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The relationship between different dimensions of alcohol use and the burden of disease—an update

Jürgen Rehm1,2,3,4,5,6 , Gerhard E. Gmel Sr1,7,8,9, Gerrit Gmel1, Omer S. M. Hasan1, Sameer Imtiaz1,3, Svetlana Popova1,3,5,10, Charlotte Probst1,6, Michael Roerecke1,5, Robin Room11,12, Andriy V. Samokhvalov1,3,4, Kevin D. Shield13 & Paul A. Shuper1,5

Institute for Mental Health Policy Research, CAMH, Toronto, Ontario, Canada,1 Campbell Family Mental Health Research Institute, CAMH, Toronto, Ontario, Canada,2

Institute of Medical Science (IMS), University of Toronto, Toronto, Ontario, Canada,3 Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada,4 Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada,5 Institute for Clinical Psychology and Psychotherapy, TU Dresden, Dresden, Germany,6

Alcohol Treatment Center, Lausanne University Hospital, Lausanne, Switzerland,7 Addiction Switzerland, Lausanne, Switzerland,8 University of the West of England, Bristol, UK,9 Factor-Inwentash Faculty of Social Work, University of Toronto, Ontario, Canada,10 Centre for Alcohol Policy Research, La Trobe University, Melbourne, Victoria, Australia,11 Centre for Social Research on Alcohol and Drugs, Stockholm University, Stockholm, Sweden12 and Section of Cancer Surveillance, International Agency for Research on Cancer, Lyon, France13

ABSTRACT

Background and aims Alcohol use is a major contributor to injuries, mortality and the burden of disease. This review updates knowledge on risk relations between dimensions of alcohol use and health outcomes to be used in global and national Comparative Risk Assessments (CRAs). Methods Systematic review of reviews and meta- analyses on alcohol consumption and health outcomes attributable to alcohol use. For dimensions of exposure: vol- ume of alcohol use, blood alcohol concentration and patterns of drinking, in particular heavy drinking occasions were studied. For liver cirrhosis, quality of alcohol was additionally considered. For all outcomes (mortality and/or morbid- ity): cause of death and disease/injury categories based on International Classification of Diseases (ICD) codes used in global CRAs; harm to others. Results In total, 255 reviews and meta-analyses were identified. Alcohol use was found to be linked causally to many disease and injury categories, with more than 40 ICD-10 three-digit categories being fully attributable to alcohol. Most partially attributable disease categories showed monotonic relationships with volume of alcohol use: the more alcohol consumed, the higher the risk of disease or death. Exceptions were ischaemic diseases and diabetes, with curvilinear relationships, and with beneficial effects of light to moderate drinking in people without heavy irregular drinking occasions. Biological pathways suggest an impact of heavy drinking occasions on additional diseases; however, the lack of medical epidemiological studies measuring this dimension of alcohol use precluded an in-depth analysis. For injuries, except suicide, blood alcohol concentration was the most important dimension of alcohol use. Alcohol use caused marked harm to others, which has not yet been researched sufficiently. Conclusions Research since 2010 confirms the importance of alcohol use as a risk factor for disease and injuries; for some health outcomes, more than one dimension of use needs to be considered. Epidemiological studies should include measurement of heavy drinking occasions in line with biological knowledge.

Keywords Alcohol use, average volume, chronic disease, injury, patterns of drinking, risk-relations, systematic review, unrecorded consumption.

Correspondence to: Jürgen Rehm, Institute for Mental Health Policy Research, CAMH, 33 Russell Street, Toronto, ON M5S 2S1, Canada. E-mail: jtrehm@gmail.com Submitted 11 November 2016; initial review completed 19 December 2016; final version accepted 9 January 2017

INTRODUCTION

Alcohol consumption has been identified as a major contributor to the burden of disease and mortality in all the global Comparative Risk Assessments (CRAs [1]) conducted thus far as part of the Global Burden of Disease (GBD) studies [2–7], and in the World Health Organization (WHO) Global Status Reports on Alcohol

and Health and their predecessors [8–10]. All CRAs restricted themselves to modifiable risk factors [11], where the modifications could be linked to reductions in the disease burden [12]. As a consequence, they have become crucial for guiding health policy [13], not only in terms of primary prevention [14–16], but also in terms of secondary prevention and health systems manage- ment [17–19].

REVIEW doi:10.1111/add.13757

This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

At the core of any CRA are the risk relations between different dimensions of exposure (in the present case, alco- hol use) and particular diseases, disorders or injuries. Each of these relative risks is then combined with the extent of the respective exposure in a particular population to create alcohol-attributable fractions (AAFs) for that population [20,21]. In most CRAs, including for alcohol, both the rel- ative risk and the prevalence of exposure are continuous functions [22]. Knowledge on and estimates of these risk relations have been evolving during the past 15 years (compare the overview from 2003 [23], and especially since 2010 when the last overview on this topic in Addiction appeared [24], which the current review will update with the latest evidence. It will follow the structure of the previous reviews [23,24]: first, we will list disease and injury categories which are 100% alcohol- attributable; secondly, we will address disease categories partly attributable to alcohol, and finally, injury categories which are partly attributable to alcohol will be discussed. In the discussion, we not only outline the limitations of our review, but also look to future research developments.

METHODS

Search strategy

For this systematic review, we (a) searched the WHO Inter- national Statistical Classification of Diseases and Related Health Problems, 10th revision (ICD-10) 2016 databank [25] for the term ‘alcohol*’ to identify disease and injury categories fully attributable to alcohol (see Table 1), and (b) updated all estimates of alcohol use–disease or injury relationships for partially attributable outcomes from the estimates in the most recent preceding publication [24], following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [26,27].

We conducted a systematic literature search on AMED, CAB Abstracts, Embase, Health and Psychosocial Instru- ments, Healthstar, OVID Medline, PsycINFO, PubMed and Social Work Abstracts databases to identify systematic reviews and/or meta-analyses. Key words were different alcohol categories and the respective outcome category, along with either ‘systematic review’ or ‘meta-analysis’. All databases were searched from January 2008, the time limit of the last review of this series [24], to October 2016. Supporting information, Appendix S1 gives an over- view on the exact search terms used and full results. To identify the appropriate studies from the search results, one author reviewed independently all titles and abstracts at the initial stage. The results were compared with previous searches and reviews conducted independently by other authors who were part of this overview for each health outcome category. Discrepancies between the authors after the title and abstract review were resolved by discussing the full text. No language or geographical

restrictions were applied. In assessing and summarizing the results of the searches, our emphasis was on causality, pathophysiology and the key meta-analyses.

Assessment of causality

We used the epidemiological definitions of causality, where alcohol had to be necessary, either alone or in combination with other antecedent conditions as a component cause [28]. This translates into AAFs for partially attributable outcome categories, i.e. for outcome categories for which alcohol is a component cause. AAFs can be interpreted as the proportion of an outcome in a specific population, which would not occur if there had been no alcohol use [11,29]. In discussing the various conditions, we also refer to the Bradford Hill criteria [30], with most emphasis on pathophysiology.

Terminology

Unless specified otherwise, we will use the term ‘heavy drinking occasion’ for consuming quantities of 60+ g of pure alcohol on one occasion. Chronic heavy drinking indicates consumption on average per day of 60+ g of pure alcohol for men and 40+ g for women (for similar thresh- olds in alcohol exposure classifications, see [31,32]). Light to moderate drinking is used to refer to drinking patterns which, on average, entail fewer than 60 g of pure alcohol per day in men and fewer than 40 g in women.

RESULTS

Disease and injury categories fully (100%) attributable to alcohol use

In the ICD-10 [25], alcohol is mentioned as part of several diseases and injuries, as well as in the chapter ‘Factors influencing health status and contact with health services’ (Z codes). Table 1 gives an overview of the over 40 codes in ICD which include ‘alcohol’ or ‘alcoholic’.

While there are more than 10000 disease and injury codes, for only a small fraction (310) of the most frequent and important categories are there global data on cause of death or morbidity. All the 100% alcohol-attributable categories in Table 1, except alcohol use disorders (F10), are too infrequent to be included in these 310 global cause of death or burden of disease statistical categories, either by the Institute for Health Metrics and Evaluation (IHME) [33] or the WHO [34]. However, GBD CRA adds estimates for alcohol poisoning (X45) and fetal alcohol syndrome (Q86.0) to this label. The WHO Global Status Reports sum- marize F10 and X45 only under alcohol use disorders. The choice of broad categories in all global CRAs is based on the availability and quality of data. For most of the population world-wide, affecting 38 million of 56 million annual

Alcohol and disease 969

deaths globally [35], there are no vital registries with cause of death information. For these deaths without vital registries, cause of death is estimated on the basis of verbal autopsies of subsamples and then scaled-up [36]. Verbal autopsy denotes a method of gathering health information concerning deceased individuals to determine their cause of death. Relevant health infor- mation and a description of symptoms and events pre- ceding the death are determined based on interviews with next of kin, neighbours or friends of the deceased. This information is then analysed by trained health professionals or computer-based algorithms to assign a probable cause of death. The resulting cause of death categories have to be broad, as it is impossible to deter- mine a detailed cause of death via verbal autopsy [37]. For any non-fatal health categories, such as morbidity or disability, the data situation is worse than for mor- tality [38].

While almost all disease or injury categories 100% attributable to alcohol cannot be included in the global CRAs, they are often assessed in high-income countries with national hospital records and vital registries and, thus, these categories should be included in national CRAs where possible. For example, alcoholic cardiomyopathy (I42.6) as a cause of death is available in approximately half of the countries as a cause of death [39], and thus could be included as part of alcohol attributable mortality in these countries.

Alcohol use disorders

For alcohol use disorders, as defined in the F10 category of ICD-10, causality is clear by definition, as there would not be alcohol use disorders without alcohol use. The most important category of alcohol use disorders in terms of public health impact is alcohol dependence (F10.2), which is linked both to regular and irregular heavy drinkingocca- sions (see the almost straight linear relationship between average level of drinking and number of symptoms for dependence [40]). The link to irregular heavy drinking occasions is most evident in drinking cultures such as those in eastern Europe, where daily drinking is not common, not even among people with alcohol dependence [41]. Alcohol dependence and other alcohol use disorders are usually assessed based on general population surveys as

Table 1 ICD-10 categories with maximal one decimal with mention of alcohol or alcoholic.

E24.4 Alcohol-induced pseudo-Cushing’s syndrome F10 Mental and behavioural disorders due to use of

alcohol F10.0 Acute intoxication F10.1 Harmful use F10.2 Dependence syndrome F10.3 Withdrawal state F10.4 Withdrawal state with delirium F10.5 Psychotic disorder F10.6 Amnesic syndrome F10.7 Residual and late-onset psychotic disorder F10.8 Other mental and behavioural disorders F10.9 Unspecified mental and behavioural disorder G31.2 Degeneration of nervous system due to alcohol G62.1 Alcoholic polyneuropathy G72.1 Alcoholic myopathy I42.6 Alcoholic cardiomyopathy K29.2 Alcoholic gastritis K29.20 Alcoholic gastritis, without mention of haemorrhage K29.21 Alcoholic gastritis, with haemorrhage K70 Alcoholic liver disease K70.0 Alcoholic fatty liver K70.1 Alcoholic hepatitis K70.2 Alcoholic fibrosis and sclerosis of liver K70.3 Alcoholic cirrhosis of liver K70.4 Alcoholic hepatic failure K70.9 Alcoholic liver disease, unspecified K85.2 Alcohol-induced acute pancreatitis K86.0 Alcohol-induced chronic pancreatitis O35.4 Maternal care for suspected damage to foetus from

alcohol P04.3 Foetus and newborn affected by maternal use of

alcohol Q86.0 Fetal alcohol syndrome (dysmorphic) R78.0 Finding of alcohol in blood T51 Toxic effect of alcohol T51.0 Ethanol T51.1 Methanol T51.2 Propanol T51.3 Fusel oil T51.8 Other alcohols X45 Accidental poisoning by and exposure to alcohol X65 Intentional self-poisoning by and exposure to alcohol Y15 Poisoning by and exposure to alcohol, undetermined

intent Y90 Evidence of alcohol involvement determined by blood

alcohol level—different subcategories as defined by thresholds in mg/100 ml

Y91 Evidence of alcohol involvement determined by level of intoxication

Y91.0 Y91.0—Mild alcohol intoxication Y91.1 Y91.1—Moderate alcohol intoxication Y91.2 Y91.2—Severe alcohol intoxication Y91.3 Y91.3—Very severe alcohol intoxication Y91.9 Alcohol involvement, not otherwise specified Z04.0 Blood-alcohol and blood-drug test Z50.2 Alcohol rehabilitation

(Continues)

Z71.4 Alcohol abuse counselling and surveillance for alcohol use disorder

Z72.1 Alcohol use Z81.1 Family history of alcohol abuse

Table 1. (Continued)

970 Jürgen Rehm et al.

part of mental disorders (such as by the World Mental Health Survey [42]). As such surveys are relatively infre- quent or absent for many countries, for most CRAs to date the prevalence of alcohol use disorders had to be estimated, often using the level of per-capita alcohol consumption or prevalence of heavy drinking predictors in the estimation [43,44].

Accidental poisoning by and exposure to alcohol

Alcohol poisoning, which is the short term for the above- specified injury category, is handled as part of alcohol use disorders in global CRAs. Alcohol poisoning is often assessed in hospitals for emergency room entries. Any blood alcohol concentration above 3 g/l should be consid- ered as potentially life-threatening, with increasing mortal- ity risks associated with increasing blood alcohol concentrations [45]; in many countries, cause of death from ‘alcohol poisoning’ may be given regularly for con- centrations above 4 g/l. However, alcohol poisonings are underestimated markedly for two main reasons. First, alco- hol use disorders in general are stigmatized, even over and above the general stigma of psychiatric disorders [46]. As a consequence, death certificates may mention more neutral categories, such as heart disease categories, as the cause of death ([47]; see also the discussion on alcoholic liver cir- rhosis below). The amount of misclassification can be sub- stantial in some countries or regions. For example, Zaridze and colleagues [48] reported that in a series of more than 22000 autopsies in a Russian city, 16% of decedents had more than 4 g/l and 8% had more than 5 g/l blood alcohol concentrations. Some of the deaths reported by Zaridze and colleagues [48] should have been coded as alcohol poison- ing instead of the other codes given, often cardiovascular deaths. Similar misclassifications were found in other re- gions of Russia and surrounding countries [49]. However, while this means that alcohol poisoning deaths have been under-reported, this effect is too small to explain the posi- tive association between heavy drinking and cardiovascu- lar mortality in countries with irregular drinking of very large amounts of alcohol, such as the eastern European countries [50,51]. The second reason for the underestima- tion of alcohol poisoning are the rules applied to classify drug overdose deaths in ICD-10 or earlier versions of the ICD [52], which give a priority for coding other substances than alcohol in case of involvement of multiple types of substance use in deaths (see also [53,54]). While polydrug use is common in drug overdose situations (e.g. [55]), and alcohol is one of the substances often present with other illicit substances, alcohol is rarely recorded as the cause of death, even when it has been specified and reported as the most toxic component by the medico-legal pathologist, and based on this should have been coded as the underly- ing cause of death [56].

Fetal alcohol spectrum disorders

Fetal alcohol spectrum disorders (FASD) are the leading known cause of preventable birth defects and develop- mental disabilities. FASD is an umbrella term that describes the full spectrum of deficits that can occur in prenatally alcohol-exposed individuals. The most severe and important form of FASD in terms of public health, fetal alcohol syndrome (FAS), is characterized by clear morphological changes, functional deficits and high prev- alence of comorbidities [57]. FAS is the only expression of FASD in the ICD-10 (see Table 1). While FASD is not yet in ICD, the 5th edition of the Diagnostic and Statistical Manual of Mental Disorders included ‘Neurobehavioral disorder associated with prenatal alcohol exposure’ under ‘conditions for further study’ as the first step before including it as a formal diagnosis for clinical use (see Supporting information, Appendix, Section III [58]). Studies by May and co-workers [59–61] give some indi- cation of the full spectrum of FASD.

While human research has not delineated, and per- haps cannot delineate fully, the pattern, amount and/or critical period of alcohol exposure necessary for struc- tural and/or functional teratogenesis, animal models have shown that all stages of embryonic development are vulnerable to the teratogenic effects of ethanol, and that the type and severity of ethanol-induced birth defects are dependent largely upon the pattern, dose and developmental stage of the embryo at the time of ethanol exposure [62,63]. Animal models demonstrate clearly that even low levels of prenatal alcohol exposure may lead to brain dysfunction which, in turn, contributes to behavioural abnormalities [64].

In human research, the link between heavy drinking occasions during pregnancy and the risk of FAS is well established [65–70]. For low amounts of alcohol (8–28 g per occasion), several studies have found that there is no increased risk of behavioural and/or develop- mental deficits in children [69,71–73]. However, there is some evidence that the consumption of 42–56 g per week during pregnancy may have adverse effects on neurodevelopment [70]. To date, however, there are no longitudinal human studies that have followed alcohol- exposed individuals over a sufficient amount of time and used FASD diagnostic criteria to establish the rela- tionship between dose and/or pattern of alcohol intake during pregnancy and FASD.

For estimation of the prevalence of FAS and FASD, Popova and colleagues developed a methodology based on the prevalence of drinking during pregnancy, which will be used in future CRAs [74]. However, disability weights [75] need to be established for both categories to estimate the burden of disease (currently only avail- able for FAS [76]).

Alcohol and disease 971

Disease and injury categories partially attributable to alcohol use

In total, 255 unique reviews and meta-analyses were iden- tified (see Supporting information, Appendix S1). Table 2 gives an overview of global cause of death and outcome categories causally impacted by alcohol, as well as the most important meta-analyses, including those used for the CRA of the upcoming WHO Global Status Report on Alcohol and Health (to be prepared in 2017; for graphs on the relationships between average level of alcohol use and disease, see Supporting information, Appendix S2).

In the following sections, we discuss the underlying reasons and pathways for major disease, injury and cause of death categories where causality has been established. An important consideration for each disease and mortality outcome are the questions of (a) which dimension of alcohol use is causally related; (b) if there are dose–response relationships within the respective dimension; and (c) whether there are gender differences (see also Supporting information, Appendix S2 for gender specific formulas). The overall results on modelled and biological relationships are summarized in Table 3.

Infectious diseases

Alcohol’s effects on the immune system

Alcohol impacts the innate and the acquired immune system and, thus, increases vulnerability to infectious disease [77,78]. Alcohol exposure impairs the functioning of phagocytes such as polymorphonuclear leucocytes (especially neutrophils) and macrophages [79]. These cells are responsible for the ingestion of dead cells and can be considered the immune system’s first responders to inflam- mation [80]. Alcohol exposure has a suppressive effect on the release of cytokines responsible for cell signalling and critical for regulation of the host defence [80,81]. This includes chemotactic signals that trigger the migration of polymorphonuclear leucocytes into the infected area. The effects of chronic alcohol use on the immune response are probably also to increase the risk of infectious disease [82,83]. Overall, the biological pathways suggest a more pronounced effect of heavy drinking occasions and, thus, more exponential pathways and a specifically high risk for alcohol use disorders.

Tuberculosis

Alcohol’s impact on the immune system described above is immediately relevant to infection with tuberculosis (TB), as approximately one-third of people in the world have been infected with Mycobacterium tuberculosis but are not yet ill and cannot transmit the disease (latent TB [84]). However, only 10% of those infected develop active TB; for the rest, the immune system will be able to fight off the infection.

Accordingly, a weakened immune system is critical for increasing susceptibility to TB infection, or for reactivation of latent TB, and alcohol plays a prominent role here [85]. As a second important pathway, alcohol use may lead to a presence in social environments that facilitate the spread of tuberculosis infection [85]. As a consequence, alcohol is one of the major risk factors for TB, especially in countries with high population densities and high infection rates of M. tuberculosis, with poverty being linked to both. Regard- ing for average level of consumption, there is clearly a dose–response relationship, with some indication that, for lower levels of consumption, the increase is less steep than for higher levels [86,87].

Given the aetiology, one may suspect an impact of patterns of drinking, especially of irregular heavy drink- ing occasions, but the empirical evidence is scarce [88]. In addition, the higher relative risks for alcohol use disor- ders or alcohol problems may serve as an indirect indica- tor [86,87], as both are usually linked to heavy drinking occasions [40,89,90].

HIV/AIDS

The status of alcohol use as a cause for HIV infection, separate from its general impact on the immune system (see above), and of the effects of alcohol use on the course of HIV/AIDS, separate from non-adherence to anti-retroviral medications [91,92], have been discussed in recent years [93–96]. Indeed, the evidence on both mechanisms was found to be non-conclusive in most publications, and also at a meeting to discuss the causal role of alcohol use in HIV/AIDS organized by the WHO and the South African Medical Research Council in 2008 [97]. However, since 2008, consider- able new scientific evidence has emerged which sup- ports a causal role of alcohol. Systematic reviews and meta-analyses are now available to allow the quantifi- cation of the impact of alcohol use on HIV/AIDS. In the following, we try to summarize recent developments (following closely [98]; see also [99]), and suggest an operationalization to quantify the causal impact of alcohol use on HIV/AIDS.

Alcohol use was found to be associated with HIV inci- dence and prevalence in systematic reviews and meta- analyses [100–106]. This association may have resulted, in part, from the causal impact of acute alcohol use on sexual decision-making [107], resulting in condomless sex [105,108–114]. Alternatively, other variables could be causally responsible for the associations between alco- hol use and HIV/AIDS, especially the effect of risk-taking behaviours and other personality traits [96,115].

