The Crisis in Nutritional Science

I've mentioned John P.A. Ioannidis on my pages many times before (the first, I think).  He's the author of what’s widely quoted as one of the most downloaded papers in history, “Why Most Published Research Findings are False 2”, in which he presents data that as much as 70% of published science is wrong.

Last year, he extended his purview to probably the richest source of bad science, nutritional epidemiology, in an article in the Journal of the American Medical Association.  Bottom line, this field has got to be fixed because it is just so far from good science that it’s dangerous. Not only is it endangering peoples’ health, it’s ruining confidence in science as a way of finding out how the world works. You can read and download the paper (2 page pdf) here.  The unusual part of getting to this article is that I bounced there from Watts Up With That, a post by frequent guest author Kip Hansen, "Epidemiology, Diet Soda, and Climate Science".  You should RTWT. 

As I always do, some quotes to get you to read it.

In recent updated meta-analyses of prospective cohort studies, almost all foods revealed statistically significant associations with mortality risk.  Substantial deficiencies of key nutrients (e.g., vitamins), extreme over consumption of food, and obesity from excessive calories may indeed increase mortality risk.  However, can small intake differences of specific nutrients, foods, or diet patterns with similar calories causally, markedly, and almost ubiquitously affect survival?

Ioannidis' clear thinking comes across very clearly here.  He's saying that when they looked at epidemiological nutrition studies, almost every food item looked at had “statistically significant associations with mortality risk” or in other words, everything we eat is killing us faster and sooner, or making us live longer, and he asks if that's even possible.  Everything?  Nothing is neutral or has no effect?

Perhaps my favorite paragraph from the whole article (emphasis added):

Assuming the meta-analyzed evidence from cohort studies represents life span–long causal associations, for a baseline life expectancy of 80 years, eating 12 hazelnuts daily (1 oz) would prolong life by 12 years (i.e., 1 year per hazelnut), drinking 3 cups of coffee daily would achieve a similar gain of 12 extra years, and eating a single mandarin orange daily (80 g) would add 5 years of life.  Conversely, consuming 1 egg daily would reduce life expectancy by 6 years, and eating 2 slices of bacon (30 g) daily would shorten life by a decade, an effect worse than smoking.  Could these results possibly be true?

Before you order your yearly 23 pounds of hazelnuts, hold on a minute.  It stretches credulity to think all of those could be true.  One year of extra life for every hazelnut eaten daily?  Or 6 years less life for one egg eaten daily? What happens if you have one hazelnut and and one egg daily?  Do the effects cancel?  Does only one year cancel, so you only die 5 years sooner?  These answer are the result of the way these meta-analyses work; they find spurious correlations.  You might recall an article I did on the King of Junk Food Science (where the adjective "junk" modifies science, not food) and a link to a FiveThirtyEight column where they post funny spurious correlations they found.  In these nutritional studies, they frequently study "all-cause mortality", but the top causes of mortality include accidents (#3) and medical mistakes (usually left out of the rankings, but numerically could be more than accidents, taking #3).  How could they be improving mortality dramatically without affecting those to some degree? 

Individuals consume thousands of chemicals in millions of possible daily combinations. For instance, there are more than 250 000 different foods and even more potentially edible items, with 300 000 edible plants alone. Seemingly similar foods vary in exact chemical signatures (e.g., more than 500 different polyphenols). Much of the literature silently assumes disease risk is modulated by the most abundant substances; for example, carbohydrates or fats. However, relatively uncommon chemicals within food, circumstantial contaminants, serendipitous toxicants, or components that appear only under specific conditions or food preparation methods (e.g., red meat cooking)may be influential. Risk-conferring nutritional combinations may vary by an individual’s genetic background, metabolic profile, age, or environmental exposures. Disentangling the potential influence on health outcomes of a single dietary component from these other variables is challenging, if not impossible.

Dr. Ioannidis concludes nutritional epidemiology is intrinsically unreliable.  It produces results that cannot be considered causal.  I hope/trust that's enough to get you interested in reading the article because I can't do it much justice without well exceeding the limits of TL:DR.  I recommend the version on WUWT rather than the original JAMA paper, if you're only going to read one.  Author Kip Hansen shows the mess that is nutritional epidemiology and then compares the field to climate science, another field with an incredible number of variables that may or may not interact with each other.

Similarly, for climate science, the object of study, the Earth’s climate system is not only exceptionally complex, but also chaotic.  First, we have to understand that, as we see in nutrition science, climate is comprised of hundreds of interacting components, each changing on time scales ranging from seconds to centuries, each being integral influencing and causal factors for the others — all correlated in ways we often (almost always) do not fully understand.  And, as in nutrition science, almost all climate variables are correlated with one another; thus, if one variable is found to be correlated to some weather/climate  outcome, many other variables will also yield significant associations in the huge present-time and historical data sets relating to Earth’s weather and climate.

Thus we find the situation, unacknowledged by most of the climate science field, that [paraphrasing Ioannidis] “Disentangling the potential influence on medium to long range climate outcomes of a single climatic factor, such as atmospheric GHG concentrations,  from these myriad other variables is challenging, if not impossible” based simply on the complexity of the climate itself.

John Ioannidis - Stanford University photo

Meet The King of Junk Food Science

Ever get overwhelmed by the "he-who" studies about food that make the news all the time?  People who eat red meat X times a week are more likely to get Y; that sort of thing?  Meet the reigning king of junk science: Buzz Feed presents the story of Brian Wansink, the head of Cornell’s prestigious food psychology research unit, the Food and Brand Lab. If any one person could be responsible for so many of us saying, "Wait!... Didn't they say that was good (or bad) for us last week?", it's Brian Wansink.

As the head of Cornell’s prestigious food psychology research unit, the Food and Brand Lab, Wansink was a social science star. His dozens of studies about why and how we eat received mainstream attention everywhere from O, the Oprah Magazine to the Today show to the New York Times. At the heart of his work was an accessible, inspiring message: Weight loss is possible for anyone willing to make a few small changes to their environment, without need for strict diets or intense exercise.

To show an example, Buzz Feed leads with a story about a young scientist from Turkey, Özge Siğirci, and the task Wansink gave her.  Earlier, Wansink's lab had performed an experiment at an all-you-can-eat buffet in an Italian restaurant.  Some customers paid $8 for the buffet, others paid half price. After their meal, they all filled out a questionnaire about who they were and how they felt about what they’d eaten.

Somewhere in those survey results, the professor was convinced, there had to be a meaningful relationship between the discount and the diners. But he wasn’t satisfied by Siğirci’s initial review of the data.

“I don’t think I’ve ever done an interesting study where the data ‘came out’ the first time I looked at it,” he told her over email.

The problem is, that's not how the statistical techniques of science work.  You don't sift through tons of data trying to find a hypothesis to publish, you have a hypothesis and then set up an experiment to try to prove or disprove it.  More specifically, you try to disprove the Null Hypothesis; which says that your experiment made no difference and any differences you found are a random event.  Disproving the null hypothesis means your experiment worked.  Wansink is going about things completely backwards: he's looking at results and trying to generate a hypothesis that matches them.  

For example, he gave Siğirci  suggestions for how to massage the data, and would later publicly praise her on his blog for being “the grad student who never said ‘no.’”

First, he wrote, she should break up the diners into all kinds of groups: “males, females, lunch goers, dinner goers, people sitting alone, people eating with groups of 2, people eating in groups of 2+, people who order alcohol, people who order soft drinks, people who sit close to buffet, people who sit far away, and so on...”

