Although we often focus on the Affordable Care Act’s Medicaid expansion and insurance exchanges, it’s important to remember that the majority of people in the United States still get their coverage from employer sponsored health insurance.

To put things in perspective, it’s worth remembering that less than 7 million people, or 2% of Americans, are covered by insurance exchange plans. Even when we hit the CBO projected 25 million people in a few years, that’s still less than 10% of nonelderly people in the United States.

Employer sponsored plans, however, cover more than 150 million people, or almost 60% of the non-elderly US population. They shouldn’t be forgotten when we discuss what’s happening with spending and reform. Recently, the Commonwealth Fund published a report which allows us to discuss what’s going on with such insurance – “National Trends in the Cost of Employer Health Insurance Coverage, 2003-2013”.

Confirming what we’ve seen from Kaiser Family Foundation data, the average family plan cost (in premiums) more than $16,000 in 2013, up 73% from 2003. Single coverage cost, on average, more than $5500, and has gone up 60%.

Many predicted that the regulations that forced family plans to start allowing children to stay on them until age 27 would force premiums to go up more than normal. But, along with overall health care spending, premiums have risen more slowly on average since the ACA was passed (4.1% per year) than before it was passed (5.1% per year). In the last few years, plans at large firms grew more slowly than those at small firms (4.0% versus 4.3%).

A concerning trend, however, has been that even though premiums have been increasing more slowly than before, they are still increased faster than family income. Remember that premiums rose 73% for families in the decade 2003-2013. In that same time period, median family income only rose 16%. That means that while premiums cost families only 15% of their income in 2003, they cost families 23% in 2013.

While most economists would argue that, in the end, employees bear the full costs of their insurance plans, the direct contributions of employees have only increased as well. In 2003, employees contributed 17% of their premium costs; in 2013, this rose to 21%. Such contributions rose from 2% of income to 4% of income over that period.

I recently wrote about underinsurance over at The Upshot, and the contributions that higher deductibles play in that discussion. Even in employer sponsored plans, that applies. Deductibles more than doubled from 2003 to 2013. In fact, in 2003, just over half of employer sponsored plans had a deductible at all. In 2013, that number had risen to more than 80%. The deductible for a family in a small firm averaged $3761 in 2013. In a large firm, it was still $2307.

In 2013, the average deductible for a single person plan was 5% of median income. That’s the definition of underinsurance. That means the average person getting an individual plan from an employer in 2013 was underinsured.

That’s on top of premiums, and also doesn’t include other forms of cost-sharing like copays and co-insurance.

There’s a lot of good news in the reports of recent health care spending slowdowns. There’s also good news in the reduction of the number of Americans who are uninsured. But we shouldn’t neglect the majority of Americans who get their coverage through employer sponsored plans. Many, if not most, are still exposed to large out of pocket spending, and they’re paying a lot of money in premiums. There’s still a lot of work to do to get this under control.




I thought the United Kingdom’s National Institute for Health and Care Excellence (NICE) strictly applied a cost-effectiveness threshold to make its care coverage recommendations for the country’s National Health Service (NHS). Helen Dakin and colleagues explain that the body is actually much more flexible. Nevertheless, it’s hard to fathom anything like it in the United States.

According to the most recent NICE methodology document, the decision to recommend a health technology that has an incremental cost effectiveness ratio (ICER) below £20,000 is normally based on cost-effectiveness. Above that threshold, other criteria are more likely come into play including uncertainty, innovation, non-health outcomes, end-of-life considerations, and stakeholder perspectives on quality of life gains. Technologies aimed at children, disadvantage populations, and severe diseases are also given special consideration.

Thus, it is not the case that every technology with an ICER below £20,000 (or any particular threshold) is covered in the U.K. and that every technology above it is not. Instead, NICE makes each recommendation based on a deliberative, committee process that exercises judgement across a range of criteria rather than applies pre-set rules.

