Outdoor light at night and depressive symptoms in male Veterans: A multicenter cross-sectional study in China
Abstract: Previous studies have indicated depression was associated with environmental exposures, but evidence is limited for the association between outdoor light at night (LAN) and depression. This study aims to examine the association between long-term outdoor LAN exposure and depressive symptoms using data from the Chinese Veteran Clinical Research platform. A total of 6445 male veterans were selected from 277 veteran communities in 18 cities of China during 2009‒2011. Depressive symptoms were evaluated using the Chinese version of the Center for Epidemiological Studies Depression scale. Outdoor LAN was estimated using the Global Radiance Calibrated Nighttime Lights data. The odds ratio and 95% confidence intervals of depressive symptoms at the high level of outdoor LAN exposure against the low level during the 1 years before the investigation was 1.49 (1.15, 1.92) with p-value for trend < 0.01, and those associated with per interquartile range increase in LAN exposure was 1.22 (1.06, 1.40).
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.