How to game mortality data

There is a great illustration in this BMJ article of what I discuss in Chapter 2 of my book: the type of mortality that matters. In the figure below from the paper, note that, as the new diagnoses of each of the cancers rise (green lines), the attendant "Deaths" (red lines) stay unchanged. If you look at the Y-axis of each graph, it tells us that the unit of measurement is "Rate per 100,000 people." So the red lines represent population mortality.

"Population mortality" means that the denominator for this value is all people in the population who are at risk for the disease in question. This means that for prostate cancer, for example, we include only those men who have a prostate and exclude all women and men who have had a prostatectomy. Population mortality stands in contradistinction to case fatality. The latter is defined as deaths among all the people diagnosed with the disease. So, for prostate cancer, case fatality would be deaths among all men who have been diagnosed with prostate cancer.

It is not difficult to see how case fatality is a somewhat circular, even self-referential, measure of our diagnostic prowess, but says very little about how well we are doing with disease treatment. If we have tests that are capable of picking up the most minute of diseases, those that are not likely to cause death in the first place, then the denominator becomes inflated with this noise, while the numerator, the actual fatalities, does not change. This leads of course to an apparent reduction in deaths from the disease, but a reduction that is an artifact of overdiagnosis. Population mortality, on the other hand, cannot be gamed this way, as you can see in the figure above. This is the only mortality that gives us honest feedback without a bias about how we are doing with our early detection and other interventions. In the case of the cancers in the figure, the answer is "not so well."

You can learn more about this in Chapter 2, where I even have a figure that illustrates this critical distinction.        

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Three wrong reasons to dismiss the azithromycin data

There is a lively discussion over at KevinMD about the azithromycin study in the New England Journal of Medicine, which I blogged about here. A certain ethos has emerged in the comments that bears unpacking. There are 3 distinct points that I am feeling unsetlled by:

#1. As a retrospective cohort study it should be ignored.
This is an intellectually indefensible position in my mind -- it is a study that uses a set of methods developed and validated for this type of data. Yes, it's complex; yes, it's messy; no, it's not to be ignored. The discipline of pharmacoepidemiology relies heavily on observational data. To expect anything more is to indulge in misapprehensions that a). it is feasible to run a RCT to detect such rare signals, and 2). that a RCT like that would give us a definitive answer.

#2. They really had to add a lot of zeros to the denominator to make the numerator seem impressive.
This is a baseless accusation, since 1 million prescriptions is not that difficult to generate. Z-pak has been on the market for over a decade, and according to this article, last year 55 million prescriptions for azithro were handed out in the US. So, just a back-of-the-envelope calculation for excess deaths per year at this rate is well over 2,000. And this is just in one year! So, as a safety signal this is not something to be trivialized.

#3. A concern that this information will keep patients from doctors' offices and delay needed treatment.   
I find this to be rather a hollow concern (though I am sure that the person putting it forward believes it wholeheartedly). As another commenter pointed out, the overuse and misuse of antibiotics is completely out of control! And yes, cardiac deaths from azithromycin are but a small part of the issue, where the elephant in the room is the evolution of resistance. It is not just these latest data that should keep patients as far away as possible from unnecessary healthcare encounters, seeing as how these encounters are the third leading cause of death in the US. Why aren't we worried that this entire monster is keeping patients away? And quite frankly, why isn't it?

So, all in all, I am very glad that Rob Lamberts chose to blog the study, and the discussion has been worthwhile. The comments have really confirmed for me that it is not only the lay public, but also healthcare professionals, who have a hard time interpreting data. And when a study is somewhat challenging, it is generally easier to let our cognitive biases run amok.      

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Septic shock doesn't need my rescue bias

Rescue bias and auxiliary hypothesis bias are tempting distractions when it comes to dealing with data that are counter to our preconceived notions. In general, the role of our cognitive biases in all kinds of discourse, including clinical and scientific, is under appreciated. For this reason I devoted fully two chapters of my book to cognitive biases.

Which makes it even more frightening that, as I read this NEJM paper on the failed drotrecogin PROWESS-SHOCK study, I am still looking for reasons why it failed, other than that just doesn't work. After so many years of trials and tribulations with Xigris, and hopes that this lone therapy ever to have been approved for treating this deadly disease, I am having a hard time letting go.

Yet the rial was meticulous, as all trials designed and overseen by B. Taylor Thompson are -- he is just a brilliant clinical researcher that we all need to learn from. The design considered all the right issues a priori -- multiple interim looks at the data, a possible need to increase the power, heterogeneous treatment effect, and others. Although there were a few numerical imbalances between the treatment and the placebo groups -- blood cultures were positive 4% more frequently and the offending pathogen overall was 5% more likely to be identified in the Xigris than in the placebo group -- I have to accept that these are not the reasons for the observed failure to improve either the 28-day or the 90-day mortality.

I have seen and even blogged about these data before -- the press release had some of them, but, more importantly, Taylor presented them at the Society of Critical Care Medicine meeting back in January. So, this is nothing new. Yet reading all of the details in the pages of the NEJM brings back that pre-conscious cognitive wall that I have to climb over in order to get to reality. And the reality is that the drug just did not work.

But the reality is also that sepsis patients have a better chance of surviving this deadly assault than they did 10 years ago. In the original PROWESS study 28-day mortality in the placebo group was 31%, and these were all kinds of sepsis patients. In the current study, among most severe sepsis patients, those with septic shock, the 28-day mortality was on the order of 25% in both arms -- that's a staggering reduction in this most ill septic population! We really need to appreciate this. In part this trend may be due to some evolution of the disease-host interaction. But in part it has to be because of the concerted effort to understand sepsis better, to study various treatment options, and, most importantly, to implement these learnings at the bedside. In no small part we owe these advances in sepsis care to the short life of drotrecogin alpha.

