Comparing the profiles of UK and Australian military veterans supported by national treatment programmes for post-traumatic stress disorder (PTSD)
Abstract: Introduction: The aim of this study was to compare and contrast the profiles of military veterans seeking formal support for post-traumatic stress disorder (PTSD) in national treatment programmes in Australia and the UK to better understand the needs of this vulnerable population. Methods: Data were extracted from 1926 participants in these treatment programmes. This consisted of 1230 from the UK who had accessed support between 2014 and early 2019, and 696 from Australia who had accessed support between 2014 and 2018. Comparison was made between a number of sociodemographic characteristics (age, sex and educational achievements), military factors (branch of military, time since leaving the military and whether participants were early service leavers or not) and health outcomes (PTSD, anger, alcohol misuse, anxiety and depression). Results: Small differences were observed, with those in the UK cohort appearing to be younger, having lower educational achievement, being more likely to be ex-Army, having longer periods of enlistment in the military and taking longer to seek help. Further, minor differences were reported in health outcomes, with those in the UK cohort reporting more severe symptoms of PTSD, anger, anxiety and depression. Conclusions: Overall, the observed differences between the cohorts were modest, suggesting that treatment-seeking veterans from the Australian and UK cohorts reported similar presentations. This provides evidence to support the establishment of international cohorts of treatment-seeking veterans to improve knowledge within this field.
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.