Then she should dig for statistical relationships between those groups and the rest of the data: “# pieces of pizza, # trips, fill level of plate, did they get dessert, did they order a drink, and so on...”

Eventually, four papers were published about the pizza study.  All four have been corrected or retracted.  It might be catching up with him.

Wansink couldn’t have known that his blog post would ignite a firestorm of criticism that now threatens the future of his three-decade career. Over the last 14 months, critics the world over have pored through more than 50 of his old studies and compiled “the Wansink Dossier,” a list of errors and inconsistencies that suggests he aggressively manipulated data. Cornell, after initially clearing him of misconduct, has opened an investigation. And he’s had five papers retracted and 14 corrected, the latest just this month.

Now, interviews with a former lab member and a trove of previously undisclosed emails show that, year after year, Wansink and his collaborators at the Cornell Food and Brand Lab have turned shoddy data into headline-friendly eating lessons that they could feed to the masses.

In correspondence between 2008 and 2016, the renowned Cornell scientist and his team discussed and even joked about exhaustively mining datasets for impressive-looking results. They strategized how to publish subpar studies, sometimes targeting journals with low standards. And they often framed their findings in the hopes of stirring up media coverage to, as Wansink once put it, “go virally big time.”

As Susan Wei, an assistant professor of biostatistics at the University of Minnesota interviewed for the article says, it's hard to tell if Wansink is stupid or corrupt.  Well, she was more polite than I am and didn't put it exactly that way:

Wei added. “He’s so brazen about it, I can’t tell if he’s just bad at statistical thinking, or he knows that what he’s doing is scientifically unsound but he goes ahead anyway.”

Longtime readers know that junk science is one of those things that really gets me mad; it's also something I've written about several times (example).  In a way, Wansink is just another example of the replication crisis hitting science, mentioned in that link.

A lot of people in the country really pay attention to these junk studies and try to adjust their life to improve their health and their family's.  There appears to be no attention in Wansink's lab to how good the science is, just that it gets lots of publicity and goes viral. 

It's a long article, but quite an interesting read if you're interested in the "replication crisis" in science, and some of the problems.  It looks closely at some studies Wansink's group is famous for and their problems, and it has interviews with some former students. 

Brian Wansink - AP Photo by Mike Groll - from Buzz Feed

The King of Junk Food Science is Out

Last February, I ran a story about Brian Wansink, whom I called the King of Junk Food Science.  According to ARS Technica yesterday, Wansink is out at Cornell University.

Brian Wansink, the Cornell nutrition researcher who was world-renowned for his massively popular, commonsense-style dieting studies before ultimately going down in flames in a beefy statistics scandal, has now resigned—with a considerably slimmer publication record.

JAMA’s editorial board retracted six studies co-authored by Wansink from its network of prestigious publications on Wednesday, September 19. The latest retractions bring Wansink’s total retraction count to 13, [Note: that page shows 35 papers retracted at this time - SiG] according to a database compiled by watchdog publication Retraction Watch. Fifteen of Wansink’s other studies have also been formally corrected.

Amid this latest course in the scandal, Cornell reported today, September 20, that Wansink has resigned from his position, effective at the end of the current academic year. In a statement emailed to Ars, Cornell Provost Michael Kotlikoff said that an internal investigation by a faculty committee found that “Professor Wansink committed academic misconduct in his research and scholarship, including misreporting of research data, problematic statistical techniques, failure to properly document and preserve research results, and inappropriate authorship.”

The important part of the article last February is what ultimately got Wansink out of his job: he wasn't doing science, he was trying to find things that would catch public attention and go viral.  The way it's supposed to work is that a researcher comes up with a hypothesis and then does an experiment to determine if their hypothesis is true; more precisely, they evaluate the null hypothesis that the experimental results were random and not due to their hypothesis.  Wansink would collect gobs of data and then try to find hypotheses that are true based on that data.

But, in a November 2016 blog post, Wansink inadvertently sank his own fame by noting that he encouraged his graduate students to go on statistical fishing trips, pushing them to net unintended conclusions from otherwise null nutrition experiment results. This is a huge red flag to researchers because such statistical fishing is a well-established method for reeling in false positives and meaningless statistical blips, like finding a link between cabbage and innie belly buttons. Moreover, many researchers see the dubious approach as fueling a crisis in social sciences in which findings from key studies—like Wansink’s—are not reproducible by other researchers, calling into question their original validity.

The blogged confession led to several other researchers sifting through Wansink’s studies and stats. Prime among those researchers is education researcher and blogger Tim van der Zee of Leiden University in the Netherlands. By last year, van der Zee and colleagues had identified at least 42 Wansink studies with alleged issues ranging from minor to severe. Those studies had collectively been cited by other researchers 3,700 times, been published in over 25 journals and eight books, and spanned 20 years of research, van der Zee noted.

As I've talked about in these pages before, there are several serious crises going on in science these days.  The biggest is reflected in the August 2005 paper by John P. A. Ioannidis which has become one of the most downloaded papers ever, "Why Most Published Research Findings are False".  Ioannidis points out that the majority of scientific papers are wrong; as much as 70% of published science is wrong.  Not just biomedical but hard sciences like particle physics. 

But maximising a single figure of merit, such as statistical significance, is never enough: witness the “pentaquark” saga. Quarks are normally seen only two or three at a time, but in the mid-2000s various labs found evidence of bizarre five-quark composites. The analyses met the five-sigma test. But the data were not “blinded” properly; the analysts knew a lot about where the numbers were coming from. When an experiment is not blinded, the chances that the experimenters will see what they “should” see rise. This is why people analysing clinical-trials data should be blinded to whether data come from the “study group” or the control group. When looked for with proper blinding, the previously ubiquitous pentaquarks disappeared.

Simply, the peer review process is broken - perhaps irreparably.

Science itself, as it currently works, may well also be badly broken. In the Spring/Summer 2016 issue of the new journal The New Atlantis, some important points were brought up.  As I excerpted in August of 2016.

As WWII came to a close, there was an acknowledgement of how much that scientific teams had contributed to the victory and a deliberate effort to keep those teams together.  Vannevar Bush, the MIT engineer called the “General of Physics” by Time Magazine, was the public face behind this push.  He pushed a vision so appealing in its imagery that everyone bought into it.

Scientific progress on a broad front results from the free play of free intellects, working on subjects of their own choice, in the manner dictated by their curiosity for exploration of the unknown.

Through example after example Sarewitz demonstrates that the progress of the late 20th century was virtually never, “free play of free intellects, working on subjects of their own choice”, but instead was almost always science being managed, being driven on specific topics for specific applications.   Scientific knowledge advances most rapidly, and is of most value to society, when it is steered to solve problems — especially those related to technological innovation.  Could it be that the War on Cancer has floundered because there's nobody in charge; nobody driving toward a goal and asking specific people specific questions? 

The typical academic scientist in a university lab may bristle at the thought of being given an assignment by a boss somewhere, and being held accountable for results.  Nevertheless, a persuasive argument can be made that this might be the way to fix science.

Five Thirty Eight did an experiment to show the kinds of spurious correlations that arise from using the typical tools of dietary studies: food frequency questionnaires and recall studies.  Their study demonstrated that eating egg rolls was strongly associated with dog ownership, and that eating cabbage was strongly associated with having an "innie bellybutton".  That's some real Brian Wansink quality science there!

