The Incidental Economist

I published a paper in 2005 with Steve Pizer and Roger Feldman on the curious and costly way regional PPOs were encouraged to enter the Medicare private plan market. The paper, "Defective Design: Regional Competition in Medicare" (Health Affairs, 2005) includes a nice summary of why the PPO, so popular in the commercial market, is less so under Medicare While at least half of commercial plan enrollment is in PPOs, local and regional Medicare PPOs attract, respectively, only 8% and 3% of enrollment in all Medicare Advantage plans.

PPOs represent a less restrictive alternative to HMOs for both enrollees and employers. They typically do not attempt to manage care, instead reducing costs by organizing networks of credentialed providers who offer discounted rates. Despite some efforts by the Medicare to involve PPOs (principally through a demonstration), enrollment to date has been small.

One intent of the 2003 Medicare Modernization Act (MMA) was to improve beneficiaries’ access to private plans offering comprehensive benefits by requiring that PPOs enter markets regionally in 2006. Twenty-six multi-state PPO regions were established, each designed to combine traditionally underserved areas with urban markets typically served by HMOs. Regional plans are constrained to offer uniform benefits and charge the same premium within each region they enter.

Because Medicare HMOs can still be offered on a county-by-county basis while PPOs must be offered regionally there is a competitive imbalance between the two plan types. HMOs are free to cherry pick the most profitable counties while PPOs cannot do so.

In addition to this competitive imbalance, there are two other reasons why the PPO model is not  adaptable to the Medicare market. First, PPOs typically pay providers more than the payment rates set by fee-for-service (FFS) Medicare, which has tremendous market power. Consequently, PPOs will have to charge a typical beneficiary a higher premium for the same benefits available through traditional Medicare, with provider-choice restrictions
not found in traditional Medicare. Second, PPOs’ commercial success is partly attributable to their popularity with self-insured employers who want help administering their plans; FFS Medicare needs no such help.

The upshot of all this is that PPOs don’t work well in Medicare. They have trouble competing on price and benefits where HMOs exist (the more urban counties) and they have trouble competing with traditional Medicare elsewhere due to their network restrictions. This is an interesting example of how a plan type that works in the commercial market fails to thrive in a different setting, one in which the rules of the game are different.

It was not broadly obvious that this was the likely fate of PPOs under Medicare. Though, my colleagues and I did predict it in our 2005 paper, published before regional PPOs were available. We did so using established econometric models of Medicare plan entry and payments. This was a triumph of economic prediction. The fact that prediction matched reality so well is affirming evidence of the value of this type of work. (Next step: getting policymakers to actually notice what we do.)

I’m an incidental economist for many reasons. One is described on the About page. Another is that while I am an economist by profession I do not have an economics degree. My undergraduate studies were in Applied and Engineering Physics at Cornell and my graduate work was in image and signal processing in the Electrical Engineering and Computer Science Department at MIT (the Stochastic Systems Group).

Maybe I’ll tell the story another time of how one seamlessly transitions from MIT’s Stochastic Systems Group to health policy. For now I’m going to tell the story of what my graduate research was about. Since this has nothing whatsoever to do with economics, health policy, law, or my current life, I would forgive readers for stopping right here. I can’t even promise this will be much fun. My main motivation for writing this post is that it finally brings my blogging about my publications up to date. With this post every single one of my publications has been described and referenced. Whew!

Onward! In graduate school I studied multiscale signal and image processing (in the spirit of, though distinct from wavelet-based multiresolution analysis). Huh? The quick-and-dirty way to think about this stuff is successive approximation.

Example 1: In an image that looks pretty much the same everywhere (like a close up picture of tree bark) there is something very different (like a big black blob). How would a machine find it? The multiscale way is to first figure out what quadrant it is in. Then, focusing on that quadrant, figure out what quadrant of that one it is in, and so on. Zoom in by successive quadrant selection. This is what I worked on for my master’s thesis. Only I didn’t have a normal image. My measurements were weirder. They were tomographic (like a CAT scan) [1].

