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Alcohol and Substance Abuse

Predictors and prevalence of hazardous alcohol use in middle-late to late adulthood in Europe

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Pages 1001-1010 | Received 05 Jan 2022, Accepted 22 Apr 2022, Published online: 31 May 2022

Abstract

Objectives: Even low to moderate levels of alcohol consumption can have detrimental health consequences, especially in older adults (OA). Although many studies report an increase in the proportion of drinkers among OA, there are regional variations. Therefore, we examined alcohol consumption and the prevalence of hazardous alcohol use (HAU) among men and women aged 50+ years in four European regions and investigated predictors of HAU.

Methods: We analyzed data of N = 35,042 participants of the European SHARE study. We investigated differences in alcohol consumption (units last week) according to gender, age and EU-region using ANOVAs. Furthermore, logistic regression models were used to examine the effect of income, education, marital status, history of a low-quality parent–child relationship and smoking on HAU, also stratified for gender and EU-region. HAU was operationalized as binge drinking or risky drinking (<12.5 units of 10 ml alcohol/week).

Results: Overall, past week alcohol consumption was 5.0 units (±7.8), prevalence of HAU was 25.4% within our sample of European adults aged 50+ years. Male gender, younger age and living in Western Europe were linked to both higher alcohol consumption and higher risks of HAU. Income, education, smoking, a low-quality parent–child relationship, living in Northern and especially Eastern Europe were positively associated with HAU. Stratified analyses revealed differences by region and gender.

Conclusions: HAU was highly prevalent within this European sample of OA. Alcohol consumption and determinants of HAU differed between EU-regions, hinting to a necessity of risk-stratified population-level strategies to prevent HAU and subsequent alcohol use disorders.

Introduction

Although the proportion of drinkers in people aged 15+ years slightly decreased worldwide, overall per capita consumption (including abstainers) of pure alcohol rose from 5.7 l in 2000 to 6.4 l in 2010 and 2016 (World Health Organization, 2018). With each unit containing 10 ml of pure alcohol, this equals an increase from 570 to 640 units a year and results in one glass of wine (125 ml) or a bottle of beer (330 ml) each day on average. This is concerning, because even low to moderate levels of alcohol consumption were linked to serious health consequences like cardiovascular diseases (Wood et al., 2018), cancer (Bagnardi et al., Citation2015) and all-cause mortality (Wood et al., 2018), and heavy drinking increases the risk for aggressive behavior (Heinz et al., Citation2011) as well as cognitive decline and dementia (Livingston et al., Citation2020; Rao et al., Citation2021).

Alcohol use changes over the life course and frequent drinking becomes especially prevalent in mid to older age (Britton et al., Citation2015). For adults aged 50+ years, studies report increasing prevalences of drinking over the past years (Bye & Moan, Citation2020; Calvo et al., Citation2020; Grucza et al., Citation2018). Calvo et al. (2020) report a decrease in the proportion of abstainers aged 50+ years in 13 of 21 examined countries from 1998 to 2016. Although the picture is less clear, findings also indicate an increase in the prevalence of harmful alcohol use among older adults (OA) in several countries, like binge drinking or heavy episodic drinking (Calvo et al., Citation2020; Han & Moore, Citation2018). For example, Grucza et al. (2018) observed an increase of 3.4% per year (2000–2016) in binge drinking in the US among OA (65+), and in Norway, the proportion of OA (60+) reporting any heavy episodic drinking within the last year rose from 17% in 1985 to 30% in 2017 (Bye & Moan, Citation2020). Calvo et al. (Citation2020) report an increase of heavy drinking among OA in Austria, China, Czech Republic, Denmark, Germany and the United States, but at the same time, prevalence decreased in Belgium, France, Italy, Mexico, Netherlands, Poland and Spain; for the remaining eight countries included in this study, rates of heavy drinking remained relatively stable. This underlines the wide variations of alcohol use among OA found across countries, which might be due to religious or cultural differences, alcohol policies (Angus et al., Citation2019; Calvo et al., Citation2021; Sudhinaraset et al., Citation2016) or other factors that affect alcohol consumption according to previous studies. For example, male gender (Bloomfield et al., Citation2006; Devaux & Sassi, Citation2016) and smoking (Paula et al., Citation2022; Wang et al., Citation2022) are associated with higher alcohol intake or risky drinking among OA, while for marital status findings are more complex and vary by gender or region (Gell et al., Citation2015; Iparraguirre, Citation2015). Higher education is associated with higher likelihood of current drinking, irrespective of gender (Bloomfield et al., Citation2006; Grittner et al., Citation2013). For binge drinking or drinking larger amounts of alcohol on a regular basis, the pattern is less clear and differs between genders and regions (Bloomfield et al., Citation2006; Devaux & Sassi, Citation2016; Grittner et al., Citation2013). Additionally, there is some evidence for a protective effect of a good parent–child relationship on adolescent (Visser et al., Citation2012) and (early) adulthood alcohol use (Boden et al., Citation2021; Mak & Iacovou, Citation2019), but less is known about this link in OA. Since adolescent drinking behavior has been associated with alcohol use in adulthood (Cable & Sacker, Citation2008; Jefferis et al., Citation2005), such an effect of parent–child relationship could potentially extend to later ages.

