It looks like the H1N1 pandemic is fading fast. I am amazed at how lucky we were, at least in the hospitals where I work. A month ago all the ICU beds were full, most of the ventilators were in use and we were wondering how we were going to triage the next batch of patients who needed advanced life support and we had none to offer. Then, right as we reached maximum capacity and had no more wiggle room, the rates plummeted. We skated right up to the edge of the precipice, looked down, and did not have to jump.

The pandemic has not been as bad as expected, but it was still no walk in the park. Nationwide H1N1 killed maybe 10,000, with 1,100 in children and 7,500 among young adults (ref). Oregon has had 1200 hospitalizations and 68 deaths. We had about 8 deaths from H1N1 in my hospital system. We would have had twice that number, but one of our hospitals is a trauma center and offers ECMO (Extra Corporeal Membrane Oxygenation) and we managed to save a number of people who would have died if they had been in a lesser hospital. The national statistics mirror our experience. None of the deaths were in the elderly. Pity the vaccine was slow to be produced as it could have prevented the majority of those deaths.

Are we done with H1N1? Will it become part of seasonal flu? Will it have a third comeback, fueled by holiday travel? Will it mutate and increase virulence? Will it recombine with avian flu to generate a new strain? Is this THE pandemic that comes every 30 years or so, and we will not see another until after I am long dead?

How am I supposed to know? I can’t see the future. Or can I? Mr. Randi, listen up: I am thinking I will be eligible for that million dollar prize. I am receiving future information from the Large Hadron Collider, curiously delivered inside a baguette. I think I can predict the next infection to sweep the US.

Measles.

Easy call, huh?

I have seen a grand total of one case of measles in my career. It was in an unimmunized young male who picked up measles traveling to Africa. I had not expected to see another case thanks to immunization. I am no longer certain that will be the case.

Measles, due to the rubeola virus, is a typical virus, with the usual fever, cough, runny nose, red eyes and a generalized, maculopapular, erythematous rash. One of many childhood infections that have plagued mankind. Measles is very infectious, with 90% of household contacts exposed developing the disease. It is one of those infections that is easy to acquire in the waiting area of a doctors office.

Case fatality rates in the West are low, about 0.3%, while in the third world it kills up to a third of infected children. About one in a thousand get encephalitis.

In the old days, everyone developed measles with about 3 million cases a year, with relatively little, but devastating, morbidity and morality.

“Before measles vaccine, nearly all children got measles by the time they were 15 years of age. Each year in the United States about 450 people died because of measles, 48,000 were hospitalized, 7,000 had seizures, and about 1,000 suffered permanent brain damage or deafness.”

Much of this is preventable by the vaccine. No vaccine is perfect, and the measles vaccine is no different. Measles vaccine is about 90-97% effective in preventing infection, depending on the population studied. Or to think of it another way, 3 to 10% of the population would remain susceptible to the disease even if we had 100% of the population vaccinated.

Thanks to Dr. Andrew Wakefield, fear of MMR induced autism is highest in Great Britain and as a result measles vaccination rates have fallen. Perhaps it should now be Mediocre Britain, at least where vaccines are concerned.

Vaccination rates have fallen in England, and at one point 20% of children were susceptible to measles, mumps and rubella. Since the English refer to vaccination as ‘the jab’ I am surprised they get anyone to take the vaccine. It’s like referring to colonoscopy as riding the python. Who would want that?

“A particularly significant decline was observed between 2000 and 2004, which can arguably be attributed to deterioration in public confidence about the safety of the MMR (Reference).”

As a result, measles boomed.

All due to Dr. Wakefield’s report in the Lancet, which evidently should have been published as work of dark humor in Punch.

“More importantly, the controversy appeared to affect parental decision-making. Uptake rates for MMR in England fell from 87.4% in 2000-01 to 79.9% in 2003-04, the lowest figure at any time since the widespread introduction of the triple vaccine in 1990-91. The decrease was especially significant given that the single vaccines alternative was only available from private medical clinics, at a cost of around £200.

The Wakefield study has been widely discredited, and MMR uptake has recovered to an extent: in 2007 vaccination rates stood at 84.6%. Meanwhile, measles notifications in 2006 and 2007 were the highest for almost a decade. (Reference)”

I wonder, as an aside, about responsibility. One of the refrains of the antivax crowd is that big pharma is protected from any liability from vaccine injury. Big pharma cannot be held responsible. I wonder, when the causes of autism are finally elucidated and vaccines are definitely exonerated as we have the answers to the etiology of autism, if Dr. Wakefield, AoA and Ms. McCarthy will assume the responsibility and liability for all the morbidity and mortality their actions caused. I am sure they will happy to step up to the plate and offer restitution to the affected families.

There was, of course, another paper out of Poland, “Lack of Association Between Measles-Mumps-Rubella Vaccination and Autism in Children,” this month exonerating the MMR as a cause of autism. Poland has an interesting history with regards the measles vaccine:

“The MMR vaccine was introduced in Poland later than in most other European countries. For the past 10 years, the MMR vaccine has been gradually replacing the single-antigen measles variety. When it was first introduced, MMR was not covered by the national health service of Poland. Parents who wished to vaccinate their children with MMR, as opposed to the single mandatory measles vaccine, had to pay extra. For this reason, few children were immunized with MMR. The Polish mandatory vaccinations schedule did not include MMR for all children until 2004.”

As a result,

“Poland’s heterogeneous population (ie, vaccinated with MMR, vaccinated against measles only, nonvaccinated) serves as a unique sample group for studying the debated association of these vaccines with autism in children.”

In comparing the three groups they found no association between MMR and autism. None. In fact, they found “a lower risk of developing autism for children vaccinated against measles, with the lowest risk being found for children vaccinated with MMR.”

This finding is dismissed by the authors as perhaps

“the decreased risk of autism among vaccinated children may be due to some other confounding factors in their health status. For example, health care workers or parents may have noticed signs of developmental delay or disease before the actual autism diagnosis and for this reason have avoided vaccination.”

Dr. Gorski also thought the finding was a fluke. Part of the argument against MMR being protective being that having one child in the family with autism would make it unlikely for other children in the family to get the vaccine out of fear of the vaccine causing autism when, in fact, it is due to perhaps inherited causes. The lack of vaccination actually being a marker for families with other predispositions to developing autism.

I am not certain that is true. As the authors report:

“This serves as evidence that, despite extensive media coverage of the debated association between MMR and autism, public acceptance of this vaccine remains very high. The situation in Poland is different to that of many European countries, where MMR vaccinations by age 2 years fell more than 10% and were followed by measles outbreaks. In this time, Poland’s already high rate of measles immunization even slightly increased.”

Seems that the Poles were immune to the anti-MMR hysteria, although I cannot say with certainty. If so, then the finding of the protective effect of vaccination, given the study population, may be valid.

Me? I think everything is due to an infectious disease. Infections are the One True Cause of All Disease. While this is the first study to demonstrate the protective effect of the MMR, remember that measles, mumps and rubella area neurotropic viruses with encephalitis a known complication. There has long been a suspicion of viral infections altering the brain to unmask schizophrenia and there is an association between borna virus and OCD. Could a subtle neurologic infection exacerbate a predilection towards autism? I do not think it is out of the question. But that is my delusion.

Vaccination rates have fallen in some segments of the US population as well. In the US, low vaccination rates are found primarily in the children of the well-to-do and often are clustered in alternative schools. There are dozens of schools with vaccination rates under 80%, with some schools having vaccination rates of 5% (reference).

Well, fine, you may say to yourself: they can get the measles or other vaccine preventable diseases. At least it will stay in the those enclaves of unvaccinated children. My kids are vaccinated and in schools where vaccine rates are high. My kids are safe. I would have thought the same thing.

Herd immunity and the models that try and predict what levels of immunity are needed to protect a population are based on the assumption that unimmunized people are randomly distributed in a population, not clustered in alternative schools.

In the Journal of Infectious Diseases this month is a description of a measles outbreak in Canada where clusters of unvaccinated populations helped perpetuate a measles outbreak even though overall community vaccination rates were high (“Long-Lasting Measles Outbreak Affecting Several Unrelated Networks of Unvaccinated Persons”):

“Despite a population immunity level estimated at ?95%, an outbreak of measles responsible for 94 cases occurred in Quebec, Canada. Unlike previous outbreaks in which most unvaccinated children belonged to a single community, this outbreak had cases coming from several unrelated networks of unvaccinated persons dispersed in the population. No epidemiological link was found for about one-third of laboratory-confirmed cases. This outbreak demonstrated that minimal changes in the level of aggregation of unvaccinated individuals can lead to sustained transmission in highly vaccinated populations. Mathematical work is needed regarding the level of aggregation of unvaccinated individuals that would jeopardize elimination.”

The graphic shows how schools acted to magnify the epidemic:

The isolated measles virus was genotyped and almost all isolates were identical, demonstrating how infectious measles can be with what was presumptively minimal contact.

