Social Determinants of Health 4 DQ 2

Attention to Local Health Burden and the Global Disparity of Health Research James A. Evans1*, Jae-Mahn Shim2, John P. A. Ioannidis3

1 Department of Sociology, Computation Institute and Center for Health and the Social Sciences, University of Chicago, Chicago, Illinois, United States of America,

2 Department of Sociology, University of Seoul, Seoul, Korea, 3 Departments of Medicine, Health Research and Policy, and Statistics, Stanford Prevention Research Center,

Stanford University, Stanford, California, United States of America

Abstract

Most studies on global health inequality consider unequal health care and socio-economic conditions but neglect inequality in the production of health knowledge relevant to addressing disease burden. We demonstrate this inequality and identify likely causes. Using disability-adjusted life years (DALYs) for 111 prominent medical conditions, assessed globally and nationally by the World Health Organization, we linked DALYs with MEDLINE articles for each condition to assess the influence of DALY-based global disease burden, compared to the global market for treatment, on the production of relevant MEDLINE articles, systematic reviews, clinical trials and research using animal models vs. humans. We then explored how DALYs, wealth, and the production of research within countries correlate with this global pattern. We show that global DALYs for each condition had a small, significant negative relationship with the production of each type of MEDLINE articles for that condition. Local processes of health research appear to be behind this. Clinical trials and animal studies but not systematic reviews produced within countries were strongly guided by local DALYs. More and less developed countries had very different disease profiles and rich countries publish much more than poor countries. Accordingly, conditions common to developed countries garnered more clinical research than those common to less developed countries. Many of the health needs in less developed countries do not attract attention among developed country researchers who produce the vast majority of global health knowledge—including clinical trials—in response to their own local needs. This raises concern about the amount of knowledge relevant to poor populations deficient in their own research infrastructure. We recommend measures to address this critical dimension of global health inequality.

Citation: Evans JA, Shim J-M, Ioannidis JPA (2014) Attention to Local Health Burden and the Global Disparity of Health Research. PLoS ONE 9(4): e90147. doi:10.1371/journal.pone.0090147

Editor: Mohammed Shamji, Toronto Western Hospital, Canada

Received January 2, 2014; Accepted January 29, 2014; Published April 1, 2014

Copyright: � 2014 Evans et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding: This study was funded by a health and health policy research for disadvantaged populations seed grant, Center for Health Administration, and by a Research Opportunity Seed grant, both from the University of Chicago. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing Interests: The authors have declared that no competing interests exist.

* E-mail: [email protected]

Introduction

Poor and minority persons, as well as those living in resource

restricted regions, are more likely to live shorter, less healthy lives

[1,2,3]. A long tradition in medicine has sought to reduce these

inequities and realize universal, global health through socio-

economic development, improved public health measures and

affordable health care [4,5,6,7]. Despite successes, there remain

concerns about the relevance and effectiveness of these efforts for

disadvantaged populations. Target populations are sometimes

resistant and non-adherent to medical intervention. This has

inspired educational projects to enhance the public understanding

of medicine [8,9] and practitioner understanding of diverse patient

cultures.

Doubts persist, however, about whether we produce sufficient

medical knowledge to provide medical care for certain conditions

in certain contexts [10,11,12,13]. Counter-intuitive findings about

emergency care for African children suffering from malaria,

septicemia, meningitis and similar infectious diseases suggest that

we know much less about diagnosis and treatment for poor

populations [14,15,16]. Moreover, effective therapies for pandem-

ics such as HIV can create unforeseen knowledge needs like how

to provide long-term medical care among HIV survivors [17,18].

Here we examine whether the global research community has

given sufficient attention to medical conditions prevailing in

globally disadvantaged populations. We demonstrate how this

concern follows from the misalignment of global disease burden

and global research attention. Specifically, we reveal the global

inequality of health research by estimating the relationship

between the health burden imposed by many important diseases

and subsequent publication of biomedical articles relevant to those

diseases. We also explore possible causes for this inequality of

health research.

Our findings highlight how poor populations not only face the

greatest burden from disease and disability, but that burden is

given the least medical research attention. We show that this

global inequality of health research follows from two processes.

