Summary Background The health risks associated with moderate alcohol consumption continue to be debated. Small amounts of alcohol might lower the risk of some health outcomes but increase the risk of others, suggesting that the overall risk depends, in part, on background disease rates, which vary by region, age, sex, and year. Methods For this analysis, we constructed burden-weighted dose–response relative risk curves across 22 health outcomes to estimate the theoretical minimum risk exposure level (TMREL) and non-drinker equivalence (NDE), the consumption level at which the health risk is equivalent to that of a non-drinker, using disease rates from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2020 for 21 regions, including 204 countries and territories, by 5-year age group, sex, and year for individuals aged 15–95 years and older from 1990 to 2020. Based on the NDE, we quantified the population consuming harmful amounts of alcohol. Findings The burden-weighted relative risk curves for alcohol use varied by region and age. Among individuals aged 15–39 years in 2020, the TMREL varied between 0 (95% uncertainty interval 0–0) and 0·603 (0·400–1·00) standard drinks per day, and the NDE varied between 0·002 (0–0) and 1·75 (0·698–4·30) standard drinks per day. Among individuals aged 40 years and older, the burden-weighted relative risk curve was J-shaped for all regions, with a 2020 TMREL that ranged from 0·114 (0–0·403) to 1·87 (0·500–3·30) standard drinks per day and an NDE that ranged between 0·193 (0–0·900) and 6·94 (3·40–8·30) standard drinks per day. Among individuals consuming harmful amounts of alcohol in 2020, 59·1% (54·3–65·4) were aged 15–39 years and 76·9% (73·0–81·3) were male. Interpretation There is strong evidence to support recommendations on alcohol consumption varying by age and location. Stronger interventions, particularly those tailored towards younger individuals, are needed to reduce the substantial global health loss attributable to alcohol. Funding Bill & Melinda Gates Foundation.
Introduction 1 Griswold MG
Fullman N
Hawley C
et al. Alcohol use and burden for 195 countries and territories, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016. , 2 Shield K
Manthey J
Rylett M
et al. National, regional, and global burdens of disease from 2000 to 2016 attributable to alcohol use: a comparative risk assessment study. , 3 Wood AM
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et al. Risk thresholds for alcohol consumption: combined analysis of individual-participant data for 599 912 current drinkers in 83 prospective studies. , 4 Rehm J
Gmel Sr, GE
Gmel G
et al. The relationship between different dimensions of alcohol use and the burden of disease-an update. , 5 Millwood IY
Walters RG
Mei XW
et al. Conventional and genetic evidence on alcohol and vascular disease aetiology: a prospective study of 500 000 men and women in China. , 6 van de Luitgaarden IAT
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Bouman EJ
et al. Alcohol consumption in relation to cardiovascular diseases and mortality: a systematic review of Mendelian randomization studies. 7 Liu Y
Nguyen N
Colditz GA Links between alcohol consumption and breast cancer: a look at the evidence. , 8 Imtiaz S
Shield KD
Roerecke M
Samokhvalov AV
Lönnroth K
Rehm J Alcohol consumption as a risk factor for tuberculosis: meta-analyses and burden of disease. , 9 Taylor B
Irving HM
Kanteres F
et al. The more you drink, the harder you fall: a systematic review and meta-analysis of how acute alcohol consumption and injury or collision risk increase together. , 10 Roerecke M
Vafaei A
Hasan OSM
et al. Alcohol consumption and risk of liver cirrhosis: a systematic review and meta-analysis. 11 Roerecke M
Rehm J Alcohol consumption, drinking patterns, and ischemic heart disease: a narrative review of meta-analyses and a systematic review and meta-analysis of the impact of heavy drinking occasions on risk for moderate drinkers. , 12 Pietraszek A
Gregersen S
Hermansen K Alcohol and type 2 diabetes. A review. , 13 Ding C
O'Neill D
Bell S
Stamatakis E
Britton A Association of alcohol consumption with morbidity and mortality in patients with cardiovascular disease: original data and meta-analysis of 48,423 men and women. 14 Sherk A
Gilmore W
Churchill S
Lensvelt E
Stockwell T
Chikritzhs T Implications of cardioprotective assumptions for national drinking guidelines and alcohol harm monitoring systems. , 15 Sherk A
Thomas G
Churchill S
Stockwell T Does drinking within low-risk guidelines prevent harm? Implications for high-income countries using the international model of alcohol harms and policies. , 16 WHO
Global status report on alcohol and health 2018. Alcohol use accounted for 1·78 million (95% uncertainty interval [UI] 1·39–2·27) deaths in 2020 and was the leading risk factor for mortality among males aged 15–49 years (Bryazka D, unpublished). The relationship between moderate alcohol use and health is complex, as shown in multiple previous studies.Alcohol consumption at any level is associated with health loss from several diseases, including liver cirrhosis, breast cancer, and tuberculosis, as well as injuries.At the same time, some studies have found that consumption of small amounts of alcohol lowers the risk of cardiovascular diseases and type 2 diabetes.As a corollary, the amount of alcohol that minimises health loss is likely to depend on the distribution of underlying causes of disease burden in a given population. Since this distribution varies widely by geography, age, sex, and time, the level of alcohol consumption associated with the lowest risk to health would depend on the age structure and disease composition of that population. 1 Griswold MG
Fullman N
Hawley C
et al. Alcohol use and burden for 195 countries and territories, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016. , 4 Rehm J
Gmel Sr, GE
Gmel G
et al. The relationship between different dimensions of alcohol use and the burden of disease-an update. , 17 Corrao G
Bagnardi V
Zambon A
La Vecchia C A meta-analysis of alcohol consumption and the risk of 15 diseases. , 18 Xi B
Veeranki SP
Zhao M
Ma C
Yan Y
Mi J Relationship of alcohol consumption to all-cause, cardiovascular, and cancer-related mortality in U.S. adults. , 19 Ma H
Li X
Zhou T
et al. Alcohol consumption levels as compared with drinking habits in predicting all-cause mortality and cause-specific mortality in current drinkers. 3 Wood AM
Kaptoge S
Butterworth AS
et al. Risk thresholds for alcohol consumption: combined analysis of individual-participant data for 599 912 current drinkers in 83 prospective studies. , 18 Xi B
Veeranki SP
Zhao M
Ma C
Yan Y
Mi J Relationship of alcohol consumption to all-cause, cardiovascular, and cancer-related mortality in U.S. adults. , 20 Patra J
Buckley C
Kerr WC
Brennan A
Purshouse RC
Rehm J Impact of body mass and alcohol consumption on all-cause and liver mortality in 240 000 adults in the United States. 1 Griswold MG
Fullman N
Hawley C
et al. Alcohol use and burden for 195 countries and territories, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016. , 21 Habtemichael LH
Djekic D
Rosengren AR
et al. Alcohol consumption in young men and risk of heart failure and all-cause mortality—a cohort study. 22 Fillmore KM
Stockwell T
Chikritzhs T
Bostrom A
Kerr W Moderate alcohol use and reduced mortality risk: systematic error in prospective studies and new hypotheses. , 23 Wallach JD
Serghiou S
Chu L
