Every time I come across an article about cancer, I'm reminded of that website that catalogued all the things that The Daily Mail said either caused or prevented cancer.
The strength of the correlation might be the only useful single variable we can have in discussions like this.
I'm starting to think that "Correlation is not causation" is a kind of slippery slope toward a world where nobody can ever prove the cause of anything. At the same time, just about everything is at least a mild carcinogen including food, the sun, air particulates, etc. But knowing that everything causes cancer isn't useful information. Knowing the strength of the correlations of each of these things is actually useful.
Not that I can see. They claim to have reverse engineered the methodology, but they have not obviously published what they came up with. They also have no explanation as to why they can find positive correlations with all kinds of locations but no negative correlations with anything.
No, but how do I know that the authors used the same methodology? The most they say about it is "we developed our own methodology by guessing what they did, and we changed stuff around until the results from our methodology matched their results", which is very unconvincing to me.
The fact that they had to guess what the original author did is already an issue in itself. What we see here is that: 1. the original author did not sufficiently document their methodology and 2. when a best effort at reproducing the results is applied and we look deeper, we then discover that the results are wacky.
When someone comes along and proclaims 2+2=5, just because someone is right that they're incorrect doesn't mean their own assertion that 2+2=3 is any more correct, or that their reasoning for believing the original person was wrong has any validity.
Being accidentally correct is only barely better than being wrong, and for many uses (such as when the reasoning is extrapolated and used elsewhere) is no better at all.
They're not claiming 2+2=3, they're showing that the same methodology that proclaimed 2+2=5 also proclaims (as best as they can determine give the flawed documentation of the methodology) that 3+3=7, 1+1=3 and 17+17=137
They didn't! They mentioned receiving "eight lines of code" and, at the end of the article, write:
> It is especially telling that no matter what landmark we applied to the methodology, we have yet to get a negative result. There is, in fact, a real chance that you simply cannot get a negative result from this method.
My guess: The code is not shareable. Maybe its method is laughably disprovable?
I hoped to see a systematic explanation of what the correlations show. As long as we don't have a viable explanation, there is something interesting there to discover. It can be that the statistical analysis is fundamentally flawed. Pointing out a flaw that tricked several serious researchers is progress. If it was because of some other factor (let's assume there are many more elderly people living around those neighborhoods, can't think of a better example now).
Cancer risk associations studies are not normally described as "proving" anything. The article being criticized uses 'risk' and 'association'. There is a long history of argument around these sorts of studies, there was a previous series of these around people living near power plants (where it seems like SES, not plant proximity, was the strongest explanatory variable, see https://www.aps.org/archives/publications/apsnews/200710/ele... ). I've seen similar "living near a freeway causes cancer" arguments. There was a massive court case by flight attendants, who have a higher rate of cancer than the regular population.
In reading the criticism, I noticed the authors keep using the term "prove" and they also keep trying to come up with mechanisms ("refueling of the plant"). Even the argument about plant worker exposure compared to people living far away doesn't completely work, because the plant workers are taking all sorts of precautions to minimize exposure to radiation, but there are still mechanisms where something could go out the cooling stacks and deliver something harmful downwind.
The biophysics of cancer causation is entirely nontrivial and looking for the actual sources of the cancer risk is challenging, and watching physics people argue with epidemiologists gets old quickly (my field is biophysics, and I've had a few physics people insist that non-ionizing radiation couldn't possibly cause cancer, "because it doesn't damage DNA". Unfortunately, that argument isn't good, because it presupposes a mechanism (DNA damage due to radiation); we know now that non-ionizing radiation causes cellular heating, stress response, and more, which are all associated (based mostly on in vitro studies) with increased rate of cancer.
Note the authors (and the institute they work for) have vested interest: Dr. Adam Stein is the Director of the Nuclear Energy Innovation program at the Breakthrough Institute, where his work centers on the technology, regulation, economics, and risk governance of advanced nuclear energy.
Deric Tilson is a Senior Nuclear Energy Innovation Analyst at The Breakthrough Institute, where he focuses on advancing nuclear energy as a critical pathway to a carbon-free and energy-abundant future.
> there was a previous series of these around people living near power plants (where it seems like SES, not plant proximity, was the strongest explanatory variable, see https://www.aps.org/archives/publications/apsnews/200710/ele... ). I've seen similar "living near a freeway causes cancer" arguments.
Social Economic Status is certainly associated with all sorts of things, but being near a freeway means being near brake dust (which used to have asbestos), being near all sorts of tailpipe nasties, being near tire dust, etc. I'd be surprised if it was all explained by money.
See- you immediately went to a mechanism that justified the result. It's all too easy to convince yourself something is true because you can see a pathway- yet that pathway might not matter.
