Hourly level analysis of the effects of temperature extremes on emergency ambulance calls

This is the authors' abstract. We don't add key points for this paper.

Methods: We obtained hourly data on EACs, weather conditions, and air pollutants from Nanjing, a megacity in eastern China, during 2018-21. We first extracted data from the cold and warm seasons to quantify the hourly impact of extreme cold and heat on EACs and potential modifying factors using a distributed lag nonlinear model. Then, we used a random-effects meta-analysis model to pool the estimates of extreme temperatures on EACs from this Nanjing study and from studies identified in the published literature. Results: The results showed that hourly exposure to extreme cold and heat increased the risk of EACs for all non-accidental causes, with relative risks (RRs) of 1.175 (95% confidence interval (CI) = 1.135, 1.216) at lag seven to 22 hours and 1.096 (95% CI = 1.048, 1.146) at lag zero to 10 hours, respectively. Stronger extreme heat-EAC associations were found between 16:00-24:00. The meta-analysis, which additionally included four prior studies, confirmed a significant association between hourly exposure to temperature extremes and EAC risk (RR = 1.155 for extreme cold and RR = 1.172 for extreme heat). Conclusions: Our findings indicate that transient exposure to temperature extremes can increase the demand for EACs within a few hours, which may have implications for improving ambulance service efficiency and developing an ambulance early warning system under extreme weather conditions.