Geographical Trends in Cancer Mortality: Using Spatial Smoothers and Methods for Adjustment

Abstract

Mapping health-related data can lead to important insights and generate hypotheses about causes and potential effects. Such data are commonly adjusted for the variables age and gender, so that inferences are not influenced by these obvious factors. In a similar fashion, data for certain diseases ought to be adjusted for known risk factors. One method of adjustment is suggested here, and insights from the adjusted data are enhanced by smoothing the data in the two dimensions (longitude and latitude). The process of adjustment and smoothing is illustrated on three sets of cancer mortality data: lung cancer (using urbanicity as the adjustor), prostate cancer in nonwhites (using percent African-American as the adjustor), and melanoma among whites (using latitude as the adjustor). In each case, the maps of the adjusted rates indicate patterns that are worthy of investigation and may contribute to the generation of hypotheses and further understanding of the etiology of the diseases.

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