Besides the bias question (which is possible and apparently likely for both sides), I realize this is a touchy subject for many but the tone of this article is rather more irritated than it really needs to be. They even mention that the authors of the Harvard paper said the study wasn't meant to show causality, but the very next section heading is "Ridiculous things you can 'prove' caused cancer mortality," which suggests that is what the authors of the original paper were trying to do. If you're going to accuse them of being deceptive, at least be open about it instead of using this sort of passive-aggressive approach.
Also, they say:
"Over the last several months, we have replicated the results of these papers. The authors supplied us with eight lines of code and answered a couple of questions about the covariates, which did not replicate the results. Most of our replication was done through first principles combined with trial and error. Once we were reasonably close to the results of the first national study on cancer mortality, we took the methodology and applied it to numerous other landmarks."
I'm no expert in experiment design, but unless I missed something, I have qualms about calling this method a true "replication."
My understanding was that they take issue with the word "attributable". They don't attempt to propose a mechanism by which living near Costco causes cancer (the most strongly associated of the landmarks they measured). Rather, the implication is that it doesn't. Like I think it's a lot of words arguing that "correlation doesn't prove causation" could be restated "association doesn't prove attribution."
Do Tilson and Stein have more or less of a vested interest than the people who wrote the studies they are criticizing? It’s not obvious to me that they are more (or less) conflicted.
I could go on a long rant on how sociologists, pyschologists, and epidemiologists all play games with data to support their own pet theories and causes, but I won't.
To answer your question: without other data, generally I would expect a person who works for an industry supporting institute to have greater vested interest than academics working at a university- academics mainly just want to get more funding for their individual research, while the industry is dealing with multi-billion-dollar industries and they get paid well to write articles like this. But now that I look carefully, I don't think their institute is funded directly by the nuclear power/plant industry.
> where something could go out the cooling stacks and deliver something harmful downwind.
This is beyond ridiculous. EVERYTHING in nuclear power plants, that can feasibly be a vector for a radiation leak, is heavily monitored. And radiation leaks into the coolant loops is the most obvious thing.
> we know now that non-ionizing radiation causes cellular heating
At levels that are not achievable outside a microwave.
Where did I say that? What came out the cooling stack was radioactive? You're presuming a mechanism. My whole point here is that the original paper just described an association. We don't know what the cause of that was or if the authors even did a competent study.
> Over the last several months, we have replicated the results of these papers. The authors supplied us with eight lines of code and answered a couple of questions about the covariates, which did not replicate the results. Most of our replication was done through first principles combined with trial and error. Once we were reasonably close to the results of the first national study on cancer mortality, we took the methodology and applied it to numerous other landmarks.
Any details of the methodology? Is it possible some mistakes were made during this "trial and error"?
Indeed, it seems quite difficult for this methodology to control for "nuclear plants are near other nasty industrial things":
> While a broad set of confounding factors with distant relationship to cancer are included in the model, sources of carcinogenic pollutants are inexplicably missing. NPPs tend to be located in industrialized and densely populated regions to minimize cost of transmission. Other industries, particular chemical and petrochemical plants are well established sources of carcinogens, yet they are not considered as a confounding factor. Occupational hazards are also excluded from the model.
As someone with experience in this field (and plenty of skepticism of geographic association studies), I find the article lacking. Is there a technical write-up?
It is quite odd that the authors say that they don’t know if the original method can yield negative results. Some of the results are expressed as relative risk, so there’s a numerator and denominator. That should be enough to begin to work it out, since all possible exclusive numerators sum to the denominator. It feels like if you don’t understand whether some areas will be lower than average, then perhaps you don’t understand the method sufficiently.
It sounds like they reverse engineered the original method, but that means they could be applying an overfit model and getting spurious results that the original wouldn’t give.
That’s all speculation, hence we need the technical write up.
This is so delicious. I’m glad that someone took the time to point out the absurdity of this class of study [1]…though nobody will ever listen.
The kind of crap “research” being criticized here makes me almost irrationally angry, because these people are at Harvard, and getting tons of international press for this slop - which they can crank out, precisely because they skip the hard parts that slow real researchers down. Meanwhile, hundreds of thousands of good researchers languish in anonymity, because it’s incredibly rare to find a high profile result using good methodology, and worse, it takes time.
In a just world, the people who get caught doing this stuff would be drummed out of academia.
I thought for sure this was going to talk about the non-scientific IARC classifications. Even group 1, the doozies, doesn't consider dose or actual risk, leading to ludicrous adjacency like processed meats and asbestos. Scientists that aren't politically motivated wouldn't classify things on a list with terms like 'probably' or 'possibly'.
