Community Behavioral Health Service Use and Criminal Recidivism of People With Mental, Substance Use, and Co-occurring Disorders.


Journal

Psychiatric services (Washington, D.C.)
ISSN: 1557-9700
Titre abrégé: Psychiatr Serv
Pays: United States
ID NLM: 9502838

Informations de publication

Date de publication:
01 12 2022
Historique:
pubmed: 18 5 2022
medline: 3 12 2022
entrez: 17 5 2022
Statut: ppublish

Résumé

This study assessed the relationship between community behavioral health service (CBHS) use and criminal recidivism in a broad sample of potential beneficiaries and by diagnostic group. Among a cohort of people on probation with any mental and/or substance use disorder (N=772), the study estimated the effect of CBHS use on rearrest with Cox proportional hazards models. Service use significantly predicted reduced recidivism among people with any mental disorder (hazard rate=0.36, p=0.008), but not among those with any substance use disorder or co-occurring disorders. CBHS use in a given week predicted a 64% reduced recidivism risk during the following week among people with any mental disorder. However, CBHS use had no clear relationship with recidivism among people with co-occurring disorders or any substance use disorder. CBHS use may reduce recidivism, depending on recipient and service characteristics.

Identifiants

pubmed: 35578804
doi: 10.1176/appi.ps.202100530
pmc: PMC9669267
mid: NIHMS1794347
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

1397-1400

Subventions

Organisme : NIAAA NIH HHS
ID : T32 AA007240
Pays : United States

Auteurs

Leah A Jacobs (LA)

School of Social Work, University of Pittsburgh, Pittsburgh (Jacobs, Greeno, Labrum); Department of Statistics and Data Science, Carnegie Mellon University, Pittsburgh (Branson); School of Social Work and Goldman School of Public Policy, University of California, Berkeley, Berkeley (Skeem).

Zach Branson (Z)

School of Social Work, University of Pittsburgh, Pittsburgh (Jacobs, Greeno, Labrum); Department of Statistics and Data Science, Carnegie Mellon University, Pittsburgh (Branson); School of Social Work and Goldman School of Public Policy, University of California, Berkeley, Berkeley (Skeem).

Catherine G Greeno (CG)

School of Social Work, University of Pittsburgh, Pittsburgh (Jacobs, Greeno, Labrum); Department of Statistics and Data Science, Carnegie Mellon University, Pittsburgh (Branson); School of Social Work and Goldman School of Public Policy, University of California, Berkeley, Berkeley (Skeem).

Jennifer L Skeem (JL)

School of Social Work, University of Pittsburgh, Pittsburgh (Jacobs, Greeno, Labrum); Department of Statistics and Data Science, Carnegie Mellon University, Pittsburgh (Branson); School of Social Work and Goldman School of Public Policy, University of California, Berkeley, Berkeley (Skeem).

Travis Labrum (T)

School of Social Work, University of Pittsburgh, Pittsburgh (Jacobs, Greeno, Labrum); Department of Statistics and Data Science, Carnegie Mellon University, Pittsburgh (Branson); School of Social Work and Goldman School of Public Policy, University of California, Berkeley, Berkeley (Skeem).

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Classifications MeSH