The Covid vaccines are said to be “safe and effective”. The second of these claims – that they’re effective – is obviously ridiculous as I have shown elsewhere. In this essay, I will consider the first claim – are the Covid vaccines safe? This is a more difficult question. The evidence on vaccine safety is not nearly as clear, at least to me, as that on vaccine efficacy. But I believe the answer is: no, they are not safe. This paper describes several reasons to believe the vaccines are, in fact, quite harmful.

This report is divided into five sections. The first is my own research. The second describes VAERS. The rest contain medical studies. The sections are:

1. Excess Mortality
2. VAERS Database
3. Severe Side Effects
4. Heart Studies
5. Conclusions

In all cases, I’ve tried to stick to statistically-valid measures of vaccine outcomes. The only exception is section 2, since the VAERS data is essentially anecdotal not statistical.

1. Excess Mortality

People who believe the Covid vaccines are deadly sometimes point to the current increase in deaths worldwide from all causes, which they claim is the result of the vaccines. Their proof is that the heavily-vaccinated countries now have higher all-cause mortality than the lightly-vaccinated countries. Where there are more vaccines, there are more deaths. So, even if the vaccines are effective at reducing Covid deaths, they may be increasing non-Covid deaths more. To see how this could be possible, imagine that the vaccines work as advertised and reduce Covid mortality by 50%. The Omicron fatality rate is at most 0.2% and in a bad pandemic year 30% of the population might catch the disease, so in the US with a population of 350M a successful vaccine would reduce the number of Covid deaths by 0.5 * 0.002 * 0.3 * 350M = 105,000 people / year. But if the all-cause mortality rate, which is about 1% in the US (1% of the population dies from all natural causes in a normal year), rises by 10% to 1.1% – as in fact it has – then the number of extra deaths would be 0.1 * 0.01 * 350M = 350,000 people / year. Not a good trade-off. But are the vaccines to blame for the large increase in deaths worldwide from causes other than Covid? In this section, I will try to answer 3 questions: 1) are there more all-cause deaths in 2022 after the vaccination campaign of 2021, 2) are the heavily-vaxxed countries suffering more deaths than the lightly-vaxxed countries, and 3) is the relationship in #2 so strong that it can be considered statistically significant? (Spoiler: yes, yes, and yes.)

I’ve read many articles which claim that highly-vaccinated countries have worse overall death rates (mostly not from Covid) than less-vaccinated countries. Most of these articles are extremely low quality – they cherry-pick just a few outlier countries to make their case. This is the problem with everything nowadays – no one is interested in the truth, they just want to promote their agenda. This is as true of the pro-vaccine shills – vaccine companies, the media, government health agencies, and practically every doctor in America – as it is of the anti-vaccine shills. Probably the best article I’ve read making the case for higher deaths in more vaccinated countries is https://igorchudov.substack.com/p/association-between-vaccines-and . It does a statistical analysis of every country which has publicly-available vaccine and mortality data. But it seems to have cherry-picked the time period it reports on. It just happened to pick the months in 2022 when the highly-vaccinated countries had higher mortality than during the rest of the year. And the author calculates his own excess mortality rates from raw death counts at the Human Mortality Database rather than using the available excess mortality figures from the World Mortality Dataset. Those calculations allow plenty of opportunity for data mining. Maybe, like everyone else, he was more interested in generating outrage than in being objective. I guess that’s how you get clicks.

All of the data I use in my analysis below comes from the website Our World in Data (OWID). They provide a very useful downloadable database with every Covid time series you could ever want at https://covid.ourworldindata.org/data/owid-covid-data.csv . This large Excel file contains a time series of Covid and related data by country for every day since 2020. I combined the daily data into yearly totals so that I could use yearly data for each country. My plan is to see if heavily-vaxxed countries do indeed have higher death rates than lightly-vaxxed countries. Specifically, I will test whether the number of vaccinations given in a country in 2021 (the year when most of the vaccinations were given) increased that country’s all-cause mortality rate in 2022 (the latest year with complete death statistics). I don’t want to cherry-pick the time periods to massage the results, so I’m just going to pick the dates before doing the analysis, and only use full years. Also, since the future obviously can’t change the past, and simultaneous events are often correlated, my models will only use 2021 events to explain 2022 outcomes.

All-cause deaths is a much more accurate statistic than Covid-only deaths. Countries’ reporting of Covid deaths is unreliable because every country has a different testing policy and some countries like the US provide financial incentives to hospitals to inflate their Covid numbers. That’s why we heard so many stories in 2020 of people dying in motorcycle accidents or of gunshot wounds who were tested for Covid in the hospital, found to be Covid-positive, and later classified as Covid deaths. Counting deaths from all-causes has none of those problems. Governments can’t do much right, but they can generally count dead bodies.

But I do need to explain the specific variable I will be analyzing – the 2022 excess mortality rate. A country’s excess mortality is how many people died minus how many people were expected to die in a normal year. Determining the number of people who died is simple, but figuring out how many were expected to die is not. The easiest method is to assume that the death rate should remain the same as in the past. A better approach would take into account how the death rate should change because of population growth, age structure, health trends like obesity, etc. There is a website https://mpidr.shinyapps.io/stmortality/ that lets you try different expected mortality calculations and see their effect on excess mortality. It’s a little disconcerting how much excess mortality goes up or down when you change your assumptions about expected mortality. It makes me not trust any excess mortality figures. But I’m not going to calculate my own excess mortality rates. I’ll just use the ones provided by OWID. Their figures come from the Human Mortality Database (HMD) for European countries and from the World Mortality Dataset (WMD) for the rest of the world. Those sources presumably use expert demographers with complicated, and hopefully accurate, mortality models.

