High-risk pooling for mitigating risk selection incentives in health insurance markets with sophisticated risk equalization: an application based on health survey information.

BACKGROUND: Despite sophisticated risk equalization, insurers in regulated health insurance markets still face incentives to attract healthy people and avoid the chronically ill because of predictable differences in profitability between these groups. The traditional approach to mitigate such incentives for risk selection is to improve the risk-equalization model by adding or refining risk adjusters. However, not all potential risk adjusters are appropriate. One example are risk adjusters based on health survey information. Despite its predictiveness of future healthcare spending, such information is generally considered inappropriate for risk equalization, due to feasibility challenges and a potential lack of representativeness. METHODS: We study the effects of high-risk pooling (HRP) as a strategy for mitigating risk selection incentives in the presence of sophisticated- though imperfect- risk equalization. We simulate a HRP modality in which insurers can ex-ante assign predictably unprofitable individuals to a 'high risk pool' using information from a health survey. We evaluate the effect of five alternative pool sizes based on predicted residual spending post risk equalization on insurers' incentives for risk selection and cost control, and compare this to the situation without HRP. RESULTS: The results show that HRP based on health survey information can substantially reduce risk selection incentives. For example, eliminating the undercompensation for the top-1% with the highest predicted residual spending reduces selection incentives against the total group with a chronic disease (60% of the population) by approximately 25%. Overall, the selection incentives gradually decrease with a larger pool size. The largest marginal reduction is found moving from no high-risk pool to HRP for the top 1% individuals with the highest predicted residual spending. CONCLUSION: Our main conclusion is that HRP has the potential to considerably reduce remaining risk selection incentives at the expense of a relatively small reduction of incentives for cost control. The extent to which this can be achieved, however, depends on the design of the high-risk pool.

Can risk rating increase the ability of voluntary deductibles to reduce moral hazard?

Several regulated health insurance markets include the option for consumers to choose a voluntary deductible. An important motive for this option is to reduce moral hazard. In return for a voluntary deductible, consumers receive a premium rebate, which is typically community rated. Under community rating, voluntary deductibles are particularly attractive for low-risk consumers. Since these people use relatively little medical care, the total moral hazard reduction might be relatively small compared to the total healthcare spending. This paper examines the potential moral hazard reduction under risk-rated premiums. We use Chile as a case study due to institutional features that make it a valid benchmark for other countries. Our simulations show that in the presence of self-selection and under a uniform percentage moral hazard reduction across risk types, the absolute moral hazard reduction from a voluntary deductible is indeed expected to be larger in a system with risk-rated premiums than in a system with community-rated premiums. Nevertheless, sensitivity checks show that this conclusion might no longer hold as the percentage moral hazard reduction is lower for high-risk individuals compared to low-risk individuals.

Selection Incentives in the Dutch Basic Health Insurance: To What Extent Does End-of-Life Spending Contribute to Predictable Profits and Losses for Selective Groups?

Existing risk-equalization models in individual health insurance markets with premium-rate restrictions do not completely compensate insurers for predictable profits/losses, confronting insurers with risk selection incentives. To guide further improvement of risk-equalization models, it is important to obtain insight into the drivers of remaining predictable profits/losses. This article studies a specific potential driver: end-of-life spending (defined here as spending in the last 1-5 years of life). Using administrative (N = 16.9 m) and health survey (N = 384 k) data from the Netherlands, we examine the extent to which end-of-life spending contributes to predictable profits/losses for selective groups. We do so by simulating the predictable profits/losses for these groups with and without end-of-life spending while correcting for the overall spending difference between these two situations. Our main finding is that-even under a sophisticated risk-equalization model-end-of-life spending can contribute to predictable losses for specific chronic conditions.

Incorporating self-reported health measures in risk equalization through constrained regression.

Most health insurance markets with premium-rate restrictions include a risk equalization system to compensate insurers for predictable variation in spending. Recent research has shown, however, that even the most sophisticated risk equalization systems tend to undercompensate (overcompensate) groups of people with poor (good) self-reported health, confronting insurers with incentives for risk selection. Self-reported health measures are generally considered infeasible for use as an explicit 'risk adjuster' in risk equalization models. This study examines an alternative way to exploit this information, namely through 'constrained regression' (CR). To do so, we use administrative data (N = 17 m) and health survey information (N = 380 k) from the Netherlands. We estimate five CR models and compare these models with the actual Dutch risk equalization model of 2016 which was estimated by ordinary least squares (OLS). In the CR models, the estimated coefficients are restricted, such that the under-/overcompensation for groups based on self-reported general health is reduced by 20, 40, 60, 80, or 100%. Our results show that CR can improve outcomes for groups that are not explicitly flagged by risk adjuster variables, but worsens outcomes for groups that are explicitly flagged by risk adjuster variables. Using a new standardized metric that summarizes under-/overcompensation for both types of groups, we find that the lighter constraints can lead to better outcomes than OLS.

