Patterns of Alcohol Consumption and Associated Factors in a Population-Based Sample of 70-Year-Olds: Data from the Gothenburg H70 Birth Cohort Study 2014–16
by
, , , , , , , and
Felicia Ahlner
1,*
,
Hanna Falk Erhag
1,
Lena Johansson
1,
Madeleine Mellqvist Fässberg
1,
Therese Rydberg Sterner
1,2,
Jessica Samuelsson
1,
Anna Zettergren
1,
Margda Waern
1,3 and
Ingmar Skoog
1,4 1
Neuropsychiatric Epidemiology Unit, Institute of Neuroscience and Physiology, Department of Psychiatry and Neurochemistry, Sahlgrenska Academy, Centre for Ageing and Health (AGECAP), University of Gothenburg, 43141 Mölndal, Sweden
2
Aging Research Center, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet, Stockholm University, 17165 Solna, Sweden
3
Region Västra Götaland, Sahlgrenska University Hospital, Psychosis Clinic, 41345 Gothenburg, Sweden
4
Region Västra Götaland, Sahlgrenska University Hospital, Psychiatry, Cognition and Old Age Psychiatry Clinic, 43141 Mölndal, Sweden
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2022, 19(14), 8248; https://doi.org/10.3390/ijerph19148248
Submission received: 31 May 2022
/
Revised: 27 June 2022
/
Accepted: 5 July 2022
/
Published: 6 July 2022
(This article belongs to the Special Issue Social Determinants of Alcohol Use and Its Consequences)
Abstract
:Older adults of today consume more alcohol, yet knowledge about the factors associated with different consumption levels is limited in this age group. Based on the data from a population-based sample (n = 1156, 539 men and 617 women) in The Gothenburg H70 Birth Cohort Study 2014–16, we examined sociodemographic, social, and health-related factors associated with alcohol consumption levels in 70-year-olds, using logistic regression. Total weekly alcohol intake was calculated based on the self-reported amount of alcohol consumed. Alcohol consumption was categorized as lifetime abstention, former drinking, moderate consumption (≤98 g/week), and at-risk consumption (>98 g/week). At-risk consumption was further categorized into lower at-risk (98–196 g/week), medium at-risk (196–350 g/week), and higher at-risk (≥350 g/week). We found that among the 1156 participants, 3% were lifetime abstainers, 3% were former drinkers, 64% were moderate drinkers, and 30% were at-risk drinkers (20% lower, 8% medium, 2% higher). Among several factors, former drinking was associated with worse general self-rated health (OR 1.65, 95% CI 1.08–2.51) and lower health-related quality of life (measured by physical component score) (OR 0.94, 95% CI 0.91–0.97), higher illness burden (OR 1.16, 95% CI 1.07–1.27), and weaker grip strength (OR 0.96, 95% CI 0.94–0.98). Higher at-risk drinkers more often had liver disease (OR 11.41, 95% CI 3.48–37.37) and minor depression (OR 4.57, 95% CI 1.40–14.95), but less contacts with health care (OR 0.32, 95% CI 0.11–0.92). Our findings demonstrate the importance of classifications beyond abstinence and at-risk consumption, with implications for both the prevention and clinical management of unhealthy consumption patterns in older adults.
1. Introduction
Alcohol use is a major contributor to the global burden of disease [1], and a huge public health challenge [2]. Recent generations of older adults have higher consumption and alcohol misuse rates than previous generations [3,4]. The aging of the population worldwide may thus lead to a substantial increase in the absolute number of older adults who consume alcohol and alcohol-related injuries, diseases, and deaths [5].
The prevalence estimates of potentially harmful drinking among older adults vary considerably among studies, due to varying study contexts, drinking culture, and intake cut-offs. While there is a widespread consensus that men consume more alcohol and account for more alcohol-related harm to self and others at all ages [6], recent studies suggests a diminishing sex gap in alcohol use in later-born cohorts of older adults [3,7]. In Sweden, the proportion of current drinkers has been stable across age groups in recent years [8]. However, the higher consumption levels found among older adults aged 65–84 years are the opposite of the trends among the younger age groups, in which the consumption levels have decreased since 2004, with the most pronounced changes among those aged 17–29 years [8,9].
Despite the growing prevalence of alcohol use and misuse, harmful drinking often remains undetected and untreated among older adults [10]. Aging is related to slower alcohol metabolism [11], lean body-mass reduction [12], decreased water-to-body weight ratio [13], increased prevalence of health conditions, and adverse interaction with prescribed drugs [14], suggesting that older adults have an increased risk for adverse effects of alcohol consumption.
Older adults constitute a heterogeneous population, with considerable variations in physical, mental, and social health and well-being. In a recent review of qualitative studies, drinking in older age has been linked with social engagement, but also with social isolation, illness, or grief [15]. However, the findings are inconsistent regarding the broader characteristics of alcohol use and nonuse in older adults. Few studies have included a sufficiently large number of variables to capture the complexity of the factors associated with alcohol consumption levels in older age.
In alcohol research, the definitions of different levels of alcohol use and nonuse play a significant role. Nonuse includes both the lifetime abstainers and former drinkers. However, it is important to separate these two, since the former drinkers may differ from those who have abstained throughout life. The sick-quitter hypothesis, first suggested in the late 1980s, states that the former drinkers, to a greater extent, quit drinking due to serious health issues (including alcohol use disorders) or interaction with prescription drugs [16]. Despite this, few studies have separately analyzed the lifetime abstainers and former drinkers. There is evidence of heterogeneity in health and well-being among older adults with a high consumption [17]. Still, relatively little is known about the factors associated with high alcohol consumption levels in this age group. Given this gap in the literature, we aimed to explore a range of sociodemographic, social, and health-related factors in relation to the different categories of alcohol use and nonuse in a cross-sectional population-based sample of 70-year-olds born in 1944 and living in Gothenburg, Sweden.
2. Materials and Methods
2.1. Sample
The data were derived from the population-based Gothenburg H70 Birth Cohort studies (H70-studies), including systematically selected samples based on specific birth dates. In 2014–16, 1203 70-year-olds born in 1944 (response rate 72.2%) were examined by trained research staff with comprehensive social, somatic, cognitive, functional, and psychiatric examinations, and a battery of laboratory tests, as described previously [18]. The information was obtained through semi-structured face-to-face interviews, questionnaires, observations, and examinations. The H70 study was approved by the Regional Ethical Review Board (approval number: 869-13). Written informed consent was obtained from all of the participants. In the present study, 26 participants were excluded due to dementia, according to the Diagnostic and Statistical Manual of Mental Disorders (DSM-III-R) criteria [19], and eleven participants due to missing data on alcohol variables. Ten participants who reported that they stopped drinking within the past 0–5 years were also excluded to ensure a strict definition of alcohol abstention, leaving a total of 1156.
2.2. Measures
2.2.1. Alcohol Variables
The information on alcohol consumption was obtained through semi-structured face-to-face interviews. The participants reported the average weekly amount and type of alcohol (beer, white wine, red wine, fortified wine, or liquor) consumed during the past month. Total weekly alcohol intake was calculated on the basis of the quantities reported. In accordance with the National Institute on Alcohol Abuse and Alcoholism (NIAAA) clinical guidelines for people aged 65 and above [20], the consumption levels among current drinkers were categorized as moderate (≤98 g/week) or at-risk (>98 g/week). In addition, at-risk drinkers were categorized into three distinct groups: lower at-risk drinkers (>98 to <196 g/week), medium at-risk drinkers (≥196 to <350 g/week), and higher at-risk drinkers (≥350 g/week). The nonusers were classified into two distinct groups: the lifetime abstainers (never-drinkers) or the former drinkers (previous drinkers, who had not consumed alcohol in the past five years).
2.2.2. Independent Variables
All of the studied factors are presented in Table 1 and have been previously described in detail [18]. The sociodemographic and social factors were self-reported and included education, income, employment status, country of birth, housing, marital status, quality of marriage, having children and grandchildren, loss of partner due to death or divorce, loss of close relatives (children, grandchildren, siblings) or friends due to death, presence of a confidant relationship, satisfaction with social relationships, loneliness, childhood circumstances, religious beliefs, smoking status, frequency of physical activity, and satisfaction with sleep. The potential consequences of current or previous alcohol use were assessed by question 9 (‘Have you or someone else been injured as a result of your drinking?’) and question 10 (‘Has a relative or friend or doctor or another health worker been concerned about your drinking or suggested you to cut down?’) from the Alcohol Use Disorders Identification Test (AUDIT) [21].
The detailed information on instrument, scales, and the system used for measurement of health status is found in Table 2. The health status measures included self-reports, performance tests, and validated instruments and scales. The self-reported measurements included information on general health, life satisfaction, the number of medications, the prevalence of diagnosed medical conditions (stroke, hypertension, angina pectoris, myocardial infarction, diabetes mellitus, liver disease, lifetime occurrence of all cancer types), and injuries (hip or femoral fractures, falls, head trauma), need of home health care, hospitalization, and other contacts with medical doctor or nurse.
The physical performance measures included grip strength, gait speed (30 m indoor at a self-selected pace with a standing start), and vital capacity (forced expiratory volume in one second divided by forced vital capacity). Body mass index (BMI) was calculated (body weight divided by height squared). Unhealthy weight was defined as BMI ≥31, based on the suggested age-adjusted cut-off for people aged 65 and over [22].
