Tag Archives: healthcare

Smoking and mirrors

The AP is reporting on a study showing that preventative medicine for obesity and smoking actually results in higher healthcare costs. For example, smoking will increase your life expectancy by about 8%, but will increase your healthcare costs by 25%. This is the result of the disproportionate amount of money spent to keep people alive at the end of their lives. Studies have shown that one third of the lifetime cost of healthcare is incurred over the age of 85 (for those living that long). From the report:

Cancer incidence, except for lung cancer, was the same in all three groups. Obese people had the most diabetes, and healthy people had the most strokes. Ultimately, the thin and healthy group cost the most, about $417,000, from age 20 on.

The cost of care for obese people was $371,000, and for smokers, about $326,000.

The results counter the common perception that preventing obesity will save health systems worldwide millions of dollars.

“This throws a bucket of cold water onto the idea that obesity is going to cost trillions of dollars,” said Patrick Basham, a professor of health politics at Johns Hopkins University who was unconnected to the study. He said that government projections about obesity costs are frequently based on guesswork, political agendas, and changing science.

What’s especially interesting and relevent about this is that both Obama and Clinton insist that much of the tremendous cost of their healthcare proposals will be paid for by better preventative healthcare, especially for obesity. It’s pretty much a given that whatever they claim the cost will be, you can pretty much double that. Anyone who doesn’t believe me can look at Massachusetts’ universal coverage initiative (or anything else the government does, for that matter). But with this new study, maybe even that is an underestimate.

Instead of trying to placate us by “lowering” health care costs by simply shifting them onto our tax costs (plus overhead) it would be nice if one candidate would come out for actually lowering the cost of healthcare in a meaningful way. Tort reform and more intelligent allocation of research funding would do a lot. There’s also the radical notion that maybe we could make some sacrifices and just say ‘no’ to some of the incredibly expensive yet only marginally more effective medical technology that we’re always paying for. A good example is 3D ultrasound. Do we really need to pay billions of dollars as a nation just to make really creepy renderings of babies? Especially when its those babies who are going to inherit the debt—and precedent—for the frivolity.

It seem to me the main problem being addressed by these proposals is that there are a lot of people who can’t afford healthcare. Why not just buy them healthcare as part of our existing welfare infrastructure? Why the need for yet another beaurocracy? We should agree to more government only with reluctance, not relish.

Studies show reading this essay will make you smarter

Recently, there was an interesting article in BusinessWeek about the flip-flop of studies on the efficacy of echinacea to cure the common cold. The article focused on the possibility of incorrectly performed studies. But, there may have been nothing wrong with any of the studies, even though they differed in their results. The statistical nature of clinical studies means there is always a small possibility that false effects will be seen. However, biases inherent to statistical research may result in a surprisingly large percentage of published studies being wrong. In fact, it has been suggested that the majority of such studies are.

First, I’ll have to briefly explain something about how statistically-based studies are done. When people do such trials, they consider as “significant” any result that would only happen by chance 1 in 20 times. In the language of statistics, they design the study so that the “null” hypothesis (e.g. that echinacea has no effect on a cold) would only be rejected falsely at most 5% of the time based on the normal random variability expected in their study. In other words, they accept that 5% of the time (at most) they will erroneously see an effect where there truly isn’t any. This 5% chance of a mistake arises from unavoidable randomness, such as the normal variation in disease duration and severity; in the case of the echinacea studies you might just happen to test your drug on a group of people who happened to get lucky and got colds which were abnormally weak.

In summary, to say a drug study is conducted at the 5% significance level, you are saying that you designed the study so that you would falsely conclude a positive effect when there were none only 5% of the time. In practice, scientists usually publish the p-value, which is the lowest significance (which you can only compute after the fact) that would have still allowed you to conclude an effect. The main point, however, is that any study that is at least significant at the 5% level is generally considered significant enough to publish.

So, being wrong at most 1 in 20 times is pretty good, right? The cost of putting a study out there that is wrong pales in comparison to the good of the 19 that actually help, right? Does it really matter if, of the 1000s of studies telling us what we should and shouldn’t do, dozens of them are wrong? In theory, there will always be an order of magnitude more studies that are truly helpful.

The problem is, this conclusion assumes a lot. Just because the average study may have a p-value of, say 2%, it doesn’t mean only 2% of the studies out there are wrong. We have no idea how many studies are performed and not published. Another way of looking at the significance level of an experiment is “How many times does this experiment have to be repeated before I have a high probability of being able to publish the result I want?” This may sound cynical, but I’m not suggesting any dishonesty. This kind of specious statistics occurs innocently all the time due to unknown repeated efforts in the community, an effect called publication bias. Scientists rarely publish null findings, and even if they do, such results are unlikely to get much attention.

Taking 5% as the accepted norm for statistical significance, this means only 14 groups need to have independently looked at the same question, in the entire history of medicine, before it’s probable that one of them will find a falsely significant result. Perhaps more problematically, consider that many studies actually look at multitudes of variables, and it becomes clear that if you just ask enough questions on a survey, you’re virtually guaranteed to have plenty of statistically significant “effects” to publish. Perhaps this is why companies find funding statistical studies so much more gratifying than funding the physical sciences.

None of what I have said so far is likely to be considered novel to anybody involved in clinical research. However, I think there is potentially another, more insidious source of bias that I don’t believe has been mentioned before. The medical research community is basically a big hypothesis generating machine, and the weirder, the better. There is fame to be found in overturning existing belief and finding counterintuitive effects, so people are biased towards attempting studies where the null hypothesis represents existing belief. However, assuming that there is some correlation between our current state of knowledge and the truth, this implies a bias towards studies where the null hypothesis is actually correct. In classical statistics, the null hypothesis can only be refuted, not confirmed. Thus, by focusing on studies that seek to overturn existing belief, there may be an inherent bias in the medical profession to find false results. If so, it’s possible that a significant percentage of published studies are wrong, far in excess of that suggested by the published significance level of the studies.

Statistical studies are certainly appropriate when attempting to confirm a scientific theory grounded in logic and understanding of the underlying mechanism. A random question, however, is not a theory, and using statistics to blindly fish for novel correlations will always produce false results at a rate proportional to the effort applied. Furthermore, as mentioned above, this may be further exacerbated by the bias towards disproving existing knowledge as opposed to confirming it. The quality expert W. Edwards Deming (1975) once suggested that the reason students have problems understanding hypothesis tests is that they “may be trying to think.” Using statistics as a primary scientific investigative tool, as opposed to merely a confirmative one, is a recipe for the production of junk science.