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Biostatistics Advance Access published online on June 29, 2006
Biostatistics, doi:10.1093/biostatistics/kxl008
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© The Author 2006. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org
Received December 19, 2005
Revised June 10, 2006
Accepted June 15, 2006

Article

Disease Mapping and Spatial Regression with Count Data

Jon Wakefield 1 *

1 Departments of Statistics and Biostatistics, Box 357232, University of Washington, Seattle, Washington 98195-7232, U.S.A.

* To whom correspondence should be addressed.
Jon Wakefield, E-mail: jonno{at}u.washington.edu


  Abstract

In this paper we provide critical reviews of methods suggested for the analysis of aggregate count data in the context of disease mapping and spatial regression. We introduce a new method for picking prior distributions, and propose a number of refinements of previously-used models. We also consider ecological bias, mutual standardization, and choice of both spatial model and prior specification. We analyse male lip cancer incidence data collected in Scotland over the period 1975-1980, and outline a number of problems with previous analyses of these data. In disease mapping studies, hierarchical models can provide robust estimation of area-level risk parameters, though care is required in the choice of covariate model, and it is important to assess the sensitivity of estimates to the spatial model chosen, and to the prior specifications on the variance parameters. Spatial ecological regression is a far more hazardous enterprise for two reasons. First, there is always the possibility of ecological bias, and this can only be alleviated by the inclusion of individual-level data. For the Scottish data we show that the previously used mean model has limited interpretation from an individual perspective. Second, when residual spatial dependence is modelled, and if the exposure has spatial structure, then estimates of exposure association parameters will change when compared with those obtained from the independence across space model, and the data alone cannot choose the form and extent of spatial correlation that is appropriate.

Keywords: Bayesian Methods; Ecological Bias; Ecological Correlation Studies; Hierarchical Models; Prior Distributions; Spatial Epidemiology; Standardization.
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