New Yorker: What Big Data can’t tell us about health care

A “measured” article.

2% of physicians account for 25% of payments

US Medicare’s release of 9million lines of 2012 outpatient payment data for all 880,000 US doctors.

Amitabh Chandra, a health economist at Harvard, noted that the release of these data may be most useful not to the public or health researchers but to private insurers. These firms keep their own data, but the Medicare dataset is far more vast than any one insurer’s figures. Insurers, Chandra said, may be able to mine these data to build smarter networks that exclude high-cost providers and include high-performing ones. This type of tiered networking, on a grand scale, could actually improve the efficiency of our delivery system. It is this version of transparency-driven tiering, Chandra believes, that could assist in our cost-containment efforts.

http://www.newyorker.com/online/blogs/currency/2014/04/the-medicare-data-dump-and-the-cost-of-care.html

APRIL 23, 2014

WHAT BIG DATA CAN’T TELL US ABOUT HEALTH CARE

Malouin is a family doctor, which is not a specialty that one typically enters hoping to get rich. Delivering primary care is seen by doctors as hard work that earns comparatively little pay, and it is a job that is only getting harder. That’s because the Affordable Care Act, with the broad ambition of containing costs while improving quality, hopes to move away from a fee-for-service model, toward one in which doctors are paid primarily for keeping their patients healthy, a responsibility that will fall largely on primary-care doctors. At this point, nobody quite knows how to make this vision a reality, but Medicare has funded various demonstration projects to test innovations in care—one of which is led by Malouin, who supervises three hundred and eighty primary-care practices that treat a million patients in Michigan. Payments for care improvement from Medicare at all these clinics are made under Malouin’s name, which is how she ended up in dozens of newspaper reports on the data dump.

Even doctors who didn’t end up making headlines like Malouin told me that they felt somewhat exposed by the release of the Medicare payments data. As one friend tweeted, “Imagine if you woke up one morning to find that every person in your profession had their income reported on the New York Times web site.” For nearly thirty-five years, the American Medical Association had worked to keep this information private, after securing a federal court injunction in 1979. Dow Jones, the parent company of the Wall Street Journal, waged a legal battle against the injunction, which was overturned by a federal judge last year.

In the march toward greater price transparency in health care, the data release represents a milestone, though perhaps one more symbolic than substantive. For those who believe that greater price transparency is the key to reining in exorbitant costs and helping patients to become more savvy “health-care consumers,” the data release is a huge victory. Indeed, the early coverage, invariably emphasizing the high spending of a small group of physicians, had a tone of triumph. According to the Times, two per cent of physicians accounted for nearly a quarter of Medicare spending. Ophthalmologists led this small group of high billers, with a large portion of their payments apparently connected to the use of an expensive treatment for macular degeneration. Charts broke down payments by specialty, showing cancer doctors in the lead, while maps of the distribution in spending confirmed long-observed geographic variations. For instance, states like Florida, Texas, and New Jersey consume a large share of Medicare resources.

The calls for price transparency, as a means of bringing down health-care costs, have certainly gained momentum in the past year. In this latest chapter, the hope is that members of the public will be empowered by their access to payments data, and will use this information to identify doctors who are behaving badly, helping to end fraud and profit-driven overuse. In fact, Medicare already conducts internal audits, and the two highest billers in 2012, an ophthalmologist and a cardiologist, both from Florida, were already under federal review. But the third-highest biller, a pathologist, directs a diagnostic company that performs tests for twenty-six other pathologists, which are all billed under his name. Similarly, an oncologist from Newport Beach, California, who billed nine million dollars, explained that all the billing at his practice, which includes five physicians, was under his name, and much of it was directed toward expensive chemotherapy drugs. (One such drug, for advanced melanoma, called ipilimumab, costs about a hundred thousand dollars for four treatments.)