After I downloaded the OWID data file, I eliminated the extremely small countries from the dataset since their data is likely to be idiosyncratic. (For instance, Monaco claims to be a real country but has only 40,000 citizens. A handful of random deaths could double its mortality rate.) I also manually dropped 2 specific countries: Russia and Hong Kong. The question I’m trying to answer is, how deadly are the Pfizer and Moderna mRNA vaccines? Russian data can’t help answer this question because they didn’t use the Pfizer or Moderna or any other mRNA vaccine. They used their own Sputnik vaccine which is non-mRNA. China also used their own non-mRNA vaccines from Sinovac and Sinopharm. But China does not provide enough mortality data for WMD to calculate its excess mortality, so I couldn’t include them in this analysis even if I wanted to. Hong Kong allowed its residents to choose between Pfizer and Sinovac, and most people chose Sinovac, so I dropped Hong Kong as well. I’m sure there are other Central Asian countries which used the Sputnik vaccine, and other East Asian countries which used Sinovac, but I don’t know which they are, so Russia and Hong Kong are the only countries that I specifically eliminated.

Since I am investigating the effect of vaccinations on mortality, I can obviously only do this for the countries which provide data on both vaccinations and death rates. Almost all countries in the world have vaccination data, but many of the smaller, poorer, especially African countries do not have enough data for HMD or WMD to compute excess mortality, so I could not use them in this analysis. That left 58 countries in the dataset – all of Europe and the Anglosphere, much of Asia, some of Latin America, and only a couple of countries in Africa. This comprises all of the developed world plus a few developing countries.

Here are some questions this dataset can answer:

1. Are there more all-cause deaths in 2022 after the vaccination campaign of 2021? The answer is, yes. The Our World in Data database provides excess mortality figures for all major developed countries in the world and they show an average 8% rise in all-cause deaths in 2022 compared to what demographers had predicted based on previous years. Most of these extra deaths were not from Covid.

2. Are the highly-vaxxed countries suffering more deaths than the less-vaxxed countries? Yes. Here is a plot, showing one point per country, of vaccinations given in 2021 on the x-axis vs. excess mortality in percent in 2022 on the y-axis. I label each point with its country name and show the best fit line over all the points in blue:

You can easily see from this graph that, except for 3 countries at the bottom, every country in the world had positive excess mortality in 2022 – from 1% above normal in low-vaccination countries like the Philippines to 20% above normal in high-vaccination countries like Chile. The US, at 9% excess mortality, is right in the middle.

The country points, and the best-fit line, clearly slope upward. In fact, the lowest vaccination country in the dataset, Kyrgyzstan, just happens to be the second lowest mortality country (lower left corner of graph), and the highest vaccination country in the dataset, Chile, happens to be the highest mortality country (upper right corner). The data is pretty damning.

3. Is the relationship in #2 so strong that it can be considered statistically significant? Almost. Below is the simplest regression model possible, showing how vaccinations given in 2021 affected excess mortality in 2022. To be clear, each data point is a different country, so the question being asked is, do countries that vaccinated a lot of their people in 2021 have more unexpected deaths in 2022:

• lm(formula = excess_mortality_percent_2022 ~ total_vaccinations_per_person_2021, data = CovidData, na.action = na.omit)

• Residual standard error: 4.5 on 56 degrees of freedom
• Multiple R-squared: 0.0663, Adjusted R-squared: 0.0497
• F-statistic: 3.98 on 1 and 56 DF, p-value: 0.051

Expressed as an algebraic equation, the formula would be

• excess_mortality_percent_2022 = 2.43 * total_vaccinations_per_person_2021 + 5.38

This regression shows that vaccinations given in 2021 affect excess mortality in 2022 with a positive coefficient of 2.43, meaning that the countries which were most vaccinated in 2021 had the most extra deaths in 2022. The T-statistic is 1.99, just below 2 – the conventional threshold for statistical significance – so this relationship is borderline significant.

There are many ways that this regression could be improved. For instance, it weights each country equally, so even though I’ve eliminated tiny countries like Monaco, there are still small countries in the dataset like New Zealand and they are all weighted as much as huge countries like the US. Ideally, the data should be weighted proportional to each country’s population:

• lm(formula = excess_mortality_percent_2022 ~ total_vaccinations_per_person_2021, data = CovidData, weights = population_2021, na.action = na.omit)

• Residual standard error: 23600 on 56 degrees of freedom
• Multiple R-squared: 0.159, Adjusted R-squared: 0.144
• F-statistic: 10.6 on 1 and 56 DF, p-value: 0.00194

Changing the regression weights makes a big difference. The T-statistic on vaccinations is now 3.25, well above the statistical significance threshold of 2, so strongly significant.

But just as there are problems with weighting each country equally, there are also problems with weighting some countries 100 times as much as others, so a useful statistical compromise is often to weight points with the square-root of the size of the sample, in this case the square-root of the country’s population. (Don’t ask me why. Just trust me on this.) Here is that regression:

• lm(formula = excess_mortality_percent_2022 ~ total_vaccinations_per_person_2021, data = CovidData, weights = sqrt(population_2021), na.action = na.omit)

• Residual standard error: 299 on 56 degrees of freedom
• Multiple R-squared: 0.116, Adjusted R-squared: 0.101
• F-statistic: 7.38 on 1 and 56 DF, p-value: 0.00877

This is my preferred simple model. It demonstrates several things:

A. Total vaccinations given in 2021 forecast excess mortality in 2022 with a positive sign, meaning more vaccines cause more deaths.