The 36-Item Short Form Health Survey (SF-36) [23] was used for a summary of health-related quality of life (HRQoL). Standard procedures were used for the coding of electrocardiographic findings. Functional independence, the burden of illness, cognitive function, and symptoms of anxiety and personality were rated with established instruments and tests. Diagnoses of depression (i.e., minor and major) were based on computerized symptom algorithms, according to criteria in the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV-TR, DSM-5) [24,25].
Table 2.
Detailed information on instrument, scales and system used for measurement of health status.
Table 2.
Detailed information on instrument, scales and system used for measurement of health status.
| Measure | Instrument, Scale or System Used for Classification | Reference | |
|---|---|---|---|
| Physical health | Self-reported general health | The 36-Item Short Form Health Survey (SF-36) | [23] |
| Physical Component Score | The 36-Item Short Form Health Survey (SF-36) | [26] | |
| Electrocardiography | The Minnesota Code Classification System | [27] | |
| Functional independence | Activities of Daily Living (ADL) | The Barthel ADL Index | [28] |
| Instrumental activities of daily living (IADL) | The Lawton IADL Scale | [29] | |
| Burden of disease | Burden of illness | Cumulative Illness Rating Scale for Geriatrics (CIRS-G) | [30] |
| Mental health | Cognitive function | Mini Mental State Examination (MMSE) | [31] |
| Anxiety | The Brief Scale for Anxiety (BSA) | [32] | |
| Depressive symptoms | The Montgomery Åsberg Depression Rating Scale (MADRS) | [33] | |
| Mental Component Score | The 36-Item Short Form Health Survey (SF-36) | [26] | |
| Minor depression disorder | The Diagnostic and Statistical Manual of Mental Disorders, 4th ed. (DSM-IV-TR) | [24] | |
| Major depression disorder | The Diagnostic and Statistical Manual of Mental Disorders, 5th ed. (DSM-5) | [25] | |
| Personality | Extroversion | The Eysenck Personality Inventory | [34] |
| Neuroticism | The Eysenck Personality Inventory | [34] |
2.3. Statistical Analysis
The sample characteristics are presented as numbers, median values, minimum (min) and maximum (max) values, and percentages. Pearson’s chi-square test was used to test differences in proportions. The Mann–Whitney U test and the Kruskal–Wallis test were used to test differences in medians. Logistic regression models were used to test associations between sociodemographic/social/health-related factors (independent variables) and alcohol consumption levels (outcome). A total of 56 factors were examined. Due to the exploratory nature of the present study, the data were analyzed without multiplicity adjustment. Statistically significant associations are presented in tables containing odds ratios (OR) adjusted for sex (Model 1) with 95% confidence intervals (CI) and p-values. The complete tables with all of the independent variables, including unadjusted OR and OR adjusted for sex and education (Model 2), can be found in Tables S1–S3, Supplementary Materials. All of the analyses were carried out using IBM SPSS Statistics 24 for Windows. A p-value < 0.05 (two-tailed) was considered statistically significant. Significant associations were further examined for potential interactions with sex to assess the need for stratified models. The interaction p-value threshold was ≤0.1.
3. Results
3.1. Sample Characteristics
The mean age of the participants (n = 1156) was 70.6 years, and 53.4% were women. Table 3 shows that, compared to men, women had a lower educational level, lower income, were less often in a relationship, more often lived alone, and were more often religious. Out of 1156 participants, 64 (5.5%) were nonusers, out of which 32 (2.8%) were lifetime abstainers and 32 (2.8%) were former drinkers. A total of 1092 (94.5%) were current drinkers, out of which 745 (64.4%) were moderate drinkers and 347 (30.0%) were at-risk drinkers. Among the at-risk drinkers, 229 (19.8%) were lower at-risk, 92 (8.0%) were medium at-risk, and 26 (2.2%) were higher at-risk drinkers. The median consumption was 32 g of alcohol per week among the moderate drinkers, and 160 g per week among the at-risk drinkers; 135 g among the lower at-risk, 249 g among the medium at-risk, and 481 g among the higher at-risk drinkers. Among those with at-risk consumption, men were more likely than women to be medium/higher at-risk drinkers, x2 (2, n = 347) = 7.19, p = 0.03.
3.2. Factors Associated with Different Alcohol Consumption Levels
Table 4, Table 5 and Table 6 show the statistically significant results of the logistic regression models adjusted for sex (Model 1). The complete result tables for Model 1 and Model 2 are presented in Tables S1–S3, Supplementary Materials.
Compared to the moderate drinkers, the lifetime abstainers had a lower income, had less often lost relatives during the last five years, were more often religious, never-smokers, and physically inactive, had less often contact with health care, but had more often minor depression (Table 4). The associations remained similar in Model 2 (Table S1, Supplementary Materials). An interaction effect was observed between sex and physical inactivity (p = 0.008), indicating that the association between physical inactivity and lifetime abstention was different in men and women. Stratified analysis showed that physical inactivity was associated with lifetime abstention among women (OR 5.95, 95% CI 1.98–17.87, p = 0.001), but not among men (OR 0.56, 95% CI 0.14–2.20, p = 0.40).
Compared to the moderate drinkers, the former drinkers had a lower income, were less often born in Sweden, had more often experienced an unhappy childhood and alcohol-related injuries, were more often smokers, physically inactive, and dissatisfied with sleep, had lower life satisfaction, and had poorer general self-rated health, a worse HRQoL in the physical component score (PCS), a higher burden of illness (CIRS-G), had more medications, more often had hypertension, were more frequently hospitalized, had more hospital admitted head injuries, weaker grip strength, slower gait speed, had unhealthy weight, a higher MADRS score, and more often had minor depression (Table 4). The associations remained similar in Model 2 (Table S1, Supplementary Materials). No interactions with sex were observed.
Compared to the moderate drinkers, the at-risk drinkers had higher education, a higher income, were more often born in Sweden, more often in a relationship, and had more often experienced partner loss (>5 years). They also showed a higher financial standard during childhood, were more often smokers, less often religious, more often acknowledged that others were concerned about their drinking, had diabetes less often, and had a higher MMSE score (Table 4). With the exception of the MMSE score, the associations remained similar in Model 2 (Table S1, Supplementary Materials). No interactions with sex were observed. Compared to the moderate drinkers, the lower at-risk drinkers had higher education, a higher income, were more often born in Sweden and in a relationship, were less likely to live alone, had more often experienced partner loss (>5 years), had a good financial standard during childhood, were more often smokers, less often religious, more often acknowledged that others were concerned about their drinking, had higher life satisfaction, had myocardial infarction and diabetes less often, had better grip strength, and had higher MMSE scores (Table 5). The associations, except for the MMSE score, remained similar in Model 2 (Table S2, Supplementary Materials). Interactions were observed between sex and smoking (p = 0.06) and sex and grip strength (p = 0.08), indicating that the associations were different in men and women. The stratified analyses showed that smoking was associated with the lower at-risk drinking among women (OR 2.31, 95% CI 1.35–3.93, p = 0.002) but not among men (OR 1.20, 95% CI 0.79–1.81, p = 0.395). A better grip strength was associated with the lower at-risk drinking among men (OR 1.02, 95% CI 1.01–1.04, p = 0.002), but not among women (OR 1.00, 95% CI 0.99–1.02, p = 0.836).
Compared to the moderate drinkers, the medium at-risk drinkers had higher education, a higher income, were more often born in Sweden, were more often smokers, less often religious, had more often acknowledged that others were concerned about their drinking, and had a higher burden of illness (CIRS-G) (Table 5). The associations, except for the MMSE score, remained similar in Model 2 (Table S2, Supplementary Materials). An interaction effect was observed between sex and smoking (p = 0.09), indicating that the association between smoking and medium at-risk drinking was different in men and women. Stratified analyses showed that smoking was related to the medium at-risk drinking among men (OR 3.17, 95% CI 1.63–6.19, p = 0.001), but not among women (OR 1.24, 95% CI 0.54–2.87, p = 0.612).
Compared to the moderate drinkers, the higher at-risk drinkers had more often had a higher financial standard during childhood and had more often acknowledged that others were concerned about their drinking, had less contact with healthcare, a higher burden of illness (CIRS-G), liver disease, and minor depression (Table 5). The associations remained similar in Model 2 (Table S2, Supplementary Materials). No interactions with sex were observed.
We then compared the two nonuser groups. Compared to the lifetime abstainers, the former drinkers had more often experienced partner loss (>5 years) and an unhappy childhood, were more often smokers and dissatisfied with sleep, had a worse HRQoL in the physical component score (PCS), a higher burden of illness (CIRS-G), had more medications, were more often hospitalized, had more hospital admissions for head injuries, weaker grip strength, and slower gait speed (Table 6). The associations remained similar in Model 2 (Table S3, Supplementary Materials). No interactions with sex were observed.
