The Incidental Economist

Since I’ve received a number of questions recently about the timing of King v. Burwell and its aftermath, I thought it was worth addressing them all in one place.

When will we get a decision?

The Court is likely to release its opinion in the last week of June. A decision could come sooner, but it probably won’t. The case was only argued in March, which is fairly late in the term, and it’s going to take time for the justices to write their opinions and to work out language with colleagues who wish to sign onto those opinions. Plus, the justices have a bunch of other opinions to write before they skip town for the summer. It’s a busy time.

When will the decision take effect?

If the government loses in King, there’s a small chance that the Court will stay its decision. If it doesn’t, however, the administration will have little choice but to comply within 25 days.

Here’s why. Per Rule 45 of the Supreme Court’s rules, an opinion takes effect 25 days after its release in any case that was appealed from a state court. King wasn’t an appeal from a state court, though. The case came from the Fourth Circuit. And Rule 45 doesn’t exactly say when a decision will take effect—in legal jargon, when the Court’s mandate will issue—with respect to the lower federal courts.

But don’t get too hung up on precisely when the mandate will issue. The executive branch’s compliance with a Supreme Court judgment is more about respecting the decision of a co-equal branch than it is about adhering to a formal judicial order. After King is decided, the Obama administration will have 25 days to consider asking the Court to rethink its decision. The administration probably won’t bother; doing so would be pointless. But after it throws in the towel, the administration couldn’t flout the Supreme Court’s decision without provoking a minor constitutional crisis.

When will people start losing coverage?

Once the administration complies with the Court’s decision, the IRS will no longer have the authority to cut subsidy checks—called “advance payment tax credits”—to insurers in 34 states. When residents in those states go on HealthCare.gov to pay their monthly premiums, perhaps on August 1, they’ll be asked to pay the full cost of their coverage.

If they don’t—and most won’t—their insurers will terminate their coverage. Those terminations will, in most states, become effective 30 days after nonpayment. Millions of people are thus likely to lose coverage by Labor Day.

There’s been some suggestion that, under the ACA, insurers must wait 90 days before terminating coverage for non-payment. But that’s wrong. The ACA does require insurers to give notice 90 days before ending a “particular type” of plan. The provision does not govern, however, where an individual’s coverage is canceled for failure to pay.

Do the states have time to transition to state-based exchanges for 2016?

Under HHS’s current rules, states that wish to operate state-based exchanges for 2016 have to secure conditional approval by mid-June—which is to say, about two weeks from now. Needless to say, no state (with the possible exception of Pennsylvania) will hit that deadline. HHS could adjust its rules, but even if it does, open enrollment is set to begin on November 1. States will thus have a scant four months to get new exchanges up and running. In most if not all states, that won’t be nearly enough time.

@nicholas_bagley

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Why do authors need to report conflicts of interest when they publish a medical study? Austin had a thoughtful post last week in response to Lisa Rosenbaum’s NEJM essays (here, here, and here) on researchers and conflicts of interest.

‘Conflict of Interest’ refers to financial relationships between the authors of a research article and the manufacturer of the intervention being studied. Rosenbaum argued that there is an unfair and unwarranted prejudice against researchers who have such relationships, because the existence of a conflict of interest does not necessarily imply that the researcher is biased. The prejudice against researchers working with industry impedes the progress of research.

Austin took her reasoning to a practical conclusion. He imagines himself reading an article and trying to evaluate its credibility. Medical journal articles typically have a footnote reporting meta data on the conflicts of interest reported by authors (e.g., “Dr. Jones was a paid consultant to the medication’s manufacturer, Big Pharma Inc.”). Austin questions whether he should even read that footnote, because

Once I gather the meta data [about the authors’ conflicts of interest], what should I do with it?

Austin’s right. Just knowing that Jones consults to Big Pharma doesn’t help you evaluate whether Jones’ study is valid. I don’t think there is a fair or even effective way for an individual reader to use meta data about authors to evaluate an individual article. I don’t read those footnotes either.

Nethertheless, it is vital that those footnotes are there. Meta data are essential for meta analyses, which are systematic reviews of the effectiveness of research. Meta analyses statistically combine the results of many studies to summarize their data into a single estimate of the effect of a treatment. Moreover, they explore the heterogeneity of treatment effects, looking for differences between studies that may explain why the treatment seemed to work better in one study than another.

Meta analyses frequently find that treatment works better in industry-funded studies than in non-industry funded studies. A recent Cochrane Review of research of the effects of industry sponsorship on research reported that:

We found that drug and device studies sponsored by the manufacturing company more often had favorable results (e.g. those with significant P values) and conclusions than those that were sponsored by other sources. The findings were consistent across a wide range of diseases and treatments.

