Pre-exposure Prophylaxis Uptake Among Men Who Have Sex With Men Who Used nPEP: A Longitudinal Analysis of Attendees at a Large Sexual Health Clinic in Montréal (Canada)
Bibliographic record
Abstract
BACKGROUND: Reducing HIV transmission using pre-exposure prophylaxis (PrEP) requires focussing on individuals at high acquisition risk, such as men who have sex with men with a history of nonoccupational post-exposure prophylaxis (nPEP). This study aims to characterize longitudinal trends in PrEP uptake and its determinants among nPEP users in Montréal. METHODS: Eligible attendees at Clinique médicale l'Actuel were recruited prospectively starting in October 2000 (nPEP) and January 2013 (PrEP). Linking these cohorts, we characterized the nPEP-to-PrEP cascade, examined the determinants of PrEP uptake after nPEP consultation using a Cox proportional-hazard model, and assessed whether PrEP persistence differed by nPEP history using Kaplan-Meier curves. RESULTS: As of August 2019, 31% of 2682 nPEP cohort participants had 2 or more nPEP consultations. Subsequent PrEP consultations occurred among 36% of nPEP users, of which 17% sought nPEP again afterward. Among 2718 PrEP cohort participants, 46% reported previous nPEP use. Among nPEP users, those aged 25-49 years [hazard ratio (HR) = 1.3, 95% confidence interval (CI): 1.1 to 1.7], with more nPEP episodes (HR = 1.4, 95% CI: 1.3 to 1.5), who reported chemsex (HR = 1.3, 95% CI: 1.1 to 1.7), with a sexually transmitted infection history (HR = 1.5; 95% CI: 1.3 to 1.7), and who returned for their first nPEP follow-up visit (HR = 3.4, 95% CI: 2.7 to 4.2) had higher rates of PrEP linkage. There was no difference in PrEP persistence between nPEP-to-PrEP and PrEP only participants. CONCLUSION: Over one-third of nPEP users were subsequently prescribed PrEP. However, the large proportion of men who repeatedly use nPEP calls for more efficient PrEP-linkage services and, among those who use PrEP, improved persistence should be encouraged.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".