We then compared the former drinkers with the at-risk drinkers. Compared to the at-risk drinkers, the former drinkers had lower education, lower income, were less often born in Sweden, less often in a relationship, were more likely to live alone, had a worse financial standard during childhood, more often had an unhappy childhood, were more often religious, physically inactive, dissatisfied with sleep, had a lower life satisfaction, poorer general self-rated health, worse HRQoL in the physical component score (PCS), were more functionally dependent (ADL), had a higher burden of illness (CIRS-G), had more medications, had angina pectoris and diabetes more often, had been hospitalized more often, had more hospital-admitted head injuries, had a weaker grip strength, and slower gait speed (Table 6). The associations, except for the financial standard during childhood and physical inactivity, remained similar in Model 2 (Table S3, Supplementary Materials). Interactions were observed between sex and higher education level (p = 0.03), sex and financial standard during childhood (p = 0.07), sex and sleep satisfaction (p = 0.02), and sex and grip strength (p = 0.07), indicating that the associations were different in men and women. The stratified analysis showed that higher education and a worse financial standard were associated with former drinking among women (education: OR 0.06, 95% CI 0.01–0.31, p = 0.001; financial standard: OR 6.38, 95% CI 1.40–29.01, p = 0.02), but not among men (education: OR 0.63, 95% CI 0.16–2.52, p = 0.51; financial standard: OR 1.14, 95% CI 0.37–3.51, p = 0.82). Sleep dissatisfaction and weaker grip strength were associated with former drinking among men (sleep dissatisfaction: OR 17.70, 95% CI 4.70–66.72, p < 0.001; grip strength: OR 0.92, 95% CI 0.88–0.96, p < 0.001), but not among women (sleep dissatisfaction: OR 1.68, 95% CI 0.43–6.64, p = 0.46; grip strength: OR 0.97, 95% CI 0.93–1.02, p = 0.22).
4. Discussion
We examined a wide range of the sociodemographic, social, and health-related factors associated with alcohol consumption levels in a population-based sample of 70-year-olds. We found that the former drinkers, and, to a certain extent, the higher at-risk drinkers, had poorer health, while there were few differences in health between the moderate drinkers and the at-risk drinkers. There were also sociodemographic and social differences between the groups. The former drinkers were more often smokers and born outside Sweden, while the at-risk drinkers had higher education, higher income, and were more often in a relationship. Our findings show the importance of differentiating between the lifetime abstainers and the former drinkers, and of differentiating between the different consumption levels among the at-risk consumers, as these subgroups differ considerably in relation to sociodemographic, social, and health-related factors.
Nearly one-third (30.0%) of Swedish 70-year-olds born in 1944 in our study exceeded the NIAAA drinking guidelines (>98 g/week), a figure which is higher than in previous studies on adults aged 65 years and over [35,36,37]. A Belgian study reported a prevalence of 20.5% [35], while a survey of older Medicare beneficiaries in the US reported a 13.0% prevalence [36], and among US primary care patients the prevalence was 7.9% [37]. The larger proportion of the higher at-risk drinkers (≥350 g/week) in our study (2.2%) was similar to that reported from a population-based study among people aged 65 and over in Japan (1%) [38], but lower than the results from a European consortium study including all ages (mean age 57 years: 7.5%) [39]. However, comparisons between the studies are difficult, due to large variations in terms of the age groups, divergent definitions of alcohol consumption groups, and cut-offs.
In our study, the former drinkers had the worst physical and medical status. This finding is in line with the sick-quitter hypothesis [16]. We found few differences between the at-risk drinkers and the moderate drinkers regarding health-related factors, even after controlling for education. However, the higher at-risk drinkers (≥350 g/week) had more liver disease and minor depression, established consequences of alcohol dependence and excessive drinking at various ages and in different settings [40,41], but rarely studied in older populations. The moderate drinkers had diabetes more often, which might be explained by the general recommendation to diabetics to drink in moderation.
We found that minor depression was associated with both nonuse (lifetime abstention and former drinking) and the higher at-risk drinking, indicating a curvilinear association, as previously reported among older adults [42,43,44]. One reason for the higher rate of depression among the abstainers may be the social aspects of drinking, in which depressed persons are less likely to be engaged [45]. However, the cross-sectional design of this study makes it impossible to speculate about the direction of this relationship. Among the higher at-risk drinkers, alcohol might be a self-medicating strategy to manage the symptoms of minor depression [46]. However, the research findings are inconsistent regarding alcohol consumption and late-life depression, and several studies find no associations [47,48,49]. Depression is related to lower life satisfaction [50]. In our study, lower life satisfaction was associated with former drinking, but no association was found with high consumption levels. Our findings are similar to a Russian population study (mean age 43.2), which reported a hump-shaped relationship (J-shaped for men, and U-shaped for women) between alcohol consumption and life satisfaction [51], but in contrast to the findings among older Jamaicans (aged 60–103 years), where current drinking was associated with both very high and very low life satisfaction [52].
Most of the differences between the lifetime abstainers and current drinkers (moderate and at-risk) involved sociodemographic characteristics. We found that lifetime abstention was associated with lower education, lower income, non-smoking, and being religious, which is similar to a study among people aged 65 and over in the US [53]. Higher consumption was related to higher education and income, and being a smoker, as also found among the Medicare beneficiaries aged 65 and over in the US [36]. We found that those with a partner were more likely to be at-risk drinkers, which is in line with findings among older adults (≥65 years) in Finland [54]. However, these findings are in contrast to studies from the US and Japan (age: ≥65 years), where being single or divorced was associated with a higher likelihood of unhealthy drinking [36,38]. These results suggest that the influence of marital status on alcohol consumption is complex and varies between geographical areas and over the life course. Additionally, the associations between alcohol consumption and the country of birth and religiousness further point to the importance of cultural influences. New findings from our study regarding sociodemographic factors include that at-risk drinking was associated with partner loss, others’ concern about the drinking, and a good financial standard during childhood.
We found that nonusers (lifetime abstainers and former drinkers) were less physically active than current drinkers, which is similar to studies among college students and in the general population [55]. Sex as a potential moderator in the relationship between physical activity and alcohol use was previously observed in a US sample of adults aged 18 and above [56]. However, that study found a stronger association in men, while the association was only apparent among women in our study, indicating an effect of age.
Overall, recent pre-clinical research has found that excessive alcohol exposure during adolescence may increase the risk of chronic health issues, especially in terms of brain damage [57,58]. Unfortunately, we have no information about the consumption pattern during adolescence. Thus, we cannot investigate whether previous alcohol habits have influenced health and function in our sample.
Strengths and Limitations
The strengths of our study include the systematically selected population-based sample, the comprehensive examinations, and the high response rate. In addition, we provide separate data for the lifetime abstainers and former drinkers. The study also has several limitations. First, the cross-sectional design limits the possibility of making causal inferences and cannot address the long-term effects of elevated drinking in old age. Second, when using self-reported alcohol consumption data, there is a risk that some individuals may underestimate or under-report their consumption to provide a more socially desirable response. However, self-reported alcohol consumption is considered to be reliable and valid [59]. To limit recall bias, participants reported the past month consumption. Underestimation was further minimized by using questions about the frequency and amounts consumed for beer, wine, and spirits separately, which yields the most realistic levels of intake [60]. However, due to the potential monthly variation in alcohol use, the reported past month consumption may not reflect the actual annual consumption pattern. Third, some medical conditions are based on self-report and were not confirmed by clinical examinations or registers. Fourth, some of the subgroups were small, leading to low statistical power and risk of false negative results (Type II errors), large risk estimates, wide confidence intervals, and the difficulties in controlling for all of the potential confounders. Although we found some interactions by sex, the absence of sex differences for other variables may be due to the small sample sizes [61]. However, the latter risk was reduced by the use of a higher p-value threshold (p < 0.1) for the interaction analyses. Fifth, this is an exploratory study and multiple comparisons were made, which may lead to false positive findings (Type I errors). However, multiplicity adjustments may increase the risk of false negative findings (Type II errors). Instead, all of the comparisons are clearly presented with individual p-values and confidence intervals. This approach enables the reader to interpret the results and decide whether the results are entirely due to multiple comparisons. Moreover, we emphasize that our findings are suggestive until further confirmed. However, we take note of the fact that the associations found are plausible and explicable [62,63]. Finally, as in all studies involving older adults, survival bias may also explain our findings, as long-term heavy users might have died before the age of 70, or might have stopped drinking at an earlier age.
5. Conclusions
In conclusion, we found that unfavorable factors were more common among the former drinkers and the individuals in the highest consumption groups. Our study underlines the wide variation in the factors associated with levels of alcohol use among older adults. Our findings suggest that there are important similarities and discrepancies within the subgroups at the extremes of alcohol use (abstainers and high consumers). These findings demonstrate the importance of accurate classification of alcohol consumption groups beyond abstinence and risk consumption in future research, as these definitions have importance for the outcome and interpretation of results. Our findings shed new light on understanding older alcohol consumers, which may help in improving prevention and treatment of individuals with unhealthy consumption patterns.
Supplementary Materials
The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/ijerph19148248/s1, Table S1: Associated factors of lifetime abstention, former drinking and at-risk consumption. Logistic regression models adjusted for sex (Model 1) and models adjusted for sex and education (Model 2) with moderate consumption (≤98 g/week) as reference category; Table S2: Associated factors of lower at-risk (>98 to <196 g/week), medium at-risk (≥196 to <350 g/week), and higher at-risk consumption (≥350 g/week). Logistic regression models adjusted for sex (Model 1) and models adjusted for sex and education (Model 2) with moderate consumption (≤98 g/week) as reference category; Table S3: Associated factors of former drinking. Logistic regression models adjusted for sex (Model 1) and models adjusted for sex and education (Model 2) with lifetime abstention and at-risk consumption (>98 g/week) as reference categories.
