The White House Blog has a post up on “How Low-cost Randomized Controlled Trials Can Drive Effective Social Spending“:

The Office of Science and Technology Policy and the Coalition for Evidence-Based Policy convened leaders from the White House, Federal agencies, Congress, philanthropic foundations, and academia this week to explore an important development in the effort to build credible evidence about “what works” in social spending: low-cost randomized controlled trials (RCTs). The goal of the conference was to help advance a broader Administration effort to promote evidence-based policy, described in the evaluation chapter of the 2014 Economic Report of the President, and the Performance and Management section of the President’s budget.

Large and rigorous RCTs are widely regarded as the most valid method of evaluating program effectiveness, but they are often perceived as too costly and burdensome for practical use in most contexts. The conference showcased a new paradigm: by measuring key outcomes using large administrative data sets already collected for other purposes – whether it be student test scores, hospitalization records, or employment and earnings data – sizeable RCTs can be conducted at low cost and low burden.

The conference showcased a number of RCTs that were conducted for between $50,000 and $350,000 (a fraction of the usual multimillion dollar cost of such studies), yet produced valid evidence that informed important policy decisions.

First of all, I’m thrilled at the idea, in general, of using data, especially those derived from randomized controlled trials, to make policy decisions. Using evidence to drive policy has always been one of the goals of this blog, as well as others I write for.

But this post focused on how it’s possible to do randomized controlled trials at an amazingly low cost. It describes, for instance, a study of Recovery Coach services to substance-abusing parents who had temporarily lost custody of their children. The cost of the nine-year trial was about $100,000, which is stunning.

But these types of opportunities are the exception, not the rule. I think it highlights a misunderstanding of where grant money goes much of the time.

If you looked at the budgets of any R01 funded randomized controlled trial I’ve had funded, the number one cost, by far, is the salaries of those who are conducting the study. Even if it’s just 10-20% of someone’s time, and it’s just a few people, the salaries add up. When you add in benefits and overhead, you can get into the hundreds of thousands of dollars pretty quickly. Also, research comes with indirect costs. The rent for people’s offices, the electricity, their phone lines, support staff, taxes, etc. There are a lot of costs.

You often need to pay for a number of people because you’re setting up some new intervention! That takes time, it takes effort, and it takes money to pay for it.

The opportunities like those described in the White House blog are relatively rare. All of the interventions discussed were already being run by personnel who were likely employed through other public lines of support. That’s great, but that doesn’t happen often. If the intervention is already paid for, if those running it are already salaried, then most of the costs of a grant are wiped clean. If the data are already collected as part of administrative data, that’s awesome, but that wasn’t free. It was paid for by someone else. Yes, that makes the “grant” cost less, but not the research itself.

It appears that all that had to be paid for in the studies presented was the cost of the analysis. That may easily be $100,000. But that’s only the last part of an RCT, usually.

It’s totally worth it to try and do research on the cheap when you can. It’s incredibly efficient. But we shouldn’t be under the illusion that research is cheap. Lots of great things can’t be done this way, and it’s important to do those things, too.

Aaron

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In spring 2014, AcademyHealth convened expert researchers and thought leaders from the disciplines of public health, health care, and health information technology to discuss a critical issue –what opportunities do new and existing sources of data bring to the research community as it continues to inform health system transformation?

This meeting was part of AcademyHealth’s Public Health Services and Systems Research (PHSSR) program, supported by the Robert Wood Johnson Foundation. To focus the discussion, AcademyHealth commissioned three papers on the thematic topics outlined in PHSSR’s national research agenda: health information technology, organization and structure, and financing. Meeting participants provided constructive feedback on the papers, and then engaged in further dialogue around these issue areas. Specifically, attendees identified priority research questions for the field of PHSSR; suggested what types of data and what elements of data are needed to answer those priority questions; proposed data resources that are so far untapped; provided strategies for cultivating new PHSSR data; and finally, debated how analytic approaches and methods could be refined, enhanced or adapted to take full advantage of all data resources for the field.

Better Leveraging Data Resources

The resulting list of research questions and topics for further exploration is below. We know that we have just begun to scratch the surface of these important questions. To continue the momentum, and keep pushing the field forward, we want your expert input. What do you think should be PHSSR’s priorities? And what data (both existing and untapped resources) are needed to answer these questions?