First, medical research activities are guided by local health needs

specific to each country rather than global health needs, and

health needs vary greatly across rich and poor populations.

Second, as medical research requires resources, a few developed

countries disproportionately produce the vast majority of biomed-

ical research. As a result, global research attention to diseases

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tracks the global market for treatment and the ability of patients to

pay for care. This has resulted in the current global inequality of

health research. To reduce this inequality of research, our analysis

recommends efforts that not only globalize the research attention

of wealthy countries [19], but also support local research in those

impoverished contexts where health knowledge is needed most.

Materials and Methods

Study Design and Key Measures We assessed the total number of biomedical articles and also the

specific number of systematic reviews, randomized controlled trials

and animal research relevant to a wide range of specific diseases

and disabilities, and then explored how much of these distributions

could be explained by 1) the global health burden imposed by

these conditions, 2) the global market for medical treatment, and

3) the local health burden. We investigated these relationships

further by assessing differences in the health profiles of developed

and less developed countries, and by measuring the association

between a country’s GDP and its production of biomedical

science.

To measure the amount of disease-specific biomedical research,

we used the total number of articles published on each disease in

MEDLINE. We also calculated the precise number of systematic

reviews, randomized controlled trials, and research performed on animal

subjects devoted to those same conditions. Each abstract in

MEDLINE is indexed with NLM Medical Subject Headings

(MeSH) [20,21]. We defined MEDLINE papers as relevant to one

or more diseases if annotated with related MeSH clinical and

disease terms. We assessed this for each country by linking

MEDLINE with Thomson Reuters’ Web of Science, which

provides full institutional information for most MEDLINE articles.

We then coded the countries of the institutions that hosted each

article author. See File S1 for details.

Number of total research articles is a reasonable indicator of

health research, but an imperfect proxy for biomedical knowledge

more generally: some diseases are harder to understand, prevent,

diagnose, and treat than others. For this reason, we also assessed

the number of different types of articles: systematic reviews,

randomized controlled clinical trials, and animal subjects research

associated with each disease. The number of systematic reviews

indicates that the biomedical community deems research on a

disease of sufficient size, and relevance that it merits secondary

evaluation and organization. The number of clinical trials is a

marker of organized research assessing the merits of interventions

for a condition across many patients in one or multiple centers.

Finally, the number of research papers performed on animals

suggests an interest at fundamental aspects of each disease.

It should be noted that medical science can possess deep

knowledge of a disease that continues to cause harm because that

knowledge has not yet disseminated to places where it is needed

most. Nevertheless, recent studies that demonstrate our limited

knowledge about treatment in resource poor environments [22]

suggest that even for diseases about which we have extensive

biological understanding, additional research into their distribu-

tion, acquisition, prevention and treatment among different

populations and in different contexts would likely produce further,

much needed medical insight. Following this, we believe that

number of total articles provides a useful purchase on relevant

health knowledge as those articles cover the range of health

research, taking biological but also behavioral, social, economic,

political and cultural factors into account, as many do here.

Number of systematic reviews, randomized control clinical trials,

and disease-relevant research performed on animal models

provide more fine-grained insight about the relationship between

health burden, research and treatment.

We used World Health Organization (WHO) data to measure

the burden of disease. The WHO introduced global and regional, but

not country-level, estimates of the disability-adjusted life years

(DALYs) for an array of common conditions through its Global

Burden of Disease (GBD) project in 1990 [23]. In 2002 and 2004,

the WHO re-estimated DALYs for 192 countries as well as

globally [24]. One DALY refers to one healthy life year lost to

disease or disability. By converting time spent in various states of

health to their ‘‘healthy-year equivalents,’’ [25] DALYs incorpo-

rate cultural values placed on different aspects of physical, mental

and social function [26]. The WHO estimates DALYs for 136

health conditions. We used 111 of the 136 conditions in our

analysis, excluding residual categories like ‘‘other infectious

diseases.’’ GBD codes for these conditions are organized into 19

categories, and 3 high-level classifications (see Table S1 in File S1

for all codes).

