Hierarchical Spatio-Temporal Mapping of Disease Rates

Published

Journal Article

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. © 1997 Taylor & Francis Group, LLC.

Full Text

Duke Authors

Cited Authors

  • Waller, LA; Carlin, BP; Xia, H; Gelfand, AE

Published Date

  • June 1, 1997

Published In

Volume / Issue

  • 92 / 438

Start / End Page

  • 607 - 617

Electronic International Standard Serial Number (EISSN)

  • 1537-274X

International Standard Serial Number (ISSN)

  • 0162-1459

Digital Object Identifier (DOI)

  • 10.1080/01621459.1997.10474012

Citation Source

  • Scopus