We are looking to learn from your experiences and expertise as we continue to define the ways in which we can move PHSSR forward! Please add your thoughts and/or suggestions in the comments section below this post.

PHSSR Research Questions:

Organization

  • What is the business case for consolidating local health departments or sharing services across health departments?

Financing

  • What is the capacity of local health departments to carry out basic financial management/ accounting? How does capacity affect agency performance?

HIT and Informatics Systems

  • What system approaches optimize public health community readiness and opportunism to effectively harness increased information flows to improve population health?
  • What technology components (e.g., sharable platforms, flexible standards or identity management tools) leverage the current information explosion, and how, to transform the current healthcare system?
  • How can the PHSSR informatics workforce be trained and maintained?
    • What deliverables are informatics staffs producing? How are the deliverables accounted for? What is the business case for moving dollars into informatics?
  • What is the minimum set of necessary data that comes out of electronic health records (EHRs)? Who defines this set? Who cleans the set? What should be done with it?
  • What are examples of successes and failures in obtaining public health information from EHRs?
  • What is the regulatory environment needed to facilitate data exchange?

Visit our website to learn more about PHSSR at AcademyHealth, and complete this form to join our Interest Group.

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I highly recommend the recent paper in The Milbank Quarterly by Harvard health economists Joseph Newhouse and Tomas McGuire on the cost, beneficiary selection (as in favorable vs. adverse), market power, and quality of Medicare Advantage (MA). Just as it sounds, it summarizes just about everything a wonk might want to know about the program. In this post, I will make just three points articulated in the paper.

Historically, the case against MA was easy: it cost too much and quality was uncertain and, possibly, suspected to be no better or worse than traditional Medicare (TM). (See the work of Miller and Luft.) More recent data compels at least a partial reassessment of the program. Newhouse and McGuire make, perhaps, one of the stronger, evidence-based cases in its favor I’ve read in a long time.

First, yes, MA plans are still overpaid relative to the cost of providing the Medicare benefit via TM. This is nicely illustrated in the chart below, from Bloomberg View.

MA vs FFS

But things are at least slightly less bad than they appear. As found by Kate Baicker, Mike Chernew, and Jacob Robbins and explained by Newhouse and McGuire, there is a spillover effect whereby MA causes offsetting savings for TM, as well as in the commercial market. (An earlier version of their publication exists as an NBER working paper as well, which I summarized.)

We find that when more seniors enroll in Medicare managed care [an MA plan], hospital costs decline for all seniors and for commercially insured younger populations. Greater managed care penetration is not associated with fewer hospitalizations, but is associated with lower costs and shorter stays per hospitalization. These spillovers are substantial – offsetting more than 10% of increased payments to Medicare Advantage plans. [...]

So, that’s not a total offset to the extra MA cost, but it’s a little off the top.

Next, Newhouse and McGuire share results from a paper by John Ayanian and colleagues that demonstrates that MA offers higher quality than TM, at least in the dimensions measured. The chart just below shows the proportion of beneficiaries in a Medicare Advantage HMO and TM receiving each of several preventive services. Women enrolled in an HMO are more likely to receive mammography screening,* those with diabetes are more likely to receive HbA1c testing, retinal exams, and those with diabetes or cardiovascular disease are more likely to receive cholesterol testing.

newhouse-mcguire 1

The next chart shows that HMO enrollees are more likely to receive flu and pneumonia vaccinations and about as likely to highly rate their personal doctor and specialists.

newhouse-mcguire 2

Finally, Newhouse and McGuire point to both published and unpublished work by Bruce Landon and colleagues that finds reduced resource use (which they call “social cost”) by beneficiaries in MA plans.

Rates of ambulatory surgery and emergency department use were 20% to 30% lower in the MA plans [relative to TM]. The difference was concentrated in elective procedures regarded as more “discretionary,” such as knee or hip replacements. Repair of a fracture of the femur, a less discretionary procedure, was actually greater in MA.