We can only see this pattern by looking across many studies using journal article meta data. Of course, the Cochrane reviewers’ conclusions can be disputed on empirical grounds. Which is, of course, the great thing about having the meta data, because with the meta data we’re not limited to our moral intuitions in evaluating the validity of the empirical literature, taken as a whole.

So here is one reason why reporting of conflicts of interest is essential: there is a substantial risk (not certainty) of industry bias in research reports. We need to track it and understand it, and we can’t do this without required disclosures of conflicts of interest. I expect that both Austin and Lisa Rosenbaum agree with me on this point.

There remains an important question about what we should do to correct for industry bias in research results, if and when it’s confirmed. Just briefly:

1. Suppose a meta analysis of a treatment for a specific drug, say, finds that (a) that the treatment effect averaged across studies is greater than zero (i.e., the treatment works), but (b) industry-funded studies tended to report bigger treatment effects. Then I’d conclude that the average treatment effect is likely an overestimate. I’d be cautious in using it. I’d also conclude that we need more studies of the topic.*
2. Suppose that many meta analyses find an association between larger treatment effects and industry funded studies, which, I believe, exists.* Then I’d conclude that we need to improve our research methodology. Such effort is already underway: many current reforms in the conduct and reporting of medical research—for example, the clinical trials registry—have been motivated in part by concerns about bias associated with industry funding.

What I wouldn’t conclude is that we should ban industry-funded clinical trials or ignore their findings entirely. Nor, without specific evidence of wrongdoing, would I assume that an industry-funded researcher is a shill or a fraud.

Let me add that Rosenbaum has raised many important questions about our moral attitudes toward researchers and their relationships with industry. We should continue to require conflict of interest reporting, but we should also have the discussion about moral attitudes that Rosenbaum calls for.

* Note that, as always, a simple correlation does not clarify the causal mechanisms that underlie it. An association between the size of a treatment effect and the study outcome needn’t imply that industry is cheating. For example, suppose that industry researchers more accurately target populations in which a treatment is likely to work or work better. Such targeting could either be viewed as “gaming” or as a means of providing useful, population-specific information. So a finding that industry-sponsored trials work better should open a question about what industry does differently. But—one more time—we can’t have that discussion without the meta data.

@Bill_Gardner

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I want to flag something meta about my Upshot post today, in which I describe a study that suggests hospital productivity has increased in recent years (through 2011). The study findings surprised me. Based on prior work and history, I am highly skeptical hospitals can maintain the high productivity growth it suggests.

Put another way, writing about the study by John Romley, Dana Goldman and Neeraj Sood the way I did was counter to confirmation bias. I’ve posted about the hospital—or health care—productivity problem many times on TIE, as I linked to in the piece. It would have been easy to cling fast to the view that hospitals can never become substantially more productive (the cost disease) and to discount the Romley et al. study for any number of reasons. (I mention caveats at the end of the piece; more have been suggested to me on Twitter.) I find it more interesting and rewarding to take the study at face value—to challenge and update my own priors, if even provisionally.

I suspect some will read the piece as Obamacare boosterism. That’s a mistake. I don’t do that. The ACA really did make a big and risky bet that hospitals could increase productivity. I’ve worried about it for years. I hope it’ll pay off, as the study suggests. We should be prepared for the possibility it won’t. While we wait, we should be brave enough to assimilate new evidence independent of what it implies about the ACA.

Stay calm and update priors as warranted.

@afrakt

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Via StuffJournalistsLike:

@afrakt

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David Sackett passed away recently. We don’t usually do obituaries here, but this one seems appropriate. This is Healthcare Triage News.

This was adapted from a piece I wrote for the AcademyHealth blog. All the references and links are there.

If you’re looking to purchase his Handbook on Evidence Based Medicine, you can find it here!

@aaronecarroll

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I’m giving him full credit right up there in the title. Twice in the last two weeks I was all riled up and feeling the need to blast out posts on how everyone needed to stop freaking out and pay attention to real risks and not the scream du jour. But  before I could even get to it, there was Christopher Ingraham in the Washington Post, doing it for me.

First up was the horrific train accident on the East Coast. Let’s acknowledge that it’s a horrible tragedy, ok? It’s also totally reasonable that it captured our attention. I can’t even fault people for being concerned that our rail infrastructure might need some updating, although I don’t think it’s clear yet that this was the cause of the crash.

But then I started hearing from people complaining that rail travel was unsafe, period. Or at least unsafe compared to other forms of travel. You hear the same sort of thing whenever there’s a plane crash, even though that’s like the safest way to travel. And you all know that I hate when people ignore that car travel is pretty much the unsafest way to go, especially since accidents are the number one killer of children.

