Regionalization with Self-Organizing Maps for Sharing Higher Resolution Protected Health Information
Brittany Krzyzanowski, Steven M. Manson
Abstract
This paper addresses the challenge of sharing finer-scale Protected Health Information (PHI) while maintaining patient privacy by using regionalization to create higher resolution HIPAA-compliant geographical aggregations. We compare four regionalization approaches in terms of their fitness for analysis and display: max-p-regions, REDCAP, and self-organizing maps (SOM) variants of each. Each method is used to create a configuration of regions that aligns with census boundaries, optimizes intra-unit homogeneity, and maximizes the number of spatial units while meeting the minimum population threshold required for sharing PHI under HIPAA guidelines. The relative utility of each configuration was assessed with measures of model-fit, compactness, homogeneity, and resolution. Adding the SOM procedure to max-p-regions resulted in statistically significant improvements for nearly all assessment measures whereas the addition of SOM to REDCAP primarily degraded these measures. These differences can be attributed to the different impacts of SOM on top-down and bottom-up regionalization procedures. Overall, we recommend REDCAP which outperformed on most measures. The SOM variant of max-p-regions (MSOM) may also be recommended as it provided the highest resolution while maintaining suitable performance on all other measures.