Coal Dust Is Claimed To Kill Old People: A New Instance of the Epidemiologist Fallacy

Coal Dust Is Claimed To Kill Old People: A New Instance of the Epidemiologist Fallacy

There are a lot of new readers, and many may not yet have heard of the epidemiologist fallacy. Few tools have been as productive at generating The Science. You know The Science. The Science is what they insist you must “follow”.

The epidemiologist fallacy (EF) is simple to describe: It is when a scientist claims X causes Y, but where he never measured X, and he “proved” the cause using his wee p.

Wee p? Which is to say, null hypothesis significance testing or Bayesian parameter posteriors, these being much the same in practice. Where the primary, or usually sole, focus is on the parameters of some ad hoc probability model. And not on the observable Y.

The double dog epidemiologist fallacy is similar: It is when a scientist claims X causes Y, but where he never measured X, and he never measure Y, and he “proved” the cause using his wee p.

Since there’s a whole lot of nothing going on—X is never measured, and sometimes Y never is, either—-you’d think we’d hear from scientists a whole lot less, because if you haven’t measured what you said caused Y, then quiet humility would seem to be the order of the day.

Alas, no. Our latest example is the peer-reviewed paper “Mortality risk from United States coal electricity generation” by Lucas Henneman and others in Science.

Our first clue something has gone wrong is the title. Risk, they say. They do not say this “Mortality from coal”, which is a definite claim. Instead, they preach about something called “risk”, which is probability by another name.

They examine a substance called “PM2.5”, which is dust. It works to cause all manner of disease and havoc, they say.

Here is a sentence from the Abstract: “We estimated the number of deaths attributable to coal PM2.5 from 1999 to 2020”.

That is causal language. That is what “attributable” means: caused by. They are claiming cause. And, though I ask you to take my word for it, they do so using standard statistical tools, all of which cannot be used to claim cause, but which here are.

We have one element of the EF. Let’s see if there are others.

Recall they said X causes Y, coal dust causes death. Did they measure X? Did they measure Y?

X: “We estimated coal PM2.5 using the HYSPLIT with Average Dispersion (HyADS) model, which accounts for date-specific atmospheric transport of PM2.5 to characterize exposure to PM2.5 from individual EGUs [coal electricity-generating units].”

So they did not measure X. X is output from some model. Still, maybe it is a good model, and gave good predictions of how much PM2.5 each individual in their study sucked in. Was it?

They say: “By averaging ZIP (postal) code levels of coal PM2.5 across the conterminous US, we found that…”

The key word, one should always look for, is exposure. It sounds nice and scientific, and intimates a measurement was made. But no. Exposure is not dose. We do not know how much PM2.5 anybody in their database sucked in.

X was not measured. A person’s zip code does not give dose. Of course, it can be used to predict dosage in a probabilistic sense. But we already have a model of PM2.5 at zip codes. Which means we need a second model of how PM2.5 predicts dosage. That’s a model of a model. Which would still be okay, as long as they carried forward the uncertainty in these models. Alas, they did not. This did not measure X.

We have just confirmed we are dealing with the epidemiologist fallacy. This paper is The Science that must be followed. Which is bad enough. But dare we look for the double dog epidemiologist fallacy?

Yes. We dare.

Recall that to get the DDEF we need to not only not measure X, but also not measure Y.

Authors: “The Medicare dataset contains records of 32.5 million deaths from 1999 to 2016 (table S1), with the annual number of deaths increasing and death rates decreasing across the study period..,”

So they did measure Y! Or did they?

No, sir, they did not. Because they measured deaths of all kinds. And not deaths from dust. A guy who slipped and fell on his covid vax counted. And only in old people.

We do have the DDEF, but a weak form of it, because they are lean on the vague and dubious claim that dust exacerbated all causes of death.

The authors, perhaps sensing this, tried to do better. Because the “attributable” deaths were not deaths after all, but “excess” deaths!

Authors: “…we estimated the excess number of deaths attributable to coal PM2.5 relative to what would have occurred assuming zero SO2 emissions from coal EGUs (i.e., coal PM2.5 = 0).”

But they can’t know how many deaths there would have been without coal plants, because, of course, there were coal plants. This is a yet another model, and one which can never be confirmed. Which doesn’t make it wrong, but it does mean there should be a lot more uncertainty.

In the end, we have a modified epidemiologist fallacy, which didn’t quite make double dog status. Sad.

Still, there is no chance that they can be anywhere near certain that coal dust killed people, because we have models of models of models. And no direct measure of dose, nor deaths caused by dust. Everything is loosey goosey correlations which they claim are cause.

