Based on the health data from Zhejiang Emergency Command Center, combined with meteorological, air pollution, land use and socio-economic data of Zhejiang Province, distributional lag nonlinear models, grouped weighted quantile and regression and Bayesian spatial models were used to explore the independent and interactive effects of the association between meteorological factors and air pollution and the number of first-aiders, to identify the related characteristics of the vulnerable populations, the types of sensitive diseases and the high-risk areas, and to elucidate the driving factors of the association between meteorological factors and air pollution and the number of first-aiders. It also clarifies the drivers of the association between meteorological factors and air pollution and the number of emergencies, so as to provide a reference for the government to take targeted measures to reduce the burden of related healthcare services.
Study Type
OBSERVATIONAL
Enrollment
1,500,000
Climate change e.g. global surface temperature rise; extreme weather events e.g. heat waves, floods
Zhejiang Provincial People's Hospital
Hangzhou, China
Number of first aiders per hour as assessed by R4.4.0
Time frame: 6 months
Vulnerable population characteristics as assessed by R4.4.0
Time frame: 6 Months
Types of Sensitive Diseases as assessed by R4.4.0
Time frame: 6 Month
High-risk areas as assessed by R4.4.0
Time frame: 6 months
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