Mental health treatment experiences of commonwealth veterans from diverse ethnic backgrounds who have served in the UK military
Abstract: Introduction: Research has shown that the likelihood of ex-military personnel developing mental health problems following service is around one in five. Little is known about the barriers to accessing mental health in veterans from diverse ethnic backgrounds. This study aims to explore mental health treatment experiences of veterans from commonwealth countries and therefore diverse ethnic backgrounds. Methods: Semi-structured interviews were conducted over the telephone with veterans from commonwealth countries. Veterans were recruited from a mental health charity and were at various stages of treatment. Results: We interviewed six veterans who were from a diverse range of commonwealth countries including St Lucia, Gambia, Ghana, Fiji and South Africa. All had served in the UK army in combat roles. Our findings consisted of key themes: (1) feeling that they are treated differently, (2) they felt as though they were unheard when reaching out for help, (3) systemic pressures such as financial difficulties, missed opportunities and lack of insight about mental health and (4) the importance of involving the wider community in care. Conclusion: Our findings highlight some distinct barriers to mental health treatment that commonwealth veterans experience. The themes reported by the participants appear to suggest they had experience signs of institutional racism. Suggesting the importance of highlighting these issues, and to help overcome these potential barriers to accessing services. Given that commonwealth veterans involvement in the UK military is significant and increasing, the findings in this study should be used to support this population by implementing service provision and policy.
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.