et al. Evaluation of confounding in epidemiologic studies assessing alcohol consumption on the risk of ischemic heart disease. Two quantities are crucially relevant when formulating effective, evidence-based guidelines and alcohol-control policies: the theoretical minimum risk exposure level (TMREL), which represents the level of consumption that minimises health loss from alcohol for a population, and the non-drinker equivalence (NDE) level, which measures the level of alcohol consumption at which the risk of health loss for a drinker is equivalent to that of a non-drinker. The majority of studies to date consider one or a small subset of health outcomes associated with alcohol consumption at a time, although several broader systematic meta-analyses have been done.Findings from these studies vary in their estimates of the TMREL. Several studies have found evidence of a J-shaped relationship between alcohol use and all-cause mortality.However, others have reported that the all-cause or attributable cause burden weighted TMREL of alcohol is zero standard drinks per day.Uncertainty about the effect of alcohol on all-cause health loss results from differences in the relative disease composition between studies, conflicting studies on individual health outcomes, differences in study covariates and methods, estimation of drinking patterns, as well as issues relating to selection bias. Research in context Evidence before this study The risks of moderate alcohol use on health outcomes have been widely studied and debated for many years. Studies have considered the health impacts associated with alcohol consumption through a variety of approaches, ranging from exploring the effects on a single disease, to considering multiple health outcomes, to using all-cause mortality as an outcome. Several systematic reviews have also been published on this topic, and in recent years several publications have used Mendelian randomisation to explore the association between alcohol use and health outcomes. Overall, the findings have varied, which partly contributes to this topic being controversial and a subject of debate. Several studies have found evidence of a J-shaped relationship between alcohol use and all-cause mortality or burden; in other words, at low levels of consumption, alcohol lowers the risk of all-cause mortality, whereas above some threshold it increases the risk. However, other studies, including a publication by the GBD 2016 Alcohol Collaborators in The Lancet in 2018, have reported that the level of alcohol consumption that minimises health loss is zero standard drinks per day. The apparent contradiction in findings across existing studies highlights the significance of continuing to study this topic and updating the evidence base as more information becomes available. Importantly, few previous studies analysing the effects of alcohol consumption on all-cause mortality have considered how the relationship between alcohol use and health is contingent on background rates of disease. We did a systematic review of the literature in which we searched PubMed and previous published meta-analyses using search terms such as “alcohol” and “drinking behavior”, terms concerning study outcomes such as “risk”, “odds ratio”, and “hazard ratio”, and terms concerning the specific causes included in the study, such as “ischemic heart disease” or “tuberculosis”. We searched for studies published up to Dec 31, 2019; the search was limited to English language publications. Added value of this study In this systematic analysis for the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2020, we estimated levels of alcohol consumption that minimise health loss using updated systematic reviews and meta-regressions, building on results from GBD 2016 and incorporating region-specific background rates of diseases and injuries within our assessment. To the best of our knowledge, this is the first study to consider the implications of background rates of disease on levels of alcohol consumption that minimise health loss. We updated the previously published systematic review and meta-regressions to consider all published studies through to December, 2019, reporting on the association between alcohol and the six alcohol-attributable health outcomes accounting for the highest number of global disability-adjusted life-years. We found insufficient evidence for an association between alcohol use and one of these outcomes and subsequently omitted it from further analysis. This analysis has yielded updated relationships on the relative risk of mortality for five causes, at various levels of alcohol consumption, which we combined with relative risk estimates from GBD 2016 for an additional 17 outcomes. We used this information, along with information on the burden of disease from these 22 diseases and injuries, to estimate the level of alcohol consumption that minimises health loss separately for each age group, sex, year, and region. In addition to estimating the level of consumption that is associated with minimising health loss, known as the theoretical minimum risk exposure level (TMREL), we also estimated the level of alcohol consumption at which the risk to health for a drinker is equivalent to that of a non-drinker—a quantity we refer to as the non-drinker equivalence. Implications of all the available evidence Our results are consistent with previous findings at the global level, and at the same time the more nuanced analysis done in this study strongly suggests that statements, guidelines, and recommendations on the optimal level of alcohol consumption need to take into consideration the background rates of diseases and injuries for each population. We provide clear evidence that the level of alcohol consumption that minimises health loss varies significantly across populations and remains zero or very close to zero for several population groups, particularly young adults. At the same time, small amounts of alcohol consumption are associated with improved health outcomes in populations that predominantly face a high burden of cardiovascular diseases, particularly older adults in many world regions. Given these findings, we recommend a modification of existing policy guidelines to focus on emphasising differential optimal consumption levels by age, rather than the current practice of recommending different consumption levels by sex. This study highlights the importance of prioritising interventions targeted at minimising alcohol consumption among young adults. 24 Dietary Guidelines Advisory Committee
Scientific Report of the 2020 Dietary Guidelines Advisory Committee: Advisory Report to the Secretary of Agriculture and Secretary of Health and Human Services. , 25 UK Department of Health
UK Chief Medical Officers' Alcohol Guidelines Review. Summary of the proposed new guidelines. , 26 Santé Publique France
Alcool et santé: améliorer les connaissances et réduire les risques. , 27 NHMRC
Australian guidelines to reduce health risks from drinking alcohol. 28 Stockwell T
Zhao J
Panwar S
Roemer A
Naimi T
Chikritzhs T Do “moderate” drinkers have reduced mortality risk? A systematic review and meta-analysis of alcohol consumption and all-cause mortality. 1 Griswold MG
Fullman N
Hawley C
