Purpose in life protects against the development of suicidal thoughts and behaviors in U.S. Veterans without a history of suicidality: a 10-year, nationally representative, longitudinal study
Abstract: To determine the incidence of suicidal ideation and suicide attempts (STBs) in veterans without an endorsed history of STBs and identify baseline predictors of these outcomes over a 10-year period. Population-based prospective cohort study of 2307 US military veterans using five waves of the 2011–2021 National Health and Resilience in Veterans Study. Baseline data were collected in 2011, with follow-up assessments conducted 2-(2013), 4-(2015), 7-(2018), and 10-years (2021) later.In total, 10.1 % (N = 203) of veterans endorsed incident suicidal ideation (SI) over the 10-year period and 3.0 % (N = 55) endorsed an incident suicide attempt (SA). Multivariable regression analyses revealed the following baseline predictors of incident SI: lower annual household income, current posttraumatic stress disorder, current alcohol use disorder (AUD), disability with activities of daily living (i.e., ADLs) or instrumental activities of daily living (i.e., IADLs), lower perceived social support, lower community integration, and lower purpose in life. Current AUD, greater cumulative trauma burden, and lower purpose in life at baseline were predictive of incident SA. Relative importance analyses revealed that lower purpose in life was the strongest predictor of both incident SI and SA. Psychosocial determinants of health, such as purpose in life, may be more reliable predictors of incident suicidal thoughts and behaviors than traditional risk factors (e.g., psychiatric distress; history of SA) in those without a history of STBs. Evidence-based interventions that facilitate purpose in life and feelings of connectedness and belonging should be examined as possible treatments for STBs.
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.