Scope and Incentives for Risk Selection in Health Insurance Markets With Regulated Competition: A Conceptual Framework and International Comparison.

In health insurance markets with regulated competition, regulators face the challenge of preventing risk selection. This paper provides a framework for analyzing the scope (i.e., potential actions by insurers and consumers) and incentives for risk selection in such markets. Our approach consists of three steps. First, we describe four types of risk selection: (a) selection by consumers in and out of the market, (b) selection by consumers between high- and low-value plans, (c) selection by insurers via plan design, and (d) selection by insurers via other channels such as marketing, customer service, and supplementary insurance. In a second step, we develop a conceptual framework of how regulation and features of health insurance markets affect the scope and incentives for risk selection along these four dimensions. In a third step, we use this framework to compare nine health insurance markets with regulated competition in Australia, Europe, Israel, and the United States.

Heteroscedasticity of residual spending after risk equalization: a potential source of selection incentives in health insurance markets with premium regulation.

Many community-rated health insurance markets include risk equalization (also known as risk adjustment) to mitigate risk selection incentives for competing insurers. Empirical evaluations of risk equalization typically quantify selection incentives through predictable profits and losses net of risk equalization for various groups of consumers (e.g. the healthy versus the chronically ill). The underlying assumption is that absence of predictable profits and losses implies absence of selection incentives. This paper questions this assumption. We show that even when risk equalization perfectly compensates insurers for predictable differences in mean spending between groups, selection incentives are likely to remain. The reason is that the uncertainty about residual spending (i.e., spending net of risk equalization) differs across groups, e.g., the risk of substantial losses is larger for the chronically ill than for the healthy. In a risk-rated market, insurers are likely to charge a higher profit mark-up (to cover uncertainty in residual spending) and a higher safety mark-up (to cover the risk of large losses) to chronically ill than to healthy individuals. When such differentiation is not allowed, insurers face incentives to select in favor of the healthy. Although the exact size of these selection incentives depends on contextual factors, our empirical simulations indicate they can be non-trivial. Our findings suggest that - in addition to the equalization of differences in mean spending between the healthy and the chronically ill - policy measures might be needed to diminish (or compensate insurers for) heteroscedasticity of residual spending across groups.

Risk Adjustment in Health Insurance Markets: Do Not Overlook the "Real" Healthy.

OBJECTIVES: The goals of this paper are (1) to identify groups of healthy people and (2) to quantify the extent to which the Dutch risk adjustment (RA) model overpays insurers for these groups. BACKGROUND: There have been strong signals that insurers in the Dutch regulated health insurance market engage in actions to attract healthy people. A potential explanation for this behavior is that the Dutch RA model overpays insurers for healthy people. METHODS: We identify healthy groups using 3 types of ex-ante information (ie, information available before the start of the health insurance contract): administrative data on prior spending for specific health care services (N = 17 m), diagnoses from electronic patient records (N = 1.3 m), and health survey data (N = 457 k). In a second step, we calculate the under/overpayment for these groups under the Dutch RA model (version: 2021). RESULTS: We distinguish eight groups of healthy people using various "identifiers." Although the Dutch RA model substantially reduces the predictable profits that insurers face for these groups, significant profits remain. The mean per person overpayment ranges from 38 euros (people with hospital spending below the third quartile in each of 3 prior years) to 167 euros (those without any medical condition according to their electronic patient record). CONCLUSIONS: The Dutch RA model does not eliminate the profitability of healthy groups. The identifiers used for flagging these groups, however, seem inappropriate for serving as risk adjuster variables. An alternative way of exploiting these identifiers and eliminating the profitability of healthy groups is to estimate RA models with "constrained regression."

Improving diagnosis-based cost groups in the Dutch risk equalization model: the effects of a new clustering method and allowing for multimorbidity.

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.

Predictable profits and losses in a health insurance market with risk equalization: A multiple-contract period perspective.

Many social health insurance systems rely on 'regulated competition' among insurers to improve efficiency. In the presence of community-rated premiums, risk equalization is an important regulatory feature to mitigate risk-selection incentives in such systems. Empirical studies evaluating selection incentives have typically quantified group-level (un)profitability for one contract period. However, due to switching barriers, a multiple contract period perspective might be more relevant. In this paper, using data from a large health survey (N≈380k) we identify subgroups of chronically ill and healthy individuals in year t and follow these groups over three consecutive years. Using administrative data covering the entire Dutch population (N≈17m), we then simulate the mean per person predictable profits and losses (i.e. spending predicted by a sophisticated risk-equalization model minus actual spending) of these groups over the three follow-up years. We find that most of the groups of chronically ill are persistently unprofitable on average, while the healthy group is persistently profitable. This implies that selection incentives might be stronger than initially thought, underscoring the necessity of eliminating predictable profits and losses for the adequate functioning of competitive social health insurance markets.

