Governments, healthcare organizations, and various agencies need precise models concerning health issues to effectively plan their initiatives. Climate change significantly affects society, including its impact on human health and mortality rates. Nevertheless, existing models that evaluate the health consequences of climate change often overlook certain environmental factors, particularly humidity, which can affect how individuals experience heat stress. This presents an opportunity for enhancement. Researchers, including a team from the University of Tokyo, have successfully integrated humidity data from numerous cities into what are known as heat stress indicators (HSIs) and evaluated how well they predict deaths caused by heat.
Public authorities, healthcare providers, and various entities necessitate reliable models regarding health topics to optimize their strategies. The effects of climate change on society are noteworthy, particularly regarding human mortality. Despite this, the existing models evaluating the impact of climate change on health fail to consider all environmental factors, especially humidity, which can affect the perception of heat stress by individuals. This indicates that there is room for enhancement. Researchers, including those from the University of Tokyo, have successfully included humidity data from hundreds of cities into heat stress indicators (HSIs) for the first time and assessed their effectiveness in predicting heat-related fatalities.
Climate change was previously referred to as global warming, and with good reason—temperature surges are occurring worldwide. However, other factors are equally significant, one of which is humidity, particularly critical in certain regions. Humidity, or the amount of moisture in the air, plays a vital role in our ability to cool down through sweating; when sweat evaporates from our skin, it cools us. In environments with high humidity, the process of evaporative cooling becomes less effective, and beyond a certain point, it no longer works.
“I was researching how irrigation around urban areas affects heat stress and its connection to human health,” explained Qiang Guo, a research fellow from the University of Tokyo’s Department of Global Health Policy. “Depending on the HSIs we examined, the outcomes and implications varied significantly. This inconsistency motivated my team and me to identify the optimal combination of temperature and humidity to more accurately estimate the heat stress experienced by people and ensure this method could be applicable across different settings.”
Guo’s team compiled daily mortality and climate data—including air temperature, relative humidity, wind speed, and solar radiation—for 739 cities across 43 countries and territories. They developed eight distinct HSIs using this climate data. Most HSIs incorporate air temperature and humidity, while some also factor in wind speed and solar radiation. Employing advanced models such as distributed lag nonlinear models and machine learning, they discovered that the key determinant for the performance of HSIs in various locations is the interplay between daily temperature and humidity.
“The success of HSIs that include humidity varies based on geographical context. We identified regions where humid heat serves as a more reliable predictor for heat-related deaths, such as coastal areas and large lakes in the U.S., Peru, South Korea, and Japan. Utilizing HSIs in these areas—like wet bulb globe temperature, which reflects human heat perception—could enhance the effectiveness of heat-health warning systems,” Guo noted. “Nevertheless, numerous other factors need to be taken into account, such as socioeconomic conditions. Our study primarily focused on developed regions due to data availability, meaning many developing areas facing extreme heat stress were excluded from our analysis. As a result, we aim to gather more data and perform analyses in the Global South in the future. Our objective is to provide aid to populations in developing economies to mitigate the health impacts associated with extreme heat stress.”
