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American Journal of Epidemiology Advance Access published online on April 19, 2006
American Journal of Epidemiology, doi:10.1093/aje/kwj150
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American Journal of Epidemiology Copyright © 2006 by the Johns Hopkins Bloomberg School of Public Health All rights reserved; printed in U.S.A.
Received June 8, 2005
Accepted January 13, 2006

ORIGINAL CONTRIBUTIONS

Bayesian Modeling of Air Pollution Health Effects with Missing Exposure Data

John Molitor 1 *, Nuoo-Ting Molitor 1, Michael Jerrett 1, Rob McConnell 1, Jim Gauderman 1, Kiros Berhane 1, and Duncan Thomas 1

1 From the Department of Preventive Medicine, University of Southern California, Los Angeles, CA

* To whom correspondence should be addressed.
John Molitor, E-mail: jmolitor{at}usc.edu


  Abstract

The authors propose a new statistical procedure that utilizes measurement error models to estimate missing exposure data in health effects assessment. The method detailed in this paper follows a Bayesian framework that allows estimation of various parameters of the model in the presence of missing covariates in an informative way. The authors apply this methodology to study the effect of household-level long-term air pollution exposures on lung function for subjects from the Southern California Children's Health Study pilot project, conducted in the year 2000. Specifically, they propose techniques to examine the long-term effects of nitrogen dioxide (NO2) exposure on children's lung function for persons living in 11 southern California communities. The effect of nitrogen dioxide exposure on various measures of lung function was examined, but, similar to many air pollution studies, no completely accurate measure of household-level long-term nitrogen dioxide exposure was available. Rather, community-level nitrogen dioxide was measured continuously over many years, but household-level nitrogen dioxide exposure was measured only during two 2-week periods, one period in the summer and one period in the winter. From these incomplete measures, long-term nitrogen dioxide exposure and its effect on health must be inferred. Results show that the method improves estimates when compared with standard frequentist approaches.

Keywords: air pollution; Bayesian analysis; bias (epidemiology).
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