Example 2: You have a one-dimensional series of data (like the S&P 500). You could model it as an autoregressive (AR) process of some order. No way you’re getting a PhD for that. Instead, consider a generalization of AR models, indexed not by the integers but by nodes of a tree (in the graph theoretic, not arboreal, sense). My work was to relate such models to wavelets [2], develop computationally efficient algorithms for estimating model parameters [3], and generalizing a famous algorithm known in the AR modeling framework (Levinson’s algorithm) and using it to solve a famous problem (covariance extension) [4].

Well, I did a few other things you can read about in my thesis, and many things you cannot read about anywhere. My years as a graduate student were wonderful and fulfilling. The proof is that among my bigger regrets is that I could only find 199 scholarly works to cite in my thesis. I had wanted to break 200 but in the final moments before submission I could not come up with even one more (these were the days before Google Scholar). Pretty trivial regret, no?

I learned a lot in graduate school, including that statistical signal and image processing were not for me. Not enough policy relevance. A month after graduation I began work in health policy and economics. Ten years later I launched this blog. I could not have guessed this trajectory in a thousand tries.

[1] Frakt AB, Karl WC, and Willsky AS, “A Multiscale Hypothesis Testing Approach Anomaly Detection and Localization From Noisy Tomographic Data,” IEEE Transactions on Image Processing, 7(6) (June 1998): 825-837.

[2] Daoudi K, Frakt AB, and Willsky AS, “Multiscale Autoregressive Models and Wavelets,” IEEE Transactions on Information Theory, 45(3) (April 1999):828-845.

[3] Frakt AB and Willsky AS, “Computationally Efficient Stochastic Realization for Internal Multiscale Autoregressive Models,” Multidimensional Systems and Signal Processing, 12(2) (April 2001): 109-142.

[4] Frakt AB, Lev-Ari H, and Willsky AS, “A Generalized Levinson Algorithm for Covariance Extension With Application To Multiscale Autoregressive Modeling,” IEEE Transactions on Information Theory, 49(2) (February 2003): 411-424.

Medicare’s drug benefit became available in 2006 through a variety of private plans. Some plan types were new (stand alone prescription drug plans (PDPs) and regional PPOs) and some were familiar (local Medicare Advantage (MA) plans). Steve Pizer and I published the first peer-reviewed paper characterizing the forms the drug benefit took under the variaty of plan types: ”A First Look at the New Medicare Prescription Drug Plans,” Health Affairs Web Exclusive (May 23, 2006): w252-w261.

A main finding was that regional PPOs were not that popular (and still aren’t). They participate in Medicare in relatively low numbers and attract relatively few enrollees. Local MA plans, where offered, are much more numerous and popular. Local MA plans also had lower premiums and deductibles on their drug benefit as compared to regional PPOs in 2006. That year, the average drug premium for regional PPO drug plans was $22 per month. About one third had a $250 annual deductible; two thirds had zero deductible. The average drug premium for local MA drug plans was $19 per month. Additionally, compared with regional PPOs, a higher proportion of local MA drug plans (three-quarters) had a zero deductible for drugs. Almost all of the remaining one quarter of local MA drug plans had a $250 annual deductible.

On average, in 2006 the monthly drug premium for a PDP was $37—well above the averages for local MA drug plans and regional PPOs. A smaller proportion of PDPs (about half) had zero deductible for drugs, relative to local MA plans and regional PPOs. About 34 percent of stand-alone PDPs had a $250 annual deductible, approximately the same as for regional PPOs but a higher percentage than for local MA drug plans. Seven percent have a $100 deductible, and 1 percent had other deductible levels ($50, $150, or $175).

The paper goes on to analyze the costs associated with the 15 national PDPs offered by six insurers in 2006. It also describes their coverage of and cost sharing for the 12 most popular brand name drugs. These results are likely be quite different today so I won’t describe them in detail.

In conclusion, where they exist, local MA plans offer lower premiums and deductibles for outpatient drug coverage relative to PDPs or regional PPO drug plans. Broad PDP characteristics do not vary much from region to region. However, within regions, characteristics vary widely, which has been reported in the media as a source of confusion for beneficiaries. Even when attention is restricted to national PDPs, beneficiaries have meaningful choices, with much variation in cost and generosity.

I’ve analyzed more recent data, though less thoroughly. Not much has fundamentally changed in the market. Local MA plans still offer the best value for drug benefits (ignoring all the other ways in which local MA plans differ from other plan types).

