Social isolation and loneliness of UK veterans: a Delphi study
Abstract: Background: Evidence increasingly acknowledges the impact of social isolation and loneliness on the lives of military veterans and the wider Armed Forces Community. Aims: The study gathered expert consensus to (i) understand if veterans are considered ‘unique’ in their experiences of social isolation and loneliness; (ii) examine perceived factors leading to social isolation and loneliness of veterans; (iii) identify ways to tackle veterans’ social isolation and loneliness. Methods: This study adopted a three-phase Delphi method. Phase 1 utilized a qualitative approach and Phase 2 and Phase 3 utilized a mixed-methods approach. Results: Several outcomes were identified across the three phases. Transition out of the military was viewed as a period to build emotional resilience and raise awareness of relevant services. It was also concluded that veterans would benefit from integrating into services within the wider community, and that social prescribing services could be a vehicle to link veterans to relevant services. Furthermore, access to, and the content of, programmes was also of importance. Conclusions: These findings illustrate various important interventional aspects to consider when funding and implementing programmes focussed on tackling social isolation and loneliness.
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.