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：
在美国，与温度有关的死亡率是与天气有关的死亡的主要原因。在这里，我们探讨了气团（AMs）-一种相对新颖和全面的环境条件测量方法-对美国61个城市的人类死亡率的影响。使用简单的描述性统计数据检查了每种AM对已灭绝和消除趋势的异常滞后死亡率的影响的地理和季节性差异，单向方差分析，超额死亡率的相对风险，和基于回归的人工神经网络(ANN)模型。结果表明，AMs与美国大多数城市的异常死亡率显着相关，在大多数季节。值得注意的是，三个凉爽的AM（凉爽和干燥凉爽）中的两个都显示出强烈的，但是在所有季节都延迟了死亡反应，在它们发生后2到4天出现峰值死亡率，干冷AM的超额死亡率风险增加近15%。湿热（HW）气团与所有季节发生后0至1天的死亡人数增加有关。在大多数季节，这些近期死亡率的增加被随后1-2周的死亡率降低所抵消；然而，在夏天,没有注意到这种减少。温暖和干燥温暖的AMs显示死亡率增加的时间稍长，尽管与HW相比强度稍低，但季节有类似的滞后结构。同时,季节性最一致的结果是过渡天气，通过冷锋与死亡率在它们发生后1天的显著下降有关，而在相同的滞后时间内，暖锋与死亡率的显着增加有关。最后,ANN建模表明，从组合荟萃分析中收集的AM-死亡率关系实际上可以比在某些单个城市上训练的模型对这些关系进行更熟练的建模，特别是在城市，由于平均每日死亡率低，这种关系可能被掩盖。
