Longevity, life expectancy, premature mortality: Are they lions and tigers and bears?

Before I set up the context for this post, I am going to throw this out to you. Think of putting rocks on a scale to achieve the weight of, let's say, 1,000 lb. And let's say that you are required to use rocks of similar size. You have a bunch of bricks -- these are your biggest "rocks," and you have a bunch of pebbles like the ones I have in my driveway (perhaps you have them in yours too). In order to get to 1,000 lb, will you need more bricks or more pebbles? I am not trying to trick you. This is just an illustration of the fact that you can get to the same magnitude of a variable (in this case weight) by either using a smaller number of more weighty components (bricks) or a larger number of lighter ones (pebbles). Keep this in mind as you read on below.

I got an interesting comment from Brad F. on my post from yesterday regarding the 10% number for the premature death avoidance attributed to access to medical care. He pointed me to a blog post on the always-informative The Incidental Economist web site which called this a "zombie statistic." Despite having a fifteen-year-old who is an avid fan of zombie fiction and film, I was not familiar with this term, but inferred its meaning pretty easily.

The gist was that when people started to look for the origins of this number, the evidence was difficult to find, and, when discovered, was at best shaky:

Thus, as Austin and Adrianna had found, the 35-year old CDC paper seems to be at the root of the often-cited 10% number; it’s “paper 0,” if you will. But those that continue to reference 10% as an estimate for health care’s contribution to health should know that the only evidence they are referencing is a survey of 40 people, done when Jimmy Carter was president. It’s not evidence-based except by the weakest notions of “evidence.” It’s really a zombie statistic.

My obvious next question was whether a more trustworthy estimate existed for the medical care's contribution to life extension in the US. In my search for a better estimate, I continued to go down the rabbit hole of links, arriving here, the AcademyHealth Blog, landing on the article called "Half of longevity gains due to health care." It was a summary of the attempt by the authors of The Incidental Economist to answer this very question. And what did they find? First, they quoted a NEJM citation from 2006, where it was claimed that 90% of the increases in life expectancy since the 1960s was due to reduction in cardiovascular and neonatal deaths. After meandering through several other sources, the authors concluded that we can attribute about 50% of the responsibility for extending our life to medical interventions.

And that's when I really confused myself. I started thinking about whether premature death and longevity are related, and how they may be related, and are we even talking about the same thing when we invoke each of them.

Premature mortality can be quantified in several ways -- 1). percentage of all deaths that are considered premature, or 2). proportion of people in a population whose death is considered premature (that would be so many cases per 100,000 population). Longevity, on the other hand, is a measure of the average life span of a population. The current life expectancy in the US is 78.8 years. This begs the question of how these two, premature deaths and life expectancy, are numerically related to each other. And can the latter go up without the former going down?

Well, if the language here is consistent with how we speak it, "premature" implies that we know what "timely" means. The definition of a "timely" death must be based on the average life expectancy in a population. This number varies according to certain characteristics of a population, of course, so different subgroups would have a different life expectancy. For example, at any given age, the life expectancy of a person with heart disease should be lower than that for a person who is perfectly healthy. If we can reduce the risk of a premature death in people with heart disease, their life expectancy should edge closer to that of a healthy individual. And, in fact, according to the literature, this has happened in cardiovascular patients, partly due to better treatment of blood pressure, and partly due to fewer people smoking and other healthful lifestyle modifications.

So, it's clear that when death due to a disease is postponed, longevity increases and, ergo, premature deaths drop. It's a bit circular, I know. But here is one interesting detail to consider. Longevity or life expectancy (I use them interchangeably) is an age average. So here is one question: Does the impact on the magnitude of life expectancy gains vary with the age of the population in which premature deaths are avoided? I know, its a clunky question. What I mean is, would you expect life expectancy to go up more, less or same amount if we manage to reduce premature deaths in infancy versus old age? If you consider that life expectancy is an average, then infant mortality attenuates this average severely (think adding a whole bunch of numbers into the denominator without contributing anything to the numerator). So you can imagine, if infant mortality goes down a lot (a big reduction in premature deaths), overall population life expectancy spikes decisively. Reducing premature deaths among the elderly, clearly, by this same calculation, will not result in nearly the same increase in life expectancy.

Another way of looking at this is to consider that a much larger reduction in premature deaths among the elderly (think driveway pebbles) than among infants (those sizable bricks) would be needed in order to reach a similar degree of longevity improvement. A less intuitive corollary of this is that we indeed can have an increase in premature mortality in a group that contributes little to longevity (the elderly) and still witness a large bump in life expectancy with a much smaller reduction in premature deaths within a group with an outsized contribution to longevity (infants). So that answers the second question I posed about these measures -- they can diverge.

Now, on to the estimated contribution of medical care to either or both of these. We have, in fact, witnessed a dramatic reduction in infant mortality. I found this report from Health Resources and Services Administration that infant mortality has dropped from 55.7 per 1,000 live births in 1935 to 6.8 per 1,000 live births in 2007. And here is what the authors cite as reasons:

...dramatic declines in infant mortality rates over the long term were due to large declines in mortality from pneumonia and influenza, birth defects, prematurity and low birthweight, respiratory distress syndrome (RDS), sudden infant death syndrome (SIDS), and injuries. Improvements in living conditions, advances in neonatal medicine and infant heath care, reductions in smoking during pregnancy, and increased access to and use of prenatal care have been suggested as factors responsible for decreases in infant mortality over the past several decades...  

 

And here is an interesting detail: the pace of this drop was a dizzying 3.1% per year on average between 1935 and 2000. However, between 2000 and 2007, the rate went down only from 6.9 to 6.8 per 1,000 live births, a staggering deceleration in this steep decline. A further detail indicates that "much of the statistically significant decline [occurred] in the neonatal period." The implication of this is that the latest declines are due to technology use, most likely among the very premature infants upon delivery. This is the very definition of access to medical care, and falls completely outside of the domain of public health.

