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Discussion 3 paragraphs

Explain the diagnostic criteria for your assigned substance-related and addictive disorder.
Explain the evidence-based psychotherapy and psychopharmacologic treatment for your assigned substance-related and addictive disorder.
Describe clinical features that you would expect to observe in a client that may have the substance-related and addictive disorder you were assigned. Align the clinical features with the DSM-5 criteria.
Support your rationale with references to the Learning Resources or other academic resources.

Prevalence of 12-Month Alcohol Use, High-Risk Drinking,
and DSM-IV Alcohol Use Disorder in the United States,
2001-2002 to 2012-2013
Results From the National Epidemiologic Survey on Alcohol
and Related Conditions
Bridget F. Grant, PhD; S. Patricia Chou, PhD; Tulshi D. Saha, PhD; Roger P. Pickering, MS; Bradley T. Kerridge, PhD; W. June Ruan, MS;
Boji Huang, MD, PhD; Jeesun Jung, PhD; Haitao Zhang, PhD; Amy Fan, PhD; Deborah S. Hasin, PhD

IMPORTANCE Lack of current and comprehensive trend data derived from a uniform, reliable,
and valid source on alcohol use, high-risk drinking, and DSM-IV alcohol use disorder (AUD)
represents a major gap in public health information.

OBJECTIVE To present nationally representative data on changes in the prevalences of
12-month alcohol use, 12-month high-risk drinking, 12-month DSM-IV AUD, 12-month DSM-IV
AUD among 12-month alcohol users, and 12-month DSM-IV AUD among 12-month high-risk
drinkers between 2001-2002 and 2012-2013.

DESIGN, SETTING, AND PARTICIPANTS The study data were derived from face-to-face
interviews conducted in 2 nationally representative surveys of US adults: the National
Epidemiologic Survey on Alcohol and Related Conditions, with data collected from April 2001
to June 2002, and the National Epidemiologic Survey on Alcohol and Related Conditions III,
with data collected from April 2012 to June 2013. Data were analyzed in November and
December 2016.

MAIN OUTCOMES AND MEASURES Twelve-month alcohol use, high-risk drinking, and DSM-IV
AUD.

RESULTS The study sample included 43 093 participants in the National Epidemiologic
Survey on Alcohol and Related Conditions and 36 309 participants in the National
Epidemiologic Survey on Alcohol and Related Conditions III. Between 2001-2002 and
2012-2013, 12-month alcohol use, high-risk drinking, and DSM-IV AUD increased by 11.2%,
29.9%, and 49.4%, respectively, with alcohol use increasing from 65.4% (95% CI,
64.3%-66.6%) to 72.7% (95% CI, 71.4%-73.9%), high-risk drinking increasing from 9.7%
(95% CI, 9.3%-10.2%) to 12.6% (95% CI, 12.0%-13.2%), and DSM-IV AUD increasing from
8.5% (95% CI, 8.0%-8.9%) to 12.7% (95% CI, 12.1%-13.3%). With few exceptions, increases in
alcohol use, high-risk drinking, and DSM-IV AUD between 2001-2002 and 2012-2013 were
also statistically significant across sociodemographic subgroups. Increases in all of these
outcomes were greatest among women, older adults, racial/ethnic minorities, and individuals
with lower educational level and family income. Increases were also seen for the total sample
and most sociodemographic subgroups for the prevalences of 12-month DSM-IV AUD among
12-month alcohol users from 12.9% (95% CI, 12.3%-17.5%) to 17.5% (95% CI, 16.7%-18.3%)
and 12-month DSM-IV AUD among 12-month high-risk drinkers from 46.5% (95% CI,
44.3%-48.7%) to 54.5% (95% CI, 52.7%-56.4%).

CONCLUSIONS AND RELEVANCE Increases in alcohol use, high-risk drinking, and DSM-IV AUD
in the US population and among subgroups, especially women, older adults, racial/ethnic
minorities, and the socioeconomically disadvantaged, constitute a public health crisis. Taken
together, these findings portend increases in many chronic comorbidities in which alcohol
use has a substantial role.

JAMA Psychiatry. 2017;74(9):911-923. doi:10.1001/jamapsychiatry.2017.2161
Published online August 9, 2017.

Editorial page 869

Author Affiliations: Epidemiology
and Biometry Branch, National
Institute on Alcohol Abuse and
Alcoholism, Rockville, Maryland
(Grant, Chou, Saha, Pickering, Ruan,
Huang, Jung, Zhang, Fan); New York
State Psychiatric Institute, New York
(Kerridge, Hasin); Department of
Psychiatry, College of Physicians and
Surgeons, Columbia University,
New York, New York (Hasin).

Corresponding Author: Bridget F.
Grant, PhD, PhD, Epidemiology and
Biometry Branch, National Institute
on Alcohol Abuse and Alcoholism,
5635 Fishers Ln, Room 3077,
Rockville, MD 20852
(bgrant@mail.nih.gov).

Research

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A lcohol use and specifically high-risk drinking, which of-ten leads to alcohol use disorder (AUD), are signifi-cant contributors to the burden of disease in the United
States and worldwide.1-7 High-risk drinking and AUD are im-
portant risk factors for morbidity and mortality from fetal al-
cohol spectrum disorders,8 hypertension,9 cardiovascular
diseases,10-15 stroke,16 liver cirrhosis,17,18 several types of
c ancer19-23 and infections,2 4-26 pancreatitis,27, 28 type 2
diabetes,29 and various injuries.30 High-risk drinking and AUD
are disabling,31,32 are associated with numerous psychiatric
comorbidities33,34 and impaired productivity and interper-
sonal functioning, and place psychological and financial bur-
dens on society as a whole and on those who misuse alcohol,
their families, friends, and coworkers,35-37 as well as through
motor vehicle crashes, violence, and property crime.38,39

In view of the seriousness of the numerous physical and
psychiatric harms of high-risk drinking and AUD, regular and
detailed monitoring of their trends over time is imperative for
the health of the nation. Historically, reliable national survey
data on alcohol use, high-risk drinking, and AUD were not avail-
able before the early 1970s.40 The few national trend surveys
conducted between the early 1970s to the early 1990s showed
stability or decreases in trends for 12-month alcohol use, vari-
ous measures of high-risk drinking, and social consequence
and alcohol dependence symptoms.41-44 Between the early
1990s and the early 2000s, 12-month alcohol consumption in-
creased from 44.0%45 to 65.4%,46 12-month high-risk drink-
ing increased from approximately 8.0%47,48 to 9.7%,49 and
DSM-IV50 AUD increased from 7.4%45 to 8.5%.32

Lack of current and comprehensive trend data derived from
a uniform source on alcohol use, high-risk drinking, and
DSM-IV AUD since the early 2000s represents a major gap in
public health information. Tracking patterns of alcohol
consumption and AUD is essential to test temporal models of
alcohol consumption behaviors and alcohol-related mor-
bidity and mortality and to estimate the effectiveness of
policy changes related to alcohol use (eg, taxes and treat-
ment entitlements). Furthermore, monitoring of alcohol
consumption patterns and AUD over time within important
sociodemographic subgroups of the US population is critical
for planning and targeting prevention and intervention
programs.

Accordingly, this study presents data for 2001-2002 and
2012-2013 on the prevalences of 12-month alcohol use, high-
risk drinking (defined as exceeding the daily drinking guide-
lines at least weekly in the past 12 months), and 12-month
DSM-IV AUD overall and among important sociodemo-
graphic subgroups of the US population. We used data from
the National Institute on Alcohol Abuse and Alcoholism’s 2001-
2002 Wave 1 National Epidemiologic Survey on Alcohol and
Related Conditions (NESARC)51 and 2012-2013 NESARC-III.52

Methods
Sample
The 2012-2013 NESARC-III is a nationally representative, face-
to-face interview survey of 36 309 US adults 18 years and older

residing in households and selected group quarters,52 with re-
spondents selected through multistage probability sampling.
The data were collected from April 2012 to June 2013. Pri-
mary sampling units were counties or groups of contiguous
counties, secondary sampling units were groups of US Census–
defined blocks, and tertiary sampling units were households
within sampled secondary sampling units within which eli-
gible adult respondents were selected, with black, Asian or Pa-
cific Islander, and Hispanic individuals oversampled. The
household response rate was 72.0%, the person-level re-
sponse rate was 84.0%, and the overall response rate was
60.0%, which were comparable with other current US na-
tional surveys.53,54 Data were adjusted for oversampling and
nonresponse and were weighted to represent the US civilian
population based on the 2012 American Community Survey.55

Weighting adjustment compensated for nonresponse.52 In-
formed consent was electronically recorded, and respon-
dents received $90 for participation. The Combined Neuro-
science Institutional Review Board of the National Institutes
of Health and Westat Institutional Review Board approved the
protocol and informed consent procedures.

