Thursday, October 17, 2024
10/17/2024
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.
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