Smoking Cessation Outcomes and Predictors Among Individuals With Co-occurring Substance Use and/or Psychiatric Disorders
Notice bibliographique
Résumé
OBJECTIVE: Individuals with substance use and psychiatric disorders have a high prevalence of tobacco use disorders and are disproportionately affected by tobacco-related morbidity and mortality. However, it is unclear how having co-occurring disorders affects tobacco cessation. Our aim was to examine smoking cessation outcomes and relevant predictors of smoking cessation among smokers with substance use and/or psychiatric disorders. METHODS: Data from medical records of 674 participants in a tobacco treatment program within mental health and addictions services in Vancouver, Canada, were analyzed. The 26-week treatment program included an 8-week structured behavioral counseling group, an 18-week support group, and 26 weeks of no-cost pharmacotherapy. Information on demographics, tobacco use and history, type of pharmacotherapy received, nicotine dependence, importance of and confidence in quitting smoking, expired carbon monoxide level, substance use and psychiatric disorder history, and total program visits were gathered. RESULTS: Approximately 67% (n = 449) of participants had co-occurring substance use and psychiatric disorders, while 20% (n = 136) had substance use disorder only, 10% (n = 67) had psychiatric disorder only, and 3% (n = 22) had tobacco dependence only. Rates of tobacco cessation (i.e., 7-day point prevalence of abstinence verified by expired carbon monoxide of ≤8 ppm) by group in the 522 people who completed treatment were as follows: 38.2% for those with co-occurring disorders, 47.1% for those with tobacco dependence only, 47.1% for those with substance use disorder only, and 41.8% for those with psychiatric disorder only. Length of treatment was a significant predictor of smoking cessation for those with co-occurring disorders and substance use disorder only. In the final stratified multivariate analysis, for individuals with co-occurring disorders, having an opiate use disorder (as compared to an alcohol use disorder) and higher nicotine dependence scores at baseline were predictive of poor cessation outcomes, while greater length of treatment was predictive of successful smoking cessation. CONCLUSIONS: Tobacco cessation treatment for individuals with co-occurring substance use and psychiatric disorders is likely to be as effective as for smokers with either disorder alone. Treatment duration predicts success among these smokers so strategies to enhance engagement and retention are needed.
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 | 0,000 | 0,000 |
| 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 | 0,001 |
| 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 ».