How to deal with persistently low/high spenders in health plan payment systems?

Health insurance markets with community-rated premiums typically include risk adjustment (RA) to mitigate selection problems. Over the past decades, RA systems have evolved from simple demographic models to sophisticated morbidity-based models. Even the most sophisticated models, however, tend to overcompensate people with persistently low spending and undercompensate those with persistently high spending. This paper compares three methods that exploit spending-level persistence for improving health plan payment systems: (1) implementation of spending-based risk adjustors, (2) implementation of high-risk pooling for people with multiple-year high spending, and (3) indirect use of spending persistence via constrained regression. Based on incentive measures for risk selection and cost control, we conclude that a combination of the last two options can substantially outperform the first, which is currently used in the health plan payment system in the Netherlands.

How the COVID-19 pandemic can distort risk adjustment of health plan payment.

The COVID-19 pandemic has led to disruptions in healthcare utilization and spending. While some changes might persist (e.g. substitution of specialist visits by online consultations), others will be transitory (e.g. fewer surgical procedures due to cancellation of treatments). This paper discusses the implications of transitory changes in healthcare utilization and spending for risk adjustment of health plan payment. In practice, risk adjustment methodologies typically consist of two steps: (1) calibration of payment weights for a given set of risk adjusters and (2) calculation of payments to insurers by combining the calibrated weights with enrollee characteristics. In this paper, we first introduce a simple conceptual framework for analyzing the (potential) distortions from the pandemic for both steps and then provide a hypothetical illustration of how these distortions can lead to under- or overpayment of insurers. The size of these under-/overpayments depends on (1) the impact of the pandemic on patterns in utilization and spending, (2) the distribution of risk types across insurers, (3) the extent to which insurers are disproportionately affected by the pandemic, and (4) features of the risk adjustment system.

Very high and low residual spenders in private health insurance markets: Germany, The Netherlands and the U.S. Marketplaces.

We study the extremely high and low residual spenders in individual health insurance markets in three countries. A high (low) residual spender is someone for whom the residual-spending less payment (from premiums and risk adjustment)-is high (low), indicating that the person is highly underpaid (overpaid). We begin with descriptive analysis of the top and bottom 1% and 0.1% of residuals building to address the question of the degree of persistence in membership at the extremes. Common findings emerge among the countries. First, the diseases found among those with the highest residual spending are also disproportionately found among those with the lowest residual spending. Second, those at the top of the residual spending distribution (where spending exceeds payments the most) account for a massively high share of the unexplained variance in the predictions from the risk adjustment model. Third, in terms of persistence, we find that membership in the extremes of the residual spending distribution is highly persistent, raising concerns about selection-related incentives targeting these individuals. As our results show, the one-in-a-thousand people (on both sides of the residual distribution) play an outsized role in creating adverse incentives associated with health plan payment systems. In response to the observed importance of the extremes of the residual spending distribution, we propose an innovative combination of risk-pooling and reinsurance targeting the predictively undercompensated group. In all three countries, this form of risk sharing substantially improves the overall fit of payments to spending. Perhaps surprisingly, by reducing the burden on diagnostic indicators to predict high payments, our proposed risk sharing policy reduces the gap between payments and spending not only for the most undercompensated individuals but also for the most overcompensated people.

Selection Incentives for Health Insurers in the Presence of Sophisticated Risk Adjustment.

This article analyzes selection incentives for insurers in the Dutch basic health insurance market, which operates with community-rated premiums and sophisticated risk adjustment. Selection incentives result from the interplay of three market characteristics: possible actions by insurers, consumer response to these actions, and predictable variation in profitability of insurance contracts. After a qualitative analysis of the first two characteristics our primary objective is to identify the third. Using a combination of claims data (N = 16.8 million) and survey information (N = 387,195), we find substantial predictable variation in profitability. On average, people in good health are profitable, while those in poor health are unprofitable. We conclude that Dutch insurers indeed face selection incentives. A complete measure of selection incentives, however, captures the correlation between individual-level profitability and consumer response to insurer-actions. Obtaining insight in this correlation is an important direction for further research.