Minimizing social cost also involves using more durable procedures if equally effective. Landon and colleagues found that coronary problems were more frequently treated with coronary bypass surgery in MA rather than the less durable percutaneous coronary intervention, suggesting not just lower long-run costs on average but a more appropriate use of services, since the patient was potentially spared a repeat procedure.

They go on to describe other work illustrating reduced social cost in MA.

In conclusion, Newhouse and McGuire wrote,

We found several reasons to maintain the level of payment to MA plans at or above the level of TM. First, the quality and appropriateness of care appear to be at least as high in MA as in TM. Second, the social cost of care in MA appears to be lower than in TM. Third, we found evidence for positive “spillovers,” meaning that higher MA enrollment in a county reduces hospital costs in TM in that county. Medicare does not immediately capture the savings, since it pays per admission (unless an admission without a procedure replaces one with a procedure). Rather, the savings would have to be captured later by a smaller update factor. Reducing the percentage of the benchmark paid to MA plans, as was done in the ACA, generates program savings for Medicare, but from the standpoint of the Medicare program’s social efficiency, cuts in MA plan payments may be shortsighted.

This is somewhat hedged, but I’d hedge further because a full welfare analysis has not been done to justify a specific MA payment level, relative to TM. It’s possible that MA is still overpaid from a social perspective, and we don’t know by how much.

There’s a lot more in the paper. I recommend you read the whole thing.

* According to the original paper, this measure applies to all women, not just those receiving diabetes care as suggested in the chart.

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.

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Hospital admissions and readmissions, being both costly and prevalent, have become a key policy issue. Furthermore, although patients may be admitted to the hospital for a variety of reasons, research shows that many admissions and readmissions are also preventable. Thus, reducing hospital readmissions is a way to both improve care and reduce avoidable costs. However, while hospital readmissions have been investigated in a variety of populations, including Medicare, there have been fewer studies in the Medicaid population.

A recent study, led by state Medicaid Medical Directors (MMD) and conducted by AcademyHealth, with funding support from the Agency for Healthcare Research and Quality (AHRQ), sought to contribute to better understanding of Medicaid readmissions and state-level efforts in addressing this issue.   Having previously investigated antipsychotic medication use in Medicaid children and adolescents, a working group of states and investigators sought to characterize acute care hospital admissions and 30-day readmissions in the Medicaid population through a retrospective analysis. The team developed detailed definitions and specifications, as well as empty template tables for data entry. The form was emailed to states and those who chose to participate filled it out and sent aggregate data back to AcademyHealth for review and analysis. Nineteen states submitted data.

The study found that 30-day Medicaid readmissions rates for the 19 participating states varied from 5.5 percent to 11.9 percent, with an average of 9.4 percent. State Medicaid payments for readmissions were substantial: they averaged $77 million per state for study states, and they represented 12.5 percent of the payments for all Medicaid hospitalizations. Five diagnostic groups appeared to drive Medicaid readmissions, accounting for 57 percent of readmissions and 49 percent of hospital payments for readmissions. These five diagnostic groups in order of their prevalence are: mental and behavioral disorders; pregnancy, childbirth and their complications; diseases of the respiratory system, diseases of the digestive system, and diseases of the circulatory system. The two most prevalent diagnostic categories — mental and behavioral disorders and diagnoses related to pregnancy, childbirth, and their complications together accounted for 31.2 percent of readmissions.

Because Medicaid has the largest number of beneficiaries among all U.S. payers and is growing even larger with the implementation of the Affordable Care Act, understanding the potential for improving hospital use in this population has particular importance. This study offers valuable new information about Medicaid hospitalizations: it allows MMDs to better understand the nature and prevalence of hospital use in the Medicaid population and provides a baseline for measuring improvement. Providing states with information about admissions and readmissions by diagnostic groupings enables a discussion of the interventions aimed specifically at these conditions. These interventions can range from better access to primary, behavioral or mental, or obstetrical care to more effective management of care transitions.

The study was published in the August issue of Health Affairs. Study investigators included MMDs, Judy Zerzan, M.D. (Colorado), and David Kelley, M.D. (Pennsylvania), as well as Tara Trudnak, Ph.D., Gerry Fairbrother, Ph.D., and Katherine Griffith, M.H.S., from AcademyHealth and Joanna Jiang, Ph.D. from AHRQ.

