The Affordable Care Act in the US, like most health care reform efforts, focuses on uninsurance. That’s fine, as people without insurance do face significant problems accessing the healthcare system in the United States. But underinsurance is a real issue, too, and it’s often ignored. Underinsurance is the topic of the week’s Healthcare Triage.
I’m writing this piece mostly to save myself time on Twitter and email. Every time I write about something like diet or nutrition, people start sending me messages on how I must have missed “this” or “that” study that proves me wrong.
The problem with using only epidemiologic data is that it’s sometimes easy to find a study in the scientific literature that supports any belief. So if you want to be “proven” right, you can certainly cherry pick to prove your point.
There’s no better example of this than a classic 2012 systematic review (also recently highlighed at Vox) that was published in the American Journal of Clinical Nutrition. They selected 40-50 common ingredients and searched the literature for studies linking them to cancer risk. Of the 264 published studies, more than 70% found an association with a risk of cancer, some higher and some lower. When meta-analyses were published, though, the results (increased or decreased) were more conservative.
That’s cause the outliers are more likely to get published. They’re also more likely to get media attention. But they’re usually that – outliers.
Look, I can’t make the point any better than Figure 1:
Any dot to the right of 1 means a study found that the food led to a higher risk of cancer. Any dot to the left found that the food was associated with a lower risk of cancer. The top half groups the studies by location of cancer. The bottom half groups them by type of food.
The take home message is that everything seems to both cause and prevent cancer. If your goal is to confirm a belief you have, you will have no trouble finding a study in the literature to “prove” that anything either causes or prevents cancer.
While there is a large distribution of individual studies around a wide variety of risks, the meta-analyses are grouped pretty tightly around “no effect”. That’s cause for most things there either really is no effect, or the actual effect is pretty small.
Now this doesn’t mean that there aren’t going to be things associated with cancer. As I acknowledge in my Upshot piece, a 2013 meta-analysis found that people who eat a lot of red meat had a 29% relative increase in all-cause mortality. But even then, you gotta dig into the details. Most of this was driven by processed red meats (ie bacon, sausage or salami), and you had to eat at least 1-2 servings (with an “s”) of red meat a DAY to be considered eating “a lot”. And it’s still epidemiological data.
So you can accuse me of many things in what I’m putting on the Upshot, but I think telling me I’m “cherry-picking” is somewhat misplaced. I’m specifically using systematic reviews and meta-analyses to avoid that. When you throw me a single study and say that proves me wrong… that’s “cherry picking”.
A paper published last year by Stryjewski et al. found that Massachusetts health reform was not associated with health improvements for patients with certain chronic conditions. A different paper, published earlier in the year by Sommers et al., found that Massachusetts health reform was associated with reductions in mortality. How could both findings be true? Find out in my AcademyHealth post.
It begins with the correct observation that innovation is the source of better, but also very often more expensive health care. This alone is not necessarily problematic as we should be willing to pay for innovation that is sufficiently valuable. What is problematic is that not all innovation that we pay for meets this condition. Health care technology is often used on the wrong patient at the wrong time. These are troubling sources of waste.
From such a beginning, many authors would then consume 150 pages explaining how we might completely revamp the health system so we achieve better and cheaper care, the politics, feasibility, and even desirability of such a thing be damned. To his credit, Robinson does not take this course.
Instead, he explains what shapes the nature and cost of health care innovation in the U.S. with a clear, useful framework. To be supplied and consumed in the system, most technology must pass over four hurdles:
The FDA must approve it for market access—a hurdle for safety and efficacy.
Insurers must cover it—a hurdle for clinical and cost effectiveness.
Physicians and hospitals must offer it—a hurdle for appropriateness and quality.
Patients must want it—a hurdle sensitive to preferences and risks.
To be sure, there are other hurdles and factors relevant to each of these, and none is ideal in its specified role. But, Robinson does not argue for removal or dramatic reform of any of these hurdles. Implicitly, he seems to accept them as necessary, if imperfect, means of guiding health care innovation.
