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Costs & Competition

Health-Adjusted Premium Subsidies In The Netherlands

Abstract

 
The Dutch government has decided to proceed with managed competition in health care. In this paper we report on progress made with health-based risk adjustment, a key issue in managed competition. In 2004 both Diagnostic Cost Groups (DCGs) computed from hospital diagnoses only and Pharmacy-based Cost Groups (PCGs) computed from out-patient prescription drugs are used to set the premium subsidies for competing risk-bearing sickness funds. These health-based risk adjusters appear to be effective and complementary. Risk selection is not a major problem in the Netherlands. Despite the progress made, we are still faced with a full research agenda for risk adjustment in the coming years.

In the past fifteen years, however, successive governments consistently worked to realize these preconditions. Much progress has been made—for example, with respect to competition policy and risk adjustment. In this paper, as a follow-up to our earlier paper, we report on progress toward health-based risk adjustment in the Netherlands in the past decade.³

First, we discuss some key elements of the Dutch financing system and the relevance of good risk adjustment. We next report on five different risk-adjustment models successively applied in the Netherlands. Finally, we discuss the health policy conclusions and some new risk-adjustment issues for the next years.

Relevance Of Health-Based Risk Adjustment

Top Editor's Notes Relevance Of Health-Based Risk... Toward Health-Based Risk... Risk Sharing New Risk-Adjustment Issues Conclusions And Policy... NOTES

 
In 1992 a radical reform took place in the Dutch mandatory sickness fund insurance, which covers some 10.5 million people with incomes below a certain threshold (66 percent of the Dutch population). During 1941–1991 all sickness funds received a retrospective, full reimbursement for all of their actual health care expenses out of the Central Fund, to which all working Dutch citizens contribute a mandatory income-related amount. Starting in 1992, sickness funds receive a prospective risk-adjusted premium subsidy per enrollee out of the Central Fund and bear the financial risk of deficits and surpluses.

The subsidy equals the (national) predicted per capita expenses in the risk group to which the enrollee belongs, minus a fixed amount. The per capita subsidy is independent of the sickness fund the consumer chooses. On average these subsidies equal 78 percent (2003) of the total expenses. In addition, consumers pay a premium contribution directly to their sickness fund. Because the risk-adjustment model was imperfect, the government required premium contributions to be the same for all enrollees in the same sickness fund. This prevents sickness funds from charging their chronically ill enrollees a high premium contribution. In addition, there is an annual open enrollment period—that is, sickness funds are not allowed to refuse new applicants. Each sickness fund is free to set its own premiums, which results in premium competition among the sickness funds.

Adverse effects of selection. Although the intended effect of the restrictions on premium contributions is to achieve solidarity (or fairness), they also create predictable losses for sickness funds on high-risk patients and thereby create incentives for risk selection. Three severe adverse effects of such incentives can be distinguished. First, sickness funds have a disincentive to respond to the preferences of high-risk consumers. Consequently, the chronically ill might receive poor quality of care or poor service. Although the findings in the literature do not warrant final conclusions, sickness funds could encounter strong financial incentives to be unresponsive to the preferences of the chronically ill, and this would be cause for concern.⁴ Second, to the extent that some sickness funds attract low-risk consumers, these selection activities result in market segmentation, wherein high-risk patients are left in plans with high premiums and low-risk patients self-select into plans with low premiums. That is, selection may threaten solidarity. Third, at least in the short run, selection will be more profitable than improving efficiency. In sum, the restrictions on premium contributions that are intended to increase solidarity instead provide incentives for selection that may threaten solidarity, quality of care, and efficiency.

Prevention of selection. The most effective strategy to avoid selection is to adjust the premium subsidy to an enrollee’s health status: health-based risk adjustment. A second strategy is risk sharing between the Central Fund and the sickness funds.⁵ Risk sharing implies that the Central Fund retrospectively reimburses the sickness funds for some of their members’ actual costs. However, risk sharing also reduces the funds’ incentive for efficiency and consequently causes a trade-off between risk selection and efficiency. Below we discuss how the Dutch government used both strategies to prevent selection.