Author Contributions
Conceptualization, F.A. and I.S.; Methodology, F.A. and I.S.; Formal Analysis, F.A.; Data Curation, F.A., H.F.E., L.J., M.M.F., T.R.S., J.S., A.Z., M.W. and I.S.; Writing—Original Draft Preparation, F.A.; Writing—Review and Editing, H.F.E., L.J., M.M.F., T.R.S., J.S., A.Z., M.W. and I.S.; Supervision, I.S.; Funding Acquisition, M.W. and I.S. All authors have read and agreed to the published version of the manuscript.
Funding
This research was funded by the Swedish state under the agreement between the Swedish government and the county councils, the National ALF agreement (grant numbers ALF 965812, ALF 716681); Systembolagets alkoholforskningsråd (grant numbers 2021-0067, 2019-0077, 2017-0051, 2016-0059); The Swedish Research Council (grant numbers 2019-01096, 2017-00639, 2016-01590, 2015-02830, 2013-8717, 2012-5041); Swedish Research Council for Health, Working Life and Welfare (grant numbers 2018-00471, 2017-1604, 2016–07097, 2013-2496, 2013-2300, 2013-1202); Konung Gustaf V:s och Drottning Victorias Frimurarestiftelse; Hjärnfonden (grant numbers FO 2020-0235, FO 2019-0163, FO 2018-0214, FO 2016-0214, FO 2014-0207); Alzheimerfonden (grant numbers AF-554461, AF-647651, AF-743701, AF-844671, AF-930868, AF-940139, AF-968441); Eivind och Elsa K:son Sylvans stiftelse; Stiftelsen Handlanden Hjalmar Svenssons forskningsfond (grant number HJSV2018007); Stiftelsen Wilhelm och Martina Lundgrens vetenskapsfond (grant number 2018-2534); and Irisstipendiet.
Institutional Review Board Statement
The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Regional Ethical Review Board (approval number: 869-13, 18 November 2013).
Informed Consent Statement
Informed consent was obtained from all of the participants involved in the study.
Data Availability Statement
The data analyzed in the present study are available on request from the corresponding author.
Acknowledgments
We would like to acknowledge the dedication of the H70 study participants and staff for making this research possible. The authors would also like to thank the members of the research group for their efforts in data collection and management.
Conflicts of Interest
The authors declare no conflict of interest. Funding sources had no role in the study design, methods, data collection, analysis, decision to publish or preparation of the manuscript.
References
- GBD 2016 Alcohol Collaborators. Alcohol use and burden for 195 countries and territories, 1990–2016: A systematic analysis for the Global Burden of Disease Study 2016. Lancet 2018, 392, 1015–1035. [Google Scholar] [CrossRef] [Green Version]
- Rehm, J. The risks associated with alcohol use and alcoholism. Alcohol Res. Health 2011, 34, 135–143. [Google Scholar] [PubMed]
- Ahlner, F.; Sigström, R.; Sterner, T.R.; Fässberg, M.M.; Kern, S.; Östling, S.; Waern, M.; Skoog, I. Increased Alcohol Consumption Among Swedish 70-Year-Olds 1976 to 2016: Analysis of Data from The Gothenburg H70 Birth Cohort Studies, Sweden. Alcohol. Clin. Exp. Res. 2018, 42, 2403–2412. [Google Scholar] [CrossRef] [PubMed]
- Breslow, R.A.; Castle, I.P.; Chen, C.M.; Graubard, B.I. Trends in Alcohol Consumption Among Older Americans: National Health Interview Surveys, 1997 to 2014. Alcohol. Clin. Exp. Res. 2017, 41, 976–986. [Google Scholar] [CrossRef] [PubMed]
- Rehm, J.; Mathers, C.; Popova, S.; Thavorncharoensap, M.; Teerawattananon, Y.; Patra, J. Global burden of disease and injury and economic cost attributable to alcohol use and alcohol-use disorders. Lancet 2009, 373, 2223–2233. [Google Scholar] [CrossRef]
- World Health Organization. Global Status Report on Alcohol and Health 2018; Licence: CC BY-NC-SA 3.0 IGO; World Health Organization: Geneva, Switerland, 2018. [Google Scholar]
- Waern, M.; Marlow, T.; Morin, J.; Ostling, S.; Skoog, I. Secular changes in at-risk drinking in Sweden: Birth cohort comparisons in 75-year-old men and women 1976–2006. Age Ageing 2014, 43, 228–234. [Google Scholar] [CrossRef] [Green Version]
- The Swedish Council for Information on Alcohol and Other Drugs, CAN. Självrapporterade alkoholvanor i Sverige 2004–2020 [Self-Reported Drinking Habits in Sweden 2004–2020]. The Swedish Council for Information on Alcohol and Other Drugs, CAN: Stockholm, Sweden, 2021. Available online: https://www.can.se/app/uploads/2021/10/can-rapport-204-sjalvrapporterade-alkoholvanor-i-sverige-2004-2020.pdf (accessed on 26 June 2022).
- Raninen, J.; Agahi, N. Trends in older people’s drinking habits 2004–2017. Nord. Stud. Alcohol. Drugs 2020, 37, 459–469. [Google Scholar] [CrossRef]
- Chhatre, S.; Cook, R.; Mallik, E.; Jayadevappa, R. Trends in substance use admissions among older adults. BMC Health Serv. Res. 2017, 17, 584. [Google Scholar] [CrossRef] [Green Version]
- Meier, P.; Seitz, H.K. Age, alcohol metabolism and liver disease. Curr. Opin. Clin. Nutr. Metab. Care 2008, 11, 21–26. [Google Scholar] [CrossRef]
- Barry, K.L.; Blow, F.C. Drinking Over the Lifespan: Focus on Older Adults. Alcohol. Res. 2016, 38, 115–120. [Google Scholar]
- Cederbaum, A.I. Alcohol metabolism. Clin. Liver Dis. 2012, 16, 667–685. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Caputo, F.; Vignoli, T.; Leggio, L.; Addolorato, G.; Zoli, G.; Bernardi, M. Alcohol use disorders in the elderly: A brief overview from epidemiology to treatment options. Exp. Gerontol. 2012, 47, 411–416. [Google Scholar] [CrossRef] [Green Version]
- Kelly, S.; Olanrewaju, O.; Cowan, A.; Brayne, C.; Lafortune, L. Alcohol and older people: A systematic review of barriers, facilitators and context of drinking in older people and implications for intervention design. PLoS ONE 2018, 13, e0191189. [Google Scholar] [CrossRef] [Green Version]
- Shaper, A.G.; Wannamethee, G.; Walker, M. Alcohol and mortality in British men: Explaining the U-shaped curve. Lancet 1988, 2, 1267–1273. [Google Scholar] [CrossRef]
- Behrendt, S.; Kuerbis, A.; Becker, U.; Mejldal, A.; Andersen, K.; Nielsen, A.S.; Tolstrup, J.; Eliasen, M.H. Distinct health-related risk profiles among middle-aged and older adults with risky alcohol use from the Danish general population. Drug Alcohol. Depend. 2021, 226, 108872. [Google Scholar] [CrossRef] [PubMed]
- Sterner, T.R.; Ahlner, F.; Blennow, K.; Dahlin-Ivanoff, S.; Falk, H.; Johansson, L.H.; Hoff, M.; Holm, M.; Hörder, H.; Jacobsson, T.; et al. The Gothenburg H70 Birth cohort study 2014–16: Design, methods and study population. Eur. J. Epidemiol. 2019, 34, 191–209. [Google Scholar] [CrossRef] [Green Version]
- American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders, 3rd ed. (revised); American Psychiatric Association: Washington, DC, USA, 1987. [Google Scholar]
- U.S. Department of Health and Human Services, National Institutes of Health National Institute on Alcohol Abuse and Alcoholism. Helping Patients Who Drink Too Much: A Clinician′s Guide. Updated 2005 Edition. Available online: https://pubs.niaaa.nih.gov/publications/practitioner/cliniciansguide2005/guide.pdf (accessed on 30 March 2022).
