Random forest analysis of the relative importance of meteorological indicators for heatstroke cases in Japan based on the degree of severity and place of occurrence.

Heatstroke is a serious health concern in Japan. To reduce heatstroke risk, the government of Japan implemented the "Heatstroke Alert" nationwide in 2021, employing the wet bulb globe temperature (WBGT) as a criterion. Although the WBGT is a useful meteorological indicator for assessing the risk of heatstroke, other important meteorological indicators must also be investigated. Therefore, using a random forest approach, this study analyzed the relative importance of several meteorological indicators, including those representing heat acclimatization, for each of the 47 Japanese prefectures. Using the generalized linear model, important meteorological indicators were employed as explanatory variables in the heatstroke prediction model to determine the predictive meteorological indicator. Heatstroke cases were evaluated separately by the degree of severity and the place of occurrence. The results showed that the relative temperature (RelTemp), which represents heat acclimatization and was calculated considering past temperature history, was the most predictive (i.e., provided the best goodness of fit) concerning the degree of severity, place of occurrence, and prefectures. RelTemp can be a complementary indicator of WBGT in countries and regions such as Japan, where seasonal differences in heat acclimatization must be considered. In addition, the findings of this study contribute to the development of a more accurate assessment of heatstroke risk.

Prediction of climate change impacts on heatstroke cases in Japan's 47 prefectures with the effect of long-term heat adaptation.

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.

Potential effect of heat adaptation on association between number of heatstroke patients transported by ambulance and wet bulb globe temperature in Japan.

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 (RRwbgt30) 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 RRwbgt30 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 RRwbgt30 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 RRwbgt30 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.

Daily temperature effects on under-five mortality in a tropical climate country and the role of local characteristics.

BACKGROUND: Climate change and its subsequent effects on temperature have raised global public health concerns. Although numerous epidemiological studies have shown the adverse health effects of temperature, the association remains unclear for children aged below five years old and those in tropical climate regions. METHODS: We conducted a two-stage time-stratified case-crossover study to examine the association between temperature and under-five mortality, spanning the period from 2014 to 2018 across all six regions in Malaysia. In the first stage, we estimated region-specific temperature-mortality associations using a conditional Poisson regression and distributed lag nonlinear models. We used a multivariate meta-regression model to pool the region-specific estimates and examine the potential role of local characteristics in the association, which includes geographical information, demographics, socioeconomic status, long-term temperature metrics, and healthcare access by region. RESULTS: Temperature in Malaysia ranged from 22 °C to 31 °C, with a mean of 27.6 °C. No clear seasonality was observed in under-five mortality. We found no strong evidence of the association between temperature and under-five mortality, with an "M-" shaped exposure-response curve. The minimum mortality temperature (MMT) was identified at 27.1 °C. Among several local characteristics, only education level and hospital bed rates reduced the residual heterogeneity in the association. However, effect modification by these variables were not significant. CONCLUSION: This study suggests a null association between temperature and under-five mortality in Malaysia, which has a tropical climate. The "M-" shaped pattern suggests that under-fives may be vulnerable to temperature changes, even with a small temperature change in reference to the MMT. However, the weak risks with a large uncertainty at extreme temperatures remained inconclusive. Potential roles of education level and hospital bed rate were statistically inconclusive.

Heat-related mortality risk model for climate change impact projection.

OBJECTIVES: We previously developed a model for projection of heat-related mortality attributable to climate change. The objective of this paper is to improve the fit and precision of and examine the robustness of the model. METHODS: We obtained daily data for number of deaths and maximum temperature from respective governmental organizations of Japan, Korea, Taiwan, the USA, and European countries. For future projection, we used the Bergen climate model 2 (BCM2) general circulation model, the Special Report on Emissions Scenarios (SRES) A1B socioeconomic scenario, and the mortality projection for the 65+-year-old age group developed by the World Health Organization (WHO). The heat-related excess mortality was defined as follows: The temperature-mortality relation forms a V-shaped curve, and the temperature at which mortality becomes lowest is called the optimum temperature (OT). The difference in mortality between the OT and a temperature beyond the OT is the excess mortality. To develop the model for projection, we used Japanese 47-prefecture data from 1972 to 2008. Using a distributed lag nonlinear model (two-dimensional nonparametric regression of temperature and its lag effect), we included the lag effect of temperature up to 15 days, and created a risk function curve on which the projection is based. As an example, we perform a future projection using the above-mentioned risk function. In the projection, we used 1961-1990 temperature as the baseline, and temperatures in the 2030s and 2050s were projected using the BCM2 global circulation model, SRES A1B scenario, and WHO-provided annual mortality. Here, we used the "counterfactual method" to evaluate the climate change impact; For example, baseline temperature and 2030 mortality were used to determine the baseline excess, and compared with the 2030 excess, for which we used 2030 temperature and 2030 mortality. In terms of adaptation to warmer climate, we assumed 0 % adaptation when the OT as of the current climate is used and 100 % adaptation when the OT as of the future climate is used. The midpoint of the OTs of the two types of adaptation was set to be the OT for 50 % adaptation. RESULTS: We calculated heat-related excess mortality for 2030 and 2050. CONCLUSIONS: Our new model is considered to be better fit, and more precise and robust compared with the previous model.