My research includes a focus on inferring the effects of Medicare payment policy on the behavior of firms offering private plans within the program. One of the chief difficulties in this type of work is controlling for the effects of cost. One expects premiums to go up if payment is reduced, but the cost of care also affects premiums. If one does not control for the cost of care one will obtain a biased estimate of the effect of payment on premiums.

Unfortunately the cost of care and other costs relevant to plan decisions (about where to enter and what to offer) are either not in the public domain or are hard to measure. Relevant correlates and proxies exist and are frequently used, for lack of anything better. However, in 2001 something unusual happened that offered an enhancement in the ability to control for costs in estimating elements of plan behavior.

In December of 2000 the Benefits Improvement and Protection Act (BIPA) was signed into law and created a natural experiment. Medicare plans had already established their premium and benefit structures for 2001 in response to expected costs and the payment rates in force prior to BIPA. BIPA changed those payment rates and  plans were permitted to adjust their 2001 premium and benefit levels in response. For each plan, both sets of premium and benefit levels (pre- and post-BIPA) were publicly available to researchers swift-footed (or fingered) enough to download them.

The last-minute change in payment rates and rapid adjustment of premiums and benefits offered the potential to reveal how plans responded to changes in payments over a compressed time frame during which underlying costs could not have changed much. The data that captured these phenomena included variation in payments, premiums, and benefits, but no significant temporal variation in underlying cost. Other dimensions of variation in cost (geographic or plan-level) are relatively easy to control for statistically.

My colleagues Steve Pizer and Roger Feldman and I took advantage of this golden opportunity and published two papers [1, 2]. One set of findings revealed the important role of competitive effects in the Medicare private plan market. These results indicated, for example, that the addition of one more plan to a market evenly divided among three existing plans would approximately offset the effects of a 10-percent reduction in payment rates [1].

Another set of findings lent support to the notion that some benefits offered by plans are less valuable to beneficiaries than they cost taxpayers (a finding also revealed in a later study). Plans are required to spend dollars received above their costs on benefits or premium reductions. In 2001 plans could not lower their premium below zero (i.e. could not offer rebates) as they can today. Consequently, plans at the zero-premium bound were forced to put the extra funds into enhanced benefits. Our statistical analysis indicated that this loss of flexibility was binding. Plans at the zero-premium bound behaved differently–offered different benefits–than plans with a positive premium. This result strongly suggests that the benefits enhancements of zero-premium plans were inefficient. That is, they were not valued by beneficiaries at or above their cost  [2].

While these results could probably be obtained with data from a period over which costs changed (using more statistical controls), that they were found in a circumstance where costs did not vary over time increases our confidence in them. Controlling for costs is hard, and the late-year passage of BIPA offered an opportunity to do some important work absent their confounding effects.

[1] Pizer SD and Frakt AB, Payment Policy and Competition in the Medicare+Choice Program,” Health Care Financing Review, 24(1) (Fall 2002): 83-94.

[2] Pizer SD, Frakt AB, and Feldman R Payment Policy and Inefficient Benefits in the Medicare+Choice Program,” International Journal of Healthcare Finance and Economics, 3(2) (June 2003): 79-93.

A well designed study can find real evidence of cost shifting. I participated in such a study and with co-authors Steve Pizer, Robert Schmitz, and Soeren Mattke wrote up the findings in a paper titled “Voluntary Partial Capitation: The Community Nursing Organization Medicare Demonstration” (Health Care Financing Review, 2005).

The Community Nursing Organization (CNO) demonstration was conducted from 1994 through 2001 to test an innovative approach to care management via the provision of community nursing and ambulatory care services for Medicare beneficiaries.The hypothesis was that provision of such community-based services would promote the timely and appropriate use of health services and to reduce the use of costly acute care services.

Organizations participating in the CNO demo were paid a fixed per-member-per-month rate (a.k.a. a capitated rate) for covered services. But the participating organizations–the CNOs–were only at risk under capitation for a subset of Medicare benefits, an arrangement called partial capitation or carve-out. The financial incentive in such an arrangement would be to minimize utilization covered under the capitated payment but not necessarily to minimize utilization of services not covered because traditional Medicare, not the CNO, would be at risk.

