Efficient mapping of California mortality fields at different spatial scales

A meaningful characterization of epidemiologic fields (mortality, incidence rate, etc.) often involves the assessment of their spatiotemporal variation at multiple scales. An adequate analysis should depend on the scale at which the epidemiologic field is considered rather than being limited by the scale at which the data are available. In many studies, for example, data are available at a larger scale (say, counties), whereas the epidemiologist is interested in a smaller-scale analysis (say, residential neighborhoods). We propose a mathematically rigorous and epidemiologically meaningful multiscale approach that uses the well-known BME theory to study important scale effects and generate informative scale-dependent maps. The approach is applied to a real-world case study involving daily mortality counts in the state of California. The approach accounts for scale effects and produces mortality predictions at the zip-code scale by downscaling data from the county scale. The multiscale approach is tested by means of a verification data set with detailed mortality information at the zip-code level for 1 day. A measure of mapping accuracy is used to demonstrate that the multiscale approach offers more accurate mortality predictions at the local scale than existing approaches, which do not account for scale effects.