- Saunders, J.B.; Aasland, O.G.; Babor, T.F.; de la Fuente, J.R.; Grant, M. Development of the Alcohol Use Disorders Identification Test (AUDIT): WHO Collaborative Project on Early Detection of Persons with Harmful Alcohol Consumption–II. Addiction 1993, 88, 791–804. [Google Scholar] [CrossRef]
- Winter, J.E.; MacInnis, R.J.; Wattanapenpaiboon, N.; Nowson, C.A. BMI and all-cause mortality in older adults: A meta-analysis. Am. J. Clin. Nutr. 2014, 99, 875–890. [Google Scholar] [CrossRef] [Green Version]
- Ware, J.E., Jr.; Sherbourne, C.D. The MOS 36-item short-form health survey (SF-36). I. Conceptual framework and item selection. Med. Care 1992, 30, 473–483. [Google Scholar] [CrossRef]
- American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders: DSM-IV-TR, 4th ed.; American Psychiatric Association: Washington, DC, USA, 2000. [Google Scholar]
- American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders, 5th ed.; American Psychiatric Association: Washington, DC, USA, 2013. [Google Scholar]
- Ware, J., Jr.; Kosinski, M.; Keller, S.D. A 12-Item Short-Form Health Survey: Construction of scales and preliminary tests of reliability and validity. Med. Care 1996, 34, 220–233. [Google Scholar] [CrossRef] [Green Version]
- Blackburn, H.; Keys, A.; Simonson, E.; Rautaharju, P.; Punsar, S. The electrocardiogram in population studies. A classification system. Circulation 1960, 21, 1160–1175. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Collin, C.; Wade, D.T.; Davies, S.; Horne, V. The Barthel ADL Index: A reliability study. Int. Disabil. Stud. 1988, 10, 61–63. [Google Scholar] [CrossRef] [PubMed]
- Lawton, M.P.; Brody, E.M. Assessment of older people: Self-maintaining and instrumental activities of daily living. Gerontologist 1969, 9, 179–186. [Google Scholar] [CrossRef] [PubMed]
- Miller, M.D.; Paradis, C.F.; Houck, P.R.; Mazumdar, S.; Stack, J.A.; Rifai, A.; Mulsant, B.; Reynolds, C.F. Rating chronic medical illness burden in geropsychiatric practice and research: Application of the Cumulative Illness Rating Scale. Psychiatry Res. 1992, 4, 237–248. [Google Scholar] [CrossRef]
- Tombaugh, T.N. Test-retest reliable coefficients and 5-year change scores for the MMSE and 3MS. Arch. Clin. Neuropsychol. 2005, 20, 485–503. [Google Scholar] [CrossRef] [Green Version]
- Tyrer, P.; Owen, R.T.; Cicchetti, D.V. The brief scale for anxiety: A subdivision of the comprehensive psychopathological rating scale. J. Neurol. Neurosurg. Psychiatry 1984, 47, 970–975. [Google Scholar] [CrossRef] [Green Version]
- Montgomery, S.A.; Asberg, M. A new depression scale designed to be sensitive to change. J. Ment. Sci. 1979, 134, 382–389. [Google Scholar] [CrossRef]
- Eysenck, H.J.; Eysenck, S.B.J. Manual of the Eysenck Personality Questionnaire; Hodder & Stoughton: London, UK, 1975. [Google Scholar]
- Hoeck, S.; Van Hal, G. Unhealthy drinking in the Belgian elderly population: Prevalence and associated characteristics. Eur. J. Public Health 2013, 23, 1069–1075. [Google Scholar] [CrossRef] [Green Version]
- Merrick, E.L.; Horgan, C.M.; Hodgkin, D.; Garnick, D.W.; Houghton, S.F.; Panas, L.; Saitz, R.; Blow, F.C. Unhealthy drinking patterns in older adults: Prevalence and associated characteristics. J. Am. Geriatr. Soc. 2008, 56, 214–223. [Google Scholar] [CrossRef] [Green Version]
- Kirchner, J.E.; Zubritsky, C.; Cody, M.; Coakley, E.; Chen, H.; Ware, J.H.; Oslin, D.W.; Sanchez, H.A.; Durai, U.N.B.; Miles, K.M.; et al. Alcohol consumption among older adults in primary care. J. General. Intern. Med. 2007, 22, 92–97. [Google Scholar] [CrossRef] [Green Version]
- Midorikawa, H.; Tachikawa, H.; Aiba, M.; Arai, T.; Watanabe, T.; Tamiya, N. Factors associated with high-risk drinking in older adults: Evidence from a national survey in Japan. Geriatr. Gerontol. Int. 2019, 19, 1260–1267. [Google Scholar] [CrossRef] [PubMed]
- Wood, A.M.; Kaptoge, S.; Butterworth, A.S.; Willeit, P.; Warnakula, S.; Bolton, T.; Paige, E.; Paul, D.S.; Sweeting, M.; Burgess, S.; et al. Risk thresholds for alcohol consumption: Combined analysis of individual-participant data for 599 912 current drinkers in 83 prospective studies. Lancet 2018, 391, 1513–1523. [Google Scholar] [CrossRef] [Green Version]
- Rehm, J.; Samokhvalov, A.V.; Shield, K.D. Global burden of alcoholic liver diseases. J. Hepatol. 2013, 59, 160–168. [Google Scholar] [CrossRef] [Green Version]
- Rehm, J.; Sr Gmel, G.E.; Gmel, G.; Hasan, O.S.M.; Imtiaz, S.; Popova, S.; Probst, C.; Roerecke, M.; Room, R.; Samokhvalov, A.V.; et al. The relationship between different dimensions of alcohol use and the burden of disease-an update. Addiction 2017, 112, 968–1001. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Gea, A.; Beunza, J.J.; Estruch, R.; Sánchez-Villegas, A.; Salas-Salvadó, J.; Buil-Cosiales, P.; Gómez-Gracia, E.; Covas, M.-I.; Corella, D.; Fiol, M.; et al. Alcohol intake, wine consumption and the development of depression: The PREDIMED study. BMC Med. 2013, 11, 192. [Google Scholar] [CrossRef] [Green Version]
- Kim, S.A.; Kim, E.; Morris, R.G.; Park, W.S. Exploring the non-linear relationship between alcohol consumption and depression in an elderly population in Gangneung: The Gangneung Health Study. Yonsei Med. J. 2015, 56, 418–425. [Google Scholar] [CrossRef] [Green Version]
- Quittschalle, J.; Pabst, A.; Löbner, M.; Luppa, M.; Heser, K.; Wagner, M.; Bussche, H.V.D.; Hajek, A.; König, H.-H.; Wiese, B.; et al. Association of Alcohol and Tobacco Consumption with Depression Severity in the Oldest Old. Results from the Age Different Old Age Cohort Platform. Int. J. Environ. Res. Public Health 2021, 18, 7959. [Google Scholar] [CrossRef]
- Fiske, A.; Wetherell, J.L.; Gatz, M. Depression in older adults. Annu. Rev. Clin. Psychol. 2009, 5, 363–389. [Google Scholar] [CrossRef]
- Bolton, J.M.; Robinson, J.; Sareen, J. Self-medication of mood disorders with alcohol and drugs in the National Epidemiologic Survey on Alcohol and Related Conditions. J. Affect. Disord. 2009, 115, 367–375. [Google Scholar] [CrossRef]
- García-Esquinas, E.; Ortolá, R.; Galán, I.; Soler-Vila, H.; Laclaustra, M.; Rodríguez-Artalejo, F. Moderate alcohol drinking is not associated with risk of depression in older adults. Sci. Rep. 2018, 8, 11512. [Google Scholar] [CrossRef]
- Hajat, S.; Haines, A.; Bulpitt, C.; Fletcher, A. Patterns and determinants of alcohol consumption in people aged 75 years and older: Results from the MRC trial of assessment and management of older people in the community. Age Ageing 2004, 33, 170–177. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Weyerer, S.; Eifflaender-Gorfer, S.; Wiese, B.; Luppa, M.; Pentzek, M.; Bickel, H.; Bachmann, C.; Scherer, M.; Maier, W.; Riedel-Heller, S.G. Incidence and predictors of depression in non-demented primary care attenders aged 75 years and older: Results from a 3-year follow-up study. Age Ageing 2013, 42, 173–180. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Strine, T.W.; Kroenke, K.; Dhingra, S.; Balluz, L.S.; Gonzalez, O.; Berry, J.T.; Mokdad, A.H. The associations between depression, health-related quality of life, social support, life satisfaction, and disability in community-dwelling US adults. J. Nerv. Ment. 2009, 197, 61–64. [Google Scholar] [CrossRef] [PubMed]
- Massin, S.; Kopp, P. Is life satisfaction hump-shaped with alcohol consumption? Evidence from Russian panel data. Addict. Behav. 2014, 39, 803–810. [Google Scholar] [CrossRef]
- Gibson, R.C.; Waldron, N.K.; Abel, W.D.; Eldemire-Shearer, D.; James, K.; Mitchell-Fearon, K. Alcohol use, depression, and life satisfaction among older persons in Jamaica. Int. Psychogeriatr. 2017, 29, 663–671. [Google Scholar] [CrossRef]
- Choi, N.G.; DiNitto, D.M.; Marti, C.N.; Choi, B.Y. Sociodemographic Characteristics and Health Status of Lifetime Abstainers, Ex-Drinkers, Bingers, and Nonbingers Among Baby Boomers and Older Adults. Subst. Use Misuse 2016, 51, 637–648. [Google Scholar] [CrossRef]
- Immonen, S.; Valvanne, J.; Pitkala, K.H. Prevalence of at-risk drinking among older adults and associated sociodemographic and health-related factors. J. Nutr. Health Aging 2011, 15, 789–794. [Google Scholar] [CrossRef]
- Leasure, J.L.; Neighbors, C.; Henderson, C.E.; Young, C.M. Exercise and Alcohol Consumption: What We Know, What We Need to Know, and Why it is Important. Front. Psychiatry 2015, 6, 156. [Google Scholar] [CrossRef] [Green Version]
- Lisha, N.E.; Martens, M.; Leventhal, A.M. Age and gender as moderators of the relationship between physical activity and alcohol use. Addict Behav. 2011, 36, 933–936. [Google Scholar] [CrossRef] [Green Version]
- Reitz, N.L.; Nunes, P.T.; Savage, L.M. Adolescent binge-type ethanol exposure in rats mirrors age-related cognitive decline by suppressing cholinergic tone and hippocampal neurogenesis. Front Behav. Neurosci. 2021, 22, 772857. [Google Scholar] [CrossRef]
- Ji, Z.; Yuan, L.; Lu, X.; Ding, H.; Luo, J.; Ke, Z.-J. Binge alcohol exposure causes neurobehavioral deficits and GSK3β activation in the hippocampus of adolescent rats. Sci. Rep. 2018, 8, 3088. [Google Scholar] [CrossRef] [Green Version]
- Del Boca, F.K.; Darkes, J. The validity of self-reports of alcohol consumption: State of the science and challenges for research. Addiction 2003, 98 (Suppl. 2), 1–12. [Google Scholar] [CrossRef] [PubMed]
- Feunekes, G.I.; van ‘t Veer, P.; van Staveren, W.A.; Kok, F.J. Alcohol intake assessment: The sober facts. Am. J. Epidemiol. 1999, 150, 105–112. [Google Scholar] [CrossRef] [PubMed]
- Rich-Edwards, J.W.; Kaiser, U.B.; Chen, G.L.; Manson, J.E.; Goldstein, J.M. Sex and Gender Differences Research Design for Basic, Clinical, and Population Studies: Essentials for Investigators. Endocr. Rev. 2018, 39, 424–439. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Bender, R.; Lange, S. Adjusting for multiple testing—When and how? J. Clin. Epidemiol. 2001, 54, 343–349. [Google Scholar] [CrossRef]
- Rothman, K.J. Modern Epidemiology; Little Brown: Boston, MA, USA, 1986. [Google Scholar]
Table 1.
