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http://dx.doi.org/10.5322/JESI.2019.28.7.617

Health Risk Estimation for Daily Maximum Temperature in the Summer Season using Healthcare Big Data  

Hwang, Mi-Kyoung (Department of Atmospheric Sciences, Pusan National University)
Kim, Yoo-Keun (Department of Atmospheric Sciences, Pusan National University)
Oh, Inbo (Environmental Health Center, University of Ulsan College of Medicine)
Publication Information
Journal of Environmental Science International / v.28, no.7, 2019 , pp. 617-627 More about this Journal
Abstract
This study investigated the relationship between heat-related illnesses obtained from healthcare big data and daily maximum temperature observed in seven metropolitan cities in summer during 2013~2015. We found a statistically significant positive correlation (r = 0.4~0.6) between daily maximum temperature and number of the heat-related patients from Pearson's correlation analyses. A time lag effect was not observed. Relative Risk (RR) analysis using the Generalized Additive Model (GAM) showed that the RR of heat-related illness increased with increasing threshold temperature (maximum RR = 1.21). A comparison of the RRs of the seven cities, showed that the values were significantly different by geographical location of the city and had different variations for different threshold temperatures. The RRs for elderly people were clearly higher than those for the all-age group. Especially, a maximum value of 1.83 was calculated at the threshold temperature of $35^{\circ}C$ in Seoul. In addition, relatively higher RRs were found for inland cities (Seoul, Gwangju, Daegu, and Daejeon), which had a high frequency of heat waves. These results demonstrate the significant risk of heat-related illness associated with increasing daily maximum temperature and the difference in adaptation ability to heat wave for each city, which could help improve the heat wave advisory and warning system.
Keywords
Heat wave; Generalized additive model; Relative risk; Healthcare big data;
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