From 1989-2002, annual screening mammography was recommended for women at low risk of breast cancer starting at age 50 in the US. In 2002, USPSTF changed that recommendation to recommend mammography every 1-2 years for women starting at age 40. This change was scientifically contentious – there were questions about the data – but endorsed by cancer organizations. This month, USPSTF changed the recommendation back to starting at age 50. You can read the published recommendation here. This change is less scientifically contentious, but has been even more upsetting to cancer organizations and, in many cases, to women.

I’m going to tackle just a few of the key decision making questions here to try to clarify what’s going on. For example, I’m going to set aside questions of cost and of insurance coverage, which are significant issues, to focus only on the health questions, and only on women at low risk for breast cancer.

Is there a threshold for screening?

Assumption: A false positive mammogram is harmful.

Why? A positive mammogram creates a decision: ignore the finding or confirm the finding? Ignoring the finding may mean ongoing anxiety, which can range from mild concern to debilitating worry. Confirming the finding requires a breast biopsy, which is uncomfortable, and, depending on the technique required, can involve small risks of breast damage, infection, anesthesia, etc. A positive biospy (which may also be false positive in rare cases) generally leads to treatment for breast cancer (lumpectomy, mastectomy, lymph node biopsy, radiation, chemotherapy, and drug therapies). Notably, some breast cancers can be very slow-growing, so that treating them at all is unnecessary (you are more likely to die with these cancers than of them), but, again, ignoring them may mean ongoing anxiety.

If you read that description and you don’t think that the possibilities of anxiety, unnecessary biopsy, and potentially unnecessary treatment of indolent cancer are harmful, then guidelines for starting ages for mammography are unnecessary — mammography should begin after puberty. After all, any chance of detecting cancer, however small, is worthwhile if there’s no harm in looking.

Most people, however, and USPSTF in particular, don’t discount those harms. Therefore, the question is: When do the benefits of screening mammography exceed the harms? As women get older, they are more likely to have breast cancer, so positive mammographies are less likely to be false positives. On the other hand, cancers found in younger women may be more beneficial to treat. (And as women get much older, they’re more likely to die of other causes, so it’s sometimes more harmful to find and treat breast cancer than to stop looking, which is why all these guidelines refer to women 75 and younger).

Put like this, the question isn’t “Is mammography a good thing?” Overall, it undoubtedly is. The question is “Knowing that there’s *some age* under which mammography is not worthwhile for the average woman, what age is that?” And, “How sure are we?”

It’s entirely possible, for example, that that age could be 45. But by convention, patients are usually studied in 10-year age ranges, so if the true “best age” for the average patient is 45, some studies will look like the right answer is 40 and some will look like the right answer is 50.

How sure are we?

So, how sure are we? In 2002, the USPSTF gave their recommendation (40-75) a certainty grade of “B”, which means (from their web site): “The USPSTF recommends the service. There is high certainty that the net benefit is moderate or there is moderate certainty that the net benefit is moderate to substantial.”

What is high certainty to USPSTF? “The available evidence usually includes consistent results from well-designed, well-conducted studies in representative primary care populations. These studies assess the effects of the preventive service on health outcomes. This conclusion is therefore unlikely to be strongly affected by the results of future studies.”

What’s moderate certainty? “The available evidence is sufficient to determine the effects of the preventive service on health outcomes, but confidence in the estimate is constrained [by concerns about the quality of the evidence]…As more information becomes available, the magnitude or direction of the observed effect could change, and this change may be large enough to alter the conclusion.”

Today, in 2009, USPSTF is effectively saying that they were overconfident in 2002. Or, if you like, they were moderately certain in 2002, and more information has become available that was large enough to alter the conclusion. The new 2009 recommendation (50-75) has a certainty grade of “C”:

“The USPSTF recommends against routinely providing the service. There may be considerations that support providing the service in an individual patient. There is at least moderate certainty that the net benefit is small.”