To exclude such alternative explanations and corrobo- rate the causal role of alcohol on HIV incidence via impacts on decision-making concerning safer sex practices, a

972 Jürgen Rehm et al.

T ab le 2

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Alcohol and disease 973

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or ra o et al ., 2 0 0 4 [1 7 0 ]; B ag n ar di et al ., 2 0 1 5 [1 6 9 ]

C R A ca lc u la ti on

s: B ag n ar di et al ., 2 0 1 5 [1 6 9 ]

St om

ac h ca n ce r

St om

ac h ca n ce r (4 1 4 )

C 1 6 – C 1 6 .9 ,D

0 0 .2 ,D

1 3 .1 ,D

3 7 .1

c C au

sa lit y: IA R C ,2

0 1 2 [1 4 6 ]: pr ob ab ly ca rc in og en ic in

h u m an

sb D et ri m en ta l

M et a- an

al ys is :B

ag n ar di

et al ., 2 0 1 5 [1 6 9 ]

C R A ca lc u la ti on

s: B ag n ar di et al ., 2 0 1 5 [1 6 9 ]; st om

ac h ca n ce r m ay

be in cl u de d in

C R A ca lc u la ti on

s w h er e th e th re sh ol d is se t to

in cl u de

‘p ro ba bl y

ca rc in og en ic ’

C ol on

an d re ct u m

ca n ce r

C ol on

an d re ct u m

ca n ce r (4 4 1 )

C 1 8 – C 2 1 .9 ,D

0 1 .0 -D 0 1 .3 ,

D 1 2 -D 1 2 .9 ,D

3 7 .3 – D 3 7 .5

c C au

sa lit y: IA R C ,2

0 1 0 ;2

0 1 2 [1 4 5 ,1 4 6 ]: su ffi ci en t ev id en ce

fo r

ca rc in og en ic it y in

h u m an

sb D et ri m en ta l

M et a- an

al ys is :C

or ra o et al ., 2 0 0 4 [1 7 0 ]; B ag n ar di et al ., 2 0 1 5 [1 6 9 ]

C R A ca lc u la ti on

s: B ag n ar di et al ., 2 0 1 5 [1 6 9 ]

Li ve r ca n ce r

Li ve r ca n ce r (4 1 7 )

C 2 2 – C 2 2 .9 ,D

1 3 .4

c C au

sa lit y: IA R C ,2

0 1 0 ;2

0 1 2 [1 4 5 ,1 4 6 ]: su ffi ci en t ev id en ce

fo r

ca rc in og en ic it y in

h u m an

sb D et ri m en ta l

M et a- an

al ys is :C

or ra o et al ., 2 0 0 4 [1 7 0 ]; B ag n ar di et al ., 2 0 1 5 [1 6 9 ]

C R A ca lc u la ti on

s: B ag n ar di et al ., 2 0 1 5 [1 6 9 ]

P an

cr ea ti c ca n ce r

P an

cr ea ti c ca n ce r (4 5 6 )

C 2 5 – C 2 5 .9 ,D

1 3 .6 – D 1 3 .7

c C au

sa lit y: IA R C ,2

0 1 2 [1 4 6 ]: pr ob ab ly ca rc in og en ic in

h u m an

sb D et ri m en ta l

M et a- an

al ys is :B

ag n ar di

et al ., 2 0 1 5 [1 6 9 ]

C R A ca lc u la ti on

s: B ag n ar di et al ., 2 0 1 5 [1 6 9 ]; pa n cr ea ti c ca n ce r h as

be en

in cl u de d in

so m e C R A ca lc u la ti on

s w h er e th e th re sh ol d w as

se t to

in cl u de

‘p ro ba bl y ca rc in og en ic ’

La ry n x ca n ce r

La ry n x ca n ce r (4 2 3 )

C 3 2 – C 3 2 .9 ,D

0 2 .0 ,D

1 4 .1 ,D

3 8 .0

c C au

sa lit y: IA R C ,2

0 1 0 ;2

0 1 2 [1 4 5 ,1 4 6 ]: su ffi ci en t ev id en ce

fo r

ca rc in og en ic it y in

h u m an

sb D et ri m en ta l

M et a- an

al ys is :C

or ra o et al ., 2 0 0 4 [1 7 0 ]; B ag n ar di et al ., 2 0 1 5 [1 6 9 ]

C R A ca lc u la ti on

s: B ag n ar di et al ., 2 0 1 5 [1 6 9 ]

T ra ch ea ,b ro n ch u s

an d lu n g ca n ce r

T ra ch ea l, br on

ch u s,

an d lu n g ca n ce r (4 2 6 )

C 3 3 – C 3 4 .9 2 ,D

0 2 .1 – D 0 2 .3 ,

D 1 4 .2 – D 1 4 .3 2 ,D

3 8 .1

c C au

sa lit y: IA R C ,2

0 1 0 ;2

0 1 2 [1 4 5 ,1 4 6 ]: n ei th er

su ffi ci en t ev id en ce

n or

pr ob ab ly ca rc in og en ic in

h u m an

sb D et ri m en ta l

M et a- an

al ys is :B

ag n ar di

et al ., 2 0 1 5 [1 6 9 ]

C R A ca lc u la ti on

s: n ot

re le va n t, as

n ot

ye t es ta bl is h ed

as ca u sa lp at h w ay

Fe m al e br ea st

ca n ce r

B re as t ca n ce r (4 2 9 )

C au

sa lit y: IA R C ,2

0 1 0 ;2

0 1 2 [1 4 5 ,1 4 6 ]: su ffi ci en t ev id en ce

fo r

ca rc in og en ic it y in

h u m an

sb D et ri m en ta l

(C on

ti n u es )

974 Jürgen Rehm et al.

T ab le 2 . (C on

ti n u ed )

D is ea se ca te go ry

G B D 2 0 1 5 C au se N am

e (C au se ID ) [3 5 4 ]

IC D – 1 0 co de s fo r ca us e of de at ha

C au sa lit y an d re fe re nc e to

m et a- an al ys es /s el ec te d sy st em

at ic re vi ew

s E ffe ct

C 5 0 – C 5 0 .9 2 9 ,D

0 5 – D 0 5 .9 2 ,

D 2 4 – D 2 4 .9 ,D

4 8 .6 – D 4 8 .6 2 ,

D 4 9 .3 ,N

6 0 – N 6 0 .9 9 c

M et a- an

al ys es :m

an y m et a- an

al ys es

w it h si m ila r re su lt s (f or

an ov er vi ew

se e

Sh ie ld et al ., 2 0 1 6 [1 5 1 ])

C R A ca lc u la ti on

s: B ag n ar di et al ., 2 0 1 5 [1 6 9 ]

O th er

n eo pl as m s

O th er

n eo pl as m s (4 8 8 )

C 1 7 – C 1 7 .9 ,C

3 – C 3 1 .9 ,C

3 7 – C 3 8 .8 ,

C 4 – C 4 1 .9 ,C

4 7 – C 5 ,C

5 1 – C 5 2 .9 ,

C 5 7 – C 5 7 .8 ,C

5 8 – C 5 8 .0 ,C

6 0 – C 6 0 .9 ,

C 6 3 – C 6 3 .8 ,C

6 6 – C 6 6 .9 ,C

6 8 .0 – C 6 8 .8 ,

C 6 9 – C 7 ,C

7 4 – C 7 5 .8 ,

D 0 7 .4 ,D

0 9 .2 – D 0 9 .2 2 ,D

1 3 .2 – D 1 3 .3 9 ,

D 1 4 .0 ,D

1 5 – D 1 6 .9 ,D

2 8 .0 – D 2 8 .1 ,D

2 8 .7 ,

D 2 9 .0 ,D

3 0 .2 – D 3 0 .2 2 ,D

3 0 .4 – D 3 0 .8 ,

D 3 1 – D 3 3 .9 ,D

3 5 – D 3 6 ,D

3 6 .1 – D 3 6 .7 ,

D 3 7 .2 ,D

3 8 .2 – D 3 8 .5 ,D

3 9 .2 ,D

3 9 .8 ,

D 4 1 .2 – D 4 1 .3 ,D

4 2 – D 4 3 .9 ,D

4 4 .1 – D 4 4 .8 ,

D 4 5 – D 4 5 .9 ,D

4 7 – D 4 7 .0 ,D

4 7 .2 – D 4 7 .9 ,

D 4 8 .0 – D 4 8 .4 ,D

4 9 .6 ,D

4 9 .8 1 ,K

3 1 .7 ,

K 6 2 .0 – K 6 2 .1 ,K

6 3 .5 ,N

8 4 .0 – N 8 4 .1

To o di ve rs e a ca te go ry

to es ta bl is h an

y ca u sa lp at h w ay s fr om

al co h ol as

a w h ol e or

to qu

an ti fy an

y ri sk -r el at io n s; th u s, th is ca te go ry

w ill n ot

be qu

an ti fi ed

as a ca u se

of de at h or

m or bi di ty

ca te go ry

ca u sa lly

im pa ct ed

by al co h ol .

D et ri m en ta l

D ia be te s m el lit u s

D ia be te s m el lit u s (5 8 7 )

E1 0 – E1

0 .1 1 ,E 1 0 .3 – E1

1 .1 ,E 1 1 .3 – E1

2 .1 ,

E1 2 .3 – E1

3 .1 1 ,E 1 3 .3 – E1

4 .1 ,E 1 4 .3 – E1

4 .9 ,

P 7 0 .0 – P 7 0 .2 ,R

7 3 – R 7 3 .9

C au

sa lit y: H ow

ar d et al ., 2 0 0 4 [1 8 8 ]

B en efi ci al or

de tr im

en ta l,

de pe n di n g on

pa tt er n s of

dr in ki n g an

d po pu

la ti on

s M et a- an

al ys es :B

al iu n as

et al ., 2 0 0 9 [1 9 1 ]; K n ot t et al ., 2 0 1 5 [1 9 2 ]; Li et al .,

2 0 1 6 [1 9 3 ]; in

ad di ti on

th er e w er e in te rv en ti on

st u di es

w it h m ix ed

re su lt s

[1 9 4 ,1 9 5 ]

C R A ca lc u la ti on

s: B al iu n as

et al ., 2 0 0 9 [1 9 1 ]; cu rr en tl y in

re vi si on

N eu ro ps yc h ia tr ic di so rd er s

A lz h ei m er ’s di se as e

an d ot h er

de m en ti as

A lz h ei m er

di se as e

an d ot h er

de m en

ti as

(5 4 3 )

F0 0 – F0

3 .9 1 ,G

3 0 – G 3 1 .1 ,G

3 1 .8 – G 3 1 .9

C au

sa lit y: C ol lin

s et al ., 2 0 0 9 [2 1 2 ] fo r po te n ti al pa th w ay s of pr ot ec ti ve

ef fe ct s

of lig h t to

m od er at e u se ;R

id le y et al ., 2 0 1 3 [2 1 0 ]; D au

la tz ai ,2

0 1 5 [2 1 1 ], fo r

m ec h an

is m

of de tr im

en ta le ffe ct s of h ea vy

u se

D et ri m en ta l; po te n ti al

be n efi ci al ef fe ct

fo r lig h t to

m od er at e dr in ki n g

M et a- an

al ys es :B

ey do u n et al ., 2 0 1 4 [2 0 7 ]

C R A ca lc u la ti on

s: n ot

ye t in cl u de d in

C R A

(C on

ti n u es )

Alcohol and disease 975

T ab le 2 . (C on

ti n u ed )

D is ea se ca te go ry

G B D 2 0 1 5 C au se N am

e (C au se ID ) [3 5 4 ]

IC D – 1 0 co de s fo r ca us e of de at ha

C au sa lit y an d re fe re nc e to

m et a- an al ys es /s el ec te d sy st em

at ic re vi ew

s E ffe ct

U n ip ol ar

de pr es si ve

di so rd er s

M aj or

de pr es si ve

di so rd er

(5 6 8 )

H as

n ot

be en

m od el le d in

G B D as

ca u se

of de at h

C au

sa lit y: R eh m

et al ., 2 0 0 4 [5 ]; B od en

& Fe rg u ss on

,2 0 1 1 [2 1 9 ];

D et ri m en ta l

M et a- an

al ys es :B

od en

& Fe rg u ss on

,2 0 1 1 [2 1 9 ]; Fo u ld s et al ., 2 0 1 5 [3 5 8 ]

C R A ca lc u la ti on

s: su gg es te d to

u se

Fe rg u ss on

et al ., 2 0 0 9 [2 2 1 ] to

be co n se rv at iv e, ba se d on

pr ev al en ce

of al co h ol u se

di so rd er s

Ep ile ps y

Ep ile ps y / Ep ile ps y

im pa ir m en t

en ve lo pe

(5 4 5 )

G 4 0 – G 4 1 .9

C au

sa lit y: B ar to lo m ei ,2

0 0 6 [3 5 9 ]; B ar cl ay

et al ., 2 0 0 8 [2 3 6 ]; Le ac h et al .,

2 0 1 2 [2 3 7 ]

D et ri m en ta l

M et a- an

al ys is an

d C R A ca lc u la ti on

s: Sa m ok h va lo v et al ., 2 0 1 0 [2 3 0 ]

C ar di ov as cu la r di se as es

H yp er te n si ve

h ea rt

di se as e

H yp er te n si ve

h ea rt

di se as e (4 9 8 )

I1 1 – I1 1 .9

C au

sa lit y: P u dd ey

& B ei lin

,2 0 0 6 [3 6 0 ]; O ’K ee fe et al ., 2 0 1 4 [2 3 9 ]; in

ad di ti on

w e h av e go od

ev id en ce

th at

in te rv en ti on

s le ad in g to

re du

ct io n s of al co h ol u se

su bs eq u en tl y le ad

to re du

ct io n s in bl oo d pr es su re an

d h yp er te n si on

[3 6 1 ,3 6 2 ]

D et ri m en ta l, m ay

de pe n d on

pa tt er n s of dr in ki n g fo r lo w

vo lu m e in

w om

en M et a- an

al ys es :C

h en

et al ., 2 0 0 8 [3 6 3 ]; T ay lo r et al ., 2 0 0 9 [2 4 1 ]; B ri as ou

lis et al ., 2 0 1 2 [2 4 2 ]

C R A ca lc u la ti on

s: T ay lo r et al ., 2 0 0 9 [2 4 1 ]; n ew

m et a- an

al ys es in pr ep ar at io n

Is ch ae m ic h ea rt

di se as e

Is ch ae m ic h ea rt

di se as e (4 9 3 )

I2 0 – I2 5 .9

C au

sa lit y: M u ka m al & R im

m ,2 0 0 1 [3 6 4 ]; C ol lin

s et al ., 2 0 0 9 [2 1 2 ]; R oe re ck e

& R eh m ,2

0 1 4 [2 4 8 ]

B en efi ci al or

de tr im

en ta l,

de pe n de n t on

le ve la n d

pa tt er n s of dr in ki n g

M et a- an

al ys es :R

on ks le y et al ., 2 0 1 1 [2 5 6 ]; R oe re ck e & R eh m ,2

0 1 1 [3 6 5 ];

R oe re ck e & R eh m ,2

0 1 2 ;2

0 1 4 [2 4 8 ,2 5 7 ]

C R A ca lc u la ti on

s: R eh m

et al ., 2 0 1 6 [2 6 8 ]

C ar di om

yo pa th y

C ar di om

yo pa th y an

d m yo ca rd it is (4 9 9 )

A 3 9 .5 2 ,B

3 3 .2 – B 3 3 .2 4 ,D

8 6 .8 5 ,

I4 0 – I4 3 .9 ,I 5 1 .4 – I5 1 .5

C au

sa lit y: Ia co vo n ie ta l., 2 0 1 0 [2 4 4 ]; G eo rg e & Fi gu

er ed o, 2 0 1 1 [3 6 6 ]; R eh m

et al ., 2 0 1 7 [3 9 ]

D et ri m en ta l

N o m et a- an

al ys es

fo u n d. T h er e is a se pa ra te

ca te go ry

fo r al co h ol ic

ca rd io m yo pa th y, w h ic h is re sp on

si bl e fo r 3 – 4 0 %

of al lc ar di om

yo pa th ie s

[2 4 4 ]. R eh m

an d co lle ag u es

re ce n tl y in tr od u ce d a m et h od

to es ti m at e A A Fs

fo r th is co n di ti on

[3 6 7 ]

C R A ca lc u la ti on

s: M an

th ey

et al ., 2 0 1 7 [3 6 7 ]

A tr ia lfi br ill at io n

an d fl u tt er

A tr ia lfi br ill at io n an

d fl u tt er

(5 0 0 )

I4 8 – I4 8 .9 2

C au

sa lit y: R os en qv is t, 1 9 9 8 [3 6 8 ]; R os en be rg

& M u ka m al ,2

0 1 2 [3 6 9 ]

D et ri m en ta l

M et a- an

al ys es :S am

ok h va lo v et al ., 2 0 1 0 [3 7 0 ]; K od am

a et al ., 2 0 1 1 [2 4 5 ];

La rs so n et al ., 2 0 1 4 [3 7 1 ]

C R A ca lc u la ti on

s: Sa m ok h va lo v et al ., 2 0 1 0 [3 7 0 ]

H ea rt fa ilu

re N o G B D ca te go ry ;I C D

co de s ar e re di st ri bu

te d

I5 0 ,I 1 1 .0 ,I 1 3 .0 ,I 1 3 .2

A lt h ou

gh th er e ar e m an

y re vi ew

s ab ou

t al co h ol u se

an d h ea rt fa ilu

re ,

in cl u di n g m et a- an

al ys es

(S u pp or ti n g in fo rm

at io n ,A

pp en di x S1

), th is do es

n ot

(C on

ti n u es )

976 Jürgen Rehm et al.

T ab le 2 . (C on

ti n u ed )

D is ea se ca te go ry

G B D 2 0 1 5 C au se N am

e (C au se ID ) [3 5 4 ]

IC D – 1 0 co de s fo r ca us e of de at ha

C au sa lit y an d re fe re nc e to

m et a- an al ys es /s el ec te d sy st em

at ic re vi ew

s E ffe ct

to ot h er

G B D ca te go ri es ,

m ai n ly to

is ch ae m ic

h ea rt di se as e

af fe ct

C R A s, as

th e ca te go ry

of ‘h ea rt fa ilu

re ’, si n ce

th e fi rs t G B D st u dy ,h

as be en

re di st ri bu

te d to

ot h er

G B D ca rd io va sc u la r ca te go ri es ,m

ai n ly to

is ch ae m ic h ea rt di se as e [3 7 2 ]

B en efi ci al or

de tr im

en ta l,

de pe n de n t on

le ve la n d

pa tt er n s of dr in ki n g

Is ch ae m ic st ro ke

(4 9 5 )

G 4 5 – G 4 6 .8 ,I 6 3 – I6 3 .9 ,

I6 5 – I6 6 .9 ,I 6 7 .2 – I6 7 .3 ,

I6 7 .5 – I6 7 .6 ,I 6 9 .3 – I6 9 .3 9 8

C au

sa lit y: P u dd ey

et al ., 1 9 9 9 [2 5 5 ]; M az za gl ia et al ., 2 0 0 1 [3 7 3 ]; C ol lin

s et al .,

2 0 0 9 [2 1 2 ]

B en efi ci al or

de tr im

en ta l,

de pe n de n t on

le ve la n d pa tt er n s

of dr in ki n g (s im

ila r to

IH D )

M et a- an

al ys es :R

ey n ol ds

et al ., 2 0 0 3 [3 7 4 ]; P at ra

et al ., 2 0 1 0 [3 7 5 ]; Zh

an g

et al ., 2 0 1 4 [3 7 6 ]

C R A ca lc u la ti on

s: P at ra

et al ., 2 0 1 0 [3 7 5 ]; R eh m

et al ., 2 0 1 6 [2 6 8 ]

H ae m or rh ag ic an

d ot h er

n on

-i sc h ae m ic

st ro ke

H ae m or rh ag ic st ro ke

(4 9 6 )

I6 0 – I6 1 .9 ,I 6 2 .0 – I6 2 .0 3 ,

I6 7 .0 – I6 7 .1 ,I 6 8 .1 – I6 8 .2 ,

I6 9 .0 – I6 9 .2 9 8

C au

sa lit y: P u dd ey

et al ., 1 9 9 9 [2 5 5 ]; M az za gl ia et al ., 2 0 0 1 [3 7 3 ];

M ai n ly de tr im

en ta l, ex ce pt

fo r

lo w do se s

M et a- an

al ys es :R

ey n ol ds

et al ., 2 0 0 3 [3 7 4 ]; P at ra

et al ., 2 0 1 0 [3 7 5 ]; Zh

an g

et al ., 2 0 1 4 [3 7 6 ]

C R A ca lc u la ti on

s: P at ra

et al ., 2 0 1 0 [3 7 5 ]

O es op h ag ea lv ar ic es

N o G B D ca te go ry

I8 5

N o m et a- an

al ys es

fo u n d

D et ri m en ta l

G lo ba lC

R A ca lc u la ti on

s: n ot

ap pl ic ab le ,a s ca te go ry

is to o sm

al l. N at io n al

C R A ca lc u la ti on

s: sh ou

ld be

do n e w it h re la ti ve

ri sk

of liv er

ci rr h os is

G as tr oi n te st in al di se as es

C ir rh os is of th e liv er

C ir rh os is an

d ot h er

ch ro n ic liv er

di se as es

(5 2 1 )

B 1 8 – B 1 8 .9 ,I 8 5 – I8 5 .9 ,I 9 8 .2 ,K

7 0 – K 7 0 .9 ,

K 7 1 .3 – K 7 1 .5 1 ,K

7 1 .7 ,K

7 2 .1 – K 7 4 .6 9 ,

K 7 4 .9 ,K

7 5 .8 – K 7 6 .0 ,K

7 6 .6 – K 7 6 .7 ,K

7 6 .9

C au

sa lit y: a ca u sa li m pa ct of al co h ol is by

de fi n it io n as

fo r m an

y liv er

di se as es

th er e ar e al co h ol ic su bc at eg or ie s in

th e IC D (s ee

T ab le 1 ); pa th og en es is :G

ao &

B at al le r, 2 0 1 1 [2 7 9 ]

