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

Using transformer-based models and social media posts for heat stroke detection

Event-based surveillance is crucial for the early detection and rapid response to potential public health risks. In recent years, social networking services (SNS) have been recognized for their potential role in this domain. Previous studies have demonstrated the capacity of SNS posts for the early detection of health crises and affected individuals, including those related to infectious diseases. However, the reliability of such posts, being subjective and not clinically diagnosed, remains a challenge. In this study, we address this issue by assessing the classification performance of transformer-based pretrained language models to accurately classify Japanese tweets related to heat stroke, a significant health effect of climate change, as true or false. We also evaluated the efficacy of combining SNS and artificial intelligence for event-based public health surveillance by visualizing the data on correctly classified tweets and heat stroke emergency medical evacuees in time-space and animated video, respectively. The transformer-based pretrained language models exhibited good performance in classifying the tweets. Spatiotemporal and animated video visualizations revealed a reasonable correlation. This study demonstrates the potential of using Japanese tweets and deep learning algorithms based on transformer networks for event-based surveillance at high spatiotemporal levels to enable early detection of heat stroke risks.

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.

Development and validation of a prognostic model of survival for classic heatstroke patients: a multicenter study

Classic heatstroke (CHS) is a life-threatening illness characterized by extreme hyperthermia, dysfunction of the central nervous system and multiorgan failure. Accurate predictive models are useful in the treatment decision-making process and risk stratification. This study was to develop and externally validate a prediction model of survival for hospitalized patients with CHS. In this retrospective study, we enrolled patients with CHS who were hospitalized from June 2022 to September 2022 at 3 hospitals in Southwest Sichuan (training cohort) and 1 hospital in Central Sichuan (external validation cohort). Prognostic factors were identified utilizing least absolute shrinkage and selection operator (LASSO) regression analysis and multivariate Cox regression analysis in the training cohort. A predictive model was developed based on identified prognostic factors, and a nomogram was built for visualization. The areas under the receiver operator characteristic (ROC) curves (AUCs) and the calibration curve were utilized to assess the prognostic performance of the model in both the training and external validation cohorts. The Kaplan‒Meier method was used to calculate survival rates. A total of 225 patients (median age, 74 [68-80] years) were included. Social isolation, self-care ability, comorbidities, body temperature, heart rate, Glasgow Coma Scale (GCS), procalcitonin (PCT), aspartate aminotransferase (AST) and diarrhea were found to have a significant or near-significant association with worse prognosis among hospitalized CHS patients. The AUCs of the model in the training and validation cohorts were 0.994 (95% [CI], 0.975-0.999) and 0.901 (95% [CI], 0.769-0.968), respectively. The model's prediction and actual observation demonstrated strong concordance on the calibration curve regarding 7-day survival probability. According to K‒M survival plots, there were significant differences in survival between the low-risk and high-risk groups in the training and external validation cohorts. We designed and externally validated a prognostic prediction model for CHS. This model has promising predictive performance and could be applied in clinical practice for managing patients with CHS.

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.

Reconstruction of the hourly fine-resolution apparent temperature (Humidex) with the aerodynamic parameters

Apparent temperature is the preferred measure of hotness or coldness expressed to depict the human sense. Spatially explicit measurement of the hourly apparent temperature is essential for capturing the threats to bioclimatic comfort and preventing potential mortality/morbidity risk from heat or cold. However, existing apparent temperature products only provide daily observations at the spatial resolution of several dozen kilometers, resulting in some substantial underestimations for some life-threatening thermal stresses highly localized in space and time. Furthermore, some data-driven models lack mechanical constraints on the turbulent exchange between the surface and the atmosphere, making some unsatisfactory accuracy. Here, we propose Humidex reconstruction model incorporating atmospheric dynamics theory and aerodynamic parameters (i.e., heat and momentum roughness lengths for natural surfaces and three urban canopy geometry parameters for artificial surfaces), capable of developing an hourly dataset at fine-grained spatial resolution (0.01° × 0.01°). In this study, a total of 2952 h in four seasons were selected to test the seasonal performance of this model, taking the Yangtze River Delta as an example. The results show that the Humidex products from this model generally outperform the existing comparable products, with the hourly population root mean square error (RMSE) ranging from 1 to 2 °C in winter and autumn and 2-3 °C in spring and summer. Moreover, the constraint of aerodynamic parameters can reduce RMSE with a significant margin for each season, up to 2 °C, especially in areas with dense woodlands or buildings. In addition, the results demonstrate the excellent performance of this model in capturing short-lived thermal health threats, which are easily overlooked when observed data only provides a daily variation. This indicates that the model can allow researchers and practitioners investigate the fine-grained spatial and temporal evolution of thermal stress and its impact on public health, tourism, learning, and work performance.