The 2001-2002 NESARC was a nationally representative,
face-to-face interview survey of 43 093 US adults, described
elsewhere in detail.51 The data were collected from April 2001
to June 2002. The target population was the US adult popu-
lation 18 years and older residing in households and selected
group quarters. Primary sampling units consisted of counties
or county equivalents from which eligible adults were
selected, with black and Hispanic individuals, and young adults
oversampled. The sampling frame response rate was 98.5%,
the household response rate was 88.5%, and the person re-
sponse rate was 93.0%, yielding an overall survey response rate
of 81.0%. Data were adjusted for oversampling and nonre-
sponse and were weighted to represent the civilian US popu-
lation based on the 2000 Decennial Census.56 The survey pro-
tocol, including written informed consent procedures, received
full ethical review and approval from the US Census Bureau
and the US Office of Management and Budget.

Key Points
Question Have the 12-month prevalences of alcohol use, high-risk
drinking, and DSM-IV alcohol use disorder increased between
2001-2002 and 2012-2013?

Findings In this study of data from face-to-face interviews
conducted in 2 nationally representative surveys of US adults,
including the National Epidemiologic Survey on Alcohol and
Related Conditions (n = 43 093) and the National Epidemiologic
Survey on Alcohol and Related Conditions III (n = 36 309),
12-month alcohol use (11.2%), high-risk drinking (29.9%), and
DSM-IV alcohol use disorder (49.4%) increased for the total US
population and, with few exceptions, across sociodemographic
subgroups.

Meaning Substantial increases in alcohol use, high-risk drinking,
and DSM-IV alcohol use disorder constitute a public health crisis
and portend increases in chronic disease comorbidities in the
United States, especially among women, older adults, racial/ethnic
minorities, and the socioeconomically disadvantaged.

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Assessments
The Alcohol Use Disorder and Associated Disabilities Inter-
view Schedule–DSM-IV Version (AUDADIS-IV)57 used in
NESARC and the AUDADIS Fifth Edition Version58 used in
NESARC-III assessed any 12-month alcohol use with identical
questions. Consistent with the US dietary guidelines,59,60 high-
risk drinking was defined as drinking 4 or more standard drinks
(a drink equals 14 g of pure alcohol) on any day for women and
as drinking 5 or more standard drinks on any day for men. In
this study, high-risk drinking was defined as exceeding the daily
drinking limits at least weekly during the prior 12 months.

An individual was considered to have a DSM-IV AUD di-
agnosis if the respondent met criteria for alcohol dependence
or abuse in the past 12 months. A diagnosis of dependence re-
quired 3 or more of the 7 DSM-IV dependence criteria, and a
diagnosis of abuse required 1 or more of the 4 abuse criteria.
Respondents with a 12-month abuse or dependence diagno-
sis were classified as having 12-month AUD.

Symptom items that assessed DSM-IV AUD diagnoses in
NESARC and NESARC-III were virtually identical. However, 3
items were slightly reworded, and 3 additional abuse ques-
tions appeared in NESARC-III. Comparisons between DSM-IV
12-month AUD diagnoses with and without the additional ques-
tions yielded almost identical prevalences (8.5% and 8.2%, re-
spectively, for NESARC and 12.7% and 12.2%, respectively, for
NESARC-III), with near-perfect concordance (κ = 0.97 for
NESARC and κ = 0.98 for NESARC-III), which suggested that
trivial differences between AUD operationalizations were not
responsible for the changes reported herein.

The test-retest reliability and validity of AUDADIS alco-
hol use, high-risk drinking, and DSM-IV AUD diagnoses are
documented in clinical and general population national
and international studies.61-71 The reliability and validity of
alcohol use, high-risk drinking, and DSM-IV AUD and their
associated criteria scales were fair to excellent.

Statistical Analysis
Data were analyzed in November and December 2016.
Weighted cross-tabulations estimated the prevalence of alco-
hol use, high-risk drinking, and DSM-IV AUD in the total sample
and in subgroups. For 2001-2002 and 2012-2013, the preva-
lences of 12-month DSM-IV AUD among 12-month alcohol us-
ers and 12-month DSM-IV AUD among 12-month high-risk
drinkers were also examined. To account for the complex
sample design of NESARC and NESARC-III, a software pro-
gram (SUDAAN, version 11.0; Research Triangle Institute72) was
used to produce standard errors of the prevalence estimates
for each survey. These data were used to test differences in
prevalences between the surveys using 2-sided t tests for in-
dependent samples. P < .05 indicated significant differences in the estimates between surveys.

Results
12-Month Alcohol Use
Twelve-month alcohol use significantly increased from 65.4%
in 2001-2002 to 72.7% in 2012-2013, a relative percentage in-

crease of 11.2% (Table 1). Significant increases, seen across all
sociodemographic subgroups, were particularly notable among
women (15.8%), racial/ethnic minorities (from 17.2% among
Hispanic to 29.1% among Asian or Pacific Islander individu-
als), adults 65 years and older (22.4%), and respondents with
lower educational level and family income (range, 11.7%-
22.3%).

12-Month High-Risk Drinking
The prevalence of 12-month high-risk drinking increased sig-
nificantly between 2001-2002 and 2012-2013 from 9.7% to
12.6% (change, 29.9%) in the total population (Table 2). Sig-
nificant increases in high-risk drinking were also seen for all
sociodemographic subgroups except Native Americans and re-
spondents residing in rural areas. Increases were most no-
table among women (57.9%), other racial/ethnic minorities
(from 40.6% among Hispanic to 62.4% among black individu-
als), adults 65 years and older (65.2%), persons previously mar-
ried (widowed, divorced, or separated) (31.9%) and married or
cohabitating respondents (34.2%), those with a high school
education (42.3%) and less than a high school education
(34.7%), those earning incomes of $19 999 or less (35.1%), and
those residing in urban areas (35.1%).

12-Month DSM-IV AUD
The prevalence of 12-month DSM-IV AUD increased signifi-
cantly from 8.5% to 12.7% (change, 49.4%) in the total popu-
lation (Table 3). Significant increases in AUD were seen in all
subgroups except Native Americans and those residing in ru-
ral areas. Notable increases were found among women (83.7%),
racial/ethnic minorities (51.9% for Hispanic and 92.8% for black
individuals), adults 65 years and older (106.7%), those with a
high school education (57.8%) and less than a high school edu-
cation (48.6%), those earning incomes of $20 000 or less
(65.9%), those living within 200% of the poverty threshold
(range, 47.1%-55.8%), and those residing in urban areas (59.5%).

12-Month DSM-IV AUD Among 12-Month Alcohol Users
Twelve-month DSM-IV AUD among 12-month alcohol users sig-
nificantly increased from 12.9% to 17.5% (change, 35.7%) in the
total population (Table 4). Increases were significant during
this time for all subgroups except Native Americans, respon-
dents who were previously married, and those residing in
rural areas. Notable increases were found among women
(59.8%), those who were black (55.8%), Asian or Pacific
Islander (36.2%), or Hispanic (29.5%), adults aged 45 to 64
years (61.9%) and 65 years and older (75.0%), those who
were married or cohabiting (45.1%), those who had a high
school education (41.2%), and those who resided in urban
areas (44.8%).