Toward Health-Based Risk Adjustment

Top Editor's Notes Relevance Of Health-Based Risk... Toward Health-Based Risk... Risk Sharing New Risk-Adjustment Issues Conclusions And Policy... NOTES

 
Since 1992 the Dutch government has used several sets of risk adjusters to calculate the premium subsidies. Here we report the results of our empirical analysis, in which we simulated five adjustment models (Exhibit 1). The empirical analyses are based on a database of 2.8 million enrollees of five sickness funds.⁶ The health expenses include the costs of inpatient room and board (except for fixed costs), inpatient and outpatient specialist care, dental care, physiotherapy, ancillary services, and prescribed drugs.

Demographic models. In 1992 the risk-adjusted premium subsidies were based only on age and sex. These risk adjusters resulted in sizable incentives for risk selection. For example, for the 10 percent of patients with the highest expenses in the previous year, the average predictable losses in the current year are 61 percent of the average actual expenses (Exhibit 1). In other words, for these patients the sickness fund’s predictable losses equal 150 percent of its revenues.

Pharmacy-based Cost Groups (PCGs). A weakness of the demographic model is that predicted expenses are not adjusted for the large differences in individual health status within each age/sex group. A way to improve the predictions is to extend the set of risk adjusters with "health status proxies" based on prior use—for example, using diagnostic information of prior hospitalization or information derived from prescription drug usage. The Dutch government decided to implement PCGs as new risk adjusters in 2002.

The basic idea is that prescribed medication can be an indication of existing chronic illness. In the Netherlands, pharmacy claims contain a code from the Anatomical Therapeutic Chemical Classification Index (ATC code).⁷ Based on these codes, we identified people with pharmacy claims that are indicative of chronic conditions. As a starting point, we used the list of chronic conditions of the revised Chronic Disease Score (CDS) as developed by Daniel Clark and colleagues.⁸ Although pharmacy data provide good predictions of future spending, they could cause problems with respect to incentives and fairness. First, the additional subsidy for a PCG-classified enrollee may be two or three times the costs of the prescribed drugs that form the basis for PCG assignment.⁹ These perverse incentives should not be allowed to frustrate sickness funds’ attempts to motivate physicians to prescribe efficiently. Second, because of the large amount of variation in prescribing practices, it would not be fair for sickness funds that selectively contract with doctors with cost-effective prescribing habits to be penalized by lower premium subsidies. Third, fairness and incentive problems would exist if a higher premium subsidy would be given for medications that are prescribed for both a major chronic disease and a minor temporary health problem.

To overcome these problems, the Dutch government has been selective in rewarding prior medication use with higher premium subsidies (Exhibit 2). First, more than half of those with an outpatient prescription (77 percent of the population) are excluded because their medication could not to a sufficient extent be uniquely related to a specific medical condition according to the Chronic Disease Score.¹⁰ Second, a person with only one or two prescriptions per year may be an incidental user rather than having a chronic condition. Therefore, to increase the probability of identifying only chronically ill people, the PCG classification is based on at least four prescriptions per year or at least 181 prescribed daily doses, both of which yielded similar results. This, again, reduced the number of PCG-classified people by more than 50 percent. Finally, some PCGs with a low additional premium subsidy were excluded because of a lack of clear consensus concerning the use of the medications involved and because of large practice variations, which may reduce their validity as indicators of chronic illness (for example, hypertension, depression, and hyperlipidemia).¹¹

In our study we did not explicitly pay attention to manipulation by "upcoding," such as if a well-controlled Type II diabetic were moved from oral blood glucose–lowering drugs to insulin. This emphasizes the need for a constant, intensive monitoring and improving of the PCG model and for regular updates. Updates are also necessary because of new drugs entering the market and because of ever-changing treatment protocols. Although this process of continuously updating and improving the risk adjusters might be considered a disadvantage because of its additional costs (which are not that high), it has a clear advantage that may outweigh its costs. The uncertainty about the precise definition of next year’s risk adjusters, in combination with the certainty that government aims at the best achievable model, reduces the sickness funds’ long-term profits from specific risk-selection strategies (such as avoiding certain groups of chronically ill patients). This year’s unprofitable patients may be next year’s profitable patients, especially in efficient sickness funds.