Just one more random thought. Reductions in infant and cardiovascular mortality, each a product of both medical and public health interventions, are one side of the life expectancy equation. The other, darker, side is the fact that in some groups and locations in the US, the overall longevity is waning. Much of this phenomenon can be attributed to poverty, environmental factors and poor health behaviors, or, in sum, a reflection of our dismal investment in public health. And, sure, there is a component of access here too. And what about this calculus: Between 1990 and 2010, mortality from cardiovascular disease dropped by about 150,000 per year. That would be an awesome contribution to increased longevity and reduced premature deaths, if it weren't offset by all the deaths (presumably premature) related to the healthcare system itself.

I know that none of this gets to the crux of the matter: What is a reliable estimate of what proportion of the increases in life expectancy can be attributed to access to medical care? But what it does make me appreciate is the complexity of each and every term, every definition, every estimate that we confront daily. This devil, as always, is in the details.


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ACOG's dysmenorrhea FAQs: Evidence of propaganda?

I have been looking up information on endometriosis for a friend of mine, and came upon this from the American College of Obstetricians and Gynecologists:

So I bit and started reading. And about half way through my reading it I realized that this really reminds me of how they taught literature in the my native USSR. The teaching consisted of stock interpretations of the great authors' works through the prism of Communist Party propaganda. In this interpretation all of the writers' messages railed against the monarchy, and all exhortations were for the purpose of freeing the proletariat. No teacher ever dared to disagree, and no student was expected to question.

Why, you ask, do these ACOG FAQs on dysmenorrhea remind me of my schooling in the old country? Well, glad you asked. Check out this gem, for example:

That's it. No follow-up questions? Good!

But really let's take it from the top. So, OK, there is the pelvic exam. I can deal with that because I am used to that as the default for anything going on "down there." Then there is the ultrasounds exam. I guess I can deal with that too because there has been so much in the news about pelvic ultrasound, and that seems to be what is done to get a better look at what is down there. A laparoscopy? Wait, isn't that a surgical procedure? Yeah, they even say it's a surgery, and it's done to get a "look inside the pelvic region." Hmmm, this sounds pretty serious. How come they don't say anything here, in these FAQs, about what they are looking for, how good this surgery is at finding it, what the chances that what they find is responsible for my dysmenorrhea, what is the treatment and how successful it is at alleviating my symptoms of dysmenorrhea, and whether or not there are alternative interventions?

(Does anyone really ask the patients what their FAQs are or are they generated by the clinicians based on what they think should be important to the patient? Or even worse, based on what they think they can give a perfunctory answer to? Just from reading these Qs and As I think it's the latter.)

You get my point. This formulation of information is beyond useless. It seems paternalistic in its "there there, dear, we will take care of everything" attitude. Perhaps I am out of touch. Perhaps women, patients in general, don't want to go beyond what their doctor tells them to do. But I happen to think that it is these FAQs that are out of touch. Granted, I am a "difficult" patient, as even a pelvic exam, let alone ultrasound and surgery, meets with questions around the evidence of its effectiveness. But even if you have only completed ePatient 101, you should know enough to ask about something as serious as a laparoscopy! How can anyone be expected to just acquiesce and, sighing, say "yes, I guess I have to have surgery." This "FAQ" is completely absurd in its willful lack of useful information. And if you read the rest of the document, you will find many places where this is true as well.

I know that some of you will read this and click away saying "oh, there she goes again." But I think you need to rethink your apathy. After all, there are well over 200,000 deaths (and possibly even more than 400,000) annually in the US that happen unnecessarily just from contact with our "healthcare" system. If you can avoid the avoidable, is it not incumbent upon you to be fully informed? You may think that all these recommendations are evidence-based, and there is not a whole lot of wiggle room in how to proceed. Well you are wrong if you think so, since the evidence, even when it is available, is rarely, if ever, unequivocal. And furthermore, in medicine no benefit comes without a risk. Are you sure you want your doctor to make these decisions for you? How is it that people who are not even willing to take wardrobe advice from their mothers wade so enthusiastically into these high-risk medical adventures with their eyes and ears closed?

I wrote Between the Lines to show just how imprecise and uncertain the science of clinical medicine is. But beyond that, I wanted to provide you with tools at least to ask the right questions. So, please, go and ask. And insist that you be included in the FAQ processes. Otherwise, we are just wasting terabytes on propaganda.            

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A FORCE against disease mongering

Have you been over to The Oransky Journal lately? If not, go and see what is happening there. What is happening is a microcosm of the larger debate we are having about detection and diagnosis of real disease versus overdiagnosis of phantom conditions whose treatment is worse than anything that the potential disease may deliver.

The issue is as follows. In his talk at TEDMED in April, Ivan gave an excellent and measured presentation about the folly of pre-disease classifications and the harm they can bring. As my readers are well aware, this is the subject of great interest to me -- after all, it is a travesty that contact with the so-called "healthcare" system is the third leading cause of death in the US, and that overtreatment costs us at least 10 cents of each healthcare dollar, and probably much more (you will find a slice of my posts on this issue here). So, Ivan's talk was timely and cogent.

After he posted the talk on his blog, he received a letter from a group called FORCE (Facing Our Risk of Cancer Empowered) who, as it turns out, coined the word "previvor," one of the many words Ivan used to illustrate the philosophy of disease mongering. The letter voiced a vigorous objection to Ivan's use of the word to "misunderstanding" its meaning. But what really happened?

Apparently, "previvor" defines a group of people who are at a heightened risk for cancer, but have not yet been diagnosed. It seems that the majority of FORCE's constituency consists of women with the BRCA gene mutations, which put them at an extraordinarily high risk of several cancers, most notably breast and ovarian. Moreover, these cancers tend to occur at an early age, and are generally quite a bit more aggressive than those not associated with these mutations. We are not talking a trivial rise in the risk either; BRCA1, for example, raises one's lifetime risk for breast cancer to about 80%! To mitigate this risk, many women with these types of mutations undergo prophylactic mastectomies and oophorectomies. These are life-changing events, and their genetic make-up hangs like a Damocles' sword over the offspring of these women as well. So, what's the problem with using whatever word suits them?