Brian Wansink in a publicity photo.  AP Photo by Mike Groll - from Buzz Feed 

Another Steaming Pile of Junk Science

In my personal "war on junk science" I can't really influence anyone who's doing the science, I can only hope to point the junk being passed on out there.  Maybe if enough of us hassle the agencies paying for this crap, things might get better.  Hah!  I make myself laugh sometimes.  We won't affect those agencies. 

In essence, this is a followup to a post from just over one year ago, The War on Meat and is based on a long post called "Why are We Basing Food Policy on Black Box Data?" from Nina Teicholz at her substack, the Unsettled Science newsletter.  Going from memory here, Nina was newspaper journalist in her early career.  At some point, her newspaper assigned her to be the food correspondent, sort of a "secret restaurant critic".  At some point, she couldn't help but notice how much better food prepared at some restaurants was and somehow learned it was because of the natural, real butter and cream they used in sauces.  Like most people, she grew up fat phobic and was afraid of it.  This led to her researching and writing a book called The Big Fat Surprise which is just full of stories of the kind of crap that goes on in food science (I've often thought if I treated test data on how some electronic system performed like Ancel Keyes treated the cholesterol vs. heart disease risk data in his famous "Seven Countries Study," I'd be in jail.)  That led her to become one of the founders and first president of The Nutrition Coalition, a grass-roots organization of people trying to clean up the US Dietary Guidelines.  

It shouldn't be a surprise, just as everywhere else and every little thing the Fed.Gov touches, industries and lobbying organizations pushing their particular agendas, spreading money around directly or indirectly.  

It's a bit on the long side, but definitely worth a read.  As I usually do, I'll post some highlights here to tease going there to read the whole thing.  The main topic is in serious errors in a study called the Global Burden of Disease study, which like so much other junk science, tries to link the harm done to people by their diets.  A note from epidemiologist John P.A. Ioannidis goes particularly well here; so well it could have been written about this study, but wasn't. 

In recent updated meta-analyses of prospective cohort studies, almost all foods revealed statistically significant associations with mortality risk.  Substantial deficiencies of key nutrients (e.g., vitamins), extreme over consumption of food, and obesity from excessive calories may indeed increase mortality risk.  However, can small intake differences of specific nutrients, foods, or diet patterns with similar calories causally, markedly, and almost ubiquitously affect survival?

As we said then, how could everything have “statistically significant associations with mortality risk”?  How could everything either lengthen or shorten our lives and nothing be benign?  That's what the GBD study is looking like.  Back to Nina Teicholz:

It turns out that a highly influential 2019 claim—that no amount of unprocessed red meat is safe for health—was completely inaccurate, according to a statement in March by the authors of the Global Burdens of Disease study (GBD), an on-going project funded by the Bill & Melinda Gates Foundation. Two years earlier, in 2017, these same authors had judged red meat to be the least likely cause of death among 15 risk factors analyzed. Then, in 2019, red-meat’s risk jumped 36-fold. A forthcoming publication will correct these errors, and the risk will drop significantly, said the lead author Christopher Murray, in an interview. Despite the inaccuracies, however, he says he does not intend to correct or retract the paper.
...
GBD has also been a collaborator with the World Health Organization since 2018, and its numbers are increasingly being used by the United Nations, including work to reduce meat consumption as part of the UN’s “Sustainable Development Goals.” The most prominent of these groups, EAT-Lancet, for which Murray was a “Commissioner,” aims for everyone on the planet to eat zero to 2.4% of calories as red meat.

Altering the world’s diet along these lines is intended to stop global warming, yet anyone can agree that global policy affecting human health ought to have a foundation in reliable data. With the still-rising epidemics of obesity and diabetes, we can’t afford false steps. In this light, GBD’s wildly fluctuating food-risk estimates look perilous.

It's not just their estimates on red meat that are problematic. 

In fact, other food risks calculated by GBD also changed dramatically from 2017 to 2019. The risk of salt dropped by 40%, while risks attributed to diets low in fruit, nuts and seeds, vegetables, seafood omega-3 fatty acids, and polyunsaturated fatty acids declined by more than 50%.

One of the sources Teicholz links to is an article from the Journal of the American Medical Association, known widely as JAMA telling the story of the attacks on a different medical journal's editor by a group calling itself the True Health Initiative (THI).  This other journal, Annals of Internal Medicine was only tangentially involved, getting some letters critical of the GBD studies.  The editor noted the hostility and tone of the THI emails (apparently she got 2000 copies of the same email) was the worst she's ever gotten.  

This gets into the way the anti-meat sources resemble all the leftist/cancel culture stories we hear.  There have been doctors who have had their lives ruined for not following the accepted stories.  When the stories are wrong and need to be corrected, groups like THI fight like mad.  Everyone knows the line that goes: "if a conservative doesn't want to eat meat, they don't eat it; if a liberal doesn't want to eat meat meat, they demand that nobody eat it and the world stop producing it."

Go read. 

How Do You Tell Junk Science from Good?

In the last week, I've linked to a few stories that touch on how science reporting - mostly bad, occasionally good - touches us.  The over-hyping of every solar flare, the poor information on radiation levels from the Fukushima disaster, and the cardiac surgeon (3) who believes we've been told all the wrong things about what to eat and what to avoid.  It raises a very important question:

How can we average people tell when we're being exposed to good science and how do we know junk science?

It used to be pretty easy; in the case of a medical study, the more people who were in the study and the longer the study ran, the better; for the harder sciences, if the study was reported in Science or Nature, or another of the big journals, it was probably as sure as anything.  If there's one thing the Hadley Climate Research Unit's emails (ClimateGate) should teach us, it's that considering the journal's reputation is useless today.  There are scientists involved in climate modeling who really are in search of the truth; it just seems that at the highest levels, it's about as corrupt as Chicago politics.  Among the highlights of those emails was how the top guys at Hadley actually controlled journals, getting editors fired if they dared publish anything that questioned the "orthodox view".  So much for judging by the journal it's published in. With the major journals controlled by the "priesthood", any advances will only show up in the smaller, less prestigious journals, inviting the sneers of the priesthood.   

Nor does it mean anything if the ideas appear well supported by other scientists.  The American Physics Society, certainly one of the great academic societies in the world, has declared "the evidence is incontrovertible" about man-made global warming.  Nobel prize-winning physicist (1973) Dr. Ivar Giaever resigned as a Fellow from the American Physical Society (APS) on September 13, 2011 in disgust over the group's promotion of man-made (anthropogenic) global warming fears. 

"In the APS it is OK to discuss whether the mass of the proton changes over time and how a multi-universe behaves, but the evidence of global warming is incontrovertible? The claim (how can you measure the average temperature of the whole earth for a whole year?) is that the temperature has changed from ~288.0 to ~288.8 degree Kelvin in about 150 years, which (if true) means to me is that the temperature has been amazingly stable, and both human health and happiness have definitely improved in this 'warming' period."

When the APS simply published a long letter from Lord Monckton, a well known skeptic about AGW, they went so far as to publish a disclaimer that this was not the APS viewpoint - something they have never done about really "out there" quantum physics.   (It's at the top of article in that link) As Dr. Giaever says; it's acceptable to talk about the Copenhagen interpretation of quantum theory, which implies that for every decision we make, a parallel universe pops into existence, but it's not acceptable to question computer models about future climates that claim accuracy to even one decimal place? 

A good rule of distinguishing good science from bad might be if they use terms like "evidence is incontrovertible", or "the science is settled", or they have a "consensus committee", it's junk science.  Look at it this way: nobody holds a consensus committee or issues statements like "incontrovertible science" about gravity, where the science, while not settled, is accepted.  Nobody calls a consensus committee unless there is no consensus.  