So I planned to make a chart on how all of these things compared to each other, but there was Christopher Ingraham, on the case already:

Yes, trains are less safe than planes, buses, or subways, but still WAY safer than driving. So deciding to cancel that 150 mile train trip and drive instead would not be rational. Thanks, Chris!

And then, this week, he took on laundry pods. those are those little prepackaged detergent things for the dishwasher or laundry. There were news stories in the fall about how kids were going to the ER in droves because they were eating them. The usual panic buttons got pushed. But, again, I wanted more information. How many is “droves”? How does this compare to other panics?

I was reminded of a bit I wrote about Plan B not too long ago, when people “worried” that would be taken inappropriately and people would overdose:

All drugs, when improperly used, carry significant effects. In 2009, there were over 70,000 calls to poison control centers for concerns about acetaminophen and more than 88,000 for ibuprofen. More than 30,000 calls were made for diphenhydramine, and 4 of those cases resulted in deaths. Just looking at kids 5 years of age and under, there were more than 130,000 calls for analgesics, 53,000 for vitamins, 48,000 for antihistamines, and 45,000 for cough and cold preparations. And yet, no one seems to be too concerned that these medications could be purchased “alongside bubble gum and batteries”. And, for the record, battery ingestions killed 4 kids in that age group that year.

It’s all about context. So I planned to write a post on how calls to poison control for laundry pods compared to other things. But there was Christopher Ingraham, on the case already:

And, of the 11,000 laundry pod calls in 2013, only 54 resulted in a major injury and only 2 resulted in death. In fact, only 29 kids aged 1-4 died of ALL accidental poisonings in 2013. Guns and assaults killed way more. Car accidents killed 454 (see above).

We need to keep these things in perspective. Chris is helping.

@aaronecarroll

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Yesterday, the Government Accountability Office (GAO) released a withering report on how Medicare sets the fee schedule for paying physicians.

The American Medical Association/Specialty Society Relative Value Scale Update Committee (RUC) has a process in place to regularly review Medicare physicians’ services’ work relative values (which reflect the time and intensity needed to perform a service). Its recommendations to [CMS], though, may not be accurate due to process and data-related weaknesses. First, the RUC’s process for developing relative value recommendations relies on the input of physicians who may have potential conflicts of interest with respect to the outcomes of CMS’s process. . . . . Second, GAO found weaknesses with the RUC’s survey data, including that some of the RUC’s survey data had low response rates, low total number of responses, and large ranges in responses, all of which may undermine the accuracy of the RUC’s recommendations. For example, while GAO found that the median number of responses to surveys for payment year 2015 was 52, the median response rate was only 2.2 percent, and 23 of the 231 surveys had under 30 respondents.

. . . [T]he evidence suggests—and CMS officials acknowledge—that the agency relies heavily on RUC recommendations when establishing relative values. For example, GAO found that, in the majority of cases, CMS accepts the RUC’s recommendations and participation by other stakeholders is limited. Given the process and data-related weaknesses associated with the RUC’s recommendations, such heavy reliance on the RUC could result in inaccurate Medicare payment rates.

This isn’t the first time the RUC has come in for serious criticism. Nor will it be the last. Rife with conflicts of interest and not especially transparent, the RUC is a specialist-dominated committee that “donates” more than $8 million of its own services each year to Medicare, presumably out of the goodness of its heart.

The RUC’s job is to tell CMS how much time and effort it takes to provide medical services in the hopes of influencing how Medicare pays physicians. Since CMS has been starved of the resources necessary to independently review physician services, the agency has little choice but to rubber-stamp most of the RUC’s recommendations.

In recent years, Congress has taken modest steps to fix the problem. The Protecting Access to Medicare Act of 2014, for example, appropriates $2 million each year to enable CMS to collect information directly from physicians about the relative value of their services. But CMS doesn’t have a plan about how it will spend that money, and in any event $2 million won’t go far when it comes to reviewing thousands of physician services.

Doing the job right would cost real money, but it’d be a pittance when compared to the $70 billion spent on physician payments in 2013. If we insist on running Medicare on a shoestring, we shouldn’t be surprised when it doesn’t work very well. Sometimes you get what you pay for.

@nicholas_bagley

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I recommend Lisa Rosenbaum’s three-part NEJM series on financial conflicts of interest (links: part 1, part 2, and part 3). Though it is thought provoking throughout, this single sentence was enough to occupy my mind for several hours:

Once moral intuitions enter the picture, the need to rationally weigh trade-offs is often eclipsed by unexamined convictions about right and wrong.

