Problem drinking and associated factors in older adults in South Africa

Abstract

Objective

Alcohol abuse poses special risks for increased morbidity and mortality among older adults. Little attention has focused on assessing alcohol use and associated factors among older adults in transitional societies such as South Africa. This study aimed to determine the prevalence of alcohol use and associated factors in older South Africans who participated in the Study of Global Ageing and Adults Health (SAGE) in 2008.

Method

We conducted a national population-based cross-sectional study with a sample of 3840 aged 50 years or older in South Africa in 2008. In this study we analysed data from all 2144 participants who were over 60 years old. The questionnaire included socio-demographic characteristics, alcohol intake as well as comorbidity. Risky drinking was defined in two ways: heavy drinkers (>7 drinks/week) and binge drinkers (>3 drinks/one occasion/week).

Results

Four percent of participants reported heavy drinking and 3.7% binge drinking. Male gender (Odds Ratio (OR) =3.79, Confidence Interval (CI) =1.38-10.37) and white population group (OR=3.01, CI=1.31-6.89) were associated with risky drinking in multivariate analysis; as well as tobacco use (OR=5.25, CI=2.20-12.52) and not being obese (OR=0.14, CI=0.05-0.35). Hypertension, diabetes and depression were not associated.

Conclusion

This study reveals moderate rates of risky drinking among older adults (60 years and more) in South Africa that puts them at risk of morbidity. Alcohol problems among older adults are commonly under-recognized, indicating a need for health care worker intervention.

Introduction

As the population of older adults continues to grow, there is an increased need to reexamine alcohol use in this population.¹ Alcohol abuse poses special risks for increased morbidity and mortality among older adults² including increased risks for accidents and injuries, hypertension, cardiac dysrhythmic events, cancers, gastrointestinal problems, liver disease, neurocognitive deficits, bone loss and depression.^3-5 Many older adults take medications that may interact negatively with alcohol.⁶

Alcohol use data of older adults from high income countries indicates that: in Japan, 47.9% of men and 10.1% of women drank alcohol almost daily⁷; in the USA, 9% of elderly Medicare beneficiaries reported unhealthy drinking, with higher prevalence in men (16%) than women (4%)⁸; and also in the USA, Schultz et al.⁹ reported that about 15% of older persons in a rural state were at risk for alcoholism. Studies from low and middle income countries found that: among older adults in Brazil, 12% reported heavy drinking behavior, while 10.4% and 2.9% were binge drinkers and alcohol dependent respectively¹⁰; among rural residents 50 years and older in Tanzania, 4.3% and 6.0% of men and women respectively, had more than one drink per day¹¹; and in a national survey in 2004 in Thailand, 13.0% of older persons (55 years and above) were hazardous or harmful alcohol drinkers.¹²

Factors associated with alcohol misuse or abuse in older adults have been identified as follows: higher or lower education and income; better health status; male sex; younger age; smoking; being white; being divorced, separated, or single; self-reported depressive symptoms; psychological distress; race and ethnicity; and religious involvement.^8,10,13-17

There are very few studies investigating alcohol use among older adults in low and middle income countries. Therefore the aim of this study was to assess the prevalence of alcohol use and associated factors in older South Africans who participated in the Study of Global Ageing and Adults Health (SAGE) in 2008.

Methods

Sample and procedure

We conducted a national population-based cross-sectional study with a sample of 3840 aged 50 years or older in South Africa in 2008. In this study we analysed data from all 2144 participants who were over 60 years of age. The SAGE sample design entails a two-stage probability sample that yields national and sub-national estimates to an acceptable precision at provincial level, by locality type (urban and rural), and by population group (including black, coloured, Indian or Asian and white). The overall response rate among those aged 50 years or older was 60%. The Global Study on Ageing (SAGE) survey was carried out in South Africa in partnership between the World Health Organization (WHO), the National Department of Health, and the Human Sciences Research Council (HSRC).

Ethics

The study was approved by the Human Sciences Research Council Research Ethics Committee and the National Department of Health.

