  In public health applications, spatial data collected are often recorded at
different spatial scales and over different correlated variables. Spatial
change of support is a key inferential problem in these applications and have
become standard in univariate settings; however, it is less standard in
multivariate settings. There are several existing multivariate spatial models
that can be easily combined with multiscale spatial approach to analyze
multivariate multiscale spatial data. In this paper, we propose three new
models from such combinations for bivariate multiscale spatial data in a
Bayesian context. In particular, we extend spatial random effects models,
multivariate conditional autoregressive models, and ordered hierarchical models
through a multiscale spatial approach. We run simulation studies for the three
models and compare them in terms of prediction performance and computational
efficiency. We motivate our models through an analysis of 2015 Texas annual
average percentage receiving two blood tests from the Dartmouth Atlas Project.
