated that dur- ng the Paleolithic times, human populations existed as small bands f nomadic foragers. The small and diffuse human groups could ot support many infectious agents. This is the original state of uman parasitism. With regard to pathoecology, this has been sup- orted by analyses of coprolites (Reinhard, 1988, 1990; Reinhard t al., 1985). From this Paleolithic baseline, human populations xperienced new infectious disease challenges with the Neolithic evolution. Therefore, the first epidemiologic transition was that etween the Paleolithic and Neolithic. For Europe, the first epidemi- logic transition established a pattern of high prevalence of disease hat lasted in all regions for centuries. Barrett et al. (1998) asserted hat permanent settlements, accumulation of human waste, animal omestication, and agricultural practices increased contact with a ariety of parasites. The Industrial Revolution, according to Bar- ett and colleagues, saw the control of infectious diseases and the mergence of chronic, noninfectious challenges. This is the second pidemiologic transition. In the archaeoparasitological record, this s seen in a reduction of contexts that contain parasite remains nd a reduction of the diversity of parasite species. For the Amer- cas, the “Paleolithic Age Baseline” is represented analogously by aleo-Indian and Archaic periods. The Neolithic revolution is rep- esented by Formative cultures and their subsequent periods. We re comparing the evidence of food-borne parasites between the mericas and the Old World of Europe with some reference to

he Old World of Asia. We are presenting our review following the ransition sequence established by Barrett et al. (1998).

For this paper, we are focusing on cultural dietary adaptations, r gastronomy in the broadest definition of the word. Therefore, e review the literature for Europe and the Americas with spe-

ific interest in parasitic evidence that is directly related to food hoice and preparation. The data set comes from a variety of sources ncluding mummies, coprolites, burial sediments, and latrines. aphonomically, these data sources are not equal. Methods have een refined over several decades to recover parasite remains effi- iently from these sources (Reinhard et al., 1986). However, the est methods cannot recover ephemeral remains from contexts, uch as open middens, that are prone to decomposition. Ephemeral emains include larvae and delicate eggs. In our combined experi- nce, there is significant decomposition of remains from latrines y fungi and arthropods as described by Reinhard et al. (1986).

n contrast, coprolites exhibit the best preservation. Mummies Reinhard and Urban, 2003) and sediments from sacra (Fugassa t al., 2008a) also show excellent preservation of delicate eggs. f the range of nematode, cestode, and trematode eggs recovered

rom archaeological sites from these sources, pinworm eggs, hook- orm eggs and larvae and threadworm larvae are differentially

usceptible to decomposition in latrines. However, they preserve ell in mummies and coprolites. In coprolites found in archaeo-

ogical layers larvae may have abandoned feces before desiccation e completed. Those nematodes that have infective larvae, such as

f Paleopathology 3 (2013) 150– 157 151

hookworms and threadworms, are rare in latrines and this may be due to poor preservation conditions. The delicate eggs of pinworm have been rarely found in latrines, partly because of decomposition and partly because few pinworm eggs are passed in feces relative to geohelminths. As of this writing, thousands of parasite samples from hundreds of sites have been analyzed from Europe, Asia, North America and South America. This provides a data base that can show the general relation between diet and parasitism for these areas. Archaeoparasitology and paleoparasitology are terms used here- after interchangeably. The first term is mainly used in association with human remains while the other has a broader spectrum, also referring to animal parasites. Both refer to parasite infections, not necessarily to diseases.

2. Parasite migration to the New World

Ancient populations arrived in the New World with an array of tightly coevolved human-specific parasites that adapted to Homo early in that genus’s evolutionary history (Araújo et al., 2008). Thus, over ten thousand years ago human groups in South and North America hosted the intestinal helminths pinworm (Enterobius vermicularis), hookworm (Ancylostoma duodenale/Necator amer- icanus), whipworm (Trichuris trichiura), and rarely roundworm (Ascaris lumbricoides). The head louse (Pediculus humanus) is also a human parasite found in South and North American archae- ological sites. Intestinal worm and louse eggs have been found associated with humans in archaeological sites dated as early as 10,000 years ago. Therefore, some common human parasites already infected prehistoric human populations in the Americas long before historic immigrants from Europe and Africa. However, all the parasites cited above (head lice, whipworm, hookworm, and roundworm) ultimately have an African origin, probably co- evolving with remote Homo ancestors. Based on mitochondrial DNA (mtDNA) data, human head lice (P. humanus) separated from Chim- panzee head lice (Pediculus schaeffi) about 5.6 million years ago (Reed et al., 2004). The world’s oldest known direct head louse association – nits on human hair – was found at a 10,000 year old archaeological site in northeast Brazil (Araújo et al., 2000). The intestinal helminths mentioned above and head lice were dispersed by human migrations to other parts of the world whenever and wherever climatic conditions allowed maintenance of the para- sites’ life cycles. Parasitological data showed that some of these parasites, especially hookworm, roundworm, and whipworm were introduced before 10,000 years ago by routes other than the Bering Land Bridge between Siberia and Alaska (Araújo et al., 2008). Pin- worm and head lice, by contrast, were able to complete their life cycles in cold conditions of Arctic and may therefore have been introduced by groups migrating across the Bering Land Bridge (Araújo and Ferreira, 1995).

3. Diet and parasitism in the New World

In the New World, dietary patterns played a key role in defin- ing parasitology in ancient people from the Paleolithic onward through the Formative and until Columbian contact (Reinhard, 1990, 1992a). Zoonotic parasites normally exist in animals but can be transmitted to humans. New studies that compare parasite diversity show that zoonotic parasites were taxonomically more diverse and with highly varied life cycles compared to human spe- cific parasites in New World prehistoric agriculturalists (Jiménez et al., 2012; Cleeland et al., 2013). In other words, Native Americans

exposed themselves to a greater variety of zoonotic parasites with a greater variety of life cycles than human-specific parasites. This was demonstrated by studies of a single site in northern Mexico. There, four zoonotic species were present, transferred by consumption

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f grain beetles, fleas and amphibians. In comparison, three human- pecific species were present, one with a direct anal-oral cycle nd two fecal transmitted species. This Formative site, Cueva de os Muertos Chiquitos, is a microcosm of the parasitic state in the rehistoric Americas before and after the first paleoepidemiologic ransition. Zoonotic parasitism dominated before and after the first aleoepidemiologic transition. In the same region, Archaic period oprolites have been analyzed as reviewed by Reinhard (1990). luke eggs consistent with Echinostoma were found. Eggs of the ame type were recovered from the maize-dependent site of Cueva e los Muertos Chiquitos. Therefore, at least this zoonotic infection ccurred before and after the first paleoepidemiologic transition. nterestingly, as evidenced in this area, two species of tapeworm

ere added to the human parasite spectrum after agriculture. One s a zoonosis associated with dogs, and the second with grain stor- ge.

Reviews of parasite evidence from New World archaeologi- al sites show that zoonotic infections from wild reservoirs were iverse and common (Beltrame et al., 2010; Fugassa and Beltrame, 009; Fugassa et al., 2010, 2011; Moore et al., 1969; Reinhard, 990; Sianto et al., 2009). In addition, diverse flukes and tapeworms

nfected prehistoric Americans, varying across local resources and aried continental ecologies (Fry, 1977; Gonç alves et al., 2003; einhard, 1990, 1992a; Sianto et al., 2009). Indeed, the majority of arasite species found in prehistoric New World site have animal rigins (Gonç alves et al., 2003; Reinhard, 1990). Humans intruded nto the endemic life cycles of a variety of parasites. By eating he parasites’ normal intermediate hosts, whether invertebrates r vertebrates, people became infected. Thus, prehistoric Ameri- an parasitism was defined by dietary habits from the Paleo-Indian nd Archaic periods onward.

A case reported by Sianto et al. (2005) showed unusual eggs f a parasite identified as Echinostoma sp., along with hookworm ggs in a human coprolite from 1200 years ago in Brazil. In this rea, humans can be involved in the Echinostoma sp. life cycle if hey ingest raw mollusks, the invertebrate host. In Asian countries oday Echinostoma sp. is an important public health problem, but he infection had never been reported in the Americas either in

odern or ancient times when Sianto and her colleagues published heir work. Carefully distinguishing true from false parasitism, the uthors argued for true parasitism due to the large number of eggs ecovered from the coprolite and the nature of the parasite life ycle, which implies intermediate host ingestion, and the pres- nce of adult worms in the human intestine passing eggs. Thus, the arasitological record anticipates possible cases of echinostomiasis mong current traditional groups in South America. Subsequently,

different Echinostoma species was found in the Cueva de los Muer- os Chiquitos.

Moniliformis clarki is a classic case of an animal parasite that as recovered from human coprolites in Utah (USA) 10,000 years

go (Fry, 1977; Moore et al., 1969) on into Formative times. It is thorny-headed worm, or acanthocephalan, related to the genus oniliformis moniliformis that can cause significant discomfort in

nfected humans. In the Great Basin region it was a consistent par- site of Archaic hunter-gatherers throughout prehistory (Reinhard, 990). Ingestion of insects, probably camel crickets, is implied in its ransmission. Fry (1977) encountered the species in Formative sites n Glen Canyon, Utah. Moore et al. (1969) discussed the possibil- ty of transmission by ingesting arthropods, linking true parasitism n humans to the evolutionary history of the parasite. Reinhard 1990) reviewed the many finds of acanthocephlans in Oregon, tah and Colorado and concluded that these were true infections.

nother thorny-headed worm genus, Macracanthorhynchus was ecently reported (Fugassa et al., 2011; Jiménez et al., 2012). In his case, humans adapted by consuming a natural anthelmintic, agebrush tea (Artemisia sp.) (Fugassa et al., 2011; Reinhard et al.,

f Paleopathology 3 (2013) 150– 157

2012). This discovery is from agricultural Ancestral Pueblo copro- lites. Habitual ingestion of raw or poorly cooked insects contributed to what seems to have been a common infection by thorny-headed worms from hunter–gatherer to Formative agricultural times (Fry, 1977; Fugassa et al., 2011; Moore et al., 1969). Most importantly, thorny-headed worm infection was present on both sides of the agricultural revolution in the region and indicates that the first paleoepidemiologic transition did not affect infection with acan- thocephalan species. This is because insects were an abundant food source for Paleo-Indian, Archaic, and Formative cultures in North America.

The reason for the continuation of zoonotic infection across the transition relates to the fact that people found small animals to be good food sources both before and after agriculture. The reliance on small animals for food in the Americas is borne out by zooarchaeological analysis of coprolites (Reinhard, 1992b, 2008; Reinhard et al., 2007). Ancient diets of the Southwest consistently included small vertebrates (Reinhard et al., 2007). Over 80% of prea- griculture coprolites and almost 50% of coprolites from agricultural sites contained small animal bones. Eating small animals, no doubt incompletely cooked, expanded the diversity of parasitism in two ways; exposure to parasites by direct ingestion of the vertebrate host and exposure of humans to vectors of the parasite. The lat- ter case has been documented for hunter–gatherers of Texas who selectively hunted wood rats and ate them in an incompletely cooked state (Reinhard et al., 2003, 2007). This exposed them to mucocutaneous transmission of the trypanosomes that cause Cha- gas disease by eating infected animals. Also, by reducing the normal vertebrate host number for the trypanosomes, the triatomine bug vectors increasingly infested human cave habitations and expose humans to the typical life cycle of trypanosomes (Reinhard et al., 2003). Thus, a New World exploitation of rodents resulted in new paleoepidemiological complications of Chagas disease (Reinhard et al., 2003).

In South America, Chagas disease can be characterized as zoono- sis due to animal domestication, including dogs and guinea pigs. A very ancient association of humans with animal parasites is exem- plified by Chagas disease. Paleoparasitological data showed that the protozoan Trypanosoma cruzi infected South American popu- lations by 9000 years ago, first recorded in Chile’s Atacama desert (Aufderheide et al., 2004). Infection was also present among inha- bitants of the Brazilian lowlands 7000 years ago (Fernandes et al., 2008; Lima et al., 2008), changing the paradigm that hypothesized an origin in the Bolivian highlands (Araújo et al., 2009; Ferreira et al., 2011).

Food availability affected zoonotic parasite prevalence over time. An example of this is the prevalence variation of fish tapeworm infection exhibited among mummies from different Chinchorro cemeteries in Arica, Chile. Diphyllobothrium pacificum is a parasite with a curious history. Jean Baer first described the parasite in living Peruvians, highlighting morphological distinc- tions with another species, Diphyllobothrium latum (Baer, 1969). D. latum, infects humans in the northern hemisphere and has a life cycle involving terrestrial mammals, freshwater crustaceans and fish (Baer, 1969). The life cycle of D. pacificum may include marine crustaceans, fish and sea mammals, while also infecting humans. Baer described cases from Peru, characterized by abdom- inal discomfort, diarrhea, and general weakness, and the finding of parasite proglottids (segmented parts of the parasite’s body) in feces. Baer hypothesized that ancient inhabitants were also infected by D. pacificum. He was unaware that Callen and Cameron (1960) had discovered Diphyllobothrium eggs in ancient copro-

lites from coastal Peru, ironically proving Baer’s hypothesis correct even before Baer developed the hypothesis. Subsequent research showed that D. pacificum was the most common parasite among prehistoric cultures in Peru and Chile at all time periods and across

K.J. Reinhard et al. / International Journal of Paleopathology 3 (2013) 150– 157 153

F hile. m percul

b t 1 a b p e p S fi p o p t i m i 6 e v p

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ig. 1. Diphyllobothrium pacificum from human remains, Chinchorro mummies, C icroscopy showing eggshell layout and (B) bright field microscopy showing the o

oth paleoepidemiologic transitions as the infection is common oday (Araújo et al., 1983; Ferreira et al., 1984; Patrucco et al., 983). Reinhard and Urban (2003) discussed aspects of the infection mong Chinchorro people from the extreme north of Chile dated etween 4000 and 5000 years ago. These findings illustrate the ersistence of food habits from prehistory to today (Fig. 1). How- ver, Arriaza et al. (2010) demonstrated a relationship between revalence of D. pacificum infections in the past and ENSO (El-Niño outhern Oscillations) events, which influence the abundance of sh as intermediate host species, alternating with other helminth arasitoses. They noticed that one cemetery exhibited a high level f parasitism while another had none. They were able to relate the revalence variation to El Niño or ENSO phenomena. Ocean water emperature variation alternatively opened or closed coastal fish- ng waters to the fish species that host the tapeworm. The use of

arine food resources in relationship to climate fluctuation thus nfluenced D. pacificum prevalence from Chinchorro times, nearly 000 years ago. ENSO phenomena influence D. pacificum prevalence ven to the present day. This shows that in some regions, climatic ariation was at least as important as behavior in defining infection revalence.

Prehistoric parasitism in the Americas challenges researchers ith the sheer diversity of extraordinary parasites in the archaeo-

ogical record. Some parasites are quite widespread due to common uman activities. For example, New World agriculture and food torage patterns further promoted zoonotic parasites by establish- ng environments for food pests and their parasites. Hymenolepidid apeworms parasitized people from the Southwest USA (Reinhard, 992b), Mexico (Jiménez et al., 2012) and the Andes (Santoro et al., 003). These tapeworms commonly infect rodents as the definitive osts and grain beetles as intermediate hosts. By ingesting grain ith grain beetles, humans became infected as well (Reinhard,

008). The analysis of zoonotic parasites is confounded by dietary

atterns that resulted in the ingestion of animal parasite eggs. hus, ancient people’s coprolites and intestines may contain eggs rom two distinctive sources: adult parasites in the intestines and ood sources that simply pass through the digestive system. Pale- parasitological studies from Patagonia (Fugassa and Beltrame, 009), the Arizona desert (Reinhard, 1990, 1992a) and the semi- rid regions of Brazil (Sianto et al., 2009) report animal parasite ggs in human coprolites. This suggests one of two alternative

xplanations: true parasitism with established infections or false arasitism whereby helminth eggs pass through the intestinal tract ithout infecting the human host (Sianto et al., 2005). Whether

rue infections or false infections, the presence of the eggs shows

These mummies date between 4000 and 5000 years ago. (A) Scanning electron um (arrow).

that humans interacted with parasite life cycles of animals, there- fore making humans susceptible to infection. This is especially evident in Patagonia through the work of Martin Fugassa and his colleagues (Fugassa and Barberena, 2006; Fugassa et al., 2008a,b, 2009, 2010) who frequently find parasites of animals in human coprolites (Fugassa and Beltrame, 2009; Fugassa et al., 2010). Some regions of the Americas today produce clinically bizarre parasites. For example, a recent case of Calodium hepaticum eggs passing through in feces was recorded in a woman living in the Amazon region (Costa et al., 2009). Her daughter gave her a carcass of a tapir that she ate everyday for more than a week. At the time of coproscopy, C. hepaticum eggs were found, initially in great num- bers, declining a few days after the end of animal consumption, and finally disappearing. This phenomenon has a documented prehis- toric counterpart. Fugassa et al. (2010) found C. hepaticum eggs in human and animal coprolites from Patagonian archaeological sites, indicating close contact between this parasite and humans during the Pleistocene–Holocene transition (13,000–9000 years ago), thus demonstrating a long history of false parasitism.

Changes in diet and parasitism can result from imperial expan- sion as shown by archaeological recovery of both parasites and food remains (Vinton et al., 2009; Santoro et al., 2003) for the Chilean Pacific coast. Vinton and her colleagues analyzed Inka Late Period (AD 1400–1532) and the pre-Inka Late Intermediate Period (AD 1100–1400) coprolites. In the Late Intermediate Period, the study area was occupied by farmers dispersed along the valley in very small communities. The Inka reorganized the population into large population centers. Santoro et al. (2003) noted an increase in D. pacificum and pinworm in the Late Inka Period. Vinton et al. (2009) noted a decline in maize during the Inka Period. This suggests that as maize became less abundant as a key food source, the people turned to fishing as an alternative dietary resource. This case exemplifies how different subsistence strategies can be associated with distinc- tive parasitological profiles as suggested by previous researchers (Ferreira et al., 1989, 1985).

4. Diet and parasitism in the Old World

Animal domestication established zoonotic parasites in the Old World. Le Bailly’s (2005) study of parasite infections from the Neolithic period to the Middle Ages in Europe revealed changes

in foods coinciding with climate conditions. Dietary changes were documented through pathoecology, which indicated shifting pat- terns of food choice. As a consequence, new parasites begin to infect humans. Zoonotic parasites were derived both from eating infected

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nimals, and also by eating plant foods contaminated with inter- ediate stages of domestic animal parasites. Gonç alves et al. (2003) summarize the distribution of the lancet

iver fluke, Dicrocoelium dendriticum in human remains. This par- site is associated with a variety of domestic animals including heep, pigs and cattle. The oldest dates from Switzerland and France re about 5000 years ago. Later, from the Roman age onward, they re found in Austria and England. These cannot be said to be true nfections. Human false infection is a consequence of eating raw or ndercooked animal liver. Eggs pass through the human digestive ract unchanged. Humans can become infected with adult worms nly If they eat ants infected by the larval stage of the parasite. Le ailly and Bouchet (2010) summarize the history of infection with . dendriticum, that is passed by ingestion of infected ants. This as a relatively common trematode parasite of Old World people,

ut whether true infection occurred is unknown. The best case for true infection is the Iron Age Roman mummy, Zweeloo Woman Searcey et al., this volume). In Zweeloo Woman, eggs were found n the liver. Without a doubt, Zweeloo Woman was truly infected

ith lancet liver flukes (see also Searcey et al., this volume). Fasciola hepatica, the sheep liver fluke, was a common trematode

n ancient humans. Humans become infected by eating vegetation ontaminated with infective cercaria. Gonç alves et al. (2003) sum- arize the archaeological distribution of this species. It is the most

ommon trematode parasite in north central Europe from 5600 ears ago onward to the Medieval period. Like the lancet liver fluke, . hepatica is a parasite associated with animal domestication after he first paleoepidemiologic transition. For archaeological context, ouchet et al. (2003a) described parasite transmission, which they ssociate with watercress intake. Although able to infect humans, his parasite is primarily a parasite of herbivores, and it is always ifficult to discriminate a human infection from an animal infection, specially when the eggs are found in archaeological sediments uch as trash midden samples where animal feces may have been eposited. However, in latrine, mummy, or burial contexts, the find f eggs signals human infection (Dittmar and Teegen, 2003).

Food practices in Europe after the first paleoepidemiologic ransition resulted in zoonotic infections. But compared to the mericas, the diversity of parasites was low. This is probably due

o the fact that after the first paleoepidemiologic transition, Euro- eans focused on a limited spectrum of domesticated animals and herefore were not exposed to a wide variety of terrestrial animal arasites. In contrast, New World cultures were dependent on a reater variety of wild vertebrates and invertebrates both before nd after the first paleoepidemiologic transition.

Research in Korea and Japan show a great diversity of para- ites derived from food animals. From Japan, Matsui et al. (2003) eported Yokogawa flukes (Metagonimus yokogawa), liver flukes Clonorchis sinensis), the beef or pork tapeworms (Taenia spp.), fish apeworm (Diphyllobothrium sp.), and the lung fluke (Paragonimus p.). From Korea, a variety of researchers reported M. yokogawai, aragonimus westermani, Gymnophalloides seoi and Taenia eggs f in ummies (Lee et al., 2011; Seo et al., 2009; Shin et al., 2009a,b,

011). Thus, food related parasites were very well established in sia. These came from a diet that included substantial amounts of sh, crustacea, and meat. There is no doubt that much more exciting ata will emerge from continued work in Korea and Japan.

. Contrasts in paleoepidemiologic transitions, Old World nd New

Parasitological data from Europe have been reviewed by several uthors, including Bouchet et al. (2003b), Le Bailly and Bouchet 2010), and Reinhard and Pucu (2013). Reviews have covered uman remains dating from before the Neolithic period to the

f Paleopathology 3 (2013) 150– 157

nineteenth century. For European cultures, the results indicate the existence of distinct first and second paleoepidemiological tran- sitions with the emergence of parasitic disease and progressive exacerbation of infections from the early Neolithic until the Indus- trial Revolution. Bouchet et al. (2003b) and Le Bailly and Bouchet (2010) argue that zoonotic parasitism may have ebbed and flowed over the centuries in Europe associated with periods of famine, when people turned to a broader range of dietary resources. During such times, humans were infected with parasites when ingesting infected raw fish, for example. Other parasites of animals, able to adapt to the human host, were also found, suggesting close con- tact between the species and the use of natural resources. There is strong evidence that the first epidemiologic transition, associated with increased prevalence of infectious diseases and poor sanita- tion, occurred in Europe (Barrett et al., 1998; Le Bailly and Bouchet, 2010). This is characterized in the parasitological record by ubiq- uitous infections with geohelminths (soil transmitted helminths) (Reinhard and Pucu, 2013). By contrast, zoonotic infections from wild reservoirs become less common as zoonotic infections from domesticated reservoirs increases (Bouchet et al., 2003b; Le Bailly and Bouchet, 2010).

For the Americas, the first paleoepidemiologic transition is not well defined archaeologically. Especially for zoonotic parasites, there seems to be no or very little change in diet-related para- sitism. Prehistoric Native Americans subsisted on a great variety of terrestrial and aquatic vertebrates and invertebrates, with each prey species having its own array of parasites potentially infec- tive to humans. This is the source of diversity of helminths in the prehistoric New World. But as far as the first transition, the only distinct parasitic change after agriculture is an enigmatic epidemic of pinworm infection among Ancestral Puebloans (Reinhard, 1988) and frontier Mesoamericans (Jiménez et al., 2012), as well as Inkas (Santoro et al., 2003).

In the New World, ubiquitous geohelminth infections did not become established until Colonial times (Leles, 2010; Leles et al., 2010). Indeed, the first and second paleoepidemiological transi- tions, as witnessed by parasitology, occurred nearly simultaneously in the Americas (Reinhard and Pucu, 2013).

6. Agriculture practices—indirect influence of subsistence on parasitism

Indirectly, agriculture had a strong impact on the state of pathoecology. In some areas, parasites overwhelmed Homo’s capac- ity for cultural evolution. This was true after the establishment of urban life and multi-tiered complex societies. In such societies, parasites proliferated among the lowest tiers of society and resulted in a vast reservoir of infection for all levels of the society. This is best seen in the European archaeological record where food-borne parasites were replaced as a major health hazard by filth-borne and crowd parasites. This is extensively reviewed and discussed by Reinhard and Pucu (2013). They summarize the archaeologi- cal studies of parasites and found that fecal-borne parasites were ubiquitous in European societies from the Iron Age onwards. They support Jones (1985) assertion that fecal-borne parasites were the common urban background fauna by medieval times.

