Chemsex and incidence of sexually transmitted infections among Canadian pre-exposure prophylaxis (PrEP) users in the l’Actuel PrEP Cohort (2013–2020)
Bibliographic record
Abstract
OBJECTIVES: Use of illicit substances during sex (chemsex) may increase transmission of HIV and other STIs. Pre-exposure prophylaxis (PrEP) is highly effective at preventing HIV transmission, providing an important prevention tool for those who practise chemsex. However, it does not prevent acquisition of other STIs. We aim to examine the impact of chemsex on STI incidence among gay, bisexual and other men who have sex with men (gbMSM), and transgender women using PrEP in Montréal, Canada. METHODS: We linked baseline sociodemographic and behavioural data with follow-up STI testing from 2013 to 2020 among PrEP users in the l'Actuel PrEP Cohort (Canada). Focusing on the 24 months following PrEP initiation, we estimated the effect of chemsex reported at baseline on cumulative incidence of gonorrhoea and chlamydia using Kaplan-Meier curves and survival analyses. We investigated the role of polysubstance use and effect modification by sociodemographic factors. RESULTS: There were 2086 clients (2079 cisgender gbMSM, 3 transgender gbMSM, 4 transgender women) who initiated PrEP, contributing 1477 years of follow-up. There were no incident HIV infections among clients on PrEP. Controlling for sociodemographic confounders, clients reporting chemsex at baseline had a 32% higher hazard of gonorrhoea/chlamydia diagnosis (adjusted HR=1.32; 95% CI: 1.10 to 1.57), equivalent to a risk increase of 8.9 percentage points (95% CI: 8.5 to 9.4) at 12 months. The effect was greater for clients who reported polysubstance use (adjusted HR=1.51; 95% CI: 1.21 to 1.89). The strength of the effect of chemsex on STI incidence varied by age, education and income. CONCLUSION: Among PrEP users, chemsex at baseline was linked to increased incidence of gonorrhoea and chlamydia. This effect was stronger for people reporting multiple chemsex substances. The high STI incidence among gbMSM who report chemsex highlights the importance of PrEP for this population and the need for integrated services that address the complexities of sexualised substance use.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".