Spatiotemporal air pollution models are increasingly being used to estimate health effects in epidemiological studies. Although such exposure prediction models typically result in improved spatial and temporal resolution of air pollution predictions, they remain subject to shared measurement error, a type of measurement error common in spatiotemporal exposure models which occurs when measurement error is not independent of exposures. A fundamental challenge of exposure measurement error in air pollution assessment is the strong correlation and sometimes identical (shared) error of exposure estimates across geographic space and time. When exposure estimates with shared measurement error are used to estimate health risk in epidemiological analyses, complex errors are potentially introduced, resulting in biased epidemiological conclusions. We demonstrate the influence of using a three-stage spatiotemporal exposure prediction model and introduce formal methods of shared, multiplicative measurement error (SMME) correction of epidemiological health risk estimates. Using our three-stage ensemble learning-based nitrogen oxides (NO x ) exposure prediction model, we quantified SMME. We conducted an epidemiological analysis of wheeze risk in relation to NO x exposure among school-aged children. To demonstrate the incremental influence of exposure modeling stage, we iteratively estimated the health risk using assigned exposure predictions from each stage of the NO x model. We then determined the impact of SMME on the variance of health risk estimates under various scenarios. Depending on the stage of the spatiotemporal exposure model used, we found that wheeze odds ratio ranged from 1.16 to 1.28 for an interquartile range increase in NO x . With each additional stage of exposure modeling, the health effect estimate moved further away from the null (OR = 1). When corrected for observed SMME, the health effects confidence intervals slightly lengthened, but our epidemiological conclusions were not altered. When the variance estimate was corrected for the potential “worst case scenario” of SMME, the standard error further increased, having a meaningful influence on epidemiological conclusions. Our framework can be expanded and used to understand the implications of using exposure predictions subject to shared measurement error in future health investigations.

中文翻译：

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空气污染研究中的暴露测量误差：共享的乘法测量误差对流行病学健康风险估计的影响


时空空气污染模型越来越多地用于估计流行病学研究中的健康影响。尽管此类暴露预测模型通常会提高空气污染预测的空间和时间分辨率，但它们仍然受到共享测量误差的影响，这是时空暴露模型中常见的一种测量误差，当测量误差不独立于暴露时就会发生。空气污染评估中暴露测量误差的一个根本挑战是跨地理空间和时间的暴露估计的强相关性，有时甚至是相同（共享）的误差。当使用具有共同测量误差的暴露估计来估计流行病学分析中的健康风险时，可能会引入复杂的误差，从而导致流行病学结论有偏差。我们证明了使用三阶段时空暴露预测模型的影响，并引入了流行病学健康风险估计的共享乘性测量误差（SMME）校正的正式方法。使用我们基于三阶段集成学习的氮氧化物 (NO x ) 暴露预测模型，我们量化了 SMME。我们对学龄儿童中与氮氧化物暴露相关的喘息风险进行了流行病学分析。为了证明暴露建模阶段的增量影响，我们使用 NO x 模型每个阶段指定的暴露预测迭代地估计了健康风险。然后我们确定了 SMME 在各种情况下对健康风险估计方差的影响。根据所使用的时空暴露模型的阶段，我们发现对于四分位数范围内的 NO x 增加，喘息优势比的范围为 1.16 至 1.28。 随着暴露模型的每一个附加阶段，健康影响估计值都远离零值（OR = 1）。当对观察到的 SMME 进行校正时，健康影响置信区间略有延长，但我们的流行病学结论没有改变。当针对 SMME 潜在的“最坏情况”修正方差估计时，标准误差进一步增加，对流行病学结论产生有意义的影响。我们的框架可以扩展并用于理解在未来的健康调查中使用受共同测量误差影响的暴露预测的影响。