This release includes more than nine million rows of numbers, encompassing more than eight hundred and eighty thousand physicians and other health-care professionals who billed under Medicare Part B—which covers care delivered in an outpatient setting—in 2012. You see their names, their addresses, the services they billed for, how much Medicare reimbursed, and many other details. There are several caveats to interpreting the payment data. First, there is a difference between what Medicare pays and what doctors earn. All doctors face overhead costs: radiation oncologists, for instance, have to pay for technicians and expensive equipment. These doctors were among the highest billers, but eighty-two per cent of their Medicare reimbursements went to covering these expenses. An analysis by the Washington Post found that Medicare paid sixty-four billion dollars to doctors in 2012, of which forty-three per cent went to office overhead, forty-one per cent went to doctor compensation, and thirteen per cent went to drug costs. Even within the Medicare system, these data provide an incomplete picture, as the reimbursements do not include payments within hospital systems (which fall under Medicare Part A) or various Medicare Advantage plans, which cover many seniors but are not included in these numbers.

Despite these caveats, members of the public were encouraged to use these data to make more informed decisions about where to seek care. Indeed, in a press briefing on Wednesday, Jonathan Blum, an administrator at the Centers for Medicare & Medicaid Services, said, “We look forward to making this important, new information available so that consumers, Medicare and other payers can get the best value for their health-care dollar.” The suggestion that these data can allow you truly to comparison-shop, however, is misleading. These data do not tell us anything about the value of care. By definition, the value of health care cannot be measured in dollars spent; it’s about what you get for those dollars, and the Medicare data, however useful, offer little new information of that sort. These numbers tell us, for example, that dermatologists receive higher reimbursements than pediatricians, that cardiologists in Oregon get paid less than their counterparts in New York, and that performing procedures pays better than talking to patients. But they cannot tell us whether doctors provided good care, because being a good doctor sometimes means doing everything, and sometimes it means doing nothing at all.

Let’s say you find that your ophthalmologist performed fewer cataract surgeries than average. It could be because he lacks experience. But it also could be because most of his patients have Medicare Advantage plans that are not included in these data. What about your vascular surgeon, who billed Medicare more than a million dollars last year? He thinks that you need a stent to open a blocked artery in your leg. Do his high numbers indicate a tendency to perform unnecessary surgeries? Maybe. But it is just as likely that his apparently high billing numbers reflect the fact that he performs procedures in his office—covered under Medicare Part B—whereas most of his peers perform similar procedures in hospitals, where their payments aren’t included in these data. And your orthopedist, who performed nearly five hundred hip replacements last year? Surely his high volume suggests that he knows what he is doing? Of all the conclusions that you can make from these data, that high procedural volume signals that better quality is the one with the most empirical backing. But even that is just one signal amid a great deal of noise.

This is not to suggest that the information released earlier this month will not play a role in making it easier for individuals to determine where they can get good care. When I spoke to Jonathan Kolstad, a professor of health-care management at Wharton, he noted that medicine has lagged far behind other industries in giving consumers data to inform their decisions—often because privacy concerns raise significant barriers. This release is a partial step, Kolstad said, but the message “is not that we should be releasing less data. It’s that we should be releasing even more.”

Amitabh Chandra, a health economist at Harvard, noted that the release of these data may be most useful not to the public or health researchers but to private insurers. These firms keep their own data, but the Medicare dataset is far more vast than any one insurer’s figures. Insurers, Chandra said, may be able to mine these data to build smarter networks that exclude high-cost providers and include high-performing ones. This type of tiered networking, on a grand scale, could actually improve the efficiency of our delivery system. It is this version of transparency-driven tiering, Chandra believes, that could assist in our cost-containment efforts.

“I think that we all agree that we have to do something about bending the cost curve,” Chandra said. “But I think we have deluded ourselves into thinking that a challenge as big as bending the cost curve can be collapsed into something as simple as transparency.” He likened the notion that transparency alone could solve the problem to imagining that we could stop global warming by driving hybrids. “We are always drawn to these tantalizing simple arguments. Transparency is just one of the ways we are seeing such aspirational thinking in health care.”

