Prediction of the number of heatstroke patients transported by ambulance in Japan’s 47 prefectures: proposal of heat acclimatization consideration

Abstract

The incidence of heatstroke is affected by various meteorological variables. However, previous studies in Japan have mainly investigated and adopted a single temperature metric or composite index for their analyses. Herein, we conducted a time series study through multivariate analysis of different weather conditions simultaneously, in order to analyze the relative importance of meteorological variables to determine the number of heatstroke patients transported by ambulance in all of Japan’s 47 prefectures. We proposed a method that considers heat acclimatization, which has been found to impact the heatstroke, by manipulating certain meteorological variables. For the heatstroke data, we utilized the secondary data provided by the Fire and Disaster Management Agency, Japan. The time period considered was from May 2015 to September 2019. All calculations were performed using R 3.5.1. For the analysis, the machine learning method of random forest (RF) was applied. The results showed that the relative temperature (RelTemp), which represents heat acclimatization, had the highest ranking among all the meteorological variables studied. Then, we developed the exponential model and the RF model to predict the number of heatstroke patients transported by ambulance by adopting the highly ranked meteorological variables including RelTemp as explanatory variables. To confirm the effectiveness of heat acclimatization, we also developed the exponential model and the RF model both without RelTemp (instead, with maximum temperature). According to the results, the R2 values of the exponential and the RF models, including RelTemp, were 0.76 and 0.74, respectively, and those of the exponential and the RF models, excluding RelTemp, were 0.68 and 0.67, respectively. We confirmed the effectiveness of considering heat acclimatization via RelTemp and found that the exponential model with RelTemp provided the higher accuracy. Better predictions by the exponential model with RelTemp would contribute to better preemptive allocation of ambulances and medical staff in medical facilities.