The issue is the group's definition of this neologism "previvor." As quoted in Oransky's post (italics mine):

“Cancer previvors” are individuals who are survivors of a predisposition to cancer but who haven’t had the disease. This group includes people who carry a hereditary mutation, a family history of cancer, or some other predisposing factor. The cancer previvor term evolved from a challenge on the FORCE main message board by Jordan, a website regular, who posted, “I need a label!” As a result, the term cancer previvor was chosen to identify those living with risk. The term specifically applies to the portion of our community which has its own unique needs and concerns separate from the general population, but different from those already diagnosed with cancer.

So, the definition is quite broad, as you can see, especially the "some other predisposing factor." Who doesn't have one? Just by virtue of being alive we have predisposing factors to many diseases, including cancer. And aging is one of the strongest predisposing factors to cancer as well. The concern is that a broadly defined term like this plays right into our national paranoia about our health and our enthusiasm for screening as the primary mode of prevention. And if you really don't feel well informed about why screening is not all it's cracked up to be, I urge you to dig through the annals of this site thoroughly (if you don't have much time, you can get a solid primer on the issue from my book). In my view, given the extent of the harm from overdiagnosis and overtreatment, Oransky's call-out of this word in the ultra-visible forum of TEDMED was a public service.

And indeed, it turned out this public service has gone well beyond just delivering the information. The discussion that ensued over the last couple of days with FORCE has shown what this organization is made of. An 80% lifetime risk of breast cancer is a grave matter, and the group is an important force in advocating for these patients and supporting their families. But as it turns out, it stands for even more than that. I commend Dr. Friedman, the Executive Director of the group, for being open to narrowing the definition of the term "previvor." This willingness signifies a real desire to do the right thing not only for her constituency, but also for the public at large. Even more, she should be proud that her organization is taking a stand against disease mongering.

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Healthfinder.gov: Education or indoctrination?

Ever heard of healthfinder.gov? It's a web site from the US Department of health and Human Services

...where you will find information and tools to help you and those you care about stay healthy.

Sounds like a laudable goal, right? Great! Now, help me! Here is the "help" that I found when I went to the page called "Colorectal Cancer Screening: Questions for the doctor":

What do I ask the doctor?

It helps to have questions for the doctor written down ahead of time. Print out these questions and take them to your next appointment. You may want to ask a family member or close friend to come with you to take notes.

So far so good. But here is the list that follows:

• What puts me at risk for colorectal cancer?

• When do I need to start getting tested?

• How often do I need to get tested?

• What screening test do you recommend? Why?

• What’s involved in screening? How do I prepare?

• Are there any dangers or side effects involved?

• How long will it take to get the results?

• What can I do to reduce my risk of colorectal cancer?

Note the wording: "When do I need to start getting tested?" "How often do I need to get tested?" And these "needs" come well before the "why?" In fact, the "why" never really comes. The oblique "why" about which test is recommended is too little too late. The real "why" is why, or even whether, I need to get tested in the first place. I am happy to see a question on the dangers of screening, but again it leaves plenty of room for the clinician to minimize and patronize.

The list of questions is built upon one (erroneous) assumption: Everyone is bound to perceive the risk-benefit equation of colorectal cancer screening the same way. We know this is false, and each person needs to make an individual decision based in what we know today and according to the values he/she places on the outcomes. The way the questions are written, they simply reinforce the bullying attitude of the screening bias, making those who swim against this tide feel irrational and unreasonable. But may I point out that some of us spoke out against universal mammography screening even before it became the main-stream recommendation? So perhaps there are good reasons to be more cautious with screening for everything, even colon cancer.

Science evolves, our knowledge evolves. What we think we know today will be modified tomorrow. I take a strong exception to this dogmatic and one-sided formulation of how to have a discussion about testing whose risk and benefit profile may not (and should not) elicit the same unbridled enthusiasm from everyone. So please, healthfinder.gov, rethink your "helpful" questions so as to educate, rather than indoctrinate.

Hat tip to @DCPatient for pointing me to this page  

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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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Can probability solve the healthcare crisis?

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



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How medicine is like quantum physics

When a patients goes to his doctor to get fixed a pivotal triad of presentation-diagnosis-treatment ensues. The three steps are as follows:
1. History, physical examination and a differential diagnosis
When the patient shows up with a complaint, a constellation of symptoms and signs, a good clinician collects this information and funnels it through a mesh of possibilities, ruling certain conditions in and others out to derive the initial differential diagnosis.
2. Diagnostic testing
Having gone through the exercise in step 1, the practitioner then decides on appropriate diagnostic testing in order to narrow down further the possible reasons for the person's state.
3. Treatment
Finally, having reviewed all the data, the clinician makes the therapeutic choice.

These three steps seem dead simple, and we have all experienced them, either as patients or clinicians or both. Yet the cause for the current catastrophic state of our healthcare system lies within the brackets of each of these three little domains.

The cause is our failure to acknowledge the vast universe of uncertainty dotted sparsely with the galaxies of definiteness, all shrouded in false confidence. And while the cause and the way to address it are conceptually simple, the remedy is not easy to implement. But I am jumping ahead; first I have to convince you that I have indeed discovered the cause of this ruin.

