Chemsex, sexually transmitted infections, and pre-exposure prophylaxis among men who have sex with men: longitudinal analyses of sexual health clinic attendees in Montréal
Notice bibliographique
Résumé
Background: Chemsex –the use of illicit substances during sex to enhance pleasure– has become more prevalent in the last decade among gay, bisexual and other men who have sex with men (gbMSM). This has raised public health concerns because chemsex can be associated with transmission of HIV and other sexually transmitted infections (STIs). Pre-exposure prophylaxis (PrEP) is highly effective at preventing HIV and is recommended for gbMSM and transgender women at elevated risk of HIV acquisition. As PrEP can provide an important prevention tool for people who practice chemsex, it is important to better understand the role of chemsex in PrEP use and STI incidence.Objectives: Understand the evolving patterns of chemsex practices among gbMSM and transgender women consulting for PrEP in Montréal (Canada) and their impact on STI transmission among PrEP users by: (1) describing temporal trends in chemsex practices and their impact on PrEP trajectories, and (2) estimating the impact of chemsex on incidence of gonorrhea and chlamydia among PrEP users.Methods: The l’Actuel PrEP Cohort was established in 2013 at the Clinique médicale l’Actuel, a major sexual health clinic in Montréal. For the purposes of this thesis, the cohort was restricted to adult (≥18 years-old) gbMSM and transgender women who consulted for PrEP between 2013–2020. This dataset includes baseline sociodemographic and behavioural data, and quarterly follow-up behavioural and STI screening data. I first described the sociodemographic profile of clients who consulted for PrEP, characterized chemsex and polysubstance use trends over time, and evaluated PrEP trajectories. Then, for the second objective, I used survival analyses to estimate the effect of chemsex at baseline on incidence of gonorrhea and chlamydia, focusing on the 24 months following PrEP initiation. I also investigated the role of polysubstance use and potential effect modification by age, education, and income.Results: A total of 2,923 clients consulted for PrEP between 2013–2020 (2,910 cisgender gbMSM, 6 transgender gbMSM, 7 transgender women). Among these clients, 24% reported any chemsex and 13% reported polysubstance use in the past year. Ecstasy (14%), GHB (13%), and cocaine (12%) were the most commonly reported chemsex substances. Prevalence of chemsex and polysubstance use decreased over time. The proportion of clients initiating PrEP (73%) was similar between groups, and the median time to discontinuation was also similar: 6.5 months (95% CI: 5.3–7.2) in the chemsex group compared to 6.9 months (95% CI: 6.3–7.5) in the no-chemsex group. There were 2,086 clients (2,079 cisgender gbMSM, 3 transgender gbMSM, 4 transgender women) who initiated PrEP, contributing 1,477 person-years of follow-up. There was no incident HIV infection among clients on PrEP. When controlling for age, education, income, PrEP regimen, and year of baseline consultation, chemsex was linked to a 32% higher hazard of gonorrhea or chlamydia diagnosis (adjusted HR=1.32; 95% CI: 1.10–1.57). This effect was stronger among clients reporting polysubstance use (adjusted HR=1.51; 95% CI: 1.21–1.89). The strength of the effect of chemsex on STI incidence varied by age, education, and income.Conclusions: One in four gbMSM consulting for PrEP at a large Canadian sexual health clinic reported chemsex in the past year. Chemsex, however, is not an obstacle to PrEP initiation or persistence. Among PrEP users, chemsex at baseline is linked to increased incidence of gonorrhea and chlamydia. Incidence was higher for people who report two or more chemsex substances. The high prevalence of chemsex practices and high STI incidence highlight the need for integrated services addressing unmet needs at the intersection of sexual health and substance use. The high STI incidence also stresses the importance of PrEP as an effective HIV prevention tool for this population
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,002 |
| Bibliométrie | 0,002 | 0,005 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».