As the discussion said:

“An important assumption of mathematical models predicting elimination, however, is the random distribution of susceptible persons in the population. In reality, unvaccinated individuals are not distributed at random. Religious groups opposed to vaccination are often tightly knit communities. Our outbreak involving 2 unrelated alternative schools attended by children whose parents were resistant to vaccination on philosophical ground demonstrated that these persons also aggregate. The spontaneous interruption of this outbreak, despite the current level of aggregation in unvaccinated children, suggests that endemicity was not likely to be reestablished in this population. The continued propagation throughout many generations of cases, however, raised the possibility that a minimal change in the overall vaccine coverage in the population or in the level of aggregation of unvaccinated individuals can lead to sustained but protracted transmission despite an immunity level near 95%.”

Lest you think this outbreak epidemiology is limited to measles, the US northeast experienced a similar outbreak with mumps, where clusters of unvaccinated populations help magnify the spread of disease.

A child with mumps came to the US from, hey, I’ll be damned, England, thank you Dr. Wakefield, where, thanks to low uptake of the MMR (the second M standing for mumps) there is a mumps epidemic. The index case went to a religious camp and gave it to the other campers, who in turn went to other collections of unvaccinated people to start their own epidemic and so on. In this case there was little spread into the wider community that “might be attributable to generally high vaccination levels and little interaction between members of the affected religious community and persons in surrounding communities.”

It appears that collections of unvaccinated people may serve to magnify the ability of diseases to spread in a community. Those unvaccinated children in the alternative schools may be unlikely to keep their infections to themselves.

My million-dollar prediction? Measles will be imported into the US in a student from Mediocre Britain. That student will visit an alternative school and start an epidemic in the school. Measles will be spread from school to school and into the community and will be difficult to control.

It will occur in 2012. The Mayans, along with the other indigenous peoples in North and South America, were killed by the millions by vaccine preventable illnesses like measles, pertussis, mumps and smallpox. The real reason the Mayan calendar ends in 2012 is the end of the world will be due to the return of vaccine-preventable diseases.

As a mother, I am a passionate advocate of breastfeeding and I breastfed my four children. As a clinician, though, I need to be mindful not to counsel patients based on my personal preferences, but rather based on the scientific evidence. While breastfeeding has indisputable advantages, the medical advantages are quite small. Many current efforts to promote breastfeeding, while well meaning, overstate the benefits of breastfeeding and distorts the risks of not breastfeeding, particularly in regard to longterm benefits.

As Joan Wolf explains in an article entitled Is Breast Really Best? Risk and Total Motherhood in the National Breastfeeding Awareness Campaign:

… Medical journals are replete with contradictory conclusions about the impact of breast-feeding: for every study linking it to better health, another finds it to be irrelevant, weakly significant, or inextricably tied to other unmeasured or unmeasurable factors. While many of these investigations describe a correlation between breast-feeding and more desirable outcomes, the notion that breast-feeding itself contributes to better health is far less certain, and this is a crucial distinction that breast-feeding proponents have consistently elided. If current research is a weak justification for public health recommendations, it is all the more so for a risk-based message that generates and then profits from the anxieties of soon-to-be and new mothers…

Wolf describes the problems with many studies of breastfeeding, particularly those that focus on long term outcomes:

In breast-feeding studies, potential confounding makes it difficult to isolate the protective powers of breast milk itself or to rule out the possibility that something associated with breast-feeding is responsible for the benefits attributed to breast milk. As the number of years between breastfeeding and the measured health outcome grows, so too does the list of possibly influential factors, which means that the challenge is magnifiedwhen trying to evaluate long-term benefits of breastfeeding… Breast-feeding, in other words, cannot be distinguished from the decision to breast-feed, which, irrespective of socioeconomic status or education,could represent an orientation toward parenting that is itself likely to have a positive impact on children’s health. In instances such as this, in which the exposure (breast-feeding) and confounder (behavior) are likely to be very highly correlated, confounding is especially difficult to detect. When behavior associated with breast-feeding has the potential to explain much of the statistical advantage attributed to breast milk, the scientific claim that breast-feeding confers health benefits … needs to be reexamined.

But even studies that may be biased show limited, if any, long term benefits of breastfeeding. The World Health Organization published a comprehensive review of the evidence  in 2007, Evidence on the long-term effects of breastfeeding, by Horta et al. According to the authors:

…[T]here is some controversy on the long-term consequences of breastfeeding. Whereas some studies reported that breastfed subjects present a higher level of school achievement and performance in intelligence tests, as well as lower blood pressure, lower total cholesterol and a lower prevalence of overweight and obesity, others have failed to detect such associations.

Objectives: The primary objective of this series of systematic reviews was to assess the effects of breastfeeding on blood pressure, diabetes and related indicators, serum cholesterol, overweight and obesity, and intellectual performance.

The authors reviewed the existing scientific literature on 5 specific claims.

1. Does breastfeeding leader to lower blood pressure?

The authors reviewed two meta analyses and three studies:

According to Owen et al, the association between breastfeeding and lower blood pressure was mainly due to publication bias, and any effect of breastfeeding was modest and of limited clinical or public health relevance. In spite of not being able to exclude residual confounding and publication bias, Martin et al concluded that breastfeeding was negatively associated with blood pressure. They argued that even a small protective effect of breastfeeding would be important from a public health perspective… Three large studies were published since the last review, two of which found no association and one found a protective effect of breastfeeding.

Both meta-analyses may have been affected by publication bias… Lack of control for confounding is another methodological issue, as pointed out by Martin et al…

In summary, the present updated meta-analyses show that there are small but significant protective effects of breastfeeding on systolic and diastolic blood pressure. Publication bias is unlikely to explain this finding because a significant protective effect was observed even among the larger studies. However, residual confounding cannot be excluded because of the marked reduction in effect size after adjustment for known confounders.

2. Does breastfeeding lead to lower cholesterol levels?

[N]o significant effect was observed in children or adolescents, mean cholesterol levels among adults who were breastfed were 0.18 mmol/L (6.9 mg/dl) lower than among non-breastfed subjects… [T]he observed reduction associated with breastfeeding corresponds to about 3.2% of [the] median.

3. Does breastfeeding reduce the risk of overweight and obesity?

The evidence suggests that breastfeeding may have a small protective effect on the prevalence of obesity. In spite of the evidence of publication bias, a protective effect of breastfeeding was still observed among the larger studies (>1500 participants),.. This effect seems to be more important against obesity than against overweight.

Because the great majority of the published studies were conducted in Western Europe and North America, we are not able to assess whether this association is present in low and middle-income settings.

4. Does breastfeeding lower the risk of type 2 diabetes?

Evidence on a possible programming effect of breastfeeding on glucose metabolism is sparse. Studies assessing the risk of type-2 diabetes reported a protective effect of breastfeeding, with a pooled odds ratio of 0.63 (95% CI: 0.45–0.89) in breastfed compared to non-breastfed subjects. On the other hand, two other studies failed to report an association between HOMA index, a measure of insulin resistance, and breastfeeding duration, and a study on fasting blood glucose levels was also negative. At this stage, it is not possible to draw firm conclusions about the longterm effect of breastfeeding on the risk of type-2 diabetes and related outcomes…

5. Does breastfeeding raise the level of school achievement or intelligence?

This meta-analysis suggests that breastfeeding is associated with increased cognitive development in childhood, in studies that controlled for confounding by socioeconomic status and stimulation at home. The practical implications of a relatively small increase in the performance in developmental tests in childhood may be open to debate. However, evidence from the only three studies on school performance in late adolescence or young adulthood suggests that breastfeeding is also positively associated with educational attainment.

The issue remains of whether the association is related to the properties of breastmilk itself, or whether breastfeeding enhances the bonding between mother and child, and thus contributes to intellectual development. Although in observational studies it is not possible to disentangle these two effects, the positive results from the randomized trial carried out by Lucas et al suggest that the nutritional properties of breastmilk alone seem to have an effect.

In the case of these five longterm outcomes, the existing scientific evidence shows that breastfeeding has either no benefit or a small benefit.

Adriano Canttaneo, an pediatric epidemiologist and enthusiastic supporter of breastfeeding, writing in the Journal of Pediatrics and Child Health in 2008 in The benefits of breastfeeding or the harm of formula feeding? cautions against making sweeping and unsupported claims about breastfeeding:

… We do not need to use weak and shaky arguments to convince mammals to breastfeed. What we need is effective care to let them breastfeed as much and as long as they wish.

Breastfeeding is desirable and beneficial, and we should promote breastfeeding as much as possible. However, breastfeeding advocates should not overstate the benefits of breastfeeding or overstate the risks of formula feeding. Rather, we should do whatever we can to allow women who wish to breastfeed to start and maintain breastfeeding for as long as they would like.

The primary reason that I and others favor science-based medicine, as opposed to the alternatives, is that science works. As Carl Sagan said, “Science delivers the good.” Science has other virtues – it is transparent and self-corrective also.

