Heatstroke risk projection in Japan under current and 1 near future climates 2

This study assesses heatstroke risk in the near future (2031-2050) under RCP8.5 scenario. 40 The developed model is based on a generalized linear model with the number of ambulance 41 transport due to heatstroke (hereafter the patients with heatstroke) as the !"#$%&’!() variable 42 and the daily maximum temperature or Wet-Bulb Globe Temperature (WBGT) as the 43 explanatory variable. With the model based on the daily maximum temperature, we 44 performed the projection of the patients with heatstroke in case of considering only climate 45 change (Case 1), climate change and population dynamics (Case 2), and climate change, 46 population dynamics, and long-term heat acclimatization (Case 3). In Case 2 , the number 47 of patients with heatstroke in the near future will be 2.3 times higher than that in the baseline 48 period (1981-2000) on average nationwide. The number of future patients with heatstroke 49 in Case 2 is about 10% larger than that in Case 1 on average nationwide despite of 50 population decline. This is due to the increase in the number of elderly people from the 51 baseline period to the near future. However, there were 21 prefectures where the number of 52 patients in Case 2 is smaller compared to Case 1. Comparing the results from Cases 1 and 53 3 reveals that the number of patients with heatstroke could be reduced by about 60% 54 nationwide by acquiring heat tolerance and changing lifestyles. Notably, given the lifestyle 55 changes represented by the widespread use of air conditioners, the number of patients with 56 heatstroke in the near future was lower than that of the baseline period in some areas. In 57 other words, lifestyle changes can be an important adaptation to the risk of heatstroke 58 emergency. All of the above results were also confirmed in the prediction model with WBGT 59 as the explanatory variable. (2 91 words, Word limit is less than 300)


Method
In this study, the six models shown in Table 1 were created and compared for accuracy. 210 The characteristics of the proposed models for the number of patients with heatstroke 211 prediction are as follows: 212 (i) The model is based on generalized linear models (GLM, Nelder and Wedderburn, 1972). 213 (ii) The predictor variable is the number of heatstroke emergency patients. 214 (iii) The default explanatory variable is the daily maximum temperature. (but, we can also 215 use WBGT instead). 216 (iv) Differences in regional, seasonal (short-term heat acclimatization), and age of 217 heatstroke risk were considered when identifying the model parameters.  (1) robustness of future climate scenario data, compared with temperature. On the other hand, 236 WBGT is possibly more suitable for explanatory variables under current climate than 237 temperature. These pros/cons are trade-off relationship for future projection; thus, we 238 compare the accuracies between the two models; the one uses the temperature as the 239 explanatory variable and the other uses the WBGT. And then, we individually predict future 240 heatstroke risk using the two models. The comparison of such models might be important 241 attempt to understand the uncertainty among prediction models.

242
Regarding (iv), it is expected that the proposed model will improve the accuracy of the future 243 projection of the number of emergency transport due to heatstroke by considering the factors 244 not limited to the meteorological field. We will describe these factors in the subsection 3.2 -  This is because the effect of short-term acclimatization is not included when using a single 259 equation as described before. Like Ikeda and Kusaka (2021), using an actual number of 260 patients with heatstroke one day before and the cumulative days from the start of summer season as explanatory variables is an example of ways to consider the short-term 262 acclimatization effect. However, the actual number of patients with heatstroke is not able to 263 use under the future climate projection. Cumulative days might be useful idea in the future 264 projection because it indicates the number of hot days experienced in one summer. However, 265 it cannot be applied to the model in this study because the timing of mid-summer may 266 change in the long term, and in that case, simple cumulative days may not be able to 267 represent this change. 268 In this study, we propose the method to divide the predicted period from June to 269 September into three sub-periods: early summer, mid-summer, and late summer, based on rises over a span of about 10days, five-day mean difference shows positive value. The 287 method of period division is as follows. The example of this method is shown in Figure 1. are not considered in this study.

346
In addition, this study did not consider the geospatial population density pattern within a 347 prefecture. However, if it is considered, the risk of heatstroke can be assessed in more 348 spatial detail. This will be useful information for the optimal allocation of medical facilities.   Table 2. Case 1 is an experiment to evaluate the increase in the risk of heatstroke due solely to 385 the increase in temperature caused by climate change. In this experiment, the number of 386 patients with heatstroke in the entire region is used as the risk indicator, but it is assumed 387 that the demographics will not change between now and the future. In other words, the increase in risk in this experiment is the same as the increase in the risk of heat stroke for 389 each individual resident.

390
Case 2 is an experiment to evaluate the variation in the risk of heatstroke by considering 391 the temperature increase due to climate change and demographic change from the baseline 392 period to the near future. In this experiment, we can obtain the projected number of patients 393 with heatstroke for the entire region at each time point in the baseline period and near future.

394
Thus, this future projection is able to assess the risks related to the burden on the emergency  In fact, the proportion of elderly people in the total population has almost tripled from 12.0% 546 to 35.3% from baseline to near future. In all prefectures, the increase rate was higher than 547 100%. We can see that the increase rate is high in the prefectures with large population that include the Tokyo metropolitan area and other major urban areas. Among these prefectures, 549 the difference in the prediction between Case 1 and Case 2 is the largest in Tokyo. In Tokyo, 550 the rate of future increase is 360.0% in Case 2, but 239.3% in Case 1. The population of 551 Tokyo as a whole increase by 16.6% from baseline to the near future, and the aging rate 552 also increases by 18.6% from baseline to the near future. In other words, in Tokyo, the risk 553 of heatstroke in Case 2 was particularly high compared to Case 1 due to two effects; total 554 population increase and increase in the aging rate from the baseline period to the near future, 555 in addition to climate change.

556
The demographic changes from the baseline to the near future can be classified into the 557 following four patterns for each prefecture.  As a result of comparing Case 2 and Case 1, we found that there were 26 prefectures 575 out of 46 prefectures where the number of patients with heatstroke was higher in Case 2.

576
Of the 26 prefectures, 6 prefectures including Tokyo were classified as type 1 (Tokyo-type).

577
In these prefectures, the number of patients with heatstroke will increase due to the following  (Table 3). The map of the near-future projection for Case 3a is shown in Figure 8(c). This figure   601 shows that the average total number of patients with heatstroke for all prefectures is 7.3 per 602 summer, with a wide range from a maximum of 14.7 per 10,000 people (Kagoshima) to a 603 minimum of 3.9 per 10,000 people (Tokyo) by prefecture.  The map of the near-future projection for Case 3b is shown in Figure 8(d). This figure   611 shows that the average total number of patients with heatstroke in the nine prefectures is             Table 2 List of future projection experiments and featured factor.