The potential danger in this data dump does not come from the new information it provides but from the old story it risks reinforcing. The existing narrative of American health care goes something like this: greedy physicians perform procedures that patients don’t need and enrich themselves in the process, which is why a third of health-care spending goes to unnecessary care. Now that members of the public can see where their tax dollars are going, they are empowered to rid the system of duplicitous doctors. Indeed, as Blum, the Medicare administrator, emphasized to the press, “We know that there’s waste in the system. We know that there’s fraud in the system. We want the public’s help to review the physician-payment data and report suspected wrongdoing.”

This narrative has many problems. First, the data suggesting the extent of unnecessary care have come under widespread criticism. Economists have also pointed out that other factors, such as high administrative costs and expensive technology, play a greater role in exorbitant costs than overuse. But, even if eliminating waste would, by itself, cure our ailing health-care system, these data do not allow us to identify waste—because waste is not the same as spending a lot. Waste is not even the same as high spending that doesn’t make people healthier. Doctors do that all the time: not because we are trying to enrich ourselves but because we are trying to help our patients. What looks wasteful in retrospect may have looked like a live-saving intervention at the time it was made—and this is as true for expensive chemotherapies that fail to save a life as it is for expensive tests that don’t reveal disease.

That’s why asking the public to use this information to identify waste belies the complexity of physician decision-making. Do physicians respond to financial incentives? Yes. Should we tolerate care that offers patients no benefit? Absolutely not. But are profit motives the primary drivers of physician behavior? My own sense is that most physicians are primarily motivated by trying to do the right thing for their patients. Combing through these data, however, creates the impression that the pecuniary trumps the humane. What else can one conclude from information that only tells you how much physicians do and what they bill?

From an experimental standpoint, unpacking non-financial drivers of physician behavior is far harder than demonstrating that physicians respond to financial incentives. But Kolstad, the Wharton health economist, recently published a study suggesting that wanting to perform better was a far more powerful motivator than wanting to earn more. The paper, which was recently awarded the prestigious Arrow Award for the best study in health economics, examines the behavior of heart surgeons in Pennsylvania. Kolstad took advantage of a report-card system implemented in Pennsylvania in 2006, which created a financial incentive for surgeons to lower their mortality rates because of a need to attract patients. He compared this incentive to simply giving surgeons feedback on their performance and showing them how they compared to their peers. This feedback was four times more powerful in improving physician performance than the financial incentives.

While much more experimentation of this type is necessary in our quest to understand how to improve quality and cut costs, it is no wonder that we cling to the story that we have told. It offers heroes and villains, fosters the American ideal of the individual over any collective authority, and, above all, provides the hopeful illusion that we don’t have to confront the hardest questions that doctors and patients grapple with every day. How much are we willing to spend to save a life? Is a groundbreaking hepatitis C drug worth eighty-four thousand dollars? What about chemotherapy that costs a hundred thousand dollars and may only prolong a life by four months?How much do we value quality of life, or patient satisfaction, against cost? We know, for instance, that M.R.I.s for patients with back pain typically leave them no healthier, but they do leave them more satisfied. Is this a cost that the American taxpayer should bear?

These types of questions are burning beneath the surface of our superficial discussions about how to improve the value of health care, but the political will to address them, from both an empirical and ethical standpoint, is decidedly absent. Indeed, Medicare is prohibited from considering cost effectiveness in coverage decisions, and, even though the Affordable Care Act does emphasize the need to fund more research comparing the efficacy of various treatments, translating these findings into practice requires a collective willingness to consider costs in coverage. Because such discussions are often conflated with rationing, any attempt to do this is a political nonstarter. Perpetuating the attribution of high health-care costs to physician greed just makes addressing these critical questions more difficult.

There is nothing wrong with trying to improve the value of health care. But better value will depend as much on doing more of what’s good as it will upon doing less of what’s bad. So much of that good isn’t captured by these numbers. You don’t bill for talking to a patient about how he wants to die. There’s no code for providing reassurance rather than ordering a test. And, for all the talk about transforming our health-care system from one that treats illness to one that promotes health, no one pays you to talk to patients about how they might lead healthier lives.

Photograph by Skynesher/Vetta/Getty.

