• Arbitration can obscure safety problems in nursing homes

    This is the third post in a series on a proposed CMS rule that would eliminate an Obama-era ban on pre-dispute arbitration for nursing home residents. For the intro, see here.

    As I explored in my last post, the research suggests that malpractice law doesn’t much improve the quality of care in nursing homes. If that’s right, then maybe it doesn’t pose safety risks to allow nursing homes to include arbitration clauses in their admissions contracts.

    But that conclusion may be too hasty. The available studies investigate how malpractice lawsuits directly change the behavior of nursing homes. They don’t purport to study how tort law, by highlighting endemic safety problems, can mobilize a policy response.

    Consider just one example. In 1986, the Institute of Medicine released a landmark report (“Improving the Quality of Care in Nursing Homes”) prompted in part by litigation in which nursing home residents “proved a variety of violations of regulatory standards, including theft of personal funds, overuse of psychotropic drugs, inadequate care resulting in decubitus ulcers, inadequate skin and nail care, inadequate bowel assistance, and sanitation problems.” That report led directly to the adoption of the Nursing Home Reform Act, which imposed minimum safety requirements and instituted mandatory inspections. The research indicates that these changes led to substantial improvements in nursing home quality. Without malpractice litigation, those improvements may never have come to pass.

    Quality remains depressingly low, however, as a follow-up report in 2000 concluded. That report, too, was prompted in part by litigation. “Concerns about problems in the quality of long-term care persist despite some improvements in recent years, and are reflected in, and spurred by, recent government reports, congressional hearings, newspaper stories, and criminal and civil court cases.” No one today thinks that we’ve addressed the concern. As Rachel Werner and Tamara Konetzka noted in a 2010 piece in Health Affairs, “ongoing quality problems and the large number of nursing home residents at risk have kept nursing home quality under scrutiny for decades.” A recent, eye-opening exposé from Jordan Rau at Kaiser Health News and the New York Times details how federal oversight of nursing homes is failing to grapple with rampant quality deficiencies.

    Lawsuits are public, and often include the sorts of graphic, shocking details that draw press attention and public outrage. For one example, drawn from a law review article by Lisa Tripp:

    Mrs. Sauer was often times found wet without being changed in four hours. She had pressure sores on her back, lower buttock, and arms on days she was found sitting in urine and excrement. A former staff member remembered seeing Mrs. Sauer at one time with a pressure sore the size of a softball, which was open. Her sores and blisters became infected. She was frequently double-padded, and even triple-padded, rather than single-padded for her incontinence problems. At times, she had no water pitcher in her room; nor did she receive a bath for a week or longer, due to there not being enough staff at the facility. She was described as “always thirsty” and her nursing notes indicated that she was heard moaning and crying. At the time she was hospitalized prior to her death, she had a severe vaginal infection. When she was in the geriatric chair, she was not “let loose” every two hours, as required by law. Finally, Mrs. Sauer was found to suffer from poor oral hygiene with caked food and debris in her mouth.

    These details only emerged because Mrs. Sauer’s family filed a lawsuit. If the Sauers had been forced into arbitration, Tripp writes, “[t]he chilling description … would have disappeared from public consciousness.”

    That secrecy may matter more than is commonly assumed. In an important book, Making Rights Real, Charles Epp examines the role that tort law played in getting police departments, employers, and park administrators to address longstanding problems in the 1980s and 1990s:

    Police departments created strict policies on the use of force and cracked down on abusive officers. Government human relations departments created and strictly enforced policies prohibiting sexual harassment. Parks administrators tore out and replaced play equipment in tens of thousands of playgrounds, designing and managing the new installations to reduce the risk of injury. I argue that these developments, and many more, came about because newly energized activist movements and liability lawyers forced agencies to face up to long-ignored problems of abuse and injury, and because managers came to recognize that these legal claims represented fundamental threats to their public and professional legitimacy.

    One of Epp’s most striking conclusions is that the financial penalties associated with tort judgments were too small to have driven the changes. What mattered, instead, was the bad publicity associated with lawsuits—and the concomitant damage to professional legitimacy. That legitimacy threat was the “engine of pressure,” not money.

