The cost and intermediary cost-effectiveness of oral HIV self-test kit distribution across 11 distribution models in South Africa.

BACKGROUND: Countries around the world seek innovative ways of closing their remaining gaps towards the target of 95% of people living with HIV (PLHIV) knowing their status by 2030. Offering kits allowing HIV self-testing (HIVST) in private might help close these gaps. METHODS: We analysed the cost, use and linkage to onward care of 11 HIVST kit distribution models alongside the Self-Testing AfRica Initiative's distribution of 2.2 million HIVST kits in South Africa in 2018/2019. Outcomes were based on telephonic surveys of 4% of recipients; costs on a combination of micro-costing, time-and-motion and expenditure analysis. Costs were calculated from the provider perspective in 2019 US$, as incremental costs in integrated and full costs in standalone models. RESULTS: HIV positivity among kit recipients was 4%-23%, with most models achieving 5%-6%. Linkage to confirmatory testing and antiretroviral therapy (ART) initiation for those screening positive was 19%-78% and 2%-72% across models. Average costs per HIVST kit distributed varied between $4.87 (sex worker model) and $18.07 (mobile integration model), with differences largely driven by kit volumes. HIVST kit costs (at $2.88 per kit) and personnel costs were the largest cost items throughout. Average costs per outcome increased along the care cascade, with the sex worker network model being the most cost-effective model across metrics used (cost per kit distributed/recipient screening positive/confirmed positive/initiating ART). Cost per person confirmed positive for HIVST was higher than standard HIV testing. CONCLUSION: HIV self-test distribution models in South Africa varied widely along four characteristics: distribution volume, HIV positivity, linkage to care and cost. Volume was highest in models that targeted public spaces with high footfall (flexible community, fixed point and transport hub distribution), followed by workplace models. Transport hub, workplace and sex worker models distributed kits in the least costly way. Distribution via index cases at facility as well as sex worker network distribution identified the highest number of PLHIV at lowest cost.

Costs of integrating HIV self-testing in public health facilities in Malawi, South Africa, Zambia and Zimbabwe.

INTRODUCTION: As countries approach the UNAIDS 95-95-95 targets, there is a need for innovative and cost-saving HIV testing approaches that can increase testing coverage in hard-to-reach populations. The HIV Self-Testing Africa-Initiative distributed HIV self-test (HIVST) kits using unincentivised HIV testing counsellors across 31 public facilities in Malawi, South Africa, Zambia and Zimbabwe. HIVST was distributed either through secondary (partner's use) distribution alone or primary (own use) and secondary distribution approaches. METHODS: We evaluated the costs of adding HIVST to existing HIV testing from the providers' perspective in the 31 public health facilities across the four countries between 2018 and 2019. We combined expenditure analysis and bottom-up costing approaches. We also carried out time-and-motion studies on the counsellors to estimate the human resource costs of introducing and demonstrating how to use HIVST for primary and secondary use. RESULTS: A total of 41 720 kits were distributed during the analysis period, ranging from 1254 in Zimbabwe to 27 678 in Zambia. The cost per kit distributed through the primary distribution approach was $4.27 in Zambia and $9.24 in Zimbabwe. The cost per kit distributed through the secondary distribution approach ranged from $6.46 in Zambia to $13.42 in South Africa, with a wider variation in the average cost at facility-level. From the time-and-motion observations, the counsellors spent between 20% and 44% of the observed workday on HIVST. Overall, personnel and test kit costs were the main cost drivers. CONCLUSION: The average costs of distributing HIVST kits were comparable across the four countries in our analysis despite wide cost variability within countries. We recommend context-specific exploration of potential efficiency gains from these facility-level cost variations and demand creation activities to ensure continued affordability at scale.

Modelling costs of community-based HIV self-testing programmes in Southern Africa at scale: an econometric cost function analysis across five countries.

BACKGROUND: Following success demonstrated with the HIV Self-Testing AfRica Initiative, HIV self-testing (HIVST) is being added to national HIV testing strategies in Southern Africa. An analysis of the costs of scaling up HIVST is needed to inform national plans, but there is a dearth of evidence on methods for forecasting costs at scale from pilot projects. Econometric cost functions (ECFs) apply statistical inference to predict costs; however, we often do not have the luxury of collecting large amounts of location-specific data. We fit an ECF to identify key drivers of costs, then use a simpler model to guide cost projections at scale. METHODS: We estimated the full economic costs of community-based HIVST distribution in 92 locales across Malawi, Zambia, Zimbabwe, South Africa and Lesotho between June 2016 and June 2019. We fitted a cost function with determinants related to scale, locales organisational and environmental characteristics, target populations, and per capita Growth Domestic Product (GDP). We used models differing in data intensity to predict costs at scale. We compared predicted estimates with scale-up costs in Lesotho observed over a 2-year period. RESULTS: The scale of distribution, type of community-based intervention, percentage of kits distributed to men, distance from implementer's warehouse and per capita GDP predicted average costs per HIVST kit distributed. Our model simplification approach showed that a parsimonious model could predict costs without losing accuracy. Overall, ECF showed a good predictive capacity, that is, forecast costs were close to observed costs. However, at larger scale, variations of programme efficiency over time (number of kits distributed per agent monthly) could potentially influence cost predictions. DISCUSSION: Our empirical cost function can inform community-based HIVST scale-up in Southern African countries. Our findings suggest that a parsimonious ECF can be used to forecast costs at scale in the context of financial planning and budgeting.

The cost effectiveness and optimal configuration of HIV self-test distribution in South Africa: a model analysis.

BACKGROUND: HIV self-testing (HIVST) has been shown to be acceptable, feasible and effective in increasing HIV testing uptake. Novel testing strategies are critical to achieving the UNAIDS target of 95% HIV-positive diagnosis by 2025 in South Africa and globally. METHODS: We modelled the impact of six HIVST kit distribution modalities (community fixed-point, taxi ranks, workplace, partners of primary healthcare (PHC) antiretroviral therapy (ART) patients), partners of pregnant women, primary PHC distribution) in South Africa over 20 years (2020-2039), using data collected alongside the Self-Testing AfRica Initiative. We modelled two annual distribution scenarios: (A) 1 million HIVST kits (current) or (B) up to 6.7 million kits. Incremental economic costs (2019 US$) were estimated from the provider perspective; assumptions on uptake and screening positivity were based on surveys of a subset of kit recipients and modelled using the Thembisa model. Cost-effectiveness of each distribution modality compared with the status-quo distribution configuration was estimated as cost per life year saved (estimated from life years lost due to AIDS) and optimised using a fractional factorial design. RESULTS: The largest impact resulted from secondary HIVST distribution to partners of ART patients at PHC (life years saved (LYS): 119 000 (scenario A); 393 000 (scenario B)). However, it was one of the least cost-effective modalities (A: $1394/LYS; B: $4162/LYS). Workplace distribution was cost-saving ($52-$76 million) and predicted to have a moderate epidemic impact (A: 40 000 LYS; B: 156 000 LYS). An optimised scale-up to 6.7 million tests would result in an almost threefold increase in LYS compared with a scale-up of status-quo distribution (216 000 vs 75 000 LYS). CONCLUSION: Optimisation-informed distribution has the potential to vastly improve the impact of HIVST. Using this approach, HIVST can play a key role in improving the long-term health impact of investment in HIVST.