Estimation of heat-related morbidity from weather data: A computational study in three prefectures of Japan over 2013-2018

Uneven heat burden in the sunshine state: Spatial patterns and socio-economic disparities of heat-related illness in Florida

Climate change has increased the frequency and severity of extreme heat events globally, adversely affecting socio-economic conditions and public health. However, extreme heat has disparate effects on different population groups and the socio-economic determinants of its health effects are not well understood. In this study, we analyzed the spatial patterns of heat-related illness (HRI) visit rates at the zip-code level in Florida and applied statistical methods to examine the relationships between HRIs and environmental and socio-economic variables. Hierarchical regression analysis was used to evaluate the socio-economic effects on HRI visit rates under the same heat conditions. This is a two-step approach: we first included heat indicators in the baseline model and then added the socio-economic variables to assess their unique contributions in predicting HRI visits. Our findings indicate that temperature can only explain a small fraction of the variance in HRI cases (R2 = 0.04, p < 0.01), while socio-economic variables show stronger associations (R2 = 0.42, p < 0.01 in urban areas and R2 = 0.20, p < 0.01 in rural areas). Notably, marginalized and disadvantaged populations (e.g., individuals in poverty, those employed in construction, and those with disabilities) are positively associated with HRIs (p < 0.01). These findings highlight the disproportionate impacts of heat-related health issues on disadvantaged groups, calling for climate justice policy interventions. Additionally, a comparative analysis between rural and urban areas revealed different determinants of HRIs. Our study enhances the understanding of the socio-economic determinants and disparities of HRIs in Florida, providing actionable insights for policymakers and health agencies to prioritize emergency services and heat resilience planning.

Projection of future heat-related morbidity in three metropolitan prefectures of Japan based on large ensemble simulations of climate change under 2 °C global warming scenarios

Recently, global warming has become a prominent topic, including its impacts on human health. The number of heat illness cases requiring ambulance transport has been strongly linked to increasing temperature and the frequency of heat waves. Thus, a potential increase in the number of cases in the future is a concern for medical resource management. In this study, we estimated the number of heat illness cases in three prefectures of Japan under 2 °C global warming scenarios, approximately corresponding to the 2040s. Based on the population composition, a regression model was used to estimate the number of heat illness cases with an input parameter of time-dependent meteorological ambient temperature or computed thermophysiological response of test subjects in large-scale computation. We generated 504 weather patterns using 2 °C global warming scenarios. The large-scale computational results show that daily amount of sweating increased twice and the core temperature increased by maximum 0.168 °C, suggesting significant heat strain. According to the regression model, the estimated number of heat illness cases in the 2040s of the three prefectures was 1.90 (95%CI: 1.35-2.38) times higher than that in the 2010s. These computational results suggest the need to manage ambulance services and medical resource allocation, including intervention for public awareness of heat illnesses. This issue will be important in other aging societies in near future.

Effects of heatwave features on machine-learning-based heat-related ambulance calls prediction models in Japan

Researchers agree that there is substantial evidence of an increasing trend in both the frequency and duration of extreme temperature events. Increasing extreme temperature events will place more pressure on public health and emergency medical resources, and societies will need to find effective and reliable solutions to adapt to hotter summers. This study developed an effective method to predict the number of daily heat-related ambulance calls. Both national- and regional-level models were developed to evaluate the performance of machine-learning-based methods on heat-related ambulance call prediction. The national model showed a high prediction accuracy and can be applied over most regions, while the regional model showed extremely high prediction accuracy in each corresponding region and reliable accuracy in special cases. We found that the introduction of heatwave features, including accumulated heat stress, heat acclimatization, and optimal temperature, significantly improved prediction accuracy. The adjusted coefficient of determination (adjusted R²) of the national model improved from 0.9061 to 0.9659 by including these features, and the adjusted R² of the regional model also improved from 0.9102 to 0.9860. Furthermore, we used five bias-corrected global climate models (GCMs) to forecast the total number of summer heat-related ambulance calls under three different future climate scenarios nationally and regionally. Our analysis demonstrated that, at the end of the 21st century, the total number of heat-related ambulance calls in Japan will reach approximately 250,000 per year (nearly four times the current amount) under SSP-5.85. Our results suggest that disaster management agencies can use this highly accurate model to forecast potential high emergency medical resource burden caused by extreme heat events, allowing them to raise and improve public awareness and prepare countermeasures in advance. The method proposed in Japan in this paper can be applied to other countries that have relevant data and weather information systems.

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

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.

