On December 13 we posted about the Big Surprise release of a research paper that showed that exercise is good for you, indeed, better than medicine. Not good news for doctors (or the corporations that own them or sell pills through them). We commented about how well known this was.
A regular reader, John Vokey, pointed out a very nice recent article in the British Medical Journal, by the arch skeptic Ben Goldacre and David Spiegelhalter, about how we know whether something that's obvious is actually true. Here is a link to that piece. He's a widely known writer and commentator, as well as a practicing physician in Britain, who has written a great deal about similar aspects of how we use data and how this affects medicine. He writes about 'Bad Pharma' to try to correct such things (see link below his picture for more).
While it is obvious that exercise is good for your health, and we have some good physiological and physical reasons and mechanisms to back up that statement, in our post we noted that the correlation between health and exercise may not be so simple. For example, you have to already be healthy to exercise so the correlation may be a result not a cause of better health.
Goldacre takes something bluntly obvious, that wearing a helmet when bicycling is good for your health (that is, in terms of injuries). He shows that even this is neither so obvious nor simple. Just to illustrate the point, if you ride more often or more often in traffic because you feel safer when you wear a helmet, even with the same per-mile (or, in Goldacre's UK, per kilometre) risk there will be more rather than fewer cycling-related injuries: the population at-risk has grown. Or drivers may cut closer to you seeing that you are helmeted. And so on. As John Vokey pointed out in his comment, that brief but to-the-point article is a fine lesson in statistical reasoning.
If something as apparently simple as the risk of cycling with vs without a helmet is not so simple, then how much more complex will other sorts of causation, epidemiological, genetic, and evolutionary are supposed cause-and-effect scenarios be? A due respect for this complexity should routinely temper conclusions from simple study designs (or, in the case of evolution, almost pure surmises about natural selection in the distant past).
Yet pressures, and perhaps natural tendencies in our boastful current culture, seem to be doing just the opposite: leading investigators to make ever-quicker and ever more grandiose claims about their findings. This is used for self-promotion in general, in seeking grant support, and in the rush to the media. And science journalists often show little, sometimes almost zero sense of skepticism or even circumspection, about such claims.
The issues we face in science are nowadays very complex and subtle, and we know from even simple examples, such as the one Goldacre used to illustrate the pitfalls of statistical reasoning, that our conclusions can be very wrong, even in very simple ways. We try to make conclusions in science, but we should do that by starting with respect for the complexity of the problem.
A new report on cancer incidence and mortality in the UK, described on the Guardian website, suggests that 40% of cancers in women and 45% in men are preventable, due to lifestyle choices. This is great news for people pushing healthy diets and exercise (perhaps not such great news for those pushing genetic causation).
Dr Rachel Thompson, deputy head of science for the World Cancer Research Fund, said: "This adds to the now overwhelmingly strong evidence that our cancer risk is affected by our lifestyles.
"We hope this study helps to raise awareness of the fact that cancer is not simply a question of fate and that people can make changes today that can reduce their risk of developing cancer in the future.
So, what is this overwhelmingly strong evidence? The authors chose 14 different risk factors, as listed in the table below (taken from the paper).
Table 1. Exposures considered, and theoretical optimum exposure level
Exposure Optimum exposure
Tobacco smoke Nil
Alcohol consumption Nil
Diet
1.Deficit in intake of fruit and veg ≥5 servings (400 g) per day
2.Red and preserved meat Nil
3.Deficit in intake of dietary fiber ≥23 g per day
4.Excess intake of salt ≤6 g per day
Overweight and obesity BMI ≤25 kb m-2
Physical exercise ≥30 min 5 times per week
Exogenous hormones Nil
Infections Nil
Radiation – ionizing Nil
Radiation – solar (UV) As in 1903 birth cohort
Occupational exposures Nil
Reproduction: breast feeding Min of 6 months
The study calculated the "population attributable fraction" of each risk factor, that is, how much excess cancer was due to exposure to the risk factor. They compared cancer incidence in those exposed with incidence in those not exposed and assumed any excess (or, in theory, deficit) was due to the risk factor.
They chose risk factors based on the following criteria:
1. There was sufficient evidence on the presence and magnitude of likely causal associations with cancer risk from high-quality epidemiological studies.