12-Month DSM-IV AUD Among 12-Month High-Risk Drinkers
Twelve-month DSM-IV AUD among 12-month high-risk drink-
ers increased 17.2% from 46.5% in 2001-2002 to 54.5% in 2012-
2013 (Table 5). Increases were significant for all sociodemo-
graphic subgroups except Native American, Asian or Pacific
Islander, previously married respondents, those with less than
a high school education, and those residing in rural areas, the

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Northeast, and the Midwest. Notable increases were seen for
women (34.7%), those who were black (25.7%) or Hispanic
(16.8%), respondents aged 45 to 64 years (34.8%) and 65 years
and older (58.1%), and those residing in urban areas (21.1%).

Discussion
Between 2001-2002 and 2012-2013, the 12-month preva-
lence of alcohol use increased 11.2% in the United States from

65.4% to 72.7%. High-risk drinking increased almost 30% from
9.7% to 12.6%, representing approximately 20.2 million and
29.6 million Americans, respectively. There was a 49.4% in-
crease in 12-month DSM-IV AUD during this time from 8.5%
to 12.7% (representing approximately 17.6 million and 29.9 mil-
lion Americans, respectively), much greater than the corre-
sponding 14.8% increase in DSM-IV AUD observed between
1991-1992 (7.4%) and 2001-2002 (8.5%).73 While the preva-
lences of AUD among both 12-month alcohol users and 12-
month high-risk drinkers increased, the prevalence of AUD

Table 1. Prevalence of and Percentage Change in 12-Month Alcohol Use by Sociodemographic Characteristics,
2001-2002 and 2012-2013

Sociodemographic Characteristic

% (95% CI)

% Change
NESARC 2001-2002
(n = 43 093)

NESARC-III 2012-2013
(n = 36 309)a

Total 65.4 (64.3-66.6) 72.7 (71.4-73.9) 11.2

Sex

Men 71.8 (70.6-73.0) 76.7 (75.5-77.9) 6.8

Women 59.6 (58.0-61.1) 69.0 (67.5-70.5) 15.8

Race/ethnicity

White 69.5 (68.2-70.8) 75.3 (73.7-76.9) 8.3

Black 53.2 (51.6-54.9) 66.1 (63.8-68.3) 24.2

Native American 58.2 (53.0-63.4) 73.9 (69.1-78.1) 27.0

Asian or Pacific Islander 48.4 (44.3-52.5) 62.5 (59.4-65.5) 29.1

Hispanic 59.9 (58.1-61.7) 70.2 (68.8-71.7) 17.2

Age, y

18-29 73.1 (71.5-74.7) 80.1 (78.8-81.3) 9.6

30-44 71.9 (70.4-73.4) 79.5 (78.1-80.8) 10.6

45-64 64.3 (62.9-65.7) 71.9 (70.3-73.5) 11.8

≥65 45.1 (43.4-46.8) 55.2 (52.8-57.6) 22.4

Marital status

Married or cohabiting 66.3 (65.0-67.6) 73.1 (71.6-74.5) 10.3

Widowed, divorced, or separated 56.8 (55.3-58.3) 67.2 (65.4-68.9) 18.3

Never married 70.1 (68.5-71.7) 76.6 (75.1-78.0) 9.3

Educational level

Less than high school 46.4 (44.8-47.9) 55.8 (53.5-58.1) 20.3

High school 60.9 (59.5-62.3) 68.0 (66.5-69.5) 11.7

Some college or higher 73.3 (72.1-74.5) 78.3 (77.1-79.5) 6.8

Family income, $

0-19 999 52.4 (51.1-53.6) 64.1 (62.2-65.9) 22.3

20 000-34 999 61.0 (59.5-62.4) 68.5 (66.8-70.1) 12.3

35 000-69 999 68.1 (66.7-69.4) 73.4 (71.8-74.9) 7.8

≥70 000 78.4 (76.8-80.0) 81.0 (79.5-82.4) 3.3

Poverty threshold, %

<100 52.1 (50.4-53.9) 64.3 (62.5-66.0) 23.4

100-200 55.2 (53.8-56.6) 66.4 (64.4-68.3) 20.3

>200 71.3 (70.0-72.5) 77.8 (76.5-79.0) 9.1

Urbanicity

Urban 67.2 (65.8-68.5) 74.0 (72.9-75.1) 10.1

Rural 58.4 (56.5-60.2) 67.9 (64.8-70.9) 16.3

Region

Northeast 70.9 (67.2-74.4) 77.1 (75.3-78.9) 8.7

Midwest 69.9 (68.4-71.4) 76.5 (74.5-78.5) 9.4

South 59.0 (57.2-60.7) 68.2 (66.0-70.4) 15.6

West 66.1 (63.5-68.7) 72.9 (69.8-75.7) 10.3

Abbreviation: NESARC, National
Epidemiologic Survey on Alcohol and
Related Conditions.
a P < .05 for all comparisons for

2001-2002 compared

with 2012-2013.

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among high-risk drinkers (46.5% in 2001-2002 and 54.5% in
2012-2013) was much greater than the prevalence of AUD
among 12-month users (12.9% in 2001-2002 and 17.5% in 2012-
2013), highlighting the critical role of high-risk drinking in the
increase in AUD between 2001-2002 and 2012-2013, which was
49.4%.46-48 The smaller increase in 12-month high-risk drink-
ing (21.3%) and the larger increase in 12-month alcohol use
(48.6%) seen between the early 1900s and the early 2000s were
associated with a much lower increase in AUD (14.9%), again

underscoring the more important influence of increases in
high-risk drinking relative to alcohol use on increases in AUD.

Increases shown in 12-month alcohol use and high-risk
drinking are consistent with other surveys during the same pe-
riod. The National Health Interview Survey showed a 6.0%
increase in 12-month alcohol use,74,75 while the National Sur-
vey on Drug Use and Health showed a 9.1% increase in 12-
month alcohol use.76,77 Trends in drinking 5 or more drinks at
least once in the past year increased 17.8% in the National

Table 2. Prevalence of and Percentage Change in 12-Month High-Risk Drinking by Sociodemographic
Characteristics, 2001-2002 and 2012-2013

Sociodemographic Characteristic
% (95% CI)
% Change
NESARC 2001-2002
(n = 43 093)

NESARC-III 2012-2013
(n = 36 309)