They do indeed insist their correlations are causations: “These results advance the growing body of evidence showing varying toxicity of PM2.5 originating from different sources.” From which they conclude not only that more regulation is needed, and so also is their continued service.

Do not follow The Science.

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  1. I’d be laughing if it weren’t so serious.

    We estimated the amount of coal dust that the average person inhaled, then estimated the number of people who died from our estimated coal dust ingestion, then claimed that it was a scientific study.


  2. Leonard

    “Growing body of evidence.” Are these scientists or lawyers?

    Indeed that is what the bulk of todays science-adjacent research is all about. There is no interest whatsoever in proof of anything, solid proof means that line of research is about to end. Instead the goal is not proof at all but merely making a case appear to not be flimsy in order to convince a judge or jury – aka the people in charge of grant money – to bless them with a continuance. (“Grant Jury”).

    The goal is to keep billing the clients without ever reaching a settlement or a final decision. Even lawyers aren’t that sleazy.

  3. Hagfish Bagpipe

    The recipe for making The Science™? is pretty simple, as Briggs has shown — no reason the process couldn’t be automated by AI. Along with making fake science the AI could also generate artificial scientists with AI headshots and bios and AI peer review and then generate AI news stories and AI social media controversy. Just one dude with a computer could generate a whole fake universe — The Fake-Verse™? — eliminating useless and redundant scientists, academics, journalists, and such. More fake bang for your fake buck. With all the fakery automated it could be ignored while the real people go back to real living. The future is bright.

  4. McChuck

    At any point, have any of these “studies” compared the number of “excess” deaths due to (eww, icky!) electric generation to the number of “excess” deaths that would be caused by a lack of electric power? No? Then we can safely ignore all of them as partisan, anti-civilizational hacks.

  5. Tars Tarkas

    There was an even more ridiculous “study” published fairly recently (I think 2005) about leaded fuel lowering the IQ of children. They literally IQ tested a group of children “upwind” of airport (most airplanes run “lowlead” (leaded) fuel) and a different group of children who lived “downwind” of the airport. Then they attributed the difference to the lead in airplane fuel.

    No demographic data was collected of the children. Ethnicity of the kids could more than explain any differences. Income level could explain any differences.

    And, of course, no blood measurements for lead. It’s possible they weren’t even the ones who administered the IQ tests. If not, that would explain why they didn’t draw blood to screen for lead.

  6. Kip Hansen

    Briggs: SO2 is not PM2. 5 == this is nonsense ““…we estimated the excess number of deaths attributable to coal PM2.5 relative to what would have occurred assuming zero SO2 emissions from coal EGUs (i.e., coal PM2.5 = 0).”

    They say, more specifically, ” We defined “coal PM2.5” as PM2.5 from coal EGU SO2 emissions” yet “Sulfur dioxide (SO2) is a gaseous air pollutant composed of sulfur and oxygen.”…”Sulfur dioxide gas can also change chemically into sulfate particles in the atmosphere, a major part of fine particle pollution, which can blow hundreds of miles away. ” They can change…but how much do they? Did they? No measured exposure again — they used SO2 as a proxy for PM2.5.

    PM means particulate matter.. ..gases are not particulates. Particulates can be captured by correctly sized filters and measured accurately.

    PM2.5s have not been shown to CAUSE illness — they have been associated with all kinds of things through statistical chicanery. Of course, cooking in a mud hut 8 hours a day over a smoking dung or stick fire is BAD for the women forced to do so. And is from that (and open coal fires for heating in Northern China, that all the insanity about PM2.5 has been derived.

  7. Kip Hansen

    Briggs ==> One of the best references on PM2.5 is James Enstrom at the UCLA and Scientific Integrity Institute.

    “Scientific distortions in fine particulate matter epidemiology”


  8. Stan Young

    I went back to one of my air quality data sets. For most US counties (>2,900), I have PM and its ten components (data from the EPA). It turns out there is a strong, non-linear, positive relationship between SO4 (from SO2/coal) and Bvoc (biogenic volatile organic compounds). So, is it the coal/SO2 or the “pine trees” that is the killer? Or, as we note in the paper, is the PM modeling component data any good?
    Here is a preprint of the paper, arXiv:2209.05461, and therein is a pointer to the data set. The authors of the Science paper do not provide their data set.

  9. Johnno

    The heat of the Sun kills far more people every day.

    When will the Expurts do something about this???

    Didn’t Bill Gates have a plan?

    Is releasing the killer mosquitoes holding it up?

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