Pediatric asthma population risk stratification using k-means clustering.

Abstract

Objectives: Incorporating social determinants of health to identify distinct pediatric asthma patient groups can help stratify populations by their risk of adverse events, improving targeted outreach and care.

Methods: Insurance claims and enrollment data from the Arkansas All-Payer Claims Database identified 22 169 children aged 5-18 years with an asthma diagnosis in 2018 and continuous Medicaid enrollment in 2018 and 2019. The clustering approach used information on comorbid conditions, asthma controller medication intensity, total controller and reliever medications filled, zip code-level Child Opportunity Index, and rural-urban classification. Binary and categorical variables were first transformed into continuous latent variables using Generalized Low-Rank Models. K-means clustering with Euclidean distance was then applied. The resulting clusters were compared based on asthma-related emergency department (ED) visits and hospitalizations in 2018.

Results: K-means clustering identified six clusters. The distribution of ED visits differed significantly across the clusters (p < 0.001) with Cluster 1 having the highest observed percentages (1 ED visit: 9.5%; ≥2 ED visits: 2.6%). This cluster consisted of 65.9% Black and had the highest proportion of children residing in neighborhoods with very low child opportunity scores: 90.5% had very low education scores, 85.5% very low health and environment scores, and 94.4% very low social and economic scores.

Conclusions: Interventions to reduce pediatric asthma disparities should address social, economic, and environmental inequities. Clustering identified children from low child opportunity areas in Arkansas, with a high percentage of Black children, as a high-risk group for asthma exacerbations, underscoring the potential of population risk stratification for tailoring interventions.