The future role of rectal and vaginal microbicides to prevent HIV infection in heterosexual populations: implications for product development and prevention
Bibliographic record
Abstract
OBJECTIVES: To compare the potential impact of rectal (RMB), vaginal (VMB) and bi-compartment (RVMB) (applied vaginally and protective during vaginal and anal intercourse) microbicides to prevent HIV in various heterosexual populations. To understand when a RMB is as useful than a VMB for women practicing anal intercourse (AI). METHODS: Mathematical model was used to assess the population-level impact (cumulative fraction of new HIV infections prevented (CFP)) of the three different microbicides in various intervention scenarios and prevalence settings. We derived the break-even RMB efficacy required to reduce a female's cumulative risk of HIV infection by the same amount than a VMB. RESULTS: Under optimistic coverage (fast roll-out, 100% uptake), a 50% efficacious VMB used in 75% of sex acts in population without AI may prevent ∼33% (27, 42%) new total (men and women combined) HIV infections over 25 years. The 25-year CFP reduces to ∼25% (20, 32%) and 17% (13, 23%) if uptake decreases to 75% and 50%, respectively. Similar loss of impact (by 25%-50%) is observed if the same VMB is introduced in populations with 5%-10% AI and for RR(RAI)=4-20. A RMB is as useful as a VMB (ie, break-even) in populations with 5% AI if RR(RAI)=20 and in populations with 15%-20% AI if RR(RAI)=4, independently of adherence as long as it is the same with both products. The 10-year CFP with a RVMB is twofold larger than for a VMB or RMB when AI=10% and RR(RAI)=10. CONCLUSIONS: Even low AI frequency can compromise the impact of VMB interventions. RMB and RVMB will be important prevention tools for heterosexual populations.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".