The data are clearly anticlimactic, and for this reason the current paper has no sex appeal -- just look at the (lack of) press coverage about it. Yet, this is a clear example of countering the traditional publication bias: Here is a manufacturer-funded negative study published in a premier peer-reviewed journal. I realize that it was always a high-profile undertaking, but let's pause for a moment to enjoy this small victory for those who have railed against the spread of biased information in the medical literature.

I don't know if and when another therapy will emerge courageous enough to brave the rough waters of sepsis and septic shock. But one thing is clear: Xigris has served out its purpose, and no amount of rescue bias from me will save it. Rest in peace.    

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"Imagine the Future of Medicine" Project

Yesterday's post hit a nerve: Who is planning the future of medicine? I don't mean the futuristic visions of single individuals, or even the palpable excitement that permeates rooms filled with health IT discussions. I am referring to an integrated vision for what we want to deliver, a vision that can then inform our actions. I am talking about strategy rather than tactics, as pointed out by Patrick Jonas in the comments here. Would you build a house without first developing a blue print? Why not? After all, we can all get excited about IKEA kitchen cabinets and stainless steel bathroom appliances. But if we don't first work with a team of people to think about the role of the structure informing its design, all we will have in the end is a pile of (shiny) rubble. So why is it that we are so averse to building a blue print for medicine? Or is it that we think it is politically impossible?

If I had to choose one most important thing I carried away from the Health Foo Camp, it would be this (as embodied by Regina Holliday): "Never doubt that a small group of thoughtful, committed people can change the world. Indeed it is the only thing that ever has." And in the age of the internet, which allows groups of committed individuals to come together in fractions of a second, there is no excuse not to try.

So, here is what I propose: How about we start having that discussion here? I will accept blog posts on the theme of "Imagine the Future of Medicine" to be posted on this site. I don't just want to hear from doctors, or those who have previously made their views known (though I encourage you to contribute too). I want to hear from everyone who has something to contribute to the discussion. I want to hear from patients, pharmacists, engineers, architects, nurses, professors, story tellers, artists, poets, anyone who can articulate their vision for what medicine of the future can and should be.

Before we start, we need to lay out the values that should inform everything in medicine. From were I sit, "do no harm" is still the most important one of all. Equally important seems to be access to safe, humane and compassionate care aligned with the patient's values, and that is guaranteed to all. Did I miss something important? Remember, we are going for high level here to refer back to when we recommend more specific details to make sure they fit this overarching purpose.

I don't want to micromanage this. I want to free-range it and see where it goes. Heck, I am not even sure that I will get any responses from all you busy people who have more important daily duties to fulfill. But I want to try. We seem to be so fixated on the trees that we are not even sure what forest we want to end up in. Let's paint that forest.

There are a couple of rules to keep in mind:
1. I will not publish any profanities, personal attacks or marketing pitches.
2. To keep the reading manageable, I ask to limit your posts to 500 words. If you feel that you really need an extra 100 or 200 for an extremely important message, I will consider that.
3. I reserve the right to do some light editing for grammar, syntax and clarity.
4. If you are unable to make yourself clear, please, request editorial help before you send in your contribution.

So, let's make Margaret Mead proud and see where this field experiment ends up.

You can e-mail me your contributions for review at healthcareetcblog AT gmail DOT com                

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Free-range thinkers create Foo for thought

I cite this favorite quote from Max Planck in my book (and every chance I get):

A new scientific truth does not triumph by convincing its opponents and making them see the light, but rather because its opponents eventually die, and a new generation grows up that is familiar with it.

I think this applies to all walks of life, not just science. Yet sometimes an argument so compelling comes along that, though reluctantly at first, one by one the old guard drop at its feet. This is what happened to me this weekend at the Health Foo Camp in Cambridge, MA.

First, what is Health Foo? Well that was my first question when I received an invitation to attend this strangely named meeting. A Foo Camp is something put together by O'Reilly, the pioneering digital media group. Started 12 years ago, these meetings are thematic gatherings of "Friends of O'Reilly," hence "Foo," intended to bring together a diversity of thought about a specific field. The camp that I attended was the second such gathering in the healthcare space, supported in part by the Robert Wood Johnson Foundation, and held at Microsoft's New England Research and Development Center in Cambridge. How can I ever thank O'Reilly, RWJF and Microsoft for this mind-shifting event?

As I mentioned in my previous post, the attendee roster was so full of luminaries that I frankly wasn't sure that the invitation had not ended up in my Inbox by mistake. But mistake or not, what a privilege to attend! I spent the weekend getting to know the faces and the substance behind such familiar names as Regina Holliday, Paul Levy, Alan Greene, Ted Eytan, Susannah Fox, Gilles Frydman and others. And what still has my mind spinning is my conversations with people I don't normally interact with -- computational scientists, game designers, food advocates and international public health movers and shakers.

The most risky aspect of this meeting was the very essence of its success: we were to free-range. No agenda was set; space, food and company were provided. The resulting sessions ran the gamut from the usual nerd porn of probability to such far-reaching topics as memory and the role of faith, poetry and the arts in medicine (my personal favorite, where I got to play in the sandbox of participatory painting led by Regina. Take that, left brain!)

I have to say I spent a part of the weekend in a bit of a fog. What is gamification of medicine? What does "deep modularity" mean? But the full impact of such diversity of knowledge did not hit me until I was heading West on the Turnpike away from the meeting in the direction of home. It felt like a deep air pocket, and for a moment I couldn't catch my breath.

