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Gary Taubes, in an article for the New York Times Magazine[0], talks about the science of epidemiology. He starts off by discussing horomone replacement therapy for women. The idea behind HRT was to ‘cure’ aging. In hindsight, it seems obvious that doing so is futile, especially given the harmful effects that we have more recently discovered. Taubes seems to think that there’s some sort of problem with the initial medical recommendations towards HRT, since we (clearly) didn’t fully understand the effects at the time and probably don’t now, but his bigger problem is that women were advised to continue taking HRT after menopause. He says that this is because of how new scientific discoveries are announced: when the media hears about some new medical discovery, despite a lack of review, they push it right out to the public as medical advice. Physicians then proceed to recommend the new treatment, the FDA doesn’t stop them because in this case HRT was already thought of as a good idea, and it was just being used differently. When more people took it doctors started to realize that it caused certain side effects, so then no one took it, until a paper was published saying that the benefits outweigh the risks – something that is true in most of medicine: when ER doctors need an emergency blood transfusion and don’t know the patient’s blood type, they go for O negative: the benefits of getting blood quickly outweigh the chance of wasting some more universal blood, when doctors have a patient in immediate danger, they medicate rather than bothering to ask the patient about potential allergies, because the benefits of saving the patient outweigh the rather slim risk of allergy, and an allergic reaction can be treated if it occurs. Even medications like Tylenol have a certain risk – but it is such a good painkiller that we use it, even the effective dose is so near to the toxic dose. These are considered acceptable risks, and rightly so, by modern medicine, so the current state of the medical opinion of HRT is at least internally consistent, however the author’s complaint is rather with the changing state of medical consensus: the classic “you were wrong before, how can we know you’re right now”. I think we’ve discussed this in the past in this class, so we’ll move on.

The author goes on to discuss the merits of preventative medicine. He makes the point of observational studies vs. controlled experiments, and how an observational conclusion can very quickly be spun into medical fact, and it’s not until the conclusion of a controlled experiment that we learn that there is no correlation. This explains the flip from a potentially good treatment back to a treatment with no positive gain with the discovery that the previously observed correlation is not a sign of any actual causation. Epidemiologists who seek to find similar links between behaviors or treatments and undesired side effects. He goes on to complain that these controlled experiments are rarely performed because they are rarely funded and lists a few cases where they were unsuccessfully. I have a few problems with that.

For one, no one is making Mr. Taubes take every supplement and perform every practice which is correlated with better health. The fact is that even strong correlations can exist between better health and slightly detrimental practices, if those practices are thought to be healthy, due to a combination of the ‘health-nut’ population and the chronically unhealthy population. The health-nuts might follow some extreme number of practices thought to be healthy, and might have a lower risk of heart disease, which the unhealthy population will be the opposite. If a study looks only at a specific remedy and ignores other factors, such as perhaps healthy eating, then the positive benefits of healthy eating may overshadow a negative effect of the remedy in question, leading to an incorrect result. Researchers can try to fix this using probabilities and things, but it is rather difficult to assign a numerical value to how healthy someone eats, and even less so to expect a large population of an observational study to do so consistently. This sounds like a support of his point, but it really isn’t: my point is that it is perhaps better to let the observational researchers do their studies, as they give actual researchers some useful leads, and just ignore the output of their observational studies. You’re allowed to do that, you know, just not take the health advice you find from some observational study, just like you don’t necessarily look at the health practices of your friends and try to emulate them. While it might feel good and cathartic to write a nine-page article in the Times Magazine condemning observational studies, it’s really not necessary. And statistically speaking, it is better, at least marginally, to take medical advice from observational researchers than from whatever anecdotal advice we get from friends, although most people tend to base their lives on the latter.

Also, it’s worth arguing that what he is talking about is not the entirety of the field of epidemiology. Epidemiology is not solely based around preventative medicine, it also does seek to analyse the causes and spread of disease, which is something it does quite well. See, I think most doctors would agree that epidemiology is merely statistical and cannot provide any actual proof, merely indicate certain probabilities of correlation and of causation. And when you’re in the middle of an epidemic – what the field is actually named for – epidemiologists are pretty good at determining its source and estimating its spread.[1] They do it every year to determine the optimal vaccination for flu, to name a single instance. Epidemiology was the field that gave us our very first understanding of disease, and it did pretty well: epidemiologists said stay away from the sick people, and that worked pretty well.

Another pretty good success case for epidemiology is certain dietary laws in religion. Many kosher laws fit this category: meat is only considered acceptable if it comes from a certain list of animals, was slaughtered in a particular way (by a qualified person, perhaps preventing people from slaughtering animals on their own if they are unqualified to determine its safety, perhaps also promoting certain cleanliness), although fish are generally permissible (many jewish communities were and are located on the water, so people were able to fish on their own, rather than relying on buying food or waiting for deliveries, and are also generally fully consumed in a single meal; these ensure freshness). The same is true of halal, which applies to Islam: both forbid pork (and so do the Scottish) and frown upon blood or carrion, they limit slaughter of animals to some specific process which ensures some measure of cleanliness and control and forbid eating of animals found dead. Some Catholics don’t eat meat on Fridays, perhaps because near the end of the week the deliveries of food are beginning to age and become less safe – fish is permitted again because it tended to be acquired locally, and dairy was forbidden unless you served in the Crusades. I’m not entirely clear on how that last one ties in with epidemiology.

Statistically speaking, the theory behind epidemiology as preventative medicine is also sound, but there are too many problems for it to be practical. Studies are all too often plagued by selection bias (people concerned about their health are more likely to participate, or more likely to engage in more ‘healthy’ behaviors, causing a bias) or subjectivity (rate your pain on a scale of one to five) or other biases in response (people claiming to exercise daily, except for the three days a week they missed, or to eat healthily, because it’s the more acceptable thing to do).[2][3]

It’s important to keep two distinctions in mind: epidemiology has a few fields, including preventative, which covers public health, like the diet and heart disease examples. There is also an aspect of analyzing an epidemic after it exists, such as its first use against diseases like cholera – here the strength of using a statistical rather than scientific approach is apparent, as it isn’t necessary to fully understand the mechanisms at work. (the downside to the statistical approach is things like witchhunts. If she floats, burn her!) In addition, there is a difference between an observational study and a controlled experiment; they fill different roles in the scientific method. An observational study exists to create potential hypotheses for further experiments, but provides no actual justification of that point.

[0] http://www.nytimes.com/2007/09/16/magazine/16epidemiology-t.html – I was able to access this article a few days ago, but as of 26 Oct 2010 it appears to be behind a registration wall.
[1] http://www.cdc.gov/excite/classroom/outbreak/steps.htm
[2] Pierre-Simon, marquis de Laplace, wrote “A Philosophical Essay on Probability” (more of a mathematical book, if you ask me) almost two hundred years ago, but it is still a good introduction to probability in practice for anyone with a bit of a scientific background.
[3] There is also some discussion of potential error sources at http://en.wikipedia.org/wiki/Epidemiology#Validity:_precision_and_bias

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