Risk equalization in The Netherlands: an empirical evaluation

Abstract

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 with Constrained Regression

“…Figure 7 shows results for the ten groups for which the initial under-or overcompensation by the base model is statistically significant (p B 0.05). 30 The results are striking: a single constraint for reducing the undercompensation for users of home care (or for users of physiotherapy) in the previous year can also substantially reduce under-or overcompensation for other omitted groups. Apparently, certain risk indicators in the RE model are correlated with both the home care (physiotherapy) group and the groups presented in Fig.…”

“…All risk indicators have been carefully developed in research programs sponsored by the Dutch Ministry of Health. For further details on these risk indicators, see van Kleef et al [30]. 22 The RE model of 2015 was estimated by a least-squares regression with medical spending in 2012 as the dependent variable and the risk classes described above as 138 independent dummy variables.…”

“…In the presence of premium regulation -as is common in regulated health plan markets -RE mitigates incentives for risk selection and thereby improves incentives for health plans to accept and serve the sick as well as the healthy [20]. 2 Recent research has shown that even state-of-the-art RE models -such as those used under the Affordable Care Act in the United States or those used under the Health Insurance Act in the Netherlands -systematically undercompensate groups of consumers in relatively poor health and overcompensate the complementary groups of consumers in relatively good health [16,30], exposing health plans and consumers to incentives for risk selection. As described by van de Ven and Ellis [28], risk selection threatens the performance of (regulated) health plan markets since it may reduce (1) the quality of care (because plans may have a disincentive to meet the preferences of the sick), (2) the efficiency of care (because risk selection may be a more cost-effective strategy for plans to reduce medical spending than improving the efficiency of care), (3) the efficient sorting of consumers among plans (when market segmentation by risk elevates premiums for particular plans), and (4) the affordability of health plans to the sick (when the same market segmentation causes the sick to face higher premiums).…”

Overpaying morbidity adjusters in risk equalization models

“…The former method allows constraining the estimated Table 2 b Since the survey groups are based on a relatively small sample of the Dutch population (N = 16,061), the undercompensations presented here are subject to random variation in actual expenses. An asterisk indicates that the undercompensation for a group is statistically significantly different from zero (p \ 0.05) c A 454.5-euro-per-person overpayment of individuals in a PCG, DCG, DMEG and/or MHCG (22 % of the population) implies a 128.2-europer-person underpayment of individuals in the complementary group (78 %) d For composition (and underlying questions) of this score see Ware et al [30] coefficients such that the over-or undercompensation of certain risk groups (e.g. XY) equals zero.…”

“…The RE model used in the Dutch basic health insurance includes morbidity adjusters based on diagnoses, drug prescriptions, durable medical equipment and prior expenses. Even these sophisticated RE models substantially undercompensate insurers for selected groups of high-risk individuals [3,4]. With undercompensation we mean that the predicted expenses (according to the relevant RE model) systematically fall below the actual expenses.…”