Adjustment Disorder in the Armed Forces: a Systematic Review
Abstract: In the UK military, adjustment disorder (AjD) is reported as one of the most diagnosed mental disorders, alongside depression, in personnel presenting to mental health services. Despite this, little is understood about what may predict AjD, common treatment or outcomes for this population. The systematic review aimed to summarise existing research for AjD in Armed Forces (AF) populations, including prevalence and risk factors, and to outline clinical and occupational outcomes. A literature search was conducted in December 2020 to identify research that investigated AjD within an AF population (serving or veteran) following the PRISMA guidelines. Eighty-three studies were included in the review. The AjD prevalence estimates in AF populations with a mental disorder was considerably higher for serving AF personnel (34.9%) compared to veterans (12.8%). Childhood adversities were identified as a risk factor for AjD. AjD was found to increase the risk of suicidal ideation, with one study reporting a risk ratio of 4.70 (95% Confidence Interval: 3.50–6.20). Talking therapies were the most common treatment for AjD, however none reported on treatment effectiveness. This review found that AjD was commonly reported across international AF. Despite heterogeneity in the results, the review identifies several literature gaps.
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.