Nonlinear relationship between extreme temperature and mortality in different temperature zones: A systematic study of 122 communities across the mainland of China.

Abstract

Numerous previous studies have reported that human health risk is extremely sensitive to temperature. Very few studies, however, have characterized the relationship between temperature and mortality in different temperature zones due to the previous conclusions deduced from a regional or administrative division. A research covers different temperature zones was indispensable to have a comprehensive understanding of regional ambient temperature effect on public health. Based on the mortality dataset and meteorological variables of 122 communities in China from 2007 to 2012, a distributed lag nonlinear model (DLNM) was utilized to estimate the temperature effect on non-accidental mortality at the community level. Then, a meta-regression analysis was applied to pool the estimates of community-specific effects in various latitude-effected temperature zones. At the community level, the mean value of relative extreme cold risk (1.63) of all 122 communities was higher than that of extreme high temperature (1.15). At regional level, we found temperature-mortality relationship (e.g., U- or J-shaped) varied in different temperature zones. Meanwhile, the minimum-mortality temperature of each zone was near the 75th percentile of local mean temperature except the north subtropics (50th percentiles). Lag effect was also obvious, especially for cold effect. An interesting M-shaped curve for the relationship between cold risk and temperature was detected, while an inverted "U" shaped with a right tail for the heat effect. Such different responses might be attributed to the difference in social-economic status of temperature zones. The temperature-mortality relationship showed a distinct spatial heterogeneity along temperature zones across the Chinese mainland. Different characteristics of mortality responding to cold and heat stress highlighted the fact that, apart from the circumstance of temperature, the social-economic condition was also linked with health risk. Our findings suggest decision-makers should take more adaptive and effective measures to reduce health risks in China.