Neither is it necessarily true that you can judge the quality of research by the connections of the scientist to some "evil" funding agency.  While this is sometimes true (famously, the tobacco companies' responses to anti-smoking studies) it has become a bogeyman used to link anyone who opposes you with an evil funding source - typically a big business .  Linking opponents of AGW with the "big oil", for example.  Everyone works for someone, and scientists who work in government-funded labs are not glowing saints free from comprises either; they often work for agencies with an agenda (the EPA for example).  In the case of the dire warnings about solar flares, do you think NASA might have a dog in the fight?  In an era when government budgets need to be slashed, do they have an interest in trying to get research money to fund more space science missions?  "Cut someone else!  We're more important!"

There's a saying that goes, "the most important words in science aren't 'Eureka, I've found it!', they're 'that's funny....' ".  Science progresses when someone notices the funny results that don't fit the current ideas and begins pulling on the loose thread.  At the beginning of the 20th century, for example, many physicists thought that everything was known about physics and their field was done.  There would be no more physicists in a few years.  All that remained was to dot a few "i"s and cross a few "t"s.  The loose thread someone pulled on led to relativity, quantum theory and an entire century of rich science that no one suspected was there. 

Dr. Dwight Lundell, the heart surgeon I mentioned the other day, appears to be a good example of the kind of guy who says, "that's funny...".  He has observed that the recommendations from the FDA and the other experts who advise us on what to eat have had unintended consequences worse than what they were trying to address - without fixing the problem.  He has written a book to tell you his findings.  Should we ignore his experience with thousands of patients and just assume he's only in it for the money?   The mere fact he's making some extra money on a book says nothing about whether he's right or wrong, and everyone has a right to make an honest living. 

Dr. Lundell is not alone.  Dr William Davis runs a "Track your Plaque" website/program for people diagnosed with actual cardiovascular disease, and his recommendations parallel Dr. Lundell's.  The whole lipid/cholesterol hypothesis is badly broken (at least, IMO), and there are probably thousands of people studying it who will tell you that (excellent summary pdf).  Dr. Duane Graveline, former NASA astronaut and M.D., has a good introductory website.  Statins may have some benefit, but those benefits likely have nothing to do with cholesterol lowering, but are from the changes to epithelial cells that they cause.  They seem to me to be extremely over prescribed. 

The whole low-fat mantra has led to a very fat industry that produces tons of heavily processed and modified foods that make them bundles of money.  In your typical grocery store, anything around the walls tends to be "whole food" (i.e., milk, cheese, butter, meats, fish, poultry, fresh fruits and veggies) that makes these companies nothing, while the other 80% of the store is filled with these processed products (i.e. breakfast cereals, cake mixes, pastas, sodas, breads, all kinds of prepared foods).  Perhaps the McGovern aides who started the whole "low fat diet as national policy" thing simply wanted to force everyone to eat like they do in Big Sur, but in the end, they got SnackWells, Honey Nut Cheerios and other highly processed junk that got a good reputation because it said "low fat" on the label.  Even the mandatory FDA labels that count a handful of nutrients, sodium, and macronutrient composition are deceptive about whether a food is a good choice to eat. 

I wouldn't trade our free market, even as badly distorted as it is, for any other system, but one of the problems with it is that industries and trade groups get together and manipulate government bodies to get their research funded (in the case of climate modeling) or to make their products to appear favorable (in the case of diet/food).  My answer to this is government is too damned big if these tactics make money, but I know that's wishing for days we'll probably never see again.

It's tempting to say that any time the news headline starts with "scientists say", ignore it.  The problem is we need to stay on top of all of this, not only for our good health, but because the behemoth Fed hydra, constantly, addictively driven by thirst for control, uses these ideas to control us.  And it doesn't have to be good science for them to use it as justification for controlling your life.  It's also tempting to say only trust things you read in small journals by honest (if not iconoclastic) researchers, but these things rarely make the news and feed back to the first point: if the news starts with "scientists say", ignore it.  And I think I stand by what I said the other day:  In general, if a government committee recommends something, do the opposite.

What If I Trust Science, But Don't Trust Big Government Science!

If you pay any attention at all to the drivel coming from the leftist media (the vast majority of media) you'll have heard the idea that anyone who questions anything from authority is a "science denier."  That's an awful term, crafted to create a subconscious link to holocaust deniers.

This has been rumbling in my mind a lot, but credit (blame?) PJ Media author Stacey Lennox for bringing it into focus today with her piece, "What If I Trust Science and Don't Trust Dr. Fauci?"  Her emphasis is on Dr. Fauci and the Kung Flu crisis, but it's broader than that.  Let me go with a few of her points for a while.

To begin with, she quotes Dr. Fauci himself from a US Department of Health and Human services podcast saying:

“One of the problems we face in the United States is that unfortunately, there is a combination of an anti-science bias that people are — for reasons that sometimes are … inconceivable and not understandable, they just don’t believe science, and they don’t believe authority,”

The problem is that this week, the same Dr. Fauci admitted that he lied to Americans about the effectiveness of masks. They decided to tell us masks didn’t work rather than tells us they were effective in preventing the spread, but please refrain from buying them until we have an adequate supply for healthcare workers.  Personally, I believe if they had simply said, "if you buy up all the masks and healthcare workers don't have them, we'll have to abandon hospitals because workers are required by law to wear them" that people would have been understanding and bought up fewer masks.

It's a lot easier to trust people who don't have a documented history of lying to you.  Could that be part of it Dr. Fauci?

Another topic that doesn't make sense and leaves me with Looney Tunes-style question marks in the air over my head is why is an old, well-known drug that was showing promise against the disease so politically divided?  The easy answer is that a couple of conservative commentators started talking about success with Hydroxychloroquine and then the president started talking it up.  Suddenly, liberal commentators couldn't acknowledge they might be right.  Dr. Fauci joined the liberal pundits against the drug.  Ms. Lennox (a Registered Nurse) says:

The debate over this generic drug that has been in use for decades is one of the most puzzling and ridiculous things about the entire pandemic. The medicine was politicized and became controversial. After researching it myself and listening to practicing physicians who were using it, I expected Dr. Fauci to step up and clarify why there was a reason to believe it may work in conjunction with the mineral zinc. He never did.

I found this odd since the drug’s older cousin, chloroquine had been demonstrated to inhibit the SARS virus, which has a 90% overlap with COVID-19. The NIH did this study in 2005, where Dr. Fauci is a director.

We report, however, that chloroquine has strong antiviral effects on SARS-CoV infection of primate cells. These inhibitory effects are observed when the cells are treated with the drug either before or after exposure to the virus, suggesting both prophylactic and therapeutic advantage.

Any doctor that was recommending the treatment recommended it be given with zinc. The properties of zinc on RNA viruses, which COVID-19 is, are also well known. Again a study from the NIH in 2010 shows that with a companion ionophore, or drug that allows more zinc to enter the cell, the mineral interferes with the replication of the virus. Both chloroquine and hydroxychloroquine are zinc ionophores.

When people see contradictory messages, they try to understand why one group of doctors currently treating patients with these combinations and reporting excellent success gets no press or negative press, while another group of doctors saying it's poisonous and will kill people gets all the media attention.  As result, people try to think of reasons and decades of experience with government at all levels brings thoughts of corruption to mind.  I really doubt that I'm the only guy who has heard people saying Dr. Fauci and the Anointed Health Experts must be in the pocket of Big Pharma.  After all, they argue, why use a very old, cheap, generic drug when there are newer, more expensive drugs, like Remdesivir that can be sold?   Why should they allow drugs that cost a couple of bucks per dose when there are drugs that cost a hundred or hundreds of bucks per dose? 

