Determining the best population-level alcohol consumption model and its impact on estimates of alcohol-attributable harms
1 Centre for Addiction and Mental Health (CAMH), Toronto, Canada
2 Department of Statistics, University of Toronto, Toronto, Canada
3 Dalla Lana School of Public Health (DLSPH), University of Toronto, Toronto, Canada
4 Addiction Info Suisse, Lausanne, Switzerland
5 Alcohol Treatment Centre, Lausanne University Hospital CHUV, Lausanne, Switzerland
6 University of the West of England, Bristol, UK
7 Institute for Clinical Psychology and Psychotherapy, Dresden University of Technology, Dresden, Germany
8 Department of Psychiatry, University of Toronto, Toronto, Canada
9 Institute of Medical Science, University of Toronto, Toronto, Canada
Population Health Metrics 2012, 10:6 doi:10.1186/1478-7954-10-6Published: 10 April 2012
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.
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.
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) (R2 = 0.9207) for women and 1.171 (95% CI: 1.144 to 1.197) (R2 = 0. 9474) for men.
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.