Category Archives: Statistics

Every food causes cancer, and cures it, research shows.

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Statistical analysis, misused, allows you to prove many things that are not true. This was long a feature of advertising: with our toothpaste you get 38% fewer cavities, etc. In the past such ‘studies’ were not published in respectable journals, and research supported by on such was not funded. Now it is published and it is funded, and no one much cares. For an academic, this is the only game in town. One result, well known, is the “crisis of replicability”– very few studies in medicine, psychology, or environment are replicable (see here for more).

In this post, I look at food health claims– studies that find foods cause cancer, or cure it. The analysis I present comes from two researchers, Schoenfeld and Ioannides, (read the original article here) who looked at the twenty most common ingredients in “The Boston Cooking-School Cook Book”. For each food, they used Pub-Med to look up the ten most recent medical articles that included the phrase, “risk factors”, the word “cancer”, and the name of the food in the title or abstract. For studies finding effect in the range of 10x risk factors to 1/10 risk factors, the results are plotted below for each of the 20 foods. Some studies showed factors beyond the end of the chart, but the chart gives a sense. It seems that most every food causes or cures cancer, often to a fairly extreme extent.

Effect estimates by ingredient. From Schoenfeld and Ioannides. Is everything we eat associated with cancer? Am J. Clin. Nutrition 97 (2013) 127-34. (I was alerted to this by Dr. Jeremy Brown, here)

A risk factor of 2 indicates that you double your chance of getting cancer if you eat this food. Buy contrast, as risk factor of 0.5 suggests that you halve your cancer risk. Some foods, like onion seem to reduce your chance of cancer to 1/10, though another study say 1/100th. This food is essentially a cancer cure, assuming you believe the study (I do not).

Only 19% of the studies found no statistically significant cancer effect of the particular food. The other 81% found that the food was significantly cancer-causing, or cancer preventing, generally of p=0.05 to 0.05. Between the many studies done, most foods did both. Some of these were meta studies (studies that combine other studies). These studies found slightly smaller average risk factors, but claimed more statistical significance in saying that the food caused or cured cancer.

0.1 0.2. 0.5 1. 2 5 10
Relative risk

The most common type of cancer caused is Gastrointestinal. The most common cancer cured is breast. Other cancers feature prominently, though: head, neck, genetilia-urinary, lung. The more cancers a researcher considers the higher the chance of showing significant effects from eating the food. If you look at ten cancers, each at the standard of one-tailed significance, you have a high chance of finding that one of these is cured or caused to the standard of p=0.05.

In each case the comparison was between a high-dose cohort and a low-dose cohort, but there was no consistency in determining the cut-offs for the cohort. Sometimes it was the top and bottom quartile, in others the quintile, in yet others the top 1/3 vs the bottom 1/3. Dose might be times eaten per week, or grams of food total. Having this flexibility increases a researcher’s chance of finding something. All of this is illegitimate, IMHO. I like to see a complete dose-response curve that shows an R2 factor pf 90+% or so. To be believable, you need to combine this R2 with a low p value, and demonstrate the same behaviors in men and woman. I showed this when looking at the curative properties of coffee. None of the food studies above did this.

From Yang, Youyou and Uzzi, 2020. Studies that failed replication are cited as often as those that passed replication. Folks don’t care.

Of course, better statistics will not protect you from outright lying, as with the decades long, faked work on the cause of Alzheimers. But the most remarkable part is how few people seem to care.

People want to see their favorite food or molecule as a poison or cure and will cite anything that says so. Irreplicable studies are cited at the same rate as replicated studies, as shown in this 2020 study by Yang Yang, Wu Youyou, and Brian Uzzi. We don’t stop prescribing bad heart medicines, or praising irreplaceable studies on foods. Does pomegranate juice really help? red wine? there was a study, but I doubt it replicated. We’ve repeatedly shown that aspirin helps your heart, but it isn’t prescribed much. Generally, we prefer more expensive blood thinners that may not help. Concerning the pandemic. It seems our lockdowns made things worse. We knew this two years ago, but kept doing it.

As Schoenfeld and Ioannides state: “Thousands of nutritional epidemiology studies are conducted and published annually in the quest to identify dietary factors that affect major health outcomes, including cancer risk. These studies influence dietary guidelines and at times public health policy… [However] Randomized trials have repeatedly failed to find treatment effects for nutrients in which observational studies had previously proposed strong associations.” My translation: take all these food studies with a grain of salt.

Robert Buxbaum, April 4, 2023

Vaccines barely worked, lockdowns seem to have made it worse.

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The first 15 months of the pandemic were grim periods of lockdown, except in Sweden where the health minister declared that lockdowns would not do anything except delay the inevitable. They chose to protect only the most vulnerable, and kept everything else open. By May, 2021, it looked like that was a mistake. Sweden had a seen a sickness and death rate that was fairly average for the world, but high compared to more locked down nordic countries, like Norway, Finland, and Germany. And now vaccines were here that were supposed to be 100% effective, both at stopping the sickness and the spread. We were at the end, opening up, and Sweden had blundered. They had ‘ignored the science,’ as Fauci put it.