Let's examine what goes on in step 1, the compilation of history and physical to generate a differential diagnosis. This is usually an implicit process that takes place mostly at a subconscious level, where the mind makes connections between the current patient and what the clinician has learned and experienced. What does that mean? It means that the clinician, within the constraints of time and the incredible shrinking appointment, has to listen, examine, elicit and put together all of the data in such a way as to cram them into a few little diagnostic boxes, many of which contain much more material than a human brain can hold all at once, even if that brain is at the right tail of human cognition (or not). What overtakes at this step is a bunch of heuristics and biases. Have we talked about those enough here? Just to review, heuristics are mental shortcuts that can serve us well, but can also lead us astray, particularly under conditions of extreme uncertainty, as in a healthcare encounter. If you want to learn more about this, read Kahneman, Slovic and Tversky's opus "Judgement under uncertainty: Heuristics and biases." As for cognitive biases, I will not belabor them, as there is enough material about them on this web site and elsewhere to overload a spaceship.

The picture that emerges at this step is one of fragments of information gathered being fit into fragments of studies and experience, stirred with mental shortcuts and poured into a bunch of baking tins shaped like specific diagnoses. Is there any room in this process for assigning objective probabilities to any of these events? Well, there is an illusion of doing so, but even this step is done by feel, rather than by computation. So while there is some awareness of a probabilistic hierarchy, it is more chaos than science. Given this picture, it's a wonder it actually works as well as it does, don't you think?

The next step in this recipe is the diagnostic workup. What ensues here is utter Wild West, particularly as new technologies are adopted at breakneck speed without any thought to the interpretation of data that they are capable of spitting out. Here the confusion of the first step gets magnified exponentially, just as it seduces us into further illusion of certainty. The uncertainties in arriving at the differential get multiplied by the imperfections of diagnostic tests to give the encounter truly quantum properties: you may know the results or you may know the patient, but you may not know both at the same time. What I mean is what I have always said on this blog: no test is perfect, and because of this simple truth, unless we know the pre-test probability of the disease in a particular patient, as well as the characteristics of the test, we have no idea about the context of these results. Taking them at face value, as we know, is a grave error.

What follows these results is frequently more diagnostic hit-or-misses, as the likelihood of harm and escalating expenditures without any added value rises. Then comes the treatment, with its many uncertainties and the potential for adverse events, and what are we left with? A pile of costly and deadly steaming manure. So, what's a doc to do?

I think that there is a very simple solution to this, and in its simplicity it will be incredibly hard to implement: education. And I don't just mean medical education. Everything that I have talked about in this post echoes back to the concept of probability. In the secondary education, at least as I remember it, probability is left to Advanced Math. By the time a student becomes eligible to take this course, she has been made to feel that she does not have the facility for math, and that, furthermore, math is boring and useless. So, while my friends in education may have a much better idea of what percentage of kids leave high school having been exposed to some probability, my guess is that it is woefully small. And those that do get exposure to it walk out of class perfectly able to bet on a game of craps or a horse race, but no clue how to apply these ideas to the world they live in.

And so those who progress into healthcare and those who don't have heard the word "probability," but cannot quite understand how it impacts them beyond their chance of winning the lottery. And unfortunately, I have to tell you that, if I relied on what I learned in medical school about probability, well, let's just say it is highly improbable that we would be having this discussion right now. This is why I do now and will for the foreseeable future harp on all of these probabilities, so that when you are faced with your own medical decisions, you will at least know the right questions to ask.

I know I need to wrap this up -- I saw that yawn! Here is the bottom line. First, we need to acknowledge the colossal uncertainties in medicine. Once we have done so, we need to understand that such uncertainties require a probabilistic approach in order to optimize care. Finally, such probabilistic approach has to be taught early and often. All of us, clinicians and patients alike, are responsible for creating this monster that we call healthcare in the 21st century. We will not train it to behave by adding more parts. The only way to train it is to train our brains to be much more critical and to engage in a conversation about probabilities. Without this shift a constructive change in how medicine is done in this country is, well, improbable.              

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Do private ICU rooms really reduce HAIs?

We have known for quite some time now that the patient's environment in a hospital matters to his/her outcomes. The concept of biophilia was applied by Roger Ulrich back in the 1980s to surgical patients in a series of experiments. Famously, this work showed that looking out your hospital room's window on a bunch trees is associated with better and less eventful post-operative recovery than staring at a brick wall, for example. We have also known for some time that some of the hospital-associated delirium can be mitigated by having the patient dwell in a room with a window and be exposed to the diurnal light changes.

Another, perhaps even more tangible outcome that can be modified by hospital design is the spread of hospital-acquired infections. This week a paper in the Archives of Internal Medicine from the group in Quebec, who brought us detailed reports of the devastating multihospital hypervirulent Clostridium difficile outbreak in the last decade, generally confirms the effectiveness of private ICU rooms in containing the spread of HAIs. There are some interesting details to point out.

For example, the intervention hospital appears to have had a higher proportion of medical patients than the control institution. Why is this important? Well, medical patients generally experience more chronic and therefore longer stays in the ICU. This gives them a greater opportunity for exposure to HAIs than their surgical counterparts. On the other hand, we know that VAP, for example, an infection very likely to be caused by one of the resistant organisms listed in Table 2 of the paper, happens much more frequently in trauma ICUs than medical ICUs.

Second, the unadjusted ICU length of stay shows some interesting results, depicted in the graph below:

So, while at the intervention hospital the raw ICU LOS has remained stable, at the comparator institution it has been slowly creeping up. Of course, the investigators adjusted for all kinds of factors that may influence this outcome, and showed that there may be a (marginal) reduction in the ICU LOS in association with the switch to private rooms. The authors note that the adjusted average ICU LOS fell by 10%, though under similar circumstances in other similar investigations there is a 95% chance that this would fall somewhere between 0% and 19% reduction. So, under the best of circumstances, if we get a 20% reduction in the 5-day ICU LOS, this translates to about 1 day. And given that transfer timing is more likely to be driven by the availability of ward beds than by the patient's clinical readiness, I question whether this is truly a staggering reduction. Additionally, if you read on, you will realize that there is very little reason to believe that this maximal reduction in ICU LOS is unlikely to be achieved by an average institution. In fact, even the 10% seen on average in this investigation may be a bar that is too high in other less well organized ICUs.  