B. Vaccinations predict mortality with a coefficient of 3.30, meaning giving every person in the country one more dose of vaccine increases the total death rate by about 3%. This is tiny. The 3% is not the percent of people dying, it’s the percent increase in people dying. So, if normally 1% of the population dies every year, vaxxing with one more shot would increase this to 1.03% of the population.

C. The T-statistic on vaccinations is 2.72 which is statistically significant.

D. The P-value is 0.0088 which means there is less than a 1% chance an effect like this would occur randomly.

E. The adjusted R-squared is 0.101 which means that vaccinations explain 10% of the variance in countries’ death rates, which is modest.

Obviously, many objections could be made to such a simple model. For instance, countries which vaccinated a lot are probably also countries which implemented other Covid controls such as lockdowns. That would obviously affect the mortality rate. Also, poor countries generally have much worse health outcomes than rich countries, and this will have a drastic effect on death rates. Plus, almost all deaths happen to old people. This is especially true of Covid deaths, but it is also true of almost every other type of death. So, death rates in countries like Egypt (average age 25) are really not very comparable to death rates in countries like Japan (average age 48). So, I should add controls for all of these factors to the model. Fortunately, Our World in Data thought about these issues and their Covid database provides almost all the health-related controls I could think of: median age, % of population over age 65, life expectancy, various health conditions, prevalence of smoking, GDP per capita, a composite measure of anti-Covid policies (lockdowns, school closures, travel bans), plus many others. I tried putting all of them in the regression and then eliminating the ones which were not statistically significant. Only two controls remained with T-statistics over (or near) 2: stringency of anti-Covid policies and GDP per capita. Here is that model:

• lm(formula = excess_mortality_percent_2022 ~ stringency_index_2021 + log(gdp_per_capita_2021) + total_vaccinations_per_person_2021, data = CovidData, weights = sqrt(population_2021), na.action = na.omit)

• Residual standard error: 286 on 54 degrees of freedom
• Multiple R-squared: 0.224, Adjusted R-squared: 0.181
• F-statistic: 5.2 on 3 and 54 DF, p-value: 0.00314

This is my preferred complex model. Controlling for the country’s Covid policies and per capita wealth, this regression shows a 6% increase in all-cause deaths for every vaccination given, a very high T-statistic of 3.50, a 0.1% chance that the vaccination’s effect on deaths is random, and a good model R^2 of 18%.

Although this regression only predicts countries’ death rates, not an individual’s chance of dying, a 6% increased risk of death country-wide means a 6% increased risk of death on average for each person in the country. So, this result for countries presumably translates into a forecast for individuals that if you decide to get another shot, you increase your chance of dying in the next year by 6% over what the risk would have been without the shot. It would not be unreasonable to conclude that for every jab you get, you are 6% more likely to die in the following year. But this is not as bad as it sounds. For people under the age of 65, who ordinarily face a chance of dying of about 0.3% per year, raising that probability by 6% to 0.318% is trivial. But for people over 80, who have a 10% risk of dying every year, raising that risk to 10.6% does seem pretty bad.

Also, since so few young people die at all, this empirical relationship between vaccinations and deaths will be dominated by anything that alters the much larger number of deaths among elderly people. So, the regression above probably doesn’t really say much about the effect of the vaccines on the young. But it strongly suggests that the vaccines are killing old people.

However, it is worth reflecting on what this regression does not show. It does not show that the vaccine is a “genocidal depopulation bioweapon” killing everyone foolish enough to get jabbed. Compare the results above to what they would be if the vaccine was truly deadly. Let’s imagine the vaccine was designed to slowly kill everyone who got fully boosted – say 4 shots kill a person over 10 years. That means the fully boosted population would experience a 10% / year mortality rate instead of the normal 1% / year, making their excess mortality 10 (a 10-fold increase). My regression formula predicts excess mortality in percent, so 4 shots would cause 1000% excess mortality, or a 250% increase per shot.

The regression model above can be simplified to

• excess_mortality_percent = 6 * total_vaccinations_per_person + other stuff

If the vaccine was truly genocidal, the regression would instead have to be

• excess_mortality_percent = 250 * total_vaccinations_per_person + other stuff

The actual relationship between vaccines given and deaths caused is nowhere near this. In fact, if the purpose really was to kill everyone who took it, the vaccine is only 6 / 250 = 2% effective at achieving this goal. Apparently, it is no more effective at exterminating the human race than it is at preventing Covid.

How bad is 2% effectiveness? Based on the regression results above, to accomplish the depopulationists’ evil plan, you’d have to give every person on the planet 4 vaccine shots, wait 10 years, each of those years would have to be as bad as 2022 (meaning that 1 shot increases excess mortality by 6%, so 4 shots increase it by 24% from 1% / year to 1.24% / year), and then after all that, you’d still only have achieved 2% of your goal of depopulating the earth. What a lame weapon of mass murder the Covid vaccine is.

