The association between involuntary alcohol treatment and subsequent emergency department visits and hospitalizations: a Bayesian analysis of treated patients and matched controls
Notice bibliographique
Résumé
BACKGROUND AND AIMS: Many nations have provisions for involuntary treatment of alcohol dependence where the person is at serious risk of harm to themselves. To date, there has been little thorough evaluation of its effectiveness. This study aimed to determine if there were differences between involuntary and voluntary treatment for alcohol dependence on subsequent emergency and hospital care. DESIGN: A retrospective cohort design using linked routinely collected administrative data on health-care utilization. SETTING: Hospital and community-based alcohol treatment, New South Wales, Australia. PARTICIPANTS: A total of 231 patients who were involuntarily treated for alcohol dependence and 231 matched controls who received treatment as usual within the period May 2012 to April 2018. INTERVENTION AND COMPARATOR: Involuntary treatment comprised a 28-day mandated hospital admission which included supervised withdrawal, comprehensive assessment, rehabilitation and support followed by voluntary aftercare support for up to 6 months. Treatment as usual comprised three not mutually exclusive forms of intensive voluntary alcohol treatment: withdrawal management, rehabilitation and pharmacotherapies for alcohol dependence. MEASUREMENTS: Outcome measures: changes in the number of emergency department (ED) visits and number of unplanned hospital admissions 12 months before and 12 months after completion of index treatment. FINDINGS: Both groups showed a reduction in ED visits (incidence rate ratio (IRR) = 0.56, 95% credible intervals (CrI) = 0.39-0.78) and unplanned hospital admissions (IRR = 0.49, 95% CrI = 0.37-0.65). There was no statistically significant difference between the two groups (IRR = 0.77, 95% CrI = 0.58-1.03 for ED visits and IRR = 0.79, 95% CrI = 0.62-1.01 for hospital admissions). The Bayes factors were 0.925 and 0.936 for ED visits and unplanned hospital admissions, respectively, interpreted as weak evidence in support of the null hypothesis of no difference between the interventions. CONCLUSIONS: Involuntary treatment of alcohol dependence was associated with reduced health service utilization in the year following treatment, and the outcomes did not differ from those of a matched control group.
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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 | 0,000 |
| 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 ».