The costs of crime during and after publicly funded treatment for opioid use disorders: a population‐level study for the state of California
Notice bibliographique
Résumé
BACKGROUND AND AIMS: Treatment for opioid use disorders (OUD) reduces the risk of mortality and infectious disease transmission; however, opportunities to quantify the potential economic benefits of associated decreases in drug-related crime are scarce. This paper aimed to estimate the costs of crime during and after periods of engagement in publicly funded treatment for OUD to compare total costs of crime during a hypothetical 6-month period following initiation of opioid agonist treatment (OAT) versus detoxification. DESIGN: Retrospective, administrative data-based cohort study with comprehensive information on drug treatment and criminal justice systems interactions. SETTING: Publicly funded drug treatment facilities in California, USA (2006-10). PARTICIPANTS: A total of 31 659 individuals admitted for the first time to treatment for OUD, and who were linked with criminal justice and mortality data, were followed during a median 2.3 years. Median age at first treatment admission was 32, 35.8% were women and 37.1% primarily used prescription opioids. MEASUREMENTS: Daily costs of crime (US$2014) were calculated from a societal perspective and were composed of the costs of policing, court, corrections and criminal victimization. We estimated the average marginal effect of treatment engagement in OAT or detoxification adjusting for potential fixed and time-varying confounders, including drug use and criminal justice system involvement prior to treatment initiation. FINDINGS: Daily costs of crime during treatment compared with after treatment were $126 lower for OAT [95% confidence interval (CI) = $116, $136] and $144 lower for detoxification (95% CI = $135, $154). Summing the costs of crime during and after treatment over a hypothetical 6-month period using the observed median durations of OAT (161 days) and detoxification (19 days), we estimated that enrolling an individual in OAT as opposed to detoxification would save $17 550 ($16 840, $18 383). CONCLUSIONS: In publicly funded drug treatment facilities in California, USA, engagement in treatment for opioid use disorders is associated with lower costs of crime in the 6 months following initiation of treatment, and the economic benefits were far greater for individuals receiving time-unlimited treatment.
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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 ».