Psychometric Properties of the PTSD Checklist for DSM-5 in Treatment-Seeking Black Veterans
Abstract: Objective: Despite widespread use of the posttraumatic stress disorder (PTSD) Checklist for Diagnostic and Statistical Manual of Mental Disorders—fifth edition (PCL-5) across various trauma-exposed populations, little is known about the psychometric properties of the instrument in certain ethnic minority groups with increased risk of trauma exposure, including Black veterans. To this end, the current study examined the internal consistency of the PCL-5, convergent validity using correlations between the PCL-5 and another measure of PTSD, and discriminant validity using correlations between the PCL-5 and other commonly occurring psychiatric symptoms, including depression as well as alcohol and substance misuse. Method: The sample was composed of 327 Black veterans (84% male, Mage = 51.87, SD = 13.72) presenting to a PTSD specialty clinic at a large Veterans Affairs hospital in the Midwest United States to receive psychological services. In addition to a diagnostic interview, veterans were asked to complete a brief battery of self-report questionnaires to assist with diagnostic clarification and treatment planning. Results: The PCL-5 demonstrated excellent internal consistency. Furthermore, the PCL-5 was significantly and positively correlated with PTSD diagnostic status, suggesting evidence of convergent validity. Finally, the PCL-5 was strongly correlated with symptoms of depression and moderately correlated with alcohol and substance misuse. Conclusions: Findings suggest that the PCL-5 is a psychometrically sound measure to assess PTSD symptoms among Black veterans. Considering the brevity of PCL-5 administration, clinicians should consider utilizing this and other psychometric tests in clinical care to reduce disparities in health equity among Black patients. (PsycInfo Database Record (c) 2023 APA, all rights reserved)
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.