This article addresses the challenge of sharing finer scale protected health information (PHI) while maintaining patient privacy by using regionalization to create higher resolution Health Insurance Portability and Accountability Act (HIPAA)-compliant geographical aggregations. We compare four regionalization approaches in terms of their fitness for analysis and display: max-p-regions, regionalization with dynamically constrained agglomerative clustering and partitioning (REDCAP), and self-organizing map (SOM) variants of each. Each method is used to create a configuration of regions that aligns with census boundaries, optimizes intraunit 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) could also be recommended, because it provided the highest resolution while maintaining suitable performance on all other measures.

本文通过使用区域化来创建更高分辨率的符合健康保险流通和责任法案 (HIPAA) 的地理聚合，解决了共享更精细的受保护健康信息 (PHI) 同时维护患者隐私的挑战。我们比较了四种区域化方法的分析和显示适应性：最大 p 区域、动态约束凝聚聚类和分区 (REDCAP) 区域化以及每种方法的自组织映射 (SOM) 变体。每种方法都用于创建与人口普查边界对齐的区域配置，优化单元内的同质性，并最大化空间单元的数量，同时满足 HIPAA 准则下共享 PHI 所需的最低人口阈值。通过模型拟合、紧凑性、均匀性和分辨率的测量来评估每种配置的相对效用。将 SOM 程序添加到 max-p-region 导致几乎所有评估指标在统计​​上均显着改善，而将 SOM 添加到 REDCAP 主要降低了这些指标的性能。这些差异可归因于 SOM 对自上而下和自下而上的区域化程序的不同影响。总体而言，我们推荐 REDCAP，它在大多数指标上都表现出色。还可以推荐 max-p-regions (MSOM) 的 SOM 变体，因为它提供了最高分辨率，同时在所有其他测量上保持适当的性能。