Geospatial Approaches to Cancer Outcomes

Abstract

Cancer epidemiology has a long history of applying geographic thinking to address longstanding place-based disparities. This dissertation adds new knowledge to geospatial approaches to social determinants of cancer outcomes. It establishes a framework consisting of three dimensions in evaluating, identifying, and prioritizing spatially heterogeneous risk factors of cancer outcomes. The first dimension is protection. Using a space-time statistic, the first study evaluated whether a non-spatial healthcare policy, Medicaid expansion, has offered protection targeting spatially vulnerable populations against adverse cancer outcomes such as breast cancer late-stage diagnosis. The second dimension is phenotype. Using a classification and regression tree, the study disentangled how risk factors of late-stage breast cancer diagnosis were conceptualized and capsulized as phenotypes that labeled groups of homogenous geographic areas. It provides a novel angle to uncover cancer disparities and to provide insights for cancer surveillance, prevention, and control. The third dimension is priority. Using a geographic random forest along with several validation methods, the study emphasized the importance of the competing effect among risk factors of cancer mortality that are specific to geographic areas. The findings from this study can be used directly for priority settings in addressing the most urgent issues associated with cancer mortality. This dissertation demonstrated that geographic methodologies and frameworks are useful and are imperative to cancer epidemiology.

Weichuan Dong

Spatial Epidemiologist

I am a lifetime learner with a commitment to using geospatial approaches in understanding health disparities. My recent interest is in investigating geographic variations of cancer diagnosis (i.e., later-stage diagnosis and access to screening services) and treatments (i.e., delayed time to treatment initiation, breast cancer conserving therapy, and breast reconstruction surgery) to understand the cause of cancer disparities using statistical, geographic, and machine learning methods.