The Incidental Economist

Reader Ryan Stevens encouraged me to take a closer look at what the ACA legislation actually says about ACOs. Fortunately, the job is made dramatically easier by HealthReformGPS. In two separate posts the amazingly thorough staff of that blog cover the nitty-gritty on ACO provisions as they pertain to Medicare and Medicaid.

I encourage interested readers to check out the posts themselves. I’m not going to attempt to summarize them because, frankly, it’s too boring. However, in the very lengthy section on open questions, the following part jumped out at me:

How will federal antitrust enforcers view the establishment of ACOs? Will ACOs be insulated from potential antitrust claims to the extent that the ACO providers collectively negotiate payments with private third-party payers outside of Medicare? Will ACO certification include a determination that ACO are “clinically integrated” and thus fall within the federal antitrust exception? Will the federal government create an express safe-harbor from antitrust scrutiny for ACO activities under certain conditions?

I’d love to know the answers to those questions.

As mentioned at the end of my prior post in the Medicaid-IV series Janet Currie and Jon Gruber published a 1996 paper on the effect of Medicaid expansion on infant mortality and birth weight. Here’s the abstract:

A key question for health care reform in the United States is whether expanded health insurance eligibility will lead to improvements in health outcomes. We address this question in the context of the dramatic changes in Medicaid eligibility for pregnant women that took place between 1979 and 1992. We build a detailed simulation model of each state’s Medicaid policy during this era and use this model to estimate (1) the effect of changes in the rules on the fraction of women eligible for Medicaid coverage in the event of pregnancy and (2) the effect of Medicaid eligibility changes on birth outcomes in aggregate Vital Statistics data. We have three main findings. First, the changes did dramatically increase the Medicaid eligibility of pregnant women, but did so at quite differential rates across the states. Second, the changes lowered the incidence of infant mortality and low birth weight; we estimate that the 30-percentage-point increase in eligibility among 15-44-year-old women was associated with a decrease in infant mortality of 8.5 percent. Third, earlier, targeted changes in Medicaid eligibility, which were restricted to specific low-income groups, had much larger effects on birth outcomes than broader expansions of eligibility to women with higher income levels. We suggest that the source of this difference is the much lower take-up of Medicaid coverage by individuals who became eligible under the broader eligibility changes. Even the targeted changes cost the Medicaid program $840,000 per infant life saved, however, raising important issues of cost effectiveness.

This study shares the same methodological approach, and many of the strengths and weaknesses of the Currie and Gruber paper I reviewed previously. So, I’m not going to repeat myself. There is one element of this study worth emphasizing, however. As stated in the abstract, the authors examined two types Medicaid expansions in the 1980s, one targeted and one broad.

The targeted expansions were essentially modest changes to Medicaid eligibility around the edges of the program’s ties to AFDC (I’m obviously grossly simplifying). The broad expansion began in 1987 and liberalized the income cutoffs for pregnant women. By 1990 all states were required to cover pregnant women with incomes up to 133% of poverty and had the option of extending coverage up to 185% of poverty with federal matching funds.

Results of the study differ across the two types of expansions. The targeted expansion had much stronger effects:

[W]e find that a 30-percentage-point increase in eligibility under targeted programs would have been associated with a highly significant 7.8 percent decline in the incidence of low birth weight; a similar increase in eligibility under the broad programs would have decreased the incidence of low birth weight by only 0.2 percent. Similarly, a 30-percentage-point increase in targeted eligibility would have been associated with an 11.5 percent decline in infant mortality, compared to a 2.9 percent decline under the broad policy changes.

The authors attribute this difference in outcomes across type of expansion to different rates of take-up. Lower take-up under the broad expansion attenuated its effect. To the extent that these findings can be generalized, they would seem to suggest that the broad Medicaid expansion under the ACA will have relatively small effects on health. However, the ACA’s expansion comes with an individual mandate, so take-up should occur at a much higher rate than under the broad expansions in the 1980s.

Next up in my “Medicaid-IV” series–in which I’m reviewing papers that use instrumental variables techniques to estimate the effects of Medicaid on health outcomes–is the widely-cited 1996 Quarterly Journal of Economics paper by Currie and Gruber on Medicaid and child health (link to ungated version).

