Michigan Policy Wonk

Last summer, members of the Michigan legislature revisited discussions of eliminating an arcane but long-standing health policy called Certificate-of-Need. Few people, or even policymakers, are familiar with state Certificate-of-Need (CON) programs, but they yield significant authority in influencing the shape of states’ immense healthcare markets. Michigan’s healthcare market, for example, accounted for an adjusted 70.4 billion dollars in expenditures in 2009 alone. And despite being in effect since the early 1970’s, CON programs continue to generate ongoing legislative debates and controversy. The most recent occurrence in Michigan being the discussion last year. This included the introduction of two House Bills (HB 4728, HB 4729), sponsored by former Rep. Gamrat, that would have repealed Michigan’s CON program entirely.

What, then, are Certificate-of-Need programs and why are they controversial? State CON programs are all different but they follow a general framework and theory. They seek to limit the supply of healthcare services or facilities in any given market from exceeding the relatively fixed, and therefore appropriate, level of demand. This is necessary, CON proponents argue, because the healthcare market is not an ordinary market where traditional market incentives serve to regulate the level of supply. Instead, the market is characterized by a multitude of factors that disrupt typical free-market dynamics. These factors include third-party insurance payment, low consumer information, supply induced demand, and other pre-existing regulations and subsidies. Without CON laws, they say, healthcare markets instead incentivize hospitals and other healthcare providers to expand the supply of healthcare beyond the level of efficiency. This ultimately results in higher costs to patients as well as even affecting issues such as healthcare quality and access.

The way CON programs limit the supply is by requiring healthcare providers to obtain permission, or a “Certificate of Need”, from the state in order to expand certain facilities or provide certain services. Should a CON request fail to meet the standards that define “need” for that regulated service, the certificate of need is denied and the provider is prohibited from building.

From this description it can be easy to see why CON programs often come under scrutiny, especially among those who favor market-driven solutions. Debates about CON have been particularly active and consequential since 1986 when the federal government lifted its requirement on states to implement a CON program. Since then, 14 states have repealed their CON programs entirely, and some others have drastically reduced the scope of their programs. For states that still maintain CON laws, the question of repeal always lingers. Within the last year alone, eight states have restarted debates about whether to repeal their CON program.[i] Before last year, Michigan considered a repeal in 2013.

Considering the controversy surrounding CON and the perennial debates, one may wonder whether cost data and quantitative studies may be able to help settle the matter. Can it help states move beyond the impasse? Each side agrees that they do settle the matter, but only in each side’s favor. This is because the conclusions from existing studies have been either mixed, or inconclusive. After reviewing much of the existing literature on CON and recently completing my own quantitative analysis of the policy, I’ve come to some thoughts about how many of the most influential CON studies actually relate to the policy and what it means moving forward.

My Study

My study took an approach similar to many of the studies cited in CON discussions, particularly from one of the most influential studies by Conover and Sloan[ii]. Using nationwide health cost data, I examined differences in health expenditures between states that have CON programs and those that do not over a 29-year period -- from 1980 to 2009. My results estimated that the presence of a Certificate of Need program in Michigan is estimated to result in an additional $181.48 per capita which translates to an increase in public and private annual spending by $1,998,921,330 (statistically significant at the 5%level).

The Problem with My Study and Others

My statistical findings were significant – meaning little statistical chance of being random - however I believe there are in fact strong reasons to doubt them, along with other often-cited quantitative studies that use the same statistical approach.[iii]^{,[iv],[v],[vi]} This includes studies both opposed and in favor of CON. The reason comes from a common error in the way studies measure, or define, CON regulation to arrive at their estimates.

There are very substantial differences among state CON programs and these differences very likely affect the end results that the programs aim to accomplish, and also that studies examine. This includes issues regarding costs, access, and quality. However, the methods used to measure CON too often use a simple binary, yes/no, observation of whether any CON law at all is present in a state. I believe this buries important differences among the many CON programs.

In a rare, in-depth qualitative analysis of CON led by Tracy Yee, researchers found, from interviewing CON stakeholders and practitioners from six states, that views on program effectiveness varied widely among them.[vii] Interviewees identified specific attributes to each state’s CON program that they believed harmed or contributed to the effectiveness of the program. These included the number of services regulated, clarity and objectivity of review standards, susceptibility of the review process to corruption, and whether standards keep up with technological and market changes. The study also found that, “In five of the six states studied—all except Michigan—the CON approval process can be highly subjective and tends to be influenced heavily by political relationships rather than policy objectives.”

This more nuanced approach to understanding CON has been supported by past CON researchers. Remarking on the then emerging consensus from quantitative studies that CON has little effect, Lawrence Brown, from the University of Michigan, warned that the method of these studies had doubtful validity. He stated that, “in general, CON labors under heavy burdens as a cost-constraining technique, but that under specific and favorable conditions it may rise above its liabilities. Neither the general burdens it bears, nor the special circumstances that surmount them, may be derived from aggregate data. In order to understand either or both, a qualitative examination of the politics of CON implementation is necessary.”[viii]

Another health policy researcher, Charles Begley, warned specifically about the practice of measuring CON with a binary measurement of its presence or absence. He wrote, “Although this approach captures the average effect of the program, nationwide, it tends to bias results against effectiveness by giving equal weight to all State programs, no matter how they have been implemented.” This approach fails to “capture the effect of programs which, by virtue of commitment, resources, and proper management, have the potential to contain growth in the industry.”[ix]

The chart above illustrates the wide variation in the scope of CON regulated services by state. As Begley notes, the binary approach captures the average effect of CON programs, nationwide, which is represented by the grey shading. However, considering the weakness of the programs in the shaded area, this simple approach may make it difficult to find clear effects from CON regulation or, as Begley believes, bias results against effectiveness. This may help explain why the body of quantitative evidence as a whole is inconclusive and sometimes statistically significant against effectiveness.

Moving Forward

What does all of this mean for CON studies and discussions moving forward, particularly for Michigan? I believe Begley was correct when he wrote that once we begin to understand what program factors may be related to effectiveness, “we may be able to test hypotheses to determine which factors are associated with different program outcomes.”[x]  Quantitative studies of CON are an old topic, but it is clear there is a need for a renewed effort in it. This will require the creation of new data sets that have never been compiled. Yee’s qualitative study offers strong indications of what these factors to be tested may be. It also reveals that Michigan’s CON program may be exceptionally suited to rise above the challenges to being an effective cost-containing policy. As the question of repeal undoubtedly lingers in Lansing, it will be important to keep in mind how the quantitative evidence actually relates to Michigan’s CON program.