Fig. Impetuous
Conditions
Brief summary
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.
Interventions
OTHERMeteorological conditions and air pollution
Climate change e.g. global surface temperature rise; extreme weather events e.g. heat waves, floods
Sponsors
Li hengjie
Study design
Observational model
COHORT
Time perspective
CROSS_SECTIONAL
Eligibility
Sex/Gender
ALL
Healthy volunteers
No
Inclusion criteria
-Emergency data of Zhejiang Emergency Command Center in the past 5 years
Exclusion criteria
* Prehospital emergency information from non-Zhejiang province domains * Emergency calls due to labor and delivery.
Design outcomes
Primary
| Measure | Time frame |
|---|---|
| Number of first aiders per hour as assessed by R4.4.0 | 6 months |
| Vulnerable population characteristics as assessed by R4.4.0 | 6 Months |
| Types of Sensitive Diseases as assessed by R4.4.0 | 6 Month |
| High-risk areas as assessed by R4.4.0 | 6 months |
Countries
China
Outcome results
None listed