Assessing Uncertainty in Support-Adjusted Spatial Misalignment Problems

Existing data from multiple sources (e.g., surveillance systems, health registries, governmental agencies) are the foundation for analysis and inference in many studies and programs. More often than not, these data have been collected on different geographical or spatial units, and each of these may be different from the ones of interest. Numerous statistical issues are associated with combining such disparate data. Florida's efforts to move toward implementation of The Centers for Disease Control and Prevention's (CDC's) Environmental Public Health Tracking (EPHT) Program aptly illustrate these issues, which are typical of almost any study designed to measure the association between environmental hazards and health outcomes. In this article, we consider the inferential issues that arise when a potential explanatory variable is measured on one set of spatial units, but then must be predicted on a different set of spatial units. We compare the results from two different approaches to linking health and environmental data on different spatial units, focusing on the need to account for spatial scale and the support of spatial data in the analysis. We compare methods for assessing uncertainty and the potential bias that arises from using predicted variables in spatial regression models. Our focus is on relatively simple methods and concepts that can be transferred to the states' departments of health, the organizations responsible for implementing EPHT.