My epiphany was this: I have been sitting in my office and analyzing, writing and thinking about how to slow down this juggernaut of digitalization in healthcare. My logic has been to identify the problems, particularly the overdiagnosis and overtreatment and the attendant harm, all in the context of an obscene price tag, and to say that not only does medicine not need the radical digital revolution that is being imposed on it, but the very definition of medicine needs to change. What I failed to consider is that medicine is a module that needs to fit into the rest of what we call our modern life. So, slamming on the brakes in hopes of stopping this locomotive before it squashes the medical system is the wrong approach.

I can hear what you are thinking. "There goes another one." "She drank the Kool-aid." "If she gets all starry-eyed about technology, there is no hope for the rest of us." Well, I am not a fan of Kool-aid, but I do like Shakespeare. My realization is as follows: If we don't start thinking about what we want medicine to be, we will continue getting medicine that looks and works like a Rube Goldberg machine, a conglomeration of unrelated levers that may or may not achieve the desired results. The stakes are too high, stakeholders too many, and the resources being used staggering.

The digital revolution will continue its break-neck pace regardless of my opinions. In its quest to take over the world, it will continue to advance into every aspect of medicine. But instead of positioning it as a confrontation between Godzilla and King Kong, I have decided that a more constructive way is to start to imagine what medicine can and should be in the future. The current model is moribund, if not altogether dead. Nature abhors a vacuum, and unless we fill it with something that heals and nurtures that has come out of a concerted multidisciplinary blueprint, it will continue to grow into a hydra that will eventually swallow us.

I know, I miss the slide rule too. But we did not get to be on Twitter by chaining ourselves to the old paradigms. Regina Holliday taught me, among so many other things this weekend, the word "chaordic." It is a neologism that combines the ideas of chaos and order into one force of nature. I think there has been enough chaos in the relentless penetration of technologies into medicine. I am tired of screaming at at the back of the digital monster like a crazy lady. We need to get ahead of it with an open mind and even some excitement, and start imagining where we can direct it for the better health of the public. Our magical thinking will not change the fact that this tidal wave will destroy us if we lack the imagination to ride it. A gathering like the Health Foo Camp is what fuels that imagination. Let's grow and harness it!


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Why I have a propensity to believe the azithromycin data

Before I take off for the Health Foo Camp in Cambridge, MA, where I will be rubbing elbows with such luminaries as Susannah Fox, Ted Eytan, e-Patient Dave deBronkart, Regina Holliday, Nick Christakis and Nancy Etcoff, I thought I'd say a few words about one of my favorite topics: antibiotics. In particular I want to talk about azithromycin. A lot has been said about it this week already in the wake of the NEJM study (alas, the full paper is behind a pay wall) indicating an increase in the risk of cardiac death among patients exposed to this drug compared to those either not getting antibiotics or those receiving a different class of antimicrobials. And a lot more will be said in the future as the FDA reviews this risk. This is not specifically what I wanted to talk about. I wanted to focus on the methods.

The study was done meticulously, as far as I can see. It was a large Medicaid database from Tennessee (this may present a generalizability problem, of course, though I cannot think of a specific reason why it would). The study design was a retrospective cohort, which, as we already know, sets the study up for all kinds of biases. So, why should we believe anything the study showed? And particularly given many people's distaste for invoking causality from observational data?

There are at least 2 reasons why we should pay attention to these results. The first is that we are talking about the ultimate harm: death. When it comes to harm, my philosophical approach leans in the direction of caution. What this means scientifically is that I accept a much lower threshold for the certainty that the data convey than when it comes to evidence of benefit. This is an extension of the precautionary principle, where the burden of proof of safety now lies with the drug.

But there is a second, possibly more important reason that I am inclined to believe the data. The reason is called succinctly "propensity scoring." This is the technique that the investigators used to adjust away as much as feasible the possibility that factors other than the exposure to the drug caused the observed effect. In my book I briefly discuss propensity scoring in Chapter 21. And here is what I say:

Propensity scoring is gaining popularity as an adjustment method in the medical literature. A propensity score is essentially a number, usually derived from a regression analysis, describing the propensity of each subject for a particular exposure. So, in terms of smoking, we can create a propensity score based on other common characteristics that predict smoking. We take advantage of the presence of some of these characteristics also in people who are non-smokers to yield a similar propensity score in the absence of this exposure. In turn, the outcome of interest can be adjusted in several ways for the propensity for smoking. One common way is to match smokers to non-smokers based on the same (or similar) propensity scores and then examine their respective outcomes. This allows us to understand the independent impact of smoking on, say, the development of coronary artery disease. 

And if you are able to access Table 1 of the paper, you will see that their propensity matching was spectacularly successful. So, although it does not eliminate the possibility that something unobserved or unmeasured is causing this increase in deaths, the meticulous methods used lower the probability of this.

One final word about azithromycin. There are data that suggest that macrolides (the class of drugs that includes erythromycin, clarithromycin and azithromycin) are actually associated with improved outcomes in the setting of community-acquired pneumonia, or CAP. This is why these drugs are in the CAP treatment guideline. The point is that again, as in everything, the benefit of using azithromycin in any individual case will have to be weighed against this newly-identified risk.

  

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Blind and toothless: The future of middle class in America?

Since it's Friday, we'll steer clear of academic subjects and just tell a story. This is a story of a good friend of mine who works in an editorial position for an interdisciplinary humanities journal. This journal is based at a prestigious private liberal arts institution, and thus she is on staff at that institution. This story is about her, but also about how well the Massachusetts health law is working for people like her. I will call her Jo.

Jo is in her early 50s, a single mom of two lovely teenage boys. Their father who is separated from Jo, although in the picture, does not provide any child support for various reasons. Thus she carries sole financial responsibility for her family.