To gain a more nuanced understanding of how NICE tends to apply cost-effectiveness and other criteria, Dakin and colleagues analyzed all 190 technology assessments that were completed by the organization through 2011 and that were informed, at least in part, by cost-effectiveness. (A smaller number of decisions are made without any cost-effectiveness data. These were not analyzed.) Their approach builds on prior work: Devlin and Parkin (2004) “found that cost-effectiveness was the key driver of NICE decisions, although uncertainty and burden of disease were also significant.” Dakin et al. (2006) “found that decisions were influenced by cost-effectiveness, clinical evidence, technology type, and patient group submissions.” Cerri et al. (2014) “found that in addition to cost-effectiveness, demonstration of statistical superiority of the primary endpoint in clinical trials, the number of pharmaceuticals appraised within the same appraisal and the appraisal year were also important.” (All quotes from Dakin et al.)

Not surprisingly, Dakin and colleagues found that the proportion of technologies recommended for approval by NICE decreases with increasing ICER. This is conveyed in their Figure 2, reproduced below (click to enlarge). Decisions are arrayed by ICER, indicated on the horizontal axis (not in a uniform scale). Recommended technologies are colored in blue and rejected ones in red.

ICER thresholds

We estimate that, in practice, the ICER at which the probability switches from more-likely-to accept to more-likely-to-reject is between £39 000 and £44 000: well above the [] £20 000–£30 000 range [typically suggested as a threshold].

This is shown in the authors’ Figure 3, shown below.

icer prob

[Apart from ICER], no other factors besides the type of condition had a significant effect on NICE decisions, although allowing for clinical evidence, alternative treatments, paediatric population, patient group involvement, publication date, type of process[...], orphan status, innovation and uncertainty improved prediction accuracy.

It should be noted that, although the other factors besides ICER did not play a statistically significant role in this analysis of NICE decisions, for any particular decision they could be decisive. The first figure in this post (the authors’ Figure 2) suggests just that.

Though NICE does not strictly apply a cost-effectiveness threshold in its decision-making process, cost-effectiveness is clearly a predominant consideration. That’s much less common in the United States (though not completely foreign), and certainly not a factor in Medicare coverage decisions. Though some economists would be happy to see cost-effectiveness more routinely considered by insurers and consumers in the U.S., David Cutler, for one, does not think it’s necessary at this time.

[T]he most important rule of health care management is this: never put care providers in a position of denying care for financial reasons.

Instead, Cutler recommends we focus on reducing purely wasteful (zero value care) first. Typically, such care arises from the application to an overly broad population of therapies that are valuable to only a subset. A classic case is the prescribing of antibiotics (which are of high value, when used appropriately) to patients with viral infections. There are many others.

As a normative matter, whether Cutler’s view is right is debatable. Nevertheless, I believe he is right to imply that cost-effectiveness is not going to be a pervasive coverage consideration in the United States for some time. Even statutorily established health care evaluative bodies that are circumscribed in doing so, like the Independent Payment Advisory Board or the Patient-Centered Outcomes Research Institute, are controversial. Notwithstanding the “wiggle room” NICE has in applying criteria other than cost-effectiveness, something like it for U.S. Medicare (and more) is hard to fathom.

Austin B. Frakt, PhD, is a health economist with the Department of Veterans Affairs and an associate professor at Boston University’s School of Medicine and School of Public Health. He blogs about health economics and policy at The Incidental Economist and tweets at @afrakt. The views expressed in this post are that of the author and do not necessarily reflect the position of the Department of Veterans Affairs or Boston University.

[Editors note: AcademyHealth also recently released an report on international perspectives on comparative effectiveness analysis and quality improvement. That report can be found here.]




In 2013, Acaroadmap_flashdemyHealth’s Translation and Dissemination Institute launched its first major activity, a “Listening Project” aimed at identifying the most pressing health services research needs of leaders in health policy and health care delivery for the coming three to five years. Its goal was, and is, to foster greater interaction among the producers, users, and funders of health services research to spur the development and use of more relevant and timely evidence. As such, it supports AcademyHealth’s work to improve health and health care by generating new knowledge and moving that knowledge into policy and practice.