D et ri m en ta l

M et a- an

al ys es

an d C R A ca lc u la ti on

s: R eh m

et al ., 2 0 1 0 [2 8 0 ]

G al lb la dd er

an d

bi le du

ct di se as e

G al lb la dd er

an d bi lia ry

di se as es

(5 3 4 )

K 8 0 – K 8 3 .9

C au

sa lit y: n ot

cl ea r fo r th e ov er al lc at eg or y (f or

ga lls to n es

se e [3 7 7 ])

P ot en ti al ly be n efi ci al ,b u t n o

re la ti on

to al co h ol u se

in th e

on ly m et a- an

al ys es

fo r

ga lls to n es

M et a- an

al ys es :S h ab an

za de h et al ., 2 0 1 6 [3 7 8 ]

C R A ca lc u la ti on

s: n ot

re le va n t, as

ca u sa lit y is n ot

cl ea r an

d th e on

ly m et a-

an al ys es

sh ow

ed n o as so ci at io n be tw

ee n al co h ol u se

an d ga lls to n es

P an

cr ea ti ti s

P an

cr ea ti ti s (5 3 5 )

K 8 5 – K 8 6 .9

C au

sa lit y: n ot

n ec es sa ry ,a s th er e ar e tw

o co n di ti on

s of pa n cr ea ti ti s w h ic h ar e

1 0 0 %

al co h ol at tr ib u ta bl e (s ee

T ab le 1 ); fo r pa th og en es is :B

ra ga n za

et al .,

2 0 1 1 [2 9 9 ]; Y ad av

et al ., 2 0 1 3 [3 0 0 ]; La n ki sc h et al ., 2 0 1 5 [3 0 1 ]; M aj u m de r

& C h ar i, 2 0 1 6 [3 0 2 ]

D et ri m en ta l

M et a- an

al ys es :I rv in g et al ., 2 0 0 9 [3 0 8 ]; Sa n ka ra n et al ., 2 0 1 5 [3 0 3 ];

Sa m ok h va lo v et al ., 2 0 1 5 [3 0 9 ]

C R A ca lc u la ti on

s: Sa m ok h va lo v et al ., 2 0 1 5 [3 0 9 ]

(C on

ti n u es )

Alcohol and disease 977

T ab le 2 . (C on

ti n u ed )

D is ea se ca te go ry

G B D 2 0 1 5 C au se N am

e (C au se ID ) [3 5 4 ]

IC D – 1 0 co de s fo r ca us e of de at ha

C au sa lit y an d re fe re nc e to

m et a- an al ys es /s el ec te d sy st em

at ic re vi ew

s E ffe ct

O th er

di ge st iv e

di se as es

O th er

di ge st iv e

di se as es

(5 4 1 )

I8 4 – I8 4 .9 ,K

2 0 – K 2 4 ,K

3 1 .0 ,

K 3 1 .8 1 – K 3 1 .8 1 9 ,K

3 8 – K 3 8 .2 ,K

5 7 – K 6 2 ,

K 6 2 .2 – K 6 2 .6 ,K

6 2 .8 – K 6 2 .9 ,K

6 4 – K 6 4 .9 ,

K 6 6 .8 ,K

6 7 ,K

6 8 – K 6 8 .9 ,K

7 5 .2 – K 7 5 .4 ,

K 7 6 .1 – K 7 6 .5 ,K

7 6 .8 – K 7 6 .8 9 ,K

7 7 – K 7 7 .8 ,

K 9 0 – K 9 0 .9 ,K

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978 Jürgen Rehm et al.

number of experiments have been conducted. Alcohol use was manipulated experimentally to assess its impact on condomless sex intentions. Systematic reviews and meta-analyses of the results of these experimental trials clearly indicated the causal impact of acute alcohol use (clearly shown for a blood alcohol concentration of 0.07 g/dl or more, but possibly even below) use on decisions/intentions about condomless sex, above and beyond the influence of expectations about alcohol and of

underlying risk-relevant personality traits [113,114]. It should be noted that these experiments have been conducted in a number of key populations, including HIV-positive people [116].

Clearly, any experimental studies on alcohol use and HIV can only use surrogate end-points, i.e. intention for unsafe (condomless) sex rather than condomless sex itself or HIV infection. However, the results of the experimental studies corroborate the results of epidemiological cohort

Table 3 Biological pathway and Comparative Risk Assessment (CRA) modelling of alcohol use and health outcomes.

Statistical model Biological pathway

Disease category General regression of alcohol use on logarithmized RR Irregular HD HD

Irregular HD

Infectious diseases Tuberculosis Linear � + + Human immunodeficiency virus/ acquired immune deficiency syndrome (HIV/AIDS)

Modelled indirectly via sexual decision making and impact on medication adherence

+ + +

Other sexually transmitted diseases Modelled indirectly via sexual decision-making + + + Lower respiratory infections: pneumonia

linear � + ?

Cancers Lip and oral cavity cancer Almost linear � � � Nasopharynx cancer Almost linear � � � Other pharynx cancer Almost linear � � � Oesophagus cancer Almost linear � � � Colon and rectum cancer Almost linear � � � Liver cancer Accelerated � � � Larynx cancer Almost linear � � � Female breast cancer Slightly accelerated � Some indications � Diabetes mellitus Diabetes mellitus Curvilinear + + ? Neuropsychiatric disorders Alzheimer’s disease and other dementias

Not clear; indications for curvilinear � + �

Unipolar depressive disorders Threshold � + ? Epilepsy Linear � + ? Cardiovascular diseases Hypertensive heart disease Accelerated � + ? Ischaemic heart disease Curvilinear + + + Cardiomyopathy Modelled indirectly via the proportion of alcoholic

cardiomyopathy to cardiomyopathy in the countries with data

+ + +

Atrial fibrillation and flutter Linear � + + Ischaemic stroke Curvilinear + + + Haemorrhagic and other non- ischaemic stroke

Linear for women; accelerated for men � + +

Gastrointestinal diseases Cirrhosis of the liver Accelerated � + � Pancreatitis Curvilinear for women; linear for men � + + Injuries Unintentional injuries Modelled mainly via drinking level in the situation + + (tolerance) + Violence Modelled mainly via drinking level in the situation + ? + Suicide Modelled based on both volume of drinking and

drinking in the situation + + +

RR: relative risk; HD: chronic heavy drinking; irregular HD: irregular heavy drinking.

Alcohol and disease 979

and cross-sectional studies with condomless sex [105,108–112,117–120], sexually transmitted diseases [121,122] or HIV incidence [102] as end-points. Moreover, there are meta-analyses that show a clear link between intentions for condomless sex and actual sexual risk behaviour [123,124], as well as between condomless sexual practices and HIV seroconversion [125–127].

Besides this pathway of sexual decision-making, there are findings of biological effects of alcohol use on HIV trans- mission and disease progression ([128] gives an overview; see also [129–131]). These include clear evidence that heavy drinking or alcohol use disorders are associated with viral load increases and/or CD4 count declines, mediated partly by treatment adherence and partly by the pharmaco- logical interactions with anti-retroviral and other medica- tions to treat comorbidities (for mechanisms see [99,128,130,132–134]; for pharmacological interactions see [135,136]). It should be noted, however, that delineation and quantification of causality in these biological pathways is difficult, as many factors interact [128,134,136,137].

The above considerations allow only a conservative operationalization of the causal impact of alcohol use on HIV/AIDS based on its causal effect on decision making, assuming that there is a threshold for alcohol’s effect on decision-making of four drinks for women and five drinks for men (approximately 48+/60+ g on one occasion). A further causal impact is the effect of alcohol on impeding adherence to anti-retroviral medications [92]. The estima- tion of relative risk based on these two mechanisms is conservative in its assumptions, and the resulting AAFs are markedly lower than those from modelling exposure with relative risk for incidence [102] using the usual methodology for CRAs (see [98] for a comparison; for usual modelling strategies see [11]).

Sexually transmitted diseases excluding HIV

Other sexually transmitted diseases have been found to be associated with alcohol use, especially with heavy drinking occasions [121]. While some specific biological pathways may vary, the general impact of alcohol use on the immune system (see above) is also relevant for the incidence of these diseases. Moreover, the behavioural causal pathway of alcohol’s impact on decision-making should be the same [98,99], so we suggest the same AAFs as for HIV/AIDS (excluding the AAF for the effect of alcohol use on mortality due to medication non-adherence). The latter effect was specific for HIV/AIDS, as missing anti-retroviral medications was shown to have marked effects on mortal- ity [92], an effect not applying to medications for other sexually transmitted diseases. Moreover, the interactions between medications for HIV/AIDS and alcohol are not observed for medications for other sexually transmitted diseases and alcohol.

Lower respiratory infections: pneumonia

The constant exchange with the environment presents a specific challenge to the immune defences of the lower respiratory tract. Apart from the general immunosup- pressive effects explained above, chronic alcohol exposure specifically impairs the immune defences and functioning of the lower respiratory tract, increasing the risk of both viral and bacterial pneumonia. Chronic alcohol exposure decreases saliva output, which leads to an increased col- onization of bacteria in the oropharynx [138]. Ciliary movement that is responsible for the transportation of trapped airborne particles and microorganisms can be impaired by heavy alcohol use, and the normal cough reflex can be weakened, increasing the risk of aspiration of oropharyngeal bacteria [80]. Finally, chronic alcohol use severely impairs alveolar macrophages that consti- tute the first line of the cellular immune defence of the lungs [79,138,139]. For an overview of the physiological mechanisms, see [138] and [140].

While the effect of alcohol use on pneumonia has been recognized since the 18th century [141], there has been a scarcity of systematic reviews and meta-analyses quantify- ing the relative risk associated with different levels of alco- hol use. The work of Samokhvalov and colleagues still seems to be the best review and quantitative summary [142]. In line with what would be expected, based on the physiological effects, heavy and prolonged alcohol use and alcohol use disorders have been linked specifically to a high risk, while evidence of the effects of lower levels of use is less clear.

Cancers

The carcinogenic effects of ethanol (the main carcinogenic compound in alcoholic beverages [143]) and its metabolites have been acknowledged by the International Agency for Research on Cancer (IARC) in three mono- graphs [144–146], as well as by the Continuous Update Project of the World Cancer Research Fund and the Amer- ican Institute for Cancer Research [147]. Specifically, the biological, animal and epidemiological evidence has re- sulted in alcohol being classified as a group 1 carcinogenic agent for humans (i.e. the highest level of evidence of car- cinogenicity; for guidelines and evaluation criteria see [148]). Furthermore, the most recent IARC monographs found sufficient animal and epidemiological evidence to conclude that alcohol consumption plays a causal role in oral cavity, pharyngeal, laryngeal, oesophageal (limited to squamous cell carcinoma (SCC), liver, colon, rectal and female breast cancers [149], as well as some evidence for a probable relationship between alcohol consumption and stomach and pancreatic cancers [146]. Lastly, there is limited epidemiological evidence of a relationship between

980 Jürgen Rehm et al.

alcohol consumption and kidney, thyroid, prostate, ovarian and endometrial cancers and Hodgkin’s and non- Hodgkin’s lymphoma [149]. Thus, the causal role of alcohol in the development of these cancers is uncertain.

There are various biological pathways by which the use of alcohol increases (and possibly decreases) the risk of can- cer; the exact pathways are often unknown and likely to vary by cancer site. Based on current evidence, the main pathway by which alcohol use is hypothesized to increase the risk of cancer is through the metabolism of ethanol into its carcinogenic metabolite acetaldehyde, which forms DNA adducts leading to the development of cancer (see review in [143]). There are at least four other pathways by which alcohol use may increase the risk of cancer. First, alcohol may alter the one carbon metabolism by inhibiting folate absorption, leading to increased homocysteine concentrations [150,151], and by inhibiting folate cycle enzyme methionine synthase and the trans-methylation enzymes methionine adenosyltransferase and DNA meth- yltransferase [150,152]. Secondly, alcohol may affect serum levels of hormones and related signalling pathways, leading to an increased risk of breast cancer, and possibly of prostate, ovarian and endometrial cancers [153–155]. Thirdly, alcohol consumption may lead to alterations in serum levels of insulin-like growth factor (IGF); however, this relationship is complex, with moderate chronic alcohol consumption increasing serum levels of IGF, and acute alcohol consumption leading to a decrease in IGF levels [156]. Lastly, alcohol also has a strong interaction with tobacco smoking, particularly in terms of its carcinogenic effects on the oral cavity and oesophagus (SCC). Specifically, alcohol acts as a solvent for tobacco carcinogens [157,158].

Conversely, alcohol may prevent the development of cancer through two biological pathways. First, by increas- ing insulin sensitivity, alcohol may decrease the risk of kidney cancer [159,160]; in contrast, insulin resistance has been observed to be a risk factor for cancer indepen- dent of other risk factors such as obesity [161,162]. Furthermore, the World Cancer Research Fund has found that there is strong evidence to suggest that alcohol con- sumption below 30 g per day on average is related causally to a decrease in the risk of developing kidney cancer [163]. Secondly, resveratrol (the ‘red wine chemical’) has gained attention for its protective effects on the development of cancer [164–166] through its ability to inhibit nuclear fac- tor kappa B (NF-κB) (thus creating an anti-inflammatory effect) and activator protein-1 (AP-1) transcription (thus inhibiting the conversion of procarcinogens into carcino- gens [167]). However, the effect of resveratrol in decreasing the risk of cancer is minimal, at best. To exhibit a protective effect against cancer (i.e. reduce the incidence of certain cancers of colon, liver and female breast) a certain mini- mum daily dose of resveratrol is required, and below this

dose there will be no possible protective effect. The amount of resveratrol in wine is approximately a factor of 100 000 or more below this minimal effective daily dose and, thus, no protective effect is to be expected from such a low dosage (this would be similar to ingesting 1/100000 of an aspirin tablet [168]).

The increase in the risk of developing cancer (stratified by cancer site) for increasing average daily amounts of alcohol consumed (measured in grams of pure alcohol consumed per day) has been observed to be linear on an exponentiated scale; however, the magnitude of these risk increases varies by cancer site [169–171]. Furthermore, as with other diseases related causally to alcohol consump- tion, the relative risks for cancer are dependent upon the systematic search strategy, inclusion and exclusion criteria, reference group (and if this includes former drinkers) of the underlying studies [172–174], use of case–control and/or cohort studies [175] and use of categorical or continuous estimates for alcohol consumption [169] (for relative risk graphs see [176] and Supporting information, Appendix S2).

No threshold for the effects of alcohol use on the risk of cancer has been detected; however, especially for breast cancer, there is ample evidence of alcohol’s effects even at low levels of average consumption [177–179]. This results in a large breast cancer burden from relatively low doses (< 21 g per day) of alcohol [179]. Furthermore, there is currently not enough epidemiological evidence to assess if the pattern of alcohol consumption modifies the risk of breast cancer [151]. The main biological pathway seems to be through overall tissue exposure to acetaldehyde, which may not be affected by drinking patterns; however, through modifications of insulin-like growth factor (IGF) serum levels, drinking patterns may have an effect on the risk of developing breast cancer (as well as other cancers, where modifications to IGF serum levels play a role [180]).

The risk relationship between alcohol consumption and the development of cancer has been shown to be modified by genetic variations in the carbon metabolism pathway and the ethanol–acetaldehyde metabolic pathways [181,182]. Specifically, genetic variations in the aldehyde dehydrogenase 2 gene have been shown to affect the risk relationship between alcohol consumption and oral cavity and oesophageal cancer [175,181,183]. As the prevalence of these genetic variations differs in different national populations, cancer is the first alcohol-attributable disease category where genetic considerations play a role in modelling the effect of alcohol use in global CRAs of the GBD and the WHO (for a first such attempt, see [184]).

Overall, the alcohol-attributable cancer disease and mortality burden is high [8,178]. However, current esti- mates of the number of cancer cases and cancer deaths caused by alcohol are limited due to the inability to incor- porate biological latency which, for many cancer sites, can be 20 years or more [185,186]. Future CRA studies

Alcohol and disease 981

will need to take into account this latency and the compet- ing risks from alcohol-related and -unrelated deaths [187].

Diabetes mellitus

There seems to be a beneficial effect of alcohol use on diabetes mellitus type 2 incidence [188], as evidenced in meta-analyses and in systematic reviews [189–193]. How- ever, this seemingly unambiguous picture must be qualified by different results by gender and ethnicity. For instance, stratification of available data in the latest and most com- prehensive meta-analyses by Knott and colleagues [192] revealed that reductions in risk may apply to women only and may be absent in studies sampled in the Asian region. In addition, Knott [192] found that some beneficial effects disappeared when compared to life-time abstainers, a prob- lem not unique to diabetes ([174]; see below and discussion in [173]). Also, intervention studies about the effects of reductions in the consumption of alcohol on glucose and insulin biomarkers in people with and without diabetes showed mixed results [194,195]. Irregular heavy drinking occasions may play a role in explaining the differences between studies and in the reviews (e.g. [196,197]), but there are not enough epidemiological studies on diabetes including this dimension of alcohol exposure to settle this question.

Whether a beneficial effect of alcohol on diabetes should be modelled in future CRAs will be a discussion in the respective technical advisory committees. This decision has important public health relevance (see [198] for addi- tional considerations), as the effect is fairly large, given the prevalence of diabetes mellitus world-wide [199,200] and the relatively high effect size found in epidemiological studies on alcohol use and the incidence of type 2 diabetes mellitus [191,192].

Neuropsychiatric disorders

Alzheimer’s disease, other dementias and cognitive decline

The relationships of alcohol use to Alzheimer’s disease, other forms of dementia and cognitive decline seem to be complex. On one hand, there is a possible protective effect of light to moderate drinking [201–203]. On the other hand, systemic reviews revealed inconsistent results about a potential protective effect of alcohol use [204,205]. Sev- eral subtypes of dementia are clearly related detrimentally and causally to heavy drinking [206], and the most com- prehensive review exhibited a J- or U-shaped relationship between the intensity of alcohol use and the direction of the effect [207]. A recent review also found evidence that heavy alcohol use predicts conversion from any type of mild cognitive impairment to dementia, and inconsistent evidence about whether moderate alcohol use predicts risk of dementia [208]. In addition, a Mendelian randomization

study did not provide any evidence of a causal impact of alcohol use on cognitive performance, although admittedly this is a more general concept than the disease categories discussed above [209].

Overall, while the negative impact of heavy drinking on dementia and cognitive functioning seems indisputable, with identified biological pathways [210,211], a protective effect of light to moderate drinking has some biological plausibility [212], but evidence on this is inconsistent. This is due partly to the multitude of methodological problems which every review describes (e.g. see discussion in [213]), such as inconsistent measurement of exposure and outcomes, inconsistent control of potential con- founders and lack of consideration of sample attrition due to mortality.

Major depressive disorders

Most mental disorders, including major depressive disor- ders, have consistent associations with alcohol use, and especially with heavy drinking and alcohol use disorders [79–81,214–217]. In addition to these associations, both the Diagnostic and Statistical Manual of Mental Disorders, 5th edition [58] and the ICD-10 ([25]; see also [218]) list alcohol-induced mental disorders, including alcohol- induced depressive disorders, thus building causality into the disorder category. However, these codes are not used in most countries (an exception is the United States, where it is a billable code for medical services), so we need to establish estimates of the causal impact of alcohol use on major depressive episodes in other ways.

There are three possible descriptions of the potential causal pathways that underlie the association between heavy alcohol use and alcohol use disorders and major de- pressive disorders [5,219]: (a) heavy drinking/alcohol use disorders cause depressive disorders; (b) depressive disor- ders increase alcohol use and cause alcohol use disorders (often discussed under the heading of a ‘self-medication’ hypothesis [220]); and (c) a reciprocal causal relationship or causation by another mechanism such as genetic vulnerability. Two reviews on this topic came to the same conclusion: that all three mechanisms are possible and probably existing, but the first mechanism—that alcohol use (especially heavy use and alcohol use disorders) causes depression—is stronger and more prevalent than the other pathways ([5,219]; see also [221,222]).

How to estimate the causal impact of alcohol use on major depressive disorders remains in question. Given the current scarcity of meta-analyses on alcohol use as a risk factor for major depressive disorders, this probably has to be performed indirectly from the risk relationships of alcohol use disorders and depressive disorders [219]. To be conservative, these risk relationships should be applied only to depressive disorders with later onset

982 Jürgen Rehm et al.

than alcohol use disorders. Alternatively, the confounder-controlled risks from Fergusson and colleagues [221] could be used [odds ratio (OR) = 1.66, 95% confidence interval (CI) = 1.08–2.55]. Both suggested solutions are conservative, as it has been demonstrated that alcohol use levels below heavy drinking are associated with higher risks than absten- tion [223].

In addition to its role in the aetiology of depressive disorders, alcohol use has been associated with worsen- ing the depression course, and worse outcomes such as suicide/death risk, social functioning and health care utilization ([214]; specifically for suicide, see section on injury below). However, the literature on this is not detailed enough to derive reliable quantitative risk relationships.

