Hierarchical Spatio-Temporal Mapping of Disease Rates

Lance A. Waller, Brad Carlin, Hong Xia, Alan E. Gelfand

Research output: Contribution to journalArticlepeer-review

341 Scopus citations

Abstract

Maps of regional morbidity and mortality rates are useful tools in determining spatial patterns of disease. Combined with sociodemographic census information, they also permit assessment of environmental justice; that is, whether certain subgroups suffer disproportionately from certain diseases or other adverse effects of harmful environmental exposures. Bayes and empirical Bayes methods have proven useful in smoothing crude maps of disease risk, eliminating the instability of estimates in low-population areas while maintaining geographic resolution. In this article we extend existing hierarchical spatial models to account for temporal effects and spatio-temporal interactions. Fitting the resulting highly parameterized models requires careful implementation of Markov chain Monte Carlo (MCMC) methods, as well as novel techniques for model evaluation and selection. We illustrate our approach using a dataset of county-specific lung cancer rates in the state of Ohio during the period 1968–1988.

Original languageEnglish (US)
Pages (from-to)607-617
Number of pages11
JournalJournal of the American Statistical Association
Volume92
Issue number438
DOIs
StatePublished - Jun 1 1997

Keywords

  • Environmental justice
  • Identifiability
  • Metropolis algorithm
  • Model selection

Fingerprint

Dive into the research topics of 'Hierarchical Spatio-Temporal Mapping of Disease Rates'. Together they form a unique fingerprint.

Cite this