The main outcome of the original papers is RR (relative risk) and the main number discussed in this article is "people living near a landmark" which is obviously nonsensical. You'd need to compute your "Costco risk index" using epidemiological risk compared with a baseline, and there's not one place here where they claim to do it. As written, this does not appear to be a serious effort.
The root cause of cancer is cellular stress. One cope mechanism with cellular stress is the cells fusing. On occasion a blood and a somatic cell will fuse. A blood cell is an organism on its own, disconnected from the electric network of the somatic cells which controls the growth and shaping into functional tissues and organs. When the programming of the fused cell combines in a way that it wants to grow and divide while disconnected from the body control network, you get a cancer.
Pretty much anything can cause cellular stress, including the normal operation of worn cells in an old body. Cataloguing the strength of the stressors is still useful.
"Over the last several months, we have replicated the results of these papers. The authors supplied us with eight lines of code and answered a couple of questions about the covariates, which did not replicate the results. Most of our replication was done through first principles combined with trial and error. Once we were reasonably close to the results of the first national study on cancer mortality, we took the methodology and applied it to numerous other landmarks."
Author implies that Alwadi et al. was not forthcoming with sharing the data underlying their studies. Because otherwise they should be able to exactly replicate their results. "Reasonably close" does not cut it.
A different theory worth investigating: hot dogs, universities, Costco, nuclear reactors, the urban environment, and modern living generally, do increase cancer risk.
Who is the quack here? Did you take this criticism article as being absolutely true? I found their argument unconvincing (I am not really qualified to judge the original paper; running a good epi study is hard, and interpreting the results even harder.
> The attributable number of deaths from this methodology is probably zero, but the attributable number of bad papers is at least four.
Brutal.
Nothing more to add, but sadly bunk or not these papers will surely be quoted by a lot of entities for a long time no matter how blatantly broken they are.
Yawn. Anybody that has looked into the data knows that nuclear is acceptably safe, particularly compared to fossil fuels.
The problem with nuclear is cost, not safety, and thus far it appears intractable in the real world rather than the fantasy world that the likes of BTI inhabit, whereas the problems and limitations of renewable energy are actually being solved.
I started noticing awhile ago that you seem to have this pipeline in scientific/academic reporting that goes academic paper -> press release -> science journalists -> regular journalists -> Facebook/Reddit.
Even if the original authors never say anything about causation, inevitably the language of correlation (related to, is linked to, associated with, correlated with, coincides with, etc.) will turn into the language of causation (leads to, increases/decreases, has an effect on, causes, etc.) somewhere in this pipeline.
Then suddenly everybody around you is talking about how going to bed with shoes on causes headaches, or goes playing tennis increases longevity. There is some atrocious science journalism out there
https://web.archive.org/web/20200221175201/http://kill-or-cu...
Too bad it's down, but here's an archive of it.
https://youtu.be/q3chJN9DCGg
I'm starting to think that "Correlation is not causation" is a kind of slippery slope toward a world where nobody can ever prove the cause of anything. At the same time, just about everything is at least a mild carcinogen including food, the sun, air particulates, etc. But knowing that everything causes cancer isn't useful information. Knowing the strength of the correlations of each of these things is actually useful.
Without correlation, what are we left with?
Mechanism. Testable causality. X causes cancer by doing y.
Saying 'Correlation isn't causation' doesn't disprove causation.
Yea..
A deer in a forest who heard the foot steps of a lion and thought "hey Correlation is not causation" would soon be a goner...
Being accidentally correct is only barely better than being wrong, and for many uses (such as when the reasoning is extrapolated and used elsewhere) is no better at all.
> It is especially telling that no matter what landmark we applied to the methodology, we have yet to get a negative result. There is, in fact, a real chance that you simply cannot get a negative result from this method.
My guess: The code is not shareable. Maybe its method is laughably disprovable?
In reading the criticism, I noticed the authors keep using the term "prove" and they also keep trying to come up with mechanisms ("refueling of the plant"). Even the argument about plant worker exposure compared to people living far away doesn't completely work, because the plant workers are taking all sorts of precautions to minimize exposure to radiation, but there are still mechanisms where something could go out the cooling stacks and deliver something harmful downwind.
The biophysics of cancer causation is entirely nontrivial and looking for the actual sources of the cancer risk is challenging, and watching physics people argue with epidemiologists gets old quickly (my field is biophysics, and I've had a few physics people insist that non-ionizing radiation couldn't possibly cause cancer, "because it doesn't damage DNA". Unfortunately, that argument isn't good, because it presupposes a mechanism (DNA damage due to radiation); we know now that non-ionizing radiation causes cellular heating, stress response, and more, which are all associated (based mostly on in vitro studies) with increased rate of cancer.
