One of the negative consequences of increased air temperatures due to global warming is the associated increase in heat-related mortality and morbidity. Studies that focused on future predictions of heat-related morbidity do not consider the effect of long-term heat adaptation measures, nor do they use evidence-based methods. Therefore, this study aimed to predict the future heatstroke cases for all 47 prefectures of Japan, by considering long-term heat adaptation by translating current geographical differences in heat adaptation to future temporal heat adaptation. Predictions were conducted for age groups of 7-17, 18-64, and ≥65 years. The prediction period was set to a base period (1981-2000), mid-21st century (2031-2050), and the end of the 21st century (2081-2100). We found that the average heatstroke incidence (number of patients with heatstroke transported by ambulance per population) in Japan under five representative climate models and three greenhouse gas (GHG) emissions scenarios increased by 2.92- for 7-17 years, 3.66- for 18-64 years, and 3.26-fold for ≥65 years at the end of the 21st century without heat adaptation. The corresponding numbers were 1.57 for 7-17 years, 1.77 for 18-64 years, and 1.69 for ≥65 years with heat adaptation. Furthermore, the average number of patients with heatstroke transported by ambulance (NPHTA) under all climate models and GHG emissions scenarios increased by 1.02- for 7-17 years, 1.76- for 18-64 years, and 5.50-fold for ≥65 years at the end of 21st century without heat adaptation, where demographic changes were considered. The corresponding numbers were 0.55 for 7-17 years, 0.82 for 18-64 years, and 2.74 for ≥65 years with heat adaptation. The heatstroke incidence, as well as the NPHTA, substantially decreased when heat adaptation was considered. Our method could be applicable to other regions across the globe.

This study analyzed the association between heatstroke incidence and daily maximum wet bulb globe temperature (WBGT) for all 47 prefectures in Japan by age group and severity using time-series analysis, controlling for confounders, such as seasonality and long-term trends. With the obtained association, the relative risk between the reference WBGT (defined as the value at which heatstroke starts to increase) and the daily maximum WBGT at 30 °C (RR_wbgt30) of each prefecture were calculated. For the heatstroke data, the daily number of heatstroke patients transported by ambulance at the prefecture level, provided by the Fire and Disaster Management Agency, was utilized. The analysis was conducted for age groups of 7-17 y, 18-64 y, and ≥65 y, and for severity of Deceased, Severe, Moderate (combined as DSM), and Mild. The analysis period was set from May 1 to September 30, 2015-2019. Finally, the correlation between RR_wbgt30 and the average daily maximum WBGT during the analysis period (aveWBGTms) of each prefecture was analyzed to examine the regionality of heatstroke incidence. The result showed that RR_wbgt30 is negatively correlated with aveWBGTms for the age group 18-64 y and ≥65 y (except for the age group 7-17 y) and for severity. The natural logarithm of the RR_wbgt30 of all 47 prefectures ranged from 2.0 to 8.2 for the age group 7-17 y, 1.1 to 4.0 for the age group 18-64 y, 1.8 to 6.0 for the age group ≥65 y, and 1.0 to 3.6 for DSM, and 0.9 to 4.0 for Mild. This regionality can be attributed to the effects of heat adaptation, where people in hotter regions are accustomed to implementing measures against hot environments and are more heat acclimatized than people in cooler regions.

Climate change poses significant threats to human health, propelling Japan to take decisive action through the Climate Change Adaptation Act of 2018. This Act has led to the implementation of climate change adaptation policies across various sectors, including healthcare. In this review, we synthesized existing scientific evidence on the impacts of climate change on health in Japan and outlined the adaptation strategies and measures implemented by the central and local governments. The country has prioritized tackling heat-related illness and mortality and undertaken various adaptation measures to mitigate these risks. However, it faces unique challenges due to its super-aged society. Ensuring effective and coordinated strategies to address the growing uncertainties in vulnerability to climate change and the complex intersectoral impacts of disasters remains a critical issue. To combat the additional health risks by climate change, a comprehensive approach embracing adaptation and mitigation policies in the health sector is crucial. Encouraging intersectoral communication and collaboration will be vital for developing coherent and effective strategies to safeguard public health in the face of climate change.

To reduce heatstroke cases, the Japanese government launched ‘Heatstroke Alert’ in 2020. In this initiative, an alert is issued when the daily maximum wet bulb globe temperature (WBGT) is expected to reach or exceed 33 °C. However, heatstroke incidents at a daily maximum of WBGT of 33 °C can vary by regions and by age groups. In this study, we analyzed the heatstroke incidence in all 47 prefectures of Japan at the daily maximum WBGT of 33 °C, considering age groups. In addition, considering variations in heatstroke incidence by prefectures and age groups, alternative ‘Heatstroke Alert’ criteria (AHAC) by each prefecture and age group were examined. We also analyzed the daily maximum WBGT at which heatstroke incidence starts to increase (the WBGT threshold), as this can be an additional metric to ‘Heatstroke Alert’ for the prevention of heatstroke. The results showed that the northern region (higher latitudes) of Japan has more patients with heatstroke per population, at a daily maximum WBGT of 33 °C, than the southern region (lower latitudes), by factors of approximately 3–5, considering each age group. The suggested AHAC among 47 prefectures were 29.8 [27.7–31.4] °C for 7–17 years, 32.4 [30.2–33.9] °C for 18–64 years, and 30.8 [28.5–33.2] °C for ≥ 65 years, respectively. The northern region exhibited a lower WBGT threshold (defined as the value at which heatstroke incidence is 0.1 persons per 10⁵ people) than the southern region by approximately 3.5°C–5°C, considering each age group. The calculated average WBGT threshold among 47 prefectures was 23.2 [20.4–25.3] °C for 7–17 years, 26.3 [24.2–28.1] °C for 18–64 years, and 23.1 [20.2–25.6] °C for ≥ 65 years, respectively. These findings are expected to contribute to the reinforcement of ‘Heatstroke Alert’ and the use of WBGT information in a more preventive manner.

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.