Evolving landscape of economic evaluations of HIV pre-exposure prophylaxis and pre-exposure prophylaxis implementation strategies: A systematic review
Bibliographic record
Abstract
Introduction: Economic evaluations of HIV pre-exposure prophylaxis (PrEP) and associated implementation strategies guide evidence-based policies, programmes, and resource allocation. Since 2015, there has been an evolution in PrEP modalities, implementation strategies, and prioritization of key populations with unmet HIV prevention needs, alongside the scale-up of other HIV prevention interventions. Our systematic review describes the evolving landscape of economic evaluations of PrEP to help identify evidence gaps relevant to the current HIV epidemic and response (PROSPERO: CRD42016038440). Methods: We searched five databases, without language restrictions, for peer-reviewed economic evaluations from inception to December 21, 2024. We describe the evolution of study characteristics over time, including perspective of analysis, region, population, PrEP modality/implementation strategy, and comparators. Results: Of 5,046 studies identified, 114 met inclusion criteria, of which 81 examined HIV epidemics in 2015 or later and 16 adopted a societal perspective. HIV epidemics studied primarily spanned countries in Sub-Saharan Africa (N=45) and in North America (N=32). Modeled populations for receipt of PrEP primarily comprised: gay, bisexual, and other men who have sex with men (N=66), female sex workers (N=23), serodifferent partnerships (N=16), and persons who inject drugs (N=12). Most evaluated oral, daily PrEP (N=70), followed by on-demand PrEP (N=12), long-acting injectable PrEP (N=12), and others (e.g., vaginal ring, topical gel; N=7). Ten studies compared different PrEP modalities with each other. Three studies evaluated different implementation strategies to increase PrEP uptake, adherence, and persistence. Of the 108 studies that compared PrEP to a combination of other HIV prevention interventions, only 25 scaled up at least part of the comparator over time. Discussion: To support decision-making, future economic evaluations should consider costs and benefits beyond the health system and consider comparators that better reflect the current HIV response across regions and populations. The increasing availability of novel PrEP modalities allows future studies to evaluate a mix of PrEP modalities and person-centered implementation strategies. Conclusions: The growing number of PrEP economic evaluations have not kept pace with emerging PrEP modalities or the current HIV epidemic/response, resulting in challenges in making evidence-based policies, programmes, and resource allocation.
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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 | 0.167 | 0.517 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.010 |
| Bibliometrics | 0.018 | 0.020 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".