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Educating congressional staffers on the importance of health services research—or even research more generally—can sometimes prove challenging:

Staffer: “Now tell me, why should I fund this research? How is it going to better my community/state/country?”

Researcher/Research Advocate: “Well, our hypothesis is that XYZ will happen, but we’re not sure yet.”

Although scientists have undoubtedly been among the core building blocks of modern society, in an era of competing political interests and limited funding, policymakers sometimes brush off studies whose practical applications are not easily recognized for those that have a more “immediate” use or effect.

Earlier this month, economists Robert Wilson, Paul Milgrom, and R. Preston McAfee received the 2014 Golden Goose Award, which “recognizes scientists and engineers whose federally funded research has had a significant human and economic benefits.” More specifically—and more intriguingly—the award highlights examples of those “seemingly obscure studies” that have made tremendous breakthroughs and led to some sort of major societal impact.

The highly theoretical research of this year’s winners on auctions and game theory—described as using mathematical models to study how people and organizations make decisions—eventually enabled the Federal Communications Commission (FCC) to allocate the nation’s telecommunications spectrum through auctions. During a spectrum auction, a government sells the rights (licenses) to send signals over specific bands of the electromagnetic spectrum and assigns its scarce resources. As Rep. Charlie Daniel (R-PA) stated in the award press release, “The theoretical work done by Professors Wilson, Milgrom, and McAfee has revolutionized federal auctions and returned the federal government’s investment many times over.”

 Including that first FCC auction in 1994, the agency has conducted 87 auctions, raising over $60 billion for the U.S. Treasury and enabling the proliferation of wireless technologies that make life convenient, safe and connected. Additionally, the basic auction process they developed has been used the world over not only for other nations’ spectrum auctions but also for items as diverse as gas stations, airport slots, telephone numbers, fishing quotas, emissions permits, and electricity and natural gas contracts.

As AcademyHealth has previously mentioned, today the “soft sciences,” such as health services research are losing ground to the “hard sciences,” such as biomedical research in the minds of some policymakers. Health services research has a definite role to play in the social and economic advancement of the country, but its genuine potential—and its true impact—may not yet be recognized.

As we advocate for the field of health services research in Washington, we can’t help but wonder, “Where are our Golden Geese?” What are the research studies that may have at first seemed overly theoretical, but have produced a similarly great impact?

AcademyHealth President and CEO Lisa Simpson posted a variation of this question on the organization’s members-only social networking site my.academyhealth.org. We welcome your thoughts.

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One of the critical questions about health care reform asks how much good reform will do to improve the quality of people’s health. Yes, insurance is about more than outcomes. It’s also about giving people peace of mind and providing them with financial security. But it’s also about making people healthier.

Does it do that? After the results of the Oregon Insurance Health Experiment, many debated this issue. Some of the papers claimed that providing people with Medicaid did little to improve chronic disease. Some, like Austin and I, argued that the study wasn’t really powered to answer these questions. But that doesn’t mean those questions shouldn’t, or can’t, be answered.

A recent paper in HSR adds to our fund of knowledge. “Effect of Massachusetts Health Reform on Chronic Disease Outcomes“:

Objective: To determine whether Massachusetts Health Reform improved health outcomes in uninsured patients with hyperlipidemia, diabetes, or hypertension.

Data Source: Partners HealthCare Research Patient Data Registry (RPDR).

Study Design: We examined 1,463 patients with hyperlipidemia, diabetes, or hypertension who were uninsured in the 3 years before the 2006 Massachusetts Health Reform implementation. We assessed mean quarterly total cholesterol, glycosylated hemoglobin, and systolic blood pressure in the respective cohorts for five follow-up years compared with 3,448 propensity score-matched controls who remained insured for the full 8-year study period. We used person-level interrupted time series analysis to estimate changes in outcomes adjusting for sex, age, race, estimated household income, and comorbidity. We also analyzed the subgroups of uninsured patients with poorly controlled disease at baseline, no evidence of established primary care in the baseline period, and those who received insurance in the first follow-up year.

