Marriage and divorce after military deployment to Afghanistan: A matched cohort study from Sweden
Abstract: Aim: To investigate the probability of marriage and divorce among Swedish military veterans deployed to Afghanistan relative to non-deployed matched comparators. Study design and setting. Participants: Military veterans were identified through Swedish military personnel registers regarding foreign deployments, and comparators from the Military Service Conscription Register (1969–2013). Of 1,882,411 eligible conscripts, 7041 had served in Afghanistan at some point in time between 2002 and 2013. To each military veteran, up to 5 non-deployed comparators who underwent conscription were matched by age, sex, psychological assessment, cognitive ability, psychiatric history and social characteristics. After matching there were 4896 (82%) unmarried and 1069 (18%) married deployed military veterans. The main outcome was marriage or divorce after deployment to Afghanistan. Data on marital status were retrieved from Statistics Sweden until December 31, 2014. Results: During a median follow-up of 4.1 years after deployment of married individuals, 124 divorces were observed among deployed military veterans and 399 in the matched non-deployed comparator cohort (277 vs. 178 per 10,000 person-years; adjusted hazard ratio 1.61, 95%CI 1.31–1.97). During a median follow-up of 4.7 years after deployment in the unmarried cohort, 827 new marriages were observed among deployed military veterans and 4363 in the matched non-deployed comparators cohort (399 vs. 444 per 10,000 person-years; adjusted hazard ratio 0.89, 95%CI 0.83–0.96). Conclusion: Military veterans were more likely to divorce and less likely to marry after deployment compared with matched non-deployed comparators.
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.