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Spatio-Temporal Hierarchical Models for Analyzing Atlanta Pediatric Asthma ER Visit Rates

  • Bradley P. Carlin
  • Hong Xia
  • Owen Devine
  • Paige Tolbert
  • James Mulholland
Part of the Lecture Notes in Statistics book series (LNS, volume 140)

Abstract

Prevalence and mortality rates of asthma among children have been increasing over the past twenty years, particularly among African American children and children of lower socioeconomic status. In this paper we investigate the link between ambient ozone and pediatric ER visits for asthma in the Atlanta metro area during the summers of 1993, 1994, and 1995. Our statistical model allows for several demographic and meteorologic covariates, spatial and spatio-temporal autocorrelation, and errors in the ozone estimates (which are obtained from a kriging procedure to smooth ozone monitoring station data). As with most recent Bayesian analyses we employ a MCMC computing strategy, highlighting convergence problems we encountered due to the high collinearity of several predictors included in early versions of our model. After providing our choice of prior distributions, we present our results and consider the issues of model selection and adequacy. In particular, we offer graphical displays which suggest the presense of unobserved spatially varying covariates outside the city of Atlanta, and reveal the value of our errors in covariates approach, respectively. Finally, we summarize our findings, discuss limitations of (and possible remedies for) both our data set and analytic approach, and compare frequentist and Bayesian approaches in this case study.

Keywords

Markov Chain Monte Carlo Ozone Level Markov Chain Monte Carlo Algorithm Pediatric Asthma Metro Area 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 1999

Authors and Affiliations

  • Bradley P. Carlin
  • Hong Xia
  • Owen Devine
  • Paige Tolbert
  • James Mulholland

There are no affiliations available

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