Measures

Alcohol use

Lifetime alcohol use was assessed with the question “Have you ever consumed a drink that contains alcohol (such as beer, wine, spirits, etc.)?” Response options were “Yes” or “No, never”. Lifetime alcohol users were asked about current (past month) alcohol use, and current alcohol users were asked “During the past 7 days, how many drinks of any alcoholic beverage did you have each day?” Finally past 12 months alcohol use frequency and average consumption was assessed; First “In the last 12 months, how frequently [on how many days] on average have you had at least one alcoholic drink?” Response options included 1=less than once a month to 4=five or more days a week; Second “In the last 12 months, on the days you drank alcoholic beverages, how many drinks did you have on average?” Response option was the number of drinks.

Risky drinking was defined in two ways: heavy drinkers (>7 drinks/week) and binge drinkers (>3 drinks/one occasion/week). These are considered ‘risky drinking’ according to the The National Institute on Alcohol Abuse and Alcoholism (NIAAA).¹⁸ Exceeding these limits is associated with interpersonal and functioning problems for elders¹⁹, who have higher sensitivity and impaired ability to metabolize alcohol.²⁰

Tobacco use

Lifetime tobacco use was assessed with the question “Have you ever smoked tobacco or used smokeless tobacco?” Lifetime tobacco users were asked “Do you currently use (smoke, sniff or chew) any tobacco products such as cigarettes, cigars, pipes, chewing tobacco or snuff?” The response options were “Yes, daily”, “Yes, but not daily” and “No, Not at all.”

Anthropometry

Height, weight, waist and hip circumferences were measured. Body mass index (BMI) was used as an indicator of obesity, and the waist/hip ratio (WHR) was used as an indicator of central obesity.

Physical activity

Physical activity was measured using the General Physical Activity Questionnaire (GPAQ). The instrument gathers information on physical activity in three domains (activity at work, travel to and from places, and recreational activities), as well as time spent on sitting. The questionnaire also assesses vigorous and moderate activities performed at work and for recreational activities.

Information on the number of days in a week spent on different activities and time spent in a typical day for each activity was also recorded.²¹

Overall self-rated health status

This was based on respondents’ assessment of their current health status on a 5-point scale in response to the question: “In general, how would you rate your health today?” Response categories were: very good, good, moderate, bad and very bad. From this a dichotomous measure was coded 0 if response was ‘very good’ or ‘good’ or ‘moderate’; and 1 if response was ‘bad’ or ‘very bad’.

Activity limitation

Difficulty an individual may have in executing tasks or actions was assessed with one item: “Overall in the last 30 days, how much difficulty did you have with work or household activities?” Response options ranged from 1=none to 5=extreme/cannot do. From this we created a dichotomous measure coded 0 if response was ‘none’ or ‘mild’ or ‘moderate’; and 1 if response was ‘severe’ or ‘extreme/cannot do.’

Symptom-based depression in the past 12 months was assessed based on the World Mental Health Survey version of the Composite International Diagnostic Interview.²² The diagnosis of depression was based on the International Classification of Diseases tenth revision (ICD-10) diagnostic criteria for research, for depressive episodes²³, and was derived from an algorithm that took into account respondents’ reporting symptoms of depression during the past 12 months.²⁴

Finally, participants were asked about a list of chronic and other illness conditions they had been diagnosed with including diabetes and hypertension.

Data analysis

The data were entered using CSPro and analysed using STATA Version 10. Data was weighted using post-stratified individual probability weights based on the selection probability at each stage of selection. Individual weights were post-stratified by province, sex and age-groups according to the 2009 Medium Mid Year population estimates from Statistics South Africa. (Available at: http://www.statssa.gov.za/publications/P0302/P03022009.pdf.) Weights were not normalised. Outliers were removed after examining the data using boxplot analyses. For physical activity, in addition to the total minutes of activity, the activity volume was also computed by weighing each type of activity by its energy requirement in metabolic equivalents (METs). One MET was defined as the energy cost of sitting quietly, and was equivalent to a caloric consumption of 1 kcal/kg/hour. A MET-minute showed the total activity volume on a weekly basis, and was calculated by multiplying time spent on each activity during a week, by the MET-values of each level of activity. MET-values for different level activities were set as 4 MET for moderate intensity physical activity, 8 MET for vigorous physical activity, and 4 MET for transport-related walking or cycling. The total physical activity for GPAQ2 was calculated as the sum of total moderate, vigorous, and transport-related activities per week. The number of days and total physical activity MET minutes per week were used to classify respondents into three categories of low, moderate, and high level of physical activities. Less than 600 MET-minutes per week was classified as low physical activity.²¹

Multivariate logistic regression identified socio-demographic and selected chronic conditions (obesity, diabetes and hypertension), depression, tobacco use and health risk (low physical activity) factors as predictors of problem drinking in the data.