Again, it's easier to distrust people who have lied to you before.

Stacey Lennox's article contains more good information related to the virus crisis, but if you take the same thoughts and expand them to wherever government big Science! is involved, you get similar answers.  The easy one to cite is climate change.  The trillions of dollars at stake have attracted the grifters that humongous sums of money always attract and every claim has to be carefully examined. 

Another example is the USDA Dietary Guidelines.  The science behind the USDA recommendations is atrociously terrible - the King of Junk Food Science is an example of the kinds of stuff they're based on.  Part of that is because it's both ferociously hard and expensive to do the kind of experiments that can give the answers people want.  While, saying, "it's too hard" is a hell of a poor thing to say, it's better that they're honest about how tenuous their data is.  The committees drafting the 2020 Guidelines have been meeting this year and they've been more (apparently) corrupt than ever, prompting federal Whistle Blowers to come forward and report bad behavior on the part of the subcommittees involved. Full disclosure: I've donated to the Nutrition Coalition and think that their work is good.

It would be better for the USDA guidelines to be shut down and the government to get out of the business of telling people what to eat, but right now that would require many laws or regulations to be revoked because the dietary guidelines influence military meals, school lunch (and breakfast) programs, hospitals, nursing homes and all sorts of institutional programs that feed people.  

Real science is a rigorous process for learning.  It's never preaching from a standpoint of "I'm all-knowing" and it's never "settled" except in the rare cases of physical law and one way you can be sure it's settled is that nobody is doing research into a field.  Nobody disputes that gravity exists; there might be research into fine details of the subject, but the fact that gravity exists is settled.

In the case of a new disease, nobody can be expected to know enough about it when it first appears.  It was quickly apparent that the doctors on front lines treating patients knew far more about it than the Experts.  Consequently, if people don't flock in admiration to the Anointed Experts, it's not that people have "an anti-science bias," it's that they know they've been lied to before or that their life's experiences tells them something funny is going on.  You see, Dr. Fauci, it's not "anti-science bias" to question things.  Questioning things is the essence of science.  Just accepting things the scientists say is the essence of religion. 

Dr. Fauci and Vice President Pence, April 19 Covid-19 press conference.  (AP Photo/Andrew Harnik)

How to Become a Famous Scientist - Just Use This One Simple Trick

I write about junk science fairly often.  There seem to be two main reasons for that.  First, it's one of my favorite topics, but behind that is the fact that there would be far less to write about if it weren't for the fact that we appear to be in the Golden Age of Junk Science.  There is far more junk science than at any point in my lifetime - or maybe I just notice it more, but don't think about that.  

Examples?  A recent example that leapt off the page at me and gathered a lot of attention is Harvard researchers have announced that eating red meat just twice a week causes diabetes.  But SiG, I hear you thinking, that's not what that headline says.  It says "may."  Yes, but junk science always uses those "hedge your bets words" like could, might, may, should and so on.  That way they never can be held accountable for misleading the world.  "We never said it would cause diabetes, we said it may.  We didn't say what the percentage chance was because we need more funding to find that out.  (Ooo! They win twice in that disclaimer!) 

Like virtually all of the food correlations you read about, they depend on Food Frequency Questionnaires (FFQs) which are notoriously unreliable.  Legendarily unreliable in fact.  FFQs have survey questions like, "list what you had for lunch in March of '22" (usually multiple choice) 

But that's not all. An honest assessor of papers like this, Dr. Zoë Harcombe, Ph.D. in Public Nutrition, did a must-read analysis of the study, but we don't always get that.  A few money quotes:

• This makes no sense. Diabetes is essentially the inability to handle glucose. Meat contains no glucose. Carbohydrates contain glucose. My immediate thought was – don’t blame the burger for what the bun, fries and fizzy drink did. 
• The definition of red meat included sandwiches and lasagna.  Lasagna is red meat?  It's only red from the tomato sauce covering it.  You just can't see that because of noodles covering it.
• As if FFQs aren't bad enough, the serving sizes have changed since the original Food Frequency Questionnaires
• Total red meat was claimed to have a higher risk than both processed red meat and unprocessed red meat. Total red meat is the sum of the other two. It can’t be worse than both.
• The relative risk numbers grabbed the headlines; the absolute risk differences were a fraction of one per cent.

Since we don't get a column like Zoë's for every study, the takeaway message about studies like this is that "correlation doesn't mean causation."  All they can possibly find is correlation.  Second to that is to know that the lobbying group with largest impact on society has got to be the vegan lobby.  It’s good to realize that it's just the latest paper from the Harvard correlation study factory.  All their papers promote plants and condemn animal foods.  And all of it, every one I've ever seen, is junk.

The easiest place to find correlations is in climate research.  Have you seen a story about climate change doing something or other and thought, "climate change; is there nothing it can't do?"  Think correlations, not causation.  For example, Watts Up With That published a story this week that says climate change is causing more allergies.  

Minnesota Public Radio (MPR) ran a segment during its local morning edition titled, “Climate change is contributing to an extended allergy season.”

The author's point is that it could be true, might even be a cause and effect relationship, but it's a negative consequence to something that's good for the world in general.  Some people will need to get more treatments, but that's not everyone (I'm one - I've had allergies since my teenage years).  By and large, allergies are treatable. 

Since correlation sells, I want to drop a simple idea that I haven't seen anywhere else.  Let's believe for the moment that global temperatures are increasing, and ignore the big questions that raises.  That means that anything else that can be found to be increasing in the time period in which temperatures are rising will be found to be directly correlated to climate change.  Conversely, anything that was found to be decreasing in time is inversely correlated to climate change.  Perhaps you could say climate change was endangering species.  Never mind.  That's been done.

In the first piece, instead of saying eating red meat causes diabetes, you can just as accurately say climate change causes diabetes.  The two things have increased in the world in the same time period.  Climate change causes microplastics in the Pacific ocean.  Without doing the research, I bet if you went back to the 1950s, let alone the late 1800s temperature reference period, you wouldn't find the word microplastics or even the concept.  Today, it's hard to go a week without seeing a microplastics story. 

Think of it!  No more need to waste time compiling fake data; if they're both increasing, one caused the other.  Food Frequency Questionnaires? Fuggedaboutit. Just ask the AI to fill it out or make it up completely.  You know people have been getting bigger and more obese in America.  It's going up, it may not go up exactly at the same slope as temperature (pictured below and far from constant) but it correlates with global temperatures so just say that climate change is causing people to get bigger and more obese.  Or red meat consumption. Your choice. If you find data that says vegetable consumption has gone up - ever notice "fruits and vegetables" has become one word, fruitsanvegetables? - you can conclude climate change caused it. You could conclude fruitsanvegetables consumption caused Americans to get bigger and fatter, but that'll get you cancelled.

University of Alabama Huntsville measure of the lower atmosphere temperature from 1979 to last month.  From Watts Up With That

Junk Science of Both Kinds

Lately I've been noticing that I have two basic reactions to science stories that I see linked to around the net.  The first reaction is "that's obviously bullshit" and the second is really the same sort of reaction except for being positive: "I hope they didn't pay too much for that study". 

