Do Alcohol Misuse, Service Utilisation, and Demographic Characteristics Differ between UK Veterans and Members of the General Public Attending an NHS General Hospital?
Abstract: The aim of this paper was to provide insights into alcohol misuse within UK veterans to inform as to whether their presentations differ from the general public. This was done by exploring differences in the severity of alcohol misuse between UK veterans and the general public admitted to a general NHS hospital over an 18 month period using retrospective data. All patients admitted to the hospital were screened for alcohol misuse. Those deemed as experiencing problems were referred for specialist nurse-led support. A total of 2331 individuals were referred for this supported and administered with a standardised assessment that included measures of the severity of alcohol difficulties (AUDIT), dependency levels (LDQ), and assessed for the presence of withdrawal symptoms (CIWA-Ar). In addition, information was collected on service utilisation, referral category (medical or mental health), other substance misuse, and demographic characteristics. No differences were found between the severity of reported alcohol difficulties between veterans and non-veterans. Evidence was found to suggest that veterans were more likely to be referred for support with alcohol difficulties at an older age and to be admitted to hospital for longer periods of time. This could have considerable cost implications for the NHS. It was more common for veterans to present at hospital with physical health difficulties prior to being referred for support for alcohol.
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.