To put this more visually, the graph of vaccinations vs. mortality shown several pages ago ranged from about 0,0 (countries which administered 0 vaccines had 0 excess deaths) to around 3,20 (3 vaccines per person caused 20% excess deaths). If the genocidal theory of the vaccines was true, the graph should have extended from 0,0 to 4,1000 (4 vaccines cause 1000% excess mortality per year). Not even close.

I will make more fun of the “vaccine as depopulation” theory later.

So, the Covid vaccines seem to be causing extra deaths in the countries which gave their citizens a lot of shots. Are there alternative theories to explain this? I think everyone acknowledges now that non-Covid deaths are up around the world. But most people don’t want to blame this on the vaccines. Two common alternative explanations for the post-Covid worldwide rise in deaths are:

A. Lockdowns. It’s a popular theory today that Covid lockdowns made people miss their doctor’s appointments, and as a result many cases of strokes, heart attacks, and cancer remained undiagnosed, and these deadly diseases, left untreated, are now killing people. It’s a very plausible theory. But the stringency index above is a measure of various anti-Covid public health policies including lockdowns. It entered negatively and significantly in the regression, meaning more of those actions in 2021 caused fewer deaths in 2022. I also tried putting the stringency index for 2020 in the regression. It also entered negatively, although insignificantly. I can’t explain why. I would expect these policies to cause more deaths and so enter the formula positively. But there’s no evidence that the harmful health effects of lockdowns and other anti-Covid measures explain the current surge in deaths worldwide.

B. Long Covid. Covid could be a much more dangerous disease than it appears to be. Covid’s only obvious effects are flu-like symptoms that last for a few days and then go away. But maybe it is actually causing lots of hidden damage to your health, like weakening your heart, and this damage shortens your life later on. This is a version of the “Long Covid” idea – permanent injuries from Covid. We can test for whether this theory is true. If it was, we’d expect the number of Covid cases in the past to positively predict all-cause deaths in the future. Specifically, more Covid cases in 2020 and 2021 would cause more excess mortality in 2022. I tried using the variables Covid_cases_per_million_2020 and Covid_cases_per_million_2021 to predict excess_mortality_2022. The effect was insignificant. In fact, it was insignificantly negative which is the opposite sign than this theory would predict. That is, countries which had more Covid cases in 2020 and 2021 had, if anything, lower excess mortality in 2022 than countries which had fewer Covid cases.

But maybe it’s not so much how many Covid cases there were, but how bad they were. So, I tried using the OWID variables Covid_deaths_per_million_2020 and Covid_deaths_per_million_2021 to predict excess_mortality_2022. The effect was again insignificant and slightly negative. Countries which had more Covid deaths in 2020 and 2021 had, if anything, less mortality in 2022.

But maybe the official counts of Covid cases and deaths are so inaccurate that the only good measurement of the Covid epidemic is the number of deaths from all causes in 2020 and 2021. To test this, I tried using excess_mortality_2020 and excess_mortality_2021 to predict excess_mortality_2022. The effect was again insignificant and negative. Countries which had more all-cause deaths in 2020 and 2021 had, if anything, fewer all-cause deaths in 2022. (These negative effects of past on future mortality are normal. All else equal, if something killed a lot of elderly people in past years, then there should be fewer elderly people around to die in future years. This demographic pattern is often called the “dry tinder effect”: a forest fire in one year clears out the dry tinder, making forest fires rarer in the following year.)

So, Covid cases, Covid deaths, and all deaths in 2020 and 2021 did not cause the increase in deaths in 2022. People having caught Covid during the pandemic is not what is killing them today.

The results above that relate countries’ vaccination rates and death rates are technically statistically significant, but having played around with the data a bit now, I believe they are brittle. There are only 58 countries in the regression, and you can always find some weird pattern in so few data points. So, I’d like to repeat this analysis on a completely different dataset to try to verify the results. Fortunately, I can do the same sort of regressions with data from the 50 US states. Our World in Data publishes Covid vaccination rates by state at https://github.com/owid/covid-19-data/blob/master/public/data/vaccinations/us_state_vaccinations.csv and the CDC publishes excess mortality rates for each state at https://data.cdc.gov/api/views/xkkf-xrst/rows.csv .

I combined the two files to run this regression of vaccinations given in 2021 on excess mortality in 2022, where each data point is a different US state:

• lm(formula = excess_mortality_percent_2022 ~ total_vaccinations_per_person_2021, data = CovidData, weights = sqrt(Population), na.action = na.omit)

• Residual standard error: 146 on 50 degrees of freedom
• Multiple R-squared: 0.0272, Adjusted R-squared: 0.00777
• F-statistic: 1.4 on 1 and 50 DF, p-value: 0.242

The sign is negative and the effect is insignificant with a T smaller than 2. Covid vaccinations had little effect on excess deaths in the states. What effect they had was the opposite sign as I previously found with countries.

Since the results above were insignificant, I didn’t make much effort to find confounding variables. The only one I tried was the state equivalent of GDP per capita which is personal income per capita:

• lm(formula = excess_mortality_percent_2022 ~ log(Per_capita_personal_income) + total_vaccinations_per_person_2021, data = CovidData, weights = sqrt(Population), na.action = na.omit)

• Residual standard error: 140 on 49 degrees of freedom
• Multiple R-squared: 0.124, Adjusted R-squared: 0.0887
• F-statistic: 3.48 on 2 and 49 DF, p-value: 0.0386

Controlling for income, the effect is basically 0. The T-statistic on vaccinations is so low, this has to be classified as extremely insignificant.