This study compared 1,463 uninsured patients and 3,448 insured patients, who had hyperlipidemia, diabetes, or hypertension, in the three years before reform in Massachueetts and the five years after. They matched them by propensity score, and controlled analyses for demographic factors and comorbidities. This isn’t a randomized controlled trial, so it’s not proof of causality, but it’s a good effort to look at whether becoming insured is associated with improvements in chronic disease management.

The study’s main findings were that patients who were uninsured before reform didn’t have a bigger improvement in total cholesterol, HbA1c level, or systolic blood pressure compared to those who were insured over the entire period. They also looked specifically at patients who had the most potential to see improvement: those who had poorly controlled disease before reform, had no established primary care before reform, and who obtained insurance in the first follow-up year after reform. Even those patients showed no significant improvement over those who were already insured.

The main conclusion of the study is correct. Health care reform in Massachusetts wasn’t associated with improvements in the care of these diseases five years later. But let’s take a pause and look at some of the underlying factors of this study.

The first thing to note is that, by design, this study matched the controls, who were insured, to the uninsured population in such a way as to make them as alike as possible. When you look at Figure 1, you see that the levels of cholesterol, HgA1c, and systolic blood pressure were remarkably similar. If that’s the case, one of two things might be true. The uninsured in Massachusetts might already be healthier than we’d expect, or the population matching caused a selection of patients from the insured population who were unhealthier than we’d expect. The former would bias this study towards the null, and latter would bias the study the other direction.

This is also a study of patients in Massachusetts. The state, even before reform was relatively generous in terms of providing uncompensated care. It had a low level of uninsurance. It had a high concentration of providers. Massachusetts is not the rest of the United States.

It’s also important to note that in the studies that have “proven” that insurance improves these factors looked at very poorly controlled individuals. The uninsured in this study don’t really qualify there. As best as I can tell from Figure 1, the baseline serum cholesterol value is about 220, the baseline HgA1c around 8, and the baseline systolic blood pressure just above 130. Those values, while high, are not the same as those in prior studies.

The more important take home, however, and one recognized by the authors as well, is this: “Interventions beyond insurance coverage might be needed to improve the health of chronically ill uninsured persons.” Insurance is necessary, but not sufficient to improve outcomes. You need it to gain access, but there’s plenty of evidence that even people in the health care system have sub-optimal outcomes. Expecting that that insurance reform alone will improve quality is somewhat short-sighted.

Aaron

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On Thursday, July 24, the Senate appropriations subcommittee with jurisdiction over health research funding released its draft appropriations legislation for fiscal year (FY) 2015. Health services research fared well in the draft bill, in which the subcommittee proposed increases for the National Institutes of Health (NIH), the Agency for Healthcare Research and Quality (AHRQ), and the Centers for Disease Control and Prevention (CDC). Yet, though the legislation looks promising–all things considered–its potential to ever see the light of a vote does not.

Within the bill, the Labor, Health and Human Services, Education and Related Agencies Subcommittee provides details on what should be funded, at what level, and how. Some important notes that are relevant to health services research include:

  • AHRQ Budget Authority. AHRQ has historically been funded through what is essentially a “tap” on other health agencies’ budgets, making the agency vulnerable as the fiscal belt has tightened and as lawmakers have sought to fund other pressing priorities. The Senate subcommittee would, for the first time in decades, provide AHRQ with its own budget authority, providing significant stability for the agency. AcademyHealth, through the leadership of the Friends of AHRQ and the Friends of NCHS (the National Center for Health Statistics) 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, NCHS, the Assistant Secretary for Planning and Evaluation (ASPE), and the Office of the National Coordinator for Health Information Technology.
  • Investigator-Initiated Research. AcademyHealth has long advocated for balance in AHRQ-funded research (IIR)–both what is funded, and how. At our recommendation, lawmakers have for many years targeted funding for investigator-initiated research grants at AHRQ to balance investments in intramural research and contracts. Again this year, the subcommittee proposes maintaining this targeted funding of $45 million and notes the importance of true IIR while urging AHRQ to avoid being too prescriptive in awarding these funds:

    “The Committee believes that IIR 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.”
  • Commission on Scientific Standing. The subcommittee proposes $1 million for the National Academy of Sciences to establish a “Blue Ribbon Commission on Scientific Standing.” The Commission would discern American public opinion on, understanding of, and faith in scientific research and make recommendations for how to improve scientific literacy and enhance Americans’ views about science.

