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. Author manuscript; available in PMC: 2021 Oct 1.
Published in final edited form as: Drug Alcohol Depend. 2020 Aug 4;215:108219. doi: 10.1016/j.drugalcdep.2020.108219

Cross-country and historical variation in alcohol consumption among older men and women: Leveraging recently harmonized survey data in 21 countries1

Esteban Calvo 1,*, José T Medina 2, Katherine A Ornstein 3, Ursula M Staudinger 4, Linda P Fried 5, Katherine M Keyes 6
PMCID: PMC7585691  NIHMSID: NIHMS1621319  PMID: 32795884

Abstract

Background:

Alcohol consumption causes greater harm in older than younger adults. As the population ages, understanding cross-country and time-varying drinking patterns of older adults is of critical importance. Available evidence relies primarily on ecological data.

Methods:

We harmonized survey data for 179,881 adults age 50+ observed repeatedly between 1998 and 2016 in 21 countries. Next, we estimated historical variation in consumption across countries (overall and stratified by gender and age group 50-64/65+).

Results:

On average, 51.95% of older adults consumed any alcohol over the observed period. For 13 countries, the proportion of older adults who drink increased (mean annual increase: 0.76 percent points). Heavy drinking (men drinks/day>3 or binge>5, women drinks/day>2 or binge>4) peaked at 23.54% for England in 2010 and lifetime abstainers at 69.65% for China in 2011. Across countries and among drinkers, consumption frequency was 2.57 days/week, the number of standard drink units when drinking was 2.57, and the average number of drinks/day over a week was 1.12. Consumption patterns varied substantially across countries and historical time. Overall probability and frequency of consumption were higher in men than women, with the largest gaps observed in 2011 for China, but gender gaps decreased (even reversed) in the young old and varied across country and time.

Conclusions:

Wide variation in older adults’ alcohol consumption across countries and time suggests that broad scale prevention and intervention efforts can be harnessed for potential population-level health benefits. Further variation by gender and age reflect physiological and social factors simultaneously shaping alcohol consumption.

Keywords: cross-national, longitudinal, historical variation, older adult, age, gender, microdata

1. INTRODUCTION

Alcohol consumption is a leading contributor to the global burden of disease (Centers for Disease Control and Prevention, 2016; Griswold et al., 2018) and has an estimated cost ranging between 1.3% and 12% of the gross domestic product (World Health Organization, 2014). Although alcohol consumption generally decreases with age, recent evidence suggests that older adults are drinking more than ever before in several countries (Breslow et al., 2017; Han et al., 2017; Kim et al., 2012; World Health Organization, 2014). Patterns of drinking are often established earlier in life (Merline et al., 2008), but drinking behavior in older age may change, can be effectively intervened (Kelly et al., 2018), and can cause greater harm than in younger ages, as alcohol is metabolized more slowly (Meier and Seitz, 2008), water as a percentage of body weight decreases (Cederbaum, 2012), medications contraindicated to alcohol become more prevalent (Han and Moore, 2018; Immonen et al., 2013), and falls and other alcohol-related injuries become more prevalent (Lehmann et al., 2018). Further, populations are aging and the majority of alcohol-related deaths occur among older adults (Rehm et al., 2006; Rehm et al., 2009; Samokhvalov et al., 2010). Therefore, understanding older adults’ cross-national and historical drinking patterns is of critical importance.

Cross-country comparisons of alcohol consumption largely rely on ecological sales and administrative data to estimate annual liters of pure alcohol consumed per capita in a country as a proxy for individual-level consumption (Poznyak et al., 2013; Rehm and Poznyak, 2015). Such data cannot disentangle frequency and quantity of drinking across individuals or by group. Survey data, in contrast, allow for teasing out abstainers from individuals who drink more or less in frequency and quantity (Slade, Tim et al., 2016). However, survey data are typically not harmonized across countries, do not focus on older adults, and are seldom used to differentiate young-old from old-old to account for likely differences in physiology, metabolism, and medicine intake (Cederbaum, 2012; Griswold et al., 2018; Han and Moore, 2018; Immonen et al., 2013). Given the decline in alcohol consumption in later life, general population consumption patterns are not an appropriate indicator of older adults’ consumption patterns and their potential harm (Lehmann et al., 2018; Rehm et al., 2006; Rehm et al., 2009; Samokhvalov et al., 2010).

Cross-national evidence on older adults’ alcohol consumption by gender suggests that men are more likely to consume alcohol and—among consumers—drink more than women (Keyes et al., 2011; Nolen-Hoeksema and Hilt, 2006; Slade, T. et al., 2016). The extent of gender differences, however, varies across countries (Bratberg et al., 2016; Obot and Room, 2006; Seedat et al., 2009; Wilsnack et al., 2000). A recent analysis of 195 countries included in the 2016 Global Burden of Disease Study found that gender differences in alcohol consumption are smaller in countries with higher socio-demographic index (summary measure based on educational attainment, fertility, and income per capita; Griswold et al., 2018). However, this study was cross-sectional and focused on the general population. Gender gaps among older adults may differ between young- and old-old, possibly due to cohort variations in women’s social status and other macro-social factors (Keyes et al., 2010; Seedat et al., 2009).

In this study, we combined and thoroughly harmonized alcohol consumption data from seven longitudinal surveys for 179,881 adults age 50 and over with repeated observations in 21 countries. After describing our harmonization approach, we present distributions of alcohol consumption across countries and over time, assessing gender differences and separating young- old (50-64) and old-old (65+) age groups.

