Costs of integrating HIV self-testing in public health facilities in Malawi, South Africa, Zambia and Zimbabwe
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".