The Importance of Context: Model Projections on How Microbicide Impact Could Be Affected by the Underlying Epidemiologic and Behavioral Situation in 2 African Settings
Bibliographic record
Abstract
In Brief Objective: The objective of this study was to explore how a microbicide’s HIV impact is affected by behavioral and epidemiologic factors in 2 African settings: Cotonou, Benin, and Hillbrow, South Africa. Methods: A mathematical model, fit to epidemiologic data from each setting, was used to estimate the HIV impact of introducing a microbicide with different HIV/sexually transmitted infection (STI) efficacies. Simulations were compared to explore how impact is affected by context. Results: Widespread microbicide use results in a greater relative reduction in HIV incidence in Cotonou, where HIV/STIs are less prevalent. Most infections averted are from commercial sex in Cotonou but noncommercial sex in Hillbrow. The microbicide’s STI efficacy is important in determining its HIV impact in both settings, but especially in Cotonou where the microbicide’s HIV impact was mainly the result of its STI efficacy. Conclusions: It is important to develop and evaluate microbicides that are efficacious against STIs. However, even with the same patterns of use, a microbicide’s impact and the importance of its STI efficacy will vary considerably between settings. The impact of widespread microbicide use differs between 2 African settings: the reduction in HIV incidence and role of the microbicide’s sexually transmitted infection efficacy being greater in Benin but more HIV-averted in South Africa.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".