Our quantitative evaluation of the CNO demonstration focused principally on the implications of the CNO treatment model for cost to the Medicare Program. Specifically, our analyses compared the costs to Medicare of services utilized by those treated under the CNO model (CNO treatment group) to those generated by a randomized control group and a population reference group (the latter consisting of beneficiaries with no known contact with a CNO). Our main finding was that Medicare spending was higher for members of the CNO treatment group as compared with both the control group and to the population reference group.

Our  conclusion that the CNO model under partial capitation led to increased Medicare costs is based on very robust findings that were consistent across several analytic approaches. The cost differences between treatment and control or reference groups persisted after the application of increasingly complex risk-adjustment methods. Moreover, the differences increased over time and were robust to changes in the way CNO participation was defined. Lastly, we found no statistically significant evidence of increase in physical or social functioning of the treatment group, as compared with the control group. CNOs cost more without providing any health benefits along dimensions measured.

Regardless of whether they affected provider decisions, the financial incentives built into the design of the CNO demonstration are the same as would accompany any partially capitated Medicare demonstration or initiative. Shifting of costs to the subset of benefits not covered under capitation benefits the organization receiving payment and increases costs to the Medicare Program. Unfortunately, the primary financial tool available to Medicare, the capitated payment rate, is ill-suited to address this problem. Decreasing capitated payment in order to recover some of the cost only increases the cost-shifting incentive. If Medicare continues toward increased privatization of risk it would be advantageous, in terms of financial incentives, to require private organizations to provide all (or as many as feasible) Medicare benefits rather than to divide benefits among different entities. Only when all benefits (or, at least, all those that could be substituted for one another) are bundled together can the capitated payment induce cost containment without cost shifting.

A few years ago my colleague Steve Pizer and I were funded to study new plan options that became available to Medicare beneficiaries in 2006, chief among them stand-alone prescription drug plans (PDPs). PDPs quickly became the most popular means within Medicare for beneficiaries to obtain drug coverage (the other option being Medicare Advantage plans that offer drug benefits). However, PDPs were not uniformly more popular. There was (and is) some geographic variation in their popularity.

We were interested in understanding what factors were associated with geographic variation in PDP enrollment. That is, why were PDPs more popular in some regions than in others? Standard economics models to investigate such a question would include measures of price (premium), competition, and demand and supply factors. We developed such a model and then did something a little unusual.

On a lark, we threw in the percent of a county’s electorate that voted for Bush in 2004. Interestingly, this turned out to be very strongly and positively correlated with proportion of beneficiaries in a county who enrolled in a PDP. Why would this be? For an answer (or a hypothesis really) we turned to behavioral economics and wrote up the results in a 2007 Health Economics paper titled “Attribute Substitution in Early Enrollment Decisions into Medicare Prescription Drug Plans.”

The key notion from behavioral economics upon which our hypothesis hangs is that of “attribute substitution.” Attribute substitution is a form of intuitive thinking in which readily accessible attributes of an object are used as proxies for the less accessible attributes relevant to a rational decision. In the case of PDPs, we hypothesized that beneficiaries might have substituted the recommendations of respected political leaders for the less accessible calculations of expected financial values of Medicare plans.

To put it bluntly, perhaps some beneficiaries heard Bush and others in his Administration touting the benefits of the new drug plans. Finding it otherwise difficult to make their own independent assessment of the relative merits of various coverage options, beneficiaries may have substituted officials’ enthusiasm for PDPs for their own prediction of its benefits. That’s a type of shortcut many of us make: we rely on the “expert” advice of others we trust rather than do our own analysis. In this case, there is geographic variation of degree of trust in the Bush Administration, which we operationalized as proportion of 2004 Bush vote.

We found that elasticity of PDP enrollment with respect to the Bush vote to be 0.14 (a 10% change in Bush vote is associated with a 1.4% change in PDP market share). To obtain a sense of the relative importance of this effect, we calculated the change in PDP enrollment due to a one-standard deviation change in each of the independent variables in our model separately. We found that the effect of the Bush vote is larger than the effect of other variables that are generally accepted to be important and relevant factors associated with enrollment decisions: premium, level of beneficiary educational attainment, county urban/rural status, provider density, income, and diagnosis based risk score.

