Bayesian Risk Modeling of Racial-spatial Interactions Among Childhood Cancer Hist

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DESCRIPTION (provided by applicant): Project Summary/Abstract. Identification of the causes of childhood cancer has been elusive with two exceptions. Geographic risks, like proximity to pollution sources, are often implicated and racial differences in risk have been profound. Interpreting how geographic exposures and individual characteristics, like race, act and interact among multiple cancer types is the current impasse. To overcome this obstacle, we need a risk modeling approach that incorporates both geographic-level and individual-level risks and can facilitate the objective pooling or parsing of cancer types. Two recent modeling developments present a promising opportunity. First, it is now possible to disentangle individual risk factors from geographic risk factors utilizing hierarchical modeling. Second, it is now possible for a single model to incorporate risks for multiple outcomes, like cancer histotypes. This application calls for developing a childhood cancer risk model that capitalizes on these two recent advances. The objective of this application is to establish a modeling approach that is both fully hierarchical and inclusive of multiple childhood cancer histotypes. The central hypothesis is that race, as the most important example of individual-level risk factors, and geographic-level exposures combine to cause multiple childhood cancer histotypes. This hypothesis will be tested by two specific aims: Specific Aim 1 is to model the small-scale spatial patterns of childhood cancer risks. The results of this specific aim will be detailed risk surfaces capable of demonstrating risk patterns and risk clusters. These modeling results will enable focused investigation, including focused risk modeling, for high-risk locations. Specific Aim 2 is to identify the spatially specific racial risks for childhood cancer. This specific aim will utilize recently developed risk modeling for spatially varying risks. The results of this specific aim will be multiple path analyses and will clarify causal pathways for childhood cancer. This application is innovative because it will exploit two new developments, fully conditional hierarchical modeling and multivariate modeling. PUBLIC HEALTH RELEVANCE: Project Narrative. The proposed research is significant because what is needed most to advance our understanding of the causes of childhood cancer is a risk model that incorporates both geographic risks and individual risks and applies to the multiple types of childhood cancer. The study will validate a risk modeling approach that will enable the elucidation of the causal mechanisms for childhood cancer.