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Mortality burden attributable to high and low ambient temperatures in China and its provinces: Results from the Global Burden of Disease Study 2019

BACKGROUND: Non-optimal temperatures are associated with mortality risk, yet the heterogeneity of temperature-attributable mortality burden across subnational regions in a country was rarely investigated. We estimated the mortality burden related to non-optimal temperatures across all provinces in C...

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Autores principales: Liu, Jiangmei, Liu, Tao, Burkart, Katrin G., Wang, Haidong, He, Guanhao, Hu, Jianxiong, Xiao, Jianpeng, Yin, Peng, Wang, Lijun, Liang, Xiaofeng, Zeng, Fangfang, Stanaway, Jeffrey D., Brauer, Michael, Ma, Wenjun, Zhou, Maigeng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9213765/
https://www.ncbi.nlm.nih.gov/pubmed/35756888
http://dx.doi.org/10.1016/j.lanwpc.2022.100493
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author Liu, Jiangmei
Liu, Tao
Burkart, Katrin G.
Wang, Haidong
He, Guanhao
Hu, Jianxiong
Xiao, Jianpeng
Yin, Peng
Wang, Lijun
Liang, Xiaofeng
Zeng, Fangfang
Stanaway, Jeffrey D.
Brauer, Michael
Ma, Wenjun
Zhou, Maigeng
author_facet Liu, Jiangmei
Liu, Tao
Burkart, Katrin G.
Wang, Haidong
He, Guanhao
Hu, Jianxiong
Xiao, Jianpeng
Yin, Peng
Wang, Lijun
Liang, Xiaofeng
Zeng, Fangfang
Stanaway, Jeffrey D.
Brauer, Michael
Ma, Wenjun
Zhou, Maigeng
author_sort Liu, Jiangmei
collection PubMed
description BACKGROUND: Non-optimal temperatures are associated with mortality risk, yet the heterogeneity of temperature-attributable mortality burden across subnational regions in a country was rarely investigated. We estimated the mortality burden related to non-optimal temperatures across all provinces in China in 2019. METHODS: The global daily temperature data were obtained from the ERA5 reanalysis dataset. The daily mortality data and exposure–response curves between daily temperature and mortality for 176 individual causes of death were obtained from the Global Burden of Disease Study 2019 (GBD 2019). We estimated the population attributable fraction (PAF) based on the exposure-response curves, daily gridded temperature, and population. We calculated the cause- and province-specific mortality burden based on PAF and disease burden data from the GBD 2019. FINDINGS: We estimated that 593·9 (95% UI:498·8, 704·6) thousand deaths were attributable to non-optimal temperatures in China in 2019 (PAF=5·58% [4·93%, 6·28%]), with 580·8 (485·7, 690·1) thousand cold-related deaths and 13·9 (7·7, 23·2) thousand heat-related deaths. The majority of temperature-related deaths were from cardiovascular diseases (399·7 [322·8, 490·4] thousand) and chronic respiratory diseases (177·4 [141·4, 222·3] thousand). The mortality burdens were observed significantly spatial heterogeneity for both high and low temperatures. For instance, the age-standardized death rates (per 100 000) attributable to low temperature were higher in Western China, with the highest in Tibet (113·7 [82·0, 155·5]), while for high temperature, they were greater in Xinjiang (1·8 [0·7, 3·3]) and Central-Southern China such as Hainan (2·5 [0·9, 5·4]). We also observed considerable geographical variation in the temperature-related mortality burden by causes of death at provincial level. INTERPRETATION: A substantial mortality burden was attributable to non-optimal temperatures across China, and cold effects dominated the total mortality burden in all provinces. Both cold- and heat-related mortality burden showed significantly spatial variations across China. FUNDING: National Key Research and Development Program.
