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://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.4.9233) 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.

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.

On the other hand, nearly everyone pays the full cost of their veterinary care, and taking your pet to the vet is a good time to think about how medical decisions should get made.

This came up for me recently. I have an 11-year-old Labrador retriever.  My wife took her to our usual vet practice because she had a strange swelling on her flew (upper lip). On this particular day, she saw a new young vet who we hadn’t seen before. He made three findings:

1. The swelling was an infection, and antibiotics were indicated. He prescribed Simplicef, which the manufacturer describes as “the first and only once-daily oral cephalosporin that’s FDA approved for veterinary use in the treatment of canine skin infections. It’s designed to be convenient and a good value for your large dog.” (from simplicef.com)
   
2. Her weight was 57 pounds, about 10 pounds lower than a year ago. He suspected a metabolic disorder (and probably also considered cancer, but didn’t say that), and ordered a panel of blood tests.
3. He heard a heart murmur, left-side, grade 2-3/6, which had been documented in her chart for several years. He recommended an ultrasound, chest x-ray, and ekg.

I like veterinarians. I think they’re good people with the interests of animals at heart. That said, in this particular consultation, several avoidable errors were made that illustrate some key principles of medical decision making (for humans as well as dogs):

1. The dog improved on the first 14-day course of Simplicef, as we all expected, but these infections typically require multiple courses. Readers of this blog will not be surprised to hear that while once-daily dosing may be more convenient than twice-daily dosing, a 14-day course of Simplicef was about $50; the equivalent course of generic cephalexin is about $8. There is no evidence of any difference in effectiveness, and cephalexin has been around a long time, and our dog has taken it before with no ill effects. We pointed this out to the vet, who was surprisingly reluctant to switch for the second course, but agreed. The infection continued to improve on cephalexin. Attention to principles of rational prescribing would have been beneficial here.
2. The blood tests were all normal, as I had predicted, because I knew something that the vet didn’t — but should have. About 10 months earlier, we changed to a prescription dog food (our dog has arthritis, the combination of glucosamine and omega-3’s we were adding to her regular dog food seemed to be helping, and the new dog food had that all built-in and was cheaper that the cost of the supplements). When we changed food, we continued to feed at the same volume, without considering differences in the caloric content of the food, which turned out to be lower. She was losing weight because she was simply not getting enough calories. I had the vet calculate the appropriate feeding amount and she was gaining weight within two weeks (although that requires titration too — her joints are less painful when she’s lighter, so we don’t want her to gain it all back). The key decision point here – one echoed by many expert human diagnosticians -  is that lab tests can’t take the place of a good history. Had this vet asked “have you changed her food?”, we would have embarked on this trial of increasing her food without the costly blood tests — which, given that she had no clinical fatigue or other symptoms, could certainly have been postponed a few weeks anyway.
3. Everyone likes to know what’s going on. Naturally, the vet would like to know the cause of the heart murmur. As we point out in the book, however, the main purpose of gathering information is to drive a decision (allaying worry might be a secondary purpose, but that should be the patient’s — or owner’s — worry, not the doctor’s). So I asked the vet what he expected to learn, and how that would affect subsequent treatment. He indicated that they would learn if her murmur was amenable to medical treatment. I pressed for exactly which drugs he was referring to, and he mentioned diuretics and ACE inhibitors. I asked if there was any evidence of benefit for an asymptomatic dog, and he assured me there were several studies. I asked where to find them; he couldn’t specifically cite any, but suggested I search Medline for canine heart murmur studies. Which I did, finding two medium-to-large trials, one showing no benefit and one showing ambiguous benefit (and with several methodological flaws). Now, the veterinary literature is considerably smaller than the human medicine literature, and many fewer well-designed large-scale randomized trials are conducted in dogs. I haven’t yet brought these studies with me to see if (a) I didn’t find some important study that the vet had in mind, (b) he was thinking of these studies and interprets the findings differently, (c) he was thinking of studies in dogs with documented heart disease, in which ACE inhibitors do appear to prolong life by several months, or (d) he was passing along a vague memory of reading these or being told that evidence supported the drugs without checking for himself. Moreover, his recommendation did not consider values in the decision beyond the value of gaining knowledge. Cardiac ultrasound requires sedation, which is a procedure that is not without risk in an older dog. Moreover, medications can have side effects that can diminish quality of life, and these may not be worth extending our dog’s life by a few months. These are crucial considerations in this decision, and this particular vet, on this particular day, missed them.

My wife is a very smart person, but, like most people, not a decision scientist – and she shouldn’t have to be. A veterinarian, like a physician, has far greater expertise and experience in health care than their clients do. When they make a recommendation, it will usually be followed – often without question. That’s one reason why veterinarians (and physicians) also need to become experts in decision making, and why owners (and patients) need to ask good questions and seek out providers who demonstrate a commitment to good decision making.

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

Baumeister reported the results of a study published earlier this year in the journal Psychological Science by Masicampo and Baumeister (19:3, pp. 255-260). To understand the motivation for the study, think about the dual-process model of decision making discussed in Medical Decision Making: A physician’s guide. We have two parallel decision systems in the brain: one intuitive and heuristic, and one analytical. The intuitive system is fast and easy, but doesn’t always get it right. The analytical system can work out the right answer, but is slower and more effortful to use. Many biases in decision making are products of the intuitive system’s heuristics, particularly in situations when the analytical system doesn’t have the opportunity to correct our intution.

Masicampo and Baumeister point out that what “effortful” means, biologically, is that analytical thinking requires and consumes more energy in the brain. Energy is provided by glucose, transported through the blood. Thus we would expect that reducing or increasing glucose might impair or enhance the brain’s capability to use the analytical system, and thus impact susceptibility to decision bias.

In the study, 120 undergraduates drank lemonade, either sweetened with sugar (which increases blood glucose a few minutes later) or with Splenda (which does affect blood glucose.) Neither the participants nor the experimenters knew which drink they were given. In addition, half the participants performed a self-control task designed to deplete their resources (the other half did a similar task that did not require self-control). Finally, all the participants made choices that are susceptible to a decision bias called the attraction effect (described in the postscript below).

Those who drank Splenda-sweetened lemonade displayed the attraction effect more strongly than the other groups. Among the depleted group, in particular, sugar drinkers outperformed Splenda drinkers and performed as well as sugar drinkers in the non-depleted group (the best-performing group).

That is, a glass of (real) lemonade a few minutes before this decision task led to better decisions, apparently by restoring depleted cognitive resources.

Yet another reason to suggest that patients — and on-call residents — should eat well to improve decision making. (And yes, Baumeister appreciates the irony of trying to apply this finding to self-control decisions around dieting or diabetic glucose control).

Postscript: The attraction effect occurs when people make choices between options which involve trade-offs (for example, one washing machine is large and low-efficiency and the other is small and high-efficiency). Imagine that people are split 50/50 on which machine they prefer. To get the attraction effect, you add a third option (the “decoy”), which is strictly worse than one of the others (for example, a medium-sized low-efficiency washing machine would be strictly worse than the first washer). No one should ever choose the decoy, and no one does, but the addition of the decoy can have a dramatic and systematic effect on the choice between the two original washers. Now people are more likely to prefer the washer that’s better than the decoy (the first one, in our example) over the other washer; instead of a 50/50 split, 80% of of people might prefer the large low-efficiency washer. If you’re in the washing machine business and some reason to want to sell more large low-efficiency washers, this has obvious applicability. This effect is also sometimes called “asymmetric dominance”.