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Estimation of heat-attributable mortality using the cross-validated best temperature metric in Switzerland and south Koreaoa mark
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Publication Year
2021-06-02
Publisher
MDPI AG
Citation
International Journal of Environmental Research and Public Health, Vol.18
Keyword
Cross validationDLNMTemperature-mortality association
Mesh Keyword
CitiesHot TemperatureMortalityRepublic of KoreaSwitzerlandTemperature
All Science Classification Codes (ASJC)
PollutionPublic Health, Environmental and Occupational HealthHealth, Toxicology and Mutagenesis
Abstract
This study presents a novel method for estimating the heat-attributable fractions (HAF) based on the cross-validated best temperature metric. We analyzed the association of eight temperature metrics (mean, maximum, minimum temperature, maximum temperature during daytime, minimum temperature during nighttime, and mean, maximum, and minimum apparent temperature) with mortality and performed the cross-validation method to select the best model in selected cities of Switzerland and South Korea from May to September of 1995–2015. It was observed that HAF estimated using different metrics varied by 2.69–4.09% in eight cities of Switzerland and by 0.61–0.90% in six cities of South Korea. Based on the cross-validation method, mean temperature was estimated to be the best metric, and it revealed that the HAF of Switzerland and South Korea were 3.29% and 0.72%, respectively. Furthermore, estimates of HAF were improved by selecting the best city-specific model for each city, that is, 3.34% for Switzerland and 0.78% for South Korea. To the best of our knowledge, this study is the first to observe the uncertainty of HAF estimation originated from the selection of temperature metric and to present the HAF estimation based on the cross-validation method.
Language
eng
URI
https://dspace.ajou.ac.kr/dev/handle/2018.oak/32076
DOI
https://doi.org/10.3390/ijerph18126413
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Type
Article
Funding
Funding: This study was supported by the National Research Foundation of Korea in the form of NRF-SNSF Researcher Exchange Program [grant number NRF-2018K2A9A1A06086694] and NSF [grant number NRF-2021R1C1C1013350].
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Lee, Jae Young  Image
Lee, Jae Young 이재영
Department of Environmental and Safety Engineering
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