2. METHODS

2.1. Data and Sample

Data from 21 countries collected between 1998 and 2016 were drawn from seven ongoing cohort studies including 179,881 individuals aged 50 and over. These countries were selected because they implemented comparable nation-wide longitudinal surveys including repeated observations for large samples of older adults. Table 1 lists countries, survey, years and span of data collection, and the sample size among those who provided alcohol information at least one time. The surveys included were: Survey of Health, Ageing and Retirement in Europe (SHARE; Austria, Belgium, Czech Republic, Denmark, Estonia, France, Germany, Israel, Italy, Netherlands, Poland, Slovenia, Spain, Sweden, Switzerland; Börsch-Supan et al., 2013), Social Protection Survey (EPS for its name in Spanish; Chile; Arenas de Mesa et al., 2008), China Health and Retirement Longitudinal Study (CHARLS; China; Zhao et al., 2012), English Longitudinal Study of Aging (ELSA; England; Steptoe et al., 2012), Korean Longitudinal Study of Aging (KLOSA; Korea; Jang, 2015), Mexican Health and Aging Study (MHAS; Mexico; Wong et al., 2015), and Health and Retirement Study (HRS; United States; Sonnega et al., 2014). A description of these surveys is provided in Table S1.2

Table 1.

Data description

Country Survey Years Year span N Age first wave Age last wave Sample aging
Austria SHARE 2007-2011-2013 6 5,751 67.11 67.46 0.35
Belgium SHARE 2007-2011-2013 6 7,578 65.35 66.17 0.82
Chile EPS 2009-2016 7 8,942 63.54 64.65 1.11
China CHARLS 2011-2013 2 16,530 62.77 63.03 0.26
Czech Republic SHARE 2007-2011-2013 6 8,177 64.46 67.06 2.60
Denmark SHARE 2007-2011-2013 6 4,990 64.53 65.50 0.97
England ELSA 2008-2010-2012-2014 6 10,597 65.43 67.71 2.28
Estonia SHARE 2011-2013 3 6,945 66.74 68.45 1.71
France SHARE 2006-2011-2013 7 6,889 65.40 67.82 2.42
Germany SHARE 2007-2011-2013 6 7,159 65.14 64.99 −0.15
Israel SHARE 2010-2013 3 3,117 68.12 68.16 0.04
Italy SHARE 2007-2011-2013 6 6,335 65.81 67.25 1.44
Korea KLOSA 2006-2008-2010-2012 6 9,766 65.30 67.23 1.93
Mexico MHAS 2001-2003-2012 11 14,817 63.34 70.56 7.22
Netherlands SHARE 2007-2011-2013 6 5,486 64.16 66.38 2.22
Poland SHARE 2007-2012 5 2,631 64.36 67.23 2.87
Slovenia SHARE 2011-2013 2 3,662 65.72 66.94 1.22
Spain SHARE 2007-2011-2013 6 7,832 67.39 68.66 1.27
Sweden SHARE 2007-2011-2013 6 5,746 66.58 68.65 2.07
Switzerland SHARE 2007-2011-2013 6 4,163 65.18 66.90 1.72
United States HRS 1998-2000-2002-2004-2006-2008-2010-2012-2014 16 32,768 67.38 69.15 1.77
Total 6 179,881 65.42 67.14 1.72
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Notes: Health and Retirement Study (HRS); English Longitudinal Study of Aging (ELSA); Survey of Health, Ageing and Retirement in Europe (SHARE), Korean Longitudinal Study of Aging (KLOSA), China Health and Retirement Longitudinal Study (CHARLS), Mexican Health and Aging Study (MHAS), Social Protection Survey (EPS for its name in Spanish).

2.2. Alcohol Consumption Measurements

Although alcohol questions and responses varied across questionnaires, we systematically developed and tested a harmonization procedure, summarized in Table 2, resulting in a drinking status variable that is comparable across countries and time.

Table 2.

Coding schema to create a harmonized drinking status variable

1. Lifetime or long-term abstainer: Lifetime abstainers never had a drink or had less than 12 drinks in their entire lifetime, while long-term abstainers have not drunk for at least four years and do not report evidence of ever drinking in the past.
2. Current abstainer: Does not drink now, but drank in the past (or we cannot rule out that they ever drank in the past).
3. Occasional drinker: Frequency less than 1 day per week, drinks per day ≥3 for men or ≥2 for women, and no binging in a single day >5 for men or >4 for women.
4. Moderate drinker: Frequency 1 or more days per week, drinks per day <3 for men or <2 for women, and no binging in a single day >5 for men or >4 for women.
5. Heavy drinker: Men have >3 drinks per day or binge >5 in a single day, and women have >2 drinks per day or binge >4 in a single day.
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All surveys ask whether the respondents drink now, and if so, how often and how much. The assessment time frame varies across surveys, but is typically anchored in the last three months or past year. Most surveys (CHARLS, KLOSA, MHAS, SHARE, and HRS in recent waves) also ask if the respondents have ever consumed alcohol, which allows us to separate current from lifetime abstainers. In surveys that do not ask this question (ELSA and EPS), we separate current from long-term abstainers (see supplementary material).3

To classify moderate, occasional, and heavy drinkers (NIAAA, 2005), we relied on harmonized measures on frequency, quantity, drinks per day, and binge drinking. Questions about how often and how much respondents drink were harmonized transforming raw values into standard frequency and quantity units. Frequency of drinking was transformed into days per week units, ranging from 0 to 7. Quantity measures for different types of beverages (beer, wine, spirits) were first transformed into standard drink units and then added into a single measure ranging from 0 to 70. One standard drink unit is equivalent to the following: a bottle or 33 cl can of beer, 12 cl glass of table wine, 8 cl glass of fortified wine, or 4 cl glass of spirits, each containing between 10 and 14 grams of ethanol (Kelly and Mozayani, 2012; NIAAA, 2015), Because quantity was assessed when last drunk or as an average over different time-frames, we multiplied these harmonized frequency and quantity measures and divided the product by seven to obtain a harmonized measure of average number of standard drinks per day that individuals drink over a week, ranging from 0 to 70. We used information on drinks per day to classify moderate, occasional, and heavy drinking status, as defined in Table 2 (NIAAA, 2005). This classification was supplemented by a question on binge drinking asked in HRS, MHAS, and SHARE. Our definition of heavy drinking combines regular heavy drinking and single episodes of binge drinking following well-validated clinical cut-points (NIAAA, 2005).