2. Data on risk factor exposure were available from nationally representative surveys.
3. There were achievable alternative exposure levels that would modify the risk.
They calculated the relative risk per unit of exposure for cancers with probable or convincing causal associations with each risk factor, based on observational epidemiological studies. These would be the same studies you see reported in the news every day, telling you that you should or shouldn't eat butter, should or shouldn't go out into the sun, should or shouldn't eat sugar.
But there is something rotten in the state of Denmark, because despite billions of dollars, hundreds of thousands of study subjects, countless studies over decades of time by the most prominent (or, that is, highly placed) epidemiologists, the actual truth about the data is very different, surprising as that may seem. Indeed, as far as we know, the only truly convincing risk factors in this list are tobacco, papilloma virus and radiation, and even there it isn't really clear how much radiation exposure is too much (and many would say no exposure is the only completely safe exposure, though some exposures may detect treatable dangerous conditions and be good in the net).
The other behavioral risk factors have been shown in some studies to account for a small fraction of risk, though the results aren't always replicable. Indeed we can assert what we've just said because, by chance we just heard a talk by Gary Taubes, a science journalist for the New York Times and Science, among other outlets, who has systematically been debunking the idea that low fat diets have been shown definitively to prevent heart disease and cancer. He says the data just aren't there and never have been, but that it's been a belief so entrenched that it can't be denied because of all the vested interest that would challenge.
Hey, we like a good heretic as much as the next guy. And Taubes has written some of the best stuff out there on why observational epidemiology can't answer basic questions about cause and effect (here and here, for example). His work on dietary fat is very convincing, and his more general point that observational epidemiology can't be the basis for dietary recommendations is equally convincing. (So it's confusing that he's now a strong advocate for the idea that processed sugar is toxic, and responsible for the obesity and diabetes epidemics all over the globe -- conclusions largely based on the same kinds of observational studies he debunks when it comes to risk factors he doesn't like.)
But we digress. We are more than willing to accept that environmental risk factors can lead to disease. If not, only genetic variation would cause disease, and that clearly isn't so! We write about this all the time on MT. We just aren't nearly as ready to accept that we know definitively what those risk factors and their associated risks are. Nor that everyone is equally at risk from every factor.
Some of the optimal exposure levels in the list probably come under the category of 'wouldn't hurt', but public health measures are, by design, meant to be population-based, and the economic costs of encouraging lifestyle changes on a population level are not trivial. Nor is the cost of lost credibility when the risk factors turn out to be less important than we've been told after all.
Worse, risk is always and necessarily estimated retrospectively by relating outcomes quantitatively to exposure histories. But what we want to know is the future risk, and we know very well that we cannot predict the mix or amount of exposures to who-knows-what risk factors in the future. This is a deeply troubling problem, since major changes in risk for many or even most complex disease have occurred, often because of unclear behaviors or exposures, just in the past 50 years or so.
So, here's the safest conclusion to date -- do (most) everything in moderation, and don't worry about it. Something will get you in the end, so try to have the best time you can before that.
Science, as practiced by scientists, has lots of flaws and fallibilities. Methods and inertia and vested interests sometimes drive what's done and how it's done. When inappropriate designs or methods are used to answer a question, or when an idea (or belief) is so strong that it can hardly be falsified by scientific evidence, science deserves criticism.
But when science gets it right and for the right reasons, this should be recognized as demonstrating that causation does actually occur in this world and can be identified when the situation is clear enough, by the methods we know how to use. Often, success comes when a single cause is strong on its own, and predictive of an effect.
There have been decades of very good evidence that smoking causes lung cancer. One can predict that a certain amount of smoking should lead to a certain amount of cancer. It's not precise, but it's clear, and shows, at least statistically (since not even most smokers get lung cancer), that smoking is a causative agent. Given what we knew of male smokers and cancer rates decades ago, information gathered when most smokers were males, it was predictable that when women started thinking that a smoke was cool they'd start joining their men friends in the cancer wards.
Women began smoking in large numbers around 25-50 or more years ago, and a new study demonstrates that it's catching up to them. Also reinforcing the causal connection, men had quit smoking in large numbers at about the same time in the past, and their rates of lung cancer have been declining as would have been--as was--predicted.
Lung cancer rates have more than doubled for women over 60 since the mid-1970s, figures show.
Cancer Research UK figures say the rate rose from 88 per 100,000 in 1975 to 190 per 100,000 in 2008, the latest year for which statistics are available.
Lung cancers in men fell, and CRUK say this is linked to smoking rates.
The proportion of male smokers peaked before 1960. But women had rising rates in the 1960s and 1970s, which would have an effect on those now over 60.
