A multiscale method for disease mapping in spatial epidemiology.
The effects of spatial scale in disease mapping are well-recognized, in that the information conveyed by such maps varies with scale. Here we provide an inferential framework, in the context of tract count data, for describing the distribution of relative risk simultaneously across a hierarchy of multiple scales. In particular, we offer a multiscale extension of the canonical standardized mortality ratio (SMR), consisting of Bayesian posterior-based strategies for both estimation and characterization of uncertainty. As a result, a hierarchy of informative disease and confidence maps can be produced, without the need to first try to identify a single appropriate scale of analysis. We explore the behaviour of the proposed methodology in a small simulation study, and we illustrate its usage through an application to data on gastric cancer in Tuscany.
Louie
Mary M
MM
Channing Laboratory, Brigham and Women's Hospital and Harvard Medical School, 181 Longwood Avenue, Boston, MA 02115, USA. mlouie@cdc.gov
Kolaczyk
Eric D
ED
D001499:Bayes Theorem; D003198:Computer Simulation; D016001:Confidence Intervals; D003710:Demography; D004812:Epidemiologic Methods; D005843:Geography; D006801:Humans; D007558:Italy / Q000453:epidemiology; D008297:Male; D015233:Models, Statistical; D009026:Mortality; D012306:Risk; D013274:Stomach Neoplasms / Q000401:mortality