Objective:To investigate the potential causes of the rising epidemic of severe fever with thrombocytopenia syndrome (SFTS) in Weifang, Shandong province.Methods:The temporal trend of SFTS epidemic was segmented using Joinpoint regression analysis. Changes in epidemiological characteristics across different periods were compared, and correlation analysis was conducted to identify meteorological factors influencing the epidemic trend.Results:Joinpoint regression revealed two distinct periods for SFTS epidemic in Weifang: 2015-2021 and 2022-2024. No significant trend was observed during 2015-2021 ( P=0.634), while a sharp annual increase of 46.69% occurred from 2022 to 2024 ( P=0.006). Spatial autocorrelation analysis demonstrated a global Moran’s I of 0.42 ( Z=8.55, P<0.001) for 2015-2021, with 15 high-high clustering areas identified. For 2022-2024, the global Moran’s I decreased to 0.37 ( Z=7.31, P<0.001), with 13 high-high clusters, including newly emerging hotspots in Anqiu and Zhucheng in the southeastern region. High-risk populations remained individuals aged ≥50 in mountainous and hilly areas, with a marked rise in incidence in these groups. The male-to-female ratio of cases was higher in plain areas than in mountainous/hilly regions. Autumn (September-November) temperatures from the preceding year showed a positive correlation with annual case numbers ( P=0.004, r=0.82). The linear regression expression is y=40.61x-580.78 (y is the annual incidence, and x is the average daily temperature of last autumn). Conclusions:The SFTS epidemic in Weifang is showing a rising trend. There is a linear correlation between the temperature of the previous autumn and the scale of SFTS epidemic in the following year. This correlation allows for predicting the subsequent year′s epidemic, thereby enabling early warning of SFTS.

Objective:To investigate the potential causes of the rising epidemic of severe fever with thrombocytopenia syndrome (SFTS) in Weifang, Shandong province.Methods:The temporal trend of SFTS epidemic was segmented using Joinpoint regression analysis. Changes in epidemiological characteristics across different periods were compared, and correlation analysis was conducted to identify meteorological factors influencing the epidemic trend.Results:Joinpoint regression revealed two distinct periods for SFTS epidemic in Weifang: 2015-2021 and 2022-2024. No significant trend was observed during 2015-2021 ( P=0.634), while a sharp annual increase of 46.69% occurred from 2022 to 2024 ( P=0.006). Spatial autocorrelation analysis demonstrated a global Moran’s I of 0.42 ( Z=8.55, P<0.001) for 2015-2021, with 15 high-high clustering areas identified. For 2022-2024, the global Moran’s I decreased to 0.37 ( Z=7.31, P<0.001), with 13 high-high clusters, including newly emerging hotspots in Anqiu and Zhucheng in the southeastern region. High-risk populations remained individuals aged ≥50 in mountainous and hilly areas, with a marked rise in incidence in these groups. The male-to-female ratio of cases was higher in plain areas than in mountainous/hilly regions. Autumn (September-November) temperatures from the preceding year showed a positive correlation with annual case numbers ( P=0.004, r=0.82). The linear regression expression is y=40.61x-580.78 (y is the annual incidence, and x is the average daily temperature of last autumn). Conclusions:The SFTS epidemic in Weifang is showing a rising trend. There is a linear correlation between the temperature of the previous autumn and the scale of SFTS epidemic in the following year. This correlation allows for predicting the subsequent year′s epidemic, thereby enabling early warning of SFTS.

Objective:To explore the relationship between input and output of occupational health funds, and to provide basis for relevant departments to make decisions.Methods:In September 2018, a state-owned iron ore in Hebei Province (mining history of more than 10 years, which can represent the general type of iron ore) was selected as the research object. Through the investigation and collection of enterprise general situation, occupational health input, loss and output related indicators, the iron mine occupational health expenditure input-output table and model were established, and the digital relationship between the investment and output was solved by MATLAB software.Results:The labor consumption in the departments of underground mining, open pit mining, crushing and rock discharging, transportation, tailings and mineral processing (taking labor wages as reference) were 756.46, 1.281.78, 987.61, 1 570.71, 50.956 and 18.9116 million yuan/year respectively. The output value of each sector is 11 207.19, 18 989.95, 15 176.40, 25 294.00, 7.704.94 and 280.1797 million yuan/year respectively. The ratio of health input to total output was 0.004 5, and the ratio of occupational health input to output was 1/0.046.Conclusion:The input-output table model of occupational health in iron mine can reflect the relationship between input and output of occupational health funds. The input situation of the coal mine is poor, and the input does not bring obvious occupational health benefits.

Objective:To explore the relationship between input and output of occupational health funds, and to provide basis for relevant departments to make decisions.Methods:In September 2018, a state-owned iron ore in Hebei Province (mining history of more than 10 years, which can represent the general type of iron ore) was selected as the research object. Through the investigation and collection of enterprise general situation, occupational health input, loss and output related indicators, the iron mine occupational health expenditure input-output table and model were established, and the digital relationship between the investment and output was solved by MATLAB software.Results:The labor consumption in the departments of underground mining, open pit mining, crushing and rock discharging, transportation, tailings and mineral processing (taking labor wages as reference) were 756.46, 1.281.78, 987.61, 1 570.71, 50.956 and 18.9116 million yuan/year respectively. The output value of each sector is 11 207.19, 18 989.95, 15 176.40, 25 294.00, 7.704.94 and 280.1797 million yuan/year respectively. The ratio of health input to total output was 0.004 5, and the ratio of occupational health input to output was 1/0.046.Conclusion:The input-output table model of occupational health in iron mine can reflect the relationship between input and output of occupational health funds. The input situation of the coal mine is poor, and the input does not bring obvious occupational health benefits.