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Projections of temperature-attributable premature deaths in 209 U.S. cities using a cluster-based Poisson approach

BACKGROUND: A warming climate will affect future temperature-attributable premature deaths. This analysis is the first to project these deaths at a near national scale for the United States using city and month-specific temperature-mortality relationships. METHODS: We used Poisson regressions to mod...

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Autores principales: Schwartz, Joel D., Lee, Mihye, Kinney, Patrick L., Yang, Suijia, Mills, David, Sarofim, Marcus C., Jones, Russell, Streeter, Richard, Juliana, Alexis St., Peers, Jennifer, Horton, Radley M.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4632409/
https://www.ncbi.nlm.nih.gov/pubmed/26537962
http://dx.doi.org/10.1186/s12940-015-0071-2
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author Schwartz, Joel D.
Lee, Mihye
Kinney, Patrick L.
Yang, Suijia
Mills, David
Sarofim, Marcus C.
Jones, Russell
Streeter, Richard
Juliana, Alexis St.
Peers, Jennifer
Horton, Radley M.
author_facet Schwartz, Joel D.
Lee, Mihye
Kinney, Patrick L.
Yang, Suijia
Mills, David
Sarofim, Marcus C.
Jones, Russell
Streeter, Richard
Juliana, Alexis St.
Peers, Jennifer
Horton, Radley M.
author_sort Schwartz, Joel D.
collection PubMed
description BACKGROUND: A warming climate will affect future temperature-attributable premature deaths. This analysis is the first to project these deaths at a near national scale for the United States using city and month-specific temperature-mortality relationships. METHODS: We used Poisson regressions to model temperature-attributable premature mortality as a function of daily average temperature in 209 U.S. cities by month. We used climate data to group cities into clusters and applied an Empirical Bayes adjustment to improve model stability and calculate cluster-based month-specific temperature-mortality functions. Using data from two climate models, we calculated future daily average temperatures in each city under Representative Concentration Pathway 6.0. Holding population constant at 2010 levels, we combined the temperature data and cluster-based temperature-mortality functions to project city-specific temperature-attributable premature deaths for multiple future years which correspond to a single reporting year. Results within the reporting periods are then averaged to account for potential climate variability and reported as a change from a 1990 baseline in the future reporting years of 2030, 2050 and 2100. RESULTS: We found temperature-mortality relationships that vary by location and time of year. In general, the largest mortality response during hotter months (April – September) was in July in cities with cooler average conditions. The largest mortality response during colder months (October–March) was at the beginning (October) and end (March) of the period. Using data from two global climate models, we projected a net increase in premature deaths, aggregated across all 209 cities, in all future periods compared to 1990. However, the magnitude and sign of the change varied by cluster and city. CONCLUSIONS: We found increasing future premature deaths across the 209 modeled U.S. cities using two climate model projections, based on constant temperature-mortality relationships from 1997 to 2006 without any future adaptation. However, results varied by location, with some locations showing net reductions in premature temperature-attributable deaths with climate change. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12940-015-0071-2) contains supplementary material, which is available to authorized users.
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spelling pubmed-46324092015-11-05 Projections of temperature-attributable premature deaths in 209 U.S. cities using a cluster-based Poisson approach Schwartz, Joel D. Lee, Mihye Kinney, Patrick L. Yang, Suijia Mills, David Sarofim, Marcus C. Jones, Russell Streeter, Richard Juliana, Alexis St. Peers, Jennifer Horton, Radley M. Environ Health Research BACKGROUND: A warming climate will affect future temperature-attributable premature deaths. This analysis is the first to project these deaths at a near national scale for the United States using city and month-specific temperature-mortality relationships. METHODS: We used Poisson regressions to model temperature-attributable premature mortality as a function of daily average temperature in 209 U.S. cities by month. We used climate data to group cities into clusters and applied an Empirical Bayes adjustment to improve model stability and calculate cluster-based month-specific temperature-mortality functions. Using data from two climate models, we calculated future daily average temperatures in each city under Representative Concentration Pathway 6.0. Holding population constant at 2010 levels, we combined the temperature data and cluster-based temperature-mortality functions to project city-specific temperature-attributable premature deaths for multiple future years which correspond to a single reporting year. Results within the reporting periods are then averaged to account for potential climate variability and reported as a change from a 1990 baseline in the future reporting years of 2030, 2050 and 2100. RESULTS: We found temperature-mortality relationships that vary by location and time of year. In general, the largest mortality response during hotter months (April – September) was in July in cities with cooler average conditions. The largest mortality response during colder months (October–March) was at the beginning (October) and end (March) of the period. Using data from two global climate models, we projected a net increase in premature deaths, aggregated across all 209 cities, in all future periods compared to 1990. However, the magnitude and sign of the change varied by cluster and city. CONCLUSIONS: We found increasing future premature deaths across the 209 modeled U.S. cities using two climate model projections, based on constant temperature-mortality relationships from 1997 to 2006 without any future adaptation. However, results varied by location, with some locations showing net reductions in premature temperature-attributable deaths with climate change. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12940-015-0071-2) contains supplementary material, which is available to authorized users. BioMed Central 2015-11-04 /pmc/articles/PMC4632409/ /pubmed/26537962 http://dx.doi.org/10.1186/s12940-015-0071-2 Text en © Schwartz et al. 2015 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
Schwartz, Joel D.
Lee, Mihye
Kinney, Patrick L.
Yang, Suijia
Mills, David
Sarofim, Marcus C.
Jones, Russell
Streeter, Richard
Juliana, Alexis St.
Peers, Jennifer
Horton, Radley M.
Projections of temperature-attributable premature deaths in 209 U.S. cities using a cluster-based Poisson approach
title Projections of temperature-attributable premature deaths in 209 U.S. cities using a cluster-based Poisson approach
title_full Projections of temperature-attributable premature deaths in 209 U.S. cities using a cluster-based Poisson approach
title_fullStr Projections of temperature-attributable premature deaths in 209 U.S. cities using a cluster-based Poisson approach
title_full_unstemmed Projections of temperature-attributable premature deaths in 209 U.S. cities using a cluster-based Poisson approach
title_short Projections of temperature-attributable premature deaths in 209 U.S. cities using a cluster-based Poisson approach
title_sort projections of temperature-attributable premature deaths in 209 u.s. cities using a cluster-based poisson approach
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4632409/
https://www.ncbi.nlm.nih.gov/pubmed/26537962
http://dx.doi.org/10.1186/s12940-015-0071-2
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