Military sexual trauma-related posttraumatic stress disorder service-connection: Characteristics of claimants and award denial across gender, race, and compared to combat trauma

Abstract: The current study characterizes a cohort of veteran claims filed with the Veterans Benefits Administration for posttraumatic stress disorder secondary to experiencing military sexual trauma, compares posttraumatic stress disorder service-connection award denial for military sexual trauma-related claims versus combat-related claims, and examines military sexual trauma -related award denial across gender and race. We conducted analyses on a retrospective national cohort of veteran claims submitted and rated between October 2017-May 2022, including 102,409 combat-related claims and 31,803 military sexual trauma-related claims. Descriptive statistics were calculated, logistic regressions assessed denial of service-connection across stressor type and demographics, and odds ratios were calculated as effect sizes. Military sexual trauma-related claims were submitted primarily by White women Army veterans, and had higher odds of being denied than combat claims (27.6% vs 18.2%). When controlling for age, race, and gender, men veterans had a 1.78 times higher odds of having military sexual trauma-related claims denied compared to women veterans (36.6% vs. 25.4%), and Black veterans had a 1.39 times higher odds of having military sexual trauma-related claims denied compared to White veterans (32.4% vs. 25.3%). Three-fourths of military sexual trauma-related claims were awarded in this cohort. However, there were disparities in awarding of claims for men and Black veterans, which suggest the possibility of systemic barriers for veterans from underserved backgrounds and/or veterans who may underreport military sexual trauma.

Read the full article
Report a problem with this article

Related articles

  • More for Researchers

    Identifying opioid relapse during COVID-19 using natural language processing of nationwide Veterans Health Administration electronic medical record data

    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.