Recently two unrelated news items have provided an opportunity to compare a scientific vs a pseudoscientific approach to the same problem – that of communicating to patients who are locked-in.

Locked-in describes those who suffer from an injury or neurological disease that mostly paralyzes them, so that they cannot move or communicate. One scenario that leads to a locked-in state is a brainstem stroke, where patients are paralyzed below the eyes – they can only blink and move their eyes, but nothing else. Widespread trauma can lead to a similar situation. ALS, which leads to progressive loss of motor neurons, can also result in total or near total paralysis.

Traditionally communication with patients who are locked in has been limited to blinking once for “yes” and twice for “no,” or perhaps twitching a finger to indicate the same. The advent of computers has lead to more sophisticated communication, such as selecting letters by looking at them displayed on a computer screen, the subject’s choice noted by a built-in camera; or by moving a joy-stick with one finger. Physicist Stephen Hawking has managed to write books using similar methods.

But these methods are slow and cumbersome. They are invaluable to the patient, and I don’t want to diminish their importance, but the point is that there is tremendous room for improvement. The ultimate goal, of course, would be to produce real-time natural communication through either speech or writing.

Facilitated Communication

Recently the case of Rom Houben has come to media attention. He is a man in his 40’s who has been in an apparent coma for the last 23 years following a car accident. However it was recently discovered that he is not in a coma or, more precisely, persistent vegetative state as was previously diagnosed but is locked in. Sometime during the last 23 years his brain recovered enough so that Mr. Houben regained consciousness. But he was mostly paralyzed by his injuries and had no way of indicating he was conscious to those taking care of him.

Eventually, a neurologist, Steven Laureys, a specialist in disorders of consciousness, reevaluated Mr. Houben with a PET scan and a more detailed clinical assessment and found that he appeared to be conscious, and therefore locked-in. Dr. Laureys reports that, although he was unable to move his eyes or hands, he could move his toe and they were able to communicate with him in the traditional but tedious yes/no method.

Then Linda Wouters came into the picture. She is a speech therapist who uses facilitated communication (FC), a technique of holding the hand of a client who cannot communicate and “helping” them type out words on a board or computer screen. However, FC has been thoroughly studies and discredited. It turns out that alleged communication through FC is simply the ideomotor effect (subconscious movements responsible for ouija board and dowsing movements) – the facilitator is doing all the communicating, even if they are not aware of it.

Video of Wouters writing with Houben’s hand tell the tale. In some videos Houben is not even looking at the screen, and may not even be awake. Wouters claims she is detecting minute movements in Houben’s hand who is guiding her to the letters – meanwhile she is flying across the computer screen with Houben’s hand. Her claims are implausible to the point that they should be rejected, barring rigorous and solid evidence to substantiate them, which is lacking.

The claims of FC are implausible, and the scientific evidence demonstrates strongly that FC does not work – it is a sad self-deception. The FC claims of Wouters are highly implausible, and the video evidence strongly suggests that she is doing all the communicating, not Houben.

What this means is that Wouters, with FC, has stolen Houben’s ability to communicate with the world. Far from providing a method of communication to a person who is locked-in, it has robbed that person of any hope of communicating, and has also inflicted a cruel fiction on them and their family. If what we strongly suspect about FC in this case is true, then Wouters is now communicating in Houben’s name.

That is the legacy of FC, a disproved method that has descended into the abyss of pseudoscience.

Brain Machine Interface

By contrast, there are those who are trying to use science to truly give the ability to communicate to those who have lost it through neurological disease or injury.

Current methods for communicating with the paralyzed primarily involve exploiting what little voluntary muscle activity they have (whether in the eyes, hands, or feet) in order to control a computer to produce words. However, some paralyzed patients lack even the minimal motor activity necessary to control such devices.

Another option is to use brain-wave measurements (electroencephalogram – EEG – measurements) to control a communication device. This could theoretically allow for a person with zero motor function to communicate by thoughts alone.

There has been steady progress in brain machine interfaces (BM() in the last decade. They involve using scalp surface electrodes, or electrodes implanted on or in the brain itself, to read the electrical signals from the brain. Those signals are then interpreted by a computer and sorted into one of a few distinct states. Computer software can then use those states to designate different letters, move a cursor around a screen, or operate equipment.

The key features of such systems are the number of different states that can be distinguished and the accuracy of the computer’s interpretation. So far such systems can only distinguish a small number of states (3-4), require extensive training, and have accuracies in the 60-90% range.

Like many new technologies, this is modest, and not any better than motor controlled systems (for those who have any motor control). It is often the case, however, that new technology does not have any advantage over older but more mature technology, except that it has more potential to improve in the future.

Recently another team has published the results of their research with BMI. A team led by Frank Guenther at Boston University have implanted electrodes in the speech area of the brain of a man who is locked in. These electrodes connect to radio transmitters which send signals to the external receiving device – so there is no need to have any wires going through the skin (a setup for infection). The implanted transmitters can be recharged through an external coil.
The computer that receives the signals interprets them as intended speech, and then makes sounds based upon its interpretation. The patient can then use the sounds as feedback, to fine tune their control.

They have now published their initial results. The patient has been trained to produce three distinct vowel sounds with his mind alone. This is an important proof of concept, but is very modest in practice. It is not sufficient for speech communication.

Other researchers have achieved similar results – distinguishing just a few states. So far it seems like the most practical application is moving a cursor across a screen. Up, down, left, and right are all that is needed in order to land the cursor on the desired letter or word.

Guenther and his team hope to increase the number of electrodes and therefore the precision of their system, to add more phonemes to its repertoire. They optimistically project that within 5 years their system will allow for some type of speech. We’ll see. I have learned to be skeptical of the 5 year optimistic projections of researchers – essentially they are saying that they will make impressive progress within the next funding cycle.

But that aside, this approach have obvious potential and progress is being made. Whether it is in 5 years or 20 or 50, progress is encouraging and it is likely we will cross significant functional thresholds in the future.

Or maybe, before this approach has a chance to mature, it will be supplanted by some other method that no one has thought of yet. The science will take time to work itself out.

Conclusion

The differences between FC and BMI are glaring. FC takes place all in the mind of the facilitator, who claims to have a highly developed skill to interpret movements that no one can see or detect. The claims are unbelievable, were made prior to meaningful research, and persist after they have been shown to be false by rigorous science.

BMI, on the other hand, is following a slow and steady research path with transparency. The claims that are being made are cautious and responsible. Sure, researchers are optimistic about the potential of their research – we expect that from researchers who need to be constantly on the prowl for competitive funding. But the only claims that matter are those they publish in the peer-reviewed literature, and they are dry and precise.

The bottom line is that getting the science right is what matters. Doing good science will help patients who are locked in. Pseudoscience will only exploit and further victimize them.

We owe it to them to remain dedicated to quality science as the standard of care in medicine.

Note: This is a slightly modified version of an article that was published in the magazine Skeptical Inquirer, Volume 34, No. 1, January/Februrary 2010. It is reprinted here with the kind permission of the Committee for Skeptical Inquiry.

—————–

Chiropractors, homeopaths, naturopaths, acupuncturists, and other alternative medicine practitioners constantly criticize mainstream medicine for “only treating the symptoms,” while alternative medicine allegedly treats “the underlying causes” of disease.

Nope. Not true. Exactly backwards. Think about it. When you go to a doctor with a fever, does he just treat the symptom? No, he tries to figure out what’s causing the fever and if it’s pneumonia, he identifies which microbe is responsible and gives you the right drugs to treat that particular infection. If you have abdominal pain, does the doctor just give you narcotics to treat the symptom of pain? No, he tries to figure out what’s causing the pain and if he determines you have acute appendicitis he operates to remove your appendix.

I guess what they’re trying to say is that something must have been wrong in the first place to allow the disease to develop. But they don’t have any better insight into what that something might be than scientific medicine does. All they have is wild, imaginative guesses. And they all disagree with one another. The chiropractor says if your spine is in proper alignment you can’t get sick. Acupuncturists talk about the proper flow of qi through the meridians. Energy medicine practitioners talk about disturbances in energy fields. Nutrition faddists claim that people who eat right won’t get sick. None of them can produce any evidence to support those claims. No alternative medicine has been scientifically shown to prevent disease or to cure it. If it had, it would have been incorporated into conventional medicine and would no longer be “alternative.”

Are these practitioners treating the underlying cause, or are they simply applying their one chosen tool to treat everything? Chiropractors treat every patient with chiropractic adjustments. What if a doctor used one treatment for everything? You have pneumonia? Here’s some penicillin. You have a broken leg? Here’s some penicillin. You have diabetes? Here’s some penicillin. Acupuncturists only know to stick needles in people. Homeopaths only know to give out ridiculously high dilutions that amount to nothing but water. Therapeutic touch practitioners only know to smooth out the wrinkles in imaginary energy fields. They are not trying to determine any underlying cause: they are just using one treatment indiscriminately.

