Geographical variation in HIV testing in South Africa: Evidence from the 2017 national household HIV survey

Abstract

Background: Identification of the geographical areas with low uptake of HIV testing could assist in spatial targeting of interventions to improve the uptake of HIV testing.Objectives: The objective of this research study was to map the uptake of HIV testing at the district level in South Africa.Method: The secondary analysis used data from the Human Sciences Research Council’s 2017 National HIV Prevalence, Incidence, Behaviour and Communication Survey, where data were collected using a multistage stratified random cluster sampling approach. Descriptive spatial methods were used to assess disparities in the proportion of those ever tested for HIV at the district level in South Africa.Results: The districts with the highest overall coverage of people ever having tested for HIV ( 85%) include West Rand in Gauteng, Lejweleputswa and Thabo Mofutsanyane in Free State, and Ngaka Modiri Molema in North-West. These provinces also had the least variation in HIV testing coverage between their districts. Districts in KwaZulu-Natal had the widest variation in coverage of HIV testing. The districts with the lowest uptake of HIV testing were uMkhanyakude (54.7%) and Ugu (61.4%) in KwaZulu-Natal and Vhembe (61.0%) in Limpopo. Most districts had a higher uptake of HIV testing amongst female than male participants.Conclusion: The uptake of HIV testing across various districts in South Africa seems to be unequal. Intervention programmes must improve the overall uptake of HIV testing, especially in uMkhanyakude and Ugu in KwaZulu-Natal and Vhembe in Limpopo. Interventions must also focus on enhancing uptake of HIV testing amongst male participants in most districts. Strategies that would improve the uptake of HIV testing include HIV self-testing and community HIV testing, specifically home-based testing.

Exploring Determinants of HIV/AIDS Self-Testing Uptake in South Africa Using Generalised Linear Poisson and Geographically Weighted Poisson Regression

“…Extant literature investigated the variation in HIV/AIDS prevalence based on human perception, behaviour patterns, attitudes, norms, and stereotypes e.g., [ 4 , 5 , 7 , 10 , 11 , 12 , 13 ]. Focusing on people’s disability status using 2012 SABSSM data, Pengpid et al [ 7 ] investigated HIV/AIDS prevalence, human behaviours, knowledge, and attitudes.…”

“…The introduction of the 95-95-95 targets initiative by the United Nations, where 95% of HIV-infected people are meant to be cognisant of their HIV/AIDS status by 2030, is an essential global programme. In South Africa, the launch of nationwide HIV/AIDS testing initiatives, including HIV testing and counselling (HTC) in 2010 and the HTC revitalisation strategy in 2013 [ 11 ] resulted in the increased uptake of much needed antiviral drugs. Despite such programmes and initiatives (HIV/AIDS testing), millions of people continue to get affected by the HIV/AIDS epidemic and most importantly, are still oblivious to their HIV status.…”

“…Various studies have explored the association of HIV testing uptake with spatial patterns, socioeconomic status, and demographics, etc., [ 2 , 3 , 4 , 7 , 11 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 ]. For instance, Ntsepe et al [ 4 ] analysed people’s perceptions of HIV testing in formal and informal urban communities from different races and age groups in select towns (Cape Town and Durban) across two South African provinces.…”

“…Using a purposive sampling survey, the study results revealed higher HIV/AIDS testing uptake among women compared to a considerably low uptake amongst men who only tested for HIV/AIDS due to convenience. Jooste et al [ 11 ] utilised a global, Generalised Linear model to assess variations in HIV/AIDS testing uptake in South Africa, reporting variation across the country. There is a need to examine the strength and weaknesses of covariates in modelling HIV/AIDS testing uptake in different districts since one variable may have a strong influence in one district but exhibit a weaker influence in a different district.…”

“…Understanding how geographical differences, as well as people’s attitudes and perception, influences HIV/AIDS self-testing uptake, may yield imperative location intelligence interventions, and tailor-made programmes to enhance uptake across the country. This study, therefore, seeks to extend the work of Jooste et al [ 11 ] by exploring relationships and scale differences in people’s attitudes and perceptions of HIV/AIDS self-testing uptake using Generalised Linear Poisson Regression (GLPR) and Geographically Weighted Poisson Regression (GWPR) models. Revelations from this study will establish the existence or non-existence of spatial relationships between HIV/AIDS self-testing uptake and its related factors at subnational level.…”