Complementary/integrative healthcare utilization in US Gulf-War era veterans: Descriptive analyses based on deployment history, combat exposure, and Gulf War Illness
Abstract: Complementary and integrative health (CIH) approaches have gained empirical support and are increasingly being utilized among veterans to treat a myriad of conditions. A cluster of medically unexplained chronic symptoms including fatigue, headaches, joint pain, indigestion, insomnia, dizziness, respiratory disorders, and memory problems, often referred to as Gulf War Illness (GWI) prominently affect US Gulf War era (GWE) veterans, yet little is known about CIH use within this population. Using data collected as part of a larger study (n = 1153), we examined the influence of demographic characteristics, military experiences, and symptom severity on CIH utilization, and utilization differences between GWE veterans with and without GWI. Over half of the sample (58.5%) used at least one CIH modality in the past six months. Women veterans, white veterans, and veterans with higher levels of education were more likely to use CIH. GWE veterans with a GWI diagnosis and higher GWI symptom severity were more likely to use at least one CIH treatment in the past six months. Over three quarters (82.7%) of veterans who endorsed using CIH to treat GWI symptoms reported that it was helpful for their symptoms. Almost three quarters (71.5%) of veterans indicated that they would use at least one CIH approach if it was available at VA. Results provide a deeper understanding of the likelihood and characteristics of veterans utilizing CIH to treat health and GWI symptoms and may inform expansion of CIH modalities for GWE veterans, particularly those with GWI.
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.