It is important to remember a couple of things: 1). In some circumstances there is unlikely to be any reduction in the ICU LOS; 2). Since LOS is not a normally distributed function, the mean value underestimates the true measure of central tendency in this outcome (this is due to the typically long right tail present in this distribution); and 3). This investigation, though not strictly speaking experimental, was done at 2 academic institutions with highly organized infrastructure and what looks like closed model ICUs (a dedicated specialized team of critical care professionals caring for all ICU patients). For this reason, a similar intervention at a less stringently streamlined institution is unlikely to produce the same magnitude of results.

But the mere fact that the rates of exogenous transmission of pathogenic organisms were reduced is itself encouraging. At the same time, by focusing on carriage rates and not just clinical infections, the authors may be overstating the clinical significance of the observed reduction. Additionally, one of the issues that does not appear to have been addressed explicitly has to do with the availability of sinks: In the intervention unit there was a plethora of sinks, missing in the pre- period and also not available in the comparator hospital. Is it possible then that simply putting in more sinks would accomplish the same for a lot less money?

And this brings me to my next issue with the paper -- cost effectiveness. Now, according to the AHA annual survey of US hospitals, the average age of the physical plant is on the order of 10 years. Given the rapid pace of change in medicine, this may well signal a time for capital investments in plant improvements. And surely from the patient's and family's perspective, private rooms are preferable. However, one must ask the pesky question of the return on such an investment in this era of much needed fiscal restraint in medicine. If the same outcomes of reducing the spread of infectious organisms can be achieved with merely adding sinks, this may be a less drastic and more immediately feasible intervention well worth considering.                

"Invisibility, inertia and income" and patient safety

Hat tip to @KentBottles for a link to this story

I spend a lot of time thinking about the quality and safety of our healthcare system, as well as our efforts to improve it. I have written a lot about it here in this blog and in some of my peer-reviewed publications. You, my reader, have surely sensed my frustration with the fact that we have been unable to put any kind of a dent in the killing that goes on within our hospitals and other healthcare encounter locations. So, it is always with much interest and appreciation that I learn that I am not alone, and that others have had it with the criminal lack of the sense of urgency to stop this medical holocaust. For this reason, I was really happy to read Michael Millenson's post on the Health Affairs Blog titled "Why We Still Kill Patients: Invisibility, Inertia and Income". I was very curious to see how he structured his argument to boil it down to these three I's, since I think that sexy slogans and memorable triplets are the way to go. So, here is how his arguments went.

First, establish the problem. And indeed, we have been killing around 100,000 people annually since the late 1970s (and probably since before then, as you actually have to look in order to find), which amounts to the total 20-year toll of 2.5 million unnecessary deaths due to healthcare in the US. This is truly appalling. And this is just up through the 1999 IoM report! Here is what I was thinking: And if we take into account not just the killing fields of the hospital, but all of life's interfaces with healthcare, we arrive at an even more frightening 400,000 deaths annually, as known back in 2000. Multiply this by 10, and now we really are talking about a killing machine of holocaust proportions! And I completely agree with Millenson that the fact that we continue to say "more research needed" and other pablum like that is utterly and completely irresponsible. However, is this really an invisible problem? The author makes a good argument for how we minimize these numbers by failing to add them up:

I laid out those numbers in a March, 2003 Health Affairs article that challenged the profession to break a silence of deed — failing to take corrective actions — and a silence of word — failing to discuss openly the consequences of that failure. This pervasive silence, I wrote:

continually distorts the public policy debate [and] gives individuals and institutions that must undergo difficult changes a license to postpone them. Most seriously of all, it allows tens of thousands of preventable patient deaths and injuries to continue to accumulate while the industry only gradually starts to fix a problem that is both long-standing and urgent.

Nearly eight years later, medical professionals now talk freely about the existence of error and loudly about the need for combating it, but silence about the extent of professional inaction and its causes remains the norm. You can see it in this latest study, which decries the continuing “patient-safety epidemic” while failing to do next what any public health professional would instinctually do: tally up the toll. Instead, we get dry language about the IOM’s goal of a 50 percent error reduction over five years not being met.

Let’s fill in the blanks: If this unchecked “epidemic” were influenza and not iatrogenesis, then from 1999 to date it would have killed the equivalent of every man, woman and child in the cities of Raleigh (this study took place in North Carolina) and Washington, D.C. Does a disaster of that magnitude really suggest that “further study” and a “refocusing of resources” are what’s needed?

I guess this makes sense -- adding up the numbers is pretty startling, yet we are reluctant to do so. At the same time I hesitate to call this "invisible", since as you saw in a paragraph above, I just multiplied by 10! Yet I am willing to concede the first "I" to Millenson, since I do see the power in these startling numbers.


On the to the next "I", inertia. I agree with Millenson generally, and we actually know this, that physicians do not practice evidence-based medicine, and, even when it does, evidence takes decades to penetrate practice. And there is every reason to be upset that the medical profession has not rushed to adopt evidence-based prevention measures that Millenson talks about. But there is a greater subtlety here than meets the eye. True, the Kestone project is frequently held as an example of a simple evidence-based bundled intervention resulting in in a huge reduction in central line-associated blood stream infections. Indeed, this is a great success and everyone should be practicing the checklist instituted in the project by Peter Pronovost's group. What is less obvious and even less talked about is that the same approach of evidence-based bundled approach to prevention of ventilator-associated pneumonia (VAP) has also been piloted by the Keystone group, yet none of us has seen any data from that. All I have is rumors at this point, but they are not good. Why is this? Well, I have discussed this before here and here: VAP is a very tricky diagnosis in a very tricky population. This is not to say that we need not work as hard as we can to prevent it. It is just to clarify that we are not sure of the best ways to accomplish this. Is this in and of itself shameful? Well, yes, if you think that medicine is a precise science. But if you have been reading my blog long enough, you know this is not the case.


