[Comorbidity of anxiety symptoms and depression symptoms among middle and high school students in Zhejiang Province].

Objective: To analyze the epidemiological patterns of comorbidity of anxiety symptoms and depression symptoms among middle and high school students in Zhejiang Province and to provide evidence for making strategy and evaluation of intervention. Methods: Through a multi-stage sampling design, 28 043 students from 376 schools in 30 counties/districts were recruited and surveyed using anonymous self-administered questionnaires in classrooms under the supervision of trained staff between April and June 2022. Anxiety symptoms were assessed using generalized Anxiety Disorders 7-Item Scale, and depression symptoms were assessed using Patient Health Questionnaire 9-Item Depression Scale. A total of 27 004 students were included in the final analysis. Results: The overall prevalence of comorbidity of anxiety symptoms and depression symptoms was 11.54% (95%CI: 10.90%-12.19%), higher among girls (15.42%, 95%CI: 14.47%- 16.38%) than boys (8.05%, 95%CI: 7.43%-8.67%) (P<0.001), higher among students living in rural areas (12.35%, 95%CI: 11.49%-13.22%) than those in urban areas (10.06%, 95%CI: 9.27%-10.86%) (P<0.001). The prevalence of comorbidity of students attending middle school, academic high school, and vocational high school was 11.73% (95%CI: 10.82%-12.64%), 12.49% (95%CI: 11.20%- 13.79%), and 9.98% (95%CI: 8.68%-11.27%), respectively (P=0.025). The prevalence of comorbidity was higher among students in divorced/widow/separated families (16.64%, 95%CI: 14.86%-18.43%) than those in intact families (10.82%, 95%CI: 10.14%-11.50%) (P<0.001). The wealthier the families, the lower the prevalence of comorbidity (P<0.001). The prevalence of comorbidity was higher among cigarettes smokers in the past 30 days (21.70%,95%CI: 18.24%- 25.16%) than non-smokers (11.13%, 95%CI: 10.51%-11.76%), higher among alcohol drinkers in the past 30 days (19.36%, 95%CI: 17.58%-21.14%) than non-drinkers (10.05%, 95%CI: 9.43%- 10.68%), higher among students engaging in physical fight in the past 12 months (18.42%, 95%CI: 16.75%-20.09%) than those without physical fight (10.45%, 95%CI: 9.81%-11.10%). The more frequently students engaged in physical exercise, the lower the prevalence of comorbidity (P<0.001). The poorer the academic performance self-reported, the higher the prevalence of comorbidity (P<0.001). Conclusion: More than one-tenth of middle and high school students in Zhejiang Province have comorbidity of anxiety symptoms and depression symptoms, and more attention should be paid to adolescent mental health.

[Spatial autocorrelation and related factors of stroke mortality in Zhejiang Province based on spatial panel model in 2015-2020].

Objective: To explore the spatial autocorrelation and macro influencing factors of stroke mortality in Zhejiang Province in 2015-2020 and provide a scientific basis for stroke prevention and control strategy. Methods: The data on stroke death were obtained from Zhejiang Chronic Disease Surveillance System. The spatial distribution of stroke mortality was explored by mapping and spatial autocorrelation analysis. The spatial panel model analyzed the correlation between stroke mortality and socioeconomic and healthcare factors. Results: From 2015 to 2020, the average stroke mortality was 68.38/100 thousand. The standard mortality of stroke was high in the areas of east and low in the west, high in the south and low in the north. Moreover, positive spatial autocorrelation was observed (Moran's I=0.274-0.390, P<0.001). Standard mortality of stroke was negatively associated with per capita gross domestic product (GDP) (ß=-0.370, P<0.001), per capita health expenditure (ß=-0.116, P=0.021), number of beds per thousand population (ß=-0.161, P=0.030). Standard mortality of ischemic stroke was negatively associated with per capita GDP (ß=-0.310, P=0.002) and standard management rate of hypertension (ß=-0.462, P=0.011). Standard mortality of hemorrhagic stroke was negatively associated with per capita GDP (ß=-0.481, P<0.001), per capita health expenditure (ß=-0.184, P=0.001), number of beds per thousand population (ß=-0.288, P=0.001) and standard management rate of hypertension (ß=-0.336, P=0.029). Conclusions: A positive spatial correlation existed between stroke mortality in Zhejiang Province in 2015-2020. We must focus more on preventing and controlling strokes in relatively backward economic areas. Moreover, to reduce the mortality of stroke, increasing the investment of government medical and health funds, optimizing the allocation of medical resources, and improving the standard management rate of hypertension are important measures.

Distribution of particulate matter and ammonia and physicochemical properties of fine particulate matter in a layer house.

High concentrations of particulate matter (PM) and ammonia (NH3) in poultry houses have adverse effects on the health of workers and animals. The present study investigated the distribution of the PM in different size fractions and NH3, as well as analyzing the physicochemical properties of fine particulate matter (PM2.5) in an enclosed layer house. The detection was taken from 5:00 to 21:00 in every 2 h for a 7-d continuous monitoring using a DustTrak II model 8532 aerosol monitor for PM concentrations and a JK40-IV portable gas detector for NH3 concentrations. The concentrations of inhalable particulate matter (PM10), total suspended particles (TSP), and NH3 were significantly higher in the rear of the chicken house compared to the front (P < 0.05). The PM10, TSP, and NH3 concentrations inside the layer house were significantly higher than outside (P < 0.001). The concentrations of PM10 and TSP were both highest at the first feeding time at 7:00. The PM2.5/PM10, PM2.5/TSP, and PM10/TSP concentration ratios at 5:00 were highest with 0.84, 0.62, and 0.74, respectively. Airflow speed showed a significant correlation with other measured microclimatic variables. Organic carbon was the primary constituent of PM2.5 collected from the layer house. It was also observed that mass percentage of elements C and O were both the highest by energy spectrum diagrams of PM2.5. The ultrastructure of PM2.5 revealed that there were mineral particles derived from feed, feces, or the ground and that smoke polymers came from ambient air. In conclusion, air quality was better at the front than at the middle and rear of the layer house. Air quality outside was better than that inside the house. Chicken activities primarily caused the PM10 and TSP concentrations to increase in the chicken house. The main components of PM2.5 in the layer house were organic matter and minerals, which were mostly derived from feed, feces, and ground dust.