Exploring the Health and Well-Being of a National Sample of U.K. Treatment-Seeking Veterans
Abstract: Military veterans experience a higher prevalence of mental health difficulties compared with the general population. Research has highlighted veterans who experience mental health difficulties have poorer treatment outcomes. Understanding veteran needs may help improve veteran mental health services and treatment outcomes. The aim of this study was to explore the complexity of health and well-being needs among a national clinical sample of veterans. In total, 989 veterans from a U.K. veterans mental health charity were invited to complete a questionnaire about their sociodemographic characteristics, military experiences, physical and mental health, and well-being. Of the invitees, 428 veterans (43.3%) completed the questionnaire. Common mental disorders, such as anxiety and depression, were the most frequently reported mental health difficulty (80.7%), followed by loneliness (79.1%) and perceived low social support (72.2%). Rates of PTSD were also high (68.7% any PTSD), with most participants experiencing complex PTSD (CPTSD; 62.5%) compared with PTSD (6.2%). Veterans with co-occurring CPTSD symptoms have poorer health due to a higher number of comorbidities, for instance between CPTSD and moral injury. Comorbidity appeared to be the norm rather than the exception within treatment-seeking veterans. As such, it seems important for veteran mental health services to take a holistic approach when supporting 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.