Model choice in time series studies of air pollution and mortality
Model choice in time series studies of air pollution and mortality
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DOI：
10.1111/j.1467-985x.2006.00410.x
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发表时间：
2006-01-01
影响因子：
2
通讯作者：
Louis, TA
中科院分区：
文献类型：
--
作者：
Peng, RD;Dominici, F;Louis, TA
对颗粒物与死亡率和发病率的多城市时间序列研究提供了证据，表明空气污染水平的每日变化与死亡率的每日变化有关。这些发现为最近审查美国国家环境空气颗粒物质量标准提供了关键的流行病学证据。因此，有关空气污染与健康之间关系的时间序列分析的方法学问题引起了科学界的关注，批评者对当前模型公式的充分性提出了担忧。关于污染和死亡率的时间序列数据一般采用对数线性泊松回归模型进行分析，以每日死亡人数为结果，以(可能滞后的)每日污染水平作为线性预测指标，并使用天气变量和日历时间的平滑函数来调整时间变化的混杂因素。世界各地的研究人员使用了不同的方法来调整混杂因素，这使得比较不同研究的结果变得困难。到目前为止，这些不同方法的统计特性还没有得到全面的比较。为了解决这些问题，我们对空气污染和死亡率的时间序列模型中的季节性和长期趋势进行调整的模型不确定性和模型选择进行了量化和表征。首先，我们进行了模拟研究，比较和描述了混杂平差常用的统计方法的特性。我们在几种混淆的情况下生成数据，并系统地比较各种方法在估计空气污染系数的均方误差方面的性能。我们发现，随着更积极的平滑，估计中的偏差通常会减小，优化预测的模型选择方法可能不适合于获得偏差较小的估计。其次，我们将建模方法与美国全国发病率、死亡率和空气污染研究数据库进行了比较，该数据库包括美国最大的100个城市1987-2000年期间几种污染物的每日时间序列、天气变量和死亡计数。当应用这些方法来调整季节性和长期趋势时，我们发现，研究对滞后1的PM10对死亡率的全国平均影响的估计大约在两个范围内变化，95%的后验区间总是排除零风险。
Multicity time series studies of particulate matter and mortality and morbidity have provided evidence that daily variation in air pollution levels is associated with daily variation in mortality counts. These findings served as key epidemiological evidence for the recent review of the US national ambient air quality standards for particulate matter. As a result, methodological issues concerning time series analysis of the relationship between air pollution and health have attracted the attention of the scientific community and critics have raised concerns about the adequacy of current model formulations. Time series data on pollution and mortality are generally analysed by using log-linear, Poisson regression models for overdispersed counts with the daily number of deaths as outcome, the (possibly lagged) daily level of pollution as a linear predictor and smooth functions of weather variables and calendar time used to adjust for time-varying confounders. Investigators around the world have used different approaches to adjust for confounding, making it difficult to compare results across studies. To date, the statistical properties of these different approaches have not been comprehensively compared. To address these issues, we quantify and characterize model uncertainty and model choice in adjusting for seasonal and long-term trends in time series models of air pollution and mortality. First, we conduct a simulation study to compare and describe the properties of statistical methods that are commonly used for confounding adjustment. We generate data under several confounding scenarios and systematically compare the performance of the various methods with respect to the mean-squared error of the estimated air pollution coefficient. We find that the bias in the estimates generally decreases with more aggressive smoothing and that model selection methods which optimize prediction may not be suitable for obtaining an estimate with small bias. Second, we apply and compare the modelling approaches with the National Morbidity, Mortality, and Air Pollution Study database which comprises daily time series of several pollutants, weather variables and mortality counts covering the period 1987-2000 for the largest 100 cities in the USA. When applying these approaches to adjusting for seasonal and long-term trends we find that the Study's estimates for the national average effect of PM10 at lag 1 on mortality vary over approximately a twofold range, with 95% posterior intervals always excluding zero risk.