Well, you say, she is on the journal's editorial staff, she is loaded, right? Nah, surely you are not so naïve as to think that this is a high-paid position at a humanities publication. Let's just say that she is not the 1%; why, she is not even in the top one-half. Would you believe me if I told you that a high-ranking editorial staffer gets a salary that is only 2x above the federal poverty level threshold? So much for those wealthy elite academic East Coast types! But she does get her healthcare insurance through her employer, and this is fortunate, right? Well, here is the rub.

Jo has a complex health condition that affects her entire body, including skin, teeth, eyes, heart, lungs and viscera. Despite this, she is optimistic, upbeat and one of the most patient parents I have ever met. But here is what gets her goat: it is the very fact that she gets her health insurance through her employer! How can this be?

Here is how. The cost of her basic restrictive HMO insurance through her employer is over $5,000 per year. And this flat rate is not affected by the salary level of the employee; in other words, the janitor and the university president get to pay the same amount of money for the same plan. In addition to this, her office visit copay is $20, and her medication co-pays are between $10 and $30 for a month's worth of medication. As you can imagine, for someone with a complicated chronic condition, these co-pays can add up quickly, and they do, to a shocking $2,500 per year. And this is before any allowance for the boys' medical needs or dental or eye care for any of them. Once you add everything up, Jo spends over 1/4 of her entire not-so-stellar income on healthcare. But if we do add dental and eye expenses into the mix (remember, her condition affects these organ systems as well), her healthcare expenditure comes to 40-50% of her annual income! And this is just for draconian restrictions of an HMO! AND, this does not cover all of the time that she spends on the phone finding providers that take her insurance and on schlepping miles away to see that single specialist that the HMO will pay for.

But wait, you say, you live in Massachusetts, the land of socialism, gay marriage and healthcare for all. Why can't she just dump this lousy and expensive employer-provided coverage and go to the Mass Connector, where everyone is equal and all get what they need for what they can pay? And furthermore, you say, isn't the PPACA, the new healthcare law of the land that is fashioned after Romneycare in MA, going to take the choice away from people and MAKE them get insurance through these exchanges rather than through their employers? Jo must be an idiot not to be taking advantage of this communist healthcare state! Hmmm, let's see now.

Jo has had a number of discussions with the staff at the Connector. And yes, you are right in that, if she switched to one of their plans, she would qualify for a plan very similar to her present one for about 1/5 of what she pays now. And the co-pays? Why those would shrink to $0 to $10. How's that for a deal? But here is the catch: According to people she has spoken with at the Connector, a person who has health insurance offered through her employer is not permitted by law to take advantage of the Connector deals if the employer plan offers coverage that meets the minimum standard in the law. And hers does. Ironic, isn't it?

So Jo goes on struggling with the financial burden of her crappy and expensive employer-provided insurance, only now she has to pick and choose: Can she afford to replace her failing dental bridge ($4,700) or should she just choose something so frivolous as feeding her children instead? This is a choice that is not a choice at all, is it? And if these are the choices that we can expect with the PPACA, well, then, we have the law that we deserve: one that will make sure that the investors in the "healthcare" marketplace continue to get handsome returns. But start getting used to people who cannot see their computer screens showing up to work without their teeth!        


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Press coverage of health data: Just like Pharma's DTC?

I am warning you now: This is going to be a rant.

Yesterday's Wall Street Journal had a front page story on observational data, and how researchers are growing more concerned about its accuracy even as the volume of such research is growing exponentially. And then today, there was this from multiple news outlets:

The HealthDay story quotes the senior author of the study thusly [emphasis mine]:

"The results of our research allow us to definitively answer the question of whether jogging is good for your health," Peter Schnohr, chief cardiologist of the long-term Copenhagen City Heart Study, said in a news release from the European Society of Cardiology. "We can say with certainty that regular jogging increases longevity. The good news is that you don't actually need to do that much to reap the benefits."

And then at the very end it says this:

The study was slated for presentation Thursday at a meeting of the European Association for Cardiovascular Prevention and Rehabilitation, called EuroPRevent2012, in Dublin.

Data and conclusions presented at meetings should be considered preliminary until published in a peer-reviewed medical journal.

So, let recap. WSJ says that observational data are of concern because they can be tough to confirm, so we should be skeptical. HealthDay and others insist that this observational study shows definitively that jogging prolongs life, so what are we supposed to believe?

The problem and the disconnect, I believe are not with the studies themselves. The problem is with the way these stories are reported and the end result of that: Just like direct-to-consumer advertising from Pharma, these stories call us to immediate action, even when the results are preliminary. We rail against Pharma's DTC, but take this kind of press coverage as a given. This type of reporting, where half-baked data are presented as the final word, disappointingly enabled by the investigators themselves (who doesn't want 15 minutes of fame?), makes observational data look like something they are not. On the one hand, we are told that here is the result. On the other, after some contemplation and peer review, we realize that the study did not show what it was said to have showed. Bingo, the sweeping conclusion is that all observational studies are bad and biased, so let's just throw out the baby with the bath water.

The press are doing a huge disservice to the public and to science itself by presenting everything in such black-and-white terms. We know that it is the initial message that grabs attention; hence "jogging adds years to your life." To make the next message stick, something powerful needs to be cooked up; hence, "Analytical Trend Troubles Scientists." Saying that "well, we overstated what the jogging study showed" isn't nearly as sexy. How about we back up a bit and say it like it is: the devil is in the details, and those details don't make nifty headlines.

I am grateful to Gary Schwitzer for slapping this kind of sloppy reporting, but he cannot eliminate it alone. We all must speak out against it. We abhor Pharma's DTC marketing practices. Why do we give the press a free pass for the same behavior? Cover accurately or don't cover at all!  

