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A limited-sample benchmark approach to assess and improve the performance of risk equalization models

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  • Stam, Pieter J.A.
  • van Vliet, René C.J.A.
  • van de Ven, Wynand P.M.M.

Abstract

A new method is proposed to assess and improve the performance of risk equalization models in competitive markets for individual health insurance, where compensation is intended for variation in observed expenditures due to so-called S(ubsidy)-type risk factors but not for variation due to other, so-called N(on-subsidy)-type risk factors. Given the availability of a rich subsample of individuals for which normative expenditures, YNORM, can be accurately determined, we make two contributions: (a) any risk equalization scheme applied to the entire population, YREF, should be evaluated through its performance in the subsample, by comparing YREF with YNORM (not by comparing YREF with observed expenditures, Y, in the entire population, as commonly done); (b) conventional risk equalization schemes can be improved by the subsample regression of YNORM, rather than Y, on the risk adjusters that are observable in the entire population. This new method is illustrated by an application to the 2004 Dutch risk equalization model.

Suggested Citation

  • Stam, Pieter J.A. & van Vliet, René C.J.A. & van de Ven, Wynand P.M.M., 2010. "A limited-sample benchmark approach to assess and improve the performance of risk equalization models," Journal of Health Economics, Elsevier, vol. 29(3), pages 426-437, May.
    • Handle: RePEc:eee:jhecon:v:29:y:2010:i:3:p:426-437
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    Cited by:

    1. Normann Lorenz, 2014. "Using quantile regression for optimal risk adjustment," Research Papers in Economics 2014-11, University of Trier, Department of Economics.
    2. Shmueli, Amir & Stam, Piet & Wasem, Jürgen & Trottmann, Maria, 2015. "Managed care in four managed competition OECD health systems," Health Policy, Elsevier, vol. 119(7), pages 860-873.
    3. Kanters, Tim A. & Brouwer, Werner B.F. & van Vliet, René C.J.A. & van Baal, Pieter H.M. & Polder, Johan J., 2013. "A new prevention paradox: The trade-off between reducing incentives for risk selection and increasing the incentives for prevention for health insurers," Social Science & Medicine, Elsevier, vol. 76(C), pages 150-158.
    4. Asthana, Sheena & Gibson, Alex, 2011. "Setting health care capitations through diagnosis-based risk adjustment: A suitable model for the English NHS?," Health Policy, Elsevier, vol. 101(2), pages 133-139, July.
    5. Bergquist, Savannah L. & Layton, Timothy J. & McGuire, Thomas G. & Rose, Sherri, 2019. "Data transformations to improve the performance of health plan payment methods," Journal of Health Economics, Elsevier, vol. 66(C), pages 195-207.
    6. France R. M. Portrait & Onno van der Galiën & Bernard Van den Berg, 2016. "Measuring Healthcare Providers' Performances Within Managed Competition Using Multidimensional Quality and Cost Indicators," Health Economics, John Wiley & Sons, Ltd., vol. 25(4), pages 408-423, April.
    7. Savannah L. Bergquist & Timothy J. Layton & Thomas G. McGuire & Sherri Rose, 2018. "Intervening on the Data to Improve the Performance of Health Plan Payment Methods," NBER Working Papers 24491, National Bureau of Economic Research, Inc.
    8. Viktor von Wyl & Konstantin Beck, 2016. "Do insurers respond to risk adjustment? A long-term, nationwide analysis from Switzerland," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 17(2), pages 171-183, March.
    9. Normann Lorenz, 2017. "Using Quantile and Asymmetric Least Squares Regression for Optimal Risk Adjustment," Health Economics, John Wiley & Sons, Ltd., vol. 26(6), pages 724-742, June.

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