The new scientific evidence since 2002 hasn’t made us hugely more certain. But it has tipped USPSTF’s beliefs about the net benefit of screening mammography for 40-49 year old women far enough to cross back over their threshold for recommendations.

Why are people so upset about it?

New evidence changes recommendations all the time, and not just in health care. In my childhood, dinosaurs were depicted as brown and scaly. Evidence now suggests many were feathered, probably colorful, and the recent discovery of fossilized melanin-producing cells makes it likely that we will know what some of those colors were. This bothers no one. What’s different about mammography?

First, of course, health evidence affects the quality and length of our lives in a way that dinosaur colors don’t.

Second, because mammograms have been urged on women for years, they are considered a valuable health good. Extending a good (as in the 2002 recommendations) seems like a gain. Removing a good (as in the 2009 recommendations) seems like a loss, and losses are psychologically more painful than equivalent gains. Of course, the debate itself is over whether mammography really is a good in younger women, not about whether we should provide goods or not.

Third, there is evidence that people may actually think well of false positives. In a 2002 study of prostate cancer screening (which may have even less benefit than mammography for women 40-49), Cantor and colleagues found that several patients were willing to endure the anxiety of a hypothetical false positive PSA test and the pain of a biospy to be reassured that they did not have prostate cancer. On the other hand, Katz and colleagues surveyed patients who had actually had a (false) positive PSA test and negative biopsy, and found increased worry and decreased sexual function among these patients as compared to those with a negative PSA test. It may be that we underestimate the downside of a false positive. (Hat tip to Rob Hamm and Scott Cantor for the references.)

Fourth, the recommendations have changed and changed back within a short enough period that the USPSTF appears indecisive and inconsistent, and this leads to distrust of their recommendations. It is easier to see the recommendations change than the evidence behind them, and it’s certainly a lot harder for USPSTF to communicate the changing evidence, but we need to develop better strategies for doing so.

Finally, USPSTF recommendations are for the population — what I’ve called the average woman. There are women, however, who had true positive mammographies in their 40’s, and on the basis of their experience are vocal supporters of earlier screening. On the other hand, the many women who had (retrospectively) unnecessary mammographies from 40-49 and didn’t experience a false positive are not a loud voice in the discussion. This returns us to the threshold question — if a single 25-year old woman turns up to say that she had a mammogram that led to discovery and treatment of a dangerous cancer that saved her life, is that sufficient reason to begin screening mammography for women in their 20’s?

With respect to individual women in their 40’s, Diana Petitti, the Vice Chair of USPSTF, says “You should talk to your doctor and make an informed decision about whether a mammography is right for you based on your family history, general health, and personal values.” That, at least, is a recommendation that has always been true.

I had also planned to let people know about SMDM President Mark Robert’s presentation at the Federal Coordinating Council for Comparative Effectiveness earlier this month.

However, fellow SMDM member David Hickam has already blogged on both, so instead, I direct you to his site, The Comparative Effectiveness Blog (and particularly the postings for June 10 and June 12). Thanks, David!

He focuses on the difference between the value of an identified life and a statistical life, as well as the impact of uncontrollability on risk perception (part of the constellation of factors Paul Slovic has referred to as being associated with “dread risk”).

The availability heuristic is also relevant here – there is very little reporting of deaths associated with seasonal flu and a lot of reporting about potential new strains. This leads to an underestimate of the risk of seasonal flu, even in the young and elderly.

Videorecordings of the symposia are now available. You can find links at http://www.sjdm.org/content/video-recordings-2008-sjdmsmdm-symposia

My response to her (revised for this blog) reviewed some evidence, much of which also appears in the Salon article:

Among doctors, there is really no question. The one published study that claimed a relationship between MMR vaccine and autism has been renounced by 10 of the 13 authors and retracted by the journal (for some relevant links see http://blog.openmedicine.ca/node/50).

