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：
10.1289/ehp.00108419
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发表时间：
2000-05
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;Dominici F;Samet JM;Schwartz J;Dockery D;Cohen A
暴露的错误分类是疾病和环境流行病学研究的公认固有局限性。对于许多感兴趣的代理人，暴露发生在一段时间内，并在多个地点;准确地估计相关的风险，为个人参与者在流行病学研究往往是艰巨的，特别是在可行性，参与者的负担和成本的限制。研究人员已经采取措施来处理测量误差的后果，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，并在统计分析中调整测量误差。在本文中，我们解决测量误差的空气污染和健康的观测研究。由于测量误差可能对解释空气污染的流行病学研究，特别是时间序列分析，有实质性的影响，我们开发了一个系统的概念制定的空气污染的流行病学研究中的测量误差的问题，然后考虑在此配方的后果。在可能的情况下，我们使用可用的相关数据对测量误差效应进行简单估计。本文概述了线性回归中的测量误差，区分两个极端的连续Berkson从经典类型的错误，和单变量从多变量预测的情况。然后，我们提出了一个概念框架，用于时间序列研究的颗粒空气污染和死亡率的对数线性回归的测量误差的评估，并确定三个主要组成部分的错误。我们提出了新的简单分析的数据暴露的空气动力学直径小于10微米的颗粒物从颗粒总暴露评估方法研究。最后，我们总结了有关测量误差的开放性问题，并提出了解决这些问题所需的其他数据。
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 deal 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 health. 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 make 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 microm 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.