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.

Abstract
 1. 서 론
 2. 재료 및 방법
  2.1. 대상기간 및 자료
  2.2. 보건통계 분석 방법
 3. 결과 및 고찰
  3.1. 온열질환 발생현황
  3.2. 기온지표와 온열질환자 상관분석
  3.3. 기온지표에 따른 상대위험도 평가
 4. 결 론
 REFERENCES

• 황미경(부산대학교 대기환경과학과) | Mi-Kyoung Hwang (Department of Atmospheric Sciences, Pusan National University)
• 김유근(부산대학교 대기환경과학과) | Yoo-Keun Kim (Department of Atmospheric Sciences, Pusan National University)
• 오인보(울산대학교 의과대학 환경보건센터) | Inbo Oh (Environmental Health Center, University of Ulsan College of Medicine) Corresponding author