The first 90 of UNAIDS 90–90–90 targets to have 90% of the people living with HIV know their status is an important entry point to the HIV treatment cascade and care continuum, but evidence shows that there is a large gap between males and females in this regard. It is therefore important to understand barriers and facilitators of achieving the first 90 target. This study examined determinants of the first 90 target among females and males in order to inform strategies aimed at improving the HIV cascade in South Africa.
The data used in the analysis were obtained from a 2017 household-based cross-sectional nationally representative survey conducted using a multi-stage stratified cluster random sampling design. A series of hierarchical multiple logistic regression models were fitted to identify the determinants of the first 90 target by gender.
Overall, 84.8% of HIV-positive individuals aged 15 years and older were aware of their HIV status. Females were significantly more aware of their HIV status compared to males (88.7% vs 78.2%, p < 0.001). Both females aged 25 to 49 years [aOR = 3.20 (95% CI 1.35–7.57), p = 0.008], and 50 years and older [aOR = 3.19 (95% CI 1.04–9.76), p = 0.042] and males aged 25 to 49 years [aOR = 3.00 (95% CI 1.13–7.97), p = 0.028], and 50 years and older [aOR = 7.25 (95% CI 2.07–25.36), p = 0.002] were significantly more likely to know their HIV status compared to those aged 15 to 19 years. Males with tertiary education level were significantly more likely to be aware of their HIV positive status [aOR = 75.24 (95% CI 9.07–624.26), p < 0.001] compared to those with no education or with primary level education. Females with secondary [aOR = 3.28 (95% CI 1.20–8.99), p = 0.021] and matric [aOR = 4.35 (95% CI 1.54–12.37), p = 0.006] educational levels were significantly more likely to be aware of their HIV positive status, compared to those with no education or with primary level education.
Significant progress has been made with regards to reaching the UNAIDS first 90 target. In this context achieving the first 90 target is feasible but there is a need for additional interventions to reach the males especially youth including those with no education or low levels of education.
The Joint United Nations Programme on HIV/AIDS’ (UANIDS’) 90–90–90 strategy is to end the HIV epidemic by 2030 by achieving three targets, 90% of all people living with HIV know their status; 90% of all people diagnosed with HIV receive sustained antiretroviral therapy (ART); and 90% of all people on ART are virally suppressed . The first 90 target is an important entry point to the HIV treatment cascade and care continuum . This includes diagnosis and linkage to care, retention in care, adherence to ART, and viral suppression needed to remain healthy and live a long life with HIV [2, 3].
The South African government also adopted this strategy and has made tremendous progress towards the UNAIDS 90–90–90 targets, where knowledge of HIV status is the first step towards progress in the HIV cascade [2, 4, 5]. Although there has been a remarkable increase in HIV testing and awareness over the past decades more remains to be done to end the HIV epidemic by 2030. In sub-Saharan Africa (SSA), men account for 41% of people living with HIV and 53% of the AIDS-related deaths in 2016 were men . Various socio-demographic, behavioural, and social characteristics have been associated with knowledge of HIV status. These include among others gender, age, marital status, educational level, employment status, socio-economic status, area of residence, stigma, and discrimination [5, 7, 8].
In Eastern and Southern African countries including South Africa, evidence points to large gaps between males and females in HIV testing and awareness including factors associated with the gender gap [5, 7, 9]. Understanding factors related to gender inequality in shaping the knowledge of HIV positive status is critical for designing interventions needed to bridge this gap, and for improving the HIV treatment and care cascade in South Africa. However, there is a paucity of nationally representative evidence. Therefore, more research is needed to understand the effects of various determinants related to gender inequality in influencing the testing and awareness of HIV status among people living with HIV. This paper examined the determinants of the first 90 target among females and males to inform strategies aimed at improving the HIV treatment and care cascade in South Africa.
