How much does HIV self-testing cost in low and middle income countries? A systematic review of evidence from economic studies
Bibliographic record
Abstract
Objectives: HIV self-testing (HIVST) has been proposed as an innovative strategy to diagnose human immunodeficiency virus (HIV). While HIVST offers the potential to broaden accessibility of early HIV diagnosis and treatment initiation, this testing strategy incurs additional cost and requires confirmatory testing and treatment. We have conducted the first systematic review to summarize the current economic literature for HIVST in low- and middle-income countries (LMICs). Design: A search strategy was developed including key terms for HIV, self-testing and cost-effectiveness and was conducted in Medline and Embase databases. Studies were included that reported costs per outcome and included both cost-effectiveness and cost-utility outcome measures. The search strategy identified publications up until August 15, 2023 were included. Abstract and full text screening was conducted and a standardized data abstraction form was used for included studies. Costs are reported in USD, 2020. Results: Our search strategy identified 536 total titles from the search strategy, which were screened down to 25 relevant studies that provided both cost and outcome data on HIVST. There was significant heterogeneity in the HIVST intervention, study population, costs and outcomes reported among included studies. Cost per person tested ranged from $1.09-155. Cost per case diagnosed ranged from $20-1,277. Cost-utility estimates ranged from cost-saving to $1846 per DALY averted. Higher cost-effectiveness estimates were associated with more expensive testing algorithms with increased support for linkage to care and post-test counseling. Conclusion: All studies considered HIVST cost-effective although major drivers were identified included underlying HIV prevalence, testing cost and linkage to care. HIVST is likely to be cost-effective in a LMIC context, however policy makers should be aware of the drivers of cost-effectiveness when implementing HIVST programs as these underlying factors can impact the overall cost-effectiveness of HIVST.
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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 | 0.023 | 0.125 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.011 |
| Bibliometrics | 0.021 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".