Estimation of the number of heat illness patients in eight metropolitan prefectures of Japan: Correlation with ambient temperature and computed thermophysiological responses

Abstract

The number of patients with heat illness transported by ambulance has been gradually increasing due to global warming. In intense heat waves, it is crucial to accurately estimate the number of cases with heat illness for management of medical resources. Ambient temperature is an essential factor with respect to the number of patients with heat illness, although thermophysiological response is a more relevant factor with respect to causing symptoms. In this study, we computed daily maximum core temperature increase and daily total amount of sweating in a test subject using a large-scale, integrated computational method considering the time course of actual ambient conditions as input. The correlation between the number of transported people and their thermophysiological temperature is evaluated in addition to conventional ambient temperature. With the exception of one prefecture, which features a different Köppen climate classification, the number of transported people in the remaining prefectures, with a Köppen climate classification of Cfa, are well estimated using either ambient temperature or computed core temperature increase and daily amount of sweating. For estimation using ambient temperature, an additional two parameters were needed to obtain comparable accuracy. Even using ambient temperature, the number of transported people can be estimated if the parameters are carefully chosen. This finding is practically useful for the management of ambulance allocation on hot days as well as public enlightenment.

Keywords: ambient heat; ambulance dispatch; global warming; heat adaptation; heat illness.

Figure 1

Location of eight prefectures in Japan selected for this study. Japan includes 47 prefectures. The Köppen climate classification in each prefecture are also presented.

Figure 2

Time course of computed daily peak core temperature and total amount of sweating in each prefecture in 2019.

Figure 3

Variation of coefficient of determination averaged over 7 years (from June 1 to September 30 from 2013 to 2019) for the number of days over which an input variable is averaged, corresponding to J in Equation (4). For the number of patients transported the indoor locations, the (A) ambient temperature, (B) amount of sweating, and (C) body core temperature increase were considered. The same evaluation was conducted for patients transported from outdoor locations in (D–F).

Figure 4

Observed and estimated number of patients with heat illness in seven prefectures (averaged over the period from 2013 to 2019) for average ambient temperature, amount of sweating, and body core temperature increase. Blue region represents the 95% confidence interval of estimation using computed daily amount of sweating.

Figure 5

Time series of MAEs per million population in Tokyo for average ambient temperature, amount of sweating, and body core temperature increase (averaged over the period from 2013 to 2019). Standard deviations are indicated by error bars.

Figure 6

Difference from observed to estimated patients per million population in the days of WBGT ≥ 31°C. This approximately corresponds to a rate of transported patients per million population of ≥10 in seven prefectures from 2013 to 2019.