Here is a plot of the 50 states (and DC and PR). The best-fit line has a slightly negative slope, which would mean more vaccinations produce fewer deaths, if it were significant:

Vaccinations had no effect on mortality in the 50 states. A disappointment for both the pro- and anti-vaccine sides. So, this 50 state data does not at all corroborate the earlier 58 country data. That’s the problem with data. It doesn’t always give you a clear answer.

But you can see from the y-axis of the graph, which has only positive excess mortality numbers, that every single state in the US had excess deaths in 2022 – from 2% above normal in Rhode Island to 22% above normal in Vermont. The country’s average excess mortality was about 9%. Something bad is going on.

Next year, I will download these vaccination and mortality data files again to see whether vaccinations in 2021 and 2022 increased mortality in 2023.

2. VAERS Database

Let’s look at the US government’s Vaccine Adverse Event Reporting System, called VAERS. The VAERS data on adverse events (side effects) cannot conclusively prove that the Covid vaccine is the worst medical product ever created. But it is amazing nonetheless.

The VAERS reporting system is simple in concept: when someone gets vaccinated and suffers an adverse event, if their doctor thinks this was caused by the vaccine, he’s supposed to report it to the government (or to his hospital which should then report it to the government). The problem is, very few doctors actually do this – they are not paid to do it and only a tiny minority of doctors do things they’re not paid for – so the data is known to be extremely incomplete. VAERS is not meant to be a full record of all the vaccine side effects that ever occurred. It is simply meant to be a sampling of potential problems.

The Vaccine Adverse Event Reporting System can be searched online at https://vaers.hhs.gov/data.html . Here are the total number of adverse events and deaths associated with all vaccines (not just the Covid vaccines) during the past 10 years (“associated with” means they were reported as believed to be, but not known with certainty to be, caused by the vaccine):

One of these years is not like the others. If this was an IQ test, the FDA – whose job it is to monitor VAERS for vaccines that cause an unusual number of side effects – would fail.

Here are the VAERS numbers for the Covid vaccines only (not all vaccines combined):

Total adverse events in 2021 and 2022 were practically all from the Covid vaccines. The numbers are much smaller in 2022 than in 2021 because most Covid vaccines were given in 2021. Of the total 666M vaccinations administered in the US by the end of 2022, 1% were given in 2020, 77% in 2021, and 22% in 2022.

Part of the “Covid misinformation” on the Internet is the idea that the vaccines cause heart problems, in particular myocarditis (inflammation of the heart muscle) and pericarditis (inflammation of the lining around the heart). Below are the VAERS reports on those particular heart problems over the past 10 years. I could show the yearly numbers for the Covid vaccines only or the yearly numbers for all vaccines combined but these numbers are essentially identical. For instance, in 2021, 99% of myocarditis reports and 98% of pericarditis reports were for the Covid vaccines. So, I will just report the yearly figures for all vaccines combined:

You’d obviously have to be crazy to think that the Covid vaccines are causing heart problems. How can people believe such misinformation? Nothing out of the ordinary is happening. Trust the experts. There is nothing to see here.

The widely-watched movie “Died Suddenly” has raised the issue of blood clots to prominence among vaccine skeptics. The medical term for blood clots is embolisms and the most common type of embolisms in VAERS are pulmonary embolisms. And with so many famous celebrities getting Bell’s Palsy lately, I’ll look for that too:

Yikes. 2021 had 1000 times all previous vaccines combined.

But this is fine. I’m sure the authorities are being perfectly honest when they say that the Covid vaccines don’t cause blood clots or any other problems. Malicious anti-vax misinformation to the contrary needs to be removed from the Internet immediately or people might get the wrong idea.

Let’s look at a few more “debunked conspiracy theories”. Because of all the false claims made about reproductive disorders that have been fabricated by anti-vaxxers, I also looked for:

Spontaneous abortions (miscarriages) – the number reported in 2021 was 50 times higher than the worst previous year.

Menstrual disorders – 2021 was 300 times larger than the worst previous year.

Heavy menstrual bleeding – 2021 was 1000 times more than the worst previous year.

The idea that the Covid vaccines are causing reproductive problems is clearly just a hoax that needs to be censored as hard as possible to prevent people from being misinformed about how this is definitely not happening.

That’s enough VAERS tables to make my point. Say what you will about VAERS’ inadequacies, it was created for a very valuable purpose: as an early warning system to flag possibly dangerous vaccines that somehow slipped through the rigorous FDA approval process which ordinarily takes about 10 years of testing before a vaccine is approved. Of course, the Covid vaccines skipped the usual 10 years of testing, which makes VAERS even more important in this case. And now the VAERS system is flashing a gigantic “danger” sign – by far the largest ever – with total adverse events in 2021 at 25 times a normal year, deaths at 150 times normal, and specific adverse events up to 1000 times normal, all caused by one new type of vaccine – mRNA. Yet in spite of this unprecedented level of warning, this time the FDA doesn’t think the danger is worth examining. Instead, they put out press releases saying “VAERS data is misinformation.”

VAERS is actually a great system if the FDA would only use it. Before Covid, they did use it. For instance, these are the adverse events in VAERS reported for RotaShield, a vaccine for rotavirus:

RotaShield was introduced in 1998, the FDA noticed too many adverse events in early 1999, and they took the vaccine off the market in late 1999. No delay. The Covid vaccines have had 1500 times as many reported adverse events and 2000 times as many reported deaths as RotaShield, but all we hear from the FDA now is that Covid adverse events are a conspiracy theory.