Results

Sample characteristics

The sample included 2144 participants (1263 women and 881 men), 61.1% were 60 to 69 years old and 38.9% 70 years or older. The largest population group consisted of Black African (70.7%), followed by Coloured (13.1%), Whites (11.6%) and Indian or Asian (4.5%). More than half (52.1%) were currently married or cohabitating, 33% widowed, 10.2% never married and 4.7% separated or divorced. About half (48.8%) had at least primary school education completed. Almost two-thirds of the sample (63.2%) resided in urban as opposed to rural areas (36.8%). In terms of alcohol use, 23.7% were lifetime and 10.7% past month or current alcohol users. Four percent of participants reported heavy drinking and 3.7% binge drinking (see Table I).

Table I. Characteristics of participants aged 60 and over (SAGE, 2008).

Socio-demographic		N=2144	%
Gender	Male	881	42.2
	Female	1263	57.8
Age (years)	60-69	1233	61.1
	70 and over	911	38.9
Population group	African Black	1134	70.7
	White	170	11.6
	Coloured	375	13.1
	Indian or Asian	165	4.5
Marital status	Single	212	10.2
	Married	1014	52.1
	Separated/Divorced	101	4.7
	Widow	779	33.0
Education level	Less than primary	1119	51.2
	Primary	492	22.5
	Secondary	415	21.5
	More than secondary	86	4.8
Wealth quintile	Lowest	399	20.0
	Second	402	17.9
	Middle	397	17.4
	Fourth	469	21.8
	Highest	462	22.9
Geolocality	Rural	749	36.8
	Urban	1392	63.2
Alcohol use	Ever used alcohol	548	23.7
	Alcohol use in past month	292	10.7
Risky drinking	Heavy drinker	106	4.0
	Binge drinker	97	3.7
Co-morbidity	Hypertension	700	36.7
	Diabetes	237	11.3
	Depression	68	3.7
	Tobacco use	435	18.6
	Obesity	839	45.8

Associations between risky drinking, socio-demographics and co-morbidity

Regarding socio-demographics, only male gender and white population group were associated with risky drinking in multivariate analysis; as well as tobacco use and not being obese. Hypertension, diabetes and depression were not associated (see Table II).

Table II. Associations between risky drinking, socio-demographics and co-morbidity.