With respect to thimerisol, it’s out of all childhood vaccines in the US right now, except for some flu shots. See http://www.cdc.gov/nip/vacsafe/concerns/thimerosal/faqs-thimerosal.htm. The one study in this area that claimed a link was also authored by a pair of docs who make their money as expert witnesses and was seriously flawed.

The purported link is, at base, no more than a scam to make profits off of people who have the misfortune to already be dealing with how to raise their autistic child and are accustomed to (rightly) standing up to recognized authority in other matters related to their children (e.g. education).

Since 2001, thimersol has been removed from vaccines, and autism rates have increased. And now measles is making a comeback.

What is known is that vaccines have made many childhood diseases
nearly extinct in the US, and saved a ton of lives and health.
E.g. http://www.cdc.gov/mmwR/preview/mmwrhtml/00056803.htm

Readers of this blog will know that I think scientific evidence is a good thing in medical decision making. But looking over my response, I wonder if I really addressed her key uncertainty: “My confidence in institutions is at an all time low”

Unfortunately, I think there are many people who are concerned about the trustworthiness of public health research and recommendations (and perhaps too few people who are concerned about the trustworthiness of pharmaceutical company research, but that’s a different story). Some of this concern is undoubtedly justified, as I wrote, because people who are raising autistic kids or living with other disabilities do have to be critical of the institutions around them, and often must make their voices heard loudly in order to secure equitable and beneficial treatment of their children and themselves in non-medical arenas. It is hard to avoid skepticism about medical science if you’re African American and familiar with the Tuskegee studies. IUD use is very low in the U.S., despite its safety and effectiveness, in part because of memories of problems with the Dalkon Shield. And sometimes things that look good, like thalidomide, do turn out to be bad.

As we wrote in the book, uncertainty is a fundamental feature of medicine; on the other hand, decisions have to be made. For vaccination, the default option (the one proposed by nearly all pediatricians) is to vaccinate, and that’s a good thing, but once a treatment has entered the public consciousness as uncertain, people will rightly want to make a more considered decision. A key question for physicians and patients alike is how much evidence, and what kind, should we expect in order to make these decisions, and how can we be sure we’re hearing reliable evidence and not biased evidence? This is a particular problem because, much of the time, good science speaks softly; bias shouts.

There are some time-honored heuristics that serve well here. Prefer evidence that you seek yourself to evidence that someone else thrusts at you. Prefer evidence that can clearly be replicated, that has received open scrutiny from other scientists, and especially findings that have been replicated repeatedly. Ask “cui bono?” Understand statistical reasoning. Recognize that in a nation of 300 million people, a one-in-a-million chance will happen about 300 times. Compare medical risks to other risks that you’re willing to undertake in daily life for similar benefit. Remember to consider the risks incurred by choosing not to choose.

Dr. Roy Poses, a well-respected physician who has done a lot of work studying physician probability judgment (one example of which is mentioned in Chapter 7 of Medical Decision Making) has posted a rebuttal on the Health Care Renewal blog. Dr. Poses demolishes the misrepresentation of EBM that appears in the original article (as well as asking some on-point questions about the author’s interests in the matter), and does it quite effectively, so I won’t repeat his criticism here. Instead, I’ll focus on some other misunderstandings in the original commentary: that cutting health care costs is at odds with the medical imperative to improve care, that patients are so biologically unique that studies of patient groups has little value, and that EBM reduces physician autonomy.

Are considerations of costs at odds with the “medical imperative”?

The commentary invites us to “Consider an overweight man who is forced to take a cheaper, less effective anti-cholesterol drug. If he ends up in the emergency room because of undertreated cardiovascular disease, this could end up costing the health-care system significantly more money.” Setting aside that the author now seems concerned with health care costs in this situation (not only with effectiveness), we could equally consider a situation in which comparative effectiveness evidence helps ensure that the patient is prescribed one of a set of anti-cholesterol drugs that evidence suggests are equally effective and safe. The physician, knowing this, chooses which of these drugs to prescribe first on the basis of judgments about the needs of the patient, which might include which of the drugs the patient can afford, which offers the most convenient dosing, etc. A patient prescribed an equally effective but more costly drug who can not afford to fill the prescription may wind up in that same emergency room, when an equally effective and more affordable generic drug might have kept him healthy.