Helminth eggs from archaeological remains of the Old World and the New World show intriguing contrasts. Whereas great num- bers of A. lumbricoides and T. trichiura eggs have been found in most archaeological sites in Europe, these eggs are rare in the pre-Columbian Western Hemisphere (Gonç alves et al., 2003; Leles

et al., 2010; Reinhard and Pucu, 2013). A variety of conditions in the ancient Americas hampered these parasites’ ability to infect new susceptible hosts (Reinhard, 1988, 1990; Reinhard and Pucu, 2013). Finding explanations for this difference is a focus of current

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nd future research at the Fundaç ão Oswaldo Cruz and the Uni- ersity of Nebraska-Lincoln. These explanations include, for Native merica, an abundance of medicinal plants, more open habita-

ions, less population concentration, absence of the humoral theory f medicine, fecal avoidance, and other behaviors. In Europe, by ontrast, after the first paleoepidemiologic transition people were ggregated in communities without sanitary facilities and in close ontact with domesticated animals. Importantly, it is likely that the umoral theory limited the response to disease. The failure of Euro- ean medicine to recognize the contagion theory until remarkably

ate in history made populations there intellectually susceptible o infection as reviewed by Reinhard and Pucu (2013). This fail- re encouraged the use of human feces as agricultural fertilizer. nder such conditions, fecal-borne parasites proliferated, including . lumbricoides and T. trichiura.

Agriculture, especially irrigation cultivation, increased the risk f vector borne disease. A full discussion of this topic is beyond he page limitations of this article. But it is worth highlighting n example such as malaria in Italy. During Renaissance times alaria, caused by Plasmodium falciparum, was prevalent in south-

rn Europe. P. falciparum is a protozoan parasite originally from frica, which infected people in southern Italy during this period, nd it has been implicated in the deaths of members of the Medici amily (Fornaciari et al., 2010). Malaria epidemics and severity were irectly linked to agricultural cycles, environmental cycles and the istribution of pernicious malaria caused by P. falciparum versus he milder form caused by Plasmodium vivax (Snowden, 2006).

. Summary

Paleoparasitological studies indicate that people living in the mericas have been infected by a variety of parasites over time. ome parasites originated in an environment occupied or trans- ormed by humans, while others were acquired from wild or omesticated animals. Additional parasite species undoubtedly ere introduced directly by humans during early migrations from frica via Asia. These parasite species had their origin in African uman ancestors, and are called heirloom parasites. By recovering eirloom parasites in archaeological sites it was possible to trace rehistoric human migrations (Araújo et al., 2008).

Therefore, pre-Columbian New World populations were nfected by parasites that were introduced into the Americas y ancient prehistoric migrations. Other parasites were acquired rom the environment, especially by ingesting raw or under- ooked meat of wild animals. With the European invasions that egan in the late 18th century, however, an enormous change ccurred. Unknown parasites, especially viruses and bacteria, were ntroduced, causing a tremendous impact upon Native Americans Desowitz, 1997/1998; Ujvari, 2008). Some of these included para- ites associated with European domestic animals. The lancet fluke, heep liver flukes, pork tapeworm, beef tapeworm and other para- ites arrived at this time. In some regions, people were obliged o live in settled villages, with increased population density. On he Brazilian coast, for example, prior to the conquest, the Tupi ccupied a vast territory (Noelli, 2008). With the Portuguese col- nization, the Indians either were assimilated or escaped into the nterior. As the Portuguese reproduced architectural models of Old

orld Middle Ages on the Brazilian coast, aggregating people with- ut sanitary measures (Fisher et al., 2007), intestinal helminth arasite loads increased (Edler, 2011).

Another instructive example is offered by Chagas disease.

lthough infection by T. cruzi was common among North and South merican prehistoric groups, prevalence rates varied according to

ifeways. Caves and rock-shelters, for example, were ideal eco- ogical niches for triatomines (triatomines are vectors of Chagas

f Paleopathology 3 (2013) 150– 157 155

disease, transmitting T. cruzi after blood sucking) and small mam- mal reservoirs. Therefore, pre-Columbian people who used caves and rock-shelters were more commonly exposed to T. cruzi trans- mission than those living in villages. However, following European immigrations, Chagas disease transmission spread throughout the continent when Europeans, especially Portuguese, introduced mud and daub dwellings, to which Triatoma infestans, and other vector species, adapted well (Araújo et al., 2009; Ferreira et al., 2011). Cha- gas disease patterns thus changed and increased during colonial times, and rural intra-household transmission became character- istic of the disease.

According to Perrin and Herbreteau (2010), human parasites are most abundant in the Palearctic realm, while the Neotropics and Australia are parasite poor. They explain low diversity in par- asite species in the Neotropics and Australia because these regions were colonized relatively recently, compared with other areas. The conclusions proposed by Perrin and Herbreteau (2010) agree with paleoparasitological data (Dittmar, 2010). Human helminth para- sites acquired evolutionarily in pre-Homo times were introduced into the Americas with the first migrants, both crossing the Bering Land Bridge or following alternative routes (Montenegro et al., 2006; Araújo et al., 2008). The first humans in the Americas occu- pied new environments, in which they were exposed to other parasites. However, when Europeans arrived, epidemiological pat- terns changed, prevalence rates of old diseases were altered, and the burden of disease increased.

With specific regard to food and parasitism, the reduced reliance on domesticated animals and increased reliance on wild animals in the prehistoric Americas produced a distinct pathoecological picture relative to Europe. In the Americas, a diversity of flukes, tapeworms, acanthocephalans and nematodes derived from wild animals make up the majority of species that infected humans. In contrast, flukes and tapeworms of domestic animals dominate European pathoecology after the first paleoepidemiologic transi- tion. However, these parasites are far less common then fecal borne parasites in Europe.

Acknowledgements

Supported by the Brazilian Agencies: CNPq (Conselho Nacional de Desenvolvimento Científico e Tecnológico); PRONEX-FAPERJ (Programa de Apoio a Núcleos de Excelência e Fundaç ão Carlos Cha- gas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro), and CAPES-CNPq (Ciência sem Fronteiras).

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dx.doi.org/10.1371/journal.pone.0008069

• Food, parasites, and epidemiological transitions: A broad perspective
• 1 Introduction
• 2 Parasite migration to the New World
• 3 Diet and parasitism in the New World
• 4 Diet and parasitism in the Old World
• 5 Contrasts in paleoepidemiologic transitions, Old World and New
• 6 Agriculture practices—indirect influence of subsistence on parasitism
• 7 Summary
• Acknowledgements
• References

Epidemiological-transition-in-Latin-America--The-case-of-C_1995_Public-Healt.pdf

Public Health (I 995), 109,431-442 © 1995 The Society of Public Health. All rights reserved 0033-3506/95 $12.00 ~

Epidemiological transition in Latin America" the case of Chile

C Albala and F Vio Institute of Nutrition and Food Technology (INTA), University of Chile

To describe Chile's stage of epidemiological transition, a descriptive study of the changes to the demographic and economic profile of this country during the last 20 years is presented. The decline in the total fertility rate from 3.4 in 1970 to 2.6 in 1992 and the important decrease in general and infant mortality rate has led to an increase of life expectancy of 8 years for men and 9 years for women. This has resulted in changes to the age structure and causes of mortality and morbidity of the population. A reduction of 82% in the proportion of deaths among children < 1 year and a 73% increase of mortality amongst those 65 years and older can be observed. In line with these changes non-communicable diseases have increased from 53.7% of all deaths in 1970 to 74.9% in 1991. In the same period mortality rates from cardiovascular causes have decreased from 189.6 to 161.1 per 100000 population, whilst their relative proportion of all causes has increased from 22.3% to 29%. High prevalence of risk factors should lead to a significant increase of chronic diseases in future years. Regarding morbidity, a high incidence rate for tuberculosis persists together with an increase of infections of the digestive system and of sexually transmitted diseases. A decrease in the rates of diseases preventable by immunisation has been noted. It is concluded that, as defined by population mortality statistics, Chile is in a post-transition stage but with a persistence of some infectious diseases corresponding to a transitional stage of development.

Keywords: epidemiological transition; fertility rates; infant mortality rate; life expect- ancy; morbidity; mortality

Introduction

I n r e c e n t d e c a d e s , L a t i n A m e r i c a n c o u n t r i e s h a v e e x p e r i e n c e d i m p o r t a n t c h a n g e s in h e a l t h c o n d i t i o n s r e l a t e d t o d e m o g r a p h i c s t r u c t u r e , a n d c h a n g i n g s o c i o e c o n o m i c a n d e n v i r o n m e n t a l c o n d i t i o n s , as a r e s u l t o f i n d u s t r i a l i s a t i o n a n d u r b a n i s a t i o n . I m p r o v e - m e n t s in m e d i c a l c a r e , in a d d i t i o n t o t e c h n o l o g i c a l a d v a n c e m e n t s in t h e h e a l t h s e c t o r , h a v e a l s o b e e n s i g n i f i c a n t . D e m o g r a p h i c c h a n g e s f r o m h i g h l e v e l s t o l o w l e v e l s o f f e r t i l i t y a n d m o r t a l i t y h a v e b e e n c a l l e d d e m o g r a p h i c t r a n s i t i o n . l

D e m o g r a p h i c , s o c i o e c o n o m i c a n d e n v i r o n m e n t a l c h a n g e s p r o d u c e t r a n s f o r m a t i o n s in t h e e p i d e m i o l o g i c a l p r o f i l e in t h e s e c o u n t r i e s , in c o m b i n a t i o n w i t h a r e d u c t i o n o f c o m m u n i c a b l e d i s e a s e s a n d a p r o g r e s s i v e i n c r e a s e o f c h r o n i c d i s e a s e s a n d i n j u r i e s as c a u s e s o f m o r t a l i t y a n d m o r b i d i t y . C o e x i s t e n c e o f c o m m u n i c a b l e d i s e a s e s w i t h c h r o n i c d i s e a s e s a n d i n j u r i e s as c a u s e s o f d e a t h is t h e m a i n c h a r a c t e r i s t i c o f t h e e p i d e m i o - l o g i c a l t r a n s i t i o n . 2 A n a l y s i s o f d e m o g r a p h i c a n d e p i d e m i o l o g i c a l c h a n g e s is c r u c i a l t o d e t e r m i n e h e a l t h p o l i c y in L a t i n A m e r i c a n c o u n t r i e s a d a p t i n g t h e h e a l t h s y s t e m t o n e w e p i d e m i o l o g i c a l s i t u a t i o n s . 3

T h e d e c r e a s i n g f e r t i l i t y a n d r e d u c t i o n o f i n f a n t m o r t a l i t y r a t e ( I M R ) in m o s t L a t i n A m e r i c a n c o u n t r i e s h a s p r o d u c e d a n i n c r e a s e in life e x p e c t a n c y as w e l l as a n i n c r e a s e in t h e p r o p o r t i o n o f t h e p o p u l a t i o n o v e r 60 y e a r s o l d , A s a c o n s e q u e n c e , t h e r e is a n i n e v i t a b l e shift o f d e a t h s f r o m c h i l d r e n t o o l d e r a g e s , p r o d u c i n g d i f f e r e n t c a u s e s o f

Correspondence: Dr Cecilia Albala, INTA, Universidad de Chile, Casilla 138-11, Santiago, Chile Accepted for publication: 30 June 1995

• C Albala and F Vio Epidemiologlcal transition in Chile 432

death such as cardiovascular diseases, cancer, injuries and congenital and metabolic conditions. 4

Chile is a country located in the south-west of South America, bordering the Pacific Ocean with m o r e than 4000 km of coastline and a mainland area of 756 626 km 2. The political administrative structure of the country is divided into 13 regions and 335 districts. The total population of Chile in 1992 was estimated to be 13559441 inhabitants. Some 11% of the population is aged between 0 and 4 years and a further 18.3% is aged less than 15; 49.0% are aged between 15 and 44 years and 15.1% of the population is aged between 44 and 65 years; 6.6% are aged over 65 years. Males account for 49.45% of the population.

A high proportion of the population (83.5%) lives in urban areas. The Metro- politan Region, where Santiago--capital of the country--is located, accommodates 40.5% of the population, followed by Bio Bio Region in the south of the country with 12.4% and Valparaiso, sited in the central coastal region, with 10.4% 5

Chile has an open m a r k e t economy equivalent to that of middle-income economies. A f t e r two severe recessions in 1975-76 and 1982-83, the trend seen in Chile's gross domestic product (GDP) per capita has been characterised by a notable recovery in real terms. Chile has had continuous economic growth in the past few years with a growth of the G D P at an average rate of 6.4% per annum during 1990-94. However, income distribution is skewed: the share of total income is 56.1% for the highest 20% of the population, and only 4.6% for the lowest 20%.

Chile's social development is impressive. While a GNP per capita of US$2726 in 1992 places Chile among middle-income countries, its social indicators closely resemble those of an industrialised country. Public investment since the 1920s in health and nutrition, as well as basic education and potable water and sanitation, have had a significant impact in reducing the incidence of communicable diseases and malnutrition, and have played a decisive role in overall health improvements. 6

Chile has had a huge decrease in infant mortality rate (IMR) caused by a reduction in diarrhoeal and respiratory diseases in the last 20 years, more than double that of most Latin A m e r i c a n countries. 7 This decrease has produced a rapid change in the epidemiological profile of the country increasing non-communicable diseases from 53.7% of all deaths in 1970 to 75.1% in 1990.

In relation to mortality, the Chilean epidemiological pattern is close to that of developed countries, in which chronic diseases account for 70 to 80% of all deaths, s With regard to morbidity, Chile still has high rates of digestive tract infections such as hepatitis and typhoid fever, A I D S and sexually transmitted diseases together with a persistence of a high incidence of tuberculosis. 6 This particular situation is to be reviewed in the current study.

The objective of the study is to analyse the Chilean epidemiological profile in relation to the epidemiological transition stages.

Methods

This is a descriptive, population-based study, aiming to analyse mortality and morbidity trends in the last 20 years in Chile including the main demographic and socioeconomic variables.

Source o f data

Secondary data were collected from official information. For mortality analysis A n n u a l D e m o g r a p h i c Reports of the National Institute of Statistics (INE) 9-~1 and

C A/bala and F Vio Epidemiological transition in Chile Y ~

population data projections from the same institute and the United Nations Latin America Demographic Center ( C E L A D E ) 12,13 were utilised.

Information arising from the Ministry of Health, 6,~4 Pan American Health Organ- ization ( P A H O / W H O ) , 15-17 The World Bank 18 and the United Nations (UN) ~9 were also used.

433

Variables

Demographic and socioeconomic changes were analysed with the following variables: life expectancy at birth, birth rates, crude and adjusted mortality rates, proportional mortality, fertility rates, age structure of the population and population growth rate.

Socioeconomic data variables were urban and rural distribution, gross domestic product (GDP) per capita, water and sanitation, literacy and food availability.

For mortality analysis, specific causes by age-groups and causes were defined by the International Classification of Diseases (ICS). Until 1981 causes of death categorised by age-group were published according to list A of the International Classification o f Diseases, eighth revision (ICD-VIII), 2° and since 1982 according to the detailed list o f 999 causes of the International Classification of Diseases ninth revision (ICD-IX). 21 For this study, both revisions were made compatible.

For comparative analysis, broad causes of mortality were grouped in three categories: category 1, 'Communicable diseases and maternal and perinatal causes', included all deaths from infectious diseases listed in ICD-9, plus influenza and pneumonia, nutritional disorders and anaemia, maternal causes of death (including abortion), and perinatal causes of death; category II, 'Injuries', included ICD-9, Section XVII E; Group III, 'Noncommunicable diseases', included all other causes of death. 18

According to W H O specifications concerning the quality of the data, ill-defined causes (780-799) are also mentioned. The study of rate tendencies of ICD-IX m a j o r group of causes was developed using adjusted mortality rates for the 1982 Chilean population. With the aim to stabilise the rate comparison, biennial rates were utilised.

Specific mortality rates by groups of age and groups of causes, and incidence rates of some infectious disease, were also analysed.

R e s u l t s

Among Latin American countries, Cuba, Chile and Costa Rica have the lowest I M R . These values are almost a third of the average in Latin American countries, but this is still twice the rate of high-income economies.

Despite a relatively low G D P per capita, I M R declined in Chile from 82.2 per thousand in 1970 to 14.3 in 1992 ( - 8 2 . 2 % ) . This decrease has been double that of the decrease in all Latin American and Caribbean countries ( - 4 6 . 3 % ) , during the same period (Table 1).

Chile has suffered a progressive urbanisation process since the 1930s, with 83.5% of the population living in urban areas in 1992. Despite G D P per capita having n o t changed dramatically in the past 20 years (only in 1993 was it over US$3000), literacy, water and sanitation coverage in urban areas and health expenditure per capita having been increasing steadily (Table 2).

In the last 20 years, the general mortality rate has decreased, but natality and total fertility rates have been maintained after a decrease in the 1970s. As a consequence, there has been an extension of life expectancy at birth, an increase of 2.6 years in the median age with stable population growth in that period (Table 3). However, population structure by groups of age has changed drastically: 39.2% of the total

C AIba[a and F Vio Epidemiological transition in Chile

434

Table 1 I n f a n t mortality rate a n d gross domestic p r o d u c t per capita in selected L a t i n - A m e r i c a n a n d developed countries 1970-91

Countries I M R * % Change G D P p . c. ** (US$) 1991

1970 1991

Chile 82.2 14.6 - 8 2 . 2 2 160 C u b a 38.7 12.0 - 6 9 . 0 n o t available Costa Rica 62.0 14.0 - 7 7 . 4 1 850 V e n e z u e l a 53 34 - 35.8 2 730 Nicaragua 106 56 - 4 7 . 2 460 Haiti 141 95 - 3 2 . 6 370 Latin A m e r i c a a n d C a r i b b e a n 82 44 - 4 6 . 3 2 390 High-income e c o n o m i e s 20 8 - 6 0 . 0 21 050

Notes: *IMR = infant mortality rate. **GDP p.c. = gross domestic product per capita. Sources: World Bank, World Development Report 1993. Washington, DC. WHO/PAHO. Health Conditions in the Americas, 1990. PAHO Scientific Publication No. 524. Republic of Chile, Ministry of Health, Health Situation in Chile 1994, ed. Antartica SA, 1994.

T a b l e 2 Chile: socioeconomic changes 1970, 1982, 1992 ( % )

Changes 1970 1982 1992 % Change 1970-92

U r b a n p o p u l a t i o n 75.1 82.2 83.5 + 11.2 U r b a n dwellings with 58 87 95.2 +64.1

drinking water U r b a n dwellings with sewers 35 62 84.4 +141.1 A d u l t illiteracy 11.0 8.2 5.7 - 4 8 . 2 G D P p.c.* (US$) 2230 2148 3020** + 3 5 . 4 H e a l t h e x p e n d i t u r e p e r 52 76 116 +123.1

capita (US$)

Notes': *GDP p.c. Gross domestic product per capita. *'1993 Sources: INE. Demographic Annual Report 1992. INE. Chilean Women. Radiograph in Numbers, 1994. INE. Census 1992. Population and Housing, General Results, 1992. World Bank. Worm Development Report 1993. Washington, DC. WHO/PAHO. Health Conditions in the Americas, 1990 edn. PAHO Scientific Publication No. 524. WHO/PAHO. Health Conditions in the Americas, 1985-86 edn. PAHO Scientific Publication No. 500.

T a b l e 3 Chile: c o m p o n e n t s of demographic changes, 1970, 1982, 1992

Year Birth I M R * Total fertility G M R * * Life expectancy Median % P o p rate rate o f age growth***

1970 26.4 82.2 3.4 8.7 60,5 66.8 27.5 1,8 1982 23.8 23.6 2.8 6.1 67.8 74.8 29.3 1.8 1992 21.6 14.3 2.6 5.4 68.7 75.8 30.1 1.6

Notes: *IMR ~ infant mortality rate. **GMR -- general mortality rate. ***%pop growth = %population growth. Source: tNE. Demographic Annual Report~, 1970, 1982, 1992.

C Albala and F Vio Epiderniological transition in Chile

p o p u l a t i o n w a s in t h e a g e - g r o u p 0 - 1 4 y e a r s in 1970, d e c r e a s i n g t o 2 9 . 4 % in 1992. I n t h e a g e - g r o u p 65 y e a r s a n d o l d e r , t h e p o p u l a t i o n i n c r e a s e d f r o m 5 % in 1970 t o 6 . 6 % in 1992. T h e p r o p o r t i o n o f d e a t h s b y g r o u p s o f a g e h a s a l s o c h a n g e d d r a m a t i c a l l y : 3 1 . 8 % o f all d e a t h s o c c u r r e d in t h e a g e g r o u p 0 - 1 4 y e a r s in 1970, d e c r e a s i n g to o n l y 7 . 8 % in 1992. O n t h e o t h e r h a n d , 3 4 . 6 % o f all d e a t h s o c c u r r e d a t o v e r 65 y e a r s in 1970, i n c r e a s i n g t o 6 0 % o f all d e a t h s in 1992 ( T a b l e 4).

T h e p e r i n a t a l m o r a l i t y r a t e ( P N M R ) h a s d e c r e a s e d s t e a d i l y s i n c e 1970, t o u n d e r 5 p e r 1000 live b i r t h s ( M R < 5). T h e r a t i o M R < 5 / P N M R h a d n o w d e c r e a s e d t o b e c l o s e t o 1, s i m i l a r t o t h a t o b s e r v e d in d e v e l o p e d c o u n t r i e s . M a t e r n a l m o r t a l i t y r a t e ( M M R ) h a s d e c r e a s e d r a p i d l y , s i m u l t a n e o u s l y w i t h i n c r e a s e d i n s t i t u t i o n a l i s e d d e l i v e r i e s , t o a l m o s t 100% in 1992. P u b l i c H e a l t h e x p e n d i t u r e as a p e r c e n t a g e o f G D P h a s b e e n m a i n t a i n e d o v e r t i m e . T o t a l h e a l t h e x p e n d i t u r e a n d h e a l t h e x p e n d i - t u r e p e r c a p i t a h a v e i n c r e a s e d in t h e s a m e p e r i o d ( T a b l e 5).

P e r i n a t a l d i s e a s e s h a v e c o n t i n u e d t o b e t h e m a i n c a u s e o f d e a t h in t h e g r o u p o f c h i l d r e n u n d e r 1 y e a r old. H o w e v e r , d i a r r h o e a l d i s e a s e s a n d n u t r i t i o n d e f i c i e n c i e s h a v e b e e n significantly r e d u c e d as c a u s e s o f d e a t h d u r i n g t h e last 20 y e a r s . C o n g e n i t a l c o n d i t i o n s m o v e d f r o m fifth t o s e c o n d p l a c e as c a u s e o f d e a t h in i n f a n t s d u r i n g t h e s a m e p e r i o d . R e g a r d i n g I M R , C h i l e w a s in a p r e - t r a n s i t i o n a l s t a g e in 1970 w i t h a h i g h p r o p o r t i o n o f d e a t h s c a u s e d b y d i a r r h o e a l a n d r e s p i r a t o r y d i s e a s e s ; as a c o n s e q u e n c e ,

Table 4 Proportion of population and deaths by groups of age, Chile, 1970, 1982, 1992

435

Groups o f % Population % Change % Deaths % Change age (years) 1970-92 1970-92

1970 1982 1992 1970 1982 1992

0 - 1 4 39.2 32.2 29.4 --25.0 31.8 13.4 7.8 --75.5 15-64 55.8 62.0 64.0 +14.7 33.6 34.4 32.2 --4.2 65+ 5.0 5.8 6.6 +32.0 34.6 57.2 60.0 +73.4

Sources: INE.Demographic Annual Report 1992 INE. Chilean Women. Radiograph in Numbers 1994.

Table 5 Economic and biomedical indicators Chile, 1970, 1980, 1992

Indicators 1970 1980 1992

IMR* 82.2 32.0 14.3 PNMR** 38.3 22.0 12.4 MR < 5/1000 LB*** 92.9 38.0 18.1 M R < 5/PNMR 2.43 1.73 1.46 MMR'~ 1.72 0.40 0.31 % Institutionalised births 85.1 96.5 99.2 Public health exp. ( % G D P ) ; 2.8 2.1 2.5 Total health exp. (% G D P ) 3.3 3.4 5.5 Health exp.US$ (1991) per capita 52 76 116

Notes: *IMR = infant mortality rate. **PNMR = perinatal mortality rate. ***MR < 5/1000 LB = mortality rate under 5 years old per 1000 live births. )MMR = maternal mortality rate. ~GDP = gross domestic producl. Sources: INE. Demographic Annual Reports', 1970, t982, 1992. World Bank, World Development Report 1993. Washington, DC.

C Albala and F Vio Epidemiological transition in Chile

436

t h e p a t t e r n o f m o r t a l i t y in i n f a n t s h a s c h a n g e d t o a p o s t - t r a n s i t i o n s t a g e in t h e last 20 y e a r s ( T a b l e 6).

