Testing the effectiveness of risk equalization models in health insurance A new method and its application ISBN 978-90-8559-152-8 © P.J.A. Stam, 2007 Cover photo credit: Marc Dietrich, Freiburg im Breisgau, Baden-Wuerttemberg, Germany Printed by: Optima Grafische Communicatie, Rotterdam, The Netherlands Testing the effectiveness of risk equalization models in health insurance A new method and its application Het toetsen van de effectiviteit van risicovereveningsmodellen voor zorgverzekeringen Een nieuwe methode en haar toepassing PROEFSCHRIFT ter verkrijging van de graad van doctor aan de Erasmus Universiteit Rotterdam op gezag van de Rector Magnificus Prof.dr. S.W.J. Lamberts en volgens besluit van het College voor Promoties. De openbare verdediging zal plaatsvinden op woensdag 17 oktober 2007 om 9.45 uur. door Pieter Johannes Adrianus Stam geboren te Schoonhoven PROMOTIECOMMISSIE Promotor: Prof.dr. W.P.M.M. van de Ven Overige leden: Prof.dr. E.K.A. van Doorslaer Prof.dr. N.S. Klazinga Prof.dr. E. Schokkaert Copromotor: Dr. R.C.J.A. van Vliet To my mother CONTENTS 1. Introduction 1.1 The purpose of this study 12 1.2 Risk equalization in the Netherlands 15 1.3 Research questions 19 1.4 Outline 20 Appendix chapter 1 A1.1 Company names of Dutch sickness funds 23 A1.2 The Dutch REF models for sickness funds 1991 – 2005 24 2. Theoretical framework and methods 2.1 A test for the effectiveness of risk-adjusted premium subsidies 30 2.1.1 Risk-adjusted premium subsidies in competitive health insurance markets 30 2.1.2 Causes for incomplete and imperfect REF adjusters 33 2.1.3 Methodological solutions for imperfect REF adjusters 37 2.1.4 Methodological solutions for incomplete REF adjusters 39 2.1.5 Additional regulations for improving the subsidies 40 2.2 Risk adjusters in the literature 43 2.3 Methods 50 2.3.1 REF predicted costs as an approximation to normative costs 50 2.3.2 The determination of normative costs 52 2.3.3 Aligning the REF weights with normative costs 56 2.3.4 Testing alternative specifications of the REF equation 60 2.3.5 Additional regulations for improving the subsidies 61 2.4 Conclusions 62 3. Data 3.1 Agis administrative data 1997-2002 66 3.2 Agis Health Survey 2001 67 3.2.1 Questionnaire design 68 3.2.2 Statistical representativeness 75 3.2.3 Selection of eligible cases for the analysis sample 80 3.3 External data sources 82 3.4 Conclusions 84 8 Contents Appendix chapter 3 A3.1 Mode of data collection 86 A3.2 Predictive power of Z&Z survey variables 90 A3.3 Data collection process 91 A3.4 Description of Agis Health Survey 2001 items 98 A3.5 Response and nonresponse analysis 100 A3.6 Summary of administrative, survey and external variables 110 4. Health status measurement 4.1 Construction of eight SF-36 health scales 114 4.1.1 Completeness 119 4.1.2 Reliability 120 4.1.3 Validity 122 4.2 Conclusions 132 Appendix chapter 4 A4.1 The SF-36 item responses 137 A4.2 Response Consistency Index 140 A4.3 The SF-36 Physical and Mental Component Scales 145 5. A derivation of normative costs 5.1 A statistical description of the S-type adjusters 148 5.2 Estimation of the normative equation 156 5.3 Conclusions 162 Appendix chapter 5 A5.1 Diagnosic Cost Group classification 164 6. Testing the REF equation for effectiveness 6.1 A comparison of REF predicted costs and normative costs 168 6.1.1 Comparing subgroups defined by the S-type adjusters 170 6.1.2 Comparing subgroups defined by the REF adjusters 176 6.2 An adjustment of the REF weights by the omitted variables approach 179 6.3 A normative adjustment of the REF weights 187 6.4 Conclusions 192 Appendix chapter 6 A6.1 A normative test of the pre-2004 Dutch REF equations 195 A6.2 A supplement to Section 6.2 and Section 6.3 198 Contents 9 7. Testing alternative REF model specifications for effectiveness 7.1 Adding new risk adjusters to the REF equation 204 7.2 Ex-post risk sharing as a supplement to the REF equation 216 7.3 The functional form and error distribution of the REF equation 219 7.4 Conclusions 232 Appendix chapter 7 A7.1 A supplement to Section 7.1 235 A7.2 A supplement to Section 7.2 236 A7.3 A supplement to Section 7.3 238 A7.4 Normative test results of Section 7.3 after the exclusion of outliers 239 8. Premium rate restrictions to improve affordability 8.1 The tradeoff between affordability and selection 244 8.2 Risk rating across Dutch provinces 250 8.3 Conclusions 252 Appendix chapter 8 A8.1 Results for socio-economic subgroups 254 9. Conclusions and discussion 9.1 Conclusions 260 9.2 Discussion 267 Glossary 275 Abbreviations 279 References 281 Samenvatting Het toetsen van de effectiviteit van risicovereveningsmodellen voor