Does service-level spending show evidence of selection across health plan types?

Ellis, Randall P.; Jiang, Shenyi; Kuo, Tzu‐Chun

doi:10.1080/00036846.2011.636023

Cited by 24 publications

(27 citation statements)

References 7 publications

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“…Consequently, given premium-rate restrictions, unpriced risk heterogeneity is still present [1,6,9,11,15,18,24]. The Dutch risk equalization model, for instance, has been greatly improved over the last decade but still leads to significant under-and overcompensations on specific groups [21].…”

Section: Introductionmentioning

confidence: 99%

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Examining unpriced risk heterogeneity in the Dutch health insurance market

2018

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A major challenge in regulated health insurance markets is to mitigate risk selection potential. Risk selection can occur in the presence of unpriced risk heterogeneity, which refers to predictable variation in health care spending not reflected in either premiums by insurers or risk equalization payments. This paper examines unpriced risk heterogeneity within risk groups distinguished by the sophisticated Dutch risk equalization model of 2016. Our strategy is to combine the administrative dataset used for estimation of the risk equalization model (n = 16.9 million) with information derived from a large health survey (n = 387k). The survey information allows for explaining and predicting residual spending of the risk equalization model. Based on the predicted residual spending, two metrics are used to indicate unpriced risk heterogeneity at the individual level and at the level of certain (risk) groups: the correlation coefficient between residual spending and predicted residual spending, and the mean absolute value of predicted residual spending. The analyses yield three main findings: (1) the health survey information is able to explain some residual spending of the risk equalization model, (2) unpriced risk heterogeneity exists both in morbidity and in non-morbidity groups, and (3) unpriced risk heterogeneity increases with predicted spending by the risk equalization model. These findings imply that the sophisticated Dutch risk equalization model does not completely remove unpriced risk heterogeneity. Further improvement of the model should focus on broadening and refining the current set of morbidity-based risk adjusters.

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Section: Introductionmentioning

confidence: 99%

“…One of the main challenges in regulated health insurance markets is to avoid risk selection [6,11,15,18]. Risk selection has been defined as 'actions by consumers and health plans to exploit unpriced risk heterogeneity and break pooling arrangements' ([13, emphasis added).…”

Section: Introductionmentioning

confidence: 99%

Examining unpriced risk heterogeneity in the Dutch health insurance market

2018

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“…By varying the maximum value of this second objective, researchers can trace out the trade-offs between fit on the included indicators and over-or undercompensation on the omitted ones. 7 Constrained regressions have been studied previously in the context of RE. Glazer et al [13] proposed using constrained regression to address selection problems, where the constraints were derived from first-order conditions for plan profit maximization, with one constraint for each service provided by the plan.…”

Section: Constrained Regressionmentioning

confidence: 99%

“…10 Cao and McGuire [4] in Medicare and Eggleston and Bir [6] in employer-based insurance find patterns of spending on various services consistent with service-level selection among competing atrisk plans. Ellis et al [7] rank services according to incentives to undersupply them. Consistent with service-level selection, they show that HMO-type plans tend to underspend on services (in relation to the average) just as the selection index predicts.…”

Section: Evaluating Incentives For Selection In Re Modelsmentioning

confidence: 99%

“…11 In an evaluation of the CMS-HCC model used in Medicare, Pope et al [22] report predictive ratios for subgroups defined by disease, numbers of prior hospitalizations, demographic characteristics, and other factors. 12 Other papers calculate the difference between RE payments and spending to assess selection incentives, with the difference being referred to as ''undercompensation'' if payments are less than spending and ''overcompensation'' 7 The form of constraints we use is written out in the ''Data and empirical methods'' Section of this paper. See, in particular, Eq.…”

Section: Evaluating Incentives For Selection In Re Modelsmentioning

confidence: 99%

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Improving Risk Equalization with Constrained Regression

Kleef

McGuire

Vliet

et al. 2015

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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 underand 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.

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

2016

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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.

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Does service-level spending show evidence of selection across health plan types?

Cited by 24 publications

References 7 publications

Examining unpriced risk heterogeneity in the Dutch health insurance market

Examining unpriced risk heterogeneity in the Dutch health insurance market

Improving Risk Equalization with Constrained Regression

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

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