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spelling pubmed-92137652022-06-23 Mortality burden attributable to high and low ambient temperatures in China and its provinces: Results from the Global Burden of Disease Study 2019 Liu, Jiangmei Liu, Tao Burkart, Katrin G. Wang, Haidong He, Guanhao Hu, Jianxiong Xiao, Jianpeng Yin, Peng Wang, Lijun Liang, Xiaofeng Zeng, Fangfang Stanaway, Jeffrey D. Brauer, Michael Ma, Wenjun Zhou, Maigeng Lancet Reg Health West Pac Articles BACKGROUND: Non-optimal temperatures are associated with mortality risk, yet the heterogeneity of temperature-attributable mortality burden across subnational regions in a country was rarely investigated. We estimated the mortality burden related to non-optimal temperatures across all provinces in China in 2019. METHODS: The global daily temperature data were obtained from the ERA5 reanalysis dataset. The daily mortality data and exposure–response curves between daily temperature and mortality for 176 individual causes of death were obtained from the Global Burden of Disease Study 2019 (GBD 2019). We estimated the population attributable fraction (PAF) based on the exposure-response curves, daily gridded temperature, and population. We calculated the cause- and province-specific mortality burden based on PAF and disease burden data from the GBD 2019. FINDINGS: We estimated that 593·9 (95% UI:498·8, 704·6) thousand deaths were attributable to non-optimal temperatures in China in 2019 (PAF=5·58% [4·93%, 6·28%]), with 580·8 (485·7, 690·1) thousand cold-related deaths and 13·9 (7·7, 23·2) thousand heat-related deaths. The majority of temperature-related deaths were from cardiovascular diseases (399·7 [322·8, 490·4] thousand) and chronic respiratory diseases (177·4 [141·4, 222·3] thousand). The mortality burdens were observed significantly spatial heterogeneity for both high and low temperatures. For instance, the age-standardized death rates (per 100 000) attributable to low temperature were higher in Western China, with the highest in Tibet (113·7 [82·0, 155·5]), while for high temperature, they were greater in Xinjiang (1·8 [0·7, 3·3]) and Central-Southern China such as Hainan (2·5 [0·9, 5·4]). We also observed considerable geographical variation in the temperature-related mortality burden by causes of death at provincial level. INTERPRETATION: A substantial mortality burden was attributable to non-optimal temperatures across China, and cold effects dominated the total mortality burden in all provinces. Both cold- and heat-related mortality burden showed significantly spatial variations across China. FUNDING: National Key Research and Development Program. Elsevier 2022-06-16 /pmc/articles/PMC9213765/ /pubmed/35756888 http://dx.doi.org/10.1016/j.lanwpc.2022.100493 Text en © 2022 The Authors https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Articles
Liu, Jiangmei
Liu, Tao
Burkart, Katrin G.
Wang, Haidong
He, Guanhao
Hu, Jianxiong
Xiao, Jianpeng
Yin, Peng
Wang, Lijun
Liang, Xiaofeng
Zeng, Fangfang
Stanaway, Jeffrey D.
Brauer, Michael
Ma, Wenjun
Zhou, Maigeng
Mortality burden attributable to high and low ambient temperatures in China and its provinces: Results from the Global Burden of Disease Study 2019
title Mortality burden attributable to high and low ambient temperatures in China and its provinces: Results from the Global Burden of Disease Study 2019
title_full Mortality burden attributable to high and low ambient temperatures in China and its provinces: Results from the Global Burden of Disease Study 2019
title_fullStr Mortality burden attributable to high and low ambient temperatures in China and its provinces: Results from the Global Burden of Disease Study 2019
title_full_unstemmed Mortality burden attributable to high and low ambient temperatures in China and its provinces: Results from the Global Burden of Disease Study 2019
title_short Mortality burden attributable to high and low ambient temperatures in China and its provinces: Results from the Global Burden of Disease Study 2019
title_sort mortality burden attributable to high and low ambient temperatures in china and its provinces: results from the global burden of disease study 2019
topic Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9213765/
https://www.ncbi.nlm.nih.gov/pubmed/35756888
http://dx.doi.org/10.1016/j.lanwpc.2022.100493
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