Characterization and impact of ambient air pollution measurement error in time-series epidemiologic studies

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Please use this identifier to cite or link to this item: http://hdl.handle.net/1853/41158

Title: Characterization and impact of ambient air pollution measurement error in time-series epidemiologic studies
Author: Goldman, Gretchen Tanner
Abstract: Time-series studies of ambient air pollution and acute health outcomes utilize measurements from fixed outdoor monitoring sites to assess changes in pollution concentration relative to time-variable health outcome measures. These studies rely on measured concentrations as a surrogate for population exposure. The degree to which monitoring site measurements accurately represent true ambient concentrations is of interest from both an etiologic and regulatory perspective, since associations observed in time-series studies are used to inform health-based ambient air quality standards. Air pollutant measurement errors associated with instrument precision and lack of spatial correlation between monitors have been shown to attenuate associations observed in health studies. Characterization and adjustment for air pollution measurement error can improve effect estimates in time-series studies. Measurement error was characterized for 12 ambient air pollutants in Atlanta. Simulations of instrument and spatial error were generated for each pollutant, added to a reference pollutant time-series, and used in a Poisson generalized linear model of air pollution and cardiovascular emergency department visits. This method allows for pollutant-specific quantification of impacts of measurement error on health effect estimates, both the assessed strength of association and its significance. To inform on the amount and type of error present in Atlanta measurements, air pollutant concentrations were simulated over the 20-county metropolitan area for a 6-year period, incorporating several distribution characteristics observed in measurement data. The simulated concentration fields were then used to characterize the amount and type of error due to spatial variability in ambient concentrations, as well as the impact of use of different exposure metrics in a time-series epidemiologic study. Finally, methodologies developed for the Atlanta area were applied to air pollution measurements in Dallas, Texas with consideration for the impact of this error on a health study of the Dallas-Fort Worth region that is currently underway.
Type: Dissertation
URI: http://hdl.handle.net/1853/41158
Date: 2011-06-28
Publisher: Georgia Institute of Technology
Subject: Spatial modeling
Exposure misclassification
Air pollution
Measurement error
Geostatistics
Time series
Geology Statistical methods
Kriging
Air Pollution
Air quality
Regression analysis
Department: Civil and Environmental Engineering
Advisor: Committee Chair: James A. Mulholland; Committee Member: Armistead G. Russell; Committee Member: Brian Stone, Jr; Committee Member: Jian Luo; Committee Member: Matthew J. Strickland; Committee Member: Michael Chang
Degree: Ph.D.

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