Exposure measurement error in time-series studies of air pollution: concepts and consequences
Exposure measurement error in time-series studies of air pollution: concepts and consequences
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DOI：
10.2307/3454382
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发表时间：
2000-05-01
影响因子：
10.4
通讯作者：
Cohen, A
中科院分区：
文献类型：
--
作者：
Zeger, SL;Thomas, D;Cohen, A
对接触的错误分类是公认的疾病和环境流行病学研究的固有局限性。对于许多感兴趣的制剂来说，暴露发生在时间和多个地点；准确估计流行病学研究中单个参与者的相关暴露通常令人望而生畏，特别是在可行性、参与者负担和成本设定的限制内。研究人员已采取步骤，通过研究设计限制误差程度，使用嵌套验证研究估计误差程度，并通过统计分析中的测量误差进行调整，从而采取措施对测量误差的后果进行DEA。在本文中，我们讨论了空气污染和健康观测研究中的测量误差。由于测量误差可能对解释空气污染流行病学研究，特别是时间序列分析具有重大影响，我们提出了空气污染流行病学研究中测量误差问题的系统概念公式，并考虑了该公式中的后果。在可能的情况下，我们使用可用的相关数据对测量误差影响进行简单估计。本文概述了线性回归中的测量误差，区分了连续统-Berkson误差和经典类型误差的两个极值，以及单变量和多变量预报器情况下的极值。然后，我们提出了一个概念框架来评估用于颗粒物空气污染和死亡率时间序列研究的对数线性回归中的测量误差，并确定了误差的三个主要组成部分。我们提出了对空气动力学直径为10微米的颗粒物暴露数据的新的简单分析，这些数据来自粒子总暴露评估方法学研究。最后，我们总结了关于测量误差的未决问题，并提出了解决这些问题所需的额外数据。
Misclassification of exposure is a well-recognized inherent limitation of epidemiologic studies of disease and the environment. For many agents of interest, exposures take place over time and in multiple locations; accurately estimating the relevant exposures for an individual participant in epidemiologic studies is often daunting, particularly within the limits set by feasibility, participant burden, and cost. Researchers have taken steps to dea with the consequences of measurement error by limiting the degree of error through a study's design, estimating the degree of error using a nested validation study, and by adjusting for measurement error in statistical analyses. In this paper, we address measurement error in observational studies of air pollution and heath. Because measurement error may have substantial implications for interpreting epidemiologic studies on air pollution, particularly the time-series analyses, we developed a systematic conceptual formulation of the problem of measurement error in epidemiologic studies of air pollution and then considered the consequences within this formulation. When possible, we used available relevant data to mace simple estimates of measurement error effects. This paper provides an overview of measurement errors in linear regression, distinguishing two extremes of a continuum-Berkson from classical type errors, and the univariate from the multivariate predictor case. We then propose one conceptual framework for the evaluation of measurement errors in the log-linear regression used for time-series studies of particulate air pollution and mortality and identify three main components of error. We present new simple analyses of data on exposures of particulate matter < 10 mu m in aerodynamic diameter from the Particle Total Exposure Assessment Methodology Study. Finally, we summarize open questions regarding measurement error and suggest the kind of additional data necessary to address them.