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

Gerrit Gmel,Tara Kehoe,Jürgen Rehm,Kevin D Shield,Hannah Frick,Gerhard Gmel

doi:10.1186/1471-2288-11-48

Abstract

BackgroundAlcohol 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.MethodsThe 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.ResultsThe 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.ConclusionsWithin reasonable computation time, the method yielded very accurate values for variances of AAFs.

Highlights

Alcohol is a major risk factor for burden of disease and injuries globally
While there are methods to calculate uncertainty around attributable fractions (AAFs) when both exposure and risk relations are derived from the same cohort [6,7], no such methods exist for the case where both exposure and risk relations stem from two different meta-analyses
Since globally morbidity and mortality can only be reliably estimated for broad disease or injury categories, the Global Burden of Disease and Injury (GBD) is restricted to 126 distinct broad disease or injury categories http://www.globalburden.org/GBD_Study_Operations_Manual_Jan_20_2009.pdf, of which 31 are causally related to alcohol [5]

Summary

Introduction

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. Alcohol consumption is a major risk factor for burden of disease and injuries globally [1,2] as demonstrated by the Comparative Risk Analyses (CRA) within the Global Burden of Disease and Injury (GBD) Studies [2,3]. We will first be using exposure measures and relative risks for disease categories from the 2005 CRA study for which a meta-analysis providing a continuous relative risk function exists to estimate AAFs [5], and will explain the methodology to construct CIs for these AAFs. This paper will focus on the Asian regions as an illustration of our results. Asia presents an interesting mix of low income and high income regions and allows us to illustrate succinctly our methodology

Objectives

Methods

Results

Conclusion