Cohort differences in PTSD symptoms and military experiences: a life course perspective

Abstract: Background and Objectives: There have been major changes in military service over the past 50 years. Most research on posttraumatic stress disorder (PTSD) among combat Veterans comes from help-seeking Vietnam and WWII cohorts; results from more recent cohort comparisons are mixed. The present study addressed these gaps by exploring cohort differences among Vietnam, Persian Gulf, and Post-9/11 combat Veterans from a life course perspective.Research Design and Methods: We recruited community-dwelling combat and war zone Veterans (N = 167), primarily from Veterans' associations in Oregon from three cohorts: Vietnam, Persian Gulf, and Post-911. Online surveys assessed current PTSD symptoms, life course (demographics and cohort membership), and experiential variables (combat severity, appraisals of military service, homecoming, and social support).Results: Cohorts were comparable in demographics and war experiences. Step one of a hierarchical regression found that PTSD symptoms were higher among Veterans of color and those with lower incomes, R-2 = 0.37, p < .001. When cohort was added, Vietnam Veterans had higher symptoms than Post-9/11; income and race/ethnicity remained significant, Delta R-2 = 0.01, p = .13. The final model added experiential variables, Delta R-2 = 0.38, p < .001; cohort and income were no longer significant, although Veterans of color still reported higher symptoms. Those with more undesirable service appraisals and who sought social support had higher symptoms, while desirable appraisals were protective.Discussion and Implications: From a life course perspective, the particular war zone that Veterans served in was less important than demographics and both service and postservice experiences, suggesting generalizability of risk and protective factors, as well as treatment modalities, across cohorts.

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.