The FDA believes, or pretends to believe, that the Covid vaccines are perfectly safe. Yet every one of the mRNA vaccines’ 870,000 adverse event reports in the VAERS database is a direct refutation, from first-hand experience, of the FDA’s “safe and effective” talking point. They all contradict the dogma of the vaccines’ harmlessness. The sheer volume of side effects that VAERS documents makes it grimly entertaining to watch the FDA try to explain away all the people reporting severe injuries from a vaccine that it claims has no dangerous side effects. It is the FDA’s job, apparently, to both collect massive numbers of reports on vaccine injuries and to issue public statements that there are no such injuries. They’re like a Soviet bureaucracy whose employees read hundreds of thousands of complaints about the government and then issue daily statements reporting that, once again, there were no complaints today.

While Soviet-style employment like that might seem detestable to anyone with integrity, the sad truth is that there were countless people in the Soviet Union who wanted those jobs. And now there are countless people in America who want jobs like that too, because those positions provide the most prestige, the highest income, the best credentials, the greatest perks, the finest reputation, the closest contact with powerful people – every signal of high status that a society can bestow is now given to the very worst liars our society can produce.

The US, of course, isn’t the only country with a vaccine monitoring system. European and Asian countries also have them. Below are the vaccine side effects collected worldwide by the WHO Programme for International Drug Monitoring (PIDM). Unfortunately, the PIDM website at http://vigiaccess.org/ has a fairly limited user interface. It can only report adverse events for specific vaccines, not all vaccines combined. With VAERS, I compared adverse events by year, knowing that 99% of the events in 2021 were from the Covid vaccine, so comparing 2021 with pre-2020 was essentially comparing the Covid vaccine with all other previous vaccines. With this database, I’ll just compare the Covid vaccine to the two most widely-used vaccines which produced the second and third largest number of side effects. Here are the Covid-vaccine-linked adverse events plus those from the flu and pneumonia vaccines since 2020:

There were more than 100 times as many adverse events reported world-wide for the Covid vaccine than for either of the other two major vaccines in 2021 and 2022. Again, one of these vaccines is not like the others. I wonder if the Europeans can figure out which one it is.

Below are some cardiac side effects reported in the PIDM database for these vaccines. Specific side effects can’t be broken down by year, so I can only report the total number of events for all years in the database. Here are the total number of various cardiac adverse events for these three major vaccines. (Cardiac disorders is the general category, myocarditis and pericarditis are specific cardiac disorders):

To make the numbers easier to compare, I’ll compute the number of cardiac disorders per year for the 3 vaccines, counting the Covid vaccine as covering 2 years (2021 and 2022) and the other vaccines as 15 and 10 years based on the time period when the WHO database has data for those vaccines. Here are the per year comparisons:

The Covid vaccines seem to be causing 300 to 1000 times as many cardiac disorders per year as the influenza vaccine and 400 to 2000 times as many as the pneumococcal vaccine worldwide.

The WHO’s PIDM data for Pulmonary Embolisms and Bell’s Palsy are similar to VAERS – there are hundreds or thousands of times more Pulmonary Embolisms and Bell’s Palsy incidents reported for the Covid vaccines than for the next-worst vaccines.

Also, pregnancy problems from the Covid vaccine are 10 to 100 times as common as from the influenza and pneumococcal vaccines, spontaneous abortions (miscarriages) are 30 to 500 times as common, reproductive problems occur 300 to 600 times more often, menstrual disorders 2000 to 10,000 times more often, and heavy menstrual bleeding 1000 to 15,000 times more often.

But other than that, it’s totally “safe and effective”. And anyone saying otherwise is a Russian bot, sent by Putin to undermine confidence in America!

I think it’s fair to say that the Covid vaccines would be considered “unsafe” by the FDA’s pre-Covid standards. According to the VAERS and WHO data, they have caused hundreds or thousands of times more injuries and deaths than other vaccines that the FDA has removed from the market because they were deemed “unsafe”. However, the FDA’s standards on safety are generally considered much too risk-averse, causing more harm from Type I errors/false positives (removing a helpful product from the market because of minor side effects) than Type II errors/false negatives (failing to remove a harmful product from the market because they ignored its side effects). So, even though the Covid vaccines are absolutely orders of magnitude more dangerous than other vaccines that the FDA has declared to be too dangerous to use, that doesn’t necessarily mean they are catastrophically dangerous.

3. Severe Side Effects

The very illuminating medical paper at https://www.scivisionpub.com/pdfs/us-covid19-vaccines-proven-to-cause-more-harm-than-good-based-on-pivotal-clinical-trial-data-analyzed-using-the-proper-scientific--1811.pdf analyzes the publicly-available clinical trial data provided by Pfizer and Moderna. It compares the vaccine benefits and side effects that were officially reported by the vaccine manufacturers themselves. The Covid clinical trials demonstrated extremely high efficacy for the vaccines (how they could have been so wrong about that is another story), but they also reported an enormous number of adverse events suffered by the trial participants. Pfizer’s and Moderna’s spin on the results was that the vaccines prevented 95% and 94% of Covid cases in those who received the vaccine instead of the placebo. They also reported, but did not emphasize, that the vaccinated group experienced many more life-threatening adverse effects than the placebo group.