Total 9.7 (9.3-10.2) 12.6 (12.0-13.2)a 29.9

Sex

Men 14.2 (13.4-14.9) 16.4 (15.7-17.3)a 15.5

Women 5.7 (5.3-6.1) 9.0 (8.4-9.7)a 57.9

Race/ethnicity

White 10.0 (9.6-10.5) 12.3 (11.6-13.0)a 23.0

Black 9.3 (8.4-10.4) 15.1 (14.0-16.2)a 62.4

Native American 12.4 (9.6-15.8) 17.4 (13.6-22.1) 40.3

Asian or Pacific Islander 4.6 (3.5-6.0) 7.2 (6.0-8.8)a 56.5

Hispanic 9.6 (8.8-10.6) 13.5 (12.5-14.6)a 40.6

Age, y

18-29 16.9 (15.7-18.2) 19.3 (18.0-20.6)a 14.2

30-44 10.8 (10.1-11.6) 14.8 (14.0-15.7)a 37.0

45-64 7.5 (6.9-8.2) 11.2 (10.5-12.1)a 49.3

≥65 2.3 (1.9-2.8) 3.8 (3.2-4.4)a 65.2

Marital status

Married or cohabiting 7.3 (6.8-7.8) 9.8 (9.2-10.5)a 34.2

Widowed, divorced, or separated 9.1 (8.3-9.9) 12.0 (11.1-13.0)a 31.9

Never married 17.4 (16.3-18.6) 20.3 (19.1-21.5)a 16.7

Educational level

Less than high school 9.5 (8.5-10.6) 12.8 (11.6-14.0)a 34.7

High school 10.4 (9.6-11.1) 14.8 (13.9-15.9) 42.3

Some college or higher 9.5 (9.0-10.0) 11.6 (10.9-12.4) 22.1

Family income, $

0-19 999 11.1 (10.3-12.0) 15.0 (13.9-16.3)a 35.1

20 000-34 999 10.3 (9.5-11.2) 12.6 (11.7-13.7)a 22.3

35 000-69 999 9.3 (8.7-10.1) 12.9 (12.1-13.7)a 38.7

≥70 000 8.4 (7.7-9.2) 10.5 (9.7-11.4)a 25.0

Poverty threshold, %

<100 11.8 (10.8-13.0) 14.2 (12.9-15.5)a 20.3

100-200 9.7 (8.9-10.7) 12.7 (11.7-13.7)a 30.9

>200 9.3 (8.8-9.8) 12.1 (11.4-12.7)a 30.1

Urbanicity

Urban 9.7 (9.2-10.3) 13.1 (12.5-13.7)a 35.1

Rural 9.6 (8.9-10.5) 10.8 (9.9-11.8) 12.5

Region

Northeast 9.3 (8.1-10.7) 12.2 (11.5-12.9)a 31.2

Midwest 11.2 (10.2-12.3) 14.7 (12.9-16.6)a 31.3

South 9.0 (8.4-9.7) 12.1 (11.1-13.1)a 34.4

West 9.7 (8.9-10.5) 11.8 (11.0-12.7)a 21.6

Abbreviation: NESARC, National
Epidemiologic Survey on Alcohol and
Related Conditions.
a P < .05 for 2001-2002 compared

with 2012-2013.
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Health Interview Survey.78 Parallel increases were also seen
in per capita alcohol consumption based on alcohol sales data,
which rose 6.4%.79 The marked increases in high-risk drink-
ing and DSM-IV AUD between 2001-2002 and 2012-2013 also
mirror recent sharp increases in morbidity and mortality from
diseases and injuries in which alcohol use has a substantial role
or deceleration of previously seen declines. Most important,
mortality rates of all cardiovascular diseases and stroke de-
celerated between 2000-2001 and 2011-2014 after 3 decades
of decline.80,81 Morbidity and mortality rates of hypertension

increased,82,83 as did hypertensive emergencies seen in emer-
gency departments (EDs).84 Age-specific death rates of liver
cirrhosis, especially alcohol-related liver cirrhosis, rose dra-
matically between 2009 and 2013 for the first time since the
early 1970s.85 Although increases in age-adjusted rates of type
2 diabetes86,87 since 2000 have largely been attributed to more
overweight and obesity,88,89 increases in high-risk drinking dur-
ing this time may have contributed, an issue that merits fur-
ther investigation. During the same period, alcohol-related ED
visits associated with falls increased, and the total number of

Table 3. Prevalence of and Percentage Change in 12-Month DSM-IV Alcohol Use Disorder
by Sociodemographic Characteristics, 2001-2002 and 2012-2013

Sociodemographic Characteristic
% (95% CI)
% Change
NESARC 2001-2002
(n = 43 093)
NESARC-III 2012-2013
(n = 36 309)

Total 8.5 (8.0-8.9) 12.7 (12.1-13.3)a 49.4

Sex

Men 12.4 (11.7-13.1) 16.7 (15.8-17.6)a 34.7

Women 4.9 (4.5-5.3) 9.0 (8.5-9.6)a 83.7

Race/ethnicity

White 8.9 (8.4-9.5) 13.1 (12.3-13.9)a 47.2

Black 6.9 (6.1-7.7) 13.3 (11.9-14.8)a 92.8

Native American 12.1 (9.3-15.6) 16.6 (12.7-21.5) 37.2

Asian or Pacific Islander 4.5 (3.5-5.9) 8.0 (6.7-9.5)a 77.8

Hispanic 7.9 (6.8-9.2) 12.0 (11.1-12.9)a 51.9

Age, y

18-29 16.2 (15.1-17.4) 23.4 (21.9-24.9)a 44.4

30-44 9.7 (9.0-10.5) 14.3 (13.3-15.3)a 47.4

45-64 5.4 (4.9-6.0) 9.8 (9.1-10.5)a 81.5

≥65 1.5 (1.2-1.8) 3.1 (2.6-3.7)a 106.7

Marital status

Married or cohabiting 6.0 (5.6-6.5) 9.7 (9.0-10.3)a 61.7

Widowed, divorced, or separated 8.1 (7.3-9.0) 10.6 (9.8-11.5)a 30.9

Never married 15.9 (14.7-17.1) 22.4 (20.9-23.9)a 40.9

Educational level

Less than high school 7.0 (6.2-8.0) 10.4 (9.3-11.7)a 48.6

High school 8.3 (7.6-9.0) 13.1 (12.2-14.0)a 57.8

Some college or higher 9.0 (8.4-9.6) 13.0 (12.3-13.8)a 44.4

Family income, $

0-19 999 8.8 (7.9-9.7) 14.6 (13.4-15.9)a 65.9

20 000-34 999 8.9 (8.2-9.7) 12.3 (11.3-13.4)a 38.2

35 000-69 999 8.1 (7.4-8.8) 12.3 (11.5-13.1)a 51.9

≥70 000 8.3 (7.6-9.1) 12.0 (11.2-12.8)a 44.6

Poverty threshold, %

<100 9.4 (8.3-10.5) 14.3 (13.0-15.6)a 52.1

100-200 7.7 (6.9-8.5) 12.0 (11.1-12.9)a 55.8

>200 8.5 (8.0-9.0) 12.5 (11.8-13.2)a 47.1

Urbanicity

Urban 8.4 (7.8-8.9) 13.4 (12.8-14.0)a 59.5

Rural 8.8 (8.0-9.7) 10.2 (9.0-11.5) 15.9

Region

Northeast 7.8 (6.7-9.0) 11.9 (10.9-12.9)a 52.6

Midwest 10.6 (9.3-11.9) 14.8 (13.2-16.5)a 39.6

South 7.3 (6.6-8.0) 11.5 (10.5-12.7)a 57.5

West 8.8 (7.9-9.7) 13.3 (12.2-14.5)a 51.1

Abbreviation: NESARC, National
Epidemiologic Survey on Alcohol and
Related Conditions.
a P < .05 for 2001-2002 compared with 2012-2013. Research Original Investigation Prevalence of Alcohol Use, High-Risk Drinking, and DSM-IV Alcohol Use Disorder

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care hours doubled, along with the intensity of care (eg, ad-
vanced imaging) received.90 Mortality among alcohol-
affected drivers who were simultaneously distracted also in-
creased between 2005 and 2009 by 63%.91

Increases in high-risk drinking and AUD among women
(57.9% and 83.7%, respectively) relative to men (15.5% and
34.7%, respectively) were alarming, consistent with earlier
studies92-96 demonstrating a narrowing of the gender gap in
these drinking patterns and AUD between 1991-1992 and 2001-
2002. Greater sensitivity to adverse health effects of heavy

drinking among women are potential biological factors influ-
encing the convergence of rates between the sexes within the
context of increasing rates of high-risk drinking and AUD.97-99

Drinking norms and values have become more permissive
among women,100,101 along with increases in educational and
occupational opportunities and rising numbers of women in
the workforce,102 all of which may have contributed to in-
creased high-risk drinking and AUD in women during the past
decade. Stress associated with pursuing a career and raising a
family may lead to inc reases in high-risk drinking and

Table 4. Prevalence of and Percentage Change in 12-Month DSM-IV Alcohol Use Disorder Among 12-Month
Alcohol Users by Sociodemographic Characteristics, 2001-2002 and 2012-2013

Sociodemographic Characteristic
% (95% CI)
% Change
NESARC 2001-2002
(n = 43 093)
NESARC-III 2012-2013
(n = 36 309)

