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Background  Alcohol 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.    Methods  We 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.    Results  Among 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).    Conclusions  The 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.

Bayesian modelling of population trends in alcohol consumption provides empirically based country estimates for South Africa