Web-Based Data to Quantify Meteorological and Geographical Effects on Heat Stroke: Case Study in China

Abstract

Heat stroke is a serious heat-related health outcome that can eventually lead to death. Due to the poor accessibility of heat stroke data, the large-scale relationship between heat stroke and meteorological factors is still unclear. This work aims to clarify the potential relationship between meteorological variables and heat stroke, and quantify the meteorological threshold that affected the severity of heat stroke. We collected daily heat stroke search index (HSSI) and meteorological data for the period 2013-2020 in 333 Chinese cities to analyze the relationship between meteorological variables and HSSI using correlation analysis and Random forest (RF) model. Temperature and relative humidity (RH) accounted for 62% and 9% of the changes of HSSI, respectively. In China, cases of heat stroke may start to occur when temperature exceeds 36°C and RH exceeds 58%. This threshold was 34.5°C and 79% in the north of China, and 36°C and 48% in the south of China. Compared to RH, the threshold of temperature showed a more evident difference affected by altitude and distance from the ocean, which was 35.5°C in inland cities and 36.5°C in coastal cities; 35.5°C in high-altitude cities and 36°C in low-altitude cities. Our findings provide a possible way to analyze the interaction effect of meteorological variables on heat-related illnesses, and emphasizes the effects of geographical environment. The meteorological threshold quantified in this research can also support policymaker to establish a better meteorological warning system for public health.

Keywords: China; heat strokes; internet search data; meteorological variables; random forest; spatial variations.

Figure 1

Spatial distribution of northern and southern cities.

Figure 2

Partial Spearman correlation coefficients (p < 0.05) between meteorological factors and heat stroke search index (HSSI). (a) Maximum temperature (b) Relative humidity (RH) (c) Evaporation (d) Sunshine duration (e) Wind speed. Points in red are positive and in blue are negative. All points shown on the Figure have passed the significance test. The table in the Figure shows the number of cities in different ranges of correlation coefficients.

Figure 3

Relative importance ranking of meteorological factors obtained from the Random Forest model. The boxes in red represent all 28 cities, boxes in green and blue represent the cities in the north and south of China, respectively. Black dots are outliers.

Figure 4

Partial dependence between meteorological variables and heat stroke search index (HSSI). The blue solid lines represent the mean of the variable across 28 cities, and the gray shades represent the standard deviation of the variable across the 28 cities.

Figure 5

Partial dependence of heat stroke search index (HSSI) on maximum temperature and relative humidity (RH). The color bar refers to values of predicted HSSI.