Correlation And Causation With Some Plausibility For Good Measure

One of the biggest problems in the battle against diseases is figuring out exactly what thing or things cause a disease. In the late 70’s and early 80’s, men and women – a lot of them being gay – started to come down with opportunistic infections at an accelerated rate. The cause was not known, but epidemiologists did come to realize that those who developed the syndrome – later to be called AIDS – were more likely to be exposed to certain behaviors and sexual preferences. That is, the personal attributes and the disease correlated, but it would be unscientific and wrong to say that one would get AIDS for the sole fact of being gay.

That sure didn’t stop the “moral majority” and others from stigmatizing an entire segment of the population. It wasn’t until HIV was isolated and discovered as the causative agent – and some heterosexual celebrities acquiring the infection – that the term GRID (Gay-Related Immune Deficiency) beam AIDS.

Of course, AIDS is not the only example.

I told you just the other day how nationalities and ethnicities are associated with certain conditions. Lou Dobbs wrongfully claimed that immigrants brought more cases of Hansen’s Disease (Leprosy) to this country than naturally occur. A discussion on recent cases of measles in Milwaukee undoubtedly turned into an immigrant bashing that would have made the most liberal KKK members blush. And God help you if you’re from Africa and trying to donate blood.

All of these examples above are instances of correlation between a disease and a person’s (or people’s) origin. Biologically speaking, it doesn’t matter where you come from. You’re still game to be infected.

But there are other examples were people have wrongly associated two things and then deduced that one caused the other – or vice-versa. For example, two non-scientists writing for an anti-vaccine blog recently published a seven-part story associating arsenic in pesticides with polio. They eyeballed data from the last 120 years and decided that arsenate in pesticides must trigger polio outbreaks because more polio outbreaks have been detected since the use of those pesticides started.

Sounds plausible, don’t it? Well, actually…

We all had a chuckle when a skeptical writer recently said that cases of autism have been on the rise since microwaveable popcorn went into the mass market. If you plot the incidence of autism and the sales of microwaveable popcorn, the lines almost overlap. Again, that’s just “eyeballing” the data without much of a scientific investigation. And that’s where a lot of assumptions about causation go badly.

Even if a study is well-designed and carried out by reputable institutions, there can be mistakes. For example, some time ago, a study was performed to look at the association between coffee and pancreatic cancer. The study concluded – with really good statistical data – that people with pancreatic cancer were more likely to be coffee drinkers. The researchers left it at that and walked away from their study, letting the public decide on whether or not to drink coffee.

Well, astute epidemiologists the world over noticed a funny thing. They noticed that the data never took into account the coffee drinkers’ smoking habits. Once the smokers and non-smokers were placed into different categories, people with pancreatic cancer were more likely to be smokers AND coffee drinkers. People without pancreatic cancer were more likely to be coffee drinkers BUT NOT smokers. Yes, it was the smoking, stupid – or the smoking stupid. The coffee industry took a while to make a comeback after that.

This brings me to biological plausibility of the whole damn thing. In the example of the coffee and cancer, there was no known biological process by which coffee could trigger pancreatic cancer. On the other hand, there was a process by which smoking could trigger pancreatic – and other forms of – cancer. In the example with arsenate in pesticides, there is no known process by which pesticides somehow make the polio virus more virulent – capable of infection – or more pathogenic – capable of causing disease. And, in the case of HIV/AIDS, one could see where certain sexual behaviors could lead to a better transmission of the virus, but there is no evidence whatsoever that one’s sexual predilections – who we’d like to shag – would make any difference to the virus. It will still infect all who are susceptible, meaning the whole of humanity if we’re not careful in how we use contaminated sharps, how we have sex with each other, and how we test blood donors (regardless of their national origin or sexual orientation).

Does the scientific data change and certain once-implausible events become plausible? Absolutely. But they’re far and few in between, and the scientists who once held them to be implausible will correct themselves and admit that there is now evidence of plausibility. And, for God’s sake, don’t just “eyeball” the data. Run through something, anything… Even MS Excel will do in a pinch!

In the case of vaccines and autism, that kind of evidence still has not come forth, despite all sorts of attempts at finding it. In fact, we have been trying to close the book on the vaccine-autism “debate”, but the anti-vaccine people won’t let it go. Once they do, we will be able to move on and help autistic people not only know why they are autistic but how to better treat them so they may live fuller lives.

I ended by talking about anti-vaccine advocates for a reason, by the way. I was asked recently if I thought anti-vaccine advocates presented an existential threat to the world or even just the country. It’s a question that will be asked of the protagonist in “The Poxes” twice. He will be asked about it on “Vaccination Day” and then again in “Five Years Since”. His answer will be much like my own five years ago and then again a few days ago. So look for that.

One thought on “Correlation And Causation With Some Plausibility For Good Measure

  1. Ahh, correlation vs. causation. This is simultaneously one of the most obvious and difficult-to-explain-to-believers-in-whatever concepts in epidemiology.

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