It is now commonplace for authors to disclose potential financial conflicts of interest (COI) to journals and institutional review boards (IRBs) before paper publication and initiation of research, respectively. You can most easily find COI statements at the end of many published papers, or accompanying them online. Here’s just a part of one COI disclosure for a paper I pulled at random from the NEJM archives:

The paper is about a drug (bevacizumab) manufactured by Genentech (as Avastin), so this particular COI disclosure for this particular author is relevant. (This author is one of 18 or so on the paper. Most of the others have no such disclosed COI, though some do.)

If I’ve ever read any COI disclosures as part of reading or evaluating a published study, it’s only been a few times. I have purposefully avoided them for many years. Why?

I worry about bias: my own. I simply don’t know what to make of COI disclosures. It’s easy to detect a potential or appearance of a COI. It’s much harder to decide how to weigh that when evaluating a study. Sure, it’s a data point that could be meaningful. So could a myriad of “irregularities” that might show up in a full body MRI on a patient with no symptoms of disease. I worry about false positives and emotional harm. How does this author’s prior financial relationship with Genentech affect the published research? Does it affect my head even more?

I do not want to worry about COI (or worry about my worry about it) when evaluating a paper’s methods.

Several years ago I received an email encouraging me to consider the work of a certain author. The work was relevant to whatever I was blogging about at the time. But I knew that author had substantial industry funding for his work, and decided I wasn’t going to read or consider his work on that basis. I emailed back as much.

I regret that decision and that email. I should have considered the work on its merits. My assessment that it could not have been worthwhile was a biased one. I don’t read COI disclosures because I want to protect myself from that bias, acknowledging that I might be blinding myself to the authors’ own biases. There’s no way to win here.

For the same reason, for years I didn’t read authors’ bios. With respect to the quality of the work, why should their institution, titles, or other credentials matter? Either their study is sound or it isn’t. If I can’t assess that from a paper’s text and figures alone (as a blinded reviewer would), then that’s a problem, but it’s not one that can be resolved by knowing an author’s pedigree any more than it can be resolved by knowing her skin color.

In fact, for years I didn’t even read authors’ names on papers. I barely knew who wrote what, until it came time to cite stuff. Then I had to know names. Over the years I came to recognize some, got to know scholars across the country.

Now I’m friends with and colleagues of many. I know where they work. I know their credentials. I consider by lines along with article titles when deciding what to read. There are some authors whose work I never want to miss. Is this a bias? Time being finite, it certainly crowds out reading others’ work.

All this meta data—names, affiliations, degrees, potential COI—can bias. Once it enters my head, I cannot tell the extent to which it does. I could argue that I’m merely being Bayesian when I use prior knowledge of the authors’ work or their institutions. (This one has a well-earned reputation for good work; this other one is from a “lesser” institution widely thought to have an ideological perspective.) And maybe that’s right. But I could also argue that I’m using—even subconsciously—this meta data to unfairly evaluate the work.

Lisa is right that once intuitions—moral and otherwise—like these enter the picture, we’re already in difficult terrain. Problems arise by unexamined convictions, she wrote. But, for me, problems arise by examined ones as well. I do think money influences, as do relationships and beliefs. But when I examine my own feelings about these, I’m no closer to understanding the extent to which I use them in my own biased way, if at all.

Once I gather the meta data, what should I do with it? What have I already done?

@afrakt

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How do we engage in debate to advance science and avoid the tendency to perpetuate the biases of our upbringing and community? I explore that question in my latest AcademyHealth post. Read it!

@afrakt

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In advance of legislative consideration of hospital DSH payments, Wisconsin’s Legislative Fiscal Bureau wrote:

Some economists have disputed the claim that low reimbursement rates paid to healthcare providers by public programs (including both Medicaid and Medicare), result in cost shifts to commercial insurance payers. They assert that the rates charged to commercial insurers by a hospital are affected primarily by market factors that are independent of the rates paid by public programs. That is, a hospital generally seeks to maximize net revenues, regardless of the mix of its commercially insured and publicly-funded patients. The extent to which hospitals increase or decrease prices charged to commercial insurers is dependent upon their market power in relation to those insurers and competing hospitals. By contrast, the idea that a hospital charges higher rates to commercial insurers in response to lower public program reimbursement rates implies that the hospital has the market power to dictate a higher price to commercial insurers that it would not otherwise exercise in the absence of low public program reimbursement rates. In support of this view, these economists cite evidence that suggests that hospitals either reduce costs in response to constrained revenues from public programs, or attempt to attract a larger pool of commercially insured patients by reducing the price charged to commercial insurers.

Some economists. Could be almost anyone.

@afrakt

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