Because self-reports are subject to measurement error, we compared country prevalence of abstainers and heavy drinkers in our sample with World Health Organization (WHO) estimates in the general population (World Health Organization, 2014). The inter-source correlation coefficients for prevalence estimates were high for abstainers (r=.86) and moderate for heavy and episode drinkers (r=.40; WHO heavy episode drinkers consume >60 grams of pure alcohol on at least one occasion), but in both cases country rank was consistent between the WHO and our own data (which is more valuable than comparison of estimates given that older adults have lower prevalence of consumption and binge drinking than the general population), indicating that the self-reported data are cross-nationally consistent and supporting the validity of our measures. We also compared the gender-specific prevalence of any drinkers in our sample with Global Burden of Disease (GBD) 2010 data and found even higher correlation coefficients (men r=0.80; women r=0.79; Institute for Health Metrics and Evaluation 2019). Other studies have validated self-reported consumption data, including among older adults (O’Connell et al., 2004), through breathalyzer, blood, and other objective indicators of alcohol use (Del Boca and Darkes, 2003; Miller and Anton, 2004; Mundle et al., 1999; Whitford et al., 2009), suggesting imperfect but adequate ranges for sensitivity (~75%-89%) and specificity (~65-78%).

All data management and analyses were carried in Stata 15 MP. For more detailed information on the harmonization procedure, see the supplementary material.4

2.3. Statistical Analysis

We first calculated point estimates and 95% confidence intervals for consumption indicators for all countries and across all waves for the whole sample and stratified by gender and age group (50-64 and 65+). We fitted violin plots showing density distributions to further differentiate alcohol consumption outcomes among drinkers. Next, we plotted historical variation across countries. Due to the inclusion of refreshment birth cohorts, these plots reflect historical time more than aging of the samples (on average we observe 6 years of historical time and 1.72 years of aging; see Table 1 and Figure S1).5 Baseline year varied from 1998 (United States) through 2011 (China, Estonia, and Slovenia). Of the 21 countries included, 14 began follow-up between 2006 and 2009. All results show year of baseline assessment on the X-axis. To further characterize alcohol consumption outcomes among drinkers, we used violin plots.

3. RESULTS

Descriptive statistics for all waves are presented in Table 3, for the whole sample and by gender and age group. On average, 51.95% (95% CI = 51.80, 52.09) of older adults aged 50 and over drink and 6.02% (95% CI = 5.95, 6.09) are heavy drinkers. Among individuals who drink, 11.59% (95% CI = 11.47, 11.73) are heavy drinkers, the frequency of drinking is 2.57 (95% CI = 2.56, 2.58) days per week, the number of standard drink units that they drink when drinking is 2.57 (95% CI = 2.56, 2.59), and the average number of drinks per day that they have over a week is 1.12 (95% CI = 1.10, 1.13). Gender differences were apparent for all consumption indicators (see Table 3). Age-group differences are more complex, as the old-old drink more frequently but less quantity relative to those 50-64 years old.

Table 3.

Descriptive statistics, overall and by gender and age group

All (N=179,881) Male (N=81,253) Female (N=98,628) 50-64 years (N=87,877) 65 years and older (N=93,768)
Variable Mean or % 95% CI Mean or % 95% CI Mean or % 95% CI Mean or % 95% CI Mean or % 95% CI
Drinking status (%)
 Heavy 6.02 5.95, 6.09 9.46 9.33, 9.59 3.33 3.27, 3.41 7.89 7.78, 8.01 4.34 4.25, 4.42
 Moderate 30.19 30.06, 30.33 39.55 39.34, 39.77 22.86 22.7, 23.03 32.37 32.17, 32.57 28.22 28.04, 28.41
 Occasional 15.73 15.63, 15.84 14.39 14.23, 14.54 16.79 16.64, 16.93 17.26 17.10, 17.42 14.36 14.22, 14.50
 Current abstainers 20.71 20.59, 20.83 20.96 20.78, 21.14 20.51 20.35, 20.67 17.38 17.22, 17.54 23.71 23.54, 23.88
 Lifetime or long-term abstainers 27.35 27.22, 27.48 15.64 15.48, 15.8 36.51 36.32, 36.7 25.10 24.92, 25.28 29.37 29.19, 29.56
Frequency (days per week) 2.57 2.56, 2.58 3.00 2.99, 3.02 2.08 2.06, 2.09 2.41 2.39, 2.42 2.76 2.74, 2.77
Quantity (number of SDUs when drinking) 2.57 2.56, 2.59 3.10 3.08, 3.13 1.96 1.94, 1.98 2.83 2.80, 2.85 2.29 2.27, 2.32
Drinks per day (average N of daily drinks per week) 1.12 1.10, 1.13 1.49 1.47, 1.51 0.69 0.67, 0.70 1.15 1.13, 1.17 1.08 1.06, 1.10
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Notes: SDUs are standard drinking units. Frequency, quantity, and drinks per day are reported among drinkers.

A more nuanced differentiation of alcohol consumption outcomes among drinkers is presented in Figure 1. The violin plots show, for example, that heavy drinkers tend to consume alcohol either daily or less than three days per week, possibly on weekends.

Figure 1.

Figure 1.