I n t h e p a t t e r n o f m o r t a l i t y f o r all a g e s in 1970, i n f e c t i o u s a n d p a r a s i t i c d i s e a s e s w e r e r e s p o n s i b l e f o r 11% o f all d e a t h s ; in 1992 this p r o p o r t i o n h a d d e c r e a s e d t o 2 . 9 % . T h e s a m e c h a n g e w a s s e e n in p e r i n a t a l d i s e a s e s : f r o m 5 % o f all d e a t h s in 1970 to 1.9% in 1992. C a r d i o v a s c u l a r d i s e a s e s a n d m a l i g n a n t t u m o u r s n o w a c c o u n t f o r t h e h i g h e s t n u m b e r s o f d e a t h s a n d b o t h h a v e i n c r e a s e d as a p r o p o r t i o n o f c a u s e s o f d e a t h . D e a t h f r o m i n j u r i e s h a s d e c r e a s e d f r o m 19% t o 12% in t h e last 20 y e a r s ( T a b l e 7). I n l i n e w i t h t h e s e c h a n g e s , n o n - c o m m u n i c a b l e d i s e a s e s h a v e i n c r e a s e d f r o m 5 3 . 7 % o f all d e a t h s in 1970 t o 7 5 . 1 % in 1990 ( F i g u r e 1).

I n t h e c a s e o f c a r d i o v a s c u l a r d i s e a s e s , t h e r a t e d e c r e a s e d f r o m 189.6 in 1970 t o 161.1 p e r 1 0 0 0 0 0 in 1992, w h i l e its r e l a t i v e p o s i t i o n a m o n g all c a u s e s h a s i n c r e a s e d

T a b l e 6 Main causes of deaths in the group under I year old/proportion of total deaths < 1 year; biennial means, Chile 1970-92

Causes o f death 1972-73 1975-76 1977-78 1979-80 1989-90 1991-92 (%) (%) (%) (%) (%) (%)

Perinatal 32.0 29.9 28.7 32.6 34.6 33.7 Congenital 4.5 6.2 7.6 11.2 22.9 26.5 Trauma 4, 8 2.9 3.5 4.9 14.8 25.1 Respiratory 25.7 20.5 16.8 16.3 15.7 14.6 Diarrhoea 14.2" 10.9 10.7 6.9 1.6 1.4 Nutr. def** 4.1 4.6 3.9 2.4 0.45 0.52 Tot deaths < 1 y*** 18628 14045 10332 8528 5184 4297

Notes: *Mean 1971-74. **Nutr def = nutrition deficiences. ***Total deaths under 1 year old. Source: INE.Demographic Anttual Reports, 1971-92.

Table 7 Deaths by groups of causes as a proportion of total deaths, Chile, 1970, 1982, 1992

Groups o f causes" 1970" 1982"/ 1992 ~

Cardiovascular diseases 22.3 27.6 29.0 (390-459)? (A80-88)*

Malignant tumours (140-208)9 12.0 16.8 20.0 (A45-59)*

Injuries (800-999)? 19.0 12.1 12.0 (AN138-150 + AE138-149)*

Respiratory (460-519)? (A89-96)* 17.4 8.5 11.1 Digestive (520-579)? (A97-104)* 6.9 8.6 6.3 Ill-defined (780-799)? (A137)* 4.5 8.8 5.6 Infectious and parasitic 10.9 3.8 2.9

(1-139)? (A1-44)* Perinatal causes 5.0 3.5 1.9

(760-779)? (A131-135)* All others 2.0 10.3 11.2 Total 100.0 100.0 100.0

Notes': *ICD-VIII. ?ICD-IX. Source: INE.Demographic Annual Reports', 1970, 1982, 1992.

C Albala and F Vie Epidemiological transition in Chile N~o.;~

N o n c o m m u n i c a b l e diseases

53.7%

F i g u r e 1

1970 Injuries

1 0 , 3 % /

1990

Injuries 12.2% Communicable

Communicable diseases

36.0%

i / d seases 12.7%

/ Noncommunicable

diseases 75.1%

C h a n g e s in p r o p o r t i o n o f m a j o r groups o f causes of d e a t h in C h i l e , 1970 a n d 1990

437

from 23.2% to 29.0% in the same period. The rate from c o r o n a r y h e a r t diseases has decreased, from 97.2 in 1970 to 80.6 per 100000 in 1992; however, for cerebrovascu- lar diseases the rate has b e e n maintained in the last 20 years (Figure 2). Total cardiovascular and ischaemic deaths in the 3 5 - 7 4 year age range have also decreased.

Malignant tumours have increased f r o m 8% in 1970 to 20% in 1992 of the total causes o f death. T h e age-adjusted mortality rate from cancer r e a c h e d 102.6 per 100000 inhabitants in 1990. Mortality rates for 15 years and over are shown in Table 8. Stomach cancer is the leading cause of cancer deaths in Chile, but its prevalence has decreased by almost 50% in the last 20 years. On the o t h e r hand, gall bladder cancer has increased by 115% during the same period of time. T h e decline ( - 2 4 . 3 % ) of mortality due to cancer of the cervix could be explained by a spread of the official

F i g u r e 2

200

180

160

140 o

120 c#

100 oC

80

60

40 _ _ _ J _ _ I I I 1 _ _ ~ t970 1975 1980 1985 1990 t 992

Years

Cardiovascular diseases Coronary heart diseases Cerebrovascular diseases

Mc, r t a l i t y b y c a r d i o v a s c u l a r d i s e a s e s , c o r o n a r y h e a r t d i s e a s e s and c e r e b r o v a s c u l a r d i s e a s e s in Chile, 1970-92 ( R a t e p e r 100 000 i n h a b i t a n t s )

• C Albala and F rio Epidemiological transition in Chile 438

Table 8 Age-adjusted* mortality rates per 100000 population 15 years and over by malignant tumours, Chile 1969 and 1989 (average rates for three years, centred at shown year)

Sites" 1969 1989 % Change 1 9 6 9 - 8 9

Stomach cancer (151) Gall bladder cancer (156) Cancer of respiratory organs

(161, 162) Female breast* cancer (174) Cervix uteri* cancer (180) Prostate* cancer (185)

54.74 27.48 - 4 9 . 8 6.55 14.09 +1.15

13.37 15.99 +19.6

13.53 16.17 + 19.5 25.23 19.10 - 24.3

9.54 10.78 +13.0

Note: *Standard population: Chile 1982. Rates from cancer of breast, cervix uteri and prostate, age- adjusted by sex-specific population. Source: INE.Demographic Annual Reports, 1968, 1969, 1970, 1988, 1989, 1990.

p r o g r a m m e o f f r e e P a p smears, T h e g r e a t d e c l i n e o f m o r t a l i t y f r o m m a l i g n a n t n e o p l a s m s o f s t o m a c h a n d t h e s t e e p i n c r e a s e o f c a n c e r o f t h e gall b l a d d e r a r e s h o w n in F i g u r e 3.

R e g a r d i n g i n f e c t i o u s diseases, despite t h e d e c r e a s e in m o r t a l i t y a n d m o r b i d i t y , t h e r e is still a p e r s i s t e n t l y high i n c i d e n c e r a t e o f t u b e r c u l o s i s (49.3 p e r 1 0 0 0 0 0 in 1990), g o n o r r h o e a a n d syphilis (92.5 p e r 1 0 0 0 0 0 ), a n d h e p a t i t i s a n d t y p h o i d f e v e r (128.7 p e r 100000). D i s e a s e s p r e v e n t a b l e b y i m m u n i s a t i o n h a v e , h o w e v e r , d e c r e a s e d significantly b y b o t h m o r t a l i t y and m o r b i d i t y d u r i n g t h e last 20 y e a r s b u t m o r b i d i t y is still high ( F i g u r e 4).

16 o

o 1 4 - c ) o ~, 1 2 ~

10,-

£E 8

20_ 18

2 - - - 4 - l I ~ ~ - 1 - - - I ~ I I 1 9 6 8 1 9 7 2 t 9 7 6 1 9 8 0 1 9 8 4 1 9 9 0

Years

-O~ S t o m a c h males

- 0 - S t o m a c h females

G a l l b l a d d e r males

G a l l b l a d d e r females

Figure3 Stomach and gall bladder cancer: mortality rates per 100000 15 years and over, by sex, Chile, 1968-90

C Albala and F Vio Epidemiological transition in Chile ~

220

200

180

160 o

140

~ 1 2 0

c~ 100

80

60

40 I 79-80

II

_ 1 I 84-85 89-90 Years

- O - Tuberculosis - t - Gonorrhea and s y p h i l i s

-lg-- Hepatitis and t y p h o i d - V - I m m u n i s a t i o n preventable

Figure4 Incidence of infectious diseases in Chile: tuberculosis, gonorrhea and syphilis, hepatitis and typhoid fever, and immunisation-preventable diseases, 1980-90 (rate per 100000

inhabitants)

439

Discussion

The change that has been produced in the pattern of disease has proceeded in two steps. The first is demographic transition, when mortality from infectious diseases declines and, partly as a result, fertility also decreases. In the second, as a consequence of declining fertility and differential rates of decline among causes of death, a co-existence of chronic diseases with infectious diseases as causes of ill health is produced. This situation is called epidemiologic transition. Some countries are in the pre-transition stage with adverse environmental conditions, such as lack of potable water and sewerage services, high incidence of infectious diseases (diarrhoeal and immunopreventable diseases), malnutrition, tubercular and malarial infections. Coun- tries in the pre-transitional stage have high fertility and mortality, illiteracy and low GDP per capita. Haiti, Bolivia and some Central American countries are in this situation, and Chile was in a pre-transition stage until the 1970s, when the improve- ment of coverage in education, water and sanitation system, immunisations, family planning, better maternal care and nutrition programmes produced a rapid decline in !MR, maternal mortality rate and malnutrition. 22

When environmental conditions are improving, and fertility and mortality decrease as a consequence of health programmes, countries are in the transition stage. In this stage, risk factors for chronic diseases (alcohol, drugs and tobacco consumption, inadequate diet, lack of exercise and others) increase owing to urbanisation. The population gets older, and the first causes of death are chronic diseases (cardio- vascular, cancer) and injuries, but infectious diseases are still important. This is the situation of most Latin American countries, and Chile was in this stage in the 1970s and 1980s. In Chile, the rapid decrease of diarrhoeal diseases, infectious diseases and malnutrition produced a change in I M R causes of death: by 1990 perinatal diseases and congenital malformations, followed by respiratory diseases and trauma were the

~ C Albala and F ¥io Epidemiological transition in Chile

440

main causes of death (see Table 7). This pattern is similar to the infant pattern of death in developed countries. With the decrease of malnutrition, other problems appear as important causes o f illness in children, such as micronutrient deficiency (leading to anaemia, goitre, retarded growth due to lack of calcium, zinc, copper, fluoride) together with metabolic and congenital diseases.

Despite cardiovascular diseases decreasing, they are still the highest cause of death. The risk of death from malignant tumours has increased, particularly for cancers related to lifestyle (diet, tobacco), such as gall bladder, prostate, breast and lung. Besides, and similar to what is happening in developed countries, mental disorders appear as important causes of morbidity, disability and as risk factors for chronic diseases (tobacco consumption, alcoholism and drug addiction with violence and injuries as sequelae).

In relation to risk factors, alcohol consumption is common in the population: 70% of adults consume alcohol, 20% of adult men are considered excessive drinkers and 5% alcoholics. 6 Smoking is a habit for 37.9% of men and 25.1% of women. Obesity also has a high prevalence: 13.2% of men and 22.7% of women are obese, particularly at low socioeconomic levels; lack of exercise is the norm in Chile. 23 In general, diet fits international recommendations, 60-65% consisting of carbohydrates, 12-18% of proteins and 20-25% of fat, with an adequate relation of saturated and non-saturated fat. The pattern of fat consumption and blood-lipid profiles is different according to socioeconomic level: at high levels fat consumption and hyperlipidemias are higher than at lower socioeconomic levels. 24

Adjusted mortality rates to the US population show a lower mortality from cardiovascular disease in Chile (ratio = 0.6), lower ischaemic heart disease but a higher mortality rate due to cerebrovascular diseases in all age-groups compared with those reported in the USA. 25 The lack of preventive programmes for early detection and treatment of hypertension, and difference in the prevalence of risk factors such as diet, hyperlipidaemia and smoking may underline this different mortality pattern from cardiovascular diseases between the USA and Chile. 23-25

There is no explanation for the decrease in mortality fi'om stomach cancer, which seems to be a worldwide p h e n o n e n o n . A number of studies have found that gall bladder cancer is much more common in Chile than in other countries and this fact does not result from misclassificationo The great majority of cases of gall bladder cancer are diagnosed in hospital by surgical biopsy, which assures the correctness of the diagnosis. On the other hand, medical certification of gall bladder cancer deaths (ICD-9 156) is 99.7%. The most significant risk factor for gall bladder cancer is gallstone disease, a prevalent condition in Chile. Although no apparent change in the prevalence of gallstone disease has occurred during the last decade, cholecystectomy rates have consistently decreased during the decade, specially among young women. 26-28 No international information has been found showing an increase in gall bladder cancer mortality like that seen in Chile. The high prevalence of gallstone disease, in particular among women, and refraining from the timely surgical elimina- tion of the gall bladder in the last two decades may explain this phenomenon. 6,29 Breast cancer follows the trend observed in developed countries. There seems to be an increase in oestrogen t r e a t m e n t of the menopause which could explain the increase in breast cancer in older women. However, other risk factors such as fewer children, first children at older ages, lack of breastfeeding and obesity have also been associated with breast cancer. ;9

In general, the epidemiological profite of mortality in Chile is in the post-transition stage. But in morbidity there is still a high incidence of TB and some infectious diseases such as typhoid fever and hepatitis, with the maintenance of a high incidence of sexually transmitted diseases (STD) and the increasing incidence of AIDS.

C Albala and F Vio Epidemiological transition in Chile ~%~

A drastic reduction of I M R in a short period of time occurred in Chile b e t w e e n 1970 and 1992. Changes w e r e p r o d u c e d by a decline in infant mortality due to diarrhoeal and respiratory diseases, as well as a decline in maternal mortality rate and malnutrition, in a period o f rapid demographic and socioeconomic changes. Now, in the 1990s, with the urbanisation process and the extension of longevity, there exists a p r e d o m i n a n c e o f chronic diseases with a pattern o f mortality according to the post-transitional stage, but some infectious diseases corresponding to a transition stage are still present.

It is concluded that, according to mortality, Chile is in a post-transition stage, with a persistence o f some infectious diseases, which corresponds to an epidemiological transition situation.

T h e Chilean situation has b e e n different from o t h e r Latin A m e r i c a n countries where a 'prolonged o r lengthy m o d e l ' is the p r o m i n e n t characteristic, with a lack of resolution of the transition process so that such countries appear to be caught in a state of mixed morbidity and mortality. 2

H o w e v e r , the rapid change f r o m a pre-transition to a post-transition stage in Chile has not been accompanied by an adequation of the health system model. T h e Chilean health system is still caught in the pre-transition model with most of the p r o g r a m m e s focused on maternal and child health, without a health policy and p r o g r a m m e s related to o t h e r population groups. T h e absence of preventive p r o g r a m m e s for chronic diseases in Chile is a high-risk situation for the country in the next 10 years. Comparative experiences in d e v e l o p e d countries have d e m o n s t r a t e d that chronic diseases can be p r e v e n t e d by the introduction of changes in the lifestyle of the population. Decreasing alcohol and tobacco consumption, i m p r o v e m e n t s in diet and physical activity and r e d u c t i o n of obesity are important challenges for developing countries in epidemiological transition.

441

Acknowledgements This work was s u p p o r t e d by a research grant (676/92) from the Chilean Scientific and Technological National R e s e a r c h Fund ( F O N D E C Y T ) .

References 1 Chakiel J Amdrica Latina: andlisis de la dindmica de la poblaci6n orientada al Sector Salud.

Perfodo 1950-2000. CELADE, Serie A-269, 1992. 2 Frenk J, Frejka T, Bobadilla JL, Stern CV, Lozano R, Sepdlveda J. The epidemiological

transition in Latin America. Boletin Oficina Sanitaria Panamericana 1991; 111: 485-496. 3 Bobadilla, JL, Possas C. H o w the Epidemiological Transition Affects Health Policy Issues in

Three Latin A m e r i c a n Countries, Policy Research Working Papers, Population, Health, and Nutrition. Population and Human Resources Department, The World Bank: Washington DC, t992.

4 Frenk J, Bobadilla JL, Sep61veda J, Lopez M. Health transition in middle-income countries: new challenges for health care. Health Policy and Planning 1989; 4: 29-39.

5 Republic of Chile, Ministry of Health. Health Situation in Chile ed. Ant~rtica SA. Ministry of Health, Republic of Chile, 1994.

6 World Bank. Chile: The A d u l t Health Policy Challange, Report No. 12681-CH, World Bank: Washington DC, 1994.

7 Taucher E. The Effect o f a Decrease in Total Ferfility Rate on Infant Mortality Rate~ Technical Report No. 57 (Studies on infant Health and Mortality). International Develop- ment Research Centre: Ottawa, Ont, 1988.

8 WHO Interhealth Programme against Diseases of Lifestyles, meeting of Interhealth Project of Directors, Helsinki, Finland, i989.

• C Albala and F Vio Epidemiological transition in Chile 442

9 National Institute of Statistics (1NE) Demographic Annual Reports, 1969, 1970, 1971, 1974, 1975, 1976, 1979, 1980, 1982, 1985, 1986, 1990, 1991, 1992. INE: Chile.

10 National Institute of Statistics (INE). Chilean Women: Radiograph in Numbers. INE: Chile, 1994.

11 National Institute of Statistics (INE) Census 1992. Population and Housing. General Results. INE: Chile, 1992.

12 National Institute of Statistics (INE). Latin American Center for Demography ( C E L A D E ) : Chile. Population Projections by Sex and Age. Total for the Country 1950-2025, F/CHI.I. INE: Santiago, Chile, 19xx.

13 CELADE. Demographic Bulletin 51. C E L A D E : Chile, 1993. 14 Republic of Chile, Ministry of Health, Coordination and Informatic Department. Annual

Report on Communicable D&eases. Ministry of Health: Santiago, Chile, 1990. 15 W H O / P A H O . Health Conditions in the Americas (1981-1984). P A H O Scientific Publication

No. 500. 16 W H O / P A H O . Health Conditions in the Americas (1990). P A H O Scientific Publication

No. 524. 17 W H O / P A H O . Health Statistics from the Americas (1991). Mortality since 1960. P A H O

Scientific Publification No. 537. 18 World Bank. World Development Report: Investing in tlealth. Oxford University Press:

Washington DC, 1993. 19 World Resources Institute, World Resources 1994-95. A Guide to the Global Environment,

The United Nations Environment Programme and The United Nations Development Programme. Oxford University Press: New York, 1994.

20 WHO. Inwrnational Classification of" Diseases, Vol. 1: 8th Revision 1965. WHO: Geneva, 1968.

21 P A H O / W H O . international Classification o f Diseases, 9th Revision 1975. Scientific Publica- tion 353-A: Washington DC, 1978.

22 Albala C, Vio F, Robledo A, Icaza G. The epidemiogical transition in Chile. Revista Mddica de Chile 1993; 121: 1446-1455.

23 Berrios X, Jadue L, Zenteno J e t al. Prevalence of risk factors for chronic diseases: a population study in the Metropolitan area of Santiago, Chile, 1986-1987. Revista Medica de Chile 1990; 118: 597-604.

24 Albala C, Villarroel P, Olivares S e t at. Diet and lipoproteins in obese women of high and low socioeconomic status. Revista Medica de Chile 1989; 117: 3-9.

25 Taucher E, Albala C, Perez P. Is there an increase in cardiovascular mortality in Chile? Revista Medica de Chile 1990; 118: 225-234.

26 Chianale J, Valdivia G, Del Pino G, Nervi F. Gallbladder carcinoma mortality rates and relation with cholecystectomy rates. Revista Mddica de Chile 1990; 118: 1284-1288.

27 Glasinovic J, Marinovic I, Vela P, Mege R, Fernfindez de la Reguera P, Tobar E, Ahumada E, Valdivia M, Alvarado A. Cholecystectomy rates among young women from South- Oriental Santiago. Comparison of two periods. Revista Mddica de Chile 1994; 122: 415-419.

28 Serra I, Calvo A, Maturana M, Decinti E. Causes of the increasing incidence of carcinoma of the gallbladder in Chile. Revista Mddica de Chile 1991; 119: 78-82.

29 Taucher E, Albala C, Icaza G. Adult mortality from chronic diseases: Chile 1968-1990. Notas de PoblaciOn, C E L A D E , 1994; 12: 141-170.

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From epidemiological transition to modern cardiovascular epidemiology: hypertension in the 21st century Jacques Blacher, Bernard I Levy, Jean-Jacques Mourad, Michel E Safar, George Bakris

In 1971, Omran formulated the theory of epidemiological transition to explain the shift in mortality and disease patterns worldwide.1 The theory begins with the major premise that mortality is a fundamental factor in population dynamics. At the beginning of time was the age of so-called pestilence and famine. Mortality was high; life expectancy around 20–30 years; and famine, injuries, and infectious diseases were common causes of death. The fi rst transition took place around 10 000 years ago which brought the world into the age of receding pandemics. Life expectancy increased to 40 years and tuberculosis, cholera, typhus, and the plague were responsible for deadly infectious pandemics. Populations shifted from foraging food to primary food production, and swelling populations, domestication of plants and animals, and progressively more sedentary lifestyles increased the prevalence of infectious diseases. The second transition happened in the 20th century when life expectancy reached 50 years. Profound changes in health and disease patterns took place in children and young women, and the primary cause of death shifted from infectious disease to chronic degenerative diseases.2

Although cardiovascular disease is the leading cause of death worldwide, death rates of people with cardiovascular disease have steadily decreased over the past three decades. In several industrialised countries, such as France, cardiovascular disease now ranks as the second most common cause of death after cancer, because of improved cardiovascular disease management rather than a rise in deaths caused by cancer. Improvements in cardiovascular mortality rates in industrialised countries are probably due to three major factors: an improvement in primary prevention of disease (better screening and treatment of cardiovascular risk factors and later onset of fi rst events); more eff ective treatment of primary cardiovascular events that resulted in a considerable reduction in case-fatality over the past 30 years; and better secondary prevention with wider use of treatments with confi rmed effi cacy.3

Two major points stand out among cardiovascular causes of death. First, median age at death has risen over the past 30 years. Life expectancy has increased year by year and was 78 years in 2008 in France, when the median age of cardiovascular death was higher, for example, the median age of death from heart failure was 88 years.4 Second, whereas coronary heart disease and stroke are predominant causes of cardiovascular death, the proportion of deaths from heart failure and other end- stage cardiovascular diseases increases each year.4 Deaths caused by heart failure were as frequent as deaths caused by coronary heart disease and stroke in populations where

the average age of death is greater than 85 years.4 Findings from our prospective study5 in a population of very elderly people (mean age 87 years) showed that cardiac systolic dysfunction and atrial fi brillation were crucial risk factors for all-cause mortality in the later stages of life.

Hypertension is associated with stiff ening and thickening of both vessel walls and ventricular walls,6 which can lead to clinical manifestations of cardiovascular and renal diseases, including coronary heart disease, stroke, arrhythmias, heart failure, and vascular dementia.

Coronary heart disease is a major cause of cardio- vascular death. Hypertension, diabetes, dyslipidaemia, and smoking are all risk factors for myocardial infarction and other forms of coronary heart disease. Observational data from more than 1 million individuals showed that risk of death from both coronary heart disease and stroke increases progressively and linearly with blood pressure measurements from as low as 115 mm Hg (systolic) and 75 mm Hg (diastolic).7 Individuals with elevated blood pressure present more frequently with other risk factors for cardiovascular disease (diabetes, smoking, dyslipid- aemia) and target organ damage. Potential interaction of risk factors increases the overall risk of patients with hypertension despite only mild or moderate blood pressure elevation.

Hypertension is the most important treatable risk factor for stroke. Stroke-related mortality in the USA over the past three decades has decreased, largely because of better blood pressure control, and achievement of specifi c targets of blood pressure levels for the prevention of primary and recurrent stroke remains an important goal.7,8 Hypertension is also the most prevalent, independent, and potentially modifi able risk factor for atrial fi brillation; up to 70% of patients with atrial fi brillation have a history of hypertension.9 Elevated blood pressure is also the most important risk factor for heart failure—roughly 75% of patients with heart failure have antecedent hypertension.8

The development of chronic kidney disease is associated with diabetes, hypertension, obesity, smoking, and low concentrations of HDL cholesterol.10 Along with diabetes, hypertension is a major risk factor for chronic renal disease and for progression to end-stage renal disease.

