Estimating the Population Size of Female Sex Workers in Three South African Cities: Results and Recommendations From the 2013-2014 South Africa Health Monitoring Survey and Stakeholder Consensus

Triangulating Truth and Reaching Consensus on Population Size, Prevalence, and More: Modeling Study

BACKGROUND

Population size, prevalence, and incidence are essential metrics that influence public health programming and policy. However, stakeholders are frequently tasked with setting performance targets, reporting global indicators, and designing policies based on multiple (often incongruous) estimates of these variables, and they often do so in the absence of a formal, transparent framework for reaching a consensus estimate.

OBJECTIVE

This study aims to describe a model to synthesize multiple study estimates while incorporating stakeholder knowledge, introduce an R Shiny app to implement the model, and demonstrate the model and app using real data.

METHODS

In this study, we developed a Bayesian hierarchical model to synthesize multiple study estimates that allow the user to incorporate the quality of each estimate as a confidence score. The model was implemented as a user-friendly R Shiny app aimed at practitioners of population size estimation. The underlying Bayesian model was programmed in Stan for efficient sampling and computation.

RESULTS

The app was demonstrated using biobehavioral survey-based population size estimates (and accompanying confidence scores) of female sex workers and men who have sex with men from 3 survey locations in a country in sub-Saharan Africa. The consensus results incorporating confidence scores are compared with the case where they are absent, and the results with confidence scores are shown to perform better according to an app-supplied metric for unaccounted-for variation.

CONCLUSIONS

The utility of the triangulator model, including the incorporation of confidence scores, as a user-friendly app is demonstrated using a use case example. Our results offer empirical evidence of the model's effectiveness in producing an accurate consensus estimate and emphasize the significant impact that the accessible model and app offer for public health. It offers a solution to the long-standing problem of synthesizing multiple estimates, potentially leading to more informed and evidence-based decision-making processes. The Triangulator has broad utility and flexibility to be adapted and used in various other contexts and regions to address similar challenges.

The HIV Cascade of Care and Service Utilisation at Sex Work Programmes Among Female Sex Workers in South Africa

Female sex workers (FSWs) in South Africa experience a uniquely high prevalence of HIV. We describe the HIV cascade of care (CoC) in FSWs in South Africa, and explored service utilisation at sex work programmes. A cross-sectional, study enrolled FSWs across 12 sites in South Africa. Participants were recruited using chain-referral method. Inclusion criteria: ≥ 18 years, cis-gender female, sold/transacted in sex, HIV positive. 1862 HIV positive FSWs were enrolled. 92% were known positive, 87% were on antiretroviral treatment (ART). Of those on ART, 74% were virally suppressed. Younger FSWs were significantly less likely to be on ART or virally suppressed. Female sex workers using HIV services from specialised programs were 1.4 times more likely to be virally suppressed than non-program users. The pre-COVID-19 pandemic HIV CoC amongst FSWs in South Africa shows striking improvement from previous estimates, and approaches achievement of 90:90:90 goals.

Persistence on oral pre-exposure prophylaxis (PrEP) among female sex workers in eThekwini, South Africa, 2016–2020

Background

Despite the established efficacy of PrEP to prevent HIV and the advantages of a user-controlled method, PrEP uptake and persistence by women in both trials and demonstration projects has been suboptimal. We utilized real-world data from an HIV service provider to describe persistence on oral PrEP among female sex workers (FSW) in eThekwini, South Africa.

Methods

We examined time from PrEP initiation to discontinuation among all FSW initiating PrEP at TB HIV Care in eThekwini between 2016–2020. We used a discrete time-to-event data setup and stacked cumulative incidence function plots, displaying the competing risks of 1) not returning for PrEP, 2) client discontinuation, and 3) provider discontinuation. We calculated hazard ratios using complementary log-log regression and sub-hazard ratios using competing risks regression.

