Abstract

Background:

Exposure measurement error is a central concern in air pollution epidemiology. Given that studies have been using ambient air pollution predictions as proxy exposure measures, the potential impact of exposure error on health effect estimates needs to be comprehensively assessed.

Objectives:

We aimed to generate wide-ranging scenarios to assess direction and magnitude of bias caused by exposure errors under plausible concentration–response relationships between annual exposure to fine particulate matter [PM $\le 2.5\mathrm{\mu }\mathrm{m}$ in aerodynamic diameter (${\mathrm{PM}}_{2.5}$)] and all-cause mortality.

Methods:

In this simulation study, we use daily ${\mathrm{PM}}_{2.5}$ predictions at $1{\text{-km}}^2$ spatial resolution to estimate annual ${\mathrm{PM}}_{2.5}$ exposures and their uncertainties for ZIP Codes of residence across the contiguous United States between 2000 and 2016. We consider scenarios in which we vary the error type (classical or Berkson) and the true concentration–response relationship between ${\mathrm{PM}}_{2.5}$ exposure and mortality (linear, quadratic, or soft-threshold—i.e., a smooth approximation to the hard-threshold model). In each scenario, we generate numbers of deaths using error-free exposures and confounders of concurrent air pollutants and neighborhood-level covariates and perform epidemiological analyses using error-prone exposures under correct specification or misspecification of the concentration–response relationship between ${\mathrm{PM}}_{2.5}$ exposure and mortality, adjusting for the confounders.

Results:

We simulate 1,000 replicates of each of 162 scenarios investigated. In general, both classical and Berkson errors can bias the concentration–response curve toward the null. The biases remain small even when using three times the predicted uncertainty to generate errors and are relatively larger at higher exposure levels.

Discussion:

Our findings suggest that the causal determination for long-term ${\mathrm{PM}}_{2.5}$ exposure and mortality is unlikely to be undermined when using high-resolution ambient predictions given that the estimated effect is generally smaller than the truth. The small magnitude of bias suggests that epidemiological findings are relatively robust against the exposure error. In practice, the use of ambient predictions with a finer spatial resolution will result in smaller bias. https://doi.org/10.1289/EHP10389

中文翻译：

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暴露测量误差对 PM2.5 长期暴露与死亡率之间估计浓度-响应关系的影响

摘要

背景：

暴露测量误差是空气污染流行病学的核心问题。鉴于研究一直使用环境空气污染预测作为代理暴露措施，需要全面评估暴露误差对健康影响估计的潜在影响。

目标：

我们的目标是生成范围广泛的情景，以评估在每年暴露于细颗粒物 [PM$\le 2.5\mathrm{\mu }\mathrm{米}$气动直径 (${\mathrm{下午}}_{2.5}$)] 和全因死亡率。

方法：

在这项模拟研究中，我们每天使用${\mathrm{下午}}_{2.5}$预测在$1{\text{-公里}}^2$估计年度的空间分辨率${\mathrm{下午}}_{2.5}$2000 年至 2016 年间美国境内居住的邮政编码的风险及其不确定性。我们考虑了我们改变错误类型（经典或伯克森）的情景以及两者之间的真实集中 - 反应关系${\mathrm{下午}}_{2.5}$暴露和死亡率（线性、二次或软阈值——即硬阈值模型的平滑近似）。在每种情况下，我们使用无错误暴露和并发空气污染物的混杂因素和邻域级协变量来生成死亡人数，并使用在正确规格或错误规格之间的浓度-反应关系下使用容易出错的暴露进行流行病学分析${\mathrm{下午}}_{2.5}$暴露和死亡率，调整混杂因素。

结果：

我们对所调查的 162 个场景中的每一个模拟了 1,000 次重复。一般来说，经典误差和伯克森误差都会使浓度-反应曲线偏向零。即使使用三倍的预测不确定性来产生误差，偏差仍然很小，并且在较高的暴露水平下相对较大。

讨论：

我们的研究结果表明，长期的因果决定${\mathrm{下午}}_{2.5}$使用高分辨率环境预测时，暴露和死亡率不太可能受到破坏，因为估计的影响通常小于事实。小幅度的偏倚表明流行病学调查结果对暴露误差相对稳健。在实践中，使用具有更精细空间分辨率的环境预测将导致更小的偏差。https://doi.org/10.1289/EHP10389