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Open Access Research article

Estimating uncertainty of alcohol-attributable fractions for infectious and chronic diseases

Gerrit Gmel12*, Kevin D Shield13, Hannah Frick4, Tara Kehoe15, Gerhard Gmel1678 and Jürgen Rehm139

Author Affiliations

1 Centre for Addiction and Mental Health (CAMH) 33 Russell Street, Toronto, Ontario, M5S 2S1, Canada

2 Ecole Polytechnique Fédérale de Lausanne, Route Cantonale, 1015 Lausanne, Switzerland

3 Dalla Lana School of Public Health (DLSPH) University of Toronto, 6thFloor, Health Sciences Building 155 College Street, Toronto, Ontario, M5T 3M7, Canada

4 Ludwig-Maximilians Universität, Geschwister-Scholl-Platz 1, 80539 München, Germany

5 Department of Statistics, University of Toronto, 100 St. George St., Toronto, Ontario, M5S 3G3, Canada

6 Addiction Info Suisse, Lausanne, Switzerland

7 Alcohol Treatment Centre, Lausanne University Hospital CHUV, Mont-Paisible 16, 1011 Lausanne, Switzerland

8 University of the West of England, Frenchay Campus Coldharbour Lane, Bristol BS16 1QY, UK

9 Institute for Clinical Psychology and Psychotherapy Technische Universität Dresden, Chemnitzer Str. 46, D-01187 Dresden, Germany

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BMC Medical Research Methodology 2011, 11:48  doi:10.1186/1471-2288-11-48

Published: 17 April 2011

Abstract

Background

Alcohol is a major risk factor for burden of disease and injuries globally. This paper presents a systematic method to compute the 95% confidence intervals of alcohol-attributable fractions (AAFs) with exposure and risk relations stemming from different sources.

Methods

The computation was based on previous work done on modelling drinking prevalence using the gamma distribution and the inherent properties of this distribution. The Monte Carlo approach was applied to derive the variance for each AAF by generating random sets of all the parameters. A large number of random samples were thus created for each AAF to estimate variances. The derivation of the distributions of the different parameters is presented as well as sensitivity analyses which give an estimation of the number of samples required to determine the variance with predetermined precision, and to determine which parameter had the most impact on the variance of the AAFs.

Results

The analysis of the five Asian regions showed that 150 000 samples gave a sufficiently accurate estimation of the 95% confidence intervals for each disease. The relative risk functions accounted for most of the variance in the majority of cases.

Conclusions

Within reasonable computation time, the method yielded very accurate values for variances of AAFs.