    Now, nursing homes aren’t government bureaucracies like the ones that Epp studied. But they’re closer than you might think. They’re dependent on public funding and they’re exquisitely attuned to the risk that public outrage could erode support for that funding. They also recognize that the outrage could lead to enhanced oversight. No less than police departments, nursing homes have an interest in avoiding threats to their professional integrity.

    A pre-dispute arbitration agreement is one technique for avoiding those threats. But it’s a technique that leaves the underlying quality problems to fester like a bedsore.

    I don’t want to overstate the case. Malpractice litigation isn’t the only way that quality problems come to light, and not all nursing homes insist on pre-dispute arbitration. (In 2011, Tripp found that 43% of North Carolina nursing homes, including all of the largest nursing-home chains, included pre-dispute arbitration agreements in their admissions contracts.) Plus, even if shifting from litigation to arbitration hampers the campaign to improve nursing home quality, the costs of litigation might still outweigh its benefits.

    But I will confess to disquiet. The deplorable quality of care in many nursing homes is a national crisis, even if it doesn’t show up on the front page every day. Is now really the time to give the industry another tool to shield its conduct from public scrutiny?


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  • Can Psychedelics Be Therapy? Allow Research to Find Out

    The following originally appeared on The Upshot (copyright 2017, The New York Times Company). 

    In the last few years, calls for marijuana to be researched as a medical therapy have increased. It may be time for us to consider the same for psychedelic drugs.

    Two general classes of such drugs exist, and they include LSD, psilocybin, mescaline and ecstasy (MDMA).

    All are illegal in the United States because they carry a high risk of abuse. They can also cause harm. The best-known adverse event is persistent flashbacks, though these are believed to be rare. More common are symptoms like increased heart rate and blood pressure, anxiety and panic.

    Some people have pointed to anecdotal evidence of positive effects. Ayelet Waldman, a novelist and former federal public defender, wrote a memoirabout how microdosing of the drugs turned her life around. But these drugs — like all drugs — carry risks that should not be ignored. With plenty of potential downsides, and no proven upsides, it’s not surprising that such drugs have been shunned.

    In recent years, however, research has begun to show promise in treating a number of ailments.

    People with life-threatening illnesses can also suffer from anxiety, which is hard to treat, especially when patients are on many other medications. In 2014, a small randomized controlled trial was published that examined if LSD could be used to improve this anxiety. The treatment included two LSD-assisted psychotherapy sessions conducted two to three weeks apart. Anxiety was significantly reduced in the intervention group for up to a year. Such results, however, could have been due in part to a placebo effect.

    More common are studies of the use of psychedelics to treat abuse or addiction to other substances. A 2012 meta-analysis of studies exploring LSD’s potential to treat alcoholism looked at six randomized controlled trials. They included more than 500 patients, with follow-up of three to 12 months. The interventions usually involved one dose of LSD, given in a supervised setting, coupled with therapy. Alcohol use and misuse were significantly reduced in the LSD group for six months; differences seemed to disappear by one year. Similar studies using psilocybin have also shown promising results.

    There was an open label study — meaning there’s no placebo or attempt to mask treatment information — of three doses of psilocybin as part of a tobacco cessation program. It found that 12 of 15 participants (who had smoked an average of more than 30 years) remained abstinent six months after the program began and 16 weeks after their last treatment. That’s a much higher rate than seen in traditional programs to help people quit smoking.

    Other uses might exist as well. Researchers examined the potential for MDMA in the treatment of chronic and treatment-resistant post-traumatic stress disorder. At two months after therapy, more than 80 percent of those in the treatment group saw a clinical improvement versus only 25 percent of those in the placebo group. These researchers later followed up with participants in the study, and found that the beneficial effects lasted for at least four years, even with no further treatment with psychedelics. Similar studies have also seen improvements in symptom scores.

    As with marijuana, though, studies like these are the exception, not the rule. It is very, very difficult to do research on psychedelic compounds because they, like pot, are classified as Schedule I controlled substances, meaning they have a very high potential for misuse and no accepted uses. Schedule II drugs also have a high potential for abuse, but are considered to have potential benefits. These include OxyContin, fentanyl, Percocet and even opium.