Here’s an example of a comparative effectiveness review from January 2007 by the US Agency for Healthcare Research and Quality (AHRQ): Comparative effectiveness of second generation antidepressants in the pharmacologic treatment of adult depression. Readers will note that this report makes no references to cost-effectiveness (it barely mentions cost at all), and focuses on reviewing the available evidence that compares the medical effectiveness of different second generation antidepressants on a variety of factors and for a variety of subgroups of patients. It points out the strength of evidence to answer different questions (which ranges from none to high), and, where evidence exists, what the evidence says (e.g. there is no difference in effectiveness between these drugs for major depressive disorder).

Is each patient biologically unique?

There is no question that individual human beings have unique genetic makeups that lead to unique biology. The question is whether this matters for medical treatment and medical research.

All medical research is based on the idea that biological similarity is as important as biological difference. When a new patient presents to a physician with the first strep throat of their lives, the physician considers prescribing an antibiotic, suggests the patient take an analgesic, but doesn’t offer an anticonvulsant. Why? Because the physicians knows the pathophysiology of strep throat, and understands that the infection can be eradicated and pain can be reduced with those medications. The physician knows this because we have studied enough people to enable us to generalize. Research on groups of people is fundamental to modern medicine.

Not every drug works as well for every person, but through well-designed research we can reduce the uncertainty and increase our confidence in how likely a drug is to work on average. We can, and do, also learn about how variable the drug’s effect is around that average, and for which patients it may perform better or worse.

As we learn more about genetic medicine, we may one day seek to tailor therapies to the specific genetic makeup of a patient. Of course, it will require extensive research on the application of these processes applied to large groups of patients before we can be confident of our ability to do this. If our knowledge is great enough that tailored therapies can complete reduce uncertainty in outcomes, the profession of medicine will experience fundamental changes. But people need health care now, too.

Does EBM reduce physician autonomy?

Physicians need the freedom to pursue effective treatments for a patient based on their knowledge — which should include available, credible research evidence — and judgment — which should include contextual factors that are individual to a patient (see, for example, this excellent article by my colleague, Dr. Saul Weiner). EBM enhances this freedom, by improving the knowledge base on which the physician relies.

Physician autonomy does not extend to prescribing ineffective cures when there is valid evidence of an effective cure. The same applies to diagnosis. No physician I know would prefer to measure fever by a hand on the forehead when a thermometer is available.

Physicians need evidence. Synthesizing evidence to review conclusions about comparative effectiveness allows the physician to reduce a major source of uncertainty, and to better inform his/her judgment in selecting a therapy. It also helps shield the physician and patient from spurious claims of effectiveness made by those with a vested interest in selling more profitable interventions — a financial imperative that can be much more at odds with the medical imperative than social imperatives to manage health care spending for the benefit of society as a whole.

The proverb captures important psychological nuances of choice. The same product – a bottle of wine or a risky medical procedure – may be perceived differently depending on its context, and it is often possible to arrange the context to influence a choice while still maintaining the decision maker’s autonomy.

The practice of structuring choices is called “choice architecture” in a brilliant and important new book, Nudge: Improving Decisions About Health, Wealth, and Happiness, by University of Chicago Distinguished Professors Richard Thaler (Business) and Cass Sunstein (Law). Nudge lays out the groundwork for the science of choice architecture in investing, insurance, health care delivery, and other areas, and argues for a “libertarian paternalism” in which choices are structured to make it more likely that a decision maker will select what is considered the most beneficial option, without impairing the ability to decision makers to select other options. For example, making enrollment in 401(k) plans automatic for new employees, with a form for opting out, is likely to result in greater retirement savings than an opt-in system, without limiting anyone’s freedom to choose.

