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Scott L. Zeger,¹ Duncan Thomas,² Francesca Dominici,¹ Jonathan M. Samet,¹ Joel Schwartz,³ Douglas Dockery,³ and Aaron Cohen⁴

¹Johns Hopkins University, School of Public Health, Baltimore, Maryland, USA
²Department of Preventive Medicine, University of Southern California School of Medicine, Los Angeles, California, USA
³Harvard University, Boston, Massachusetts, USA
⁴Health Effects Institute, Cambridge, Massachusetts, USA

Abstract

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 µm 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. Key words: air pollution, design methods, exposure, measurement error, time-series. Environ Health Perspect 108:419-426(2000) . [Online 24 March 2000]

http://ehpnet1.niehs.nih.gov/docs/2000/108p419-426zeger/ abstract.html

Address correspondence to S.L. Zeger, Johns Hopkins University, School of Public Health, 615 North Wolfe Street, Baltimore, MD 21205 USA. Telephone: (410) 955-3067. Fax: (410) 955-0958. E-mail: szeger@jhsph.edu

Research described in this article was conducted under contract to the Health Effects Institute (HEI) , an organization funded jointly by the U.S. EPA (EPA R824835) and automotive manufacturers. Funding was also provided by the Johns Hopkins Center in Urban Environmental Health (5P30 ESO 3819-12) .

The contents of this article do not necessarily reflect the views and policies of HEI, the EPA, or automotive manufacturers.

Received 1 July 1999 ; accepted 16 November 1999.