Indicators for climate change-driven urban health impact assessment

Climate change can cause multiply potential health issues in urban areas, which is the most susceptible environment in terms of the presently increasing climate volatility. Urban greening strategies make an important part of the adaptation strategies which can ameliorate the negative impacts of climate change. It was aimed to study the potential impacts of different kinds of greenings against the adverse effects of climate change, including waterborne, vector-borne diseases, heat-related mortality, and surface ozone concentration in a medium-sized Hungarian city. As greening strategies, large and pocket parks were considered, based on our novel location identifier algorithm for climate risk minimization. A method based on publicly available data sources including satellite pictures, climate scenarios and urban macrostructure has been developed to evaluate the health-related indicator patterns in cities. The modelled future- and current patterns of the indicators have been compared. The results can help the understanding of the possible future state of the studied indicators and the development of adequate greening strategies. Another outcome of the study is that it is not the type of health indicator but its climate sensitivity that determines the extent to which it responds to temperature rises and how effective greening strategies are in addressing the expected problem posed by the factor.

Heatstroke-related ambulance dispatch risk before and during COVID-19 pandemic: Subgroup analysis by age, severity, and incident place

BACKGROUND

In summer 2020 under the COVID-19 pandemic, the Ministry of Health, Labour and Welfare has made public warnings that specific preventive measures such as maskwearing and stay-at-home orders, may increase heatstroke risk. In our previous work, we found a lower risk of heatstroke-related ambulance dispatches (HSAD) during the COVID-19 period, however, it is uncertain whether similar risk reductions can be observed in different vulnerable subgroups. This study aimed to determine the HSAD risk during the COVID-19 pandemic by age, severity, and incident place subgroups.

METHOD

A summer-specific (June-September), time-series analysis was performed, using daily HSAD and meteorological data from 47 Japanese prefectures from 2017 to 2020. A two-stage analysis was applied to determine the association between HSAD and COVID-19 pandemic, adjusting for maximum temperature, humidity, seasonality, and relevant temporal adjustments. A generalized linear model was utilized in the first stage to estimate the prefecture-specific effect estimates. Thereafter, a fixed effect meta-analysis in the second stage was implemented to pool the first stage estimates. Subsequently, subgroup analysis via an interaction by age, severity, and incident place was used to analyze the HSAD risk among subgroups.

RESULTS

A total of 274,031 HSAD cases was recorded across 47 Japanese prefectures. The average total number of HSAD in the pre-COVID-19 period was 69,721, meanwhile, the COVID-19 period was 64,869. Highest reductions in the risks was particularly observed in the young category (ratio of relative risk (RRR) = 0.54, 95% Confidential Interval (CI): 0.51, 0.57) compared to the elderly category. Whereas highest increment in the risks were observed in severe/death (RRR = 1.25, 95% CI: 1.13, 1.37) compared to the mild category.

CONCLUSION

COVID-19 situation exhibited a non-uniform change in the HSAD risk for all subgroups, with the magnitude of the risks varying by age, severity, and incident place.

Establishment and characterization of immortalized sweat gland myoepithelial cells

Sweat glands play an important role in thermoregulation via sweating, and protect human vitals. The reduction in sweating may increase the incidence of hyperthermia. Myoepithelial cells in sweat glands exhibit stemness characteristics and play a major role in sweat gland homeostasis and sweating processes. Previously, we successfully passaged primary myoepithelial cells in spheroid culture systems; however, they could not be maintained for long under in vitro conditions. No myoepithelial cell line has been established to date. In this study, we transduced two immortalizing genes into primary myoepithelial cells and developed a myoepithelial cell line. When compared with primary sweat gland cells, the immortalized myoepithelial cells (designated "iEM") continued to form spheroids after the 4th passage and expressed α-smooth muscle actin and other proteins that characterize myoepithelial cells. Furthermore, treatment with small compounds targeting the Wnt signaling pathways induced differentiation of iEM cells into luminal cells. Thus, we successfully developed an immortalized myoepithelial cell line having differentiation potential. As animal models are not useful for studying human sweat glands, our cell line will be helpful for studying the mechanisms underlying the pathophysiology of sweating disorders.