The association between long-term exposure to outdoor artificial light at night and poor sleep quality among Chinese veterans: A multi-city study
Abstract: A handful of previous studies have reported the association between exposure to outdoor artificial light at night (ALAN) and sleep problems. However, evidence for such association is limited in low- and middle-income countries. This study aimed to examine the association between outdoor ALAN exposure and sleep quality in veterans across different regions of China. Within the network of the Chinese Veteran Clinical Research Platform, we selected 7258 participants from 277 veteran communities in 18 cities across China during December 2009 and December 2011, using a multi-stage stratified cluster sampling strategy. Face-to-face interviews with the participants were conducted by trained investigators. We used the Pittsburgh Sleep Quality Index (PSQI) to assess participants' sleep quality. We defined poor sleep quality as a PSQI global score >7. The 3-year average exposure to outdoor ALAN prior to the baseline interview was calculated using satellite imagery data, according to participants’ geolocation information. The association of ALAN exposure with sleep quality was examined using the mixed-effects logistic regression models with natural cubic splines. The exposure-response curve for sleep quality associated with ALAN exposure was nonlinear, with a threshold value of 49.20 nW/cm2/sr for the 3-year average exposure to outdoor ALAN prior to the baseline interview. Higher ALAN exposure above the threshold was associated with increased risk of poor sleep quality. After adjusting for potential confounders, the odds ratios (and 95%CI, 95% confidence intervals) were 1.15 (0.97, 1.36) and 1.45 (1.17, 1.78) at the 75th and 95th percentiles of ALAN against the threshold. The association of ALAN exposure with poor sleep quality was more pronounced in veterans with depression than those without. Higher OR of poor sleep quality at the 75th percentile of ALAN against the threshold was observed in veterans with depression than those without [2.09 (1.16, 3.76) vs. 1.09 (0.92, 1.30)]. Long-term exposure to outdoor ALAN was associated with higher risk of poor sleep quality in Chinese veterans. Effective outdoor ALAN management may help to reduce the burden of sleep disorders in Chinese veterans.
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.