Past dynamics of HIV transmission among men who have sex with men in Montréal, Canada: a mathematical modelling study
Bibliographic record
Abstract
Abstract Background Gay, bisexual, and other men who have sex with men (gbMSM) experience disproportionate risks of HIV acquisition/transmission. In 2017, Montréal became the first Canadian Fast-Track city, setting the 2030 goal of zero new HIV infections. To inform local elimination efforts, we estimate the evolving role of prevention/risk behaviours and HIV transmission dynamics among gbMSM in Montréal between 1975-2019. Methods Data from local bio-behavioural surveys were analyzed to develop, parameterize, and calibrate an agent-based model of sexual HIV transmission. Partnership dynamics, the HIV natural history, and treatment and prevention strategies were considered. The model simulations were analyzed to estimate the fraction of HIV acquisitions/transmissions attributable to specific age-groups and unmet prevention needs. Results The model-estimated HIV incidence peaked in 1985 (2.2%; 90%CrI: 1.3-2.8%) and decreased to 0.1% (90%CrI: 0.04-0.3%) in 2019. Between 1990-2017, the majority of HIV acquisitions/transmissions occurred among men aged 25-44 years, and men aged 35-44 thereafter. The unmet prevention needs of men with >10 annual anal sex partners contributed 92-94% of transmissions and 63-73% of acquisitions annually. The primary stage of HIV played an increasing role over time, contributing to 12%-27% of annual transmissions over 1990-2019. In 2019, approximately 75% of transmission events occurred from men who had discontinued, or never initiated ART. Conclusions The evolving HIV landscape has contributed to the recent low HIV incidence among MSM in Montréal. The shifting dynamics identified in this study highlight the need for continued population-level surveillance to identify unmet prevention needs and core groups on which to prioritize elimination efforts.
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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 | 0.002 |
| Science and technology studies | 0.002 | 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) | 0.006 | 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".