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使用基于聚类的泊松方法对美国209个城市因温度导致的过早死亡进行预测。

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

作者信息

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

机构信息

Department of Environmental Health, Harvard School of Public Health, Boston, MA, USA.

Department of Epidemiology, Harvard University, Boston, MA, USA.

出版信息

Environ Health. 2015 Nov 4;14:85. doi: 10.1186/s12940-015-0071-2.

Abstract

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.

摘要

背景

气候变暖将影响未来与温度相关的过早死亡。本分析首次在美国近乎全国的尺度上,利用特定城市和月份的温度-死亡率关系预测这些死亡情况。

方法

我们使用泊松回归模型,将与温度相关的过早死亡率作为209个美国城市按月份划分的日平均温度的函数。我们利用气候数据将城市分组为集群,并应用经验贝叶斯调整来提高模型稳定性,计算基于集群的特定月份温度-死亡率函数。利用来自两个气候模型的数据,我们计算了在代表性浓度路径6.0情景下每个城市未来的日平均温度。将人口维持在2010年的水平不变,我们将温度数据和基于集群的温度-死亡率函数相结合,预测多个未来年份特定城市与温度相关的过早死亡情况,这些年份对应于一个单一报告年。然后对报告期内的结果进行平均,以考虑潜在的气候变异性,并报告为2030年、2050年和2100年未来报告年份相对于1990年基线的变化。

结果

我们发现温度-死亡率关系因地点和一年中的时间而异。一般来说,在较热月份(4月至9月),平均条件较凉爽的城市中,7月的死亡率反应最大。在较冷月份(10月至3月),死亡率反应最大的是该时期的开始(10月)和结束(3月)。利用来自两个全球气候模型的数据,我们预测与1990年相比所有未来时期内,这209个城市汇总的过早死亡人数将出现净增加。然而,变化的幅度和迹象因集群和城市而异。

结论

基于1997年至2006年不变的温度-死亡率关系且不考虑任何未来适应措施,我们利用两个气候模型预测发现,美国209个建模城市未来过早死亡人数将会增加。然而,结果因地点而异,一些地点显示气候变化导致与温度相关的过早死亡人数出现净减少。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/adb9/4632409/0331204bad2a/12940_2015_71_Fig1_HTML.jpg

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