Just as important as what is in the bill is what is not. The bill does not include restrictions on the use of research, or prohibitions on comparative effectiveness research and health economics. AcademyHealth has been watching for such troubling language closely, since it first appeared in a draft House appropriations spending bill two years ago.

The release of the draft spending bill provides some transparency into the priorities of the Senate subcommittee with jurisdiction over health spending. However, it’s about all we can expect to see for the remainder of this Congress. The Senate Appropriations Committee cancelled its full committee markup of this spending bill among concerns about Affordable Care Act (ACA)-related amendments. The House has not–and likely will not–scheduled a markup of its health spending bill given the political sensitivities surrounding it (e.g., ACA, Title X funding, etc.).

With the month-long August recess upon us, lawmakers will not take any further action on the FY 2015 spending bills until they return in September, at which point they are expected to pass a stop-gap continuing resolution to keep the government running at current levels until December. Whether or not Congress will be able to negotiate an actual spending bill for the Department of Health and Human Services (HHS)–or will take the easy way out with a year-long continuing resolution–will largely depend on which party retains control of the Senate after the mid-term elections.

For a copy of the Senate’s draft legislation, click here, and for a copy of the report, click here. A summary of the subcommittee’s funding allocations can be found here.

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Earlier this week, I had the pleasure of participating in TEDMED’s “Facing the Facts of Childhood Obesity” Google Hangout, part of the community’s Great Challenges of Health and Medicine series, which convenes individuals from various disciplines to explore “20 of the most complex, multifaceted, and insufficiently understood challenges to the health and health care of Americans.”

As a pediatrician and a passionate advocate for advancing the nation’s health, I was thrilled to join Risa Lavisso-Mourey, Nancy Brown, Don Schwarz, and Elissa Epel for this conversation. As tends to happen when I’m in a room full of great minds, I came away with my wheels churning and with our mission to improve health and health care through the application of evidence reaffirmed. Reflecting on this discussion, I want to emphasize one point I made during the Hangout that I find critical to the field of health services research more broadly:

To build a culture of health, we must first build a “culture of learning.”

To clarify, when I say “culture,” I mean an expectation, a new normal, where every innovation, every intervention, every program, and every policy is monitored and evaluated to establish whether it is actually delivering the intended impact. A new normal where every actor engaged in a program or policy development—whether a provider, program manager, policymaker, or participant—naturally asks, “How will we know if this intervention worked? How will we know that we had the effect we intended to have?”

It is only in a culture of learning that we will be able to scale up and spread those interventions that do work, adapting them to the many diverse communities in which they’re needed and—just as importantly—discarding those which do not. This will maximize our valuable, and not to mention already limited resources, to the benefit of better health and health care.

However, to accomplish this fundamental shift in thinking, we need to become better at building bridges between our scientific/evidence communities and our policy/practice communities. You’ll note that I made ‘communities’ plural, and I do so intentionally. Currently, there are too many silos within the science community; they’re bounded by disciplines and sectors, and the same is true for practice and policy.

What the obesity epidemic, both child and adult, makes abundantly clear is that we must work across these silos to succeed. We, as health services researchers, must not only bridge the traditional evidence and policy/program divide, which is already far too wide, but we must also create a vibrant, expansive, and mutually supportive community of evidence producers. We need to expand our proverbial “toolkit” of measures and study designs to enable more real-time knowledge generation, helping to drive mid-course corrections rather than [click to continue…]

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On Tuesday, July 15, the Coalition for Health Funding hosted its annual Public Health 101 congressional briefing. During this year’s briefing, “Faces of Austerity: How Budget Cuts Hurt America’s Health,” longtime AcademyHealth member Glen P. Mays, F. Douglas Scutchfield Endowed Professor in Health at the University of Kentucky, spoke on the economic impact of cuts to public health funding.

The new work presented by Dr. Mays complemented his 2011 study, published in Health Affairs, which demonstrated that investments in local public health departments improved rates of preventable causes of death, including infant mortality, cardiovascular disease, cancer, and diabetes. That study, too, saw public health having a greater impact in low-resource communities.