Finally, cognitive decline, one of the most devastating signs of ageing and vascular disease, is rapidly becoming a substantial cause of disability and mortality worldwide. Increasing evidence shows that hypertension, which causes damage to small and large cerebral vessels, is the most important, modifi able, vascular risk factor for the development and progression of cognitive decline and dementia.11

Lancet 2016; 388: 530–32

Published Online February 5, 2016

http://dx.doi.org/10.1016/ S0140-6736(16)00002-7

Diagnosis and Therapeutic Centre, Hôtel-Dieu University

Hospital, Assistance Publique-Hôpitaux de Paris,

Paris-Descartes University, Paris, France (Prof J Blacher MD, Prof M E Safar MD); Vessels and

Blood Institute, Lariboisière University Hospital, Paris,

France (Prof B I Levy MD); Paris Cardiovascular Research

Centre, INSERM U970, P aris, France (Prof B I Levy);

Department of Internal Medicine, Avicenne University

Hospital, Assistance Publique-Hôpitaux de Paris,

University Paris 13, Bobigny, France (Prof J-J Mourad MD);

and Department of Medicine, American Society of

Hypertension Comprehensive Hypertension Center, University of Chicago

Medicine, IL, USA (Prof G Bakris MD)

Correspondence to: Prof Jacques Blacher, Diagnosis

and Therapeutic Centre, Hôtel-Dieu University Hospital,

Assistance Publique-Hôpitaux de Paris, Paris-Descartes University,

75004 Paris, France [email protected]

http://crossmark.crossref.org/dialog/?doi=10.1016/S0140-6736(16)00002-7&domain=pdf

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www.thelancet.com Vol 388 July 30, 2016 531

Hypertension is, therefore, a major cause of coronary heart disease, stroke, arrhythmias, heart failure, renal disease, and dementia. Since the 1980s, mortality rates related to stroke and coronary heart disease have decreased in industrialised countries, partly because of improved blood pressure control. Life expectancy has increased, which provides the time and opportunity needed for the development of end-stage cardiovascular disease (such as arrhythmias, heart failure, renal disease, and dementia) and cancer.

Consequently, hypertension can lead to all stages of the cardiovascular continuum, independently of the previous stage. Hypertension left untreated could be considered as the origin of cardiovascular diseases. Research into future therapeutic strategies should give priority ranking to this epidemiological transition in hypertension.

Increases in life expectancy and end-stage cardio- vascular disease events in patients with hypertension highlight the need for new risk-reduction strategies to reduce the burden of degenerative diseases. The following proposals all have little data at present and, therefore, need confi rmation in dedicated therapeutic trials.

The fi rst strategy is associated with the notion of residual risk in patients treated for hypertension.12 Treated patients who reached the systolic blood pressure target of 140 mm Hg have a higher cardiovascular risk than do patients with a spontaneous systolic blood pressure reading of 140 mm Hg. This observation appears to substantiate the need for more ambitious target blood pressure levels for patients with hypertension, particularly within the fi rst years of diagnosis.13 Intensive treatment at this early stage could reduce potentially irreversible arterial damage in individuals with hypertension, such as vascular and cardiac fi brosis.

The second proposal is that some of the residual risk could be attributable to the treatment that is too little and too late. Results from several studies suggest that prompt blood pressure reduction is preferable to delayed interventions,14 even in patients aged 80 years or older.15

The third strategy focuses on the optimum blood pressure measurement. Previous studies have established that treatment choice should be based on systolic blood pressure or pulse pressure in elderly people, rather than mean or diastolic blood pressure.16 Central blood pressure has been shown to relate more closely to white matter lesions, cognitive decline, and hypertension-related dementia than brachial blood pressure.17 Nevertheless, studies focusing on central blood pressure as the factor for titration of antihypertensive drugs are still lacking.

The fourth proposal is a fundamental shift. Existing guidelines suggest that the same baseline treatment be used for all patients with hypertension, that fi rst-line treatment depends on baseline characteristics of the patient, or that no compelling factor exists in favour of one antihypertensive drug class or another.18 Not all

patients with hypertension are at the same risk of developing all potential complications, so why should the choice of antihypertensive drug not also focus on the diseases that are to be prevented? For example, renin- angiotensin-aldosterone system (RAAS) blockers probably prevent atrial fi brillation to a greater extent than blood pressure reduction.19

The fi fth proposal is that antihypertensive drug combinations should be used more frequently, both as fi rst-line and second-line treatments. In addition to easier and more rapid achievement of blood pressure goals when time is undoubtedly important,18 combination therapies could help to prevent disease to a greater extent than blood pressure reduction.18,19

The sixth new strategy that we propose relates to the temporality of antihypertensive drug treatment. Hormonal systems are not constant throughout life. The RAAS, for example, is known to be aff ected by ageing,20 and thus modifi es the eff ects of antihypertensive drugs over time. This eff ect is seen with RAAS blockers and β blockers, which are both less eff ective with increasing age.18 If the choice of drug is aff ected by specifi c pathologies to be prevented, blood pressure lowering drug treatment should be modifi ed during the life of the patient, independently of blood pressure control.

In summary, in terms of epidemiological transition, causes of cardiovascular death have clearly evolved over the past three decades: end-stage cardiovascular disease (atrial fi brillation, renal disease, dementia, and heart failure) is becoming more frequent than coronary heart disease and stroke. Because hypertension is the most prevalent cardiovascular risk factor for all these diseases, modifi cation of antihypertensive strategies could have a considerable eff ect in delaying these degenerative diseases, thus further improving life expectancy. New strategies for the management of hypertension should focus on early initiation of blood pressure lowering drug treatment, more ambitious blood pressure goals in the initial stages of hypertension, and use of emerging technologies for blood pressure measurement. Maintenance of optimum blood pressure levels will prevent associated disorders, encourage wider use of blood pressure lowering drug combinations in all disease stages, and, fi nally, allow the application of the notion of temporality in blood pressure lowering treatment—ie, consideration of physiological changes over time as a factor in the choice of blood pressure lowering treatment. Contributors JB had the original idea and drafted this Viewpoint. All authors had substantial input to the content, reviewed the fi nal version, and approved its publication.

Declaration of interests JB has received research support from Servier; has served on advisory boards for Sanofi and Daiichi Sankyo; and has served as a speaker for Amgen, AstraZeneca, Bayer, Bouchara, Daiichi Sankyo, Menarini, MSD, Novartis, Pierre Fabre, Pileje, Sanofi , Servier, and Takeda. BIL has received research support from Servier, has served on advisory boards for Servier, and has served as a speaker for Bayer and Servier. J-JM has

Viewpoint

532 www.thelancet.com Vol 388 July 30, 2016

served on advisory boards for Servier and Daiichi Sankyo, and has served as a speaker for Daiichi Sankyo, Menarini, and Servier. MES has received research support from Servier, and has served on advisory boards for Novartis and Servier. GB has served on advisory boards for AbbVie, Bayer, Daiichi-Sankyo, Janssen, Medtronic, Novartis, Relypsa, and Takeda.

Acknowledgments We thank Moyra Barbier for editorial assistance.

References 1 Omran AR. The epidemiologic transition: a theory of the

epidemiology of population change. 1971. Milbank Q 2005; 49: 509–38.

2 Omran AR. The epidemiologic transition theory revisited thirty years later. World Health Stat Q 1998; 51: 99–119.

3 Ford ES, Ajani UA, Croft JB, et al. Explaining the decrease in U.S. deaths from coronary disease, 1980–2000. N Engl J Med 2007; 356: 2388–98.

4 Aouba A, Eb M, Rey G, Pavillon G, Jougla E. Mortality data in France: the main causes of death in 2008 and trends since 2000. Bull Epidemiol Hebd 2011; 22: 249–55.

5 Zhang Y, Protogerou AD, Iaria P, Safar ME, Xu Y, Blacher J. Prognosis in the hospitalized very elderly: the PROTEGER study. Int J Cardiol 2013; 168: 2714–19.

6 O’Rourke MF, Safar ME, Dzau V. The Cardiovascular Continuum extended: aging eff ects on the aorta and microvasculature. Vasc Med 2010; 15: 461–68.

7 Lewington S, Clarke R, Qizilbash N, Peto R, Collins R, for the Prospective Studies Collaboration. Age-specifi c relevance of usual blood pressure to vascular mortality: a meta-analysis of individual data for one million adults in 61 prospective studies. Lancet 2002; 360: 1903–13.

8 Go AS, Mozaff arian D, Roger VL, et al, for the American Heart Association Statistics Committee and Stroke Statistics Subcommittee. Heart disease and stroke statistics—2013 update: a report from the American Heart Association. Circulation 2013; 127: e6–245.

9 Benjamin EJ, Levy D, Vaziri SM, D’Agostino RB, Belanger AJ, Wolf PA. Independent risk factors for atrial fi brillation in a population-based cohort The Framingham Heart Study. JAMA 1994; 271: 840–44.

10 Bakris GL. Protecting renal function in the hypertensive patient: clinical guidelines. Am J Hypertens 2005; 18: 112S–19S.

11 Gąsecki D, Kwarciany M, Nyka W, Narkiewicz K. Hypertension, brain damage and cognitive decline. Curr Hypertens Rep 2013; 15: 547–58.

12 Blacher J, Evans A, Arveiler D, et al, for the PRIME study group. Residual coronary risk in men aged 50–59 years treated for hypertension and hyperlipidaemia in the population: the PRIME study. J Hypertens 2004; 22: 415–23.

13 Verdecchia P, Staessen JA, Angeli F, et al. Usual versus tight control of systolic blood pressure in non-diabetic patients with hypertension (Cardio-Sis): an open-label randomised trial. Lancet 2009; 374: 525–33.

14 Julius S, Kjeldsen SE, Weber M, et al, for the VALUE trial group. Outcomes in hypertensive patients at high cardiovascular risk treated with regimens based on valsartan or amlodipine: the VALUE randomised trial. Lancet 2004; 363: 2022–31.

15 Beckett N, Peters R, Tuomilehto J, et al, for the HYVET study group. Immediate and late benefi ts of treating very elderly people with hypertension: results from active treatment extension to Hypertension in the Very Elderly randomised controlled trial. BMJ 2011; 344: d7541.

16 Blacher J, Staessen JA, Girerd X, et al. Pulse pressure not mean pressure determines cardiovascular risk in older hypertensive patients. Arch Intern Med 2000; 160: 1085–89.

17 Verger A, van der Gucht A, Guedj E, et al. Central pulse pressure is a determinant of heart and brain remodeling in the elderly: a quantitative MRI and PET pilot study. J Hypertens 2015; 33: 1378–85.

18 Blacher J, Halimi JM, Hanon O, et al, for the French Society of Hypertension. Management of hypertension in adults: the 2013 French Society of Hypertension guidelines. Fundam Clin Pharmacol 2014; 28: 1–9.

19 Marott SC, Nielsen SF, Benn M, Nordestgaard BG. Antihypertensive treatment and risk of atrial fi brillation: a nationwide study. Eur Heart J 2014; 35: 1205–14.

20 Weidmann P, De Myttenaere-Bursztein S, Maxwell MH, de Lima J. Eff ect of aging on plasma renin and aldosterone in normal man. Kidney Int 1975; 8: 325–33.

• From epidemiological transition to modern cardiovascular epidemiology: hypertension in the 21st century
• Acknowledgments
• References

Meeting-the-global-demands-of-epidemiologic-transition---The_2013_Molecular-.pdf

M O L E C U L A R O N C O L O G Y 7 ( 2 0 1 3 ) 1 e1 3

available at www.sciencedirect.com

www.elsevier.com/locate/molonc

Review

Meeting the global demands of epidemiologic

transition e The indispensable role of cancer prevention

Silvia Franceschi, Christopher P. Wild*

International Agency for Research on Cancer, 150 Cours Albert Thomas, 69372 Lyon Cedex 08, France

A R T I C L E I N F O

Article history:

Received 11 October 2012

Accepted 22 October 2012

Available online 17 November 2012

Keywords:

Cancer prevention

Cancer burden

Risk factors

Epidemiology

Abbreviations: COPD, chronic obstructive C virus; H. pylori, Helicobacter pylori; HPV, hum KSHV, Kaposi sarcoma herpes virus; NCDs World Health Organization. * Corresponding author. Tel.: þ33 (0) 4 72 73 E-mail addresses: [email protected] (S.

1574-7891/$ e see front matter ª 2012 Feder http://dx.doi.org/10.1016/j.molonc.2012.10.01

A B S T R A C T

The number of new cancer cases each year is projected to rise worldwide by about 70% by

2030 due to demographic changes alone, with the largest increases in the lower-income

countries. Wider adoption of specific aspects of westernized lifestyles would translate to

still greater increases in certain cancer types. In many countries the burden of cancer

and other non-communicable diseases will add to communicable diseases and malnutri-

tion to impose a “double burden” on the poorest. These trends represent major challenges

to health, poverty, sustainable development and equality. Prevention is, however, possible

based on implementing existing knowledge about risk factors and the natural history of

the disease. Both primary and secondary cancer prevention offer therefore many opportu-

nities to combat the projected increases. Tobacco control, reductions in obesity and phys-

ical inactivity, reduced consumption of alcohol, vaccination against hepatitis B and human

papilloma viruses, safe sex, avoidance of environmental and occupational carcinogens and

excessive sun exposure as well as the early detection and screening for breast, cervix and

colorectal cancers would all make significant contributions. At the same time, for a number

of major cancers (e.g., colon, prostate, kidney, pancreas, brain, lympho-haematological

malignancies) research is needed to identify as yet unknown risk factors whilst for existing

prevention strategies additional work is needed on their implementation into health ser-

vices. Finally, there is a remarkable opportunity for advances in understanding the molec-

ular basis of carcinogenesis to provide new tools and insights into aetiology and

prevention. It is only by complementing efforts to improve treatment with those aimed

at prevention that the impending epidemic of this disease can be addressed.

ª 2012 Federation of European Biochemical Societies. Published by Elsevier B.V. All rights reserved.

pulmonary disease; HDI, Human Development Index; HBV, hepatitis B virus; HCV, hepatitis an papillomavirus; IARC, International Agency for Research on Cancer; KS, Kaposi sarcoma; , non-communicable diseases; PSA, prostate-specific antigen; UN, United Nations; WHO,

85 77; fax: þ33 (0) 4 72 73 85 64. Franceschi), [email protected] (C.P. Wild). ation of European Biochemical Societies. Published by Elsevier B.V. All rights reserved. 0

mailto:[email protected]

mailto:[email protected]

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www.sciencedirect.com/science/journal/15747891

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http://dx.doi.org/10.1016/j.molonc.2012.10.010

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http://dx.doi.org/10.1016/j.molonc.2012.10.010

M O L E C U L A R O N C O L O G Y 7 ( 2 0 1 3 ) 1 e1 32

1. Introduction age adjustment indicating that Africa’s elevated mortality

The burden of cancer as well as that of other non-

communicable diseases (NCDs) is increasing globally. NCDs

(including cancer but also cardiovascular diseases, stroke, di-

abetes, chronic obstructive pulmonary disease (COPD), etc.) in

2005 were estimated to have caused more than 60% (35 mil-

lion) of all deaths worldwide (United Nations, 2012). Without

prevention and control actions, the figure is expected to in-

crease to 41 million in 2015. This phenomenon is mainly a con-

sequence of the so-called epidemiologic transition, i.e., a shift

from infectious to NCDs (Omran, 1971; Maule and Merletti,

2012). One consequence, certainly in the lower-income coun-

tries, is the implausibility of treating our way out of the NCD

epidemic.

In the present review we will focus on needs, knowledge

and opportunities in cancer prevention cognisant of the inti-

mate link between prevention of cancer and prevention of

other NCDs. Cancer burden will be put in a global perspective

and knowledge on established risk factors summarised. While

cancer is a global problem it is not a uniform one. There are

distinct patterns of the types of cancer at a regional and na-

tional level. These differences reflect heterogeneity in under-

lying risk factors and hence imply the need for cancer

control strategies tailored to specific regional challenges.

Any strategy that can prevent NCDs as a whole is, obviously,

attractive, but some cancer-specific preventive tools can be

extremely effective and potentially cost-effective.

2. The epidemiologic transition

The second part of the twentieth century witnessed enormous

progress in improving health and survival around the world.

Life expectancy at birth for the world population rose from

48 years in 1950e1955 to 68 years in 2005e2010 (United

Nations, 2012). In a number of countries that have transited

towards the highest levels of human development (e.g., Aus-

tralia, Canada, France, Italy, Spain, Norway, Sweden, Switzer-

land, Israel, Japan and the Republic of Korea), life expectancy

at birth exceeded 80 years in 2005e2010. Epidemiologic transi-

tion is characterised by initial declines in communicable dis-

eases (Group I) followed by subsequent increases in crude

and proportional mortality attributable to NCDs (Group II).

Enormous disparities exist across regions in the stage of epi-

demiologic transition attained, but there are no countries

where the present and future challenge posed by NCDs can

be ignored.

Figure 1 shows the ranking of world regions in 2008 by the

three main Groups of causes of death including, in addition to

Group I and II, Group III, i.e., injuries. The non-standardised

(or crude) death rate reflects the burden to the population

from the number of deaths from a disease or group of dis-

eases. Conversely, age-standardised rates account for the dif-

ferent age structures observed in different populations.

Africa’s mortality rate for Group I was nearly four-times

higher than in Asia and over 20-fold higher than in more de-

veloped regions (excluding Eastern Europe). The relationship

between regional rates for Group I deaths is not modified by

for Group 1 diseases is not attributable to a very young popu-

lation but rather to other factors (i.e., very high mortality from

malaria and diarrhoea in children and from HIV infection

among young adults). If Africa’s mortality rates due to com-

municable diseases were to be reduced to those observed in

the longest-lived world populations, the region would achieve

a 17-year increase in life expectancy at birth, from 55 to 72

years. Conversely, age-adjustment substantially modifies the

ratios between Group II deaths across regions (Figure 1). If

the underlying population age structure was equal, Africa, de-

veloping Oceania (i.e., Oceania except Australia and New Zea-

land), and Eastern Europe would have the highest NCD

mortality followed by Asia and Latin America. Perhaps con-

trary to common perception, more developed regions tend to

have relatively low age-standardised rates of NCDs. To a lesser

extent, deaths from injuries follow a pattern similar to NCDs,

with the highest age-standardised mortality rates in Africa

and Eastern Europe. It is, therefore, essential to bear in mind

that less developed countries experience a “double burden”

of infectious diseases and NCDs compared to more developed

ones and are potentially extremely vulnerable to additional

NCD increases.

The possibility to live a long and healthy life is a fundamen-

tal aspect of human development and the epidemiologic

transition in more developed countries was associated with

improved socioeconomic conditions (that in turn improved

hygiene and nutrition) earlier than medical advances (Maule

and Merletti, 2012). Medical advances in disease prevention

and treatment came at a later stage but they can now, in prin-

ciple, be made available globally.

3. Global cancer burden: mortality and incidence

This section reviews the current burden of cancer in 2008 and

cancer projections for 2030 (GLOBOCAN http://globoca-

n.iarc.fr) (Ferlay et al., 2010). It also provides the latest esti-

mates on the number, and rates of global deaths from

cancer. A description of the methods used to produce these

estimates is provided in GLOBOCAN. Data are also discussed

and presented according to the Human Development Index

(HDI) groups (Bray et al., 2012a).

A total of 7.6 million deaths from cancer are estimated to

have occurred in 2008 (21% of all NCD deaths). Premature

death is a major consideration when evaluating the impact

of cancer on a given population. Cancer accounted for 27%

of NCD deaths below age 70 (World Health Organization, 2011).

Large variations in both cancer incidence and mortality are

observed, overall and in relation to the major forms of cancer,

in different regions of the world (Ferlay et al., 2010). Figure 2

presents the most frequent types of cancer diagnosis (based

on the number of new cases per year) in each country, for

men and women.

The geographical variation in cancer distribution is mir-

rored on examination of the number of new cases and deaths

for the most common cancers in relation to the HDI of coun-

tries (Bray et al., 2012a)(Figure 3). It is worth bearing in

mind that the population in very high-, high-, medium-, and

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Figure 1 e Non-standardised and age-standardised death rates by group of causes (see text) for selected regions, 2008 (United Nations, 2012).

M O L E C U L A R O N C O L O G Y 7 ( 2 0 1 3 ) 1 e1 3 3

low-HDI countries was of very different size, i.e., 1.0, 0.9, 4.4

and 0.4 billion, respectively. Figure 3 is, therefore, only useful

to evaluate the relative importance of different cancer types

by HDI. In all countries, other than those in the low HDI cate-

gory, men have a heavier burden of all types of cancer com-

bined than women. The exception of low HDI countries is

most likely explained by the high rates of cervical cancer

among women in the African Region.

Within high- and very high-HDI countries, prostate and

breast cancers are the most commonly diagnosed in males

and females respectively, with lung and colorectal cancers

representing the next most common type (Figure 3). These

cancers also represent the most frequent types of cancer-

related deaths in these countries although lung cancer is the

most common cause of cancer death in both sexes. Within

low-HDI countries, the absolute burden of cancer is lower,

and while prostate and breast cancers remain among the

most common diagnoses and types of cancer-related deaths,

cancers of the cervix, stomach, liver, and Kaposi sarcoma

(KS) are also among the leading types e all of which are can-

cers with infection-related aetiology. Medium-HDI countries

are intermediate with respect to their patterns of cancer bur-

den, reflecting an on-going transition from infection-related

cancers to those most frequently diagnosed in countries

with the highest HDI. The three most common types of cancer

in medium-HDI countries are lung, stomach and liver cancers

in males, and breast, cervix and lung cancer in females

(Figure 3).

Concerning the overall prevalence of cancer in 2008, the es-

timated proportion of adults (>15 years old) living with cancer

varies from one in 60 people in the very high HDI countries to

just one in 450 in the low HDI countries (data not shown) (Bray

et al., 2012b).

Future planning of service provision is an integral part of

cancer control programmes. Considering the projected

growth in cancer morbidity, important differences can be ob-

served in relation to HDI groups. Without any changes in the

prevalence and distribution of underlying known or putative

risk factors (i.e., based only on anticipated demographic

changes alone), between 10 and 11 million cancers will be di-

agnosed annually in 2030 in the low- and medium-HDI coun-

tries (Figure 4). The estimated percentage increase in cancer

incidence by 2030 (compared with 2008) will be greater in

low- (93%) and medium-HDI countries (78%) compared to

high- (60%) and very high-HDI (39%) countries.

4. Risk factors for NCDs and cancer

For more than a decade the World Health Organization (WHO)

has been raising awareness of the need to place higher priority

on NCDs. This culminated in the United Nations (UN) high-

level meeting on NCDs in September 2011 and the Political

Declaration that emerged. WHO is now engaged in establish-

ing a Global Monitoring Framework for NCDs. The WHO As-

sembly in May 2012 agreed on the first global target: to

reduce premature (30e70 years) mortality from NCDs by 25%

by 2025 (“25 by 25”). The UN also called upon the WHO, in col-

laboration with the Member States, UN agencies (including

IARC) and other relevant organisations, to prepare before the

end of 2012 recommendations for a set of voluntary global tar-

gets and indicators. These should permit the monitoring of

trends and thus an assessment of progress made in the imple-

mentation of national strategies and plans on NCDs.

There are different ways to establish priorities and achiev-

able targets in the prevention of cancer and the focus of WHO

has been on risk factors shared across the spectrum of key

NCDs. The criteria for selecting risk factors included: 1) to be

leading causes of disease globally or regionally; 2) not to be too

specific (e.g., not a single environmental pollutant); 3)

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Figure 2 e Most frequently diagnosed cancers worldwide, by country and sex, 2008 (GLOBOCAN).

M O L E C U L A R O N C O L O G Y 7 ( 2 0 1 3 ) 1 e1 34

availabilityofreasonablycompletedataonexposure,risklevels,

and evidence of causality; and 4) being potentially modifiable.

Along these lines, Table 1 is a modified version of the widely

quoted work by the Institute for Health Metrics and Evaluation,

Seattle, USA (Ezzati et al.,2006). Table 1 is, however, restricted to

risk factors for NCDs that are also relevant to cancer. For each

risk factor, it also shows a theoretical-minimum-risk exposure

that would be desirable to reach in any population.

Not surprisingly, Table 1 includes many of the most impor-

tant cancer risk factors given NCDs share key modifiable behav-

ioural risk factors like tobacco use, the harmful use of alcohol,

overweight and obesity, unhealthy diet, and lack of physical ac-

tivity. There is, therefore, a substantial overlap between Table 1

and several past attempts to quantify lifestyle and environmen-

tal factors that contribute the most to cancer incidence and

mortality. Danaei et al. (2005) used the same approach as in

Ezzatiet al. (2006)and estimatedglobalmortality from12cancer

types attributable to risk factors listed in Table 1. Altogether, the

ninerisk factors accountedfor 35% ofworldcancer(37%inhigh-

income countries and 34% in low-and-middle-income coun-

tries). Smoking was associated with a larger attributable

fraction in high-income countries than in low-and-middle-

income countries (29% versus 18%, respectively). A smaller

differencewasalsoreportedforthefractionattributabletoover-

weight and obesity (3% versus 1%). Cancer-causing infections

were not included in Danaei et al. (2005), except for an indirect

mention to safe sex as a way to prevent cervical cancer and HIV.