Total 12.9 (12.3-17.5) 17.5 (16.7-18.3)a 35.7

Sex

Men 17.2 (16.3-18.2) 21.7 (20.6-22.9)a 26.2

Women 8.2 (7.5-8.9) 13.1 (12.4-13.8)a 59.8

Race/ethnicity

White 12.8 (12.1-13.6) 17.4 (16.4-18.4)a 35.9

Black 12.9 (11.6-14.3) 20.1 (18.2-22.2)a 55.8

Native American 20.8 (16.3-26.0) 22.5 (17.3-28.7) 8.2

Asian or Pacific Islander 9.4 (7.3-11.9) 12.8 (10.9-15.1)a 36.2

Hispanic 13.2 (11.4-15.2) 17.1 (15.9-18.3)a 29.5

Age, y

18-29 22.2 (20.7-23.7) 29.2 (27.5-31.0)a 31.5

30-44 13.5 (12.5-14.6) 17.9 (16.8-19.2)a 32.6

45-64 8.4 (7.6-9.3) 13.6 (12.7-14.6)a 61.9

≥65 3.2 (2.6-4.0) 5.6 (4.8-6.6)a 75.0

Marital status

Married or cohabiting 9.1 (8.5-9.8) 13.2 (12.4-14.1)a 45.1

Widowed, divorced, or separated 14.2 (12.9-15.7) 15.8 (14.7-17.1) 11.3

Never married 22.6 (20.9-24.4) 29.2 (27.6-30.9)a 29.2

Educational level

Less than high school 15.2 (13.4-17.2) 18.7 (16.7-20.9)a 23.0

High school 13.6 (12.4-14.8) 19.2 (18.0-20.5)a 41.2

Some college or higher 12.2 (11.5-13.0) 16.7 (15.8-17.6)a 36.9

Family income, $

0-19 999 16.7 (15.2-18.3) 22.8 (21.2-24.4)a 36.5

20 000-34 999 14.7 (13.5-15.9) 17.9 (16.6-19.3)a 21.8

35 000-69 999 11.8 (11.0-12.8) 16.7 (15.7-17.8)a 41.5

≥70 000 10.6 (9.7-11.5) 14.8 (13.8-15.8)a 39.6

Poverty threshold, %

<100 17.9 (16.1-20.0) 22.2 (20.5-24.0)a 24.0

100-200 13.9 (12.6-15.4) 18.0 (16.9-19.2)a 29.5

>200 11.9 (11.3-12.6) 16.0 (15.2-16.9)a 34.5

Urbanicity

Urban 12.5 (11.7-13.2) 18.1 (17.3-19.0)a 44.8

Rural 15.1 (13.7-16.6) 15.0 (13.5-16.7) -0.7

Region

Northeast 11.0 (9.7-12.4) 15.4 (14.3-16.6)a 40.0

Midwest 15.1 (13.4-17.0) 19.3 (17.3-21.5)a 27.8

South 12.3 (11.3-13.4) 16.9 (15.7-18.2)a 37.4

West 13.2 (12.0-14.6) 18.3 (16.6-20.1)a 38.6

Abbreviation: NESARC, National
Epidemiologic Survey on Alcohol and
Related Conditions.
a P < .05 for 2001-2002 compared with 2012-2013. Prevalence of Alcohol Use, High-Risk Drinking, and DSM-IV Alcohol Use Disorder Original Investigation Research

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AUD103,104 among women, results that were consistent with
substantial increases in these patterns of alcohol use among
married individuals and those residing in urban areas found
in this study. A narrowing of the gender gap in high-risk drink-
ing and AUD may portend substantial future increases in the
types of alcohol-related morbidity and mortality to which
women are more vulnerable, especially breast cancer105,106 and
liver cirrhosis,17,18,107 as well as increases in fetal alcohol spec-
trum disorder and exposure to violence.108 Women are also

more likely than men to take prescription drugs109 that can in-
crease the risk of severe adverse reactions when combined with
alcohol.

Older adults have had consistently lower rates than oth-
ers of alcohol use, high-risk drinking, and AUD over the past
40 years.32,40,45 However, between 2001-2002 and 2012-
2013, increases in alcohol use (22.4%), high-risk drinking
(65.2%), and AUD (106.7%) among older adults were substan-
tial and unprecedented relative to earlier surveys.73 Older

Table 5. Prevalence of and Percent Change in 12-Month DSM-IV Alcohol Use Disorder Among 12-Month
High-Risk Drinkers by Sociodemographic Characteristics, 2001-2002 and 2012-2013

Sociodemographic Characteristic
% (95% CI)
% Change
NESARC 2001-2002
(n = 43 093)
NESARC-III 2012-2013
(n = 36 309)

Total 46.5 (44.3-48.7) 54.5 (52.7-56.4)a 17.2

Sex

Men 50.7 (47.9-53.4) 57.4 (55.0-59.8)a 13.2

Women 36.9 (33.4-40.5) 49.7 (46.8-52.6)a 34.7

Race/ethnicity

White 47.3 (44.5-50.0) 56.6 (54.1-59.0)a 19.7

Black 40.4 (35.8-45.2) 50.8 (46.9-54.6)a 25.7

Native American 63.1 (51.0-73.8) 55.2 (41.8-67.9) −12.5

Asian or Pacific Islander 52.5 (38.5-66.2) 55.0 (45.8-64.0) 4.8

Hispanic 42.3 (37.4-47.4) 49.4 (46.0-52.9)a 16.8

Age, y

18-29 56.6 (53.0-60.2) 64.6 (61.0-68.0)a 14.1

30-44 45.0 (41.6-48.4) 52.3 (49.2-55.4)a 16.2

45-64 37.1 (33.0-41.3) 50.0 (47.0-53.0)a 34.8

≥65 19.8 (13.7-27.8) 31.3 (24.8-38.7)a 58.1

Marital status

Married or cohabiting 38.1 (35.1-41.2) 48.6 (45.7-51.5)a 27.6

Widowed, divorced, or separated 50.8 (46.0-55.6) 53.4 (50.0-56.8) 5.1

Never married 55.0 (51.4-58.5) 62.5 (58.9-66.0)a 13.6

Educational level

Less than high school 47.2 (42.0-52.4) 51.4 (46.7-56.1) 8.9

High school 46.6 (42.7-50.5) 55.7 (52.6-58.8)a 19.5

Some college or higher 46.3 (43.7-48.9) 54.6 (52.0-57.3)a 17.9

Family income, $

0-19 999 49.3 (45.4-53.2) 58.8 (55.2-62.4)a 19.3

20 000-34 999 49.6 (45.2-53.9) 55.7 (51.9-59.4)a 12.3

35 000-69 999 43.4 (39.7-47.1) 52.7 (49.4-56.0)a 21.4

≥70 000 44.4 (40.0-48.9) 51.2 (47.3-55.1)a 15.3

Poverty threshold, %

<100 48.7 (43.4-54.0) 58.2 (54.3-62.0)a 19.5

100-200 46.2 (41.7-50.8) 55.2 (51.4-59.0)a 19.5

>200 46.0 (43.5-48.6) 52.9 (50.4-55.4)a 15.0

Urbanicity

Urban 45.5 (43.0-48.0) 55.1 (53.0-57.1)a 21.1

Rural 50.7 (46.2-55.1) 52.2 (47.0-57.4) 3.0

Region

Northeast 46.4 (42.2-50.6) 51.7 (47.6-55.8) 11.4

Midwest 48.6 (43.4-53.9) 54.4 (49.9-58.9) 11.9

South 44.9 (41.3-48.6) 53.8 (50.9-56.7)a 19.8

West 46.4 (41.8-51.2) 58.1 (54.4-61.8)a 25.2

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adults are at higher risk for disability, morbidity, and mortal-
ity from many alcohol-related chronic diseases110,111 that have
increased over the past 15 years.86,87 Older adults are at par-
ticular risk for falls and injuries,112 and the unintentional in-
jury death rate,113 ED-treated falls,114 hospitalized fall rates,115

and fall-related traumatic brain injury deaths116 have risen sig-
nificantly over the past decade. Alcohol-interactive prescrip-
tion medicine use is highly prevalent among older adults,117,118

especially among current drinkers,119 and recent trend data sug-
gest that ED visits for adverse drug reactions involving alco-
hol use are on the rise.120 Even if the rates among older adults
remain stable, the projected increase in the size of this seg-
ment of the population (from 40 million in 2010 to 80 million
in 2030)121 could produce a substantial increase in the abso-
lute number of older adults with high-risk drinking and AUD,
w i t h c o n c o m i t a n t i n c r e a s e s i n a l c o h o l – r e l a t e d
multimorbidities.122