We matched GBD codes to MeSH terms through the mediation

of ICD-9 (International Statistical Classification of Diseases and

Related Health Problems) codes. ICD-9 codes are sufficiently

general that we mapped them onto GBD codes with very little

ambiguity. We then linked ICD-9 codes to MeSH through NLM’s

Unified Medical Language System (UMLS) metathesaurus.

Following this approach, we regrouped MeSH disease terms

according to the 111 GBD codes and so estimated the number of

articles in MEDLINE relevant to a particular disease category (see

File S1 for alternate linkages).

We measured the global market for treatment associated with each

disease. First, we multiplied the number of disability-adjusted life

years (DALYs) for each disease in each country by gross national

income per capita (GNI) at purchasing power parity (PPP) in that

country. This product equals the value of the revenue that could

be generated if everyone afflicted by the condition in question was

restored to full health, or the size of a national market for

treatment, if people in that country were willing to spend all

money that could be gained from health on health. With the same

disease profile, different countries have different markets, depend-

ing on their GNI. We used the World Bank’s World Development

Indicators for GNI (PPP) data for each country [27]. By summing

all national markets for treatment for a given condition, we

computed the global market for treatment for that condition. The

global market for a condition common in developed countries is

much greater than the market for a condition prevalent only

among less developed countries.

Statistical Analysis We used regression-based analyses to estimate the association

between the burden of disease and the market for treatment on the

quantity of medical research produced. Counts for each type of

disease-relevant article are not normally distributed: they are

discrete and widely skewed with a few diseases like breast cancer

and AIDS attracting a disproportionate share of research attention

while others like Chagas disease and leishmaniasis attracting little

[19]. This recommended the use of negative binomial regression

models.

First, we analyzed the relationship between the global burden of

disease for 111 diseases and disabilities in one year (2002 and

2004) and the global number of articles published relevant to those

conditions in the subsequent year. We subsequently analyzed the

relationship of the market for treatment on the quantity of

subsequently published science. This analysis involved 4,703,021

disease and disability assignments to 3,771,604 distinct articles.

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Our next analysis evaluated the correlation of burden of disease

within countries on the number of subsequently published medical

articles, systematic reviews, randomized-controlled clinical trials,

and animal model studies relevant to each disease by researchers

from those countries. We estimated these models with data from

the 167 countries for which total article information was complete

and 155 countries for which information about systematic reviews,

randomized-controlled clinical trials and animal models studies

was complete.

Finally, we explored the relationship between poverty, disease

and disease-relevant science revealed by our regression analyses.

We examined the difference in disease distribution between

developed and less developed countries by calculating the relative

burden of disease for each country and then graphing and

modeling its relationship with that country’s gross domestic

product (GDP). We also assessed the relationship between wealth

and the amount of research published by researchers in each

country.

Results

Our world-level analysis reveals that the global burden of

disease accounts for none of the distribution of total health research

or the controlled trials published in the subsequent year (see

Table 1). For randomized controlled trials and animal model

studies, more global need is actually associated with less global

research. Systematic reviews responds positively to global DALYs,

but only when the 19 broad disease and disability categories are

statistically controlled for. Figure 1 summarizes the alignment of

health burden with health research, as grouped by broad disease

and disability category. This illustrates how malignant neoplasms

(cancers), endocrine disorders (including diabetes), and skin

diseases are overrepresented in biomedical research, dispropor-

tionate to the global health burden they exact. In contrast,

infectious parasitic disorders, respiratory infections and perinatal

conditions are underrepresented in the research relative to their

burden.

In contrast, the global market for treatment significantly impacts

health research. Table 1 shows that for every $10 billion lost to a

disease or disability, which might have been put toward care,

biomedical articles of all types of controlled trials devoted to that

disease increased by approximately 3–5% in the subsequent year,

controlling for health burden. Randomized control trials, which

are most expensive and closest to marketable health products,

increase most—by 5.2% the following year. These statistical

relationships between the number of articles of various types, the

burden of disease, and the global market for treatment persist

when we controlled for the total cumulative number of articles

relevant to each condition and the proportion of those articles

published in the prior five years, but they attenuate when we

include indicator variables for 19 coarse disease and disability

categories. This means that much of the positive effect of market

size on published research is attributable to different categories of

disease, which are associated with larger and smaller markets.