Overall, the number of women diagnosed with lung cancer has risen from around 7,800 cases in 1975 to more than 17,500 in 2008.
Figures for men went from 23,400 over-60s diagnosed in 1975, falling to 19,400 in 2008, with rates showing a similar large drop.
Strong evidence, to go with laboratory and molecular/biochemical evidence about the nasty ingredients in smoke and what it does to DNA to transform nice, pink healthy lung cells to charred, ugly cancerous one (anybody who's taken a gross anatomy class in a medical school has probably seen the coal-bag lungs of cadavers of former smokers).
Famous people, most notoriously RA Fisher, one of the founders of modern statistics, have tried to find reasons why this association was due to confounding--some true cause other than Virginia's finest, but that was correlated with smoking. But the evidence has piled up the other way (despite the effects of other exposures).
Other predictions
So this is prediction about the future made from past observations. But what about the other kind of prediction? A scientific theory can be really convincing if it can make some additional predictions that would be a consequence of the hypothesis. So, what if you go not to smokers and non-smokers and follow their exposure rates, but go to lung cancer wards and ask whether the patients were smokers? You'd expect to find that most of them were, and that is what the evidence shows. Even with twists, such as in Utah, where the population is heavily Mormon. Mormons don't believe in smoking, but the cancer wards in Utah suggest that Mormon lung cancer patients had apparently not adhered to their religion's teaching.
Understandably, attention is on the gruesome outcome of lung cancer. But we can make another prediction, and we guess some of the data are probably already in hand. In many studies, perhaps largely of men since they were the main smokers, a high fraction of smoking-attributed death and disease was not due to lung cancer, but involved many other systems--heart attack, emphysema, and many others. Lung cancer is only a minority, perhaps a small minority of these consequences. So we can predict that these traits have diminished in men (we think they have), but should be increasing, along with lung cancer, in women. If that turns out not to be the case, then we have to revisit much that we think we know about smoking.
Given both the prospective prediction and retrospective assessment, our ideas about cause and effect receive strong, persuasive scientific support. No weakling GWAS evidence here! Yet, why given this strong and clear support for smoking as a sledge-hammer kind of risk factor, do so many people--even college students who learn about these facts in a reasonably rigorous way, still smoke? It raises questions about the efficacy of education, about understanding of statistics and risk, and of the impact (or not) of scientific knowledge.
Because today, only the tobacco industry would still claim that smoking was just plain innocent fun.
The subtitle reads: "Neanderthals really did act like Neanderthals, new research suggests, as our early relatives were found to be more aggressive, competitive and promiscuous than modern man."
So what did the study actually find?
Lucky for you and me, the article is open access so anyone with internet access can see for themselves.
P.S. On a related note, here's a funny little question-and-answer about finger size and use.
A few weeks ago we posted about the confusion over research into Chronic Fatigue Syndrome (CFS), or myalgic encephalitis (ME), which is or isn't caused by the recently discovered virus, XMRV, which also does or does not cause a form of hereditary prostate cancer.
Science reports that a meeting of several hundred researchers took place two weeks ago to discuss the controversy surrounding the role of this virus in disease. But, as one of the organizers of the workshop concluded, the field remains "a zone of chaos". The presence of XMRV has been confirmed in men with prostate cancer by some groups in the US, but not in Europe, and in some people with ME but not others -- by some labs. Some labs have definitively documented contamination with XMRV, and thus can't confirm its role in disease, while others report finding it in most cases they've tested but few controls, suggesting it's real and not contaminant. The ability of all labs that have reported results of tests for the virus, whether positive or negative, to detect the virus has been tested and all were able to, so it seems that the possibility that there's an easy answer to this, and the conflicting results are due to varying laboratory techniques is not to be.
The head of the National Institute of Allergy and Infectious Diseases, Anthrony Fauci, has asked an epidemiologist at Columbia to test blood samples from 100 CFS patients from four different areas of the US and 100 controls for the presence or absence of XMRV. It's unlikely that throwing the results from yet another lab, whether positive or negative, into the mix will convince anyone on either side of this issue, however. Meanwhile, we conclude this post as we did our previous post on the subject -- it's possible that the only way the controversy over whether XMRV causes disease will be sorted out is if CFS patients can be successfully treated with antiretroviral medications.