The trend of increasing prevalences of alcohol consumption in OA is concerning, since drinking can be especially harmful for older people. First, OA react more sensitive to alcohol due to age-related physiological changes (Barry & Blow, Citation2016; Oneta et al., Citation2001). Second, medication use and polypharmacy are common within OA and the combination of drugs and alcohol can be particularly harmful (Cousins et al., Citation2014; Han & Moore, Citation2018). Third, the effect of alcohol related injuries and falls can be detrimental for OA, as falls tend to have worse consequences for older compared to younger people (Chatha et al., Citation2018) and the additional influence of alcohol can lead to worse injuries (Shakya et al., Citation2020).

Europe is the region with the highest proportion of current drinkers and the highest per capita consumption worldwide (World Health Organization, Citation2018). However, since alcohol consumption and the influence of its contributing factors vary regionally, partly because of differences in alcohol policies, taxation, country-level SES or social and cultural contexts (Angus et al., Citation2019; Grittner et al., Citation2013; Sudhinaraset et al., Citation2016), it is likely that prevalences and predictors of alcohol use differ regionally. Therefore, we aimed to examine and compare alcohol consumption and the prevalence of hazardous alcohol use (HAU) as well as its predictors among men and women aged 50+ years in four European regions (Northern, Eastern, Southern, Western Europe).

Materials & methods

Data were drawn from the longitudinal Survey of Health, Aging and Retirement in Europe (SHARE) (Börsch-Supan et al., Citation2013) in the sense of a retrospective cohort study. SHARE collects data on health, SES, and social networks of individuals aged 50+ years and their partners. Nearly 140,000 participants from 28 European countries and Israel participated between 2004 (Wave 1) and 2017 (Wave 7) (Bergmann et al., Citation2019). SHARE applies probability-based sampling. Where possible, sampling is based on an official population register. If such a register is not available, the best alternative sampling frame resource is used (i.e. telephone directory and screening procedure). Our analysis is based on Wave 6 of SHARE and uses an additional predictor from Wave 7 (see ; for more details, see next section). We grouped countries to EU-regions according to the United Nations Statistics Divisions’ list of geographic regions (United Nations Statistics Division, n.d.): Northern Europe (Denmark, Estonia, Sweden), Eastern Europe (Czech Republic, Poland), Southern Europe (Croatia, Greece, Italy, Portugal, Slovenia, Spain) and Western Europe (Austria, Belgium, France, Germany, Luxembourg, Switzerland). We did not include participants from Israel.

Figure 1. Flow chart of the study sample. Note: * Some participants’ partners were additionally interviewed, regardless of their age.

Figure 1. Flow chart of the study sample. Note: * Some participants’ partners were additionally interviewed, regardless of their age.

Measurements

Most variables, including information on alcohol consumption within the last week and binge drinking, were taken from Wave 6 of SHARE in 2015 (Börsch-Supan, Citation2018). Parent–child relationship quality (concerning participants’ own childhood) was obtained from SHARElife Wave 7 (2017) (Börsch-Supan, Citation2019).

Outcome variables

Current alcohol consumption was indicated by alcohol units (10 ml of pure alcohol) consumed in the past seven days. It was assessed via self-reported consumption of standard drinks in the past week with approximately 1.4 units of pure alcohol per drink (Alcohol Units, Citation2018).

We defined risky alcohol consumption, according to Wood et al. (2018), as the consumption of >12.5 units alcohol per week (7.5 bottles of beer (330 ml) or six glasses of wine (175 ml).

Binge drinking was defined as consuming six or more standard drinks on at least one occasion within the last three months (World Health Organisation, Citation2014).

We compared the original data on number of drinks during the past week as well as binge drinking, and checked for plausibility and missing data. Where possible, we derived the information from the other variable if either was missing. If both items were answered, but substantially contradicted each other, we used the higher value, because social desirability more likely leads to underreporting of alcohol consumption.

We defined hazardous use of alcohol (HAU) as either binge drinking at least once in the past 3 months or risky alcohol consumption in the past week (>12.5 units/week), as both drinking habits carry the risk of adverse health consequences in adults (Wood et al., 2018). Since Wave 6 interviews contained no global question on current drinking behavior, but only asked for binge drinking and alcohol consumption during the past week, it was impossible to identify abstainers.

Other variables

We categorized age into four categories (50–59, 60–60, 70–79 and 80+ years).

Income per capita (€) was calculated by dividing the total annual household net income by the number of household members. Marital status, Educational years (full-time) and Smoking status are self-reported information. A positive smoking status was present when participants ever smoked cigarettes, cigars, cigarillos or a pipe daily for a period of at least one year.

We extracted information on the relationship quality to participants’ parents or guardian during childhood and adolescence. We dichotomized the available categories in order to contrast the lowest quality relationships (‘poor’ relationship with mother/father/both) against higher quality relationships (‘excellent’ to ‘fair’ relationships).

Statistical analyses

We replaced missing values in marital status, income, years of education and smoking status by multiple imputations provided within the SHARE dataset (de Luca, Citation2018; de Luca et al., Citation2015). Participants with an alcohol consumption (units last week) that exaggerated the group mean by more than three standard deviations were considered outliers and were excluded. All analyses were performed using SPSS27. All tests were performed two-sided and considered significant at p < .05. Income was transformed using a natural logarithm to improve the distribution characteristics.

We conducted a 2 × 4 × 4 factorial analysis of variance (ANOVA) to analyze main effects of gender, age and EU-region on alcohol consumption. Due to variance heterogeneity in our ANOVA data, we compared our results to robust parameters based on a regression model (Hayes & Cai, Citation2007). Post hoc tests were conducted via Fisher’s Least Significant Difference (LSD). A hierarchical binary logistic regression analysis was used to investigate predictors of HAU. All full regression models were stratified by gender and EU-region, stratified analyses were corrected using the Bonferroni–Holm method (Holm, Citation1979).