These patterns remained unchanged in a supplementary analysis

using global DALY data from 1990 and 2004 (eTable 2). In this

analysis, disease burden remains insignificant, but growth in

market size (by $10 billion) leads to an increase of relevant articles

by more than 10%, which attenuates when controlling for disease

categories. These results together show that diseases prevailing in

poor populations are given less overall research attention than

those common in wealthy populations. Although market forces

appear to be implicated in the publication of research articles of all

types, in the following analyses we show how they are likely not the

root cause of unequal health knowledge, but themselves a

consequence of global health and wealth inequality.

Our next analysis shows that within countries, disease burden

has a strong, significant association with many forms of health

research. For each 10 million DALYs lost to a disease within a

country, the number of articles published by researchers in that

country increased by 73.9% (see Table 2). The effect of local

burden is highest for randomized controlled clinical trials, where a

million DALYs lost to a disease results in 367.9% more such trials

in that year. Only the number of systematic reviews on a disease

do not vary significantly with recent DALYs lost to that disease

within country. Interestingly, systematic reviews do not respond

more to the amount of previous national or global research than

other kinds of research.

Global burden of disease has a small, independent association

with the publication of all research articles within countries, and

with review articles and all clinical research when controlling for

broad disease categories. This suggests that whether or not

researchers and funding agencies factor global health needs into

their research, the influence of local needs exerts much more

influence on their work. Alternative specifications of country-

authorship produced the same pattern of results (i.e., all countries

with participating authors are assigned the article versus only the

wealthiest country, which restricts the measure to indigenous

research; see File S1.

In order to reconcile the presence of a national association

between health burden and health research with the absence of a

global one, we explored how disease profiles and health research

are correlated with national wealth.

Figure 2 illustrates the striking difference in disease profiles

among populations of rich and poor countries. These are evident

at the level of coarse disease classifications, but the differences are

much larger at the level of individual conditions (see Table S3 and

Figure S1 in File S1). For example, consider the relative burden of

infectious and malignant neoplasms (cancers) in rich and poor

countries. Infectious diseases like diarrheal diseases, malaria and

HIV naturally levy a much higher toll in less developed countries,

while cancers incur a larger burden in more developed countries

with longer life spans. Respiratory infections, perinatal conditions

and injuries disproportionately afflict less developed countries,

while neuro-psychiatric conditions like depression and schizophre-

nia and musculoskeletal diseases like arthritis and back pain

represent a greater burden in wealthy countries. Note the

conditions that most afflict poor populations only lightly affect

the rich (e.g., infectious diseases, respiratory infections, perinatal

conditions), while diseases that most afflict rich populations also

levy a substantial toll on poor ones (e.g., cancers, neuro-psychiatric

and musculoskeletal disorders). Figure 2 also shows the regional

dispersion of these health burden differences. The world’s least

developed countries are located in Africa, and to a lesser extent

South Asia and South America: so also disease burden clusters

regionally, largely correlated with country wealth.

There are, however, striking disparities among countries in the

capacity to produce health research. Figure 3 plots the relationship

between country wealth and the publication of biomedical

research. This figure illustrates how wealthy countries publish

much more biomedical research than less wealthy countries.

National disparities in research are not surprising, as biomedical

research requires substantial resources. Nevertheless, combined

with the responsiveness to local health needs demonstrated

previously, research disparities result in the overrepresentation of

conditions burdening developed countries and the underrepresen-

tation of those afflicting less developed countries in the research

literature. The inequality of research limits current quality of care

Local Health Burden and Global Research Disparity

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Figure 1. 2004 global disability-adjusted life years (DALYs) and 2005 research articles categorized by 19 broad WHO disease and disability categories. This correspondence suggests the loose relationship between burden of disease and health knowledge (see Figure S1 in File S1 for the distribution of different types of articles by disease). doi:10.1371/journal.pone.0090147.g001

Table 1. Estimated Association of Global Biomedical Articles with Global Health Burden (2002, 2004) a .