Excess mortality January 26 2020 to March 1, 2023, from Our World in Data. I focus on excess deaths here, rather than COVID specifically, because death is a metric that is hard to fudge.

Unfortunately, it quickly became clear that the vaccines were far less than 100% effective. The current estimate is that 2 shots are 24% effective at preventing the disease and 0% effective at preventing the spread. This is a problem in much of medical science these days: successful results tend to be irreproducible, I discuss the reason here. The disease had evolved, and somehow the experiments had not noticed. What’s more they had side-effects (all drugs do). People were dying at a faster rate than before in the US, and in many European countries (see graph below). There was no flattening of the curve suggesting that the vaccine didn’t work. By last year, I had noticed that US COVID deaths did not decrease with the advent of vaccines. Strangely, deaths did not increase as fast in Sweden. By 2022, Sweden was doing better than its lock-down peers. As of today, it’s doing much better. So, what have we learned?

The results of the 6 month Pfizer trial already suggested there might be a problem -that perhaps the vaccine did more harm than good. The above were the results in the Biologics Licence Application (BLA) report (page 23), submitted to the Food and Drug Administration (FDA) to apply for vaccine approval, November, 2021. The vaccine decreased COVID but increased other cause death even more. Suspicious.

The fact that the death rate did generally not go down when a majority were vaccinated and the most vulnerable were already dead suggests that vaccination does not help much. That excess death increased in some countries (Norway, Finland, Germany) suggests that either the side-effects of the vaccine are worse than the disease itself or that some other aspect of the treatment (lockdowns?) were worse than the disease. The vaccine still may be shown to have helped, but it doesn’t look like it helped much. The fact that lock-down countries are doing worse than Sweden suggests that lockdowns actually hurt. This is significant. One thing to learn is that you have a right to not trust medical science: you have a right to be wrong. Mr Spock never trusted Bones’s medicine. You have a particularly strong right to doubt when you have evidence as strong as the map below (excess deaths in Europe as it stood in December 2021). Already Sweden was doing well and the experts were looking very wrong.

A map of excess deaths in Europe as of December 2021. Already many countries had passed Sweden. Eastern and Southern Europe were particularly hard hit.

I can now speculate on the mechanism; why might lockdowns hurt or kill? I suggested it’s loneliness. Perhaps it’s inaction, or mental distress. People would rather get an electric shock than sit a think without doing anything. It might be that lockdowns prevented other medical treatment. Whatever the mechanism, you’d think that our government would have acknowledged, by early 2022, that lockdowns were not working. Instead virtually every state continued lockdowns through a good chunk of 2022 with school closures, limited seating, etc.

I suspect that “long COVID” may be a form of lock-down depression plus associated noxious behaviors: increased drug and alcohol use, lack of exercise, and avoiding treatment for health problems. I suggested iodine hand wash (and gargle) to stop the disease spread (I imagine it’s on surfaces), and still think it’s a good idea. Iodine is cheap and it definitely kills all germs. Other anti-isolation nostrums include exercise, lithium, aspirin, letters, and hydroxycholoroquine. There was reasonable statistical evidence for several of these things helping, though Fauci denied it. Perhaps they only helped via ‘the placebo effect’. But placebo cures are real, especially for mental problems.

Robert Buxbaum, March 30, 2023. As an add-on (April 2, 2013), I’d like to show the decline in life-expectancy in the US compared to other countries. Isolation is a killer. A lot of the blame goes to Fauci for continuing to push socially isolating solutions as “the science”, while blasting any who say otherwise. We’ve lost 3 years of life-span in 3 years — preventably avoided — when other countries have lost zero or one. There could be no greater inditement of the health management.

This is from the Financial Times. The US is doing worst of all in terms of lives lost to the pandemic and it continues. Isolating people is torture. We then blame them for feeling distrust. I blame Biden and Fauci.

Social science is irreproducible, drug tests nonreplicable, and stoves studies ignore confounders.

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Efforts to replicate the results of the most prominent studies in health and social science have found them largely irreproducible with the worst replicability appearing in cancer drug research. The figure below, from “The Reproducibility Project in Cancer Biology, Errington et al. 2021, compares the reported effects in 50 cancer drug experiments from 23 papers with the results from repeated versions of the same experiments, looking at a total of 158 effects.

Graph comparing the original, published effect of a cancer drug with the replication effect. The units are whatever units were used in the original study, percent, or risk ratio, etc. From “Investigating the replicability of preclinical cancer biology,”
Timothy M Errington et al. Center for Open Science, United States; Stanford University, Dec 7, 2021, https://doi.org/10.7554/eLife.71601.

