Barriers to mental health seeking in army aviation
This research explores barriers to mental health seeking, self-reported symptoms, and perspectives on self-help mental-wellness options among U.S. Army Aviation Personnel. Safe aviation operations require constant focus and mental clarity. These requirements expand when considering the implications and added stress of military operations, especially in combat scenarios. Yet, recent studies demonstrate that aviation personnel avoid seeking healthcare due to fears of losing their medical certification. This report provides preliminary results from the first known study on barriers to mental health seeking among U.S. Army aviation personnel. Utilizing an anonymous survey instrument, facilitated primarily through Social Media recruiting of current and former Army aircrew members, air traffic controllers, and uncrewed aircraft, these data provide insight into the influence of attitudinal, instrumental, and stigma barriers among the more than 300 respondents. Additionally, the study provides perspectives on the use of three evidence-based, self-help, mental-wellness options both with and without explicit FAA and DoD approval. Finally, survey results provide details on self-reported anxiety, depression, and posttraumatic stress symptoms among Army Aviation members. These results, along with the forthcoming final analysis, provide insight into the mental wellness of aviation operations on military personnel, barriers they experience when considering mental health care, and possible options for early intervention strategies.
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.