In their seminal report on the causes of cancer Doll and Peto

(1981) showed that each type of cancer that was common in one

world populationwas rare in another. They argued that because

these differences were not chiefly genetic, wherever one type of

cancer was common there were likely to be potentially avoid-

able causes. The proportion of cancer theoretically avoidable

was higher (approximately 75%, Table 2) than in Danaei et al.

(2005). The difference was not accounted for completely by ei-

ther the population under study (United States in 1978 versus

world in 2001), the choice of minimal achievable risk level, or

fractions attributed to individual risk factors. As an example,

30% (range of acceptable estimates: 25e40) of cancer deaths

were attributed by Doll and Peto to tobacco smoking and 3%

(range of acceptable estimates: 2e4) to alcohol drinking, com-

pared to 21% and 5% in the report on the world (Danaei et al.,

2005). The cancer fraction attributed to diet (35%; range of ac-

ceptable estimates: 10e70) by Doll and Peto would be probably

considered too large nowadays but it had extremely wide range

of acceptable estimates and encompassed, in its definition,

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Figure 3 e Total population and estimated annual number of new cases and deaths for the most common cancers, by Human Development Index

groups and by sex, 2008 (GLOBOCAN).

M O L E C U L A R O N C O L O G Y 7 ( 2 0 1 3 ) 1 e1 3 5

different items from Table 1 (overweight and obesity; physical

inactivity; and low fruit and vegetable intake). Doll and Peto’s

larger fraction of theoretically avoidable cancer depended in

part on the fact that they included a broader range of cancer

types and risk factors than in Danaei et al. (2005). Some risk fac-

tors are specific to certain cancer sites and not necessarily rele-

vant to other NCDs or avoidable. An update of Doll and Peto’s

workbyJulianPeto(2001)showedseparateestimatesforcurrent

smokers and non-smokers. The cancer fraction attributed to

smoking increased to 60% among current smokers. The fraction

due to overweight was larger among non-smokers (10%) than

current smokers (4%) and so was the fraction of cancers pres-

ently unavoidable on account of insufficient knowledge (50%

and 25%, respectively).

After Doll and Peto’s report substantial knowledge of can-

cer causes has accumulated, particularly with respect to sev-

eral chronic infections (de Martel et al., 2012) and the

benefits of cancer screening are recognised (IARC, 2002,

2005; Segnan, 2010). A rigorous update of their estimates is be-

yond the scope of the present review but comments in Table 2

(Scope for updates) address the issues that would have to

be revised to-day. Doll and Peto’s conclusions, however,

still provide a strong reminder of the existence of many

specific features of cancer aetiology and, hence, prevention

opportunities.

5. Diversity in cancer aetiology and cancer prevention strategies

Cancer differs from the other NCDs by including a wide range

of cancer sites and types that vary substantially with respect

to their aetiology and natural history of disease. Although

some risk factors, notably smoking, can increase the risk of

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Figure 4 e Estimated annual number of new cancer cases 2008 and

predicted 2030, by Human Development Index groups (Bray et al.,

2012a).

M O L E C U L A R O N C O L O G Y 7 ( 2 0 1 3 ) 1 e1 36

a large number of cancer types (IARC, 2012a, 2012b, 2012c,

2012d) there is no universal risk factor for cancer. In addition,

several different risk factors, including those affecting early-

stage and late-stage, are typically involved in the causation

of cancer in any given site (Day, 1990).

The International Agency for Research on Cancer (IARC)

Monograph series has classified carcinogens for more than

40 years. Recently, IARC has provided up-to-date information

on cancer sites associated with more than 100 carcinogenic

agents (Cogliano et al., 2011). Initially, IARC classified an agent

as carcinogenic to humans only when sufficient epidemiolog-

ical evidence in humans supported a causal association. Sci-

entific understanding of the mechanisms of carcinogenesis,

accompanied by the development of assays for studying

mechanistic events involved in carcinogenesis, have given re-

searchers new ways of establishing whether an agent is carci-

nogenic. Agents that have been classified as carcinogenic

to humans based on mechanistic and other relevant

non-human data include many that typically occur in com-

plex exposures making it difficult for epidemiologic studies

to attribute causality to specific components (Cogliano et al.,

2011).

It needs to be noted in this context that for some cancer

types the aetiology remains poorly understood and requires

more research, preferably in regions with the highest or low-

est incidence of those cancers. In addition, most cancers

grow slowly and occur only decades after initiation. The full

benefit of many primary prevention strategies, therefore,

will often only be seen decades after their introduction. At

the same time, the long induction time of many precancerous

lesions and cancers allows early detection and screening

(see section on Screening) and consequent prognostic

improvements.

Avoidance of risk factors shared by cancer and NCDs (Table

1) is of paramount importance but should be complemented

by additional approaches in order to diminish cancer burden,

especially in low-income countries. NCD risk factors men-

tioned above are highly relevant to the prevention of cancer

of the lung (smoking), and fairly relevant to the one or other

of cancers of the breast and colon (overweight and lack of

physical activity, and alcohol). These three cancer sites have

lower incidence and mortality in low-income countries than

in high-income countries, but they nonetheless impose

a heavy disease burden everywhere and are increasing in

low-income countries because of less favourable trends in

smoking than in high-resource countries (Giovino et al.,

2012); and more recent changes in lifestyle and reproductive

habits (decrease and postponement of childbearing). Because

of the long induction time of cancers such prevention strate-

gies should ideally be implemented now even in regions

where those cancers are at present relatively uncommon, be-

cause the full extent of cancers related to those risk factors

will appear only in the future and to reverse such a trend takes

up to several decades.

Among the four cancer sites that show much more ele-

vated incidence and mortality in low- than high-income coun-

tries (cervical, liver, stomach, and oesophagus) all except

cancer of the oesophagus are predominantly caused by

chronic infections, although tobacco and/or alcohol consump-

tion also play a role (IARC, 2012b).

5.1. Infections and cancer

Conservative estimates showed that about 2 million cancer

cases per year (16% of the global cancer burden) are attribut-

able to a few chronic infections (de Martel et al., 2012). This

fraction is substantially larger in low-resource countries

(that include low- and medium-HDI countries) (26%) than in

high-resource countries (8%) making the prevention or eradi-

cation of these infections a powerful tool to overcome in-

equalities in cancer incidence between poor and rich

populations. Variations are also seen by continent and coun-

try (Table 3). The fraction of cancer attributable to infections

is largest in sub-Saharan Africa (33%) and China (26.1%) and

smallest in Australia and New Zealand and in North America

(�4%). The principal infectious agents, each responsible for ap-

proximately 5% of cancers worldwide, are human papilloma-

virus (HPV) (100% of cancer of the cervix, the majority of

cancers of the ano-genital tract in each sex, and between 13

and 56% of cancer of the oro-pharynx depending upon the

population); hepatitis B virus (HBV) and hepatitis C virus

(HCV) (responsible for 77% of hepatocellular carcinoma world-

wide); and Helicobacter pylori (H. pylori) (that causes 75% of non-

cardia carcinomas of the stomach) (de Martel et al., 2012).

The prevalence of cancer-associated infections varies sub-

stantially in different populations in a way that closely resem-

bles the geographic distribution of the incidence of the

corresponding cancer types. The prevalence of cervical HPV

infection in women, for instance, varies more than 10-fold

according to IARC population-based HPV surveys: from less

than 3% to more than 30% in some sub-Saharan African pop-

ulations (Franceschi et al., 2006; Keita et al., 2009). Large vari-

ations are also seen for H. pylori (EUROGAST, 1993), HBV, and

HCV infection (Raza et al., 2007). The transmission of HCV in-

fection has been substantially reduced in high-income coun-

tries, where major epidemics had taken place in the last

decades (e.g., Japan and Italy) but not so in many low-resource

countries (e.g., Egypt, Pakistan, Mongolia) where it is still

mainly sustained by contaminated needles and unsafe trans-

fusions (Raza et al., 2007).

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Table 1 e Risk factors for cancer that are also leading causes of disease burden globally or regionally and are potentially modifiable (adapted from Ezzati et al., 2006).

Risk factor Minimum-risk exposure

Cancer Other major diseases

Smoking No smoking Cancer of the lung,

upper aero-digestive

tract; liver; pancreas;

cervix uteri; bladder;

kidney; and leukaemia

IHD; stroke; COPD

and other respiratory

diseases

Alcohol No use Cancer of the upper

aero-digestive tract;

liver, and breast

IDH; stroke; diabetes;

cirrhosis

Overweight

and obesity

BMI ¼ 21 SD 1 kg/m2

Cancer of the colon;

gallbladder; breast

(post-menopausal);

endometrium; and

kidney

IHD; stroke; diabetes;

osteo-arthritis

Physical inactivity �2.5 h/wk Cancer of the colon - rectum; breast; and

prostate

IHD; diabetes;

osteoporosis; osteoarthritis

Low fruit and

vegetable intake

600 gr/day

SD 50 g

Cancer of the upper

aero-digestive tract;

stomach; colon; and

lung

IHD; stroke

Urban air pollution 7.5 mg/m3 for PM2.5

1.5 mg/m3 for PM10

Cancer of the lung Mortality from respiratory

and cardiovascular diseases

Indoor smoke

from solid fuels

No solid fuel use Cancer of the lung COPD, respiratory infections

in children

Unsafe sex No unsafe sex Cancer of the cervix,

other anogenital tract,

and oropharynx

HIV/AIDS; sexually-

transmitted diseases

Contaminated

injections in

health care setting

No contaminated

injections

Liver cancer; non-

Hodgkin lymphoma

Infection with HBV; HCV;

HIV, cirrhosis,

BMI ¼ body mass index; COPD: chronic obstructive pulmonary disease; HBV: hepatitis B virus; HCV: hepatitis C virus; HIV: human immunode- ficiency virus; IDH: ischemic heart disease; SD: standard deviation.

M O L E C U L A R O N C O L O G Y 7 ( 2 0 1 3 ) 1 e1 3 7

Some infections globally less frequent than HPV, HBV,

HCV, and H. pylori, are, however, extremely important in cer-

tain low-income countries. One of the most frequent cancers

in Africa (KS) is caused by a virus (Kaposi sarcoma herpes vi-

rus, KSHV) notably in combination with HIV-induced immu-

nosuppression (IARC, 2012b; de Martel et al., 2012). Epstein

Barr virus (lymphoma and nasopharyngeal cancer) and some

parasites are associated with a high cancer burden in parts

of Asia and Africa (de Martel et al., 2012).

Estimates of infection-attributable cancer in de Martel

et al. (2012) did not separate the contribution of HIV from

that of a few cancer-associated viruses (KSHV, EBV, HPV,

and, probably, HBV and HCV) whose carcinogenetic potential

is greatly enhanced by HIV-induced immunosuppression.

HIV is one of the world’s leading infectious killers, claiming

more than 25 million lives over the past three decades. There

were approximately 34 million people living with HIV in 2010.

The introduction of highly active (combined) anti-retroviral

therapy in 1996 rapidly changed the outcome of the infection

in Western countries making HIV infection a chronic, al-

though still not curable, disease. By 2010, around 6.6 million

people living with HIV were receiving antiretroviral therapy

in low- and middle-income countries, but over 7 million

others are waiting for access to treatment. Although anti-

retroviral treatment led to a reduction of KS and non-

Hodgkin lymphomas, a beneficial effect on HPV-associated

cancer has not yet been seen, possibly because of the long la-

tency of these malignancies (Franceschi et al., 2010). The bur-

den of cancer in long-living HIV-positive individuals is

certainly going to increase, and so should efforts to decrease

cancer-associated infections and high-risk habits (smoking)

among them.

The eradication of many cancer-associated infections is

possible and often affordable. Control measures include

avoidance of contaminated blood and needles (HBV, HCV,

HIV); safe sex practices (HBV and HIV); early detection of pre-

cancerous or cancerous lesions; or treatment of the infection

(e.g., detection and treatment of HPV-related precancerous

cervical lesions in screening programmes and antibiotic treat-

ment of H. pylori). The availability of highly effective and safe

vaccines against HBV and HPV infection is a special asset.

Historically, only clean water has performed better than

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Table 2 e Doll and Peto’s tabulation of proportions of all cancer deaths attributable to various different risk factors in the USA in 1978 (Doll and Peto, 1981).

Factor or class of factors % of all cancer deaths Scope for updates

Best estimate Acceptable range

Tobacco 30a 25e40 Going down in more developed and up in less developed countries

Alcohol 3 2e4 Much higher in some countries; it did not include breast cancer

Diet 35 10e70 Still uncertain but probably lower, even including overweight and

obesity, physical activity, salt, and low fruit and vegetable intake

Food additives <1 �5 to 2b Reproductive and sexual

behaviours

7 1e13 Increasingly important in many less developed countries in which

childbearing is being delayed and family size reduced. Sexual

behaviour should be partly moved to Infection

Occupation 4 2e8

Pollution 2 <1e5

Industrial products <1 <1e2

Medicines and medical

procedures

1 0.5e3 On the rise due to increases in use of ionising radiation in imaging

and cancer treatment and of drugs with immunosuppressive and

carcinogenic effects

Geophysical factorsc 3 2e4 It did not include radon

Infection 10? 1e? Only HBV and EBV were well-established carcinogens.

Very different by countryd

Unknown ? ?

a 60% in current smokers (Peto, 2001).

b Allowing for a possibly protective effect of antioxidants and other preservatives.

c About 1%, not 3%, could reasonably be described as “avoidable” (see text). Geophysical factors also cause a much greater proportion of non-

fatal cancers (up to 30% of all cancers, depending on ethnic mix and latitude) because of the importance of UV light in causing the relatively non-

fatal basal cell and squamous cell carcinomas of sunlight-exposed skin.

d See Table 3 for current best estimates.

M O L E C U L A R O N C O L O G Y 7 ( 2 0 1 3 ) 1 e1 38

vaccination to reduce disease burden (Andre et al., 2008). If

high coverage can be achieved, vaccines can reduce inequal-

ities in health more than other medical interventions. Immu-

nisation programmes require funding for infrastructure,

purchase of vaccines and adequate staffing. However, the

mortality and morbidity prevented translates into long-term

savings and potential economic growth. In addition, “herd

protection” of the unvaccinated occurs when a sufficient pro-

portion of the population is immune (Andre et al., 2008).

Vaccination against HBV should prevent the majority of

hepatocellular carcinomas in many world regions, as already

observed in Taiwan (Chang, 2003) and being studied in an

on-going trial in The Gambia (Kirk et al., 2004). It is noteworthy

that the carcinogenic aflatoxins, common dietary contami-

nants in HBV endemic areas, are more potent among chronic

HBV carriers. Thus vaccination against HBV may also reduce

the cancer risks associated with aflatoxins (Wild and Gong,

2010).

HPV16 and 18, the two oncogenic HPV types included in

currently available vaccines, are responsible for at least 70%

of cervical cancers in each world region (Guan et al., 2012).

Findings from randomised vaccine trials suggested that the

percentage of cervical cancer preventable by current HPV vac-

cines may be substantially higher than 70% because of cross-

protection against other oncogenic HPV types (Lehtinen et al.,

2012). WHO recommended in 2009 that routine HPV vaccina-

tion of adolescent girls should be included in national immu-

nisation programmes, provided cervical cancer constitutes

a public health priority and cost effectiveness can be shown

(World Health Organization, 2009). The price of HPV vaccines

was initially far too high to be affordable in developing

countries but it is rapidly decreasing. In addition, GAVI opened

in April 2012 a funding window to support HPV vaccine intro-

duction in GAVI-eligible countries. The cost-effectiveness of

HPV vaccination programmes is therefore improving in both

developing and developed countries provided high and equi-

table coverage of adolescent girls (>70%) can be achieved.

A remaining outstanding priority for research is to estab-

lish the most appropriate mechanism for H. pylori eradication

and the impact on stomach cancer risk. IARC classified this

bacterium as a human carcinogen in 1986, exposure is wide-

spread globally, antibiotic treatments are available and yet

to date the best prevention strategy remains undefined

(IARC, 2012b).

5.2. Cancer and the environment

Environmental causes of cancer, encompassing environmen-

tal contaminants or pollutants, naturally occurring toxins,

occupationally-related exposures and radiation, can make

substantial contributions to specific cancers or cancer clusters

on a smaller scale. These exposures can also be amenable to

low-cost modification by regulation, thus reducing the burden

of some very lethal cancers with straightforward operable

measures. In addition, environmental causes add to health in-

equality in low-income countries where it is often the most

vulnerable groups that are affected most by those cancers.

Approximately 50 occupational agents and work-related

exposure circumstances are carcinogenic to humans

(Cogliano et al., 2011), for example arsenic, benzene, chromium

VI compounds, nickel compounds, polycyclic aromatic hydro-

carbons, silica, soot, vinyl chloride, and several pesticides, and

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Table 3 e Number of new cancer casesa in 2008 attributable to infectious agents, by geographic region (de Martel et al., 2012).

Number of new

cases in 2008

Number attributable to infection

PAF (%)

Africa

Sub-Saharan Africa 550,000 180,000 32.7%

North Africa and

west Asia

390,000 49,000 12.7%

Asia

India 950,000 200,000 20.8%

Other central Asia 470,000 81,000 17.0%

China 2,800,000 740,000 26.1%

Japan 620,000 120,000 19.2%

Other east Asia 1,000,000 230,000 22.5%

America

South Americab 910,000 150,000 17.0%

North America 1,600,000 63,000 4.0%

Europe 3,200,000 220,000 7.0%

Oceania

Australia and

New Zealand

130,000 4200 3.3%

Other Oceania 8800 1600 18.2%

More developed

regionsc 5,600,000 410,000 7.4%

Less developed

regionsd 7,100,000 1,600,000 22.9%

World 12,700,000 2,000,000 16.1%

PAF ¼ population attributable fraction. a Numbers are rounded to two significant digits.

b Includes Mexico.

c Total for Japan, North America, Europe, and Australia and New

Zealand.

d Total for all other regions.

M O L E C U L A R O N C O L O G Y 7 ( 2 0 1 3 ) 1 e1 3 9

in occupations such as aluminium production, chimney

sweeping, coke production, painters or rubber processing. In

the United Kingdom, 5.3% of cancers were estimated to be at-

tributable to occupation, including breast cancer due to shift

work (Rushton et al., 2012). Occupation-attributable fraction

may be, however, higher in countries with less stringent stan-

dards of worker protection, less attention to industrial hygiene

or with child labour.

Many of these occupation-related chemicals or exposures

also occur as environmental pollutants in air, soil or drinking

water, at generally lower exposure levels than for workers but

often appearing as highly localised pollution due to insuffi-

cient waste management or inadequate protection of the pub-

lic or the environment. Outdoor air pollution due to industry

or traffic has high local variability. Specifically in some low-

and middle- income countries indoor air pollution is relevant

for cancer prevention when solid fuels are used for cooking or

heating in insufficiently ventilated places (Cogliano et al.,

2011) (see also Table 1). Asbestos accounts for almost all of

the mesothelioma burden worldwide today (IARC, 2012e), pre-

dominantly via occupational exposure, and is also account-

able for at least as many lung cancers.

Naturally-occurring carcinogenic chemicals are also

among those which merit a regional priority for targeted can-

cer prevention. Aflatoxins contaminate grain and nuts in the

field and during storage in humid environments (IARC,

2012e) and were estimated to have a causative role in 5e28%

of all global hepatocellular cancers (Wild and Gong, 2010; Liu

and Wu, 2010). Low-cost methods to diminish aflatoxin con-

tamination exist. Arsenic is another naturally-occurring envi-

ronmental carcinogen that can contribute to cancers of the

lung, skin, and bladder (IARC, 2012e).

Ionising radiation, or more specifically X-rays and gamma

radiation, neutrons, and alpha- and beta-particle emitting ra-

dionuclides, are risk factors for several cancer types (IARC,

2012d; UNSCEAR, 2011). Diagnostic X-rays were estimated to

contribute between 0.5 and 3% to the overall cancer burden

in high-income countries (Berrington de and Darby, 2004).

Risk related to radon is high in miners and residential radon

has been estimated to cause 2% of cancer deaths in Europe

with particularly high risks among smokers (Darby et al.,

2005). Protection against solar radiation and avoidance of UV

tanning devices are effective cancer prevention strategies es-

pecially in populations of people with light-coloured skin

(IARC, 2012d).

6. Early diagnosis and screening

The majority of cancers have a long latent phase and are pre-

ceded by pre-neoplastic lesions. Early detection and treatment

of cancer or precancerous lesions allowed substantial declines

in cancer mortality in high-resource countries and would

greatly improve survival in low-resource countries where ac-

cess to expensive cancer treatment is limited

(Sankaranarayanan and Swaminathan, 2011). Firm evidence

of efficacy of screening programmes in the reduction of cancer

mortality exists for three cancer sites: the cervix uteri, breast,

and colon-rectum (IARC, 2002, 2005; Segnan, 2010).

Cervical cancer screening stands out compared to other

screenings as it allows the recognition and treatment of pre-

cancerous lesions using relatively inexpensive and minimally

invasive tests (Pap smear, visual inspection techniques, and

HPV-testing) that can be chosen according to different country

settings. The superiority of HPV-testing compared to cytology

in terms of sensitivity, duration of negative predictive value

and reproducibility of test results across different diagnostic

laboratories has been demonstrated by a number of rando-

mised clinical trials and prospective data in high- and low-

resource countries (Franceschi et al., 2011). A consensus exists

that cytology has a better role to play in the additional evalu-

ation (triage) of HPV-positive women than as a primary

screening test (Franceschi et al., 2011). Visual inspection

methods are less sensitive and specific than either HPV-

testing or good-quality cytology but they may be useful to es-

tablish a first cervical screening infrastructure in low-resource

countries and inform treatment after HPV-testing. Problems

remain, however, with respect to the cost of HPV testing and

the management of HPV-positive women. A simple and cheap

HPV test, careHPV� (Qiagen), has proved to be very accurate (Qiao et al., 2008) but, unfortunately, is not yet available.

Breast cancer screening is an obvious priority on account of

the high frequency of the disease in high- and very high HDI

countries and the rise of breast cancer in medium- and low-

HDI countries. The only screening test of demonstrated effi-

cacy is mammography (IARC, 2002). Mammography-based

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M O L E C U L A R O N C O L O G Y 7 ( 2 0 1 3 ) 1 e1 310

screening programmes are well-established in high-resource

countries but major discrepancies exist in the literature on

screening efficiency. The number of women who would

need to be screened to prevent one death from breast cancer

varied by 5-fold according to different studies (Beral et al.,

2011). The randomised evidence indicates, however, that in

high-HDI countries, around one breast cancer death would

be prevented in the long term for every 400 women aged

50e70 years regularly screened over a 10-year period (Beral

et al., 2011). Mammography-based screening, however, re-

quires larger introduction and running costs than the tests

available for cervical cancer screening and it is not consis-

tently recommended in low- and medium-HDI countries in

which breast cancer incidence rates are lower than in high-

HDI countries. However, due to the substantial survival ad-

vantages of down-staging, a broad range of strategies may

be envisaged to anticipate breast cancer diagnosis according

to different country settings (Harford, 2011). These may ini-

tially include improvements in breast cancer awareness

among women and health workers; facilitation of access of

women with clinically detectable lumps to high-quality diag-

nostic facilities and of women with breast cancer to effective

systemic treatment (Sankaranarayanan et al., 2011).

For colorectal cancer screening in high-resource countries

theissueisnotwhethertoscreenbutratherhowtoscreen.Rand-

omised controlled trials showed that faecal occult blood tests

and flexible sigmoidoscopy reduce colorectal cancer mortality,

but there is much less evidence about the additional benefit

from colonoscopy (Harris and Kinsinger, 2011; Segnan, 2010).

Colorectal cancer screening using flexible sigmoidoscopy offers

the double benefit of early cancer detection and removal of pre-

cancerous lesions (relatively large polyps). Analogous to breast

cancer screening, transfer to low-and medium- HDI-countries

is not yet advocated on account of the high cost and quality re-

quirements of the screening process.

Evidence of mortality benefit from prostate cancer screen-

ing using prostate-specific antigen (PSA) is weak. A large US-

based randomised controlled trial showed no significant re-

duction in prostate cancer mortality after a 13-year follow

up (Andriole et al., 2012). Another similar European-based trial

found a 20% mortality reduction, possibly linked to higher-

standard treatment of cases diagnosed within the interven-

tion arm than in the control group (Wolters et al., 2010). Differ-

ent use of PSA in the control group (85% and 24%, respectively,

in US- and the Europe-based study) may also partly explain

the discrepancy between the two trials (D’Amico, 2012).

Yet, the high uptake of PSA since the late 1980s in the United

States and, later, in Australia, Canada, and many European

countries led to a sharp rise in prostate cancer incidence rates

followed by a much more modest mortality decline. It is of

note that countries that did not start large-scale screening

showed some decline in prostate cancer mortality without

the harm of over-diagnosis (Shibata and Whittemore, 2001).

Experts argued that large-scale use of PSA was adopted pre-

maturely (Brawley, 2009). Notably, contrary to the other effec-

tive cancer screening programmes, prostate cancer screening

spread before consensus on the best treatment for localised

prostate cancer. Many treatment options exist and some of

these treatments are very expensive and have serious and

long-lasting side effects. As noted by Brawley (2009), every

treatment looks good when more than 90% of men getting it

do not need it.

