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Christopher H. Holloman,¹ Steven M. Bortnick,¹ Michele Morara,¹ Warren J. Strauss,¹ and Catherine A. Calder²

¹Statistics and Data Analysis Systems, Battelle Memorial Institute, Columbus, Ohio, USA; ²Department of Statistics, The Ohio State University, Columbus, Ohio, USA

Abstract
Considerable attention has been given to the relationship between levels of fine particulate matter (particulate matter 2.5 µm in aerodynamic diameter ; PM_2.5) in the atmosphere and health effects in human populations. Since the U.S. Environmental Protection Agency began widespread monitoring of PM_2.5 levels in 1999, the epidemiologic community has performed numerous observational studies modeling mortality and morbidity responses to PM_2.5 levels using Poisson generalized additive models (GAMs) . Although these models are useful for relating ambient PM_2.5 levels to mortality, they cannot directly measure the strength of the effect of exposure to PM_2.5 on mortality. In order to assess this effect, we propose a three-stage Bayesian hierarchical model as an alternative to the classical Poisson GAM. Fitting our model to data collected in seven North Carolina counties from 1999 through 2001, we found that an increase in PM_2.5 exposure is linked to increased risk of cardiovascular mortality in the same day and next 2 days. Specifically, a 10-µg/m³ increase in average PM_2.5 exposure is associated with a 2.5% increase in the relative risk of current-day cardiovascular mortality, a 4.0% increase in the relative risk of cardiovascular mortality the next day, and an 11.4% increase in the relative risk of cardiovascular mortality 2 days later. Because of the small sample size of our study, only the third effect was found to have > 95% posterior probability of being > 0. In addition, we compared the results obtained from our model to those obtained by applying frequentist (or classical, repeated sampling-based) and Bayesian versions of the classical Poisson GAM to our study population. Key words: exposure simulator, fine particulate matter, SHEDS-PM, spatial modeling, Stochastic Human Exposure and Dose Simulation. Environ Health Perspect 112:1282-1288 (2004) . doi:10.1289/ehp.6980 available via http://dx.doi.org/ [Online 3 June 2004]

Address correspondence to C. Holloman, Battelle Memorial Institute, 505 King Ave., Columbus, Ohio 43201-2693 USA. Telephone: (614) 424-4946. Fax: (614) 424-4611. E-mail: hollomanc@battelle.org

We thank H. Özkaynak and R. Williams of the U.S. Environmental Protection Agency for many helpful suggestions regarding exposure modeling and health end points.

This work was funded by a Battelle internal research and development grant for fiscal year 2003.

The authors declare they have no competing financial interests.

Received 23 January 2004 ; accepted 3 June 2004.