et al. Alcohol use and burden for 195 countries and territories, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016. , 29 Rehm J Why the relationship between level of alcohol-use and all-cause mortality cannot be addressed with meta-analyses of cohort studies. Importantly, no study to date has examined the variation in the theoretical minimum risk of alcohol consumption by geography, age, sex, and time, conditioned on background rates of disease. National dietary guidelines on low-risk drinking, such as those in the USA, UK, France, and Australia, base recommendations on studies of the risk of alcohol use on all-cause mortality and some cause-specific outcomes.This complicates interpretation of the risk of alcohol use on mortality, given three aspects of all-cause mortality. First, causal pathways between alcohol use and cause-specific outcomes can differ, creating multiple confounding structures that are not readily adjustable when embedded within models analysing the effects of alcohol use on all-cause mortality.Second, all-cause mortality includes non-causally related outcomes, further increasing the threat to internal validity for evidence produced from analysing the effects of alcohol use on all-cause mortality. Third, and most importantly for the present study, the composition of causes within all-cause mortality can differ substantially between populations, changing the proportional risk of mortality due to alcohol use across these populations.In tandem, these features limit the applicability of determining minimum risk exposures on the basis of observational data on alcohol use and all-cause mortality. In this study, we used the distribution of causes of disability-adjusted life-years (DALYs) in each population, along with alcohol consumption patterns from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2020, to estimate the TMREL and NDE for each region, age group, sex, and year from 1990 to 2020. Using these estimates, we quantified the proportion of the population consuming alcohol in amounts exceeding these thresholds by location, age, sex, and year, serving as a guide for targeting alcohol control efforts. 30 Institute for Health Metrics and Evaluation
Protocol for the global burden of diseases, injuries, and risk factors study (GBD). This manuscript was produced as part of the GBD Collaborator Network and in accordance with the GBD Protocol.
Methods Overview 31 Murray CJL
Aravkin AY
Zheng P
et al. Global burden of 87 risk factors in 204 countries and territories, 1990–2019: a systematic analysis for the Global Burden of Disease Study 2019. 32 Stevens GA
Alkema L
Black RE
et al. Guidelines for Accurate and Transparent Health Estimates Reporting: the GATHER statement. GBD is the most comprehensive effort to date to understand the changing health challenges around the world.In the most recent revision, GBD 2020, estimates were produced for the mortality and health burden from 287 causes of death, 370 diseases and injuries, and 88 risk factors in 204 countries and territories by 5-year age groups and sex from 1990 to 2020. As part of GBD 2020, we estimated the TMREL and NDE of alcohol consumption for 21 regions by 5-year age group, sex, and year for individuals aged 15–95 years and older from 1990 to 2020 (Bryazka D, unpublished). Using the comparative risk assessment framework, we also quantified the population consuming alcohol in harmful amounts, by 5-year age group, country or territory, sex, and year. In the following sections, we provide an overview of our methods. This study adheres to the Guidelines for Accurate and Transparent Health Estimates Reporting (GATHER) statement. Estimating dose–response relative risks 1 Griswold MG
Fullman N
Hawley C
et al. Alcohol use and burden for 195 countries and territories, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016. 1 Griswold MG
Fullman N
Hawley C
et al. Alcohol use and burden for 195 countries and territories, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016. , 33 Flaxman AD An integrative metaregression framework for descriptive epidemiology. As part of GBD, a previous systematic literature review and meta-analysis was published in 2018 that included 592 cohort and case-control studies across 23 outcomes associated with alcohol use.These outcomes included ischaemic stroke, intracerebral haemorrhage, ischaemic heart disease, hypertensive heart disease, atrial fibrillation and flutter, lip and oral cavity cancer, nasopharynx cancer, other pharynx cancer, oesophageal cancer, larynx cancer, colon and rectum cancer, breast cancer, liver cancer, type 2 diabetes, cirrhosis and other chronic diseases of the liver, pancreatitis, idiopathic epilepsy, tuberculosis, lower respiratory infection, transport injuries, unintentional injuries, self-harm, and interpersonal violence. As part of this previous meta-analysis, dose–response relative risk curves for each of these outcomes were estimated through use of a Bayesian meta-regression tool, DisMod ODE. For GBD 2020, we updated this review for the six alcohol-attributable outcomes that accounted for the greatest number of global DALYs: ischaemic heart disease, ischaemic stroke, intracerebral haemorrhage, type 2 diabetes, tuberculosis, and lower respiratory infection. Through the update, we included 71 additional studies. After evaluating all available evidence, we found insufficient evidence for a relationship between alcohol use and lower respiratory infection. Based on these results, we removed this as a risk–outcome pair for GBD 2020 and from this analysis, resulting in 22 remaining relative risk curves. Further details of the systematic review, including search strings, inclusion criteria, Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flow diagrams, and relative risk curves are provided in appendix 1 (pp 18–47 ). 1 Griswold MG
Fullman N
Hawley C
et al. Alcohol use and burden for 195 countries and territories, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016. Using the updated data for these five outcomes, we revised the relative risk curves associated with each outcome using the meta-regression Bayesian, regularised, trimmed (MR-BRT) meta-regression tool. MR-BRT is a tool that is well suited to the complex task of estimating the dose–response risk association between alcohol and health because it is does not enforce a log-linear functional form, instead parameterising the log relative risk as a B-spline (Zheng P, Institute for Health Metrics and Evaluation, personal communication). It uses an ensemble approach for knot selection of splines based on level of exposure, and incorporates unexplained between-study heterogeneity into the uncertainty of the relative risk estimates. To adjust for aspects of study design that contribute to bias in relative risks, we included covariates for study reference group, adjustment for sick quitter bias, sex, age, population representativeness, outcome reporting method, exposure measurement timing, geographical representativeness, outcome measure (incidence versus mortality), and adjustment for confounders in risk estimation. The MR-BRT tool uses a generalised Lasso approach to select the most relevant bias covariates to adjust for in the final model. A full list of the confounders tested and included in each of these five models is summarised on in appendix 1 (p 15 ). Consistent with the previous systematic review,we utilised a reference group of non-drinkers. We estimated parameter uncertainty using 1000 draws from the posterior distribution, sampled at 1 g intervals of pure alcohol consumption between 0 g and 100 g per day. Further details of the meta-regression approach are available in appendix 1 (pp 14–16 ). Estimating TMREL and NDE Figure 1 Exemplifying a weighted all-attributable cause alcohol relative risk curve Show full caption Points mark the theoretical minimum risk exposure level and non-drinker equivalence level. The shaded areas denote consumption levels with a lower risk (green) and greater risk (purple), compared to no consumption. The solid line indicates the mean aggregate relative risk