Importantly, the behavior of each is shaped by organizational structure and financial incentives, which are malleable. Indeed, today they are changing, in some cases in helpful ways. The overriding message of the book is that the era of “cost-unconsciousness” is ending and one emphasizing “value” is underway.
How can we enhance that value? Each of the four hurdles is covered in at least one chapter (the second and third hurdles received two each) that explains its role and address that question.
The FDA chapter was the first thing I’ve read about the agency’s process that I could follow. Perhaps I’ve been reading the wrong things, but most accounts of the FDA I’ve seen either assume I know how it works or only explain a small part of what it does, then move quickly to how it fails. Robinson is more thorough and balanced, specifying the purpose and value of the FDA’s activities, but also indicating where it might make some useful changes to avoid both over- and under-regulating.
The insurance chapters address how Medicare and commercial market insurers make coverage and payment decisions. I suspect most health policy wonks will find much (though perhaps not all) of the material in these chapters familiar: Medicare’s national and regional coverage decision processes, the roles of comparative and cost effectiveness, medical/utilization management, selective- and value-based design and contracting, the variety of reimbursement and payment mechanisms (fee for service, per case, per diem, per episode, capitation, and the like).
My favorite chapters of the book were on hospital purchasing. Robinson doesn’t specify which hurdle plays the most significant role and is most rapidly changing, but my sense is that this is it. Whereas hospitals once competed with one another by offering all of the latest and greatest technology (at high expense) to attract physicians and their referrals, the new paradigm is for hospitals to at least attempt to manage technology purchasing to drive down costs. The basic approaches include technology assessment committees and volume purchasing that forces suppliers to compete more vigorously on price and quality. How do hospitals perform at these?
It’s a big struggle, mostly because it’s difficult to convince a large number of physicians, potentially across disparate facilities in a system, to come to (or accept) consensus on technology. The chapter that looks directly at how Orange County, CA health systems (including Kaiser) do this and the extent to which they succeed is fascinating. I’ve never read a health economist write in such specificity about how hospitals are run. (If I’d read these chapters on hospitals without knowing the author, I’d have guessed he was a physician or hospital administrator, not a health economist. I’d love for doctors who work in hospitals or hospital managers to read chapters 4 and 5 and let me know how accurately it reflects their experiences.)
The book then turns back to more familiar territory: how different varieties of cost sharing and patient engagement affect patient demand and behavior. Robinson concludes with ways in which the FDA, insurers, providers, and consumers can collaborate today to motivate development and use of more valuable technology tomorrow.
For the FDA, this implies lower barriers to initial market access, more extensive postmarket surveillance, and a willingness to retract authorization for products found to be unsafe or ineffective. For insurers, it implies rapid coverage and generous pricing for breakthrough products, thereby allowing evidence to accumulate and products to improve with experience, coupled with price discounting for follow-on therapies and medical management for inappropriate uses. For physicians and hospitals, it implies methods of payment that reward improved product assessment, procurement, and use. For consumers, it implies a structure of cost sharing that encourages adherence to evidence-based care and discourages demand for overpriced services.
I’m grateful and honored Robinson sent me a copy of his book. Though I didn’t have to pay for it, I would have. It’s an innovation worth the price.
[C]onsider a healthy consumer facing the risk of developing Parkinson’s disease in the years before the discovery of treatments that reduced the disease’s impacts on quality of life. Suppose we measure the quality of one year of life as some percentage of a year spent in perfect health. In the absence of a treatment, contracting Parkinson’s might reduce quality of life from, say, 80% of perfect health to 40%. Consider the introduction of a new medical treatment that costs roughly $5,000 per year and increases quality of life for Parkinson’s patients from 40% to 70%. If the value of perfect health for one year is $50,000, this increase in quality of life is worth $15,000 annually but costs only $5,000 annually. The traditional approach in health economics compares these two numbers to arrive at the net value of the treatment, which in this case would be $10,000 annually.