Thaler and Sunstein apply the principles of choice architecture to a few problems in health care (How could Medicare part D be improved? How can organ donation rates be increased? Why shouldn’t patients be allowed to waive their right to sue for medical negligence in return for cheaper health care?) But the concepts in the book go beyond their specific examples and could prove very useful to practicing clinicians, who, they note, are often in the position of being choice architects for their patients.

Their principles of choice architecture (paraphrased by me and focused on physicians helping patients make decisions) are:

• Make sure incentives are aligned with desired outcomes
• Help patients map outcomes of different alternatives into formats they can understand (a major focus of Medical Decision Making as well)
• Arrange default options to favor better health. Pediatricians have done a good job of making vaccination a default option.
• Provide timely and relevant feedback about choices and outcomes. A patient seeking to lose weight needs to experience feedback in the form of measurable progress soon enough that they are not discouraged.
• Expect error and develop systems to prevent, detect, and minimize it. For example, pill cases and inhalers with dosage counters are simple and valuable ways to reduce the frequent errors people make in remembering medication. Psychological research provides direction as to what kinds of errors are to be expected when people are making decisions.
• Structure complex choices to reduce the difficulty of making good decisions. In many ways, that’s what medical decision making — and Medical Decision Making — is about.

I highly recommend Nudge. It’s a great read, and has the potential to change the way you think about clinical practice. Here’s a link to the Nudge web site and blog for more information.

As a result of a $430 million settlement between drug manufacturer Warner-Lambert and the U.S. government, several projects have been funded to study and address issues in the marketing of pharmaceuticals. One such project, Formulary Leveraged Improved Prescribing (FLIP) is centered across the street from my office, and is a joint effort between the University of Illinois at Chicago and the John H. Stroger, Jr., Hospital of Cook County.

As its name suggests, FLIP is directed at members of formulary committees of hospitals and health plans and is intended to encourage them to take a critical look at their drugs choices for the formulary and to become an important source of provider education. Both patients and physicians may be particularly interested in FLIP’s 24 Principles for Rational Prescribing, a set of guidelines for increasing the overall safety of prescribing while lowering costs. Many of these, not surprising, involve using non-drug therapies and favoring older, well-established drugs over newer.

While decision scientists have traditionally emphasized utilitarian approaches to decision making, bioethicists have questioned whether one should focus on the consequences of decisions or their intrinsic features (as in deontological approaches to ethics). They have also asked such questions as whether morality is a feature of actions or a set of virtues, how moral claims should be justified, how to balance contextually-specific decisions with universal principles, and how health care ethics related to ethics in other endeavors (Khushf 2004).

Like decision scientists, ethicists, particularly those that recognize multiple ethical principles, virtues, or rights, regularly consider situations in which all of their goals can not be fully met. Decision science approaches this problem through prioritization and trade-off of goals. Ethics is more likely to use a process of specification, in which each principle is made more contextual and concrete in order to provide guidance for the particular decision in question (Beauchamp and DeGrazia 2004). Indeed, some philosophers advocate case-based approaches to bioethics, just as the judicial system in most Western nations interprets the law through the analysis of cases and the development of precedence (Boyle 2004).

Although it would be natural to expect these two perspectives to converge and these two groups of theorists to share ideas, historically these questions have been answered through entirely separate academic endeavors, and have resulted in separate communities of scholarship (Stiggelbout, Elstein et al. 2006).

Two recent studies have compared the thinking of decision scientists and bioethicists. In a survey by Ubel, et al. (1996), 568 prospective jurors, 74 members of the American Association of Bioethics and 73 members of the Society for Medical Decision Making were asked to choose between providing a more effective screening program to half a population (resulting in a greater number of deaths prevented overall) or a less effective program to the complete population (resulting in fewer deaths prevented overall). Decision scientists were less likely than others to favor the less effective (but arguably more equitable) program.[i]

A more extensive study by Stiggelbout, et al. (2006) surveyed 327 members of the American Society for Bioethics and Humanities and 77 members of the Society for Medical Decision Making, presenting each participant with two clinical cases, one concerning refusal of a beneficial treatment, and one concerning surrogate decision making about futile care at the end-of-life. The authors found substantial similarities in the opinions of the two groups, although decision scientists were more likely to emphasize outcomes and trade-offs and ethicists were more likely to emphasize patient autonomy. Notably, both groups called for greater exploration of patient goals and values, and ongoing theme in this book.

