College of Public Health and Human Sciences

Spatial modeling of rare diseases, such as childhood cancer, has been hampered by imprecise risk estimates. Recent developments in Bayesian hierarchical modeling include the ability to adjust a disease risk estimate to be fully conditional for covariance among neighboring locations and for covariance among multiple diseases within each location. This joint modeling approach is called Multivariate Intrinsic Conditional Autoregressive. The objective of this study was to evaluate the spatial and histotype covariance among childhood cancer histotypes, in Texas. Results will be valuable for selecting appropriate models to support more specific etiologic studies of environmental factors for childhood cancer.