Musculoskeletal Injuries and United States Army Readiness Part I: Overview of Injuries and their Strategic Impact
Abstract: Noncombat injuries (“injuries”) greatly impact soldier health and United States (U.S.) Army readiness; they are the leading cause of outpatient medical encounters (more than two million annually) among active component (AC) soldiers. Noncombat musculoskeletal injuries (“MSKIs”) may account for nearly 60% of soldiers’ limited duty days and 65% of soldiers who cannot deploy for medical reasons. Injuries primarily affect readiness through increased limited duty days, decreased deployability rates, and increased medical separation rates. MSKIs are also responsible for exorbitant medical costs to the U.S. government, including service-connected disability compensation. A significant subset of soldiers develops chronic pain or long-term disability after injury; this may increase their risk for chronic disease or secondary health deficits potentially associated with MSKIs. The authors will review trends in U.S. Army MSKI rates, summarize MSKI readiness-related impacts, and highlight the importance of standardizing surveillance approaches, including injury definitions used in injury surveillance.
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.