Loneliness and Social Isolation of Military Veterans: Systematic Narrative Review
Abstract: Loneliness and social isolation are being increasingly recognized as influencing both physical and mental health. There is limited research carried out with military veterans and, to date, there is no review of existing evidence. To synthesize and examine the evidence exploring aspects of social isolation and loneliness of military veterans, using a systematic narrative review strategy. A database search was carried out utilizing relevant search criterion. Seven databases were searched for publications with no date restrictions. Articles were included if they involved veterans and either social isolation or loneliness. The initial search returned 484 papers, after exclusions, removal of duplications, and a reference/citation search, 17 papers remained and were included in this review. The retrieved papers examined four areas of loneliness and social isolation: prevalence of loneliness in the veteran population, experiences related to military service as impacting loneliness or social isolation, the relationship between mental health and loneliness or social isolation, and interventions to combat loneliness and social isolation. Differences between the experiences of younger and older veterans were also highlighted. It is evident that military veterans present unique experiences of loneliness and social isolation, especially older veterans. This requires specific attention outside of campaigns targeted at the nonmilitary population.
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.