It’s seen that virtually none of the drugs are found to work the same as originally reported. Those below the dotted, horizontal line behaved the opposite in the replication studies. About half, those shown in pink, showed no significant effect. Of those that showed positive behavior as originally published, mostly they show about half the activity with two drugs that now appear to be far more active. A favorite web-site of mine, retraction watch, is filled with retractions of articles on these drugs.

The general lack of replicability has been called a crisis. It was first seen in the social sciences, e.g. the figure below from this article in Science, 2015. Psychology research is bad enough such that Nobel Laureate, Daniel Kahneman, came to disown most of the conclusions in his book, “Thinking, Fast and Slow“. The experiments that underly his major sections don’t replicate. Take, for example, social printing. Classic studies had claimed that, if you take a group of students and have them fill out surveys with words about the aged or the flag, they will then walk slower from the survey room or stand longer near a flag. All efforts to reproduce these studies have failed. We now think they are not true. The problem here is that much of education and social engineering is based on such studies. Public policy too. The lack of replicability throws doubt on much of what modern society thinks and does. We like to have experts we can trust; we now have experts we can’t.

From “Estimating the reproducibility of psychological science” Science, 2015. Social science replication is better than dance drug replication, about 35% of the classic social science studies replicate to some, reasonable extent.

Are gas stoves dangerous? This 2022 environmental study said they are, claiming with 95% confidence that they are responsible for 12.7% of childhood asthma. I doubt the study will be reproducible for reasons I’ll detail below, but for now it’s science, and it may soon be law.

Part of the replication problem is that researchers have been found to lie. They fudge data or eliminate undesirable results, some more some less, and a few are honest, but the journals don’t bother checking. Some researchers convince themselves that they are doing the world a favor, but many seem money-motivated. A foundational study on Alzheimers was faked outright. The authors doctored photos using photoshop, and used the fake results to justify approval of non-working, expensive drugs. The researchers got $1B in NIH funding too. I’d want to see the researchers jailed, long term: it’s grand larceny and a serious violation of trust.

Another cause of this replication crisis — one that particularly hurt Daniel Kahneman’s book — is that many social science researchers do statistically illegitimate studies on populations that are vastly too small to give reliable results. Then, they only publish the results they like. The graph of z-values shown below suggest this is common, at least in some journals, including “Personality and social psychology Bulletin”. The vast fraction of results at ≥95% confidence suggest that researchers don’t publish the 90-95% of their work that doesn’t fit the desired hypothesis. While there has been no detailed analysis of all the social science research, it’s clear that this method was used to show that GMO grains caused cancer. The researcher did many small studies, and only published the one study where GMOs appeared to cause cancer. I review the GMO study here.

From Ulrich Schimmack, ReplicationIndex.com, January, 2023, https://replicationindex.com/2023/01/08/which-social-psychologists-can-you-trust/. If you really want to get into this he is a great resource.

The chart at left shows Z-scores, were Z = ∆X √n/σ. A Z score above 1.93 generally indicates significance, p < .05. Notice that almost all the studies have Z scores just over 1.93 that is almost all the studies proved their hypothesis at 95% confidence. That makes it seem that the researchers were very lucky, near prescient. But it’s clear from the distribution that there were a lot of studies that done but never shown to the public. That is a lot of data that was thrown out, either by the researchers or by the publishers. If all data was published, you’d expect to see a bell curve. Instead the Z values are of a tiny bit of a bell curve, just the tail end. The implication is that these studies with Z= >1.93 suggest far less than 95% confidence. This then shows up in the results being only 25% reproducible. It’s been suggested that you should not throw out all the results in the journal, just look for Z-scores of 3.6 or more. That leaves you with the top 23%, and these should have a good chance of being reproducible. The top graph somewhat supports this, but it’s not that simple.

Another classic way to cook the books, as it were, and make irreproducible studies provide the results you seek is to ignore “confounders.” This leads to association – causation errors. As an example, it’s observed that people taking aspirin have more heart attacks than those who do not, but the confounder is that aspirin is prescribed to those with heart problems; the aspirin actually helps, but appears to hurt. In the case of stoves, it seems likely that poorer, sicker people own gas, and that they live in older, moldy homes, and cook more at home, frying onions, etc. These are confounders that the study to my reading ignores. They could easily be the reason that gas stove owners get more asthma toxins than the rich folks who own electric, induction stoves. If you confuse association, you seem to find that owning the wrong stove causes you to be poor and sick with a moldy home. I suspect that the stove study will not replicate if they correct for the confounders.

I’d like to recommend a book, hardly mathematical, “How to Lie with Statistics” by Darrell Huff ($8.99 on Amazon). I read it in high school. It gives you a sense of what to look out for. I should also mention Dr. Anthony Fauci. He has been going around to campuses saying we should have zero tolerance for those who deny science, particularly health science. Given that so much of health science research is nonreplicable, I’d recommend questioning all of it. Here is a classic clip from the 1973 movie, ‘Sleeper’, where a health food expert wakes up in 2173 to discover that health science has changed.

