Abstract
BackgroundAlcohol use has widespread effects on health and contributes to over 200 detrimental conditions. Although the pattern of heavy episodic drinking independently increases the risk for injuries and transmission of some infectious diseases, long-term average consumption is the fundamental predictor of risk for most conditions. Population surveys, which are the main source of data on alcohol exposure, suffer from bias and uncertainty. This article proposes a novel triangulation method to reduce bias by rescaling consumption estimates by sex and age to match country-level consumption from administrative data.MethodsWe used data from 17 population surveys to estimate age- and sex-specific trends in alcohol consumption in the adult population of South Africa between 1998 and 2016. Independently for each survey, we calculated sex- and age-specific estimates of the prevalence of drinkers and the distribution of individuals across consumption categories. We used these aggregated results, together with data on alcohol production, sales and import/export, as inputs of a Bayesian model and generated yearly estimates of the prevalence of drinkers in the population and the parameters that characterise the distribution of the average consumption among drinkers.ResultsAmong males, the prevalence of drinkers decreased between 1998 and 2009, from 56.2% (95% CI 53.7%; 58.7%) to 50.6% (49.3%; 52.0%), and increased afterwards to 53.9% (51.5%; 56.2%) in 2016. The average consumption from 52.1 g/day (49.1; 55.6) in 1998 to 42.8 g/day (40.0; 45.7) in 2016. Among females the prevalence of current drinkers rose from 19.0% (17.2%; 20.8%) in 1998 to 20.0% (18.3%; 21.7%) in 2016 while average consumption decreased from 32.7 g/day (30.2; 35.0) to 26.4 g/day (23.8; 28.9).ConclusionsThe methodology provides a viable alternative to current approaches to reconcile survey estimates of individual alcohol consumption patterns with aggregate administrative data. It provides sex- and age-specific estimates of prevalence of drinkers and distribution of average daily consumption among drinkers in populations. Reliance on locally sourced data instead of global and regional trend estimates better reflects local nuances and is adaptable to the inclusion of additional data. This provides a powerful tool to monitor consumption, develop burden of disease estimates and inform and evaluate public health interventions.
Highlights
Alcohol has widespread, and pervasively harmful, effects on health; its consumption has been identified as a contributing factor for over 200 detrimental conditions, ranging from liver disease and road injuries, to cancers, cardiovascular diseases, psychiatric disorders, Cois et al Popul Health Metrics (2021) 19:43 tuberculosis and HIV/AIDS [1]
In most cases this relationship is monotonic but there is evidence of J-shaped relationships for some cardiovascular diseases and for diabetes, where low levels of consumption are accompanied by beneficial effects [3, 5, 6]
Model checking procedures supported the conclusion that the model reached convergence, with acceptable values of effective sample size and Montecarlo standard error (ESS > 539, MCSE < 5% of the posterior standard deviation for all parameters)
Summary
Pervasively harmful, effects on health; its consumption has been identified as a contributing factor for over 200 detrimental conditions, ranging from liver disease and road injuries, to cancers, cardiovascular diseases, psychiatric disorders, Cois et al Popul Health Metrics (2021) 19:43 tuberculosis and HIV/AIDS [1]. Despite the solid and growing evidence of the independent role of drinking patterns in determining the health risk associated with alcohol use, the long-term average quantity of alcohol consumed by an individual remains the fundamental predictor of risk [3]. A clear dose-response relationship exists between quantity of alcohol consumed and risk of negative health consequences [4] In most cases this relationship is monotonic (with higher quantity of alcohol associated with greater risk and no consumption associated with the minimum risk) but there is evidence of J-shaped relationships for some cardiovascular diseases and for diabetes, where low levels of consumption are accompanied by beneficial effects [3, 5, 6]. This article proposes a novel triangulation method to reduce bias by rescaling consumption estimates by sex and age to match country-level consumption from administrative data
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