Regional incidence risk of heat stroke in elderly individuals considering population, household structure, and local industrial sector

This study aims to clarify the regional characteristics of heat stroke incidence patterns inside and outside residences among the elderly from the perspective of working and living conditions. The study area comprised 41 municipalities belonging to Hyogo Prefecture in Japan. Based on information on heat stroke emergency medical evacuees in each municipality from 2011 to 2020, the regional differences in the incidence risk of heat stroke were analyzed. The results revealed that the number of cases and the proportion of males and females among them were related to the demographic structure of each municipality. A grouping analysis was conducted to classify the characteristics of each municipality based on the relationship between the incidence risk of heat stroke and the industrial structure. A factor analysis and binomial logistic regression analysis were also conducted to investigate the effect of demographic structure on the incidence risk of heat stroke. The results indicate that the incidence risk of heat stroke is correlated with industrial and demographic structures, and the risk is likely to vary regionally.

Trends in ambulance dispatches related to heat illness from 2010 to 2019: An ecological study

Heatstroke is a serious heat-related illness that can even cause death. Heat alert systems play an important role in reducing the number of patients experiencing heat illness, as they encourage preventive actions such as the use of air conditioning, hydration, or other strategies. However, to date, the Japanese hazard classification has not considered seasonal and regional variations, despite clear differences in meteorological conditions across different regions in Japan. Moreover, several studies have reported a difference in thermoregulation between older and younger adults, implying that the hazard classification should also consider age differences. This study examined the relationship between the number of ambulance dispatches related to heat illness (ADRHI) and the Japanese heat hazard classification from 2010 to 2019, focusing on monthly and regional differences. Data from 47 prefectures during the 10-year period were collected and analyzed. ADRHI and wet bulb globe temperature (WBGT) data were collected from Japan’s Ministry of Internal Affairs and Communications and the Ministry of the Environment Heat Illness Prevention Information website, respectively. The findings showed a significant relationship between ADRHI and WBGT_max (p < 0.05, r = 0.74). ADRHI per 100,000 people showed significant differences across months. The post hoc test detected the first steep increase in ADRHI at a WBGT_max of 23°C than at 22°C in June, and at a WBGT_max of 26°C, 27°C, and 25°C in July, August, and September, respectively. Moreover, the first significant increase in ADRHI per 100,000 people at WBGT_max differed across each region, at a WBGT_max of 24°C in Hokkaido-Tohoku, 25°C in Kanto, Kansai, and Chugoku, 26°C in Chubu, 27°C in Shikoku, and 28°C in Kyushu-Okinawa. Further, Poisson regression analysis revealed that the relative risks differed across each region and month. These results imply that the hazard classification should be adjusted according to region and month in Japan.

Baseline scenarios of heat-related ambulance transportations under climate change in Tokyo, Japan

Background

Predictive scenarios of heatstroke over the long-term future have yet to be formulated. The purpose of the present study was to generate baseline scenarios of heat-related ambulance transportations using climate change scenario datasets in Tokyo, Japan.

Methods

Data on the number of heat-related ambulance transportations in Tokyo from 2015 to 2019 were examined, and the relationship between the risk of heat-related ambulance transportations and the daily maximum wet-bulb globe temperature (WBGT) was modeled using three simple dose-response models. To quantify the risk of heatstroke, future climatological variables were then retrieved to compute the WBGT up to the year 2100 from climate change scenarios (i.e., RCP2.6, RCP4.5, and RCP8.5) using two scenario models. The predicted risk of heat-related ambulance transportations was embedded onto the future age-specific projected population.

Results

The proportion of the number of days with a WBGT above 28°C is predicted to increase every five years by 0.16% for RCP2.6, 0.31% for RCP4.5, and 0.68% for RCP8.5. In 2100, compared with 2000, the number of heat-related ambulance transportations is predicted to be more than three times greater among people aged 0-64 years and six times greater among people aged 65 years or older. The variance of the heatstroke risk becomes greater as the WBGT increases.

Conclusions

The increased risk of heatstroke for the long-term future was demonstrated using a simple statistical approach. Even with the RCP2.6 scenario, with the mildest impact of global warming, the risk of heatstroke is expected to increase. The future course of heatstroke predicted by our approach acts as a baseline for future studies.