    To engage in research in Schedule I drugs, scientists have to get approval from the Drug Enforcement Administration. To obtain a license, research labs must have inspections to prove that they are capable of storing the drugs and protecting them from misuse. In Britain, the added costs of licensing and security can cost a lab about £5,000 a year, or nearly $6,500. Unfortunately, the costs in the United States are not as well documented.

    Because of this, much of the research on these drugs is old; a lot of it took place before the United States and other countries categorized these drugs in the 1960s. What research has occurred since has often taken place in countries that are more permissive in their experiments.

    Given the potential dangers inherent in these drugs, it’s important to stress that research would need to be closely monitored. Although the drugs are relatively safe compared with substances like heroin or cocaine, and aren’t nearly as addicting, they still pose psychological and physical risks.

    People with a family or personal history of psychotic or psychiatric disorders should be particularly wary, and perhaps be excluded from trials. Research requires safety monitoring, careful planning and significant support throughout. We need to watch carefully for adverse outcomes, both expected and unexpected. We need to make sure protocols are transparent and reproducible.

    We also need to acknowledge that we need more research before anyone attempts to use these drugs as medicine. They’re typically coupled with professional therapy in studies, and we still aren’t sure there are benefits.

    But it may be time to time to reconsider our current classification of controlled substances. Clearly we must continue to be vigilant about whether drugs pose physical harm to patients. But we could assess drugs using additional measurements, including the potential for dependence; social costs through damaged family and social life; and financial costs through health care, social care and the need for police involvement.

    Using these metrics, it’s hard to argue that alcohol and tobacco should be legal for adults while marijuana and psychedelics should be considered so dangerous they’re hard to study. Likewise, opioids are considered widely acceptable in practice, yet appear to do far more harm.

    With the potential to help curb more serious addictions and ease the symptoms of mental illnesses, it seems odd to continue to make it nearly impossible to research the therapeutic potential of psychedelics.


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  • Seriously? Still with the Vitamin D?

    This time they’re trying to prevent upper respiratory infections in kids. I swear, I have no idea who thinks this stuff up. “Effect of High-Dose vs Standard-Dose Wintertime Vitamin D Supplementation on Viral Upper Respiratory Tract Infections in Young Healthy Children“:

    Importance: Epidemiological studies support a link between low 25-hydroxyvitamin D levels and a higher risk of viral upper respiratory tract infections. However, whether winter supplementation of vitamin D reduces the risk among children is unknown.

    Objective: To determine whether high-dose vs standard-dose vitamin D supplementation reduces the incidence of wintertime upper respiratory tract infections in young children.

    Design, Setting, and Participants: A randomized clinical trial was conducted during the winter months between September 13, 2011, and June 30, 2015, among children aged 1 through 5 years enrolled in TARGet Kids!, a multisite primary care practice–based research network in Toronto, Ontario, Canada.

    Interventions: Three hundred forty-nine participants were randomized to receive 2000 IU/d of vitamin D oral supplementation (high-dose group) vs 354 participants who were randomized to receive 400 IU/d (standard-dose group) for a minimum of 4 months between September and May.

    Main Outcome Measures: The primary outcome was the number of laboratory-confirmed viral upper respiratory tract infections based on parent-collected nasal swabs over the winter months. Secondary outcomes included the number of influenza infections, noninfluenza infections, parent-reported upper respiratory tract illnesses, time to first upper respiratory tract infection, and serum 25-hydroxyvitamin D levels at study termination.

    We start, of course, with the epidemiological data that low levels of Vitamin D are associated with higher risks of URIs. I can’t find the absolute risk in the paper, and I’m too tired to go look myself. I assume it’s statistically significant, although I reserve the right to snarkily assume it’s clinically insignificant and probably confounded.

