Reaching the hard-to-reach: A probability sampling method for assessing prevalence of driving under the influence after drinking in alcohol outlets

Abstract

Drinking alcoholic beverages in places such as bars and clubs may be associated with harmful consequences such as violence and impaired driving. However, methods for obtaining probabilistic samples of drivers who drink at these places remain a challenge - since there is no a priori information on this mobile population - and must be continually improved. This paper describes the procedures adopted in the selection of a population-based sample of drivers who drank at alcohol selling outlets in Porto Alegre, Brazil, which we used to estimate the prevalence of intention to drive under the influence of alcohol. The sampling strategy comprises a stratified three-stage cluster sampling: 1) census enumeration areas (CEA) were stratified by alcohol outlets (AO) density and sampled with probability proportional to the number of AOs in each CEA; 2) combinations of outlets and shifts (COS) were stratified by prevalence of alcohol-related traffic crashes and sampled with probability proportional to their squared duration in hours; and, 3) drivers who drank at the selected COS were stratified by their intention to drive and sampled using inverse sampling. Sample weights were calibrated using a post-stratification estimator. 3,118 individuals were approached and 683 drivers interviewed, leading to an estimate that 56.3% (SE = 3,5%) of the drivers intended to drive after drinking in less than one hour after the interview. Prevalence was also estimated by sex and broad age groups. The combined use of stratification and inverse sampling enabled a good trade-off between resource and time allocation, while preserving the ability to generalize the findings. The current strategy can be viewed as a step forward in the efforts to improve surveys and estimation for hard-to-reach, mobile populations. © 2012 de Boni et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.