Heatstroke predictions by machine learning, weather information, and an all-population registry for 12-hour heatstroke alerts

This study aims to develop and validate prediction models for the number of all heatstroke cases, and heatstrokes of hospital admission and death cases per city per 12 h, using multiple weather information and a population-based database for heatstroke patients in 16 Japanese cities (corresponding to around a 10,000,000 population size). In the testing dataset, mean absolute percentage error of generalized linear models with wet bulb globe temperature as the only predictor and the optimal models, respectively, are 43.0% and 14.8% for spikes in the number of all heatstroke cases, and 37.7% and 10.6% for spikes in the number of heatstrokes of hospital admission and death cases. The optimal models predict the spikes in the number of heatstrokes well by machine learning methods including non-linear multivariable predictors and/or under-sampling and bagging. Here, we develop prediction models whose predictive performances are high enough to be implemented in public health settings.

In the context of climate change, heatstroke is expected to become an increasingly relevant public health concern. Here, the authors develop and validate prediction models for the number of all heatstroke cases in different cities in Japan.

Heat health risk assessment analysing heatstroke patients in Fukuoka City, Japan

Background

Climate change, as a defining issue of the current time, is causing severe heat-related illness in the context of extremely hot weather conditions. In Japan, the remarkable temperature increase in summer caused by an urban heat island and climate change has become a threat to public health in recent years.

Methods

This study aimed to determine the potential risk factors for heatstroke by analysing data extracted from the records of emergency transport to the hospital due to heatstroke in Fukuoka City, Japan. In this regard, a negative binomial regression model was used to account for overdispersion in the data. Age-structure analyses of heatstroke patients were also embodied to identify the sub-population of Fukuoka City with the highest susceptibility.

Results

The daily maximum temperature and wet-bulb globe temperature (WBGT), along with differences in both the mean temperature and time-weighted temperature from those of the consecutive past days were detected as significant risk factors for heatstroke. Results indicated that there was a positive association between the resulting risk factors and the probability of heatstroke occurrence. The elderly of Fukuoka City aged 70 years or older were found to be the most vulnerable to heatstroke. Most of the aforementioned risk factors also encountered significant and positive associations with the risk of heatstroke occurrence for the group with highest susceptibility.

Conclusion

These results can provide insights for health professionals and stakeholders in designing their strategies to reduce heatstroke patients and to secure the emergency transport systems in summer.

The Effect of Minimum and Maximum Air Temperatures in the Summer on Heat Stroke in Japan: A Time-Stratified Case-Crossover Study

An increase in the global surface temperature and changes in urban morphologies are associated with increased heat stress especially in urban areas. This can be one of the contributing factors underlying an increase in heat strokes. We examined the impact of summer minimum air temperatures, which often represent nighttime temperatures, as well as a maximum temperature on a heat stroke. We collected data from the records of daily ambulance transports for heat strokes and meteorological data for July and August of 2017–2019 in the Tottori Prefecture, Japan. A time-stratified case-crossover design was used to determine the association of maximum/minimum air temperatures and the incidence of heat strokes. We used a logistic regression to identify factors associated with the severity of heat strokes. A total of 1108 cases were identified with 373 (33.7%) calls originating in the home (of these, 59.8% were the age of ≥ 75). A total of 65.8% of cases under the age of 18 were related to exercise. Days with a minimum temperature ≥ 25 °C had an odds ratio (95% confidence interval) of 3.77 (2.19, 6.51) for the incidence of an exercise-related heat stroke (reference: days with a minimum temperature < 23 °C). The odds ratio for a heat stroke occurring at home or for calls for an ambulance to the home was 6.75 (4.47, 10.20). The severity of the heat stroke was associated with older age but not with air temperature. Minimum and maximum air temperatures may be associated with the incidence of heat strokes and in particular the former with non-exertional heat strokes.

Model-based approach for analyzing prevalence of nuclear cataracts in elderly residents

Recent epidemiological studies have hypothesized that the prevalence of cortical cataracts is closely related to ultraviolet radiation. However, the prevalence of nuclear cataracts is higher in elderly people in tropical areas than in temperate areas. The dominant factors inducing nuclear cataracts have been widely debated. In this study, the temperature increase in the lens due to exposure to ambient conditions was computationally quantified in subjects of 50-60 years of age in tropical and temperate areas, accounting for differences in thermoregulation. A thermoregulatory response model was extended to consider elderly people in tropical areas. The time course of lens temperature for different weather conditions in five cities in Asia was computed. The temperature was higher around the mid and posterior part of the lens, which coincides with the position of the nuclear cataract. The duration of higher temperatures in the lens varied, although the daily maximum temperatures were comparable. A strong correlation (adjusted R² > 0.85) was observed between the prevalence of nuclear cataract and the computed cumulative thermal dose in the lens. We propose the use of a cumulative thermal dose to assess the prevalence of nuclear cataracts. Cumulative wet-bulb globe temperature, a new metric computed from weather data, would be useful for practical assessment in different cities.