Between 2001-2002 and 2012-2013, increases in alcohol use,
high-risk drinking, and AUD were generally much greater among
minorities than among white individuals, results that are con-
sistent with substantial increases among individuals with the
lowest educational levels and family incomes seen in this study.
Wealth inequality between minorities and whites has wid-
ened during and after the 2008 recession,123,124 possibly lead-
ing to increased stress and demoralization. Adversities that dis-
proportionately affect racial/ethnic minorities include family
income and educational disparities, unemployment, residen-
tial segregation, discrimination, decreased access to health care,
and increased stigma associated with drinking.125-127 These dis-
parities may have accumulated over recent years, leading to in-
creased negative coping behaviors, such as high-risk drinking
and the development of AUD.125-131 Reasons for the widening
of the racial/ethnic gap in alcohol use, high-risk drinking, and
AUD are complex, historically rooted in racial/ethnic discrimi-
nation and persistent socioeconomic disadvantage both at the
individual and community levels.132-137 Future research is war-
ranted to understand the interplay of socioeconomic, psycho-
social, cultural, and biological factors that have contributed to
the widening of the racial/ethnic gap in alcohol use, high-risk
drinking, and AUD in recent years, with particular attention to
the development of subracial/subethnic prevention and inter-
vention strategies.

Limitations and Strengths
Limitations of this study are noted. NESARC and NESARC-III
lacked biological testing for substance use. Like other na-
tional surveys,53,54 some population segments were not cov-
ered in either survey (eg, the homeless and those who are in-
carcerated), potentially leading to underestimation of alcohol

use, high-risk drinking, and DSM-IV AUD. AUDADIS interview-
ers were not clinicians, but a NESARC-III validation substudy
comparing AUDADIS and clinician diagnoses of 12-month AUD
showed similar prevalence and good concordance.68 The NE-
SARC-III response rate was acceptable (60.1%) but was lower
than that of NESARC (81.0%). Weighting that compensated for
nonresponse facilitated comparisons between the surveys.51,52

The validity of increases shown between NESARC and NESARC-
III is supported by their coherence with the other studies noted
above showing increases in alcohol-related indicators over the
same period.

These limitations are balanced by the numerous strengths
of the Wave 1 NESARC and NESARC-III, including their large
sample sizes and detailed measures of alcohol use, high-risk
drinking, and DSM-IV AUD that have been extensively tested
and validated,61-71 in addition to their rigorous epidemiologic
study methods. These 2 surveys are also unique in providing
a uniform source of alcohol information and AUD to examine
trends over time.

Conclusions
Between 2001-2002 and 2012-2013, an increase in alcohol use,
high-risk drinking, and AUD occurred in the total US popula-
tion and across almost all sociodemographic subgroups, es-
pecially women, older adults, racial/ethnic minorities, and the
socioeconomically disadvantaged. These increases consti-
tute a public health crisis that may have been overshadowed
by increases in much less prevalent substance use (mari-
juana, opiates, and heroin)138-140 during the same period. Treat-
ment rates for AUD remain low (<10%)141 despite the signifi- cant rise in high-risk drinking and AUD and the existence of a broad spectrum of evidence-based and effective behavioral and pharmacological approaches.142-152 The results of this study call for a broader effort to address the individual, biological, en- vironmental, and societal factors that influence high-risk drink- ing and AUD and their considerable consequences and eco- nomic costs to society ($250 billion)153 to improve the health, safety, and well-being of the nation. The development of pre- vention and intervention strategies both at the population level and those targeted at high-risk subgroups of the population identified in this study154-159 will be paramount to achieving this goal. Most important, the findings herein highlight the ur- gency of educating the public, policymakers, and health care professionals about high-risk drinking and AUD,160 destigma- tizing these conditions and encouraging those who cannot re- duce their alcohol consumption on their own, despite sub- stantial harm to themselves and others, to seek treatment.

ARTICLE INFORMATION

Accepted for Publication: May 28, 2017.

Published Online: August 9, 2017.
doi:10.1001/jamapsychiatry.2017.2161

Author Contributions: Dr Saha and Mr Pickering
had full access to all of the data in the study and
take responsibility for the integrity of the data and
the accuracy of the data analysis.

Study concept and design: All authors.
Acquisition, analysis, or interpretation of data: All
authors.
Drafting of the manuscript: Grant.
Critical revision of the manuscript for important
intellectual content: All authors.
Statistical analysis: All authors.
Obtained funding: Grant, Chou, Saha, Pickering,
Ruan, Huang, Jung, Zhang.

Administrative, technical, or material support:
Kerridge, Ruan, Huang, Zhang, Fan.

Conflict of Interest Disclosures: None reported.

Funding/Support: This study was sponsored by
the National Institute on Alcohol Abuse and
Alcoholism, with supplemental funding from the
National Institute on Drug Abuse, and by grant

Prevalence of Alcohol Use, High-Risk Drinking, and DSM-IV Alcohol Use Disorder Original Investigation Research

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K05AA014223 from the National Institutes of
Health (Dr Hasin).

Role of the Funder/Sponsor: The funding sources
had no role in the design and conduct of the study;
collection, management, analysis, and
interpretation of the data; preparation, review, or
approval of the manuscript; and decision to submit
the manuscript for publication.

Disclaimer: The views and opinions expressed in
this report are those of the authors and should not
be construed to represent the views of any of the
sponsoring organizations or agencies or the US
government.

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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
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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 development

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.

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it
te
d
di
se
as
es

ex
cl
u
di
n
g
H
IV

(3
9
3
)

A
5
0
–
A
5
8
,A

6
0
–
A
6
0
.9
,A

6
3
–
A
6
3
.8
,

B
6
3
,I
9
8
.0
,K

6
7
.0
–
K
6
7
.2
,M

0
3
.1
,

M
7
3
.0
–
M
7
3
.1
,N

7
0
–
N
7
1
.9
,

N
7
3
–
N
7
4
.8

C
au

sa
lit
y:
C
oo
k
&
C
la
rk
,2

0
0
5
[1
2
1
]

D
et
ri
m
en
ta
l
M
et
a-
an

al
ys
es
,C

R
A
ca
lc
u
la
ti
on

s:
th
e
be
h
av
io
u
ra
lc
au

sa
lp
at
h
w
ay

vi
a

al
co
h
ol
’s
im

pa
ct

on
de
ci
si
on

m
ak
in
g

sh
ou

ld
be

th
e
sa
m
e
[9
8
,9
9
],
so

w
e

su
gg
es
t
th
e
sa
m
e
A
A
Fs

as
fo
r
H
IV
/A

ID
S,
bu

t
w
it
h
ou

t
th
e
ef
fe
ct
of
al
co
h
ol
u
se

on
m
or
ta
lit
y
vi
a
m
ed
ic
at
io
n
n
on

-a
dh
er
en
ce

Lo
w
er

re
sp
ir
at
or
y

in
fe
ct
io
n
s:
pn

eu
m
on

ia
Lo
w
er

re
sp
ir
at
or
y

in
fe
ct
io
n
s
[3
2
2
]

A
4
8
.1
,A

7
0
,J
0
9
–
J1
5
.8
,J
1
6
–
J1
6
.9
,

J2
0
–
J2
1
.9
,P

2
3
.0
–
P
2
3
.4

C
au

sa
lit
y:
Sa
m
ok
h
va
lo
v
et
al
.,
2
0
1
0
[1
4
2
];
T
ra
ph

ag
en

et
al
.,
2
0
1
5
[3
5
6
],
fo
r

h
ea
vy

dr
in
ki
n
g
u
n

d
al
co
h
ol
u
se

di
so
rd
er

s:
Si
m
et

&
Si
ss
on

,2
0
1
5
[3
5
7
]

D
et
ri
m
en
ta
l
M
et
a-
an

al
ys
is

C
R
A
ca
lc
u
la
ti
on

s:
Sa
m
ok
h
va
lo
v
et
al
.,
2
0
1
0
[1
4
2
]

