The National Center for Health Statistics has released updated estimates for the percentage of persons under age 65 who were in families having problems paying medical bills. The updated estimates are based on data from the National Health Interview Survey (NHIS) and are based on five consecutive six-month periods from January-June 2011 to January-June 2013. Selected highlights from “Problems Paying Medical Bills: Early Release of Estimates from the National Health Interview Survey, January 2011-June 2013,” include the following:

  • The percentage of persons under the age of 65 who were in families having problems paying medical bills decreased from 21.7 percent (57.6 million) in the first six months of 2011 to 19.8 percent (52.8 million) in the first six months of 2013.
  • Within each six-month period from January 2011 through June 2013, children ages 0-17 years old were more likely than adults ages 18-64 to be in families having problems paying medical bills.
  • The percentage of children who were 0-17 years old who were in families having problems paying medical bills decreased from 23.7 percent in the first six months of 2011 to 21.3 percent in the first six months of 2013.
  • In the first six months of 2013, 28.6 percent of poor, 33.3 percent of near poor, and 14.3 percent of not poor persons under age 65 were in families having problems paying medical bills in the past 12 months.



Appointment audit studies, like the one I wrote about yesterday, have limitations. They assess the degree to which doctors’ offices will accept new patients, by insurance status. Some might call that “access,” but it’s not the full story. Audit study results do not directly reveal the extent to which variations in new appointment availability translate into variations in receipt of necessary or appropriate care. “Access” is not so simple a concept.

Recall that based on the findings from the JAMA Internal Medicine (JAMA IM) study I summarized, to achieve the same level of success at making a primary care appointment, a new Medicaid patient, calling offices at random, would have to call about two offices for every one that a new privately insured patient called. Is this a big deal?

A companion study led by Genevieve Kenney and including some of the same researchers as the JAMA IM paper, addresses this question. They used 2012 National Health Interview Survey (NHIS) data to examine access issues for low income adults with Medicaid coverage, private, employer-sponsored insurance (ESI), or no insurance. The NHIS is a nationally representative survey of 35,000 households, focusing on the non-institutionalized, civilian, U.S. population.

For low-income (<250% of the federal poverty level) adults with new coverage, their findings corroborate those of the JAMA IM paper. New Medicaid enrollees were more likely to have trouble finding a doctor than those with new ESI.  However, even there, just 11.3 percent of new Medicaid enrollees  said that they had difficulties finding a general doctor or provider and only 2.8 percent said that they could not find a general doctor or provider with availability. [click to continue…]



Adequate access to primary care is necessary to achieve many of the population health improvement goals of the Affordable Care Act. However, the ACA’s expansion of health insurance is somewhat at odds with increasing that access: lower out-of-pocket costs will facilitate access to care, but dramatic expansion of coverage could also stress the supply of primary care providers, limiting access. To assess how the ACA affects access to primary care, we need baseline (pre-expansion) measures of it.

In a new study published in JAMA Internal Medicine (JAMA IM), Karin Rhodes and colleagues provide the latest estimates of that baseline.* It’s a well-crafted study, documenting access challenges facing Medicaid beneficiaries in particular. I will warn you up front, however, it’s only a partial view of access issues. An analysis published separately by many of the same investigators provides a different and seemingly contrasting view. I will comment on that separate analysis in a subsequent post later this week.

With a phone call, audit-based (i.e., simulated patients) approach, Rhodes and colleagues examined pre-ACA primary care access by insurance status—Medicaid, private coverage, and uninsured—over the period November 13, 2012 to April 4, 2013 and in ten states: Arkansas, Georgia, Illinois, Iowa, Massachusetts, Montana, New Jersey, Oregon, Pennsylvania, and Texas. These states account for between one-quarter and one-third of each of the nonelderly population, Medicaid enrollees, and the uninsured.

Simulated patients called (“audited”) primary care offices seeking new visits. Nearly 11,400 appointments were attempted across the ten states, about 5,400 by a private plan enrollee, about 4,400 Medicaid, and about 1,600 uninsured (all simulated and with a standardized protocol, of course).

