Chronic pain: The Canadian Armed Forces Members and Veterans Mental Health Follow-up Survey
Abstract: Chronic pain is pain that has lasted three to six months or longer. Many people with back pain, migraines, arthritis, and gastrointestinal conditions such as irritable bowel syndrome, have chronic pain. The experience of chronic pain may have various negative effects on individuals. Pain may prevent a person from doing everyday tasks such as household chores. Chronic pain is an understudied area of research among military members and Veterans. Thus, the authors explored chronic pain in the Canadian military population. This study looked at the differences in chronic pain conditions among serving personnel and Veterans. The results show that a majority of serving members and Veterans experience chronic pain conditions. Veterans also reported experiencing more chronic pain than serving members.
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.