Model 1: All research articles % change 95% C.I. % change 95% C.I. % change 95% C.I.

Global DALYs (10 millions) 25.1 210.7–1.0 23.9 29.3–1.7 3.3 23.1–10.2

Market size ($10 billions) 3.6** 2.2–5.0 3.0** 1.6–4.4 1.3 {

20.1–2.8

Cumulative Global articles (10 thousands)

3.0** 2.2–3.8 10.3** 7.0–13.6

Model 2: Systematic reviews

Global DALYs (10 millions) 20.9 210.3–9.4 0.8 28.6–10.2 11.6 {

21.8–25.1

Market size ($10 billions) 2.7** 0.8–4.6 2.0* 0.1–3.9 0.4 21.8–2.7

Cumulative Global articles (10 thousands)

23.5** 14.0–33.8 12.5** 5.5–19.9

Model 3: Clinical trials

Global DALYs (10 millions) 211.2* 220.9–20.3 27.1 213.1–8.1 6.6 26.0–20.9

Market size ($10 billions) 5.2** 2.5–7.9 3.2* 21.6–3.0 1.1 21.5–3.8

Cumulative Global articles (10 thousands)

561.8** 275.8–1065.7 341.2** 142.2–703.6

Model 4: Animal subjects

Global DALYs (10 millions) 28.0{ 215.8–0.5 24.7 214.0–3.6 1.1 27.4–10.2

Market size ($10 billions) 3.8** 1.1–6.5 1.9 {

20.4–3.0 1.0 21.3–3.3

Cumulative Global articles (10 thousands)

35.9** 26.7–45.8 29.1** 19.1–39.9

Controlling for disease `

a Models in 1A contain 222 cases (111 diseases in 2002 and 2004). `Models control for the 19 broad disease/disability categories listed in Figure 2 (including the 2 in the footnote). {

p ,.10; * p ,.05; ** p ,.01 doi:10.1371/journal.pone.0090147.t001

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in less developed countries, but it also limits the next generation of

care there, as the science and technology that could be transferred

to developing countries are less relevant to their most pressing

health needs.

Discussion

Our analysis demonstrates that the production of health

research in the world correlates with the market for treatment

and not the burden of disease. While we expected to find a weak

relationship between global health and medical research, we find

no relationship. One prior study that found a modest correlation

between the amount of systematic review papers and global

burden of disease [11], our research reveals the fragile nature of

this relationship. Clinical trails and case reports have no

relationship to global burden, and systematic reviews only post a

small influence when disease categories are held constant. This

means that existing global health research is less relevant to the

needs of poor populations.

More importantly, we show how this global pattern is related to

the local processes of health research: 1) local health needs within a

country draw the attention of researchers and research resources

of the country more than global health needs [13,19], 2) developed

countries and less developed countries have divergent health

profiles, and 3) developed countries produce much more health

research of all kinds than less developed countries [10]. In short,

health needs from less developed countries do not attract much

attention among rich country researchers. Ultimately, this article

stresses that poor populations are in double jeopardy: they

experience the greatest health burdens but their diseases have

been studied least and even researchers from wealthy countries

often lack secure knowledge for context-relevant treatments.

Systematic reviews, which are not driven by local needs, attend

to research slightly more relevant to global health needs, but this

correction is very small.

These findings have relevance for international development

and health policy. The primary focus of international health efforts

has been to extend health care innovations from developed

countries to less developed and comparatively less healthy

countries. This is good policy: as we demonstrate, conditions that

incur the highest health burden in wealthy countries like cancers

and musculoskeletal disorders are relevant also to poor countries.

They are, however, not the most burdensome health challenges for

those countries. Even global health initiatives, which often target

specific diseases relevant to less developed countries and have

succeeded in reducing some health inequities, are not always

sufficiently aligned with country priorities or countries’ burdens of

disease [28,29].

Others argue that the biggest health challenges are the result of

inferior environmental contexts (e.g., increased air and water

pollution or sanitation), and so efforts to reduce global health

inequity should focus on economic development and public health.