estimate, whereas the shaded area reflects the 95% uncertainty interval of the aggregate relative risk estimate. One standard drink is equivalent to 10 g of pure ethanol. The TMREL and NDE are based on aggregate, burden-weighted relative risk curves across health outcomes associated with alcohol use. Burden was quantified with DALY rates for each region, age, sex, and year obtained from GBD 2020 (Bryazka D, unpublished). DALYs are the sum of years of life lost (capturing the effect of premature mortality) and years lived with disability (capturing the effect of morbidity). For each region, age, sex, and year, we produced all-attributable cause relative risk curves as a weighted average of cause-specific relative risk curves, with weights based on the share of the overall DALY rates from each cause. The step-by-step process and formula for computing the weighted all-attributable cause curves are provided in appendix 1 (p 16 ). Using these estimates, we computed the TMREL and NDE by region, age, sex, and year. Uncertainty in the relative risk curve, based on 1000 draws of each cause-specific relative risk curve and 1000 draws of DALY rates used for weighting, was propagated to the estimates of TMREL and NDE. All estimates are presented to three significant figures. An example of a weighted all-attributable cause alcohol relative risk curve, for all 22 alcohol associated causes combined, is shown in figure 1 Since alcohol use contributes to the DALY rates that are used as weighting factors when constructing the TMREL and NDE, we did a sensitivity analysis that utilised risk-deleted DALY rates as alternative weights. We generated risk-deleted DALY rates by multiplying the DALY rate of each cause by the complement of the cause-specific population-attributable fraction due to alcohol (Bryazka D, unpublished). Additionally, our weighted attributable-cause relative risk curves were based on only 22 of 24 health outcomes since no relative risk curves could be computed for alcohol use disorder or alcoholic cardiomyopathy due to the paucity of studies on dose–response relative risks. To assess whether inclusion of these two outcomes could potentially affect the TMREL and NDE levels, we did a second sensitivity analysis in which we generated conservative hypothetical relative risk functions for alcohol use disorder and alcoholic cardiomyopathy and re-computed TMREL and NDE levels that reflect all 24 alcohol-associated outcomes. Additional details of the sensitivity analyses are presented in the appendix (p 17) Estimating prevalence of alcohol use and alcohol consumption 1 Griswold MG
Fullman N
Hawley C
et al. Alcohol use and burden for 195 countries and territories, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016. 1 Griswold MG
Fullman N
Hawley C
et al. Alcohol use and burden for 195 countries and territories, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016. To estimate the proportion of the population consuming alcohol in excess of the NDE, estimates of alcohol consumption in units of grams of pure ethanol consumed per day, on average, by current drinkers for 204 countries and territories, by age, sex, and year, were obtained from GBD 2020 (Bryazka D, unpublished). Briefly, this process combines supply-side data, household survey data, and administrative data, which allows us to adjust for under-reporting due to self-report bias in surveys, account for unrecorded alcohol consumption, and adjust for consumption among tourists. Current drinkers were defined as individuals who had consumed at least one standard drink in the past 12 months. To facilitate interpretation, we report estimates in terms of standard drinks per day, where one standard drink is defined as 10 g of pure ethanol, consistent with previous GBD publications (Bryazka D, unpublished).Further details on estimation of the prevalence of alcohol use and alcohol consumption have been published previously. Role of the funding source The funder of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report.
Results Figure 2 Relative proportions of DALYs for causes associated with alcohol use, by GBD super-region, age group, and sex, in 2020 Show full caption The proportions represent the weights associated with each cause-specific relative risk curve when constructing each all-cause relative risk curve. The green shades signify causes with a lower risk at low levels of consumption, compared to no consumption. The red and purple shades signify causes with an entirely harmful effect at all levels of consumption. The black line separates causes for which moderate alcohol use lowers risk from causes with an entirely harmful effect. Diabetes includes only type 2 diabetes. Cancers include lip and oral cavity cancer, nasopharynx cancer, other pharynx cancer, oesophageal cancer, larynx cancer, colon and rectum cancer, breast cancer, and liver cancer. Cirrhosis includes cirrhosis and other chronic diseases of the liver. Infectious disease includes tuberculosis. Injuries includes transport injuries, unintentional injuries, self-harm, and interpersonal violence. Other causes include pancreatitis, idiopathic epilepsy, hypertensive heart disease, and atrial fibrillation and flutter. DALY=disability-adjusted life-year. Figure 3 Mean theoretical minimum risk exposure levels (A) and non-drinker equivalence levels (B), in units of standard drinks per day, by region, age group, and sex, in 2020 Show full caption One standard drink is equivalent to 10 g of pure ethanol. The distribution of DALYs arising from outcomes associated with alcohol by GBD super-region, age, and sex for 2020 are shown in figure 2 . The TMREL and NDE by region, age, and sex for 2020 are shown in figure 3 . Overall, we found that the TMREL remained low regardless of geography, age, sex, or time, varying between 0 (95% UI 0–0) and 1·87 (0·500–3·30) standard drinks per day. As a result of the differences in the cause distributions across world regions, both the TMREL and NDE varied by region. The TMREL and NDE did not vary significantly by sex or year. There was significant variation in the TMREL and the NDE across ages, with younger age groups having much lower TMREL and NDE levels than older adults. In 2020, the TMREL varied between 0 (0–0) and 0·603 (0·400–1·00) standard drinks per day among individuals aged 15–39 years and between 0·114 (0–0·403) and 1·87 (0·500–3·30) standard drinks per day among individuals aged 40 years and older. The NDE varied between 0·002 (0–0) and 1·75 (0·698–4·30) standard drinks per day among individuals aged 15–39 years and between 0·193 (0–0·900) and 6·94 (3·40–8·30) standard drinks per day among individuals aged 40 years and older. This result was mainly driven by differences in the major causes of death and disease burden across ages, as seen in figure 2 . Overall, we did not observe any significant differences in the TMREL and NDE between males and females in any age group. In all super-regions, among individuals aged 15–39 years, injuries accounted for the majority of alcohol-related DALYs in 2020. Globally, in this age range, all injuries accounted for 66·3% (95% UI 65·1–67·5) of alcohol-related DALYs for males and 47·9% (46·0–49·8) of alcohol-related DALYs for females; transport injuries comprised 25·9% (25·0–27·0) of alcohol-related DALYs among males and 12·7% (12·0–13·4) among females, self-harm comprised 11·7% (10·1–13·3) of alcohol-related DALYs among males and 12·3% (10·8–13·8) among females, and interpersonal violence comprised 12·4% (11·8–13·0) of alcohol-related DALYs among males and 6·70% (5·90–7·69) among females. The TMREL