Precedent isn't very helpful. Theories of disease come and go, and viral theories are no exception. At one point that was cancer's general cause, but not cervical cancer which was due to promiscuity. Ulcer was due to stress, not bacteria. And so on. It is not clear how we can know with confidence, but in some examples like these there is an answer, and a rather clear one. In others, the theory simply fades away. It will be very interesting to see how this one goes.
After years of study and millions (or tens of millions) invested, a report shows that we cannot yet tell whether the plastic molecule BPA (bisphenol-A) , to which we are all exposed, in bottles and can linings, is safe. BPA apparently acts like estrogen in experimental animals and cell culture. But is it harmful to humans?
Some scientists, whose job it is to try to look out for public safety, are concerned and say that we should limit exposure, and they want to prove that there's good reason for that. Some Republicans, whose job it is to look out for private pocketbooks, say that this is like yet another Commie plot, and they don't need any evidence, thanks very much (and, by the way, perhaps nowadays it's an Evolutionist or Islamic plot).
We don't know the answer. Sometimes in issues like these, the safe and sane thing to do is ban the questioned substance and just do without what usually minor loss it would lead to (except in the pockets of a few in industry who make whatever-it-is). In this case, however, the issues are less clear. Food packaging is important or even central to our general health and well-being. We have to have at least some food transported from large distances, and hence preserved in some way to make the trip without perishing. It's not all junk food that's involved. Most of us live in cities and know more about growing tired than growing carrots. Indeed, without our distribution system, would we live any longer in the wild than an escaped laboratory mouse?
So these issues go beyond objection to private self-interest by industry. But if science is so powerful, why can't we know the truth?
One response is that people, exposures and people's responses to exposures are highly variable. And, it's difficult to measure exposure, or even to know how to measure it, and people's responses can be so variable that it it's almost impossible to come to a conclusion about cause and effect. The evidence is weak because the net effect, the average effect, is small. However, for some people or some exposures, the effect could be huge. Maybe they have an unusual combination of exposures, or an unusually susceptible (but rare) genotype. It's very hard to know.
Alternatively, the effect could be similar to all of us but very, very small. In that case, what we would need to know is whether any alternative to using BPA would be without at least as many negative consequences. For example, if some people would have lessened nutrition and hence higher disease rates, if they were not able to obtain inexpensive foods preserved in part by the use of BPA. Even if a small number were helped, is that number larger than those who might be negatively affected?
Maybe when the risks appear to be very small, the best policy is not to worry. The risk may change year to year with changes in exposures or other factors. It may not be evaluable. On the other hand, what if there is some cumulative or long-delayed effect (a concern with genetically modified plants and the evolution of resistance, for example). Then we should try to determine this now, and stop the exposure.
We are constantly faced with such issues in evaluating causation. We have no answers. Unfortunately, nobody else does, either.
Epidemiologists often follow a set of criteria called the "Hill Criteria" to determine what causes a disease. The list includes things like consistency, and strength of the cause and effect relationship -- the cause should reliably produce the effect, and the effect should increase as the cause increases. And so on. Unfortunately for epidemiology, there are many situations in which these criteria aren't met, even with known causes, but there is one criterion that is pretty reliable and that's the one that says that cause should come before effect. But it's a problem when you can't tell which is the cause and which the effect.
The BBC reports that scientists have found the "clue to getting a good night's sleep." The paper they are reporting on, from Current Biology, summarizes it this way:
Common experience suggests that [the] fragility of sleep is highly variable between people, but it is unclear what mechanisms drive these differences. Here we show that it is possible to predict an individual's ability to maintain sleep in the face of sound using spontaneous brain rhythms from electroencephalography (EEG). The sleep spindle is a thalamocortical rhythm manifested on the EEG as a brief 11–15 Hz oscillation and is thought to be capable of modulating the influence of external stimuli. Its rate of occurrence, while variable across people, is stable across nights. We found that individuals who generated more sleep spindles during a quiet night of sleep went on to exhibit higher tolerance for noise during a subsequent, noisy night of sleep. This result shows that the sleeping brain's spontaneous activity heralds individual resilience to disruptive stimuli.
So they believe they've found the brain's way to tune out noise during sleep, and it works something like this: During sleep, when the brain receives sensory input like noise or light, the thalamus interacts with the cortex, and this produces " transient fluctuations of the brain's electric field visible on the EEG as rhythmic spindles" (seen in part A of the figure). Some have proposed that these spindles are signals of the brain interfering with the transmission of sensory input to the cortex, where they are perceived -- meaning that you become consciously aware of them -- a process that isn't normally disrupted when we're awake. If this is true, then the more spindles you make, the less likely your sleep is to be disturbed by external stimuli.