Heat-related mortality and ambulance transport after a power outage in the Tokyo metropolitan area.

Background: Air conditioners can prevent heat-related illness and mortality, but the increased use of air conditioners may enhance susceptibility to heat-related illnesses during large-scale power failures. Here, we examined the risks of heat-related illness ambulance transport (HIAT) and mortality associated with typhoon-related electricity reduction (ER) in the summer months in the Tokyo metropolitan area. Methods: We conducted event study analyses to compare temperature-HIAT and mortality associations before and after the power outage (July to September 2019). To better understand the role of temperature during the power outage, we then examined whether the temperature-HIAT and mortality associations were modified by different power outage levels (0%, 10%, and 20% ER). We computed the ratios of relative risks to compare the risks associated with various ER values to the risks associated without ER. Results: We analyzed the data of 14,912 HIAT cases and 74,064 deaths. Overall, 93,200 power outage cases were observed when the typhoon hit. Event study results showed that the incidence rate ratio was 2.01 (95% confidence interval [CI] = 1.42, 2.84) with effects enduring up to 6 days, and 1.11 (95% CI = 1.02, 1.22) for mortality on the first 3 days after the typhoon hit. Comparing 20% to 0% ER, the ratios of relative risks of heat exposure were 2.32 (95% CI = 1.41, 3.82) for HIAT and 0.95 (95% CI = 0.75, 1.22) for mortality. Conclusions: A 20% ER was associated with a two-fold greater risk of HIAT because of summer heat during the power outage, but there was little evidence for the association with all-cause mortality.

Non-optimal temperature-attributable mortality and morbidity burden by cause, age and sex under climate and population change scenarios: a nationwide modelling study in Japan.

Background: Future temperature effects on mortality and morbidity may differ. However, studies comparing projected future temperature-attributable mortality and morbidity in the same setting are limited. Moreover, these studies did not consider future population change, human adaptation, and the variations in subpopulation susceptibility. Thus, we simultaneously projected the temperature-related mortality and morbidity by cause, age, and sex under population change, and human adaptation scenarios in Japan, a super-ageing society. Methods: We used daily mean temperatures, mortality, and emergency ambulance dispatch (a sensitive indicator for morbidity) in 47 prefectures of Japan from 2015 to 2019 as the reference for future projections. Future mortality and morbidity were generated at prefecture level using four shared socioeconomic pathway (SSP) scenarios considering population changes. We calculated future temperature-related mortality and morbidity by combining baseline values with future temperatures and existing temperature risk functions by cause (all-cause, circulatory, respiratory), age (<65 years, ≥65 years), and sex under various climate change and SSP scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5). Full human adaptation was simulated based on empirical evidence using a fixed percentile of minimum mortality or morbidity temperature (MMT), while no adaptation was simulated with a fixed absolute MMT. Findings: A future temporal decline in mortality burden attributable to non-optimal temperatures was observed, driven by greater cold-related deaths than heat-related deaths. In contrast, temperature-related morbidity increased over time, which was primarily driven by heat. In the 2050s and 2090s, under a moderate scenario, there are 83.69 (95% empirical confidence interval [eCI] 38.32-124.97) and 77.31 (95% eCI 36.84-114.47) all-cause deaths per 100,000 population, while there are 345.07 (95% eCI 258.31-438.66) and 379.62 (95% eCI 271.45-509.05) all-cause morbidity associated with non-optimal temperatures. These trends were largely consistent across causes, age, and sex groups. Future heat-attributable health burden is projected to increase substantially, with spatiotemporal variations and is particularly pronounced among individuals ≥65 y and males. Full human adaptation could yield a decreasing temperature-attributable mortality and morbidity in line with a decreasing population. Interpretation: Our findings could support the development of targeted mitigation and adaptation strategies to address future heat-related impacts effectively. This includes improved healthcare allocations for ambulance dispatch and hospital preventive measures during heat periods, particularly custom-tailored to address specific health outcomes and vulnerable subpopulations. Funding: Japan Science and Technology Agency and Environmental Restoration and Conservation Agency and Ministry of the Environment of Japan.

Machine learning analysis and risk prediction of weather-sensitive mortality related to cardiovascular disease during summer in Tokyo, Japan.