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Distribution of alcohol consumption outcomes among drinkers, by gender

Types of Drinkers and Abstainers

While overall a slight majority of older adults consume alcohol (51.95%, 95% CI = 51.80, 52.09), there is considerable variation in alcohol consumption patterns across countries and time. Types of drinkers and abstainers across country and time are plotted in Figure 2. The highest proportion of heavy drinkers was observed for England in 2010 (23.54%, 95% CI = 22.53, 24.58), followed by Czech Republic in 2013 (13.37%, 95% CI = 12.46, 14.32), Korea in 2010 (13.26%, 95% CI = 12.49, 14.06), and Belgium in 2007 (11.15%, 95% CI = 10.06, 12.31). In contrast, the highest proportion of lifetime abstainers was observed for China in 2011 (69.65%, 95% CI = 68.83, 70.45), followed by Israel in 2010 (59.77%, 95% CI = 57.78, 61.75) and Korea in 2006 (59.15%, 95% CI = 58.07, 60.22). Prevalence of abstainers and heavy drinkers are only moderately associated (see Figure S2).6

Figure 2.

Figure 2.

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Historical variation in types of drinkers and abstainers, by country

Variation over historical time is also noteworthy. We observed increases in the prevalence of any consumption among older adults in 13 countries (Austria, Belgium, Chile, China, Czech Republic, France, Germany, Israel, Netherlands, Slovenia, Spain, Sweden, and the United States) and decreases in eight countries (Denmark, England, Estonia, Italy, Korea, Mexico, Poland, and Switzerland). Confidence intervals suggest percent point increases in any alcohol consumption from the first to the last wave in Austria (4.69, 95% CI = 1.70, 7.65), Chile (1.98, 95% CI = 0.40, 3.57), China (6.18, 95% CI = 5.20, 7.16), Czech Republic (5.44, 95% CI = 3.18, 7.71), France (5.88, 95% CI = 3.87, 7.89), Slovenia (4.67, 95% CI = 2.05, 7.26), and the United States (6.43, 95% CI = 5.43, 7.42). In contrast, confidence intervals suggest percent point decreases in any consumption from the first to the last wave in England (−1.76, 95% CI = −3.04, −0.47), Estonia (−5.24, 95% CI = −7.00, −3.48), Italy (−8.77, 95% CI = −11.07, −6.46), Mexico (−11.85, 95% CI = −12.95, −10.75), and Poland (−6.26, 95% CI = −9.40, −3.16).

Among heavy drinkers, the changes were of smaller magnitude, but confidence intervals suggested increases in six countries (Austria, China, Czech Republic, Denmark, Germany, and Unites States) and decreases in seven countries (Belgium, France, Italy, Mexico, Netherlands, Poland, and Spain). The largest percent point increases in heavy drinkers were observed for Czech Republic (5.38, 95% CI = 4.11, 6.65), Austria (2.39, 95% CI = 1.26, 3.52), and Denmark (1.65, 95% CI = 1.12, 2.17), while the largest percent point decreases were observed for Italy (−4.23, 95% CI = −5.39, −3.07), Spain (−3.18, 95% CI = −4.38, −1.98), and France (−2.93, 95% CI = −4.19, −1.67).

Frequency and Quantity of Alcohol Consumption

Historical variation in frequency and quantity of alcohol consumption among drinkers and by age group is plotted in Figure 3. Frequency indicates the average number of days per week that older adults drank. Quantity indicates the number of standard drink units that older adults drank on average when drinking or when last drank. Overall, there is more cross-country variation in frequency than quantity of alcohol consumption. Two very distinct types of countries emerge from this visualization: a group—including Austria, Belgium, China, Denmark, England, France, Germany, Israel, Italy, Netherlands, Slovenia, Spain, Sweden, Switzerland, and United States—in which frequency is higher than quantity of drinking, and another group—including Chile, Czech Republic, Estonia, Korea, Mexico, and Poland—in which quantity is higher than frequency of drinking. Another pattern in Figure 3 is that the 50-64 age group tends to drink more quantity but less often, while the 65+ age group tends to drink less quantity but more frequently.

Figure 3. Historical variation in frequency and quantity of alcohol consumption among drinkers, by age and country.

Figure 3.

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Notes: Frequency and quantity are reported for drinkers. Quantity was top-coded at 7 for 4.86% of the observations that had extreme values.

Any Alcohol Consumption

Historical variation in the percentage of older adults who drink, stratified by gender, is plotted in Figure 4. In Chile, China, Czech Republic, Israel, Italy, Korea, Mexico, Poland, Slovenia, and Spain, men were more likely to consume any alcohol than women. This gender gap slightly widened over historical time for Italy, Mexico, and Poland, while it became narrower for Chile, China, Korea, Slovenia, and Spain, and completely disappeared for Czech Republic. The gender gap was greatest for China in 2011, with 87.98% (95% CI = 86.61, 89.25) of men reporting current alcohol use compared with 12.02% (95% CI = 10.75, 13.38) of women (gender ratio 7.32:1.00). In contrast, a similar percentage of males and females consume any alcohol in Australia, Belgium, Denmark, England, Estonia, France, Germany, Netherlands, Sweden, Switzerland, and the United States. Overall, the percentage of drinkers is always higher for males than females, but among individuals age 50-64 (see Figure S3),7 the prevalence of drinking can be higher among women than men (in Sweden and for the most recent years in Austria, Belgium, Denmark, England, France, Germany, Netherlands, Poland, Switzerland, and the United States).

Figure 4.

Figure 4.

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Percentage of drinkers for males and females, by country

Drinks per Day

Historical variation in drinks per day among drinkers, stratified by gender, is plotted in Figure 5. Regardless of age, men consumed more average drinks per day than women in all countries, yet there was substantial cross-country variation. Gender differences in average drinks per day ranged from the largest for Czech Republic in 2013 (average among men: 3.39 [95% CI = 2.91, 3.87], average among women: 1.10, 95% CI = 0.83, 1.37) to the smallest for Israel in 2013 (average among men: 0.47, 95% CI = 0.34, 0.60; average among women: 0.26, 95% CI = 0.16, 0.36).

Figure 5. Average drinks per day for males and females among drinkers, by country.

