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

Abstract

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.

The association between preterm labour, perinatal mortality and infant death (during the first year) in Bishop Lavis, Cape Town, South Africa

“…[28] According to another study, the prevalence for women aged 15 -49 years is 23.3%. [29] The reasons for the low prevalence rate in our study are unclear. It is most likely that exposure to HIV among pregnant women in this community is low, but it is also possible that a higher resistance to HIV could have played a role.…”

Analytical methods used in estimating the prevalence of HIV/AIDS from demographic and cross-sectional surveys with missing data: a systematic review

“…We observe an increase of the HIV prevalence estimates after adjusting for the missing data, demonstrating the presence of downward bias if complete case analysis is used The differences were significant in some studies [58,71], and this suggests there might be underestimating of HIV prevalence if missing data are ignored.…”

“…All the applied methods had the shortcoming of its application considering the mechanism followed since there is no proof that missing data were MAR or MNAR. Heckman's selection models and application of instrumental variables where the methods tried to explore the deviation of MAR to the possibility of MNAR assumption although a lack of suitable selection or instrumental variable impacts their applicability [57,71]. The use of doubly robust methods and extension of Heckman's selection models are the current methods identified as suitable when data are assumed to be MNAR.…”

The influence of interviewers on survey responses among female sex workers in Zambia

“…In order for interviewers to have such an effect, both exposure and outcome must be susceptible to interviewer influence. This is clearly the case when both variables are self-reported, but can also arise when interviewers are also asking individuals to take a test – a topic that has been substantively investigated in the context of HIV testing within population studies [33, 34]. Our results highlight the need to consider interviewer identity as a possible confounder in associational as well as prevalence analyses.…”