Results

The number of initiations increased each year from 155 (9.3%, n = 155/1659) in 2016 to 1224 (27.5%, n = 1224/4446) in 2020. Persistence 1-month after initiation was 53% (95% CI: 51%-55%). Younger women were more likely to discontinue PrEP by not returning compared with those 25 years and older. Risk of discontinuation through non-return declined for those initiating in later years. Despite the COVID-19 pandemic, a greater number of initiations and sustained persistence were observed in 2020.

Conclusions

Low levels of PrEP persistence were observed, consistent with data among underserved women elsewhere. Encouragingly, the proportion of women persisting increased over time, even as the number of women newly initiating PrEP and staff workload increased. Further research is needed to understand which implementation strategies the program may have enacted to facilitate these improvements and what further changes may be necessary.

Comparative performance of multiple-list estimators of key population size

Estimates of the sizes of key populations (KPs) affected by HIV, including men who have sex with men, female sex workers and people who inject drugs, are required for targeting epidemic control efforts where they are most needed. Unfortunately, different estimators often produce discrepant results, and an objective basis for choice is lacking. This simulation study provides the first comparison of information-theoretic selection of loglinear models (LLM-AIC), Bayesian model averaging of loglinear models (LLM-BMA) and Bayesian nonparametric latent-class modeling (BLCM) for estimation of population size from multiple lists. Four hundred random samples from populations of size 1,000, 10,000 and 20,000, each including five encounter opportunities, were independently simulated using each of 30 data-generating models obtained from combinations of six patterns of variation in encounter probabilities and five expected per-list encounter probabilities, producing a total of 36,000 samples. Population size was estimated for each combination of sample and sequentially cumulative sets of 2-5 lists using LLM-AIC, LLM-BMA and BLCM. LLM-BMA and BLCM were quite robust and performed comparably in terms of root mean-squared error and bias, and outperformed LLM-AIC. All estimation methods produced uncertainty intervals which failed to achieve the nominal coverage, but LLM-BMA, as implemented in the dga R package produced the best balance of accuracy and interval coverage. The results also indicate that two-list estimation is unnecessarily vulnerable, and it is better to estimate the sizes of KPs based on at least three lists.

shinyrecap: A Shiny Application for Population Size Estimation from Capture-Recapture Data (Preprint)

Background

Population size estimates (PSE) provide critical information in determining resource allocation for HIV services geared toward those at high risk of HIV, including female sex workers, men who have sex with men, and people who inject drugs. Capture-recapture (CRC) is often used to estimate the size of these often-hidden populations. Compared with the commonly used 2-source CRC, CRC relying on 3 (or more) samples (3S-CRC) can provide more robust PSE but involve far more complex statistical analysis.

Objective

This study aims to design and describe the Shiny application (shinyrecap), a user-friendly interface that can be used by field epidemiologists to produce PSE.

Methods

shinyrecap is built on the Shiny web application framework for R. This allows it to seamlessly integrate with the sophisticated CRC statistical packages (eg, Rcapture, dga, LCMCR). Additionally, the application may be accessed online or run locally on the user’s machine.

Results

The application enables users to engage in sample size calculation based on a simulation framework. It assists in the proper formatting of collected data by providing a tool to convert commonly used formats to that used by the analysis software. A wide variety of methodologies are supported by the analysis tool, including log-linear, Bayesian model averaging, and Bayesian latent class models. For each methodology, diagnostics and model checking interfaces are provided.

Conclusions

Through a use case, we demonstrated the broad utility of this powerful tool with 3S-CRC data to produce PSE for female sex workers in a subnational unit of a country in sub-Saharan Africa.

Getting resources to those who need them: the evidence we need to budget for underserved populations in sub-Saharan Africa

INTRODUCTION

In recent years, many countries have adopted evidence-based budgeting (EBB) to encourage the best use of limited and decreasing HIV resources. The lack of data and evidence for hard to reach, marginalized and vulnerable populations could cause EBB to further disadvantage those who are already underserved and who carry a disproportionate HIV burden (USDB). We outline the critical data required to use EBB to support USDB people in the context of the generalized epidemics of sub-Saharan Africa (SSA).

