SPATIOTEMPORAL MODELS WITH ERRORS IN COVARIATES - MAPPING OHIO LUNG-CANCER MORTALITY

In estimating spatial disease patterns, as well as in related assessme nts of environmental equity, regional morbidity and mortality rate map s are widely used. Hierarchical Bayes methods are increasingly popular tools for creating such maps, since they permit smoothing of the fitt ed rates toward spatially local mean values, with more unreliable esti mates (those arising in low-population regions) receiving more smoothi ng. In this paper we blend methods for spatial-temporal mapping with t hose for handling errors in covariates in a single hierarchical model framework. Estimated posterior distributions for the resulting highly- parameterized models are obtained via Markov chain Monte Carlo (MCMC) methods, which also play a key role in our approach to model evaluatio n and selection. We apply our approach to a data set of county-specifi c lung cancer rates in the state of Ohio during the period 1968-1988. Our model uses age-adjusted death rates, and incorporates recent infor mation regarding smoking prevalence, population density, and the socio -economic status of the counties. This information is critical to unde rstanding the role played by a certain depleted uranium fuel processin g facility on the elevated lung cancer rates in the counties that neig hbour it. (C) 1998 John Wiley & Sons, Ltd.