Date of Award


Document Type

Open Access Dissertation



First Advisor

Susan L. Cutter


While cancer rates have shown promising trends over the last few decades, not all populations have experienced the same levels of decrease in cancer incidence in mortality rates. Identifying populations suffering from the impacts of the disparities has become a major goal in cancer research.

Most research has focused on the influence of single variables on cancer disparities or on small-scale case studies. Using the information from these analyses, the research conducted in this dissertation tests the relationship of selected variables to an outcome measure, the mortality to incidence ratio (MIR) in search of spatial relationships between the indicators and the MIR. The goal is to identify influential variables in addition to determining whether variables consistently express the same influence over the MIR.

In order to achieve the goal, three separate analyses are conducted to correspond with the primary research questions. To answer the first research question, involving the identification of predominant socio-spatial indicators driving cancer disparities in the US, a regression model is run, using thirty-four potential variables as independent variables and the MIR as a dependent variable. The second research question, based on the identification of broad-based factors accounting for disparities in cancer outcomes, is answered through both theoretical and inductive grouping of the indicators using a priori knowledge and principle components analysis. A second regression tests the predictive ability of the two grouping methods and the contribution of each group to the MIR. A path analysis is conducted as well to determine how factors influence each other and interact to yield cancer outcomes. The third research question, intending to identify whether the relationship of the broad-based factors to cancer outcomes remains consistent across the United States, is conducted using spatial methodologies. This final step involves a combination of hot spot mapping, geographically weighted regression analysis, and a bivariate Moran’s I to establish regions where disparities exist as well as identifying differences in the contribution of variables to the disparities.

The findings of the research reveal a complex interaction of variables and a level of dependence between the aggregated groups. The results of the first research question revealed obesity as the most highly correlated indicator to the MIR. Counties with higher rural populations were second, while social indicators including the percentage of single parent households and unmarried population also factored very highly into the model. When the indicators were grouped via theoretical models, health and behavioral characteristics along with social characteristics displayed most of the variance and had the highest correspondence to the MIR. The final research question, looking for spatial patterns, revealed a significant hot spot in the Southeast United States for both the MIR, social, health, and access factors. Similarities are most evident between the spatial patterns of the MIR in comparison to social and health characteristics. With the presence of definitive regional patterns and clear connections between the MIR and societal groupings, the finding from this research suggest a need to shift to sub-regional analysis in order to determine whether the same patterns hold up at a local level.