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Direct versus indirect standardization in risk adjustment

Author

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  • SCHOKKAERT, Erik
  • VAN DE VOORDE, Carine

Abstract

Direct and indirect standardization procedures aim at comparing differences in health or in health care expenditures between subgroups of the population after controlling for observable morbidity differences. There is a close analogy between this problem and the issue of risk adjustment in health insurance. Traditional methods of risk adjustment are analogous to indirect standardization. They are equivalent to the so-called conditional egalitarian mechanism in social choice. In general, they do not remove incentives for risk selection, even if the effect of non-morbidity variables is correctly taken into account. A method of risk adjustment based on direct standardization does remove the incentives for risk selection, but at the cost of violating a neutrality condition, stating that insurers should receive the same premium subsidy for all members of the same risk group. Direct standardization is equivalent to the egalitarian-equivalent (or proportional) mechanism in social choice. The conflict between removing incentives for risk selection and neutrality is unavoidable if the health expenditure function is not additively separable in the morbidity and efficiency variables.
(This abstract was borrowed from another version of this item.)

Suggested Citation

  • SCHOKKAERT, Erik & VAN DE VOORDE, Carine, 2009. "Direct versus indirect standardization in risk adjustment," LIDAM Reprints CORE 2139, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
  • Handle: RePEc:cor:louvrp:2139
    DOI: 10.1016/j.jhealeco.2008.10.012
    Note: In : Journal of Health Economics, 28, 361-374, 2009
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    Cited by:

    1. FLEURBAEY, Marc & SCHOKKAERT, Erik, 2011. "Equity in health and health care," LIDAM Discussion Papers CORE 2011026, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    2. Marco Doretti & Giorgio E. Montanari, 2024. "Classification and estimation of case-mix adjusted performance indices for binary outcomes," Annals of Operations Research, Springer, vol. 342(3), pages 2201-2225, November.
    3. Vincenzo Atella & Federico Belotti & Ludovico Carrino & Andrea Piano Mortari, 2017. "The future of Long Term Care in Europe. An investigation using a dynamic microsimulation model," CEIS Research Paper 405, Tor Vergata University, CEIS, revised 08 May 2017.
    4. Kristensen, Troels & Rose Olsen, Kim & Sortsø, Camilla & Ejersted, Charlotte & Thomsen, Janus Laust & Halling, Anders, 2013. "Resources allocation and health care needs in diabetes care in Danish GP clinics," Health Policy, Elsevier, vol. 113(1), pages 206-215.
    5. Erwin Ooghe & Erik Schokkaert, 2016. "School accountability: can we reward schools and avoid pupil selection?," Social Choice and Welfare, Springer;The Society for Social Choice and Welfare, vol. 46(2), pages 359-387, February.
    6. S. Veen & R. Kleef & W. Ven & R. Vliet, 2015. "Improving the prediction model used in risk equalization: cost and diagnostic information from multiple prior years," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 16(2), pages 201-218, March.
    7. Armstrong, John & Paolucci, Francesco & McLeod, Heather & van de Ven, Wynand P.M.M., 2010. "Risk equalisation in voluntary health insurance markets: A three country comparison," Health Policy, Elsevier, vol. 98(1), pages 39-49, November.
    8. Laura Anselmi & Yiu-Shing Lau & Matt Sutton & Anna Everton & Rob Shaw & Stephen Lorrimer, 2022. "Use of past care markers in risk-adjustment: accounting for systematic differences across providers," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 23(1), pages 133-151, February.

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