Table of Contents
ISRN Obstetrics and Gynecology
Volume 2013 (2013), Article ID 387452, 9 pages
http://dx.doi.org/10.1155/2013/387452
Research Article

Air Pollution Metric Analysis While Determining Susceptible Periods of Pregnancy for Low Birth Weight

1Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599-7420, USA
2Department of Statistics, North Carolina State University, Raleigh, NC 27695-8203, USA
3Birth Defects Epidemiology and Surveillance Branch, Texas Department of State Health Services, Austin, TX 78714-9347, USA

Received 16 December 2012; Accepted 3 January 2013

Academic Editors: M. Friedrich, S. Palomba, and C. J. Petry

Copyright © 2013 Joshua L. Warren et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Multiple metrics to characterize air pollution are available for use in environmental health analyses in addition to the standard Air Quality System (AQS) pollution monitoring data. These metrics have complete spatial-temporal coverage across a domain and are therefore crucial in calculating pollution exposures in geographic areas where AQS monitors are not present. We investigate the impact that two of these metrics, output from a deterministic chemistry model (CMAQ) and from a spatial-temporal downscaler statistical model which combines information from AQS and CMAQ (DS), have on risk assessment. Using each metric, we analyze ambient ozone's effect on low birth weight utilizing a Bayesian temporal probit regression model. Weekly windows of susceptibility are identified and analyzed jointly for all births in a subdomain of Texas, 2001–2004, and results from the different pollution metrics are compared. Increased exposures during weeks 20–23 of the pregnancy are identified as being associated with low birth weight by the DS metric. Use of the CMAQ output alone results in increased variability of the final risk assessment estimates, while calibrating the CMAQ through use of the DS metric provides results more closely resembling those of the AQS. The AQS data are still preferred when available.