Not surprisingly, the authors do a superb job of explaining their approach and interpreting their results. So, I’m going to liberally quote from the paper. Let’s start with the abstract just to get an overview, then I’ll hit some important issues not fully revealed by such a brief summary.

We study the effect of public insurance for children on their utilization of medical care and health outcomes by exploiting recent expansions of the Medicaid program to low-income children. These expansions doubled the fraction of children eligible for Medicaid between 1984 and 1992. … [E]ligibility for Medicaid significantly increased the utilization of medical care, particularly care delivered in physicians’ offices. Increased eligibility was also associated with a sizable and significant reduction in child mortality.

By “exploiting recent expansions of the Medicaid program” the authors mean they use state-year variations in those expansions to construct an instrument that is not correlated with individual characteristics but is correlated with Medicaid eligibility, and therefore with Medicaid enrollment. The instrument and how it works are mind-benders (I didn’t get it upon first encounter). It’s the average Medicaid eligibility rate under each of the year-state Medicaid rules where the average is computed over a year-but-not-state-varying population of kids. (I know that’s hard to grok. I could spend a whole post explaining it further, but I won’t. You’ll have to trust me that it is a valid instrument and has become standard technique for instrumenting for Medicaid status. Or you can read the paper. This is advanced material!)

A good question is, “Why the focus on kids?” Currie and Gruber have a great answer:

A potential problem with utilization measures, however, is that they confound access and morbidity. For example, the Medicaid expansions may have increased access to hospitals, but at the same time they could have increased the use of preventive care, improving health status and reducing the demand for hospital care. One way to surmount this problem is to focus on utilization that is explicitly preventative, and therefore unaffected by morbidity. Pediatric guidelines recommend at least one doctor’s visit per year for most children in our sample, so that the absence of a doctor’s visit in the previous year is suggestive of a true access problem, regardless of underlying morbidity.

The results are well-summarized by the abstract quoted above, but I want to highlight a few things. The authors find that Medicaid eligibility cuts the probability in half that a child will go a year without seeing a physician in any setting. Much of this is due to increased visits to doctors’ offices. They also find that the 15.1 percentage point rise in Medicaid eligibility during the study period reduced child mortality by 5.1 percent. Their sub-analysis of mortality is sharp:

If Medicaid eligibility reduces deaths by improving the utilization of care, then we would expect deaths due to “internal causes” (such as disease) to fall more than deaths due to “external causes” (such as accidents, homicides, suicides, and other external causes). [The results] show that this is indeed the case: increases in eligibility are correlated with a significant reduction in deaths due to internal causes, but have no significant effect on deaths due to external causes.

I’ve saved the most puzzling finding for last, Medicaid eligibility was found to increase hospital visits. That sounds bad, and maybe it is. But the mechanism could be benign, as the authors explain.

[H]ospitals may be better equipped to assist patients in claiming benefits. Potential eligibles for Medicaid must complete lengthy and complex application forms, provide extensive documentation (such as birth certificates, pay stubs, and confirmation of child care costs), and attend several interviews with caseworkers. … In response, many hospitals have established special offices, or contract with private companies, to assist Medicaid eligibles in completing these procedures. … The nontrivial costs of providing these services may be beyond the means of private doctors and clinics, leading them to recommend that potential eligibles seek care in a hospital setting.

Before closing, it is worth noting two things. One, the control variables in the regressions do not include health status. That’s important since health status could be an outcome of Medicaid enrollment. (Inclusion of an outcome as a control variable leads to bias.) Second, as the authors point out, Medicaid expansions have two effects. They encourage additional Medicaid enrollment and discourage private coverage. Some new Medicaid enrollees had been privately insured, am effect known as “crowding out.” The estimates include all effects of Medicaid expansion on outcomes, including that due to crowding out, but do not distinguish among them.

Finally, there is a question of generality of the findings. This is a study of Medicaid expansions that targeted children about 20 years ago. The ACA’s Medicaid expansion is far broader and occurring in four years from now. Can one generalize the findings of Currie and Gruber to other populations and eras? It’s hard to say. The authors published another paper that used the same techniques and focussed on the effect of Medicaid expansions for pregnant women, finding they lowered infant mortality and increased birth weight. So, the positive effects of Medicaid expansions on outcomes apply to more than one population, which strengthens claims of generality.