Prostatecanceristhebestknownbutnottheonlyexampleof

the potential risk of an intensive search for early lesions accom-

panied by a lack of adequate knowledge of the biology of the tar-

geted cancer and the way to treat conservatively screen-

detected lesions. Cancer over-diagnosis (i.e. diagnosis of a can-

cer that would otherwise not go on to cause symptoms or death,

Welch and Black, 2010) occurs when there is a vast disease res-

ervoir and activities leading to its detection. Detection can bein-

tentional (e.g., visual inspection for the detection of melanoma

and non-melanoma skin cancer) or unintentional (detailed im-

agingofthebrain,thorax,abdomen,andpelvisintendedtoeval-

uate symptoms that are not cancer-specific).

Thyroid cancer is another important, though little dis-

cussed, example of possible over-diagnosis. In high-resource

countries, small suspicious thyroid nodules are typically dis-

covered by manual palpation of the gland or using ultrasound.

Complaints that are especially often reported to the doctor by

young women (e.g., weight variation, fatigue, irritability, etc)

can also lead to the evaluation of the thyroid gland. Thyroid

cancer incidence rates have been rapidly rising over the last

two or three decades in many countries (Kilfoy et al., 2009;

Dal Maso et al., 2011). Papillary carcinoma accounts for approx-

imately 75% of thyroid cancer cases in high-resource countries

(Kilfoy et al., 2009; Dal Maso et al., 2011). In 2008, thyroid cancer

was the ninth most frequent cancer in women and the six-

teenth in men of all ages worldwide. However, in women

aged 15e45 years thyroid cancer was the second most frequent

cancer in more developed countries (age-standardised inci-

dence rate ¼ 10.1/100000) and the third most frequent in less developed countries (age-standardised incidence rate ¼ 2.8/ 100000) (Table 4) (Ferlay et al., 2010). Only incidence rates of

breast cancer and, in less developed countries, cervical cancer

were more elevated in women aged 15e45 years. Incidence-to-

mortality (I/M ) ratios tend to be relatively favourable for all

cancer types in young women, except for leukaemia in less de-

veloped countries, but I/M ratios for thyroid cancer were by far

the largest: 1 death out of 203 incident cases in more developed

countries and 1 out of 19 in less developed countries. It is note-

worthy that, despite excellent survival, thyroid cancer treat-

ment (thyroidectomy, radioactive iodine, and lifelong thyroid

hormone replacement therapy) is associated with substantial

side effects (Singer et al., 2012).

The existence of an especially large reservoir of slowly

growing tumours in the thyroid gland is also demonstrated

by the frequent detection of small papillary carcinoma in au-

topsy series (Riboli and Delendi, 1991). In a Finnish study of

101 thyroids in children and adults younger than 40 years,

36% harboured one foci of papillary carcinoma or more, with

little variation by sex or age (Harach et al., 1985). Sixty-seven

percent of tumours were below 1 mm.

7. The future for prevention

Whilst innovative cancer treatments and improved access to

affordable and effective cancer treatments are required

(Farmer et al., 2010), it is difficult to envisage dealing with

a doubling in cancer numbers in just two decades by improved

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Table 4 e Age-standardised incidence (I ) and mortality (M ) rates of the most frequent cancer types in women aged 15-to-44 years in more developed and less developed countries (GLOBOCAN).

Cancer No of new cancers Age-standardised incidence rate (per 100,000)

Age-standardised mortality rate (per 100,000)

I/M ratio

a) More developed countries

Breast 78,001 27.7 4.1 7

Thyroid 26,251 10.1 0.0a 203

Cervix uteri 25,638 9.7 1.9 5

Melanoma of skin 20,675 7.9 0.5 14

Ovary 11,105 4.1 1.0 4

Colorectum 10,896 3.9 1.1 4

b) Less developed countries

Breast 207,679 16.1 4.3 4

Cervix uteri 152,496 11.8 3.8 3

Thyroid 36,282 2.8 0.1 19

Ovary 35,381 2.7 1.1 3

Leukaemia 30,735 2.4 1.9 1

Colorectum 29,076 2.3 1.1 2

a Corresponding to an estimate of 129 deaths.

M O L E C U L A R O N C O L O G Y 7 ( 2 0 1 3 ) 1 e1 3 11

efficacy, effectiveness or access to treatment alone, particu-

larly in the low-and medium-HDI countries. This report illus-

trates what is known and what could be done if the evidence-

base for cancer prevention was put into practice now. Clearly

such approaches need to be tailored to the particular chal-

lenges faced by a given country or region. By consequence,

a focus only on shared common risk factors for NCDs (i.e., to-

bacco use, unhealthy diet, physical inactivity, and the harmful

use of alcohol) would miss some special prevention opportu-

nities (Wild, 2012).

In addition, whilst much could already be done, notably re-

ducing tobacco- and infection-associated cancers, there are

significant gaps in knowledge which limit cancer prevention.

First, for a number of common cancers the major risk factors

remain unidentified. Second, in relation to screening, there is

an inability to characterise which screen-detected cancers

will progress and result in morbidity and mortality for the pa-

tient, leading to a risk of over-diagnosis. Third, whilst a number

of prevention strategies have been demonstrated in clinical or

community-based trials, the implementation of these into

health services remains sub-optimal or incomplete. Each of

these areas should be a priority for future research (Wild, 2012).

One area of great promise is to use the rapid advances in un-

derstanding carcinogenesis and the associated laboratory tools

(e.g. biomarkers) to provide new opportunities for primary and

secondary cancer prevention. Among these opportunities are:

improved exposure assessment; elucidation of mechanistic

pathways related to defined exposures; identification of molec-

ular markers which indicate risk of disease progression; and

stratification of cancer cases by molecular subtype in relation

to specific exposures. An especially important development

would be the identification of pre-neoplastic conditions at

stages that are still reversible and for which medical treat-

ments may either be curative or at least able to stop disease

progression. Such progress would diminish the current gap be-

tween rather expensive and potentially harmful screening ap-

proaches in cancer (i.e., early detection of neoplastic or pre-

neoplastic lesions that require surgical ablation) and the

more effective screening approaches in cardio-vascular

diseases (i.e., detection and medical treatment of predisposing

conditions such as hypertension, and hypercholesterolemia).

For these opportunities to be realised, however, basic sci-

ence must be driven towards application to epidemiology and

public health. Achieving this would require a multi-faceted ap-

proach involving education and training, infrastructure

(including co-location of disciplines), resource allocation by

funders and political prioritisation. Nevertheless, there is a re-

sponsibility at least as far as publically-funded research is con-

cerned, to translate the “common soil” of basic science to

prevention in analogous fashion to the translation into better

treatment, hence our call for “two-way” translational cancer

research (Wild, 2010, 2011). Policymakers and funders must pri-

oritise translational research into the causes and prevention of

cancer on a scale not yet seen by comparison to translational

research from bench to beside. The longer-term benefits of pre-

vention in reducing years of life lost and the economic cost of

cancer would counterbalance this investment.

This broader concept of translational cancer research and

its potential to inform cancer prevention stands at an exciting

but critical point in time. The causes of many cancers remain

to be discovered, limiting primary prevention. Secondary pre-

vention is available for some cancers, but improved manage-

ment of individuals after a positive screening test is needed.

Advances in basic science offer clear opportunities to unravel

the complex aetiology of the disease and provide mechanism-

based entry points to disrupt the progression of the disease to-

wards frank malignancy. However, if the benefits for society

are to be realised then a fresh, innovative approach is required

through interdisciplinary research and a robust, more bal-

anced funding of research in cancer prevention and cancer

treatment. To miss this opportunity will be to fail future gen-

erations, particularly those which are amongst the most vul-

nerable, commonly residing in poor countries.

Funding source

None.

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M O L E C U L A R O N C O L O G Y 7 ( 2 0 1 3 ) 1 e1 312

Acknowledgements

The authors would like to acknowledge the helpful comments

provided by Freddie Bray, Jacques Ferlay, David Forman, and

Joachim Sch€uz and technical assistance by Ms Veronique Cha-

banis and Ms Laurence Marnat.

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• Meeting the global demands of epidemiologic transition – The indispensable role of cancer prevention
• 1. Introduction
• 2. The epidemiologic transition
• 3. Global cancer burden: mortality and incidence
• 4. Risk factors for NCDs and cancer
• 5. Diversity in cancer aetiology and cancer prevention strategies
• 5.1. Infections and cancer
• 5.2. Cancer and the environment
• 6. Early diagnosis and screening
• 7. The future for prevention
• Funding source
• Acknowledgements
• References

Understanding and Applying Medical Anthropology Chapter9&10.pdf

Total life expectancy at age 45

Life expectancy at birth

Year

Li fe

e xp

ec ta

n cy

1870 1890 1910 1930 1950 1970

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Year 1870 1890 1910 1930 1950 1970

50

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150

M o

rt al

it y

ra te

(p er

t h

o u

sa n

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1840 1860 1880 1900 1920 1940 1960

100

0

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700

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900

Year

D ea

th ra

te (p

er m

ill io

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h ild

re n

)

Free vaccination available

Compulsary vaccination of infants

Vaccination laws enforced

Causal organism identified

Antitoxin first used in treatment

National immunization campaign begun

Smallpox Diphtheria Scarlet Fever

Whooping cough Measles

1850 1860 1870 1880 1890 1900 1910

Year

1920 1930 1940 1950 1960 1970

D ea

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h ild

re n

)

0

500

1000

1500

2000

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Streptococcus identified

Causal organism identified

Sulfa drugs

Antibiotics

Immunization generally available

Immunization begun

Tuberculosis Bronchitis, pneumonia, & influenza

1850 1860 1870 1880 1890 1900 1910 Year

1920 1930 1940 1950 1960 19701840

D ea

th ra

te (p

er m

ill io

n )

0

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Tubercle bacillus identified

Chemotherapy

BCG vaccination

01_Booth_Epidemiologic_Transition_in_2016.pdf

23

Canadian Studies in Population 43, no. 1–2 (2016): 23–47.

Epidemiologic Transition in Australia: The last hundred years

Heather Booth1 Leonie Tickle Jiaying Zhao

Abstract

Mortality change in Australia since 1907 is analysed in the light of Epidemiologic Transition theory. Australia began the twentieth century in the second age of the Epidemiologic Transition, the Age of Receding Pandemics. Australia probably moved to the third, the Age of Degenerative and Man-Made Diseases before 1946, which is slightly in advance of most Western countries. Transition to the fourth, the Age of Delayed Degenerative Diseases, is clearly marked by a downturn, in about 1970, in circulatory disease mortality, concurrent with other Western countries.

Keywords: mortality, trends, decomposition, life expectancy, differentials. Australia.

Résumé

La théorie de la transition épidémiologique sert de base pour une analyse des changements de mortalité en Australie depuis 1907. Au début du XXe siècle, l’Australie était dans la deuxième phase de la transition épidémiologique, celle du recul des pandémies. Néanmoins, l'Australie entrait probablement avant 1946 dans la troisième phase, celle des maladies dégénératives, ce qui est légèrement en avance sur la plupart des pays occidentaux. La transition vers la quatrième phase, celle des maladies dégénératives retardées, est clairement marqué par un ralentissement depuis environ 1970 dans la mortalité par maladies circulatoires, en même temps que chez d'autres pays occidentaux.

Mots-clés : mortalité, tendances, décomposition, espérance de vie, écarts, Australie.

Introduction

Australia enjoys a life expectancy that is among the highest in the world. In 2011–13, life expect- ancy at birth among females was 84.3 years, and 80.1 years among males (ABS 2014a). The recently- released United Nations World Population Prospects 2015 (UNPD 2015) shows that for life expectancy at birth in 2010–15, Australian males rank eighth internationally and Australian females rank tenth. The top five ranked countries for males are Hong Kong, Iceland, Switzerland, Italy, and Israel, and for females they are Hong Kong, Japan, Singapore, Italy, and Spain.

1. Corresponding author: Prof. Heather Booth, School of Demography, The Australian National University, Canberra ACT 2601, Australia, e-mail: [email protected]; Prof. Leonie Tickle, Faculty of Business and Economics, Macquarie University, Sydney; and Dr. Jiaying Zhao, School of Demography, The Australian National University.

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This paper examines changing mortality in Australia since 1907, six years after the Commonwealth of Australia was created. The focus is at the national level, with some discussion of differentials. The theoretical framework of the analysis is the Epidemiologic Transition (Omran 1971; Olshansky and Ault 1986). As noted by de Looper (2015), the more recent Epidemiologic Transition in Australia has not been addressed as such, though studies of twentieth-century mortality decline there do exist (e.g., Taylor and Lewis 1998; Taylor et al. 1998; Booth 2003). This paper remedies the omission.

The paper is organized as follows. After a discussion of Epidemiologic Transition theory and a description of the data and methods employed, the paper examines trends in life expectancy at birth and at selected ages by sex. Cause of death, in conjunction with age, is then explored through a series of decompositions of temporal change in life expectancy over the course of the lengthy period con- sidered. The following section addresses age patterns of change and focuses on infant mortality, the adolescent and young adult mortality hump, and old age mortality. The penultimate section presents geographic, indigeneity and socio-economic mortality differentials, and the paper concludes with a discussion of the findings in relation to Epidemiologic Transition theory.

Epidemiologic Transition theory

The theory of Epidemiologic Transition (Omran 1971, 1983) describes health changes during the process of modernisation as a series of three successive stages of transition or ‘Ages’. The first is the ‘Age of Pestilence and Famine’, characterised by low and fluctuating life expectancy in the range 20–40 years. The second is the ‘Age of Receding Pandemics’ when life expectancy increases steadily from an average of about 30 years to 50 (Omran 1971) or 55 (Omran 1983) years, largely as a result of less frequent epidemics and the decline of infectious diseases; the underlying causes were primarily socio-economic, ‘augmented by the sanitary revolution in the late nineteenth century and by medical and public health progress in the twentieth century’ (Omran 1971, reprint p.753). The third ‘Age of Degenerative and Man-Made Diseases’ is characterised by a slow increase in life expectancy due to the balancing effects of the disappearance of infectious diseases and the rise of ‘degenerative and man-made’ or non-communicable diseases such as heart disease, stroke, cancers, and external causes. At the time of publication of the theory, the general consensus was that there was a limit to life expectancy which would soon be reached (see Meslé and Vallin 2011); for example, United Na- tions (1975) took this limit to be 75 years.

In response to renewed mortality decline from the 1970s, Olshansky and Ault (1986) proposed a fourth ‘Age of Delayed Degenerative Diseases’ characterised by the decline of cardiovascular and other non-communicable diseases at increasingly older ages, due to advances in medical technology and improved health programs. Rogers and Hackenberg (1987) also proposed a fourth ‘hubristic’ (or ‘hybristic’) stage giving prominence to the decline of social pathologies arising from individual behav- iour and lifestyle, which are driven by ‘hubris’ or notions of excessive self-confidence and invincibility. These two proposed extensions of Omran’s Epidemiologic Transition theory address different aspects of the same stage.2 The Epidemiologic Transition theory has been criticised by Robine (2001) and by Meslé and Vallin (2006), particularly in regard to the distinction between the third and fourth Ages.

Omran (1971) defined three models of Epidemiologic Transition, in recognition of the differing dates of onset and speeds of transition among countries. The Classical or Western model applies to the populations of Europe and North America. Compared with this, the Accelerated model involves

2. Proposals of fifth and sixth stages exist; these are not considered.

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a more rapid transition such as occurred in Japan. The Contemporary or Delayed model applies to the populations of developing countries.

While Epidemiologic Transition theory was developed to explain global patterns, it can be used in the study of mortality decline in individual countries (e.g., Caselli, Meslé and Vallin 2002; Lussier, Bourbeau and Choinière 2008). Several exceptions to the overall theory have been identified (Caselli, Meslé and Vallin 2002). A limitation is that Omran did not provide clear guidelines to determine when successive Ages begin and end (Mackenbach 1994). It has been argued that the approach is overly broad, and that there is a need to take greater account of how population subgroups experi- ence epidemiologic transitions differently (Gaylin and Kates 1997).

The early years of the Epidemiologic Transition in the settler3 population of Australia have been comprehensively documented by de Looper (2015) who notes that the Age of Pestilence and Famine was absent in Australia.4 Though there was no shortage of epidemics in the second half of the nineteenth century, famine was almost entirely absent, and life expectancy was always above the defining threshold of 40 years for transition to the second stage (Omran 1971). Thus, the second Age of Receding Pandemics characterises the start of the ‘truncated’ Epidemiologic Transition in Australia, confirmed by life expectancies in the 1860s of 45 years for males and 49 years for females. Further, de Looper (2015) concluded that, although there was rapid mortality decline in the period 1885 to 1903, there was no evidence of transition to the third Age of Degenerative and Man-Made Diseases because the major causes of death (infectious diseases, non-communicable diseases, and external causes) declined proportionately. Commencing in 1907, this analysis therefore begins in the second Age of Receding Pandemics.

Data and methods

Data for international trends and comparisons of life expectancies are from the Human Mortal- ity Database (HMD 2015). The analyses use five-year averages from 1920–24 to the present. For Aus- tralia, HMD covers 1921 to 2011, so that the first period is 1921–24.The countries for comparison are Canada, England and Wales, France, Japan, and the United States, selected on the basis of high income and either historical links and cultural similarities to Australia (Canada, England and Wales, United States) or recent leading-edge mortality experience (France, Japan). HMD data are also used for the examination of trends at specific ages.

Australian cause of death data are from the Australian Institute of Health and Welfare (AIHW) General Record of Incidence of Mortality (GRIM) books (AIHW 2015a), which contain mortality rates by five-year age groups for ages 0 to 84 and for the age 85+, from 1907 to 2012. Over this per- iod, the International Classification of Diseases underwent numerous revisions (WHO 1992), leading to inconsistencies in cause of death classification and discontinuities in time series of data. These potential problems have been largely mitigated in this analysis by considering only the major cause of death categories. The six major causes of death employed are infectious diseases, neoplasms, circulatory diseases, respiratory diseases, external causes, and ‘all other’ causes. Note that in the early part of the century,

3. The first British settlers arrived in Australia in 1788. Comprehensive mortality data have been compiled from 1856, when registration began, by de Looper (2015).

4. Smith (1980) and Gray (1985) suggest that the historic Indigenous population was stationary prior to settlement. Therefore, this population would not have been subject to the fluctuation defining the Age of Pestilence and Famine. Thus, neither the Indigenous nor settler population appears to have experienced Omran’s first Age.

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many ‘all other’ causes of death were indicated as ‘ill-defined’, but this classification was reduced to near zero by 1960 (Lancaster 1990).

The cause of death analysis uses standardized mortality rates and life expectancy decomposition. Standardized mortality rates are computed by sex and the six major causes of death, using the 1981 total Australian population (both sexes) by five-year age groups as the standard. Life expectancy decomposition uses the Arriaga (1984) method to attribute differences in life expectancy at birth to mortality change by age and major cause simultaneously. To facilitate discussion of the Epidemio- logic Transition, decomposition analyses were conducted for four periods:5 1922–46, 1946–70, 1970– 94, and 1994–2011. The periods were identified on the basis of internal consistency of patterns; that they are of roughly equal length assists in their comparison.

Trends in life expectancy

Australia in international context

Figure 1 compares historic male and female Australian life expectancies at birth with those of Canada, England and Wales, France, Japan, and the United States. The upward trends confirm the experience of Epidemiologic Transition. Deaths of Australian military personnel during World War II were excluded from national mortality statistics (Taylor et al. 1998), accounting for the absence of a downward spike in male life expectancy observed for some other countries.

In 1921–24, life expectancy in Australia was the highest among the six selected countries. Figure 1 shows that for both males and females, Australian life expectancy exceeded that of the second- highest of this group by as much as four years. Over the next two to three decades, this advantage diminished, and in the 1950s and 1960s Australia fell behind other countries. In the 1960s, for males, only US life expectancy was less than Australian life expectancy, while for females Australian life ex- pectancy was as low as any other at this time. Australian life expectancy has since recovered, ranking first among the selected group for males, and third for females, in the most recent period.

It may be observed in Figure 1 that the life expectancies of the selected countries largely con- verged in the 1960s. Convergence among the five Western countries persisted for about two decades, and coincided with the period when Japan overtook the Western countries. In recent decades, all six countries have tended to diverge, with differences of as much as five years occurring in the most recent decade. This pattern of convergence and divergence is consistent with wider trends (Meslé and Vallin 2011). Cardiovascular diseases, which are of key importance in the transition from the Age of Degenerative and Man-Made Diseases to the Age of Delayed Degenerative Diseases, played a dominant role in both the convergence and divergence of countries over the entire period (Meslé and Vallin 2011).

Japanese life expectancy increased rapidly in the post–World War II years, in keeping with its characterization as undergoing Accelerated Epidemiologic Transition (Omran 1971; Zhao et al. 2014). Japan has been a leader in life expectancy since the 1970s, particularly for females. In 2011, Japanese females had a 1.6 year advantage over Australian females, though Japanese males were at a slight disadvantage compared with Australian males (HMD 2015). In contrast, the United States has generally ranked last—since the mid-1960s for males, and since the early 1990s for females (Figure 1); this has been attributed to higher prevalences of smoking, obesity, and violence, as well as restricted access to health care (Caselli et al. 2014: 231).

5. 1921 was omitted owing to a large increase in that year.

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The sex difference in life expectancy at birth for the selected countries is shown in Figure 2. Ignoring war-related deviations, all countries experienced a general increase in the sex difference, followed by a downturn, as male improvements began to exceed female improvements. The turning point differs among countries, occurring first for England and Wales followed by the United States, Canada, and Australia, France, and finally (only around a decade ago) Japan. This same pattern has been found for high-income countries more generally, and has been attributed in most countries primarily to sex differences in the age pattern of mortality, rather than declining sex ratios in mortal- ity (Glei and Horiuchi 2007). A decomposition analysis of the G7 countries over the three decades to 2000 found that the main causes of death contributing to narrowing of the sex difference were circulatory diseases and accidents, violence, and suicide (Trovato and Heyen 2006).

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Figure 1. Life expectancy at birth for males and females, selected countries, 1920–24 to present. Source: Human Mortality Database (HMD 2015) Life tables, five-year.

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Australia currently has a sex difference in life expectancy of 4.3 years; this is broadly similar to Canada, the United States, and England and Wales, and lower than France and Japan, where the turning points occurred later. In Australia, the sex difference increased from 3.9 years in 1921–24 to a maximum of 7.0 years in 1975–79, and has since declined linearly. More detailed decomposition analyses appear in Pollard (1996), Trovato and Lalu (1997), Booth (2003), and Tickle (2016).

The Australian experience

Over the period from 1921–24 to 2010–11, life expectancy at birth in Australia increased from 60.2 to 79.9 for males, an average of 2.2 years per decade, and from 64.1 to 84.3 for females, an average of 2.3 years per decade. However, as already indicated, improvements in life expectancy were not uni- form over this period. After increases from 1921 to 1960 averaging 1.9 years per decade for males and 2.5 years per decade for females, mortality levels stagnated during the 1960s. The earlier, more rapid in- creases in life expectancy are characteristic of the second Age of Receding Pandemics, while the slow increases are characteristic of the third Age of Degenerative and Man-Made Diseases as described by Omran (1971). Mortality decline resumed in the early 1970s, with subsequent average improvement rates per decade of 3.0 years for males and 2.3 years for females. This post-1970 experience accords with the fourth Age of Delayed Degenerative Diseases, as described by Olshansky and Ault (1986).

Life expectancies for Australian males and females at ages 0, 50, 65, and 85 from 1921 to 2011 are shown in Figure 3 and Table 1. It is evident that virtually all of the improvement in male life expectancy at birth between 1921 and 1970 was due to mortality decline at ages less than 50: life ex- pectancy at age 50 remained roughly constant over this period and actually declined during the 1930s and 1960s. In contrast, female life expectancy at age 50 improved before 1970, although the 1930s and 1960s were periods of stagnation. Since 1970, gains at the older ages have been rapid, particularly for males. This pattern is consistent with the Epidemiologic Transition, in that it describes mortality improvement as first occurring among children and young women, followed later by reductions in chronic and non-communicable diseases among older people. The age and cause-of-death groups

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Figure 2. Sex difference in life expectancy at birth in years, selected countries, 1920–24 to present. Source: Human Mortality Database (HMD 2015) Life tables, five-year.

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contributing to changing life expectancy in Australia are discussed in Section 5; for a more detailed analysis for the period since 1979, see Tickle (2016).

The widening and then narrowing pattern in the sex difference in Australian life expectancy at birth generally also applies at the older ages, as Figure 3 and Table 1 show. The sex differences in life expectancy at ages 0, 50, and 65 all increased to maxima—7.1, 5.7, and 4.2 years, respectively—at around 1980. In contrast, at the oldest ages increases in female advantage persisted longer; the sex difference in life expectancy at age 85 widened to a maximum of 1.4 years in the mid-1990s, before narrowing began.