For an example of the first one, a story started making the rounds 10 days ago that this week eggs are bad for us again.  You may be one of the folks who feel like this old Sidney Harris cartoon:

Being a curious guy, I noticed there was next to nothing in the original reports.  It finally made it online a few days later so it was easy to take a quick look at the abstract for some details. 

Let me start with the disclaimer that I'm not a medical doctor or a statistician, but I'm a lot closer to being a statistician than an MD and that's all I need to find what's wrong with this study. 

Leading with the thing about the study that's good, they have a good number of subjects, nearly 30,000.  The big problem with the study is that it's a meta-analysis of six other studies.  Whenever a statistician gathers data from multiple experiments there's a mountain of hurdles they need to overcome, especially if the original studies were of something other than the effect being studied.  Practically, that means you'd want the risk ratio (the amount that the risk of bad outcome increases) to be large - like twice the risk or three times the risk of "non-treatment" group.  This study showed a relative risk increase of 17%.  While I know there's a whole crisis going on over statistical significance limits, I don't think a 17% relative risk increase is likely to be real.

Like the vast majority of these "he-who" studies, "he who eats N eggs per week" relies on food questionnaires.  The accuracy of these surveys has been widely criticized from the standpoint of memory accuracy (how many eggs did you eat per week last March?) and other things.  In this study, the six datasets being pooled might have handled things like recipes that use whole eggs differently with some counting them and some not counting.  Of course, no study measuring correlation can prove causation and that's another big problem with this study.  Finally, for a look at just how bad some of this can be, read my first post on the King of Junk Food Science.

(Table from Five Thirty Eight)

In the other class, "I hope they didn't pay too much for this", yesterday's Daily Mail is reporting that male and female brains are actually different!  Not only that, the differences are apparently observable while the baby humans are still in utero; during the second half of pregnancy.   I'll say it's interesting the difference is detectable, but is there really any doubt that there are differences between the brains of males and females? 

As you can imagine, the people saying that sexual differences are all learned behaviors are upset over this study, calling it ‘unfounded conclusions’. 

What I want to ask those people is if they've ever just sat back and observed male and female cats, dogs, or other animals.  We have a male and a female cat and they are so obviously different one would have to be extremely non-observant to not see it.  Furthermore, they're both neutered and were both neutered young, so it's not their current levels of testosterone or estrogen.  Influence on the brain before birth or up until they were neutered is completely logical.

I guarantee you our male, Mojo, has never played with toy trucks, hot wheels, or Nerf guns.  Likewise, our female, Aurora, has never played with Barbie dolls or tea sets - and while I think she'd use a tea set if she had one, she doesn't have a thing for shoes.  Their behaviors are absolutely not influenced by TV, advertising, or any of those cultural factors the people saying "they're learned behaviors" claim. 

The War on Meat

While it turns out that the widely reported story that the latest Biden "infrastructure" bill had provisions to limit beef consumption to a few ounces a month was a fake news story tracked to the UK Daily Mail, that's a minor distraction.  There really is an all out war on eating meat, especially beef, which is grounded in nothing but pseudoscience and propagated rumor.  It has been going on for years and if you're like most people, you've probably have heard some of the arguments so long you tend to think they're true. 

We've covered some of this sort of stuff here.  Junk science is a pet peeve of mine and you'll hardly find an area of science more filled with junk than diet recommendations.  I'll link to this piece because it carries a great table of spurious correlations of the kind that show up in what I've called "he-who" studies:  "he who eats (or does) X is more likely to get Y;" that sort of thing.  There's a great deal of desire on the part of many people to know what they should eat.  Simply saying, "eat what your grandparents ate, not industrial foods" which is honestly as a good a recommendation as anything, doesn't get accepted well.  The alternative, real, randomized controlled experiments that would last for decades, is prohibitively expensive, hard to do, and nobody wants to wait.  As we noted while going through my wife's cancer 24 years ago, it takes five years to get five year survival data; extrapolate that to it takes a lifetime to get life extension data. 

The rest of the world does appear to want to institute a carbon tax on meat because of grossly exaggerated figures on the amount of impact animal farming has on methane production.  First off, the methane from cows is 1.8% of the greenhouse gas emissions in the US.  Second off, methane doesn't come from cattle farts, it comes from cattle burps.  I realize that might be a minor distinction, but the EPA, those high priests of junk science, jumped on the "regulate cattle farts" bandwagon under Obama.  The UN claims cattle create 18% of global greenhouse gas emissions - more than comes from transportation - but they're lumping in all livestock, not just cattle, to include poultry, lamb and all sources of meat.  They're also including the effects of animal feed production, feed harvesting, feeding the animals, the farm vehicles that tend to these animals and everything up to the emissions from the slaughterhouse.  A third of that 18% is blamed on deforestation specifically in Brazil.

Both of those summaries are dishonest.  First, it's not fair to blame methane production in chicken farming on cattle farming, and it's unfair to include everything that the goes into food production to just the tailpipe emissions of vehicles rather than the equivalent entire life cycle associated with transportation.  Second, the part about deforestation is dishonest for two reasons; the easiest being that there's no equivalent deforestation in the US, or in other parts of the world.  In the US the story is reforestation.  We have more trees today than a hundred years ago.  The other reason is that not all grassland could be forest and not all forest can convert to grasslands.  There is some relation between the two, but it's not simple subtraction.  Simply, much of the planet can't be dense forest and can only be grassland. 

Chances are, you've heard until you're subconsciously convinced that low fat foods are healthier.  That data was always suspect, but that cynical observation applies that says old science theories don't go away because the weight of evidence pushes them aside; they go away because old scientists who support them die off.  Since about 2000 there have been many good quality meta-analyses of all the studies that have been done before and concluded the evidence is just too weak to matter.  The diet-heart hypothesis that lifetimes of eating fatty foods and having elevated cholesterol levels led to heart attacks has had conflicting data, like that in older adults higher LDL is associated with longer life, long enough for studies to have essentially concluded the diet-heart hypothesis is dead. 

What about vegetarianism?  It's another belief that has far more faith behind it than evidence.  Seven years ago, I ran a review on a book I'd read by health writer Denise Minger, called "Death by Food Pyramid."  Denise was a 17 year old who had thought she should become a raw food vegan but was unaware of the constant effort required to not destroy her health.  Vitamin B12, for example, just doesn't come in plant matter, at least not to any level that eliminates the need for supplementation.  In Denise's case, she simply needed 17 teeth fixed.  At 17, she went to the dentist and after way too many disconcerting "hmm" sounds, heavy sighs, and pokes with pointy metal objects, found she needed to have 17 teeth worked on - coming from never having had dental problems before she became a vegetarian.  In the space of one year. 

In all of these struggles over diet, we have the same conflicts of interest of special interests that we've had with the Covid fiasco.  Everyone pushes to get their favorite industries pushed by the USDA Dietary Guidelines.  The vegetarian movement is largely pushed by the Seventh Day Adventist church, and some influential doctors they've won over to their side, like Dean Ornish, a diet book author and M.D., and Walter Willet, the very influential head of Harvard's School of Public Health.  The lowfat crowd is pushed by the grain and cereal industry.  The push to get people to eat less meat and saturated fat is pushed by the vegetable seed oil industry, which may well be the absolutely worst things in our processed foods. 

Someone who has spent the last several years fighting to get the USDA Dietary Guidelines fixed is Nina Teicholz, who went from being a low-fat, vegetarian food writer to an omnivore heading the Nutrition Coalition, an organization trying to get the dietary guidelines to more honestly assess science that has been pouring in within the last 20 years.  This an hour long, but very worthwhile talk on many of these topics. 

