I’m always on about Experts, which are credentialed trained individuals who support the regime, so I thought it well to show Experts in action. One way is through the use of the Epidemiologist Fallacy, which happens when a scientist says X causes Y, but where X is never measured, and the cause is confirmed by a wee p-value.
Now there is nothing more embarrassing in science than a researcher running around, simpering “Look at my wee p! Look at my wee p!” Yet it happens all the time. I won’t here prove that every use of a p-value contains a (separate) fallacy, but it is so. For proof go here.
You’d also think claims like “fracking natural gas kills people” would be based on measuring the cause, in this case measuring fracking natural gas exposure or, rather, intake. But you’d be wrong. Neither natural gas exposure or intake in the study I’m about to describe was measured. At all.
Yet the researchers still claimed that natural gas killed old people.
How do they get away with it? I’ll tell you. You won’t believe me, but I swear to you the reason I give is true. They get away with it because everybody else commits the Epidemiologist Fallacy, too. And misery loves company. Here are many examples.
Our example today is the peer-reviewed paper “Exposure to unconventional oil and gas development and all-cause mortality in Medicare beneficiaries” by Longxiang Li and a host of others in Nature Energy. Following a depressing trend, most of the paper is shunted to supplementary material. You cannot understand what is happening in papers anymore.
Here’s their main claim:
We found evidence of a statistically significant higher mortality risk associated with living in proximity to and downwind of unconventional oil and gas wells. Our results suggest that primary air pollutants sourced from unconventional oil and gas exploration can be a major exposure pathway with adverse health effects in the elderly.
The paper is constructed from the Epidemiologist Fallacy. They want to say X (natural gas) causes Y (death in the elderly) but they can’t measure actual exposure to natural gas or its effluvia.
So what do they do?
Like many before them, they use exposure to zip codes: “For each beneficiary’s ZIP code of residence and year in the cohort, we calculated a proximity-based and a downwind-based pollutant exposure.”
Zip codes are used to classify people who live within some distance to a natural gas source. Address is taken to be exposure/intake.
Were people at their zip codes the entire time? Did some have their windows open, some closed? Did some use air filters and others not? What about those who ate what the government recommended (not a wise idea) and those who didn’t? What about the actual level of exposure to natural gas and its combustion products?
Were the people who lived right next to gas wells or fires poorer than those living in more placid lands? And therefore, being poorer, suffer more for various other reasons besides gas?
We’ll never know.
Now it is not impossible that somebody sniffing natural gas fumes will live a shorter life next to somebody who breathes only pristine air. But there must be some great uncertainty in measuring the distance from a gas source and saying that distance is exposure or intake, even if intake does in fact lead to shorter lives.
I don’t know what this uncertainty is, but it cannot be small. But it exists and should be applied to any claims of cause. Here’s what I mean. If they say that there’s a certain percent chance you’ll die sniffing gas or its byproducts because of the gas, but don’t measure exposure but only distance, you must multiply that percent chance by the uncertainty in the proxy.
Suppose they say you have a 1% chance of dying from sniffing gas. But they don’t measure sniffing, but only distance. And the chance that distance truly represents sniffing is, say, also 1% (a plausible number).
That makes the real chance (based on this evidence) that gas kills you 1% x 1% = 0.01%.
The real result doesn’t sound as exciting, does it.
Our authors’ result, even before doing this necessary calculation, sounds even worse. I won’t explain the model, but it’s a regression stuffed aplenty with, well, stuff, one thing of which is distance/zip code.
Before that, here’s a table of their raw data, to show you the effect of modeling (and recalling all models only say what they are told to say).
The “PE” is “exposure”, i.e. zip code. And the “DE+/-” is wind direction “exposure” (read the paper). You can see the raw mortality rates. They barely differ across the levels of “exposure”. There certainly isn’t any strong signal that greater “exposure” leads to more death.
To get that, they had to use a model, the output of which was a “hazard ratio”, which is a multiplicative effect to the chance of dying right now, at this moment. The lowest “exposure” had a HR of about 1.01. The highest “exposure” had an HR of just over 1.02, which rose to almost 1.03 in the downwind category (see their Table 4; supplementary Table 2). But these differences, because of the massive size of the data, gave them wee p-values to wave around.
All this means the effect, even if genuine, is tiny. Before accounting for the uncertainty in the proxy as cause (and the over-certainty in this parametric and not predictive analysis). Once we do that, and recognize that in the raw data there was no signal at all, we must conclude that THERE IS NOTHING TO SEE HERE.
Back to Experts. The paper we now see says nothing. Yet the regime picked it up and touted it. Here’s a headline from Inside Climate News: “For the First Time, a Harvard Study Links Air Pollution From Fracking to Early Deaths Among Nearby Residents.” Science Daily: “Living near or downwind of unconventional oil and gas development linked with increased risk of early death”.
Linked is the supreme weasel word in Science. It’s like convicted in court on purely circumstantial evidence.
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