The implementation of Bayesian structural additive regression models in multicity time series air pollution and human health studies
 LungChang Chien,
 Shrikant I. Bangdiwala
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In this study, a novel Bayesian semiparametric structural additive regression (STAR) model is introduced in multicity time series air pollution and human health studies. This modeling approach can simultaneously take into account the fixed effects, random effects, nonlinear smoothing functions and spatial functions in an integrated model framework. This study focuses on examining the powerful functionalities of this approach in modeling air pollution and mortality data of 100 U.S. cities from 1987 to 2000. Compared with previous studies, the modeling approach used in this study yields consistent findings of nationlevel and citylevel PM_{10} (particulate matter less than 10 μm) effects on mortality. Notably, cities with significantly elevated mortality rates were concentrated in the Northeastern U.S. This modeling approach also emphasizes the important functionality of the spatial function in visualizing disease mapping. Model diagnostics were performed to confirm the availability of the STAR model. We also found consistent findings by using different hyperparameters in the sensitivity analysis. To sum up, the implementation of this modeling approach has achieved the goals of applying a spatial function and obtaining robust results in the multicity time series air pollution and human health study.
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 Title
 The implementation of Bayesian structural additive regression models in multicity time series air pollution and human health studies
 Journal

Stochastic Environmental Research and Risk Assessment
Volume 26, Issue 8 , pp 10411051
 Cover Date
 20121201
 DOI
 10.1007/s0047701205624
 Print ISSN
 14363240
 Online ISSN
 14363259
 Publisher
 SpringerVerlag
 Additional Links
 Topics

 Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences
 Computational Intelligence
 Earth Sciences, general
 Probability Theory and Stochastic Processes
 Math. Appl. in Environmental Science
 Waste Water Technology / Water Pollution Control / Water Management / Aquatic Pollution
 Keywords

 Structural additive regression models
 Air pollution
 Mortality
 Spatiotemporal analysis
 Industry Sectors
 Authors

 LungChang Chien ^{(1)}
 Shrikant I. Bangdiwala ^{(2)}
 Author Affiliations

 1. Department of Internal Medicine, Division of Health Behavior Research, Washington University School of Medicine, 4444 Forest Park Ave, Suite 6700, St. Louis, MO, 63108, USA
 2. Department of Biostatistics, University of North Carolina at Chapel Hill, 137 E. Franklin St., Suite 203, Chapel Hill, NC, 27514, USA