So, an administration’s enthusiasm and popularity can have a significant impact on the early response to a new program. That’s a fairly intuitive result, and it is nice to see it is supported by the data. This paper was an interesting walk through a small tract of behavior economics. It is something I’d like to pursue further but not something for which I’m funded. So it will likely be a long time before I try anything like this again.

In 2006 stand-alone prescription drug plans (PDPs) became a new source for prescription drug coverage for Medicare beneficiaries. PDPs weren’t just new to Medicare, they were a new type of insurance product in general. No such thing had previously existed in the commercial market. Since level of prescription drug use is relatively easy for an individual to predict there was good reason to worry that PDPs would fail due to severe adverse selection. In a paper by Steve Pizer, me, and Roger Feldman we predicted the likely selection experience of PDPs and found that they could weather the degree of adverse selection they would experience (Pizer, Frakt, Feldman. (2008). Predicting Risk Selection Following Major Changes in Medicare. Health Economics 17(4).)

Some background is warranted to explain the paper’s findings in detail. Adverse selection occurs when an insurance product is purchased by individuals who are on average more risky than expected. The insurer sets the product’s premium based on the expected level of risk (expected claims). If actual risk of those who purchase the product is higher (adverse selection), the product is at risk of failure since it does not have enough revenue to cover claims.

Adverse selection is the reason stand-alone drug plans didn’t exist before. Prescription drug use is not like use of other health services in that it is very predictable. Individuals generally know their pattern of drug use much more accurately than they know if they’ll be hospitalized, for example. Therefore, to insure the expenses of drug use is a dangerous game. It is very likely that only individuals with high expected use will purchase stand-alone drug coverage. The insurer will experience adverse selection, raise its premiums in response, thereby driving out the relatively lower risks, and experiencing selection more adverse. This is the classic insurer death spiral.

For this reason, Medicare’s drug program (Part D) includes a variety of measures that reduce the amount and consequences of adverse selection. One is a late enrollment penalty. Others include risk adjustment, risk sharing, and reinsurance. In addition, PDP premiums are subsidized at a rate of 74.5% (of basic coverage, not including enhancements), making a PDP a good value even for beneficiaries with relatively low drug use.

In the paper, we predicted that PDPs would experience adverse selection. In fact, drug expenditures for PDP enrollees were predicted to be 78% higher than those for Medicare HMO enrollees. However, we also found that the PDP market would likely be able to absorb the degree of adverse selection that we predicted they would experience: on the whole the PDP market will be stable. Death spirals, if they occur, are likely to be seen only for plans that offer extensive enhanced benefits (such as the Humana Complete plan that entered the market in 2006 with full brand name coverage in the gap–it subsequently raised premiums and dropped brand name gap coverage.)

Careful readers may have noticed that I used terms like “predicted” and “likely” in the foregoing paragraph. Why didn’t we use actual enrollment to see what plan selection experience has been? We couldn’t. The data weren’t available at the time of the study. Moreover, they’re still not available. The only way to estimate PDP selection experience is to simulate it based on models of beneficiary decision making among other plan types for which data are available. That’s precisely what we did.

Using Medicare Current Beneficiary Survey data, we developed a model of Medicare beneficiary insurance decisions based on plans that existed in 1998-2001. Those plans include HMOs, Medigap supplements, and traditional fee for service (FFS) Medicare. The characteristics of choices available today are included among the choices that existed in 1998-2001, only mixed up in different ways. That is, there were choices then (HMOs) and now (still HMOs) that restrict provider choice. There were choices then (FFS, Medigap+FFS) and now (FFS, FFS+PDP, FFS+Medigap+PDP) that do not restrict choice. There were choices then (some HMO and Medigap plans) that offered drug coverage and the same is true today (some HMOs and all PDPs).

By estimating beneficiary response to the characteristics of options in 1998-2001 it is possible to predict their response to new options with the same characteristics bundled differently. That’s, more or less, what we did in the paper. Someday, when the data are (finally) available, we’ll be able to re-estimate our model and see the extent to which predictions of beneficiary decision making differ in the pre- versus post-Part D era.

This post summarizes a 2008 article I coauthored with Steve Pizer and Ann Hendricks titled “Controlling Prescription Drug Costs: Regulation and the Role of Interest Groups in Medicare and the Veterans Health Administration” (Journal of Health Policy, Politics and Law 33(6), December).