At the risk of overstating the obvious, If a Government Committee Recommends Something, Do The Opposite, as I said here.  If they tell you to limit red meat, maybe you should eat more of it.

Going After the Junk Science

Tonight’s ramble is going to be my take on the MAHA movement.  My view is heavily influenced by the years (in the early 1970s) when I studied biochemistry through my junior year of college.  I imagine some people will throw that out as being too “establishment,” but I think there are good things to talk about and ideas to spread around down this road.

The first thing I stumbled across that made me pay attention to RFK Jr. was him saying that in the past, autism struck something like 1 in 10,000 kids while today it’s 1 in 34.  Put another way, it has gone from a 0.0001 portion (0.01% of kids) to (1/34 or 0.0294 (2.94%)  At almost 300 times the previous percentage, that’s a monstrous increase and it really needs to be investigated.  

The problem is that we don’t know, as proven by any real science, why this has happened.  Some people will say vaccinations, but we have just as much proof of that as we do that it was chem trails or that chem trails are simply jet engine exhaust, or anything else.  So how do we establish a cause with as little doubt as we can?  How do we prove if one specific thing causes an effect? 

As I quipped the other day, junk science is a favorite topic of mine, but we have enough now.  We don't need to add volumes more junk in the effort to improve the many widely quoted statistics. 

The gold standard way to really prove causation is double-blinded, randomized, controlled trials (I’ll just call them RCTs because that seems to be common) – and potentially a LOT of those RCTs.  The golden rule here is the bigger the population being experimented on the better.  That makes these sorts of studies hard to do, take a long time, and burn dumpsters full of money.

So what are RCTs?  A controlled trial is an experiment with two groups: the experimental group that gets the thing being tested and a second group called the control that gets something expected to have no effect at all, usually called a placebo.  (While many people envision something like a sugar pill, sugar clearly has effects on some things so the placebo has to carefully chosen – a placebo for an injection might be “normal saline” or saltwater.)  Randomized means that a group chosen to be used in the study is chosen to be as identical as possible, and exactly which group a subject goes into (experimental or control) is chosen randomly.  Blinding a study means either the subject or the experimenter that gives them their treatment knows which group they’re in; double-blinding means that neither the subject getting the treatment or the person giving them the treatment can know if it’s the real treatment or the placebo. 

I hope you’re seeing a big problem here.  Let’s say we want to find if giving a particular vaccination causes autism.  We need two big groups – the bigger the better – to experiment on.  Then we have to monitor them for however long we think it takes to be able to say “if they haven’t gone autistic by now, they’re not going to.”  How long?  Here’s where the question might not be as long as it could be for other things.  Maybe there’s evidence that if they don’t start showing signs in the first couple months they never do; maybe it’s more like if they don’t show signs in five years they won’t, and maybe it’s 10 years or fully adults.

Now it gets harder to run the tests.  Nobody gets one vaccination; today’s kids get larger numbers than even 30 years ago.  In the RCTs, we can test whether getting two specific vaccines staggered in time however the protocols assign them can cause the autism.  They can’t get any other vaccines or anything the control group doesn’t get.  We need more huge groups to experiment on.  

And it gets even harder; astronomically harder.  In probability and statistics classes they cover how to compute how many possible combinations there are.  It’s worse than this, but let’s assume kids get 15 vaccines and we want to test every combination of two out of the 15 in an RCT.  How many RCTs does it take?

That shows that to test 15 vaccines 2 at a time, takes 105 RCTs.  That would be like 1 vs 2, 1 v 3, up to 1 vs 15 then 2 vs every other, 3 vs every other and so on.  If it’s 30 vaccines, twice that “N” in the calculated number, that 105 jumps to 435.  The last time I did any research on this question, the results were that there have never been any tests like even one of these about interactions between combinations of vaccines, but it has been some years since I looked. 

The shear number and cost of those tests could be one of the reasons it has never been done, however just vaccinating everyone instead of testing it rigorously and carefully should not be the way to approach this.  

This is one of the reasons why science is in such deep trouble these days.  Now think of a harder thing to do an RCT on: dietary guidance.  An example some people might be interested in would be something along the lines of “if I eat something they say is bad for me once a week, let’s say bacon, is that going to shorten my life compared to never eating it.”  To do a rigorous RCT, you’d need to get a couple of groups of lots of people that are genetically similar (to rule out effects from that) and study them from childhood throughout their entire lives.  These two groups would need to eat exactly the same thing as each other at every meal for their entire lives before a conclusion could be reached.  How could they be sure it was that one food unless a number in the test group (that ate the food being tested) died that was statistically higher than the number in the control group? 

This experiment is unethical, to say the least.  The experimenters would have to commit a group of children to being experimented on for their entire lives – long before they could make that decision.  Whoever is paying for the test would have to pay for every single meal for both groups for up to a hundred years.  Kids growing up in either group would have to be isolated.  No going out to meals with friends, no just going out for a late night pizza or any sort of “social eating.”  Not to mention not having a conclusion until long past everyone associated with starting the experiment has passed away.

So what are the alternatives to doing a lifelong RCT?  The approach appears to be to study some number of people who get the treatment and then see if the number is close enough to the general population’s incidence of early death (or whatever they’re interested in).  This is relatively easy; the numbers of people are smaller, they aren’t really subjected to getting a test substance, and they don’t need to be housed separately or cared for differently.  In the case of eating the bacon, we’ll give a group of people forms to record what they eat and when.  The typical way of doing this a questionnaire that’s filled out in retrospect, called a Food-Frequency Questionnaire or FFQ.   It’s not quite the same as someone asking, “what did you have for lunch on March 10, 2023?” two years after the fact, but it’s close.  In processing data from the FFQs, the software could separate out those who claimed to have eaten bacon from those who didn’t claim to and see if their rates of death correlated with the general population. 

As I’ve said over and over, it then becomes a matter of correlations, what I call “he-who” studies: he-who eats 3 ounces of bacon/day correlates with the group that lived the expected lifespan, or lived longer or shorter.  To stretch the example to absurdity, let’s say in the past 50 years, life expectancy in the US has gone up.  Anything that has also increased, or has become more common in the same time range can be correlated; we could say that since global temperature has gone up, global warming is extending lifespans.  The standard method of testing whether that correlation is good enough to claim possible causation is to compare the rate of change (slopes) of the two things.  If they’re within a certain range (typically 5%) the correlation is considered good enough to rule out agreement by random chance.  It’s simply not robust enough, IMO. 

Do you see the immediate problem here?  If autism had increased dramatically at the same time that the number of vaccines increased dramatically, as it did, that's automatically a correlation.  One which could mean exactly nothing.

I've mentioned John P. A. Ioannidis on my pages many times before.  He's the author of what’s widely quoted as one of the most downloaded papers in history, “Why Most Published Research Findings are False,” in which he presents data that as much as 70% of published science is wrong.  One of the features of that document is a list of his ROTs (Rules of Thumb) for what makes papers more likely to be good or bad.  Allow me to post two of them here; I think they're relevant:

Corollary 5: The greater the financial and other interests and prejudices in a scientific field, the less likely the research findings are to be true. 

Corollary 6: The hotter a scientific field (with more scientific teams involved), the less likely the research findings are to be true.

Both of those seem to explain a lot of "newspaper reported science" perfectly.

