The spatial association between community air pollution and mortality: A new method of analyzing correlated geographic cohort data
The spatial association between community air pollution and mortality: A new method of analyzing correlated geographic cohort data
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DOI：
10.2307/3434784
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发表时间：
2001-06-01
影响因子：
10.4
通讯作者：
Krewski, D
中科院分区：
文献类型：
--
作者：
Burnett, R;Ma, RJ;Krewski, D
我们提出了一种新的统计模型，用于将环境空气污染的空间变化与死亡率联系起来。该模型纳入了在个人层面（例如吸烟）和空间层面（例如空气污染）测量的风险因素。我们证明，社区死亡率的空间自相关性（表明未完全表征空气污染与死亡率关联的潜在混杂风险因素）可以通过在评估空气污染对死亡率影响的模型中包含位置来解释。我们的方法通过对美国癌症协会队列的分析来说明，以确定全因死亡率是否与硫酸盐颗粒的浓度相关。根据 Cox 比例风险生存模型，假定受试者具有统计独立性，所有死因的硫酸盐分布四分位数范围为 4.2 杯/米 (3) 相关的相对风险为 1.051（95% 置信区间 1.036-1.066）。包含基于社区的随机效应产生的相对风险为 1.055 (1.033, 1.077)，这表明与 Cox 模型估计的相比，残差方差增加了一倍。随机效应模型的残差显示出空间自相关的有力证据 (p = 0.0052)。随着位置表面复杂性的增加，进一步包含位置表面会降低硫酸盐相对风险和自相关证据，相对风险范围为 1.055-1.035。我们得出的结论是，这些数据显示了外变量和空间自相关，这是 Cox 生存模型未捕获的特征。如果不考虑外变量和空间自相关，可能会导致低估空气污染与死亡率之间的不确定性。
We present a new statistical model for linking spatial variation in ambient air pollution to mortality. The model incorporates risk factors measured at the individual level, such as smoking, and at the spatial level, such as air pollution. We demonstrate that the spatial autocorrelation in community mortality rates, an indication of not fully characterizing potentially confounding risk factors to the air pollution-mortality association, can be accounted for through the inclusion of location in the model assessing the effects of air pollution on mortality. Our methods are illustrated with an analysis of the American Cancer Society cohort to determine whether all cause mortality is associated with concentrations of sulfate particles. The relative risk associated with a 4.2 mug/m(3) interquartile range of sulfate distribution for all causes of death was 1.051 (95% confidence interval 1.036-1.066) based on the Cox proportional hazards survival model, assuming subjects were statistically independent. Inclusion of community-based random effects yielded a relative risk of 1.055 (1.033, 1.077), which represented a doubling in the residual variance compared to that estimated by the Cox model. Residuals from the random-effects model displayed strong evidence of spatial autocorrelation (p = 0.0052). Further inclusion of a location surface reduced the sulfate relative risk and the evidence for autocorrelation as the complexity of the location surface increased, with a range in relative risks of 1.055-1.035. We conclude that these data display both extravariation and spatial autocorrelation, characteristics not captured by the Cox survival model. Failure to account for extravariation and spatial autocorrelation can lead to an understatement of the uncertainty of the air pollution association with mortality.