Federal statute authorizes private plans offering a drug benefit under Medicare to negotiate with drug manufacturers for volume discounts, and it prohibits Medicare as a whole from doing so. While the prohibition on direct negotiation by Medicare has received considerable attention there is another important limitation imposed by law on the administration of the Medicare drug benefit: a minimum number of drugs in each class must be included on formularies (some classes must be open to “all or substantially all” drugs on the market).

Some have pointed out, correctly in my view, that providing Medicare the authority to negotiate directly with manufacturers would not lead to price reductions on its own. To achieve savings Medicare would also need the ability to exclude drugs from its formulary. This ability to tighten the formulary would provide the leverage to negotiate bargains.

Medicare’s inability to negotiate prices and to freely restrict drugs from its formulary is in stark contrast to another large public provider of prescription drug benefits, the Veterans Health Administration (VA), which negotiates directly with drug manufacturers and obtains very low prices.

This raises two interesting questions. First, why is Congress comfortable with the VA prescription drug benefit but not willing to authorize something similar under Medicare? Second, given the limitations on Medicare, is there a lower-resistance path to getting VA-like drug prices for more Medicare beneficiaries? Both questions are addressed in our “Controlling Prescription Drug Costs” paper, and the answer to the first question suggests one to the second.

The paper explains the differences between the two drug benefit designs by observing that Congress acts as an agent for multiple interest groups. We conclude that important limitations on the Medicare drug benefit probably arose from the advocacy of drug manufacturers and retail pharmacies, among others. Relative to Medicare policy, these interest groups are less involved in VA policy.

This suggests a practical approach to reducing the cost of providing a prescription drug benefit. A drug program that is more directly under the VA’s purview but that builds on the financing structure of the new drug-only Medicare plans may not immediately arouse the kind of effective interest group opposition that typically restricts the options of Congress with respect to Medicare. Moreover, a drug program of this kind is likely to receive the combined support of Medicare and VA beneficiary advocacy groups, which increases the political cost to opposition relative to policy proposals that receive the support of only one or the other of these groups. We develop this idea in more detail and show that a combination of VA and Medicare could achieve improved access and lower costs for some Medicare-enrolled veterans.

In particular, a VA-Medicare prescription drug plan (PDP) could be made available to certain Medicare-enrolled veterans. Such a plan has the potential to provide a rich drug benefit to a large number of beneficiaries. Of the 43 million Medicare beneficiaries, about 10 million are also veterans. While about 3 million Medicare-eligible veterans already receive drug and nondrug benefits from the VA, the rest do not. A VA-Medicare PDP would be another prescription drug coverage option for these beneficiaries, one that likely would be more comprehensive and less costly than any other available to them.

The VA-Medicare PDP discussed in the article would offer advantages to both programs and beneficiaries. Much as Medicare currently subsidizes private drug plans (whether employer offered or individually purchased), Medicare could subsidize the VA-Medicare PDP on a per-beneficiary basis. These funds would permit the VA to broaden the numbers and types of veterans it serves. Since the VA receives steeper discounts for prescription drugs than Medicare drug plans do, the per-beneficiary subsidy could be set lower than for private plans, producing savings to Medicare.

A VA-Medicare PDP would not be implemented without challenges, which are acknowledged and explored in the article. Of course, above all, it is political considerations that make prospects for this kind of integration uncertain.

High-risk pools have come up again (see Ezra Klein and Jonathan Cohn). Their best application may be as a transitional measure, to fill the gap between health reform passage and full implementation. In this regard, the House health reform bill has the right idea.

Under the House bill subsidies for a national high-risk pool for the medically uninsurable would be established immediately and patch a hole in the safety net, remaining in place until 2013 at which point pre-existing exclusion restrictions would be abolished.  As explained by Timothy Jost, this high-risk pool

would cover persons who are not eligible for Medicaid, Medicare or SCHIP and who have been uninsured for six months or more, or who have been denied coverage or been offered limited or unaffordable coverage because of a preexisting condition. … Premiums could cost up to 125% of the prevailing rate for individual coverage in the market and deductibles of up to $1500 and out-of-pocket limits of up to $5000 individual/$10,000 family could be imposed, so the coverage would not be a bargain.