Influence of Absolute Humidity, Temperature and Population Density on COVID-19 Spread and Decay Durations: Multi-Prefecture Study in Japan

This study analyzed the spread and decay durations of the COVID-19 pandemic in different prefectures of Japan. During the pandemic, affordable healthcare was widely available in Japan and the medical system did not suffer a collapse, making accurate comparisons between prefectures possible. For the 16 prefectures included in this study that had daily maximum confirmed cases exceeding ten, the number of daily confirmed cases follow bell-shape or log-normal distribution in most prefectures. A good correlation was observed between the spread and decay durations. However, some exceptions were observed in areas where travelers returned from foreign countries, which were defined as the origins of infection clusters. Excluding these prefectures, the population density was shown to be a major factor, affecting the spread and decay patterns, with R² = 0.39 (p < 0.05) and 0.42 (p < 0.05), respectively, approximately corresponding to social distancing. The maximum absolute humidity was found to affect the decay duration normalized by the population density (R² > 0.36, p < 0.05). Our findings indicate that the estimated pandemic spread duration, based on the multivariate analysis of maximum absolute humidity, ambient temperature, and population density (adjusted R² = 0.53, p-value < 0.05), could prove useful for intervention planning during potential future pandemics, including a second COVID-19 outbreak.

Clinical characteristics, prognostic factors, and outcomes of heat-related illness (Heatstroke Study 2017-2018)

Aim

Heat‐related illness is common, but its epidemiology and pathological mechanism remain unclear. The aim of this study was to report current clinical characteristics, prognostic factors, and outcomes of heat‐related illness in Japan.

Methods

We undertook a prospective multicenter observational study in Japan. Only hospitalized patients with heat‐related illness were enrolled from 1 July to 30 September 2017 and 1 July to 30 September 2018.

Results

A total of 763 patients were enrolled in the study. Median age was 68 years (interquartile range, 49–82 years) and median body temperature on admission was 38.2°C (interquartile range, 36.8–39.8°C). Non‐exertional cause was 56.9% and exertional cause was 40.0%. The hospital mortality was 4.6%. The median Japanese Association for Acute Medicine disseminated intravascular coagulation (JAAM DIC), Sequential Organ Failure Assessment (SOFA), and Acute Physiology and Chronic Health Evaluation II (APACHE II) scores on admission were 1 (0–2), 4 (2–6), and 13 (8–22), respectively. To predict hospital mortality, areas under the receiver operating characteristic curves were 0.776 (JAAM DIC score), 0.825 (SOFA), and 0.878 (APACHE II). There were 632 cases defined as heatstroke by JAAM heat‐related illness criteria, 73 cases diagnosed as having DIC. A total of 16.6% patients had poor neurological outcome (modified Rankin Scale ≥ 4) at hospital discharge. In the multivariate analysis, Glasgow Coma Scale and platelets were independent predictors of mortality. Type of heatstroke, Glasgow Coma Scale, and platelets were independent predictors of poor neurological outcome. Body temperature was not associated with mortality or poor neurological outcome.

Conclusions

In this study, hospital mortality of heat‐related illness was <5%, one‐sixth of the patients had poor neurological outcome. The APACHE II, SOFA, and JAAM DIC scores predicted hospital mortality. Body temperature was not associated with mortality or poor neurological outcome.

Estimation of Time-Course Core Temperature and Water Loss in Realistic Adult and Child Models with Urban Micrometeorology Prediction

Ambient conditions may change rapidly and notably over time in urban areas. Conventional indices, such as the heat index and wet bulb globe temperature, are useful only in stationary ambient conditions. To estimate the risks of heat-related illness, human thermophysiological responses should be followed for ambient conditions in the time domain. We develop a computational method for estimating the time course of core temperature and water loss by combining micrometeorology and human thermal response. We firstly utilize an urban micrometeorology prediction to reproduce the environment surrounding walkers. The temperature elevations and sweating in a standard adult and child are then estimated for meteorological conditions. With the integrated computational method, we estimate the body temperature and thermophysiological responses for an adult and child walking along a street with two routes (sunny and shaded) in Tokyo on 7 August 2015. The difference in the core temperature elevation in the adult between the two routes was 0.11 °C, suggesting the necessity for a micrometeorology simulation. The differences in the computed body core temperatures and water loss of the adult and child were notable, and were mainly characterized by the surface area-to-mass ratio. The computational techniques will be useful for the selection of actions to manage the risk of heat-related illness and for thermal comfort.