Categorization of included variables; type, values, and collection method.
| Variable | Type | Value | Variable | Type | Value |
|---|---|---|---|---|---|
| Sociodemographic factors | Physical health cont. | ||||
| Education a | Categorical | ≤Primary *, Secondary, Higher | Medications a | Continuous | Number of medications |
| Income a | Categorical | <Sample median, ≥Sample median * | Stroke a,* | Categorical | No *, Yes |
| Employed a | Categorical | No *, Yes | Hypertension a,* | Categorical | No *, Yes |
| Born in Sweden a | Categorical | No, Yes * | Angina pectoris a,* | Categorical | No *, Yes |
| Special housing b | Categorical | No *, Yes | Myocardial infarction a,* | Categorical | No *, Yes |
| Having partner a | Categorical | No *, Yes | Diabetes a,* | Categorical | No *, Yes |
| Happy relationship a | Categorical | No *, Yes | Liver disease a,* | Categorical | No *, Yes |
| Living alone a | Categorical | No *, Yes | Cancer (any) a,* | Categorical | No *, Yes |
| Having children a | Categorical | No *, Yes | Hip or femoral fracture preceding 10 years a | Categorical | No *, Yes |
| Having grandchildren a | Categorical | No *, Yes | Have fallen preceding 1 year a | Categorical | No *, Yes |
| Loss of partner due to death or divorce a | Categorical | No *, Yes, more than 5 years ago, Yes, 0–5 years ago | Home care ≥1/week a | Categorical | No *, Yes |
| Lost relatives or friends due to death a | Categorical | No *, Yes, more than 5 years ago, Yes, 0–5 years ago | Hospitalization preceding 10 years a | Categorical | No *, Yes |
| Having ≥ 1 confidant a | Categorical | No, Yes * | Hospital admitted head injury a | Categorical | No *, Yes |
| Feeling alone a | Categorical | No *, Yes | Health care in preceding y a | Categorical | No *, Yes |
| Financial standard during childhood a | Categorical | Very good/Good *, Average or Poor/Very poor | Physical performance | ||
| Unhappy childhood a | Categorical | No *, Yes | Grip strength c | Continuous | Kilogram |
| Parent having alcohol problem a | Categorical | No *, Yes | Gait speed c | Continuous | meter/second |
| Being religious a | Categorical | No *, Yes/Undecided | ECG abnormalities c | Categorical | 1 = No, 2 = Yes |
| Smoking status a | Categorical | Never-smoker *, Ever-smoker (i.e., past and current) | FEV1/FVC ratio (FEV%) c | Continuous | Volume first second/Liters |
| Physically active a | Categorical | Never, ≥1 times/month * | Body Mass Index (unhealthy weight) c | Continuous | No, BMI ≤ 30, Yes, BMI ≥ 31 |
| Overall satisfaction with sleep a | Categorical | Dissatisfied, Satisfied * | Mental health | ||
| Alcohol-related injuries to others a | Categorical | No *, Yes | Mini Mental State Examination score c | Continuous | 0 to 30 (maximum score) |
| Others concern about drinking a | Categorical | No *, Yes | Mental Component Summary score e | Continuous | 0 (Poor) to 100 (Highest) |
| Physical health | Montgomery Åsberg Depression Rating score e | Continuous | 0 to 60 (greater symptom burden) | ||
| Life satisfaction a | Continuous | 1 (Completely satisfied) to 7 (Dissatisfied) | Brief Scale of Anxiety e | Continuous | 0 to 60 (greater symptom burden) |
| General self-rated health a | Continuous | 1 (Excellent) to 5 (Poor) | Minor depression f | Categorical | No *, Yes |
| Physical Component Summary score e | Continuous | 0 (Poor) to 100 (Highest) | Major depression f | Categorical | No *, Yes |
| Activities of Daily Living score d | Continuous | 0 (Lowest) to 8 (Highest) | Personality traits | ||
| Instrumental Activities of Daily Living score d | Continuous | 0 (Lowest) to 8 (Highest) | Neuroticism score e | Continuous | 0 to 24 (maximum score) |
| CIRS-G score d | Continuous | 0 (Lowest) to 56 (Highest) | Extroversion score e | Continuous | 0 to 24 (maximum score) |
CIRS-G, the Cumulative Illness Rating Scale for Geriatrics; FEV1/FVC, forced expiratory volume in one second divided by forced vital capacity. a Self-report (interview or questionnaire); b National registration; c Test/examination; d Rating by research nurse; e Summary score; f Diagnosed by computerized symptom algorithms. * Reference in logistic regression analyses.
Table 3.
Sample characteristics, 70-year-olds born in 1944 (examined 2014–16) stratified by sex (n = 1156).
Table 3.
Sample characteristics, 70-year-olds born in 1944 (examined 2014–16) stratified by sex (n = 1156).
| All | Men | Women | Sex Difference | |
|---|---|---|---|---|
| % (no. of cases/total sample) | 100.0 (1156) | 46.6 (539/1156) | 53.4 (617/1156) | |
| p-value a | ||||
| Education | 0.012 * | |||
| Primary education | 14.3 (165/1154) | 16.7 (90/539) | 12.2 (75/615) | |
| Secondary education | 39.4 (455/1154) | 28.8 (155/539) | 48.8 (300/615) | |
| Higher education | 46.3 (534/1154) | 54.5 (294/539) | 39.0 (240/615) | |
| Monthly income SEK, median (min, max) | 14,000 (1300, 100,000) | 16,915 (3795, 100,000) | 12,000 (1300, 50,000) | <0.001 * |
| Employed | 21.6 (248/1147) | 23.4 (125/534) | 20.1 (123/613) | 0.170 |
| Born in Sweden | 85.3 (983/1153) | 83.3 (449/539) | 87.0 (534/614) | 0.080 |
| Special housing | 1.4 (16/1145) | 1.3 (7/538) | 1.5 (9/607) | 0.794 |
| Having partner | 73.3 (846/1154) | 83.7 (451/539) | 64.2 (395/615) | <0.001 * |
| Living alone | 36.0 (416/1154) | 27.6 (149/539) | 43.4 (267/615) | <0.001 * |
| Being religious | 24.7 (273/1104) | 20.9 (107/511) | 28.0 (166/593) | <0.001 * |
| Smoking | 0.053 | |||
| Never-smoker | 38.1 (440/1155) | 37.0 (199/538) | 39.1 (241/617) | |
| Current smoker | 8.9 (103/1155) | 7.1 (38/538) | 10.5 (65/617) | |
| Past smoker | 53.0 (612/1155) | 55.9 (301/538) | 50.4 (311/617) | |
| Alcohol consumption level | <0.001 * | |||
| Lifetime abstainer | 2.8 (32/1156) | 1.9 (10/539) | 3.6 (22/617) | |
| Former drinker | 2.8 (32/1156) | 2.6 (14/539) | 2.9 (18/617) | |
| Moderate drinker (≤98 g/week) | 64.4 (745/1156) | 52.5 (283/539) | 74.9 (462/617) | |
| At-risk drinker (>98 g/week) | 30.0 (347/1156) | 43.0 (232/539) | 18.6 (115/617) |
a Pearson’s chi-square for categorical variables. Mann–Whitney U test for medians. * p < 0.05.
Table 4.
Sociodemographic, social, and health-related factors associated with lifetime abstention, former drinking and at-risk consumption.
Table 4.
