Researchers devise accurate way to predict alcohol drinking habits in South Africa
Researchers developed a computer model that uses survey data and sales numbers to predict alcohol intake in South Africa, which could help the country monitor and manage excessive drinking. They found that the percentage of people that drink, and the amount they drink, is generally decreasing.
Drinking alcohol can increase the risk of getting over 200 illnesses, including liver disease, cancer, tuberculosis, and HIV/AIDS.
Using people’s alcohol intake over the long term is the best way to predict their risk. For most illnesses, a higher intake gives a higher risk. But, the risk increase depends on a person’s sex and age. It is easier for doctors to help people manage this risk if they know how much alcohol people drink.
The main way researchers get data on how much alcohol people drink is through surveys, especially questionnaires that people fill out themselves. Unfortunately, people tend to underestimate how much alcohol they drink. For example, some studies suggest that people think they drink between 50% and 20% of what they actually drink.
Thus, researchers wanted to find a way to make accurate predictions of alcohol intake despite this underestimation.
These researchers made a computer model that uses survey and alcohol sales data to predict alcohol intake. They used data from 17 surveys of South Africans 15 years and older, taken between 1998 and 2016. Their model predicted what percentage of people drink alcohol and how much alcohol they drink.
The model seemed to work well and gave predictions similar to previous studies. The model predicted that a higher percentage of men drink alcohol than women, and that men drink more alcohol than women.
The percentage of men that drink decreased from 56% in 1998 to 51% in 2009, and increased again to 54% in 2016. The percentage of women drinkers increased from 19% to 20%. During this same period, the amount that men drink decreased from 52 to 43 grams of pure alcohol per day. The amount that women drink decreased from 33 to 26 grams per day.
Some of the largest changes in drinking habits were in young people. The percentage of people aged between 15 and 34 years who drink increased by about 10% from 1998 to 2009.
Because survey data is often inaccurate, previous studies used global alcohol intake patterns to help make predictions in specific countries. But, the researchers’ new method of using local alcohol sales will likely give more accurate predictions for South Africa, and potentially other countries. These new and more precise alcohol intake estimates could enable doctors to manage people’s health better.
The researchers want to improve the model by checking some of their assumptions, using more data, and extending the model to predict how illness caused by alcohol affects the economy.
They also suggest that research be done on how to make survey data more accurate.
These South African researchers focused on their own country but the model may work for other countries. In general, however, they say we need more information on alcohol intake in low- and middle-income countries, many of which are in Africa.
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.
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.
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).
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.
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