Unprovoked seizures and epilepsy

The association between alcohol use and seizures has been known since ancient times, with alcohol withdrawal seizures being the best studied and described aspect [224,225]. However, in terms of public health, the effect of alcohol use on the development of epilepsy and seizures not resulting directly from alcohol withdrawal is more im- portant ([224–228]; for an exact definition see [229]). A meta-analysis of the data on unprovoked seizures from six available studies showed an overall association between alcohol use and the risk of epilepsy with a pooled relative risk (RR) of 2.19 (95% CI = 1.83–2.63). In addition, there was a dose–response relationship, with RRs of 1.81 (95% CI = 1.59–2.07), 2.04 (95% CI = 2.00–2.97) and 3.27 (95% CI 2.52–4.26) for consuming 48, 72 and 96 g pure alcohol per day, respectively [224,230]. Alcohol use also fulfilled other Bradford Hill criteria, such as temporality and biological plausibility [225,228]. The time for develop- ing epilepsy or repetitive unprovoked seizures in heavy drinkers is 10 or more years [228]. The most plausible bio- logical pathway is described by the ‘kindling effect’, which postulates that repeated withdrawals, even subclinical, may lead to gradual lowering of the seizure threshold and eventually to the development of epilepsy, or unprovoked seizures that occur even in those who no longer consume alcohol [231,232]. Other theories postulate cerebral atrophy, cerebrovascular infarctions, lesions, traumas, neuroplasticity and chronic electrolyte imbalances as lead- ing to the onset of seizures [233–235]. In addition, alcohol use may affect the clinical course of pre-existing epilepsy ei- ther by changes in anti-epileptic drug pharmacokinetics or by non-compliance with prescribed medication [236,237].

Cardiovascular diseases

The relationship between alcohol use and cardiovascular disease outcomes is complex, as different dimensions

play a role for different outcomes [238–240]. Clearly, chronic heavy drinking is detrimental (for blood pressure/hypertension [241,242]; ischaemic heart disease [243]; cardiomyopathy [244]; atrial fibrillation and flutter [245]; all types of stroke [246]), but there is also evidence for an increased risk associated with irregular heavy drink- ing, even in people who are on average light to moderate drinkers (ischaemic heart disease [247–249]; ischaemic stroke [250]; all types of stroke [251]; different cardiovas- cular outcomes [252]). For the effects of irregular heavy drinking occasions on cardiovascular disease, there are potentially four main mechanisms [253]. First, irregular heavy drinking increases the risk of coronary artery disease via unfavourable impacts on blood lipids. Secondly, there are effects on clotting, increasing the risk of thrombosis. Thirdly, irregular heavy drinking affects the conducting system, leading to a greater risk of arrhythmias [254]. Finally, any heavy drinking increases blood pressure, leading to acute or sustained hypertension [255].

With respect to non-heavy drinking, there are benefi- cial and detrimental effects. Beneficial effects are seen mainly for ischaemic diseases, i.e. ischaemic heart disease and ischaemic stroke [256,257]. While these beneficial effects have been put into question for different reasons (e.g. [174,258,259]), and while they may be overestimated using standard epidemiological methodol- ogy because of biased comparison groups [260], biological pathways corroborate some protective effect. The basic biological pathways for beneficial effects on ischaemic diseases are favourable changes in several surrogate biomarkers for cardiovascular risk, such as higher levels of high density lipoprotein cholesterol and adiponectin and lower levels of fibrinogen [255,261,262]. However, the situation may be more complex, as there are indications that the beneficial effect on ischaemic out- comes cannot be found in certain countries such as India [263,264]. It remains to be seen if this reflects different drinking patterns among those who are, on average, light to moderate drinkers, or if there are genetic influences on the biological pathways leading to cardioprotection of light to moderate alcohol use (see also [249]).

As different dimensions of alcohol use impact upon car- diovascular outcomes, instrumental variable approaches such as Mendelian randomization cannot answer ques- tions of causality easily, as they assume linear relations with one dimension (for Mendelian randomizations studies see [259,265]; for a discussion of different dimensions of alcohol use with divergent predictions see [266]).As a result, modelling of alcohol use on cardiovascular disease outcomes also has to take different dimensions of exposure into account. In the most recent CRAs, this was solved as follows [22,267]: • For hypertensive heart disease, ischaemic heart dis- ease and both stroke types, the risk relations are

Alcohol and disease 983

specified for fatal and non-fatal outcomes. Moreover, for ischaemic diseases, we used age-specific risk rela- tions [268].

• For countries in eastern Europe (Russia and surrounding countries with similar drinking patterns), different rela- tive risk estimates were used ([269], based on [270]). In particular, no beneficial effect was modelled because of detrimental drinking patterns and higher relative risk per heavy drinking occasion, as the average quantity per heavy drinking occasion in these countries is higher (see [41,271–273] as background).

• For all countries, for ischaemic heart disease and ischae- mic stroke, we used risk relations which changed the risk function below 60 g of pure alcohol per day based on the presence or absence of heavy drinking occasions [268]. Modelling the impact of alcohol use this way for all

countries in the WHO European Region between 1990 and 2014 revealed that alcohol-attributable cardiovascu- lar mortality was key to understanding the trends in alcohol-attributable mortality as a whole [178,274]. For most countries in the region, alcohol-attributable cardio- vascular mortality was close to zero, as the detrimental effects on hypertensive heart disease, atrial fibrillation and haemorrhagic stroke more or less balanced the benefi- cial effects on ischaemic heart disease and ischaemic stroke [178]. However, for countries with more heavy drinking occasions in the eastern part of the region, there was con- siderable alcohol-attributable cardiovascular mortality; in some countries such as Russia, this even constituted the highest category of alcohol-attributable mortality ([178]; see also [275]).

Gastrointestinal diseases

Liver cirrhosis

Liver cirrhosis and the wider GBD category with other liver diseases is a major cause of death globally [200], even though it has not been included into the WHO targets for non-communicable disease [276]. Liver disease is linked clearly to alcohol [277], evidenced by several ICD codes for alcoholic liver diseases (Table 1), including simple alcoholic steatosis, hepatitis, fibrosis and cirrhosis and superimposed hepatocellular carcinoma, which is part of alcohol-attributable cancers (see above). Globally, approxi- mately half of all liver cirrhosis deaths and disability- adjusted life years were estimated to be attributable to alcohol in 2012 [8].

The pathogenesis of specific forms of alcoholic liver dis- ease can be summarized as follows [278,279]. Alcohol use, especially heavy drinking occasions, induces changes in lipid metabolism (increases lipogenesis and mobilization of lipids and simultaneously decreases hepatic lipid catabo- lism), resulting in the accumulation of lipids in hepatocytes

called fatty liver. Alcohol use can also cause an inflamma- tory response known as alcoholic hepatitis, or steatohepatitis if it is accompanied by hepatic lipid deposi- tion. Although hepatic steatosis does not normally cause irreversible hepatic changes, persistence and severity of alcoholic hepatitis or steatohepatitis leads eventually to fibrosis and sclerotic changes in the liver that result in insidious replacement of hepatocytes with connective tissue (liver cirrhosis) and subsequent liver failure.

The dose–response relationship between average vol- ume of alcohol use and liver cirrhosis is exponential, with the curve more pronounced for mortality than for non- fatal morbidity [280]. The more accelerated dose–response curve for mortality is due to the fact that liver damage can have different aetiologies (most prominently, hepatitis B or C [281]), but if the liver is damaged continuation of alcohol use, even at relatively low quantities, can lead to death. Most research about the relationship between alcohol use and liver disease examined the overall tissue exposure (i.e. overall volume of alcohol consumption) following the tradition of Lelbach [282]. However, there are also indica- tions that patterns of drinking matter [283]. More specifi- cally, given the same amount of overall alcohol exposure, days without any alcohol consumption (‘liver holidays’) have been shown to be associated with a lower risk than daily drinking [284,285].

Another dimension of alcohol use has been discussed specifically for liver cirrhosis: the quality of the alcoholic beverage, and particularly potential problems with hepato- toxic ingredients in unrecorded consumption (e.g. [286]. Unrecorded consumption denotes all alcohol that is not registered and thus not controlled by routine state activi- ties, such as home-made, illegally produced or smuggled alcohol (for a definition see [287]). While there have been some instances where ingredients of unrecorded alcohol have been found which could cause liver problems over and above the impact of ethanol [288,289] these instances are limited, and the overall conclusion of relevant reviews has been that there is not sufficient evidence to link a sizable portion of liver cirrhosis mortality to unrecorded alcohol ([290,291]; see also [292]).

Another issue is the fact that alcoholic liver disease can- notbemeasuredreliablyviausualdeathregistriesor viaver- bal autopsies, as the assessment of whether a liver disease is due to alcohol use or other risk factors is impacted highly by socio-cultural factors, in particular by stigma [46]. In their seminal study in 12 cities in 10 countries, Puffer & Griffith [293] found that after triangulating data on death certifi- cates with data from hospital records and interviews of attending physicians or family members, the number of deaths with alcoholic liver cirrhosis more than doubled, withthemajorityofnewcasesbeingrecodedfromcategories of cirrhosis which do not mention alcohol. This under- reporting of alcoholic liver cirrhosis has persisted in later

984 Jürgen Rehm et al.

studies [294–296]; this seems to be the case for all disease categoriesfullyattributabletoalcoholuse[296,297]includ- ing,butnotlimitedto,thedisclosureofalcoholusedisorders. As a consequence, in national CRAs based on death registries, estimations of alcohol-attributable liver diseases should not be based on routine data from these registries, but estimated indirectly via measures which have no or less bias (such as attributable fractions of liver cirrhosis or liver disease in general). Exceptions should be made only for countries where there had been empirical studies on the validity of alcoholic liver disease as a cause of death.

Pancreatitis

As is the case for liver diseases (see above), there are ICD-10 codes for alcoholic pancreatitis (see Table 1). The patho- genesis is different for acute and chronic pancreatitis, but alcohol use has a significant impact on the pathophysiol- ogy of both [298–302] and in the transition from acute to chronic pancreatitis (see [303]). Specifically, in chronic pancreatitis, metabolism of alcohol leads to production of reactive oxygen species [304] and fatty acid ethyl esters [305,306] that activate stellate cells and damage acinar cells of the pancreas. This process is mediated by sustained elevation of the cytosolic Ca2+ levels [307] and results ultimately in releasing pancreatic enzymes into the interstitium and in chronic inflammation [299]. In acute pancreatitis a similar cascade of intra- and extracellular reactions leads to fatty acid ethyl esters (FAEE)-induced increase of the Ca2+ release which results in massive necrosis of pancreatic acinar cells [307] and acute inflammation.

Regarding epidemiological results, the dose–response relationship seems to be accelerated for higher doses [308,309], more pronounced in women, and in acute pancreatitis. There were not enough data to evaluate the impact of irregular heavy drinking occasions in those who are on average light to moderate drinkers, however.

Injuries

Alcohol use has long been identified as a major contributor to injuries of all kinds, with established causal links (for de- tails see previous reviews [23,24]). Blood alcohol concen- tration is the most important dimension to impair vision, psychomotor skills/abilities and reaction-time; all these processes and others in the central nervous system can be affected negatively, starting at as low as 0.03% blood alcohol concentration by volume [310]. In addition, as already mentioned above, judgement about risk-taking and other behavioural actions is impacted by alcohol use, again dose-dependent. The dose–response relationship between acute alcohol use, measured through the blood alcohol concentration and injury, seem exponential for all

injury types, albeit varying slightly by type of injury [311–313].

However, there is also interindividual heterogeneity, based in part on usual drinking habits. For instance, Krüger and colleagues found that for any given blood alcohol concentration, the risk for traffic injury would be lower for a driver who is a regular heavy drinker than for a light drinker [314]. In other words, average volume of alcohol use also plays a role, even though this complexity of an interaction between acute and typical alcohol use is not modelled in current CRAs [315] or in other modelling of alcohol-attributable injury harm [316].

The impact of alcohol use on suicide may be different from other types of injury, as it seems to be determined more by long-term drinking patterns, such as heavy drink- ing or alcohol use disorders (e.g. [317,318], even though there are also acute effects of alcohol use, e.g. on judge- ment [319,320]. Thus, it should be considered to model suicide in future CRAs differently from other types of injury, with more emphasis on chronic patterns of drinking, in particular heavy drinking.

Current modelling of alcohol-attributable injuries in CRAs takes into account the number of drinking occasions of different sizes and the relative risks associated with these different exposures (for the most comprehensive analyses on risk relations see [311]; for others see [312,313]; for the exact methodologies see [321,322]). The last estima- tion, as part of the larger study for the WHO European Region estimating alcohol-attributable mortality in more than 50 countries for 25 years, revealed [178] that alcohol-attributable injury rates did not decrease in the time-period in the same way as injuries in general [323].

The final consideration about alcohol-attributable injury is the estimation of harm to others than the drinker from injuries, which is described below.

Overview on biological pathways and CRA modelling strategies for each cause of death

Table 3 gives an overview of biological reasoning and CRA modelling for all partially attributable disease and injury categories. To explain further how to interpret this Table, let us give one example: haemorrhagic and other non-ischaemic stroke. As indicated, the current statistical model is based on average volume of alcohol consumption only [375]; see also the graphs in Supporting information, Appendix S2). However, the biological pathways (see above and Table 2) would clearly indicate an additional role for irregular heavy drinking occasions which could not be included to date into the model due to lack of data.

As can be seen, for several disease categories biological pathways would suggest more complex statistical models, which cannot be realized via the usual meta-analytical

Alcohol and disease 985

procedures because of lack of data from underlying medical epidemiological studies.

Overview on different dimensions of alcohol use and disease and injury outcomes

Figure 1 tries to summarize our knowledge about the strength of the relationships between volume of alcohol consumption, on one hand, and specific heavy drinking oc- casions, on the other hand, and major disease categories. On one end of the spectrum are cancers, which all show a more or less linear relationship between alcohol use and risk of cancer as expressed in logarithmized relative risk compared to life-time abstention: the higher the (aver- age) volume of alcohol use, the higher the risk for cancer. The use of logarithmic scales for risk relations is customary for the statistical techniques used, meaning that a linear relationship in logarithmized relative risks actually trans- lates into exponential risk relations in the real scales.

At the other end of the spectrum are ischaemic diseases (i.e. ischaemic heart disease and ischaemic stroke), where there is a curvilinear relationship between average volume of alcohol use and risk, which is modified by heavydrinking occasions. Heavy drinking occasions seem primarily to

determine the adverse risk and subsequent harm. In socie- ties where most of the alcohol is consumed in non-heavy drinking occasions, we expect an overall beneficial rela- tionship of alcohol use on ischaemic diseases and an overall very small net impact of alcohol use on cardiovascular dis- ease and mortality; in societies where most of the alcohol is consumed via heavy drinking occasions, the overall rela- tionship should be detrimental for ischaemic disease, and even more so for cardiovascular disease and death. This hy- pothesis was also corroborated by the recent 25-year trend analyses on alcohol-attributable mortality in 52 countries of the WHO European Region [178]. While such a hypoth- esis is based on individual-level studies, it could not always be confirmed in ecological analyses such as time–series analyses (for confirmation see [5,324]; for essentially no re- lations in a number of countries in the European Union, see [325]; for a result contrary to the hypothesis, see [326]). However, ecological analysis may be impacted by other factors which cannot be controlled [327]. For the disease categories in between, the ranking from top to bottom may be interpreted as deviation from a straight line (linear relationship) between alcohol use and relative risk of the respective disease category: the higher the impact of heavy drinking occasions, the more accelerated is the curve.

Figure 1 The impact of volume of alcohol use and heavy drinking upon major attributable dis- ease outcomes.

986 Jürgen Rehm et al.

T ab le 4

C h an

ge s to

th e la st re vi ew

in pa rt ia lly

at tr ib u ta bl e di se as e ca te go ri es .

D is ea se ca te go ry

C au sa lit y

R is k re la ti on s

C om

m en ts (c ha ng es su gg es te d fo r G SR

A H 2 0 1 7 ve rs us

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H u m an

im m u n od efi ci en cy

vi ru s/

A cq u ir ed

im m u n e de fi ci en cy

sy n dr om

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ID S)

N ew

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on es ta bl is h in g ca u sa lit y fo r in ci de n ce

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m et h od ol og y

In ci de n ce

su gg es te d to

be ad di ti on

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C u rr en tl y in

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A H 2 0 1 7

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D is cu ss io n ba se d on

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C u rr en tl y in

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to ev al u at e th e n ew

ev id en ce ;p ro ba bl y to o la te

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G SR

A H 2 0 1 7

U n ip ol ar

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di so rd er s

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s N ew

m et a- an

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N ew

di se as e ca te go ry

su gg es te d to

be in cl u de d fo r G SR

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Ep ile ps y

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(c on

du ct ed

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t n ot

in cl u de d in

[2 4 ])

N ew

m et a- an

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(c on

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t n ot

in cl u de d in

[2 4 ]

A s in

G SR

A H 2 0 1 4

C ar di ov as cu la r di se as es

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h ea rt di se as e

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A H 2 0 1 4

Is ch ae m ic h ea rt di se as e

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re vi ew

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A H 2 0 1 7

A tr ia lfi br ill at io n an

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A s in

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H ea rt fa ilu

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2 0 1 0

A s in

2 0 1 0

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in cl u de d, as

ca se s ar e di st ri bu

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m et a- an

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H ae m or rh ag ic an

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(C on

ti n u es )

Alcohol and disease 987

Summary of changes since the last review

Table 4 gives an overview of changes for partially attribut- able disease categories since the 2010 review [24]. Fewer changes can be seen for injury, although there have been new meta-analyses (see above) which are to be included in the planned new Global Status Report. Alcohol epidemi- ology is clearly a fast-moving field, and our knowledge about alcohol’s impact upon disease and mortality has in- creased. Clearly, as there have been no major updates in the ICD during the time from the last review, these catego- ries have been stable.

Health harm to others

Like tobacco, alcohol has a marked impact upon the health of others than the drinker [328–331]. Drinking of others as an external cause is usually not measured in health system classifications [332], so these impacts have to be estimated otherwise. In terms of CRAs, minimally three categories need estimation: • The impact of alcohol use during pregnancy on the health of the child: this can be captured mainly via FASD and FAS, as described above, and new algorithms for estimating incidence and prevalence of these condi- tions based on mother’s drinking during pregnancy have been developed [74]. Prevalence can then be multiplied with disability weights to derive burden (see above). Regarding fatal outcomes of FAS: while a recent study has found a life expectancy of 34 years [333], the overwhelming majority of these deaths are coded as resulting from comorbidities [57], and are not coded to FAS as a cause of death.

• Alcohol use of others can have marked impact on all unintentional injuries. For instance, drinking by a pa- rental care-giver increases the chances of uninten- tional injury to a toddler [334], and parental alcohol misuse is a powerful predictor of a child’s traumatic brain injury [335]. Although others’ drinking can im- pact upon a wide variety of unintentional injuries, it has been studied most fully in the context of driving and other traffic participation under the influence of alcohol (e.g. [329,336]). The burden in traffic injuries and fatalities, at least, can now be estimated more ac- curately, as there are global statistics by sex of driver and average number of passengers in each car [337].

• The impact of alcohol on aggression and violence to others has been well established [23,338,339]. How- ever, its quantification becomes extremely compli- cated, as drinking of the victim [311,340,341] and drinking of the perpetrator seem to impact upon the risk and severity of violent acts [340,342], the latter possibly in a curvilinear fashion [340]. More- over, the impact of alcohol use on violence is mediated by other variables [342,343], including byTa

bl e 4 . (C on

ti n u ed )

D is ea se ca te go ry

C au sa lit y

R is k re la ti on s

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m en ts (c ha ng es su gg es te d fo r G SR

A H 2 0 1 7 ve rs us

2 0 1 4 )

G al lb la dd er

an d bi le du

ct di se as e

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in cl u de d as

in 2 0 1 0

N ot

in cl u de d as

in 2 0 1 0

N ei th er

in G SR

A H 2 0 1 4 n or

su gg es te d fo r G SR

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P an

cr ea ti ti s

A s in

2 0 1 0

N ew

m et a- an

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ri sk

re la ti on

s su gg es te d to

be in cl u de d fo r G SR

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di se as e ca te go ri es

co n si de re d

P so ri as is

N ew

re vi ew

s bu

t ca u sa lit y n ot

ye t es ta bl is h ed

N ot

re le va n t

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in G SR

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su gg es te d fo r G SR

A H 2 0 1 7

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N ew

re vi ew

s N o m et a- an

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in G SR

A H 2 0 1 4 n or

su gg es te d fo r G SR

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bi rt h co m pl ic at io n s

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re vi ew

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m et a- an

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ifi ca n t ef fe ct

of al co h ol – >

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ld n ot

be in cl u de d

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in th is se ri es

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A H = W or ld H ea lt h O rg an

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A lc oh

ol an

d H ea lt h .

988 Jürgen Rehm et al.

culture [344]. While all these mediating and moder- ating variables complicate estimation (for a first try within the framework of the CRAs see [345]), the estimates found so far seem to indicate large effect sizes: thus, English and colleagues estimated that ap- proximately half the hospitalizations due to assault were attributable to alcohol [31], and male homicide deaths in the Soviet Union dropped by 40% when per capita consumption dropped by 25% [346].

DISCUSSION

This systematic review has shown that many disease and mortality outcomes are impacted causally by alcohol, most often in an accelerated dose–response fashion. Since the last review [24], many new reviews and meta- analyses have appeared (see Table 4 and Supporting infor- mation, Appendix S1 for a complete listing), but while new alcohol-attributable disease categories have been added, the general picture of alcohol use being a major contributor to the burden of mortality and disease has not changed.

