Assessing Oral Health in U.S. Children and Its Impact on Disparities of Overall Health and Health Expenditures: An Application Using Two-Stage Clustering

Abstract

We assess children’s oral and overall health using an innovative clustering methodology. Prior studies have documented the link between oral health and overall health, such as dental caries, which could lead to infections and pain, or periodontal disease, which is associated with other conditions, such as diabetes, heart disease, cardiovascular disease, and adverse pregnancy outcomes. Using the nationally representative Medical Expenditure Panel Study data, we link children’s dental care services and other control variables with various health outcomes using a two-stage clustering method. In the first stage, we cluster similar children using their social determinants of health, medical conditions, and dental care utilization as input variables. In the second stage, we identify groups of similar clusters using a metric called cosine similarity. External variables, rather than those used to create the clusters, are used to assess the effectiveness of the clusters. An outcome score is created based on children’s favorable overall and mental health statuses and absence of behavioral issues at home, school, and in general. We find that the first-stage clusters are separated into three main groups in the second stage, where one cluster from the first stage is very different from all of the other clusters, a second group has more favorable outcome scores, and the third group has lower outcome scores. Lower dental expenditures are associated with lower outcome scores. Clusters are useful for cost management purposes to identify subgroups for interventions to improve population health. Comparisons of access and outcomes of children between different insurance types can provide beneficial information for insurance design and public policy. Our approach can be applied to other areas of practice where there is an interest in grouping similar individuals and relating these groups to other quantities. The two-stage clustering method is a versatile method with a broader appeal to empirical research, especially when there are big data, a large number of clusters, and many input variables.