AGGREGATION OF EXISTING GEOGRAPHIC REGIONS TO DIMINISH SPURIOUS VARIABILITY OF DISEASE RATES

The availability of large data sets together with the growth in power and storage capabilities of computers have made the analysis of the sp atial distribution of disease rates an increasingly important tool in public health research. Use of existing geographic divisions or groupi ngs tends to result either in unstable estimates of disease rates if t he corresponding populations are small or in loss of spatial resolutio n if the areas are unnecessarily large. This paper describes a compute r algorithm for combining existing geographic areas into regions with populations large enough to diminish spurious variability in disease r ates while limiting the loss in resolution. The method is demonstrated using Medicare hospital admissions data for pneumonia and central ner vous system cancer. Disease rates were calculated for both predefined regions and those generated by the algorithm and their frequency distr ibutions were compared. The algorithm produces more stable rates over a variety of diseases and provides substantially more flexibility than the use of predefined aggregations.