Millenson further sites his reading of the Joint Commission Journal, which has been documenting the progress within one large Catholic healthcare system, Ascension, in its efforts to reduce infections, falls and other common iatrogenic harms. By the system's account, they are now able to save over 2,000 lives annually with these measures. This is impressive. But is it trustworthy? Unfortunately, without reading the primary studies I cannot comment on the latter. However, I did publish a review of studies from this very journal on VAP prevention efforts, and here is what I found:

A systematic approach to understanding this research revealed multiple shortcomings. First, since all of the papers reported positive results and none reported negative ones, there is a potential for publication bias. For example, a recent story in a non-peer-reviewed trade publication questioned the effectiveness of bundle implementation in a trauma ICU, where the VAP rate actually increased directionally from 10 cases per 1,000 MV days in the period before to 11.9 cases per 1,000 MV days in the period after implementation of the bundle (24). This was in contradistinction to the medical ICU in the same institution, which achieved a reduction from 7.8 to 2.0 cases per 1,000 MV days with the same intervention (24). Since the results did not appear in a peer-reviewed form, it is difficult to judge the quality or significance of these data; however, the report does highlight the need for further investigation, particularly focusing on groups at heightened risk for VAP, such as trauma and neurological critically ill (25).             

Second, each of the four reported studies suffers from a great potential for selection bias, which was likely present in the way VAP was diagnosed. Since all of the studies were naturalistic and none was blinded, and since all of the participants were aware of the overarching purpose of the intervention, the diagnostic accuracy of VAP may have been different before as compared to after the intervention. This concern is heightened by the fact that only one study reports employing the same team approach to VAP identification in the two periods compared (23). In other studies, although all used the CDC-NNIS VAP definition, there was either no reporting of or heterogeneity in the personnel and methods of applying these definitions. Given the likely pressure to show measurable improvement to the management, it is possible that VAP classification suffered from a bias.

Third, although interventional in nature, naturalistic quality improvement studies can suffer from confounding much in the same way that observational epidemiologic studies do. Since none of the studies addressed issues related to case mix, seasonal variations, secular trends in VAP, and since in each of the studies adjunct measures were employed to prevent VAP, there is a strong possibility that some or all of these factors, if examined, would alter the strength of the association between the bundle intervention and VAP development. Additional components that may have played a role in the success of any intervention are the size and academic affiliation of the hospital. In a study of interventions aimed at reducing the risk of CRBSI, Pronovost et al. found that smaller institutions had a greater magnitude of success with the intervention than their larger counterparts (26). Similarly, in a study looking at an educational program to reduce the risk of VAP, investigators found that community hospital staff were less likely to complete the educational module than the staff at an academic institution; in turn, the rate of VAP was correlated with the completion of the educational program (27). Finally, although two of the studies included in this review represent data from over 20 ICUs each (20, 22), the generalizability of the findings in each remains in question. For example, the study by Unahalekhaka and colleagues was performed in the institutions in Thailand, where patient mix and the systems of care for the critically ill may differ dramatically from those in the US and other countries in the developed world (22). On the other hand, while the study by Resar and coworkers represents a cross section of institutions within the US and Canada, no descriptions are given of the particular ICUs with respect to the structure and size of their institutions, patient mix or ICU care model (e.g., open vs. closed; intensivists present vs. intensivists absent, etc.) (20). This aggregate presentation of the results gives one little room to judge what settings may benefit most and least from the described interventions. The third study includes data from only two small ICUs in two community institutions in the US (21), while the remaining study represents a single ICU in a community hospital where ICU patients are not cared for by an intensivist (23).  Since it is acknowledged that a dedicated intensivist model leads to improved ICU outcomes (28, 29), the latter study has limited usefulness to institutions that have a more rigorous ICU care model.           

So, not to toot my own horn here, and not expecting you to read the long-winded Discussion, suffice it to say that we found many methodologic errors in this body of research from the Joint Commission's own journal to invalidate potentially nearly all of the reported findings. My point is again to reiterate that unless you read each study with a critical eye and then put it into the larger context, do not believe someone else's cursory reference to the staggering improvements. I guess pertinent to our discussion, inertia, while present, is a more nuanced issue than we are led to believe.


And finally, income. I do agree that it is annoying that economic arguments are even necessary to promote a culture of prevention and safety. What I disagree with is that these economic fallacies of the C-suite impact in any way the implementation of the needed prevention systems. Most of the evidence-based preventions are pretty low tech. And although they do require teams and commitment and systems to implement broadly, small demonstrations at the level of individual clinicians are possible. Also, I shudder at the thought that a group of dedicated clinicians could not persuade a group of equally dedicated administrators to do the right thing, even at the risk of losing some revenue. 


Bottom line? While I like Millenson's sexy little "three I's of safety", I think the solutions, as is always the case when you start looking under the hood, are more complicated and nuanced. In a recent post I cited 5 potential solutions to our quality problem, and I will repeat them here:

1. Empower clinicians to provide only care that is likely to produce a benefit that outweighs risks, be they physical or emotional.
2. Reward the signal and not the noise. I wrote about this here andhere.
3. Reward clinicians with more time rather than money. Although I am not aware of any data to back up this hypothesis, my intuition is that slowing down the appointment may result not only in reduction of harm by cutting out unnecessary interventions, but also in overall lowering of healthcare expenditures. It is also sure to improve the crumbling therapeutic relationship.
4. We need to re-engineer our research enterprise for the most important stakeholder in healthcare: the clinician-patient dyad. We need to make the data that are currently manufactured and consumed for large scale policy decisions more friendly at the individual level. And as a corollary, we need to re-think how we help information diffuse into practice and adopt some of the methods of the social sciences.
5. Let's get back to the tried and true methods of public health, where an ounce of prevention continues to be worth a pound of cure. Yes, let's strive for reducing cancer mortality, but let us invest appropriately in stuffing that tobacco horse back into its barn -- getting people to stop smoking will reduce lung cancer mortality by 85% rather than 0.3%, and at a much lower cost with no complications or false positives. Same goes for our national nutrition and physical activity struggles. Our social policies must support these well-recognized and efficient population interventions.

