Cost Patterns of Multiple Chronic Conditions: A Novel Modeling Approach Using a Condition Hierarchy.

This article presents a novel hierarchical modeling approach to estimate healthcare costs associated with multiple chronic conditions, addressing the complexity arising from multimorbidity and interactions among diseases. The method models the cost contribution of each chronic condition as a function of its rank in a cost hierarchy—specifically, the number of other, more expensive chronic conditions a patient has—using large-scale insurance claims data covering 69 chronic conditions. Employing a generalized linear model with a gamma distribution and log link, combined with a backward aggregation algorithm to retain statistically significant variables while minimizing information loss, the study identifies distinct cost patterns: some conditions’ cost contributions increase with additional more expensive comorbidities, while others decrease. The approach offers scalable, interpretable insights into chronic disease cost dynamics that can inform healthcare policy, intervention design, and resource allocation, and it may be applicable to other domains involving complex factor interdependencies.