zorgverzekeringen 291 Curriculum Vitae 313 Acknowledgments 315 1 INTRODUCTION Chapter 12 Chapter 1 1.1 THE PURPOSE OF THIS STUDY In several European countries (Belgium, Germany, Switzerland and The Neth- erlands), competition among health insurers is used to stimulate efficiency and responsiveness to consumers’ preferences in the health care sector (Van de Ven et al. 2003). The ultimate goal is to stimulate health insurance companies to act as prudent purchasers or providers of care for their members. In an unregulated competitive health insurance market, however, insurers will risk rate their premi- ums according to an individual consumer’s risk profile: sick people will pay higher insurance premiums for a specified benefit package than will healthy people. This is called the “equivalence principle”. Individual health insurance may not be af- fordable for those at high risk if premium differences among individuals are rather extreme. It is, therefore, a major challenge to combine efficiency and financial -ac cess to coverage in the health system reforms that take place in many countries. In practice, risk-rated premiums have been observed to differ across subgroups of insured people which are defined by rating factors such as age, gender, family size, geographic area, occupation, length of contract period, the level of deductible, health status at time of enrollment, health habits (smoking, drinking, exercising) and — via differentiated bonuses for multi-year no-claim — to prior costs (Van de Ven et al. 2000). Financial transfers are needed in order to avoid problems of fi- nancial access to coverage for those at high risk. The first and best solution in this case is to find a so-called sponsor who organizes compensation for those at high risk by setting up a regulatory system of risk-adjusted premium subsidies (Van de Ven et al. 2000). In the aforementioned European countries, the role of the spon- sor is played by a government agency that organizes a system of risk-adjusted premium subsidies in the form of risk equalization among health insurers.1 A sponsor may not want to subsidize all premium rate variation observed in prac- tice. For example, if premiums are rated across geographic areas, a sponsor may desire to equalize observed premium rate variation only up to the extent that this variation is caused by regional health status differences and not by regional differences in input prices. In general, the total set of risk factors that insurers use to rate their premiums can be divided into two categories: the subset of risk factors that causes premium rate variation which the sponsor decides to subsidize, the S(ubsidy)-type risk factors, and the subset that causes premium rate variation which the sponsor does not want to subsidize, the N(on-subsidy)-type risk fac- tors (Van de Ven and Ellis 2000, p. 768-769). In most countries, up to a certain 1. See the glossary for a broader definition of the term sponsor. Introduction 13 extent, gender, health status, and age will probably be considered as S-type risk factors. Examples of potential N-type risk factors are a high propensity for medical consumption, living in a region with high prices and/or overcapacity resulting in supply-induced demand, or using providers with an inefficient practice-style (Van de Ven et al. 2000). The sponsor determines the specific categorization of S-type and N-type risk factors. Ultimately, if the sponsor is a national government, this categorization is determined by value judgments in society. The risk-adjusted premium subsidies should compensate for cost variation caused by the S-type risk factors alone. Given the specific categorization of S-type and N-type risk factors, adequate measures of these S-type risk factors should be found in order to be able to implement a system of risk-adjusted premium subsidies in practice. It often turns out to be quite a challenge to find such ad- equate measures of the S-type risk factors at the individual level for the total population of insured people. Although age and gender of insured people may be easily implemented, the empirical possibilities to find adequate measures of health status are often limited because of feasibility concerns and a complex political and legal environment in which the scheme must operate. Amongst others, feasibil- ity implies that the health status measure be available routinely for all insured people, both healthy and unhealthy. In the absence of adequate measures of the S-type risk factors, it may be the case that, in practice, incomplete and/or imperfect measures of the S-type risk factors are used instead to calculate the risk-adjusted premium subsidies.