among males aged 15–39 years in 2020 was 0·136 (0–0·400) standard drinks per day. Among females aged 15–39 years in 2020, the TMREL was 0·273 (0–0·500) standard drinks per day. The NDE was 0·249 (0–1·00) standard drinks per day among males and 0·546 (0–1·30) standard drinks per day among females. The differences in the TMREL and the NDE between females and males were not statistically significant. In individuals aged 40–64 years, the health outcomes contributing to the alcohol-related burden shifted to chronic health conditions, including cardiovascular disease and cancer. In this population, ischaemic heart disease comprised 24·1% (95% UI 23·0–25·3) of alcohol-related DALYs among males and 19·5% (18·0–21·0) among females, and intracerebral haemorrhage comprised 10·3% (9·61–10·9) of alcohol-related DALYs among males and 11·7% (10·7–12·8) among females, whereas injuries, such as transport or unintentional injuries, remained significant sources of burden, comprising 23·0% (21·7–24·4) of alcohol-related DALYs among males and 16·7% (15·3–18·3) of alcohol-related DALYs among females. Health outcomes for which moderate alcohol use is associated with a lower risk constituted an increasing portion of the cause distribution in this age group, resulting in a higher TMREL and NDE than in individuals aged 15–39 years. The global TMREL among individuals aged 40–64 years in 2020 was 0·527 (0·400–1·00) standard drinks per day among males and 0·562 (0·400–0·800) standard drinks per day among females. The global NDE in 2020 was 1·69 (0·800–3·20) standard drinks per day among males and 1·82 (1·00–3·10) standard drinks per day among females. As in the younger age group, the differences in the TMREL and the NDE between females and males aged 40–64 years were not statistically significant. Among individuals aged 65 years and older, the major causes of disease burden were cardiovascular diseases. In 2020, ischaemic heart disease was responsible for 31·5% (95% UI 30·3–32·7) of all alcohol-related DALYs among males and 29·7% (28·2–31·2) among females, intracerebral haemorrhage was responsible for 11·6% (10·9–12·4) of all alcohol-related DALYs among males and 10·9% (10·1–11·8) among females, and ischaemic stroke was responsible for 14·2% (13·5–14·9) of all alcohol-related DALYs among males and 16·0% (15·2–16·7) among females. As a result, in this population the TMREL was higher than in the younger age groups and was estimated to be 0·636 (0·500–1·00) standard drinks per day among males and 0·656 (0·500–1·00) standard drinks per day among females, whereas the NDE was estimated to be 3·19 (1·50–5·20) standard drinks per day among males and 3·51 (1·70–5·50) standard drinks per day among females. The differences in the TMREL and the NDE between males and females aged 65 years and older were not significant. The distribution of the causes of disease burden for a given age group varied substantially across regions, resulting in regional variations in TMRELs and NDEs, particularly in individuals aged 40 years and older. For example, among individuals aged 55–59 years in north Africa and the Middle East, 30·7% (95% UI 27·3–34·6) of alcohol-related DALYs were due to cardiovascular disease, 12·6% (11·0–14·3) were due to cancers, and 0·37% (0·27–0·55) were due to tuberculosis. By contrast, in this same age group in central sub-Saharan Africa, 20·1% (17·2–23·8) of alcohol-related DALYs were due to cardiovascular disease, 9·80% (8·31–11·7) were due to cancers, and 10·1% (6·03–14·1) were due to tuberculosis. As a result, the TMRELs for this age group were 0·876 (0·500–2·00) standard drinks per day in north Africa and the Middle East and 0·596 (0·300–2·00) standard drinks per day in central sub-Saharan Africa. The NDEs also varied, with an NDE of 3·89 (1·50–5·90) standard drinks per day in north Africa and the Middle East and 1·53 (0·600–4·70) standard drinks per day in central sub-Saharan Africa. The TMRELs and NDEs for each region by age and sex for 1990, 2000, 2010, and 2020 are shown in appendix 2 (pp 3–31 ). Figure 4 Proportion of the population consuming harmful amounts of alcohol, defined as consumption in excess of the mean non-drinker equivalence level, by sex and age group, in 2020 The distribution of the major causes of DALYs varied slightly between sexes, with injuries making up a larger share of distributions for males than for females. This resulted in mean TMRELs and NDEs that were larger among females compared to males of the same region, age, and year. When taking uncertainty into account, these differences were not significant. However, a larger proportion of males compared to females consume alcohol, and their average level of consumption is also significantly higher. As a result, young males stood out as the group with the highest level of harmful alcohol consumption ( figure 4 ). Table 1 Number and proportion of population consuming in excess of the non-drinker equivalence, and percentage change since 1990 by country, age group, and sex, for 2020 Females Males Number (thousands) Proportion of population (%) Percentage change since 1990 (%) Number (thousands) Proportion of population (%) Percentage change since 1990 (%) Global 15–39 years 195 000 (124 000 to 273 000) 13·2% (8·34 to 18·4) −3·89% (−6·02 to 2·53) 595 000 (489 000 to 658 000) 39·0% (32·1 to 43·2) −4·62% (−6·27 to −2·64) 40–64 years 98 600 (60 200 to 137 000) 9·22% (5·63 to 12·8) −4·22% (−6·54 to −0·279) 363 000 (274 000 to 441 000) 34·0% (25·7 to 41·4) −2·55% (−5·65 to 3·30) ≥65 years 18 400 (8990 to 32 600) 4·49% (2·20 to 7·99) −1·96% (−3·63 to −0·448) 69 900 (47 000 to 98 500) 20·6% (13·8 to 29·0) −1·39% (−5·12 to 2·61) Central Asia 15–39 years 2200 (1320 to 3610) 11·9% (7·16 to 19·5) −2·93% (−6·61 to 0·104) 8420 (6710 to 9580) 44·5% (35·4 to 50·6) −4·11% (−9·08 to 0·506) 40–64 years 824 (494 to 1280) 6·52% (3·90 to 10·1) −0·985% (−3·34 to 0·956) 3740 (2720 to 4910) 32·2% (23·4 to 42·3) 3·29% (−2·37 to 9·14) ≥65 years 27·3 (8·21 to 91·9) 0·841% (0·253 to 2·83) −0·147% (−0·881 to 0·407) 263 (171 to 477) 12·0% (7·81 to 21·8) 2·42% (0·0992 to 4·88) Armenia 15–39 years 64·8 (30·9 to 126) 11·8% (5·63 to 23·1) 1·36% (−3·88 to 6·39) 325 (242 to 383) 59·3% (44·1 to 69·9) −5·32% (−13·7 to 2·88) 40–64 years 22·5 (9·50 to 44·6) 4·50% (1·90 to 8·89) 1·68% (−0·424 to 3·81) 137 (87·4 to 199) 31·9% (20·3 to 46·3) 7·96% (−0·927 to 16·6) ≥65 years 0·657 (0·0590 to 3·52) 0·285% (0·0256 to 1·53) 0·176% (0·00923 to 0·877) 13·9 (6·82 to 32·1) 8·84% (4·33 to 20·4) 3·63% (0·491 to 6·98) Azerbaijan 15–39 years 191 (124 to 278) 9·19% (5·97 to 13·4) −1·76% (−5·24 to 1·44) 890 (743 to 1010) 41·0% (34·2 to 46·6) 0·163% (−5·15 to 5·35) 40–64 years 77·9 (43·9 to 113) 4·75% (2·67 to 6·91) −0·919% (−3·33 to 1·22) 502 (357 to 624) 33·3% (23·7 to 41·3) 2·46% (−3·73 to 8·66) ≥65 years 2·92 (0·698 to 6·65) 0·806% (0·193 to 1·84) −0·0175% (−0·786 to 0·629) 45·1 (25·0 to 69·5) 16·8% (9·31 to 25·9) 1·92% (−3·77 to 6·91) Georgia 15–39 years 133 (55·0 to 270) 23·6% (9·76 to 48·0) 2·46% (−7·88 to 12·8) 427 (340 to 486) 71·9% (57·3 to 81·7) 1·18% (−6·60 to 9·21) 40–64 years 37·6 (13·0 to 82·6) 6·04% (2·09 to 13·3) 2·61% (−1·19 to 6·51) 235 (161 to 321) 42·6% (29·2 to 58·2) 13·5% (2·02 to 25·7) ≥65 years 1·14 (0·0390 to 6·83) 0·342% (0·0117 to 2·05) 0·190% (−0·0872 to 1·11) 34·6 (17·7 to 71·7) 17·2% (8·82 to 35·6) 8·13% (0·630 to 15·7) Kazakhstan 15–39 years 933 (570 to 1530) 26·9% (16·5 to 44·0) −5·14% (−13·0 to 3·37) 1910 (1490 to 2220) 55·6% (43·3 to 64·6) −4·33% (−12·1 to 2·87) 40–64 years 386 (227 to 590) 14·4% (8·45 to 22·0) −3·39% (−10·1 to 2·64) 871 (599 to 1190) 36·5% (25·1 to 