To test the association of spindle number with sleep disruption, the researchers monitored the sleep of 12 healthy individuals for 3 nights with EEGs. The first night was quiet, while they introduced noise at regular intervals during the 2nd and 3rd nights (regularity could be a problem here, though, as people tend to acclimate to regular sound, but oh well). They found a statistically significant association of spindle rate with sleep stability. They found spindle count to be consistent over 3 nights, and "suitable for predicting sleep continuity under noisy conditions", and they suspect that the biological relationship reflects "the cumulative effects of spindle and sound collision" -- that is, spindles themselves inhibit the perception of sound during sleep. Naturally, future studies are needed, primarily of "interventions" to promote spindle production in light sleepers (i.e., drugs).
So, are these guys on to something? Will they soon be marketing a Viagra equivalent for the sleep-deprived?
Well, here's what a sleep researcher unrelated to this study, and quoted in the BBC story, has to say (this is a consideration not mentioned in the actual paper, so we applaud the BBC for adding it to their story).
Professor Jim Horne of the Sleep Research Centre at Loughborough University added: "Sleep spindles certainly help to block outside noise. There are other interpretations for this study, though, as those people with fewer spindles may simply be 'lighter sleepers' and more likely to wake up with the noise - hence less sleep and fewer spindles.
So, what's cause and what's effect? That's indeed a problem.
But here's another one -- do those of us who toss and turn really need to be diagnosed with a 'condition' or even a 'disease', that demands doctors' attention and health-care funds? And whose reframing could make some people potentially very rich? Why do we turn every toss into a topic for 'research', and publish our handkerchief every time we blow our nose? We have a lot of other reasons to lose sleep, but not over this.
Our culture, and certainly our science culture, firmly believes in the concept of causation that is directed in time order. Only some rather esoteric physicists toy with the idea that time is reversible, or doesn't exist, or is some sort of illusion. In everyday life, at least, cause comes first, then effect. Always. The horse always pulls the cart, never the reverse.
But that may not allow us to identify which comes first, at least if we think sloppily as seems to happen daily in the science press and hence also in the journals the reporters are reporting on. Just-So stories of how something came to be are just too appealing.
So, in the New York Times on Jan 27, there was a story about aging and running....or is it running and aging? It turns out--at least it's reported--that older men who run have bodies like young studs. They may look old on the surface, but if you gaze pruriently at the ends of their....chromosomes, you find that those chromosome ends are capped by structures, called telomeres, that are as long as younger guys' telomeres. Size matters!
Telomeres protect chromosomes from chemical degradation in the cell, so they're good for genetic function. But generally they are reported to shorten with age, and this is argued to be one cause of biological aging and senescence.
In general, telomere loss was reduced by approximately 75 percent in the aging runners. Or, to put it more succinctly, exercise, [the principle investigator] says, ‘‘at the molecular level has an anti-aging effect.’’
If runners' telomeres are longer for their age than non-runners, running must be good for you, right? And then, indeed, we must be able to find an evolutionary explanation for that.
Well, not necessarily. This may be a case of causal order, of carts and horses. Does running inhibit telomere degradation, or do longer telomeres let older men run better? That would be easy to explain: if the whole idea of telomeres and aging has merit, maybe guys with damaged telomeres feel lethargic or in other ways are not inclined to run, or can't run comfortably. One would see the same association: older runners have longer telomeres. In this case, telomeres are the horse, the running man the cart.
Of course it is also possible, though not easy to understand, that running boosts your telomere length, so if older guys run, they maintain their telomeric health. How a cell knows that you're running and how that leads it to keep up telomere maintenance is a critical subject if the man is the horse and the telomere the cart.
But all that assumes there really is a causal connection between running and telomere length, whichever way it may go. Because another widely practiced fault, an obvious one everyone knows about but a temptation few can resist, is to equate correlation with causation. Telomere length may be correlated with running in one's dotage, but there need be no causal connection between them. Older running men probably are (on average) more educated and more into health cultures; they probably also watch more Public Television than slothful older guys, who probably watch more online poker or wrestling. But we doubt that PBS shows lengthen telomeres.....or might they? What if thinking harder has that effect?
