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大气团对美国相邻各州人口死亡率的影响。

The influence of air masses on human mortality in the contiguous United States.

机构信息

Kent State University, Department of Geography, ClimRISE Laboratory, 433 McGilvrey Hall, 325 S. Lincoln St., Kent, OH, 44242, USA.

出版信息

Int J Biometeorol. 2024 Nov;68(11):2281-2296. doi: 10.1007/s00484-024-02745-y. Epub 2024 Aug 5.

Abstract

Temperature-related mortality is the leading cause of weather-related deaths in the United States. Herein, we explore the effect of air masses (AMs) - a relatively novel and holistic measure of environmental conditions - on human mortality across 61 cities in the United States. Geographic and seasonal differences in the effects of each AM on deseasonalized and detrended anomalous lagged mortality are examined using simple descriptive statistics, one-way analyses of variance, relative risks of excess mortality, and regression-based artificial neural network (ANN) models. Results show that AMs are significantly related to anomalous mortality in most US cities, and in most seasons. Of note, two of the three cool AMs (Cool and Dry-Cool) each show a strong, but delayed mortality response in all seasons, with peak mortality 2 to 4 days after they occur, with the Dry-Cool AM having nearly a 15% increased risk of excess mortality. Humid-Warm (HW) air masses are associated with increases in deaths in all seasons 0 to 1 days after they occur. In most seasons, these near-term mortality increases are offset by reduced mortality for 1-2 weeks afterwards; however, in summer, no such reduction is noted. The Warm and Dry-Warm AMs show slightly longer periods of increased mortality, albeit slightly less intensely as compared with HW, but with a similar lag structure by season. Meanwhile, the most seasonally consistent results are with transitional weather, whereby passing cold fronts are associated with a significant decrease in mortality 1 day after they occur, while warm fronts are associated with significant increases in mortality at that same lag time. Finally, ANN modeling reveals that AM-mortality relationships gleaned from a combined meta-analysis can actually lead to more skillful modeling of these relationships than models trained on some individual cities, especially in the cities where such relationships might be masked due to low average daily mortality.

摘要

与温度相关的死亡率是美国与天气相关死亡的主要原因。在此,我们探讨了大气团(AMs)——一种相对新颖和全面的环境条件衡量标准——对美国 61 个城市的人类死亡率的影响。使用简单描述性统计、单向方差分析、超额死亡率的相对风险以及基于回归的人工神经网络(ANN)模型,研究了每种大气团对去季节化和去趋势异常滞后死亡率的影响的地理和季节差异。结果表明,大气团与美国大多数城市的异常死亡率密切相关,而且在大多数季节都是如此。值得注意的是,三种凉爽大气团中的两种(Cool 和 Dry-Cool)在所有季节都表现出强烈但延迟的死亡率反应,在它们发生后 2 到 4 天达到峰值,Dry-Cool 大气团的超额死亡率风险几乎增加了 15%。湿热大气团(HW)与它们发生后 0 到 1 天所有季节的死亡人数增加有关。在大多数季节,这种短期死亡率的增加被随后 1 到 2 周的死亡率降低所抵消;然而,在夏季,没有注意到这种降低。温暖和干燥温暖的大气团显示出稍长的死亡率增加期,尽管与 HW 相比,强度略低,但与季节的滞后结构相似。与此同时,与过渡性天气相关的结果最具季节性一致性,即冷锋过境后 1 天,死亡率显著降低,而暖锋过境后,死亡率显著增加。最后,ANN 建模显示,从综合荟萃分析中得出的大气团与死亡率的关系实际上可以导致对这些关系的建模更有技巧,而不是基于某些个别城市的模型进行训练,尤其是在这些关系可能由于平均每日死亡率低而被掩盖的城市。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ef40/11519110/eabcccb4c3d6/484_2024_2745_Fig1_HTML.jpg

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