Janes Holly, Dominici Francesca, Zeger Scott L
Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland 21205, USA.
Epidemiology. 2007 Jul;18(4):416-23. doi: 10.1097/EDE.0b013e31806462e9.
We propose a method for diagnosing confounding bias under a model that links a spatially and temporally varying exposure and health outcome. We decompose the association into orthogonal components, corresponding to distinct spatial and temporal scales of variation. If the model fully controls for confounding, the exposure effect estimates should be equal at the different temporal and spatial scales. We show that the overall exposure effect estimate is a weighted average of the scale-specific exposure effect estimates. We use this approach to estimate the association between monthly averages of fine particles (PM2.5) over the preceding 12 months and monthly mortality rates in 113 US counties from 2000 to 2002. We decompose the association between PM2.5 and mortality into 2 components: (1) the association between "national trends" in PM2.5 and mortality; and (2) the association between "local trends," defined as county-specific deviations from national trends. This second component provides evidence as to whether counties having steeper declines in PM2.5 also have steeper declines in mortality relative to their national trends. We find that the exposure effect estimates are different at these 2 spatiotemporal scales, which raises concerns about confounding bias. We believe that the association between trends in PM2.5 and mortality at the national scale is more likely to be confounded than is the association between trends in PM2.5 and mortality at the local scale. If the association at the national scale is set aside, there is little evidence of an association between 12-month exposure to PM2.5 and mortality.
我们提出了一种在将时空变化的暴露因素与健康结果相联系的模型下诊断混杂偏倚的方法。我们将该关联分解为正交分量，分别对应不同的空间和时间变化尺度。如果模型完全控制了混杂因素，那么在不同的时间和空间尺度上，暴露效应估计值应该是相等的。我们表明，总体暴露效应估计值是特定尺度暴露效应估计值的加权平均值。我们使用这种方法来估计2000年至2002年期间美国113个县前12个月细颗粒物（PM2.5）月平均值与月死亡率之间的关联。我们将PM2.5与死亡率之间的关联分解为两个分量：（1）PM2.5“全国趋势”与死亡率之间的关联；（2）“局部趋势”之间的关联，“局部趋势”定义为各县相对于全国趋势的特定偏差。第二个分量提供了证据，表明PM2.5下降幅度更大的县相对于其全国趋势而言，死亡率下降幅度是否也更大。我们发现，在这两个时空尺度上，暴露效应估计值是不同的，这引发了对混杂偏倚的担忧。我们认为，与局部尺度上PM2.5趋势与死亡率之间的关联相比，全国尺度上PM2.5趋势与死亡率之间的关联更有可能存在混杂。如果将全国尺度上的关联搁置一旁，几乎没有证据表明12个月暴露于PM2.5与死亡率之间存在关联。
