Long-term health outcomes of traumatic brain injury in Veterans

Abstract: Traumatic brain injury (TBI) has become one of the most prevalent battle wounds among veterans of the Iraq and Afghanistan wars. According to the Defense and Veterans Brain Injury Center, nearly 414 000 service members worldwide sustained TBI between 2000 and late 2019. Numerous psychiatric and medical consequences arise after TBI, including posttraumatic stress disorder, depression, suicidal thoughts, cognitive deficits, chronic pain, and unemployment. TBI severity appears to be linked to certain outcomes. Given this backdrop, the analysis by Stewart et al. provides a timely probe of associations between TBI severity and subsequent risk of brain cancer in this vulnerable patient cohort. The commentary of this study in question commentary concludes that the research provides meaningful data clarifying associations between combat-related TBI severity and subsequent brain cancer risk among post-9/11 veterans. It highlights that elucidating potential connections between battlefield trauma and longer-term health outcomes is imperative to inform prevention and care approaches for those who have served. In particular, because the PACT Act expands access and benefits for veterans affected by illness that may relate to hazardous exposures, understanding contributors is critical. In addition, an improved scientific understanding of how traumatic injury forces propagate through brain tissue to trigger neurodegeneration and other pathological conditions may shed further light on the observed links between TBI and elevated brain cancer rates.

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.