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Risk-adjusted capitation payments: how well do principal inpatient diagnosis-based models work in the German situation? Results from a large data set

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  • Corinne Behrend
  • Florian Buchner
  • Michael Happich
  • Rolf Holle
  • Peter Reitmeir
  • Jürgen Wasem

Abstract

The Risk Adjustment Reform Act of 2001 mandates that a health-status-based risk adjustment mechanism has to be implemented in Germany's Statutory Health Insurance system by January 1, 2007. German parliament decided this as with the existing demographic risk adjustment model, that means there is cream skimming and sickness funds hesitate to engage in managing care for the chronical ill. Four approaches were used to test the feasibility of incorporating use of diagnosis as a proxy measure for health status in a German risk adjustment formula. The first two models used standard demographic and socio-demographic variables. The other two models are separately incorporating a simple binary indicator for hospitilization and Hierarchical Coexisting Conditions (HCCs: DxCG® Risk Adjustment Software Release 6.1) using inpatient diagnosis. Age and gender grouping accounted for 3.2% of the variation in total expenditures for concurrent as well as prospective models. The current German risk adjusters age, sex, and invalidity status account for 5.1% and 4.5% of the variance in the concurrent and prospective models respectively. There are substantial increases in explanatory power, however, when HCCs are added. Age, gender, invalidity status and HCC covariates explain about 37% of the variations of the total expenditures in a concurrent model and roughly 12% of the variations of total expenditures in a prospective model. For high-risk (cost) groups, substantial underprediction remains; conversely, for the low-risk group, represented by enrolees who did not show any health care expense in the base year, all of the models over-predict expenditure.
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Suggested Citation

  • 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.
  • Handle: RePEc:spr:eujhec:v:8:y:2007:i:1:p:31-39
    DOI: 10.1007/s10198-006-0004-7
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    1. Buchner, Florian & Wasem, Jurgen, 2003. "Needs for further improvement: risk adjustment in the German health insurance system," Health Policy, Elsevier, vol. 65(1), pages 21-35, July.
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    1. Schillo, Sonja & Lux, Gerald & Wasem, Juergen & Buchner, Florian, 2016. "High cost pool or high cost groups—How to handle high(est) cost cases in a risk adjustment mechanism?," Health Policy, Elsevier, vol. 120(2), pages 141-147.
    2. Manuel García-Goñi & Pere Ibern & José María Inoriza, 2009. "Hybrid risk adjustment for pharmaceutical benefits," Working Papers, Research Center on Health and Economics 1139, Department of Economics and Business, Universitat Pompeu Fabra.
    3. Greß, Stefan, 2004. "Competition in Social Health Insurance: A Three-Country Comparison," IBES Diskussionsbeiträge 135, University of Duisburg-Essen, Institute of Business and Economic Studie (IBES).
    4. Wasem, Jürgen & Buchner, Florian & Lux, Gerald & Schillo, Sonja, 2017. "High Cost Pool in a Health Status Based Risk Adjustment System – Some Conceptional and Empirical Considerations," VfS Annual Conference 2017 (Vienna): Alternative Structures for Money and Banking 168122, Verein für Socialpolitik / German Economic Association.
    5. Manuel García-Goñi & Pere Ibern & José Inoriza, 2009. "Hybrid risk adjustment for pharmaceutical benefits," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 10(3), pages 299-308, July.
    6. Conor Keegan & Conor Teljeur & Brian Turner & Steve Thomas, 2017. "Addressing Market Segmentation and Incentives for Risk Selection: How Well Does Risk Equalisation in the Irish Private Health Insurance Market Work?," The Economic and Social Review, Economic and Social Studies, vol. 48(1), pages 61-84.
    7. 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.
    8. 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.
    9. Göpffarth Dirk, 2007. "Theorie und Praxis des Risikostrukturausgleichs / Risk Adjustment in Theory and Practice," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 227(5-6), pages 485-501, October.

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