Not since the RAND Health Insurance Experiment (HIE) has there been a randomized controlled experiment of the effect of insurance on health outcomes. Finally, a second one is underway, the Oregon Health Study (OHS). It’s being conducted by Heidi Allen, Katherine Baicker, Amy Finkelstein, Sarah Taubman, Bill J. Wright, and the Oregon Health Study Group who report on the study design in the most recent edition of Health Affairs.

[T]he Oregon Health Study [is] a randomized controlled trial that will be able to shed some light on the likely effects of [Medicaid] expansions. In 2008, Oregon randomly drew names from a waiting list for its previously closed public insurance program. Our analysis of enrollment into this program found that people who signed up for the waiting list and enrolled in the Oregon Medicaid program were likely to have worse health than those who did not. However, actual enrollment was fairly low, partly because many applicants did not meet eligibility standards.

Get excited! But don’t get too excited. The study runs through 2010 and no outcome results are available yet.

The paper includes a nice summary of observational, quasi-experimental, and experimental studies of the effects of public insurance programs on outcomes. As I’ve written before, observational studies are not of primary interest to me. There’s only been one other experimental study (RAND HIE). About the quasi-experimental studies, the authors write,

Some studies have found evidence that public health insurance reduces mortality among infants and children [8-10] and improves some outcomes—although not mortality—among the elderly. [11–14] Although they are much more persuasive than observational studies, quasi-experimental studies are not truly randomized. Thus, investigators must rely on the assumption that the people whose health insurance was affected by environmental or policy changes are otherwise identical to the people in the comparison group.

The authors’ references 8-14 are listed below. References 8 and 9 pertain to Medicaid and are on my list of papers as part of the “Medicaid-IV” project.

References

8. Currie J, Gruber J. Saving babies: the efficacy and cost of recent expansions of Medicaid eligibility for pregnant women. J Polit Econ. 1996;104(6):1263–96.

9. Currie J, Gruber J. Health insurance eligibility, utilization of medical care, and child health. Q J Econ. 1996;111(2):431–66.

10. Hanratty MJ. Canadian national health insurance and infant health. Am Econ Rev. 1996;86(1):276–84.

11. Card D, Dobkin C, Maestas N. The impact of nearly universal health care coverage on health care utilization: evidence from Medicare. Am Econ Rev. Forthcoming.

12. McWilliams JM, Meara E, Zaslavsky AM, Ayanian JZ. Health of previously uninsured adults after acquiring Medicare coverage. JAMA. 2007; 298(24):2886–94.

13. McWilliams JM, Meara E, Zaslavsky A, Ayanian JZ. Use of health services by previously uninsured Medicare beneficiaries. New Engl J Med. 2007; 357(2):143–53.

14. Finkelstein A, McKnight R.What did Medicare do? The initial impact of Medicare on mortality and out of pocket medical spending. J Public Econ. 2008;92(7):1644–69.

An individual’s health status affects Medicaid enrollment (the ill are more likely to enroll). Medicaid enrollment affects an individual’s health status too (one can argue about which way, for the better or worse). The two are simultaneous. That makes inferring the causal effect of Medicaid on health outcomes difficult.

A few weeks ago I described the right way to tease out the causal effect of Medicaid enrollment on health outcomes:

There are undoubtedly studies that consider Medicaid vs. uninsured outcomes using the random variations provided by the natural experiment that is Medicaid. Characteristics of the program vary by state and year, making it a perfect set-up for such an analysis of this issue. This second I can’t point to a study. But I know where to look.

I’ve started to look and will begin to describe the relevant literature as I read the papers. I’m not going to filter or cherry pick papers based on their findings. All that matters to me is the quality of the methods applied. Feel free to send me links to papers you think qualify (look for peer-reviewed, natural or randomized experiments and/or instrumental variables approaches; the run-of-the-mill observational study that controls for observable individual characteristics won’t do). There may be many posts in this series of paper reviews. They’ll all be under the “Medicaid-IV” tag. When I think I’ve summarized them all, I’ll post a conclusion that reports on the full body of evidence.

