Distributed lag models have been used to identify critical pregnancy periods of exposure (i.e. critical exposure windows) to air pollution in studies of pregnancy outcomes. However, much of the previous work in this area has ignored the possibility of spatial variability in the lagged health effect parameters that may result from exposure characteristics and/or residual confounding. We develop a spatially varying Gaussian process model for critical windows called ‘SpGPCW’ and use it to investigate geographic variability in the association between term low birth weight and average weekly concentrations of ozone and PM_2.5 during pregnancy by using birth records from North Carolina. SpGPCW is designed to accommodate areal level spatial correlation between lagged health effect parameters and temporal smoothness in risk estimation across pregnancy. Through simulation and a real data application, we show that the consequences of ignoring spatial variability in the lagged health effect parameters include less reliable inference for the parameters and diminished ability to identify true critical window sets, and we investigate the use of existing Bayesian model comparison techniques as tools for determining the presence of spatial variability. We find that exposure to PM_2.5 is associated with elevated term low birth weight risk in selected weeks and counties and that ignoring spatial variability results in null associations during these periods. An R package (SpGPCW) has been developed to implement the new method.

中文翻译：

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空间变化的分布滞后模型，应用于空气污染和足月低出生体重研究。

在妊娠结局研究中，已使用分布式滞后模型来确定暴露于空气污染的关键怀孕时期（即关键暴露窗口）。但是，该领域的许多先前工作都忽略了由于暴露特征和/或残留混杂而导致的滞后健康影响参数空间变异的可能性。我们为关键窗口开发了一个空间变化的高斯过程模型，称为“ SpGPCW”，并使用它来研究足月出生低体重与每周平均臭氧浓度和PM _2.5之间的关联中的地理变异性怀孕期间使用北卡罗来纳州的出生记录。SpGPCW旨在适应滞后健康影响参数与整个妊娠风险估计中的时间平滑度之间的区域空间相关性。通过仿真和实际数据应用，我们表明忽略滞后健康影响参数中的空间变异性的后果包括对参数的可靠推断和识别真实关键窗口集的能力降低，并且我们研究了现有贝叶斯模型比较的使用技术作为确定空间变异性存在的工具。我们发现暴露于PM _2.5在选定的几周和各县，与长期低出生体重的风险升高有关，而忽略空间变异性会导致在这些时期内无效关联。已经开发了R软件包（SpGPCW）来实现新方法。