Stochastic Environmental Research and Risk Assessment

, Volume 26, Issue 8, pp 1041–1051

The implementation of Bayesian structural additive regression models in multi-city time series air pollution and human health studies

Original Paper

Abstract

In this study, a novel Bayesian semiparametric structural additive regression (STAR) model is introduced in multi-city 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 nation-level and city-level PM10 (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 multi-city time series air pollution and human health study.

Keywords

Structural additive regression models Air pollution Mortality Spatio-temporal analysis 

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Copyright information

© Springer-Verlag 2012

Authors and Affiliations

  1. 1.Department of Internal Medicine, Division of Health Behavior ResearchWashington University School of MedicineSt. LouisUSA
  2. 2.Department of BiostatisticsUniversity of North Carolina at Chapel HillChapel HillUSA

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