Examining unpriced risk heterogeneity in the Dutch health insurance market.

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.

Can premium differentiation counteract adverse selection in the Dutch supplementary health insurance? A simulation study.

Most health insurers in the Netherlands apply community-rating and open enrolment for supplementary health insurance, although it is offered at a free market. Theoretically, this should result in adverse selection. There are four indications that adverse selection indeed has started to occur on the Dutch supplementary insurance market. The goal of this paper is to analyze whether premium differentiation would be able to counteract adverse selection. We do this by simulating the uptake and premium development of supplementary insurance over 25 years using data on healthcare expenses and background characteristics from 110,261 insured. For the simulation of adverse selection, it is assumed that only insured for whom supplementary insurance is expected not to be beneficial will consider opting out of the insurance. Therefore, we calculate for each insured the financial profitability (by making assumptions about the consumer's expected claims and the premium set by the insurer), the individual's risk attitude and the probability to opt out or opt in. The simulation results show that adverse selection might result in a substantial decline in insurance uptake. Additionally, the simulations show that if insurers were to differentiate their premium to 28 age and gender groups, adverse selection could be modestly counteracted. Finally, this paper shows that if insurers would apply highly refined risk-rating, adverse selection for this type of supplementary insurance could be counteracted completely.

Exploring the predictive power of interaction terms in a sophisticated risk equalization model using regression trees.

This study explores the predictive power of interaction terms between the risk adjusters in the Dutch risk equalization (RE) model of 2014. Due to the sophistication of this RE-model and the complexity of the associations in the dataset (N = ~16.7 million), there are theoretically more than a million interaction terms. We used regression tree modelling, which has been applied rarely within the field of RE, to identify interaction terms that statistically significantly explain variation in observed expenses that is not already explained by the risk adjusters in this RE-model. The interaction terms identified were used as additional risk adjusters in the RE-model. We found evidence that interaction terms can improve the prediction of expenses overall and for specific groups in the population. However, the prediction of expenses for some other selective groups may deteriorate. Thus, interactions can reduce financial incentives for risk selection for some groups but may increase them for others. Furthermore, because regression trees are not robust, additional criteria are needed to decide which interaction terms should be used in practice. These criteria could be the right incentive structure for risk selection and efficiency or the opinion of medical experts.

A voluntary deductible in health insurance: the more years you opt for it, the lower your premium?

Adverse selection regarding a voluntary deductible (VD) in health insurance implies that insured only opt for a VD if they expect no (or few) healthcare expenses. This paper investigates two potential strategies to reduce adverse selection: (1) differentiating the premium to the duration of the contract for which the VD holds (ex-ante approach) and (2) differentiating the premium to the number of years for which insured have opted for a VD (ex-post approach). It can be hypothesized that premiums will decrease with the duration of the contract or the number of years for which insured have opted for a VD, providing an incentive to insured to opt for a deductible also in (incidental) years they expect relatively high expenses. To test this hypothesis, we examine which premium patterns would occur under these strategies using data on healthcare expenses and risk characteristics of over 750,000 insured from 6 years. Our results show that, under the assumptions made, only without risk equalization the premiums could decrease with the duration of the contract or the number of years for which insured have opted for a VD. With (sophisticated) risk equalization, decreasing premiums seem unfeasible, both under the ex-ante and ex-post approach. Given these findings, we are sceptical about the feasibility of these strategies to counteract adverse selection.

A method to simulate incentives for cost containment under various cost sharing designs: an application to a first-euro deductible and a doughnut hole.

Many health insurance schemes include deductibles to provide consumers with cost containment incentives (CCI) and to counteract moral hazard. Policymakers are faced with choices on the implementation of a specific cost sharing design. One of the guiding principles in this decision process could be which design leads to the strongest CCI. Despite the vast amount of literature on the effects of cost sharing, the relative effects of specific cost sharing designs-e.g., a traditional deductible versus a doughnut hole-will mostly be absent for a certain context. This papers aims at developing a simulation model to approximate the relative effects of different deductible modalities on the CCI. We argue that the CCI depends on the probability that healthcare expenses end up in the deductible range and the expected healthcare expenses given that they end up in the deductible range. Our empirical application shows that different deductible modalities result in different CCIs and that the CCI under a certain modality differs across risk-groups.