This position is not unreasonable, but understates the possibility

that we lack appropriate knowledge to intervene in impoverished

environments or those simply different from rich countries. For

example, recent research demonstrates that child hydration, a

long-promoted emergency care measure for children suffering

from infectious disease in resource poor sub-Saharan Africa

Table 2. Estimated Association of National Biomedical Articles with National Health Burden (2002, 2004)a.

Model 1: All research articles % change 95% C.I. % change 95% C.I. % change 95% C.I.

National DALYs (10 millions) 73.9** 17.4–130.5 72.4** 18.0–126.7 68.4** 15.1–121.7

Global DALYs (10 millions) 1.0* 20.9–2.8 0.7** 21.1–2.5 5.0** 3.0–7.0

Cumulative National articles (thousands) 1.1** 0.8–1.3 1.2** 1.0–1.4

Cumulative Global articles (thousands) 0.3** 0.2–0.3 0.3** 0.2–0.3

Model 2: Systematic reviews

National DALYs (10 millions) 27.4 241.6–96.4 23.9 241.4–89.2 23.5 241.7–88.7

Global DALYs (10 millions) 0.2 23.0–3.4 20.1 23.2–3.0 7.5** 3.9–11.1

Cumulative National articles (thousands) 7.3** 5.0–9.5 8.9** 6.6–11.2

Cumulative Global articles (thousands) 2.5** 2.0–2.9 1.7** 1.1–2.3

Model 3: Clinical trials

National DALYs (10 millions) 367.9** 92.3–1038.6 297.6** 80.8–774.6 285.6** 81.7–718.3

Global DALYs (10 millions) 3.1 {

20.1–6.3 1.9 21.1–4.9 7.9** 4.5–11.5

Cumulative National articles (thousands) 57.7** 41.7–75.5 73.9** 56.0–93.8

Cumulative Global articles (thousands) 26.9** 23.2–30.7 16.9** 11.9–22.0

Model 4: Animal subjects

National DALYs (10 millions) 90.8** 242.5–90.4 87.1** 20.3–190.9 81.1** 17.0–180.3

Global DALYs (10 millions) 0.6 22.9–3.4 0.4 21.8–2.6 1.4 21.1–3.9

Cumulative National articles (thousands) 10.7** 9.3–12.2 11.1** 9.6–12.5

Cumulative Global articles (thousands) 1.4** 1.1–1.7 1.2** 0.7–1.6

Controlling for disease`

a Models in 2B contain 8102 cases (up to 111 diseases and 192 countries in 2002 and/or 2004). `Models control for the 19 broad disease/disability categories listed in Figure 2 (including the 2 in the footnote). {

p,.10; * p,.05; ** p,.01. doi:10.1371/journal.pone.0090147.t002

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Figure 2. Broad disease categories, the global DALYs they exact, and the relationship between country health burden and wealth for broad disease categories. Disease subcategories (e.g., HIV/AIDS) are listed in order from those that incur the largest global health burden. Scatter plots graph country DALY rate (DALYs per 1000 people) of conditions by GDP per capita, plotted on a log scale; slopes represent this as a linear relationship (the estimated OLS coefficient of logged GDP per capita regressed on logged DALY rate). The global map illustrates country differences in disease burden by plotting the difference between DALY rate for infectious diseases and cancers, categories with the most negative and positive relationship with country wealth. doi:10.1371/journal.pone.0090147.g002

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increases short-term mortality [22]. One conclusion voiced by

Ugandan doctor Peter Oluput-Oluput is that ‘‘we need to do more

research in Africa for Africans.’’ [30] This suggests the importance

of transferring not only health technology like tertiary care

facilities to the least developed countries, but also helping to

transfer health research technology to impoverished locations with

health burdens that differ most from wealthy countries. These

recommendations do not presume what less developed countries

should want or how they should spend their limited resources and

balance urgent with long-term health needs. Neither do they

address the material inequalities that lie behind inequality in both

health and health knowledge. Our research simply highlights the

potential impact of more health research relevant to the needs of

the poorest populations.