Any systematic review is limited by the underlying liter- ature. While the depth and quality of the literature varies by disease and mortality category, it is unfortunately still true that exposure measurement in many epidemiological studies is restricted to one measure of average volume of consumption, e.g. from a food frequency questionnaire or from simple quantity–frequency measures (for an explana- tion of these measures and their strengths see [347]). Even though in recent years there have been more attempts to quantify other dimensions such as irregular heavy drink- ing occasions, these changes have come slowly, and for many outcomes meta-analyses on patterns of drinking are not possible. Moreover, many studies measure alcohol use only once at baseline, and no changes of use over time can be incorporated into the models. Finally, the compari- son group still is a problem [174]: while using last-year ab- stention may bias results by introducing sick-quitters [348], life-time abstention may be the theoretically pre- ferred measure but has been proven to be unreliable [173], and in many high-income countries life-time abstainers are special groups which also differ on other outcome-relevant measures. In summary, very little has changed since 2000, when these points had been already listed as barriers for improving knowledge on alcohol use and mortality outcomes [349]. Mendelian randomization studies were added to our methodological arsenal [224,259], but their assumptions are problematic if two dimensions are to be analysed simultaneously with one instrumental variable, as in the analyses on the impact of alcohol use on ischaemic heart disease ([266]; see also the discussion in the British Medical Journal [259]).Improv- ing measurement of alcohol exposure (including but not

limited to measurement of chronic and irregular heavy drinking), as described in the limitations above, should be one of the research priorities. Other research priorities (see also [1]) include: • Improving incorporating time lags [186] into future CRAs: this applies not only to effects of alcohol use, but also to all risk factors, as CRAs need to be comparative.

• Improving our knowledge about risk relations: as indi- cated above, for most countries with the exception of Russia and surrounding countries [269], we assume that risk relations taken from the most comprehensive meta-analysis are applicable. Given the genetic and envi- ronmental differences, we would expect some differences in risk relations between alcohol use and disease/ mortality outcomes in different regions (see the example of genetically based varying cancer risks described above, which had marked implications for the population-level burden of oesophagus cancer in Japan [175]; see also some indications that alcohol use has different risk for cardiovascular events in Asians versus non-Asians [263,350]). The biggest difference in risk relations will probably be found in injury outcomes, as these depend more upon environment than disease [311,344]. However, for any regional differences in risk, it has to be checked if these cannot be ascribed to differ- ences in drinking patterns first, before they are applied to CRAs.

• Improvingour knowledge on health harm to others: cur- rently, only a few studies exist on harm to others which can be translated into a CRA framework, and this should be a priority for future research. In particular, efforts to improve the recording of alcohol’s involvement in injuries in hospital or emergency service records (e.g. [351,352]) should include attention to the involvement of others’ drinking in the occurrence of the injury. We would like to finish this review with a reminder that

while the alcohol-attributable burden of disease and mortality is large, it is only part of the harm of alcohol use. Social harm outside of health harm is impacted by similar dimensions of alcohol use (e.g. [90,353]), and should be included in any considerations of the overall impact of alcohol use in our societies.

Declaration of interests

None.

Acknowledgements

The current review was supported in part by the WHO Collaboration Centre on Mental Health and Addiction as part of the monitoring effort on alcohol use and health. We would like to thank Dr Vincenzo Bagnardi for allowing us to use and publish the formulas on the

Alcohol and disease 989

dose–response relationship between level of alcohol use and cancer based on [169]. Finally, we would like to thank contributors for sending more than 100 mails and requests regarding the previous review, which helped to shape the current version.

References

1. Rehm J., Imtiaz S. A narrative reviewof alcohol consumption as a risk factor for global burden of disease. Subst Abuse Treat Prev Policy 2016; 11: 37.

2. Global Burden of Disease (GBD) 2015. Risk Factors Collabo- rators Global, regional, and national comparative risk assessment of 79 behavioral, environmental and occupa- tional, and metabolic risks or clusters of risks, 1990–2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet 2016; 388: 1659–724.

3. Forouzanfar M. H., Alexander L., Anderson H. R., Bachman V. F., Biryukov S., Brauer M. et al. Global, regional, and national comparative risk assessment of 79 behavioural, en- vironmental and occupational, and metabolic risks or clusters of risks in 188 countries, 1990–2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet 2015; 386: 2287–323.

4. Lim S. S., Vos T., Flaxman A. D., Danaei G., Shibuya K., Adair-Rohani H. et al. A comparative risk assessment of bur- den of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990–2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet 2012; 380: 2224–60.

5. Rehm J., Room R., Monteiro M., Gmel G., Graham K., Rehn N. et al. Alcohol use. In: Ezzati M., Lopez A. D., Rodgers A., Murray C. J. L., editors. Comparative Quantification of Health Risks: Global and Regional Burden Of Disease Attributable To Selected Major Risk Factors. Geneva, Switzerland: World Health Organization; 2004, pp. 959–1109.

6. Ezzati M., Lopez A. D., Rodgers A. D., Vander Horn S., Murray C. J. L., Comparative Risk Assessment Collaborating Group Selected major risk factors and global and regional burden of disease. Lancet 2002; 360: 1347–60.

7. Murray C. J. L., Lopez A. Global mortality, disability, and the contribution of risk factors: global burden of disease study. Lancet 1997; 349: 1436–42.

8. World Health Organization. Global Status Report on Alcohol and Health. Geneva, Switzerland: World Health Organization; 2014.

9. World Health Organization. Global Status Report on Alcohol and Health. Geneva, Switzerland: World Health Organization; 2011.

10. Rehm J., Mathers C., Popova S., Thavorncharoensap M., Teerawattananon Y., Patra J. Global burden of disease and injury and economic cost attributable to alcohol use and alcohol use disorders. Lancet 2009; 373: 2223–33.

11. Murray C. J. L., Ezzati M., Lopez A. D., Rodgers A., Vander Hoorn S. Comparative quantification of health risks: concep- tual framework and methodological issues. In: Ezzati M., Lopez A. D., Rodgers A., Murray C. J. L., editors. Comparative Quantification of Health Risks: Global and Regional Burden of Disease Attributable to Selected Major Risk Factors. Geneva, Switzerland: World Health Organization; 2004, pp. 1–38.

12. Rehm J., Taylor B., Patra J., Gmel G. Avoidable burden of dis- ease: conceptual and methodological issues in substance

abuse epidemiology. Int J Methods Psychiatr Res 2006; 15: 181–91.

13. Disease Control Priorities. About Disease Control Priorities, 3rd edn. University of Washington; 2016. Available at: http://dcp-3.org/disease-control-priorities-third-edition/ Web (accessed 11 October 2016) (Archived at http://www. webcitation.org/6n7sGJLT0).

14. Anderson P., Braddick F., Conrod P., Gual A., Hellman M., Matrai S. et al. The New Governance of Addictive Substances and Behaviours. Oxford, UK: Oxford University Press; 2017.

15. Chisholm D., Rehm J., van Ommeren M., Monteiro M. Reducing the global burden of hazardous alcohol use: a comparative cost-effectiveness analysis. J Stud Alcohol 2004; 65: 782–93.

16. Chisholm D., Doran C., Shibuya K., Rehm J. Comparative cost-effectiveness of policy instruments for reducing the global burden of alcohol, tobacco and illicit drug use. Drug Alcohol Rev 2006; 25: 553–65.

17. Medina-Mora M. E., Monteiro M., Room R., Rehm J., Jernigan D., Sanchez-Moreno D. et al. Alcohol Use and Alcohol Use Disorders. In: Patel V., Chisholm D., Dua T., Laxminarayan R., Medina-Mora M. E., editors. Mental, Neurological, and Substance Use Disorders. Washington, DC: The World Bank; 2015, pp. 127–44.

18. Petersen I., Evans-Lacko S., Semrau M., Barry M., Chisholm D., Gronholm P. et al. Population and community platform interventions. In: Patel V., Chisholm D., Dua T., Laxminarayan R., Medina-Mora M. E., editors. Mental, Neurological, and Substance Use Disorders. Washington, DC: The World Bank; 2015, pp. 183–200.

19. Shidhaye R., Lund C., Chisholm D. Health care platform in- terventions. In: Patel V., Chisholm D., Dua T., Laxminarayan R., Medina-Mora M. E., editors. Mental, Neurological, and Substance Use Disorders. Washington, DC: The World Bank; 2015, pp. 201–18.

20. Walter S. D. The estimation and interpretation of attribut- able risk in health research. Biometrics 1976; 32: 829–49.

21. Walter S. D. Prevention of multifactorial disease. Am J Epidemiol 1980; 112: 409–16.

22. Rehm J., Kehoe T., Gmel G., Stinson F., Grant B., Gmel G. Statistical modeling of volume of alcohol exposure for epidemiological studies of population health: the example of the US. Popul Health Metrics 2010; 8: 3.

23. Rehm J., Room R., Graham K., Monteiro M., Gmel G., Sempos C. The relationship of average volume of alcohol consumption and patterns of drinking to burden of disease —an overview. Addiction 2003; 98: 1209–28.

24. Rehm J., Baliunas D., Borges G. L., Graham K., Irving H. M., Kehoe T. et al. The relation between different dimensions of alcohol consumption and burden of disease—an overview. Addiction 2010; 105: 817–43.

25. World Health Organization. International Statistical Classifi- cation of Diseases and Related Health Problems 10th Revision [internet]; 2016. Available at: http://apps.who. int/classifications/icd10/browse/2016/en/Web (accessed 12 October 2016) (Archived at http://www.webcitation. org/6n7rylGgR).

26. Moher D., Liberati A., Tetzlaff J., Altman D. G., The Prisma Group Preferred Reporting Items for Systematic Reviews and Meta-Analyses: the PRISMA Statement. PLoS Med 2009; 6: e1000097.

27. Moher D., Shamseer L., Clarke M., Ghersi D., Liberati A., Petticrew M. et al. Preferred Reporting Items for Systematic

990 Jürgen Rehm et al.

Review and Meta-Analysis Protocols (PRISMA-P) 2015 statement. Syst Rev 2015; 4: 1.

28. Rothman K. J., Greenland S., Lash T. L. Modern Epidemiology. Philadelphia, PA: Lippincott Williams & Wilkins; 2008.

29. Rehm J., Monteiro M., Room R., Gmel G., Jernigan D., Frick U. et al. Steps towards constructing a global comparative risk analysis for alcohol consumption: determining indicators and empirical weights for patterns of drinking, deciding about theoretical minimum, and dealing with different consequences. Eur Addict Res 2001; 7: 138–47.

30. Hill A. B. The environment and disease: association or causation? Proc R Soc Med 1965; 58: 295–300.

31. EnglishD.,HolmanC.,MilneE.,WinterM.,HulseG.,CoddeG., et al. The Quantification of Drug Caused Morbidity and Mortal- ity in Australia 1995. Canberra, Australia: Commonwealth Department of Human Services and Health; 1995.

32. World Health Organization. International Guide for Monitor- ing Alcohol Consumption and Related Harm. Geneva, Switzerland: World Health Organization; 2000.

33. Institute for Health Metrics and Evaluation. Global Burden of Disease (GBD) [internet]; 2013. Available at: http://www. healthdata.org/gbd/Web (accessed 19 July 2016) (Archived at http://www.webcitation.org/6n7rq1J9N).

34. World Health Organization. Global Health Estimates (GHE) [internet]; 2016. Available at: http://www.who.int/healthinfo/ global_burden_disease/en/Web (accessed 19 July 2016) (Archived at http://www.webcitation.org/6n7rhyHx1).

35. World Health Organization. Civil registration: why counting births and deaths is important [Internet]; 2014. Available at: http://www.who.int/mediacentre/factsheets/fs324/en/Web (accessed 19 July 2016) (Archived at http://www.webcita- tion.org/6n7rXjl8y).

36. Murray C. J., Lozano R., Flaxman A. D., Serina P., Phillips D., Stewart A. et al. Using verbal autopsy to measure causes of death: the comparative performance of existing methods. BMC Med 2014; 12: 5.

37. Rehm J., Mathers C. Global comparative risk assessment— what level of detail can be achieved? Lancet 2009; 374: 477.

38. Goerdt A., Koplan J., Robine J., Thuriaux M., van Ginneken J. Non-fatal health outcomes: Concepts, instru- ments and indicators. In: Murray C., Lopez A., editors. The Global Burden of Disease: A Comprehensive Assessment of Mortality and Disability from Diseases, Injuries and Risk Factors in 1990 and Projected to 2020. Boston, MA: Harvard School of Public Health on behalf of the World Health Organization and the World Bank; 1996, pp. 99–117.

39. Rehm J., Hasan O. S. M., Imtiaz S., Neufield M. Quantifying the contribution of alcohol to cardiomyopathy: a systematic review. Alcohol 2017; in press.

40. Rehm J., Anderson P., Gual A., Kraus L., Marmet S., Room R. et al. The tangible common denominator of substance use disorders: a reply to commentaries to Rehm et al. (2013). Alcohol Alcohol 2014; 49: 118–22.

41. Zaridze D., Lewington S., Boroda A., Scélo G., Karpov R., Lazarev A. et al. Alcohol and mortality in Russia: prospective observational study of 151,000 adults. Lancet 2014; 383: 1465–73.

42. Kessler R. C., Haro J. M., Heeringa S. G., Pennell B. E., Ustun T. B. The World Health Organization World Mental Health Survey Initiative. Epidemiol Psychiatr Soc 2006; 15: 161–6.

43. Rehm J. How should prevalence of alcohol use disorders be assessed globally? Int J Methods Psychiatr Res 2016; 25: 79–85.

44. Flaxman A. D., Vos T., Murray C. J. L. An Integrative Metaregression Framework for Descriptive Epidemiology. Washington, DC: University of Washington Press; 2015.

45. National Institute on Alcohol Abuse and Alcoholism. Alcohol overdose: the dangers of drinking too much [inter- net]; 2015. Available at: http://pubs.niaaa.nih.gov/ publications/AlcoholOverdoseFactsheet/Overdosefact.htm/ Web (accessed 7 November 2016) (Archived at http://www. webcitation.org/6n7r72mxL).

46. Schomerus G., Lucht M., Holzinger A., Matschinger H., Carta M. G., Angermeyer M. C. The stigma of alcohol depen- dence compared with other mental disorders: a review of population studies. Alcohol Alcohol 2011; 46: 105–12.

47. Hudson P. The medical examiner looks at drinking. In: Ewing J. A., Rouse B. A.,. e., editors. Drinking: Alcohol in American Society—Issues and Current Research. Chicago, IL: Nelson-Hall; 1978, pp. 71–92.

48. Zaridze D., Maximovitch D., Lazarev A., Igitov V., Boroda A., Boreham J. et al. Alcohol poisoning is a main determinant of recent mortality trends in Russia: evidence from a detailed analysis of mortality statistics and autopsies. Int J Epidemiol 2009; 38: 143–53.

49. Rahu K., Palo E., Rahu M. Diminishing trend in alcohol poisoning mortality in Estonia: reality or coding peculiarity? Alcohol Alcohol 2011; 46: 485–9.

50. Leon D. A., Shkolnikov V. M., McKee M., Kiryanov N., Andreev E. Alcohol increases circulatory disease mortality in Russia: acute and chronic effects or misattribution of cause? Int J Epidemiol 2010; 39: 1279–90.

51. Sidorenkov O., Nilssen O., Nieboer E., Kleshchinov N., Grijibovski A. M. Premature cardiovascular mortality and al- cohol consumption before death in Arkhangelsk, Russia: an analysis of a consecutive series of forensic autopsies. Int J Epidemiol 2011; 40: 1519–29.

52. World Health Organization International Statistical Classifica- tion of Diseases and Related Health Problems, 10th revision, vol. 2. Instruction Manual. Geneva, Switzerland: World Health Organization; 2011.

53. Lahti R. A., Vuori E. Fatal alcohol poisoning: medico-legal practices and mortality statistics. Forensic Sci Int 2002; 126: 203–9.

54. Landen M., Castle S., Nolte K., Gonzales M., Escobedo L., Chatterjee B. et al. Methodological issues in the surveillance of poisoning, illicit drug overdose, and heroin overdose deaths in New Mexico. Am J Epidemiol 2003; 157: 273–8.

55. Darke S. Heroin overdose. Addiction 2016; 111: 2060–3. 56. Lahti R. A., Sajantila A., Korpi H., Poikolainen K., Vuori E.

Under-recording of ethanol intoxication and poisoning in cause-of-death data: causes and consequences. Forensic Sci Int 2011; 212: 121–5.

57. Popova S., Lange S., Shield K. D., Mihic A., Chudley A. E., Mukherjee R. A. S. et al. Co-morbidity of Fetal Alcohol Spectrum Disorder: a systematic literature review and metaanalysis. Lancet 2016; 387: 978–87.

58. American Psychiatric Association Diagnostic and Statistical Manual of Mental Disorders, 5th edn. Philadelphia, PA: American Psychiatric Association; 2013.

59. May P. A., Blankenship J., Marais A.-S., Gossage J. P., Kalberg W. O., Barnard R. et al. Approaching the prevalence of the full spectrum of fetal alcohol spectrum disorders in a South African population-based study. Alcohol Clin Exp Res 2013; 37: 818–30.

60. May P. A., de Vries M., Marais A.-S., Buckley D., Kalberg W. O., Adnams C. M. et al. The continuum of fetal alcohol

Alcohol and disease 991

spectrum disorders in four rural communities in South Africa: prevalence and characteristics. Drug Alcohol Depend 2016; 159: 207–18.

61. May P. A., Marais A.-S., de Vries M. M., Kalberg W. O., Buckley D., Hasken J. M. et al. The continuum of fetal alcohol spectrum disorders in a community in South Africa: preva- lence and characteristics in a fifth sample. Drug Alcohol Depend 2016; 168: 274–86.

62. O’Leary-Moore S. K., Parnell S. E., Lipinski R. J., Sulik K. K. Magnetic resonance-based imaging in animal models of fetal alcohol spectrum disorder. Neuropsychol Rev 2011; 21: 167–85.

63. Sulik K. K. Fetal alcohol spectrum disorder: pathogenesis and mechanisms. Handb Clin Neurol 2014; 125: 463–75.

64. Hamilton D. A., Barto D., Rodriguez C. I., Magcalas C. M., Fink B. C., Rice J. P. et al. Effects of moderate prenatal alcohol exposure and age on social behavior, spatial response persev- eration errors and motor behavior. Behav Brain Res 2014; 269: 44–54.

65. Abel E. L. Fetal Alcohol Abuse Syndrome. New York: Plenum Press; 1998.

66. Alvik A., Torgersen A. M., Aalen O. O., Lindermann R. Binge alcohol exposure once a week in early pregnancy predicts temperament and sleeping problems in the infant. Early Hum Dev 2011; 87: 827–33.

67. Jacobson J. L., Jacobson S. W. Drinking moderately and preg- nancy. Effects on child development. Alcohol Res Health 1999; 23: 25–30.

68. Jones K. L., Smith D. W., Ulleland C. N., Streissguth A. P. Pattern of malformation in offspring of chronic alcoholic mothers. Lancet 1973; 1: 1267–71.

69. Kelly Y., Sacker A., Gray R., Kelly J., Wolke D., Quigley M. A. Light drinking in pregnancy, a risk for behavioural problems and cognitive deficits at 3 years of age? Int J Epidemiol 2009; 38: 129–40.

70. Sood B., Delaney-Black V., Covington C., Nordstrom-Klee B., Ager J., Templin T. et al. Prenatal alcohol exposure and child- hood behavior at age 6 to 7 years: I. Dose–response effect. Pediatrics 2001; 108: 1–9.

71. Gray R., Henderson J. Review of the Fetal Effects of Prenatal Alcohol Exposure. Oxford, UK: University of Oxford; 2006.

72. Linnet K. M., Dalsgaard S., Obel C., Wisborg K., Henriksen T. B., Rodriguez A. et al. Maternal lifestyle factors in pregnancy risk of attention deficit hyperactivity disorder and associated behaviors: review of the current evidence. Am J Psychiatry 2003; 160: 1028–40.

73. Testa M., Quigley B. M., Eiden R. D. The effects of prenatal alcohol exposure on infant mental develop- ment: a metaanalytical review. Alcohol Alcohol 2003; 38: 295–304.

74. Popova S., Lange S., Probst C., Gmel G., Rehm J. Estimation of national, regional and global prevalence of alcohol use during pregnancy and fetal alcohol syndrome: a systematic review and meta-analysis. Lancet Glob Health 2017; DOI: 10.1016/S2214-109X(17)30021-9.

75. Rehm J., Frick U. Valuation of health states in the U.S. study to establish disability weights: lessons from the literature. Int J Methods Psychiatr Res 2010; 19: 18–33.

76. Salomon J. A., Haagsma J. A., Davis A., de Noordhout C. M., Polinder S., Havelaar A. H. et al. Disability weights for the Global Burden of Disease 2013 study. Lancet Glob Health 2015; 3: e712–e723.

77. Szabo G., Saha B. Alcohol’s effect on host defense. Alcohol Res 2015; 37: 159–70.

78. Cook R. T. Alcohol abuse, alcoholism, and damage to the immune system—a review. Alcohol Clin Exp Res 1998; 22: 1927–42.

79. Zhang P., Bagby G. J., Happel K. I., Raasch C. E., Nelson S. Alcohol abuse, immunosuppression, and pulmonary infec- tion. Curr Drug Abuse Rev 2008; 1: 56–67.

80. Moss M. Epidemiology of sepsis: race, sex, and chronic alcohol abuse. Clin Infect Dis 2005; 41: S490–S497.

81. Boe D. M., Nelson S., Zhang P., Bagby G. J. Acute ethanol intoxication suppresses lung chemokine production follow- ing infection with Streptococcus pneumoniae. J Infect Dis 2001; 184: 1134–42.

82. Szabo G., Mandrekar P. A recent perspective on alcohol, immunity, and host defense. Alcohol Clin Exp Res 2009; 33: 220–32.

83. Goral J., Karavitis J., Kovacs E. J. Exposure-dependent effects of ethanol on the innate immune system. Alcohol 2008; 42: 237–47.

