The Impact and Spillover Effects of HIV Self-Test Technology on HIV Outcomes of the South African Working Class

Abstract

Background: South Africa recently adopted HIV self-test technology (HIVST) to improve HIV testing and encourage earlier treatment initiation in working populations with a low uptake of conventional testing approaches. This study investigates the impact of HIVST on testing outcomes, focusing on both frequent and infrequent working-class testers. The paper also examines the spillover effect of HIVST on antiretroviral (ART) treatment initiation. To identify these effects, the author focused on South Africa and exploited the HIVST distribution data of 6259 beneficiaries of HIVST. Methods: The author used a two-stage least-squared model to quantify the impact of the HIVST on these vulnerable working populations. Results: The results show that HIVST fosters a 27.6% higher testing uptake in infrequently testing workers compared to frequently testing workers, and that the uptake of HIVST is 11.5% higher in rural regions than in urban settings, as well as 14.5% more prominent in infrequent male testers than infrequent female testers. Notably, the positive effects of HIVST are also confirmed by the presence of positive spillover effects in workers screening positive for HIV. The paper documents a 7.6% increase in ART initiation in infrequent testers. Conclusions: There is a case for adopting this technology to improve the uptake of HIV testing and ART initiation as the country seeks to attain the UNAIDS 95–95–95 targets by 2030.

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“…Hence, we did not use an OLS model (we have provided OLS results see the Appendix Table 7 , Table 8 , Table 9 ). Instead, we opted for a Two-stage Least Squared Model (2SLS) – like other health policy studies done in South Africa ( Mostert and Vall, 2020 , Mostert, 2021 , Mostert, 2021 , Mostert, 2021 , Mostert, 2022 ). The 2SLS models are more robust to biases than other models.…”

“…Thus, the year (or cohort) fixed effects account for any trend in the outcome variable across 60-year cohorts, and the province fixed effects control for any baseline (time-invariant) difference in the outcome variables across provinces ( Mostert and Vall, 2020 ). In all model estimations, one needs two assumptions to be fulfilled: first, the instrument must be relevant in explaining the probability of being treated, and this will be corroborated by the F-test of the first stage equation; and second, the exclusion restriction needs to hold, that is, the instrument should not influence the primary outcome directly through any channel other than treatment ( Mostert and Vall, 2020 , Mostert, 2021 , Mostert, 2021 , Mostert, 2021 , Mostert, 2022 ). In this case, this assumption means that differences in health outcomes between the treated and control groups can only be due to exposure to the 2008 pension reform.…”

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