Hysterectomy and Bilateral Salpingo-Oophorectomy: Variations by History of Military Service and Birth Cohort
Abstract: Introduction: Little is known about hysterectomy and bilateral salpingo-oophorectomy (BSO), which are associated with both health risks and benefits, among women Veterans. Purpose of the Study: To compare the prevalence of hysterectomy with or without BSO, and early hysterectomy, between postmenopausal Veterans and non-Veterans. Design and Methods: We used baseline data from the Women’s Health Initiative Clinical Trial and Observational Study. Multinomial logistic regression models examined differences in the prevalence of hysterectomy (neither hysterectomy nor BSO, hysterectomy without BSO, and hysterectomy with BSO) between Veterans and non-Veterans. Generalized linear models were used to determine whether early hysterectomy (before age 40) differed between Veterans and non-Veterans. Analyses were stratified by birth cohort (<65, ≥65 years at enrollment). Results: The unadjusted prevalence of hysterectomy without BSO was similar among Veterans and non-Veterans in both birth cohorts (<65: 22% vs 21%; ≥65: 22% vs 21%). The unadjusted prevalence of hysterectomy with BSO was equivalent among Veterans and non-Veterans in the >65 cohort (21%), but higher among Veterans in the <65 cohort (22% vs 19%). In adjusted analyses, although no differences were observed in the >65 cohort, Veterans in the <65 cohort had higher odds of hysterectomy without BSO (odds ratio [OR] 1.18, 95% confidence interval [CI] 1.03, 1.36) and with BSO (OR 1.26, 95% CI 1.10, 1.45), as well as elevated risk of early hysterectomy (relative risk 1.32, 95% CI 1.19, 1.47), compared with non-Veterans. Implications: Aging women Veterans may have higher prevalence of hysterectomy and BSO than non-Veterans. This information contributes to understanding the health needs and risks of women Veterans and can inform clinical practice and policy for this population.
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.