The first Listening Project report, published in February 2014, focused on the questions facing Medicare policymakers and identified research and data needs around three themes:

  • Research is needed to address new organizational structures like Accountable Care Organizations and consolidated markets, and to address persistent issues like health care costs.
  • Data gaps impede the study of health care quality, Medicare Advantage and physician practice.
  • The way research is conducted is changing; there’s a need to better understand the promise and potential pitfalls of electronic data, rapid-cycle research, and comparative effectiveness research.

The report also offered advice to the community of health services researchers producing evidence on Medicare, including a call to better understand the impact of politics, role of relationships, and the need for timely evidence and information.

In response to what we heard from Medicare policymakers, AcademyHealth today released a series of Evidence Roadmaps that identify existing resources related to the identified gaps. These Roadmaps represent a selected, minimal set of key resources rather than a comprehensive list of relevant research. As such, the Roadmaps are intended to help policy analysts and other research users better understand whether a perceived research gap represents an actual lack of evidence or failure of existing evidence to reach the policy arena, that is, a failure of adequate translation and dissemination.

For the Medicare project, we found at least some existing data and literature to support nine Roadmaps, which include a mix of data sources, systematic reviews, individual studies, grey literature, and ongoing research. While the evidence presented in the Roadmaps supports our hypothesis that the field of health services research can do to more get existing research results to policymakers, they also confirm the Listening Project report’s conclusion that Medicare policy questions offer important opportunities for researchers to contribute new evidence to the policy process.

The series of Roadmaps offering a glimpse of what is currently known about Medicare includes:

  1. Understanding Medicare Cost Growth
  2. Care Coordination
  3. Medicare ACOs and the Health Care Marketplace
  4. Medicare Beneficiary Behavior and Decision Making
  5. End-of-Life Care and Medicare’s Hospice Benefit
  6. Changes in Physician Practice
  7. Medicare Trends in Specialty Drug Costs and Use
  8. Medicare Advantage
  9. The Internal Workings of Medicare Accountable Care Organizations

Going forward, we intend this body of work – the Listening Project reports and follow-on Roadmaps – to offer a platform for linking policymaker needs to available evidence, and a resource for the community of evidence producers to identify potentially relevant research questions. In the coming weeks, look for a new Listening Project report that the Institute has undertaken with support from the Robert Wood Johnson Foundation and the Medicaid and Chip Access and Payment Commission (MACPAC) focused on the research needs of Medicaid policymakers.

You can review the Roadmaps and learn more on our website.



[Editors Note: Elizabeth McGlynn, Kaiser Permanente, Timothy Ferris, Partners HealthCare, Robert Galvin, The Blackstone Group, Rebekah Gee, Louisiana Department of Health and Hospitals, and Eve Kerr, University of Michigan/VA Ann Arbor Healthcare System, will discuss innovative approaches to quality measurement, leveraging new sources and methods of acquiring data, and harmonizing measurement efforts across payers and organizations at the 2015 AcademyHealth National Health Policy Conference on February 9, 2015. Learn more and register here. Online registration ends January 30.]

I have written many times before, both here and in other media, about how pay for performance is failing to live up to its promise. That doesn’t mean that we can’t identify areas where things could be improved. Two recent Perspectives pieces in the NEJM can help us with this effort.

The first, “Getting More Performance from Performance Measurement”, identifies some important barriers to success:

Many observers fear that a proliferation of measures is leading to measurement fatigue without commensurate results. An analysis of 48 state and regional measure sets found that they included more than 500 different measures, only 20% of which were used by more than one program. Similarly, a study of 29 private health plans identified approximately 550 distinct measures, which overlapped little with the measures used by public programs.

There are so many metrics that systems must measure that they must devote huge numbers of resources to gathering them. Moreover, as we’ve discussed before, many of those metrics are not associated with actual outcomes. Therefore, by definition, collecting many of them may be wasteful.

