Abstract
Health insurance markets with community-rated premiums typically use risk equalization (RE) to compensate insurers for predictable profits on people in good health and predictable losses on those with a chronic disease. Over the past decades RE models have evolved from simple demographic models to sophisticated health-based models. Despite the improvements, however, non-trivial predictable profits and losses remain. This study examines to what extent the Dutch RE model can be further improved by redesigning one key morbidity adjuster: the Diagnosis-based Cost Groups (DCGs). This redesign includes (1) revision of the underlying hospital diagnoses and treatments (‘dxgroups’), (2) application of a new clustering procedure, and (3) allowing multi-qualification. We combine data on spending, risk characteristics and hospital claims for all individuals with basic health insurance in the Netherlands in 2017 (N = 17 m) with morbidity data from general practitioners (GPs) for a subsample (N = 1.3 m). We first simulate a baseline RE model (i.e., the RE model of 2020) and then modify three important features of the DCGs. In a second step, we evaluate the effect of the modifications in terms of predictable profits and losses for subgroups of consumers that are potentially vulnerable to risk selection. While less prominent results are found for subgroups derived from the GP data, our results demonstrate substantial reductions in predictable profits and losses at the level of dxgroups and for individuals with multiple dxgroups. An important takeaway from our paper is that smart design of morbidity adjusters in RE can help mitigate selection incentives.
Original language | English |
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Pages (from-to) | 303-324 |
Number of pages | 22 |
Journal | International Journal of Health Economics and Management |
Volume | 23 |
Issue number | 2 |
DOIs | |
Publication status | Published - Jun 2023 |
Bibliographical note
Funding Information:The authors gratefully acknowledge the valuable comments on earlier versions of this paper by Wynand van de Ven, Erik Schut, Frank Eijkenaar and the participants of the Dutch advisory board for scientific research on insurer data (March, 2021). The authors are also grateful to the Dutch Ministry of Health, Welfare and Sports and the Association of Health Insurers for access to (anonymized) claims data. They also thank the Netherlands Institute for Health Services Research (NIVEL) for access to anonymized morbidity information from General Practitioners
Publisher Copyright:
© 2023, The Author(s).