Geostatistical analysis of health data with different levels of spatial aggregation

This paper presents a geostatistical approach to combine two geographical sets of area-based data into the mapping of disease risk, with an application to the rate of prostate cancer late-stage diagnosis in North Florida. This methodology is used to combine individual-level data assigned to census tracts for confidentiality reasons with individual-level data that were allocated to ZIP codes because of incomplete geocoding. This form of binomial kriging, which accounts for the population size and shape of each geographical unit, can generate choropleth or isopleth risk maps that are all coherent through spatial aggregation. Incorporation of both types of areal data reduces the loss of information associated with incomplete geocoding, leading to maps of risk estimates that are globally less smooth and with smaller prediction error variance.