Predictive Modeling of Costs for a Chronic Disease with Acute High-Cost Episodes

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Abstract
Chronic diseases account for 75% of U.S. national health care expenditures as estimated by the Centers for Disease Control. Many chronic diseases are punctuated by acute episodes of illnesses that occur randomly and create cost spikes in utilization from one year to the next. Modeling to account for these random events provides better estimates of (1) future costs and (2) their variability. A Bayesian statistical model is used to predict the incidence and cost of hospitalizations for one chronic disease. A two-part statistical model is described that separately models the utilization and cost of hospitalization. Individual demographic characteristics are included as well as a simple biological classification system to adjust for the severity of disease among individuals. Results by child, as well as by calendar year, are presented. Using a simple approach to incorporate severity, the model produces reasonable estimates of the number of hospitalizations and cost of hospitalization for the group in total, as well as for a separate group of High Utilizers. The study reflects real-world experiences of persons entering and leaving a group. Modeling at an individual level provides a way to adjust for individual-level severity. The ability to model uneven and unpredictable occurrence of utilization, and potential cost, would be beneficial in the design of insurance programs or for disease management programs.

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