Climate-sensitive diseases developing from heat or cold stress threaten human health. Therefore, the future health risk induced by climate change and the aging of society need to be assessed. We developed a prediction model for mortality due to cardiovascular diseases such as myocardial infarction and cerebral infarction, which are weather or climate sensitive, using machine learning (ML) techniques. We evaluated the daily mortality of ischaemic heart disease (IHD) and cerebrovascular disease (CEV) in Tokyo and Osaka City, Japan, during summer. The significance of delayed effects of daily maximum temperature and other weather elements on mortality was previously demonstrated using a distributed lag nonlinear model. We conducted ML by a LightGBM algorithm that included specified lag days, with several temperature- and air pressure-related elements, to assess the respective mortality risks for IHD and CEV, based on training and test data for summer 2010-2019. These models were used to evaluate the effect of climate change on the risk for IHD mortality in Tokyo by applying transfer learning (TL). ML with TL predicted that the daily IHD mortality risk in Tokyo would averagely increase by 29% and 35% at the 95th and 99th percentiles, respectively, using a high-level warming-climate scenario in 2045-2055, compared to the risk simulated using ML in 2009-2019.

A Nationwide Comparative Analysis of Temperature-Related Mortality and Morbidity in Japan.

BACKGROUND: The impact of temperature on morbidity remains largely unknown. Moreover, extensive evidence indicates contrasting patterns between temperature-mortality and temperature-morbidity associations. A nationwide comparison of the impact of temperature on mortality and morbidity in more specific subgroups is necessary to strengthen understanding and help explore underlying mechanisms by identifying susceptible populations. OBJECTIVE: We performed this study to quantify and compare the impact of temperature on mortality and morbidity in 47 prefectures in Japan. METHODS: We applied a two-stage time-series design with distributed lag nonlinear models and mixed-effect multivariate meta-analysis to assess the association of temperature with mortality and morbidity by causes (all-cause, circulatory, and respiratory) at prefecture and country levels between 2015 and 2019. Subgroup analysis was conducted by sex, age, and regions. RESULTS: The patterns and magnitudes of temperature impacts on morbidity and mortality differed. For all-cause outcomes, cold exhibited larger effects on mortality, and heat showed larger effects on morbidity. At specific temperature percentiles, cold (first percentile) was associated with a higher relative risk (RR) of mortality [1.45; 95% confidence interval (CI): 1.39, 1.52] than morbidity (1.33; 95% CI: 1.26, 1.40), as compared to the minimum mortality/morbidity temperature. Heat (99th percentile) was associated with a higher risk of morbidity (1.30; 95% CI: 1.28, 1.33) than mortality (1.04; 95% CI: 1.02, 1.06). For cause-specific diseases, mortality due to circulatory diseases was more susceptible to heat and cold than morbidity. However, for respiratory diseases, both cold and heat showed higher risks for morbidity than mortality. Subgroup analyses suggested varied associations depending on specific outcomes. DISCUSSION: Distinct patterns were observed for the association of temperature with mortality and morbidity, underlying different mechanisms of temperature on different end points, and the differences in population susceptibility are possible explanations. Future mitigation policies and preventive measures against nonoptimal temperatures should be specific to disease outcomes and targeted at susceptible populations. https://doi.org/10.1289/EHP12854.

Net impact of air conditioning on heat-related mortality in Japanese cities.

BACKGROUND: Air conditioning (AC) presents a viable means of tackling the ill-effects of heat on human health. However, AC releases additional anthropogenic heat outdoors, and this could be detrimental to human health, especially in urban communities. This study determined the excess heat-related mortality attributable to anthropogenic heat from AC use under various projected global warming scenarios in seven Japanese cities. The overall protection from AC use was also measured. METHODS: Daily average 2-meter temperatures in the hottest month of August from 2000 to 2010 were modeled using the Weather Research and Forecasting (WRF) model with BEP+BEM (building effect parameterization and building energy model). Risk functions for heat-mortality associations were generated with and without AC use from a two-stage time series analysis. We coupled simulated August temperatures and heat-mortality risk functions to estimate averted deaths and unavoidable deaths from AC use. RESULTS: Anthropogenic heat from AC use slightly augmented the daily urban temperatures by 0.046 °C in Augusts of 2000-2010 and up to 0.181 °C in a future with 3 °C urban warming. This temperature rise was attributable to 3.1-3.5 % of heat-related deaths in Augusts of 2000-2010 under various urban warming scenarios. About 36-47 % of heat-related deaths could be averted by air conditioning use under various urban warming scenarios. DISCUSSION: AC has a valuable protective effect from heat despite some unavoidable mortality from anthropogenic heat release. Overall, the use of AC as a major adaptive strategy requires careful consideration.

Mortality Risk of Hot Nights: A Nationwide Population-Based Retrospective Study in Japan.

