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.
Highlights
Excessive heat exposure is a well-known public health problem
Metzger et al (2009) revealed that the maximum apparent temperature was better at predicting heat-related mortality in New York City, as compared to the daily mean, minimum, or maximum temperature based on the deviance explained metric [22]
This study modeled the relationship of temperature and mortality in selected cities of Switzerland and South Korea, based on eight temperature metrics
Summary
Excessive heat exposure is a well-known public health problem. Several studies have examined the association between daily temperature and mortality based on historical data [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17]. The best predictor of heat-related mortality has been questioned and studied [18,19,20,21,22]. 107 US cities, various temperature indices have the same predictive ability based on crossvalidated residual [18]. Hajat et al (2006) showed that in three European cities (London, Budapest, and Milan), the daily mean temperature was a better predictor of mortality than the daily maximum or daily minimum temperature because it characterized the complete profile of daily exposure [19]. Metzger et al (2009) revealed that the maximum apparent temperature was better at predicting heat-related mortality in New York City, as compared to the daily mean, minimum, or maximum temperature based on the deviance explained metric [22]
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