How do you define “cause”? We don’t know what “causes” gravity, but we understand enough about how it works to overcome it with elevators, airplanes, and rockets to the moon. We may not know what ultimately “causes” asthma, but we know enough about the causes of airway constriction and inflammation to devise effective treatments.

Let’s take a simple example: strep throat. The symptom is throat pain. Doctors don’t just treat the pain – they do a throat culture, they determine that a strep infection is causing the pain, and they treat the infection with an antibiotic. But what caused the strep infection? The body had to host the bacteria and respond to their presence by developing symptoms; the bacteria had to be capable of multiplying in the human body. The patient had to be exposed to another person who had a strep infection, who in turn had caught it from someone else – involving a chain of social and epidemiologic causes. The bacteria had to evolve from ancestor bacteria and the human from ancestor animals. In order for the life to develop, the circumstances on the early Earth had to be propitious. Keep going. The Earth had to have formed and cooled. Stars had to have created the necessary carbon, nitrogen, and other elements that were not present in the early universe. Keep going, and you will end up saying these words from the theme song of a popular TV show: “It all started with the Big Bang.” If you really want to be picky, you could go one step further and say the real cause was whatever caused the Big Bang. (But what caused that cause? Turtles all the way down?)

So you see, “cause” involves a chain of causation and there can even be several simultaneous causes. “Cause” can mean pretty much anything you want it to. But however you look at it, doctors definitely do not “just treat symptoms.”

Philosophy has studied causation. Aristotle said everything had four causes: material, formal, efficient, and final. And he introduced complications: proper (prior) causation and accidental (chance) causation. Potential or actual, particular or generic. Reciprocal or circular causality as a relation of mutual dependence or influence of cause upon effect. The same thing as the cause of contrary effects when its presence and absence result in different outcomes. He recognized that the subject of causation was complicated.

Alternative providers are more “simple” minded. They often claim to know the one true cause of all disease. That is curious, because medical science defines several categories of causes falling under the mnemonic VINDICATE:

V – Vascular
I – Infectious/inflammatory
N – Neoplastic
D – Drugs/toxins
I – Intervention/iatrogenic
C – Congenital/developmental
A – Autoimmune
T – Trauma
E – Endocrine/metabolic.

And sometimes more than one cause is involved: a traumatic injury gets infected. Where science finds complexity, alternative medicine imagines simplicity. As H.L. Mencken said:

For every complex problem, there is an answer that is clear, simple—and wrong.

Some homeopaths claim to treat “genetic” illness, tracing its origins to 6 main genetic causes: Tuberculosis, Syphilis, Gonorrhoea, Psora (scabies), Cancer, Leprosy. Bet you didn’t know tuberculosis was genetic! Neither did I. Science classifies all these as infectious except for cancer, which is neoplastic. Homeopathy disregards science and redefines “genetic” to suit its own inscrutable purposes.

Science finds many causes for disease and sometimes more than one cause for a given disease. Pseudoscience has identified the one true cause of all disease – many times. I did an Internet search and found the following 69 one causes of all disease. This is not an exhaustive list but rather an exhausted list (I stopped when I got tired of searching).

Toxemia
Subluxations
Oxygen deficiency
Psora
“Fearful, tight and negative minds”
Obstruction of ch’i along the meridians
Refined sugar
“Fault of awareness”
Grains in the diet
False beliefs and fears
“Imbalance”
Ama due to aggravated doshas
Stress
Anger
Modern medicine
Some morbid agent, producing irritation and inflammation
Arrogance
A “non-perceivable but very real attachment to the material aspect of creation”
Inadequate nutrition
A congested colon
“All disease is a learned experience which we can un-learn.”
“All illness is in our minds,” and we can cure it with faith in God, meditation, or whatever.
Spiritual vital force and its dynamic derangement
Holding on to energy within the physical, emotional and spiritual bodies that is not in harmony with us
Impairment of movement of the bones of the skull
Bad health habits
Nerves too tense or too slack
God
Lack of life
Witchcraft
Miasms
Poor sanitation
A shock experience that catches us completely off guard
Cellular memories
An excess or insufficiency of nervous tension
Poor digestion
Weak digestive fire
Exogenous toxins
Morbid matter
An unbalanced life style
Disharmony in the equilibrium of Yin and Yang
A breakdown of the immune system
A weak “immine” system
Malnutrition
Free radicals
An imbalance of electrons in the cellular atoms
Emotion
Sin
Food abuse
Allergies
Ignorance of reality
Dis-ease on any level (physical, emotional, mental, soul or spiritual) is incorrect vibratory rate(s), patterns which are not appropriate, or blocked energy pathway(s) within or between the various levels of existence
Repletion
The blood
Morbid humors
Poisonous chemicals
Emotional trauma
“Allurement” of the mind by sense objects and its “willfulness” in gratifying these desires
Toxic metals
Cold
Blocked nerves
Our inability to adapt
Overeating
Poverty
Food acidity
Violation of natural law
Liver flukes
Breaking taboos

And my favorite: “the United KKK States of America is the root cause of all disease…”

It never seems to bother them that others have found different one true causes. In his book Voodoo Science, Bob Park describes a press conference following a meeting to discuss government funding for alternative medicine research:

Perhaps the strangest part of the press conference consisted of brief statements by individual members of the editorial review board of what they saw as the most important issues for the Office of Alternative Medicine. One insisted that the number-one health problem in the United States is magnesium deficiency; another was convinced that the expanded use of acupuncture could revolutionize medicine; and so it went around the table, with each touting his or her preferred therapy. But there was no sense of conflict or rivalry. As each spoke, the others would nod in agreement. The purpose of the OAM, I began to realize, was to demonstrate that these disparate therapies all work. It was my first glimpse of what holds alternative medicine together: there is no internal dissent in a community that feels itself besieged from the outside.

When scientists encounter two mutually exclusive claims, it bothers them. They experience cognitive dissonance and try diligently to find evidence to reject one of the hypotheses and leave a winner. They eventually reach a consensus. Alternative medicine pseudoscientists don’t seem to mind cognitive dissonance. They are content to look for evidence to support their own chosen treatment while blithely disregarding competing claims. They don’t want to look for evidence that something doesn’t work. While each claims to know the one cause of disease, they don’t seem interested in looking for the one truth.

Live and let live? Create your own reality? Truth is only relative? The same thing may be simultaneously true for me and false for you? Maybe it boils down to a mutual tolerance of delusions (OK, I’ll believe that you are Jesus if you believe that I’m Napoleon). For the cynical, follow the money: “I won’t interfere with your livelihood if you don’t interfere with mine.”

I can play the cause-finding game too. I’ve discovered the one cause of all the “one cause” theories: a deficiency of critical thinking skills combined with an overactive imagination. And, of course, a failure to test beliefs using the scientific method.

Credibility alert: the following post contains assertions and speculations by yours truly that are subject to, er, different interpretations by those who actually know what the hell they’re talking about when it comes to statistics. With hat in hand, I thank reader BKsea for calling attention to some of them. I have changed some of the wording—competently, I hope—so as not to poison the minds of less wary readers, but my original faux pas are immortalized in BKsea’s comment.

Lies, Damned Lies, and…

A few days ago my colleague, Dr. Harriet Hall, posted an article about acupuncture treatment for chronic prostatitis/chronic pelvic pain syndrome. She discussed a study that had been performed in Malaysia and reported in the American Journal of Medicine. According to the investigators,

After 10 weeks of treatment, acupuncture proved almost twice as likely as sham treatment to improve CP/CPPS symptoms. Participants receiving acupuncture were 2.4-fold more likely to experience long-term benefit than were participants receiving sham acupuncture.

The primary endpoint was to be “a 6-point decrease in NIH-CSPI total score from baseline to week 10.” At week 10, 32 of 44 subjects (73%) in the acupuncture group had experienced such a decrease, compared to 21 of 45 subjects (47%) in the sham acupuncture group. Although the authors didn’t report these statistics per se, a simple “two-proportion Z-test” (Minitab) yields the following:

I won’t repeat everything from that post; rather, I’ll try to amplify the central problem of “frequentist statistics” (the kind that we’re all used to), and of the P-value in particular. Goodman explained that it is logically impossible for frequentist tools to “both control long-term error rates and judge whether conclusions from individual experiments
[are] true.” In the first article he quoted Neyman and Pearson, the creators of the hypothesis test:

…no test based upon a theory of probability can by itself provide any valuable evidence of the truth or falsehood of a hypothesis.

But we may look at the purpose of tests from another viewpoint. Without hoping to know whether each separate hypothesis is true or false, we may search for rules to govern our behaviour with regard to them, in following which we insure that, in the long run of experience, we shall not often be wrong.

Goodman continued:

It is hard to overstate the importance of this passage. In it, Neyman and Pearson outline the price that must be paid to enjoy the purported benefits of objectivity: We must abandon our ability to measure evidence, or judge truth, in an individual experiment. In practice, this meant reporting only whether or not the results were statistically significant and acting in accordance with that verdict.

