The association of deployment stressors and PTSD and depression symptoms in military mothers

Abstract: Previously deployed mothers report higher levels of posttraumatic stress and depression symptoms than non-deployed mothers. However, the specific stressors encountered during deployment that account for elevated clinical symptoms are not well understood including the impact of Military Sexual Trauma (MST) in the context of other deployment-related stressors. This study examined whether MST during deployment, degree of combat exposure, and length of deployment will each be associated with posttraumatic stress and depression symptoms among previously deployed mothers. Participants included 113 mothers (86.6% White) who had previously been deployed to Iraq or Afghanistan during the post 9/11 conflicts. Logistic regressions revealed that mothers who experienced MST during deployment were five times more likely to report clinically significant posttraumatic stress symptoms and two times more likely to report clinically significant depression symptoms. When controlling for MST, degree of combat exposure and length of deployment were not significantly associated with posttraumatic stress or depression symptoms. The present study fills an important gap in the literature and implicates MST as an important correlate of post-deployment functioning for military mothers. Findings from this study can be used to inform both prevention and intervention efforts.

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.