Moreover, some metrics can backfire:

One example of a measurement effort that had unintended consequences was the CMS quality measure for community-acquired pneumonia. This metric assessed whether providers administered the first dose of antibiotics to a patient within 6 hours after presentation, since analyses of Medicare databases had shown that an interval exceeding 4 hours was associated with increased in-hospital mortality. But the measure led to inappropriate antibiotic use in patients without community-acquired pneumonia, had adverse consequences such as Clostridium difficile colitis, and did not reduce mortality.

Not all actions are entirely beneficial. In this case, encouraging antibiotics, and making it a quality metric, led to an inappropriate overuse of antibiotics, and even led to an increase in C. diff colitis. The take-home message of this piece is that we should think carefully about metrics. Only those with proven outcome association should be used. Others consume money and time needlessly and can lead to worsening quality.

The second piece, “Reimagining Quality Measurement”, takes this and goes a bit further:

A fruitful alternative approach, in our view, would be guided by three principles: quality measurement should be integrated with care delivery rather than existing as a parallel, separate enterprise; it should acknowledge and address the challenges that confront doctors every day — common and uncommon diseases, patients with multiple coexisting illnesses, and efficient management of symptoms even when diagnosis is uncertain; and it should reflect individual patients’ preferences and goals for treatment and health outcomes and enable ongoing development of evidence on treatment heterogeneity.

The authors argue that first, our quality measurement should be part of patient care, rather than something which must be added to it. This would reduce the resources necessary to implement QI programs and pay for performance systems.

Second, they maintain that metrics should be more cognizant of how medicine is practiced in the real world. They should acknowledge that many patients have a number of problems, with guidelines that overlap and sometimes even conflict with each other. They should also recognize that many patients, and the decisions for their care, aren’t as cut and dry as many guidelines make them to be.

Finally, they ask that metrics reflect patient preferences as well as those the system values. There has been a real thrust in research recently, to recognize that patient preferences and patient-centered outcomes matter. It’s hard not to argue that quality metrics should, too.

These pieces are short, and worth consideration. Go read!





There are many arguments as to why Medicaid is a good thing for children. Many studies have been done comparing outcomes for children with and without Medicaid. Many more have looked at how access to the health care system is different for kids with Medicaid.

But concerns about Medicaid, and arguments about whether to expand it, inevitably devolve to the cost. Implicit in that concern is whether it’s “worth it” to have children on Medicaid. Are the benefits worth the cost? Might they be achieved by more efficient means? Perhaps money put into Medicaid could be used for other things.

Many of these discussions, however, ignore some of the potential long-term return on investment of the program for children. In a recent NBER paper, David Brown, Amanda Kowalski, and Ithai Lurie attempted to get at that question. “Medicaid as an Investment in Children: What is the Long-Term Impact on Tax Receipts?”:

We examine the long-term impact of expansions to Medicaid and the State Children’s Health Insurance Program that occurred in the 1980′s and 1990′s. With administrative data from the IRS, we calculate longitudinal health insurance eligibility from birth to age 18 for children in cohorts affected by these expansions, and we observe their longitudinal outcomes as adults. Using a simulated instrument that relies on variation in eligibility by cohort and state, we find that children whose eligibility increased paid more in cumulative taxes by age 28. These children collected less in EITC payments, and the women had higher cumulative wages by age 28.

Does enrollment in Medicaid or SCHIP have a long-term return on investment? First the researchers used data from the IRS to figure out health insurance eligibility for kids so that they could examine their later outcomes when they were adults. They created a simulation of children with different levels of eligibility and found that kids with more eligibility paid more money in taxes by the time they were 28. That’s a fiscal positive return for the government. They also found that these children took less from the government in terms of EITC payments.

In other words, children eligible for Medicaid or SCHIP were more “productive” members of society. They paid more in taxes and took less in entitlements.

Going further, they calculated that the government spent $872 in 2011 dollars for each year of Medicaid eligibility added in the expansion for kids. They estimated, though, that the government would likely get 56% of this investment returned to them (including discounting) by the time those kids hit 60 years of age.