84. World Health Organization. Tuberculosis—fact sheet [inter- net]; 2016. Available at: http://www.who.int/mediacentre/ factsheets/fs104/en/Web (accessed 13 October 2016) (Archived at http://www.webcitation.org/6n7r72mxL).

85. Rehm J., Samokhvalov A. V., Neuman M. G., Room R., Parry C. D., Lönnroth K. et al. The association between alcohol use, alcohol use disorders and tuberculosis (TB). A systematic re- view. BMC Public Health 2009; 9: 450.

86. Lönnroth K., Williams B., Stadlin S., Jaramillo E., Dye C. Alcohol use as a risk factor for tuberculosis—a systematic review. BMC Public Health 2008; 8: 289.

87. Imtiaz S., Roerecke M., Shield K. D., Samokhvalov A. V., Lönnroth K., Rehm J. Alcohol use, alcohol use dose and alcohol-related problem as risk factors for tuberculosis: meta-analyses and burden of disease. Toronto, ON: Centre for Addiction and Mental Health; 2016.

88. Coker R., McKee M., Atun R., Dimitrova B., Dodonova E., Kuznetsov S. et al. Risk factors for pulmonary tuberculosis in Russia: case–control study. BMJ 2006; 332: 85–7.

89. Rehm J., Marmet S., Anderson P., Gual A., Kraus L., Nutt D. J. et al. Defining substance use disorders: do we really need more than heavy use? Alcohol Alcohol 2013; 48: 633–40.

90. Rehm J., Gmel G. Patterns of alcohol consumption and social consequences. Results from an eight-year follow-up study in Switzerland. Addiction 1999; 94: 899–912.

91. Hendershot C. S., Stoner S. A., Pantalone D. W., Simoni J. M. Alcohol use and antiretroviral adherence: review and meta-analysis. J Acquir Immune Defic Syndr 2009; 52: 180–202.

92. Gmel G., Shield K., Rehm J. Developing a methodology to de- rive alcohol-attributable fractions for HIV/AIDS mortality based on alcohol’s impact on adherence to antiretroviral medication. Popul Health Metrics 2011; 9: 5.

93. Hahn J. A., Woolf-King S. E., Muyindike W. Adding fuel to the fire: alcohol’s effect on the HIV epidemic in sub- Saharan Africa. Curr HIV/AIDS Rep 2011; 8: 172–80.

94. Fritz K., Morojele N., Kalichman S. Alcohol: the forgotten drug in HIV/AIDS. Lancet 2010; 376: 398–400.

95. Rehm J., Parry C. D. Alcohol consumption and infectious diseases in South Africa. Lancet 2009; 374: 2053.

96. Shuper P. A., Neuman M., Kanteres F., Baliunas D., Joharchi N., Rehm J. Causal considerations on alcohol and HIV/AIDS —a systematic review. Alcohol Alcohol 2010; 45: 159–66.

97. Parry C. D. H., Rehm J. R., Poznyak V., Room R. Alcohol and infectious diseases: are there causal linkages? Addiction 2009; 104: 331–2.

992 Jürgen Rehm et al.

98. Rehm J., Probst C., Shield K. D., Shuper P. A. Does Alcohol Use Have a Causal Effect on HIV Incidence and Disease Progression? A Review of the Literature and a Modelling Strategy for Quanti- fying the Effect. Toronto, ON: Centre for Addiction and Mental Health; 2016.

99. Williams E. C., Hahn J. A., Saitz R., Bryant K., Lira M. C., Samet J. H. Alcohol use and Human Immunodefi- ciency Virus (HIV) infection: Current knowledge, implications, and future directions. Alcohol Clin Exp Res 2016; 40: 2056–72.

100. Lan C. W., Scott-Sheldon L. A., Carey K. B., Johnson B. T., Carey M. P. Prevalence of alcohol use, sexual risk behavior, and HIV among Russians in high-risk settings: a systematic review and meta-analysis. Int J Behav Med 2016; DOI: 10.1007/s12529-016-9596-1.

101. Schensul J. J., Singh S. K., Gupta K., Bryant K., Verma R. Alcohol and HIV in India: a review of current research and intervention. AIDS Behav 2010; 14: S1–S7.

102. Baliunas D., Rehm J., Irving H., Shuper P. Alcohol consump- tion and risk of incident human immunodeficiency virus infection: a meta-analysis. Int J Public Health 2010; 55: 159–66.

103. Pithey A., Parry C. Descriptive systematic review of sub- Saharan African studies on the association between alcohol use and HIV infection. SAHARA J 2009; 6: 155–69.

104. Van H., Koblin B. A. HIV, alcohol, and noninjection drug use. Curr Opin HIVAIDS 2009; 4: 314–18.

105. Woolf S. E., Maisto S. A. Alcohol use and risk of HIV infection among men who have sex with men. AIDS Behav 2009; 13: 757–82.

106. Fisher J. C., Bang H., Kapiga S. H. The association between HIV infection and alcohol use: a systematic review and meta-analysis of African studies. Sex Transm Dis 2007; 34: 856–63.

107. Steele C., Josephs R. Alcohol myopia: its prized and danger- ous effects. Am Psychol 1990; 45: 921–33.

108. Scott-Sheldon L. A., Walstrom P., Carey K. B., Johnson B. T., Carey M. P. Alcohol use and sexual risk behaviors among in- dividuals infected with HIV: a systematic review and meta- analysis 2012 to early 2013. Curr HIV/AIDS Rep 2013; 10: 314–23.

109. Woolf-King S. E., Maisto S. A. Alcohol use and high-risk sexual behavior in sub-Saharan Africa: a narrative review. Arch Sex Behav 2011; 40: 17–42.

110. Sales J. M., Brown J. L., Vissman A. T., DiClemente R. J. The association between alcohol use and sexual risk behaviors among African American women across three developmen- tal periods: a review. Curr Drug Abuse Rev 2012; 5: 117–28.

111. Kalichman S. C., Simbayi L. C., Kaufman M., Cain D., Jooste S. Alcohol use and sexual risks for HIV/AIDS in sub-Saharan Africa: systematic review of empirical findings. Prev Sci 2007; 8: 141–51.

112. Shuper P. A., Joharchi N., Irving H., Rehm J. Alcohol as a correlate of unprotected sexual behavior among people liv- ing with HIV/AIDS: review and meta-analysis. AIDS Behav 2009; 13: 1021–36.

113. Scott-Sheldon L. A., Carey K. B., Cunningham K., Johnson B. T., Carey M. P. Alcohol use predicts sexual decision- making: a systematic review and meta-analysis of the exper- imental literature. AIDS Behav 2016; 20 Suppl 1: S19–39.

114. Rehm J., Shield K. D., Joharchi N., Shuper P. Alcohol con- sumption and the intention to engage in unprotected sex: systematic review and meta-analysis of experimental stud- ies. Addiction 2012; 107: 51–9.

115. Shuper P. A., Joharchi N., Rehm J. Personality as a predictor of unprotected sexual behavior among people living with HIV/AIDS: a systematic review. AIDS Behav 2014; 18: 398–410.

116. Shuper P. A., Joharchi N., Rehm J. Protocol for a controlled experiment to identify the causal role of acute alcohol consumption in condomless sex among HIV- positive MSM: study procedures, ethical considerations, and implications for HIV prevention. AIDS Behav 2015; 20: 173–84.

117. Cooper M. L. Alcohol use and risky sexual behavior among college students and youth. J Stud Alcohol 2002; 14: 101–17.

118. Halpern-Felsher B. L., Millstein S. G., Ellen J. M. Relationship of alcohol use and risky sexual behavior: a review and analysis of findings. J Adolesc Health 1996; 19: 331–6.

119. Heath J., Lanoye A., Maisto S. A. The role of alcohol and substance use in risky sexual behavior among older men who have sex with men: a review and critique of the current literature. AIDS Behav 2011; 16: 578–89.

120. Leigh B. C., Stall R. Substance use and risky sexual behavior for exposure to HIV. Issues in methodology, interpretation, and prevention. Am Psychol 1993; 48: 1035–45.

121. Cook R. L., Clark D. B. Is there an association between alcohol consumption and sexually transmitted diseases? A systematic review. Sex Transm Dis 2005; 32: 156–64.

122. Li Q., Stanton B. Alcohol use and sexual risk behaviors and outcomes in China: a literature review. AIDS Behav 2010; 14: 1227–36.

123. Sheeran P., Abraham C., Orbell S. Psychosocial correlates of heterosexual condom use: a meta-analysis. Psychol Bull 1999; 125: 90–132.

124. Sheeran P., Orbell S. Do intentions predict condom use? Meta-analysis and examination of six moderator variables. Br J Soc Psychol 1998; 37: 231–50.

125. Detels R., English P., Visscher B. R., Jacobson L., Kingsley L. A., Chmiel J. S. et al. Seroconversion, sexual activity, and condom use among 2915 HIV seronegative men followed for up to 2 years. J Acquir Immune Defic Syndr 1989; 2: 77–83.

126. Pinkerton S. D., Abramson P. R. Effectiveness of condoms in preventing HIV transmission. Soc Sci Med 1997; 44: 1303–12.

127. Smith D. K., Herbst J. H., Zhang X., Rose C. E. Condom effec- tiveness for HIV prevention by consistency of use among men who have sex with men in the United States. J Acquir Immune Defic Syndr 2015; 68: 337–44.

128. Pandrea I., Happel K. I., Amedee A., Bagby G. J., Nelson S. Alcohol’s role in HIV transmission and disease progression. Alcohol Res Health 2010; 33: 203–18.

129. Amedee A. M., Nichols W. A., Robichaux S., Bagby G. J., Nelson S. Chronic alcohol abuse and HIV disease progres- sion: studies with the non-human primate model. Curr HIV Res 2014; 12: 243–53.

130. Hahn J. A., Samet J. H. Alcohol and HIV disease progression: weighing the evidence. Curr HIV/AIDS Rep 2010; 7: 226–33.

131. Justice A. C., McGinnis K. A., Tate J. P., Braithwaite R. S., Bryant K. J., Cook R. L. et al. Risk of mortalityand physiologic injury evident with lower alcohol exposure among HIV in- fected compared with uninfected men. Drug Alcohol Depend 2016; 161: 95–103.

132. Bagby G. J., Amedee A. M., Siggins R. W., Molina P. E., Nelson S., Veazey R. S. Alcohol and HIV effects on the immune system. Alcohol Res 2015; 37: 287–97.

Alcohol and disease 993

133. Kumar S., Jin M., Ande A., Sinha N., Silverstein P. S., Kumar A. Alcohol consumption effect on antiretroviral therapy and HIV-1 pathogenesis: role of cytochrome P450 isozymes. Expert Opin Drug Metab Toxicol 2012; 8: 1363–75.

134. Molina P. E., Amedee A. M., Winsauer P., Nelson S., Bagby G., Simon L. Behavioral, metabolic, and immune conse- quences of chronic alcohol or cannabinoids on HIV/AIDs: studies in the non-human primate SIV model. J Neuroimmune Pharmacol 2015; 10: 217–32.

135. Neuman M. G., Monteiro M., Rehm J. Drug interactions between psychoactive substances and antiretroviral therapy in individuals infected with human immunodefi- ciency and hepatitis viruses. Subst Use Misuse 2006; 41: 1395–463.

136. Neuman M. G., Schneider M., Nanau R. M., Parry C. HIV- antiretroviral therapy induced liver, gastrointestinal, and pancreatic injury. Int J Hepatol 2012; 2012: 760706.

137. Schneider M., Neuman M., Chersich M., Parry C. Alcohol and antiretroviral therapy—a lethal cocktail. J AIDS Clin Res 2011; S1: 005.

138. Mehta A. J., Guidot D. M. Alcohol abuse, the alveolar macro- phage and pneumonia. Am J Med Sci 2012; 343: 244–7.

139. Siggins R. W., Melvan J. N., Welsh D. A., Bagby G. J., Nelson S., Zhang P. Alcohol suppresses the granulopoietic response to pulmonary Streptococcus pneumoniae infection with enhancement of STAT3 signaling. J Immunol 2011; 186: 4306–13.

140. Bhatty M., Pruett S. B., Swiatlo E., Nanduri B. Alcohol abuse and Streptococcus pneumoniae infections: consideration of vir- ulence factors and impaired immune responses. Alcohol 2011; 45: 523–39.

141. Rush B. An Inquiry Into the Effects of Ardent Spirits Upon the Human Body and Mind: With an Account of the Means of Preventing, and of the Remedies for Curing Them, 8th edn. Reprint. Exeter: NH: Richardson; 1785.

142. Samokhvalov A. V., Irving H. M., Rehm J. Alcohol consump- tion as a risk factor for pneumonia: systematic review and meta-analysis. Epidemiol Infect; 138: 1789–95.

143. Pflaum T., Hausler T., Baumung C., Ackermann S., Kuballa T., Rehm J. et al. Carcinogenic compounds in alcoholic bever- ages: an update. Arch Toxicol 2016; 90: 2349–67.

144. International Agency for Research on Cancer. Alcohol Drink- ing. Lyon, France: International Agency for Research on Cancer, World Health Organization; 1988.

145. International Agency for Research on Cancer. IARC Mono- graphs on the Evaluation of Carcinogenic Risks to Humans: Alcohol Consumption and Ethyl Carbamate. Lyon, France: International Agency For Research On Cancer; 2010.

146. International Agency for Research on Cancer. IARC Mono- graphs on the Evaluation of Carcinogenic Risks to Humans 100E: Personal Habits and Indoor Combustions. Lyon, France: International Agency for Research on Cancer; 2012.

147. World Cancer Research Fund International Continuous Update Project (CUP), WCRF International. Available at: http://www.wcrf.org/int/research-we-fund/continuous- update-project-cup/Web (accessed 21 October 2016) (Archived at http://www.webcitation.org/6n7qgzug3).

148. International Agency for Research on Cancer. IARC Monographs on the Evaluation of Carcinogenic Risks to Humans [internet]; 2016. Available at: http://mono- graphs.iarc.fr/ENG/Preamble/currentb6evalrationale0706. php/Web (accessed 21 October 2016) (Archived at http:// www.webcitation.org/6n7qT2PxS).

149. Baan R., Straif K., Grosse Y., Secretan B., El Ghissassi F., Bouvard V. et al. Carcinogenicity of alcoholic beverages. Lancet Oncol 2007; 8: 292–3.

150. Varela-Rey M., Woodhoo A., Martinez-Chantar M. L., Mato J. M., Lu S. C. Alcohol, DNA methylation, and cancer. Alcohol Res 2013; 35: 25.

151. Shield K. D., Soerjomataram I., Rehm J. Alcohol use and breast cancer: a critical review. Alcohol Clin Exp Res 2016; 40: 1166–81.

152. Choi S. W., Frisco S. Interactions between folate and aging for carcinogenesis. Clin Chem Lab Med 2005; 43: 1151–7.

153. Cauley J. A., Lucas F. L., Kuller L. H., Stone K., Browner W., Cummings S. R. Elevated serum estradiol and testosterone concentrations are associated with a high risk for breast cancer. Ann Intern Med 1999; 130: 270–7.

154. Key T. J., Appleby P. N., Reeves G. K., Travis R. C., Alberg A. J., Barricarte A. et al. Sex hormones and risk of breast cancer in premenopausal women: a collaborative reanalysis of indi- vidual participant data from seven prospective studies. Lancet Oncol 2013; 14: 1009–19.

155. Hankinson S. E., Eliassen A. H. Circulating sex steroids and breast cancer risk in premenopausal women. Horm Cancer 2010; 1: 2–10.

156. Yu H., Berkel H. Do insulin-like growth factors mediate the effect of alcohol on breast cancer risk? Med Hypotheses 1999; 52: 491–6.

157. International Agency for Research on Cancer. IARC Mono- graphs on the Evaluation of Carcinogenic Risks to Humans: Volume 96—Alcohol Consumption and Ethyl Carbamate. Lyon, France: International Agency for Research on Cancer; 2007.

158. Talamini R., Bosetti C., La Vecchia C., Dal Maso L., Levi F., Bidoli E. et al. Combined effect of tobacco and alcohol on la- ryngeal cancer risk: a case–control study. Cancer Causes Control 2002; 13: 957–64.

159. Park S. M., Lim M. K., Shin S. A., Yun Y. H. Impact of prediagnosis smoking, alcohol, obesity, and insulin resis- tance on survival in male cancer patients: National Health Insurance Corporation Study. J Clin Oncol 2006; 24: 5017–24.

160. Lee J. E., Hunter D. J., Spiegelman D., Adami H. O., Albanes D., Bernstein L. et al. Alcohol intake and renal cell cancer in a pooled analysis of 12 prospective studies. J Natl Cancer Inst 2007; 99: 801–10.

161. Chow W. H., Dong L. M., Devesa S. S. Epidemiology and risk factors for kidney cancer. Nat Rev Urol 2010; 7: 245–57.

162. Zhang G. M., Zhu Y., Ye D. W. Metabolic syndrome and renal cell carcinoma. World J Surg Oncol 2014; 12: 236.

163. World Cancer Research Fund (WRCF). International, Amer- ican Institute for Cancer Research. Continuous Update Project report: Food, Nutrition, Physical Activity, and Kidney Cancer. London, UK: WRCF International; 2015.

164. Langcake P., Pryce R. J. The production of resveratrol by Vitis vinifera and other members of the Vitaceae as a response to infection or injury. Physiol Plant Path 1976; 9: 77–86.

165. Baur J. A., Mai A. Revelations into resveratrol’s mechanism. Nat Med 2012; 18: 500–1.

166. Siemann E. H., Creasy L. L. Concentration of the phytoalexin resveratrol in wine. Am J Enol Vitic 1992; 43: 49–52.

167. Kraft T. E., Parisotto D., Schempp C., Efferth T. Fighting can- cer with red wine? Molecular mechanisms of resveratrol. Crit Rev Food Sci Nutr 2009; 49: 782–99.

994 Jürgen Rehm et al.

168. Lachenmeier D. W., Godelmann R., Witt B., Riedel K., Rehm J. Can resveratrol in wine protect against the carcinogenicity of ethanol? A probabilistic dose–response assessment. Int J Cancer 2014; 134: 144–53.

169. Bagnardi V., Rota M., Botteri E., Tramacere I., Islami F., Fedirko V. et al. Alcohol consumption and site-specific cancer risk: a comprehensive dose–response meta-analysis. Br J Cancer 2015; 112: 580–93.

170. Corrao G., Bagnardi V., Zambon A., La Vecchia C. A meta- analysis of alcohol consumption and the risk of 15 diseases. Prev Med 2004; 38: 613–19.

171. Scoccianti C., Straif K., Romieu I. Recent evidence on alcohol and cancer epidemiology. Future Oncol 2013; 9: 1315–22.

172. Fillmore K. M., Kerr W. C., Stockwell T., Chikritzhs T., Bostrom A. Moderate alcohol use and reduced mortality risk: systematic error in prospective studies. Addict Res Theory 2006; 14: 101–32.

173. Rehm J., Irving H., Ye Y., Kerr W. C., Bond J., Greenfield T. K. Are lifetime abstainers the best control group in alcohol ep- idemiology? On the stability and validity of reported lifetime abstention. Am J Epidemiol 2008; 168: 866–71.

174. Zeisser C., Stockwell T. R., Chikritzhs T. Methodological biases in estimating the relationship between alcohol consumption and breast cancer: the role of drinker misclassification errors in meta-analytic results. Alcohol Clin Exp Res 2014; 38: 2297–306.

175. Roerecke M., Shield K. D., Higuchi S., Yoshimura A., Larsen E., Rehm M. X. et al. Estimates of alcohol-related oesopha- geal cancer burden in Japan: systematic review and meta- analyses. Bull World Health Organ 2015; 93: 329–38.

176. Shield K. D., Parry C., Rehm J. Chronic diseases and condi- tions related to alcohol use. Alcohol Res 2013; 35: 155–71.

177. Rehm J. Light or moderate drinking is linked to alcohol re- lated cancers, including breast cancer. BMJ 2015; 351: h4400.

178. Shield K. D., Rylett M., Rehm J. Public health successes and missed opportunities. In: Trends in Alcohol Consumption and Attributable Mortality in the WHO European Region, 1990– 2014. Copenhagen, Denmark: WHO European Region; 2016.

179. Bagnardi V., Rota M., Botteri E., Tramacere I., Islami F., Fedirko V. et al. Light alcohol drinking and cancer: a meta- analysis. Ann Oncol 2013; 24: 301–8.

180. Chen W. Y., Rosner B., Hankinson S. E., Colditz G. A., Willett W. C. Moderate alcohol consumption duringadult life, drink- ing patterns, and breast cancer risk. JAMA 2011; 306: 1884–90.

181. Brooks P. J., Enoch M. A., Goldman D., Li T. K., Yokoyama A. The alcohol flushing response: an unrecognized risk factor for esophageal cancer from alcohol consumption. PLOS Med 2009; 6: 258–63.

182. Boffetta P., Hashibe M. Alcohol and cancer. Lancet Oncol 2006; 7: 149–56.

183. Yokoyama A., Omori T. Genetic polymorphisms of alcohol and aldehyde dehydrogenases and risk for esophageal and head and neck cancers. Alcohol 2005; 35: 175–85.

184. Praud D., Rota M., Rehm J., Shield K., Zatonski W., Hashibe M. et al. Cancer incidence and mortality attributable to alco- hol consumption. Int J Cancer 2016; 138: 1380–7.

185. Holmes J., Meier P. S., Booth A., Guo Y., Brennan A. The tem- poral relationship between per capita alcohol consumption and harm: a systematic review of time lag specifications in aggregate time series analyses. Drug Alcohol Depend 2012; 123: 7–14.

186. Rehm J., Patra J., Popova L. Alcohol drinking cessation and its effect on oesophageal and head and neck cancers: a pooled analysis. Int J Cancer 2007; 121: 1132–7.