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Figure 3. Life expectancy at ages 0, 50, 65 and 85 for males (solid line) and females (dotted line),

Australia, 1921 to 2011

Source: Human Mortality Database (HMD 2015) Life tables, five-year.

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Figure 3. Life expectancy at ages 0, 50, 65 and 85 for males (solid line) and females (dotted line), Australia, 1921 to 2011. Source: Human Mortality Database (HMD 2015) Life tables, five-year.

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Causes of death

Long-term changes in major causes of death

Australia experienced substantial changes in cause-specific mortality over the period 1907 to 2012 (AIHW 2006). Figure 4 shows that mortality from infectious diseases decreased substantially during the first half of the twentieth century: in 1907, infectious diseases accounted for 16 per cent of the total standardised mortality rate for males and 23 per cent for females, but by 1946 accounted for less than 6 per cent for both sexes, and decreased to insignificant levels by 1960. The initial de- cline of infectious diseases as a cause of death is characteristic of the Age of Receding Pandemics, while their ongoing decline and virtual disappearance is a characteristic of the Age of Degenerative and Man-Made Diseases. The decreasing prevalence of tuberculosis contributed significantly to the decline of infectious diseases as a cause of death in Australia.

The most recent epidemic significantly affecting Australia was Spanish Influenza, a respiratory disease. Though it began in Spain in 1918, this epidemic did not reach Australia until 1919. The oc- currence of this epidemic would indicate that Australia experienced the Age of Receding Pandemics until at least 1920. Apart from this epidemic, the mortality rate from respiratory diseases generally decreased over the whole period, and in relative terms it declined from 14 per cent of the total stan- dardised mortality rate in 1921 to 10 per cent or less since 1946. Respiratory diseases contributed substantially to excess male over female mortality, especially in the second half of the 20th century, partly as a result of smoking-related diseases such as COPD, which increased in relative terms.

The pattern of change in circulatory disease mortality involved a substantial increase, beginning in about 1920, before decline commenced, as seen in Figure 4. In 1907, circulatory diseases accounted for 19 per cent of male and 20 per cent of female mortality. For males, mortality from circulatory dis- eases increased to the late 1960s, reaching 54 per cent of the total standardized mortality rate in 1970. The increase for females was much less pronounced, and rates stagnated in the 1950s and 1960s, but circulatory disease still accounted for 58 per cent of the total standardized mortality rate in 1970. This pattern would suggest that the transition to the Age of Degenerative and Man-Made Diseases began in 1920 or soon thereafter. For both sexes, circulatory disease mortality declined rapidly from about 1970, marking the beginning of the transition to the fourth Age of Delayed Degenerative Diseases. Circulatory diseases remain both a leading cause of death and a leading cause of excess male mortality.

The increase in circulatory disease mortality has been attributed to high blood pressure, smoking, elevated blood cholesterol, and dietary factors (particularly the consumption of saturated fat and salt; AIHW 2000). Other risk factors include socio-economic status, obesity and physical inactivity, and the harmful use of alcohol (AIHW 2014). The decrease in circulatory diseases from 1970 has been sub- stantially attributed to medical advances and health service improvement, as there has been little change

Table 1. Life expectancy at ages 0, 50, 65 and 85 for males and females and the sex difference, Australia, selected years

Year

Life expectancy at age 0 age 50 age 65 age 85

Male Female Diff. Male Female Diff. Male Female Diff. Male Female Diff. 1922 60.9 65.1 4.2 22.6 25.3 2.7 12.4 14.0 1.6 4.1 4.5 0.4 1946 65.9 70.2 4.3 22.7 26.1 3.4 12.2 14.5 2.3 3.9 4.5 0.6 1970 67.4 74.2 6.8 22.6 27.8 5.2 12.0 15.7 3.7 4.1 5.1 1.0 1994 74.9 80.8 5.9 27.9 32.6 4.7 15.6 19.4 3.8 4.9 6.3 1.4 2011 80.0 84.3 4.3 32.1 35.7 3.6 19.2 22.1 2.9 6.0 7.1 1.1 Source: Human Mortality Database (HMD 2015) Life tables, single years of age.

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in physical activity levels and a significant increase in the prevalence of overweight (AIHW 2000, 2014). Thus, the Australian experience of circulatory disease is consistent with Olshansky and Ault (1986) in that the main agent for delayed non-communicable morbidity and mortality has been advances in med- ical technology (Weisfeldt and Zieman 2007), health care programs for the older population, and reduc- tion of risk factors in communities. The Australian experience also supports the hubristic hypothesis of Rogers and Hackenberg (1987) in that lifestyle factors serve to limit and delimit mortality decline.

Deaths from neoplasms accounted for 8 per cent of male and 9 per cent of female mortality in 1907.6 Though rates remained fairly constant during the first half of the century, declines in overall mortality were such that by the 1950s, neoplasms ranked second among leading causes of death. Mortality from neoplasms subsequently increased, especially among males; this has been attributed to changes in smoking behaviour, diet, and environmental factors (AIHW 2000). Despite decreases from about 1990, neoplasms have ranked first among the leading causes of death since 2005. Deaths from neoplasms currently account for around one-third of total age-standardized mortality rates. The causes of cancer are not yet fully understood, but it has been estimated that in high-income countries, smoking, alcohol use, and overweight and obesity were the most important causes at the turn of the century (Danaei et al. 2005). In terms of the Epidemiologic Transition, the Australian experience of neoplasms since 1990 is characteristic of the Age of Delayed Degenerative Diseases.

Mortality rates from external causes were fairly constant over much of the century, although changes occurred for specific external causes such as motor vehicle accidents and suicides. Rates were consistently higher for males than for females, while for both sexes decreases occurred in recent decades. The relative contribution of external causes of death to overall mortality increased slightly, especially for males. Deaths due to external causes are discussed in greater detail below.

The contribution of ‘all other’ causes of death to overall mortality declined substantially over time, particularly before 1950, due largely to improvements in the classification of specific conditions and an associated reduction in the ‘ill-defined’ causes (Lancaster 1990). It is clearly the case that if these deaths had been otherwise classified, the early pattern of mortality decline by cause of death could have looked somewhat different. This observation extends to the causes of excess male over fe- male mortality, most of which is attributed to all other causes in the early part of the period (Figure 4).

These trends in 20th-century Australian mortality rates by major cause of death are broadly similar to those in other Western countries (Meslé and Vallin 2011; Zhao et al. 2014; Bourbeau and Ouellette 2016; this volume). The decline in infectious disease mortality and the timing of Spanish Influenza were contemporaneous across countries. As noted, the transition to the Age of Degenerative and Man- Made Diseases began in Australia in 1920 at the earliest. The increase in circulatory disease mortality also began in about 1920, though it is unknown to what degree improved classification influenced the early trend. It is clear that mortality levels stagnated during the 1960s, due to the combined effect of declining infectious disease mortality and, until around 1970, increasing circulatory disease mortality (Taylor et al. 1998). This pattern of counterbalancing causes of death is characteristic of the third Age of Degenerative and Man-Made Diseases, as described by Omran (1971). The ensuing rapid decline in circulatory disease mortality, combined with declines in respiratory disease mortality from the 1970s, and with declines in deaths from neoplasms from the 1990s, marked the transition to the Age of Delayed Degenerative Diseases, when life expectancy resumed its increase. This decline in circulatory disease mortality was more pronounced in Australia than in other Western countries, including Canada and the US (Barbieri and Ouellette 2012) and England and Wales (Griffiths and Brock 2003).

6. These are probably underestimates as in 1907 the diagnosis and certifying of cause of death for cancer was problematic.

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Contribution of changes in age-cause-specific mortality to changes in life expectancy at birth

To further identify the roles of the six major causes of death by age group during the Epidemio- logic Transition, decomposition analyses of changes in life expectancy were conducted for the four selected periods (see the section on ‘Data and methods’). Life expectancies for relevant years are shown in Table 1. The decompositions by age and cause of death are shown in Figure 5 for males and Figure 6 for females.

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Figure 4. Standardized mortality rate (per 100,000) for males and females by major cause of death, Australia, 1907 to 2012. Source: Authors’ calculations based on data from AIHW (2015a).

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From 1922 to 1946, life expectancy increased by 5.0 years for both sexes. Mortality reductions at ages 0 to 4 were responsible for a life expectancy gain of 2.6 years for males and 1.9 years for females, half of which was attributable to infectious diseases. Reductions in infectious and respiratory disease mortality at ages 5 and older also contributed to the life expectancy gains, but at older adult ages were counterbalanced by increases in circulatory disease mortality (see Figures 5 and 6). Thus, ages 65 and older for males and 85 and older for females contributed negatively to the life expectancy gains.

In terms of the contribution of deaths from different causes to the increases in life expectancy, infectious diseases contributed 2.3 years for males and 2.1 years for females, while respiratory dis- eases contributed 1.1 years for males and 0.9 years for females. In contrast, rising circulatory disease mortality at ages 45 and older produced negative contributions of 1.5 years for males and 1.0 year for females. It is noted that reduced deaths from all other causes (which were partly due to better classification) made substantial positive contributions in this period, particularly for females; this may account for some of the negative contribution of circulatory disease mortality (Lancaster 1990). In terms of the Epidemiologic Transition, this analysis would indicate that 1922–46 was the period of transition from the Age of Receding Pandemics to the Age of Degenerative and Man-Made Dis- eases. The substantial increase in life expectancy over this period would, however, indicate that the transition occurred rather late in the period.

The changes in life expectancy from 1946 to 1970 are smaller than those occurring in the previ- ous period, and differ substantially by sex: life expectancy for females increased by 4.0 years, while that for males increased by only 1.6 years (Table 1). Again, the mortality decline at ages 0 to 4 con- tributed significantly to the overall increase in life expectancy—by 1.2 years for both sexes. The large sex difference in the net gain in life expectancy is accounted for by a much larger decrease in female than male mortality at ages 15 and older. For males, an increase in circulatory disease mortality (oc- curring at ages 35 and older) resulted in an overall 0.6-year loss of life expectancy, while an increase in deaths from neoplasms accounted for a 0.4-year loss. In contrast, for females, reduced deaths from circulatory diseases (except at ages 75 and older) and neoplasms contributed to overall gains of 0.3 and 0.2 years, respectively. Further, the life expectancy gains due to respiratory disease mortality were larger for females than for males. Deaths from external causes contributed negatively to the change in life expectancy for both sexes, particularly among males and those aged 15 to 29, attributable to the emergence of the accident hump. Again, reduced deaths from all other causes contributed sub- stantially and positively to life expectancy. During this period, Australia exhibited characteristics of the Age of Degenerative and Man-Made Diseases, especially among males for whom circulatory, respiratory, and external causes—associated with lifestyle and man-made factors such as smoking and motor vehicles—were key. While female mortality was less affected by such factors and continued to decline, albeit at a slower pace than previously, male mortality stabilized and at some ages increased. For both sexes, as shown in Figure 4, non-communicable diseases became dominant, with circulatory diseases becoming the primary killer.

The period 1970–94 was one of renewed acceleration in mortality decline, with life expectancy increasing by 7.5 years for males and 6.6 years for females. The decline in mortality at ages 0 to 4 contributed 1.3 years for males and 1.0 year for females, but these were no longer dominant. Declines at ages 45 to 84 made much larger positive contributions to life expectancy, primarily due to reduced circulatory disease mortality, but also due to reductions in respiratory disease mortality. Mortality from external causes also declined, notably among males aged 15 to 24, contributing to higher life expectancy. In contrast, mortality from neoplasms at ages 65 and older made a small negative con- tribution to life expectancy, 0.1 years for both sexes. This pattern of change is consistent with the

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Booth et al.: Epidemiologic Transition in Australia – The last hundred years

35

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Canadian Studies in Population 43, no. 1–2 (Spring/Summer 2016): Special issue on Canada and Australia

36

Age of Delayed Degenerative Diseases, which is characterised by a significant decline in circulatory disease mortality (Olshansky and Ault 1986). In Australia, fully 4.3 years of the net gain in both male and female life expectancy was due to reduced deaths from circulatory diseases.

During the final period considered, 1994–2011, life expectancy increased by 5.1 years for males and 3.5 years for females. These increases were mainly attributable to declines in mortality at ad- vanced ages. Indeed, life expectancy at age 65 increased by 3.0 years for males and 2.4 years for females. In terms of causes of death, circulatory diseases were still the primary contributor, ac- counting for 2.7 years for males and 2.6 years for females. In contrast to the previous period, reduced mortality from neoplasms—mainly at ages 50 to 79 years for males and 45 to 74 for fe- males—contributed positively to changes in life expectancy, though rates continued to increase at older ages. This pattern is consistent with the notion of delayed non-communicable diseases. For males, reduced mortality from respiratory diseases, mainly at older ages and from external causes at young ages, each produced a gain of 0.4 years in life expectancy, while for females, smaller gains of 0.2 and less than 0.1 years, respectively, were produced. The emergence of a loss in life expectancy due to ‘all other’ causes at age 85 and older, particularly for females, is due to the increased incidence and better reporting of neurological diseases such as dementia.7 Generally, over this period, the pat- terns of change in the major causes of death are characteristic of the Age of Delayed Degenerative Diseases, in that the rapid decline in death rates is concentrated mostly at advanced ages (Olshansky and Ault 1986).

Comparison of the four decompositions throws further light on the evolution of the Epidemio- logic Transition in Australia. Being chosen on the basis of internal consistency of age-by-cause patterns, these time periods help to identify the processes taking place in the transition. Comparing 1922–46 and 1946–70, it is clear that circulatory disease mortality was more important in the earlier period in limiting life expectancy gains. During this period, gains due to infectious disease mortality, which was concentrated at ages 0 to 4, were counterbalanced by losses due to circulatory disease mor- tality at older ages. Thus the transition moved into the Age of Degenerative and Man-Made Diseases during this period. For male mortality, the decomposition for 1946–70 shows not only a continua- tion of this pattern but also life expectancy losses due to increased mortality from external cause (see below subsection on the adolescent and young adult mortality hump), and from neoplasms and respiratory diseases, both of which are associated with smoking. The near-absence of these losses in female life expectancy during this period is largely attributable to the later and more-restricted uptake of smoking among females (AIHW 2000).

It is clear from comparison of the decompositions for 1946–70 and 1970–94 that 1970 marked a turning-point in cause-of-death patterns in Australia. Pre-1970 life expectancy losses, due principally to circulatory disease mortality, became large positive post-1970 gains. Thus, 1970 can be regarded as a watershed between the Age of Degenerative and Man-Made Diseases and the Age of Delayed De- generative Diseases. Comparison of the decompositions for 1970–94 and 1994–2011 demonstrates how gains in life expectancy due to circulatory disease mortality have moved to older ages, which is a characteristic of the Age of Delayed Degenerative Diseases. Comparison of males with females in 1970–94 and 1994–2011 shows that female gains occur at older ages than male; given higher female life expectancy, this is consistent with the Age of Delayed Degenerative Diseases, in which deaths are progressively delayed. In general terms, it can be said that the Epidemiologic Transition is more advanced for females than for males.

7. Alzheimer’s disease was introduced into the ICD in 1979.

Booth et al.: Epidemiologic Transition in Australia – The last hundred years

37

Age patterns of change

The broad theoretical approach of the Epidemiologic Transition is complemented in this section using two approaches. First, age patterns of mortality change are examined over time. Second, we focus on three ages that are important in determining the shape of the mortality schedule and the evolution of life expectancy: these are infancy, adolescence and young adulthood, and older age (or senescence).

The age patterns of mortality change themselves change over time. This is seen in Figure 7 which shows the average annual percentage decline in age-specific mortality rates for the four selected time periods. These curves are similar to the overall patterns shown in Figures 5 and 6, but enable direct comparison by age and period in the speed of decline.

In 1922–46, mortality decline was most rapid at childhood (but not infant) ages and at about age 30. In contrast, there was very little change at older adult ages, where in fact some increases occurred, especially for males. This echoes the counterbalancing trends in infectious and circulatory diseases, and provides supporting evidence that the transition to the Age of Degenerative and Man-Made Diseases occurred during this period.

The second period, 1946–70, is notable for the substantial sex difference in the patterns of mortality decline at adult ages. Though for both sexes the rate of decline was relatively low at age 20, for males the rate was negative around this age, indicating increased mortality that, in combination with modest increases at older ages, resulted in the mortality stagnation of the last decade or so of this period. At older ages, negative rates of decline for males reflect the increasing mortality from circulatory and respiratory diseases and neoplasms that characterizes the Age of Degenerative and Man-Made Diseases. Infant and early childhood mortality declined relatively slowly during this per- iod, again consistent with the Age of Degenerative and Man-Made Diseases.

Figure 7. Average annual percentage decline in age-specific mortality rates by sex by period, Australia, 1922 to 2011. Source: Authors’ calculations based on data from HMD (2015).

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In 1970–94, infant and childhood mortality resumed a more rapid decline. At post-childhood ages, the most rapid declines occurred at ages 40 to 80 among females and at 50 to 60 among males. This was the period when circulatory disease mortality declined rapidly and life expectancy increases resumed, indicative of transition to the Age of Delayed Degenerative Diseases, as described by Ol- shansky and Ault (1986). The fact that little change occurred at ages 20–30, especially among males, supports the hubristic hypothesis of Rogers and Hackenberg (1987).

Finally, in 1994–2011, the most rapid declines in adult mortality were at ages 50 to 90 years. The rapid shift of the modal age of mortality decline is a characteristic of the Age of Delayed Degenera- tive Diseases. While high rates of decline still occurred in infancy and early childhood, they apply to very low rates, reducing their overall impact.

Infant mortality

In common with high-income countries generally, over the last century rapidly declining infant mortality accounted for a very significant component of the increase in Australian life expectancy at birth. This was particularly so during the earlier part of this period, when Australia experienced a rapid decline in infectious disease mortality. de Looper (2015, Chapter 7) noted that deaths in infancy occurred at a rate of about 1 in 10 in 1900, and that they declined sharply after 1903, thanks largely to social and environmental factors.

Figure 8 shows the infant mortality rate, defined as annual deaths of infants under one year of age per 1,000 live births in the same year, over the period 1921–2011. While the decline has been fairly continuous, more rapid declines occurred in the early 1940s—following the introduction of sulfa drugs in the 1930s to combat infection, notably in childbirth—and again in the mid-to-late 1970s—coincident with both the introduction of the Medibank (now Medicare) universal health in- surance scheme (Taylor and Lewis 1998) and the operation of new neonatal intensive care units (Tay- lor and Lewis 1998). By the mid-1970s, infant mortality rates had declined to one-quarter of 1921 levels, and by 2011 to only 6 per cent of 1921 levels. Infant mortality rates in Australia are now such that over 99.5 per cent of infants survive to their first birthday. Although further dramatic reductions are therefore not possible, scope for significant gains is indicated by even lower rates in numerous countries—including Singapore and Hong Kong, where current infant mortality rates are less than half of those in Australia (UNPD 2015). It is noted, however, that many among these countries do not have remote or inaccessible areas such as those in Australia, and are therefore in a better position to deliver universal health services and risk factor reduction programs.

Adolescent and young adult mortality hump

A notable feature of changing Australian mortality over the last century was the appearance and subsequent diminution of the so-called accident hump at late teenage and early adult ages. Figure 9 illustrates this phenomenon, showing the emergence of a pronounced hump for males in the 1960s and 1970s8 and its transformation into a plateau or ‘shoulder’ by the early 1990s, and the later emergence of a much smaller accident hump for females (Pollard 1996). Since the early 1990s, mortality rates have declined further at all ages, and the plateau shape has been retained. It is ques- tionable whether this feature will persist or eventually disappear. It is noted that historic mortality patterns do not display this feature, and that for Swedish females it has all but disappeared (Booth and Tickle 2008).

8. The main increase (for males) or stagnation (for females) in the probability of death occurred in the 1960s.

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39

Pollard (1996) found that the factors behind the change in the Australian male accident hump included a decline in motor vehicle accident mortality in late teenage and early twenties, due to public health measures—including seat belt and random breath testing legislation—as well as improvements in road systems and in vehicle design. Thus, the accident hump is a feature of the Age of Degen- erative and Man-Made Diseases, and its diminution may be viewed as characteristic of the Age of Delayed Degenerative Diseases.

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Figure 9. Probability of death at ages 10 to 30 for selected periods for males and females, Australia. Source: Australian Government Actuary Australian Life Tables (various dates).

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Mortality at advanced ages

One of the remarkable features of the Australian mortality transition is the speed at which mor- tality at advanced ages has declined in recent decades. It has already been demonstrated in Figure 7 that the most rapid rates of decline moved to older ages over time, and that rates of decline at older ages increased substantially. This transition was underway in 1970–94, and by 1994–2011, mortality at ages 50 to 90 was declining rapidly. Such a pattern of change is characteristic of the Age of De- layed Degenerative Diseases (Olshansky and Ault 1986). Figure 7 also shows that in the two most recent periods, the speed of decline at advanced ages (80 years and older) is very similar for males and females, which is also characteristic of this Age. Additionally, Figures 5 and 6 have demonstrated that deaths due to non-communicable diseases—circulatory diseases and neoplasms—have shifted to progressively older ages, which is also characteristic. This is further evidence that the Australian mortality experience from about 1970 is consistent with the Age of Delayed Degenerative Diseases.

Differentials in mortality

Though the Epidemiologic Transition is concerned with broad developments, usually addressed at the national level, the Age of Degenerative and Man-Made Diseases and the Age of Delayed Degenerative Diseases may be experienced at different speeds or times by different subpopulations within a nation. As noted by Caselli, Meslé, and Vallin (2002), the later stages of the transition de- pend progressively on personal responsibility for one’s own health. Many public health messages, initiatives, and services are effective only to the degree of personal compliance. Education, income, occupation, residential environment, and cultural factors all play important roles in health and health behaviour, influencing diet, physical activity, smoking, alcohol consumption, and risky behaviour (AIHW 2014). Government also plays a role through legislation, taxation, and the creation of equit- able health-promoting environments. In Australia, substantial differentials in mortality can be found by geographic area and by socio-economic characterization of area, but the largest differential is by indigeneity. This section examines three sources of heterogeneity in mortality based on life expect- ancy at birth: states and territories, indigeneity, and socio-economic factors.

Mortality for states and territories

Australia consists of six states—New South Wales (NSW), Victoria, Queensland, Western Aus- tralia, South Australia, and the island state of Tasmania—and two territories, the Northern Territory and the Australian Capital Territory (ACT).9 Life expectancy at birth by state and territory since 1971 is shown in Figure 10.

For males, ACT residents clearly have the highest life expectancy at birth, with a consistent ad- vantage over the next-ranked state or territory, averaging just less than one year since the early 1980s. NSW, Victoria, Queensland, South Australia, and Western Australia are ranked next and have similar levels of male life expectancy, differing by at most one year since the early 1990s. Tasmania currently lags behind the lowest of this group by about one year, and there is then a gap of four years to the Northern Territory. For females, similar patterns apply, but the ACT has a smaller lead, Tasmania has a larger lag, and the Northern Territory is currently three years below Tasmania. These patterns of mortality decline indicate that the Epidemiologic Transition in Australia is led by the ACT, with the Northern Territory being a significant laggard.

9. The ACT is a small territory enclaved within NSW and containing the capital city, Canberra.

Booth et al.: Epidemiologic Transition in Australia – The last hundred years

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It is also seen in Figure 10 that the gap in life expectancy between the Northern Territory and other states and territories has narrowed considerably for females. Compared with the Australian average, Northern Territory life expectancy for females was about 10 years lower in the 1970s and is currently about four years lower. For males, there is much less narrowing in evidence, and the gap between the Northern Territory and the Australian average has remained at roughly six years since the 1990s. For both sexes, Northern Territory mortality in 2011was equivalent to that experienced in 1990–95 in other parts of Australia.

A number of factors account for the state and territory differences. Low life expectancy in the Northern Territory reflects at least in part a relatively high proportion of Indigenous (Aboriginal and Torres Strait Islander) peoples, and a significant proportion of the Australian population living in

45

Figure 10. Life expectancy at birth (years) for males and females by state/territory, Australia, 1971

to 2012

Source: Australian Bureau of Statistics (2014b). Figures for 1971 to 1993 are for single years;

figures for 1994–2012 are three-year averages.

Figure 10. Life expectancy at birth (years) for males and females by state/territory, Australia, 1971 to 2012 Source: Australian Bureau of Statistics (2014b). Figures for 1971 to 1993 are for single years; figures for 1994–2012 are three-year averages.

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remote areas. Indigenous mortality is higher than non-Indigenous mortality (see subsection on ‘In- digenous mortality’), while mortality in remote areas generally exceeds that in regional areas, which in turn generally exceeds that in major cities (AIHW 2003). Indigenous mortality is a major contributor to higher mortality in remote areas; and people living in regional and remote areas tend to have lower levels of access to health services (AIHW 2007a). Socio-economic factors are also relevant to state and territory differences: for example, that average weekly adult full-time earnings are highest in the ACT and lowest in Tasmania (ABS 2015) accords with observed mortality differentials.

