Determining the best population-level alcohol consumption model and its impact on estimates of alcohol-attributable harms

Abstract

Background The goals of our study are to determine the most appropriate model for alcohol consumption as an exposure for burden of disease, to analyze the effect of the chosen alcohol consumption distribution on the estimation of the alcohol Population- Attributable Fractions (PAFs), and to characterize the chosen alcohol consumption distribution by exploring if there is a global relationship within the distribution. Methods To identify the best model, the Log-Normal, Gamma, and Weibull prevalence distributions were examined using data from 41 surveys from Gender, Alcohol and Culture: An International Study (GENACIS) and from the European Comparative Alcohol Study. To assess the effect of these distributions on the estimated alcohol PAFs, we calculated the alcohol PAF for diabetes, breast cancer, and pancreatitis using the three above-named distributions and using the more traditional approach based on categories. The relationship between the mean and the standard deviation from the Gamma distribution was estimated using data from 851 datasets for 66 countries from GENACIS and from the STEPwise approach to Surveillance from the World Health Organization. Results The Log-Normal distribution provided a poor fit for the survey data, with Gamma and Weibull distributions providing better fits. Additionally, our analyses showed that there were no marked differences for the alcohol PAF estimates based on the Gamma or Weibull distributions compared to PAFs based on categorical alcohol consumption estimates. The standard deviation of the alcohol distribution was highly dependent on the mean, with a unit increase in alcohol consumption associated with a unit increase in the mean of 1.258 (95% CI: 1.223 to 1.293) (R 2 = 0.9207) for women and 1.171 (95% CI: 1.144 to 1.197) (R 2 = 0. 9474) for men. Conclusions Although the Gamma distribution and the Weibull distribution provided similar results, the Gamma distribution is recommended to model alcohol consumption from population surveys due to its fit, flexibility, and the ease with which it can be modified. The results showed that a large degree of variance of the standard deviation of the alcohol consumption Gamma distribution was explained by the mean alcohol consumption, allowing for alcohol consumption to be modeled through a Gamma distribution using only average consumption.

A narrative review of alcohol consumption as a risk factor for global burden of disease

“…While the overall methodology for CRAs has been fairly stable (for a description see [105, 106]) based on original epidemiological concepts of the 1980s [107, 108], there are important shifts in details:The first CRAs until and including the 2010 study were based on discrete categories of exposure and associated relative risks, whereas the latter were based on a continuous distribution of both exposure and risk (for theoretical background: [109]; for a comparison of both methods in the same sample: [110])This implicated different ways to define exposure and to triangulate between national per capita consumption and surveys (see [16, 111] and [110, 112] for the categorical, and the continuous approach respectively). However, the differences between the categorical and the continuous approach for alcohol as a risk factor in CRAs are not that large [109].…”

On monitoring unrecorded alcohol consumption

“…The main mechanism for gathering data is regular surveys (i.e., iterations of the WHO Global Survey on Alcohol and Health) to all WHO membership countries, where information on alcohol exposure (surveys, recorded per capita consumption as derived from regular statistics), alcohol-attributable harm and alcohol policy are collected [12]. This information is complemented by regular systematic searches of the literature, and statistical analyses to estimate missing data (see methods section of the Global Status Report [3], also [16]), and to triangulate different data sources [2,17,18].…”

Global burden of HIV/AIDS in 2004 resulting from alcohol attributable non-adherence to medication regimes

“…Data on drinking status were obtained from large representative population surveys taken in the 2000s (WHO, 2011). The prevalence of heavy drinkers, defined as those consuming on average 40 or more grams of alcohol per day for women (3.33 standard drinks or more per day) and 60 or more grams of alcohol per day for men (5 standard drinks or more) was calculated based on methods developed by Kehoe and colleagues, whereby the distribution of average daily consumption of alcohol is estimated based on 80% of the mean alcohol consumption for men and women (allowing 20% for alcohol not consumed due to spillage, breakage, and waste, and to account for the underreporting of alcohol consumption commonly seen in surveys (Kehoe et al, 2012;.…”