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Fast science: No time for uncertainty

Reading Barbara Ehrenreich's "Bright-Sided" has been liberating in that is has given me permission to let my pessimistic nature out of the closet. Well, it's not exactly that I am pessimistic, but certainly I am not given over to brightness and cheer all the time. My poison is worry. Yes, I am a worrier, in case you had not noticed. So, imagine how satisfying it is for me to find new things to worry about. As if climate change were not enough, lately I started to worry about science.

No, my anxiety about how we do clinical science overall is not new; this blog is overrun with it. However, the new branch of that anxiety relates to something I have termed "fast science." Like fast food it fills us up, but the calories are at best empty and at worst detrimental. What I mean is that science is a process more than it is a result, and this process cannot and should not be microwaved. Don't believe me? Let me give you a couple of instances where slow science may be the answer to our woes.

1. Lies and damned lies
Remember this story in the Atlantic that rattled us with its incendiary message? Researcher John Ioannidis has been making headlines with his assertion that most, if not all, of what we know in medicine is in doubt, given how we do and publish research. And how we do and publish research has everything to do with the speed of "progress." Academic careers are made with positive results, to sell news the media demand positive results, and to respond to this demand academic journals prefer only to publish positive results (this last phenomenon is referred to as "publication bias," and is something Ben Goldacre rails against at length). A further manifestation of this fast science is that "no replicators need apply." I am, of course, referring to an extension of the publications bias, whereby journals are not interested in publishing even a positive study that replicates a previous finding -- this is simply not sexy. Thus, results have to be quick and positive to grab a share of our attention and sell academic prestige, journals and news.

2. Science output to drive business profits
In his book Supercapitalism, Robert Reich describes the growing demand by investors over the last several decades to squeeze ever-growing profits. It is clear that this chase after short-term profits has resulted in job loss in the US through outsourcing, the widening of the economic gap, and even the crash of the world economy following the collapse of the mortgage-backed securities house of cards. Much of the profit can be counted on to come through scientific innovations which may or may not improve our quality of life.
In medicine, where scientific progress is applied to our fragile being, being reasonably sure of our findings seems pretty important. Yet speed is once again the order of the day. I will grant you that speed is of importance in such diseases as advanced cancer, for example, where we may and should accept a level of uncertainty that we would ordinarily run away from in other circumstances. But doesn't it make sense to be much more cautious before broadly accepting an intervention that happens before one gets sick, one that is meant to diagnose either early disease or a precursor to one? Should we not demand slower science before we allow anyone to medicalize such normal events in life as menopause and aging? Should this caution also not apply to screening for diseases that may or may not impact us in the long term, yet the chase could hurt us substantially in the immediate future?
But this is not the way to stimulate the economy or to make a profit. The half-life of a medical device, for example, is less than 1 year. After that a new "improved" version of the device is expected, whether it does or does not improve outcomes. For decades we were told to get screening mammography after the age of 40, only to find out now that the risks of this may well outweigh its benefits for many. The American Lung Association has just endorsed CT screening for lung cancer among current or former heavy smokers, yet the jury on its risk-benefit-uncertainty equation should still be in the thick of deliberations.

3. Science denialism
We hear a lot about how people are turning away from science. The state of Tennessee is about to descend back into the dark ages when superstitions instead of scientific theories dominated the classroom. A strong and largely anti-scientific lobby wants to bury any mention of human-driven climate change; fortunately, it looks like they are not succeeding. The anti-vaccination groups are getting more instead of less vocal following repeated debunking of any link between vaccination and autism. Science denialism is so rampant that there was even a need for a conference on how to address it. What gives?
While blaming everything on fast science alone may be reductionist, fast science in the setting of our growing societal innumeracy is a recipe for disaster, as we are seeing unfold. Our schools have failed spectacularly in their duty to educate kids about the process of science, while at the same time arming them with the "single-right-answer-to-every-question" attitude toward knowledge. This pernicious combination, along with the publication and reporting of sexy science at the expense of the more thorough analytic and introspective approach, seals the impression that the roller coaster of scientific knowledge represents not the very essence of how science should be done, but that science (and scientists) has failed.
Is slow science the answer to this fiasco? Only in part, I am afraid. Without altering fundamentally how we teach science at all levels, it would not be the cure, even if it were possible to execute. No, I am afraid that without teaching what science is, it is not even possible to get it to slow down.

Let me reiterate: the pace of scientific discovery is slow. This does not mean that we need to hide every step of it from view until we get the results that we deem worthy of sharing. On the contrary, I agree with those who think that sharing at the more interim steps can only improve what we do. Yet the innumeracy, fame and fortune are forces that put such free sharing in peril by misrepresenting it as the final answer to everything. And when the answer is changed, which is not only expected, but indeed desired in scientific pursuits, the public opinion punishes science.

Let me end with a quote I read on one of my favorite web sites, Brain Pickings, in a review of the book boldly entitled Ignorance: How It Drives Science:

Are we too enthralled with the answers these days? Are we afraid of questions, especially those that linger too long? We seem to have come to a phase in civilization marked by a voracious appetite for knowledge, in which the growth of information is exponential and, perhaps more important, its availability easier and faster than ever.

[...]There are a lot of facts to be known in order to be a professional anything — lawyer, doctor, engineer, accountant, teacher. But with science there is one important difference. The facts serve mainly to access the ignorance… Scientists don’t concentrate on what they know, which is considerable but minuscule, but rather on what they don’t know…. Science traffics in ignorance, cultivates it, and is driven by it. Mucking about in the unknown is an adventure; doing it for a living is something most scientists consider a privilege. 

So, let's celebrate uncertainty. Let's take time to question, answer and question again. Slow down, take a deep breath, cook a slow meal and think.

