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Exploring the predictive power of interaction terms in a sophisticated risk equalization model using regression trees

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  • S. H. C. M. van Veen
  • R. C. van Kleef
  • W. P. M. M. van de Ven
  • R. C. J. A. van Vliet

Abstract

This study explores the predictive power of interaction terms between the risk adjusters in the Dutch risk equalization (RE) model of 2014. Due to the sophistication of this RE‐model and the complexity of the associations in the dataset (N = ~16.7 million), there are theoretically more than a million interaction terms. We used regression tree modelling, which has been applied rarely within the field of RE, to identify interaction terms that statistically significantly explain variation in observed expenses that is not already explained by the risk adjusters in this RE‐model. The interaction terms identified were used as additional risk adjusters in the RE‐model. We found evidence that interaction terms can improve the prediction of expenses overall and for specific groups in the population. However, the prediction of expenses for some other selective groups may deteriorate. Thus, interactions can reduce financial incentives for risk selection for some groups but may increase them for others. Furthermore, because regression trees are not robust, additional criteria are needed to decide which interaction terms should be used in practice. These criteria could be the right incentive structure for risk selection and efficiency or the opinion of medical experts.

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  • S. H. C. M. van Veen & R. C. van Kleef & W. P. M. M. van de Ven & R. C. J. A. van Vliet, 2018. "Exploring the predictive power of interaction terms in a sophisticated risk equalization model using regression trees," Health Economics, John Wiley & Sons, Ltd., vol. 27(2), pages 1-12, February.
    • Handle: RePEc:wly:hlthec:v:27:y:2018:i:2:p:e1-e12
    DOI: 10.1002/hec.3523
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    1. Van de ven, Wynand P.M.M. & Ellis, Randall P., 2000. "Risk adjustment in competitive health plan markets," Handbook of Health Economics, in: A. J. Culyer & J. P. Newhouse (ed.), Handbook of Health Economics, edition 1, volume 1, chapter 14, pages 755-845, Elsevier.
    2. van de Ven, Wynand P.M.M. & Beck, Konstantin & Van de Voorde, Carine & Wasem, Jurgen & Zmora, Irit, 2007. "Risk adjustment and risk selection in Europe: 6 years later," Health Policy, Elsevier, vol. 83(2-3), pages 162-179, October.
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    4. Corinne Behrend & Florian Buchner & Michael Happich & Rolf Holle & Peter Reitmeir & Jürgen Wasem, 2007. "Risk-adjusted capitation payments: how well do principal inpatient diagnosis-based models work in the German situation? Results from a large data set," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 8(1), pages 31-39, March.
    5. Joseph P. Newhouse, 1996. "Reimbursing Health Plans and Health Providers: Efficiency in Production versus Selection," Journal of Economic Literature, American Economic Association, vol. 34(3), pages 1236-1263, September.
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    Cited by:

    1. Alexandre Vimont & Henri Leleu & Isabelle Durand-Zaleski, 2022. "Machine learning versus regression modelling in predicting individual healthcare costs from a representative sample of the nationwide claims database in France," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 23(2), pages 211-223, March.
    2. Anell, Anders & Dackehag, Margareta & Dietrichson, Jens & Ellegård, Lina Maria & Kjellsson, Gustav, 2022. "Better Off by Risk Adjustment? Socioeconomic Disparities in Care Utilization in Sweden Following a Payment Reform," Working Papers 2022:15, Lund University, Department of Economics, revised 12 Mar 2024.
    3. Marica Iommi & Savannah Bergquist & Gianluca Fiorentini & Francesco Paolucci, 2022. "Comparing risk adjustment estimation methods under data availability constraints," Health Economics, John Wiley & Sons, Ltd., vol. 31(7), pages 1368-1380, July.

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