Not only environmental but the biological context of disease is

likely to be different in less developed countries. Research

suggesting that treatment for some cancers may be less effective

in certain U.S. minority populations suggests that current

therapies may have been ‘‘overfit’’ to a biased sample of genes

and bodies [31]. A growing collection of related findings have been

framed as evidence that biological factors play a role in health

disparities [32,33,34,35,36], but they also implicate the differential

relevance of health knowledge produced by biomedical research

for the health of different groups [37]. In short, the same care may

not always be equal. In this way, the inequality of biomedical

research that our analysis demonstrates likely understates its true

inequality.

By estimating the particular inequality of health conditions in

relation to national wealth (see Figure S1 in File S1), our study

highlights those most likely to be underserved given the national

focus and global inequality of research funding. For example,

malaria, tetanus, Chagas disease, measles, Vitamin A deficiency,

lymphatic filariasis, schistosomiasis, and diphtheria most dispro-

portionately afflict poor populations. Other conditions also inflict a

greater burden in less developed countries, including fires,

violence, drowning, and poisoning, as also glaucoma, peptic ulcers

and ear infections.

Our study has several limitations. The national-level burden of

disease data are only for two years, two years apart, which does

not provide sufficient change to isolate a causal effect of health

burden on research (see File S1). The lag between burden of

disease and disease-relevant publication may also not be long

enough to demonstrate the total influence—we had only one year

of subsequent citation data available to us. Moreover, we did not

have data on the economic value of diseases within countries, and

so we were unable to explore to what degree the same dynamic

that occurs across countries occurs inside them. Finally, we neglect

several other institutions that likely influence health research,

independent of global health needs. These include national

funding priorities, activism in disease communities, the scientific

maturity or generality of research on some disorders over others,

etc. Nevertheless, we believe that our analysis sheds light on the

global inequality of health research and suggests that attention to

local disease is likely a primary influence. To address global health

Figure 3. Relationship between the national GDP per capita in 2004 and the quantity of research published by researchers in 2005, by country, plotted on a logarithmic scale (to spread out countries for visual inspection). Each three character string corresponds to the unique ISO 3166-1 alpha-3 code associated with each country (see Table S4 in File S1 for complete list). doi:10.1371/journal.pone.0090147.g003

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inequality, we propose the development of measures to globalize

the research attention of wealthy countries and to support local

research in impoverished contexts where health knowledge is

needed most.

Supporting Information

File S1 Supporting information, figures, and tables. Figure S1, 2004 global disability-adjusted life years (DALYs) and 2005 reviews, clinical trials and animal studies categorized by 19 broad WHO disease and disability categories. This correspondence the loose relation- ship between burden of disease and health knowledge (see

Figure 1). Figure S2, Relationship between national disease burden and wealth. Scatterplots of national DALY rate (DALYs per 1000 people) and GNI per capita for each of 96

specific health conditions, where each point is a country. Also

shown is the estimated influence (or regression slope) of logged

DALY rate on logged GNI per capita, by condition, computed

using ordinary least-squares (OLS) regression. Figure S3, Relationship between the national GDP per capita in 2004 and the quantity of reviews, clinical trials and animal studies published by researchers in 2005, by country, plotted on a logarithmic scale (to spread out countries for visual inspection). Each three character string

corresponds to the unique ISO 3166-1 alpha-3 code associated

with each country (see Figure 3 and Table S4 in File S1 for

complete list). Table S1, Complete list of WHO Global Burden of Disease Categories. Table S2, Estimated Change in Global Number of Biomedical Articles with Changes in Global Health Burden (1990, 2004). Table S3, Estimated Change in Regional Number of Biomedical Articles with Changes in Regional Health Burden (1990, 2004). Table S4, Disease or Disease Category exacting the most DALYs. Table S5, Countries and their 3- Character Codes from Figure 3.

(DOCX)

Acknowledgments

We thank Jane Rosov from the National Library of Medicine for many

helpful discussions about details of MEDLINE’s annotation history with

Medical Subject Headings (MeSH), and the Unified Medical Language

System (UMLS); and John Schneider, Funmi Olopade and Jacob Foster for

helpful comments on the paper.

Author Contributions

Conceived and designed the experiments: JAE JMS JPI. Analyzed the data:

JMS. Wrote the paper: JAE JMS. Critically revised the study: JPI.

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