    Anyway, it doesn’t matter, because here is the RCT to test the association for causation. Researchers gathered kids age 1-5 years in the winters from 2011-2015 in Canada. They randomized them to get either 2000 IU/d or 400 IU/d of Vitamin D, because I suppose it would be criminal not to supplement everyone with at least some Vitamin D these days. The main outcome of interest was laboratory-confirmed viral URI. Secondary outcomes included individual infections and Vitamin D levels.

    More than 700 kids took part in this study, and nearly all completed it (well done, researchers). In the high-dose group, kids got an average 1.05 infections, and in the standard-dose group they got 1.03 infections. Fewer with less Vitamin D, but not statistically significant. There was also no significant differences in the median time to the first laboratory-confirmed infection or the number of parent-reported URIs between groups.

    When the study ended, the group getting high-dose Vitamin D had levels of 48.7 ng/mL while the standard-dose group had levels of 36.8 ng/mL, although I’m still unclear on why we should care. The IOM says that anything over 20 ng/mL is “Generally considered adequate for bone and overall health in healthy individuals” and when you get over 50 ng/mL  “Emerging evidence links potential adverse effects to such high levels”.

    I do not understand why we keep looking for Vitamin D to be some sort of wonder drug. It’s seriously baffling to me.


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  • Healthcare Triage: New Studies Adjust Our Thinking on Spinal Manipulation

    This episode was adapted from a column I wrote for the Upshot. Links to sources and further reading can be found there.


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  • Marketplace networks

    Daniel Polsky, Yuehan Zhang, Laura Yasaitis, and Janet Weiner of Penn’s Leonard Davis Institute have been doing the foundational work of tracking and analyzing marketplace plans’ network extent. Late last year, they published a Data Brief on the state of marketplace plans’ networks in 2016, with comparison to findings from 2014.

    For each marketplace plan, they quantified network size as the ratio of the number of participating physicians to the number of physicians eligible to participate in the plan’s service area. They did this for all physicians and by specialty. They categorized network extent as follows:

    • x-small: <10% of physicians participating
    • small: 10%-25% of physicians participating
    • medium: 25%-40% of physicians participating
    • large: 40%-60% of physicians participating
    • x-large: ≥60% of physicians participating

    Across all plans and marketplaces, 12% of networks are x-small, 19% are small, 24% are medium, 31% are large, and 15% are x-large. That’s a lot of numbers. Since consumers, wonks, and policymakers are probably most concerned about narrow networks, it may be simpler just to pay attention to the proportion that are either x-small or small: 31%.

    According to their analysis, for the most part, there is little correlation between network extent and metal tiers. As shown in the chart below, within metal tier, the distribution of plans’ network extent is fairly stable. The one exception is platinum plans, which have a substantially higher proportion of narrow networks, with 41% x-small or small. However, platinum plans only attract 5% of enrollees.

    The Data Brief also breaks down network extent by physician specialty. Prior work suggests that narrow network plans help control health care spending so long as they don’t disrupt access to primary care physicians (PCPs) and do reduce network extent of specialists. The 2016 results show that network extent across primary care and other specialists is largely similar.

    There are two exceptions. First, networks for psychiatrists tend to be much more narrow (45% x-small or small) than other specialists (e.g., 31% x-small or small for PCPs). This raises concerns about adequate access to mental health care in marketplace plans. Second, hospital-based physician networks are extremely narrow: 72% x-small or small. As the authors point out, “This is notable given that this is the group of physicians most likely to lead to a surprise out-of-network bill.”

    Network extent varies tremendously by state. The chart below shows the percent of networks in each state that are x-small or small. It would be valuable to understand what accounts for such variation and its implications. How does it relate to provider and insurer market power, for example? What patterns of care and outcomes correlate with network extent? I’d also like to know how geographic variation of network extent looks by specialty, again with the implications for access and health care outcomes.

    If you’re concerned about narrow networks, you might want to know how their prevalence has evolved over time. The authors compared network extent from 2014 to 2016 for silver-rated plans. By and large, the proportion of narrow network plans didn’t change, though there was a shift from small networks (declining from 31% to 29%) to x-small networks (increasing from 6% to 12%).

