Long-term trajectories of depressive symptoms by military affiliation

Abstract: Objectives: Using a national sample of Americans, this study estimated and compared patterns of depressive symptom trajectories stratified by military service. This study then examined associations between sociodemographic factors theorized to shape entry into military service and trajectory patterns. Method: Data came from the National Longitudinal Study of Adolescent to Adult Health, a nationally representative study that followed participants from adolescence (1994-1995) through midlife (2016-2018). Latent growth mixture modeling was used to estimate depressive symptom trajectories among civilians (n=17,644) and participants who served in the military (n=1,266). Associations between trajectory membership and sociodemographic factors were tested with multinomial regression. Results: Trajectories were best represented by 4-class linear models. “Low” was the largest class, representing 74.4% of civilians and 70.4% of those who served. The second largest class, “low then increasing,” was comprised of 13.6% of civilians and 19.6% of service members. The third smallest class, “high then decreasing” class, included 8.8% of civilians and 4.5% of service members. An “increasing” class emerged with high depressive symptoms by midlife, comprised of 3.2% of civilians and 5.5% of those who served. Gender and family structure had robust associations with trajectory membership. Discussion: A larger percentage of those who served were in the “increasing” trajectory characterized by concerningly high depressive symptoms by midlife, underscoring the need for continued screening in depressive symptoms throughout the life course. Associations between family structure and gender on depressive symptoms support calls for conceptualizing family structure as a social determinant of health and continued investment in women’s health.

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.