Lifetime Prevalence and Comorbidity of Mental Disorders in the Two-wave 2002-2018 Canadian Armed Forces Members and Veterans Mental Health Follow-up Survey (CAFVMHS)
Abstract: The current study used the Canadian Armed Forces Members and Veterans Mental Health Follow-up Survey (CAFVMHS) to (1) examine the incidence and prevalence of mental disorders and (2) estimate the comorbidity of mental disorders over the follow-up period. The CAFVMHS (2018) is a longitudinal study with two time points of assessment. The sample is comprised of 2,941 Canadian Forces members and veterans who participated in the 2002 Canadian Community Health Survey: Canadian Forces Supplement. The World Health Organization Composite International Diagnostic Interview (WHO-CIDI) was utilized to diagnose Diagnostic and Statistical Manual-IV post-traumatic stress disorder (PTSD), major depressive episode (MDE), generalized anxiety disorder, social anxiety disorder (SAD), and alcohol abuse and dependence. Self-report health professional diagnoses were assessed for attention deficit hyperactivity disorder (ADHD), mania, obsessive compulsive disorder (OCD), and personality disorder. We established weighted prevalence of mental disorders and examined the association between mental disorders using logistic regression. In 2018, lifetime prevalence of any WHO-CIDI-based or self-reported mental disorder was 58.1%. Lifetime prevalence of any mood or anxiety disorder or PTSD was 54.0% in 2018. MDE (39.9%), SAD (25.7%), and PTSD (21.4%) were the most common mental disorders. There was a substantial increase in new onset or recurrence/persistence of mental disorders between the two measurement points (16-year assessment gap); 2002–2018 period prevalences were 43.5% for mood and anxiety disorder and 16.8% for alcohol abuse or dependence. The prevalence of self-reported ADHD, OCD, any personality disorder, and mania were 3.3%, 3.0%, 0.8%, and 0.8%, respectively. Comorbidity between mental disorders increased over the follow-up. This study demonstrates a high burden of mental disorders among a large Canadian military and veteran cohort. These findings underscore the importance of prevention and intervention strategies to reduce the burden of mental disorders and alcohol use disorders in these populations.
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.