    You’ll find even more stats in the Brief. Though the Leonard Davis Institute investigators have made aggregate marketplace network extent more transparent to policymakers and the public, making network extent — including within specialty — transparent to consumers at the time of plan purchase is an ongoing challenge. Also particularly troubling is

    [t]he high prevalence of narrow networks among hospital-based physicians […]. Given that these physicians are the ones most likely to send surprise out-of-network bills, this remains a concern for those with narrow network plans and broad plans.


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  • Medicaid by the numbers

    All of this is cribbed from a Viewpoint today in JAMA by Ben Sommers and David Grabowski.

    Medicaid beneficiaries in 2017:

    • Total: 77 million people
      • Kids: 34 million
      • Elderly: 6 million
      • Blind and disabled: 9 million
      • Pregnant women: 2 million
      • Parents and childless adults: 27 million

    For those of you keeping track at home, this means that only 35% of beneficiaries aren’t kids, old, blind, disabled, and/or pregnant. Remember that when people should say that Medicaid recipients should “try harder”. Also, of that 35%, many already have jobs. Some are stay-at-home parents. So the number of people who could “try harder” isn’t as many as lots of people think.

    Under the ACA, it’s projected that 86 million beneficiaries would exist in Medicaid. If repeal and replace happens, it would go down to 77 million.

    • Share of spending (2014)
      • Kids: 19%
      • Elderly: 21%
      • Blind and disabled: 40%
      • Parents and childless adults: 19%

    And if you cut, where will it come from? Pregnant women are included in the 19% of parents and childless adults. There’s not that much fat. So do you cut care for kids? For the blind and disabled? For the elderly?

    The numbers don’t add up easily. There’s no magic here. People will get “sent” to private plans with huge deductibles. It won’t be better.


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  • Healthcare Triage Breaking News: The Senate and Obamacare, Part Deux

    The Senate is trying once again to repeal and replace Obamacare, and we’ve got the news on what’s in the updated bill. Spoiler Alert: not much has changed. There are still big cuts to Medicaid, and some small changes to the proposed tax cuts of their previous bill.

    We’re trying to make HCT News more “newsy”. Hope you like it!


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  • Don’t Assume That Private Insurance Is Better Than Medicaid

    The following originally appeared on The Upshot (copyright 2017, The New York Times Company). It was coauthored by Aaron Carroll and Austin Frakt. It also appeared on page A14 of the July 14, 2017 print edition

    As we recently wrote, it’s better for patients to have Medicaid than to be uninsured, contrary to critics of the program. But is having Medicaid, as those critics also say, much worse than having private insurance?

    This idea has become a talking point for conservatives who back big changes to Medicaid, as the Senate health bill proposes. The poor would benefit simply by being ushered off Medicaid and onto private insurance, they write.

    But it’s far from proven that Medicaid is worse than private insurance. A lot depends on what kind of insurance is compared with Medicaid, and how they are compared.

    Many studies that measure Medicaid against private insurance suffer from the same flaws that compare Medicaid with being uninsured. They’re terribly confounded, and can show only associations, not causation. People with private insurance are healthier and wealthier than those on Medicaid, and in ways not fully controlled for in statistical analyses. These factors almost certainly predispose someone on Medicaid to have worse outcomes than someone with private insurance.

    Perhaps the most convincing way to compare Medicaid and private insurance would be with a randomized controlled trial that pits them head to head. No such trials exist. Recall that the Oregon Medicaid study randomly offered, via a lottery, the opportunity for low-income adults to enroll in Medicaid. It did not have another study arm that offered private insurance.

    But we do have a decades-old trial that looked at varying levels of cost-sharing: the RAND Health Insurance Experiment. This is relevant because one substantial difference between Medicaid and most private coverage is the level of cost-sharing. Medicaid is nearly free. Most private coverage comes with deductibles and co-payments.

    The RAND study randomly assigned 2,750 families to one of four health plans. One had no cost-sharing whatsoever — kind of like Medicaid. The other three had cost-sharing (money people had to pay out-of-pocket for care) at levels of 25, 50 or 95 percent — capped at $1,000 at the time, which is about an inflation-adjusted $6,000 today. This level of personal liability acts like a deductible, making the plan with a 95 percent level of cost-sharing comparable to a “Bronze” plan on the Affordable Care Act’s exchanges today.

