Alcohol misuse, health-related behaviors, and burnout among clinical therapists in China during the early Covid-19 pandemic: A Nationwide survey.

Objectives: This study aimed to assess the extent of alcohol use and misuse among clinical therapists working in psychiatric hospitals in China during the early COVID-19 Pandemic, and to identify associated factors. Methods: An anonymous nationwide survey was conducted in 41 tertiary psychiatric hospitals. We collected demographic data as well as alcohol use using the Alcohol Use Disorders Identification Test-Consumption (AUDIT-C) and burnout using the Maslach Burnout Inventory Human Services Survey. Results: In total, 396 clinical therapists completed the survey, representing 89.0% of all potential participants we targeted. The mean age of participants was 33.8 years old, and more than three-quarters (77.5%) were female. Nearly two-fifths (39.1%) self-reported as current alcohol users. The overall prevalence of alcohol misuse was 6.6%. Nearly one-fifth (19.9%) reported symptoms of burnout with high emotional exhaustion in 46 (11.6%), and high depersonalization in 61 (15.4%). Multiple logistic regression showed alcohol use was associated with male gender (OR = 4.392; 95% CI =2.443-7.894), single marital status (OR = 1.652; 95% CI =0.970-2.814), smoking habit (OR = 3.847; 95%CI =1.160-12.758) and regular exercise (OR = 2.719; 95%CI =1.490-4.963). Alcohol misuse was associated with male gender (OR = 3.367; 95% CI =1.174-9.655), a lower education level (OR = 3.788; 95%CI =1.009-14.224), smoking habit (OR = 4.626; 95%CI =1.277-16.754) and high burnout (depersonalization, OR = 4.848; 95%CI =1.433-16.406). Conclusion: During the COVID-19 pandemic, clinical therapists' alcohol consumption did not increase significantly. Male gender, cigarette smoking, and burnout are associated with an increased risk of alcohol misuse among clinical therapists. Targeted intervention is needed when developing strategies to reduce alcohol misuse and improve clinical therapists' wellness and mental health.

Interpretable tourism volume forecasting with multivariate time series under the impact of COVID-19.

This study proposes a novel interpretable framework to forecast the daily tourism volume of Jiuzhaigou Valley, Huangshan Mountain, and Siguniang Mountain in China under the impact of COVID-19 by using multivariate time-series data, particularly historical tourism volume data, COVID-19 data, the Baidu index, and weather data. For the first time, epidemic-related search engine data is introduced for tourism demand forecasting. A new method named the composition leading search index-variational mode decomposition is proposed to process search engine data. Meanwhile, to overcome the problem of insufficient interpretability of existing tourism demand forecasting, a new model of DE-TFT interpretable tourism demand forecasting is proposed in this study, in which the hyperparameters of temporal fusion transformers (TFT) are optimized intelligently and efficiently based on the differential evolution algorithm. TFT is an attention-based deep learning model that combines high-performance forecasting with interpretable analysis of temporal dynamics, displaying excellent performance in forecasting research. The TFT model produces an interpretable tourism demand forecast output, including the importance ranking of different input variables and attention analysis at different time steps. Besides, the validity of the proposed forecasting framework is verified based on three cases. Interpretable experimental results show that the epidemic-related search engine data can well reflect the concerns of tourists about tourism during the COVID-19 epidemic.

Interpretable tourism volume forecasting with multivariate time series under the impact of COVID-19

This study proposes a novel interpretable framework to forecast the daily tourism volume of Jiuzhaigou Valley, Huangshan Mountain, and Siguniang Mountain in China under the impact of COVID-19 by using multivariate time-series data, particularly historical tourism volume data, COVID-19 data, the Baidu index, and weather data. For the first time, epidemic-related search engine data is introduced for tourism demand forecasting. A new method named the composition leading search index–variational mode decomposition is proposed to process search engine data. Meanwhile, to overcome the problem of insufficient interpretability of existing tourism demand forecasting, a new model of DE-TFT interpretable tourism demand forecasting is proposed in this study, in which the hyperparameters of temporal fusion transformers (TFT) are optimized intelligently and efficiently based on the differential evolution algorithm. TFT is an attention-based deep learning model that combines high-performance forecasting with interpretable analysis of temporal dynamics, displaying excellent performance in forecasting research. The TFT model produces an interpretable tourism demand forecast output, including the importance ranking of different input variables and attention analysis at different time steps. Besides, the validity of the proposed forecasting framework is verified based on three cases. Interpretable experimental results show that the epidemic-related search engine data can well reflect the concerns of tourists about tourism during the COVID-19 epidemic.

Alcohol Use and Misuse Among Chinese Psychiatrists During the Early COVID-19 Pandemic.

Aim: Survey alcohol use and misuse among Chinese psychiatrists during the Coronavirus diseases 2019 (COVID-19) pandemic. Methods: We conducted a large-scale, nationwide online survey of psychiatrists regarding their alcohol use during the pandemic. The Alcohol Use Disorder Identification Test-Concise (AUDIT-C) was used to assess alcohol use and misuse. Results: Of 3,815 psychiatrists who completed the survey, alcohol use and misus were 47.5% and 8.2%, respectively, and both were significantly higher in males. The majority (59%) reported no change in alcohol use during the pandemic, one-third (34.5%) reported a decrease, and 6.5% reported an increase. Alcohol misuse was associated with middle-age (OR = 1.418), male sex (OR = 5.089), Northeast China (OR = 1.507), cigarette-smoking (OR = 2.335), insomnia (OR = 1.660), and regular exercise (OR = 1.488). A master's degree (OR = 0.714) and confidence in clinical work (OR = 0.610) were associated with less alcohol misuse. Those who reported a decrease in alcohol use during the pandemic were more likely to be male (OR = 2.011), located in Northeast China (OR = 1.994), and feel confident in their clinical work (OR = 1.624). Increased alcohol use was significantly associated with insomnia (OR = 3.139). Conclusions: During the COVID-19 pandemic, alcohol use and misuse among Chinese psychiatrists declined. While males were more likely to misuse alcohol, they were also more likely to have reduced their intake. Age, location, and lifestyle factors also predicted alcohol use and misuse. Further examination of specific factors that reduced alcohol use and misuse may help guide public health efforts to sustain the lower rates beyond the pandemic.