…the question is whether we can use a single number, a probability, to represent both the strength of the evidence against the null hypothesis and the frequency of false-positive error under the null hypothesis. If so, then Neyman and Pearson must have erred when they said that we could not both control long-term error rates and judge whether conclusions from individual experiments were true. But they were not wrong; it is not logically possible.

The P Value Fallacy

The idea that the P value can play both of these roles is based on a fallacy: that an event can be viewed simultaneously both from a long-run and a short-run perspective. In the long-run perspective, which is error-based and deductive, we group the observed result together with other outcomes that might have occurred in hypothetical repetitions of the experiment. In the “short run” perspective, which is evidential and inductive, we try to evaluate the meaning of the observed result from a single experiment. If we could combine these perspectives, it would mean that inductive ends (drawing scientific conclusions) could be served with purely deductive methods (objective probability calculations).

These views are not reconcilable because a given result (the short run) can legitimately be included in many different long runs…

It is hard to overstate the importance of that passage. When applied to the acupuncture study under consideration, what it means is that the observed difference between the two proportions, 26%, is only one among many “outcomes that might have occurred in hypothetical repetitions of the experiment.” Look at the Minitab line above that reads “95% CI for difference: (0.0642303, 0.456982).” CI stands for Confidence Interval: in the words of BKsea, “in 95% of repetitions, the 95% CI (which would be different each time) would contain the true value.” We don’t know where, in the 95% CI generated by this trial (between 6.4% and 45.7%), the true difference lies; we don’t even know that the true difference lies within that interval at all (if we did, it would be a 100% Confidence Interval)! Put a different way, there is little reason to believe that the “center” of the Confidence Interval generated by this study, 26%, is the true proportion difference. 26% is merely a result that “can legitimately be included in many different long runs…”

Hence, the P-value fallacy. It is that point—26%—that is used to calculate the P-value, with no basis other than its being as good an estimate, in a sense, as any: it was observed here, so it can’t be impossible; when looked at from the point of view of whatever the true proportion difference is, it has a 95% chance of being within two standard deviations of that value, as do all other possible outcomes (which is why we can say with ‘95% confidence’ that the true proportion is within two standard deviations of 26%). You can see that the CI is a better way to report the statistic based on the data, because it doesn’t “privilege” any point within it (even if many people don’t know that), but the CI will also steer us away from being wrong only in “the long run of experience.” CIs, of course, will be different for each observed outcome. The P-value should not be used at all.

Now let’s look at a graph from the acupuncture report:

Hmmm. I dunno about you, but at first glance what I see are two curves that are pretty similar. They differ “significantly” at only two of the six observation times: week 10 and week 34. Why would there be a difference at 10 weeks (when the treatments ended), no difference at weeks 14 and 22, and then suddenly a difference again? Is it plausible that the delayed reappearance of the difference is a treatment effect? The “error bars” don’t even represent what you’re used to seeing: the 95% CI. Here they represent one standard deviation, not two, and thus only about a 68% CI. Not very convincing, eh?

OK, I’m gonna give this report a little benefit of the doubt. The graph shown here is of mean scores for each group at each time (lower scores are better). That is different from the question of how many subjects benefited in each group, because there could have been a few in the sham group who did especially well and a few in the ‘true’ group who did especially poorly. It is bothersome, though, that this is the only graph in the report, and that the raw data were not reported. Do you find it odd that the number of ‘responders’ in each group diminished over time, even as the mean scores continued to improve?

Just for fun, let’s see what we get if we use the Bayes Factor instead of the P-value as a measure of evidence in this trial. Now we’ll go back to the primary endpoint, not the mean scores. Look at Goodman’s second article:

Minimum Bayes factor = e^-Z²/2

At 10 weeks, according to our statistics package, Z = 2.60. Thus the Bayes factor is 0.034, which in Bayes reasoning is “moderate to strong” evidence against the null hypothesis. Not bad, but hardly the “P = 0.009″ that we have been raised on and most of us still cling to. The Bayes factor, of course, is used together with the Prior Probability to arrive at a posterior probability. If you look on p. 1008 of Goodman’s second paper, you’ll see that as strong as this evidence appears at first glance, it would take a prior probability estimate of the acupuncture hypothesis being true of close to 50% to result in a posterior probability (of the null being true) of 5%, our good-ol’ P-value benchmark. Some might be willing to give it that much; I’m not.

Now for the Hard Part

This has been a slog and I’m sure there are only 2-3 people reading at this point. Nevertheless, here’s a plug for previous discussions of topics that came up in the comments following Harriet’s piece about this study:

Science, Reason, Ethics, and Modern Medicine, Part 5: Penultimate Words

Science, Reason, Ethics, and Modern Medicine, Part 4: is “CAM” the only Alternative?

Science, Reason, Ethics, and Modern Medicine, Part 3

Science, Reason, Ethics, and Modern Medicine, Part 2: the Tortured Logic of David Katz

Science, Reason, Ethics, and Modern Medicine Part 1

A while back I wrote about rethinking how we screen for breast cancer using mammography. Basically, the USPSTF, an independent panel of physicians and health experts that makes nonbinding recommendations for the government on various health issues, reevaluated the evidence for routine screening mammography and concluded that for women at normal risk for breast cancer, mammography before age 50 should not be recommended routinely and should be ordered on an individualized basis, and that routine formalized breast self-examination (BSE) should also not be routinely recommended. In addition, for women over 50, it was recommended that they undergo mammography every other year, rather than every year. These recommendations were based on a review of the literature, including newer studies.

To say that these new recommendations caused a firestorm in the breast cancer world is an understatement. The USPSTF was accused of misogyny; opponents of health care reform leapt on them as evidence that President Obama really is preparing “death panels”; and HHS secretary Kathleen Sebelius couldn’t run away from the guidelines fast enough. Meanwhile, a society I belong to (the American Society of Breast Surgeons) issued a press release accusing the USPSTF of sending us back to the “pre-mammography” days when, presumably women only found breast cancer after it had grown to huge size (just like Europe and Canada, I guess, given that the recommendations for screening there closely mirrors those recommended by the USPSTF). Meanwhile, in the most blatant example of protecting its turf I’ve seen in a very long time, the American College of Radiology went full mental jacket with a press release that was as biased as it was insulting. Meanwhile some physicians even likened the recommendations to going back to being like Africa, Southeast Asia and China as far as breast screening goes in that he actually speculated that he’d now become very busy treating advanced, neglected breast cancers. Unfortunately, as Val pointed out, the communication of the USPSTF guidelines to the public was almost a perfect case study in how not to do it. Even though the science was in general sound and the USPSTF recommendations were in essence close to identical to what other industrialized nations do, they were communicated in just such a way as to produce maximum misunderstanding and misuse for political purposes.

Despite all the hysterical and in some cases disingenuous attacks on the new guidelines, there is one criticism that actually resonates with me because I work at a cancer center in a very urban environment with a large population of African-American women. Last week I heard on NPR this story:

Many African-American women don’t fit the profile of the average American woman who gets breast cancer. For them, putting off the first mammogram until 50 — as recommended by a government task force — could put their life in danger.

“One size doesn’t fit all,” says Lovell Jones, director of the Center for Research on Minority health at Houston’s M.D. Anderson Cancer Center. Jones says the guidelines recently put out by the U.S. Preventive Services Task Force covered a broad segment of American women based on the data available. “Unfortunately,” he says, “the data on African-Americans, Hispanics and to some extent Asian-Americans is limited.”

So while the recommendations may be appropriate for the general population, he says, it could have a deleterious affect on African-American women who appear to have a higher risk of developing very deadly breast cancers at early in life.

And this is actually true. Some of the studies used to develop the latest mammographic guidelines were performed in Scandanavian countries, and in the others arguably African-American women were underrepresented. As the article points out:

When you look at the death statistics for breast cancer in African-American women and compare them to white women, it’s stunning. Beginning in their 20s, into their 50s, black women are twice as likely to die of breast cancer as white women who have breast cancer. In older black women, cases of breast cancer decline, but the high death rates persist.

Overall, breast cancer deaths have been declining for nearly a decade (by 2 percent annually), yet deaths of African-American women have been dropping at a much slower pace. In 2009, an estimated 40,170 women will die from breast cancer. Nearly 6,000 will be African-American women.

Why this disparity exists is unclear. One potential reason is that, for whatever reason, African-American women tend to develop a more aggressive form of breast cancer known as “triple negative” cancer. What triple negative means is that the tumor is estrogen receptor negative [ER(-)], progesterone receptor negative [PR(-)], and HER2/neu negative [HER2/neu(-)]. The lack of estrogen receptor means that these tumors don’t respond to antiestrogen drugs, while the lack of HER2/neu means that they don’t respond to Herceptin. In other words, there are no targeted therapies for these tumors, only cytotoxic chemotherapy or nothing.