Diagnostic Cost Groups (DCGs). PCGs, which are based on outpatient prescriptions, can be considered an outpatient health status proxy. Therefore, DCGs, as an inpatient health status proxy, might be an ideal complement to PCGs.

We adapted the methodology developed by Greg Pope and colleagues.¹³ People with multiple hospital admissions in a year were assigned to only the most costly DCG of their various diagnoses. So we use the principal inpatient (PIP) DCGs. Hospital admissions of one or two days were excluded to prevent undesired substitution of outpatient or day surgery care for inpatient care. In our database, 5.5 percent of the population had at least one hospitalization of at least three days. About 40 percent of them were classified in one of twenty-three DCGs (Exhibit 3). The other 60 percent of admissions were not included in the DCGs because either (1) the diagnosis was highly discretionary or vague (such as stomachache), (2) it was a temporary health problem (such as a broken leg), or (3) the diagnosis was not the main reason for hospitalization (such as fever or headache).

Adding DCGs to the demographic model greatly increased the predictive accuracy. The R2 doubled, in particular as a result of a better prediction of inpatient expenses. The predictable losses for the 10 percent of people with the highest expenses in the previous year were reduced by 30 percent (Exhibit 1).

PCG + DCG model. The simultaneous predictive accuracy of PCGs and DCGs is illustrated in Exhibit 1. In comparison to the demographic model, the predictable losses for the 10 percent of people with the highest expenses in the previous year were reduced by 50 percent, and the R2 nearly tripled. In addition, PCGs and DCGs are quite complementary in their ability to predict future expenses. The complementary character of PCGs and DCGs is also illustrated in Exhibit 4. Exhibit 3 presents the additional annual premium subsidies for people classified in PCGs and DCGs as calculated in our "PCG + DCG" model.

Top Editor's Notes Relevance Of Health-Based Risk... Toward Health-Based Risk... Risk Sharing New Risk-Adjustment Issues Conclusions And Policy... NOTES

New Risk-Adjustment Issues

Top Editor's Notes Relevance Of Health-Based Risk... Toward Health-Based Risk... Risk Sharing New Risk-Adjustment Issues Conclusions And Policy... NOTES

 
Further improving the risk adjusters. A challenging question is, How can the health-based risk-adjustment model be improved even more? One option would be to take diagnoses in the DCG model from all medical encounters, not just from hospitalizations. Another strategy could be to use multiyear diagnostic information from prior hospitalizations.¹⁶ Or we could use a better indicator of invalidity—for example, an indicator based on the prior use of certain medical aids or appliances.

Acceptable costs. By acceptable costs we mean the cost of the set of services and intensity of treatment that government has decided is acceptable for subsidy.¹⁷ In the past the basic benefit package and acceptable costs were clearly defined. In recent years, however, individual sickness funds got more and more flexibility to determine the entitlements of their enrollees. In the coming years, when sickness funds will become more active in managed care, this flexibility is expected to increase further. The question then becomes how to define the acceptable costs—that is, the national cost level that forms the basis for calculating the premium subsidies.

