Academic journal article Environmental Health Perspectives

Exposure Measurement Error in Time-Series Studies of Air Pollution: Concepts and Consequences

Academic journal article Environmental Health Perspectives

Exposure Measurement Error in Time-Series Studies of Air Pollution: Concepts and Consequences

Article excerpt

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 [is less than] 10 [micro]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. 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

Misclassification of exposure has long been recognized as an inherent limitation of epidemiologic studies of the environment and disease (1). For many agents of interest, exposures take place over time and in multiple locations so that it is difficult to accurately estimate the relevant exposures for individual study participants, particularly within the limits set by feasibility, participant burden, and cost. In general, exposure measurement error tends to blunt the sensitivity of epidemiologic studies for detecting the effects of environmental agents, although the specific impact of exposure error on effect estimates depends on several factors including the study design, the types of error, and the relationships between the outcome and the independent variables (1,2). As the problem of exposure error has become well recognized, researchers have taken steps to control its consequences by limiting the degree of error through careful study design and data collection, by estimating the degree of error using a nested validation study, and by making adjustments for measurement error in statistical analyses.

In this paper, we address the problem of exposure error in observational ecologic time-series studies of air pollution and health. The pollution of outdoor air is a public health concern throughout the world. For decades, epidemiologic studies have been a cornerstone of our approach to investigating the health effects of air pollution and have been a principal basis for setting regulations to protect the public against adverse health effects. Two broad types of observational study designs have been used in research on air pollution: ecologic or aggregate-level studies, either cross-sectional or time-series in design, and individual-level studies, primarily of the cross-sectional or cohort designs. …

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