Evaluation of a hospital-initiated tobacco dependence treatment service: uptake, smoking cessation, readmission and mortality
Notice bibliographique
Résumé
BACKGROUND: The National Health Service in England aims to implement tobacco dependency treatment services in all hospitals by 2024. We aimed to assess the uptake of a new service, adapted from the Ottawa Model of Smoking Cessation, and its impact on 6-month quit rates and readmission or death at 1-year follow-up. METHODS: We conducted a pragmatic service evaluation of a tobacco dependency service implemented among 2067 patients who smoked who were admitted to 2 acute hospitals in London, England, over a 12-month period from July 2020. The intervention consisted of the systematic identification of smoking status, automatic referral to tobacco dependence specialists, provision of pharmacotherapy and behavioural support throughout the hospital stay, and telephone support for 6 months after discharge. The outcomes were (i) patient acceptance of the intervention during admission, (ii) quit success at 6 months after discharge, (iii) death, or (iv) readmission up to 1 year following discharge. Multivariable logistic regression was used to estimate the impact of a range of clinical and demographic variables on these outcomes. RESULTS: The majority (79.4%) of patients accepted support at the first assessment. Six months after discharge, 35.1% of successfully contacted patients reported having quit smoking. After adjustment, odds of accepting support were 51-61% higher among patients of all non-White ethnicity groups, relative to White patients, but patients of Mixed, Asian, or Other ethnicities had decreased odds of quit success (adjusted odds ratio (AOR) = 0.32, 95%CI = 0.15-0.66). Decreased odds of accepting support were associated with a diagnosis of cardiovascular disease or diabetes; however, diabetes was associated with increased odds of quit success (AOR = 1.88, 95%CI = 1.17-3.04). Intention to make a quit attempt was associated with a threefold increase in odds of quit success, and 60% lower odds of death, compared to patients who did not intend to quit. A mental health diagnosis was associated with an 84% increase in the odds of dying within 12 months. CONCLUSIONS: The overall quit rates were similar to results from Ottawa models implemented elsewhere, although outcomes varied by site. Outcomes also varied according to patient demographics and diagnoses, suggesting personalised and culturally tailored interventions may be needed to optimise quit success.
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 | 0,002 | 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 | 0,000 |
| 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 ».