Insurance status and hospital care for myocardial infarction, stroke, and pneumonia, by Omar Hasan, E. John Orav, and LeRoi S. Hicks

Despite widely documented variations in health care outcomes by insurance status, few nationally representative studies have examined such disparities in the inpatient setting. [Our objective is to] determine whether there are insurance-related differences in hospital care for 3 common medical conditions. … For each diagnosis, we compared in-hospital mortality, length of stay (LOS), and cost per hospitalization for Medicaid and uninsured patients with the privately insured. Compared with the privately insured, in-hospital mortality among AMI and stroke patients was significantly higher for the uninsured (adjusted odds ratio [OR] 1.52, 95% confidence interval [CI] [1.24-1.85] for AMI and 1.49 [1.29-1.72] for stroke) and among pneumonia patients was significantly higher for Medicaid recipients (1.21 [1.01-1.45]). Excluding patients who died during hospitalization, LOS was consistently longer for Medicaid recipients for all 3 conditions (adjusted ratio 1.07, 95% CI [1.05-1.09] for AMI, 1.17 [1.14-1.20] for stroke, and 1.04 [1.03-1.06] for pneumonia), although costs were significantly higher for Medicaid recipients for only 2 of the 3 conditions (adjusted ratio 1.06, 95% CI [1.04-1.09] for stroke and 1.05 [1.04-1.07] for pneumonia). … Americans hospitalized for 3 common medical conditions, significantly lower in-hospital mortality was noted for privately insured patients compared with the uninsured or Medicaid recipients. Interventions to reduce insurance-related gaps in inpatient quality of care should be investigated.

Regression Discontinuity Designs in Economics, by David S. Lee and Thomas Lemieux

This paper provides an introduction and “user guide” to Regression Discontinuity (RD) designs for empirical researchers. It presents the basic theory behind the research design, details when RD is likely to be valid or invalid given economic incentives, explains why it is considered a “quasi-experimental” design, and summarizes different ways (with their advantages and disadvantages) of estimating RD designs and the limitations of interpreting these estimates. Concepts are discussed using examples drawn from the growing body of empirical research using RD.

Building Bridges between Structural and Program Evaluation Approaches to Evaluating Policy, by James J. Heckman

This paper compares the structural approach to economic policy analysis with the program evaluation approach. It offers a third way to do policy analysis that combines the best features of both approaches. I illustrate the value of this alternative approach by making the implicit economics of LATE explicit, thereby extending the interpretability and range of policy questions that LATE can answer.

Better LATE Than Nothing: Some Comments on Deaton (2009) and Heckman and Urzua (2009), by Guido W. Imbens

Two recent papers, Deaton (2009) and Heckman and Urzua (2009), argue against what they see as an excessive and inappropriate use of experimental and quasi-experimental methods in empirical work in economics in the last decade. They specifically question the increased use of instrumental variables and natural experiments in labor economics and of randomized experiments in development economics. In these comments, I will make the case that this move toward shoring up the internal validity of estimates, and toward clarifying the description of the population these estimates are relevant for, has been important and beneficial in increasing the credibility of empirical work in economics. I also address some other concerns raised by the Deaton and Heckman-Urzua papers.

Instruments, Randomization, and Learning about Development, by Angus Deaton

There is currently much debate about the effectiveness of foreign aid and about what kind of projects can engender economic development. There is skepticism about the ability of econometric analysis to resolve these issues or of development agencies to learn from their own experience. In response, there is increasing use in development economics of randomized controlled trials (RCTs) to accumulate credible knowledge of what works, without overreliance on questionable theory or statistical methods. When RCTs are not possible, the proponents of these methods advocate quasi-randomization through instrumental variable (IV) techniques or natural experiments. I argue that many of these applications are unlikely to recover quantities that are useful for policy or understanding: two key issues are the misunderstanding of exogeneity and the handling of heterogeneity. I illustrate from the literature on aid and growth. Actual randomization faces similar problems as does quasi-randomization, notwithstanding rhetoric to the contrary. I argue that experiments have no special ability to produce more credible knowledge than other methods, and that actual experiments are frequently subject to practical problems that undermine any claims to statistical or epistemic superiority. I illustrate using prominent experiments in development and elsewhere. As with IV methods, RCT-based evaluation of projects, without guidance from an understanding of underlying mechanisms, is unlikely to lead to scientific progress in the understanding of economic development. I welcome recent trends in development experimentation away from the evaluation of projects and toward the evaluation of theoretical mechanisms.

