The Impact of Antiretroviral Therapy on the HIV Epidemic in South Africa

The Human Immunodeficiency Virus (HIV) is the cause of Acquired Immune Deficiency Syndrome (AIDS), a lethal disease characterized by the destruction of the immune system. At the end of 2010, there were about 34 million people infected with HIV worldwide. The epicenter of the HIV pandemic lies in sub-Saharan Africa (figure 1.1): with only 12% of the world’s population, 68% of all people living with HIV live in the subcontinent. Although the number of new infections worldwide declined over the past decades, there were an estimated 2.7 million acquired the infection in 2010 [3]. Especially countries in Eastern and Southern Africa are heavily affected by the pandemic. In countries like Botswana or Swaziland prevalence levels in the adult population are as high as 20 to 25%. South Africa is the country with the largest HIVinfected population worldwide. With prevalence levels of about 15% in the population aged over 15 years, the country has about 6 million people living with HIV. Over the past few years, the number of new infections in the country declined, yet incidence levels remain considerable. In mid-2010, about 1.5 million people infected with HIV were receiving antiretroviral therapy (ART), while a further 1.5 million are estimated to be eligible for treatment

Human resources needs for universal access to antiretroviral therapy in South Africa: a time and motion study

<p>Background - Although access to life-saving treatment for patients infected with HIV in South Africa has improved substantially since 2004, treating all eligible patients (universal access) remains elusive. As the prices of antiretroviral drugs have dropped over the past years, availability of human resources may now be the most important barrier to achieving universal access to HIV treatment in Africa. We quantify the number of HIV health workers (HHWs) required to be added to the current HIV workforce to achieve universal access to HIV treatment in South Africa, under different eligibility criteria.</p> <p>Methods - We performed a time and motion study in three HIV clinics in a rural, primary care-based HIV treatment program in KwaZulu-Natal, South Africa, to estimate the average time per patient visit for doctors, nurses, and counselors. We estimated the additional number of HHWs needed to achieve universal access to HIV treatment within one year.</p> <p>Results - For universal access to HIV treatment for all patients with a CD4 cell count of ≤350 cells/μl, an additional 2,200 nurses, 3,800 counselors, and 300 doctors would be required, at additional annual salary cost of 929 million South African rand (ZAR), equivalent to US$141 million. For universal treatment (‘treatment as prevention’), an additional 6,000 nurses, 11,000 counselors, and 800 doctors would be required, at an additional annual salary cost of ZAR 2.6 billion (US$ 400 million).</p> <p>Conclusions - Universal access to HIV treatment for patients with a CD4 cell count of ≤350 cells/μl in South Africa may be affordable, but the number of HHWs available for HIV treatment will need to be substantially increased. Treatment as prevention strategies will require considerable additional financial and human resources commitments.</p&gt

Equity in utilization of antiretroviral therapy for HIV-infected people in South Africa: a systematic review

INTRODUCTION: About half a million people in South Africa are deprived of antiretroviral therapy (ART), and there is little systematic knowledge on who they are – e.g. by severity of disease, sex, or socio-economic status (SES). We performed a systematic review to determine the current quantitative evidence-base on equity in utilization of ART among HIV-infected people in South Africa. METHOD: We conducted a literature search based on the Cochrane guidelines. A study was included if it compared for different groups of HIV infected people (by sex, age, severity of disease, area of living, SES, marital status, ethnicity, religion and/or sexual orientation (i.e. equity criteria)) the number initiating/adhering to ART with the number who did not. We considered ART utilization inequitable for a certain criterion (e.g. sex) if between groups (e.g. men versus women) significant differences were reported in ART initiation/adherence. RESULTS: Twelve studies met the inclusion criteria. For sex, 2 out of 10 studies that investigated this criterion found that men are less likely than women to utilize ART, while the other 8 found no differences. For age, 4 out of 8 studies found inequities and reported less utilization for younger people. For area of living, 3 out of 4 studies showed that those living in rural areas or certain provinces have less access and 2 out of 6 studies looking at SES found that people with lower SES have less access. One study which looked at the marital status found that those who are married are less likely to utilize ART. For severity of disease, 5 out of 6 studies used more than one outcome measure for disease stage and reported within their study contradicting results. One of the studies reported inconclusive findings for ethnicity and no study had looked at religion and sexual orientation. CONCLUSION: It seems that men, young people, those living in certain provinces or rural areas, people who are unemployed or with a low educational level, and those being unmarried have less access to ART. As studies stem from different contexts and use different methods conclusions should be taken with caution

Changes in context, typology and programme outcomes between early and recent periods of sex work among young female sex workers in Mombasa, Kenya: a cross-sectional study