C
an

ce
rs

Li
p
an

d
or
al
ca
vi
ty

ca
n

ce
r

Li
p
an
d
or
al
ca
vi
ty

ca
n
ce
r

(4
4
4
)

C
0
–
C
0
8
.9
,D

0
0
.0
0
–
D
0
0
.0
7
,

D
1
0
.0
–
D
1
0
.5
,D

1
1
–
D
1
1
.9
,

D
3
7
.0
1
–
D
3
7
.0
4
,D

3
7
.0
9
c

C
au

sa
lit
y:
In
te
rn
at
io
n
al
A
ge
n
cy

fo
r
R
es
ea
rc
h
on

C
an

ce
r
(I
A
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
]

N
as
op
h
ar
yn

x
ca
n
ce
r

N
as
op
h
ar
yn
x
ca
n
ce
r

(4
4
7
)

C
1
1
–
C
1
1
.9
,D

0
0
.0
8
,D

1
0
.6
,D

3
7
.0
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
]
O
th
er

ph
ar
yn

x
ca
n
ce
r
O
th
er
ph
ar
yn
x
ca
n
ce
r

(4
5
0
)

C
0
9
–
C
1
0
.9
,C

1
2
–
C
1
3
.9
,D

1
0
.7

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

s
b

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
]

(C
on

ti
n
u
es
)

Alcohol and disease 973

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
es
op
h
ag
u
s
ca
n
ce
r

O
es
op
h
ag
ea
lc
an

ce
r

(4
1
1
)

C
1
5
–
C
1
5
.9
,D

0
0
.1
,D

1
3
.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
]

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

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

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

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

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

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

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

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
i

s
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

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

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

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.

to
ot
h
er

G
B
D
ca
te
go
ri
es
,

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

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

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

ly
m
et
a-

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

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

9
2
.8
–
K
9
2
.8
9

To
o
br
oa
d
a
ca
te
go
ry

fo
r
qu

an
ti
fy
in
g
th
e
im

pa
ct

of
al
co
h
ol
u
se
;t
h
er
e
ar
e
n
o

st
u
di
es

on
th
e
im

pa
ct
of
al
co
h
ol
u
se

on
th
is
sp
ec
ifi
c
gr
ou

p
of
di
se
as
es

M
ai
n
ly
de
tr
im
en
ta
l
O
th
er

di
se
as
e
ca
te
go
ri
es

co
n
si
de
re
d

P
so
ri
as
is

P
so
ri
as
is
(6
5
5
)

N
ot

a
ca
u
se

of
de
at
h
in

G
B
D
C
au

sa
lit
y:
Fa
rk
as

&
K
em

én
y,
2
0
1
0
[3
7
9
];
B
re
n
au

t
et
al
.,
2
0
1
3
[3
8
0
];
R
ic
h
ar
d

et
al
.,
2
0
1
3
[3
8
1
];
ev
en

th
ou

gh
al
co
h
ol
u
se

h
as
be
en
sh
ow

n
to

af
fe
ct
th
e

im
m
u
n
e
sy
st
em

in
ge
n
er
al
,t
h
e
co
n
cl
u
si
on

h
as
be
en

th
at
ca
u
sa
lit
y
fo
r
ps
or
ia
si
s

h
as
n
ot

ye
t
be
en

fu
lly

es
ta
bl
is
h
ed
(s
ee

al
so

[3
8
2
])
.M

os
t
st
u
di
es

ar
e
n
ot

ab
ou
t
al
co
h
ol
u
se

as
a
ri
sk

fa
ct
or

fo
r
ps
or
ia
si
s,
bu

t
ab
ou

t
co
m
or
bi
di
ty
of
ps
or
ia
si
s
an

d
al
co
h
ol
u
se

di
so
rd
er
s
an

d
in
cr
ea
se
d
ri
sk

of
m
or
ta
lit
y
[3
8
3
].
A
la
rg
e
co
h
or
t

st
u
dy

fo
u
n
d
h
ig
h
ex
ce
ss
m
or
ta
lit
y
of
pe
op
le
w
it
h
ps
or
ia
si
s
m
ai
n
ly
w
it
h

al
co
h
ol
-a
tt
ri
bu

ta
bl
e
ca
u
se

of
de
at
h
s
[3
8
4
]

D
et
ri
m
en
ta
l
M
et
a-
an

al
ys
is
:Z
h
u
et
al
.,
2
0
1
2
[3
8
2
]

C
R
A
ca
lc
u
la
ti
on
s:
n
ot
re
le
va
n
t,
as

ca
u
sa
lit
y
h
as

n
ot
be
en
es
ta
bl
is
h
ed

A
bo
rt
io
n

M
at
er
n
al
ab
or
ti
on

,
m
is
ca
rr
ia
ge
,a
n
d
ec
to
pi
c

pr
eg
n
an

cy
[3
7
1
]

N
9
6
,O

0
0
-O
0
7
.9

W
h
ile

th
er
e
ar
e
a
n
u
m
be
r
of
re
vi
ew

s,
n
o
qu

an
ti
ta
ti
ve

m
et
a-
an

al
ys
es
h
av
e
be
en

ca
rr
ie
d
ou

t
on
th
is
ca
te
go
ry
(s
ee

Su
pp
or
ti
n
g
in
fo
rm

at
io
n
,A
pp
en
di
x
S1
fo
r

de
ta
ils
)

D
et
ri
m
en
ta
l

P
re
te
rm

bi
rt
h

co
m
pl
ic
at
io
n
s

N
eo
n
at
al
pr
et
er
m

bi
rt
h

co
m
pl
ic
at
io
n
s
[3
8
1
]

P
0
1
.0
-P
0
1
.1
,P

0
7
-P
0
7
.3
9
,P

2
2
-P
2
2
.9
,

P
2
5
-P
2
8
.9
,P

6
1
.2
,P

7
7
-P
7
7
.9

T
h
e
on

ly
m
et
a-
an

al
ys
es

on
pr
et
er
m

bi
rt
h
co
m
pl
ic
at
io
n
s

co
ve
re
d
lo
w

bi
rt
h

w
ei
gh

t,
pr
et
er
m

bi
rt
h
an

d
sm

al
lf
or

ge
st
at
io
n
al
ag
e
[3
8
5
],
an

d
th
e
re
la
ti
ve

ri
sk

fo
r
pr
et
er
m

bi
rt
h
w
as

n
ot

si
gn

ifi
ca
n
t

D
et
ri
m
en
ta
lf
or

so
m
e
co
m
pl
ic
at
io
n
s
C
R
A
ca
lc
u
la
ti
on
s:
n
ot
re
le
va
n
t,
as
re
la
ti
ve
ri
sk
is
n
ot
si
gn
ifi
ca
n
t

a
IC
D
co
de
s
fo
r
n
on

-f
at
al
di
se
as
e
ou

tc
om

es
ar
e
sl
ig
h
tl
y
di
ffe
re
n
ti
n
th
e
G
lo
ba
lB
u
rd
en

of
D
is
ea
se
(G
B
D
),
bu

tf
or

th
is
ov
er
vi
ew

ta
bl
e
w
e
di
d
n
ot
w
an

t
to
in
tr
od
u
ce

th
is
di
st
in
ct
io
n
(f
or

th
e
re
sp
ec
ti
ve

IC
D
co
de
s
by

th
e
G
B
D
,s
ee

[3
8
6
];

b
fo
r
de
fi
n
it
io
n
s,
se
e

[1
4
8
].

c T
h
e
re
la
ti
on

sh
ip
s
be
tw

ee
n
al
co
h
ol
u
se

an
d
th
e
re
sp
ec
ti
ve

ca
n
ce
r
si
te
s
ar
e
ba
se
d
on

st
u
di
es

w
it
h
IC
D
-1
0
C
co
de
s;
th
e
D
co
de
s
w
er
e
lis
te
d
on

ly
,a
s
w
e
w
an

te
d
to
sh
ow

co
m
pa
ti
bi
lit
y
w
it
h
th
e
G
B
D
;S
h
ad
ed

ro
w
s
in
di
ca
te
a
ca
u
sa
li
m
pa
ct
of

al
co
h
ol
,w

h
et
h
er

or
n
ot

th
e
re
la
ti
on

sh
ip
co
u
ld
be

qu
an

ti
fi
ed
.C

R
A
=

C
om

pa
ra
ti
ve

R
is
k
A
ss
es
sm

en
t;
H
IV

=
h
u
m
an

im
m
u
n
od
efi
ci
en
cy

vi
ru
s;
A
A
F
=
al
co
h
ol
-a
tt
ri
bu

ta
bl
e
fr
ac
ti
on

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

2
0
1
4
)

