A rapid review of pre-exposure prophylaxis for HIV in the Asia–Pacific region: recommendations for scale up and future directions
Bibliographic record
Abstract
Decline in new HIV infections in the Asia-Pacific region (APAC) continues to be slow, emphasising the importance of scaling up new HIV prevention strategies, such as pre-exposure prophylaxis (PrEP). To help inform PrEP rollout in APAC, we conducted a rapid review of published literature on PubMed from 2015 to 2020, to assess feasibility, implementation strategies, cost-effectiveness, and availability of national policies and guidelines; for the latter, we also did an expanded Internet search. This review focussed on nine countries contributing >95% of new infections in this region. A total of 36 PrEP-related studies conducted among men who have sex with men, female sex workers, and transgender women were included, of which 29 were quantitative, six were qualitative and one was a mixed-method study. Most of the studies have addressed the availability and acceptability of PrEP, whereas cost-effectiveness of any approach was assessed by limited studies. Limited published information was available about national PrEP policies and guidelines; of the selected nine countries, five have adopted the recommended World Health Organization PrEP policy of which four have integrated it in their national HIV response. HIV risk perception concerns about safety, side-effects, stigma, and affordability were major challenges to PrEP acceptance. Community-based implementation has the potential to address these. Limited evidence suggested merging PrEP implementation with ongoing targeted intervention and treatment programs could be a cost-effective approach. To stem the epidemic, newer effective prevention strategies, like PrEP, should be urgently adopted within the context of combination HIV prevention approaches.
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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 | 0.021 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.024 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".