title: Disease mapping with errors in covariates.
abstract: We describe Bayesian hierarchical-spatial models for disease mapping with imprecisely observed ecological covariates. We posit smoothing priors for both the disease submodel and the covariate submodel. We apply the models to an analysis of insulin Dependent Diabetes Mellitus incidence in Sardinia, with malaria prevalence as a covariate.
authors.affiliation: Dipartimento di Scienze Sanitarie Applicate, University of Pavia, Italy.
authors.forename: L
authors.initials: L
authors.lastname: Bernardinelli
authors.forename: C
authors.initials: C
authors.lastname: Pascutto
authors.forename: N G
authors.initials: NG
authors.lastname: Best
authors.forename: W R
authors.initials: WR
authors.lastname: Gilks
mesh_terms: D001499:Bayes Theorem; D015982:Bias; D015897:Comorbidity; D003922:Diabetes Mellitus, Type 1 / Q000453:epidemiology; D004812:Epidemiologic Methods; D006801:Humans; D015994:Incidence; D007558:Italy / Q000453:epidemiology; D008288:Malaria / Q000453:epidemiology; D008377:Maps as Topic; D008390:Markov Chains; D015995:Prevalence; D015203:Reproducibility of Results; D012111:Residence Characteristics