A related question is, Should all risk factors that are observed by the Central Fund be used to calculate the risk-adjusted subsidies? In December 2000 the Dutch Social Economic Council explicitly advised the government to adjust the premium subsidies only for age and health status, and not for other risk factors, such as providers’ (in)efficiency, overcapacity, and the price levels of contracted providers. A fundamental policy question then is this: Are the sickness fund allowed to differentiate their premium contributions based on the observed risk factors for which the premium subsidies are not adjusted? If not, sickness funds have incentives for selection based on these risk factors. To answer this question, policymakers must make a trade-off between solidarity and (the adverse effects of) selection.

Similar questions arise in the case of a voluntary deductible, as recently (again) proposed by the Dutch government. If it appears that the deductibles greatly reduce moral hazard, the issue then is whether the acceptable cost level should be based on those with a deductible or those without. In other words, are the additional expenses attributable to moral hazard acceptable to be subsidized or not?¹⁸

Pay-for-performance. If (in the future) sickness funds pay providers for their performance—for example, measured by the change in health status of their patients over time—there might be an incentive problem. The better the providers perform in terms of improving health status, the more a sickness fund pays to providers but the lower the next year’s premium subsidies that the sickness fund receives.¹⁹ In sum, despite the progress the Dutch have made so far, we are still faced with a full research agenda for health adjustment in the coming years.

Conclusions And Policy Implications

Top Editor's Notes Relevance Of Health-Based Risk... Toward Health-Based Risk... Risk Sharing New Risk-Adjustment Issues Conclusions And Policy... NOTES

 
In the Netherlands, as in several other countries, proposals for managed competition in health care have been launched. To combine managed competition with solidarity requires an appropriate health-based risk adjustment model. If sickness funds encounter strong financial incentives to be unresponsive to the preferences of chronically ill people, the disadvantages of a competitive sickness-fund market may outweigh its advantages. Good health adjustment is the only effective strategy to reduce these incentives for risk selection without reducing solidarity and efficiency and without disturbing competition among risk-bearing sickness funds.

Adequate health adjustment. In the past decade much progress has been made toward implementing health adjustment. Good health adjustment not only reduces the predictable losses from high-risk consumers, but it also increases the sickness funds’ costs of risk selection as well as the uncertainty about their net benefits of selection. Although not perfect, the "PCG + DCG" model in combination with some risk sharing between the Central Fund and the sickness funds appears adequate to the task of managing competition among risk-bearing sickness funds. Along with improvements in the risk adjustment model, the financial risk of the competing sickness funds has risen from 3 percent in 1993 to 53 in 2004, without any evidence of adverse effects resulting from risk selection.

Toward managed competition, as soon as justifiable. In the past five years the Dutch government increasingly encountered problems with the classic way of controlling health care expenses: government’s central capacity planning and price regulation. The public no longer accepts the long waiting lists and "public poverty, with private wealth." Because of several court rulings, the macro budget for health care is no longer effective as tool to enforce cost containment by creating a "centrally planned scarcity." According to the declaration of policy of the Dutch government that took office in May 2003, "The central planning by government has failed and will be replaced by managed competition as soon as justifiable." With these last four words, government on the one hand stresses the urgent need for reform and on the other hand indicates that not all preconditions for managed competition are yet fulfilled. Only time will tell whether, after fifteen years of health care reform toward managed competition, the halfway point has already been passed.

Editor's Notes

Top Editor's Notes Relevance Of Health-Based Risk... Toward Health-Based Risk... Risk Sharing New Risk-Adjustment Issues Conclusions And Policy... NOTES

 
Wynand van de Ven (vandeven{at}bmg.eur.nl) is a professor of health insurance at Erasmus University Rotterdam, The Netherlands. René van Vliet is an associate professor of health insurance, and Leida Lamers, an assistant professor of medical technology assessment there.

The authors thank three anonymous referees for their valuable comments on a previous draft. Only the authors are responsible for the content of this paper.

NOTES

Top Editor's Notes Relevance Of Health-Based Risk... Toward Health-Based Risk... Risk Sharing New Risk-Adjustment Issues Conclusions And Policy... NOTES

 

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