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Bayesian 2-Stage Space-Time Mixture Modeling With Spatial Misalignment of the Exposure in Small Area Health Data

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Abstract

We develop a new Bayesian two-stage space-time mixture model to investigate the effects of air pollution on asthma. The two-stage mixture model proposed allows for the identification of temporal latent structure as well as the estimation of the effects of covariates on health outcomes. In the paper, we also consider spatial misalignment of exposure and health data. A simulation study is conducted to assess the performance of the 2-stage mixture model. We apply our statistical framework to a county-level ambulatory care asthma data set in the US state of Georgia for the years 1999–2008.

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Authors and Affiliations

  1. Division of Biostatistics and Epidemiology, College of Medicine, Medical University of South Carolina, Charleston, SC, 29403, USA

    Andrew B. Lawson & Jungsoon Choi

  2. Department of Epidemiology and Biostatistics, University of South Carolina, Columbia, SC, 29208, USA

    Bo Cai & Jihong Liu

  3. Division of Biostatistics and Epidemiology, Cincinnati Childrens Hospital and Medical Center, University of Cincinnati College of Medicine, Cincinnati, OH, 45229, USA

    Monir Hossain

  4. Department of Community and Family Health, College of Public Health, University of South Florida, Tampa, FL, USA

    Russell S. Kirby

Authors
  1. Andrew B. Lawson
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  2. Jungsoon Choi
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  3. Bo Cai
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  4. Monir Hossain
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  5. Russell S. Kirby
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  6. Jihong Liu
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Correspondence to Andrew B. Lawson.

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Lawson, A.B., Choi, J., Cai, B. et al. Bayesian 2-Stage Space-Time Mixture Modeling With Spatial Misalignment of the Exposure in Small Area Health Data. JABES 17, 417–441 (2012). https://doi.org/10.1007/s13253-012-0100-3

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  • DOI: https://doi.org/10.1007/s13253-012-0100-3

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