More importantly, there is something about the biology of these tumors that makes them more aggressive. They may respond well initially to chemotherapy but they tend to relapse rapidly and kill quickly. This subtype of tumor generally makes up around 15% of cancers among women who are not black, but among African Americans it makes up nearly 40% of tumors. This is a striking difference, and five year survival for women with triple negative cancer is considerably lower than for other types of breast cancer, particularly for young, premenopausal African-American women.

With that background, it’s not unreasonable to ask what “normal” risk for breast cancer is for purposes of recommending a program of screening mammography. On the one hand, if young African-American women are at a higher risk for breast cancer, then beginning their screening at an earlier age makes sense because it is the lower risk of breast cancer in women in their 40s that led the USPSTF to conclude that the risk-benefit ratio of mammography was less favorable in this age range. On the other hand, the more aggressive nature of breast cancer in young, premenopausal African-American women means that length bias becomes a consideration. Basically, length bias means that mammographic screening tends preferentially to pick up slower-growing, more indolent tumors. Faster-growing, more aggressive tumors tend to “pop up” between screening intervals. So, even if screening were started earlier for African-American women, it’s not clear that the benefits would be as dramatic as we might hope. Indeed, the NPR story alludes to this:

Sheppard even wonders if the old guideline of routine screening every year beginning at age 40 is good enough. “The tumors are growing fast and the intervals that we prescribe may not work,” she says. “How can we have better diagnostic tools, better screening tools that can capture the women that aren’t the average woman?”

A blogger going by the pseudonym of Isis the Scientist brought up this very issue the other day has a point when she wonders:

The other thing I wonder about is the effect these recommendations will have on the perception of health care equity. A black woman is more likely to develop aggressive cancer than a white woman before age 50, yet the USPSTF has recommended not to actively screen women less than. I wonder how this will be interpreted by that community? Black women experience a distrust of scientists performing clinical trials (second reference here), operate within a healthcare that is not always sensitive to their needs, and use mammography as a resource less frequently than white women. Will these new recommendations foster feelings of distrust and reinforce the notion that the current health care system does not adequately meet their needs?

There is a legitimate concern that the USPSTF guidelines may not be a good fit to African-American women because not only do they tend to have more aggressive disease at a younger age but they have been underrepresented in many of the large screening trials that have been used to formulate the recommended mammography guidelines. For that reason, upon further reflection I don’t think that the USPSTF guidelines should be used to determine how and when African-American women should undergo screening, as I consider them to be at a high enough risk that screening beginning at 40 makes sense.

However, as much as she did raise a valid point when she questioned whether the current mammography guidelines should apply to African-American women, still I must remonstrate with Isis and point out that the article by Nicholas Kristof that she cited in support of her speculations is dubious at best and a load of grade-A woo at worst. For example, Kristof states:

Dr. Philip Landrigan, the chairman of the department of preventive medicine at Mount Sinai, said that the risk that a 50-year-old white woman will develop breast cancer has soared to 12 percent today, from 1 percent in 1975. (Some of that is probably a result of better detection.)

What’s very important to realize is that 12% of women do not get invasive breast cancer as compared to 1% in the past. Moreover, “some of that” is not “probably” a result of better detection. Most of it is almost certainly a result of better detection of earlier breast cancer, including pre-invasive lesions like ductal carcinoma in situ (DCIS), through widespread mammography screening programs. Indeed, as this report by the American Cancer Society shows, the incidence of invasive breast cancer per 100,000 women is not increasing nearly that fast. In fact, it’s not increasing at all. On the contrary, since 2002 breast cancer incidence has actually declined, very likely due to the massive decrease in hormone replacement therapy use in the wake of the 2002 report from the Women’s Health Initiative showing that HRT doesn’t decrease cardiovascular risk but does increase the risk of breast cancer. Figure 1 in particular shows this trend, while Figure 2 shows what’s really driving the apparent increase in breast cancer diagnoses, a massive increase in the incidence of preinvasive DCIS.

We’ve known for quite some time that what’s driving this increase is nearly all mammographic screening; indeed, the article speculates that we may have finally reached the plateau in the increase of DCIS cases with the widespread use of mammographic screening over the last 20 years. That 12% figure is not just invasive cancer; it includes DCIS. While invasive cancer diagnoses are more or less stable, diagnoses of DCIS skyrocketed due to mammography. Indeed, this dovetails nicely with my earlier discussions of overdiagnosis due to mammography, because this is exactly what I’m talking about. Mammography picks up early cancers that may or may not ever threaten the life of the woman; that’s what overdiagnosis is. Moreover, overdiagnosis leads to overtreatment, as we don’t have a good handle on what percentage of DCIS lesions progress to life-threatening breast cancers if left alone. So we treat them all with surgery, nearly all of them with radiation, and most of them with Tamoxifen after surgery and radiation.

Unfortunately, in the article Kristof takes a somewhat reasonable suspicion and runs right off the dock with it into woo land, and Isis appears not to have been skeptical enough about his claims, given that she then used Kristof’s article as the basis for speculation that maybe African-American women, tending to be of lower socioeconomic status than Caucasian women, are exposed to more toxic chemicals and endocrine disruptors. Unfortunately, as Peter Lipson characterized his article, Kristof has clearly fallen for the “one true cause” fallacy so beloved of practitioners of woo, labeling endocrine disruptors such as BPA as a major environmental cause of the apparent increase in breast cancer diagnoses. Don’t get me wrong, there may well be something there in that BPA and endocrine disruptors may contribute to breast cancer, but almost certainly not to the extent that Kristof claims, even given the evidence he cites.

In addition, if there’s one thing about breast cancer, it’s that no single environmental exposure has been found to be strongly correlated with it; most of the correlations other than family history and exposure to hormone replacement therapy, including both positive and negative correlations, have been in general pretty weak. Indeed, I was recently peripherally involved in an effort to design a project to study environmental influences in breast cancer, and there are amazingly few validated environmental factors that increase the risk of breast cancer. Also, timing is very important; it may well be that it is exposure to these factors in adolescence or childhood in a “window” of susceptibility, not in adulthood, which is when they are normally studied. Right now, that’s where the current research efforts seem to be focused. In addition, breast density, which is primarily genetically determined, is a known risk factor for breast cancer as well, and investigators are actually planning to study that at our institution. All in all, it’s a hideously complicated business combining genetic and environmental factors that I am only beginning to wrap my brain around, while Kristof’s article was simplistic and alarmist in the extreme. For better information, I recommend a report from the Endocrine Society for the more sober, balanced perspective, and a report from the Breast Cancer Fund for arguments more explicitly in favor of a link. More information, including the chemical industry’s viewpoint (if you’re interested), can be found here.

I firmly believe that the recommendations for how we screen for breast cancer were overdue for an overhaul. Badly. However, the USPSTF guidelines may have gone too far too fast, at least for public consumption in light of the years of urging by the government and private advocacy organizations for all women over 40 to be screened, recommendations that say that mammography before age 50 may not be particularly beneficial were a hard pill to swallow. As I think about it more, though, one big flaw in the guidelines is that there was little consideration of how changing screening recommendations would impact special populations that may be at higher risk, such young African-American women. Worst of all, the USPSTF recommendations are an example of some of the astoundingly worst science communication I’ve seen in a long time. No groundwork was laid to prepare the public; the guidelines were just announced; and the spokespeople for the USPSTF looked like deer in the headlights when they showed up in the media to defend the guidelines. Specialty groups protecting their interests such as the American Radiologial Society and its President Dr. Kopans ate them for lunch and then laughed at their discomfiture. Meanwhile high ranking government officials couldn’t distance themselves fast enough, and lawmakers and ideologues had a field day playing politics with the guidelines.

In the end, while I still think that the new guidelines are reasonable for most non-black women, after thinking about it I doubt that they should be applied to African Americans. Finally, I’m now convinced more than ever that screening will only have limited effects in decreasing mortality from breast cancer, regardless of the test used, as long as we have so poor an understanding of the aspects of breast cancer biology that govern which early cancerous lesions will progress, which will not, and which will regress. Until we do, if there were developed a test to replace mammography, the same problems of overdiagnosis and overtreatment would remain. More than ever, we need to develop an understanding of the biology of breast cancer sufficiently advanced that it permits us to develop imaging tools and biological markers that can differentiate breast cancers that will progress and those that are not going to threaten the life of the woman. At the very least we need better indicators of risk. Until we have these things, screening will remain a highly imperfect tool that doesn’t save as many lives as it has the potential to.