Digital Therapeutics – Omada Health

The world is finally entering a new era of effective, scalable, and life-saving change, all delivered through the other end of an internet connection. For three out of four of us, that change can’t come soon enough.

http://www.forbes.com/sites/sciencebiz/2014/04/17/what-if-doctors-could-finally-prescribe-behavior-change/

BUSINESS 4/17/2014 @ 5:31PM |3,232 views

What If Doctors Could Finally Prescribe Behavior Change?

Three out of four Americans will die of a disease that could be avoided—if only they could re-route their unhealthy habits. A new category of medicine, digital therapeutics, wants to change the course of these conditions — and of history.

Doctors have known for decades that, in order to prevent disease or its complications, they were going to have to get into people’s living rooms and convince them to change everyday behaviors that would very likely kill them. To that end, back in the early ’90s, health institutions started trying to intervene largely via the cutting-edge technology that existed at the time: phone calls. At-risk populations were dialed up and encouraged to take steps that could ward off heart disease, diabetes complications, lung cancer and other avoidable conditions that cause 75% of Americans to die prematurely.

As you can imagine, these calls largely flopped. A phone interaction led by a stranger who interrupts your dinner hour, no matter how well-intentioned, felt like more like an intrusion than meaningful
support.

The more we discover about behavioral science, the more naïve those calls seem in retrospect. Whether it’s for weight loss, smoking cessation, diabetes, or otherwise, the best research shows that meaningful behavior change outcomes require not just guidance from a trusted health professional, but also positive social support, easy-to-digest insights about their condition, a carefully orchestrated timeline, and a process that follows validated behavioral science protocols. That’s hard to squeeze into a phone call. Or a doctor’s visit, for that matter.

The world urgently needs better ways to bring behavior change therapies to the masses, and advancements in digital tech are finally enabling us to orchestrate the necessary ingredients to make that happen in a clinically meaningful way.

That’s doesn’t make it easy. In fact, it’s effectively pioneering a new class of medicine, often dubbed “digital therapeutics.” But any clinically-meaningful digital therapeutic needs to clear two significant
hurdles. One, it needs to genuinely engage and inspire the patient, both initially and over time. Two, it must also unequivocally demonstrate efficacy to the medical community by rooting itself in the best science and by producing clinically-significant outcomes, just as any traditional drug is expected to do.

That’s why, until recently, most available health apps couldn’t truly be categorized as digital therapeutics. For instance, a study in 2012 showed that very few of the top 50 smoking cessation apps available at the time abided by evidence-based protocols. This high-tech snake oil was not deliberate, but it is a side effect of the fact that very few of the leading behavioral science researchers knowing how to program in Objective C or Ruby on Rails. Companies looking to truly pioneer in this new category must both establish and exceed the highest scientific standards while building exceptional online experiences. The good news is that is starting to happen.

Emerging in the white hat category are a handful of medically-minded visionaries who have put real clinical rigor into every aspect of their design. For instance, David Van Sickle, a former CDC “epidemiologist intelligence officer,” and now the CEO and Co-Founder of Propeller Health, built a GPS-enabled sensor for asthma inhalers that links to an elegantly designed app — every puff is mapped and time-stamped, allowing patients and doctors to spot patterns in ‘random’ attacks and identify previously unknown triggers.

Another example is Jenna Tregarthen, a PhD candidate in clinical psychology and eating disorder specialist. She rallied a team of engineers, entrepreneurs, and fellow psychologists to develop Recovery Record, a digital therapy that helps patients gain control over their eating disorder by enabling them to self-monitor for destructive thoughts or actions, follow meal plans, achieve behavior goals, and message a therapist instantly when they need support.

Momentum for the promise of digital therapeutics is building. A massive surge in digital health investing reflects how rapidly confidence in this space is growing. In ten years, we have no doubt that your doctor will recommend a digital program for your depression either instead of, or in addition to, a pill. Your insomnia, kidney stones, or lower back pain might be treated by an experience centered around an iOS app. We can clearly see a future where a doctor’s prescription sends you to an immersive online experience as often as it does to a pharmacy.

