  Epidemiologists commonly use regional aggregates of health outcomes to map
mortality or incidence rates and identify geographic disparities. However, to
detect health disparities across regions, it is necessary to identify
"difference boundaries" that separate neighboring regions with significantly
different spatial effects. This can be particularly challenging when dealing
with multiple outcomes for each unit and accounting for dependence among
diseases and across areal units. In this study, we address the issue of
multivariate difference boundary detection for correlated diseases by
formulating the problem in terms of Bayesian pairwise multiple comparisons by
extending it through the introduction of adjacency modeling and disease graph
dependencies. Specifically, we seek the posterior probabilities of neighboring
spatial effects being different. To accomplish this, we adopt a class of
multivariate areally referenced Dirichlet process models that accommodate
spatial and interdisease dependence by endowing the spatial random effects with
a discrete probability law. Our method is evaluated through simulation studies
and applied to detect difference boundaries for multiple cancers using data
from the Surveillance, Epidemiology, and End Results Program of the National
Cancer Institute.