Risk equalization in competitive health insurance markets: Identifying healthy individuals on the basis of multiple-year low spending.

OBJECTIVE: To study the extent to which risk equalization (RE) in competitive health insurance markets can be improved by including an indicator for being healthy. STUDY SETTING/DATA SOURCES: This study is conducted in the context of the Dutch individual health insurance market. Administrative data on spending and risk characteristics (2011-2014) for the entire population (N = 16.6 m) as well as health survey data from a large sample (N = 387 k) are used. STUDY DESIGN: The indicator for being healthy is low spending in three consecutive prior years. "Low spending" is defined in three ways: belonging to the bottom 60%, 70%, or 80% of the annual spending distribution. Versions of the Dutch RE model 2017 with and without the indicator are compared on individual-level payment fit and, using the survey data, group-level payment fit. PRINCIPAL FINDINGS: All three alternative models outperform the Dutch RE model 2017. However, significant unpriced risk heterogeneity remains. Compared with the 60% threshold, the 80% threshold comes with a larger improvement in fit but identifies a less selective group. CONCLUSIONS: The performance of the RE model can be improved by adding an indicator for being healthy based on multiple-year low spending. However, risk-selection potential remains, warranting high priority to further improvement of RE.

Deriving risk adjustment payment weights to maximize efficiency of health insurance markets.

Risk-adjustment is critical to the functioning of regulated health insurance markets. To date, estimation and evaluation of a risk-adjustment model has been based on statistical rather than economic objective functions. We develop a framework where the objective of risk-adjustment is to minimize the efficiency loss from service-level distortions due to adverse selection, and we use the framework to develop a welfare-grounded method for estimating risk-adjustment weights. We show that when the number of risk adjustor variables exceeds the number of decisions plans make about service allocations, incentives for service-level distortion can always be eliminated via a constrained least-squares regression. When the number of plan service-level allocation decisions exceeds the number of risk-adjusters, the optimal weights can be found by an OLS regression on a straightforward transformation of the data. We illustrate this method with the data used to estimate risk-adjustment payment weights in the Netherlands (N = 16.5 million).

Diagnosis-based Cost Groups in the Dutch Risk-equalization Model: Effects of Clustering Diagnoses and of Allowing Patients to be Classified into Multiple Risk-classes.

BACKGROUND: The risk-equalization (RE) model in the Dutch health insurance market has evolved to a sophisticated model containing direct proxies for health. However, it still has important imperfections, leaving incentives for risk selection. This paper focuses on refining an important health-based risk-adjuster in this model: the diagnosis-based costs groups (DCGs). The current (2017) DCGs are calibrated on "old" data of 2011/2012, are mutually exclusive, and are essentially clusters of about 200 diagnosis-groups ("dxgroups"). METHODS: Hospital claims data (2013), administrative data (2014) on costs and risk-characteristics for the entire Dutch population (N≈16.9 million), and health survey data (2012, N≈387,000) are used. The survey data are used to identify subgroups of individuals in poor or in good health. The claims and administrative data are used to develop alternative DCG-modalities to examine the impact on individual-level and group-level fit of recalibrating the DCGs based on new data, of allowing patients to be classified in multiple DCGs, and of refraining from clustering. RESULTS: Recalibrating the DCGs and allowing enrolees to be classified into multiple DCGs lead to nontrivial improvements in individual-level and group-level fit (especially for cancer patients and people with comorbid conditions). The improvement resulting from refraining from clustering does not seem to justify the increase in model complexity this would entail. CONCLUSIONS: The performance of the sophisticated Dutch RE-model can be improved by allowing classification in multiple (clustered) DCGs and using new data. Irrespective of the modality used, however, various subgroups remain significantly undercompensated. Further improvement of the RE-model merits high priority.

Improving risk equalization with constrained regression.

State-of-the-art risk equalization models undercompensate some risk groups and overcompensate others, leaving systematic incentives for risk selection. A natural approach to reducing the under- or overcompensation for a particular group is enriching the risk equalization model with risk adjustor variables that indicate membership in that group. For some groups, however, appropriate risk adjustor variables may not (yet) be available. For these situations, this paper proposes an alternative approach to reducing under- or overcompensation: constraining the estimated coefficients of the risk equalization model such that the under- or overcompensation for a group of interest equals a fixed amount. We show that, compared to ordinary least-squares, constrained regressions can reduce under/overcompensation for some groups but increase under/overcompensation for others. In order to quantify this trade-off two fundamental questions need to be answered: "Which groups are relevant in terms of risk selection actions?" and "What is the relative importance of under- and overcompensation for these groups?" By making assumptions on these aspects we empirically evaluate a particular set of constraints using individual-level data from the Netherlands (N = 16.5 million). We find that the benefits of introducing constraints in terms of reduced under/overcompensations for some groups can be worth the costs in terms of increased under/overcompensations for others. Constrained regressions add a tool for developing risk equalization models that can improve the overall economic performance of health plan payment schemes.

