Research
Statistical modeling of volume of alcohol exposure for epidemiological studies of population health: the US example
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* Corresponding author: Jürgen Rehm jtrehm@aol.com
1 Centre for Addiction and Mental Health (CAMH), 33 Russell Street, Toronto, Ontario, M5S 2S1, Canada
2 Dalla Lana School of Public Health, University of Toronto, 6th Floor, Health Sciences Building, 155 College Street, Toronto, Ontario, M5T 3M7, Canada
3 Institute for Clinical Psychology and Psychotherapy, Technische Universität Dresden, Chemnitzer Str. 46 D-01187 Dresden, Germany
4 Department of Statistics, University of Toronto, 100 St. George St, Toronto, Ontario, M5S 3G3, Canada
5 National Institute on Alcohol Abuse and Alcoholism/NIH, Laboratory of Epidemiology and Biometry, Division of Intramural Clinical and Biological Research, 5635 Fishers Lane, Rockville MD 20852, USA
6 Swiss Institute for the Prevention of Alcohol and Drug Problems PO Box 870, 1001 Lausanne, Switzerland
7 Alcohol Treatment Center, Lausanne University Hospital, Mont-Paisible 16, 1011 Lausanne, Switzerland
8 University of the West of England, Frenchay Campus Coldharbour Lane, Bristol BS16 1QY, UK
Population Health Metrics 2010, 8:3 doi:10.1186/1478-7954-8-3
Published: 4 March 2010Abstract
Background
Alcohol consumption is a major risk factor in the global burden of disease, with overall volume of exposure as the principal underlying dimension. Two main sources of data on volume of alcohol exposure are available: surveys and per capita consumption derived from routine statistics such as taxation. As both sources have significant problems, this paper presents an approach that triangulates information from both sources into disaggregated estimates in line with the overall level of per capita consumption.
Methods
A modeling approach was applied to the US using data from a large and representative survey, the National Epidemiologic Survey on Alcohol and Related Conditions. Different distributions (log-normal, gamma, Weibull) were used to model consumption among drinkers in subgroups defined by sex, age, and ethnicity. The gamma distribution was used to shift the fitted distributions in line with the overall volume as derived from per capita estimates. Implications for alcohol-attributable fractions were presented, using liver cirrhosis as an example.
Results
The triangulation of survey data with aggregated per capita consumption data proved feasible and allowed for modeling of alcohol exposure disaggregated by sex, age, and ethnicity. These models can be used in combination with risk relations for burden of disease calculations. Sensitivity analyses showed that the gamma distribution chosen yielded very similar results in terms of fit and alcohol-attributable mortality as the other tested distributions.
Conclusions
Modeling alcohol consumption via the gamma distribution was feasible. To further refine this approach, research should focus on the main assumptions underlying the approach to explore differences between volume estimates derived from surveys and per capita consumption figures.