Figure 5.

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Notes: Drinks per day reported for drinkers and top-coded at 7 for 1.37% of the observations that had extreme values.

4. DISCUSSION

The present study documents both cross-country and historical variation in alcohol consumption among older adults, emphasizing differences by gender and age group. Given that alcohol consumption causes greater morbidity and mortality at older than younger ages (Lehmann et al., 2018; Rehm et al., 2006; Rehm et al., 2009; Samokhvalov et al., 2010), the combination, harmonization, and analysis of older adult samples across seven longitudinal surveys advances the field of alcohol epidemiology by providing a rigorous dataset documenting that a slight majority of older adults consumes alcohol and that the proportion of older adults who drink increased in 13 of the 21 countries analyzed. These data are congruent with multiple sources showing historical increases in heavy drinking (Breslow et al., 2017; Han et al., 2017; Kim et al., 2012; World Health Organization, 2014) and alcohol disorders among older adults (Grant et al., 2017) across many countries.

We find, however, interesting differences between countries: both moderate and heavy drinking in later life are increasing in Austria, China, Czech Republic, and Unites States, but decreasing in Italy, Mexico, and Poland. Identifying countries in which older adults are drinking in more or less harmful ways is a first step towards promoting healthy aging in a context of rapid demographic change (Fried, 2016; Staudinger, 2015). Further research should consider the interplay of potential drivers of changing alcohol consumption patterns among older adults, including health (Han et al., 2017), loneliness (Canham et al., 2016), unemployment and economic downturns (Kalousova and Burgard, 2014), and a variety of alcohol-related policies (Anderson et al., 2009; Hahn et al., 2010; Middleton et al., 2010; Popova et al., 2009; Wagenaar et al., 2009).

Consistent with previous studies (Bloomfield et al., 2003), we find evidence for two types of drinking cultures across countries: one in which individuals drink frequently but in small quantities, and another in which they drink large quantities but not very often. Our results, however, also show that individuals age 50-64 consume higher quantities of alcohol but less often, while individuals age 65+ consume lower amounts but more frequently. This pattern requires further research to comprehensively understand underlying drivers of differences across older age, and may be due to young-old adults having healthier physiologic, metabolic, and medication profiles that allow them to drink higher quantities (Cederbaum, 2012; Han and Moore, 2018; Immonen et al., 2013), whereas old-old adults may have more available time to drink frequently, especially on weekdays after retiring (Gauthier and Smeeding, 2003). Further study of patterns and timing of alcohol consumption by age group and across countries are needed to disentangle these relationships.

Our findings document wide variation across countries and over historical time and underscore the need to investigate the macro-social causes of alcohol use around the world. For example, alcohol policies such as taxes and minimum prices may contribute to reduce harmful patterns of consumption (Anderson et al., 2009; Wagenaar et al., 2009). Restrictions on access and opportunities for consumption, including opening and closing times as well as hours and days of sale for alcohol (Hahn et al., 2010; Middleton et al., 2010; Popova et al., 2009), are also associated with changes in consumption. Examining cross-country and historical variation in consumption using high-quality survey data is a first step in a long-term research agenda aiming to identify population-level strategies to control harmful drinking, which may operate differently depending on the context of alcohol consumption. Indeed, countries such as Korea, which exhibited among the highest levels of alcohol abstention in 2006 (and has historically had high levels of abstention; Edenberg, 2007) but among the highest levels of heavy drinking in 2010 provide a foundation to example contextual factors that drive change. Alcohol abstention in China is also declining substantially, with a growing gender differences between men and women. Given that mobility patterns as well as global economic position and policy have been rapidly shifting in recent decades, further research into how these macro trends funnel to population health behavior such as alcohol consumption and gender differences is warranted for public health.

Across numerous countries, gender differences in alcohol consumption and alcohol-related problems have been converging in recently born cohorts (Keyes et al., 2011; Slade, T. et al., 2016). Our results suggest that among older adults, gender differences are also highly variable across countries and time. We further find that among adults aged 50-64 years, the percentage of women that consumes any alcohol can even be higher relative to men. While gender differences in alcohol consumption are partly due to physiologic differences (Nolen-Hoeksema and Hilt, 2006), they also respond to women’s social status and equality (Keyes et al., 2010; Seedat et al., 2009). Because heavy alcohol consumption has more detrimental health consequences for women than men (Hanna et al., 1997; Schweinsburg et al., 2003), the convergences and cross-overs in gender differences portend a greater risk for chronic disease and medical care among older-adult female drinkers, which may be clinically underdiagnosed and undertreated.

Cross-nationally harmonized observational data are subject to limitations, including varying baseline years, misclassification, and missing data. Self-reports are also subject to error and recall bias. However, we note that: current clinical standards are to query patients directly about current drinking practices (deriving clinical applications and successful interventions) (Kelly et al., 2018), the surveys we used minimized recall bias by focusing on current consumption among drinkers, the harmonized dataset had only 2.31% of data points missing, and many studies have validated self-reported consumption data, including among older adults (O’Connell et al., 2004), through objective indicators of alcohol use such as breathalyzer and blood data (Del Boca and Darkes, 2003; Miller and Anton, 2004; Mundle et al., 1999; Whitford et al., 2009). These studies have provided bounds around the minimum and maximum expected sensitivity (~75%-89%) and specificity (~65-78%) of self-reported drinking, suggesting that self-reports are a valid, if imperfect indicator of alcohol consumption; further, while prevalence estimates may include some error, the historical variation we document is likely reliable.

Large scale survey studies with cross-national and longitudinal information on alcohol consumption are a major advance in the field of alcohol epidemiology, despite their limitations. Wide variation in alcohol consumption across countries and historical time suggests that prevention and intervention efforts on a broad scale can be harnessed for potential population-level health benefits, and variation across gender and age groups reflect both the physiological and social factors that contribute to alcohol-related health. Internationally harmonized longitudinal survey data provide a remarkable opportunity to deepen our understanding of alcohol consumption among older adults, calling for further investigation of what drives observed patterns and providing the basis for studying individual age-trajectories in alcohol consumption and its health implications.

