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.
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.
Authors: Anton M Palma; Giampiero Marra; Rachel Bray; Suzue Saito; Anna Colletar Awor; Mohamed F Jalloh; Alexander Kailembo; Wilford Kirungi; George S Mgomella; Prosper Njau; Andrew C Voetsch; Jennifer A Ward; Till Bärnighausen; Guy Harling Journal: J Int AIDS Soc Date: 2022-08 Impact factor: 6.707
Authors: Neema R Mosha; Omololu S Aluko; Jim Todd; Rhoderick Machekano; Taryn Young Journal: BMC Med Res Methodol Date: 2020-03-14 Impact factor: 4.615