No, they are not simple, they are not sexy, and most importantly they may be painful. Yet, what is the alternative? We must stop this massive bleeder before the American public starts thinking that the cure is worse than the disease.     

Evidence of harm

We alluded recently to some of the perils of our healthcare system -- the errors, the unnecessary deaths, healthcare-associated infections, and even the idea that we have not done a good job accounting all of the harm that goes with our interventions. It occurred to me that, since you are not immersed in this stuff all day every day the way that I am, it might be helpful actually to put the issue in perspective through transparent data. Mind you, these data are not easy to gather or clean or interpret. So, the point estimates that are widely cited may be somewhat off. But think about it: even the problem is only one-half the magnitude reported, it is still mammoth!  


When we search the Centers for Disease Control and Prevention web site for latest statistics on mortality, we get to this page, where the familiar numbers confront us. These numbers do not change drastically year after year, and indicate that in 2007 the top four cause of death in the US remained cardiovascular disease (616,067), cancer (562,875), stroke (135,952), and lung disease (127,924). 

Here is something shocking, though: in the CDC's hierarchical accounting of the causes of death in the US, one of top three causes is incorrect. And this baffles me because for a decade we have known the third leading cause of death in the US to be something other than stroke. The cause is what I call fulminant iatrogenesis. What is fulminant iatrogenesis? Well, in Greek, the word for "doctor" is "iatre". In our medicalese we have adopted this root to describe complications arising from healthcare as "iatrogenic". We all know that there are adverse events, mistaken identities and such, and we even have an idea that there are somewhere in the range of 50,000-100,000 deaths attributed to hospital care itself, thanks to a well publicized report from the Institute of Medicine from 1999 with a memorable title of "To Err is Human". And since then we have heard echoes of other rather large numbers of deaths due to hospital-acquired infections, also somewhere in the vicinity of 100,000. But details and holistic picture of these numbers, whether there are there overlaps or are they really as outrageously high as they seem, have not been clarified well by the popular press. But is has been addressed in the medical literature, a decade ago. And while we continue to hear a constant din of hoofbeats of the safety police, it is still questionable what effect they have had.           

According to a paper that can be considered vintage at this point, appearing in JAMA in 2000 by Barbara Starfield, MD, MPH, from Johns Hopkins University in Baltimore, our US healthcare system is not so healthy. An accounting of deaths that can be attributed to various forms of contact with the healthcare system is shocking:  

Iatrogenic deaths/year in US hospitals

12,000: unnecessary surgery

7,000: medication errors in hospitals

20,000: other errors in hospital

80,000: nosocomial infections in hospitals

106,000: non-error medication adverse events

These numbers add up to 225,000 deaths/year from iatrogenic causes, and this broadly represents the third leading cause of death in the US after cardiovascular disease and cancer, and accounting for twice as many deaths as the CDC-listed 3rd cause, cerebrovascular disease. And please note that these numbers are actually far lower than those presented in the 1999 IOM for hospital deaths related to errors (48,000 -- 99,000), and at least a bit lower than the most recent CDC estimates for deaths from hospital-acquired infections (99,000). I call this "fulminant iatrogenesis". According to Starfield, what the above sum total of 225,000 fails to take into account is the additional 199,000 deaths related to adverse effects of outpatient treatment. So, putting these together, there are 424,000 iatrogenic deaths in the US annually. Now, this is something to shout about! We know that in the US we do not do a very good job following evidence, even when there is such available, and even when it is relatively solid. So, with all of the caveats and care that I regularly discuss on this site, we really need to do a better job streamlining processes and implementing best available evidence as appropriate. This frightening number of nearly 1/2 million deaths due to contact with the healthcare system needs to become branded onto our national psyche. What a travesty that in a nation that outspends every other in healthcare, that has possibly the worst access to care of all developed nations, we should have such a staggering number of potentially preventable deaths from iatrogenic causes.  How many lives does this kind of system have to be responsible for saving in order to balance this risk-benefit equation?   

Another premier cause of death hidden in the CDC numbers is smoking. In fact, back in the late 1990s the estimate was that the annual death toll in the US from smoking-related causes is 440,000. Of course this is not on top of the causes of death listed, but overlaps with many of the proximal causes listed. Add to this our growing obesity epidemic, and the new data that by 2050 we can expect 1 in 3 Americans to have diabetes mellitus, and you have yourself a tidy public health catastrophe. 

Now, some people may consider this to be a "tu quoque" logical fallacy. Or that this is a straw man. They may say that we are already applying evidence-based methods to address this monster. And they will, of course, be correct, to an extent. The methods that we are applying are not always evidence-based and the incentives system growing up around these measures is far from the latest and greatest in what social sciences tell about human nature and successful behavior change. Most importantly, 10 years later the evidence indicates that we have not moved this hospital death meter, not one little bit. 

So, my response to criticism of my logic is: yeah, whatever. A straw man this massive could eat Tokyo. To me these are startling numbers. We need everyone committed to science and evidence to participate in finding solutions, and constantly reflect on the medical errors that are so pervasive and so deadly. If you pay attention to the evidence of harm, there is no escaping the conclusion that our house is indeed made of glass.   

When the black box of evidence is empty

A close friend of mine gave birth a few days ago to her second child. My friend is a slight woman whose infant came out at 6 lb 9 oz, even though she was calculated to be at 36 weeks' gestation. The infant and the mom have done great with absolutely no complications.

