Exposure measurement error in time-series studies of air pollution: concepts and consequences

Misclassification of exposure is a well-recognized inherent limitation of e pidemiologic studies of disease and the environment. For many agents of int erest, exposures take place over time and in multiple locations; accurately estimating the relevant exposures for an individual participant in epidemi ologic studies is often daunting, particularly within the limits set by fea sibility, 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 va lidation study, and by adjusting for measurement error in statistical analy ses. In this paper, we address measurement error in observational studies o f air pollution and heath. Because measurement error may have substantial i mplications for interpreting epidemiologic studies on air pollution, partic ularly the time-series analyses, we developed a systematic conceptual formu lation of the problem of measurement error in epidemiologic studies of air pollution and then considered the consequences within this formulation. Whe n possible, we used available relevant data to mace simple estimates of mea surement error effects. This paper provides an overview of measurement erro rs in linear regression, distinguishing two extremes of a continuum-Berkson from classical type errors, and the univariate from the multivariate predi ctor case. We then propose one conceptual framework for the evaluation of m easurement errors in the log-linear regression used for time-series studies of particulate air pollution and mortality and identify three main compone nts of error. We present new simple analyses of data on exposures of partic ulate matter < 10 mu m in aerodynamic diameter from the Particle Total Expo sure Assessment Methodology Study. Finally, we summarize open questions reg arding measurement error and suggest the kind of additional data necessary to address them.