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Witness results from the Boston Area Community Health (BACH) survey, as presented by John McKinlay and Carol Link at this year’s AcademyHealth Annual Research Meeting. The BACH is a survey of a representative sample of Boston residents ages 30-79. (In the figure below, region between each adjacent pair of vertical bars is 20 percentage points.)

The bars illustrate how health insurance coverage changed with health reform in Massachusetts. Though Hispanics experienced the greatest gains in coverage (15.3 percentage points), they are still the group least likely to be covered (10.9% uninsured). Why?

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In Massachusetts, the individual mandate requiring state residents to buy health insurance is working. Yet, a similar requirement remains among the more controversial elements of the new national health reform law. Opponents of the mandate resent being required to purchase a product they may not want. Proponents claim that the mandate is necessary to prevent an unraveling of the broader set of reforms in the law. But will it work? It is in Massachusetts, and that should give reform advocates some confidence.

First of all, what does it mean for the mandate to “work?” The purpose of the mandate is to counter a potential threat to health insurers’ stability when they are required to accept all comers, even those with preexisting conditions. If individuals have access to insurance coverage whenever they please but are not required to have it all year, some will choose to enroll only when sick and then drop coverage when healthy. If too many people were to do just that, then insurers would be on the hook for more health care expenses than they could cover with collected premiums.

No insurer could survive a sufficiently severe level of such “adverse selection” (policyholders’ health care costing much more than their collective premiums can cover), and the individual mandate is designed to ensure that they won’t face one. Requiring individuals to purchase coverage–and pay premiums–even when they’re healthy guarantees that insurers have sufficient funds to cover the claims of the sick.

The individual mandate is working in Massachusetts because it is preventing a destabilizing level of adverse selection. Although there are individuals gaming the system in the state—by waiting to purchase insurance until they need it–the overall coverage rate is high (about 96% insured) and the associated degree of adverse selection is very low.

In a recent report released by the Massachusetts Division of Insurance, actuaries estimated that part-year insurance purchasing in Massachusetts’ combined individual and small group market increased premiums by 0.5 percent to 1.5 percent. Based on an average individual premium in Massachusetts of about $5,000 per year, that translates into an annual premium increase of $25 to $75, far too low to have a major impact on the market. Insurance companies can pass that level of premium increase on to consumers without many of them dropping coverage.

This is good news for Massachusetts and for the country. The penalty for lack of compliance with the state’s mandate is slightly lower on average than what the fully phased-in penalties will be (in 2016) under the new national health reform law. (I’ve estimated them to be about $674 per person per year under the national law and $537 under Massachusetts law.) Thus, all else being equal, individual mandate compliance ought to be at least as high under the new law as it is in Massachusetts. The results seen in the state imply that little gaming should be expected nationally.

Still, one should not be too complacent. Not every state is like Massachusetts (as some might rejoice). It is possible that individuals in conservative states where the mandate is not popular would be more likely to make short-term insurance purchases. However, the new health reform law has one thing that the Massachusetts reforms lack, the ability for insurance exchanges to impose open enrollment periods, something Massachusetts Gov. Deval Patrick (D) and Massachusetts Senate President Therese Murray (D) both advocate for their state’s version of the exchange.

Year-round access to insurance facilitates gaming the system, so limiting access to certain months should reduce it. It may also reduce levels of coverage overall, so there is a catch. Another perfectly reasonable reform that avoids this trade-off would be to increase the penalties for non-compliance.

