A comparison of probable post-traumatic stress disorder and alcohol consumption among active female members of the UK Police Service and UK Armed Forces
Abstract: Background: The British Police Service and Armed Forces are male-dominated occupations, characterised by frequent trauma exposure and intensive demands. Female police employees and military personnel may have unique experiences and face additional strains to their male counterparts. This analysis compared the levels of post-traumatic stress disorder (PTSD), hazardous/harmful alcohol consumption, and comorbidity in female police employees and military personnel. Methods: Police data were obtained from the Airwave Health Monitoring Study (N=14,145; 2007–2015) and military data from the Health and Wellbeing Cohort Study (N=928; phase 2: 2007–2009 and phase 3: 2014–2016). Multinomial/logistic regressions analysed sample diferences in probable PTSD, hazardous (14–35 units per week) and harmful (35+units per week) alcohol consumption, and comorbid problems. We compared covariate adjustment and entropy balancing (reweighting method controlling for the same covariates) approaches. Results: There were no signifcant diferences in probable PTSD (police: 3.74% vs military: 4.47%) or hazardous drinking (police: 19.20% vs military: 16.32%). Female military personnel showed signifcantly higher levels of harmful drinking (4.71%) than police employees (2.42%; Adjusted Odds Ratios [AOR]=2.26, 95% Confdence Intervals [CIs]=1.60–3.21), and comorbidity (1.87%) than police employees (1.00%, AOR=2.07, 95% CI=1.21–3.54). Entropy balancing and covariateadjustments obtained the same results. Conclusions: Comparable levels of probable PTSD were observed, which are slightly lower than estimates observed in the female general population. Future research should explore the reasons for this. However, female military personnel showed higher levels of harmful drinking than police employees, emphasising the need for alcohol interventions in military settings.
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.