Guo Qiang, Phung Vera Ling Hui, Ng Chris Fook Sheng, Oka Kazutaka, Honda Yasushi, Hashizume Masahiro
Department of Global Health Policy Graduate School of Medicine The University of Tokyo Tokyo Japan.
Center for Climate Change Adaptation National Institute for Environmental Studies Ibaraki Japan.
Geohealth. 2025 Apr 1;9(4):e2024GH001257. doi: 10.1029/2024GH001257. eCollection 2025 Apr.
The increasing threat of heat stress poses significant risks to human health globally. To quantify heat exposure more effectively, integrated heat stress indicators (HSIs) have been developed to simplify the classification of heat stress severity and assist in public heat warnings. However, their ability to accurately predict daily heat stroke cases has not been fully assessed. In this study, we evaluated the performance of multiple HSIs in forecasting the number of heat stroke-related emergency ambulance dispatches (HT-EADs) across 47 prefectures in Japan and compared their accuracy to models using raw meteorological variables. Our results indicate that, while HSIs simplify the process of assessing heat stress, they generally show lower performances than models based on raw meteorological data. Among the eight HSIs tested, the Wet Bulb Globe Temperature ( ) showed the strongest predictive power, with median values of 0.77 and 0.70 for the calibration and validation periods, respectively. However, models incorporating air temperature, relative humidity, wind speed, and solar radiation outperformed , achieving values of 0.85 and 0.74. We also observed spatial variability in HSI performance, particularly in cooler regions like Hokkaido, where HSIs provided no improvement over temperature alone. Given these findings, we recommend that HSIs be rigorously evaluated with local health data before being used in heat warning systems for specific locations. For predictions requiring high accuracy, raw meteorological variables could be prioritized to ensure greater precision.
热应激威胁的不断增加给全球人类健康带来了重大风险。为了更有效地量化热暴露，已开发出综合热应激指标（HSIs），以简化热应激严重程度的分类并协助发布公共热警报。然而，它们准确预测每日中暑病例的能力尚未得到充分评估。在本研究中，我们评估了多个HSIs在预测日本47个县中暑相关紧急救护车派遣（HT-EADs）数量方面的表现，并将其准确性与使用原始气象变量的模型进行了比较。我们的结果表明，虽然HSIs简化了评估热应激的过程，但它们的表现通常低于基于原始气象数据的模型。在测试的八个HSIs中，湿球黑球温度（）显示出最强的预测能力，在校准期和验证期的中位数分别为0.77和0.70。然而，纳入气温、相对湿度、风速和太阳辐射的模型表现优于，其值分别为0.85和0.74。我们还观察到HSIs性能的空间变异性，特别是在北海道等较凉爽的地区，在这些地区HSIs相对于单独的温度并没有改进。鉴于这些发现，我们建议在将HSIs用于特定地点的热警报系统之前，应使用当地健康数据对其进行严格评估。对于需要高精度的预测，可以优先考虑原始气象变量以确保更高的精度。