No doubt tweaks to the Massachusetts and the national law such as these will be made. But it is reassuring that they’re just that: tweaks. The fundamental structure of both laws and the role of the individual mandate they include appear to be sound. It’s working in Massachusetts. That’s good news for all of us.

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In 2006 San Francisco adopted major health reform, becoming the first city to implement a pay-or-play employer health spending mandate. It also created Healthy San Francisco, a “public option” to promote affordable universal access to care. Using the 2008 Bay Area Employer Health Benefits Survey, we find that most employers (75%) had to increase health spending to comply with the law, yet most (64%) are supportive of the law. There is substantial employer demand for the public option, with 21% of firms using Healthy San Francisco for at least some employees, yet there is little evidence of firms dropping existing insurance offerings in the first year after implementation.

A Review of War Costs in Iraq and Afghanistan, by Ryan D. Edwards

As of this writing, the wars in Iraq and Afghanistan are in their eighth and tenth years, having accrued nearly a trillion dollars in direct military costs. I review the history of cost forecasts for these ongoing engagements, highlighting the differences across them in scope and accuracy, assessing the methods and practice of cost forecasting, and exploring the implications of the war costs themselves. Besides the unanticipated length and breadth of the military conflicts themselves, a related and equally important component of costs is the life cycle of costs associated with caring for veterans. The forecasts we have of such costs imply high levels of public spending per veteran and very high levels of costs associated with pain and suffering per veteran, as high as 10 to 25 percent of lifetime wealth. I also discuss the methods and motivations associated with war cost forecasts by comparing them with other types of aggregate forecasts, which are prone to similar types of errors. The history of war cost forecasts suggests that increasing their frequency and transparency may improve their usefulness in guiding policy.

What if the Federal Government Negotiated Pharmaceutical Prices for Seniors? An Estimate of National Savings, by Walid F. Gellad, Sebastian Schneeweiss, Phyllis Brawarsky, Stuart Lipsitz and Jennifer S. Haas

The government is prohibited from directly negotiating drug prices for Medicare Part D, resulting in substantial policy debate. However, the government has an established mechanism for setting prices with pharmaceutical manufacturers for certain other federal programs – the Federal Supply Schedule (FSS). [H]ow much could be saved nationwide if prices equivalent to the 2006 FSS were achieved for the top 200 drug formulations dispensed to seniors[?] … The potential annual savings with FSS prices would be $21.9 billion [95% confidence interval (CI), $21.1 billion to $22.8 billion]. If FSS prices were substituted for only the top ten drugs, the annual savings would be $5.9 billion (95% CI, $5.7 billion, $6.1 billion). … Extension of existing price setting mechanisms to Medicare could save tens of billions of dollars if prices similar to those already achieved by other federal programs could be reached. Whether or not this is a political or economic possibility, the magnitude of these savings cannot be ignored.

Provider networks and primary-care signups: Do they restrict the use of medical services? by Partha Deb and Pravin Trivedi

This article analyzes the effect of gatekeeper and network restrictions on use of health-care services using simulation-based estimation methods. Data from the Community Tracking Survey (1996–1997) show significant evidence of selection into plans with gatekeeper and/or network restrictions. Enrollees in plans with networks of physicians have fewer office-based visits to non-physician medical professionals, but more emergency room visits and hospital stays. Individuals in plans that require signups with a primary-care provider have more visits to nonphysician providers of care, more surgeries and hospital stays but substantially fewer emergency room visits. Enrollees of plans that do not pay for out-of-network services have more office-based and emergency room visits, but less surgeries and hospitalizations.

Imperfect information in a quality-competitive hospital market, by Hugh Gravellea and Peter Sivey

We examine the implications of policies to improve information about the qualities of profit-seeking duopoly hospitals which face the same regulated price and compete on quality. We show that if hospital costs of quality are similar then better information increases the quality of both hospitals. However, if the costs are sufficiently different improved information will reduce the quality of both hospitals. Moreover, even when quality increases, better information may increase or decrease patient welfare depending on whether an ex post or ex ante view of welfare is taken.