And that doesn’t take into account changes in mortality or college attendance. If more kids grow up to become earners, and potentially better earners, that’s better for tax receipts, too. And it turns out that increased Medicaid eligibility is associated with both of these things as well. Moreover, not all eligible children make use of the program. Therefore, the ROI per actual beneficiary may be even higher.

Sure, it’s not all returned to the government. But Medicaid and SCHIP aren’t meant to be cost-saving. They’re providing increased access, and hopefully better health outcomes, to children – for a price. But that price may be less than people think, if we account for returns on investment for the programs.




As we await the case to be heard by the Supreme Court as to whether premium tax credits can be provided to consumers purchasing plans on the federal exchange, let’s revisit the role of those tax credits. A RAND report by Christine Eibner and Evan Saltzman, published in October, spells out that role and what would happen without them. (See also this analysis by the Urban Institute.)

Recall that the Affordable Care Act (ACA) offers premium tax credits to consumers enrolled in exchange (aka Marketplace) plans if their incomes are between one and four times the federal poverty level (FPL). At the low end of this income range, consumers contribute between 2 percent of their incomes toward the cost of the second-lowest silver plan, rising to 9.5 percent at the high end. Consumers can pay more if they purchase a more expensive plan or less if they purchase a cheaper one.

The value of such subsidies to individuals is clear: they reduce the price they pay for health insurance, shifting some of it to the federal government. What’s slightly less obvious is that these subsidies are valuable even to people who don’t receive them: the premium tax credits reduce the price of insurance for everyone.

Though true, this is a little weird if you only know enough basic microeconomics to get into trouble. Basic micro teaches us that the provision of a subsidy cannot decrease the price. In fact, to some degree (depending on the slopes of supply and demand curves), it should raise it, along with the quantity bought and sold. This is shown as an increase in quantity from QE to Qand a corresponding increase in price from PE to PS in the figure below, taken from my microeconomics book, coauthored by Mike Piper.


What the figure shows is that, because of the subsidy, though the full transaction price goes up to PS, the consumer only pays a fraction of it: the subsidy reduces the consumer’s price but not the total price received by the producer. What the figure doesn’t quite show is that the full transaction price, PS, is the same for everyone, regardless of how much subsidy received. That’s so unless the supplier of the good can price discriminate based on subsidy (or anything correlated with it, like income). In the market we’re talking about — the individual market for health insurance — such price discrimination is explicitly outlawed by the Affordable Care Act’s modified community rating provisions. (Premiums can vary by age and smoking status, but only with constraints. Insurers aren’t likely to be able to exploit this limited flexibility to effectively price discriminate on the basis of subsidy, if at all.)

Were this an exam, I’d give the foregoing analysis a D. It’s correct for most goods, but not for health insurance. What’s different about health insurance — and left out of the analysis above — is that its cost changes with the composition of purchasers. A health insurance product’s premiums is largely the average health care cost of the risk pool it attracts, plus a bit more for overhead and profit, which we can ignore for our purposes. Rational (or even somewhat rational) consumers compare the premium to their expected costs (those they’d have to pay out-of-pocket without coverage) when deciding to purchase coverage. When premiums go up, healthier individuals with relatively lower expected costs are less likely to buy coverage. (I’m sidestepping the fact that people rationally buy coverage to protect themselves against the risk of catastrophic costs well above those that are expected. But even accounting for that, it’s true that, in general, those with lower expected costs are less likely to buy coverage as premium rises.)

Given this, what the Affordable Care Act’s subsidies do is pull lower expected cost (healthier) consumers into the market. Those eligible for a subsidy are less price sensitive, since their contribution toward premium (at least that of the second-cheapest silver rated plan) is a fixed percentage of their income. As more healthy individuals buy coverage, insurers’ per person costs (claims) come down — the risk pool is said to become more “favorable” — which reduces premiums for everyone in the risk pool, even those who do not receive a subsidy. (For a more thorough, graphical explanation, see this post by Matthew Martin.)