You can find Pfizer’s and Moderna’s clinical trial reports online at https://pubmed.ncbi.nlm.nih.gov/33301246/ and https://pubmed.ncbi.nlm.nih.gov/33378609/ . The idea of this paper is simply to compare the number of severe Covid cases prevented by the vaccines with the number of severe adverse events caused by the vaccines. If “severe” means the same thing in both cases, then we can say which is worse – Covid or the vaccine. Of course, this assumes that getting the vaccine prevents Covid. That is what the original clinical trials found – 95% vaccine effectiveness – something that nobody, even the vaccine makers, believes anymore. If the vaccines don’t prevent Covid, then vaxxing gives you both the side effects and Covid. But let’s take the Pfizer and Moderna clinical trial results at face value – assume the vaccines prevent 95% of Covid infections, but also cause the side effects documented in these reports, so the Covid effects and the side effects can be compared.

Before I get to the paper’s main results on severe side effects, let’s start simply with the total number of Covid cases prevented and the total number of adverse events caused by the vaccines, as reported in the clinical trials:

“Covid cases prevented” is the number of Covid cases in the placebo group (162 for Pfizer) minus the number of Covid cases in the vaccinated group (8). “Adverse events caused” is the number of adverse events in the vaccinated group (5770 for Pfizer) minus the number of adverse events in the placebo group (2638).

Moderna reported many more adverse events than Pfizer did, but this is mostly because of how they counted adverse events. Pfizer only counted “unsolicited” adverse events, which means the clinical trial participants had to contact Pfizer to tell them about the problem. Moderna counted “solicited” adverse events, which means that Moderna contacted every participant and asked them if they had any problems. Pfizer made it difficult and Moderna made it easy for people to report side effects. (I got the definite impression from reading through these clinical trials that Pfizer and Moderna had fundamentally different philosophies about the disclosure of problems. The Pfizer report contains 1 page on side effects. The Moderna report has 20 pages, breaking down all side effects in great detail. Obviously, the Pfizer philosophy – hide as much bad news as possible – has been much more lucrative for them financially. I think most people today have the impression that Pfizer’s vaccine has fewer side effects than Moderna’s.)

Obviously, these raw totals are not very important. Most side effects were extremely mild. According to Pfizer, they were mainly “pain at the injection site, fatigue, and headache”. Of course, most Covid cases were mild too, some even asymptomatic. But still, comparing all side effects to all Covid cases is mostly comparing mild side effects to mild Covid cases, so this doesn’t really matter. I just wanted to give some context to the more important numbers below.

Of much more importance than all effects are severe effects:

“Severe” in this case has a specific medical meaning. The FDA classifies adverse events from drugs and vaccines on a 4-point scale. “Severe adverse events” are level 3 (defined as “prevents daily activity and requires medical intervention”) and level 4 (“potentially life threatening, ER visit, or hospitalization”). Severe Covid cases and severe adverse events are presumably comparable in severity. So, according to Pfizer’s and Moderna’s own reports, their vaccines cause 13 to 102 times as many severe problems as they prevent.

This is the main result presented in this paper. I went through the two clinical trial reports online to verify the numbers. The paper computes the statistical significance of the net effect – the benefit of preventing severe Covid minus the cost of suffering severe side effects. It reports the p values for both the Pfizer and the Moderna results as p < 0.00001. That is, there is a 99.999% chance that these negative net effects are real, not the result of random chance. I have not recalculated the p values myself because I can never remember how to do that.

Of course, deadly side effects are an even greater concern than severe side effects. Here are the reported deaths from these original clinical trial reports:

There were more total deaths in the placebo groups than in the vaccinated groups. However, there were so few deaths in both groups that the difference between vaccine deaths and placebo deaths was not statistically significant. Also, these were the initial reports, rushed out as soon as they reached their efficacy goals. (Actually, Pfizer reached their efficacy goals in October 2020, but the FDA told them not to release their results publicly until after the election, because they didn’t want to “influence” the election. Scum.) The trials continued for several months after these initial reports. By then, many more participants had died, mostly not from Covid, several from heart attacks in the vaccinated group – which should have caught someone’s attention. The final death figures for the Pfizer and Moderna clinical trials were:

To make it clearer, here are the total number of deaths from all causes in the vaccine and placebo groups:

None of these death figures are statistically significant, but they suggest that the vaccines could be causing between 1.5 and 5 deaths from side effects for every 1 life saved by preventing a deadly case of Covid. The amazing thing about all these numbers is that Pfizer and Moderna made so much damning information about their vaccines publicly available, but since every official expert – the companies, media doctors, public health authorities, the government – chose not to mention them, the press simply didn’t report the problems. This information is hiding in plain sight, forever available on the Internet, in the very documents that Pfizer, Moderna, and the FDA trumpeted as such triumphs. Here are the final versions of both clinical trial reports, with the updated deaths counts: https://www.nejm.org/doi/full/10.1056/NEJMoa2110345 and https://www.nejm.org/doi/10.1056/NEJMoa2113017 .

In summary, the positive and negative effects of the vaccine can be grouped into 3 categories:

1) All Covid cases prevented by the vaccine vs. all side effects caused by the vaccine. The latter outnumbered the former, and the difference is statistically significant, but these mild effects are not important enough to care about.

2) Severe Covid cases prevented by the vaccine vs. severe side effects caused by the vaccine. The latter outnumbered the former, the difference is statistically significant, and severe impacts are important.