	Heavy drinker		Binge drinker
	UOR (95% CI)	AOR (%% CI)	UOR (95% CI)	AOR (%% CI)
Gender
Female	1.00	1.00	1.00	1.00
Male	3.80 (1.64-8.83)**	3.55 (1.05-12.02)*	3.53 (1.52-8.20)**	3.79 (1.38-10.37)*
Age
60-69	1.00	1.00	1.00	1.00
70 and over	0.66 (0.36-1.23)	0.59 (0.30-1.16)	0.48 (0.22-1.05)	0.41 (0.16-1.04)
Population group
African Black	1.00	1.00	1.00	1.00
White	1.55 (0.54-4.44)	1.95 (0.75-5.10)	2.10 (0.84-5.28)*	3.01 (1.31-6.89)*
Coloured	0.63 (0.18-2.15)	0.49 (0.17-1.46)	0.53 (0.20-1.46)	0.48 (0.20-1.18)
Indian or Asian	0.47 (0.12-1.93)	0.40 (0.06-2.85)	0.52 (0.13-2.14)	0.51 (0.08-3.30)
Marital status
Single	1.00	1.00	1.00	---
Married	2.59 (1.33-5.04)**	1.84 (0.70-4.88)	1.63 (0.74-3.55)
Separated/Divorced	1.39 (0.35-5.55)	0.87 (0.14-5.46)	1.90 (0.46-7.90)
Widow	1.12 (0.53-2.37)	1.05 (0.47-2.34)	0.83 (0.33-2.10)
Educational level
Less than primary	1.00	1.00	1.00	1.00
Primary	0.92 (0.48-1.79)	1.00 (0.49-2.05)	0.77 (0.39-1.51)	0.82 (0.43-1.57)
Secondary	0.80 (0.33-1.98)	1.44 (0.41-5.00)	1.06 (0.46-2.46)	1.26 (0.45-3.49)
Wealth
Low	1.00	1.00	1.00	1.00
Medium	0.66 (0.37-1.17)	0.62 (0.32-1.18)	0.59 (0.32-1.09)	0.54 (0.27-1.09)
High	0.75 (0.27-2.11)	0.61 (0.23-1.18)	0.86 (0.33-2.26)	0.70 (0.28-1.73)
Geolocality
Rural	1.00	---	1.00	---
Urban	1.14 (0.57-2.31)		1.32 (0.64-2.70)
Co-morbidity
Hypertension	0.72 (0.30-1.71)	---	0.92 (0.41-2.08)	---
Diabetes	0.12 (0.03-0.45)**	0.28 (0.06-1.27)	0.40 (0.13-1.24)	---
Depression	1.11 (0.48-2.59)	---	0.88 (0.29-2.66)	---
Tobacco use	7.27 (3.09-17.12)***	6.35 (2.47-16.33)***	5.26 (2.31-11.96)***	5.25 (2.20-12.52)***
Obesity	0.12 (0.06-0.23)***	0.13 (0.05-0.32)***	0.12 (0.06-0.27)***	0.14 (0.05-0.35)***
Subjective health status	1.03 90.49-2.14)		0.94 (0.44-2.01)
Activity limitation	0.96 (0.62-1.50)		0.90 (0.59-1.39)
Physical activity
High	1.00		1.00
Moderate	0.67 (0.30-1.47)		0.48 (0.19-1.25)
Low	0.65 (0.31-1.38)		0.59 (0.26-1.36)

Both heavy and binge drinking were adjusted mutually by gender, age, educational level, wealth and depression

Discussion

The study found a moderate rate of risky drinking among persons 60 years and older in South Africa. Other studies from low and middle income countries seemed to have identified high alcohol problem drinking among older adults.^10-12 Public health interventions should be targeted at alcohol problem drinking in older adults.

The study further found that regarding socio-demographics, only male gender and white population group were associated with risky drinking in multivariate analysis; while among co-morbidities, tobacco use and not being obese were associated with risky drinking. Hypertension, diabetes and depression were not associated. In agreement with other studies this study found significantly higher problem drinking among men than women.^8,10,14,17 This gender difference may be in some way attributed to socio-cultural norms that hinder women to engage in alcohol drinking. The study found that there was a decrease of alcohol use with age but this was not significant, as found in some other studies.^8,14 This would indicate that daily drinking would pose an increasing problem as with ageing and would require specific attention. Further, unlike in other studies^15,16, depression symptoms and psychological distress were in this study not related with risky drinking. In concordance with other studies^25-27, this study found an association between tobacco use and hazardous or harmful drinking. Public health interventions should address multiple substance use risk behaviour. The finding that obesity was protective of risky drinking in both older women and men is less clear. Other studies show different relationships between obesity and risky drinking. For example, drinking alcohol increased the odds of being overweight or obese in Chinese older adults²⁸: lower and higher levels of alcohol use²⁹ and women who were heavy drinkers, were less likely to be obese than women who were non-drinkers.³⁰

Limitations of the study

This study had several limitations. Firstly, the self-report of alcohol use should be interpreted with caution; it is possible that respondents underreported alcohol use, especially females. A number of risk factors associated with alcohol misuse or abuse in older adults reported in other studies (such as medication intake which may interact negatively with alcohol and religious involvement^{3,4,8,13,14,17}), were not assessed and should be assessed in future studies. For co-morbidities, given the limitations of self-reported morbidity, symptom questions and a related diagnostic algorithm were used to ascertain possible presence of co-morbidity conditions. Furthermore, this study was based on data collected in a cross-sectional survey. We cannot, therefore, ascribe causality to any of the associated factors in the study. Prospective studies are required to follow up alcohol use and associated factors.

Conclusion

This study reveals moderate rates of risky drinking among older adults (60 years and older) in South Africa that puts them at risk of morbidity. Alcohol problems among older adults are commonly under-recognized, suggesting a need for health care worker intervention. Evidence is needed as the capacity of some African health systems to respond to multiple co-morbidities is limited.