How can the regulator show evidence of (no) risk selection in health insurance markets? Conceptual framework and empirical evidence.

If consumers have a choice of health plan, risk selection is often a serious problem (e.g., as in Germany, Israel, the Netherlands, the United States of America, and Switzerland). Risk selection may threaten the quality of care for chronically ill people, and may reduce the affordability and efficiency of healthcare. Therefore, an important question is: how can the regulator show evidence of (no) risk selection? Although this seems easy, showing such evidence is not straightforward. The novelty of this paper is two-fold. First, we provide a conceptual framework for showing evidence of risk selection in competitive health insurance markets. It is not easy to disentangle risk selection and the insurers' efficiency. We suggest two methods to measure risk selection that are not biased by the insurers' efficiency. Because these measures underestimate the true risk selection, we also provide a list of signals of selection that can be measured and that, in particular in combination, can show evidence of risk selection. It is impossible to show the absence of risk selection. Second, we empirically measure risk selection among the switchers, taking into account the insurers' efficiency. Based on 2-year administrative data on healthcare expenses and risk characteristics of nearly all individuals with basic health insurance in the Netherlands (N > 16 million) we find significant risk selection for most health insurers. This is the first publication of hard empirical evidence of risk selection in the Dutch health insurance market.

Risk Selection Threatens Quality Of Care For Certain Patients: Lessons From Europe's Health Insurance Exchanges.

Experience in European health insurance exchanges indicates that even with the best risk-adjustment formulas, insurers have substantial incentives to engage in risk selection. The potentially most worrisome form of risk selection is skimping on the quality of care for underpriced high-cost patients--that is, patients for whom insurers are compensated at a rate lower than the predicted health care expenses of these patients. In this article we draw lessons for the United States from twenty years of experience with health insurance exchanges in Europe, where risk selection is a serious problem. Mistakes by European legislators and inadequate evaluation criteria for risk selection incentives are discussed, as well as strategies to reduce risk selection and the complex trade-off among selection (through quality skimping), efficiency, and affordability. Recommended improvements to the risk-adjustment process in the United States include considering the adoption of risk adjusters used in Europe, investing in the collection of data, using a permanent form of risk sharing, and replacing the current premium "band" restrictions with more flexible restrictions. Policy makers need to understand the complexities of regulating competitive health insurance markets and to prevent risk selection that threatens the provision of good-quality care for underpriced high-cost patients.

Risk selection in a regulated health insurance market: a review of the concept, possibilities and effects.

The Dutch basic health insurance is based on the principles of regulated competition. This implies that insurers and providers compete on price and quality while the regulator sets certain rules to achieve public objectives such as solidarity. Two regulatory aspects of this scheme are that insurers are not allowed to risk rate their premiums and are compensated for predictable variation in individual medical expenses (i.e., risk equalization). Research, however, indicates that the current risk equalization is imperfect, which confronts insurers and consumers with incentives for risk selection. The goal of this paper is to review the concept, possibilities and potential effects of risk selection in the Dutch basic health insurance. We conclude that the possibilities for risk selection are numerous and a potential threat to solidarity, efficiency and quality of care. Regulators should be aware that measurement of risk selection is a methodological and data-demanding challenge.

Risk equalization in The Netherlands: an empirical evaluation.

The Netherlands relies on risk equalization to compensate competing health insurers for predictable variation in individual medical expenses. Without accurate risk equalization insurers are confronted with incentives for risk selection. The goal of this study is to evaluate the improvement in predictive accuracy of the Dutch risk equalization model since its introduction in 1993. Based on individual-level claims data (n = 15.6 million), we estimate the risk equalization models that have been successively applied in The Netherlands since 1993. Using individual-level survey data (n = 8735), we examine the average under-/overcompensation by these models for several relevant subgroups in the population. We find that in the course of years, the risk equalization model has been substantially improved. Even the current model (2012), however, does not eliminate incentives for risk selection completely. To achieve the public objectives, further improvement of the Dutch risk equalization model is crucial.

Improving risk equalization using multiple-year high cost as a health indicator.

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.