Indigenous mortality

There is a very large gap between Indigenous and non-Indigenous life expectancy. Indeed, in 2009, the Council of Australian Governments (COAG) adopted, among a number of targets to ad- dress Indigenous disadvantage, the intention ‘to close the life expectancy gap within a generation’ (COAG 2009).

Assessment of the gap in life expectancy between Indigenous and non-Indigenous peoples is problematic because of unreliable estimates of Indigenous life expectancy. Both deaths and popula- tion data suffer from problems in the reporting of indigeneity: Indigenous deaths may not be iden- tified as Indigenous by the family, health worker, or funeral director, while Indigenous population counts vary according to changes in the propensity to identify as Indigenous (AIHW 2015b). The Australian Bureau of Statistics makes allowances for this, based on an Indigenous deaths and census records linkage study (ABS 2014c); others have linked death records and hospital and other data to obtain different estimates (Neville et al. 2011; AIHW 2015b; Madden et al. 2012). The Australian Institute of Health and Welfare recently estimated the indigeneity gap as 10.6 years for males and 9.5 years for females (AIHW 2015b).

This ten-year difference between Indigenous and non-Indigenous life expectancy places the In- digenous population at mortality levels experienced by the non-Indigenous population some 30 to 40 years ago (see Figure 3, noting that the Indigenous population comprises less than 3 per cent of the total population of Australia). In other words, Indigenous life expectancy is equivalent to that experienced by the non-Indigenous population in the 1970s. Two-thirds of the gap in life expectancy is estimated to be due to deaths from circulatory diseases, endocrine, metabolic and nutritional disor- ders (including diabetes), cancer, and respiratory diseases (AIHW 2015b). This suggests that the In- digenous population may still be experiencing the third stage of the Epidemiologic Transition—the Age of Degenerative and Man-Made Diseases—and may be best described as undergoing Delayed Epidemiologic Transition (Omran 1971).

Socio-economic and geographic differentials

The collection of data on the characteristics of deceased persons at time of registration of death is not comprehensive in Australia, limiting the availability of data on socio-economic and other dif- ferentials in mortality. The method adopted by official agencies for measuring socio-economic dif- ferentials relies on area-based socio-economic indices (SEIFA) constructed from census data (ABS 2013). These indices are used to classify geographic areas (generally postcodes) into the quintiles of the socio-economic distribution. As both deaths and population estimates are available by geographic area, the SEIFA scores enable the estimation of mortality differentials. This method involves in- accuracies, in that the SEIFA score is an average for the area and cannot reflect the range of personal socio-economic characteristics in the area.

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Table 2 shows life expectancy by socio-economic quintile for 200310 (AIHW 2007b). The gradient in mortality by socio-economic quintile demonstrates that the Epidemiologic Transition is led by high- er socio-economic areas. The difference in life expectancy between the low and high socio-economic quintiles is greater for males (4.0 years) than for females (2.2 years). Further, the sex difference in life expectancy at the low socio-economic level (5.4 years) is greater than at the high socio-economic level (3.6 years). The Epidemiologic Transition is least advanced among low socio-economic males.

Table 2 also shows life expectancy differences by geographic area: major cities, regional areas, and remote areas. The major city–remote area differences are 3.4 years for males and 2.0 years for fe- males; these are smaller than the socio-economic differences. Sex differences in remote and regional areas are larger than in major cities. These differences demonstrate that the Epidemiologic Transition is led by major cities, and that males in remote areas lag considerably.

Discussion

This analysis has found that the Australian mortality experience over the last hundred years is broadly consistent with the second and third Ages of the Classical or Western model of the Epidemi- ologic Transition defined by Omran (1971, 1983) and with the fourth Age of Delayed Degenerative Diseases, as described by Olshansky and Ault (1986) and Rogers and Hackenberg (1987). However, the relatively high life expectancy in the early decades of the twentieth century is somewhat anomal- ous in the Epidemiologic Transition framework.

As noted, the first Age of the Epidemiologic Transition was essentially absent in Australia. From first settlement in 1788, health and mortality conditions were best described as characteristic of the second Age of Receding Pandemics. After a slow decline between the mid-1850s and mid-1880s, mortality declined more rapidly. Based on cause of death analysis, de Looper (2015) concluded that there was no evidence of epidemiologic transition before 1906.

By the early 1920s, Australia enjoyed a life expectancy close to the highest in the world. In 1921– 24, life expectancy exceeded the threshold for transition to the Age of Degenerative and Man-Made Diseases by as much as ten years, suggesting a more advanced transition than in other Western popu- lations, where the Age of Receding Pandemics is generally viewed as continuing until mid-century

10. These area-based analyses of mortality are not routinely available.

Table 2. Life expectancy by sex by socio-economic quintile and remoteness, Australia, 2003

Males Females Persons Sex difference Socio-economic quintile

High 80.9 84.5 82.7 3.6 Moderately high 79.0 83.5 81.2 4.5 Average 77.7 82.7 80.2 5.0 Moderately low 77.4 82.8 80.0 5.4 Low 76.9 82.3 79.6 5.4

Remoteness Major cities 78.8 83.5 81.2 4.7 Regional 77.5 82.7 80.0 5.2 Remote 75.4 81.5 78.1 6.1 Australia 78.3 83.2 80.7 4.9

Source: AIHW (2007b) Chapter 5.

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(e.g., Robine 2001; Lussier et al. 2008). Further, what was to be Australia’s last epidemic—Spanish Influenza in 1919—could be seen as having been precipitated by the particular circumstances of the First World War and its aftermath, and the widespread movement of people in 1918–19 (Oxford et al. 2005), such that the Age of Receding Pandemics was exceptionally prolonged.11

The analyses in this paper provide partial support for the proposition of a relatively advanced Epidemiologic Transition in Australia, but not to the extent implied by life expectancy levels. Dur- ing 1922–46, significant decreases in infectious and respiratory disease mortality occurred, as well as substantial increases in circulatory disease mortality. While Figure 4 shows increasing circulatory disease mortality from about 1920, it is possible that this trend is influenced by the improved clas- sification of cause of death (Lancaster 1990), such that the increase is exaggerated. However, the disappearance of ill-defined causes (Lancaster 1990) would imply that by 1946, circulatory disease mortality was much more reliably reported, such that some increase can be confirmed. It can there- fore be concluded that Australian mortality was transitioning to the third Age of Degenerative and Man-Made Diseases during this period, but it is not possible to be more precise about timing. Thus, the Australian Epidemiologic Transition may indeed have been slightly advanced in comparison with other industrialized countries at this time.

Whenever its beginnings, the Age of Degenerative and Man-Made Diseases can be said to have endured until about 1970, in keeping with other industrialized countries. From about 1950, increasing circulatory disease mortality began to slow among males, and rates declined among females, a result of prior changes in risk factor behaviours. Over the period 1946–70, circulatory disease mortality made a small positive contribution to the change in female life expectancy, but a sizeable negative contribution in male life expectancy. Thus, for males especially, overall mortality stagnated in the 1960s. This pattern of slow decline and the predominance of circulatory disease mortality is characteristic of the Age of Degenerative and Man-Made Diseases. Life expectancy in 1970 was about 71 years, close to the limit of 70–75 years that was accepted wisdom at the time (Olshansky and Ault 1986).

The beginnings of the Age of Delayed Degenerative Diseases seem clear. Life expectancy in- creased with renewed vigour from about 1970, driven largely by rapid declines in circulatory disease mortality, in line with the ‘cardiovascular revolution’ (Meslé and Vallin 2011). Ongoing declines oc- curred in mortality from circulatory and respiratory diseases from the 1970s and in neoplasms from the 1980s, such that mortality decline is greatest at increasingly older ages, roughly equally for the sexes. This fourth stage continues in the 21st century.

Acknowledgment

We thank Michael de Looper and two anonymous referees for useful comments.

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11. While Spanish Influenza affected many populations, their lower life expectancies were more in keeping with the Age of Receding Pandemics.

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———. 2014c. Census Data Enhancement project: An update, 2011. ABS Cat. No. 2062.0. Canberra: ABS.

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Quaternary Science Reviews 26 (2007) 2036–2041

Rapid communication

Catastrophic early Holocene sea level rise, human migration and the Neolithic transition in Europe

Chris S.M. Turney a,b,�

, Heidi Brown a School of Geography, Archaeology and Earth Resources, University of Exeter, Exeter, EX4 4RJ, UK

b GeoQuEST Research Centre, School of Earth and Environmental Sciences, University of Wollongong, Wollongong, NSW 2522, Australia

Received 10 April 2007; accepted 11 July 2007

Abstract

The collapse of the Laurentide Ice Sheet and release of freshwater 8740–8160 years ago abruptly raised global sea levels by up to 1.4 m.

The effect on human populations is largely unknown. Here we constrain the time of the main sea level rise and investigate its effect on the

onset of the Neolithic across Europe. An analysis of radiocarbon ages and palaeoshoreline reconstruction supports the hypothesis that

flooding of coastal areas led to the sudden loss of land favoured by early farmers and initiated an abrupt expansion of activity across

Europe, driven by migrating Neolithic peoples.

r 2007 Elsevier Ltd. All rights reserved.

1. Introduction

Understanding human responses to climatic and environ- mental variability during the early Holocene can provide important lessons for mitigating the effects of future change (Church et al., 2001). Between 8740 and 8160 calendar years before present (relative to AD 1950; BP), the remnant Laurentide Ice Sheet collapsed (Barber et al., 1999), resulting in the largest single North Atlantic freshwater pulse of the past 100,000 years (Clarke et al., 2003), raising global sea levels by up to 1.4 m (Clarke et al. 2004; Törnqvist et al., 2004) and culminating in hemispheric cooling (Alley et al., 1997; von Grafenstein et al., 1999; Rohling and Pälike, 2005; Rasmussen et al., 2006). At around the same time in Europe, the archaeological record indicates there was an abrupt expansion of Neolithic farming into areas previously inhabited by Mesolithic hunter–gatherers (van Andel and Runnels, 1995; Gkiasta et al., 2003; Ammerman et al., 2006). Due to chronological uncertainties, however, it is unclear what effect sea level rise and/or subsequent cooling had on the spread of farming.

The earliest transition from Mesolithic hunter–gathering to Neolithic farming has been identified in the Near East

e front matter r 2007 Elsevier Ltd. All rights reserved.

ascirev.2007.07.003

ing author. Tel.: +44 1392 263341; fax: +44 1392 263342.

ess: [email protected] (C.S.M. Turney).

between 13 and 11.5 cal ka BP (before the production of pottery; Pre-Pottery Neolithic A and B) (Pinhasi et al., 2005; Renfrew, 2006). Archaeological evidence indicates that around approximately 9.2 cal ka BP, a range of domesticated plants and animals were transmitted from the Near East to Greece via Anatolia (Renfrew, 2006). Radiocarbon dating of the first evidence for food produc- tion in Europe, however, suggests that after its arrival in the southeast, expansion largely ceased for a thousand years (Gkiasta et al., 2003; Pinhasi et al., 2005; Renfrew, 2006). Sometime around 8 cal ka BP, an abrupt enlarge- ment of farming practice and pottery production then took place across the Balkans (van Andel and Runnels, 1995; Gkiasta et al., 2003; Ammerman et al., 2006), which was apparently followed by a progressively later transition to the Neolithic in a north-westerly direction across the continent (Childe, 1925; Ammerman and Cavalli-Sforza, 1971, 1973; Forenbaher and Miracle, 2005; Pinhasi et al., 2005; Fig. 1). A range of different models has been proposed to explain

these observations. At one end of the spectrum, the onset of the Neolithic has been argued to be the result of a cultural diffusion of ideas (‘indigenism’), independent of migration and with little replacement of the original population (Dennell, 1983). Alternatively, local population increase has been regarded as a causal factor, driving

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Fig. 1. Location of sites recording the first evidence for the Neolithic across Europe, Anatolia and the Near East (Forenbaher and Miracle, 2005; Pinhasi

et al., 2005) based on median probability calibrated radiocarbon ages (see Supplementary Data). The coloured dots indicate new sites that were established

during each time slice; grey dots represent pre-existing sites established during earlier time slices.

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Neolithic migration from the earliest areas of established farming and assimilating or displacing local hunter– gatherers in newly settled lands (‘demic diffusion’) (Childe, 1925; Ammerman and Cavalli-Sforza, 1973). In southeast Europe, it has been observed that domesticated cereals and animals moved together as a package, implying some form of early demic diffusion from Anatolia (van Andel and Runnels, 1995; Renfrew, 2006), but there is little evidence for increasing population pressure in this region (van Andel and Runnels, 1995). In southeast Europe, a modified version of demic diffusion has been proposed: early farmers had a preference for river floodplains and the

margins of seasonally rising and falling lakes as a buffer against drought years, providing a reliable harvest for sizeable populations; when these areas reached carrying capacity, migration into new lands took place (van Andel and Runnels, 1995). Although the modified model explains the original migration out of Anatolia, the cause of the abrupt expansion around 8 cal ka BP remains unresolved.

2. The Black Sea

The Black Sea lies in a strategic position on the edge of mainland Europe, potentially playing an important

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role in controlling the spread of farming from Anatolia (Ryan et al., 1997). During glacial periods, the Bosporus Sill (30 m below current sea level (mbsl); Georgievski and Stanev, 2006) formed an effective barrier to the Mediterranean, exposing a continental shelf that was characterised by meandering river valleys, deltas and wavecut beaches (Ryan et al., 1997). Underwater surveys, dredging and coring studies suggest that sometime between 9.4 and 7.5 cal ka BP, the most recent lacu- strine phase ended abruptly (Ryan et al., 1997; Ballard et al., 2000; Major et al., 2006); the Bosporus Sill was breached and the lake catastrophically flooded with marine water.

Within the Black Sea, a clear sequence of events has been identified. On the continental shelf, freshwater molluscs have been obtained from the palaeoshoreline around 155 mbsl. The youngest reported radiocarbon age for this unit is 7460755 BP (Ballard et al., 2000), providing a maximum age for the flood event. Above this shoreline, a distinct winnowed and reworked unit containing complete and smashed shells of mixed age (the ‘shell hash’) is preserved across a range of depths (49–129 mbsl) (Major et al., 2006), marking the lacustrine–marine boundary. Overlying the shell hash, salinity-tolerant molluscs have been identified in marine sediments; the oldest radiocarbon age from the base of this unit is 7940775 BP (Major et al., 2006), providing a minimum age for the marine incursion into the Black Sea.

The above radiocarbon ages were analysed in OxCal using the IntCal04 and Marine04 calibration datasets (Bronk Ramsey, 2001; Hughen et al., 2004; Reimer et al., 2004) to construct a Bayesian model to date the time of the flood event. A mean DR of 50763 yr for the Black Sea (http://calib.qub.ac.uk/marine/) was assumed for the oldest marine mollusc. No hard water effect has been identified for the early Holocene lake surface (Bahr et al., 2005) so no correction has been made to the freshwater sample. The model accounts for the stratigraphic order of the samples (which implies a chronological sequence) and calculates a boundary age for the marine incursion. The modelled age estimate for the marine incursion of the Black Sea is constrained by the calibrated age ranges of the ecologically distinctive molluscs and is calculated to have taken place between 8350 and 8230 cal yr BP at 1 s (Fig. 2B). This range falls within the relatively large bounding ages for the final collapse of the Laurentide Ice Sheet (8740–8160 cal yr BP; Barber et al., 1999), and precedes the Northern Hemisphere cooling event of 8260–8100 ice-core yr BP (Rasmussen et al., 2006; Fig. 2A), supporting the view that the flooding of the Black Sea occurred due to the input of melted freshwater ice into the North Atlantic. The timing suggests that on the back of global postglacial sea level rise (Lambeck and Chappell, 2001), the associated increase of up to 1.4 m (Clarke et al. 2004; Törnqvist et al., 2004) was sufficient to breach the Bosporus Sill and catastrophically flood the freshwater lake of the Black Sea.

3. Onset of the European Neolithic

To test for any effect on the spread of farming into Europe, an analysis was made of 575 radiocarbon ages that date the first occurrence of the Neolithic in European sites (Forenbaher and Miracle, 2005; Pinhasi et al., 2005). Southeast Europe (encompassing the Balkans) records the earliest evidence of farming on the continent (Gkiasta et al., 2003; Pinhasi et al., 2005; Renfrew, 2006), occurring in low-lying areas prior to and after the events of the early Holocene. Settled inhabitants of this region would there- fore have been the most vulnerable to a sea level rise or climatic change, and as a result, the associated radiocarbon ages were analysed separately from the rest of Europe. The radiocarbon ages were calibrated using IntCal04 in

Calib 5.0 (Reimer et al., 2004) and their probabilities summed, providing a measure of fluctuations in the spread of farming (Gkiasta et al., 2003; Fig. 2C and D; see Supplementary Data). In the Near East and Anatolia, where agriculture was already firmly established before the onset of the Holocene, there was a relatively constant rate of Neolithic settlement, commencing around 12.9 cal ka BP (see Supplementary Data). In contrast, the onset of farming in Europe started in Greece from around 9.2 cal ka BP (Fig. 2 and see Supplementary Data), but no new sites appear to have been established between 8.3 and 8.2 cal ka BP. From 8.2 cal ka BP, the number of Neolithic sites rapidly increased in coastal and inland areas across the Balkans, peaking 7.8 cal ka BP. This expansion appears to have carried on into other parts of Europe, commencing around 8 cal ka BP and reaching a peak 7.3 cal ka BP. A second major period of farming expansion is also observed, centred on 5.7 cal ka BP, and is dominated by sites in northwestern Europe (concentrated in Denmark and the British Isles; Fig. 1 and Supplementary Data). This later phase may have been relatively delayed due to difficulties associated with crossing sea barrier(s), a new wave of migration driven by increasing population pressure and/or reflecting indigenism in local Mesolithic populations. In southeast Europe, the downturn in Neolithic settle-

ment led the cooling event recorded in the Greenland ice (Rasmussen et al., 2006) (Fig. 2A and D), implying that climatic change at this time was not a significant control on the spread of farming. The period 8.3–8.2 cal ka BP, however, is indistinguishable from the timing of the sea level rise recorded in the Black Sea (Fig. 2B and C).

4. Modelling the impact on early Neolithic populations

To investigate the impact of flooding during the early Holocene, we reconstructed the palaeoshoreline of the Mediterranean and Black Sea before and after the rise in sea level. As a worst-case scenario, we assumed the Mediterranean rose 1.4 m (the maximum reconstructed rise associated with the early Holocene event; Törnqvist et al., 2004) from 30 mbsl (the depth at which the Bosporus Sill was breached; Georgievski and Stanev, 2006), and the

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Fig. 2. Timing of the flooding, climatic cooling and onset of the Neolithic across Europe during the early Holocene. (A) The d18O record from North GRIP (using the recently developed Greenland Ice Core Chronology 2005; Rasmussen et al., 2006); the age scale has been adjusted, relative to AD 1950.

The dashed boundary represents the age range of the collapse of the Laurentide Ice Sheet and the associated freshwater pulse into the North Atlantic

(Barber et al., 1999). (B) Bounded prior (unfilled) and posterior (filled) calibrated age distributions for the flooding of the Black Sea. Radiocarbon ages for

the lacustrine and marine phases were obtained from mollusc shells (Ballard et al., 2000; Major et al., 2006). The grey band denotes the age range of the

flooding of the Black Sea at 1 s. Summed probability distributions of calibrated radiocarbon ages for the onset of the Neolithic in sites across (C) southeast and (D) the rest of Europe (Forenbaher and Miracle, 2005; Pinhasi et al., 2005).

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freshwater lake in the Black Sea taken to be 155 mbsl (Ballard et al., 2000). To quantify the areas flooded, we used the 2006 National Geophysical Data Center Global Digital Elevation Model (ETOPO2) that represents gridded (2 min by 2 min) elevation and bathymetry for the world (www.ngdc.noaa.gov/mgg/image/2minrelief. html). We reprojected the ETOPO2 dataset onto an equal area projection and then determined the areas encom- passed by the contours set at 155, 30 and 28.6 mbsl (Supplementary Data).

The area of land lost to an increase in sea level in the Mediterranean and Black Sea is shown in Table 1. In the established farming areas of the eastern Mediterranean (southeast Europe, Anatolia, Cyprus and the Near East), only around 1120 km

2 would have been flooded with a sea

level rise of 1.4 m. Assuming the early farming population density in the region was two people per km

2 (Ammerman

and Cavalli-Sforza, 1984; Bar-Yosef and Meadow, 1995), this would suggest that around 2240 individuals were displaced as a result of flooding in the eastern Mediterra- nean. Although the quantified Mesolithic population of Europe is uncertain (Shennan and Edinborough, 2007), we consider the low number of displaced individuals calcu- lated here as unlikely to have driven the large expansion of farming observed after 8.2 ka cal BP (Fig. 2C and D).

Alternatively, it has been proposed that the flooding of the Black Sea may have driven the onset of farming across Europe (Ryan et al., 1997, 2003; Ryan and Pitman, 2000). Our results indicate that up to 72,700 km

2 was flooded

when the Bosporus Sill was breached. Recent modelling results indicate that it would have taken around 34 years to fill the Black Sea from 155 mbsl (Siddall et al., 2004). If early farmers were indeed present along the Black Sea, the total number of people displaced from here could have been as high as 145,000, migrating at a rate of around 4300 individuals per year.

Although early populations would have been able to adjust to postglacial rising sea levels, the results suggest that the preference of early farmers for low lying areas (van Andel and Runnels, 1995) made them particularly vulner- able to extreme and abrupt flooding from the sea. The rapid drowning of large areas of the continental shelf would have significantly altered the coastline, disrupted sea routes and provided an instant population pressure in neighbouring regions as migrants sought to rapidly

Table 1

Reconstructed Mediterranean and Black Sea areas flooded during the sea

level rise between 8350 and 8230 cal yr BP

Area flooded (km 2 )

Black Sea 72,677

Eastern Mediterranean 1122

Southeast Europe 761

Anatolia, Cyprus and Near East 361

Total Mediterranean 4022

See Supplementary Data for the defined boundaries of the above regions.

distance themselves from the area of flooding, initiating the long-term spread of farming across Europe. There is little evidence of newly established Neolithic populations to the immediate west of the Black Sea after the flooding (Fig. 1; Yanko-Hombach et al., 2007); the majority of the sites lie along the Mediterranean coastline (see Supplemen- tary Data), suggesting migration was largely driven by seafaring populations. Although it is uncertain whether Neolithic people were active along the shores of the freshwater Black Sea due to the inherent difficulties of underwater excavations (Ballard et al., 2000), our results support the hypothesis that flooding of coastal areas may have driven the rapid expansion of the Neolithic around and across Europe (Ryan et al., 1997, 2003; Ryan and Pitman, 2000). Coincident with the expansion of farming across Europe

was a disruption of established sites in neighbouring areas. For instance, the early Neolithic site of C- atalhöyük East declined after 8200 years ago, and preceded a dispersed settlement pattern across the Konya Plain (Fairbairn, 2005). Considered to be the result of disaffected groups challenging the existing social order (Fairbairn, 2005), the sudden influx of migrants from flooded areas would have provided a major impetus for change. Furthermore, on Cyprus, there is evidence for abandonment from this time (Todd, 1987). Memories of the deluge may have been passed down through subsequent generations, creating the numerous flood ‘myths’ found in the surrounding regions (Dalley, 1989).

5. Conclusions

Here we constrain the age of early Holocene sea level rise and demonstrate that the associated flooding played a significant role in the onset of the Neolithic across Europe. High-precision dating of the marine flooding of the freshwater Black Sea places the main rise in global sea level to between 8350 and 8230 calendar years BP, preceding the 8.2 ka BP cooling event. No Neolithic sites were established in Europe during the main period of sea level rise but following this event there was an abrupt expansion along coastal and inland routes that ultimately led to the continental-wide establishment of farming. The results support a model of migrating Neolithic people into Europe and suggest that land lost from rising sea levels can drive mass migration and cultural change on a regional scale.

Acknowledgements

C.S.M.T. would like to thank the Australian Research Council for awarding a Queen Elizabeth II Fellowship to the author. Numerous colleagues kindly advised on the chronologies of early Neolithic sites; these included Staso Forenbaher, Preston Miracle and Ruth Whitehouse. Many thanks to Chris Stringer and an anonymous reviewer who provided useful suggestions and comments.

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Appendix A. Supplementary Materials

Supplementary data associated with this article can be found in the online version at doi:10.1016/j.quascirev. 2007.07.003.

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dx.doi.org/10.1029/2004GL021429.3d

• Catastrophic early Holocene sea level rise, human migration and the Neolithic transition in Europe
• Introduction
• The Black Sea
• Onset of the European Neolithic
• Modelling the impact on early Neolithic populations
• Conclusions
• Acknowledgements
• Supplementary Materials
• References