We performed collinearity diagnostics for the regression models by analyzing the Variance Inflation Factors (VIF) and the tolerance statistics, which did not reveal any multicollinearity for our variables (all VIF < 1.16, average VIF = 1.084, all tolerance scores > .86).

Results

The distribution of participants and their age, gender, living status, income and education by EU-regions are presented in .

Table 1. Sample characteristics.

Effects of EU-region, age and gender on alcohol consumption

Mean units of alcohol consumption in the last week by EU-region, age and gender are displayed in and and . There was a statistically significant main effect of age (brackets) on the units of alcohol consumed (F3.35042 = 95.88, p < .001, ηp2 = .008). Post hoc tests revealed significantly less units of alcohol in older compared to younger age brackets (all p < .001), except for higher alcohol consumption in the age bracket 60–69 years compared to age bracket 50–59 years (p = .126). However, this effect is due to a differential gender distribution in the age brackets and disappears in gender-stratified analyses (see ). EU-regions also showed a significant main effect on units of alcohol consumed (F3.35042 = 294.64, p < .001, ηp2 = .025). Post hoc tests revealed differences in alcohol consumption between all four EU-regions (all p < .001), except for the comparison between Southern Europe and Northern Europe (p = .279) and highest alcohol consumption in Western Europe. Furthermore, there was a significant main effect for gender on consumed alcohol units (F1.35042 = 3577.30, p < .001, ηp2 = .093) with lower alcohol consumption in female participants.

Figure 2. Alcohol consumption in men and women by EU-region and age (Overall sample: N = 35,042).

Figure 2. Alcohol consumption in men and women by EU-region and age (Overall sample: N = 35,042).

Figure 3. (a) Alcohol consumption by EU-region and age in women (N = 20,125). (b) Alcohol consumption by EU-region and age in men (N = 14,917).

Figure 3. (a) Alcohol consumption by EU-region and age in women (N = 20,125). (b) Alcohol consumption by EU-region and age in men (N = 14,917).

Table 2. Alcohol consumption in middle-late to late adulthood in Europe.

Prevalence of hazardous alcohol use

Prevalences of HAU (binge drinking in the past three months or risky alcohol consumption (>12.5 units/week) by gender, age and EU-region are shown in . All categories (gender, age, EU-region) showed significant differences at p < .001 (chi-squared tests).

Table 3. Hazardous alcohol use in middle-late to late adulthood in Europe.

Predictors of hazardous alcohol use

The hierarchical binary logistic regression models for hazardous versus non-hazardous alcohol use revealed statistically significant odds ratios (OR) for the following variables or categories (see , Model 1): female gender (OR = .318, CI = .301–.336, p < .001), age categories 50–59, 60–69, 70–79 (reference category 80+, all ps < .001), education (OR = 1.035, CI = 1.029–1.041, p < .001), annual household income (OR = 1.137, CI = 1.116–1.158, p = .008), ‘married, not living with spouse’ and ‘divorced’ (reference category ‘married, living with spouse’; OR=.1.392, CI = 1.113–1.741, p = .004 and OR = 1.093, CI = 1.001–1.194, p = .046, respectively), history of a low-quality parent–child relationship (OR = .894, CI = .804–.995, p = .041), and smoking (OR = 1.487, CI = 1.411–1.567, p < .001).

Table 4. Hierarchical binary logistic regression: Predictors of hazardous versus nonhazardous alcohol use.

Apart from being ‘divorced’, all these predictors stayed significant when entering EU-regions as additional predictors in model 2. In this model, also ‘widowed’ (OR = .885, CI = .805–.973, p = .012) predicted HAU. Compared to living in Southern Europe (reference category), living in Western Europe, in Northern or Eastern Europe (all p < .001) were a significant risk factors for HAU.

Gender-stratified predictors of hazardous alcohol use

In the gender stratified full model (), age brackets remained significant predictors of HAU. ORs for age brackets were slightly higher in men. Furthermore, higher educational years increased the risk for HAU slightly stronger in women (OR = 1.033, CI = 1.022–1.043, p < .001) than in men (OR = 1.017, CI = 1.009–1.026, p < .001). Similarly, higher income increased the risk of HAU stronger in women (OR = 1.098 CI = 1.059–1.138, p < .001) than in men (OR = 1.053, CI = 1.026–1.080, p < .001). Regarding marital status, the category ‘married, not living with spouse’ was only associated with a significantly higher risk of HAU in men (OR = 1.449, CI = 1.072–1.960, p = .032), whereas being widowed only was significantly and negatively associated in women (OR = .807, CI = .713–.912, p = .001). A low-quality parent–child relationship significantly predicted HAU in women (OR = .833, CI = .715–.970, p = .019) and men (OR = .853, CI = .732–.993, p = .040). Female smokers showed a slightly stronger increased risk of HAU (OR = 1.576, CI = 1.454–1.709, p < .001) compared to male smokers (OR = 1.372, CI = 1.279–1.472, p < .001).

Regarding EU-regions, again Western, Northern and Eastern Europe showed higher risks of HAU compared to Southern Europe, with the highest OR in Eastern European men and women. For Western and Northern Europe, the risks were stronger increased in women (OR = 2.327, CI = 2.064–2.623, p < .001; OR = 2.223, CI = 1.969–2.511, p < .001) than in men (OR = 1.808, CI = 1.645–1.987, p < .001; OR = 1.525, CI = 1.382–1.683, p < .001).