49·9) −2·92% (−10·6 to 5·52) ≥65 years 16·6 (4·32 to 55·5) 1·91% (0·496 to 6·38) −0·957% (−3·73 to 0·747) 68·2 (39·9 to 130) 13·9% (8·15 to 26·5) −3·78% (−10·9 to 2·50) Kyrgyzstan 15–39 years 139 (75·6 to 253) 10·4% (5·63 to 18·8) 0·00207% (−3·95 to 4·51) 529 (415 to 619) 39·0% (30·6 to 45·7) −1·29% (−6·83 to 4·25) 40–64 years 51·8 (27·7 to 85·8) 6·58% (3·52 to 10·9) 1·05% (−1·44 to 3·64) 221 (161 to 290) 31·2% (22·7 to 40·8) 1·90% (−4·13 to 8·73) ≥65 years 0·834 (0·137 to 3·28) 0·418% (0·0687 to 1·64) 0·0640% (−0·315 to 0·567) 12·7 (7·97 to 22·2) 9·82% (6·16 to 17·1) 1·19% (−2·05 to 4·50) Mongolia 15–39 years 157 (100 to 243) 25·0% (16·0 to 38·7) 5·71% (−1·72 to 12·0) 350 (282 to 398) 55·1% (44·5 to 62·7) 7·58% (1·06 to 14·4) 40–64 years 62·5 (38·4 to 95·1) 14·4% (8·86 to 22·0) 7·08% (2·93 to 11·3) 163 (122 to 210) 41·9% (31·3 to 53·9) 21·2% (12·8 to 29·5) ≥65 years 1·57 (0·462 to 4·64) 1·88% (0·555 to 5·58) 1·48% (0·476 to 3·54) 8·92 (5·45 to 16·6) 16·5% (10·1 to 30·8) 12·0% (7·81 to 16·8) Tajikistan 15–39 years 28·8 (17·2 to 45·0) 1·42% (0·847 to 2·22) −0·182% (−0·845 to 0·466) 489 (397 to 578) 23·4% (19·0 to 27·6) −0·934% (−5·56 to 3·47) 40–64 years 6·72 (3·98 to 10·6) 0·671% (0·397 to 1·06) −0·0827% (−0·441 to 0·245) 172 (131 to 221) 17·6% (13·5 to 22·6) −1·54% (−5·75 to 2·40) ≥65 years 0·137 (0·0370 to 0·364) 0·0814% (0·0219 to 0·216) −0·0218% (−0·133 to 0·0600) 3·75 (2·25 to 6·18) 2·54% (1·52 to 4·18) −0·259% (−1·38 to 0·969) Turkmenistan 15–39 years 123 (64·2 to 218) 12·7% (6·62 to 22·4) 2·66% (−2·83 to 7·36) 533 (417 to 619) 48·3% (37·7 to 56·0) 5·39% (−1·05 to 12·2) 40–64 years 44·4 (20·8 to 78·1) 6·92% (3·25 to 12·2) 3·70% (0·910 to 6·99) 210 (146 to 284) 34·0% (23·5 to 45·8) 15·4% (7·25 to 23·4) ≥65 years 0·974 (0·126 to 3·74) 0·620% (0·0802 to 2·38) 0·482% (0·0614 to 1·70) 14·5 (8·06 to 27·3) 13·0% (7·23 to 24·4) 8·29% (4·05 to 13·3) Uzbekistan 15–39 years 426 (234 to 728) 6·20% (3·41 to 10·6) −0·215% (−3·48 to 2·46) 2970 (2340 to 3460) 42·4% (33·4 to 49·3) −0·720% (−7·28 to 5·87) 40–64 years 135 (72·7 to 228) 3·12% (1·68 to 5·28) 0·981% (−0·604 to 2·44) 1220 (876 to 1650) 30·4% (21·7 to 40·9) 7·90% (−0·290 to 16·2) ≥65 years 2·43 (0·436 to 9·45) 0·289% (0·0519 to 1·12) 0·194% (0·0193 to 0·683) 61·4 (35·3 to 113) 9·73% (5·60 to 17·9) 4·90% (1·64 to 8·69) Central Europe 15–39 years 8440 (5820 to 10 900) 49·6% (34·2 to 63·7) 2·04% (−3·03 to 7·63) 14 000 (12 200 to 14 900) 78·3% (68·1 to 83·5) 3·80% (0·745 to 7·05) 40–64 years 5350 (3770 to 8180) 26·8% (18·9 to 41·0) 5·49% (1·43 to 10·2) 11 900 (9880 to 14 400) 60·6% (50·4 to 73·7) 10·3% (3·79 to 17·4) ≥65 years 769 (341 to 1620) 6·00% (2·66 to 12·6) 2·01% (0·632 to 3·48) 2980 (2160 to 4330) 33·9% (24·5 to 49·2) 7·44% (3·57 to 10·9) Albania 15–39 years 112 (58·3 to 158) 24·3% (12·7 to 34·4) −1·18% (−9·18 to 6·39) 289 (238 to 319) 57·0% (46·9 to 62·7) 6·40% (−0·567 to 13·6) 40–64 years 33·5 (16·5 to 65·2) 7·41% (3·64 to 14·4) 3·86% (1·16 to 7·14) 169 (123 to 227) 40·6% (29·5 to 54·4) 21·2% (12·6 to 27·5) ≥65 years 2·35 (0·353 to 7·73) 1·12% (0·168 to 3·68) 0·993% (0·164 to 2·97) 36·6 (19·5 to 65·2) 18·9% (10·1 to 33·7) 14·8% (8·78 to 20·9) Bosnia and Herzegovina 15–39 years 125 (82·4 to 166) 24·2% (16·0 to 32·3) 3·65% (−2·87 to 10·1) 357 (311 to 389) 65·1% (56·8 to 71·1) 6·16% (0·147 to 11·9) 40–64 years 69·6 (43·7 to 114) 11·5 (7·21 to 18·8) 4·92 (0·916 to 8·92) 284 (233 to 353) 48·6 (39·8 to 60·4) 17·0 (7·31 to 26·1) ≥65 years 6·95 (2·39 to 15·8) 2·01% (0·690 to 4·56) 1·44% (0·417 to 3·14) 62·9 (42·8 to 93·5) 24·7% (16·8 to 36·7) 13·9% (8·01 to 19·5) Bulgaria 15–39 years 488 (351 to 624) 52·1% (37·5 to 66·6) 3·64% (−4·86 to 12·2) 798 (706 to 851) 79·9% (70·7 to 85·2) 4·62% (0·386 to 9·03) 40–64 years 382 (283 to 541) 31·1% (23·0 to 44·0) 8·55% (3·07 to 14·6) 798 (680 to 947) 64·8% (55·3 to 77·0) 13·4% (5·56 to 22·4) ≥65 years 62·3 (28·3 to 120) 6·98% (3·17 to 13·5) 3·19% (0·735 to 6·12) 230 (170 to 326) 38·7% (28·6 to 54·7) 11·4% (4·57 to 17·3) Croatia 15–39 years 315 (193 to 417) 50·8% (31·1 to 67·4) −0·297% (−8·93 to 8·88) 513 (435 to 555) 79·2% (67·2 to 85·7) 1·08% (−3·76 to 5·60) 40–64 years 183 (111 to 290) 24·5% (14·9 to 39·0) 1·78% (−5·68 to 9·38) 401 (313 to 512) 55·0% (42·9 to 70·2) 4·54% (−3·74 to 14·7) ≥65 years 28·3 (8·87 to 68·4) 5·47% (1·72 to 13·2) 0·141% (−3·90 to 3·53) 110 (67·5 to 175) 30·4% (18·6 to 48·4) 1·72% (−6·72 to 10·1) Czech Republic 15–39 years 963 (704 to 1210) 66·2% (48·4 to 82·9) −0·371% (−7·38 to 7·53) 1310 (1170 to 1390) 85·3% (76·2 to 90·6) 1·81% (−1·91 to 5·41) 40–64 years 708 (521 to 1040) 38·3% (28·2 to 56·1) 5·25% (−0·408 to 11·6) 1300 (1100 to 1540) 68·1% (58·0 to 81·1) 7·53% (1·20 to 15·1) ≥65 years 127 (60·0 to 250) 10·1% (4·75 to 19·8) 2·87% (−0·351 to 6·19) 391 (291 to 546) 42·5% (31·6 to 59·3) 6·30% (0·884 to 11·4) Hungary 15–39 years 695 (474 to 898) 50·7% (34·6 to 65·5) −2·77% (−10·5 to 5·60) 1100 (972 to 1180) 77·4% (68·2 to 82·8) −0·0753% (−4·81 to 4·02) 40–64 years 450 (309 to 716) 25·4% (17·4 to 40·3) −0·0407% (−6·20 to 6·71) 1010 (833 to 1220) 59·5% (49·1 to 71·9) 3·11% (−2·87 to 10·1) ≥65 years 73·2 (30·1 to 155) 5·99% (2·46 to 12·6) −0·294% (−3·88 to 2·47) 262 (179 to 379) 34·6% (23·6 to 50·1) 1·74% (−4·33 to 7·33) Montenegro 15–39 years 33·9 (22·2 to 45·6) 33·4% (21·8 to 44·8) −2·75% (−10·6 to 5·04) 70·1 (60·3 to 76·1) 65·6% (56·4 to 71·2) −0·548% (−5·57 to 5·10) 40–64 years 18·0 (11·3 to 28·9) 17·1% (10·7 to 27·4) 1·20% (−4·59 to 6·82) 52·5 (41·4 to 65·8) 51·5% (40·6 to 64·5) 6·14% (−2·11 to 16·3) ≥65 years 2·37 (0·774 to 5·54) 4·36% (1·42 to 10·2) 1·42% (−1·12 to 4·38) 11·9 (7·55 to 18·3) 29·5% (18·7 to 45·3) 6·39% (−2·19 to 15·5) North Macedonia 15–39 years 115 (68·7 to 162) 36·1% (21·5 to 50·8) −4·09% (−12·4 to 5·66) 251 (212 to 272) 76·7% (64·8 to 83·3) −0·507% (−6·11 to 4·17) 40–64 years 45·2 (24·5 to 86·3) 14·0% (7·60 to 26·7) −1·37% (−7·27 to 4·30) 169 (131 to 218) 54·9% (42·6 to 70·7) 0·976% (−6·26 to 8·99) ≥65 years 3·54 (0·728 to 10·2) 2·37% (0·487 to 6·81) −0·276% (−2·89 to 1·63) 34·0 (21·5 to 53·5) 29·3% (18·6 to 46·2) −0·672% (−7·80 to 6·31) Poland 15–39 years 3330 (2270 to 4340) 54·8% (37·4 to 71·6) 4·15% (−3·89 to 12·3) 5150 (4460 to 5530) 81·8% (70·9 to 87·9) 5·91% (1·45 to 10·7) 40–64 years 1970 (1350 to 3060) 29·7% (20·3 to 46·1) 8·70% (3·00 to 14·8) 4060 (3390 to 4900) 63·0% (52·5 to 76·0) 13·8% (5·48 to 22·4) ≥65 years 254 (105 to 551) 6·04% (2·49 to 13·1) 3·32% (1·09 to 5·88) 939 (685 to 1360) 33·6% (24·5 to 48·7) 11·8% (5·68 to 16·7) Romania 15–39 years 1310 (862 to 1740) 48·5% (31·9 to 64·3) 2·36% (−5·77 to 11·3) 2290 (2010 to 2460) 80·2% (70·4 to 85·9) 3·29% (−1·10 to 7·50) 40–64 years 921 (619 to 1420) 26·9% (18·0 to 41·6) 6·36% (−0·371 to 13·0) 2110 (1750 to 2560) 61·9% (51·1 to 75·0) 11·0% (2·97 to 19·9) ≥65 years 141 (54·9 to 300) 6·32% (2·46 to 13·4) 2·37% (−0·162 to 5·16) 519 (350 to 757) 34·4% (23·2 to 50·2) 8·33% (1·91 to 14·7) Serbia 15–39 years 285 (194 to 384) 21·2% (14·5 to 28·6) −1·90% (−8·11 to 4·37) 904 (792 to 980) 63·1% (55·3 to 68·4) 2·53% (−2·55 to 7·65) 40–64 years 186 (125 to 277) 12·6% (8·46 to 18·8) 1·47% (−4·04 to 6·49) 763 (629 to 921) 53·6% (44·2 to 64·7) 9·12% (−0·412 to 21·3) ≥65 years 30·0 (11·8 to 61·6) 3·26% (1·28 to 6·70) 1·18% (−1·52 to 3·55) 212 (151 to 297) 30·0% (21·4 to 42·1) 6·74% (−2·46 to 16·6) Slovakia 15–39 years 524 (353 to 684) 61·5% (41·4 to 80·3) −2·67% (−10·5 to 6·55) 724 (625 to 781) 80·9% (69·8 to 87·2) −0·197% (−5·04 to 3·74) 40–64 years 309 (209 to 483) 32·1% (21·7 to 50·1) 1·92% (−3·90 to 9·21) 600 (497 to 735) 62·8% (52·0 to 76·9) 2·45% (−3·26 to 9·04) ≥65 years 31·4 (10·3 to 77·9) 5·73% (1·88 to 