Below I’ll discuss a 2001 paper in the Journal of the American Statistical Association by Dana Goldman et al., Effect of Insurance on Mortality in an HIV-Positive Population in Care. Before I get to the paper, just in case it isn’t clear, by exploiting the variations in state-year Medicaid eligibility I’m talking about instrumental variables (IV) analysis, about which I’ve written considerably.* The sense in which those variations are random is that an individual’s characteristics cannot affect them. As far as an individual is concerned, the Medicaid policy in effect in his state and at a particular time is random. But Medicaid policy does affect Medicaid enrollment (it affects private enrollment too), so it can be exploited to infer the causal effect of Medicaid (or insurance in general) on health outcomes free of the confounding effects of health on Medicaid.

Goldman and colleagues do just that using a nationally representative cohort of HIV-infected persons and sound IV methods. The abstract summarizes the highlights. It’s a bit of dense reading so if you wish to skip it just trust me that it communicates that the authors are following standard techniques for causal inference:

A naïve single-equation model confirms the perverse result found by others in the literature—that insurance increases the probability of death for HIV+ patients. We attribute this finding to a correlation between unobserved health status and insurance status in the mortality equation for two reasons. First, the eligibility rules for Medicaid and Medicare require HIV+ patients to demonstrate a disability, almost always defined as advanced disease, to qualify. Second, if unobserved health status is the cause of the positive correlation, then including measures of HIV+ disease as controls should mitigate the effect. Including measures of immune function (CD4 lymphocyte counts) reduces the effect size by approximately 50%, although it does not change sign. To deal with this correlation, we develop a two-equation parametric model of both insurance and mortality. The effect of insurance on mortality is identified through the judicious use of state policy variables as instruments (variables related to insurance status but not mortality, except through insurance). The results from this model indicate that insurance does have a beneficial effect on outcomes, lowering the probability of 6-month mortality by 71% at baseline and 85% at follow-up. The larger effect at followup can be attributed to the recent introduction of effective therapies for HIV infection, which have magnified the returns to insurance for HIV+ patients (as measured by mortality rates). (Bold mine.)

The reason to read the paper, or the first few pages of it anyway, is to get a sense of how to do Medicaid-health outcome studies properly. Importantly, the authors used arguably exogenous instruments–features of state Medicaid and AIDS drug assistance programs–and subjected them to power and falsification tests, which they passed. One can still argue that the instruments are not valid, but it would require an argument so contorted I cannot fathom what it could be.

The reason to take the study with a couple of big grains of salt is that there are a few potential and actual problems, not least of which is that the results I made bold above are not statistically significant. In that sense, the findings are inconclusive about whether or not insurance reduced mortality for HIV patients.

A second limitation is that it is not specifically a study of Medicaid. It’s a study of insurance, of any type. The authors lump patients with different types of insurance (public, private) together. That’s a big problem because characteristics of state Medicaid programs affect Medicaid enrollment and private coverage rates, but in opposite directions. It is also possible that Medicaid coverage and private insurance have opposite effects on outcomes. Ultimately, it is hard to draw policy conclusions with a study that mixes the two insurance types. If mortality improves is it due to public or private coverage? It’s impossible to tell. They acknowledge this limitation and correctly describe a more complex model that would separately identify the effects of public and private insurance on mortality. They wrote that such a model was a computational challenge. Today it would not be.

A final critique is that the preferred model specifications include a measure of disease burden, the lowest ever CD4 count as of the baseline year. To the extent that Medicaid causes poor outcomes (due to, say the poor quality care it could plausibly promote) it is possible that the lowest ever CD4 count is itself an outcome of insurance coverage. It’s a big no-no to include an outcome as a control variable. So, the authors need to make an argument that including lowest ever CD4 count is OK. They didn’t, and I don’t know enough about AIDS to make the argument for them.

* If you’re already puzzled, stop right here and go read some of my posts on IV and/or Steve Pizer’s tutorial paper. I am not exaggerating by suggesting that anyone who wants to understand research in social science and particularly anyone who is going to interpret that research for a wider audience really ought to take the time to understand the issues pertaining to IV, why it is used, and why many (though not all) observational studies that do not consider and deal with those issues are potentially flawed.

