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Refusal Bias in the Estimation of HIV Prevalence

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  • Wendy Janssens
  • Jacques Gaag
  • Tobias Rinke de Wit
  • Zlata Tanović

Abstract

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. Copyright Population Association of America 2014

Suggested Citation

  • Wendy Janssens & Jacques Gaag & Tobias Rinke de Wit & Zlata Tanović, 2014. "Refusal Bias in the Estimation of HIV Prevalence," Demography, Springer;Population Association of America (PAA), vol. 51(3), pages 1131-1157, June.
    • Handle: RePEc:spr:demogr:v:51:y:2014:i:3:p:1131-1157
    DOI: 10.1007/s13524-014-0290-0
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    Cited by:

    1. Mark E. McGovern & Kobus Herbst & Frank Tanser & Tinofa Mutevedzi & David Canning & Dickman Gareta & Deenan Pillay & Till Bärnighausen, 2016. "Do Gifts Increase Consent to Home-based HIV Testing? A Difference-in-Differences Study in Rural KwaZulu-Natal, South Africa," CHaRMS Working Papers 16-05, Centre for HeAlth Research at the Management School (CHaRMS).
    2. McGovern, Mark E. & Canning, David & Bärnighausen, Till, 2018. "Accounting for non-response bias using participation incentives and survey design: An application using gift vouchers," Economics Letters, Elsevier, vol. 171(C), pages 239-244.
    3. Bruno Arpino & Elisabetta De Cao & Franco Peracchi, 2011. "Using panel data to partially identify HIV prevalence When HIV status is not missing at random," Working Papers 048, "Carlo F. Dondena" Centre for Research on Social Dynamics (DONDENA), Università Commerciale Luigi Bocconi.
    4. Ricardo Maertens & Alessandro Tarozzi & Kazi Matin Ahmed & Alexander van Geen, 2018. "Demand for Information on Environmental Health Risk, Mode of Delivery, and Behavioral Change: Evidence from Sonargaon, Bangladesh," Working Papers id:12934, eSocialSciences.
    5. Mark McGovern & David Canning & Till Bärnighausen, 2018. "Accounting for Non-Response Bias using Participation Incentives and Survey Design," CHaRMS Working Papers 18-02, Centre for HeAlth Research at the Management School (CHaRMS).
    6. Giampiero Marra & Rosalba Radice & Till Bärnighausen & Simon N. Wood & Mark E. McGovern, 2017. "A Simultaneous Equation Approach to Estimating HIV Prevalence With Nonignorable Missing Responses," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(518), pages 484-496, April.

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