Spatial clustering of adult cancer prevalence in the United States

Abstract

BACKGROUND AND AIM: Understanding the geographic distribution of cancer prevalence at neighborhood level may help guide cancer control and prevention measures from the perspective of neighborhoods, as opposed to individuals. We examined the spatial heterogeneity of cancer prevalence across census tracts in the contiguous United States. METHODS: We previously identified predictors of census tract-level cancer prevalence by applying Bayesian additive regression trees to a combined database of tract-level population health, environmental exposure, and socioeconomic data from multiple sources. In this study, we examined the association between the selected predictors (percentage of adults 65 years old or over, prevalence of routine checkup, percentage of non-Hispanic white, percentage of housing built before 1960, and percentage of individuals below poverty) and cancer prevalence using a linear regression model. Spatial clustering were identified based on weighted normal scan statistics (SatScan V9.7) using the tract-level cancer prevalence and the regression residuals, respectively, with the population for aged≥65 years as the weight. RESULTS:The mean cancer prevalence was 6.7% (standard deviation 1.8%). Using cancer prevalence, we identified 4 circular-shaped high-risk clusters in Florida (Latitude Longitude and radius: 27.817765 N 81.396327 W, 216km), Arizona (33.631195 N 112.350663 W, 9km), New Jersey (39.958259 N 74.345696 W, 11km), and a region encompassing parts of Michigan, Ohio, Pennsylvania, and New York (43.268491 N 78.823785 W, 468km). Their estimated cancer prevalence was 8.1%, 10.1%, 13.8%, and 7.3%, respectively. Using residuals, we identified one dominant high-risk region covering parts of Montana, Wyoming, North Dakota, South Dakota, Nebraska, Minnesota, and Iowa (47.723678 N 104.100726 W, 1834km), and four smaller clusters (radius 20km) in Florida and Maryland. CONCLUSIONS:Variations in high-risk clustering patterns based on prevalence and residual values suggest that the identified five predictors can explain much of the initial variation in tract-level cancer prevalence. However, the high-risk regions based on residuals require further investigation. KEYWORDS: Cancer prevalence, United States, Geospatial analysis, Cancer high-risk clusters