Population-level effectiveness of pre-exposure prophylaxis for HIV prevention among men who have sex with men in Montréal: a modelling study of surveillance and survey data
Bibliographic record
Abstract
Abstract Background HIV pre-exposure prophylaxis (PrEP) has been recommended and partly subsidized in Québec since 2013. We aimed to evaluate the population-level impact of PrEP on HIV transmission among men who have sex with men (MSM) in Montréal over 2013-2021. Methods We used an agent-based mathematical model of sexual HIV transmission to estimate the fraction of HIV acquisitions averted by PrEP compared to a counterfactual scenario without PrEP. The model was calibrated to local MSM survey and cohort data and accounted for COVID-19 pandemic impacts on sexual activity, prevention, and care. To assess potential optimization strategies, we modelled hypothetical scenarios prioritizing PrEP to MSM with high sexual activity or aged ≤45 years, increasing coverage to levels achieved in Vancouver (where PrEP is free-of-charge), and improving retention. Results Over 2013-2021, the estimated annual HIV incidence decreased from 0.4 (90% credible interval [CrI]: 0.3-0.6) to 0.2 (90%CrI: 0.1-0.2) per 100 person-years. PrEP coverage in HIV-negative MSM remained low until 2015 (<1%). Afterward, coverage increased to a maximum of 10% (15% of those eligible for PrEP) and the cumulative fraction of HIV acquisitions averted over 2015-2021 was 20% (90%CrI: 11%-30%). The hypothetical scenarios modelled showed that PrEP could have averted up to 63% (90%CrI: 54%-70%) of acquisitions if coverage reached 10% in 2015 and 30% in 2019, like in Vancouver. Interpretation PrEP reduced population-level HIV transmission among Montréal MSM. However, our study suggests missed prevention opportunities and provides support for public policies that provide PrEP free-of-cost to MSM at high risk of HIV acquisition.
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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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 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".