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How to avoid the "Titanic effect" in Pharma

Today I was going to tell you the tale of my son's broken wrist (he is fine now, this happened in January, but the insurance issues are fascinating), but I got distracted thinking about another fascinating subject that many do not understand well: confounding by indication. I especially started thinking about it in the context of how decisions and policies are made, and how not having the right data at the right time leads to this "Titanic effect" for a technology. What do I mean by this? Well, let me explain.

Some say the Titanic sank simply because of poor preparation -- not enough life boats, not enough training on the evacuation procedure, in other words "not enough imagination" to plan for a catastrophe. It was derailed in its course by an entirely predictable natural calamity that had not been planned for adequately, even though the risk was obvious in retrospect. Was this just on of those "unintended consequences" that could have been avoided with more clear vision? Perhaps, but the Titanic is, ahem, water under the bridge. But we can focus on some more mundane and current potential missteps and make some guesses.

Let's talk about medical technologies, and drugs in particular. Let us say that there is a new sepsis drug that has been tested among patients with sepsis but without organ failure. This drug appears to prevent organ failure in a fraction of the treated patients, and also reduces mortality by 6%. The only obstacle to widespread use of this drug is its acquisition cost, which is much higher than what the hospital's critical care pharmacist is used to paying for other drugs. Because of this high cost, the drug, despite being on the formulary, gets administered only to those patients who have developed not one, but two organ failures. The savvy pharmacist looks at the outcomes of these patients and, after comparing them to those of the patients who did not receive the drug, concludes that the new sepsis drug, instead of saving lives, actually kills. The P&T committee discusses this, dumps the drug from the formulary and other hospitals follow suit. What's wrong with this picture?

Several fallacies are at work here, including an overly broad inference of causality and bias. But the most important lesson is to do with confounding: because of its apparent expense, the drug has been niched into a population of patients who a). were not the ones that exhibited the evidence of benefit in the trials, and b). have a very high risk of mortality at baseline. So, not only is it not valid to conclude that the drug killed these patients, but it is not even valid to say that the drug does not work -- it may well work in the populations that it was shown to work in, but not in this, much more ill, population. You see the difference? It is like saying that you umbrella failed to keep you dry when you opened it only after you already got soaked.

So confounding by indication is one reason that drugs "fail" -- they are given to people who are by definition not going to do well, and the confirmation bias pushes us to say see, it's expensive and doesn't work. So how do we overcome this phenomenon and make sure that appropriate patients get access to useful technologies? I believe I have a very simple answer: don't squeeze the toothpaste out of the tube if you don't want to have to cram it back in. Huh?

In other words, do what I always advocate: be ready with the relevant data before the train leaves the station, before the cat gets out of the bag, before the horse gets out of the barn. It is very well known that cognitive biases, once established, are difficult to overcome. The pharmacist's first concern is for being able to use his very limited resources efficiently, and to guard from spending his monthly budget on a potentially useless intervention in a single patient only to be left with no resources to care for all of the other patients. Yet many manufacturers at launch send their reps to the pharmacist with two virtually unrelated stories: one about efficacy and the other about the acquisition price and its impact on his budget. When the drug is expensive, the efficacy pales in comparison to the price tag, and the pharmacist has no choice but to restrict the use of the drug, thereby consigning it to failure by confounding by indication. Sound familiar?

Is there a way to avoid this scenario? I think so. It is self-evident that you have to have good data. The surprising thing is that good data are necessary, but not sufficient: the timing of these data is critical as well. It is easier to help people form an opinion where none exists than to change one that is already there. So, to be successful, the manufacturer with a good technology must have a coherent effectiveness and cost-effectiveness proposition right out of the gate. Not only that, but it is imperative to help the clinician understand what patients might benefit from the technology (no, not all patients should be on your drug). This is the kind of a collaboration that will ultimately benefit all stake holders: 1). Appropriate patients will get the opportunity at better outcomes, 2). The pharmacist will understand up front the value proposition and the potential scope of use, and 3). The manufacturer will profit from providing a beneficial service. Isn't this the intent of all this drug development?

If all this seems all too obvious, it is because this is not rocket science. But why, then, do I see so many companies get into trouble with this very scenario? Is it just the case of "best laid plans" or is it a real blind spot that needs to be illuminated? You tell me. Given the investment that goes into drug development, I think it makes sense to approach this gap earnestly, instead of just shuffling the deck chairs on the Titanic.      

 
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Do complex problems always require complex solutions?

A mind-blowing talk by Gerd Gigerenzer on making decisions under conditions of uncertainty -- this is for you, heuristics mavens! (Note an interesting nuance about medical decisions at about 16:15).



A big h/t to @Medskep

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To solve complex problems, tinker!

A fabulous TED talk by the economist and writer Tim Harford (@TimHarford) about the virtue of making good mistakes (aka tinkering, which I have written about in the past). This is why science, at least in theory, works so well -- trial and error move our knowledge forward. Also a great reminder of why our educational system is failing.

A big h/t to Kent Anderson of The Scholarly Kitchen for posting this on their web site.

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Can probability solve the healthcare crisis?

Here is the video of my Ignite Boston 9 talk from March 29.



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How I fell in love at TEDMED

Over the exhilarating four days this past week, we all fell in love a little bit -- with the city, the Center, the meeting, the ideas, and one another. The city was Washington, DC, a touch past its cherry-blossom blush; the meeting was, of course, TEDMED. The ideas were of about honoring our health, environment, food, and about making health and healthcare efficient and kind for all.

I fell in love with dreamers. Though their dreams were varied, their paths to fulfilling them all converged into the same stream. Like a trip down the Amazon that the biggest dreamer of all, Jay Walker, the curator and the force behind the meeting used as a metaphor for TEDMED 2012, they accepted their tortuous and demanding journeys and, much to our delight and benefit, made a stop at the Kennedy Center. And although I will only mention a few, many others will stay with and inspire me for the months to come until TEDMED 2013.

