Background: More and more competitive health insurance markets use risk equalization to compensate health plans for the predictable high costs of chronically ill enrollees. In the presence of premium rate restrictions, an important goal of risk equalization is to reduce incentives for selection, while maintaining incentives for efficiency. The literature shows, however, that even the most sophisticated risk equalization models-which include both diagnoses-based and pharmacy-based indicators of health status-do not reduce incentives for selection sufficiently. Objectives: The goal of this study is to examine the extent to which a sophisticated risk-equalization model can be improved by using multiple-year high cost as a health indicator. The idea is that health plans receive an additional compensation for enrollees whose costs were in the top-15% in each of the 3 preceding years, assuming that this group contains a substantial overrepresentation of people with a chronic condition. Research Design: We examine 3 types of additional compensation: (1) retrospective compensation, (2) fixed prospective compensation, and (3) continuous prospective compensation. Subjects: We use individual-level information on medical costs and risk characteristics from the period 2004 to 2007 for almost the entire Dutch population. Measures: The effect on selection incentives is measured by predictive ratios for subgroups of enrollees who were undercompensated in previous years. The effect on efficiency incentives is quantified by the relationship between cost and compensation. Results and conclusions: All 3 modalities substantially reduce incentives for selection, but-to some extent-also reduce incentives for efficiency. With respect to these criteria, the continuous prospective compensation outperforms the other 2 modalities.

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Medical Care
Erasmus MC: University Medical Center Rotterdam

van Kleef, R., & van Vliet, R. (2012). Improving risk equalization using multiple-year high cost as a health indicator. Medical Care, 50(2), 140–144. doi:10.1097/MLR.0b013e31822ebf8b