A scoping review of military and Veteran families within international suicidality and suicide prevention research
Abstract: The impacts of suicidality on families are well known, which is particularly relevant in at-risk populations, such as active duty military personnel and Veteran communities. This scoping review describes how military and Veteran families have been conceptualized within suicide prevention research. A systematic, multi-database search was conducted, and 4,835 studies were screened. All included studies underwent quality assessment. Bibliographic, participant, methodological, and family-relevant data was extracted and descriptively analyzed into Factors, Actors, and Impacts. In total, 51 studies (2007 – 2021) were included. Most studies focused on suicidality rather than suicide prevention. Factor studies described family constructs as a suicidality risk or protective factor for military personnel or Veterans. Actor studies described families’ roles or responsibilities to act in relation to the suicidality of military personnel or Veterans. Impacts studies described the impacts of suicidality on military and Veteran family members. The search was limited to English language studies. There were few studies on suicide prevention interventions for or including military and Veteran family members. Family was typically considered peripheral to the military personnel or Veteran experiencing suicidality. However, there was also emerging evidence of suicidality and its consequences in military-connected family 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.