The world is finally entering a new era of effective, scalable, and life-saving change, all delivered through the other end of an internet connection. For three out of four of us, that change can’t come soon enough.

 

RWJF Report: Personal Data for the Public Good

Solid report on personal health data. Interesting observation re. (lack of) alignment between research and business objectives… i.e. public vs private goods?

http://www.rwjf.org/en/research-publications/find-rwjf-research/2014/03/personal-data-for-the-public-good.html

Report: http://www.rwjf.org/content/dam/farm/reports/reports/2014/rwjf411080

PDF:

1. Executive Summary
Individuals are tracking a variety of health-related data via a growing number of wearable devices and smartphone apps. More and more data relevant to health are also being captured passively as people communicate with one another on social networks, shop, work, or do any number of activities that leave “digital footprints.”
Almost all of these forms of “personal health data” (PHD) are outside of the mainstream of traditional health care, public health or health research. Medical, behavioral, social and public health research still largely rely on traditional sources of health data such as those collected in clinical trials, sifting through electronic medical records, or conducting periodic surveys.
Self-tracking data can provide better measures of everyday behavior and lifestyle and can fill in gaps in more traditional clinical data collection, giving us a more complete picture of health. With support from the Robert Wood Johnson Foundation, the Health Data Exploration (HDE) project conducted a study to better understand the barriers to using personal health data in research from the individuals who track the data about their own personal health, the companies that market self-tracking devices, apps or services and aggregate and manage that data, and the researchers who might use the data as part of their research.
Perspectives
Through a series of interviews and surveys, we discovered strong interest in contributing and using PHD for research. It should be noted that, because our goal was to access individuals and researchers who are already generating or using digital self-tracking data, there was some bias in our survey findings—participants tended to have more education and higher household incomes than the general population. Our survey also drew slightly more white and Asian participants and more female participants than in the general population.
Individuals were very willing to share their self-tracking data for research, in particular if they knew the data would advance knowledge in the fields related to PHD such as public health, health care, computer science and social and behavioral science. Most expressed an explicit desire to have their information shared anonymously and we discovered a wide range of thoughts and concerns regarding thoughts over privacy.

Equally, researchers were generally enthusiastic about the potential for using self-tracking data in their research. Researchers see value in these kinds of data and think these data can answer important research questions. Many consider it to be of equal quality and importance to data from existing high quality clinical or public health data sources.
Companies operating in this space noted that advancing research was a worthy goal but not their primary business concern. Many companies expressed interest in research conducted outside of their company that would validate the utility of their device or application but noted the critical importance of maintaining their customer relationships. A number were open to data sharing with academics but noted the slow pace and administrative burden of working with universities as a challenge.
In addition to this considerable enthusiasm, it seems a new PHD research ecosystem may well be emerging. Forty-six percent of the researchers who participated in the study have already used self-tracking data in their research, and 23 percent of the researchers have already collaborated with application, device, or social media companies.
The Personal Health Data Research Ecosystem
A great deal of experimentation with PHD is taking place. Some individuals are experimenting with personal data stores or sharing their data directly with researchers in a small set of clinical experiments. Some researchers have secured one-off access to unique data sets for analysis. A small number of companies, primarily those with more of a health research focus, are working with others to develop data commons to regularize data sharing with the public and researchers.
SmallStepsLab serves as an intermediary between Fitbit, a data rich company, and academic researchers via a “preferred status” API held by the company. Researchers pay SmallStepsLab for this access as well as other enhancements that they might want.
These promising early examples foreshadow a much larger set of activities with the potential to transform how research is conducted in medicine, public health and the social and behavioral sciences.

Opportunities and Obstacles
There is still work to be done to enhance the potential to generate knowledge out of personal health data:

Privacy and Data Ownership: Among individuals surveyed, the dominant condition (57%) for making their PHD available for research was an assurance of privacy for their data, and over 90% of respondents said that it was important that the data be anonymous. Further, while some didn’t care who owned the data they generate, a clear majority wanted to own or at least share ownership of the data with the company that collected it.