ADDITIONAL READING ON MAMMOGRAPHY, BREAST CANCER, AND CANCER SCREENING:

1. The early detection of cancer and improved survival: More complicated than most people think
2. Early detection of cancer, part 2: Breast cancer and MRI
3. Do over one in five breast cancers detected by mammography alone really spontaneously regress?
4. PSA – To Screen or Not to Screen
5. Are one in three breast cancers really overdiagnosed and overtreated?
6. The cancer screening kerfuffle erupts again: “Rethinking” screening for breast and prostate cancer
7. The USPSTF recommendations for breast cancer screening: Not the final word
8. The Mammogram Post-Mortem

It’s been about a year and a half since I’ve written about this topic; so I thought I’d better update the disclaimer that I wrote at the beginning:

Before I start into the meat of this post, I feel the need to emphasize, as strongly as I can, four things:

1. I do not receive any funding from the telecommunications industry in general, or wireless phone companies in particular. None at all. In other words, I’m not in the pocket of “big mobile” any more than I am in the pocket of big pharma.
2. I don’t own any stock in telecommunications companies, other than as parts of mutual funds in which my retirement funds are invested that purchase shares in many, many different companies, some of which may or may not be telecommunications companies.
3. None of my friends or family work for cell phone companies.
4. I don’t have a dog in this hunt. I really don’t.

There. That’s better. Hopefully that will, as it did last time, serve as a shield against the “shill” argument, which is among the frequent accusations I hear whenever I venture into this particular topic area. So, as I did back in 2008, I just thought I’d clear that up right away in order (hopefully) to preempt any similar comments after this post. Unfortunately, as I have known for a long time, I’m sure someone will probably show his or her lack of reading comprehension and post one of those very criticisms of me. It’s almost inevitable, either here or elsewhere. Posting such disclaimers never seems to work against the “pharma shill” gambit when I write about vaccines or dubious cancer cures. Even so, even after nearly ten years involved in skepticism and promoting science-based medicine, hope still springs eternal.

There are two reasons that I think the issue of mobile phones and cancer needs an update on our blog: First, it has been a year and a half since I last wrote about it. At that time I castigated Dr. Ronald B. Herberman, who at that time was director of the University of Pittsburgh Cancer Institute for what I viewed as fear mongering over cell phones and cancer based on at best flimsy evidence. Second, there have been two fairly high profile studies looking at whether there is a link between mobile phone use and cancer. One of these our fearless leader Steve Novella has already discussed, but there was another one that he didn’t see because it didn’t get quite as much publicity, possibly because the corresponding author is based in Korea. I will take this opportunity to discuss them both.

Over the last several years, as cell phones have become not only ubiquitous but have morphed into in essence pocket computers with Internet connections, there has developed a cottage industry of cell phone “shields” that allegedly protect people from horrible microwaves emitted by cell phones that supposedly cause cancer. These shields, of course, do nothing of the sort; objective tests of many of them show that they don’t even do a good job of blocking electromagnetic radiation emitted by cell phones. However, the data looking at the question of whether cell phones can cause some form of cancer has been mixed at best and consistent with no detectable association or possibly a very tiny association that just rises above the background noise, but even that is arguable.

One reason we have a lot of doubts over whether cell phone radiation can actually cause cancer goes back to a longtime focus of this blog and one reason why we emphasize science-based medicine rather than evidence-based medicine, namely scientific plausibility. From a biological standpoint, a strong link between cell phone use and brain cancer (or any other cancer) is not very plausible at all; in fact, it’s highly implausible. Cell phones do not emit ionizing radiation; they emit electromagnetic radiation in the microwave spectrum whose energy is far too low to cause the DNA damage that leads to mutations that lead to cancer. True, it is possible that perhaps heating effects might contribute somehow to cancer, but most cell phones, at least ones manufactured in the last decade or so, are low power radio transmitters. It is also possible that there is an as yet undiscovered biological mechanism by which low power radio waves can cause cancer, perhaps epigenetic or other, but the evidence there is very weak to nonexistent as well. Worse, as has been pointed out many times, epidemiological evidence for people who have used cell phones heavily for more than 10 years is sparse.

Moreover, it’s not possible to study the issue by randomized studies, because it is impractical to the point of being virtually impossible to randomize people into groups that do and do not use cell phones given how essential cell phones have become to most people in industrialized nations and then to follow them for the 20 years or so that it would take to identify a link. That leaves retrospective data, with all the perils and pitfalls inherent in retrospective studies. One potential approach to such a study is to formulate a simple hypothesis. If mobile phone use causes cancer, then after the widespread introduction of cell phones into a population there should be a detectable significant increase in the incidence of tumors that could potentially be due to cell phone use, particularly 10 to 20 years later. That is the hypothesis that the investigators who published recent Danish study (Deltour et al, Time Trends in Brain Tumor Incidence Rates in Denmark, Finland, Norway, and Sweden, 1974–2003) decided to examine. It is a good population to examine this question in because the populations of these countries is homogeneous, cell phone use became widespread earlier than it did in the U.S., and these countries have nationalized health systems that allow centralized collection of cancer data in national cancer registries.

The basic design of the study was as follows. Investigators examined the cancer registries of these nations for the first incidence of brain tumors in patients aged 20-79, noting this background:

Previous investigations in Denmark, Finland, Norway, and Sweden found that the incidence of glioma was relatively stable from 1983 to 1998 ( 7 ) and that the incidence of meningioma increased from 1968 to 1997, more so for women than for men (8). Time trends in brain tumor incidence after 1998 are likely to be relevant for evaluating possible associations with respect to radio frequency exposure from mobile phones after 5 – 10 years of exposure. We investigated time trends in brain tumor incidence rates in these four Nordic countries to evaluate whether trends in the incidence of brain tumor changed in Denmark, Finland, Norway, and Sweden from 1998 to 2003.

This information is important to know, because if brain tumor incidence were rising before the widespread use of cell phones, then to find a correlation that might indicate causation, there would have to be a more rapid increase in brain cancer, starting with an appropriate lag time after the use of cell phones became so prevalent, likely at least five to ten years. This is very much like the evidence for an epidemiological link between smoking tobacco and lung cancer, except that for the tobacco-lung cancer link there was a 20-30 year lag between the introduction of inexpensive, mass-marketed cigarettes and the increase in lung cancer incidence. In any case, the resultant population examined in this study was, in essence, the entire populations of these four countries, where 59,984 brain tumor cases that were diagnosed from 1974 to 2003 among 16 million adults aged 20 –79 years. They say that a picture is worth a thousand words; so here is Figure 1 from the paper:

Panels A and B present incidence rates for gliomas and meningiomas, respectively, in men; panels C and D present incidence rates for gliomas and meningiomas, respectively, in women. Circles indicate rates for those aged 20 – 39 years, squares indicate rates for those aged 40–59 years, triangles indicate rates for those aged 60–79 years, and a solid line indicates the regression curve. As described in the paper:

During this time, the incidence rate of cancers known as gliomas increased gradually by 0.5% per year among men and by 0.2% per year among women.

For cancers known as meningioma, the incidence rate increased by 0.8% among men and, after the early 1990’s, by 3.8% among women.

This more rapid change for women was driven, the researchers say, by the 60-79 year age group.

In other words, there was a slow rate of increase in these tumors that did not change in the 1998-2003 cohort. The reasons for this slow rate of increase are unclear, but because it began before the widespread insinuation of mobile phones into the population it is almost certainly not due to mobile phone radiation. One possibility that could have contributed to this is the increasing use and sophistication of imaging technology like CT and MRI, which, as the authors pointed out in the introduction, can lead to an increased apparent incidence without any changes in etiological factors through the detection of asymptomatic meningioma, for example. (Sound familiar?) In any case, there was no detectable evidence of an uptick in the incidence of these brain tumors after 1998. The lack of evidence for a change in the rate of increase of these tumors is consistent with three conclusions. Either there is no link between cell phone use and these brain tumors; the “lag time” for such an effect is greater than 5-10 years; or the effect is too small to be detected in an overall population level. One area where I will disagree with our fearless leader (somewhat) is that I don’t consider this study to be weak evidence. It is, in fact, strong evidence that, if a link between cell phone use and brain cancer exists, it is almost certainly weak and small. Add to that the biological implausibility of a link, given our current knowledge about cancer, and I remain less than impressed with the claims that cell phones cause brain cancer. I would be happy to change my mind if new evidence, either in the form of a biological mechanism being discovered that could explain how long term exposure to low energy radio signals could cause cancer or epidemiological evidence showing a clear association between cell phone use and cancer (preferably both), came to light.

Unfortunately, the second study does not qualify as either form of evidence. (How’s that for a segue?) In fact, from my perspective, it is one of the best examples of how meta-analyses can be tortured to find tenuous correlations where none probably exist. The study, which appeared in the November 20 issue of the Journal of Clinical Oncology, apparently slipped by my notice when it first appeared as an online publication on October 13. In any case, the study (Myung et al, Mobile Phone Use and Risk of Tumors: A Meta-Analysis) is, as the title says, a meta-analysis, meaning it’s a formal way of combining multiple studies that may or may not have statistically significant results on their own, that may or may not have found a correlation, and trying to see if the weight of the evidence suggests a correlation between mobile phone use and cancer. In this case, 465 articles were winnowed down to 22 articles using specific selection criteria, which were:

We included epidemiologic studies that met all of the following criteria: case-control study (to date, no randomized controlled trials and only one retrospective cohort study published in four different articles have been reported; therefore, we included only case-control studies in this study); investigated the associations between the use of mobile phones, cellular phones, or cordless phones and malignant or benign tumors; reported outcome measures with adjusted odds ratios and 95% CIs, crude odds ratios and 95% CIs, or values in cells of a 2(1)2 table (from which odds ratios could be calculated). If data were duplicated or shared in more than one study, the first published or more comprehensive study was included in the analysis.