BACKGROUND: The health effects of heat are well documented; however, limited information is available regarding the health risks of hot nights. Hot nights have become more common, increasing at a faster rate than hot days, making it urgent to understand the characteristics of the hot night risk. OBJECTIVES: We estimated the effects of hot nights on the cause- and location-specific mortality in a nationwide assessment over 43 y (1973-2015) using a unified analytical framework in the 47 prefectures of Japan. METHODS: Hot nights were defined as days with a) minimum temperature ≥25°C (HN25) and b) minimum temperature ≥95th percentile (HN95th) for the prefecture. We conducted a time-series analysis using a two-stage approach during the hot night occurrence season (April-November). For each prefecture, we estimated associations between hot nights and mortality controlling for potential confounders including daily mean temperature. We then used a random-effects meta-analytic model to estimate the pooled cumulative association. RESULTS: Overall, 24,721,226 deaths were included in this study. Nationally, all-cause mortality increased by 9%-10% [HN25 relative risk (RR)=1.09, 95% confidence interval (CI): 1.08, 1.10; HN95th RR=1.10, 95% CI: 1.09, 1.11] during hot nights in comparison with nonhot nights. All 11 cause-specific mortalities were strongly associated with hot nights, and the corresponding associations appeared to be acute and lasted a few weeks, depending on the cause of death. The strength of the association between hot nights and mortality varied among prefectures. We found a higher mortality risk from hot nights in early summer in comparison with the late summer in all regions. CONCLUSIONS: Our findings support the evidence of mortality impacts from hot nights in excess of that explicable by daily mean temperature and have implications useful for establishing public health policy and research efforts estimating the health effects of climate change. https://doi.org/10.1289/EHP11444.

Enhancing health resilience in Japan in a changing climate.

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.

Environmental variable importance for under-five mortality in Malaysia: A random forest approach.

BACKGROUND: Environmental factors have been associated with adverse health effects in epidemiological studies. The main exposure variable is usually determined via prior knowledge or statistical methods. It may be challenging when evidence is scarce to support prior knowledge, or to address collinearity issues using statistical methods. This study aimed to investigate the importance level of environmental variables for the under-five mortality in Malaysia via random forest approach. METHOD: We applied a conditional permutation importance via a random forest (CPI-RF) approach to evaluate the relative importance of the weather- and air pollution-related environmental factors on daily under-five mortality in Malaysia. This study spanned from January 1, 2014 to December 31, 2016. In data preparation, deviation mortality counts were derived through a generalized additive model, adjusting for long-term trend and seasonality. Analyses were conducted considering mortality causes (all-cause, natural-cause, or external-cause) and data structures (continuous, categorical, or all types [i.e., include all variables of continuous type and all variables of categorical type]). The main analysis comprised of two stages. In Stage 1, Boruta selection was applied for preliminary screening to remove highly unimportant variables. In Stage 2, the retained variables from Boruta were used in the CPI-RF analysis. The final importance value was obtained as an average value from a 10-fold cross-validation. RESULT: Some heat-related variables (maximum temperature, heat wave), temperature variability, and haze-related variables (PM10, PM10-derived haze index, PM10- and fire-derived haze index, fire hotspot) were among the prominent variables associated with under-five mortality in Malaysia. The important variables were consistent for all- and natural-cause mortality and sensitivity analyses. However, different most important variables were observed between natural- and external-cause under-five mortality. CONCLUSION: Heat-related variables, temperature variability, and haze-related variables were consistently prominent for all- and natural-cause under-five mortalities, but not for external-cause.

Downscaling Global Emissions and Its Implications Derived from Climate Model Experiments.

In climate change research, future scenarios of greenhouse gas and air pollutant emissions generated by integrated assessment models (IAMs) are used in climate models (CMs) and earth system models to analyze future interactions and feedback between human activities and climate. However, the spatial resolutions of IAMs and CMs differ. IAMs usually disaggregate the world into 10-30 aggregated regions, whereas CMs require a grid-based spatial resolution. Therefore, downscaling emissions data from IAMs into a finer scale is necessary to input the emissions into CMs. In this study, we examined whether differences in downscaling methods significantly affect climate variables such as temperature and precipitation. We tested two downscaling methods using the same regionally aggregated sulfur emissions scenario obtained from the Asian-Pacific Integrated Model/Computable General Equilibrium (AIM/CGE) model. The downscaled emissions were fed into the Model for Interdisciplinary Research on Climate (MIROC). One of the methods assumed a strong convergence of national emissions intensity (e.g., emissions per gross domestic product), while the other was based on inertia (i.e., the base-year remained unchanged). The emissions intensities in the downscaled spatial emissions generated from the two methods markedly differed, whereas the emissions densities (emissions per area) were similar. We investigated whether the climate change projections of temperature and precipitation would significantly differ between the two methods by applying a field significance test, and found little evidence of a significant difference between the two methods. Moreover, there was no clear evidence of a difference between the climate simulations based on these two downscaling methods.