Abstract
Mapping of disease incidence has long been
of importance to epidemiology and public health. In this paper, we consider identification
of clusters of spatial units with elevated disease rates and develop a new
approach that estimates the relative disease risk in association with potential
risk factors and simultaneously identifies clusters corresponding to elevated
risks. A heterogeneity measure is proposed to enable the comparison of a
candidate cluster and its complement under a pair of complementary models. A
quasi-likelihood procedure is developed for estimating the model parameters and
identifying the clusters. An advantage of our approach over traditional spatial
clustering methods is the identification of clusters that can have arbitrary
shapes due to abrupt or non-contiguous changes while accounting for risk
factors and spatial correlation. Asymptotic properties of the proposed
methodology are established and a simulation study shows empirically sound
finite-sample properties. The mapping and clustering of enterovirus 71
infection in Taiwan are carried out for illustration.