The association between perceived social support and mental health in combat-injured and uninjured male UK (ex-)military personnel: A cross-sectional study

Abstract: Social support is a key determinant of mental health across multiple populations and contexts. Little is known about social support among UK (ex-)military personnel, especially those with combat injuries following deployment to Afghanistan. This study aimed to investigate the level of perceived social support and its associations with mental health among injured and uninjured UK (ex-)military personnel. An analysis of baseline data from the Armed Services Trauma Rehabilitation Outcome (ADVANCE) prospective cohort study was performed. A representative sample of male UK combat-injured personnel was compared with a frequency-matched sample of uninjured personnel. Validated questionnaires were completed including the Multidimensional Scale of Perceived Social Support (MSPSS). MSPSS score was transformed using linear splines with a knot at ≥ 55. Multivariable logistic regression analyses examined associations between perceived social support and mental health. In total, 521 combat-injured participants (137 with amputations) and 515 uninjured participants were included. Median MSPSS score was 65 (interquartile range [IQR] 54-74). Injured and uninjured participants reported similar MSPSS scores, as did those injured with amputations, and non-amputation injured participants. For each one unit increase in MSPSS score (for scores ≥55), the odds of post-traumatic stress disorder decreased (adjusted odds ratio [AOR] 0.93, 95% confidence interval [CI] 0.91 to 0.96). No such association was found with MSPSS scores below 55 (AOR 0.99, 95% CI 0.97 to 1.01). Similar results were observed for depression and anxiety. Perceived social support may be a target for intervention within this population, irrespective of injury status.

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.