14·2) 0·852% (−2·00 to 3·55) 142 (101 to 204) 39·1% (27·7 to 56·1) 0·980% (−3·64 to 6·44) Slovenia 15–39 years 151 (77·7 to 220) 54·0% (27·8 to 78·7) −5·61% (−17·7 to 8·13) 234 (180 to 265) 77·4% (59·7 to 87·8) −3·83% (−13·5 to 2·26) 40–64 years 68·3 (23·4 to 145) 18·6% (6·37 to 39·6) −5·52% (−14·1 to 3·61) 165 (88·8 to 257) 43·0% (23·1 to 66·9) −9·03% (−20·6 to 1·11) ≥65 years 5·77 (0·255 to 20·7) 2·35% (0·104 to 8·41) −2·20% (−7·29 to 0·192) 34·3 (8·90 to 71·5) 18·8% (4·88 to 39·2) −11·3% (−20·7 to −3·09) Eastern Europe 15–39 years 18 200 (12 300 to 22 700) 54·7% (36·8 to 68·1) −5·33% (−16·7 to 4·98) 25 000 (21 300 to 27 000) 73·5% (62·5 to 79·4) −2·15% (−8·36 to 2·10) 40–64 years 9930 (6560 to 15 500) 25·6% (16·9 to 39·9) 2·92% (−2·26 to 8·65) 17 000 (13 200 to 22 100) 51·3% (39·9 to 66·7) 3·75% (−2·51 to 11·2) ≥65 years 408 (127 to 1420) 1·87% (0·583 to 6·51) 0·237% (−0·514 to 1·32) 2210 (1510 to 4190) 19·7% (13·4 to 37·3) 1·09% (−2·53 to 4·42) Belarus 15–39 years 907 (592 to 1130) 61·8% (40·3 to 77·2) −2·17% (−15·9 to 10·1) 1200 (982 to 1300) 78·8% (64·5 to 85·4) −0·148% (−7·94 to 5·27) 40–64 years 556 (359 to 853) 30·9% (19·9 to 47·4) 3·69% (−3·19 to 11·6) 825 (610 to 1100) 53·4% (39·5 to 71·2) 3·12% (−4·07 to 12·0) ≥65 years 32·2 (10·6 to 105) 3·27% (1·07 to 10·7) 0·184% (−1·79 to 2·53) 107 (67·5 to 208) 21·8% (13·7 to 42·4) 0·262% (−5·63 to 5·59) Estonia 15–39 years 137 (97·8 to 163) 71·3% (50·9 to 84·5) −1·86% (−14·0 to 9·83) 175 (152 to 187) 84·9% (73·5 to 90·3) 0·709% (−5·10 to 5·39) 40–64 years 79·9 (55·3 to 119) 35·9% (24·8 to 53·4) 11·9% (4·74 to 18·3) 120 (92·0 to 155) 57·6% (43·9 to 74·3) 13·8% (4·88 to 23·1) ≥65 years 4·77 (1·25 to 17·7) 2·74 (0·720 to 10·2) 1·68 (0·402 to 5·12) 19·0 (12·3 to 39·1) 20·6 (13·3 to 42·6) 9·21 (4·63 to 14·2) Latvia 15–39 years 179 (130 to 212) 66·8% (48·4 to 79·1) −4·22% (−15·4 to 7·48) 232 (205 to 245) 82·6% (73·0 to 87·1) 0·817% (−3·58 to 5·69) 40–64 years 122 (85·0 to 175) 35·5% (24·6 to 50·8) 12·7% (6·14 to 18·5) 187 (151 to 232) 61·4% (49·7 to 76·2) 17·2% (6·96 to 27·6) ≥65 years 9·25 (3·42 to 29·6) 3·54% (1·31 to 11·3) 2·51% (0·974 to 6·09) 37·0 (27·8 to 61·4) 28·6% (21·6 to 47·6) 14·3% (9·87 to 18·6) Lithuania 15–39 years 296 (201 to 355) 73·8% (50·0 to 88·4) −2·41% (−15·1 to 9·58) 359 (307 to 382) 85·3% (72·9 to 90·7) 1·01% (−4·81 to 5·79) 40–64 years 173 (113 to 268) 33·4% (21·8 to 51·5) 8·67% (1·56 to 15·5) 259 (196 to 343) 57·2% (43·3 to 75·8) 11·2% (1·82 to 20·8) ≥65 years 12·3 (3·87 to 44·5) 3·35% (1·05 to 12·1) 1·79% (0·415 to 4·74) 40·9 (26·9 to 82·4) 21·8% (14·3 to 43·8) 8·46% (4·09 to 13·0) Moldova 15–39 years 366 (257 to 447) 58·9% (41·3 to 71·9) −1·16% (−13·8 to 9·36) 526 (447 to 565) 80·8% (68·7 to 86·8) −0·983% (−8·09 to 4·18) 40–64 years 211 (147 to 303) 31·1% (21·6 to 44·6) 0·128% (−7·71 to 7·63) 334 (255 to 438) 55·7% (42·6 to 73·1) −0·201% (−8·14 to 9·58) ≥65 years 13·0 (4·50 to 40·4) 3·91% (1·35 to 12·1) −1·29% (−4·80 to 1·65) 45·3 (28·3 to 89·8) 22·1% (13·8 to 43·9) −5·49% (−13·4 to 2·44) Russia 15–39 years 12 400 (8350 to 15 600) 53·1% (35·7 to 66·8) −5·67% (−16·6 to 5·08) 17 100 (14 700 to 18 400) 71·6% (61·7 to 77·4) −2·71% (−8·47 to 2·07) 40–64 years 6720 (4460 to 10 500) 25·0% (16·6 to 39·0) 2·57% (−3·44 to 8·74) 11 900 (9490 to 15 100) 52·0% (41·5 to 66·2) 3·08% (−3·45 to 10·7) ≥65 years 243 (57·8 to 875) 1·64% (0·390 to 5·91) 0·263% (−0·680 to 1·74) 1550 (1050 to 2820) 20·5% (13·9 to 37·3) 0·925% (−3·79 to 5·54) Ukraine 15–39 years 3900 (2420 to 4980) 56·4% (35·0 to 71·9) −4·74% (−18·3 to 7·58) 5470 (4350 to 6030) 76·5% (60·9 to 84·4) −0·693% (−8·78 to 4·87) 40–64 years 2070 (1280 to 3370) 24·5% (15·1 to 39·9) 2·95% (−3·55 to 9·68) 3380 (2340 to 4790) 47·3% (32·8 to 67·0) 4·14% (−4·15 to 13·0) ≥65 years 92·6 (24·6 to 320) 1·89% (0·501 to 6·53) 0·0302% (−1·30 to 1·45) 410 (223 to 921) 16·0% (8·69 to 35·9) −0·119% (−5·36 to 5·13) Australasia 15–39 years 3910 (3290 to 4130) 77·7% (65·3 to 82·0) −4·37% (−12·8 to 1·58) 4210 (3600 to 4400) 83·2% (71·1 to 86·9) −2·38% (−9·26 to 0·990) 40–64 years 2700 (1870 to 3650) 57·0% (39·5 to 77·2) 4·33% (−6·08 to 14·6) 2830 (2140 to 3630) 62·3% (47·2 to 80·0) 7·69% (−1·03 to 15·4) ≥65 years 536 (283 to 865) 20·3% (10·7 to 32·7) 10·5% (2·07 to 16·7) 772 (504 to 1230) 33·6% (21·9 to 53·4) 8·12% (0·525 to 15·0) Australia 15–39 years 3280 (2740 to 3490) 78·1% (65·3 to 83·2) −3·65% (−12·7 to 2·99) 3500 (3000 to 3670) 83·5% (71·5 to 87·4) −2·07% (−9·33 to 1·66) 40–64 years 2230 (1530 to 3050) 56·7% (38·7 to 77·5) 4·88% (−6·07 to 15·6) 2380 (1810 to 3050) 63·0% (47·9 to 80·7) 8·04% (−0·866 to 15·9) ≥65 years 439 (226 to 726) 19·7% (10·2 to 32·6) 10·7% (2·85 to 17·2) 656 (431 to 1040) 34·0% (22·3 to 53·8) 8·35% (0·612 to 15·4) New Zealand 15–39 years 634 (546 to 665) 75·6% (65·1 to 79·3) −7·92% (−14·7 to −2·74) 709 (605 to 742) 81·7% (69·8 to 85·5) −3·88% (−10·5 to −0·412) 40–64 years 463 (348 to 607) 58·2% (43·6 to 76·2) 1·44% (−8·62 to 10·8) 446 (330 to 589) 58·7% (43·5 to 77·6) 6·04% (−3·22 to 13·7) ≥65 years 97·8 (57·5 to 149) 23·4% (13·7 to 35·7) 9·97% (−0·197 to 17·0) 116 (72·2 to 189) 31·5% (19·7 to 51·5) 6·85% (−1·20 to 14·4) High–income Asia Pacific 15–39 years 12 800 (7820 to 15 200) 51·3% (31·4 to 61·1) 0·211% (−12·8 to 10·9) 17 800 (12 400 to 20 000) 66·9% (46·6 to 75·1) −3·96% (−19·1 to 4·23) 40–64 years 12 000 (9160 to 16 700) 36·5% (27·7 to 50·7) −2·79% (−8·44 to 3·88) 17 700 (14 100 to 22 600) 52·3% (41·8 to 66·9) −6·77% (−13·3 to −0·928) ≥65 years 3390 (2210 to 4870) 13·2% (8·61 to 19·0) −0·103% (−3·77 to 3·58) 6110 (4340 to 8250) 30·6% (21·7 to 41·3) −2·03% (−7·53 to 3·57) Brunei 15–39 years 2·35 (1·14 to 3·29) 2·50% (1·22 to 3·52) −1·01% (−2·44 to 0·248) 6·36 (2·75 to 8·58) 5·78% (2·50 to 7·79) −4·43% (−7·49 to −2·20) 40–64 years 1·02 (0·395 to 1·66) 1·68% (0·655 to 2·76) −1·04% (−2·42 to 0·0405) 2·08 (0·331 to 3·84) 3·22% (0·511 to 5·94) −4·92% (−7·44 to −2·81) ≥65 years 0·0632 (0·00798 to 0·119) 0·533% (0·0673 to 1·01) −0·397% (−1·01 to 0·158) 0·135 (0·00400 to 0·263) 1·23% (0·0364 to 2·40) −2·31% (−3·88 to −0·898) Japan 15–39 years 9850 (5760 to 11 900) 62·2% (36·4 to 75·2) 1·84% (−16·3 to 15·2) 12 100 (7770 to 13 800) 73·2% (47·0 to 83·6) −2·86% (−21·8 to 6·97) 40–64 years 9370 (6930 to 13 400) 43·9% (32·5 to 62·9) 0·924% (−6·35 to 9·81) 12 000 (9070 to 16 000) 55·6% (42·1 to 74·3) −5·46% (−12·7 to 1·52) ≥65 years 2910 (1820 to 4260) 14·1% (8·81 to 20·7) 0·197% (−3·97 to 4·47) 4920 (3280 to 6840) 30·7% (20·5 to 42·7) −2·14% (−7·99 to 4·04) South Korea 15–39 years 2630 (1860 to 3230) 33·0% (23·4 to 40·5) −0·638% (−9·15 to 8·51) 5280 (4370 to 5810) 58·7% (48·5 to 64·5) −3·27% (−11·3 to 3·06) 40–64 years 2520 (2000 to 3230) 23·7% (18·8 to 30·4) −1·21% (−7·71 to 5·20) 5460 (4810 to 6240) 49·4% (43·4 to 56·3) −3·98% (−9·71 to 1·87) ≥65 years 469 (337 to 627) 10·1% (7·25 to 13·5) −0·0438% (−3·93 to 3·88) 1160 (960 to 1380) 32·1% (26·6 to 38·2) −0·965% (−7·45 to 5·41) Singapore 15–39 years 286 (126 to 384) 28·8% (12·8 to 38·8) 8·01% (−3·57 to 17·4) 435 (200 to 568) 42·4% (19·4 to 55·2) −1·38% (−18·8 to 8·97) 40–64 years 157 (98·3 to 262) 15·3% (9·61 to 25·6) 4·07% (−0·107 to 10·2) 260 (154 to 431) 23·0% (13·6 to 38·1) −1·70% (−8·43 to 4·16) ≥65 years 10·9 (5·86 to 17·9) 2·85% (1·54 to 4·71) 0·826% (−0·460 to 2·36) 27·7 (14·2 to 44·3) 8·08% (4·13 to 12·9) 0·941% (−2·00 to 4·00) High–income North America 15–39 years 32 200 (21 700 to 38 700) 53·0% (35·6 to 63·7) −7·72% (−14·1 to −1·78) 41 000 (34 300 to 44 300) 66·2% (55·4 to 71·5) −7·17% (−11·3 to −4·16) 40–64 years 18 500 (13 500 to 26 000) 30·9% (22·5 to 43·4) 2·00% (−5·74 to 6·46) 25 700 (19 800 to 33 100) 44·6% (34·4 