Progress towards the 2020 targets for HIV diagnosis and antiretroviral treatment in South Africa

Background: The UNAIDS targets for 2020 are to achieve a 90% rate of diagnosis in HIV-positive individuals, to provide antiretroviral treatment (ART) to 90% of HIV-diagnosed individuals and to achieve virological suppression in 90% of ART patients.Objectives: To assess South Africa’s progress towards the 2020 targets and variations in performance by province.Methods: A mathematical model was fitted to HIV data for each of South Africa’s provinces, and for the country as a whole. Numbers of HIV tests performed in each province were estimated from routine data over the 2002–2015 period, and numbers of patients receiving ART in each province were estimated by fitting models to reported public and private ART enrolment statistics.Results: By the middle of 2015, 85.5% (95% CI: 84.5% – 86.5%) of HIV-positive South African adults had been diagnosed, with little variation between provinces. However, only 56.9% (95% CI: 55.3% – 58.7%) of HIV-diagnosed adults were on ART, with this proportion varying between 50.8% in North West and 72.7% in Northern Cape. In addition, 78.4% of adults on ART were virally suppressed, with rates ranging from 69.7% in Limpopo to 85.9% in Western Cape. Overall, 3.39 million (95% CI: 3.26–3.52 million) South Africans were on ART by mid-2015, equivalent to 48.6% (95% CI: 46.0% – 51.2%) of the HIV-positive population. ART coverage varied between 43.0% in Gauteng and 63.0% in Northern Cape.Conclusion: Although South Africa is well on its way to reaching the 90% HIV diagnosis target, most provinces face challenges in reaching the remaining two 90% targets

HIV epidemic drivers in South Africa: A model-based evaluation of factors accounting for inter-provincial differences in HIV prevalence and incidence trends

Background: HIV prevalence differs substantially between South Africa’s provinces, but the factors accounting for this difference are poorly understood.Objectives: To estimate HIV prevalence and incidence trends by province, and to identify the epidemiological factors that account for most of the variation between provinces.Methods: A mathematical model of the South African HIV epidemic was applied to each of the nine provinces, allowing for provincial differences in demography, sexual behaviour, male circumcision, interventions and epidemic timing. The model was calibrated to HIV prevalence data from antenatal and household surveys using a Bayesian approach. Parameters estimated for each province were substituted into the national model to assess sensitivity to provincial variations.Results: HIV incidence in 15–49-year-olds peaked between 1997 and 2003 and has since declined steadily. By mid-2013, HIV prevalence in 15–49-year-olds varied between 9.4% (95% CI: 8.5%–10.2%) in Western Cape and 26.8% (95% CI: 25.8%–27.6%) in KwaZulu-Natal. When standardising parameters across provinces, this prevalence was sensitive to provincial differences in the prevalence of male circumcision (range 12.3%–21.4%) and the level of nonmarital sexual activity (range 9.5%–24.1%), but not to provincial differences in condom use (range 17.7%–21.2%), sexual mixing (range 15.9%–19.2%), marriage (range 18.2%–19.4%) or assumed HIV prevalence in 1985 (range 17.0%–19.1%).Conclusion: The provinces of South Africa differ in the timing and magnitude of their HIV epidemics. Most of the heterogeneity in HIV prevalence between South Africa’s provinces is attributable to differences in the prevalence of male circumcision and the frequency of nonmarital sexual activity

A quantitative assessment of the consistency of projections from five mathematical models of the HIV epidemic in South Africa: a model comparison study

Background: Mathematical models are increasingly used to inform HIV policy and planning. Comparing estimates obtained using different mathematical models can test the robustness of estimates and highlight research gaps. As part of a larger project aiming to determine the optimal allocation of funding for HIV services, in this study we compare projections from five mathematical models of the HIV epidemic in South Africa: EMOD-HIV, Goals, HIV-Synthesis, Optima, and Thembisa. // Methods: The five modelling groups produced estimates of the total population, HIV incidence, HIV prevalence, proportion of people living with HIV who are diagnosed, ART coverage, proportion of those on ART who are virally suppressed, AIDS-related deaths, total deaths, and the proportion of adult males who are circumcised. Estimates were made under a “status quo” scenario for the period 1990 to 2040. For each output variable we assessed the consistency of model estimates by calculating the coefficient of variation and examining the trend over time. // Results: For most outputs there was significant inter-model variability between 1990 and 2005, when limited data was available for calibration, good consistency from 2005 to 2025, and increasing variability towards the end of the projection period. Estimates of HIV incidence, deaths in people living with HIV, and total deaths displayed the largest long-term variability, with standard deviations between 35 and 65% of the cross-model means. Despite this variability, all models predicted a gradual decline in HIV incidence in the long-term. Projections related to the UNAIDS 95–95-95 targets were more consistent, with the coefficients of variation below 0.1 for all groups except children. // Conclusions: While models produced consistent estimates for several outputs, there are areas of variability that should be investigated. This is important if projections are to be used in subsequent cost-effectiveness studies

