Abstract: This report explores the additional needs of Service families living with disability. The Forces Additional Needs and Disability Forum consists of serving Forces families who have a close loved one with an additional need or disability. Established in 1989, the FANDF have lived experience and knowledge of the challenges families face, helping to give a voice to Forces families with additional needs and disabilities. To mark the 30th anniversary of the creation of the forum, the FANDF committee conducted a survey of issues faced by serving Forces families with additional needs and disability. This is the largest known survey into MOD families with additional needs or disabilities in the UK Armed Forces. The resulting report, Families Fighting On, provides a platform for the families to explain in their own words their lived experiences and the issues they face on a daily basis.
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.