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In public health applications, spatial data collected are often recorded atdifferent spatial scales and over different correlated variables. Spatialchange of support is a key inferential problem in these applications and havebecome standard in univariate settings; however, it is less standard inmultivariate settings. There are several existing multivariate spatial modelsthat can be easily combined with multiscale spatial approach to analyzemultivariate multiscale spatial data. In this paper, we propose three newmodels from such combinations for bivariate multiscale spatial data in aBayesian context. In particular, we extend spatial random effects models,multivariate conditional autoregressive models, and ordered hierarchical modelsthrough a multiscale spatial approach. We run simulation studies for the threemodels and compare them in terms of prediction performance and computationalefficiency. We motivate our models through an analysis of 2015 Texas annualaverage percentage receiving two blood tests from the Dartmouth Atlas Project.

Bayesian Hierarchical Modeling for Bivariate Multiscale Spatial Data with Application to Blood Test Monitoring