Combat exposure, social support, and posttraumatic stress: A longitudinal test of the stress-buffering hypothesis among Veterans of the wars in Afghanistan and Iraq

Abstract: Purpose: While social support is widely viewed as a protective factor against posttraumatic stress disorder (PTSD), few studies have directly tested whether social support buffers the long-term effects of pre-existing PTSD symptoms or baseline combat exposure among Veterans (i.e., the stress-buffering hypothesis). Methods: To address this gap, the current study tested perceived social support as a moderator of the effects of baseline PTSD symptoms and combat exposure on PTSD symptoms at 10-year follow up in a sample of post-911 Veterans (N = 783). Results: Higher levels of combat exposure and baseline PTSD symptoms predicted elevated PTSD symptoms at 10-year follow-up. Perceived social support moderated these effects, such that the impacts of baseline symptoms and combat exposure were attenuated for Veterans with high levels of perceived support. However, buffering effects were less evident at higher levels of combat exposure and were not significant at very high levels of baseline PTSD symptoms. Conclusion: While findings are broadly consistent with the stress-buffering hypothesis, results of the present study suggest that the benefits of perceived social support may be less evident at higher levels of combat exposure. Results also offer preliminary evidence that perceived social support is less protective for Veterans with severe pre-existing symptoms.

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.