So, what happens when those subsidies go away? The risk pool becomes more “adverse” — it’s comprised of relatively sicker and more costly individuals and fewer people overall. Premiums rise. But how much?

To answer this question, the RAND study authors used a simulation model based on the economics sketched above and nationally representative survey data. They found that eliminating premium tax credits would result in a large decrease in enrollment and a sharp increase in premiums due to adverse selection. Enrollment would fall by 68% and premiums would increase by 43%. What’s likely to happen under these circumstances is that far fewer insurers would offer plans; the market would not support them. Thus, consumers wishing to purchase individual-market products would suffer two kinds of harms: much higher premiums and loss of choice. (Never mind the additional harms of being uninsured, for those who would otherwise wish to be covered.)

Of course, premium tax credits aren’t the only provision that encourages purchase of coverage. So does the individual mandate. However, there is a hardship exemption to that mandate; those for whom the lowest-cost insurance product is above 8% of income are not subject to it. Larry Levitt estimated that 83% of subsidy-eligible individuals would receive such a hardship exemption if subsidies are withdrawn. As premiums increased for everyone, some proportion of those not eligible for subsidies would also be exempt. Thus, though the mandate would still apply, it would apply to fewer people. To the extent that it motivates healthier people to purchase coverage, removal of subsidies has an indirect effect on making the risk pool more adverse.

It is pure speculation how the Supreme Court will rule. What is not speculation is the economic consequences to the new health insurance markets the Affordable Care Act was intended to encourage. They will be large and severe.

Austin B. Frakt, PhD, is a health economist with the Department of Veterans Affairs and an associate professor at Boston University’s School of Medicine and School of Public Health. He blogs about health economics and policy at The Incidental Economist and tweets at @afrakt. The views expressed in this post are that of the author and do not necessarily reflect the position of the Department of Veterans Affairs or Boston University.



The combination of rising health care costs, efforts to achieve universal or near-universal coverage globally, and growing drive for better outcomes brings an urgent demand to spend health care funds efficiently and in accordance with each country’s priorities. A response to such demand requires, first, an understanding of what technologies and interventions (drugs, devices, procedures, diagnostics, and health care services) increase the quality and value of health care and, second, knowledge of the policy levers that encourage health care systems to adopt appropriate technologies. Comparative Effectiveness Research (CER) and Health Technology Assessment (HTA) are important tools used in different ways by countries to achieve these goals.

A new analysis–led by AcademyHealth Senior Scholars Gerry Fairbrother, PhD and Ellen O’Brien, PhD, with Rosina Pradhananga, MPH and Kalipso Chalkidou, MD, PhD, a collaborator from the National Institute for Health and Care Excellence (NICE) in the United Kingdom—explores these issues and describes ways in which the United States and other high-income countries assess effectiveness of new drugs, devices, procedures, diagnostics, and health care services and make coverage decisions based on these assessments. The report, which was supported by the Kaiser Permanente Institute for Health Policy and the National Institute for Health Care Management Foundation, also provides an overview of HTA activities in Europe, Canada, and Australia and examines the new public investments in CER in the United States.

Read the full report, Improving Quality and Efficiency in Health Care through Comparative Effectiveness Analyses: An International Perspective. 



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Several of our members—among them Dr. Austin Frakt, health services researcher, cofounder of the The Incidental Economist (TIE) and a frequent blogger here —have alerted us to some troubling developments related to data access. Word is that the Centers for Medicare and Medicaid Services (CMS) has been directed by the Substance Abuse and Mental Health Services Administration (SAMHSA) to henceforth exclude identifiable data on claims related to substance use disorder diagnoses and treatments from its Medicare and Medicaid datasets.

Needless to say, this is a huge blow for behavioral health services research and health services research in general, as well as the providers and patients who need evidence on what treatments work best, for whom, and in what circumstances. You can find extensive chronicling of the developments from Austin and University of Michigan health law professor Nicholas Bagley , and more on the dire implications for researchers, providers, and patients via TIE here.

