Examining provincial PrEP coverage and characterizing PrEP awareness and use among gay, bisexual and other men who have sex with men in Vancouver, Toronto and Montreal, 2017–2020
Bibliographic record
Abstract
INTRODUCTION: Accessibility of pre-exposure prophylaxis (PrEP) in Canada remains complex as publicly funded coverage and delivery differs by province. In January 2018, PrEP became publicly funded and free of charge in British Columbia (BC), whereas PrEP coverage in Ontario and Montreal is more limited and may require out-of-pocket costs. We examined differences over time in PrEP uptake and assessed factors associated with PrEP awareness and use. METHODS: Gay, bisexual and other men who have sex with men (GBM) were recruited through respondent-driven sampling (RDS) in Toronto, Vancouver and Montreal, Canada, in a prospective biobehavioural cohort study. We applied generalized estimating equations with hierarchical data (RDS chain, participant, visit) to examine temporal trends of PrEP use and correlates of PrEP awareness and use from 2017 to 2020 among self-reported HIV-negative/unknown GBM. RESULTS: Of 2008 self-identified HIV-negative/unknown GBM at baseline, 5093 study visits were completed from February 2017 to March 2020. At baseline, overall PrEP awareness was 88% and overall PrEP use was 22.5%. During our study period, we found PrEP use increased in all cities (all p<0.001): Montreal 14.2% during the first time period to 39.3% during the last time period (p<0.001), Toronto 21.4-31.4% (p<0.001) and Vancouver 21.7-59.5% (p<0.001). Across the study period, more Vancouver GBM used PrEP than Montreal GBM (aOR = 2.05, 95% CI = 1.60-2.63), with no significant difference between Toronto and Montreal GBM (aOR = 0.90, 95% CI = 0.68-1.18). CONCLUSIONS: Full free-of-charge public funding for PrEP in BC likely contributed to differences in PrEP awareness and use. Increasing public funding for PrEP will improve accessibility and uptake among GBM most at risk of HIV.
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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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".