187. Kontis V., Mathers C. D., Rehm J., Stevens G. A., Shield K. D., Bonita R. et al. Contribution of six risk factors to achieving the ‘25 × 25’ NCD mortality reduction target. Lancet 2014; 384: 427–37.

188. Howard A. A., Arnsten J. H., Gourevitch M. N. Effect of alco- hol consumption on diabetes mellitus: a systematic review. Ann Intern Med 2004; 140: 211–19.

189. Koppes L. L. J., Bouter L. M., Dekker J. M., Heine R. J., Hendricks H. F. J. Moderate alcohol consumption lowers the risk of type 2 diabetes. A meta-analysis of prospec- tive observational studies. Diabetes Care 2005; 28: 719–25.

190. Carlsson S., Hammar N., Grill V. Alcohol consumption and type 2 diabetes meta-analysis of epidemiological studies in- dicates a U-shaped relationship. Diabetologia 2005; 48: 1051–4.

191. Baliunas D., Taylor B., Irving H., Roerecke M., Patra J., Mohapatra S. et al. Alcohol as a risk factor for type 2 diabe- tes—a systematic review and meta-analysis. Diabetes Care 2009; 32: 2123–32.

192. Knott C., Bell S., Britton A. Alcohol consumption and the risk of type 2 diabetes: a systematic review and dose– response meta-analysis of more than 1.9 million individuals from 38 observational studies. Diabetes Care 2015; 38: 1804–12.

193. Li X. H., Yu F. F., Zhou Y. H., He J. Association between alco- hol consumption and the risk of incident type 2 diabetes: a systematic review and dose–response meta-analysis. Am J Clin Nutr 2016; 103: 818–29.

194. Schrieks I. C., Heil A. L., Hendriks H. F., Mukamal K. J., Beulens J. W. The effect of alcohol consumption on insulin sensitivity and glycemic status: a systematic review and meta-analysis of intervention studies. Diabetes Care 2015; 38: 723–32.

195. Hirst J. A., Aronson J. K., Feakins B. G., Ma C., Farmer A. J., Stevens R. J. Short- and medium-term effects of light to mod- erate alcohol intake on glycaemic control in diabetes mellitus: a systematic review and meta-analysis of random- ized trials. Diabet Med 2016; DOI: 10.1111/dme.13259.

196. Heianza Y., Arase Y., Saito K., Tsuji H., Fujihara K., Hsieh S. D. et al. Role of alcohol drinking pattern in type 2 diabetes in Japanese men: the Toranomon Hospital Health Manage- ment Center Study 11 (TOPICS 11). Am J Clin Nutr 2013; 97: 561–8.

197. Vaeth P. A., Caetano R., Durazo E. M. Ethnicity and alcohol consumption among US adults with diabetes. Ann Epidemiol 2014; 24: 720–6.

198. Babor T., Rehm J., Jernigan D., Vaeth P., Monteiro M., Lehman H. Alcohol, diabetes and public health interven- tions in the Americas. Rev Panam Salud Publ/Pan Am J Public Health 2012; 32: 151–5.

199. Global Burden of Disease (GBD) DALYs and HALE Collabora- tors (2016). Global, regional, and national disability- adjusted life-years (DALYs) for 315 diseases and injuries and healthy life expectancy (HALE), 1990–2015: a system- atic analysis for the Global Burden of Disease Study 2015. Lancet 2015; 388: 1603–58.

200. Global Burden of Disease (GBD) Mortality and Causes of Death Collaborators (2016). Global, regional, and national life expectancy, all-cause mortality, and cause-specific mortality for 249 causes of death, 1980–2015: a

Alcohol and disease 995

systematic analysis for the Global Burden of Disease Study 2015. Lancet 2015; 388: 1459–544.

201. Letenneur L. Moderate alcohol consumption and risk of developing dementia in the elderly: the contribution of propsective studies. Ann Epidemiol 2007; 17: S43–5.

202. Peters R., Peters J., Warner J., Beckett N., Bulpitt C. Alcohol, dementia and cognitive decline in the elderly: a systematic review. Age Ageing 2008; 37: 505–12.

203. Anstey K. J., Mack H. A., Cherbuin N. Alcohol consumption as a risk factor for dementia and cognitive decline: meta- analysis of prospective studies. Am J Geriatr Psychiatry 2009; 17: 542–55.

204. Di Marco L. Y., Marzo A., Muñoz-Ruiz M., Ikram M. A., Kivipelto M., Ruefenacht D. et al. Modifiable lifestyle factors in dementia: a systematic review of longitudinal observa- tional cohort studies. J Alzheimers Dis 2014; 42: 119–35.

205. Lafortune L., Martin S., Kelly S., Kuhn I., Remes O., Cowan A. et al. Behavioural risk factors in mid-life associ- ated with successful ageing, disability, dementia and frailty in later life: a rapid systematic review. PLoS One 2016; 11: e0144405.

206. Gupta S., Warner J. Alcohol-related dementia: a 21st- century silent epidemic? Br J Psychiatry 2008; 193: 351–3.

207. Beydoun M. A., Beydoun H. A., Gamaldo A. A., Teel A., Zonderman A. B., Wang Y. Epidemiologic studies of modifi- able factors associated with cognition and dementia: systematic review and meta-analysis. BMC Public Health 2014; 24: 643.

208. Cooper C., Sommerlad A., Lyketsos C. G., Livingston G. Mod- ifiable predictors of dementia in mild cognitive impairment: a systematic review and meta-analysis. Am J Psychiatry 2015; 172: 323–34.

209. Kumari M., Holmes M. V., Dale C. E., Hubacek J. A., Palmer T. M., Pikhart H. et al. Alcohol consumption and cognitive per- formance: a Mendelian randomization study. Addiction 2014; 109: 1462–71.

210. Ridley N. J., Draper B., Withall A. Alcohol-related dementia: an update of the evidence. Alzheimers Res Ther 2013; 5: 3.

211. Daulatzai M. A. ‘Boomerang neuropathology’ of late-onset Alzheimer’s disease is shrouded in harmful ‘BDDS’: breath- ing, diet, drinking, and sleep during aging. Neurotox Res 2015; 28: 55–93.

212. Collins M. A., Neafsey E. J., Mukamal K. J., Gray M. O., Parks D. A., Das D. K. et al. Alcohol in moderation, cardioprotection, and neuroprotection: epidemiological con- siderations and mechanistic studies. Alcohol Clin Exp Res 2009; 33: 206–19.

213. Piazza-Gardner A. K., Faffud T. J., Barry A. E. The impact of alcohol on Alzheimer’s disease: a systematic review. Aging Ment Health 2013; 17: 133–46.

214. Sullivan L. E., Fiellin D. A., O’Connor P. G. The prevalence and impact of alcohol problems in major depression: a sys- tematic review. Am J Med 2005; 118: 330–41.

215. Kessler R. C., Crum R. M., Warner L. A., Nelson C. B., Schulenberg J., Anthony J. C. Lifetime co-occurrence of DSM-III-R alcohol abuse and dependence with other psychi- atric disorders in the National Comorbidity Survey. Arch Gen Psychiatry 1997; 54: 313–21.

216. Regier D., Farmer M., Rae D., Locke B., Keith S., Judd L. et al. Comorbidityof mental disorders with alcohol and other drug abuse: results from the Epidemiological Catchment Area (ECA) Study. JAMA 1990; 264: 2511–8.

217. Helzer J. E., Pryzbeck T. R. The co-occurrence of alcoholism with other psychiatric disorders in the general population

and its impact on treatment. J Stud Alcohol 1988; 49: 219–24.

218. World Health Organization. Alcohol dependence with alcohol-induced mood disorder - F10.24 [internet]; 2016. Available at: http://icd10coded.com/cm/ch5/F10-F19/ F10/F10.24/Web (accessed 31 October 2016) (Archived at http://www.webcitation.org/6n7pFo5Vx).

219. Boden J. M., Fergusson D. M. Alcohol and depression. Addiction 2011; 106: 906–14.

220. Bolton J. M., Robinson J., Sareen J. Self-medication of mood disorders with alcohol and drugs in the National Epidemio- logic Survey on Alcohol and Related Conditions. J Affect Disord 2009; 115: 367–75.

221. Fergusson D. M., Boden J. M., Horwood L. J. Tests of causal links between alcohol abuse or dependence and major depression. Arch Gen Psychiatry 2009; 66: 260–6.

222. Falk D. E., Yi H. Y., Hilton M. E. Age of onset and temporal sequencing of lifetime DSM-IV alcohol use disorders relative to comorbid mood and anxiety disorders. Drug Alcohol De- pend 2008; 94: 234–45.

223. Haynes J. C., Farrell M., Singleton N., Meltzer H., Araya R., Lewis G. et al. Alcohol consumption as a risk factor for anxiety and depression. Br J Psychiatry 2005; 187: 544–51.

224. Samokhvalov A. V., Farid A. K., Selby P., Rehm J. Alcohol-related seizure disorders. In: Reuber M., Schachter S. C., editors. Borderland of Epilepsy Revisited. New York, NY: Oxford University Press; 2012, pp. 144–54.

225. Bartolomei F., Suchet L., Barrie M., Gastaut J. L. Alcoholic epilepsy: a unified and dynamic classification. Eur Neurol 1997; 37: 13–17.

226. Hattemer K., Knake S., Oertel W. H., Hamer H. M., Rosenow F. Recurrent alcohol-induced seizures in a patient with chronic alcohol abuse. Epileptic Disord 2008; 10: 162–4.

227. Hillbom M., Pieninkeroinen I., Leone M. Seizures in alcohol- dependent patients: epidemiology, pathophysiology and management. CNS Drugs 2003; 17: 1013–30.

228. Devetag F., Mandich G., Zaiotti G., Toffolo G. G. Alcoholic ep- ilepsy: review of series and and proposed classification and etiopathogenesis. Ital J Neurol Sci 1983; 4: 275–84.

229. Fisher R. S., van Emde Boas W., Blume W., Elger C., Genton P., Lee P. et al. Epileptic seizures and epilepsy: definitions pro- posed by the international league against epilepsy (ILAE) and the international bureau for epilepsy (IBE). Epilepsia 2005; 46: 470–2.

230. Samokhvalov A. V., Irving H., Mohapatra S., Rehm J. Alcohol consumption, unprovoked seizures and epilepsy: a systematic review and meta-analysis. Epilepsia 2010; 51: 1177–84.

231. Ballenger J. C., Post R. M. Kindling as a model for alcohol withdrawal syndromes. Br J Psychiatry 1978; 133: 1–4.

232. Gonzalez L. P., Veatch L. M., Ticku M. K., Becker H. C. Alcohol withdrawal kindling: mechanisms and implications for treatment. Alcohol Clin Exp Res 2001; 25: 197s–201s.

233. Dam A. M., Fuglsang-Frederikse A., Svarre-Olsen U., Dam M. Late-onset epilepsy: etiologies, types of seizure, and value of clinical investigation, EEG, and computerized tomography scan. Epilepsia 1985; 26: 227–31.

234. Freedland E. S., McMicken D. B. Alcohol-related seizures, part I: pathophysiology, differencial diagnosis and evalua- tion. J Emerg Med 1993; 11: 463–73.

235. Rathlev N. K., Ulrich A. S., Delanty N., D’Onofrio D., D’Onofrio G. Alcohol-related seizures. J Emerg Med 2006; 31: 157–63.

996 Jürgen Rehm et al.

236. Barclay G. A., Barvour J., Stewart S., Day C. P., Gilvarry E. Adverse physical effects of alcohol misuse. Adv Psychiatr Treat 2008; 14: 139–51.

237. Leach J. P., Mohanraj R., Borland W. Alcohol and drugs in epilepsy: pathophysiology, presentation, possibilities, and prevention. Epilepsia 2012; 53: 48–57.

238. Roerecke M., Rehm J. Alcohol intake revisited: risks and benefits. Curr Atheroscler Rep 2012; 14: 556–62.

239. O’Keefe J. H., Bhatti S. K., Bajwa A., DiNicolantonio J. J., Lavie C. J. Alcohol and cardiovascular health: the dose makes the poison... or the remedy. Mayo Clin Proc 2014; 89: 382–93.

240. Klatsky A. L. Alcohol and cardiovascular diseases: where do we stand today? J Intern Med 2015; 278: 238–50.

241. Taylor B., Irving H. M., Baliunas D., Roerecke M., Patra J., Mohapatra S. et al. Alcohol and hypertension: gender differ- ences in dose–response relationships determined through systematic review and meta-analysis. Addiction 2009; 104: 1981–90.

242. Briasoulis A., Agarwal V., Messerli F. H. Alcohol consump- tion and the risk of hypertension in men and women: a systematic review and meta-analysis. J Clin Hypertension 2012; 14: 792–8.

243. Roerecke M., Rehm J. Chronic heavy drinking and ischaemic heart disease: a systematic review and meta-analysis. Open Heart 2014; 1: e000135.

244. Iacovoni A., De Maria R., Gavazzi A. Alcoholic cardiomyop- athy. J Cardiovasc Med 2010; 11: 884–92.

245. Kodama S., Saito K., Tanaka S., Horikawa C., Saito A., Heianza Y. et al. Alcohol consumption and risk of atrial fibrillation: a meta-analysis. J Am Coll Cardiol 2011; 57: 427–36.

246. Patra J., Taylor B., Irving H., Roerecke M., Baliunas D., Mohapatra S. et al. Alcohol consumption and the risk of morbidity and mortality from different stroke types—a systematic review and meta-analysis. BMC Public Health 2010; 10: 258.

247. Roerecke M., Rehm J. Irregular heavy drinking occasions and risk of ischemic heart disease: a systematic review and meta-analysis. Am J Epidemiol 2010; 171: 633–44.

248. Roerecke M., Rehm J. Alcohol consumption, drinking pat- terns, and ischemic heart disease: a narrative review of meta-analyses and a systematic review and meta-analysis of the impact of heavy drinking occasions on risk for moder- ate drinkers. BMC Med 2014; 12: 182.

249. Leong D. P., Smyth A., Teo K. K., McKee M., Rangarajan S., Pais P. et al. Patterns of alcohol consumption and myocardial infarction risk: observations from 52 countries in the INTERHEART case–control study. Circulation 2014; 130: 390–8.

250. Guiraud V., Amor M. B., Mas J. L., Touzé E. Triggers of ische- mic stroke: a systematic review. Stroke 2010; 41: 2669–77.

251. O’Donnell M. J., Chin S. L., Rangarajan S., Xavier D., Liu L., Zhang H. et al. Global and regional effects of potentially mod- ifiable risk factors associated with acute stroke in 32 countries (INTERSTROKE): a case–control study. Lancet 2016; 388: 761–75.

252. Mostofsky E., Chahal H. S., Mukamal K. J., Rimm E. B., Mittleman M. A. Alcohol and immediate risk of cardiovascu- lar events: a systematic review and dose–response meta- analysis. Circulation 2016; 133: 979–87.

253. McKee M., Britton A. The positive relationship between alco- hol and heart disease in Eastern Europe: potential physiological mechanisms. J R Soc Med 1998; 91: 402–7.

254. Tonelo D., Providência R., Gonçalves L. Holiday heart syn- drome revisited after 34 years. Arq Bras Cardiol 2013; 101: 183–9.

255. Puddey I. B., Rakic V., Dimmitt S. B., Beilin L. J. Influence of pattern of drinking on cardiovascular disease and cardiovas- cular risk factors—a review. Addiction 1999; 94: 649–63.

256. Ronksley P. E., Brien S. E., Turner B. J., Mukamal K. J., Ghali W. A. Association of alcohol consumption with selected car- diovascular disease outcomes: a systematic review and meta-analysis. BMJ 2011; 342: d671.

257. Roerecke M., Rehm J. The cardioprotective association of av- erage alcohol consumption and ischaemic heart disease: a systematic review and meta-analysis. Addiction 2012; 107: 1246–60.

258. Stockwell T., Greer A., Fillmore K., Chikritzhs T., Zeisser C. How good is the science. BMJ 2012; 344: e2276.

259. Holmes M. V., Dale C. E., Zuccolo L., Silverwood R. J., Guo Y., Ye Z. et al. Association between alcohol and cardiovascular disease: Mendelian randomisation analysis based on individ- ual participant data. BMJ 2014; 349: g4164.

260. Emberson J. R., Bennett D. A. Effect of alcohol on risk of cor- onary heart disease and stroke: causality, bias, or a bit of both? Vasc Health Risk Manag 2006; 2: 239–49.

261. Rehm J., Sempos C., Trevisan M. Average volume of alcohol consumption, patterns of drinking and risk of coronary heart disease—a review. J Cardiovasc Risk 2003; 10: 15–20.

262. Brien S. E., Ronksley P. E., Turner B. J., Mukamal K. J., Ghali W. A. Effect of alcohol consumption on biological markers associated with risk of coronary heart disease: systematic re- view and meta-analysis of interventional studies. BMJ 2011; 342: d636.

263. Yusuf S., Hawken S., Ounpuu S., Dans T., Avezum A., Lanas F. et al. Effect of potentially modifiable risk factors associated with myocardial infarction in 52 countries (the INTERHEART study): case–control study. Lancet 2004; 364: 937–52.

264. Roy A., Prabhakaran D., Jeemon P., Thankappan K. R., Mohan V., Ramakrishnan L. et al. Impact of alcohol on cor- onary heart disease in Indian men. Atherosclerosis 2010; 210: 531–5.

265. Tolstrup J. S., Wium-Andersen M. K., Ørsted D. D., Nordestaard B. G. Alcohol consumption and risk of atrial fi- brillation: Observational and genetic estimates of association. Eur J Prev Cardiol 2016; 23: 1514–23.

266. Frick U., Rehm J. Can we establish causality with statistical analyses? The example of epidemiology. In: Wiedermann W., von Eye A., editors. Statistics and Causality: Methods for Applied Empirical Research. Hoboken, NJ: Wiley; 2016, pp. 407–32.

267. Kehoe T., Gmel G. Jr., Shield K., Gmel G. Sr., Rehm J. Deter- mining the best population-level alcohol consumption model and its impact on estimates of alcohol-attributable harms. Popul Health Metrics 2012; 10: 6.

268. Rehm J., Shield K. D., Roerecke M., Gmel G. Modelling the impact of alcohol consumption on cardiovascular disease mortality for comparative risk assessments: an overview. BMC Public Health 2016; 16: 363.

269. Shield K., Rehm J. Russia-specific relative risks and their ef- fects on the estimated alcohol-attributable burden of disease. BMC Public Health 2015; 15: 482.

270. Zaridze D., Brennan P., Boreham J., Boroda A., Karpov R., Lazarev A. et al. Alcohol and cause-specific mortality in Rus- sia: a retrospective case–control study of 48,557 adult deaths. Lancet 2009; 373: 2201–14.

Alcohol and disease 997

271. Malyutina S., Bobak M., Kurilovitch S., Ryizova E., Nikitin Y., Marmot M. Alcohol consumption and binge drinking in Novosibirsk, Russia, 1985–95. Addiction 2001; 96: 987–95.

272. Bobak M., Room R., Pikhart H., Kubinova S., Malyutina S., Pajak A. et al. Contribution of drinking patterns to differ- ences in rates of alcohol related problems between three urban populations. J Epidemiol Community Health 2004; 58: 238–48.

273. Rehm J. What can we learn from Russia about alcohol epide- miology and alcohol policy? Lancet 2014; 383: 1440–2.

274. Shield K. D., Rylett M., Rehm J. Public health gains and missed opportunities. Trends in alcohol consumption and attributable mortality in the WHO European Region, 1990–2014: a report to the WHO European Region, Toronto, Canada: Centre for Addiction and Mental Health; 2016.

275. Britton A., McKee M. The relation between alcohol and cardiovascular disease in Eastern Europe: explaining the paradox. J Epidemiol Community Health 2000; 54: 328–32.

276. Lopez A. D., Williams T. N., Levin A., Tonelli M., Singh J. A., Burney P. G. J. et al. Remembering the forgotten non- communicable diseases. BMC Med 2014; 12: 200.

277. Rehm J., Samokhvalov A. V., Shield K. D. Global burden of al- coholic liver diseases. J Hepatol 2013; 59: 160–8.

278. European Association for the Study of the Liver (EASL). EASL clinical practical guidelines: management of alcoholic liver disease. J Hepatol 2012; 57: j399–j420.

279. Gao B., Bataller R. Alcoholic liver disease: pathogenesis and new therapeutic targets. Gastroenterology 2011; 141: 1572–85.

280. Rehm J., Taylor B., Mohapatra S., Irving H., Baliunas D., Patra J. et al. Alcohol as a risk factor for liver cirrhosis - a sys- tematic review and meta-analysis. Drug Alcohol Rev 2010; 29: 437–45.

281. Mokdad A. A., Lopez A. D., Shahraz S., Lozano R., Mokdad A. H., Stanaway J. et al. Liver cirrhosis mortality in 187 countries between 1980 and 2010: a systematic analysis. BMC Med 2014; 12: 145.

282. Lelbach W. K. Epidemiology of alcoholic liver disease. Prog Liver Dis 1976; 5: 515.

283. Rehm J., Roerecke M. Patterns of drinking and liver cirrhosis —what do we know and where do we go? J Hepatol 2015; 62: 1061–7.

284. Marugame T., Yamamoto S., Yoshimi I., Sobue T., Inoue M., Tsugane S. Patterns of alcohol drinking and all-cause mor- tality: results from a large-scale population-based cohort study in Japan. Am J Epidemiol 2007; 165: 1039–46.