As I wrote last month about a similar provision in the Senate Finance bill, at least in concept, if not in detail, this is a sensible plan. First, outlawing pre-existing exclusion restrictions is the right thing to do. But it can’t be done overnight. It will take time to implement this and other insurance reforms and to allow for the individual insurance market and exchanges to develop. Therefore, it is sensible to provide some transitional assistance for individuals who are in desperate need of health insurance coverage but who cannot obtain it (the medically uninsurable).

My own research on high-risk pools makes a few important contributions. In 2000, high-risk pool enrollment was a small proportion of the number of medically uninsurable individuals: 8% nationally, with state variation between 1% and 54%. Recent reports suggest not much has changed in this regard. So, high-risk pools are making a dent, but only a small one. The main limitation to greater enrollment is low funding. Some states cap enrollment due to limitations of funding. And premium subsidies are, of course, subject to funding limitations.

The paper’s main policy conclusion was that an injection of federal funds, accompanied by appropriate regulation, could dramatically increase the affordability of high-risk pool plans and provide much needed assistance to medically uninsurable individuals. This appears to be exactly what the House leadership intends to do.

High-risk pools have received brief mention in the media lately. They were included in a bipartisan nod by Obama and are part of the Senate Finance Committee’s transitional plan toward universal coverage. Having participated in a study on high-risk pools and published a paper on it (Health Care Financing Review, Winter 2004-2005, with Steve Pizer and Marian Wrobel), I know a bit about them.

Scott Hensley of NPR explains that that under the Senate Finance Committee’s plan, until 2013 insurance companies could still deny coverage based on pre-existing conditions. Individuals uninsured for six months for this reason would be eligible for coverage through state high-risk pools, which would be subsidized with $5 billion in federal funding. This is a transitional measure, and by 2013 pre-existing exclusion restrictions would be illegal. My interpretation of language in the Chairman’s Mark (bottom of page 2) is that plan premiums would be subsidized so that participants paid no more than a healthy individual would.

At least in concept, if not in detail, this is a sensible plan. First, outlawing pre-existing exclusion restrictions is the right thing to do. But it can’t be done overnight. It will take time to implement this and other insurance reforms and to allow for the individual insurance market and exchanges to develop. Therefore, it is sensible to provide some transitional assistance for individuals who are in desperate need of health insurance coverage but who cannot obtain it (the medically uninsurable).

Second, high risk pools already exist in 35 states and can be set up in the other states relatively quickly. Where they exist they’re already designed to accommodate individuals with substantial medical needs. Many, if not all, include participation of patient advocacy and consumer groups. In short, if one is looking to quickly assist the medically uninsurable, leveraging existing high-risk pool organizations is a good way to do it.

Third, directing funds toward coverage of those otherwise medically uninsurable is an efficient use of taxpayers’ dollars. It steers the money and the benefits toward individuals who need it most.

What about that six month waiting period? Presumably it is included to avoid crowd-out of unsubsidized plans. The concern, no doubt, is that people who aren’t really uninsurable will cause themselves to appear so in order to obtain subsidized coverage from the high-risk pool. Therefore, forcing individuals to be uninsured for six months before eligibility for high-risk pool coverage protects the pool from gaming, albeit at the expense of additional suffering by those who badly need the coverage.

And what does my research say about high-risk pools? The paper made the following main contributions:

In 2000, high-risk pool enrollment was a small proportion of the number of medically uninsurable individuals: 8% nationally, with state variation between 1% and 54%. So, high-risk pools were making a dent, but only a small one. Recent reports suggest not much has changed in this regard. The main limitations to greater enrollment were enrollment caps and affordability. These are really two symptoms of the same thing: low levels of funding. Some states capped enrollment due to limitations of funding. And premium subsidies were, of course, subject to funding limitations.

We estimated that high-risk pool premiums were above 25% of family income for 29% of the medically uninsurable population. That is, even when high-risk pool enrollment was possible, for a large minority of medically uninsurable individuals, it was unaffordable. We simulated the effect of lowering high-risk pool premiums to 125% of the individual market rate and found that doing so would increase enrollment by 33%.

The main policy conclusion was that an injection of federal funds, accompanied by appropriate regulation, could dramatically increase the affordability of high-risk pool plans and provide much needed assistance to medically uninsurable individuals. This appears to be exactly what the Senate Finance Committee intends to do.