# Treatment Utilization During Community Reentry of Opioid Use Disorder Inpatients

> **NIH NIH F31** · UNIVERSITY OF RHODE ISLAND · 2024 · $48,974

## Abstract

Project Summary/Abstract
Nearly 400,000 people with an opioid use disorder (OUD) receive OUD treatment each year (19.7% of all
people with an OUD). Community re-entry following residential OUD treatment is a critically vulnerable time, as
risk of return to use, overdose, and death are increased in the 30 days following discharge.
Continuity of care
during re-entry is vital in supporting recovery outcomes, but little is known about treatment utilization during this
high-risk period. Individual and socio-structural factors can influence treatment utilization; however, there is a
dearth of information regarding the factors that predict treatment utilization during re-entry. Characterizing
treatment utilization and identifying predictors of treatment engagement is necessary to detect those who are
at risk for not engaging with treatment during community re-entry and thus address critical gaps in the OUD
treatment pipeline. By defining treatment broadly, inclusive of harm reduction strategies, and combining
intensive longitudinal methods (ecological momentary assessment [EMA]) with innovative machine learning
analyses, this study will both characterize treatment utilization with greater precision and improve prediction of
treatment engagement during community re-entry. Our findings aim to identify (in residential OUD treatment)
those at risk for not engaging with treatment during community re-entry (to inform preventative interventions)
as well as to pinpoint proximal facilitators and barriers to treatment utilization during community re-entry.
 The proposed secondary data analysis aims to characterize treatment utilization and identify individual and
structural facilitators and barriers to, and predictors of, OUD treatment during community re-entry. The target
population is adults with OUD who discharged from residential OUD treatment (N=150). This study uses
innovative methods and cutting-edge data analyses to maximize existing data from the Sponsor’s NIH-funded
study (P20GM125507). Aim 1 uses EMA to accurately and reliably describe experiences with treatment
utilization during community re-entry. Aim 2 applies innovative machine learning modeling approaches to 1)
socio-structural and clinical data gathered during residential treatment to identify those at risk for not engaging
with treatment during community re-entry, and 2) symptoms and behaviors gathered during community re-entry
with EMA to identify facilitators and barriers to treatment utilization the re-entry period. Findings will inform
continuity of care, evidence-based tools to prevent and/or delay return to opioid use, and reduce harms
associated with community re-entry. This proposal addresses critical gaps in my training and knowledge
and will be necessary to develop an independent, NIH-funded research program focused on opioid use
and OUD treatment utilization during high-risk transitional periods, such as community re-entry.

## Key facts

- **NIH application ID:** 10997506
- **Project number:** 1F31DA060010-01A1
- **Recipient organization:** UNIVERSITY OF RHODE ISLAND
- **Principal Investigator:** Noam Newberger
- **Activity code:** F31 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $48,974
- **Award type:** 1
- **Project period:** 2024-09-01 → 2026-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10997506

## Citation

> US National Institutes of Health, RePORTER application 10997506, Treatment Utilization During Community Reentry of Opioid Use Disorder Inpatients (1F31DA060010-01A1). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10997506. Licensed CC0.

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