To be in the sample for Medicaid, the office had to have a contract with a Medicaid plan, whether primary care case management or a Medicaid managed care plan. If asked, Medicaid callers identified their insurance status as that of the specific plan accepted by the office called. Private plan callers identified themselves as covered by the private plan with the highest market share in the office’s county, again if asked. If the office did not accept that plan, a second call was made during which the caller identified him/herself with the plan with second highest market share if asked. Offices that did not accept either of the top two plans were removed from the private plan sample.

The chart below, from the paper, shows the new patient appointment rates by state for Medicaid and private insurance and averaged over all ten states. On average, private plan callers were offered an appointment 85% of the time and Medicaid callers only 58% of the time. This suggests significantly higher primary care access barriers for Medicaid patients relative to privately insured ones.

Appointment Availability for Private and Medicaid Callers

appointment avail

A common knock on Medicaid is that it’s worse than being uninsured (it isn’t), and the above results would seem to support that claim. But, in fact, this assertion is difficult to test in a study like this one. One can’t fairly compare Medicaid and uninsured populations without controlling for income, which can’t be done in an audit study for which the unit of analysis is the office, not the patient. What the investigators did, however, was examine the rate at which uninsured callers could obtain appointments at full and reduced cost, where “reduced” meant $75 or less at the time of visit. The thinking here is that appointments requiring more than $75 at time of visit would be financially difficult for uninsured patients with Medicaid-like incomes. (The $75 was selected because it was half the median cost; a weakness of this approach is that this cut-off is not grounded in any particular affordability standard.)

The chart below, also from the paper, shows the results. On average uninsured callers were 79% successful at obtaining an appointment, much better than Medciaid. But, only about 15% of uninsured callers were offered an appointment that would cost $75 or less at the time of visit, which is much worse than Medicaid and an arguably fairer comparison to it.

Appointment Availability for Uninsured Callers

appointment uninsured

The investigators also found that median wait times were between 5-8 days for private and Medicaid callers alike except in Massachusetts where they were 13 days for private and 15 days for Medicaid callers. This, perhaps, indicates that coverage expansion to the extent experienced in Massachusetts could substantially increase waiting times, though it is unclear whether this degree of increase is clinically significant. It’s worth noting, however, that new patients without an established provider are more of a rarity in Massachusetts—one of the states with the highest percentage of adults saying they have a usual source of care (see Exhibit 11, here). The experiences of patients who already have established care are likely to be different.

Some conclusions: The study results suggest that the primary care system has the capacity to take on new, privately insured patients. They seem to suggest that Medicaid enrollees may face greater access problems than privately insured patients, though not as severe as (arguably) comparable uninsured patients. What they really show is that Medicaid patients (and comparable uninsured patients) will need to make better targeted or more calls to get appointments. According to the results above, to achieve the same rate of success at securing an appointment, Medicaid patients who were calling offices at random would, on average, have to call about two offices for every one a privately insured patient would. (See also the invited commentary about this work by Andrew Bindman and Janet Coffman.)

But, we should ask, do Medicaid patients call offices randomly? Is the additional effort they may have to exert to obtain appointments an unreasonable burden? Does it affect outcomes? My next post on related work will shed some light on these questions.

* Several other papers relevant to access to care by Medicaid patients and Medicaid expansion were also published in JAMA IM today: Chima Ndumele and colleagues found that adult Medicaid enrollees in ten states that expanded Medicaid between 2000-2009 experienced no loss in access to care or increased emergency department use, relative to 14 neighboring control states that did not initiate expansions. This paper, among others, is summarized in an editorial by Mitchell Katz. Finally, using National Health Interview Survey data from 2010-2012, Sandra Decker and colleagues found that low-income adults in states not expanding Medicaid in 2014 were in worse health and had poorer access to care, relative to expansion states. I.e., they had more to gain from coverage expansion than states that are expanding Medicaid.





On March 4, 2014, President Obama’s administration announced a new five-year program intended to reduce the over-prescription of psychotropic medications to children in the foster care system. [i] Recent research from Rutgers University and its six partner states addresses the issue of over-prescribing in the Medicaid population. Lessons from their work can be found in a new resource guide, and may be instructive for other states seeking to affect similar changes.

