Gender differences in rates and predictors of individual psychotherapy initiation and completion among Veterans Health Administration users recently diagnosed with PTSD
Abstract: Objective: Most veterans with posttraumatic stress disorder (PTSD) who receive care from the Veterans Health Administration (VHA) do not receive individual psychotherapy. The purpose of this study was to explore gender differences in initiation and completion of a sufficient course (defined as attending 8 or more sessions) of individual psychotherapy among male and female VHA users recently diagnosed with PTSD. Method: Participants (N 7,218) were veterans in a prospective national cohort survey of VHA users diagnosed with PTSD; oversampling was used to increase representation of women and minority veterans. Results: Forty-two percent of the sample (40.1% of men, 52.3% of women) initiated individual psychotherapy within 6 months of their index PTSD diagnosis. Of those who initiated, 12.1% (10.8% of men, 17.7% of women) completed a sufficient course of individual psychotherapy. Women were generally more likely than men to initiate individual psychotherapy. However, we found an interaction between gender and age, such that younger men were more likely to initiate psychotherapy than older men; age was not significantly associated with initiation among women. Regarding completion of individual psychotherapy, an interaction between gender and beliefs about psychotherapy was found, such that men were less likely to complete individual psychotherapy when they held more negative beliefs about psychotherapy; these beliefs did not significantly impact female veterans’ likelihood of completing psychotherapy. Conclusions: Overall, while female veterans are more likely than male veterans with PTSD to initiate individual psychotherapy, rates of initiation and completion of individual psychotherapy for both genders remain relatively low. Interventions are needed to increase engagement in individual psychotherapy, particularly for male veterans with PTSD.
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.