This suggests I’ve not made something clear. I’ll try again. Aaron Carroll said it well.

A lot of it shows that people with private insurance do better than those with public insurance or those without insurance; that should not be a surprise.  Most people (and most of your docs) would rather have private insurance than Medicaid.  But would you really rather have no insurance than Medicaid?  If so, that is everyone’s right.  Don’t get the Medicaid.

He’s right. Medicaid is not mandatory.

Let’s dig deeper. You’re uninsured. Thus, with your own money you can select and pay for whatever care you can afford. You get the quality of care you choose to buy with the resources you have. That’s your lot.

You wake up one day in possession of a magic (or maybe evil) Medicaid card. Let’s presume it permits you to visit for no charge a small number of lower quality health care providers. You can still choose to leave the card at home and visit any provider you could before and pay the same prices you had been paying.

Will the quality of care you receive go up or down with the possession of the Medicaid card? This is an empirical question, but let’s first explore the theoretical possibilities. If it helps, replace “care” above with some other type of good like “food” or “clothes” and “Medicaid card” with a “discount card.”

Consider the options. If quality goes up (your outcomes improve) then we would believe that reducing your out-of-pocket price of care, even for lower quality providers, improves outcomes. We’d say, “Medicaid works!”

On the other hand, if quality goes down (your outcomes get worse) what can we say? What causes this? My best explanation would be that you are such a poor judge of your health care needs that you are seduced by lower out-of-pocket cost, Medicaid care and are harmed by its lower quality. Having access to cheap care induces you to use more care and more low quality care. Making care cheaper, but only for certain providers, actually makes outcomes worse. You’d be better off with no insurance because it imposes resource constraints causing you to receive less care overall and thereby avoid the low quality care offered by providers accepting Medicaid. (Do you believe this explanation? Can you suggest a better one without appealing to selection bias (I’m getting to that)?)

I bet you’d say, “Oh no, not me. I’m smarter than the typical Medicaid beneficiary. I would know not to get more care and, above all, to avoid low quality providers. I would not be seduced by the discount the Medicaid card provides.”

To say that suggests that there is something about the Medicaid population that is different from you. I bet you think you’re different than the typical uninsured individual too (provided you aren’t one). By the same token, it is reasonable to presume there are differences between Medicaid and uninsured populations as well. Some are observable and can be controlled for in a multivariate analysis. Some are not, requiring an instrumental variable analysis, exploitation of a natural experiment, or a randomized trial to obtain unbiased results.  Note that the relevant differences pertain to individual characteristics, not those of the providers they visit. The selection of providers is an effect of the Medicaid discount (again, assuming you don’t know enough about your health care needs to make more informed decisions).

There are undoubtedly studies that consider Medicaid vs. uninsured outcomes using the random variations provided by the natural experiment that is Medicaid. Characteristics of the program vary by state and year, making it a perfect set-up for such an analysis of this issue. This second I can’t point to a study. But I know where to look. One place to start would be to examine the literature cited by Stan Dorn on Ezra Klein’s blog at the Washington Post (tinyurl.com/StanDorn), Harold Pollack on The New Republic’s The Treatment blog (tinyurl.com/HPollack), and by J. Michael McWilliams on this blog (tinyurl.com/JMMcWill).

That’s it.  That’s my position, and it always has been. If you read carefully you ought to notice that I didn’t actually condemn or praise Medicaid. I didn’t actually say how it should be reformed. I just listed the possibilities. Which you believe depends on a combination of your personal views and your interpretation of the literature. What can actually happen depends on political forces so strong my opinion hardly matters.

Aaron Carroll has some wise words to keep in mind when interpreting studies of the Medicaid or uninsured populations:

Insurance doesn’t equal care. Insurance can affect how likely you are to get care and how quickly you might get it. But any study that looks at insurance has to adjust for many, many other variables in order to get the true effect of insurance. …

Surgery is different than other types of care (like emergency care) in that it is harder to refuse. So it may be that the uninsured are getting care on a compassionate basis. Few would provide a screening mammogram or yearly colonoscopy to someone uninsured, however, and you would get that with Medicaid.