Poppin' a Cap In the Nanny's Ass

The Nanny State is at it again.  The first Nanny-ism I commented on was the Fed.gov's ill-considered war on salt.  Then, of course, there's the First Lady's attack on children's menus.  Now, I'm sure most of you saw that the San Francisco Board of Supervisors has banned any child's meal that comes with a toy: McDonald's Happy Meal is the archetype that got the headlines. 

"Supporters of the ban claim it will help protect children from obesity, while opponents see it as just the latest example of the nanny state run wild and say it's the parents' right and responsibility -- not the government's -- to choose what's right for their children."

I'll take B.  Allow me a slight detour first. 

Down on the lower right of the top this page is a small, random selection of perhaps 20 books I've entered into the Library Thing web site.  One of the books that will show up from time to time is "Good Calories, Bad Calories" by Gary Taubes, a science writer who has won several prestigious awards in that field: his article "The Soft Science of Dietary Fat" turned the popular perception of this area on its ear.  As someone who has fought the "battle of the bulge" since I was about 15, I've been studying this subject in as much depth as I can for as long as I can recall. "GC/BC" is a very well researched look at much of the common wisdom about diet; about the dangers of eating fat, the role of westernization in the spread of disease, and how the culprit might well be refined carbohydrates rather than fats.  If you have any interest in this field at all, it's really a good read. 

Getting back to the Nanny State, what they won't tell you is that these sorts of efforts have been ongoing for some years now and they are not effective.  If you take the french fries away, kids don't magically get slimmer.  You can really eat anything, anything, and lose weight if you follow some common rules. CNN reports on a professor of nutrition who ate a junk food diet, of " Twinkies. Nutty bars. Powdered donuts.", and lost 27 pounds.

"For 10 weeks, Mark Haub, a professor of human nutrition at Kansas State University, ate one of these sugary cakelets every three hours, instead of meals. To add variety in his steady stream of Hostess and Little Debbie snacks, Haub munched on Doritos chips, sugary cereals and Oreos, too."

Adding interest to the story is that many of the blood tests routinely run to monitor health improved for Professor Haub.  The good Professor is reluctant to say he's healthier, but your blood doesn't lie.  If the numbers mean anything to begin with, if you improve the numbers, you've improved your health. 

"Haub's 'bad' cholesterol, or LDL, dropped 20 percent and his 'good' cholesterol, or HDL, increased by 20 percent. He reduced the level of triglycerides, which are a form of fat, by 39 percent."

A simple explanation is that losing weight probably made these blood profiles better.  As I pointed out in my long piece on corporate wellness programs, these indicators don't show that you're "well" so much as they point out that you're young and healthy. 

It doesn't matter that the Nannies behind the Happy Meal ban are well-intentioned.  Banning toys is not going to have the desired effect and personal liberty continues the long slide down the drain. Obesity is a big, complicated topic, and simply removing options is not going to fix everything.  I always get the feeling that these nannies would walk into a village in the third world with people lying around starving to death and think, "my, look how nice and lean they look!"

Perhaps the best introduction to the topic of obesity for the intelligent lay-person is Adiposity 101, a continuously updated paper online since the early 1990s.  It might just turn some notions of yours upside down.

Bottom-Feeder Lawyer Frenzy Over Roundup Seems to be Ending

Every now and then, I surprise myself by searching for something I'm absolutely sure I've written about before and don't find it.  Sometimes it has shown up when searching for totally off-the-wall search terms (can't think of an example) but ordinarily I search for the topic and find something I'm sure I've written. 

Not this time. I can find no evidence of having written about the bottom-feeding lawyer race to the bottom that has been going on over the weed killer Roundup.

Yesterday at Townhall, occasional columnist Angela Logomasini passed on the news that the bottom feeders seem to be moving on to something else to sue over.  It's an interesting story, if you know the background that no carefully controlled study has ever concluded that Roundup (glyphosate) causes cancer, nor has any country declared it a carcinogen.  Even the EPA hasn't ruled Roundup to be dangerous and you've got to know the EPA would love to regulate as much as they possibly can.  Ms. Logomasini put it this way:

All these cases are built on a single, discredited hazard assessment produced by a United Nations outfit known as the International Agency for Research on Cancer or IARC. IARC classified Roundup’s active ingredient—glyphosate—as a known carcinogen despite contrary findings by most governmental and nongovernmental entities around the world.

Yet IARC does not even attempt to determine if real-world exposures pose risks, they just consider the theoretical possibility of risk at some unspecified level.

I'm sure you know what's referred to as the First Law of Toxicology, which is "the dose makes the poison," right?  IARC totally ignores that.  If you look into IARC rulings, it's even more bizarre.  IARC places plutonium in their Group 1, the same cancer category as Chinese-style salty fish, leather and wood dusts.  I think everyone considers plutonium a carcinogen; the salty fish and sawdust, not so much.  They're hard to take seriously. 

The reason there's a feeding frenzy over suing Bayer AgroSciences, parent company of Monsanto, is that in some jury case a suit was successful and thus became a legal precedent.  In an attempt at self-preservation, Bayer established a policy of just paying out on these claims, but that sent the message to the lawyers that the gravy train had arrived. All they had to do was file and Bayer would pay out.

As the money has been paying out and the number of new cases is going down, the sharks are looking for a new place to feed.  They've found one.  Again, to Ms. Logomasini:

As Roundup cases hopefully winds down, there are a growing number of lawsuits focused on ethylene oxide (EO) on the horizon. EO is a chemical used to sterilize more than 50 percent of the nation’s medical supplies—including masks, bandages, ventilators, and more. The U.S. Environmental Protection Agency aided and abetted the trial lawyers on this one in 2016 by producing an absurd assessment on the chemical’s risk.

I've long considered the EPA to be the High Priests of Junk Science, and this time it's Junk Science in the extreme.  The EPA has a program called the Integrated Risk Information System or (IRIS) (pdf warning on the link), and IRIS assigned a safe exposure limit of 0.1 parts per trillion for EO.  For perspective, the American Chemistry Council (ACC) says this is the equivalent of taking one drop of water and spreading it into 200 Olympic-sized swimming pools.  Must be powerful stuff, right? 

Here's where the EPA really screwed the pooch on this subject.  See, your body produces EO at levels 19,000 times greater than the EPA's 0.1 ppt.  All day, everyday.  Further, since this is part of the intricately regulated biochemistry of our bodies, the body clears EO quickly, with a half life (that is, levels falling by 50%) of 42 minutes.  If the EPA was right, people would be dropping like flies from the EO in their own bodies, inhaling it in the air, and more. 

Since the EPA declared it such a ridiculously potent poison, the lawyers have followed. 

Despite these realities, EPA’s air quality office used the IRIS number in a 2018 report that suggested people in communities near medical sterilization plants might face elevated cancer risks. Sensationalist news headlines followed, whipping up panic in several communities leading local and state governments to shut down several plants during 2019 and into 2020.

These closures exacerbated medical supply shortages  (pdf warning) just when the novel corona virus crisis started. Fortunately, in March and April of 2020, the Food and Drug Administration was able to get states and localities to open all but one of the facilities to help address shortages, but this issue is far from over.

The problem, I'm sure you can see, is that if the EPA doesn't reclassify EO, the lawyers may get lucky and get into a court with a dumb enough jury to award money, which will trigger a Roundup-like feeding frenzy that could shut down all the medical production that relies on it.  Instead of hurting one corporation (and the millions that depend on Bayer), they'll hurt everyone dependent on the medical facilities that sterilize with EO.  Which hurts everyone, especially in the days of the Rona. 

Typical lawyer attempt at trolling.