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).
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 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.
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.
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?
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.
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.
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.
"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.
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
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.
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).
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.
https://web.archive.org/web/20200221175201/http://kill-or-cu...
Too bad it's down, but here's an archive of it.
Is this whole article (the article in this Hacker News post, to be clear) an example of
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.
Did I miss it, or do the authors not bother to explain how the original methodology is wrong? Do we just take their word for it?
Do you think Costco really is associated with 2.2 million deaths?
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.
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?
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.
I thought the papers author's nearly ten year employment at `Petrofac` is interesting and probably warrents a mention.
> 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/
he was a construction engineer, not a policymaker.
Doesn't matter; his paycheck depended on him not understanding these things.
> 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."
Attributable risk is a specific term in epi, the FDA uses it, and it doesn't imply causality.
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.
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.
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 like this article: Does X cause Y? An in-depth evidence review (https://www.cold-takes.com/does-x-cause-y-an-in-depth-eviden...)
"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.
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
I've seen plenty of papers that assume causation in the summary, with no evidence whatsoever, so bad science can occur at the source, too.
I love seeing quacks get shredded by articles like this.
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.
Me too, but how many quacks are going to read it? And if they do, cancel their Costco membership?
There was a time when encountering truly naked propaganda was something of a rarity. Clearly those days are behind us.
Are you referring to the original study, or the rebuttal article?
When was that? Serious question, obvious propaganda seems to be common at pretty much every point in history across multiple cultures.