Supplementary Material

1

HIGHLIGHTS.

  • Alcohol-related morbidity and mortality is greater at older than younger age

  • Older adults’ alcohol consumption data were harmonized across countries and time

  • Alcohol consumption patterns vary widely across countries and historical time

  • Later life alcohol consumption is increasing in most but not all countries

  • Variation across gender and age reflects both physiological and social determinants

Acknowledgements:

The authors would like to thank Kasim Allel, Alvaro Castillo-Carniglia, Jan Copeland, Cynthia Córdova, Rosario Donoso, and two anonymous reviewers for their insightful recommendations on this study. The final content the article is solely the responsibility of the authors and does not necessarily represent the views of the people or institutions that supported this study.

Role of Funding Source: This work was supported by the Robert N Butler Columbia Aging Center; the National Institutes of Health to KMK [K01AA021511]; and Comisión Nacional de Investigatión Científica y Tecnológica to EC [FONDECYT Regular #1181009]. These sponsors did not have any involvement in: study design; collection, analysis and interpretation of data; writing of the report; decision to submit the article for publication.

Footnotes

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Conflict of Interest Statement: None declared.

1

Supplementary material can be found by accessing the online version of this paper at http://dx.doi.org and by entering doi: https://doi.org/10.1016/j.drugalcdep.2020.108219.

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Supplementary material can be found by accessing the online version of this paper at http://dx.doi.org and by entering doi: https://doi.org/10.1016/j.drugalcdep.2020.108219.

3

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4

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5

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6

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Contributor Information

Esteban Calvo, Department of Epidemiology and Robert N. Butler Columbia Aging Center, Mailman School of Public Health, Columbia University, New York, NY, United States of America. Society and Health Research Center and Laboratory on Aging and Social Epidemiology, School of Public Health and Facultad de Humanidades, Universidad Mayor, Santiago, Chile.

José T Medina, Laboratory on Aging and Social Epidemiology, Universidad Mayor, Santiago, Chile.

Katherine A Ornstein, Geriatrics and Palliative Medicine and Institute for Translational Epidemiology, Icahn School of Medicine at Mount Sinai, New York, NY, United States of America.

Ursula M Staudinger, Robert N. Butler Columbia Aging Center and Department of Sociomedical Sciences, Mailman School of Public Health, Columbia University, New York, NY, United States of America.

Linda P Fried, Department of Epidemiology and Robert N. Butler Columbia Aging Center, Mailman School of Public Health, Columbia University, New York, NY, United States of America.

Katherine M Keyes, Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, United States of America. Society and Health Research Center, Facultad de Humanidades, Universidad Mayor, Santiago, Chile.

