The Together Programme: Supporting Caregiving Partners of veterans
Abstract: In a recent analysis conducted by Combat Stress of UK, partners living alongside veterans with mental health difficulties, rates for depression and PTSD were higher compared to the external population (depression 39% Vs 20%, PTSD 17% Vs 3%. (Murphy, Palmer & Busuttil, 2016). As such this suggests the high burden of need within this group. The support currently available here in the UK mainly comprises of peer based support. Whilst research indicates the positive impact peer led groups can have, the clinical severity of partners symptoms implies a need for more structured, bespoke and evidence based intervention. To this end, a bid for funding to support the development of an evidence based intervention ‘The Together Programme’ for UK veterans partners was made and kindly awarded by The Royal British Legion in 2016. Based on review findings, two US psychoeducational programmes, SAFE and Homefront Strong (see glossary) which have been found to be effective and well accepted within the US military population were selected as the most appropriate base to devloop a UK specific injtervention to support military partners.
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.