Ageing, limb-loss, and military veterans: A systematic review of the literature
Abstract: The impact of losing a limb in military service extends well beyond initial recovery and rehabilitation, with long-term consequences and challenges requiring healthcare commitments across the life-course. This paper presents a systematic review of the current state of knowledge regarding the long-term impact of ageing and limb-loss in military veterans. Key databases were systematically searched including: ASSIA, CINAHL, Cochrane Library, Medline, Web of Science, PsycArticles/PsychInfo, ProQuest Psychology and ProQuest Sociology Journals, and SPORTSDiscus. Empirical studies which focused on the long-term impact of limb-loss and/or healthcare requirements in veterans were included. The search process revealed 30 papers relevant for inclusion. These papers focused broadly on four themes: 1) long-term health outcomes, prosthetics use, and quality of life; 2) long-term 2 psycho-social adaptation and coping with limb-loss; 3) disability and identity, and; 4) estimating the long-term costs of care and prosthetic provision. Findings present a compelling case for ensuring the long-term care needs and costs of rehabilitation for older limbless veterans are met. A dearth of information on the lived experience of limb-loss, and the needs of veterans’ families calls for further research to address these important issues.
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.