He also cautions against taking conference abstracts too seriously:

[L]ess than 45%of the research presented [at the 1998 and 1999 Pediatric Academic Society meetings] was published in a peer-reviewed journal in the next four to five years. So over half of what was presented at the meeting never was “really” published.

I’m not saying the results … aren’t valid. I’m saying I can’t tell. And neither can you, without more information. The peer review for a meeting just isn’t the same as for full publication. You have less time, different criteria, and almost nothing by which to judge the work. Ideally, meetings would stop publicizing abstracts as if they were full studies, but neither they, nor the press, seem likely to do so.

So, be wary of conference abstracts. Actually, be wary of peer-reviewed publications too. The strongest conclusions are based on a body of work collected in an unbiased fashion. It’s not uncommon for papers to draw conflicting conclusions. But if an overwhelming majority of papers in an area point in the same direction then it’s reasonable to think they’re on to something. Of course, methodological technique matters. It is possible that scholars are all doing something wrong and only a few recent papers actually get it right. So, it is no easy task to interpret the academic literature.

That’s a good segue to this interesting set of results from a recent paper in the Journal of Hospital Medicine, “Insurance status and hospital care for myocardial infarction, stroke, and pneumonia,” by Omar Hasan, E. John Orav, and LeRoi S. Hicks. Click on the following figure to enlarge and study it carefully.

Ignore model 2. Model 1 is adjusted for age group, sex, race, income, emergency admission, and weekend admission and for hospitals’ bed size, control, region, and teaching status. That sounds like a lot of risk adjustment. But model 3 has a lot more. It also adjusts for comorbidities, severity of principal diagnosis, and the proportion of uninsured and Medicaid patients in each hospital.

Now take a close look at the first set of results for acute myocardial infarction. The relative sizes of the odds ratio of uninsured vs privately insured and Medicaid vs. privately insured switch between models 1 and 3. A lot of the other results change quite a bit between the two models too. There are two lessons here. One is that risk adjustment really matters. The other is that we can’t be certain that even model 3 has enough. There may be unobservable characteristics that bias the results.

The only way to fully address the bias due to selection into the Medicaid, uninsured, and privately insured groups is to find a source of exogenous random variation. That could be supplied by a randomized trial or with sound instrumental variables techniques.

Another thing to note is that in the results presented, Medicaid patients have better mortality outcomes than the uninsured for stroke and AMI (model 3). So, from this study alone, one might be tempted to conclude that Medicaid isn’t as bad as some other studies may suggest. But again, should we make a big deal out of this cherry-picked study? No, we certainly should not.

Sometimes when I critique an element of a broader set of proposals or raise a question about some research it is mistaken for a wholesale dismissal of those proposals or a rejection of the research. If that’s my aim, I’ll say so. If I raise a narrow point or ask a pointed question, that’s my point, that’s my question.

Sometimes people think I’m engaged in an ideological fight. I’m not. I’m continually trying to understand our health system, how it works (and doesn’t ), for whom it works (and doesn’t), and how it might be improved. I don’t claim to have all the answers. Anyone who does is deluded or selling something. I also don’t think there is a unique solution to every problem.

I say all this because it relates to my recent blog-to-blog conversation with Avik Roy (his three posts, my two), on which some of my readers have commented. He responds in a new post to my comments and to those of my readers (yes, he quotes you guys). Avik finds it odd that I might want to spend more money on Medicaid. Of course I don’t want to spend more. I want everyone to have access to affordable, quality health care. The question is how to do it, and especially for those with low incomes and, in some cases, serious health needs. More to the point, how do we make things better for folks with politically realistic ideas and sooner rather than later (we’re talking death here)?

I will confess, I don’t know how to do it. If the literature Avik cited is to be believed, we can better assist Medicaid beneficiaries by making Medicaid more like no insurance at all. We can assist them even more by making it more like private insurance. That’s the unavoidable conclusion if one believes the studies. (The other interpretation is that they failed to sufficiently control for all the relevant differences between the three populations–the uninsured, Medicaid, and the privately insured. But I am not claiming that, I’m just mentioning it as a possibility. I haven’t read the studies, nor done a comprehensive literature review.)