DISCUSSION

To be considered in an EBB cycle, an intervention needs at a minimum to have an estimate of a) the average cost, typically per recipient of the intervention; b) the effectiveness of the intervention and c) the size of the intervention target population. The methods commonly used for general populations are not sufficient for generating valid estimates for USDB populations. USDB populations may require additional resources to learn about, access, and/or successfully participate in an intervention, increasing the cost per recipient. USDB populations may experience different health outcomes and/or other benefits than in general populations, influencing the effectiveness of the interventions. Finally, USDB population size estimation is critical for accurate programming but is difficult to obtain with almost no national estimates for countries in SSA. We explain these limitations and make recommendations for addressing them.

CONCLUSIONS

EBB is a strong tool to achieve efficient allocation of resources, but in SSA the evidence necessary for USDB populations may be lacking. Rather than excluding USDB populations from the budgeting process, more should be invested in understanding the needs of these populations.

Estimating the Population Size of Female Sex Workers in Zimbabwe: Comparison of Estimates Obtained Using Different Methods in Twenty Sites and Development of a National-Level Estimate

National-level population size estimates (PSEs) for hidden populations are required for HIV programming and modelling. Various estimation methods are available at the site-level, but it remains unclear which are optimal and how best to obtain national-level estimates.

Estimating the contribution of key populations towards HIV transmission in South Africa

INTRODUCTION

In generalized epidemic settings, there is insufficient understanding of how the unmet HIV prevention and treatment needs of key populations (KPs), such as female sex workers (FSWs) and men who have sex with men (MSM), contribute to HIV transmission. In such settings, it is typically assumed that HIV transmission is driven by the general population. We estimated the contribution of commercial sex, sex between men, and other heterosexual partnerships to HIV transmission in South Africa (SA).

METHODS

We developed the "Key-Pop Model"; a dynamic transmission model of HIV among FSWs, their clients, MSM, and the broader population in SA. The model was parameterized and calibrated using demographic, behavioural and epidemiological data from national household surveys and KP surveys. We estimated the contribution of commercial sex, sex between men and sex among heterosexual partnerships of different sub-groups to HIV transmission over 2010 to 2019. We also estimated the efficiency (HIV infections averted per person-year of intervention) and prevented fraction (% IA) over 10-years from scaling-up ART (to 81% coverage) in different sub-populations from 2020.

RESULTS

Sex between FSWs and their paying clients, and between clients with their non-paying partners contributed 6.9% (95% credibility interval 4.5% to 9.3%) and 41.9% (35.1% to 53.2%) of new HIV infections in SA over 2010 to 2019 respectively. Sex between low-risk groups contributed 59.7% (47.6% to 68.5%), sex between men contributed 5.3% (2.3% to 14.1%) and sex between MSM and their female partners contributed 3.7% (1.6% to 9.8%). Going forward, the largest population-level impact on HIV transmission can be achieved from scaling up ART to clients of FSWs (% IA = 18.2% (14.0% to 24.4%) or low-risk individuals (% IA = 20.6% (14.7 to 27.5) over 2020 to 2030), with ART scale-up among KPs being most efficient.

CONCLUSIONS

Clients of FSWs play a fundamental role in HIV transmission in SA. Addressing the HIV prevention and treatment needs of KPs in generalized HIV epidemics is central to a comprehensive HIV response.

Population Size Estimation Methods: Searching for the Holy Grail

Accurate size estimates of key populations (eg, sex workers, people who inject drugs, transgender people, and men who have sex with men) can help to ensure adequate availability of services to prevent or treat HIV infection; inform HIV response planning, target setting, and resource allocation; and provide data for monitoring and evaluating program outcomes and impact. A gold standard method for population size estimation does not exist, but quality of estimates could be improved by using empirical methods, multiple data sources, and sound statistical concepts. To highlight such methods, a special collection of papers in JMIR Public Health and Surveillance has been released under the title “Key Population Size Estimations.” We provide a summary of these papers to highlight advances in the use of empirical methods and call attention to persistent gaps in information.