Note the authors (and the institute they work for) have vested interest: Dr. Adam Stein is the Director of the Nuclear Energy Innovation program at the Breakthrough Institute, where his work centers on the technology, regulation, economics, and risk governance of advanced nuclear energy.
Deric Tilson is a Senior Nuclear Energy Innovation Analyst at The Breakthrough Institute, where he focuses on advancing nuclear energy as a critical pathway to a carbon-free and energy-abundant future.
> Petrofac designs, builds, manages and maintains oil, gas, refining, petrochemicals and renewable energy infrastructure.
https://hsph.harvard.edu/profile/yazan-alwadi/
https://www.linkedin.com/in/yazan-alwadi-ab1b112b/
Social Economic Status is certainly associated with all sorts of things, but being near a freeway means being near brake dust (which used to have asbestos), being near all sorts of tailpipe nasties, being near tire dust, etc. I'd be surprised if it was all explained by money.
Also, they say:
"Over the last several months, we have replicated the results of these papers. The authors supplied us with eight lines of code and answered a couple of questions about the covariates, which did not replicate the results. Most of our replication was done through first principles combined with trial and error. Once we were reasonably close to the results of the first national study on cancer mortality, we took the methodology and applied it to numerous other landmarks."
I'm no expert in experiment design, but unless I missed something, I have qualms about calling this method a true "replication."
To answer your question: without other data, generally I would expect a person who works for an industry supporting institute to have greater vested interest than academics working at a university- academics mainly just want to get more funding for their individual research, while the industry is dealing with multi-billion-dollar industries and they get paid well to write articles like this. But now that I look carefully, I don't think their institute is funded directly by the nuclear power/plant industry.
No, there aren't.
> where something could go out the cooling stacks and deliver something harmful downwind.
This is beyond ridiculous. EVERYTHING in nuclear power plants, that can feasibly be a vector for a radiation leak, is heavily monitored. And radiation leaks into the coolant loops is the most obvious thing.
> we know now that non-ionizing radiation causes cellular heating
At levels that are not achievable outside a microwave.
Any details of the methodology? Is it possible some mistakes were made during this "trial and error"?
edit: Some aggregated data is available from https://pmc.ncbi.nlm.nih.gov/articles/PMC12929679/ , but far from enough to reproduce: https://media.springernature.com/original/springer-static/es...
https://pubpeer.com/publications/AEA87585FB0365719098E2C0D06...
> While a broad set of confounding factors with distant relationship to cancer are included in the model, sources of carcinogenic pollutants are inexplicably missing. NPPs tend to be located in industrialized and densely populated regions to minimize cost of transmission. Other industries, particular chemical and petrochemical plants are well established sources of carcinogens, yet they are not considered as a confounding factor. Occupational hazards are also excluded from the model.
It is quite odd that the authors say that they don’t know if the original method can yield negative results. Some of the results are expressed as relative risk, so there’s a numerator and denominator. That should be enough to begin to work it out, since all possible exclusive numerators sum to the denominator. It feels like if you don’t understand whether some areas will be lower than average, then perhaps you don’t understand the method sufficiently.
It sounds like they reverse engineered the original method, but that means they could be applying an overfit model and getting spurious results that the original wouldn’t give.
That’s all speculation, hence we need the technical write up.
The kind of crap “research” being criticized here makes me almost irrationally angry, because these people are at Harvard, and getting tons of international press for this slop - which they can crank out, precisely because they skip the hard parts that slow real researchers down. Meanwhile, hundreds of thousands of good researchers languish in anonymity, because it’s incredibly rare to find a high profile result using good methodology, and worse, it takes time.
In a just world, the people who get caught doing this stuff would be drummed out of academia.
[1] uncontrolled observational research
https://m.youtube.com/watch?v=EWNjw3pg9sA
Pretty much anything can cause cellular stress, including the normal operation of worn cells in an old body. Cataloguing the strength of the stressors is still useful.
Author implies that Alwadi et al. was not forthcoming with sharing the data underlying their studies. Because otherwise they should be able to exactly replicate their results. "Reasonably close" does not cut it.
Brutal.
Nothing more to add, but sadly bunk or not these papers will surely be quoted by a lot of entities for a long time no matter how blatantly broken they are.
The problem with nuclear is cost, not safety, and thus far it appears intractable in the real world rather than the fantasy world that the likes of BTI inhabit, whereas the problems and limitations of renewable energy are actually being solved.
Even if the original authors never say anything about causation, inevitably the language of correlation (related to, is linked to, associated with, correlated with, coincides with, etc.) will turn into the language of causation (leads to, increases/decreases, has an effect on, causes, etc.) somewhere in this pipeline.
Then suddenly everybody around you is talking about how going to bed with shoes on causes headaches, or goes playing tennis increases longevity. There is some atrocious science journalism out there