In direct response to Austin’s and Nicholas’s persistent (near daily) coverage of this evolving issue, SAMHSA issued an official response to TIE, explaining its decision to direct CMS to now withhold these previously available, identifiable substance abuse data. The decision is rooted in what SAMHSA views as the letter of federal law (42 USC 290dd-2) and the implementing regulation (42 CFR Part 2 (Part 2), which guarantee the confidentiality of patient records of individuals receiving substance abuse treatment services from federally assisted alcohol and drug abuse programs.

As Nicholas notes in his blog post, it seems that “SAMHSA’s position jibes with the text of the rules but is at odds with the spirit in which they were adopted.”

SAMHSA notes that they are, in consultation with CMS and other HHS agencies, “examining ways in which Part 2 may be updated in accordance with SAMHSA’s statutory authority.” That’s the good news. The bad news is that any change in the regulation will take some time, assuming SAMHSA has the legal authority to do it. If it’s determined that they don’t, new legislation granting such authority would be needed. Either way, we don’t see an easy (or fast) fix to help regain access to these data.

AcademyHealth has alerted our advocacy partners in the behavioral sciences and treatment communities, and we will continue to collaborate with them on a coordinated advocacy strategy. In the meantime, we welcome and need your input!

Please share stories of research findings and evidence-based, improvements in care that would not have been possible, or will not be possible in the future, without these identifiable data at! We will use these examples as we make a compelling case to the administration and other policymakers for these data’s release.




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Emily Holubowich, Senior Vice President at CRD Associates, is AcademyHealth’s Washington representative and leads the organization’s advocacy efforts in the nation’s capital.

On December 16, President Obama signed the bipartisan Consolidated and Further Continuing Appropriations Act, 2015, or “CRomnibus”–part continuing resolution (CR), part omnibus legislation. The massive spending bill includes a CR through February 27, 2015 for the Department of Homeland Security and 11 separate spending bills for the remaining months of the fiscal year, including the Departments of Labor, Health and Human Services, Education and Related Agencies Appropriations Act (Labor-HHS).

This typically controversial bill, which includes spending for the National Institutes of Health (NIH), the Agency for Healthcare Research and Quality (AHRQ), and other health agencies, generally holds funding ‘flat’ for public health and health research activities. Specifically, the CRomnibus provides NIH a $150 million increase to chip away at funding lost due to sequestration in 2013. AHRQ’s budget is held flat when taking into account the approximately $100 million automatic transfer of funding from the Patient-Centered Outcomes Research Trust Fund. The National Center for Health Statistics (NCHS) is also held flat at $155 million; NCHS has not recouped its 2014 loss of mandatory Prevention and Public Health Fund dollars that were used to expand several surveys.

Flat funding roughly represents the best we can hope for in this austere fiscal environment. However, if you look closely at the details of the legislation and its accompanying report, the CRomnibus does include important ‘wins’ for the field of health services research–wins that may not have been if not for AcademyHealth:

  • Budget Authority for AHRQ and NCHS. The base budgets of AHRQ and NCHS have historically been funded through what is essentially a “tap” on other health agencies’ budgets, making the agencies vulnerable as the fiscal belt has tightened and as lawmakers have sought to fund other pressing priorities (see our blog about one such threat, here). The CRomnibus, for the first time in decades, funds the base budgets of AHRQ and NCHS with full budget authority. Direct budget authority with funds from the U.S. Treasury—rather than relying on other agencies’ budgets—will provide significant stability for the agencies going forward.Earlier this year AcademyHealth, through the leadership of our Friends of AHRQ and the Friends of NCHS coalitions, initiated the “Mind the Tap” campaign to educate lawmakers and staff about the evaluation tap and the important work funded by it, including AHRQ and NCHS, as well as the Assistant Secretary for Planning and Evaluation or “ASPE” and the Office of the National Coordinator for Health Information Technology, among others.
  • Investigator-Initiated Research. AcademyHealth has long advocated for balance in AHRQ-funded research–both what is funded, and how. At our recommendation, lawmakers have for many years targeted funding for investigator-initiated research (IIR) grants at AHRQ to balance investments in intramural research and contracts. Again this year, the CRomnibus includes targeted funding of $45 million for IIR and notes the importance of true IIR while urging AHRQ to avoid being too prescriptive in awarding these funds:

    “Investigator-Initiated Research should not be targeted to any specific area of health services research in order to generate the best unsolicited ideas from the research community about a wide variety of topics.”