Sociodemographic, social, and health-related factors associated with lifetime abstention, former drinking and at-risk consumption.
| Moderate Consumption a | Lifetime Abstention | Former Drinking | At-Risk Consumption b | |||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Variables | Adjusted OR | 95% CI | p | Adjusted OR | 95% CI | p | Adjusted OR | 95% CI | p | |
| Secondary education | 1.00 (reference) | 1.14 | 0.40–3.22 | 0.801 | 0.64 | 0.26–1.61 | 0.344 | 1.58 | 1.01–2.46 | 0.045 * |
| Higher education | 1.00 (reference) | 0.79 | 0.27–2.33 | 0.670 | 0.49 | 0.19–1.26 | 0.139 | 1.99 | 1.30–3.04 | 0.002 * |
| Income < median | 1.00 (reference) | 2.86 | 1.14–7.17 | 0.026 * | 4.33 | 1.33–14.12 | 0.015 * | 0.57 | 0.42–0.78 | 0.001 * |
| Born outside Sweden | 1.00 (reference) | 2.16 | 0.97–4.81 | 0.059 | 3.75 | 1.78–7.90 | 0.001 * | 0.42 | 0.27–0.65 | <0.001 * |
| Having partner | 1.00 (reference) | 0.99 | 0.46–2.16 | 0.982 | 0.50 | 0.24–1.05 | 0.066 | 1.48 | 1.06–2.05 | 0.021 * |
| Lost partner >5 years | 1.00 (reference) | 0.65 | 0.29–1.45 | 0.292 | 2.09 | 1.00–4.40 | 0.051 | 1.46 | 1.11–1.93 | 0.008 * |
| Lost relatives or friends preceding 5 years | 1.00 (reference) | 0.35 | 0.14–0.85 | 0.021 * | 1.07 | 0.40–2.88 | 0.892 | 0.97 | 0.70–1.35 | 0.872 |
| Alcohol-related injuries to others | 1.00 (reference) | n.a. | 12.66 | 2.30–69.83 | 0.004 * | 2.52 | 0.75–8.39 | 0.133 | ||
| Unhappy childhood | 1.00 (reference) | 0.62 | 0.19–2.08 | 0.438 | 3.61 | 1.67–7.84 | 0.001 * | 1.24 | 0.85–1.83 | 0.268 |
| Being religious | 1.00 (reference) | 4.19 | 1.99–8.79 | <0.001 * | 2.13 | 0.95–4.79 | 0.066 | 0.47 | 0.33–0.67 | <0.001 * |
| Ever-smoker | 1.00 (reference) | 0.10 | 0.04–0.29 | <0.001 * | 3.79 | 1.44–9.94 | 0.007 * | 1.76 | 1.32–2.33 | <0.001 * |
| Physically inactive | 1.00 (reference) | 2.50 | 1.19–5.26 | 0.016 * | 2.36 | 1.08–5.19 | 0.032 * | 0.86 | 0.67–1.13 | 0.291 |
| Dissatisfied with sleep | 1.00 (reference) | 0.73 | 0.17–3.11 | 0.666 | 4.08 | 1.73–9.59 | 0.001 * | 0.77 | 0.46–1.30 | 0.332 |
| Others concern about drinking | 1.00 (reference) | n.a. | 3.39 | 0.73–15.70 | 0.118 | 4.48 | 2.39–8.39 | <0.001 * | ||
| Poor financial standard during childhood | 1.00 (reference) | 1.38 | 0.61–3.13 | 0.435 | 1.65 | 0.70–3.86 | 0.251 | 0.65 | 0.50–0.86 | 0.003 * |
| Life satisfaction | 1.00 (reference) | 1.08 | 0.82–1.42 | 0.566 | 1.39 | 1.10–1.76 | 0.005 * | 0.89 | 0.79–1.00 | 0.051 |
| General self-rated health | 1.00 (reference) | 1.20 | 0.82–1.77 | 0.347 | 1.65 | 1.08–2.51 | 0.020 * | 0.92 | 0.79–1.06 | 0.238 |
| Physical Component Summary score | 1.00 (reference) | 0.99 | 0.95–1.02 | 0.408 | 0.94 | 0.91–0.97 | <0.001 * | 1.00 | 0.99–1.02 | 0.850 |
| CIRS-G score | 1.00 (reference) | 1.02 | 0.93–1.11 | 0.715 | 1.16 | 1.07–1.27 | <0.001 * | 1.02 | 0.99–1.06 | 0.205 |
| Medications | 1.00 (reference) | 0.97 | 0.88–1.08 | 0.624 | 1.18 | 1.09–1.27 | <0.001 * | 1.01 | 0.97–1.05 | 0.751 |
| Hypertension | 1.00 (reference) | 0.90 | 0.44–1.83 | 0.760 | 2.20 | 1.05–4.63 | 0.038 * | 1.29 | 0.99–1.68 | 0.063 |
| Diabetes | 1.00 (reference) | 0.84 | 0.25–2.84 | 0.784 | 1.77 | 0.71–4.44 | 0.224 | 0.49 | 0.30–0.80 | 0.004 * |
| Hospitalization preceding 10 years | 1.00 (reference) | 0.75 | 0.36–1.55 | 0.431 | 4.41 | 1.78–10.95 | 0.001 * | 1.06 | 0.81–1.38 | 0.686 |
| Health care in preceding year | 1.00 (reference) | 0.27 | 0.11–0.65 | 0.004 * | 2.23 | 0.30–16.68 | 0.436 | 0.65 | 0.41–1.04 | 0.070 |
| Hospital admitted head injury | 1.00 (reference) | 0.47 | 0.11–2.00 | 0.306 | 3.04 | 1.39–6.65 | 0.005 * | 1.01 | 0.97–1.05 | 0.813 |
| Grip strength | 1.00 (reference) | 1.01 | 0.99–1.04 | 0.353 | 0.96 | 0.94–0.98 | <0.001 * | 1.01 | 1.00–1.02 | 0.116 |
| Self-selected gait speed (m/s) | 1.00 (reference) | 0.85 | 0.08–9.09 | 0.896 | 3.28 | 1.06–10.19 | 0.040 * | 0.67 | 0.25–1.81 | 0.434 |
| Body Mass Index ≥ 31 | 1.00 (reference) | 0.70 | 0.23–2.64 | 0.698 | 2.58 | 1.12–5.95 | 0.026 * | 1.08 | 0.72–1.62 | 0.728 |
| Mini Mental State Examination score | 1.00 (reference) | 0.95 | 0.73–1.23 | 0.672 | 1.08 | 0.79–1.47 | 0.639 | 1.15 | 1.03–1.28 | 0.016 * |
| Montgomery Åsberg Depression Rating score | 1.00 (reference) | 1.04 | 0.98–1.10 | 0.162 | 1.06 | 1.01–1.12 | 0.032 * | 1.02 | 0.99–1.04 | 0.249 |
| Minor depression | 1.00 (reference) | 2.82 | 1.03–7.75 | 0.044 * | 3.11 | 1.13–8.56 | 0.028 * | 1.24 | 0.71–2.17 | 0.449 |
Logistic regression models adjusted for sex with moderate consumption as reference category. CIRS-G, Cumulative Illness Rating Scale for Geriatrics. a Moderate consumption: ≤98 g/week; b At-risk consumption: >98 g/week. * p < 0.05.
Table 5.
Sociodemographic, social, and health-related factors associated with lower at-risk, medium at-risk, and higher at-risk consumption.
Table 5.
Sociodemographic, social, and health-related factors associated with lower at-risk, medium at-risk, and higher at-risk consumption.
| Moderate Consumption a | Lower At-Risk Consumption b | Medium At-Risk Consumption c | Higher At-Risk Consumption d | |||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Variables | Adjusted OR | 95% CI | p | Adjusted OR | 95% CI | p | Adjusted OR | 95% CI | p | |
| Secondary education | 1.00 (reference) | 1.36 | (0.82–2.26) | 0.240 | 3.06 | (1.23–7.60) | 0.016 * | 1.06 | (0.34–3.31) | 0.923 |
| Higher education | 1.00 (reference) | 1.85 | (1.14–2.99) | 0.012 * | 3.20 | (1.33–7.75) | 0.010 * | 0.87 | (0.30–2.57) | 0.806 |
| Income below median | 1.00 (reference) | 0.65 | (0.45–0.92) | 0.016 * | 0.31 | (0.16–0.59) | <0.001 * | 1.05 | (0.42–2.61) | 0.919 |
| Born outside Sweden | 1.00 (reference) | 0.39 | (0.23–0.67) | 0.001 * | 0.25 | (0.10–0.63) | 0.004 * | 1.55 | (0.63–3.82) | 0.346 |
| Having partner | 1.00 (reference) | 1.67 | (1.13–2.47) | 0.010 * | 1.02 | (0.60–1.74) | 00.94 | 2.06 | (0.60–7.10) | 0.251 |
| Living alone | 1.00 (reference) | 0.66 | (0.47–0.92) | 0.014 * | 1.30 | (0.82–2.07) | 00.26 | 0.52 | (0.19–1.40) | 0.195 |
| Lost partner preceding 5 years | 1.00 (reference) | 0.89 | (0.43–1.84) | 0.754 | 1.76 | (0.72–4.30) | 0.217 | 16.40 | (2.11–127.27) | 0.007 * |
| Lost partner >5 years | 1.00 (reference) | 1.41 | (1.03–1.94) | 0.032 * | 1.43 | (0.89–2.23) | 0.136 | 1.91 | (0.86–4.26) | 0.113 |
| Poor financial standard during childhood | 1.00 (reference) | 0.69 | (0.50–0.94) | 0.019 * | 0.67 | (0.42–1.05) | 0.080 | 0.44 | (0.20–0.98) | 0.046 * |
| Being religious | 1.00 (reference) | 0.50 | (0.33–0.74) | 0.001 * | 0.44 | (0.23–0.83) | 0.012 * | 0.38 | (0.11–1.28) | 0.117 |
| Ever-smoker | 1.00 (reference) | 1.55 | (1.13–2.14) | 0.007 * | 2.29 | (1.36–3.84) | 0.002 * | 2.34 | (0.92–5.97) | 0.074 |
| Others concern about drinking | 1.00 (reference) | 3.33 | (1.63–6.80) | 0.001 * | 5.54 | (2.43–12.63) | <0.001 * | 9.68 | (3.23–29.01) | <0.001 * |
| Life satisfaction | 1.00 (reference) | 0.82 | (0.71–0.95) | 0.009 * | 1.05 | (0.88–1.25) | 0.577 | 0.85 | (0.59–1.24) | 0.398 |
| CIRS-G score | 1.00 (reference) | 0.99 | (0.85–1.03) | 0.721 | 1.07 | (1.01–1.13) | 0.018 * | 1.10 | (1.00–1.21) | 0.043 * |
| Myocardial infarction | 1.00 (reference) | 0.28 | (0.10–0.79) | 0.016 * | 1.45 | (0.66–3.19) | 0.351 | 1.04 | (0.23–4.67) | 0.957 |
| Diabetes | 1.00 (reference) | 0.48 | (0.27–0.86) | 0.013 * | 0.45 | (0.19–1.08) | 0.075 | 0.80 | (0.23–2.76) | 0.722 |
| Liver disease | 1.00 (reference) | 0.17 | (0.02–1.31) | 0.089 | 1.346 | (0.38–4.79) | 0.646 | 11.41 | (3.48–37.37) | <0.001 * |
| Healthcare in preceding year | 1.00 (reference) | 0.69 | (0.40–1.17) | 0.167 | 0.68 | (0.31–1.46) | 0.318 | 0.32 | (0.11–0.92) | 0.035 * |
| Grip strength | 1.00 (reference) | 1.01 | (1.00–1.02) | 0.011 * | 0.99 | (0.98–1.01) | 0.290 | 1.01 | (0.98–1.03) | 0.738 |
| Mini Mental State Examination score | 1.00 (reference) | 1.18 | (1.03–1.34) | 0.015 * | 1.17 | (0.96–1.42) | 0.119 | 0.89 | (0.69–1.15) | 0.365 |
| Minor depression | 1.00 (reference) | 0.84 | (0.41–1.73) | 0.638 | 1.77 | (0.75–4.21) | 0.196 | 4.57 | (1.40–14.95) | 0.012 * |
Logistic regression models adjusted for sex with moderate consumption as reference category. CIRS-G, Cumulative Illness Rating Scale for Geriatrics. a Moderate consumption: ≤98 g/week; b Lower at-risk consumption: >98 to <196 g/week; c Medium at-risk consumption: ≥196 to <350 g/week; d Higher at-risk consumption: ≥350 g/week. * p < 0.05.
