Background With the rapid development of the food delivery industry, take-away deliverymen become an essential component of urban logistics. However, high labor intensity, unstable income, and extended working hours place them at considerable risk of occupational burnout. Available studies have paid insufficient attention to the mental health of this population, and effective predictive or preventive approaches remain limited. Objective To understand the status of occupational burnout among take-away deliverymen, identify influencing factors based on restricted cubic spline analysis, and develop a predictive model to provide a theoretical basis for improving their mental health. Methods A cross-sectional survey was conducted among full-time take-away deliverymen registered to the "Ele.me" and "Meituan" platforms in Hangzhou between September 1 and November 30, 2024, using both online and offline approaches. A questionnaire covered sociodemographic, household, and occupational information, and the Maslach Burnout Inventory–General Survey were used in this survey. Univariate analyses and logistic regression were used to identify factors associated with burnout and to construct a predictive model. Model performance was evaluated using receiver operating characteristic (ROC) curve and calibration curve. Furthermore, restricted cubic spline was used to further explore the relationship between age, working hours, and occupational burnout. Results Among the 1192 participants, 735 (61.66%) experienced occupational burnout. The logistic regression model indicated that older age (OR=1.021, P=0.010), educational attainment of college level or above compared to junior high school or below, (OR=2.837, P<0.001), and longer daily working hours (OR=1.081, P=0.001) were positively associated with occupational burnout. In contrast, work experience of 3−5 years (OR=0.666, P=0.016) and more than 5 years (OR=0.382, P<0.001) compared to those with <3 years, as well as providing three social insurances (OR=0.655, P=0.006) or five social insurances (OR=0.478, P<0.001) compared to those without any social insurances coverage, were negatively associated with occupational burnout. The predictive model based on these variables achieved an area under the curve (AUC) of 0.783 (95% CI: 0.756~0.809), and good agreement between calibration curves. The restricted cubic spline analysis revealed that the risk of occupational burnout was highest among delivery workers aged 33−46 years. Those with daily working hours between 3.5 and 8.1 h had a lower risk, while the risk significantly increased when working hours exceeded 8.1 h per day. Conclusion The prevalence of reporting occupational burnout among take-away deliverymen in Hangzhou is high. The developed predictive model provides valuable insights for the prevention and control of burnout in this population. Measures such as optimizing social security systems, rational work schedule allocation, and enhancing psychological support may help reduce occupational burnout among take-away deliverymen.

Objective: To investigate the epidemiological characteristics of varicella outbreaks in primary and middle schools, and to establish a risk predictive model, so as to provide scientific guidance for the prevention of varicella outbreaks in schools. Methods: Based on a nested case-control study, primary and middle schools in 4 districts of Shanghai (Yangpu District and Jingan District) and Hangzhou (Xiaoshan District and Linping District) from January to December 2023 were selected to observe the status of varicella outbreaks. Associated factors of varicella outbreaks were investigated and used for establishing the predictive model, which was evaluated by the Hosmer-Lemeshow(H-L) goodness of fit test, receiver operating characteristic (ROC) curve, Calibration curve, decision curve analysis (DCA). Results: A total of 98 varicella outbreaks were included, with 195 schools without varicella outbreaks during the same period as controls. Eight factors, including the availability of warm water in restroom, availability of hand soap in restroom, average class size, duration of student attendance at school per day, presence of a fulltime school doctor, hesitancy of the school principal towards varicella vaccination, and rates of first and second doses of varicella vaccination, were identified as potential factors for school varicella outbreaks, with statistically significant differences (χ2/Z=10.01, 20.49, 17.43, 9.74, 32.17, 6.60, 2.20, 3.39, P<0.05). The 8 variables above were employed to construct a risk predictive model, and Hosmer-Lemeshow goodness of fit test yielded a χ2 value of 5.863 (P>0.05); the area under the ROC curve (AUC) was 0.846 (95%CI=0.799-0.893); Calibration curve analysis indicated good consistency between predicted and actual values of the model. DCA demonstrated favorable predictive performance of the model over a wide range. Conclusions The predictive model for school varicella outbreaks demonstrates satisfactory accuracy and efficacy. It suggested to make good use of this prediction model and take relevant measures to reduce the risk of varicella transmission in schools.

Bipolar disorder with mixed features is currently a prevalent clinical phenomenon,with patients exhibiting a heightened risk of suicidal behaviour,suboptimal therapeutic outcomes,poor prognostic indicators and frequently accompanied by significant impairment in psychosocial functioning.The current body of research on the pathological mechanisms of bipolar disorder with mixed features has proposed several mechanisms including neurotransmitter imbalance leading to abnormal emotion regulation,hypothalamic-pituitary-adrenal(HPA)dysfunction triggering an excessive stress response,circadian rhythm disorder affecting the sleep pattern,abnormal functional connectivity of the cerebral cortex and limbic system,independent and multidirectional changes in four dimensions,such as behaviour,cognition,mood,and sleep.Further studies are required to integrate the results of neurobiology,imaging,genetics and other multidisciplinary fields in order to provide more precise targets for the diagnosis and treatment of bipolar disorder with mixed features.