Social Network Cohesion among Veterans Living in Recovery Homes
Abstract: Recovery homes for individuals with substance use disorders (SUD) called Oxford House (OH) have been shown to improve the prospects of a successful recovery across different sub-populations, and these homes may be particularly beneficial for veterans in recovery. An estimated 18% of OH residents are veterans; however, not much is known about their experiences living in these homes. Participants included 85 veterans and non-veterans living in 13 OHs located in different regions of the United States. Using social network analysis and multi-level modeling, we investigated whether the social networks of veterans residing with other veterans were more cohesive compared to veterans living with only non-veterans. Results indicated that veterans residing with other veterans had stronger relationships with other OH residents than veterans who reside with all non-veterans. The implications for theory and practice are discussed. Further research is needed to determine if greater social network cohesion leads to better recovery outcomes for veterans.
Abstract: Novel and automated means of opioid use and relapse risk detection are needed. Unstructured electronic medical record data, including written progress notes, can be mined for clinically relevant information, including the presence of substance use and relapse-critical markers of risk and recovery from opioid use disorder (OUD). In this study, we used natural language processing (NLP) to automate the extraction of opioid relapses, and the timing of these occurrences, from veteran patients' electronic medical record. We then demonstrated the utility of our NLP tool via analysis of pre-/post-COVID-19 opioid relapse trends among veterans with OUD. For this demonstration, we analyzed data from 107,606 veterans OUD enrolled in Veterans Health Administration, comparing a pandemic-exposed cohort (n = 53,803; January 2019-March 2021) to a matched prepandemic cohort (n = 53,803; October 2017-December 2019). The recall of our NLP tool was 75% and our precision was 94%, demonstrating moderate sensitivity and excellent specificity. Using the NLP tool, we found that the odds of opioid relapse postpandemic onset were proportionally higher compared to prepandemic trends, despite patients having fewer mental health encounters from which to derive instances of relapse postpandemic onset. In this research application of the tool, and as hypothesized, we found that opioid relapse risk was elevated postpandemic. The application of NLP Methods: to identify and monitor relapse risk holds promise for future surveillance, risk prevention, and clinical outcome research.