Informed Consent: Researchers are concerned about the privacy of PHD as well as respecting the rights of those who provide it. For most of our researchers, this came down to a straightforward question of whether there is informed consent. Our research found that current methods of informed consent are challenged by the ways PHD are being used and reused in research. A variety of new approaches to informed consent are being evaluated and this area is ripe for guidance to assure optimal outcomes for all stakeholders.

Data Sharing and Access: Among individuals, there is growing interest in, as well as willingness and opportunity to, share personal health data with others. People now share these data with others with similar medical conditions in online groups like PatientsLikeMe or Crohnology, with the intention to learn as much as possible about mutual health concerns. Looking across our data, we find that individuals’ willingness to share is dependent on what data is shared, how the data will be used, who will have access to the data and when, what regulations and legal protections are in place, and the level of compensation or benefit (both personal and public).

Data Quality: Researchers highlighted concerns about the validity of PHD and lack of standardization of devices. While some of this may be addressed as the consumer health device, apps and services market matures, reaching the optimal outcome for researchers might benefit from strategic engagement of important stakeholder groups.

We are reaching a tipping point. More and more people are tracking their health, and there is a growing number of tracking apps and devices on the market with many more in development. There is overwhelming enthusiasm from individuals and researchers to use this data to better understand health. To maximize personal data for the public good, we must develop creative solutions that allow individual rights to be respected while providing access to high-quality and relevant PHD for research, that balance open science with intellectual property, and that enable productive and mutually beneficial collaborations between the private sector and the academic research community.

Healthy Ageing Japan-style

 

http://www.abc.net.au/radionational/programs/saturdayextra/japan27s-aging-population/5397864

Japan’s ageing population

Saturday 26 April 2014 8:30AM

A quarter of Japanese people are now aged over 65, with predictions that nearly half the population will reach that age by the end of the century.

In Japan people don’t just live longer, they work longer, stay healthier and approach old age in some interesting and innovative ways.

One policy initiative is old age day care which is well used and well organised in Japan.

Guests

Professor John Creighton Campbell
Visiting scholar, Institute of Gerontology at Tokyo University

Credits

Presenter
Dr Norman Swan
Producer
Kate Pearcy

Hmmmmm….

seriously freakin’ weird what the kids are getting up to these days…

 

http://www.buzzfeed.com/mrloganrhoades/43-tumblr-comments-that-make-you-go-hmmmm

 

43 Tumblr Comments That Make You Go “Hmmmm”

“pigeons can just fly straight to disneyland yet here they are eating breadcrumbs off the pavement”posted on 

1. Allergies:

Allergies:

2. Deep thoughts:

Deep thoughts:

3. Fictional morning:

Fictional morning:

4. Shitty dolphins:

Shitty dolphins:

5. Disappointing playground:

Disappointing playground:

6. How to survive in the rainforest:

How to survive in the rainforest:

7. A million dollar idea:

A million dollar idea:

43 Tumblr Comments That Make You Go "Hmmmm"

8. How to win a debate:

How to win a debate:

9. The funky boogeyman:

The funky boogeyman:

10. Screw you, China:

Screw you, China:

11. Catch 22:

Catch 22:

12. Clap on, clap off:

Clap on, clap off:

13. A rough(age) day:

A rough(age) day:

14. Bagel graffiti:

Bagel graffiti:

15. Tiny astronaut:

Tiny astronaut:

16. Bumpy globes:

Bumpy globes:

17. Sweet dragon:

Sweet dragon:

18. Jungle river:

Jungle river:

19. Quick, easy and free:

Quick, easy and free:

20. Bug categories:

Bug categories:

21. Snail effort:

Snail effort:

22. Sick dog:

Sick dog:

23. Stupid pigeons:

Stupid pigeons:

24. That makes sense:

That makes sense:

25. Flawless chocolate logic:

Flawless chocolate logic:

26. A really, really good point:

A really, really good point:

27. Push it to the limit (physically and financially):

Push it to the limit (physically and financially):

28. Drag Queens:

Drag Queens:

29. Dial 666:

Dial 666:

30. Damnit Scooby:

Damnit Scooby:

31. Shh shh:

Shh shh:

32. Bedroom door bravery:

Bedroom door bravery:

33. Pokemom:

Pokemom:

34. Never gonna give you up:

Never gonna give you up:

35. Selling kids:

Selling kids:

36. Perfume commercials:

Perfume commercials:

37. Netflix dude:

Netflix dude:

38. Tater Tots:

Tater Tots:

39. At least there’s that:

At least there's that:

40. Worldy things:

Worldy things:

41. How dare you:

How dare you:

42. I don’t even know:

I don't even know:

43. Some M. Night Shyamalan shit:

Some M. Night Shyamalan shit:

NEJM: Mammography doesn’t pass muster…

Mammography = bad, according to the Swiss…

http://www.nejm.org/doi/full/10.1056/NEJMp1401875?query=TOC

Perspective

Abolishing Mammography Screening Programs? A View from the Swiss Medical Board

Nikola Biller-Andorno, M.D., Ph.D., and Peter Jüni, M.D.

April 16, 2014DOI: 10.1056/NEJMp1401875

Article

References
Comments (26)

In January 2013, the Swiss Medical Board, an independent health technology assessment initiative under the auspices of the Conference of Health Ministers of the Swiss Cantons, the Swiss Medical Association, and the Swiss Academy of Medical Sciences, was mandated to prepare a review of mammography screening. The two of us, a medical ethicist and a clinical epidemiologist, were members of the expert panel that appraised the evidence and its implications. The other members were a clinical pharmacologist, an oncologic surgeon, a nurse scientist, a lawyer, and a health economist. As we embarked on the project, we were aware of the controversies that have surrounded mammography screening for the past 10 to 15 years. When we reviewed the available evidence and contemplated its implications in detail, however, we became increasingly concerned.

First, we noticed that the ongoing debate was based on a series of reanalyses of the same, predominantly outdated trials. The first trial started more than 50 years ago in New York City and the last trial in 1991 in the United Kingdom.1 None of these trials were initiated in the era of modern breast-cancer treatment, which has dramatically improved the prognosis of women with breast cancer. Could the modest benefit of mammography screening in terms of breast-cancer mortality that was shown in trials initiated between 1963 and 1991 still be detected in a trial conducted today?

Second, we were struck by how nonobvious it was that the benefits of mammography screening outweighed the harms. The relative risk reduction of approximately 20% in breast-cancer mortality associated with mammography that is currently described by most expert panels2 came at the price of a considerable diagnostic cascade, with repeat mammography, subsequent biopsies, and overdiagnosis of breast cancers — cancers that would never have become clinically apparent. The recently published extended follow-up of the Canadian National Breast Screening Study is likely to provide reliable estimates of the extent of overdiagnosis. After 25 years of follow-up, it found that 106 of 484 screen-detected cancers (21.9%) were overdiagnosed.3 This means that 106 of the 44,925 healthy women in the screening group were diagnosed with and treated for breast cancer unnecessarily, which resulted in needless surgical interventions, radiotherapy, chemotherapy, or some combination of these therapies. In addition, a Cochrane review of 10 trials involving more than 600,000 women showed there was no evidence suggesting an effect of mammography screening on overall mortality.1 In the best case, the small reduction in breast-cancer deaths was attenuated by deaths from other causes. In the worst case, the reduction was canceled out by deaths caused by coexisting conditions or by the harms of screening and associated overtreatment. Did the available evidence, taken together, indicate that mammography screening indeed benefits women?

Third, we were disconcerted by the pronounced discrepancy between women’s perceptions of the benefits of mammography screening and the benefits to be expected in reality. The figureU.S. Women’s Perceptions of the Effects of Mammography Screening on Breast-Cancer Mortality as Compared with the Actual Effects. shows the numbers of 50-year-old women in the United States expected to be alive, to die from breast cancer, or to die from other causes if they are invited to undergo regular mammography every 2 years over a 10-year period, as compared with women who do not undergo mammography. The numbers in Panel A are derived from a survey about U.S. women’s perceptions,4 in which 717 of 1003 women (71.5%) said they believed that mammography reduced the risk of breast-cancer deaths by at least half, and 723 women (72.1%) thought that at least 80 deaths would be prevented per 1000 women who were invited for screening. The numbers in Panel B reflect the most likely scenarios according to available trials1-3: a relative risk reduction of 20% and prevention of 1 breast-cancer death. The data for Switzerland, reported in the same study, show similarly overly optimistic expectations. How can women make an informed decision if they overestimate the benefit of mammography so grossly?

