Non-fatal self-harm in Scottish military veterans: a retrospective cohort study of 57,000 veterans and 173,000 matched non-veterans (Scotland)
Abstract: Purpose: Although suicide risk in veterans has been widely studied, there is little information on the risk of non-fatal self-harm in this population. We used data from the Scottish Veterans Health Study to conduct an epidemiological analysis of self-harm in veterans, in comparison with people who have never served. Methods: We conducted a retrospective, 30-year cohort study of 56,205 veterans born 1945–1985, and 172,741 people with no record of military service, and used Cox proportional hazard models to examine the association between veteran status and cumulative risk of non-fatal self-harm, overall and stratified by birth cohort, sex and length of service. We also examined mental and physical comorbidities, and association of suicide with prior self-harm. Results: There were 1620 (2.90%) first episodes of self-harm in veterans, compared with 4212 (2.45%) in non-veterans. The difference was statistically significant overall (unadjusted HR 1.27, 95% CI 1.21–1.35, p < 0.001). The risk was highest in the oldest veterans, and in the early service leavers who failed to complete initial training (unadjusted HR 1.69, 95% CI 1.50–1.91, p < 0.001). The risk reduced with longer service and in the intermediate birth cohorts but has increased again in the youngest cohort. Conclusions: The highest risk of non-fatal self-harm was in veterans with the shortest service, especially those who did not complete training or minimum engagement, and in the oldest birth cohorts, whilst those who had served the longest were at reduced risk. The risk has increased again in the youngest veterans, and further study of this subgroup is indicated.
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.