This is why, when I was talking to the Dad this morning, I was shocked to hear that the baby had to undergo a test I had never heard of: something to do with putting the child in its car seat and monitoring its heart rate and oxygen saturation for one hour. Mind you, my youngest is 9 years old, and I had never hear of this test, so I looked it up.

Turns out, this is a test formally known as a "pre-discharge car seat challenge". I went to the American Academy of Pediatrics web site to look for their take on it, and here it is:

Hospitals should develop a policy to ensure provision of a period of observation in a car safety seat before hospital discharge for each infant born at <37 weeks' gestation to monitor for possible apnea, bradycardia, or oxygen desaturation.

I went on a circuitous journey to trace the origin of this recommendation. Turns out, it is based on a consensus statement from an APP committee from 1996, the reference for which is this paper from 1993 about implementation of the 1990 AAP recommendations. Looking at the 1990 version, however, does not help in the least, as there is no mention at all of the test in question, or anything to do with infants under 37 weeks' gestation. So, this is where the dubious evidence trail ends. Period. No evidence near as I can see. [If someone knows of a more complete statement that does include the information I am interested in, please, point me to it].

Why did I go after this piece of information? Well it seemed to me to arise out of the "more is better" fallacy, and I wanted to understand a) the evidence that led to the recommendation, b) how the test alters practice, and finally, and most importantly, c) does this practice impact the desired outcomes (i.e., prevent something bad).

So, I went to one of the most trusted sources in evidence synthesis, the Cochrane Collaborative, to see if they have examined this issue. Lo' and behold, I found this 2006 review, whose findings were summarized thusly:

There is no evidence that undertaking a pre-discharge "car seat challenge" benefits preterm infants. The "car seat challenge" assesses whether preterm infants who are ready for discharge home are prone to episodes of apnoea (stopping breathing), bradycardia (slow heart rate), or desaturation (low oxygen levels) when seated in their car seat. However, it is not clear whether the level of oxygen desaturation, apnoea, or bradycardia detected in the car seat challenge is actually harmful for preterm infants. Additionally there is concern that the use of the car seat challenge may cause undue parental anxiety about the safety of transporting their infant in a car seat. Despite these uncertainties, and despite the widespread use of the test, we have not identified any randomised controlled trials that assessed whether undertaking a car seat challenge is beneficial or harmful to preterm infants.

So, here is a kid, born at supposedly 36 weeks gestation, but looking and acting like a full-term infant, having to sit in her car seat for 1 hour to undergo monitoring that has absolutely no evidence behind it. Putting aside the inconvenience, the false positives, the parental anxiety, let's think about the costs. I could not find published reimbursement rates for this useless test. However, if you think about the person-power, the equipment used, and the potential prolongation of hospitalization that it may induce, the price tag is likely not trivial.

This case is instructive to me. As I have said before, a pitiful minority of medical practice has good evidence behind it. It is precisely this type of testing, done to comply with what looks to be a self-propagating yet outdated recommendation, that healthcare reformers in Washington need to be addressing. Furthermore, when evidence is kept in a black box, we are likely to find that the box is indeed empty.a,

bradycardia, or oxygen desaturation. An appropriate

hospital staff person should conduct the observation.

Hospitals should develop policies to

  

Is breast cancer really the WORST enemy?

I got a chain e-mail from a good friend of mine urging me to get my screening mammogram done. The message contained cute comics about mammography (mostly about the lasting effects of squishing the boobs, you can imagine), and at the end it had the following message:

Mail this to 13 other women. Now, don't break the chain! One female broke the chain, her plumbing became so bad, she now has an outhouse! OK gals, now that you have had your laugh, remember... Breast Cancer Awareness... Go have those boobs checked out and stay healthy! Pass the message on to your mothers, sisters, daughters, aunts, cousins, friends, and even your enemies. Because the WORST enemy is Breast Cancer.

Now, by now you,my reader, know that I am a great skeptic of over-diagnosis and over-treatment; I think it causes more harm than good, though we have not really bothered to quantify the harm yet. But a recent study from Europe fueled my fires on the mammo front. This study, a systematic review of the literature on the subject, concluded that about 1 in 3 cancers in a screened population is an over-diagnosis. In an accompanying editorial, H. Gilbert Welch from Dartmouth, a perennial skeptic of the "more is better" approach, provided a sober view of the dilemma:

Mammography is one of medicine’s "close calls"—a delicate balance between benefits and harms—where different people in the same situation might reasonably make different choices. Mammography undoubtedly helps some women but hurts others. No right answer exists, instead it is a personal choice. (emphasis mine)

Dr. Welch, one of the foremost experts on over-diagnosis in mammography, in fact provided this very useful, in my opinion, table:

Draft balance sheet for screening mammography in 50 year old women*

Credits Debits 1 woman will avoid dying from breast cancer 2-10 women will be overdiagnosed and treated needlessly 10-15 women will be told they have breast cancer earlier than they would otherwise have been told, but this will not affect their prognosis 100-500 women will have at least one "false alarm" (about half of these women will undergo a biopsy)

*For every 1000 women undergoing annual mammography for 10 years

This tally is chilling, given that the end-result is that, to avoid 1 breast cancer death, 2 to 10 women will be treated unnecessarily with disfiguring surgery, toxic chemo and radiation therapies, and 100 to 500 others will be subjected to follow-up testing, some of it invasive. Now, you may be saying that it is all worth it to avoid dying from breast cancer. Unfortunately, what we do not have a solid idea about is how much harm, in the form of complications and deaths from the unnecessary work-up and treatment, comes from this "better-safe-than-sorry" approach. We also have very little knowledge of the long-term effect of the radiation exposure from mammo. 


So, the bottom line is that, if a woman decides against the current dogma to have annual screening mammography, don't make her into a pariah -- she is exercising her good judgment. Peer pressure in this situation is not only unwarranted, but may be detrimental. We as the medical and public health profession need to start developing a much more nuanced message about cancer screening in general, and mammography in particular. There is too much evidence now that one size does not fit all.