Predictors of hazardous alcohol use stratified by EU-region

Stratified by EU-region, analyses revealed gender, age and education as significant predictors in all four regions in Europe (see ), except for 70–79 years. compared to 80+ years. in Eastern Europe. Female gender was associated with a stronger decreased risk for HAU in Southern and Eastern Europe (OR = .239, CI = .213–.268, p < .001; OR = .243, CI = .204–.290, p < .001) than in Western and Northern Europe (OR = .345, CI = .317–.376, p < .001; OR = .377, CI = .338–.420, p < .001). Years of education were positively and stronger associated with an increased risk of HAU in Eastern Europe (OR = 1.065, CI = 1.037–1.095, p < .001) than in all other EU-regions. The increased risk in the youngest compared to the oldest age bracket was stronger in Northern Europe compared to all other European regions. Additionally, higher household income was associated with an increased risk for HAU in Western and Northern Europe (OR = 1.177, CI = 1.121–1.236, p < .001; OR = 1.278, CI = 1.214–1.346, p < .001), but not in Southern and Eastern Europe. Categories of marital status were not significantly associated with HAU in the EU region stratified analyses after Bonferroni–Holm correction (see ).

Table 5. Binary logistic regression: Predictors of hazardous alcohol use stratified by EU-region.

A low-quality parent–child relationship only significantly decreased the risk in Western Europe (OR = .772, CI = .667–.893, p = .002). Lastly, smoking was associated with in increased risk for HAU in all EU-regions. Analyses stratified for gender and EU-region are displayed in Supporting Information Tables S1 and S2.

Discussion

The present study investigated determinants of alcohol consumption and HAU in a large European sample. Regarding alcohol consumption (alcohol units last week), age showed an effect narrowly below a small effect, EU-Region had a small effect, whereas gender had a medium effect size. Alcohol consumption decreased with increasing age and was substantially lower in women. We found a higher alcohol consumption in Western Europe compared to all other EU regions. Northern Europe showed lower alcohol consumption data compared to Western and Eastern Europe, but no difference in alcohol consumption compared to Southern Europe.

HAU was present in 25.4% of our overall sample of European OA. We found differences for gender, age and EU-region in prevalences of HAU, which was more than twice as prevalent in men compared to women and more than twice as prevalent in the youngest age bracket compared to the oldest.

The logistic regression model confirmed these effects of gender and age on HAU. The risks for HAU in younger age brackets compared to the oldest were slightly higher in men than in women, hinting to a slightly stronger difference across age brackets in men versus women. This could in part be due to the fact that overall fewer women engage in HAU compared to men (Britton et al., Citation2015), which means that there is less scope for reduction over the years. Stratification by region and gender (see Supporting Information ) confirmed this pattern for all EU-regions but Southern Europe, where the ORs for women are slightly higher than among men across all age groups. Also, the effect of age differed between EU-regions and was largest in Northern Europe, with higher risks for HAU among younger participants (see ).

Additionally, SES variables like education and income were associated with HAU: Years of education were positively associated with a slightly increasing risk for HAU. This was also true for both genders and all EU-regions. In the gender- and region-stratified analyses this effect only remained significant in Western and Eastern European women. While this association of higher education and higher prevalence of HAU among women has been reported for several countries (Bloomfield et al., Citation2006; Grittner et al., Citation2013), for men, this observation is interesting, since usually the pattern is described as being reversed in men (Devaux & Sassi, Citation2016). This might be explained by our sample of adults aged 50+ years, a population in which alcohol use has previously been associated with higher SES (Bonevski et al., Citation2014). Since the harmful consequences of heavy alcohol consumption more strongly affect people of lower SES (Katikireddi et al., Citation2017; Probst et al., Citation2020), lower educated people might reduce or stop drinking earlier due to health consequences, while persons with higher education may tend to stay relatively healthy and maintain their drinking habits. Within the region-stratified analysis, income was significantly and positively associated with HAU in Northern and Western Europe, but had no significant effect on HAU in Southern and Eastern Europe. The significant positive associations of income and HAU in Northern and Western Europe likely reflect the higher affordability of alcohol for people with higher income, especially in regions with higher alcohol prices (Wagenaar et al., Citation2009): where alcohol prices are high (e.g. because of higher alcohol duty rates (Angus et al., Citation2019)), alcohol is more affordable for people with higher income. Although alcohol policies and taxation differ significantly between countries, most Northern and some Western European countries have higher alcohol duty rates than Eastern and Southern European countries (Angus et al., Citation2019), possibly leading to a higher alcohol consumption among wealthier people in these countries.

Regarding marital status, being ‘widowed’ decreased the risk of HAU, while being ‘married, not living with spouse’ and being ‘divorced’ increased the risk. Gender-stratified analyses revealed that being widowed only decreased the risk in women, while being married, but not living with the spouse significantly increased the risk of HAU in men. Previous studies have shown that single living arrangements often increased the risk of hazardous drinking in older men, while there was mostly no or even a protective effect in older women (Bosque-Prous et al., Citation2017; Iparraguirre, Citation2015; Watt et al., Citation2014). A possible explanation are fewer social contacts (Iliffe et al., Citation2007; Kharicha et al., Citation2007; Shimada et al., Citation2014) and a different influence on drinking habits for men and women. Indeed, a study from Sweden found that women but not men aged 65+ with lower levels of social contacts were less likely to regularly consume alcohol (Agahi et al., Citation2019). This pattern could be explained by conventional gender norms that potentially prevent older women from drinking alone (Agahi et al., Citation2019).