Robert Buxbaum , February 7, 2023.

How to tell who is productive if work is done in groups

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It is a particular skill of management to hog the glory and cast the blame; if a project succeeds, executives will make it understood that the groups’ success was based on their leadership (and their ability to get everyone to work hard for low pay). If the project fails, a executive will cast blame typically on those who spotted the problem some months early. These are the people most likely to blame the executive, so the executive discredits them first.

This being the dynamic of executive oversight, it becomes difficult to look over the work of a group and tell who is doing good and who is coasting. If someone’s got to be fired in the middle of a project, or after, who do you fire? My first thought is that, following a failure, you fire the manager and the guy at the top who drew the top salary. That’s what winning sports teams do. It seems to promote “rebuilding” it’s a warning to those who follow. After the top people are gone, you might get an honest appraisal of what went wrong and what to do next.

A related problem, if you’re looking to hire is who to pick or promote from within. In the revolutionary army, they allowed the conscripts to pick some of their commanders, and promoted others based on success. This may not be entirely fair, as there are many causes to success and failure, but it seemed to work better than the British system, where you picked by birth or education. Here’s a lovely song about the value of university education in a modern major general.

A form of this feedback about who knows what he’d doing and who does not, is to look at who is listened to by colleagues. When someone speaks, do people who know listen. It’s a method I’ve used to try to guess who knew things in a field outside my own. Bull-shitters tend to be ignored when they speak. The major general above is never listened to.

In basketball or hockey, the equivalent method is to see who the other players pass to the most, and who steals the most from the other side. It does not take much watching to get a general sense, but statistics help. With statistics, one can set up a hierarchical system based on who listens to whom, or who passes to whom with a logistic equation as used for chess and dating sites. A lower-paid person at the center-top is a gem who you might consider promoting.

In terms of overall group management, it was the opinion of W Edwards Deming, the name-sake of the Deming prize for quality, that overall group success was typically caused by luck or by some non-human cause. Thus that any manager would be as good as any other. Deming had a lovely experiment to show why this is likely the case– see it here. If one company or team did better year after year, it was common that they were in the right territory, or at the right time. As an example, the person who succeeded selling big computers in New York in the 1960s was not necessarily a good salesman or manager. Anyone could have managed that success. To the extent that this is true, you should not fire people readily, but neither worry that your highest paid manager or salesman is irreplaceable.

Robert Buxbaum, October 9, 2022

Curing my heart fibrillation with ablation.

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Two years ago, I was diagnosed with Atrial fibrillation, A-Fib in common parlance, a condition where my heart would sometimes speed up to double its normal speed. I was prescribed metopolol and then atenolol, common beta blockers, and a C-Pap for sleep apnea. None of this seemed to help, as best I could tell from occasional pulse measurements with watch and a finger pulse-oxometer. Besides, the C-Pap was giving me cough and the beta blockers made me dizzy. And the literature on C-Pap did not impress.

So, some moths ago, I bought an iWatch. The current versions allows you to take EKGs and provides a continuous record of your heart rate. This was very helpful, as I saw that my heart rate was transitioning to chaos. While it was normally predictable, it would zoom to 130 or so at some point virtually every day. Even more alarming, it would slow down to the mid 30s at some point during the night, bradycardia, and I could see it was getting worse. At that point, I agreed to go on eliquis, a blood thinner, and agreed to a catheter ablation. The doctor put a catheter into my heart by way of a leg vein, and zapped various nerve centers in the heart. The result is that my heart is back into normal behavior. See the heart-rate readout from my iWatch below; before and after are dramatically different.

My heart rate for the last month, very variable before the ablation treatment, 2 weeks ago; a far less variable range of heart rates in the two weeks following the treatment. Heart rate data is from my iPhone and iwatch — a good investment, IMHO.

The reason I chose ablation over drugs or no therapy was that I read health-studies on line. I’ve go a PhD, and that training helps me to understand the papers I’ve read, but you should read them too. They are not that hard to understand. Though ablation didn’t appear as a panacea, it was clearly better than the alternatives. Particularly relevant was the CABANA study on life expectancy. CABANA stands for “Catheter ABlation vs ANtiarrhythmic Drug Therapy for Atrial Fibrillation – CABANA”. https://www.acc.org/latest-in-cardiology/clinical-trials/2018/05/10/15/57/cabana.