I fell in love with Bryan Stevenson, who spoke about his grandmother and identity and justice.

I fell in love with Rebecca Onie, who, while transforming the care of the urban poor is also transforming the face of student activism.

I fell in love with Traces, a Montreal performance group who made my heart stop with their daring acts of precision. Our healthcare system can learn a lot from these young people.

I fell in love with Jacob Scott and Sandeep Kishore, both of them young, energetic and passionately committed to changing the face of medical education.

I fell in love with Ed Gavagan, who told the story of his confrontation with death with courage, humor and honesty.

And yes, I fell in love with and was made to weep by Robert Gupta's transcendent violin and Stephen Petronio's defiant vulnerability.

TEDMED 2012 was a feast, and now I am back to the journey of my real life: calls to make, e-mails to return, analyses to do, papers to write, talks to give, a book to get to market. It all seems just a little drab compared to the four days I spent in this intellectual and emotional climax. But like a great yoga session, TEDMED was restorative, rejuvenating, and remarkably inspirational. The mix of hard core science, the arts, history and frank curiosity sparked personal ideas and renewed personal commitments to executing my dreams for a better society. Spurred by Sekou Andrews' and Steve Connell's raw poetry performance, like a youngster in love for the first time, I am ready to GO! So I am off to do what E.O. Wilson suggested a scientist needs to do: think like a poet and work like a bookkeeper.              

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Five ways to tame the risk-benefit-uncertainty troika

There was a story on NPR this morning that sent me in a radical direction. It discussed the increase in use of brachytherapy for localized breast cancer. The idea is that this is a concentrated dose delivered much more locally and rapidly (over 5 days) than the conventional external beam radiation (over 6 weeks). The issue is that it has not been tested rigorously in a randomized controlled trial yet, and some oncologists are concerned about its outcomes as they compare to the conventional approach. One of the concerns stems from an increase in the rates of subsequent mastectomies, which are double in brachytherapy relative to conventional. At the same time, there are clear benefits, not the least of which is the period of exposure and the hassle associated with daily trips for radiation for 6 weeks.

Several points popped up in my head in response to the story:
1. When discussing this predominantly women's disease, the expert voices mostly heard from were male (5 of the 6 doctors quoted). Does this matter? Not sure.
2. The priorities addressed by the experts were the traditional outcomes -- survival, recurrence, metastasis. The priorities described by the patient were about her time and convenience today.
3. The concern about the procedure stems from the observed doubling of the need for mastectomy within 5 years among patients treated with brachytherapy, an event "rare no matter what what kind of radiation women got."

So what's my point? Do I think that more rigorous testing is not indicated? Not at all; we need a more rigorous evaluation of the technique. No, what I am wondering is at what point should a procedure like this (or any intervention, for that matter) become available to patients as an option to be considered? Should its availability be determined in a dark room by bespectacled men around a conference table, or should it be put on the menu of choices, along with its risks, benefits and uncertainties, as soon as it looks safe enough, whatever that looks like?

The larger question this raises is what is the degree of uncertainty that we are willing to accept around interventions that become available, be it a drug or a procedure or a device? How do we incorporate patients' priorities for outcomes that are important to them into these decisions? Remember ACT-UP and how they moved the FDA to make more rapid decisions about treatments for HIV/AIDS? Have we swung too far in the opposite direction today, whereby we want a virtual guarantee of safety before a technology is approved?

I offer these 5 potential question to help with making these decisions:
1. How severe/deadly is the disease in question?
2. What is (are) the known potential benefit(s) of the intervention?
3. What is (are) the known potential risk(s) of the intervention?
4. What uncertainties bracket this risk-benefit equation?
5. How does the patient feel about the extent of this risk-benefit-uncertainty balance in the context of her condition?

I think that the first four are the questions that the FDA struggles with every day. They are the gatekeepers for the availability of new technologies, and, therefore, for the relevance of the fifth question. What I am wondering is whether it is not better to start bringing a lot more of the public perspective to the discussion much earlier, so that the patient can have the option of evaluating more choices sooner. I know I may be treading on thin ice here, but I am ignoring any market forces or special interests for the moment. The question I am asking is "In the best of all possible worlds, where no one is trying to sell you anything, when is the best time to give the patient an opportunity to accept or reject an intervention, given the risk-benefit-uncertainty profile?"

Bottom line: There are no guarantees. Just because something is available on the market does not mean that it is completely safe or completely effective. Most importantly, it does not mean that we come even close to being certain about these attributes. As a corollary, just because there are uncertainties about the risks and benefits of an intervention, does it mean that it should not be available as an option for a patient? My guess is that there is a balance of this troika of properties that may be optimal on average, but I am also guessing that that this average balance will miss a substantial volume of outliers. Just as some people thrive on the thrill of bungee jumping while others clamp down just at the mere thought of it, so some patients may surprise us with their position on this risk-benefit-uncertainty continuum.

I apologize if my argument is not clear -- I am definitely thinking about this stuff actively. The one thing I am absolutely sure of is this: Unless the public and clinicians are educated about how to have these conversations, we will always have to rely on and, consequently, blame someone else for making decisions for us.

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Infographics: devil in details

I got an e-mail yesterday asking me if I would be interested in displaying an info graphic on my blog. After a few back and forths, I decided to do it. After all, a picture is worth a thousand words. But as with everything, buyer beware: the devil is in the details.

