최근 기후변화로 인한 평균기온이 지속적으로 상승함에 따라 폭염으로 인한
건강피해가 기상재해 중 가장 큰 것으로 밝혀졌다. 따라서 세계 각국에서 폭염으 로 인한 건강피해를 최소화하기 위한 폭염예경보시스템과 예경보 발령의 기준설 정에 관한 연구를 진행하고 있다. 우리나라는 2008년부터 서울지역의 초과사망률 에 대한 일 최고기온이 전국의 기준 기온으로 적용되고 있다. 한편 2010년부터 응급의료기관 기반 폭염 건강피해 사례감시체계의 운영되었다. 그에 따라 고온에 따른 온열질환자에 관한 연구가 보고되기 시작했다. 지금까지 국내 사망 자료를 이용한 폭염과 사망률 간 연관성 연구는 많았지만 폭염으로 인한 사망과 온열질 환 발생률 예측을 비교 분석한 연구는 없었다는 점에서 본 연구는 의미가 있다 고 할 수 있다.
본 연구 결과, 폭염감시체계에 가장 유용한 회귀모형은 일 최고기온 상승에
따른 온열질환 발생률 모형이며 기온 상승에 따른 사망률 모형보다 온열질환 발 생률 모형에서 기온의 상승시 건강영향을 민감하게 예측할 수 있었다. 그리고 현 재 폭염특보의 기준이 본 연구의 여러 모형에 대한 역치기온 보다 높아 폭염예 경보시스템의 보완이 필요할 것으로 판단된다.
또한 폭염과 응급의료기관 기반 온열질환자의 연관성을 좀 더 정확히 분석하
기 위해 혼란변수(대기오염, 지역간 비교시 지역특성)를 보정할 수 있는 역학 연 구와 폭염예경보 발령시기의 기준에 관한 좀 더 광범위한 연구가 필요할 것으로 사료된다.
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부 록
Appendix 1. Daily death counts and incidence rate during the summer (June-August) by age group in Korea for each year (2001-2011).
Year Daily death counts Incidence rate
Cause all ① Cause accident ② ②/①×100 ( % )
0-19 13,604 5,476 40.3
20-64 208,154 54,958 26.4
65- 421,842 28,951 6.9
Total* 643,600 89,385 13.9
* Excluding missing values
Appendix 2. Estimated threshold temperature and relative risk(RR) of accidental mortality associated with 1℃ increment of maximum temperature above threshold during the summer (June-August) by age group in South Korea, 2001-2011.
Age (year) Threshold (℃) RR 95% CI p value Adjusted R2
0-19 28.1 1.060 1.050-1.071 <0.001 0.100
20-64 21.6 1.006 1.004-1.010 <0.001 0.014
65- 25.1 1.011 1.007-1.015 <0.001 0.014
CI: confidence interval
R2: coefficient of determination
Appendix 3. Occurrence frequency (in days %) by year when daily maximum
temperature is over threshold temperature for daily patient counts with heat related illness, daily death counts maximum temperature, advisory and warning of heatwave during the summer (June-August) in South Korea, 2001-2012.
Year
TT1) 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2001-2012 30.52) 32.6 21.7 5.4 32.6 30.4 28.3 23.9 27.2 8.7 27.2 14.1 29.3 23.5
323) 14.1 3.3 1.1 20.7 12.0 17.4 14.1 10.9 1.1 9.8 1.1 22.8 10.7
334) 7.6 - - 14.1 3.3 13.0 2.2 1.1 - 5.4 - 13.0 5.0
355) - - - - - - - - - - - 1.1 0.1
1) TT : Threshold temperature (℃)
2) Threshold in daily patient counts with heat related illness 3) Threshold in daily death counts
4) Threshold in hea twave advisory 5) Threshold in heat wave warning
Appendix 4. Observed daily maximum temperature (line) during the summer
(June-August) in South Korea, 2001-2012 vs Threshold criteria level for advisory and warning of heatwave, daily patient counts with heat related illness, daily death counts maximum temperature.
[ABSTRACT]
A Comparison of the Prediction Models for the Impact of Heat Wave on Total Mortality and Incidence of Heat-related Illness
in the Republic of Korea
Hyun-Young Kim
Department of Biomedical Sciences The Graduate School, Ajou University
(Supervised by Associate Professor Jae-Yeon Jang)
Heatwave surveillance systems of patients with heat-related illness generated by the Korea Centers for Disease Control and Prevention have been reported since 2010. So, there are few studies about relationship between incidence of heat-related illness and temperature during heat wave in Korea. This study is to select the most appropriate prediction model for the impact of heat wave on total mortality and incidence of heat-related illness in Korea.
Using piecewise regression model, the study was selected the best goodness of fit with the model which were calculated by adjusted R2 between mortality during the summer (1 June to 31 August) from 2001 to 2011 and incidence of heat-related illness during the summer (1 June to 31 August) in 2011 and 2012. Then, threshold temperature showing the minimum Akaike's information criterion and the relative risk (RR) above the threshold temperature were calculated.
The adjusted R2 on the model of incidence of the heat-related illness with daily maximum temperature (0.836) was higher than that on the model of the mortality with daily maximum temperature (0.058). The threshold temperature on the model of the incidence of the heat-related illness with daily maximum temperature(30.5℃) was lower than that on the
model of the mortality with daily maximum temperature(32℃). Meanwhile, the threshold temperatures on the model of the incidence of heat-related illness with daily maximum temperature in Seoul, Gyeonggi-do and Chungcheong-do were higher than that of the model of the mortality with daily maximum temperature, having the explanation power of 0.841, 0.724, and 0.721, respectively. The threshold temperature of women (31.9℃) on the model of the heat-related illness with daily maximum temperature which was the best goodness of fit by sex was greater than that of men (30℃). The RR was the highest in the ≥65 age group on the model of the heat-related illness with daily maximum temperature which was the best goodness of fit by age group (1.803 [95% CI, 1.723-1.887]).
The model built by the daily maximum temperature and heat-related illness was the most regression model. There were differences in the threshold temperature and relative risk between the models of mortality and incidence of heat-related illness with the daily maximum temperature by regions, genders, and age groups. Average daily maximum temperature was 5 percent higher than heat wave warning issued 33 ℃. And there was no temperature threshold of research model is higher than heat wave warning issued 35 ℃.
Therefore it may be needed to establish relative criteria on heat wave warning system in Korea.
Key words: Heat wave, Mortality, Heat-related illness, Threshold temperature, Piecewise regression model