The resource guide titled, Implementing a State-level Quality Improvement Collaborative: A Resource Guide from the Medicaid Network for Evidence-based Treatment, aims to provide state agencies and policymakers with guidance on how to implement a quality improvement collaborative to address a clinical concern, such as the one President Obama included in his 2015 budget. This resource guide outlines the core components of such a collaborative, including the importance of collaborative learning and making policy decisions based on evidence and sound data.

As the resource guide describes, one of the first steps towards building a quality improvement collaborative is identifying and describing the issue at hand. This collaborative, which began as a collaboration of the Medicaid Medical Directors Learning Network (MMDLN), and now the Medicaid Medical Directors Network,[ii] offers the opportunity to disseminate lessons learned and collaborate on specific clinical topics and review policy solutions.[iii] Specifically, the MMDLN conducted a study of 16 states’ data in 2010 and found that 193,178 children or adolescents enrolled in Medicaid in 16 states received antipsychotic (AP) medications, which are used to treat serious mental illnesses (a 10 percent relative increase since 2004). The study also found that children in foster care (12.4 percent) were prescribed AP medications at much higher rates than those who were not in foster care (1.4 percent).[iv] Given the serious potential side effects and significant cost of these medications, many were concerned that the expanded use of these medications, frequently off-label, often outpaced the evidence base.[v]

In response to this need, Rutgers University’s Institute for Health, Health Care Policy, and Aging Research (IHHCPAR) and AcademyHealth coordinated MEDNET, a three-year, multi-state consortium that focused on increasing the utilization of evidence-based clinical and delivery system practices in mental health treatment for Medicaid beneficiaries. The learning collaborative included California, Maine, Missouri, Oklahoma, Texas, and Washington.

This guide provides a much needed “how to” and can be used by others in the field that aim to improve the delivery of care. Highlights from the resource guide include:

  1. Develop and Implement a Stakeholder and Data-Driven Quality Improvement Initiative: This section provides a step-by-step description of each stage of developing a collaborative with guiding questions at each stage.
  2. Identify a Champion, Project Lead, and Core Staff Team: This section describes what management resources are needed for strong teams, and as a result, successful programs.
  3. Engage Stakeholders and Partners: This section describes the importance of engaging key stakeholders and how to involve them in the activities throughout the collaborative.
  4. Ensure a Data-Driven Process: Data and information sharing are critical for quality improvement collaboratives to identify the issue, measure progress and provide evidence to stakeholders to initiate action.
  5. Develop a Data-Driven, Iterative and Actionable Quality Improvement Plan: This section goes into the specifics of how to develop a quality improvement plan.
  6. Implement Policy and Quality Interventions: This section provides examples of policy and quality interventions employed by states in this area that can inform other programs.
  7. Host Collaborative Activities: This section provides practical information on how to convene participants since regular communication is essential to successful programs.
  8. Disseminate: This section describes how to share information gathered and work products developed from the initiative.
  9. Bonus: Cross-State Collaboratives: This section provides an example of cross-state collaboratives as a potential mechanism to address issues that affect states broadly.

As this resource guide describes, developing a successful quality improvement collaborative is a significant investment and its success requires thoughtful implementation.

You can download a copy of the report here.

Acknowledgement: Both the Medicaid Medical Directors Learning Network (MMDLN) and the Medicaid Network for Evidence-Based Treatment (MEDNET) were funded by the Agency for Healthcare Research and Quality.


[i] Cheney, Kyle. Obama Budget Funds New Initiative to Limit Psychotropic Drugs for Foster Kids. Politico Pro. Accessed at:
[ii] In 2013, the MMDLN moved under the auspices of the National Association of Medicaid Directors as a clinical arm of the national association of state Medicaid programs and became the Medicaid Medical Directors Network.
[iii] Medicaid Medical Directors Learning Network Fact Sheet. Agency for Healthcare Research and Quality. Accessed April 2014. Available at:
[iv] Medicaid Medical Directors Learning Network and Rutgers Center for Education and Research on Mental Health Therapeutics. Antipsychotic Medication Use in Medicaid Children and Adolescents: Report and Resource Guide from a 16-State Study. MMDLN/Rutgers CERTs Publication #1. July 2010. Distributed by Rutgers CERTs at
[v] Crystal S, Olfson M, Huang C, Pincus H, Gerhard T. Broadened use of atypical antipsychotics: safety, effectiveness, and policy challenges. Health Affairs. Sep-Oct 2009;28(5):w770-781.

