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Introduction: In South Africa, older adults make up a growing proportion of people living with HIV. HIV programmes are likely to reach older South Africans in home-based interventions where testing is not always feasible. We evaluate the accuracy of self-reported HIV status, which may provide useful information for targeting interventions or offer an alternative to biomarker testing. Methods: Data were taken from the Health and Aging in Africa: A Longitudinal Study of an INDEPTH Community in South Africa (HAALSI) baseline survey, which was conducted in rural Mpumalanga province, South Africa. A total of 5059 participants aged ≥40 years were interviewed from 2014 to 2015. Self-reported HIV status and dried bloodspots for HIV biomarker testing were obtained during at-home interviews. We calculated sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) for self-reported status compared to "gold standard" biomarker results. Logbinomial regression explored associations between demographic characteristics, antiretroviral therapy (ART) status and sensitivity of self-report. Results: Most participants (93%) consented to biomarker testing. Of those with biomarker results, 50.9% reported knowing their HIV status and accurately reported it. PPV of self-report was 94.1% (95% confidence interval (CI): 92.0-96.0), NPV was 87.2% (95% CI: 86.2-88.2), sensitivity was 51.2% (95% CI: 48.2-54.3) and specificity was 99.0% (95% CI: 98.7-99.4). Participants on ART were more likely to report their HIV-positive status, and participants reporting false-negatives were more likely to have older HIV tests. Conclusions: The majority of participants were willing to share their HIV status. False-negative reports were largely explained by lack of testing, suggesting HIV stigma is retreating in this setting, and that expansion of HIV testing and retesting is still needed in this population. In HIV interventions where testing is not possible, self-reported status should be considered as a routine first step to establish HIV status. [ABSTRACT FROM AUTHOR]

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Introduction: A substantial number of patients with HIV in South Africa have failed first-line antiretroviral therapy (ART). Although individual predictors of first-line ART failure have been identified, few studies in resource-limited settings have been large enough for predictive modelling. Understanding the absolute risk of first-line failure is useful for patient monitoring and for effectively targeting limited resources for second-line ART. We developed a predictive model to identify patients at the greatest risk of virologic failure on first-line ART, and to estimate the proportion of patients needing second-line ART over five years on treatment. Methods: A cohort of patients aged ≥18 years from nine South African HIV clinics on first-line ART for at least six months were included. Viral load measurements and baseline predictors were obtained from medical records. We used stepwise selection of predictors in accelerated failure-time models to predict virologic failure on first-line ART (two consecutive viral load levels >1000 copies/mL). Multiple imputations were used to assign missing baseline variables. The final model was selected using internal-external cross-validation maximizing model calibration at five years on ART, and model discrimination, measured using Harrell’s C-statistic. Model covariates were used to create a predictive score for risk group of ART failure. Results: A total of 72,181 patients were included in the analysis, with an average of 21.5 months (IQR: 8.8–41.5) of follow-up time on first-line ART. The final predictive model had a Weibull distribution and the final predictors of virologic failure were men of all ages, young women, nevirapine use in first-line regimen, low baseline CD4 count, high mean corpuscular volume, low haemoglobin, history of TB and missed visits during the first six months on ART. About 24.4% of patients in the highest quintile and 9.4% of patients in the lowest quintile of risk were predicted to experience treatment failure over five years on ART. Conclusions: Age, sex, CD4 count and having any missed visits during the first six months on ART were the strongest predictors of ART failure. The predictive model identified patients at high risk of failure, and the predicted failure rates over five years closely reflected actual rates of failure. [ABSTRACT FROM AUTHOR]