Social security during COVID-19: The experiences of military veterans
Abstract: Research published prior to COVID-19 has illustrated some of the difficulties that veterans can experience within the benefits system (Scullion et al, 2018; 2019; Scullion and Curchin, 2021). For example, those with Service-attributed mental health conditions can face challenges interacting with various aspects of the system from Work Capability Assessments (WCAs) through to Work Focused Interviews (WFIs) (Scullion and Curchin, 2021). Accounts within pre-COVID-19 research also highlight the significant role of informal peer networks and third sector organisations in supporting veterans in relation to both benefits processes but also wider issues relating to health and wellbeing, particularly where there is an absence of close family connections and relationships (Scullion et al, 2018; 2019). Drawing on emerging findings from interviews with veterans undertaken during COVID-19, this chapter revisits some of these pre-COVID-19 issues around mental health, benefits processes, and support networks to explore the impact of the pandemic.
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.