The use of x-rays to detect breast cancer, known as mammography, started around 1960. The idea was that x-rays could give an in-depth picture of the breast that would be superior to palpation for detecting small tumors that had not yet become obvious or symptomatic. It seemed like a very good idea that could save many lives, and became not just widespread but formally recommended as part of preventive care.
This was based on the belief, or perhaps even dogma, that tumors are 'transformed' cells that are out of control and will continue dividing without the normal context-specific restraint. The tumors induced vascularization that nourished its cells, and eventually cells flake off into the blood or lymph systems, to be carried along to other sites where they would eventually lodge, spreading the tumor (this is called metastasis). If anything, treatment or just competition would lead this distributing clone of transformed cells to gain an increasing evolutionary advantage over the woman's (and, in much rarer instances, men's) normal tissue: tumor cells would continue to accumulate mutations at the regular or even an accelerated rate, that would give them even further growth advantage.
Sometimes tumors seemed to regress, but this was difficult to explain and often it was thought that perhaps the initial diagnosis was wrong. If the tumor had escaped immune destruction when it was only a single or few cells large, what could then later make it regress?
Thus the general dogma in cancer biology that the earlier it was caught, the less likely it would spread. That also meant the earlier in life one was screened, the better. Local surgery could then cure the disease.
But there was a problem: the same x-radiation used to detect different cell densities between tumor and normal tissue, is also a very well-known mutagen and cause of cancer!
Worse, the more actively dividing cells were, the more liable to mutation and thus transmission to increased numbers of a descendant line of daughter cells in the tissue. Since breast tissue grows every menstrual cycle, pre-menopausal women would be particularly vulnerable to iatric carcinogenesis. Yet the idea was that earlier screening was better!
Even further, early onset cases are more likely to be or to become bilateral (both breasts) or multiclonal (more independent tumors), and it was suspected and is now known that some of this, at least, is due to inherited susceptibility mutations (in BRCA1 and BRCA1 and a few other genes). These mutations put a woman at very high risk, so earlier and more frequent screening--but higher total radiation doses!--could be important.
Especially after the atomic bombing of Japan in World War II, and the subsequent fallout from nuclear reactors and bomb tests, and the proliferation of diagnostic x-rays, many extensive studies were done to document the risk, and for example chest x-rays used in routine tuberculosis screening were shown to be a risk for cancers including breast cancer.
So, to screen or not to screen? The obvious answer to this Hobson's choice was a grim cost-benefit analysis: how many cancers are detected and cured vs those that are caused by mammographic screening? Even grimmer, this could be evaluated by age, so that recommendations could be made based on a judgment as to how favorable the age-specific balance between cause and cure was. And there's more: radiation-induced carcinomas take years to develop before they would appear as clinically detectable tumors, so evaluating and attributing risk was (and is) not easy.
Breast cancer is unfortunately quite common, but the differences being considered, among many additional variables known and unknown, are small. That means very large, long-term studies needed even to come to a tentative rational ('evidence-based') conclusion. The result was recommendations of occasional mammograms for women in their 40's, with more frequent screens in 50's and beyond.
This made sense....until a few studies recently began to appear with curious results. Several studies showed that the number of cancers in women not screened was lower than those in women who had been screened. How can this be? The answer appears to be that screening leads to detection, reporting, and treatment of tumors that would eventually disappear on their own. So screening led to interventions of various types, some rather grim in themselves, in a substantial fraction of cases that would go away without any treatment with its associated cost and trauma.
The same has been found recently in PSA testing of men for prostate cancer, so it's not a fluke of the study design. Scars of remitted tumors have been found, showing clearly that they regressed without diagnosis or treatment.
So now a panel of experts has recommended backing off, and doing screening less often (except in those who, in a grim kind of good luck, know they carry a high-risk mutation and hence need to be checked carefully, and often, where early detection can more clearly be effective).
Now if that isn't 'evidence' what is? Yet this is controversial, because it goes against accepted practice. In the Wednesday NY Times it's reported that some physicians don't plan to change their recommendations (what will insurance companies, our most noble citizens, and the entities that will actually drive this decision, do?). The NIH Secretary also backed away from this new report. This is curious to say the least and relevant, of course, to the notion of 'evidence based' medicine that we discussed in a recent post, and why we think the notion of evidence is actually rather slippery.
This strikes close to home for many of us, who have very close relatives who have died of breast cancer. For us, research on this subject could hardly be more important. If you're a young woman you face these grim or even terrifying choices. But in real life, rather than fairy stories, there's no easy answer.