By: Emily Moore, Research Assistant , AcademyHealth



As the Affordable care Act matures, many states still have refused to expand the Medicaid program. This has left millions of Americans without insurance this year, as the law contains no means to provide subsidies for those making less than the federal poverty line to buy private insurance in the exchanges. Some states continue to negotiate with the Obama administration for waivers to build some sort of compromise, but things have stalled in many areas of the country.

One of the reasons held up as a means to refuse the expansion is that costs will be too much for state governments to bear. They believe that the uninsured, as a class, are unhealthy. Indeed, much of the rhetoric of reform has focused on the fact that those with chronic conditions have been refused policies in the past; when able to obtain them, they have been prohibitively expensive. Therefore, it’s not a great leap to believe that the health care expenses of the newly insured will be large.

Moreover, those who have been previously eligible for Medicaid, but uninsured, will not be covered by the federal government at the same 100% rate that the newly eligible will be. Their costs will be born much more at the state level, adding an additional disincentive for many states to work to expand Medicaid coverage.

A new study in Health Affairs adds to our knowledge base concerning the relative health of the uninsured:

We used simulation methods and data from the Medical Expenditure Panel Survey to compare nondisabled adults enrolled in Medicaid prior to the ACA with two other groups: adults who were eligible for Medicaid but not enrolled in it, and adults who were in the income range for the ACA’s Medicaid expansion and thus newly eligible for coverage. Although differences in health across the groups were not large, both the newly eligible and those eligible before the ACA but not enrolled were healthier on several measures than pre-ACA enrollees.

What they found is somewhat surprising, given the previously mentioned rhetoric. Adults who were newly eligible for Medicaid had equal or better physical health than those who were covered by Medicaid already. They also had equal or better mental health and fewer depressive symptoms. Moreover, even the previously eligible, but not covered adults, were equally in the same or better mental and physical health. They also had less asthma, diabetes, and obesity than those already covered by Medicaid.

In some ways, this makes a fair amount of sense. Medicaid does not function in the same way that private insurance does. Since people do not “pay” for it, there’s much less of a reason for healthy people who are eligible to get it. There’s much less of a financial penalty to be paid for signing up late than with private insurance.

Given this fact, it’s possible that the sicker eligible patients have already obtained coverage. Therefore, those who might obtain coverage now may be healthier in general.

This is great news for states that are worried about Medicaid costs. Remember that Medicaid spending is almost entirely related to actual health spending. Those that consume no care really cost nothing to the state. So “signing up” healthy people adds no burden to the state, unless people need care.

This is not the first study to find this result. Much of the recent literature supports these findings. Expanding Medicaid eligibility would provide millions of Americans with the peace of mind that they are protected should they need care. It would provide them with necessary access if they get ill. It’s great news that the expansion might cost less than predicted, and states should take this into consideration as they consider their options.



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For a number of years, we’ve seen overall health care spending increasing at a slower rate than in the years before. But one of the things that frustrates me about these types of reports is that they rarely focus on children.

One of the things that’s drummed into the mind of every pediatric trainee is that “children are not little adults.” We cannot assume that we understand kids and health by taking what we know about adults and just shrinking it. This is true with spending as well.

The Health Care Cost Institute recently released a report entitled Children’s Health Spending: 2009-2012 that provides us with a lot of specific information in this area. Let’s start with the fact that health care spending per child age 18 years of age or less was $2437 in 2012. This was a $363 increase from 2009, for an average growth of about 5.5% per year.

This growth remains higher than that seen in adults. But there’s still a lot of room for movement. The amount we spend per child is less than a third of what we spend on every adult.

Spending varies greatly by age as well. Infants and toddlers up to age three cost $4446 each, which is about half of what we spend on adults. The vast majority of spending in this age group is on inpatient hospitalizations and professional procedures.

Children age 4-8 years, on the other hand, cost $1653 each, making them really cheap. Prescriptions constitute a larger share of spending, especially those treating infectious diseases and central nervous system disorders.

In general, each child spends 54.6 days each year covered by a generic drug prescription, and another 19.5 days covered by a brand name drug prescription.

