American clusters: using machine learning to understand health and health care disparities in the United States

Abstract

Health and health care access in the U.S. is plagued by high inequality. While machine learning (ML) is increasingly used in clinical settings to inform health care delivery decisions and predict health care utilization, using ML as a research tool to understand health care disparities in the U.S. and how these are connected to health outcomes, access to health care, and health system organization is less common. We utilized over 650 variables from 24 different databases aggregated by the Agency for Healthcare Research and Quality (AHRQ) in their Social Determinant of Health Database (SDOH). We used k-means–a non-hierarchical ML clustering method–to cluster county level data. Principal factor analysis created county level index values for each SDOH domain and two health care domains–health care infrastructure and health care access. Logistic regression classification was used to identify the primary drivers of cluster classification. The most efficient cluster classification consists of 3 distinct clusters in the U.S.; the cluster having the highest life expectancy comprised only 10% of counties. The most efficient ML clusters do not identify the clusters with the widest health care disparities. ML clustering, using county level data, shows that health care infrastructure and access are the primary drivers of cluster composition.