to 57·5) 5·93% (−1·10 to 11·0) ≥65 years 4110 (2270 to 7140) 12·0% (6·63 to 20·9) 6·43% (3·67 to 9·71) 6120 (4050 to 9890) 22·0% (14·6 to 35·6) 7·62% (3·52 to 11·5) Canada 15–39 years 3420 (2170 to 4240) 60·1 (38·1 to 74·3) 2·39 (−7·63 to 12·5) 4340 (3540 to 4710) 75·1 (61·4 to 81·6) 1·33 (−4·50 to 5·65) 40–64 years 2060 (1460 to 3010) 32·8% (23·3 to 47·8) 2·60% (−4·85 to 8·87) 2870 (2170 to 3730) 47·5% (36·0 to 61·6) 7·20% (0·309 to 12·3) ≥65 years 425 (209 to 780) 11·5% (5·67 to 21·2) 5·77% (2·53 to 9·72) 708 (453 to 1170) 22·7% (14·5 to 37·5) 7·51% (3·07 to 12·1) Greenland 15–39 years 5·26 (3·31 to 6·50) 52·9% (33·3 to 65·4) −5·57% (−15·5 to 4·19) 7·13 (5·76 to 7·77) 67·8% (54·9 to 74·0) −3·25% (−9·06 to 1·69) 40–64 years 2·40 (1·42 to 3·62) 27·9% (16·5 to 42·0) 0·312% (−11·2 to 11·3) 4·41 (2·86 to 5·87) 43·3% (28·0 to 57·6) 6·42% (−5·08 to 19·4) ≥65 years 0·249 (0·0861 to 0·475) 10·5% (3·62 to 20·0) 6·04% (−0·0437 to 12·6) 0·615 (0·307 to 1·02) 22·8% (11·3 to 37·6) 10·4% (−1·09 to 21·3) USA 15–39 years 28 800 (19 500 to 34 700) 52·2% (35·4 to 63·0) −8·78% (−14·9 to −2·88) 36 600 (30 600 to 39 600) 65·3% (54·6 to 70·5) −8·04% (−12·3 to −4·89) 40–64 years 16 500 (12 000 to 23 000) 30·7% (22·4 to 43·0) 1·93% (−5·70 to 6·52) 22 800 (17 600 to 29 400) 44·3% (34·1 to 57·1) 5·78% (−1·65 to 11·1) ≥65 years 3690 (2040 to 6340) 12·1% (6·69 to 20·8) 6·51% (3·70 to 9·77) 5420 (3570 to 8690) 21·9% (14·5 to 35·2) 7·62% (3·29 to 11·7) Southern Latin America 15–39 years 7500 (5340 to 8610) 59·0% (42·0 to 67·8) 4·31% (−3·75 to 12·4) 9690 (8560 to 10 200) 76·7% (67·7 to 80·5) −0·186% (−3·91 to 3·05) 40–64 years 3200 (2350 to 4670) 32·6% (24·0 to 47·6) −1·25% (−6·45 to 5·38) 4790 (3790 to 6170) 52·3% (41·4 to 67·4) −3·31% (−7·73 to 1·70) ≥65 years 556 (322 to 863) 12·2% (7·05 to 18·9) −0·0526% (−3·56 to 3·17) 979 (666 to 1360) 29·1% (19·8 to 40·6) −3·24% (−7·62 to 1·76) Argentina 15–39 years 5290 (3750 to 6190) 60·5% (42·9 to 70·7) 3·21% (−6·09 to 12·6) 6700 (5890 to 7060) 77·7% (68·4 to 81·9) −1·40% (−5·71 to 2·81) 40–64 years 2050 (1460 to 3060) 32·3% (23·0 to 48·2) −3·06% (−9·65 to 4·67) 3070 (2400 to 3980) 51·6% (40·3 to 67·0) −6·09% (−11·3 to 0·455) ≥65 years 343 (187 to 565) 11·5% (6·27 to 18·9) −1·72% (−5·99 to 2·50) 603 (397 to 856) 27·8% (18·3 to 39·5) −6·87% (−12·1 to −1·57) Chile 15–39 years 1950 (1370 to 2280) 57·9% (40·7 to 67·8) 5·69% (−3·64 to 15·6) 2600 (2310 to 2740) 76·0% (67·5 to 80·1) 1·58% (−3·14 to 6·33) 40–64 years 1000 (752 to 1410) 34·3% (25·7 to 48·1) 2·19% (−4·80 to 8·83) 1490 (1210 to 1860) 54·8% (44·5 to 68·5) 2·21% (−2·46 to 7·34) ≥65 years 178 (106 to 268) 14·1% (8·38 to 21·2) 3·83% (−0·526 to 7·63) 318 (223 to 431) 32·5% (22·8 to 44·2) 4·90% (−0·805 to 10·6) Uruguay 15–39 years 264 (189 to 314) 43·6% (31·2 to 51·9) 4·20% (−5·20 to 13·5) 395 (345 to 423) 66·2% (57·9 to 71·0) 0·745% (−4·29 to 5·94) 40–64 years 146 (107 to 214) 27·5% (20·1 to 40·4) 1·67% (−4·43 to 8·32) 231 (183 to 294) 47·8% (37·9 to 60·9) 1·51% (−3·45 to 7·00) ≥65 years 34·6 (19·3 to 55·4) 11·1% (6·16 to 17·7) 2·03% (−2·02 to 5·89) 57·9 (39·5 to 80·4) 27·4% (18·7 to 38·1) 1·88% (−3·43 to 7·51) Western Europe 15–39 years 41 000 (28 300 to 47 000) 64·3% (44·3 to 73·6) −4·40% (−15·8 to 3·08) 52 600 (44 500 to 56 000) 79·3% (67·1 to 84·5) −3·15% (−9·01 to −0·0908) 40–64 years 36 000 (26 600 to 50 900) 48·0% (35·4 to 67·8) −1·03% (−7·36 to 5·93) 45 300 (36 700 to 57 300) 61·4% (49·7 to 77·6) −0·157% (−4·75 to 4·95) ≥65 years 9340 (6000 to 13 900) 18·9% (12·1 to 28·1) 4·92% (0·0743 to 8·75) 14 300 (10 200 to 20 700) 36·5% (25·9 to 52·7) 2·80% (−1·86 to 6·59) Andorra 15–39 years 8·28 (5·59 to 9·89) 66·9% (45·1 to 79·8) −5·56% (−19·7 to 4·71) 10·5 (8·71 to 11·4) 80·5% (66·6 to 87·4) −3·86% (−10·3 to 0·358) 40–64 years 8·56 (6·04 to 12·2) 50·5% (35·7 to 72·1) −1·18% (−10·6 to 8·82) 11·4 (9·00 to 14·6) 61·5% (48·7 to 78·9) −0·478% (−7·38 to 6·96) ≥65 years 1·26 (0·751 to 1·94) 19·3% (11·5 to 29·7) 4·90% (−2·99 to 11·6) 2·28 (1·53 to 3·42) 35·6% (23·8 to 53·3) 2·85% (−5·91 to 11·8) Austria 15–39 years 874 (612 to 1030) 63·8% (44·6 to 75·3) −6·59% (−18·1 to 3·50) 1160 (982 to 1250) 79·9% (67·7 to 85·9) −3·48% (−9·90 to 0·603) 40–64 years 774 (567 to 1070) 49·1% (35·9 to 68·1) −1·47% (−9·42 to 7·35) 959 (780 to 1200) 61·4% (50·0 to 76·7) −0·293% (−5·47 to 5·32) ≥65 years 187 (116 to 275) 19·5% (12·1 to 28·7) 3·68% (−2·40 to 8·83) 259 (179 to 378) 35·3% (24·5 to 51·5) 2·06% (−3·46 to 7·57) Belgium 15–39 years 1160 (810 to 1370) 66·8% (46·5 to 78·5) −3·76% (−16·0 to 5·81) 1400 (1190 to 1500) 79·1% (67·7 to 85·0) −2·61% (−8·21 to 1·53) 40–64 years 966 (700 to 1380) 50·9% (36·9 to 72·9) 0·230% (−8·05 to 10·0) 1160 (923 to 1470) 60·6% (48·3 to 76·8) −0·459% (−5·74 to 5·14) ≥65 years 247 (146 to 381) 20·1% (11·9 to 30·9) 5·15% (−0·207 to 10·8) 336 (222 to 503) 34·5% (22·9 to 51·7) 1·25% (−4·32 to 6·50) Cyprus 15–39 years 136 (84·0 to 171) 54·8% (33·9 to 68·8) −6·12% (−19·5 to 5·30) 212 (172 to 230) 83·1% (67·4 to 90·1) −0·761% (−8·12 to 3·36) 40–64 years 81·6 (49·8 to 126) 36·1% (22·1 to 55·8) 0·596% (−8·51 to 10·1) 124 (96·9 to 162) 61·1% (47·8 to 79·8) 3·41% (−2·93 to 9·34) ≥65 years 11·3 (5·66 to 18·4) 11·2% (5·60 to 18·2) 4·60% (0·0601 to 8·89) 30·1 (19·6 to 44·6) 33·0% (21·5 to 48·9) 7·14% (0·266 to 13·0) Denmark 15–39 years 666 (439 to 779) 75·1% (49·5 to 87·9) −3·91% (−18·5 to 6·36) 790 (645 to 852) 85·2% (69·6 to 91·9) −2·19% (−9·90 to 1·69) 40–64 years 525 (358 to 775) 55·4% (37·8 to 81·9) −2·26% (−10·8 to 8·23) 564 (417 to 774) 59·3% (43·9 to 81·3) −1·42% (−7·31 to 5·42) ≥65 years 144 (83·8 to 230) 23·0% (13·4 to 36·7) 6·78% (−0·0593 to 13·2) 180 (110 to 292) 33·8% (20·7 to 54·9) 2·35% (−3·87 to 8·27) Finland 15–39 years 572 (379 to 676) 70·6% (46·8 to 83·4) −4·73% (−18·5 to 5·81) 683 (562 to 741) 79·6% (65·4 to 86·2) −3·33% (−9·16 to 0·782) 40–64 years 442 (307 to 658) 51·1% (35·6 to 76·1) 1·40% (−7·09 to 10·2) 514 (398 to 680) 58·7% (45·5 to 77·7) 3·64% (−2·83 to 9·90) ≥65 years 112 (59·6 to 184) 15·9% (8·42 to 26·0) 7·64% (2·59 to 12·6) 155 (91·0 to 250) 27·8% (16·3 to 44·8) 7·77% (1·20 to 13·5) France 15–39 years 6510 (4450 to 7700) 65·4% (44·8 to 77·4) −5·31% (−17·1 to 4·05) 8040 (6870 to 8590) 81·0% (69·2 to 86·5) −3·18% (−8·52 to 0·837) 40–64 years 5420 (3890 to 7720) 50·2% (36·0 to 71·5) −1·65%% (−9·67 to 7·07) 6640 (5410 to 8370) 64·0% (52·1 to 80·7) −0·712% (−5·72 to 4·54) ≥65 years 1500 (897 to 2270) 19·5% (11·7 to 29·6) 4·73% (−1·33 to 10·0) 2430 (1740 to 3470) 41·4% (29·6 to 59·0) 1·29% (−4·39 to 6·92) Germany 15–39 years 8470 (6000 to 9820) 70·8% (50·2 to 82·1) −5·45% (−17·4 to 3·86) 11 100 (9550 to 11 700) 83·6% (72·2 to 88·8) −2·97% (−8·45 to 0·596) 40–64 years 8080 (6090 to 10 900) 54·9% (41·4 to 74·1) −1·41% (−8·98 to 6·50) 9900 (8350 to 11 900) 66·7% (56·2 to 80·1) −0·556% (−5·44 to 4·74) ≥65 years 2440 (1620 to 3580) 23·8% (15·7 to 34·9) 5·24% (−1·12 to 11·0) 3450 (2550 to 4700) 42·7% (31·6 to 58·3) 1·53% (−3·99 to 7·04) Greece 15–39 years 999 (629 to 1210) 67·0% (42·2 to 81·1) −4·87% (−19·6 to 6·49) 1240 (1020 to 1350) 81·1% (66·6 to 88·2) −3·18% (−11·3 to 1·61) 40–64 years 778 (481 to 1240) 40·6% (25·1 to 64·5) 0·187% (−8·23 to 10·6) 1070 (815 to 1410) 58·3% (44·6 to 77·0) −0·685% (−7·00 to 5·94) ≥65 years 123 (56·6 to 220) 9·64% (4·44 to 17·3) 2·13% (−2·39 to 5·91) 304 (189 to 482) 29·5% (18·3 to 46·7) −0·997% (−6·15 to 4·53) Iceland 15–39 years 38·5 (24·4 to 46·1) 66·5% (42·1 to 79·6) −4·38% (−18·6 to 9·44) 48·0 (38·6 to 52·4) 77·3% (62·1 to 84·3) 0·215% (−5·41 to 7·05) 40–64 years 26·0 (17·6 to 38·9) 49·1% (33·3 to 73·4) 9·38% (−1·35 to 18·2) 30·5 (22·8 to 41·0) 56·6% (42·3 to 76·1) 18?