I’m not going to spend too much more time reporting results, because you can go read the report for yourself. But there are a few points I’d like to highlight. The first is that it’s easy to see that much like we can’t treat children as adults, we also can’t treat all children the same. Infants and toddlers are not like elementary school children. And they aren’t like preteens or teenagers. Each group has different needs, and different drivers of spending.

Children don’t constitute a huge part of health care spending. But they do constitute a large part of Medicaid. And as children’s health care spending increases, Medicaid spending will follow. That’s a real concern to state governments, and to the federal government as well.

Finally, the last page of the report details this:

In 2010, children’s outpatient visits to emergency rooms (ERs) declined, with the most pronounced declines for younger children. ER visits per 1,000 children fell by 9.4 percent for preteens, 8.2 percent infants and toddlers (Table A6), and 6.5 percent for teens. In 2011, children’s visits to the ER rose, increasing 1.8 percent for infants and toddlers, 5.2 percent for younger children, 2.0 percent for pre-teens, and 1.8 percent for teens. However, by 2012, visits to the ER for all children remained below 2009 levels.

It’s not like children’s health vastly improved over this time such that they didn’t need as much care. One of the possible reasons for this was a delayed effect of the recession. In other words, some children might not have received care because of cost. We can’t know at this time whether this affected the quality of children’s health care or health, but it bears watching in the future.



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When Daniel Liebman reviewed the literature on the role of health insurance in preventing bankruptcy and financial hardship, he asked if the ACA will “put a dent in the bankruptcy rate.” Examining the experience in Massachusetts is a particularly good way to approach the question since its coverage expansion law closely resembles the ACA. Unfortunately, when he prepared the post, Daniel only found one Massachusetts-relevant study.

One study [Himmelstein, Thorne, and Woolhandler, 2011] found that the medical bankruptcy rate did not decline in Massachusetts immediately following its health reform effort, though there could be some confounding due to the Great Recession.

Daniel also pointed out that bankruptcy is only one, extreme measure of financial hardship. Using it to assess financial strain is a bit like using mortality as a health outcome. It’s undeniably bad, but so much else bad can happen before one gets to that point.

[M]edical bankruptcy, whatever its “true” rate, represents the tip of a larger iceberg of medical debt and heath care costs in this country.

Examining Massachusetts and using different methods and data, a new working paper by Bhash Mazumder and Sarah Miller (ungated) comes to a different conclusion than Himmelstein and colleagues. The investigators also consider a wide array of financial outcomes, including credit card balances, credit balance past due (over 29 days), fraction of debt past due, third-party collections, credit risk score, and bankruptcy.

The source of data is the Federal Reserve Bank of New York Consumer Credit Panel data set, from which the investigators extracted quarterly data from 1999-2012 for five million and one million individual-year observations in Massachusetts and other New England states, respectively. Their analytic approach is a “triple difference” strategy, probing whether financial outcomes changed more in counties with a higher pre-reform rate of uninsurance as the Massachusetts reform was implemented, relative to other New England states. The results can be interpreted causally under the assumption that

any change in financial outcomes among the more-affected individuals [in higher uninsurance rate counties] in Massachusetts relative to other New England states over the period of the reform is [due to] the reform.

The authors found that

the reform significantly improved credit scores, reduced the total amount past due, reduced the fraction of debt past due, and reduced the probability of personal bankruptcy. We find particularly pronounced reductions in the probability of having a large delinquency of over $5,000. These effects tend to be larger among individuals whose credit scores were low at the time of the reform, suggesting that the greatest gains in financial security occurred among those who were already struggling financially. Furthermore, our analysis yields some suggestive evidence that the reform may have also reduced total debt and the amount of third party collections.

The paper includes many charts such as the following, that illustrate how the coefficient on the pre-reform rate interacted with a Massachusetts indicator changes over time. The vertical lines indicate the period during which reform was implemented. As can be seen below, the reform led to a decrease in the likelihood of bankruptcy. (Dotted lines indicate 95% confidence intervals.) The two-year bankruptcy rate fell 20%. In addition, credit balance past due fell 22%, fraction of debt past due fell 10%, and credit scores rose 0.4%. I recommend downloading the paper for results and charts for other financial outcomes.