2,204 individuals with persistent AF were followed for 5 years after treatment, 37% female, 63% male, average age 67.5. Prior hospitalization for AF: 39%. The results were as follows:

  • Death: 5.2% for ablation vs. 6.1% for drug therapy (p = 0.38)
  • Serious stroke: 0.3% for ablation vs. 0.6% for drug therapy (p = 0.19)
  • All-cause mortality: 4.4% for ablation vs. 7.5% for drug therapy (p = 0.005)
  • Death or CV hospitalization: 51.7% for ablation vs. 58.1% for drug therapy (p = 0.002)
  • Pericardial effusion with ablation: 3.0%; ablation-related events: 1.8%
  • First recurrent AF/atrial flutter/atrial tachycardia: 53.8% vs. 71.9% (p < 0.0001)

I found all of this significant, including the fact that 27.5% of those on the drug treatment crossed over to have ablation while only 9.2% on the ablation side crossed to have the drug treatment.

I must give a plug for doctor Ahmed at Beaumont Hospital who did the ablation. He does about 200 of these a year, and does them well. Do not go to an amateur. I was less-than impressed with him pushing the beta-blocker hard; I’ll write about that. Also, get an iWatch if you think you may have A-Fib or any other heart problem. You see a lot, just by watching, so to speak.

Robert Buxbaum, August 3, 2022.

Three identical strangers, and the genetics of personality

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Inheritability of traits is one of the greatest of insights; it’s so significant and apparent, that one who does not accept it may safely be called a dullard. Personal variation exists, but most everyone accepts that if your parents are tall, you are likely to be tall; If they are dark, you too will likely be dark, etc., but when it comes to intelligence, or proclivities, or psychological leanings, it is more than a little impolite to acknowledge that genetics holds sway. This unwillingness is glaringly apparent in the voice-over narration of a popular movie about three identical triplets who were raised separately without knowing of one another. The movie is “Three identical strangers”, and it recounts their meeting, and their life afterwards.

Triplets, raised separately, came out near identical.

As one might expect, given my introduction, though raised separately, the three showed near identical intelligence, and near identical proclivities: two of them picked the same out-of-the way college. All of them liked the same sort of clothes and had the same taste in women. There were differences as well: one was a more outgoing, one was depressed, but in many ways, they were identical. Meanwhile, the voice-over kept saying things like, “isn’t it a shame that we never saw any results on nature/nurture from this study.” Let me clear this us: genetics applies to psychology too. It’s not all genetics, but it is at least as influential as upbringing/ nurture.

This movie also included pairs of identical twins, raised separately, they also showed strong personality similarities. It’s a finding that is well replicated in broader studies involving siblings raised separately, and unrelated adoptees raised together. Blood, it seems, is stronger than nurture. See for example the research survey paper, “Genetic Influence on Human Psychological Traits” Journal of the American Psychological Society 13-4, pp 148-151 (2004). A table from that paper appears below. Genetics plays a fairly strong role in all personal traits including intelligence, personality, self-control, mental illness, criminality, political views (even mobile phone use). The role is age-dependent, though so that intelligence (test determined) is strongly environment-dependent in 5 year olds, almost entirely genetic in 25-50 year olds. One area that is not strongly genetic, it seems, is religion.

In a sense, the only thing surprising about this result is that anyone is surprised. Genetics is accepted as crucial for all things physical, so why not mental and social. As an example of the genetic influence on sports, consider Jewish chess genius, Lazlo Polgar: he decided to prove that anyone could be great at chess, and decided to train his three daughters: he got two grand masters and an international master. By comparison, there are only 2 chess grand masters in all of Finland. Then consider that there are five all-star, baseball players named Alou, all from the same household, including the three brothers below. The household has seven pro baseball players in all.

Most people are uncomfortable with such evidence of genetic proclivity. The movie has been called “deeply disturbing” as any evidence of proclivity contradicts the promise of education: that all men are equal, blank slates at birth that can be fashioned into whatever you want through education. What we claim we want is leaders — lots of them, and we expect that education will produce equal ratios of woman and men, black and white and Hispanic, etc. and we expect to be able to get there without testing for skills, — especially without blind testing. I notice that the great universities have moved to have testing optional, instead relying on interviews and related measures of leadership. I think this is nonsense, but then I don’t run Harvard. As a professor, I’ve found that some kids have an aptitude and a burning interest, and others do not. You can tell a lot by testing, but the folks who run the universities disagree.

The All star Alou brothers share an outfield.

University heads claim that blind testing is racist. They find that some races score poorly on spacial sense, for example, or vocabulary suggesting that the tests are to blame. There is some truth to these concerns, but I find that the lack of blind testing is more racist. Once the test is eliminated, academia finds a way to elevate their friends, and the progeny of the powerful.

The variety of proclivities plays into an observation that you can be super intelligent in one area, and super stupid in others. That was the humor of some TV shows: “Big Bang Theory” and “Fraser”. That was also the tragedy of Bobby Fischer. He was brilliant in chess (and the child of brilliant parents), but was a blithering idiot in all other areas of life. Finland should not feel bad about their lack of great chess players. The country has produced two phone companies, two strong operating systems, and the all time top sniper.

