Modeling the potential impact of pre-exposure prophylaxis for HIV among men who have sex with men in Cameroon
Bibliographic record
Abstract
BACKGROUND: Men who have sex with men (MSM) are consistently burdened by HIV at higher levels than other adults. While HIV prevention programs for MSM are growing in coverage and quality, HIV incidence remains high. In response, pre-exposure prophylaxis (PrEP) was introduced in 2019 to support HIV risk reduction among MSM in Cameroon. Understanding how PrEP initiation programs will change the HIV prevalence among MSM in Cameroon is important to developing effective programs. METHODS: This study uses a mathematical model to simulate population-level HIV transmission among MSM in the cities of Yaoundé and Douala, Cameroon. PrEP is incorporated into the model at rates that equal 25%, 50%, or 75% coverage after twenty years to assess the potential effects on HIV prevalence among MSM, requiring annual initiation rates of 2.5%, 6.8%, and 17.2% for Yaoundé and 2.2%, 5.6%, and 13.4% for Douala, respectively. The data utilized for this model are from a cross sectional study which recruited MSM through respondent-driven sampling of MSM in two major cities in Cameroon: Yaoundé and Douala. RESULTS: The model estimated an HIV prevalence of 43.2% among MSM, annual HIV diagnoses of 300 per 10,000 MSM and antiretroviral therapy (ART) coverage of 53.9% in Yaoundé. In Douala, estimated prevalence is 26.5% among MSM, 167 per 10,000 MSM annual diagnoses and ART coverage of 72.0%. Standalone PrEP interventions aimed at 50% coverage at the end of a 20-year program would reduce the prevalence from 43.2% to 35.4% in Yaoundé and from 26.5 to 20.1% in Douala. Combining PrEP with a 10% increase in HIV testing would decrease the number of MSM living with HIV and unaware of their status from 9.8 to 6.0% in Yaoundé and from 8.7 to 4.6% in Douala. CONCLUSIONS: PrEP would be beneficial in reducing prevalence even at varying initiation and coverage levels. Combination of PrEP and increased HIV testing further decreased the number of undiagnosed MSM. This study supports the utility of implementing PrEP as part of comprehensive HIV prevention programming among MSM in Cameroon.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".