Examining Veterans’ Interactions with the UK Social Security System through a Trauma-Informed Lens
Abstract: This paper uses the principles of trauma-informed care – safety, collaboration, choice, trustworthiness, and respect – to reflect on the quality of veterans’ treatment within the UK social security system. Drawing upon new data from qualitative longitudinal research with veterans in four geographical locations across England, UK, it explores their experiences within the social security system, highlighting specific issues relating to their interactions with the Work Capability Assessment (WCA) but also the conditionality inherent within the UK benefits system. Overall, it is evident that there is a lack of understanding of the impact of trauma on people’s psychosocial functioning and, as a result, veterans are treated in ways which are variously perceived as disrespectful, unfair or disempowering and in some cases exacerbate existing mental health problems. We propose that the application of traumainformed care principles to the UK social security system could improve interactions within this system and avoid re-traumatising those experiencing on-going or unresolved trauma. The paradigm of trauma-informed care has been used internationally to examine health, homelessness, prison and childcare services, but ours is the first exploration of its application to the delivery of social security.
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.