Seasonal analyses of air pollution and mortality in 100 US cities
Seasonal analyses of air pollution and mortality in 100 US cities
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DOI：
10.1093/aje/kwi075
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发表时间：
2005-03-15
影响因子：
5
通讯作者：
Samet, JM
中科院分区：
文献类型：
--
作者：
Peng, RD;Dominici, F;Samet, JM
将空气污染水平的短期变化与每日死亡人数联系起来的时间序列模型通常假设空气污染对对数相对死亡率的影响不随时间而变化。然而，这些短期影响可能会因季节而异。空气污染源和气象学的变化可导致空气污染混合物在不同季节的特征发生变化。作者开发了贝叶斯半参数层次模型，用于估计多地点时间序列研究中污染对死亡率的时变影响。该方法被应用到全国死亡率和死亡率空气污染研究，其中包括100个美国城市的数据，为1987年至2000年期间的数据库。在全国范围内，空气动力学直径小于10微米的颗粒物每增加10微克/立方米，滞后1天，（95%后验间隔（PI）：-0.08，0.39），0.14%冬季、春季、夏季和秋季的死亡率分别增加0.36%（95%PI：-0.11，0.61）和0.14%（95%PI：-0.06，0.34）。按地理区域进行的分析发现，东北部的季节性模式很强（夏季达到高峰），而该国南部地区的季节性变化很小。这些结果提供了有用的信息，了解颗粒物的毒性和指导未来的分析颗粒物成分的数据。
Time series models relating short-term changes in air pollution levels to daily mortality counts typically assume that the effects of air pollution on the log relative rate of mortality do not vary with time. However, these short-term effects might plausibly vary by season. Changes in the sources of air pollution and meteorology can result in changes in characteristics of the air pollution mixture across seasons. The authors developed Bayesian semiparametric hierarchical models for estimating time-varying effects of pollution on mortality in multisite time series studies. The methods were applied to the database of the National Morbidity and Mortality Air Pollution Study, which includes data for 100 US cities, for the period 1987-2000. At the national level, a 10-mug/m(3) increase in particulate matter less than 10 mum in aerodynamic diameter at a 1-day lag was associated with 0.15% (95% posterior interval (PI): -0.08, 0.39), 0.14% (95% PI: -0.14, 0.42), 0.36% (95% PI: 0.11, 0.61), and 0.14% (95% PI: -0.06, 0.34) increases in mortality for winter, spring, summer, and fall, respectively. An analysis by geographic region found a strong seasonal pattern in the Northeast (with a peak in summer) and little seasonal variation in the southern regions of the country. These results provide useful information for understanding particle toxicity and guiding future analyses of particle constituent data.