Showcasing Cancer Incidence and Mortality in Rural ZCTAs Using Risk Probabilities via Spatio-Temporal Bayesian Disease Mapping

Abstract

Health departments are seeking new ways to determine when and where limited resources should be allocated to achieve maximum benefit for the population. In this work, we demonstrate how one state health department worked to create relative risk measures of cancer incidence, late-stage cancer incidence and mortality incidence displayed in an easy to read map using spatio-temporal statistical tools. The data included age, sex, cancer type and stage, and ZIP Code Tabulation Area (ZCTA) for every incidence and death from 2004 to 2015. Eight types of cancer were selected for analysis - breast, cervical, colorectal, liver, lung, non-Hodgkin lymphoma (NHL), prostate, and melanoma. The risk maps were designed to illustrate areas of the state where risk for developing or dying from certain cancers was higher than the state average, and to show how trends are evolving over time. A hierarchical Bayesian log-normal Poisson regression model, with effects for ZCTA, time period, and a space-time interaction was implemented. The spatial effects accounted for spatial correlation using an intrinsic conditional auto-regressive model, and the time effects used an autoregressive model. Through the model, we were able to achieve reliable estimates of relative risk per ZCTA and time period, even for small population ZCTAs with few, if any, cases during the time period. Furthermore, we calculated a measure of risk probability for each ZCTA, relative to the state average. Results from two cancers are discussed in this manuscript, but all 24 results are available on the project website.