At risk in uniform: investigating the risks of alcohol use disorders among army personnel

Abstract: Background: Military populations have higher prevalence rates of alcohol consumption and engage in heavier drinking compared to the general population. Alcohol Use Disorders (AUD) is a serious negative consequence of alcohol consumption often affecting military personnel. The objective of this study was to determine the risk factors of AUDs among male military personnel in the Sri Lanka Army. Methods: This was a case control study. The data were derived from a larger cross-sectional study conducted among male army personnel in active service (n = 1337) deployed in battalions in Sri Lanka. Cases scored 16 or higher from the Alcohol Use Disorders Identification Test (AUDIT) and were clinically confirmed to have Alcohol Use Disorders by the Consultant Psychiatrist (n = 90). Controls scored 0 for AUDIT (n = 180). Exposure data were collected from a self-administered questionnaire. Bivariate logistic regression analysis was conducted which controlled for confounding. Results: When controlled for confounding ever smoker (AOR 3.14, 95% CI 1.35-7.27), current cannabis user (AOR 12.29, 95% CI 4.86-31.08), mental distress (AOR 8.20, 95% CI 3.37-19.93), sleep disturbances (AOR 3.17, 95% CI 1.03-9.72) and sex with a commercial sex worker (AOR 4.06, 95% CI 1.31-12.52) had a higher risk of AUD. Conclusions: The Sri Lanka Army medical staff should focus on the risk factors for AUDs during clinical assessments. Health promotion efforts on AUD prevention need to be integrated with initiatives addressing smoking, cannabis use, high-risk sexual behaviours, mental well-being, and quality sleep.

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.

    Mental Health Peer reviewed