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Title of Journal: Demography

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Refusal Bias in the Estimation of HIV Prevalence

Authors: Wendy Janssens, Jacques van der Gaag, Tobias F. Rinke de Wit, Zlata Tanović,

Publish Date: 2014/05/02
Volume: 51, Issue:3, Pages: 1131-1157
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In 2007, UNAIDS corrected estimates of global HIV prevalence downward from 40 million to 33 million based on a methodological shift from sentinel surveillance to population-based surveys. Since then, population-based surveys are considered the gold standard for estimating HIV prevalence. However, prevalence rates based on representative surveys may be biased because of nonresponse. This article investigates one potential source of nonresponse bias: refusal to participate in the HIV test. We use the identity of randomly assigned interviewers to identify the participation effect and estimate HIV prevalence rates corrected for unobservable characteristics with a Heckman selection model. The analysis is based on a survey of 1,992 individuals in urban Namibia, which included an HIV test. We find that the bias resulting from refusal is not significant for the overall sample. However, a detailed analysis using kernel density estimates shows that the bias is substantial for the younger and the poorer population. Nonparticipants in these subsamples are estimated to be three times more likely to be HIV-positive than participants. The difference is particularly pronounced for women. Prevalence rates that ignore this selection effect may be seriously biased for specific target groups, leading to misallocation of resources for prevention and treatment.This work was supported by the Dutch Ministry of Development Cooperation (Grant No. 13298) and the Dutch Organization of Scientific Research (NWO) (Rubicon Grant No. 446-08-004 to W.J.). The survey data used in this article were collected by the University of Namibia (UNAM) and the National Institute of Population (NIP), with technical assistance from PharmAccess International and the Amsterdam Institute of International Development. Special thanks are due to Ingrid De Beer, Gert van Rooy, and Christa Schier for organizing the fieldwork and providing detailed insights into the data collection process. We are also grateful to Chris Elbers, Angus Deaton, and Aico van Vuren for helpful discussions on technical aspects of the estimation. We would like to thank participants at the 2007 AIID workshop on “The Economic Consequences of HIV/AIDS,” the 2008 Tinbergen Annual Conference in Amsterdam, the 2009 CSAE Conference in Oxford, and the 2012 Scientific EUDN Conference in Paris for useful comments and suggestions. Finally, we thank the editors of this journal and three anonymous reviewers for the positive and constructive feedback on earlier versions of this article.



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