First of all, I want to flag the use of the term “value” or “net value” here. It’s consistent with what Uwe Reinhardt endorses: the difference—not ratio—of benefit and price. (Click through to the full text of his remarks.) Cost-benefit ratios (or their reciprocols), for instance, are also called “value” by some, but, as Reinhardt noted, that’s weird and inconsistent with the notion of “value” generally used in economics.
Notice that this calculation neglects the way the medical treatment’s introduction also compresses the variance in the quality of life between the Parkinson’s and non‐Parkinson’s states. Prior to the availability of treatment, Parkinson’s was a gamble that lowered quality of life by 40% of a perfectly healthy year, or a loss of approximately $20,000 per year; the treatment transforms the disease into a new gamble that lowers quality of life by just 10% of a perfectly healthy year, or a loss of just $5,000 per year. This compression in quality of life outcomes generates value for consumers who dislike risk.
It is true that the reduction in the variance of health outcomes is mitigated by an increase in the variance of healthcare spending. Before the availability of treatment, the individual may have faced no financial risk from falling ill with Parkinson’s; after its introduction, she faces the risk of a $5,000 per year expenditure. However, if the treatment is priced to generate consumer surplus, the ex post improvement in health outcomes will outweigh its financial cost. Thus, it should come as no surprise that this medical treatment lowers total risk in our example. Prior to the development of treatment, Parkinson’s imposes a risk of losing $20,000 in reduced health. After development, the risk of disease is transformed into a $5,000 financial risk plus a $5,000 health risk. In sum, this medical treatment cut the total risk of Parkinson’s in half. Furthermore, the nascent financial risk associated with purchasing treatment can be mitigated or even eliminated by health insurance.
I’ve put in bold a key assumption (or focus) that the authors apply in their analysis. They are considering only treatments that are priced such that consumer surplus is positive, in the absence of insurance. Given the widespread take-up of insurance, how many treatments are really priced this way? I guess it depends on whose consumer surplus one examines. For many treatments and many patients (but not for all), prices for the uninsured are above that which would generate positive consumer surplus for the uninsured. That fact is the source of moral hazard. Put it this way, at current prices, what’s the market for Sovaldi or proton beam treatments look like without insurance? I think they’re priced precisely to account for insurance. Indeed, I think these products wouldn’t exist without insurance, which is tantamount to saying that there’d be no technology-sustaining consumer surplus positive price.
Am I raising a limitation of the work here? I don’t know. (I will admit to not tracing this through all the math. Think of this as a point I’d raise in a seminar, and then I’d like to hear others who know the work better tell me what’s if what I’ve raised is important. UPDATE: Lead author Darius Lakdawalla responded to this. You’ll find that response below. It’s a good one.)
Even if a consumer has no health insurance, technology can reduce the physical risk she faces. In the Parkinson’s example, she faced a health risk of $20,000 prior to the technology but just a $10,000 risk after it, even if no health insurance is available. Adding health insurance to the analysis would cause the risk to fall even lower, to just $5,000. [… P]roviding consumers with access to better medical technology by encouraging medical innovation may reduce risk more efficiently than providing them with health insurance.
Their conclusion (after analysis),
New medical technologies provide substantial insurance value above and beyond standard consumer surplus. Under plausible assumptions, the insurance value is roughly equal to the conventional value. Accounting for risk thus doubles the value of medical technology over and above conventional calculations.
The ability of medical innovation to function as an insurance device influences not just the level of value, but also the relative value of alternative medical technologies. The conventional framework understates the value of technologies that treat the most severe illnesses, compared to technologies that treat mild ailments. This helps explain why health technology access decisions driven by cost‐effectiveness considerations alone often seem at odds with public opinion. For example, survey evidence suggests that representative respondents evaluating equally “cost‐effective” technologies strictly prefer paying for the one that treats the most severe illness.
I really like this because it aligns how humans tend to feel about the value of medical technologies with economic analysis, explaining why standard cost-effectiveness approaches seem wrong to us. This observation is what gives rise to the rule of rescue.