    The RAND study found that the more cost-sharing was imposed on people, the less health care they used — and therefore the less was spent on their care. The study also found that, over all, people’s health didn’t suffer from lower health care use and spending.

    Lower spending and no decline in health — these are the results that everyone cites to justify increased cost-sharing, and to justify shifting people from Medicaid to private plans with high deductibles.

    But the results of the RAND study, like so much in health care, are complicated. A deeper dive into the data shows that people decreased their consumption of necessary health care in equal measure to unnecessary health care. As a rule, people are terrible discriminators of what care is needed and what’s not. Since most people under the age of 65 are healthy, even in the RAND study, that doesn’t matter much.

    But even if most people are healthy, some are not (and particularly those on Medicaid). In the RAND study, poorer and sicker people — exactly the kind more likely to be on Medicaid — were slightly more likely to die with cost-sharing.

    Free care also resulted in improvements in vision and blood pressure for those with low income. As an influential 1983 New England Journal of Medicine paper put it: “Free care does make a difference.”

    One limitation of the RAND study is its age. It took place between 1971 and 1982. There have been no studies of cost-sharing to rival it since. Still, the best recent evidence we have is that giving free care to poorer and sicker people improves health and saves lives. It is reasonable to conclude that switching them to a plan with high cost-sharing (even a private plan) would do the opposite.

    Some of the more recent studies were nicely summarized in a paper by Katherine Swartz for the Robert Wood Johnson Foundation’s Synthesis project. She found that increased cost-sharing for low-income populations was associated with a shift toward more costly services, like increased emergency room visits because people skipped taking their drugs. She also found that increased cost-sharing affects poor people differently than everyone else, confirming RAND’s findings. A more recent study found that enrollment in plans with high deductibles led to reductions in necessary care, which would have consequences for the poor and sick.

    Austin wrote previously herehow increased cost-sharing may lead people to take fewer drugs for their high cholesterol, hypertension and diabetes. In his first Upshot column, Aaron wrote that parents delay taking their children for asthma treatment when cost-sharing rises.

    Even small premiums can lead to problems. A $10 increase in monthly Medicaid premiums was followed by a 6.7 percent reduction in Medicaid and coverage of CHIP (Children’s Health Insurance Program) for people just above the poverty line.

    Unquestionably, private coverage can work very well for many people. Take us, for instance. The insurance that we each have from our employers is probably better for us than Medicaid would be. Though these plans come with cost-sharing, we have incomes that can handle it. Our plans cover things that Medicaid often does not, like dental checkups.

    Our plans have great networks, and they reimburse well for the care we receive. Just like Medicaid enrollees, we also receive support from the federal government, which waives tax collections on dollars contributed to premiums. That tax break is higher than the cost of Medicaid in many cases.

    We’re also relatively healthy and would probably be fine on any plan (unless and until our health deteriorates).

    But because our plans require considerable cost-sharing, even Medicaid enrollees would struggle on them. More important, neither House nor Senate repeal and replace bills offer poor Medicaid enrollees plans as generous as ours.

    The Senate’s health care plan, for example, would offer much less generous plans. A 64-year-old woman with an income of $11,400 would face a deductible of at least $6,000. For her, such a plan is not better than Medicaid; it is most likely much worse if she is also sick. Because of the deductible, the care she’d need would be financially out of reach.

    recent paper in Health Affairs documented that outcomes in Arkansas, which allowed poor people to buy private plans on the exchanges, were similar to those in Kentucky, which expanded access to poor people through Medicaid. But those private plans came with significant cost-sharing subsidies, which would be stripped away by the Senate’s bill. Even so, the evidence did not suggest that the private coverage of Arkansas was better than the public coverage of Kentucky.

    There are certainly private plans for poor and sick Americans that are better than Medicaid. But plans with very high cost-sharing — which are the ones being offered in Congress as A.C.A. replacements — are not among them.