Interestingly, a low-quality parent–child relationship slightly decreased the risk for HAU only for Western European participants. Further region- and gender-stratified analyses revealed that this effect was significant only in Western European men. Again, since a poor parent–child relationship can lead to an early and rather harmful use of alcohol in younger years (Boden et al., Citation2021), such individuals may stop or reduce drinking in later life due to health consequences.

The risk for HAU in smokers was higher in women than men, which is in line with findings of a recent review (Wilsnack et al., Citation2018). The authors assume that changing gender stereotypes might lead to gender differences in the association of binge drinking and tobacco use. Interestingly, smoking showed a weaker association with HAU in Eastern Europe.

EU-regions had an additional predictive value on HAU in our study. All other EU-regions had higher risks of HAU compared to Southern Europe, and Eastern Europe showed the highest risk for HAU. Interestingly, the risk increased in Western and Northern Europe compared to Southern Europe higher for women compared to men. Likewise, the effect of gender was stronger in Western and Northern Europe than in Southern and Eastern Europe.

Strengths & limitations

The large sample of middle- to older-aged participants from all over Europe represents a strength of this study as it allows comparisons throughout large parts of the European continent and it allows stratification for gender and European regions. Therefore, the large sample enabled us to analyze important gender- and EU-region-sensitive predictors of HAU as a crucial public health issue.

Due to a lack of assessing abstinence in Wave 6, we were not able to identify abstainers. Since differences in the proportion of abstainers between regions and age groups are likely (Calvo et al., Citation2020), it is important to notice that the mean units of alcohol last week reflect the consumption per capita rather than the average amount of alcohol consumed within the group of drinkers, which may vary heavily. However, this shortage does not affect the outcomes of the prediction of HAU. Since mental health variables like anxiety or depression are potential confounders of the association between risk or protective factors and alcohol consumption, further research should consider statistical adjustments for mental health. Furthermore, we chose 12.5 units of alcohol per week as the cut-off point for risky alcohol use irrespective of gender, since a higher consumption is associated with increased risk of morbidity and lower life-expectancy in both, men and women (Wood et al., 2018). Compared to most drinking guidelines, this is a rather low threshold (Kalinowski & Humphreys, Citation2016), which might contribute to the high proportion of HAU within our data. But since it is associated to harmful effects on health in men and women (Wood et al., 2018) and was proposed as an threshold especially for older people (Royal College of Psychiatrists, 2018), this seems to be a suitable definition for HAU in OA. Conversely, the commonly used cut-off of five drinks for men and four drinks for women on one occasion for binge drinking (Han et al., Citation2019) would possibly lead to higher HAU prevalences in our sample and could also impact findings on our HAU predictors.

Conclusion

Regional differences for HAU in European OA were stronger in women than in men and were present beyond established risk factors for alcohol misuse, and therefore, not solely explained by social and socioeconomic differences between EU-regions.

Our data showed that HAU is highly prevalent in European OA. Markers of higher SES were associated with a higher risk of HAU. We observed an additional regional effect on HAU in European OA, with the highest risk of HAU in Eastern Europe.

Because of these regional differences in HAU and its predictors in European OA, population-level strategies need to be adjusted to vulnerable groups in the targeted region and not just adopted from other regions. Future research should focus on a deeper understanding of those regional differences to further inform risk-stratified prevention.

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Acknowledgement

This paper uses data from SHARE Waves 6 and 7 (DOIs: 10.6103/SHARE.w6.710, 10.6103/SHARE.w7.711), see Börsch-Supan et al. (Citation2013) for methodological details.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the German Ministry of Education and Research (Forschungsnetz AERIAL 01EE1406A, 01EE1406B, 01EE1406I) and the Deutsche Forschungsgemeinschaft DFG (SFB 940). The SHARE data collection has been funded by the European Commission, DG RTD through FP5 (QLK6-CT-2001-00360), FP6 (SHARE-I3: RII-CT-2006-062193, COMPARE: CIT5-CT-2005-028857, SHARELIFE: CIT4-CT-2006-028812), FP7 (SHARE-PREP: GA No. 211909, SHARE-LEAP: GA No. 227822, SHARE M4: GA No. 261982, DASISH: GA No. 283646) and Horizon 2020 (SHARE-DEV3: GA No. 676536, SHARE-COHESION: GA No. 870628, SERISS: GA No. 654221, SSHOC: GA No. 823782) and by DG Employment, Social Affairs & Inclusion through VS 2015/0195, VS 2016/0135, VS 2018/0285, VS 2019/0332, and VS 2020/0313. Additional funding from the German Ministry of Education and Research, the Max Planck Society for the Advancement of Science, the U.S. National Institute on Aging (U01_AG09740-13S2, P01_AG005842, P01_AG08291, P30_AG12815, R21_AG025169, Y1-AG-4553-01, IAG_BSR06-11, OGHA_04-064, HHSN271201300071C, RAG052527A) and from various national funding sources is gratefully acknowledged (see www.share-project.org).