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The individual mandate is working in Massachusetts because it is preventing a destabilizing level of adverse selection. Although there are individuals gaming the system in the state—by waiting to purchase insurance until they need it–the overall coverage rate is high (about 96% insured) and the associated degree of adverse selection is very low.

In a recent report released by the Massachusetts Division of Insurance, actuaries estimated that part-year insurance purchasing in Massachusetts’ combined individual and small group market increased premiums by 0.5 percent to 1.5 percent. Based on an average individual premium in Massachusetts of about $5,000 per year, that translates into an annual premium increase of $25 to $75, far too low to have a major impact on the market. Insurance companies can pass that level of premium increase on to consumers without many of them dropping coverage.

Read the rest here.

Just to make it clear, the purpose of the individual mandate is to protect insurers and full-year premium paying consumers from severe adverse selection that would substantially increase premiums and destabilize the market. Even though there is some “gaming” of the system in Massachusetts, it’s not leading to these potential problems.

Based on this, there is reason to think gaming won’t be an issue with the national mandate. First, the ACA’s penalties for lack of compliance with the mandate are actually higher than Massachusetts‘. Second, exchanges will have open enrollment periods, which doesn’t exist for the Massachusetts version of an exchange right now. There are, of course, differences between Massachusetts and other states that may cause results to vary.

Note that I am not saying that everything about the health care system in Massachusetts is wonderful. The Bay State still has a health care cost problem and no agreed upon solution to it, for example. (For a particularly pessimistic view that highlights other issues, see today’s other KHN column by Grace-Marie Turner.) Nevertheless, the individual mandate is functioning as designed. That should give us hope that it can work well elsewhere, though it doesn’t guarantee that it will.

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The McKinsey report also tells us what’s driving that growth. The authors break it out into five components, but I think it’s really just four:

1. Growth in provider capacity (outpatient care generates big profits, encouraging the provision of more of it),
2. Information asymmetry (providers can sell more than is necessary, induce demand),
3. Technological innovation (newer means more expensive),
4. Price-insensitive patients (out-of-pocket costs are low).

Assuming for the moment those are the main drivers of cost, any approach to reining in cost growth must address them. Thus, broadly speaking, the following is what must be done.

1. Restrain capacity. This will happen naturally if margins for outpatient care go down. The following ideas should help.
2. Stop paying for volume. Use capitation and quality/cost incentives.
3. Evaluate new technology with comparative effectiveness research. Implement value-based insurance designs so consumers pay more out-of-pocket for treatments that are of lower value relative to cost.
4. In general, raise out-of-pocket costs but in ways that are sensitive to income and value. That is cost-sharing should be lower for lower income individuals and should be lower for cheaper and more effective treatments (again, value-based insurance design).

None of these ideas are new, and I’m not saying any of them are easy to implement. I’m just saying we’ve identified the problem and solutions exist. Someday we’ll put them together.

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Jason concludes,

In fact, the authors note the following: “If anything, estimates suggested that gaining prescription drug insurance was associated with worsening health.” Could this be because some physicians overprescribe drugs which could actually worsen outcomes? Is functional status and self-reported health sufficiently precise to capture the benefit of these drugs? Are the benefits of pharmaceuticals generally confined to short-run benefits?

Another possibility is that it isn’t the increased drug utilization that is producing this effect, it’s the decrease in outpatient utilization, which the authors also find (or did they control for that in the outcome model?). Suffice it to say, this begs quite a few questions similar to these, does it not?