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  • The most influential health care studies, according to Twitter

    In an interview, a journalist asked me for the health care studies with greatest policy influence. I said the RAND Health Insurance Experiment and the Oregon Medicaid Study. I added there are certainly more worthy to be named, but this is not a thing my brain does so readily.

    And so I put it to Twitter:

    The replies overwhelmed me, so I asked if anyone would compile them for a post. Nisarg Patel, a DMD Candidate at Harvard University and delivery system innovation researcher at Boston Children’s Hospital, obliged. (He’s on Twitter @nxpatel).

    Below is the list, in no particular order. Just so you can debate these and add more, comments open for one week. (I won’t be going back to Twitter to pull in more replies there, so if you want yours in the TIE record you’ll have to add them here.)

    UPDATE: Brian Rahmer (@brianrahmer) put the PDFs of these studies in Dropbox. Go get them!


    National Research Council. America’s uninsured crisis: consequences for health and health care. Washington, DC: The National Academic. 2009.

    Baicker K, Staiger D. Fiscal shenanigans, targeted federal health care funds, and patient mortality. The quarterly journal of economics. 2005;120(1):345-86.

    Kane TJ, Orzsag P, Gunter DL. State fiscal constraints and higher education spending: The role of Medicaid and the business cycle. 2003.

    Blumberg LJ, Buettgens M, Holahan J, Garrett B, Wang R. State-by-State Coverage and Government Spending Implications of the Better Care Reconciliation Act. 2017.

    McGlynn EA, Asch SM, Adams J, Keesey J, Hicks J, DeCristofaro A, Kerr EA. The quality of health care delivered to adults in the United States. New England journal of medicine. 2003;348(26):2635-45.

    Baicker K, Taubman SL, Allen HL, Bernstein M, Gruber JH, Newhouse JP, Schneider EC, Wright BJ, Zaslavsky AM, Finkelstein AN. The Oregon experiment—effects of Medicaid on clinical outcomes. New England Journal of Medicine. 2013;368(18):1713-22.

    Wagnerman K, Alker J, Hoadley J, Holmes M. Medicaid in Small Towns and Rural America: A Lifeline for Children, Families, and Communities. 2017.

    Arrow KJ. Uncertainty and the Welfare Economics of Medical Care. The American Economic Review. 1963;53(5): 941-973

    Summers LH. Some simple economics of mandated benefits. The American Economic Review. 1989;79(2):177-83.

    Berwick DM, Nolan TW, Whittington J. The triple aim: care, health, and cost. Health affairs. 2008;27(3):759-69.

    Barnett ML, Sommers BD. A National Survey of Medicaid Beneficiaries’ Expenses and Satisfaction With Health Care. JAMA Intern Med. Published online July 10, 2017. doi:10.1001/jamainternmed.2017.3174

    Sommers BD, Gawande AA, Baicker K. Health Insurance Coverage and Health—What the Recent Evidence Tells Us. New England Journal of Medicine (2017).

    Frean M, Gruber J, Sommers BD. Disentangling the ACA’s coverage effects—lessons for policymakers. New England Journal of Medicine. 2016;375(17):1605-8.

    Luntz F. The Language of Healthcare 2009. Politico.

    Wasserman J, Manning WG, Newhouse JP, Winkler JD. The effects of excise taxes and regulations on cigarette smoking. Journal of health economics. 1991;10(1):43-64.

    Ridley DB, Grabowski HG, Moe JL. Developing drugs for developing countries. Health Affairs. 2006;25(2):313-24.

    Marmot M. Social determinants of health inequalities. The Lancet. 2005;365(9464):1099-104.

    Nelson A. Unequal treatment: confronting racial and ethnic disparities in health care. Journal of the National Medical Association. 2002;94(8):666.


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  • Private insurance isn’t always better than Medicaid

    Austin and I have a new column up on The Upshot today. We’ll post it here in its entirety on Friday, per our routine, but it’s important for you to go read it today before the new Senate bill is likely released tomorrow. A new talking point is that while Medicaid may be better than being uninsured (what people used to say), it’s certainly far, far worse than private insurance.

    We take that apart over at the NYT. Go read it.


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