References

  1. Anderson P, Chisholm D, Fuhr DC, 2009. Effectiveness and cost-effectiveness of policies and programmes to reduce the harm caused by alcohol. Lancet 373, 2234–2246. [DOI] [PubMed] [Google Scholar]
  2. Arenas de Mesa A, Bravo D, Behrman JR, Mitchell OS, Todd PE, 2008. The Chilean pension reform turns 25: Lessons from the Social Protection Survey, in: Kay SJ, Sinha T (Eds.), Lessons from Pension Reform in the Americas. Oxford University Press, New York. [Google Scholar]
  3. Bloomfield K, Stockwell T, Gmel G, Rehn N, 2003. International comparisons of alcohol consumption. Alcohol Res. Health 27, 95–109. [PMC free article] [PubMed] [Google Scholar]
  4. Börsch-Supan A, Brandt M, Hunkler C, Kneip T, Korbmacher J, Malter F, Schaan B, Stuck S, Zuber S, 2013. Data resource profile: The Survey of Health, Ageing and Retirement in Europe (SHARE). Int. J. Epidemiol 42, 992–1001. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Bratberg GH, S CW, Wilsnack R, Havas Haugland S, Krokstad S, Sund ER, Bjorngaard JH, 2016. Gender differences and gender convergence in alcohol use over the past three decades (1984–2008), The HUNT Study, Norway. BMC Public Health 16, 723. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Breslow RA, Castle IP, Chen CM, Graubard BI, 2017. Trends in alcohol consumption among older Americans: National Health Interview Surveys, 1997 to 2014. Alcohol Clin. Exp. Res 41, 976–986. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Canham SL, Mauro PM, Kaufmann CN, Sixsmith A, 2016. Association of alcohol use and loneliness frequency among middle-aged and older adult drinkers. J. Aging Health 28, 267–284. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Cederbaum AI, 2012. Alcohol metabolism. Clin. Liver Dis 16, 667–685. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Centers for Disease Control and Prevention, 2016. Excessive alcohol use: Preventing a leading risk for death, disease, and injury, Atlanta, GA [Google Scholar]
  10. Del Boca FK, Darkes J, 2003. The validity of self-reports of alcohol consumption: State of the science and challenges for research. Addiction 98 Suppl 2, 1–12. [DOI] [PubMed] [Google Scholar]
  11. Edenberg HJ, 2007. The genetics of alcohol metabolism: Role of alcohol dehydrogenase and aldehyde dehydrogenase variants. Alcohol Res. Health, 30, 5–13. [PMC free article] [PubMed] [Google Scholar]
  12. Fried LP, 2016. Investing in health to create a third demographic dividend. Gerontologist 56 Suppl 2, S167–177. [DOI] [PubMed] [Google Scholar]
  13. Gauthier AH, Smeeding TM, 2003. Time use at older ages: Cross-national differences. Res. Aging 25, 247–274. [Google Scholar]
  14. Global Burden of Disease Collaborative Network. Global Burden of Disease Study 2016 (GBD 2016) Alcohol Use Estimates 1990-2016. Seattle, United States: Institute for Health Metrics and Evaluation (IHME), 2018. [Google Scholar]
  15. Grant BF, Chou SP, Saha TD, Pickering RP, Kerridge BT, Ruan WJ, Huang B, Jung J, Zhang H, Fan A, Hasin DS, 2017. 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. JAMA Psychiatry. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Griswold MG, Fullman N, Hawley C, Arian N, Zimsen SRM, Tymeson HD, Venkateswaran V, Tapp AD, Forouzanfar MH, Salama JS, Abate KH, Abate D, Abay SM, Abbafati C, Abdulkader RS, Abebe Z, Aboyans V, Abrar MM, Acharya P, Adetokunboh OO, Adhikari TB, Adsuar JC, Afarideh M, Agardh EE, Agarwal G, et al. , 2018. Alcohol use and burden for 195 countries and territories, 1990–2016: A systematic analysis for the Global Burden of Disease Study 2016. Lancet 392, 1015–1035. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Hahn RA, Kuzara JL, Elder R, Brewer R, Chattopadhyay S, Fielding J, Naimi TS, Toomey T, Middleton JC, Lawrence B, 2010. Effectiveness of policies restricting hours of alcohol sales in preventing excessive alcohol consumption and related harms. Am J. Prev. Med 39, 590–604. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Han BH, Moore AA, 2018. Prevention and screening of unhealthy substance use by older adults. Clin. Geriatr. Med 34, 117–129. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Han BH, Moore AA, Sherman S, Keyes KM, Palamar JJ, 2017. Demographic trends of binge alcohol use and alcohol use disorders among older adults in the United States, 2005–2014. Drug Alcohol Depend. 170, 198–207. [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Hanna EZ, Chou SP, Grant BF, 1997. The relationship between drinking and heart disease morbidity in the United States: Results from the National Health Interview Survey. Alcohol. Clin. Exp. Res 21, 111–118. [PubMed] [Google Scholar]
  21. Immonen S, Valvanne J, Pitkala KH, 2013. The prevalence of potential alcohol-drug interactions in older adults. Scand. J. Prim. Health Care 31, 73–78. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Jang S-N, 2015. Korean Longitudinal Study of Ageing (KLoSA): Overview of research design and contents, in: Pachana NA (Ed.) Encyclopedia of Geropsychology. Springer Singapore, Singapore, pp. 1–9. [Google Scholar]
  23. Kalousova L, Burgard SA, 2014. Unemployment, measured and perceived decline of economic resources: contrasting three measures of recessionary hardships and their implications for adopting negative health behaviors. Soc. Sci. Med 106, 28–34. [DOI] [PubMed] [Google Scholar]
  24. Kelly AT, Mozayani A, 2012. An overview of alcohol testing and interpretations in the 21st century. J. Pharm. Pract 25, 30–36. [DOI] [PubMed] [Google Scholar]
  25. Kelly S, Olanrewaju O, Cowan A, Brayne C, Lafortune L, 2018. Interventions to prevent and reduce excessive alcohol consumption in older people: A systematic review and meta-analysis. Age Ageing 47, 175–184. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Keyes KM, Li G, Hasin DS, 2011. Birth cohort effects and gender differences in alcohol epidemiology: a review and synthesis. Alcohol Clin. Exp. Res 35, 2101–2112. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Keyes KM, Martins SS, Blanco C, Hasin DS, 2010. Telescoping and gender differences in alcohol dependence: new evidence from two national surveys. Am. J. Psychiatry 167, 969–976. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Kim JW, Lee DY, Lee BC, Jung MH, Kim H, Choi YS, Choi IG, 2012. Alcohol and cognition in the elderly: A review. Psychiatry Investig. 9, 8–16. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Lehmann SW, Longo DL, Fingerhood M, 2018. Substance-use disorders in later life. N. Engl. J. Med 379, 2351–2360. [DOI] [PubMed] [Google Scholar]
  30. Meier P, Seitz H, 2008. Age, alcohol metabolism and liver disease. Curr. Opin. Clin. Nutr. Metab. Care 11, 21–26. [DOI] [PubMed] [Google Scholar]