Study data and sampling
The data used in the analysis were obtained from a nationally representative population-based household survey that was conducted in 2017 using a multi-stage stratified random cluster sampling design described in detail elsewhere . A total of 1000 small area layers (SALs) were used as the primary sampling unit, drawn from the master sample through stratified, disproportionate sampling. The selection of SALs was stratified by province, locality type (urban areas, rural informal and formal areas), and race group. A total of 15 visiting points (VPs) were randomly selected from each of 1000 SALs, targeting 15,000 VPs. Of these, 12,435 (82.9%) VPs were approached. Among these VPs, 11,776 (94.7%) were valid VPs. A household response rate of 82.2% was achieved from the valid VPs (Simbayi et al. 2019). All consenting members of the selected household formed the ultimate sampling unit.
The survey collected data using a household questionnaire and three age-appropriate questionnaires were administered to consenting individuals. For those younger than 18 years of age, consent was given by parents/guardians and assent by the participant. The interview instruments solicited information among others on socio-demographic characteristics, HIV-related knowledge, attitudes, and behaviours, including questions on HIV testing. The questionnaires were fieldworker administered and electronically captured using CSPro software on Mercer tablets. Fieldworkers also collected dried blood specimen samples from participants using a finger prick.
Fieldworkers also collected dried blood specimen samples from participants using a finger prick. Samples were sent to a centralised laboratory for HIV antibodies testing using an algorithm with three different enzyme immunoassays (EIAs). All samples testing HIV positive during the first two EIAs (Roche Elecys HIV Ag/Ab assay, Roche Diagnostics, Mannheim, Germany and Genescreen Ultra HIV Ag/Ab assay, Bio-Rad Laboratories, California, USA) were subjected to a nucleic acid amplification test (COBAS AmpliPrep/Cobas Taqman HIV-1 Qualitative Test, v2.0, Roche Molecular Systems, New Jersey, USA) for the final interpretation of test results. Testing for exposure to antiretroviral drugs (ARVs) in HIV-positive specimens was performed using High-Performance Liquid Chromatography (HPLC) coupled with Tandem Mass Spectrometry.
The survey protocol was approved by the Human Sciences Research Council (HSRC) Research Ethics Committee (REC: 4/18/11/15), and both the Division of Global HIV and TB (DGHT) and the Center for Global Health (CHG) of the Centers for Disease Control and Prevention (CDC). Ethical clearance was also obtained from the University of KwaZulu-Natal’s Biomedical Research Ethics Committee (BE 646/18). Verbal or written informed consent was sought before undertaking both the behavioural data and blood specimen collection.
The primary outcome variable, the first 90 of the UNAIDS 90–90–90 targets  was defined as people who have been diagnosed HIV positive in the central laboratory and knew their status or were exposed to antiretrovirals, dichotomized as diagnosed and aware of HIV status = 1 and diagnosed and not aware of HIV status = 0.
Explanatory variables were socio-demographic and HIV related behavioural characteristics. Socio-demographic characteristics included age group (15–19, 20–24, 25–49, 50 years and older), race groups (African and other race groups), marital status (married, never married), level educational qualification (no education/primary, secondary, matric, tertiary), employment (yes, no), and locality type (urban areas, rural informal/tribal areas, rural formal/farms). HIV related behaviour characteristics included condom use last sex act (yes, no), correct HIV knowledge and myth rejection (yes, no), and self-perceived risk of HIV infection (yes, no).
Descriptive statistics were used to summarize the sample characteristics. Proportion tests “prtest” command were used to test for differences between the explanatory variables and the first 90 target by gender. A series of hierarchical multiple logistic regression models structured by sex (males and females) were fitted, and the estimates of the contributions of each independent variable were computed against the dependent variable in successive models. The best-fitting models with variables that reliably predict the first 90 target were determined by assessing changes in R-squared (R2) values and best predictors by adjusted odds ratios (aOR) with 95% confidence intervals (CIs) and p ≤ 0.05. The ‘svy’ command was used to introduce weights that take into account the complex design of the survey. All data analyses were conducted using STATA version 15.0 (STATACORP, College Station, TX) software.
Figure 1 shows the sub-sample of those who tested for HIV and the breakdown of the first 90 the primary outcome of interest in the study.