INTRODUCTION: The sex work context and typology change continuously and influence HIV related risk and vulnerability for young female sex workers (YFSW). We sought to describe changes in the context and typology of sex work between the first (early) and past month (recent) of sex work among YFSW to inform HIV prevention programming for sex workers. METHODS: We used data from a cross-sectional survey (April-November 2015), administered using physical location-based sampling to 408 cis-women, aged 14-24 years, who self-identified as sex workers, in Mombasa, Kenya. We collected self-reported data on the early and recent month of sex work. The analysis focused on changes in a) sex work context and typology (defined by setting where sex workers practice sex work) where YFSW operated, b) primary typology of sex work, and c) HIV programme outcomes among YFSW who changed primary typology, within the early and recent month of sex work. We analysed the data using a) SPSS27.0 and excel; b) bivariate analysis and χ2 test; and c) bivariate logistic regression models. RESULTS: Overall, the median age of respondents was 20 years and median duration in sex work was 2 years. Higher proportion of respondents in the recent period managed their clients on their own (98.0% vs. 91.2%), had sex with >5 clients per week (39.3% vs.16.5%); were able to meet > 50% of living expenses through sex work income (46.8% vs. 18.8%); and experienced police violence in the past month (16.4% vs. 6.5%). YFSW reported multiple sex work typology in early and recent periods. Overall, 37.2% reported changing their primary typology. A higher proportion among those who used street/ bus stop typology, experienced police violence, or initiated sex work after 19 years of age in the early period reported a change. There was no difference in HIV programme outcomes among YFSW who changed typology vs. those who did not. CONCLUSIONS: The sex work context changes even in a short duration of two years. Hence, understanding these changes in the early period of sex work can allow for development of tailored strategies that are responsive to the specific needs and vulnerabilities of YFSW

Priority setting for universal health coverage: We need evidence-informed deliberative processes, not just more evidence on cost-effectiveness

Priority setting of health interventions is generally considered as a valuable approach to support low- and middle-income countries (LMICs) in their strive for universal health coverage (UHC). However, present initiatives on priority setting are mainly geared towards the development of more cost-effectiveness information, and this evidence does not sufficiently support countries to make optimal choices. The reason is that priority setting is in reality a value-laden political process in which multiple criteria beyond cost-effectiveness are important, and stakeholders often justifiably disagree about the relative importance of these criteria. Here, we propose the use of ‘evidence-informed deliberative processes’ as an approach that does explicitly recognise priority setting as a political process and an intrinsically complex task. In these processes, deliberation between stakeholders is crucial to identify, reflect and learn about the meaning and importance of values, informed by evidence on these values. Such processes then result in the use of a broader range of explicit criteria that can be seen as the product of both international learning (‘core’ criteria, which include eg, cost-effectiveness, priority to the worse off, and financial protection) and learning among local stakeholders (‘contextual’ criteria). We believe that, with these evidence-informed deliberative processes in place, priority setting can provide a more meaningful contribution to achieving UHC

Mapping and characterising areas with high levels of HIV transmission in sub-Saharan Africa: A geospatial analysis of national survey data

BACKGROUND: In the generalised epidemics of sub-Saharan Africa (SSA), human immunodeficiency virus (HIV) prevalence shows patterns of clustered micro-epidemics. We mapped and characterised these high-prevalence areas for young adults (15-29 years of age), as a proxy for areas with high levels of transmission, for 7 countries in Eastern and Southern Africa: Kenya, Malawi, Mozambique, Tanzania, Uganda, Zambia, and Zimbabwe. METHODS AND FINDINGS: We used geolocated survey data from the most recent United States Agency for International Development (USAID) demographic and health surveys (DHSs) and AIDS indicator surveys (AISs) (collected between 2008-2009 and 2015-2016), which included about 113,000 adults-of which there were about 53,000 young adults (27,000 women, 28,000 men)-from over 3,500 sample locations. First, ordinary kriging was applied to predict HIV prevalence at unmeasured locations. Second, we explored to what extent behavioural, socioeconomic, and environmental factors explain HIV prevalence at the individual- and sample-location level, by developing a series of multilevel multivariable logistic regression models and geospatially visualising unexplained model heterogeneity. National-level HIV prevalence for young adults ranged from 2.2% in Tanzania to 7.7% in Mozambique. However, at the subnational level, we found areas with prevalence among young adults as high as 11% or 15% alternating with areas with prevalence between 0% and 2%, suggesting the existence of areas with high levels of transmission Overall, 15.6% of heterogeneity could be explained by an interplay of known behavioural, socioeconomic, and environmental factors. Maps of the interpolated random effect estimates show that environmental variables, representing indicators of economic activity, were most powerful in explaining high-prevalence areas. Main study limitations were the inability to infer causality due to the cross-sectional nature of the surveys and the likely under-sampling of key populations in the surveys. CONCLUSIONS: We found that, among young adults, micro-epidemics of relatively high HIV prevalence alternate with areas of very low prevalence, clearly illustrating the existence of areas with high levels of transmission. These areas are partially characterised by high economic activity, relatively high socioeconomic status, and risky sexual behaviour. Localised HIV prevention interventions specifically tailored to the populations at risk will be essential to curb transmission. More fine-scale geospatial mapping of key populations,-such as sex workers and migrant populations-could help us further understand the drivers of these areas with high levels of transmission and help us determine how they fuel the generalised epidemics in SSA