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At first I was amazed to see that outpatient care represents such a large portion of total spending (41%) and a huge fraction of spending above what would be expected given the nation’s wealth (68%). By comparison, inpatient care is a meager 22% of total spending. Wow! Perhaps hospitals are not to blame for the vast majority of health spending after all. But something didn’t make sense …

Don’t be fooled. “Outpatient” doesn’t mean “non-hospital” and “inpatient” doesn’t represent all that occurs in a hospital. According to a 2009 Health Affairs press release, hospital-based spending accounted for $1.4 trillion of the $2.4 trillion in 2008 U.S. health spending (58%). So a whole lot of stuff is happening in hospitals. In fact, the Jenson/Mendonca article tells us that one of “[t]he two largest categories of outpatient spending [is] same-day hospital care.”

The report upon which the Jenson/Mendonca article is based has a nice figure that illustrates this:

Yes, a lot is going on in hospitals. But physicians offices are also a major source of excess spending. (By the way, that report and the accompanying figures are loaded with interesting descriptive analysis.)

The article goes on to tell us why outpatient care costs so much. It’s not the volume of visits. It’s per visit costs.

Per visit cost shave been surging due to more care being delivered in each visit, a shift toward more complex and expensive procedures including CT and MRI scans, and absolute price increases for equivalent procedures. A marked shift in visits from general practitioners to specialists is also likely playing a role in boosting costs.

Fee-for-service payment arrangements, which predominate the health care industry, are one of the major factors driving the increased service intensity.

So, it may still be fair to call hospitals the 800 pound gorilla. But a more nuanced view is that it is outpatient care, much of it in hospital settings, that is the real cost issue. However, since outpatient care is both highly profitable and relatively easy to peal away from hospitals, the future of cost control may not be justifiably focused on hospitals.

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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.

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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. [summary]

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

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. [summary]

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. [summary]

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

6. 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. [summary]

7. Frakt AB, Pizer SD, and Wrobel MV, “High Risk Pools for Uninsurable Individuals: Recent Growth, Future Prospects,” Health Care Financing Review, 26(2) (Winter 2004):73-87. [summary]

8. Frakt AB, Pizer SD, Schmitz R, and Mattke S, “Partial Capitation in the CNO Medicare Demonstration,” Health Care Financing Review, 26(4) (Summer 2005): 21-37. [summary]

9. Pizer SD, Feldman R, and Frakt AB, “Defective Design: Regional Competition in Medicare,” Health Affairs Web Exclusive, (August 23, 2005): w5-399-411. [summary]

10. Frakt AB and Pizer SD, “A First Look at the New Medicare Prescription Drug Plans,” Health Affairs Web Exclusive (May 23, 2006): w252-w261. [summary]

11. Frakt AB and Pizer SD, “Attribute Substitution in Early Enrollment Decisions into Medicare Prescription Drug Plans,” Health Economics, 17(4) (April 2008): 513-521. [summary]

12. Pizer SD, Frakt AB, and Feldman R, “Predicting Risk Selection Following Major Changes in Medicare,” Health Economics, 17(4) (April 2008): 453 – 468. [summary]

13. Frakt AB, Pizer SD, and Hendricks AM, “Controlling Prescription Drug Costs: Regulation and the Role of Interest Groups in Medicare and the Veterans Health Administration,” Journal of Health Politics, Policy and Law, 33(6) (December 2008). [summary]

14. Pizer SD, Frakt AB, and Feldman R, “Nothing for Something? Estimating Cost and Value for Beneficiaries from Recent Medicare Spending Increases on HMO Payments and Drug Benefits,” International Journal of Healthcare Finance and Economics, 9(1) (March 2009):59-81. [mentioned in many posts: 1, 2, 3, 4]

15. Frakt AB and Pizer SD, “Beneficiary Price Sensitivity in the Medicare Prescription Drug Plan Market,” Health Economics 19(1) (January 2010). [summary]

16. Frakt AB, Pizer SD, and Feldman R, “Payment Reduction and Medicare Private Fee for Service,” Health Care Financing Review, 30(3), Spring 2009. [summary]

17. Pizer SD, Frakt AB, Iezzoni LI, “Uninsured Adults with Chronic Conditions or Disabilities: Gaps in Public Insurance Programs,” Health Affairs, (online October 2009). [summary]

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