References

  • Agahi, N., Dahlberg, L., & Lennartsson, C. (2019). Social integration and alcohol consumption among older people: A four-year follow-up of a Swedish national sample. Drug and Alcohol Dependence, 196, 40–45. https://doi.org/10.1016/j.drugalcdep.2018.12.011
  • Alcohol units. (2018, April 26). Nhs.Uk. https://www.nhs.uk/live-well/alcohol-support/calculating-alcohol-units/
  • Angus, C., Holmes, J., & Meier, P. S. (2019). Comparing alcohol taxation throughout the European Union. Addiction (Abingdon, England), 114(8), 1489–1494. https://doi.org/10.1111/add.14631
  • Bagnardi, V., Rota, M., Botteri, E., Tramacere, I., Islami, F., Fedirko, V., Scotti, L., Jenab, M., Turati, F., Pasquali, E., Pelucchi, C., Galeone, C., Bellocco, R., Negri, E., Corrao, G., Boffetta, P., & La Vecchia, C. (2015). Alcohol consumption and site-specific cancer risk: A comprehensive dose-response meta-analysis. British Journal of Cancer, 112(3), 580–593. https://doi.org/10.1038/bjc.2014.579
  • Barry, K. L., & Blow, F. C. (2016). Drinking over the lifespan: Focus on older adults. Alcohol Research: Current Reviews, 38(1), 115–120.
  • Bergmann, M., Kneip, T., de Luca, G., Scherpenzeel, A. (2019). Survey participation in the Survey of Health, Ageing and Retirement in Europe (SHARE), Wave 1-7 | Munich Center for the Economics of Aging—MEA (Survey participation in the Survey of Health, Ageing and Retirement in Europe (SHARE), Wave 1-7: Based on Release 7.0.0. SHARE Working Paper Series 41-2019). SHARE-ERIC. https://www.mpisoc.mpg.de/sozialpolitik-mea/publikationen/detail/publication/survey-participation-in-the-survey-of-health-ageing-and-retirement-in-europe-share-wave-1-7/
  • Bloomfield, K., Grittner, U., Kramer, S., & Gmel, G. (2006). Social inequalities in alcohol consumption and alcohol-related problems in the study countries of the EU concerted action “Gender, culture and alcohol problems: A multi-national study.” Alcohol and Alcoholism (Oxford, Oxfordshire). Supplement, 41(1), i26–i36. https://doi.org/10.1093/alcalc/agl073
  • Boden, J. M., Crossin, R., Cook, S., Martin, G., Foulds, J. A., & Newton-Howes, G. (2021). Parenting and home environment in childhood and adolescence and alcohol use disorder in adulthood. The Journal of Adolescent Health, 69(2), 329–334. https://doi.org/10.1016/j.jadohealth.2020.12.136
  • Bonevski, B., Regan, T., Paul, C., Baker, A. L., & Bisquera, A. (2014). Associations between alcohol, smoking, socioeconomic status and comorbidities: Evidence from the 45 and Up Study. Drug and Alcohol Review, 33(2), 169–176. https://doi.org/10.1111/dar.12104
  • Börsch-Supan, A. (2018). Survey of health, ageing and retirement in Europe (SHARE) Wave 6 (7.1.0) [Data set]. SHARE–ERIC. https://doi.org/10.6103/SHARE.W6.710
  • Börsch-Supan, A. (2019). Survey of health, ageing and retirement in Europe (SHARE) Wave 7 (7.1.1) [Data set]. SHARE–ERIC. https://doi.org/10.6103/SHARE.W7.711
  • Börsch-Supan, A., Brandt, M., Hunkler, C., Kneip, T., Korbmacher, J., Malter, F., Schaan, B., Stuck, S., Zuber, S., & SHARE Central Coordination Team. (2013). Data Resource Profile: The Survey of Health, Ageing and Retirement in Europe (SHARE). International Journal of Epidemiology, 42(4), 992–1001. https://doi.org/10.1093/ije/dyt088
  • Bosque-Prous, M., Brugal, M. T., Lima, K. C., Villalbí, J. R., Bartroli, M., & Espelt, A. (2017). Hazardous drinking in people aged 50 years or older: A cross-sectional picture of Europe, 2011-2013. International Journal of Geriatric Psychiatry, 32(8), 817–828. https://doi.org/10.1002/gps.4528
  • Britton, A., Ben-Shlomo, Y., Benzeval, M., Kuh, D., & Bell, S. (2015). Life course trajectories of alcohol consumption in the United Kingdom using longitudinal data from nine cohort studies. BMC Medicine, 13(1), 47. https://doi.org/10.1186/s12916-015-0273-z
  • Bye, E., & Moan, I. (2020). Trends in older adults’ alcohol use in Norway 1985–2019. Nordic Studies on Alcohol and Drugs, 37(5), 444–458. https://doi.org/10.1177/1455072520954325
  • Cable, N., & Sacker, A. (2008). Typologies of alcohol consumption in adolescence: Predictors and adult outcomes. Alcohol and Alcoholism (Oxford, Oxfordshire), 43(1), 81–90. https://doi.org/10.1093/alcalc/agm146
  • Calvo, E., Allel, K., Staudinger, U. M., Castillo-Carniglia, A., Medina, J. T., & Keyes, K. M. (2021). Cross-country differences in age trends in alcohol consumption among older adults: A cross-sectional study of individuals aged 50 years and older in 22 countries. Addiction (Abingdon, England), 116(6), 1399–1412. https://doi.org/10.1111/add.15292
