National South African HIV prevalence estimates robust despite substantial test non-participation
Background. South African (SA) national HIV seroprevalence estimates are of crucial policy relevance in the country, and for the worldwide HIV response. However, the most recent nationally representative HIV test survey in 2012 had 22% test non-participation, leaving the potential for substantial bias in current seroprevalence estimates, even after controlling for selection on observed factors.
Objective. To re-estimate national HIV prevalence in SA, controlling for bias due to selection on both observed and unobserved factors in the 2012 SA National HIV Prevalence, Incidence and Behaviour Survey.
Methods. We jointly estimated regression models for consent to test and HIV status in a Heckman-type bivariate probit framework. As selection variable, we used assigned interviewer identity, a variable known to predict consent but highly unlikely to be associated with interviewees’ HIV status. From these models, we estimated the HIV status of interviewed participants who did not test.
Results. Of 26 710 interviewed participants who were invited to test for HIV, 21.3% of females and 24.3% of males declined. Interviewer identity was strongly correlated with consent to test for HIV; declining a test was weakly associated with HIV serostatus. Our HIV prevalence estimates were not significantly different from those using standard methods to control for bias due to selection on observed factors: 15.1% (95% confidence interval (CI) 12.1 - 18.6) v. 14.5% (95% CI 12.8 - 16.3) for 15 - 49-year-old males; 23.3% (95% CI 21.7 - 25.8) v. 23.2% (95% CI 21.3 - 25.1) for 15 - 49-year-old females.
Conclusion. The most recent SA HIV prevalence estimates are robust under the strongest available test for selection bias due to missing data. Our findings support the reliability of inferences drawn from such data.
Guy Harling, Research Department of Infection and Population Health, Institute for Global Health, University College London, UK; Department of Global Health and Population, Harvard T.H. Chan School of Public Health, Boston, USA
Sizulu Moyo, Human Sciences Research Council, Cape Town, South Africa
Mark E McGovern, Centre for Health Research at the Management School (CHaRMS), Queen’s University Belfast, UK; UKCRC Centre of Excellence for Public Health (Northern Ireland), UK
Musawenkosi Mabaso, Human Sciences Research Council, Cape Town, South Africa
Giampiero Marra, Department of Statistics, University College London, UK
Till Bärnighausen, Department of Global Health and Population, Harvard T.H. Chan School of Public Health, Boston, USA; Institute of Public Health, University of Heidelberg, Germany; Africa Health Research Institute, Mtubatuba, KwaZulu-Natal, South Africa
Thomas Rehle, Human Sciences Research Council, Cape Town, South Africa; Centre for Infectious Disease Epidemiology, School of Public Health and Family Medicine, Faculty of Health Sciences, University of Cape Town, South Africa
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Date published: 2017-06-30
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