As the Stiggelbout study suggests, the concerns of bioethics and decision science are often in alignment; the ethical principle of autonomy, for example, is often naturally reflected in the importance of properly eliciting and considering patient preferences in decision analysis. Bioethical principles can also be naturally incorporated into decision making as personal or societal constraints on possible choice alternatives: in a society which espouses the natural right of people to be free from becoming unwilling subjects of medical procedures, a decision alternative which results in forcing a family member to undergo genetic screening might be simply unacceptable.

Atul Gawande’s excellent 2002 collection Complications: A Surgeon’s Notes on an Imperfect Science struck a responsive chord with medical decision scientists with its insightful examination of medical error. Gawande has now collected 11 new essays in Better: A Surgeon’s Notes on Performance (Metropolitan Books, 2007), which shift the focus to how some physicians come to excel in their craft (in his terms, becoming “positive deviants”). He asks “what does it take to become good at something in which failure is so easy, so effortless?”

Gawande focuses on three areas in which he characterize good physicians: diligence, doing right, and ingenuity.

Part I of the book, on diligence, comprises three essays. The first reviews the tremendous impact of physician hand-washing behavior on patient outcomes, and the ongoing efforts of some dedicated health workers to reinforce the need for doctors to wash their hands consistently and thoroughly. The second chronicles WHO’s approach to containing a new polio case in India, a massive vaccination effort organized by volunteer health care workers. The third demonstrates the remarkable progress in the effectiveness of military medicine through a look at the organization of American combat hospitals in Iraq. The lesson of this part is that performance improvement demands painstaking attention to detail and a continuous effort to get it right – despite sometimes insurmountable constraints.

Part II of the book, on doing right, includes five essays. These cover such apparently disparate topics as conducting pelvic and rectal exams, malpractice litigation (which he considers replacing with a system modeled after the National Vaccine Injury Compensation program, or New Zealand’s malpractice system), physician billing and income, the role of physicians and nurses in state-ordered executions, and how aggressively physicians should treat patients with terminal diagnoses. The overall theme of this part of the book is that medicine is intimately pervaded by questions of social ethics, personal morality, and individual preference, and that “doing right” necessarily requires facing up to difficult choices — and making them.

Part II of the book, on ingenuity, contains three final essays. The first illustrates the impact of measurement on results by looking at the impact of the Apgar score on obstetrical outcomes. By providing a clinical score that measures the status of a newborn, can be universally applied, and is a natural basis for comparing practices, Gawande argues, the Apgar has
driven performance improvements in labor and delivery. The second examines the considerable variability in success among cystic fibrosis treatment centers and looks at some of the factors that may contribute to the performance of the most successful centers. The third provides a look at surgeons in rural India who, lacking access to specialists, develop expertise far broader than Western general surgeons.

Gawande concludes with an afterword based on a lecture to medical students offering five tips to becoming a positive deviant in medicine: “ask an unscripted question”, “don’t complain”, “count something”, “write something”, and “change”. It’s clear in this book that Gawande is not merely espousing platitudes, but is charting a course toward betterment for physicians and following that course.

The writing is clear, forceful, and engaging, and once I picked up the book, it was difficult to put down. Although the essays (other than the afterword) tend to raise questions – sometime troubling ones – rather than offer prescriptions, the questions themselves are important and demand attention from contemporary practitioners. Gawande may not be able to tell physicians how to make the choices that will face them, but he does a great service in highlighting both the content of some of those choices and the need for informed judgment itself. For decision scientists, there is a wealth of material here that calls for further descriptive and prescriptive study in the lab and in the field.