    The bill’s report language continues:

    “For this reason, the agreement rejects the administration’s request to target $15 million of the investigator-initiated grants to health economics. No funds are included for this purpose.”It’s important to note that this sentence does not represent a prohibition on AHRQ-funded health economics research, merely, the targeting of IIR grant funding specifically for health economics. AHRQ may still fund health economics through an open IIR solicitation and/or through other areas of its budget. In this regard, this language maintains the true spirit of IIR—competition in the free market of ideas.

AcademyHealth welcomes the CRomnibus for not only what it includes, but what it does not:

  • The CRomnibus includes no bans on patient-centered outcomes research; troubling language that has popped up in draft spending bills of years past.
  • The bill does not prohibit health economics research at NIH, as previous draft spending proposals have. It is important to note that the CRomnibus does not include funding for research “within the [Office of the Director’s] Common Fund specifically related to health care financing reform and insurance incentive activities related to the Affordable Care Act.” This is not an outright prohibition agency-wide; Institutes themselves may continue to fund health economics.
  • The CRomnibus does not include language to restrict behavioral and social sciences at the National Science Foundation. Funding for NSF’s Directorate of Social, Behavioral, and Economics Sciences or “SBE” is an important barometer of policymakers’ attitudes about scientific disciplines other than the basic, clinical, and physical sciences, including health services research (for more on the ongoing “War on Social Science” click here and here).

AcademyHealth worked diligently with our partners in the social and behavioral science community to educate Members of Congress about the value of such research and the federal role in funding it, and the CRomnibus represents the fruits of these efforts.

With the 113th Congress now adjourned, all eyes turn toward January 6 when the new Congress returns and we embark on a new appropriations season in earnest. We will continue our efforts to educate policymakers about the value of health services research and its important place in the federal health research continuum. We were successful in winning the FY 2015 battle and protecting our priorities, but the CRomnibus does include clear signs—particularly around health economics—that we have not yet won the war. We will be calling on you, our members, to help us make an effective case for funding health services research and health data collection.

For more information about funding levels for your specific priorities, please click here for a copy of the legislation, and click here for a copy of the explanatory statement or “report language” (health is in Division G) that includes more specificity about the funding levels.


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A new study in the December issue of Health Affairs—led by Medicaid Medical Directors in conjunction with AcademyHealth and with funding support from the Agency for Healthcare Research and Quality, Centers for Medicare and Medicaid Services, and the Health Resources and Services Administration—examines the role state Medicaid programs are playing in reducing the number of early elective deliveries.

In this study, 22 state Medicaid programs with their maternal and child health and vital statistics state colleagues sought to coordinate quality improvement efforts related to early elective deliveries in the Medicaid population. The study finds that almost 9 percent of the 1.8 million-plus Medicaid births each year are early elective deliveries resulting in a higher rate of neonate and NICU admissions or transfers compared to full-term elective deliveries. Furthermore, these deliveries contribute to increased morbidity rates and costs.

While the study also found that early elective delivery rates among Medicaid births dropped between 2007-2012, they still need to be reduced more. Finally, the study identifies policies implemented in states to reduce the rate of these births, including prior authorization (i.e., patients getting permission from their Medicaid plan to have an early, elective birth) and “hard stop” policies (i.e., hospitals prohibiting such procedures), as well as education and feedback efforts targeting patients and physicians.
To access the Health Affairs article:

Additional analysis of the 22-states’ data can be found here:



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