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Health Risk Estimation for Daily Maximum Temperature in the Summer Season using Healthcare Big Data
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보건의료빅데이터를 이용한 여름철 일최고기온에 대한 건강위험도 평가

논문 기본 정보

Type
Academic journal
Author
Mi-Kyoung Hwang (부산대학교) Yoo-Keun Kim (부산대학교) Inbo Oh (울산대학교)
Journal
The Korean Environmental Sciences Society Journal of Environmental Science International Vol.28 No.7 KCI Accredited Journals
Published
2019.7
Pages
617 - 627 (11page)

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Health Risk Estimation for Daily Maximum Temperature in the Summer Season using Healthcare Big Data
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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 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.
#Heat wave #Generalized additive model #Relative risk #Healthcare big data

Contents

Abstract
1. 서론
2. 재료 및 방법
3. 결과 및 고찰
4. 결론
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