Quantitative Assessment of the Contribution of Meteorological Variables to the Prediction of the Number of Heat Stroke Patients for Tokyo

Abstract

This study reveals the best combination of meteorological variables for the prediction of the number of emergency transport due to heat stroke over 64 years old in Tokyo metropolis based on a generalized linear model using 2008-2016 data. Temperature, relative humidity, wind speed, and solar radiation were used as candidates of the explanatory variables. The variable selection with Akaike's information criterion (AIC) showed that all the four meteorological elements were selected for the prediction model. Additional analysis showed that the combination of daily mean temperature, maximum relative humidity, maximum wind speed, and total solar radiation as explanatory variables gives the best prediction, with approximately 19% less error than the conventional single-variable model which only uses the daily mean temperature. Finally, we quantitatively estimated the relative contribution of each variable to the prediction of the daily number of heat stroke patients using standardized partial regression coefficients. The result reveals that temperature is the largest contributor. Solar radiation is second, with approximately 20% of the temperature effect. Relative humidity and wind speed make relatively small contributions, each contributing approximately 10% and 9% of the temperature, respectively. This result provides helpful information to propose more sophisticated thermal indices to predict heat stroke risk.