As Dr. Mays noted during the briefing, historically, the U.S. public health system is a great success story. However, public health receives only 3 percent of the $2.3 trillion spent on health care. He cautioned that recent cuts to the chronically underfunded system could prevent public health from doing its traditionally “invisible” work to increase life expectancy, prevent disease and death, and contribute to healthier communities.

Among the statistics provided during his presentation, Dr. Mays noted that, between 2006 and 2012, the average community in the United States lost roughly 5 percent of its public health protection, including efforts to monitor community health status, investigate and control disease outbreaks, and educate the public about health risks and prevention strategies. Those hardest hit experienced reductions of more than 25 percent. When looking at the numbers, research reveals that the communities that experienced the largest increases in unemployment and the largest reductions in public health spending during the economic recession saw the most severe fall in local public health delivery. Also related to unemployment, the research found that a doubling of unemployment rate was associated with a 6.3 percent decline in the availability of public health activities in the average community. As drastic as these cuts appear at their surface, Dr. Mays told attendees that their full impact is yet to be recognized.

Public health is operating under the tyranny of short-term thinking. A desire to reduce funding now is resulting in shortsightedness when it comes to the future of the United States health care system. When considering the mounting list of demands and challenges facing health care today, including increasing costs and an aging population, continued cuts will do more harm than good. Despite what policymakers may think, “Cuts to the nation’s health are certainly not trivial,” said Dr. Mays. “Research shows they will have a direct impact.”

Read more about Dr. Mays’s research in the one pager “Evidence Links Public Health Services to Stronger Communities,” and follow along with his team’s research through his blog.

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When tort reform comes up in discussions of health policy, it’s almost always pitched as a way to reduce health care spending in general. I’ve written extensively on how this seems very unlikely, given what we know from data and evidence. Changing physician behavior is hard, and the threat of lawsuits is not the only motivator in how physicians order tests or do procedures.

That said, there are many other questions. Such as how might malpractice reform change the supply of physicians? And would it be more likely to move high or low quality physicians? A recent study published in the Journal of Law and Economics is on point:

Malpractice reforms tend to reduce physicians’ liability for harming patients. Because these reforms are passed at the state level, the costs of harming patients vary widely by geographic location. In this paper, I test whether malpractice reforms affect where physicians choose to practice and whether physicians who relocate in response to reforms are particularly prone to commit malpractice. Because a state’s own reforms cannot separately distinguish moral hazard from adverse selection, and because those reforms are likely to have direct effects on measures of malpractice via the legal market, I focus attention on neighboring states’ reforms.

This study looked at how reforms passed in adjacent states affected physician movement, since it’s much easier to move from one state to another right across a border. The analysis got down to the county level to look at ease of moving when a neighboring state passed a law making malpractice lawsuits harder.

The first analysis looked at whether doctors relocated their practices after a nearby state changed its malpractice laws. The second analysis examined the how changes to nearby states’ malpractice laws changed the local rates for malpractice insurance. The authors felt that this was a decent proxy for detecting whether doctors who remained or moved were more or less likely to have been sued in the future.

The first analysis found that physician migration was related to nearby changes to malpractice laws. The study found that the supply of doctors decreased about 4.4% when a neighboring state passed tort reform with caps on non-economic damages. It appears that physicians are more likely to practice in states with reforms than states without.

The second analysis was also interesting. If a state passing malpractice reform caused the malpractice rates in neighboring areas to go up, that would imply that higher risk doctors remained, and lower risk doctors left the area. If rates went down in neighboring areas, then it would imply that it was the high risk doctors who moved.

The latter was seen. When states passed tort reform with caps on non-economic damages, malpractice rates in neighboring areas dropped 4.5%. Moreover, the sensitivity analyses showed that it’s unlikely that this is the result of certain specialties moving. It seems that physicians who are more likely to commit malpractice are the ones most likely to move.

Now, I admit that it’s hard to know what the actual damages and risks are here. Malpractice is still very uncommon, and these changes aren’t huge. But when policymakers argue that passing tort reform will increase the physician supply, they should know both the magnitude of that difference, and the quality of the physicians they might attract. This study gives us a little more information on both those questions.

Aaron

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