Menstrual Function, Eating Disorders, Low Energy Availability, and Musculoskeletal Injuries in British Servicewomen
Abstract: This study aimed to investigate associations between menstrual function, eating disorders, and risk of low energy availability with musculoskeletal injuries in British servicewomen. All women younger than 45 yr in the UK Armed Forces were invited to complete a survey about menstrual function, eating behaviors, exercise behaviors, and injury history. A total of 3022 women participated; 2% had a bone stress injury in the last 12 months, 20% had ever had a bone stress injury, 40% had a time-loss musculoskeletal injury in the last 12 months, and 11% were medically downgraded for a musculoskeletal injury. Menstrual disturbances (oligomenorrhea/amenorrhea, history of amenorrhea, and delayed menarche) were not associated with injury. Women at high risk of disordered eating (Female Athlete Screening Tool score >94) were at higher risk of history of a bone stress injury (odds ratio (OR; 95% confidence interval (CI)), 2.29 (1.67–3.14); P < 0.001) and time-loss injury in the last 12 months (OR (95% CI), 1.56 (1.21–2.03); P < 0.001) than women at low risk of disordered eating. Women at high risk of low energy availability (Low Energy Availability in Females Questionnaire score ≥8) were at higher risk of bone stress injury in the last 12 months (OR (95% CI), 3.62 (2.07–6.49); P < 0.001), history of a bone stress injury (OR (95% CI), 2.08 (1.66–2.59); P < 0.001), a time-loss injury in the last 12 months (OR (95% CI), 9.69 (7.90–11.9); P < 0.001), and being medically downgraded with an injury (OR (95% CI), 3.78 (2.84–5.04); P < 0.001) than women at low risk of low energy availability. Eating disorders and risk of low energy availability provide targets for protecting against musculoskeletal injuries in servicewomen.
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.