I’ll cut to the chase right now. This meta-analysis does not–I repeat, does not–show any correlation between cell phone use and cancer, at least not in the overall results. The authors even say so:

As shown in Figure 2, the overall use of mobile phones (use v never or rarely use) was not significantly associated with the risk of tumors in a random-effects model meta-analysis of all 23 case-control studies (odds ratio 0.98; 95% CI, 0.89 to 1.07).

Let me repeat that again. The overall meta-analysis did not find any significant association between mobile phone use and tumors.

Of course, whenever an investigator does a meta-analysis and finds a result like this, he can never rest there. He has to slice and dice the data to try to find a group for which there is a correlation. There’s nothing wrong with that in and of itself. In prospective studies, post hoc subgroup analyses that were not planned in the original protocol right from the beginning are generally frowned upon because they have a high tendency to find associations that are usually spurious. I tend to look at it as being a lot like making multiple comparisons but not controlling for them. By doing a study to look at one population and a set of outcome measures but then, upon finding a negative result, going back and doing subgroup analyses, one is basically doing multiple comparisons without correcting for multiple comparisons. Anyone who’s been a regular reader of this blog should know that if one doesn’t correct for multiple comparisons, the more comparisons the higher the likelihood of finding one or more false positives.

Even so, it’s not necessarily scientifically dubious to do subgroup analyses if one looks at it as a hypothesis-generating exercise, rather than any actual conclusions. Correlations that are found may or may not be “real,” but strong correlations may be worth further investigation. What bothers me about this study is not so much that it did subgroup analysis on the populations studied, but rather how it did a sort of dubious “subgroup analysis” on the actual studies themselves:

However, a significant positive association (ie, harmful effect) was observed in eight studies ^{7,12,14-16,18,23} and one study by another group¹⁰) using blinding (odds ratio1.17; 95% CI, 1.02 to 1.36), whereas a significant negative association (ie, protective effect) was observed in 15 studies (nine INTERPHONE-related studies^{17,20-22,24-28} and six studies by other groups^8,9,11,13,19) not using blinding (odds ratio 0.85; 95% CI, 0.80 to 0.91). No publication bias was observed in the selected studies (Begg’s funnel plot was symmetric; Egger’s test, P for bias .21; Fig 3)

[...]

Subgroup meta-analyses by methodologic quality of study revealed a significant positive association in the high-quality studies (odds ratio 1.09; 95% CI, 1.01 to 1.18), whereas a negative association was observed in the low-quality studies. In subgroup meta-analyses by malignancy of tumor, no significant association was observed for malignant tumors. However, a significant negative association was observed for benign tumors. Neither the use of analog phones nor the use of digital phones was associated with the risk of tumors.

First, note that these odds ratios are barely statistically significant, ranging from 1.02 to 1.36 and 1.01 to 1.18; in other words, the 95% confidence interval barely misses overlapping with 1.0. More importantly, I was very puzzled by the way that they chose to differentiate “high” quality studies from “low” quality studies. Basically, although it is only one of the eight criteria used in the Newcastle-Ottawa Scale (NOS) for Assessing the Quality of Case-Control Studies to evaluate study quality, for some reason the Myung et al decided to focus primarily on whether the studies were blinded or not, specifically whether the status of patient cases and controls was blinded at interview (blinded or not blinded/no description). This struck me as most curious. So studies that were blinded showed a positive association between cell phone use and brain tumors upon meta-analysis of just their results. The authors also report that their subgroup analysis demonstrated that studies of higher methodological quality (greater than or equal 7 points) also showed a positive association between cell phone use and cancer for just these studies.

So does it mean anything that lumping the studies together that are of “high quality” produces a positive result where the low quality studies don’t? Normally, I’d think that it might. After all, the studies showing the positive result are considered to be methodologically rigorous, although it should be noted that several of them individually didn’t find a significant association. In this case, I doubt it means much of anything, and here’s why. Seven of the eight “high quality” studies were all by a single group of researchers, Dr. Lennart Hardell’s group in Sweden. Whenever one group of researchers keeps finding a result that no other group seems able to replicate or that otherwise disagrees with what everyone else is finding, that’s a huge red flag for me. Remove those studies, and even the wisp of a hint of a shadow of the association between cell phone use and cancer found in this study disappears. I’d have a lot more confidence in this seeming association in “high quality” studies if the association didn’t depend upon a single researcher and if this researcher was not also known for being an expert witness in lawsuits against mobile phone companies. Don’t get me wrong; these studies could be correct, but replication is one of the checks on research in science-based medicine. If other groups can’t replicate Dr. Hardell’s work, I wonder why. Is it something about the methodology? Is it something specific about Swedes? Is it something about the population? Are there confounders unique to his work that aren’t operative elsewhere? Until I see other researchers independent of Dr. Hardell and using a variety of different analyses find the same results, I don’t have a lot of confidence in them. Even the authors acknowledge that a weakness of their study is that “we did not explore potential confounding factors in the studies by Hardell et al that reported positive results not found by other study groups.”

There was another passage in this paper that I also found most curious:

We feel the need to mention the funding sources for each research group because it is possible that these may have influenced the respective study designs and results. According to the acknowledgments that appeared in the publications, the Hardell et al group was supported by grants from the Swedish Work Environment Fund, Orebro Cancer Fund, Orebro University Hospital Cancer Fund, and so on. Most of the INTERPHONE-related studies were mainly supported by the Quality of Life and Management of Living Resources program of the European Union and the International Union Against Cancer; the International Union Against Cancer received funds for those studies from the Mobile Manufacturers Forum and the Global System for Mobile Communication Association.

I don’t know about you, but I’ve never seen a passage like this in a research paper or meta-analysis before. It stands out like the proverbial sore thumb, and I can only think that it was placed there to try to cast aspersions, representing Dr. Hardell as the exemplar of Truth, Justice, and Science while painting the studies that failed to find an association between cell phone use and cancer as hopelessly biased, the product of Big Cellular. At least that’s how it came across to me. It struck me as gratuitous.

So where do we stand now? My interpretation of the evidence thus far is that we can say with some confidence that there is no short term risk of brain cancer from cell phone use. However, after more than ten years the evidence is less clear but trends towards either no detectable risk or a very small risk that barely rises above the noise. It’s possible that there may well be a risk; it’s possible that there is hitherto undiscovered biology that provides a mechanism by which non-ionizing radiation like the radio waves from cell phones could over time induce cancer beyond ten years. Because more and more people are using cell phones over longer and longer periods of time, it’s worth studying this issue. If there is an increased risk, it may be possible to mitigate that risk by using headsets or through the design of phones that use less energy. Mobile phone technology is a relatively new technology, though, and has only been widely available since the mid-1990s. In the U.S., it didn’t become truly ubiquitous until the early part of this decade. Consequently, there hasn’t been enough lag time for us to be truly confident of studies showing little or no risk. On the other hand, it is reassuring that early studies are pretty resoundingly negative and that there really is no good biological mechanism that we have been able to find by which cell phone radiation could cause cancer. In the next 5-10 years, more studies will be done, and, over that time, I expect evidence will answer the question one way or the other.

In the meantime, it’s useful to keep things in perspective. Each and every year, there are approximately 40,000 to 45,000 fatalities due to auto collisions. Getting in a car and driving to work every day is among the most dangerous things the average person does. Yet we accept this risk because automobiles are such an incredibly useful tool in modern life; indeed, they are indispensable to most people. Cell phones are clearly in that category as well; so even if cell phones are ultimately found to increase the risk of brain cancer by, for example, 10-20% after 10-20 years, it would most likely be a risk that most people would end up living with in order to be able to use these incredibly useful devices. My personal reading of the data is that there probably isn’t a significant risk of brain tumors due to the use of cell phones, but I am not sufficiently certain to make any blanket statements. I, like everyone else, will have to wait for the evidence to settle things one way or the other.

REFERENCES:

Myung, S., Ju, W., McDonnell, D., Lee, Y., Kazinets, G., Cheng, C., & Moskowitz, J. (2009). Mobile Phone Use and Risk of Tumors: A Meta-Analysis Journal of Clinical Oncology, 27 (33), 5565-5572 DOI: 10.1200/JCO.2008.21.6366

Deltour, I., Johansen, C., Auvinen, A., Feychting, M., Klaeboe, L., & Schuz, J. (2009). Time Trends in Brain Tumor Incidence Rates in Denmark, Finland, Norway, and Sweden, 1974-2003 JNCI Journal of the National Cancer Institute DOI: 10.1093/jnci/djp415