My Portuguese is very limited, but I’ve flipped through it and I think the translator, Marcio Moacyr de Vasconcelos, a Pediatric neurology fellow at George Washington University and Adjunct Professor of Pediatrics at Universidade Federal Fluminense, has done a creditable job. I’ll try to get a picture of the cover somewhere on this web site soon.

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.

Four times a year I lead a group third-year medical students through afternoon seminar on using test results in the diagnostic process. Although one could make this topic very broad, the focus of the seminar is essentially Bayes Theorem. There is plenty of evidence that this is a challenging topic for students in the health sciences (and practicing physicians). I certainly found it challenging when it was introduced to me in medical school. However, once mastered I had to wonder why students could not see how obvious Bayes Theorem is; it is only a simple mathematic transformation.  When teaching Bayes Theorem it always seemed to take me multiple attempts at the computation and providing explanations until a few of the group grasped the concept of probability revision. Most would leave bewildered.

In frustration, I searched for a better approach- I thought my students should be able to experience the wonder of probability revision and not the pain of elementary mathematics. The search led to a wonderful report about simplifying bayesian inference by making it visual. (S Krauss, L Martignon, U Hoffrage. Simplifying Bayesian Inference. Conference on Model-Based Reasoning in Scientific Discovery, 1998. http://www.mpib-berlin.mpg.de/en/institut/dok/full/martignon/kssbimbri/kssbimbri.pdf) Students still are required to perform simple mathematical computations but the visual presentation of Bayes Theorem allows the students to see where they are in the process. Once completed students can easily go back and review the steps they took.  This simple visual approach has turned afternoons of student frustration into afternoons of discovery where they come to “see” the importance of pre-test probability in interpreting a test result and “see” the importance of not only sensitivity but also specificity.

If you are facing a similar challenge in your teaching, I highly recommend that you take a look at this approach.

“a thoughtful exposition of the breadth of the medical decision issues to which the analyses of decision theory have often been applied. The authors’ approach to medical decision making ensures that readers from different backgrounds understand the concepts by expressing them in words, elaborated with concrete numerical examples and graphs, instead of expecting symbolic formulas to communicate….This book has the potential for teaching practicing physicians to make good decisions and to make decisions well.”
–Doody’s Review Service

We’ll be following the blog, and Dr. Paciotti’s interest in how health care is distributed in the U.S. and how the Internet will affect the delivery of health care and medical education.

Session Description

Will decision aids have a vital role in health care reform? Patient decision support tools are well-validated and effective, but under used. The symposium will address this problem by examining the keys to success of decision support tools used in the US outside of health care.  Investigators will present results of an in-depth investigation of four successful tools used to present evidence and promote informed choices by consumers.

Consumer Reports’s annual car buying guide, the FDA Nutrition Facts Panel, eBay, and US News’s America’s Best Colleges all provide decision support, some mandated by government, some entrepreneurial. Representatives from health care patient decision support producers will comment. The symposium will present key variables for success, and debate the implications for consumer and patient decision involvement in health care decisions.

Speakers

• Margaret Holmes-Rovner, PhD, Professor – Health Services Research, Michigan State University, East Lansing, MI
• Dale Shaller, MPA, Principal, Shaller Consulting, Stillwater, MN
• Dorothy Jeffress, M.B.A., M.S.W., M.A., Executive Director, The Center for Advancing Health, Washington, DC
• Richard Wexler, MD, Director, Patient Support Strategies, The Foundation for Informed Medical Decision Making, Boston, MA
• Representative, Consumer Reports Health Rating (Invited)

Registration

Information about registration for the Society for Medical Decision Making meeting, including pre-meeting symposia and short courses, will soon be available at http://www.smdm.org/2009meeting.shtml

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.

The article does a fine job of reviewing and highlighting key research in patient risk communication. It offers clinically-relevant guidance for physicians seeking to educate their patients that is very much in the MDM:APG spirit. It also links to Dr. Chris Cates’s Visual Rx web site, which provides a form that can generate pictograms for representing number needed to treat or number needed to harm data (Dr. Cates, if you’re reading this, I’d love to see version that can combine both efficacy and adverse event data and produce a unified plot of NNT/NNH – something I’ve been meaning to do with my own (nonvisual) NNT/NNH calculator).

It’s great to see this kind of discussion appearing in publications like these that reach a large community of practice.

The study of risk communication is an active one. Determinants of whether or not lay people achieve adequate comprehension of risk information are multifaceted, and include how the risk communicator understands the risk information, his or her mental model of what the lay person knows, the format and context of the risk communication, the lay person’s own numeracy and health literacy, the human cognitive processes involved in judgment and decision making, and cultural expectations around the discussion of medical and environmental risk.

Due to the growing requirements to communicate numerical information to lay individuals, there has recently been a significant push to conduct research that can provide direct guidance regarding how to best present numerical information in order to increase the likelihood of informed decisions. Accuracy is one key goal; evaluability (the ability to make a meaningful interpretation of risk so that it can be used in judgments and decisions- cf. Hsee C, The Evaluability Hypothesis: An Explanation for Preference Reversals between Joint and Separate Evaluations of Alternatives. Organ Behav Hum Decis Process, 1996;67:3) is a conceptually distinct goal, and both are important. Look for a lot more research on these topics in the next few years.