National South African HIV prevalence estimates robust despite substantial test non-participation

SOURCE: South African Medical Journal
OUTPUT TYPE: Journal Article
TITLE AUTHOR(S): G.Harling, S.Moyo, M.E.McGovern, M.Mabaso, G.Marra, T.Barnighausen, T.Rehle
DEPARTMENT: Human and Social Capabilities (HSC)
Print: HSRC Library: shelf number 10177
HANDLE: 20.500.11910/11675

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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. The objective of this study was 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. 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. 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. 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.