UPDATE: Here’s Lakdawalla’s response:
In fact, this is not a strong assumption, even for a high-cost drug like your Sovaldi example. To take one example, even the UK’s notoriously stingy health technology assessment agency thinks Sovaldi meets that bar quite easily.
To understand why, it helps to be a bit more literal about the issue. Drugs that generate surplus in the sick state generate a health benefit whose value exceeds the full price of the drug. That is, the gain in quality-adjusted life-years (QALYs) multiplied by the value of a QALY exceeds the full price of the drug. This is the same as saying that the cost-effectiveness ratio of a drug exceeds the value of a QALY. In the case of Sovaldi, the UK concluded that its cost-effectiveness exceeds $50K. Since a QALY is almost surely worth more than that, it follows that Sovaldi generates surplus in the sick state, even when its full price is considered, and even according to the UK.
One caveat is that drugs are priced to hit cost-effectiveness thresholds in markets that perform this analysis — like the UK — but not necessarily in the US. However, most of the time, this ends up being largely a wash. Let’s stick with the Sovaldi example to illustrate. Sovaldi costs $58K in the UK. Large private insurers in the US are probably paying 10-30% more than this, depending on their size and bargaining leverage. This is a pretty typical price differential between UK and US payers. However, the UK’s threshold of $50K/QALY is almost surely much less than 30% below the revealed preference willingness to pay for a QALY in the US. (For example, the labor literature says the value of a statistical life-year is about $200-300K. We have some work showing that metastatic cancer patients are willing to pay about $300K per life year. Etc.) Thus, on balance, Sovaldi is generating surplus in the sick state even at US prices.
Of course, the spirit of your point is still correct, because there are non-trivial numbers of drugs that fail to meet this bar. In addition, if sick people were better insured against the financial risk of illness, more drugs would generate surplus in the sick state, because the willingness to pay for health would go up among the sick. This is the sense in which financial insurance and medical technology are complements.
Antibiotic resistance is a complex social problem, with alarming global implications. That’s why it is exceedingly good news that the White House released the full National Action Plan today. We’ve known that antibiotic resistance is a problem for more than 70 years, but today is the first time any Administration has taken the threat this seriously. It’s the boldest move by any President on this issue. Ever.
Do I wish it had gone further? Sure, but I’m an academic researcher. I always have new questions to explore. The report needs more heft on what happens to reimbursement after FDA approval, for instance.
But look at the solid targets across many areas, the goals set in agriculture, the emphasis on global and regional coordination, and the significant attention to diagnostics. Not just a good first step, but a dozen good steps.
Congress should fund this as an insurance policy against a post-antibiotic era.
Almost two years ago, John Green did a Vlogbrothers video on why health care costs so much in the United States. It relied heavily on TIE’s series on the same topic, and if you haven’t seen it, then you’re odd, because it’s had almost 6.5 million views:
Needless to say, this video – more than any other thing – led to Healthcare Triage being possible. Anyway, this week, John did a video on “whether Obamacare is working” 5 years later, and it also relies on some Upshot and other TIE-related material. It’s worth a watch as well:
I’ve been on record, both here and on Twitter, being skeptical a doc fix might ever pass. I’ve also been skeptical, both at the Upshot and in talks, that this Congress could pass a CHIP extension. Evidently, the House is doing everything in its power to prove me wrong:
The House overwhelmingly approved sweeping changes to the Medicare system on Thursday, in the most significant bipartisan policy legislation to pass through that chamber since the Republicans regained a majority in 2011.
The measure, which would establish a new formula for paying doctors and end a problem that has bedeviled the nation’s health care system for more than a decade, has already been blessed by President Obama, and awaits a vote in the Senate. The bill would also increase premiums for some higher income beneficiaries and extend a popular health insurance program for children.
The legislation, which passed on a 392-to-37 vote, embodies a rare and significant agreement negotiated by Speaker John A. Boehner and the House Democratic leader, Representative Nancy Pelosi of California, two leaders who are so often at odds with each other.
It’s been so long since I’ve see a bipartisan effort to pass anything substantial that I really don’t know how to process it. I’m literally stunned.