The mechanisms by which health reform confers financial protection are several, as the authors point out. First and most obviously, coverage expansion directly decreases exposure to the cost of care for the previously uninsured. Second, more generous coverage (e.g., for those previously underinsured) also offers greater protection against health care costs. Finally, these two effects can spillover to others whose coverage didn’t change by reducing the need for them to pitch in when under- or uninsured family members require costly care.

All in all, Mazumder and Miller offer a plausible and convincing case that health reform in Massachusetts decreased financial hardship. It is therefore likely that the ACA will do the same.

-Austin Frakt



“It’s not about the size of the data, it’s what we do with it”-  Elizabeth McGlynn, Kaiser Permanente

A crowded room of attendees chewed on this quote and many, many more during a session titled “Using Big Data to Advance Healthcare” at AcademyHealth’s 2014 National Health Policy Conference in February. What follows is a summary of the session.   

The session featured an all-star panel of data users, holders and advocates and dove into hot topics like reengineering EMRs and incorporating microbiome information into the research repertoire.    

Dwayne Spradlin moderated the day’s session and led by framing the pressing “need to put data to work.”  In a world of data overload, Dwayne posited that health and health care information is the most useful of all.  If so, the good news is there’s plenty of it:  claims data, cost data, clinical data, research data, vital statistics, environmental data, social data, person-level data, etc. Unfortunately, much of this data is locked in silos and not easily available for useful consumption. 

He then spoke about “big data” and what it really means.  In short, it’s a “buzzword” describing a massive volume of unstructured data.  He referenced the McKinsey & Company study articulating that the benefits of big data are plenty, but, he noted, the elephant in the room is how we realistically transform big data into it’s full potential – to ultimately improve health and health care.    

Elizabeth McGlynn, Kaiser Permanente, cautioned the audience that decisions around big data should be made using a “value” lens – we shouldn’t do something because it’s new; rather, it must have a value proposition to be worthwhile.   Related to value, she articulated promising uses of big data such as care delivery (personalized medicine), operations, public health, and research. 

She stated that EMRs of tomorrow will include big data (and more complete data), offer global data access for researchers, and provide real-time decision support. In closing, she noted that the “future of big data lies in its ability to support the safest, highest quality, most individualized care without constraint of borders and boundaries.”

Gregory Moore, Geisinger Health System, echoed the need to put big data into action.   He highlighted four enablers to using big data. First, effective use relies on the talent (e.g., clinical leadership, data scientists).   Well-versed personnel are crucial to maximizing the full potential of data.  Then, proper tools must be in place (e.g., functioning data architecture, meaningful clinical rules.)  Next, a process must be implemented to validate the quality of data (e.g., data scrubbing.) Lastly, patients must be engaged and activated in order for maximize the benefits of big data usage and integration into the health and healthcare ecosystem. 

He went on to provide the audience a brief peek into Geisinger’s efforts using big data streams to conduct real-time modeling.   Right now, Geisinger has produced automated data models of emergency rooms, everything from clinicians to hospital beds. Researchers then alter ratios of these resources (e.g., removing or adding hospital beds) to test its efforts on care delivery, resource allocation and health outcomes.  This is a real-world example of systems level data in action. 

Lastly, Larry Smarr, California Institute for Telecommunications and Information Technology, spoke about his in-depth experience observing, measuring, and evaluating nearly all aspects of his health (e.g., what nutrients he requires, what happens when he sleeps, etc.) through the use of gadgets (e.g., heart rate monitors). He has recorded tens of billions of data, all of which are not included in traditional medical records.  

He stated that current medical care is only treating 10% of the patient’s makeup, as the remainder is the composed of the microbiome. Though the NIH has funded work in this area (Human Microbiome Project), Larry believes that more needs to be done to quantify how changes in a person’s microbiome relate to health, and how this data should be used in health care delivery. This is yet another example of the potential of big data.   

The session wrapped with a rapid series of audience questions. The tone of the room was energized from start to finish, and as the session formally ended, the dialogue, thought-provoking discussion, and lively debate continued into the hallway.

By: Jessica Winkler, Senior Associate, AcademyHealth



For the last year, AcademyHealth has been fighting alongside our partners in the social and behavioral science community in the ongoing “war on social science,” where some in Congress attempt to micromanage discovery and pit discipline against discipline. Now, we’re gearing up for the latest skirmish. This Thursday, the House Committee on Science, Space, and Technology’s Subcommittee on Research and Technology will mark up the Frontiers in Innovation, Research, Science and Technology Act of 2014 (H.R. 4186), or “FIRST Act.”