  • Calvo, E., Medina, J. T., Ornstein, K. A., Staudinger, U. M., Fried, L. P., & Keyes, K. M. (2020). Cross-country and historical variation in alcohol consumption among older men and women: Leveraging recently harmonized survey data in 21 countries. Drug and Alcohol Dependence, 215, 108219. https://doi.org/10.1016/j.drugalcdep.2020.108219
  • Chatha, H., Sammy, I., Hickey, M., Sattout, A., & Hollingsworth, J. (2018). Falling down a flight of stairs: The impact of age and intoxication on injury pattern and severity. Trauma (London, England), 20(3), 169–174. https://doi.org/10.1177/1460408617720948
  • Cousins, G., Galvin, R., Flood, M., Kennedy, M.-C., Motterlini, N., Henman, M. C., Kenny, R.-A., & Fahey, T. (2014). Potential for alcohol and drug interactions in older adults: Evidence from the Irish longitudinal study on ageing. BMC Geriatrics, 14, 57. https://doi.org/10.1186/1471-2318-14-57
  • de Luca, G. (2018). Imputations. In SHARE release guide 6.1.1 (pp. 42–47). Munich: MEA, Max Planck Institute for Social Law and Social Policy
  • de Luca, G., Celidoni, M., & Trevisan, E. (2015). Item nonresponse and imputation strategies in SHARE Wave 5. In F. Malter & A. Börsch-Supan (Eds.), SHARE Wave 5: Innovations & methodology (pp. 85–100). Munich: MEA, Max Planck Institute for Social Law and Social Policy
  • Devaux, M., & Sassi, F. (2016). Social disparities in hazardous alcohol use: Self-report bias may lead to incorrect estimates. European Journal of Public Health, 26(1), 129–134. https://doi.org/10.1093/eurpub/ckv190
  • Gell, L., Meier, P. S., & Goyder, E. (2015). Alcohol consumption among the over 50s: International comparisons. Alcohol and Alcoholism (Oxford, Oxfordshire), 50(1), 1–10. https://doi.org/10.1093/alcalc/agu082
  • Grittner, U., Kuntsche, S., Gmel, G., & Bloomfield, K. (2013). Alcohol consumption and social inequality at the individual and country levels—Results from an international study. European Journal of Public Health, 23(2), 332–339. https://doi.org/10.1093/eurpub/cks044
  • Grucza, R. A., Sher, K. J., Kerr, W. C., Krauss, M. J., Lui, C. K., McDowell, Y. E., Hartz, S., Virdi, G., & Bierut, L. J. (2018). Trends in adult alcohol use and binge drinking in the early 21st-century United States: A meta-analysis of 6 national survey series. Alcoholism, Clinical and Experimental Research, 42(10), 1939–1950. https://doi.org/10.1111/acer.13859
  • Han, B. H., & Moore, A. A. (2018). Prevention and screening of unhealthy substance use by older adults. Clinics in Geriatric Medicine, 34(1), 117–129. https://doi.org/10.1016/j.cger.2017.08.005
  • Han, B. H., Moore, A. A., Ferris, R., & Palamar, J. J. (2019). Binge drinking among older adults in the United States, 2015 to 2017. Journal of the American Geriatrics Society, 67(10), 2139–2144. https://doi.org/10.1111/jgs.16071
  • Hayes, A. F., & Cai, L. (2007). Using heteroskedasticity-consistent standard error estimators in OLS regression: An introduction and software implementation. Behavior Research Methods, 39(4), 709–722. https://doi.org/10.3758/bf03192961
  • Heinz, A. J., Beck, A., Meyer-Lindenberg, A., Sterzer, P., & Heinz, A. (2011). Cognitive and neurobiological mechanisms of alcohol-related aggression. Nature Reviews. Neuroscience, 12(7), 400–413. https://doi.org/10.1038/nrn3042
  • Holm, S. (1979). A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics, 6(2), 65–70.
  • Iliffe, S., Kharicha, K., Harari, D., Swift, C., Gillmann, G., & Stuck, A. E. (2007). Health risk appraisal in older people 2: The implications for clinicians and commissioners of social isolation risk in older people. The British Journal of General Practice: The Journal of the Royal College of General Practitioners, 57(537), 277–282.
  • Iparraguirre, J. (2015). Socioeconomic determinants of risk of harmful alcohol drinking among people aged 50 or over in England. BMJ Open, 5(7), e007684. https://doi.org/10.1136/bmjopen-2015-007684
  • Jefferis, B. J. M. H., Power, C., & Manor, O. (2005). Adolescent drinking level and adult binge drinking in a national birth cohort. Addiction (Abingdon, England), 100(4), 543–549. https://doi.org/10.1111/j.1360-0443.2005.01034.x
  • Kalinowski, A., & Humphreys, K. (2016). Governmental standard drink definitions and low-risk alcohol consumption guidelines in 37 countries. Addiction (Abingdon, England), 111(7), 1293–1298. https://doi.org/10.1111/add.13341
  • Katikireddi, S. V., Whitley, E., Lewsey, J., Gray, L., & Leyland, A. H. (2017). Socioeconomic status as an effect modifier of alcohol consumption and harm: Analysis of linked cohort data. The Lancet. Public Health, 2(6), e267–e276. https://doi.org/10.1016/S2468-2667(17)30078-6
  • Kharicha, K., Iliffe, S., Harari, D., Swift, C., Gillmann, G., & Stuck, A. E. (2007). Health risk appraisal in older people 1: Are older people living alone an ‘at-risk’ group? The British Journal of General Practice, 57(537), 271–276.