Table 6.
Sociodemographic, social, and health-related factors associated with former drinking.
| Lifetime Abstention | Former Drinking | At-Risk Consumption a | Former Drinking | |||||
|---|---|---|---|---|---|---|---|---|
| Variables | Adjusted OR | 95% CI | p | Adjusted OR | 95% CI | p | ||
| Secondary education | 1.00 (reference) | 0.52 | (0.14–2.01) | 0.344 | 1.00 (reference) | 0.40 | (0.15–1.08) | 0.070 |
| Higher education | 1.00 (reference) | 0.53 | (0.12–2.24) | 0.384 | 1.00 (reference) | 0.24 | (0.09–0.64) | 0.005 * |
| Income below median | 1.00 (reference) | 1.43 | (0.33–6.29) | 0.633 | 1.00 (reference) | 7.25 | (2.22–23.65) | 0.001 * |
| Born outside Sweden | 1.00 (reference) | 2.00 | (0.68–5.86) | 0.205 | 1.00 (reference) | 8.78 | (3.79–20.35) | 0.000 * |
| Having partner | 1.00 (reference) | 0.50 | (0.17–1.46) | 0.205 | 1.00 (reference) | 0.34 | (0.16–0.72) | 0.005 * |
| Living alone | 1.00 (reference) | 2.35 | (0.84–6.58) | 0.105 | 1.00 (reference) | 2.45 | (1.16–5.15) | 0.018 * |
| Lost partner >5 years | 1.00 (reference) | 3.22 | (1.09–9.51) | 0.034 * | 1.00 (reference) | 1.41 | (0.66–3.04) | 0.377 |
| Poor financial standard during childhood | 1.00 (reference) | 1.35 | (0.41–4.46) | 0.621 | 1.00 (reference) | 2.51 | (1.05–6.01) | 0.038 * |
| Unhappy childhood | 1.00 (reference) | 6.20 | (1.47–26.13) | 0.013 * | 1.00 (reference) | 2.83 | (1.26–6.39) | 0.012 * |
| Being religious | 1.00 (reference) | 0.55 | (0.18–1.64) | 0.282 | 1.00 (reference) | 4.42 | (1.89–10.37) | 0.001 * |
| Ever-smoker | 1.00 (reference) | 43.43 | (9.62–196.12) | <0.001 * | 1.00 (reference) | 2.11 | (0.78–5.66) | 0.139 |
| Physically inactive | 1.00 (reference) | 1.15 | (0.37–3.56) | 0.805 | 1.00 (reference) | 2.66 | (1.19–5.97) | 0.017 * |
| Dissatisfied with sleep | 1.00 (reference) | 5.42 | (1.02–28.82) | 0.048 * | 1.00 (reference) | 4.87 | (1.90–12.48) | 0.001 * |
| Life satisfaction | 1.00 (reference) | 1.31 | (0.91–1.90) | 0.152 | 1.00 (reference) | 1.56 | (1.21–2.01) | 0.001 * |
| General self-rated health | 1.00 (reference) | 1.38 | (0.78–2.42) | 0.267 | 1.00 (reference) | 1.79 | (1.17–2.75) | 0.008 * |
| Physical Component Summary score | 1.00 (reference) | 0.95 | (0.90–1.00) | 0.031 * | 1.00 (reference) | 0.93 | (0.90–0.97) | <0.001 * |
| Activities of Daily Living score | 1.00 (reference) | 0.96 | (0.88–1.04) | 0.323 | 1.00 (reference) | 0.91 | (0.85–0.97) | 0.004 * |
| CIRS-G score | 1.00 (reference) | 1.13 | (1.00–1.27) | 0.044 * | 1.00 (reference) | 1.15 | (1.05–1.26) | 0.004 * |
| Medications | 1.00 (reference) | 1.23 | (1.06–1.42) | 0.005 * | 1.00 (reference) | 1.22 | (1.11–1.34) | 0.000 * |
| Angina pectoris | 1.00 (reference) | 3.05 | (0.30–31.45) | 0.350 | 1.00 (reference) | 6.50 | (1.43–29.61) | 0.015 * |
| Diabetes | 1.00 (reference) | 2.33 | (0.52–10.40) | 0.270 | 1.00 (reference) | 3.56 | (1.30–9.76) | 0.013 * |
| Hospitalization preceding 10 y | 1.00 (reference) | 6.00 | (1.92–18.78) | 0.002 * | 1.00 (reference) | 3.80 | (1.51–9.60) | 0.005 * |
| Hospital admitted head injury | 1.00 (reference) | 6.41 | (1.22–33.55) | 0.028 * | 1.00 (reference) | 3.08 | (1.35–7.05) | 0.008 * |
| Grip strength | 1.00 (reference) | 0.96 | (0.92–0.99) | 0.025 * | 1.00 (reference) | 0.94 | (0.91–0.97) | <0.001 * |
| Self-selected gait speed | 1.00 (reference) | 2241.05 | (5.84–860659.48) | 0.011 * | 1.00 (reference) | 48.14 | (4.85–477.34) | 0.001 * |
Logistic regression models adjusted for sex with lifetime abstention and at-risk consumption as reference categories. CIRS-G, Cumulative Illness Rating Scale for Geriatrics. a At-risk consumption: >98 g/week. * p < 0.05.
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Ahlner, F.; Falk Erhag, H.; Johansson, L.; Mellqvist Fässberg, M.; Rydberg Sterner, T.; Samuelsson, J.; Zettergren, A.; Waern, M.; Skoog, I. Patterns of Alcohol Consumption and Associated Factors in a Population-Based Sample of 70-Year-Olds: Data from the Gothenburg H70 Birth Cohort Study 2014–16. Int. J. Environ. Res. Public Health 2022, 19, 8248. https://doi.org/10.3390/ijerph19148248
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Ahlner F, Falk Erhag H, Johansson L, Mellqvist Fässberg M, Rydberg Sterner T, Samuelsson J, Zettergren A, Waern M, Skoog I. Patterns of Alcohol Consumption and Associated Factors in a Population-Based Sample of 70-Year-Olds: Data from the Gothenburg H70 Birth Cohort Study 2014–16. International Journal of Environmental Research and Public Health. 2022; 19(14):8248. https://doi.org/10.3390/ijerph19148248
Chicago/Turabian StyleAhlner, Felicia, Hanna Falk Erhag, Lena Johansson, Madeleine Mellqvist Fässberg, Therese Rydberg Sterner, Jessica Samuelsson, Anna Zettergren, Margda Waern, and Ingmar Skoog. 2022. "Patterns of Alcohol Consumption and Associated Factors in a Population-Based Sample of 70-Year-Olds: Data from the Gothenburg H70 Birth Cohort Study 2014–16" International Journal of Environmental Research and Public Health 19, no. 14: 8248. https://doi.org/10.3390/ijerph19148248
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