Robert Buxbaum, May 15, 2022

C-PAPs do not help A-Fib, and seem to make heart health worse.

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In this blog-post, I’d like to report on the first random study of patients with Atrial fabulation, A-Fib, and sleep apnea, comparing the health outcome of those who use a C-PAP, a “Continuous Positive Airway Pressure” device, to the outcome those who do not. The original study was published in May, 2021 (read it here) in the American Journal of Respiratory and Critical Care Medicine. The American Journal, Pulmonary Advisor published a more-popular version here.

As a background, if you are over 65 and overweight, there is a 25% chance or so that your heart rate will begin to surge semi-randomly, and that it will flutter. This is Atrial fabulation, A-Fib. It tends to get worse and tends to lead to heart attacks and strokes. People with A-fib tend to be treated with drugs, aspirin, warfarin, beta blockers, and anti arrhythmics. They also tend to be prescribed a C-PAP because overweight, older folks tend to snore and wake up a lot during the night (several times per hour: apnea).

A C-PAP definitely stops the snoring and the Apnea, and the assumption was that it would help your heart as well, if only by giving you a better night’s sleep. As it turns out, the C-PAP seems to decrease heart health — significantly.

For this study, adult patients between 18 and 75 years old diagnosed with paroxysmal A-Fib (that’s occasional AF) were screened for moderate to severe sleep apnea. Those who agreed to participate were randomly assigned to either a treatment of C-PAP plus usual care (drugs mostly) or just usual care for the next 5 months. Of the 109 who enrolled in the study, 55 got the C-PAP plus usual care, 54 got usual care alone. The outcome was that there were 9 serious, adverse heart events (strokes and heart attacks); 7 were in the C-PAP group.

The CPAP pressure was, on average, 6.8 cm H2O; mean time of use was 4.4±1.9 hours per night. The C-PAPs did their jobs on the apnea too, reducing residual apnea-hypopnea to 2.3±1.9 events per hour for those in the C-PAP group.

There was a non-statistically significant reduction is AF among the C-PAP group. They reduced their time in AF by 0.6 percentage points compared to the control group  (95% CI, -2.55 to 1.30; P =.52). That not a statistically significant difference, and is most likely random.

There was a statistically significant decrease in heart health, though. A total of 7 serious adverse events occurred in the C-PAP group and only 2 in the control group. A total of 9 is a relatively small number of events, but there is a strong statistical difference between 7 and 2.

The authors conclude: “CPAP treatment does not seem to reduce or prevent paroxysmal AF.” They should also have concluded that it reduced heart health with a statistical confidence of ~82%: (1-2(36+10)/512) =82%. See more on this type of statistics.

A possible explanation of why a C-PAP would would make heart health worse is an outcome of the this recent sleep study (link here). It appears that the C-PAP helps restore breathing, but by doing so, it interferes with a mechanism the body uses to deal with A-fib. It seems that, for people with A-Fib, their bodies use breathing stoppages to get their heart back into rhythm. For these people, many of their breathing stoppage are not obstructive, but a bio-pathway to raise the CO2 level in the blood and thus regulate heart rate. The use of a C-PAP prevents this restorative mechanism and this seem to be the reason it is destructive to the heart-health of patients with A-fib. On the other hand, a C-PAP does improve the sleep those patients whose apnea is obstructive. It seems to me that sleep studies should do a better job distinguishing the two causes of apnea. C-PAPs seem counter-indicated for patients with A-fib.

Robert Buxbaum, March 30, 2022. I was diagnosed with apnea and A-Fib some years ago. The sleep doctor prescribed a C-PAP and was adamant that I had to use it to keep my heart healthy. There were no random studies backing him up or contradicting him until now.

COVID is 1/50 as deadly in China, Hong Kong, Taiwan, Korea…

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I may be paranoid, but that doesn’t mean I’m crazy. COVID-19 shows a remarkably low death rate in Asia, particularly Eastern Asia, compared to the US or Europe or South America. As of this month, there have been 734,600 US deaths from COVID-19, representing 0.22% of all Americans. Another way of stating this is 2.2 deaths per thousand population. In one year, COVID has lowered the life expectancy of US men by 2.1 years; with the decline worst among hispanic men. The COVID death rate is very similar in Europe, and higher in South America (in Peru 0.62%), but hardly any deaths in East Asia. In China only 4,636 people, 0.003% of the population. That’s 1/700th the rate in the US, and almost all of these deaths are in western China. They no longer bother with social distancing.