Introduced by Congressman Larry Bucshon (R-IN), the bill serves as reauthorization legislation for the National Science Foundation (NSF). Unfortunately, the bill includes a number of problematic provisions. Of particular concern is the proposal to cut the authorized funding level for NSF’s Social, Behavioral and Economic (SBE) sciences directorate by nearly 42 percent. In addition, the bill seeks to micromanage the grant application process and limit the number of awards that can be made to principal investigators, which would undermine the merit review process that successfully determines the best and brightest science.

Organizations in the science community are lining up to oppose the bill, including the Association of American Universities (AAU). As AAU explains in their official statement:

“…the bill does little to close the nation’s innovation deficit, but it also does some things to widen it, including significant funding cuts to social, behavioral, and economic research. The social and behavioral sciences play a vital role in this nation’s research portfolio. They contribute significantly to understanding and solving our nation’s economic, health, and security challenges, and they increase the efficiency and efficacy of the cures, technologies, and discoveries made in other disciplines.”

AcademyHealth joins our colleagues in the scientific community in opposition to this bill, and we urge our members to do the same. If you’d like to lend your voice to the debate, you can send a message to Congress via the Consortium of Social Science Associations by clicking here.

You can also Tweet about the issue using #VoteNoHR4186.


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On Tuesday, President Barack Obama released the proposed budget for fiscal year (FY) 2015, including the “Budget-in-Brief” for the Department of Health and Human Services (HHS). While the president’s budget request is widely viewed by Washington insiders as “dead-on-arrival,” a “non-starter,” and “on the road to nowhere” on Capitol Hill, it does provide important clues into the administration’s thinking and how it would prioritize health and health research if the constitutional “power of the purse” were in the hands of the Executive Branch.

As with any budget document, the devil is in the details. And to fully understand the administration’s vision for health services research we’ll need to wait until later this week when agencies publicly release their budget justifications to appropriators in Congress (more on that to come). Nevertheless, the Budget-in-Brief indicates a major win for the field of health services research–even if just the first step in a winding process of negotiation between the executive and legislative branches.

Beginning more than a decade ago, the Agency for Healthcare Research and Quality (AHRQ) started changing the way it did business, moving away from competitive research grants toward contracts as the funding mechanism of choice. By 2013, new and competing grants comprised only 5 percent of AHRQ’s total budget. In fact, the ratio of total grants to total contracts in the budget has gone from 1.35:1 in 2002 to 0.51:1 in 2013.

AcademyHealth members have been vocal in their concerns, and AcademyHealth responded by making a concerted advocacy effort to restore balance to both what AHRQ funds, and how it funds that work. Beginning five years ago, AcademyHealth was successful in convincing Congress to create a small set-aside in AHRQ’s budget for “investigator initiated research” (IIR). This is ironic given that IIR is the centerpiece of research funding at the National Institutes of Health and has been seen as a key strategy to have the best and brightest scientists compete for funding based on the strength of their science and the potential impact of their work. But beginning with the earliest of President Obama’s budgets, the administration has each year proposed cutting funding for IIR, or eliminating it altogether.

AcademyHealth fought back by educating policymakers on the Hill and in the administration about the value of research competition and innovation afforded by R01s. Congress has listened, and consistently supported the funding for IIR. Over time, this set-aside has grown slowly to a new high watermark of nearly $46 million in the FY 2014 spending bill passed by Congress and signed by President Obama in January.

Based on the president’s FY 2015 budget request, it seems AcademyHealth’s message has finally reached the administration as well. The president explicitly requests $40 million for AHRQ’s IIR portfolio. The request includes $20 million for new and competing grants, and of this, $15 million for health economics research. A first!

The administration’s budget request is just that, a request. It will be up to congressional appropriators to decide how much, if any, funding to dedicate to AHRQ’s IIR portfolio. However, the administration’s acknowledgement of the value of IIR by prioritizing it in the FY 2015 budget remains a small but significant victory for the field of health services research, and demonstrates importance of persistence in advocacy.