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The influence of temperature on mortality and its Lag effect: a study in four Chinese cities with different latitudes

BACKGROUND: Global climate change is one of the most serious environmental issues faced by humanity, and the resultant change in frequency and intensity of heat waves and cold spells could increase mortality. The influence of temperature on human health could be immediate or delayed. Latitude, relat...

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Autores principales: Bao, Junzhe, Wang, Zhenkun, Yu, Chuanhua, Li, Xudong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4855424/
https://www.ncbi.nlm.nih.gov/pubmed/27146378
http://dx.doi.org/10.1186/s12889-016-3031-z
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author Bao, Junzhe
Wang, Zhenkun
Yu, Chuanhua
Li, Xudong
author_facet Bao, Junzhe
Wang, Zhenkun
Yu, Chuanhua
Li, Xudong
author_sort Bao, Junzhe
collection PubMed
description BACKGROUND: Global climate change is one of the most serious environmental issues faced by humanity, and the resultant change in frequency and intensity of heat waves and cold spells could increase mortality. The influence of temperature on human health could be immediate or delayed. Latitude, relative humidity, and air pollution may influence the temperature–mortality relationship. We studied the influence of temperature on mortality and its lag effect in four Chinese cities with a range of latitudes over 2008–2011, adjusting for relative humidity and air pollution. METHODS: We recorded the city-specific distributions of temperature and mortality by month and adopted a Poisson regression model combined with a distributed lag nonlinear model to investigate the lag effect of temperature on mortality. RESULTS: We found that the coldest months in the study area are December through March and the hottest months are June through September. The ratios of deaths during cold months to hot months were 1.43, 1.54, 1.37 and 1.12 for the cities of Wuhan, Changsha, Guilin and Haikou, respectively. The effects of extremely high temperatures generally persisted for 3 days, whereas the risk of extremely low temperatures could persist for 21 days. Compared with the optimum temperature of each city, at a lag of 21 days, the relative risks (95 % confidence interval) of extreme cold temperatures were 4.78 (3.63, 6.29), 2.38 (1.35, 4.19), 2.62 (1.15, 5.95) and 2.62 (1.44, 4.79) for Wuhan, Changsha, Guilin and Haikou, respectively. The respective risks were 1.35 (1.18, 1.55), 1.19 (0.96, 1.48), 1.22 (0.82, 1.82) and 2.47 (1.61, 3.78) for extreme hot temperatures, at a lag of 3 days. CONCLUSIONS: Temperature–mortality relationships vary among cities at different latitudes. Local governments should establish regional prevention and protection measures to more effectively confront and adapt to local climate change. The effects of hot temperatures predominantly occur over the short term, whereas those of cold temperatures can persist for an extended number of days. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12889-016-3031-z) contains supplementary material, which is available to authorized users.
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spelling pubmed-48554242016-05-05 The influence of temperature on mortality and its Lag effect: a study in four Chinese cities with different latitudes Bao, Junzhe Wang, Zhenkun Yu, Chuanhua Li, Xudong BMC Public Health Research Article BACKGROUND: Global climate change is one of the most serious environmental issues faced by humanity, and the resultant change in frequency and intensity of heat waves and cold spells could increase mortality. The influence of temperature on human health could be immediate or delayed. Latitude, relative humidity, and air pollution may influence the temperature–mortality relationship. We studied the influence of temperature on mortality and its lag effect in four Chinese cities with a range of latitudes over 2008–2011, adjusting for relative humidity and air pollution. METHODS: We recorded the city-specific distributions of temperature and mortality by month and adopted a Poisson regression model combined with a distributed lag nonlinear model to investigate the lag effect of temperature on mortality. RESULTS: We found that the coldest months in the study area are December through March and the hottest months are June through September. The ratios of deaths during cold months to hot months were 1.43, 1.54, 1.37 and 1.12 for the cities of Wuhan, Changsha, Guilin and Haikou, respectively. The effects of extremely high temperatures generally persisted for 3 days, whereas the risk of extremely low temperatures could persist for 21 days. Compared with the optimum temperature of each city, at a lag of 21 days, the relative risks (95 % confidence interval) of extreme cold temperatures were 4.78 (3.63, 6.29), 2.38 (1.35, 4.19), 2.62 (1.15, 5.95) and 2.62 (1.44, 4.79) for Wuhan, Changsha, Guilin and Haikou, respectively. The respective risks were 1.35 (1.18, 1.55), 1.19 (0.96, 1.48), 1.22 (0.82, 1.82) and 2.47 (1.61, 3.78) for extreme hot temperatures, at a lag of 3 days. CONCLUSIONS: Temperature–mortality relationships vary among cities at different latitudes. Local governments should establish regional prevention and protection measures to more effectively confront and adapt to local climate change. The effects of hot temperatures predominantly occur over the short term, whereas those of cold temperatures can persist for an extended number of days. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12889-016-3031-z) contains supplementary material, which is available to authorized users. BioMed Central 2016-05-04 /pmc/articles/PMC4855424/ /pubmed/27146378 http://dx.doi.org/10.1186/s12889-016-3031-z Text en © Bao et al. 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Bao, Junzhe
Wang, Zhenkun
Yu, Chuanhua
Li, Xudong
The influence of temperature on mortality and its Lag effect: a study in four Chinese cities with different latitudes
title The influence of temperature on mortality and its Lag effect: a study in four Chinese cities with different latitudes
title_full The influence of temperature on mortality and its Lag effect: a study in four Chinese cities with different latitudes
title_fullStr The influence of temperature on mortality and its Lag effect: a study in four Chinese cities with different latitudes
title_full_unstemmed The influence of temperature on mortality and its Lag effect: a study in four Chinese cities with different latitudes
title_short The influence of temperature on mortality and its Lag effect: a study in four Chinese cities with different latitudes
title_sort influence of temperature on mortality and its lag effect: a study in four chinese cities with different latitudes
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4855424/
https://www.ncbi.nlm.nih.gov/pubmed/27146378
http://dx.doi.org/10.1186/s12889-016-3031-z
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