Perceived stigma and barriers to care in UK Armed Forces personnel and veterans with and without probable mental disorders
Abstract: Background: Previous studies have found that perceptions of mental health related stigma can negatively impact help-seeking, particularly in military samples. Moreover, perceptions of stigma and barriers to care can vary between individuals with different psychiatric disorders. The aim of this study was to examine whether perceptions of stigma and barriers to care differed in a UK military sample between those with and without a current likely mental health diagnosis. Method: Structured telephone interviews were carried out with 1432 service personnel and veterans who reported recent subjective mental ill health in the last 3 years. Participants completed self-reported measures relating to perceived stigma, barriers to care and psychological wellbeing. Results: Those meeting criteria for probable common mental disorders (CMD) and PTSD were significantly more likely to report concerns relating to perceived and internalised stigma and barriers to care compared to participants without a likely mental disorder. Compared to individuals with likely CMD and alcohol misuse, those with probable PTSD reported higher levels of stigma-related concerns and barriers to care – although this difference was not significantly different. Conclusions: These results indicate that perceptions of stigma continue to exist in UK serving personnel and military veterans with current probable mental disorders. Efforts to address particular concerns (e.g. being seen as weak; difficulty accessing appointments) may be worthwhile and, ultimately, lead to improvements in military personnel and veteran wellbeing.
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.