Clustering the prevalence of pediatric chronic conditions in the United States using distributed computing

Abstract

This research paper presents an approach to clustering the prevalence of chronic conditions among children with public insurance in the United States. The data consist of prevalence estimates at the community level for 25 pediatric chronic conditions. We employ a spatial clustering algorithm to identify clusters of communities with similar chronic condition prevalences. The primary challenge is the computational effort needed to estimate the spatial clustering for all communities in the U.S. To address this challenge, we develop a distributed computing approach to spatial clustering. Overall, we found that the burden of chronic conditions in rural communities tends to be similar but with wide differences in urban communities. This finding suggests similar interventions for managing chronic conditions in rural communities but targeted interventions in urban areas.