In
fe
ct
io
u
s
di
se
as
es

T
u
be
rc
u
lo
si
s

A
s
in

2
0
1
0

N
ew

m
et
a-
an
al
ys
is
N
ew
ri
sk
re
la
ti
on

su
gg
es
te
d
to

be
in
cl
u
de
d

fo
r
G
SR

A
H
2
0
1
7
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

e
(H
IV
/A

ID
S)
N
ew

da
ta

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

N
ew

m
et
h
od
ol
og
y

In
ci
de
n
ce

su
gg
es
te
d
to

be
ad
di
ti
on

al
ly
in
cl
u
de
d
fo
r
G
SR

A
H
2
0
1
7
O
th
er
se
xu
al
ly
tr
an

sm
it
te
d
di
se
as
es

N
ew
da
ta
on
es
ta
bl
is
h
in
g
ca
u
sa
lit
y
fo
r
in
ci
de
n
ce
N
ew
m
et
h
od
ol
og
y
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
A
H
2
0
1
7
Lo
w
er

re
sp
ir
at
or
y
in
fe
ct
io
n
s:

pn
eu
m
on

ia
A
s
in

2
0
1
0
A
s
in
2
0
1
0
A
s
in
G
SR

A
H
2
0
1
4

C
an
ce
rs

A
ll

ca
n
ce
r
ca
te
go
ri
es

ca
n
ce
r

N
o
ch
an

ge
in

ca
n
ce
r
ca
te
go
ri
es

w
it
h
su
ffi
ci
en
t
ev
id
en
ce

fo
r
ca
rc
in
og
en
ic
it
y
in
h
u
m
an

s;
tw

o
n
ew

ca
te
go
ri
es

w
h
er
e
ev
id
en
ce

in
di
ca
te
s
pr
ob
ab
ly
re
la
ti
on

sh
ip
s
(s
to
m
ac
h
,p
an

cr
ea
ti
c
ca
n
ce
r)

N
ew
m
et
a-
an
al
ys
es

D
is
cu
ss
io
n
w
h
et
h
er

n
ew

ly
es
ta
bl
is
h
ed

ca
te
go
ri
es

w
h
er
e
al
co
h
ol
h
as

be
en

ju
dg
ed

as
pr
ob
ab
ly
ca
rc
in
og
en
ic
in

h
u
m
an

s
sh
ou

ld
be

in
cl
u
de
d;
n
ew

ri
sk
re
la
ti
on

su
gg
es
te
d
fo
r
G
SR

A
H
2
0
1
7
D
ia
be
te
s
m
el
lit
u
s
D
ia
be
te
s
m
el
lit
u
s

D
is
cu
ss
io
n
ba
se
d
on

n
ew
re
vi
ew
s
an

d
m
et
a-
an

al
ys
es
N
ew
m
et
a-
an
al
ys
es

C
u
rr
en
tl
y
in

re
vi
si
on

to
ev
al
u
at
e
th
e
n
ew

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

fo
r
G
SR
A
H
2
0
1
7

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

D
is
cu
ss
io
n
ba
se
d
on
n
ew
re
vi
ew
s
an
d
m
et
a-
an
al
ys
es
N
ew
m
et
a-
an
al
ys
es
C
u
rr
en
tl
y
in
re
vi
si
on
to
ev
al
u
at
e
th
e
n
ew
ev
id
en
ce
;p
ro
ba
bl
y
to
o
la
te
fo
r
G
SR
A
H
2
0
1
7
U
n
ip
ol
ar
de
pr
es
si
ve
di
so
rd
er
s
N
ew
re
vi
ew

s
N
ew

m
et
a-
an
al
ys
es
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
A
H
2
0
1
7
Ep
ile
ps
y
N
ew
re
vi
ew

(c
on

du
ct
ed

in
2
0
1
0

t
n
ot

in
cl
u
de
d
in

[2
4
])

N
ew
m
et
a-
an
al
ys
is
(c
on
du
ct
ed
in
2
0
1
0
bu
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
H
yp
er
te
n
si
ve
h
ea
rt
di
se
as
e
N
ew
re
vi
ew

s
A
s
in

2
0
1
0
A
s
in
G
SR
A
H
2
0
1
4

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

N
ew
re
vi
ew
s
N
ew
m
et
a-
an
al
ys
es
N
ew
ri
sk
re
la
ti
on

s
su
gg
es
te
d
to

be
in
cl
u
de
d
fo
r
G
SR
A
H
2
0
1
7
C
ar
di
om
yo
pa
th
y
N
ew
re
vi
ew
s
N
ew
m
et
a-
an
al
ys
es
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
A
H
2
0
1
7
A
tr
ia
lfi
br
ill
at
io
n
an
d
fl
u
tt
er
A
s
in
2
0
1
0
A
s
in
2
0
1
0
A
s
in
G
SR
A
H
2
0
1
4
H
ea
rt
fa
ilu

re
A
s
in

2
0
1
0
A
s
in
2
0
1
0
N
ot

in
cl
u
de
d,
as

ca
se
s
ar
e
di
st
ri
bu

te
d
to

cl
ea
re
r
di
se
as
e
an

d
ca
u
se

of
de
at
h
ca
te
go
ri
es
Is
ch
ae
m
ic
st
ro
ke
N
ew
re
vi
ew
s
N
ew
m
et
a-
an
al
ys
es
N
ew
ri
sk
re
la
ti
on
s
su
gg
es
te
d
to
be
in
cl
u
de
d
fo
r
G
SR
A
H
2
0
1
7
H
ae
m
or
rh
ag
ic
an
d
ot
h
er
n
on

–
is
ch
ae
m
ic
st
ro
ke

A
s
in
2
0
1
0
N
ew
m
et
a-
an
al
ys
es
A
s
in
G
SR
A
H
2
0
1
4
G
as
tr
oi
n
te
st
in
al
di
se
as
es
C
ir
rh
os
is
of
th
e
liv
er
A
s
in
2
0
1
0
A
s
in
2
0
1
0
A
s
in
G
SR
A
H
2
0
1
4
(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
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

2
0
1
4
)
G
al
lb
la
dd
er

an
d
bi
le
du

ct
di
se
as
e
N
ot

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
A
H
2
0
1
7
P
an
cr
ea
ti
ti
s
A
s
in
2
0
1
0
N
ew
m
et
a-
an
al
ys
is
N
ew
ri
sk
re
la
ti
on
s
su
gg
es
te
d
to
be
in
cl
u
de
d
fo
r
G
SR
A
H
2
0
1
7
O
th
er
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

N
ei
th
er
in
G
SR
A
H
2
0
1
4
n
or
su
gg
es
te
d
fo
r
G
SR
A
H
2
0
1
7
A
bo
rt
io
n
N
ew
re
vi
ew

s
N
o
m
et
a-
an

al
ys
es
N
ei
th
er
in
G
SR
A
H
2
0
1
4
n
or
su
gg
es
te
d
fo
r
G
SR
A
H
2
0
1
7
P
re
te
rm
bi
rt
h
co
m
pl
ic
at
io
n
s
N
ew
re
vi
ew
N
ew
m
et
a-
an
al
ys
is

N
o
si
gn

ifi
ca
n
t
ef
fe
ct

of
al
co
h
ol
–
>

sh
ou

ld
n
ot

be
in
cl
u
de
d

T
h
e
la
st
re
vi
ew

in
th
is
se
ri
es

w
as

pu
bl
is
h
ed

in
2
0
1
0
[2
4
].
G
SR

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

iz
at
io
n
G
lo
ba
lS
ta
tu
s
R
ep
or
ts
on

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.

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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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