The Swiss Medical Board’s report was made public on February 2, 2014 (www.medical-board.ch). It acknowledged that systematic mammography screening might prevent about one death attributed to breast cancer for every 1000 women screened, even though there was no evidence to suggest that overall mortality was affected. At the same time, it emphasized the harm — in particular, false positive test results and the risk of overdiagnosis. For every breast-cancer death prevented in U.S. women over a 10-year course of annual screening beginning at 50 years of age, 490 to 670 women are likely to have a false positive mammogram with repeat examination; 70 to 100, an unnecessary biopsy; and 3 to 14, an overdiagnosed breast cancer that would never have become clinically apparent.5 The board therefore recommended that no new systematic mammography screening programs be introduced and that a time limit be placed on existing programs. In addition, it stipulated that the quality of all forms of mammography screening should be evaluated and that clear and balanced information should be provided to women regarding the benefits and harms of screening.

The report caused an uproar and was emphatically rejected by a number of Swiss cancer experts and organizations, some of which called the conclusions “unethical.” One of the main arguments used against it was that it contradicted the global consensus of leading experts in the field — a criticism that made us appreciate our unprejudiced perspective resulting from our lack of exposure to past consensus-building efforts by specialists in breast-cancer screening. Another argument was that the report unsettled women, but we wonder how to avoid unsettling women, given the available evidence.

The Swiss Medical Board is nongovernmental, and its recommendations are not legally binding. Therefore, it is unclear whether the report will have any effect on the policies in our country. Although Switzerland is a small country, there are notable differences among regions, with the French- and Italian-speaking cantons being much more in favor of screening programs than the German-speaking cantons — a finding suggesting that cultural factors need to be taken into account. Eleven of the 26 Swiss cantons have systematic mammography screening programs for women 50 years of age or older; two of these programs were introduced only last year. One German-speaking canton, Uri, is reconsidering its decision to start a mammography screening program in light of the board’s recommendations. Participation in existing programs ranges from 30 to 60% — variation that can be partially explained by the coexistence of opportunistic screening offered by physicians in private practice. At least three quarters of all Swiss women 50 years of age or older have had a mammogram at least once in their life. Health insurers are required to cover mammography as part of systematic screening programs or within the framework of diagnostic workups of potential breast disease.

It is easy to promote mammography screening if the majority of women believe that it prevents or reduces the risk of getting breast cancer and saves many lives through early detection of aggressive tumors.4 We would be in favor of mammography screening if these beliefs were valid. Unfortunately, they are not, and we believe that women need to be told so. From an ethical perspective, a public health program that does not clearly produce more benefits than harms is hard to justify. Providing clear, unbiased information, promoting appropriate care, and preventing overdiagnosis and overtreatment would be a better choice.

The views expressed in this article are those of the authors and do not necessarily reflect those of all members of the expert panel of the Swiss Medical Board.

Disclosure forms provided by the authors are available with the full text of this article at NEJM.org.

This article was published on April 16, 2014, at NEJM.org.

SOURCE INFORMATION

From the Institute of Biomedical Ethics, University of Zurich, Zurich (N.B.-A.), and the Institute of Social and Preventive Medicine and Clinical Trials Unit Bern, Department of Clinical Research, University of Bern, Bern (P.J.) — both in Switzerland; and the Division of Medical Ethics, Department of Global Health and Social Medicine, Harvard Medical School, Boston (N.B.-A.). Dr. Biller-Andorno is a member of the expert panel of the Swiss Medical Board; Dr. Jüni was a member of the panel until August 30, 2013.