A Two-Part Model of the Individual Costs of Chronic Kidney Disease

Brian Hartman, Courtney Larson, Christopher Kunkel, Cason Wight and Richard L. Warr

North American Actuarial Journal, 2024, vol. 28, issue 1, 139-186

Abstract: Chronic kidney disease (CKD) affects many lives and has a large impact on health systems around the world. To better understand and predict costs for insurance plan people with CKD in the United States, we built a new model of their individual costs. Our model is the first to explicitly model both the CKD stage transition process and the distribution of costs given those stages. Additionally, it incorporates numerous covariates and comorbidities. We applied the models to two large and rich datasets, one commercial insurance and the other Medicare fee-for-service, totaling about 40 million beneficiary months. We found that the XGBoost models best predict both stage transitions and costs. If XGBoost models are unavailable, a multivariate logistic regression model with regularization to predict stage and a logit-gamma model of the costs given the stage best predicted the people’s health care costs in the next month.

Date: 2024
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DOI: 10.1080/10920277.2023.2177676

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