Alcohol consumption, substance use, and depression in relation to HIV Pre-Exposure Prophylaxis (PrEP) nonadherence among gay, bisexual, and other men-who-have-sex-with-men
Notice bibliographique
Résumé
Abstract Background Although HIV pre-exposure prophylaxis (PrEP) substantially diminishes the likelihood of HIV acquisition, poor adherence can decrease the HIV-protective benefits of PrEP. The present investigation sought to identify the extent to which alcohol consumption, substance use, and depression were linked to PrEP nonadherence among gay, bisexual, and other men-who-have-sex-with-men (gbMSM). Methods gbMSM (age ≥ 18, prescribed PrEP for ≥3 months) were recruited from two clinics in Toronto, Canada for an e-survey assessing demographics; PrEP nonadherence (4-day PrEP-focused ACTG assessment); hazardous and harmful alcohol use (AUDIT scores of 8–15 and 16+, respectively); moderate/high risk substance use (NIDA M-ASSIST scores > 4); depression (CESD-10 scores ≥10); and other PrEP-relevant factors. The primary outcome, PrEP nonadherence, entailed missing one or more PrEP doses over the past 4 days. A linear-by-linear test of association assessed whether increasing severity of alcohol use (i.e., based on AUDIT categories) was linked to a greater occurrence of PrEP nonadherence. Univariate logistic regression was employed to determine factors associated with PrEP nonadherence, and factors demonstrating univariate associations at the p < .10 significance level were included in a multivariate logistic regression model. Additive and interactive effects involving key significant factors were assessed through logistic regression to evaluate potential syndemic-focused associations. Results A total of 141 gbMSM (Mean age = 37.9, white = 63.1%) completed the e-survey. Hazardous/harmful drinking (31.9%), moderate/high risk substance use (43.3%), and depression (23.7%) were common; and one in five participants (19.9%) reported PrEP nonadherence. Increasing alcohol use level was significantly associated with a greater likelihood of nonadherence (i.e., 15.6, 25.0, and 44.4% of low-risk, hazardous, and harmful drinkers reported nonadherence, respectively (χ 2 (1) = 4.79, p = .029)). Multivariate logistic regression demonstrated that harmful alcohol use (AOR = 6.72, 95%CI = 1.49–30.33, p = .013) and moderate/high risk cocaine use (AOR = 3.11, 95%CI = 1.01–9.59, p = .049) independently predicted nonadherence. Furthermore, an additive association emerged, wherein the likelihood of PrEP nonadherence was highest among those who were hazardous/harmful drinkers and moderate/high risk cocaine users (OR = 2.25, 95%CI = 1.19–4.25, p = .013). Depression was not associated with nonadherence. Conclusions Findings highlight the need to integrate alcohol- and substance-focused initiatives into PrEP care for gbMSM. Such initiatives, in turn, may help improve PrEP adherence and reduce the potential for HIV acquisition among this group.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».