JOURNAL ARTICLE

Implementation of Jail and Prison-Based Medication Treatment for Opioid Use Disorder Programs: A Narrative Synthesis.

  • Published In: Medical Care Research & Review, 2025, v. 82, n. 6. P. 439 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Lenz, Cosima; Song, Minna; Bandara, Sachini; Kennedy Hendricks, Alene; Kramer, Camille; Sufrin, Carolyn; Fingerhood, Michael; Saloner, Brendan 3 of 3

Abstract

This article focuses on the implementation of medications for opioid use disorder (MOUD) programs in U.S. jails and prisons, synthesizing evidence from 36 studies published between 2019 and 2023. Using the Exploration, Preparation, Implementation, Sustainment (EPIS) framework, the review highlights that MOUD delivery in carceral settings requires substantial resources, infrastructure, and staffing, with diversion of medications and stigma—especially toward pregnant individuals—posing significant challenges. Effective coordination between carceral facilities and community providers is critical for treatment continuity postrelease, and the COVID-19 pandemic accelerated innovations such as telehealth use in MOUD programs. The review underscores the variability in program implementation across facilities, the need for sustainable funding and policies, and calls for future research on scaling MOUD programs, evaluating outcomes, and addressing systemic barriers to improve care for justice-involved individuals with opioid use disorder.

Additional Information

  • Source:Medical Care Research & Review. 2025/12, Vol. 82, Issue 6, p439
  • Document Type:Literature Review
  • Subject Area:Politics and Government
  • Publication Date:2025
  • ISSN:1077-5587
  • DOI:10.1177/10775587251345018
  • Accession Number:188807223
  • Copyright Statement:Copyright of Medical Care Research & Review is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Looking to go deeper into this topic? Look for more articles on EBSCOhost.