Military experience strongly influences post-service eating behavior and BMI status in American veterans
Abstract: In-depth interviews were conducted with veterans (n = 64) with an average age of 57 years to investigate eating behavior and food insecurity during military service and examine if it affects post-war eating behavior, and if this contributes to the high incidence of obesity found in veterans. About half of the subjects served during the Vietnam War, while smaller numbers served in WWII, the Korean War, Desert Storm, or other conflicts. The mean BMI was 30.5 ± 6.7 kg/m2. Only 12.5% of participants were classified as normal weight, while 37.5% were overweight, 46.9% were obese, and 3.1% were classified as excessively obese. Five major themes were identified including, (a) military service impacts soldier’s food environment, (b) food insecurity influences eating behavior and food choices, (c) military impacts weight status during and post-service, (d) military service has health consequences, and (e) post-service re-adjustment solutions are needed to ease re-entry into civilian life.
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.