A multi-state model of treatment states in an antiretroviral treatment programme cohort in Cape Town

Introduction A recent systematic review estimated that almost a quarter of patients in low- and middle-income countries are not retained on antiretroviral treatment (ART) beyond one year. Further, it is difficult to determine whether a patient who is not retained in care has interrupted their treatment, transferred to another treatment facility, or died. Previous studies have been deterministic in classifying loss to follow-up and treatment interruption. This study investigates treatment interruption and resumption rates when accounting for uncertainty in the occurrence of interruptions. The primary objective is to estimate the rate at which ART is interrupted and the rate at which ART is resumed after an interruption. Methods We fitted a multi-state model to data from the Khayelitsha cohort of the International Epidemiologic Databases to Evaluate AIDS. Between 2001 and 2012, 6796 adult patients starting ART were included. Potential treatment interruption periods were defined between contact points 3 or more months apart. To aid the model in determining if a patient truly interrupted treatment a CD4 count model was used. CD4 counts were modelled to drop to baseline by 3 months after the start of a treatment interruption. Bayesian estimation and Markov chain Monte Carlo were used to obtain posterior distributions of parameters. Several scenarios were used in sensitivity testing, including varying the threshold used to define potential treatment interruption periods, and either adjusting or excluding the data of those with CD4 counts that drop below baseline. Results The baseline annual rate of treatment interruption had a posterior mean of 0.060 (95% CI 0.038- 0.087) which is significantly lower than the prior distribution that had a mean of 0.145 (95% CI 0.080-0.229). The posterior distribution of the baseline annual rate of treatment resumption (mean 1.09; 95% CI 0.68-1.65) was consistent with the prior distribution (mean 1.46; 95% CI 0.21-3.90). The posterior distributions of the parameters related to treatment interruption and resumption did not change significantly in sensitivity testing. Conclusion This study indicates that treatment interruption rates may be significantly lower than previously estimated. The methodology of this study may be useful to those measuring retention within ART programmes. An important limitation was that the CD4 count model did not allow for CD4 counts to fall below baseline during periods of treatment interruption. This limits the generalisability of the posterior estimates of the parameters of the CD4 count model. Further research may require a more flexible CD4 count model

Do Increasing Rates of Loss to Follow-up in Antiretroviral Treatment Programs Imply Deteriorating Patient Retention?

In several studies of antiretroviral treatment (ART) programs for persons with human immunodeficiency virus infection, investigators have reported that there has been a higher rate of loss to follow-up (LTFU) among patients initiating ART in recent years than among patients who initiated ART during earlier time periods. This finding is frequently interpreted as reflecting deterioration of patient retention in the face of increasing patient loads. However, in this paper we demonstrate by simulation that transient gaps in follow-up could lead to bias when standard survival analysis techniques are applied. We created a simulated cohort of patients with different dates of ART initiation. Rates of ART interruption, ART resumption, and mortality were assumed to remain constant over time, but when we applied a standard definition of LTFU, the simulated probability of being classified LTFU at a particular ART duration was substantially higher in recently enrolled cohorts. This suggests that much of the apparent trend towards increased LTFU may be attributed to bias caused by transient interruptions in care. Alternative statistical techniques need to be used when analyzing predictors of LTFU-for example, using "prospective" definitions of LTFU in place of "retrospective" definitions. Similar considerations may apply when analyzing predictors of LTFU from treatment programs for other chronic diseases

A quantitative assessment of the consistency of projections from five mathematical models of the HIV epidemic in South Africa: a model comparison study

Abstract Background Mathematical models are increasingly used to inform HIV policy and planning. Comparing estimates obtained using different mathematical models can test the robustness of estimates and highlight research gaps. As part of a larger project aiming to determine the optimal allocation of funding for HIV services, in this study we compare projections from five mathematical models of the HIV epidemic in South Africa: EMOD-HIV, Goals, HIV-Synthesis, Optima, and Thembisa. Methods The five modelling groups produced estimates of the total population, HIV incidence, HIV prevalence, proportion of people living with HIV who are diagnosed, ART coverage, proportion of those on ART who are virally suppressed, AIDS-related deaths, total deaths, and the proportion of adult males who are circumcised. Estimates were made under a “status quo” scenario for the period 1990 to 2040. For each output variable we assessed the consistency of model estimates by calculating the coefficient of variation and examining the trend over time. Results For most outputs there was significant inter-model variability between 1990 and 2005, when limited data was available for calibration, good consistency from 2005 to 2025, and increasing variability towards the end of the projection period. Estimates of HIV incidence, deaths in people living with HIV, and total deaths displayed the largest long-term variability, with standard deviations between 35 and 65% of the cross-model means. Despite this variability, all models predicted a gradual decline in HIV incidence in the long-term. Projections related to the UNAIDS 95–95-95 targets were more consistent, with the coefficients of variation below 0.1 for all groups except children. Conclusions While models produced consistent estimates for several outputs, there are areas of variability that should be investigated. This is important if projections are to be used in subsequent cost-effectiveness studies