Keller Joshua P, Chang Howard H, Strickland Matthew J, Szpiro Adam A
From the aDepartment of Biostatistics, University of Washington, Seattle, WA; bDepartment of Biostatistics and Bioinformatics, Emory University, Atlanta, GA; and cSchool of Community Health Sciences, University of Nevada Reno, Reno, NV.
Epidemiology. 2017 May;28(3):338-345. doi: 10.1097/EDE.0000000000000623.
Air pollution cohort studies are frequently analyzed in two stages, first modeling exposure then using predicted exposures to estimate health effects in a second regression model. The difference between predicted and unobserved true exposures introduces a form of measurement error in the second stage health model. Recent methods for spatial data correct for measurement error with a bootstrap and by requiring the study design ensure spatial compatibility, that is, monitor and subject locations are drawn from the same spatial distribution. These methods have not previously been applied to spatiotemporal exposure data.
We analyzed the association between fine particulate matter (PM2.5) and birth weight in the US state of Georgia using records with estimated date of conception during 2002-2005 (n = 403,881). We predicted trimester-specific PM2.5 exposure using a complex spatiotemporal exposure model. To improve spatial compatibility, we restricted to mothers residing in counties with a PM2.5 monitor (n = 180,440). We accounted for additional measurement error via a nonparametric bootstrap.
Third trimester PM2.5 exposure was associated with lower birth weight in the uncorrected (-2.4 g per 1 μg/m difference in exposure; 95% confidence interval [CI]: -3.9, -0.8) and bootstrap-corrected (-2.5 g, 95% CI: -4.2, -0.8) analyses. Results for the unrestricted analysis were attenuated (-0.66 g, 95% CI: -1.7, 0.35).
This study presents a novel application of measurement error correction for spatiotemporal air pollution exposures. Our results demonstrate the importance of spatial compatibility between monitor and subject locations and provide evidence of the association between air pollution exposure and birth weight.
空气污染队列研究通常分两个阶段进行分析，首先对暴露进行建模，然后在第二个回归模型中使用预测的暴露量来估计健康影响。预测暴露量与未观测到的真实暴露量之间的差异在第二阶段的健康模型中引入了一种测量误差形式。最近的空间数据方法通过自抽样法并要求研究设计确保空间兼容性来校正测量误差，即监测点和研究对象的位置来自相同的空间分布。这些方法以前尚未应用于时空暴露数据。
我们利用2002 - 2005年期间估计受孕日期的记录（n = 403,881）分析了美国佐治亚州细颗粒物（PM2.5）与出生体重之间的关联。我们使用复杂的时空暴露模型预测孕期特定的PM2.5暴露量。为了提高空间兼容性，我们将研究对象限制为居住在设有PM2.5监测点的县的母亲（n = 180,440）。我们通过非参数自抽样法考虑了额外的测量误差。
在未校正的分析中（暴露量每相差1μg/m，出生体重降低2.4g；95%置信区间[CI]：-3.9，-0.8）以及自抽样法校正后的分析中（降低2.5g，95%CI：-4.2，-0.8），孕晚期PM2.5暴露与较低的出生体重相关。无限制分析的结果有所减弱（降低0.66g，95%CI：-1.7，0.35）。
本研究展示了对时空空气污染暴露进行测量误差校正的新应用。我们的结果证明了监测点和研究对象位置之间空间兼容性的重要性，并提供了空气污染暴露与出生体重之间关联的证据。