The low death rate in East Asia. was noted by the BBC over a year ago. Based on today’s data from Worldometer, here, the low death rates continue throughout East Asia, as graphed at right. In Hong-Kong the death rate is 0.03 per thousand, or 1/70th the US rate. In Taiwan, 0.04 per thousand; in Singapore, 0.01 per thousand; in S. Korea 0.04 per thousand; Cambodia and Japan, 0.1 per thousand. The highest of these countries shows 1/20 the death rate of the US. This disease kills far fewer East Asians than Westerners. This difference shows up, for example in a drop in the lifespan of male Americans by 2.16 years. The lifespan of male Hispanics dropped more, by 4.58 years. In China, Japan, and Korea the lifespans have continued to increase.

Life expectancy for US males has dropped by 2.16 years. It’s dropped more for Hispanic and Black Americans. Data for women is similar but not as dramatic.

My suspicion is that this was a racially targeted bio-weapon. But perhaps the targeting of westerners reflects a cultural lifestyle difference. Mask use has been suggested, but I don’t think so. In many high mask countries the death rate is high, while in low mask Taiwan and Korea it’s low, only 0.04 COVID deaths per thousand. Even Sweden, with no masks reports only 1.4 per thousand deaths; that’s 2/3 the death rate of the US. Masks do not seem to explain the difference.

Another lifestyle difference is obesity; Americans are fat. Then again, Peru was hit far worse than we were, and Peruvians are thin. Meanwhile, in Hong Kong, folks are fat, but the death rate is small. Another cultural difference is medicine, but I don’t believe Sweden, Germany, and France have worse healthcare than Taiwan or Cambodia. Cambodia saw 1/20 the US COVID death rate.

My suspicion is that this disease targets by race because it was designed that way. If it isn’t a bio weapon, it certainly behaves like one. I may be paranoid here, but that’s the way it seems.

As a side issue, perhaps related, I note that China keeps pushing for the to close its manufacturing in the interest of CO2 abatement, while they keep building coal burning power plants to fill the manufacturing need that we abandon. I also notice that they hit us with tariffs while protesting our tariffs, that they steal our intellectual property, and that they are building islands in the sea between China and Japan. There is war-tension between our countries, and Western-targetting virus appears right outside of China’s top-security virus lab — their only level 4 lab — I’m guessing it’s not a total coincidence.

Robert Buxbaum, October 12, 2021

The delta variant is no big deal if you’re young or vaccinated.

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The toll of COVID-19 has been terrible: 660,000 dead by my count, based on excess deaths, graph below, or 620,000 according to the CDC based on hospital records. Death rates appear to have returned to pre-pandemic levels, more or less*, but folks are still getting very sick and going to the hospital, mostly for “the delta variant.”

Weekly US death rates since October 2015.

As the following chart shows, severe symptoms of COVID are now almost entirely in the old, and unvaccinated. The risk to the young and middle aged is low, but even there, vaccination helps. According to the CDC, 72.2% of the adult US population is vaccinated with at least one shot. The vaccination, doesn’t prevent you from getting the delta variant nor from spreading it; it just protects from the most serious consequences of the disease. It seems a previous infection has the same effect, though less so.

Vaccination helps prevent hospitalization – at all ages (Israeli data)

If you’re over 60 and unvaccinated, I recommend getting vaccinated with at least one shot; the inconvenience and side-effects are few, and the benefit is large. The second shot seemswothshile too, and for all I know a third will too. Sooner or later there is a diminishing return. The benefit of masks seems is smaller, as I judge things. I notice that the disease is spreading at about the same rate in masked and unmasked states, and that the death numbers are as high, or higher in heavily masked, blue states as in red. New York and NJ are the top COVID death states, with Michigan not far behind. Masks seem to help, just not very much.

For those who want further advice, I can suggest dilute iodine gargle. I did this when I got a sore throat, I also suggest got a pneumonia vaccination, and take and adult aspirin every other day for COVID and heart-attack prevention. I also take a vitamin D tablet every few days.

If you wish to check my analysis, go here to get the raw data: https://gis.cdc.gov/grasp/fluview/mortality.html. Then, to calculate the COVID effect, I subtracted the weekly death rates in 2020 and 2021 from the corresponding week rates in 2019, correcting the deaths by 1%/year for population growth and aging. *I find that there are about 500 excess deaths per week, and I assume those are among the unvaccinated. If you are vaccinated, I’d worry about something else besides COVID-delta: heart attack, cancer, suicide, or Afghanistan.

Robert Buxbaum August 18, 2021. I made a video of cute iodine reactions, including the classic “iodine clock”, where I use vitamin C as as the anti-oxidant (reducing agent).

The equation behind Tinder, J-swipe, and good chess matchups.

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Near the beginning of the movie “The social network”, Zuckerberg asks his Harvard roommate, Saverin, to explain the chess rating system. His friend writes an equation on the window, Zuckerberg looks for a while, nods, and uses it as a basis for Facemash, the predecessor of Facebook. The dating site, Tinder said it used this equation to match dates, but claims to have moved on from there, somewhat. The same is likely true at J-swipe, a jewish coating site, and Christian mingle.