Avik has another idea, or is it really different than either of the options I suggested?

I instead favor, as a start, what Mitch Daniels has accomplished with the Medicaid program in Indiana (before PPACA destroys it): subsidized health savings accounts combined with consumer-driven health plans. And Indiana covers people at up to 200% of the poverty line, compared to Obamacare’s 133%. Instead of covering more people, I would more heavily subsidize those at or below the poverty line, in order to bring Medicaid’s low physician payments in line with those of the private sector. Ideally, we would move to a modified version of the Swiss model, in which everyone purchases consumer-driven plans in the individual market, with graduated subsidies for lower-income households. (Bold mine.)

This sounds a lot like making Medicaid more like private insurance in terms of what it pays providers (that’s gonna cost something though!). I have no doubt that something more like health savings accounts (in general, higher cost sharing) is in our future. The Swiss model may well work and it is the direction we’re going elsewhere in the system, just not for Medicare and Medicaid (yet).

As for the HSAs in Medicaid, here’s the guts of it from the source Avik cites,

Healthy Indiana establishes a Personal Wellness and Responsibility (POWER) account valued at $1,100 per adult to pay for initial medical costs. This is similar to a health savings account (HSA) and is used to fund the deductible. The state pays 95 percent and individuals pay the rest.

Is this a money saver? Does it lead to better outcomes? How do Medicaid beneficiaries use that $1,100? I don’t know the literature on it so I’m asking honest questions here.

Avik goes on to write sentences I cannot disagree with.

Ultimately, the goal should be to minimize the number of people who require subsidized insurance. This requires comprehensive health reform aimed at reducing the cost of health care: de-linking employment from insurance; broadening the reach of consumer-driven care; creating a national insurance market; aggressive antitrust enforcement against providers; medical tourism; transparency; malpractice reform; and Medicare reform.

Which of these can pass Congress and in what form? That’s another serious question. I have argued that the ACA is about as much reform as the politics would allow. I want more. So does Avik. Get cracking legislators!

Just in case folks don’t read comments, here are some good ones by steve and another by Jay related to my recent posts about Medicaid coverage, outcomes, and policy implications. In particular steve asks some great questions:

I would bet almost anything that they do not look at all of the social factors that would contribute to worse outcomes for a Medicaid population. Docs dont generally look for those. If you are uninsured, how long have you been uninsured? Are you working if you are uninsured? What is the functional capability of someone on Medicaid not working vs someone uninsured who is working? Which group is more likely to have communication problems? Which group is more likely to give a better history? Which group is more likely to get family support? Which group is staff more likely to dislike?

I look forward to some answers.

Avik Roy responds to my post. He lists several more studies that find Medicaid patients have far worse outcomes than privately insured ones and the uninsured too. I’m not going to undertake a literature review. I don’t have time. So, I’m not really debating the merits of the studies Avik Roy cites or whether they are representative of the entire body of work in this area.

So, let’s presume they are credible and representative, then what is the implication? Should we make Medicaid more like private insurance or more like no insurance? Should we Federalize the program?

I believe that everyone should have access to affordable insurance that facilitates access to affordable, high-quality care. (I’m happy to skip the insurance part and just get everyone access to the care, but that’s not how our system works right now.) If Medicaid doesn’t fill that role for low-income individuals, some of whom are very sick and/or disabled, then it should be reformed. That probably means spending more money on it.

I’m not getting the sense that’s what Roy has in mind. He writes that “most people can afford to take on more responsibility for their own care, and indeed would be far better off doing so.” That sounds like he wants to make Medicaid more like no insurance.

Why is it that folks on Medicaid don’t supplement it with their own spending? If they do, why is that supplementation not sufficient for them to have better outcomes than the uninsured? One has to answer those question in a way that doesn’t also imply that if they didn’t have Medicaid and were uninsured, they’d spend their money more wisely and achieve better results than they can with the support of Medicaid.

Is Medicaid forcing them to receive worse care? That suggests severe information asymmetry. How does being uninsured fix that problem?