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Uncomfortable Arithmetic — Whom to Cover versus What to Cover

Posted Dec 17 2009 11:33am
Katherine Baicker, Ph.D., and Amitabh Chandra, Ph.D.

Much of the current debate about expanding health insurance coverage avoids addressing an uncomfortable trade-off: with a limited budget, making benefits more generous means being able to cover fewer people. Moreover, designing insurance benefits that are limited to coverage of higher-value care but are extended to more people will generate greater improvements in health than providing unlimited care for fewer people. Policymakers and patient advocates are reluctant to acknowledge that in a world of scarce resources it will not be enough to eliminate waste: we will have to make active choices in our public insurance programs between increasing the number of people covered and increasing the generosity of that coverage.

Table 1 illustrates the most basic of these choices: the more generous the insurance policy, the fewer the people who can be covered with a given budget. It shows the amount that it would cost to cover a certain number of people with policies of a certain level of generosity (as indicated by the per-person premium). We chose these values on the basis of the distribution of premiums for individual coverage in the employer-sponsored health insurance market today, using data for employers with more than 50 workers from the 2008 Medical Expenditure Panel Survey (MEPS) conducted by the Agency for Healthcare Research and Quality.1 The median premium was $4,200, the premium at the 25th percentile was $3,500, and the premium at the 75th percentile was $5,100. This dispersion reflects many factors besides the generosity of policies, including geographic variation and enrollee characteristics (although basing the analysis on premiums paid by larger employers mitigates the effects of these characteristics). One could also think of the less generous policies as reflective of the typical premiums of a decade ago (for example, the 25th-percentile premium in 2008 was similar to the average premium in 2000, which was $3,500 after adjustment for inflation).

Baicker_F1

This analysis demonstrates an obvious trade-off: a fixed budget of $180 billion per year could cover 30 million people with a policy whose annual premium was $6,000, or it could cover more than 50 million people with a $3,500 policy. With a fixed budget, the “cost” of moving from providing a plan at the 25th percentile of generosity to one at the 90th percentile is leaving 20 million people uninsured. Of course, some might say that the health care budget should not be fixed: we should spend as much as it takes to cover everyone. However, this argument neglects the financial reality that with uncapped benefits, health care programs could grow so quickly that there would be no public funds left for anything else, from food to housing to education.

This discussion implicitly assumes that more expensive health plans offer benefits that improve health more, but there is disagreement on this point. Some would argue that more expensive plans provide virtually no advantage over less expensive ones, and that eliminating duplicated tests, unnecessary procedures, and therapies with unproven benefit would be sufficient to stem the growth of health care spending and provide care for the uninsured. But even after such waste is eliminated, we will still be confronted with the same stark choice between using public resources to provide all people with some care and using those resources to provide some people with all care (or using some of those resources for things like education).

Our very real need to decide how generous publicly subsidized insurance policies should be highlights a related trade-off: when dollars are focused on covering higher-value care, they produce greater aggregate health gains than when they are spread a cross care of mixed value. Evidence suggests that the benefits that are gained from various kinds of health care spending vary widely.

Table 2 presents examples of health care interventions and their associated benefit. Here, too, we have simplified a complex landscape: the effectiveness of all these interventions clearly ranges beyond the categories in which we have placed them, but the examples illustrate the point that dollars can be stretched to produce much greater improvements in health if they are focused on certain uses rather than others. The table shows the reduction in the number of quality-adjusted life-years (QALYs) gained as we cover services that are less cost-effective: an annual budget of $180 billion can offer a gain of at least 1.8 million QALYs if those resources are devoted exclusively to services that cost less than $100,000 per QALY. But if the threshold is raised to include less effective services so that the average cost per QALY gained is $300,000, the same budget will result in only 600,000 additional QALYs. (Of course, cost-effectiveness should be only one of many criteria used to design public health insurance programs: the purpose of health insurance is to reduce financial uncertainty and increase access in the case of large and uncertain medical expenses.)

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Although the benefits of successfully targeting limited resources could be dramatic, the mechanisms by which spending might be targeted toward the highest-value uses are complex. One state offers a potential template: in Oregon, a commission that includes both patients and providers ranks treatments according to their effectiveness, with the goal of having the public insurance program cover only services whose value is above a certain threshold.2 In practice, however, there has been very little limiting of services on the basis of these rankings — a fact that highlights the tremendous political difficulties of making such trade-offs explicit.

Mandating what is covered and what is not isn’t the only approach for increasing the reach of limited public dollars. Competition among private plans for enrollees (who could receive government-subsidized vouchers based on their income and health risks) is another strategy for moving people into plans that offer higher-value care. Lessons from the behavioral economics literature, however, imply that unregulated competition alone is unlikely to result in patients’ choosing the highest-value plans, suggesting that there is a powerful role for more nuanced plan design. All these strategies, however, will involve implicit or explicit trade-offs between the generosity of subsidies and the number of people who are eligible for them, as well as the resources that will be available for other public programs.

Unfortunately, the mere recognition of the existence of trade-offs does not tell us how best to make them. There are no easy solutions in which all people receive all care that might potentially benefit their health. There is only 100% of Gross Domestic Product to go around, whereas we could theoretically spend a virtually unlimited amount of money on health care. As medical technology advances, there will continue to be new treatments that will offer incremental improvements in health at increasingly high costs, and we will have to decide how to allocate scarce resources among treatments and among people. To date, there has been little debate in Congress about the generosity of public benefit packages, except for whether such benefits should cover abortion. But eventually, we will have to engage in the difficult discussions required to choose whom and what our public insurance programs should cover. Some might call this rationing, but the reality is that millions of Americans now have no access to lifesaving medical technologies at the same time that public resources are being devoted to covering less-effective therapies for less-serious conditions. We find that sort of rationing hard to justify.

Financial and other disclosures provided by the authors are available with the full text of this article at NEJM.org.

Source Information

From the Harvard School of Public Health, Boston (K.B.); and the John F. Kennedy School of Government, Harvard University, Cambridge, MA (A.C.).

This article (10.1056/NEJMp0911074) was published on December 16, 2009, at NEJM.org.

References

  1. Agency for Healthcare Research and Quality, Center for Financing, Access and Cost Trends. 2008 Medical Expenditure Panel Survey — insurance component. Rockville, MD: AHRQ. (Accessed December 7, 2009, at http://www.meps.ahrq.gov/mepsweb/data_stats/summ_tables/insr/national/series_1/2008/tig1.pdf.)
  2. Bodenheimer T. The Oregon Health Plan — lessons for the nation. N Engl J Med 1997;337:651-5, 720. [Free Full Text]
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