Scene from the social network, Saverin shows Zuckerberg the equations for the expected outcome of a chess match between players of different rankings, Ra and Rb.

I’ll explain how the original chess ranking system worked, and then why it works also for dating. If you’ve used Tinder or J-swipe, you know that they provide fairly decent matches based on a brief questionnaire and your pattern of swiping left or right on pictures of people, but it is not at all clear that your left-right swipes are treated like wins and losses in a chess game: your first pairings are with people of equal rating.

Start with the chess match equations. These were developed by Anand Elo (pronounced like hello without the h) in the 1950s, a physics professor who was the top chess player in Wisconsin at the time. Based on the fact that chess ability changes relatively slowly (usually) he chose to change a persons rating based on a logistic equation, sigmoid model of your chances of winning a given match. He set a limit to the amount your rating could change with a single game, but the equation he chose changed your rating fastest when you someone much better than you or lost to someone much weaker. Based on lots of inaccurate comparisons, the game results, you get a remarkably accurate rating of your chess ability. Also, as it happens, this chess rating also works well to match people for chess games.

The knowledge equation, an S curve that can be assumed to relate to the expected outcome of chess matchups or dating opportunities.

For each player in a chess match, we estimate the likelihood that each player will win, lose or tie based on the difference in their ratings, Ra -Rb and the sigmoid curve at left. We call these expected outcome Ea for player A, and Eb for player B where Ea = Eb = is 50% when Ra = RB. It’s seen that Ea never exceeds 1; you can never more than 100% certain about a victory. The S-graph shows several possible estimates of Ea where x= Ra-Rb, and k is a measure of how strongly we imagine this difference predicts outcome. Elo chose a value of k such that 400 points difference in rating gave the higher ranked player a 91% expectation of winning.

To adjust your rating, the outcomes of a game is given a number between 1 and 0, where 1 represents a win, 0 a loss, and 0.5 a draw. Your rating changes in proportion to the difference between this outcome and your expected chance of winning. If player A wins, his new rating, Ra’, is determined from the old rating, Ra as follows:

Ra’ = Ra + 10 (1 – Ea)

It’s seen that one game can not change your rating by any more than 10, no matter how spectacular the win, nor can your rating drop by any more than 10 if you lose. If you lose, Ra’ = Ra – 10 Ea. New chess players are given a start ranking, and are matched with other new players at first. For new players, the maximum change is increased to 24, so you can be placed in a proper cohort that much quicker. My guess is that something similar is done with new people on dating sites: a basic rating (or several), a basic rating, and a fast rating change at first that slows down later.

As best I can tell, dating apps use one or more ratings to solve a mathematical economics problem called “the stable marriage problem.” Gayle and Shapely won the Nobel prize in economics for work on this problem. The idea of the problem is to pair everyone in such a way that no couple is happier by a swap of partners. It can be shown that there is always a solution that achieves that. If there is a singe, understood ranking, one way of achieving this stable marriage pairing is by pairing best with best, 2nd with second, and thus all the way down. The folks at the bottom may not be happy with their mates, but neither is there a pair that would like to switch mates with them.

Part of this, for better or worse, is physical attractiveness. Even if the low ranked (ugly) people are not happy with the people they are matched with, they may be happy to find that these people are reasonably happy with them. Besides a rating based on attractiveness, there is a rating based on age and location; sexual orientation and religiosity. On J-swipe and Tinder, people are shown others that are similar to them in attractiveness, and similar to the target in other regards. The first people you are shown are people who have already swiped right for you. If you agree too, you agree to a date, at least via a text message. Generally, the matches are not bad, and having immediate successes provides a nice jolt of pleasure at the start.

Religious dating sites, J-swipe and Christian Mingle work to match men with women, and to match people by claimed orthodoxy to their religion. Tinder is a lot less picky: not only will they match “men looking for men” but they also find that “men looking for women” will fairly often decide to date other “men looking for women”. The results of actual, chosen pairings will then affect future proposed pairings so that a man who once dates a man will be shown more men as possible dates. In each of the characteristic rankings, when you swipe right it is taken as a win for the person in the picture, if you swipe left it’s a loss: like a game outcome of 1 or 0. If both of you agree, or don’t it’s like a tie. Your rating on the scale of religion or beauty goes up or down in proportion to the difference between the outcome and the predictions. If you date a person of the same sex, it’s likely that your religion rating drops, but what do I know?

One way or another, this system seems to work at least as well as other matchmaking systems that paired people based on age, height, and claims of interest. If anything, I think there is room for far more applications, like matching doctors to patients in a hospital based on needs, skills, and availability, or matching coaches to players.

Robert Buxbaum, December 31, 2020. In February, at the beginning of the COVID outbreak I claimed that the disease was a lot worse than thought by most, but the it would not kill 10% of the population as thought by the alarmist. The reason: most diseases follow the logistic equation, the same sigmoid.