A couple of caveats:
1. I cannot back up all of the numbers myself, and the references at the bottom seem to represent single source data, rather than the totality of the evidence. So take with a grain of salt.
2. There are a couple of notable errors in the graphics:
a. The graphic on other first world nations' spending says that the US spends 2x what they do in Japan. What the graph shows is that this is the case as the proportion of the GDP (also the x-axis is not labeled as such). So, the statement is not entirely accurate.
b. In hospitals overcharging, the caption states that hospitals charge 200% more for meds compared to ex US. The number adds up to 100% more, which is 2x.
3. In general, there are actually some valid reasons why we pay more for stuff in the US. I do not, for example, know if the international data are adjusted for various economic factors, such as purchasing power.
4. The statement that doctors are overpaid is a laugh -- take it from someone who has been in the trenches. I worked dawn till dusk and beyond, nearly every day of every week. I was making barely enough to keep food on the table and a roof over our heads. No lavish vacations, no BMWs, etc. It is not the MDs that are overpaid. Go to the C-suites and corporations, and then you are getting warmer.

Despite the disagreements that I have with the data, I thought it might be a good point of discussion. Would love to hear your thoughts about it.


Created by: Medical Billing and Coding Certification
 
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Happy to be in the "top three"

So, I must be in the "top three" then :)


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How to make safer decisions in medicine

I love when an article I read first thing in the morning gets me to think about itself all through my morning chores and then erupts into a blog post. So it was with this little gem in the statistical publications "Significance." The author suggests making gambling safer by placing realistic odds estimates right on the poker machines in casinos. He even goes through the generation of the odds of winning and losing and how much based on really transparent assumptions. In fact, what he has in effect constructed is a cost-benefit model for the decision to engage in the game of poker on these machines. Seems pretty simple, right? Just a few assumptions about how long the person will play, some objective inputs about the probabilities, and PRESTO, you have a transparent and realistic model of what is probable.

In medicine, there is a discipline known as Medical Decision Making, and what it does is exactly what you see in the "Significance" article: its practitioners construct risk- (and, hence, cost-) benefit models for decisions that we make in medicine. To be sure,these turn out to be rather more complex, since the inputs for them have to come from a large and complete sampling of the clinical literature addressing the risks and the benefits. But that's the meat; the skeleton upon which this meat hangs is a simple decision tree with "if this then that" arguments. In this way these models synthesize everything that we know about a specific course of action and put it together into a number driven by probability.

They usually go something like this. We have a group of women between 40 and 49 years of age with no apparent risk factors for breast cancer. What is the risk-benefit balance for mammography screening in this specific age layer? One way to approach this is to take a hypothetical cohort of 1,000 women who fit this description and put it through a decision tree. The first decision node here is whether to perform a screening or not. What follows are limbs stretching out toward particular outcomes. Obviously, some of these outcomes will be desirable (e.g., saving lives), while some will be undesirable, ranging from worry about false positive results to unnecessary surgery, chemotherapy, radiation, and even death. Because these outcomes are so heterogeneous, we try to convert everything to monetary costs per quality of life (quality because there are outcomes worse than death, as it turns out). But what underlies all of these models is the mathematics derived from clinical studies, not pulled out of thin air. This is the most useful synthesis of the best evidence available.

To be sure, MDM models are rather more complicated than the poker example. They require a little more undivided attention to follow and understand. Furthermore, I personally did not get a whole lot of exposure to them in my training, but perhaps that has changed. Like anything to do with probability, these models tend to be off-putting in a society that has consigned itself to wide-spread innumeracy. And doctors are certainly not immune from misunderstanding probability. Yet without them perceptions rule, and our healthcare becomes a reckless gamble. In our ignorance we collude to build profits that come with medicalizing small deviations from the perceived normality. Sadly, the primary interests that drive these profits are not usually doing so with probabilistic forethought either, but rather on the basis of red hot conviction that they are right.

Doctors and e-patients need to lead a radical transformation in how we handle decisions in healthcare. It is very clear that willful ignorance has not served us well, and we are all too easily led into panic about every pimple. Resilience can only come when we question our assumptions. Alas, our intuitive brain is almost certain to mislead us when faced with complex information; why else would we need explicit odds listed on poker machines? The absurd complexity of information in medicine deserves no less. It's time to start the probability revolution!

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My solution to the healthcare crisis

Here is my talk from Ignite Boston last night -- I solved the healthcare crisis!

Zilberberg igniteboston2012

View more PowerPoint from murzee

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Why the PPACA hearing is the very definition of insanity

I haven't said much about the SCOTUS hearing of the PPACA, but it is time to break my silence. I have been listening to some of the details of the arguments, and I cannot help but be nauseated. It feels to me like the Justices are behaving like my teen: they take literalness to its absurd limit. Health insurance and cell phones, really? This betrays a complete disregard for probability. What are the chances that you will within your lifetime in the US need to dial 911 in the absence of a landline or another human with a cell phone within shouting distance? And what are the odds that you will at some point in your life require a medical consultation? I rest my case.

The more I think about it, the more convinced I am that the bill should have introduced single payer a priori -- funding our access to medicine through a tax. Yes, a tax. Perhaps the government is not the most efficient agent of this, and the overhaul could have been accomplished through some public-private hybrid model. In the end, as I have said here, our priorities are misaligned, as are our perceptions of what is important in this debate. We spend 97% of all the healthcare money on medicine, and we spend well over 97% of our national discussion about health on access to healthcare and medical interventions, which can only make a 10% difference in our health. The real money, so-to-speak, is in public health, which contributes 60% to our true health and gets only 3% of the expenditures and practically no conversational energy.

So, once again I find myself turning to the wisdom of Albert Einstein, who defined insanity as "doing the same thing over and over again, and expecting different results." The SCOTUS circus is the poster child for this insanity. Whatever the outcome, and I am not at all optimistic about the individual mandate, my sense is that nothing will change until we start paying attention to the root causes of our collective illness.

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