285. Askgaard G., Grønbæk M., Kjaer M. S., Tjønneland A., Tolstrup J. S. Alcohol drinking pattern and risk of alcoholic liver cirrhosis: a prospective cohort study. J Hepatol 2015; 62: 106–7.current issue

286. Szücs S., Sarvary A., McKee M., Adany R. Could the high level of cirrhosis in central and eastern Europe be due partly to the quality of alcohol consumed? An exploratory investi- gation. Addiction 2005; 100: 536–42.

287. Lachenmeier D. W., Gmel G., Rehm J. Unrecorded alcohol consumption. In: Boyle P., Boffetta P., Lowenfels A. B. et al., editors. Alcohol: Science, Policy, and Public Health. Oxford, UK: Oxford University Press; 2013, pp. 132–42.

288. Leitz J., Kuballa T., Rehm J., Lachenmeier D. W. Chemical analysis and risk assessment of Diethyl Phthalate in alco- holic beverages with special regard to unrecorded alcohol. PLoS One 2009; 4: e8127.

289. Solodun Y. V., Monakhova Y. B., Kuballa T., Samokhvalov A. V., Rehm J., Lachenmeier D. W. Unrecorded alcohol con- sumption in Russia: toxic denaturants and disinfectants pose additional risks. Interdiscip Toxicol 2011; 4: 198–205.

290. Rehm J., Kanteres F., Lachenmeier D. W. Unrecorded con- sumption, quality of alcohol and health consequences. Drug Alcohol Rev 2010; 29: 426–36.

291. Lachenmeier D. W., Monakhova Y. B., Rehm J. Influence of unrecorded alcohol consumption on liver cirrhosis mortal- ity. World J Gastroenterol 2014; 20: 7217–22.

292. Rehm J., Kailasapillai S., Larsen E., Rehm M. X., Samokhvalov A. V., Shield K. D. et al. A systematic review of the epidemiology of unrecorded alcohol consumption and the chemical composition of unrecorded alcohol. Addic- tion 2014; 109: 880–93.

293. Puffer R. R., Griffith G. W. Patterns of Urban Mortality: Report of the Inter-American Investigation of Mortality. Washington, DC: Pan American Health Organization; 1967.

294. Prytz H., Anderson H. Underreporting of alcohol-related mortality from cirrhosis is declining in Sweden and Denmark. Scand J Gastroenterol 1988; 23: 1035–43.

295. Haberman P. W., Weinbaum D. F. Liver cirrhosis with and without mention of alcohol as cause of death. Br J Addict 1990; 85: 217–22.

296. Tuusov J., Lang K., Väli M., Pärna K., Tõnisson M., Ringmets I. et al. Prevalence of alcohol-related pathologies at autopsy: Estonian forensic study of alcohol and premature death. Ad- diction 2014; 109: 2018–26.

297. Pollock D. A., Boyle C. A., DeStefano F., Moyer L. A., Kirk M. L. Underreporting of alcohol-related mortality on death cer- tificates of young U.S. army veterans. JAMA 1987; 258: 345–8.

298. Yadav D., Hawes R. H., Brand R. E., Anderson M. A., Money M. E., Banks P. A. et al. Alcohol consumption, cigarette smoking, and the risk of recurrent acute and chronic pan- creatitis. Arch Intern Med 2009; 169: 1035–45.

299. Braganza J., Lee S., McCloy R., McMahon M. Chronic pan- creatitis. Lancet 2011; 377: 1184–97.

300. Yadav D., Lowenfels A. B. The epidemiology of pancrea- titis and pancreatic cancer. Gastroenterology 2013; 144: 1252–61.

301. Lankisch P., Apte M., Banks P. Acute pancreatitis. Lancet 2015; 386: 85–96.

302. Majumder S., Chari S. T. Chronic pancreatitis. Lancet 2016; 387: 1957–66.

303. Sankaran S. J., Xiao A. Y., Wu L. M., Windsor J. A., Forsmark C. E., Petrov M. S. Frequency of progression from acute to chronic pancreatitis and risk factors: a meta-analysis. Gas- troenterology 2015; 149: 1490–500.

304. Gonzalez F. J. Role of cytochromes P450 in chemical toxicity and oxidative stress: studies with CYP2E1. Mutat Res 2005; 569: 101–10.

305. Pandol S. J., Raraty M. Pathobiology of alcoholic pancreati- tis. Pancreatology 2007; 7: 105–14.

306. Witt H., Apte M. V., Keim V., Wilson J. S. Chronic pancre- atitis: challenges and advances in pathogenesis, genetics, diagnosis, and therapy. Gastroenterology 2007; 132: 1557–73.

307. Gerasimenko J. V., Gerasimenko O. V., Petersen O. H. The role of Ca(2+) in the pathophysiology of pancreatitis. J Physiol 2014; 592: 269–80.

308. Irving H. M., Samokhvalov A., Rehm J. Alcohol as a risk fac- tor for pancreatitis. A systematic review and meta-analysis. J Pancreas 2009; 10: 387–92.

998 Jürgen Rehm et al.

309. Samokhvalov A. V., Rehm J., Roerecke M. Alcohol consump- tion as a risk factor for acute and chronic pancreatitis: a systematic review and a series of meta-analyses. EBioMedicine 2015; 2: 1996–2002.

310. Eckardt M., File S., Gessa G., Grant K., Guerri C., Hoffman P. et al. Effects of moderate alcohol consumption on the central nervous system. Alcohol Clin Exp Res 1998; 22: 998–1040.

311. Cherpitel C., Ye Y., Bond J., Borges G., Autonoma M., Monteiro M. et al. Alcohol attributable fraction for injury morbidity from the dose–response relationship of acute alco- hol consumption: emergency department data from 18 countries. Addiction 2015; 110: 1724–32.

312. Taylor B., Irving H. M., Kanteres F., Room R., Borges G., Cherpitel C. et al. The more you drink, the harder you fall: a systematic review and meta-analysis of how acute alcohol consumption and injury or collision risk increase together. Drug Alcohol Depend 2010; 110: 108–16.

313. Taylor B., Rehm J. The relationship between alcohol consumption and fatal motor vehicle injury: high risk at low alcohol levels. Alcohol Clin Exp Res 2012; 36: 1827–34.

314. Krüger H. P., Kazenwadel J., Vollrath M. Grand Rapids ef- fects revisited: accidents, alcohol and risk. In: Kloeden C. N., McLean A. J., editors. Alcohol, Drugs, and Traffic Safety: Proceedings of the 13th International Conference on Alcohol, Drugs and Traffic Safety, Adelaide. Adelaide: NHMRC Road Accident Research Unit, University of Adelaide; 1995, pp. 222–30.

315. Rehm J. Commentary on Cherpitel et al. (2015): improving global estimates of alcohol-attributable injury. Addiction 2015; 110: 1733–4.

316. Rafia R., Brennan A. Modelling methods to estimate the po- tential impact of lowering the blood alcohol concentration limit from 80 mg/100 ml to 50 mg/100 ml in England and Wales: Report to the National Institute for Health and Clinical Excellence [Internet]. The University of Sheffield; 2010. Available at: https://www.nice.org.uk/media/de- fault/About/what-we-do/NICE-guidance/NICE-guidelines/ Public-health-guidelines/Additional-publications/Blood-al- cohol-content-road-traffic-modelling.pdf/Web (accessed 3 November 2016) (Archived at http://www.webcitation. org/6n7oz91nt).

317. Darvishi N., Farhadi M., Haghtalab T., Poorolajal J. Alcohol- related risk of suicidal ideation, suicide attempt, and com- pleted suicide: a meta-analysis. PLoS One 2015; 10: e0126870.

318. Norström T., Rossow I. Alcohol consumption as a risk factor for suicidal behavior: a systematic review of associations at the individual and at the population level. Arch Suicide Res 2016; 20: 489–506.

319. Cherpitel C. J., Borges G. L., Wilcox H. C. Acute alcohol use and suicidal behavior: a review of the literature. Alcohol Clin Exp Res 2004; 28: 18S–28S.

320. Borges G., Loera C. R. Alcohol and drug use in suicidal behaviour. Curr Opin Psychiatry 2010; 23: 195–204.

321. Taylor B., Shield K., Rehm J. Combining best evidence: a novel method to calculate the alcohol-attributable fraction and its variance for injury mortality. BMC Public Health 2011; 11: 265.

322. Shield K. D., Gmel G. Jr., Patra J., Rehm J. Global burden of injuries attributable to alcohol consumption in 2004: a novel way of calculating the burden of injuries attrib- utable to alcohol consumption. Popul Health Metrics 2012; 10: 9.

323. Haagsma J. A., Graetz N., Bolliger I., Naghavi M., Higashi H., Mullany E. C. et al. The global burden of injury: incidence, mortality, disability-adjusted life years and time trends from the Global Burden of Disease study 2013. Inj Prev 2016; 22: 3–18.

324. Gmel G., Rehm J., Frick U. Trinkmuster, Pro-Kopf-Konsum von Alkohol und koronare Mortalitat [Drinking patterns, per capita consumption of alcohol and coronary mortality]. Sucht 2003; 49: 95–104.

325. Hemström Ö. Per capita alcohol consumption and ischaemic heart disease mortality. Addiction 2001; 96: S93–S112.

326. Pun V. C., Lin H., Kim J. H., Yip B. H., Chung V. C., Wong M. C. et al. Impacts of alcohol duty reductions on cardio- vascular mortality among elderly Chinese: a 10-year timeseries analysis. J Epidemiol Community Health 2013; 67: 514–18.

327. Morgenstern H. Ecological studies. In: Rothman K., Green- land S., Lash T., editors. Modern Epidemiology, 3rd edn. Philadelphia, PA: Lippincott, Williams and Wilkins; 2008, pp. 511–31.

328. Gell L., Ally A., Buykx P., Hope A., Meier P. Alcohol’s Harm to others: An Institute of Alcohol Studies report. London, UK: In- stitute of Alcohol Studies; 2015.

329. Laslett A. M., Catalano P., Chikritzhs T., Dale C., Doran C., Ferris J. et al. The Range and Magnitude of Alcohol’s Harm to Others. Fitzroy, Australia: Turning Point Alcohol and Drug Centre; 2010.

330. Laslett A. M., Room R., Ferris J., Wilkinson C., Livingston M., Mugavin J. Surveying the range and magnitude of al- cohol’s harm to others in Australia. Addiction 2011; 106: 1603–11.

331. Navarro H. J., Doran C. M., Shakeshaft A. P. Measuring costs of alcohol harm to others: a review of the literature. Drug Al- cohol Depend 2011; 114: 87–99.

332. Laslett A., Waleewong O., Obot I., Benegal V., Hettige S., Florenzano R. et al. Scoping response system management of alcohol’s harm to others in lower middle income coun- tries. Nord Stud Alcohol Drugs 2016; 33: 515–36.

333. Thanh N. X., Jonsson E. Life expectancy of people with fetal alcohol syndrome. J Popul Ther Clin Pharmacol 2016; 23: e53–9.

334. Damashek A., Williams N. A., Sher K., Peterson L. Relation of caregiver alcohol use to unintentional childhood injury. J Pediatr Psychol 2009; 34: 344–53.

335. Winqvist S., Jokelainen J., Luukinen H., Hillbom M. Parental alcoholmisuseisapowerfulpredictorfor theriskoftraumatic brain injury in childhood. Brain Inj 2007; 21: 1079–85.

336. Quinlan K., Shults R. A., Rudd R. A. Child passenger deaths involving alcohol-impaired drivers. Pediatrics 2014; 133: 966–72.

337. World Health Organization. Global Status Report on Road Safety 2015. Geneva, Switzerland: World Health Organiza- tion; 2015.

338. Bushman B., Cooper H. Effects of alcohol on human aggres- sion: an integrative research review. Psychol Bull 1990; 107: 341–54.

339. Graham K., West P. Alcohol and Crime. In: Heather N., Peters T. J., Stockwell T., editors. International Handbook of Alcohol Dependence and Problems. London: John Wiley & Sons; 2001, pp. 439–70.

340. Abbey A., Clinton-Sherrod A. M., McAuslan P., Zawacki T., Buck P. O. The relationship between the quantity of alcohol consumed and the severity of sexual assaults committed by college men. J Interpers Violence 2003; 18: 813–33.

Alcohol and disease 999

341. Clausen T., Martinez P., Towers A., Greenfield T., Kowal P. Alcohol consumption at any level increases risk of in- jury caused by others: data from the study on Global Ageing and adult health. Subst Abuse Res Treat 2016; 9: 125–32.

342. Hull J. G., Bond C. F. J. Social and behavioral consequences of alcohol consumption and expectancy: a meta-analysis. Psychol Bull 1986; 99: 347–60.

343. Ito T. A., Miller N., Pollock V. E. Alcohol and aggression: a meta-analysis on the moderating effects of inhibitory cues, triggering events, and self-focused attention. Psychol Bull 1996; 120: 60–82.

344. Room R., Rossow I. The share of violence attributable to drinking. J Subst Use 2001; 6: 218–28.

345. Shield K. D., Rylett M. J., Gmel G., Rehm J. Part 1. Trends in alcohol consumption and alcohol-attributable mortality in the EU in 2010. In: World Health Organization Regional Of- fice for Europe, editor. Status Report on Alcohol and Health in 35 European Countries 2013. Copenhagen: WHO Regional Office for Europe; 2013, pp. 3–14.

346. Shkolnikov V. M., Nemtsov A. V. The anti-alcohol campaign and variations in Russian mortality. In: Bobadilla J. L., Costello C. A., Mitchell F., editors. Premature Death in the New Independent States. Washington, DC: National Academy Press; 1997, pp. 239–61.

347. Rehm J. Measuring quantity, frequency and volume of drink- ing. Alcohol Clin Exp Res 1998; 22: 4S–14S.

348. Shaper A., Wannamethee G., Walker M. Alcohol and mor- tality in British men: explaining the U-shaped curve. Lancet 1988; 2: 1267–73.

349. Rehm J. Alcohol consumption and mortality. What do we know and where should we go? Addiction 2000; 95: 989–95.

350. Chen X., Zhou L., Zhang Y., Yi D., Liu L., Rao W. et al. Risk factors of stroke in Western and Asian countries: a system- atic review and meta-analysis of prospective cohort studies. BMC Public Health 2014; 14: 776.

351. McKenzie K., Harrison J. E., McClure R. J. Identification of al- cohol involvement in injury-related hospitalisations using routine data compared to medical record review. Aust J Pub- lic Health 2010; 34: 146–52.

352. Saunders J. B., Room R. Enhancing the ICD system in re- cording alcohol’s involvement uin disease and injury. Alcohol Alcohol 2012; 47: 216–18.

353. Klingemann H., Gmel G. Mapping Social Consequences of Alco- hol Consumption. Dordrecht, the Netherlands: Kluwer Academic Publishers; 2001.

354. Institute of Health Metrics and Evaluation. GBD 2015 Code- book, Institute of Health Metrics and Evaluation. 2015. Available at: http://ghdx.healthdata.org/sites/default/files/ ihme_query_tool/IHME_GBD_2015_CODEBOOK.zip/Web (accessed 25 October 2016) (Archived at http://www. webcitation.org/6n7o8JnWs).

355. Patra J., Jha P., Rehm J., Suraweera W. Tobacco smoking, al- cohol drinking, diabetes, low body mass index and the risk of self-reported symptoms of active tuberculosis: individual par- ticipant data (IPD) meta-analyses of 72,684 individuals in 14 high tuberculosis burden countries. PLoS One 2014; 9: E96433.

356. Traphagen N., Tian Z., Allen-Gipson D. Chronic ethanol ex- posure: pathogenesis of pulmonary disease and dysfunction. Biomolecules 2015; 5: 2840–53.

357. Simet S. M., Sisson J. H. Alcohol’s effects on lung health and immunity. Alcohol Res 2015; 37: 199–208.

358. Foulds J. A., Adamson S. J., Boden J. M., Williman J. A., Mulder R. T. Depression in patients with alcohol use disor- ders: systematic review and meta-analysis of outcomes for independent and substance-induced disorders. J Affect Disord 2015; 1: 47–59.

359. Bartolomei F. Epilepsy and alcohol. Epileptic Disord 2006; 8: S72–8.

360. Puddey I. B., Beilin L. J. Alcohol is bad for blood pressure. Clin Exp Pharmacol Physiol 2006; 33: 847–52.

361. Xin X., He J., Frontini M. G., Ogden L. G., Motsamai O. J., Whelton P. K. Effects of alcohol reduction on blood pressure: a meta-analysis of randomized controlled trials. Hypertension 2001; 38: 1112–7.

362. Roerecke M., Kaczorowski M., Tobe S. W., Gmel G., Hasan O. S. M., Rehm J. The effect of a reduction in alcohol consump- tion on blood pressure: a systematic review and meta- analysis of trial data. Lancet Public Health 2017; in press.

363. Chen L., Smith G. D., Harbord R. M., Lewis S. J. Alcohol in- take and blood pressure: a systematic review implementing a Mendelian Randomization Approach. PLoS Med 2008; 5: 461–71.

364. MukamalK.J.,RimmE.B.Alcohol’seffectsontheriskforcor- onary heart disease. Alcohol Res Health 2001; 25: 255–61.

365. Roerecke M., Rehm J. Ischemic heart disease mortality and morbidity in former drinkers: a meta-analysis. Am J Epidemiol 2011; 173: 245–58.

366. George A., Figueredo V. M. Alcoholic cardiomyopathy: a re- view. J Card Fail 2011; 17: 844–9.

367. Manthey J., Imtiaz S., Neufeld J., Rylett M., Rehm J. Quanti- fying the Global Contribution of Alcohol Consumption to Cardiomyopathy. Toronto, ON, Canada: 2017.

368. Rosenqvist M. Alcohol and cardiac arrhythmias. Alcohol Clin Exp Res 1998; 22: 318s–322s.

369. Rosenberg M. A., Mukamal K. J. The estimated risk of atrial fibrillation related to alcohol consumption. J Atr Fibrillation 2012; 5: 83–92.

370. Samokhvalov A. V., Irving H. M., Rehm J. Alcohol as a risk factor for atrial fibrillation: a systematic review and meta- analysis. Eur J Cardiovasc Prev Rehabil 2010; 17: 706–12.

371. Larsson S. C., Drca N., Wolk A. Alcohol consumption and risk of atrial fibrillation: a prospective study and dose– response meta-analysis. J Am Coll Cardiol 2014; 64: 281–9.

372. Murray C. J. L., Lopez A. Mortality by cause for eight regions of the world: Global Burden of Disease Study. Lancet 1997; 349: 1269–76.

373. Mazzaglia G., Britton R., Altmann D. R., Chenet L. Exploring the relationship between alcohol consumption and non- fatal or fatal stroke: a systematic review. Addiction 2001; 96: 1743–56.

374. Reynolds K., Lewis B., Nolen J., Kinney G., Sathya B., He J. Alcohol consumption and risk of stroke: a meta-analysis. JAMA 2003; 289: 579–88.

375. Patra J., Taylor B., Irving H., Roerecke M., Baliunas D., Mohapatra S. et al. Alcohol consumption and the riskof mor- bidity and mortality for different stroke types—a systematic reviewandmeta-analysis.BMC Public Health 2010; 10: 258.

376. Zhang C., Qin Y. Y., Chen Q., Jiang H., Chen X. Z., Xu C. L. et al. Alcohol intake and risk of stroke: a dose–response meta-analysis of prospective studies. Int J Cardiol 2014; 174: 669–77.

377. Tseng M., Everhart J. E., Sandler R. S. Dietary intake and gall- bladderdisease:areview.PublicHealthNutr1999;2:161–72.

378. Shabanzadeh D. M., Sørensen L. T., Jørgensen T. Determi- nants for gallstone formation—a new data cohort study

1000 Jürgen Rehm et al.

and a systematic review with meta-analysis. Scand J Gastroenterol 2016; 51: 1239–48.

379. Farkas A., Kemény L. Psoriasis and alcohol: is cutaneous ethanol one of the missing links? Br J Dermatol 2010; 162: 771–16.

380. Brenaut E., Horreau C., Pouplard C., Barnetche T., Paul C., Richard M. A. et al. Alcohol consumption and psoriasis: a systematic literature review. J Eur Acad Dermatol Venereol 2013; 27: 30–5.

381. Richard M. A., Barnetche T., Horreau C., Brenaut E., Pouplard C., Aractingi S. et al. Psoriasis, cardiovascular events, cancer risk and alcohol use: evidence-based recom- mendations based on systematic review and expert opinion. J Eur Acad Dermatol Venereol 2013; 27: 2–11.

382. Zhu K. J., Zhu C. Y., Fan Y. M. Alcohol consumption and psoriatic risk: a meta-analysis of case–control studies. J Dermatol 2012; 39: 770–3.

383. Adamzik K., McAleer M. A., Kirby B. Alcohol and psoriasis: sobering thoughts. Clin Exp Dermatol 2013; 38: 819–22.

384. Poikolainen K., Karvonen J., Pukkala E. Excess mortality related to alcohol and smoking among hospital-treated patients with psoriasis. Arch Dermatol 1999; 135: 1490–3.

385. Patra J., Bakker R., Irving H., Jaddoe V. W. V., Malini S., Rehm J. Dose–response relationship between alcohol consumption before and during pregnancy and the risks of low birthweight, preterm birth and small for gestational age (SGA)—a systematic review and meta-analyses. Int J Obstet Gynaecol 2011; 118: 1411–21.

386. Global Burden of Disease Study. Global Burden of Disease Study 2015. Causes of death and Nonfatal Causes Mapped to ICD codes. Seattle, WA: Institute for Health Metrics and Evaluation; 2016.

Supporting Information

Additional Supporting Information may be found in the online version of this article at the publisher’s web-site:

Appendix S1 Results of the systematic searches. Appendix S2 Dose Response-relationships between average volume of alcohol use and relative risk for mortality for partially alcohol-attributable disease categories.

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