Medical chart-reported alcohol consumption, substance use, and mental health issues in association with HIV pre-exposure prophylaxis (PrEP) nonadherence among gay, bisexual, and other men-who-have-sex-with-men
Notice bibliographique
Résumé
BACKGROUND: Although some evidence suggests that alcohol, substance use, and mental health issues diminish adherence to HIV Pre-Exposure Prophylaxis (PrEP) among gay, bisexual, and other men-who-have-sex-with-men (gbMSM), findings are somewhat inconsistent and have primarily derived from studies involving non-random samples. Medical chart extraction can provide unique insight by in part surmounting sampling-related limitations, as data for entire PrEP clinic populations can be examined. Our investigation entailed comprehensive chart extraction to assess the extent to which chart-reported alcohol, substance use, and mental health issues were associated with chart-reported PrEP nonadherence. METHODS: Data from medical charts of gbMSM at two PrEP clinics in Toronto, Canada were extracted for a retrospective 12-month period (02/2018-01/2019). Charts were reviewed for all patients who were 1) ≥ 18 years old; 2) gbMSM; 3) prescribed PrEP ≥ 3 months, and 4) not in a PrEP-related drug trial. Information regarding PrEP, alcohol, substance use, mental health, and sexual behavior was extracted. PrEP adherence was classified in terms of (1) any reported nonadherence, and (2) 'suboptimal adherence,' reflecting nonadherence patterns indicative of insufficient pharmacological protection from HIV. Multivariate logistic regression was employed to identify factors associated with adherence outcomes. RESULTS: Data were extracted from 4,292 clinic visits among 501 eligible patients (age: M = 39.1; duration on PrEP: M = 17.4 months; daily PrEP regimen = 93.8%). Hazardous/harmful drinking, club drug use, and mental health issues were reported among 8.8%, 22.2%, and 26.1% of patients, respectively. Any nonadherence and suboptimal adherence were reported among 37.5% and 12.4% of patients, respectively. Factors significantly associated with any nonadherence included age < 25 (AOR = 3.08, 95%CI = 1.54-6.15, p < .001), club drug use (AOR = 2.71, 95%CI = 1.65-4.47, p < .001), and condomless sex (AOR = 1.83, 95%CI = 1.19-2.83, p = .006). For suboptimal adherence, significant factors included age < 25 (AOR = 4.83, 95%CI = 2.28-10.22, p < .001), non-daily PrEP regimens (AOR = 2.94, 95%CI = 1.19-7.22, p = .019), missing PrEP appointments (AOR = 1.97, 95%CI = 1.09-3.55, p = .025), and club drug use (AOR = 1.97, 95%CI = 1.01-3.68, p = .033). Neither alcohol nor mental health issues were associated with nonadherence outcomes. CONCLUSIONS: Chart-indicated suboptimal adherence was present among a small subgroup of PrEP-prescribed gbMSM. Adherence-related interventions should target gbMSM who use club drugs, are younger, experience challenges attending PrEP care, and are prescribed non-daily regimens. Offering long-acting injectable PrEP when available and feasible may also improve PrEP's HIV-preventive impact among 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 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,003 | 0,004 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 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 ».