  • Livingston, G., Huntley, J., Sommerlad, A., Ames, D., Ballard, C., Banerjee, S., Brayne, C., Burns, A., Cohen-Mansfield, J., Cooper, C., Costafreda, S. G., Dias, A., Fox, N., Gitlin, L. N., Howard, R., Kales, H. C., Kivimäki, M., Larson, E. B., Ogunniyi, A., … Mukadam, N. (2020). Dementia prevention, intervention, and care: 2020 report of the Lancet Commission. Lancet (London, England), 396(10248), 413–446. https://doi.org/10.1016/S0140-6736(20)30367-6
  • Mak, H. W., & Iacovou, M. (2019). Dimensions of the parent-child relationship: Effects on substance use in adolescence and adulthood. Substance Use & Misuse, 54(5), 724–736. https://doi.org/10.1080/10826084.2018.1536718
  • Oneta, C. M., Pedrosa, M., Rüttimann, S., Russell, R. M., & Seitz, H. K. (2001). Age and bioavailability of alcohol. Zeitschrift Für Gastroenterologie, 39(9), 783–788. https://doi.org/10.1055/s-2001-17196
  • Paula, T. C. S., Chagas, C., Henrique, A. E. G., Castro-Costa, E., Lima-Costa, M. F., & Ferri, C. P. (2022). Alcohol consumption among older adults: Findings from the ELSI-Brazil study. International Journal of Geriatric Psychiatry, 37(1), 1-7. https://doi.org/10.1002/gps.5655
  • Probst, C., Kilian, C., Sanchez, S., Lange, S., & Rehm, J. (2020). The role of alcohol use and drinking patterns in socioeconomic inequalities in mortality: A systematic review. The Lancet. Public Health, 5(6), e324–e332. https://doi.org/10.1016/S2468-2667(20)30052-9
  • Rao, R., Creese, B., Aarsland, D., Kalafatis, C., Khan, Z., Corbett, A., & Ballard, C. (2021). Risky drinking and cognitive impairment in community residents aged 50 and over. Aging & Mental Health, 12, 1–8. https://doi.org/10.1080/13607863.2021.2000938
  • Royal College of Psychiatrists. (2018). Our invisible addicts (No. CR211).
  • Shakya, I., Bergen, G., Haddad, Y. K., Kakara, R., & Moreland, B. L. (2020). Fall-related emergency department visits involving alcohol among older adults. Journal of Safety Research, 74, 125–131. https://doi.org/10.1016/j.jsr.2020.06.001
  • Shimada, K., Yamazaki, S., Nakano, K., Ngoma, A. M., Takahashi, R., & Yasumura, S. (2014). Prevalence of social isolation in community-dwelling elderly by differences in household composition and related factors: From a social network perspective in urban Japan. Journal of Aging and Health, 26(5), 807–823. https://doi.org/10.1177/0898264314531616
  • Sudhinaraset, M., Wigglesworth, C., & Takeuchi, D. T. (2016). Social and cultural contexts of alcohol use. Alcohol Research : Current Reviews, 38(1), 35–45.
  • United Nations Statistics Division. (n.d). Standard country or area codes for statistical use (M49): Geographic Regions. https://unstats.un.org/unsd/methodology/m49/
  • Visser, L., de Winter, A. F., & Reijneveld, S. A. (2012). The parent-child relationship and adolescent alcohol use: A systematic review of longitudinal studies. BMC Public Health, 12, 886. https://doi.org/10.1186/1471-2458-12-886
  • Wagenaar, A. C., Salois, M. J., & Komro, K. A. (2009). Effects of beverage alcohol price and tax levels on drinking: A meta-analysis of 1003 estimates from 112 studies. Addiction, 104(2), 179–190. https://doi.org/10.1111/j.1360-0443.2008.02438.x
  • Wang, Q., Zhang, Y., & Wu, C. (2022). Alcohol consumption and associated factors among middle-aged and older adults: Results from China Health and Retirement Longitudinal Study. BMC Public Health, 22(1), 322. https://doi.org/10.1186/s12889-022-12718-8
  • Watt, R. G., Heilmann, A., Sabbah, W., Newton, T., Chandola, T., Aida, J., Sheiham, A., Marmot, M., Kawachi, I., & Tsakos, G. (2014). Social relationships and health related behaviors among older US adults. BMC Public Health, 14(1), 533. https://doi.org/10.1186/1471-2458-14-533
  • Wilsnack, R. W., Wilsnack, S. C., Gmel, G., & Kantor, L. W. (2018). Gender differences in binge drinking. Alcohol Research: Current Reviews, 39(1), 57–76.
  • Wood, A. M., Kaptoge, S., Butterworth, A. S., Willeit, P., Warnakula, S., Bolton, T., Paige, E., Paul, D. S., Sweeting, M., Burgess, S., Bell, S., Astle, W., Stevens, D., Koulman, A., Selmer, R. M., Verschuren, W. M. M., Sato, S., Njølstad, I., Woodward, M., … Danesh, J. (2018). Risk thresholds for alcohol consumption: Combined analysis of individual-participant data for 599 912 current drinkers in 83 prospective studies. The Lancet, 391(10129), 1513–1523. https://doi.org/10.1016/S0140-6736(18)30134-X
  • World Health Organisation. (2014). Global Information system on alcohol and health: Indicator Code Book 2014. World Health Organization
  • World Health Organization, World Health Organization, World Health Organization, & Management of Substance Abuse Team. (2018). Global status report on alcohol and health 2018.