  31. Merline A, Jager J, Schulenberg JE, 2008. Adolescent risk factors for adult alcohol use and abuse: stability and change of predictive value across early and middle adulthood. Addiction 103 Suppl 1, 84–99. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Middleton JC, Hahn RA, Kuzara JL, Elder R, Brewer R, Chattopadhyay S, Fielding J, Naimi TS, Toomey T, Lawrence B, 2010. Effectiveness of policies maintaining or restricting days of alcohol sales on excessive alcohol consumption and related harms. Am. J. Prev. Med 39, 575–589. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Miller PM, Anton RF, 2004. Biochemical alcohol screening in primary health care. Addict Behav. 29, 1427–1437. [DOI] [PubMed] [Google Scholar]
  34. Mundle G, Ackermann K, Gunthner A, Munkes J, Mann K, 1999. Treatment outcome in alcoholism - a comparison of self-report and the biological markers carbohydrate-deficient transferrin and gamma-glutamyl transferase. Eur. Addict. Res 5, 91–96. [DOI] [PubMed] [Google Scholar]
  35. NIAAA, 2015. What is a standard drink? Available at: https://www.niaaa.nih.gov/alcohol-health/overview-alcohol-consumption/what-standard-drink. Accessed on September 3rd, 2017.
  36. NIAAA, 2005. Helping Patients Who Drink Too Much: A Clinician’s Guide. Available at: https://pubs.niaaa.nih.gov/publications/Practitioner/CliniciansGuide2005/guide.pdf. Accessed on September 3rd, 2017.
  37. Nolen-Hoeksema S, Hilt L, 2006. Possible contributors to the gender differences in alcohol use and problems. J. Gen. Psychol 133, 357–374. [DOI] [PubMed] [Google Scholar]
  38. O’Connell H, Chin AV, Hamilton F, Cunningham C, Walsh JB, Coakley D, Lawlor BA, 2004. A systematic review of the utility of self-report alcohol screening instruments in the elderly. Int. J. Geriatr. Psychiatry 19, 1074–1086. [DOI] [PubMed] [Google Scholar]
  39. Obot IS, Room R, 2006. Alcohol, gender and drinking problems: Perspectives from low and middle income countries. World Health Organization, Geneva, Switzerland. [Google Scholar]
  40. Popova S, Giesbrecht N, Bekmuradov D, Patra J, 2009. Hours and days of sale and density of alcohol outlets: impacts on alcohol consumption and damage: a systematic review. Alcohol Alcohol. 44, 500–516. [DOI] [PubMed] [Google Scholar]
  41. Poznyak V, Fleischmann A, Rekve D, Rylett M, Rehm J, Gmel G, 2013. The World Health Organization’s global monitoring system on alcohol and health. Alcohol Res. 35, 244–249. [PMC free article] [PubMed] [Google Scholar]
  42. Rehm J, Greenfield TK, Kerr W, 2006. Patterns of drinking and mortality from different diseases - an overview. Contemp. Drug Probl 33, 205–235. [Google Scholar]
  43. Rehm J, Mathers C, Popova S, Thavorncharoensap M, Teerawattananon Y, Patra J, 2009. Global burden of disease and injury and economic cost attributable to alcohol use and alcohol-use disorders. Lancet 373, 2223–2233. [DOI] [PubMed] [Google Scholar]
  44. Rehm J, Poznyak V, 2015. On monitoring unrecorded alcohol consumption. Alcoholism and Drug Addiction 28, 79–89. [Google Scholar]
  45. Samokhvalov AV, Irving HM, Rehm J, 2010. Alcohol consumption as a risk factor for atrial fibrillation: A systematic review and meta-analysis. Eur. J. Cardiovasc. Prev. Rehabil 17,706–712. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Schweinsburg BC, Alhassoon OM, Taylor MJ, Gonzalez R, Videen JS, Brown GG, Patterson TL, Grant I, 2003. Effects of alcoholism and gender on brain metabolism. Am. J. Psychiatry 160, 1180–1183. [DOI] [PubMed] [Google Scholar]
  47. Seedat S, Scott KM, Angermeyer MC, Berglund P, Bromet EJ, Brugha TS, Demyttenaere K, De Girolamo G, Haro JM, Jin R, 2009. Cross-national associations between gender and mental disorders in the World Health Organization World Mental Health Surveys. JAMA Psychiatry 66, 785–795. [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Slade T, Chapman C, Swift W, Keyes K, Tonks Z, Teesson M, 2016. Birth cohort trends in the global epidemiology of alcohol use and alcohol-related harms in men and women: Systematic review and metaregression. BMJ Open 6(10), e011827. [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Slade T, Chiu W-T, Glantz M, Kessler RC, Lago L, Sampson N, Al-Hamzawi A, Florescu S, Moskalewicz J, Murphy S, Navarro-Mateu F, Torres de Galvis Y, Viana MC, Xavier M, Degenhardt L, 2016. A cross-national examination of differences in classification of lifetime alcohol use disorder between DSM-IV and DSM-5: Findings from the World Mental Health Survey. Alcohol Clin. Exp. Res 40, 1728–1736. [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Sonnega A, Faul JD, Ofstedal MB, Langa KM, Phillips JW, Weir DR, 2014. Cohort profile: The health and retirement study (HRS). Int. J. Epidemiol 43, 576–585. [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Staudinger U, 2015. The future of aging: How I will live, in: Barysch K (Ed.) Our world and us: how our environment and our societies will change. Allianz SE, Munich, Germany, pp. 137–149. [Google Scholar]
  52. Steptoe A, Breeze E, Banks J, Nazroo J, 2012. Cohort profile: The English Longitudinal Study of Ageing. Int. J. Epidemiol 42, 1640–1648. [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Wagenaar AC, Salois MJ, Komro KA, 2009. Effects of beverage alcohol price and tax levels on drinking: a meta-analysis of 1003 estimates from 112 studies. Addiction 104, 179–190. [DOI] [PubMed] [Google Scholar]
  54. Whitford JL, Widner SC, Mellick D, Elkins RL, 2009. Self-report of drinking compared to objective markers of alcohol consumption. Am. J. Drug Alcohol Abuse 35, 55–58. [DOI] [PubMed] [Google Scholar]
  55. Wilsnack RW, Vogeltanz ND, Wilsnack SC, Harris TR, Ahlstrom S, Bondy S, Csemy L, Ferrence R, Ferris J, Fleming J, Graham K, Greenfield T, Guyon L, Haavio-Mannila E, Kellner F, Knibbe R, Kubicka L, Loukomskaia M, Mustonen H, Nadeau L, Narusk A, Neve R, Rahav G, Spak F, Teichman M, et al. , 2000. Gender differences in alcohol consumption and adverse drinking consequences: Cross-cultural patterns. Addiction 95, 251–265. [DOI] [PubMed] [Google Scholar]
  56. Wong R, Michaels-Obregon A, Palloni A, 2015. Cohort profile: The Mexican Health and Aging Study (MHAS). Int. J. Epidemiol 46, e2–e2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. World Health Organization, 2014. Global status report on alcohol and health. Geneva, Switzerland. [Google Scholar]
  58. Zhao Y, Hu Y, Smith JP, Strauss J, Yang G, 2012. Cohort profile: The China Health and Retirement Longitudinal Study (CHARLS). Int. J. Epidemiol 43, 61–68. [DOI] [PMC free article] [PubMed] [Google Scholar]

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