3) Covid deaths prevented by the vaccine vs. deadly side effects caused by the vaccine. The latter outnumbered the former, deaths are the most important outcome, but the difference is not statistically significant.

Taking the Covid vaccine caused more harm than good in all 3 categories, but this paper focused on category #2 because severe medical problems are both important and statistically significant.

The bottom line is that the Pfizer and Moderna clinical trials made it clear from the beginning that both vaccines cause much more harm than good. Everyone in authority knew this in November 2020, but they all said the opposite. Although these figures were published in 2020, most people are not able to read medical papers so they rely on the media to tell them what the results mean. The public was never told about the side effects described in these reports. Yet despite, or perhaps because of, the press constantly lying, most people are now suspicious of the vaccines. They may not be able to cite the statistics, but they can sense that they’re being lied to. The experts have been so relentlessly dishonest over the past 3 years, no one who has a brain cell working believes the official story anymore. Perhaps someday people will begin to realize that it wasn’t just the public health experts on TV who lied to them, it was also their own personal physicians.

In fact, I have a lot more sympathy for the CEOs of Pfizer and Moderna than I do for the average doctor who recommended the vaccine to his patients. Albert Bourla, CEO of Pfizer, made about $100M in compensation and stock profits because of the Pfizer vaccine. Stephane Bancel, CEO and major shareholder of Moderna, made over $1B. Of course they lied to make that money. Wouldn’t you? I believe there are two kinds of people in this world – those who would lie for $100M and admit it, and those who would lie for $100M but will not admit it which is another lie. So, I forgive Albert Bourla and Stephane Bancel for their lies. I don’t forgive the average MD. They had very little to lose by telling the truth to the patients who trusted them. Maybe they would have gotten in some trouble, perhaps even losing their privileges at their hospital, if word got out that they recommended their young and healthy patients not take the vaccine. So, maybe the financial cost to them would have amounted to thousands of dollars. But it’s pathetic to sell your soul for thousands of dollars. That’s like trading your birthright for a mess of pottage. Those doctors are despicable. Selling your soul for millions or billions of dollars, like Albert Bourla and Stephane Bancel did, is just human nature. You can’t really blame them for doing that.

The Pfizer and Moderna vaccines were approved by the FDA based on the clinical trial data presented above. The FDA’s criterion for approval was whether the vaccines reduced Covid infections, which the data showed they did. But this was the wrong criterion for approving a medical treatment. Chemotherapy drugs are not approved based simply on whether they cure cancer. They are approved only if their cancer-curing benefits are greater than their health-destroying side effects. If a chemotherapy drug cured cancer 50% of the time but killed the patient 100% of the time, that drug would not be approved because its net benefit would be negative. Similarly, the FDA knew that the Covid vaccine prevented 1 severe Covid case for every 10 to 100 severe non-Covid side effects that it caused, so its net benefit was extremely negative, and it should never have been approved. The FDA was fully aware of all these facts when they rushed through the vaccines in 2020.

Vaccines used to take 10 years for approval because the FDA wanted to know about their long-term side effects. But because Covid was an “emergency”, the process was shortened to under 1 year. The clinical trials themselves lasted only 6 months. At that point, the FDA was so impressed with the results, they declared that it would be “unethical” to leave the placebo group unvaccinated and in danger of contracting Covid. So, after 6 months, the entire placebo group was given the vaccine, destroying any possibility of doing a longer-term study of the vaccines’ positive or negative effects.

So, the clinical trials can’t tell us anything about either the long-term benefits or the long-term harms caused by the vaccines. In particular, if Long Covid is real, then perhaps preventing Covid is more beneficial than a 6-month-long trial could show. On the other hand, if the vaccines cause long-term side effects, then maybe they are even worse than these trials indicate.

For instance, there was a study of 300 school students in Thailand who received the Pfizer vaccine, which can be found at https://www.mdpi.com/2414-6366/7/8/196 . Instead of just asking the children whether they felt any symptoms after the injection, they measured the students’ cardiac biomarkers for heart problems. They found assorted minor heart issues in many children after vaccination: “cardiovascular manifestations were found in 29% of patients, ranging from tachycardia or palpitation to myopericarditis.” 3% of the boys, and none of the girls, had subclinical (asymptomatic) myocarditis or pericarditis. Subclinical myocarditis has no symptoms so just asking patients if they feel bad after the vaccine – let alone counting myocarditis hospitalizations like the heart studies in the next section do – won’t find it. But subclinical myocarditis can apparently be tested for. Asymptomatic myocarditis can damage heart muscle and weaken the heart which increases the chance of having heart failure later in life. So, will vaccinated boys have more heart attacks 20 years from now? Maybe. But there is no money to be made in finding out, so unless the political calculus changes, we will never be told. The authorities don’t want to know. The public doesn’t want to know. People who got vaccinated are not clamoring for studies which might show that they willingly killed themselves or their children for no reason except cowardly conformity. There will be no government grants to fund those studies.

In hindsight, anyone concerned about their heart probably should have avoided the Covid vaccine at all costs, including dropping out of college or quitting their job if necessary.

4. Heart Studies

The Florida Department of Health did its own study of vaccine-caused deaths at https://floridahealthcovid19.gov/wp-content/uploads/2022/10/20221007-guidance-mrna-covid19-vaccines-analysis.pdf . They employed what’s called the “self-controlled case series” (SCCS) method to eliminate the problem of confounders. SCCS compares a person at one time with the same person at a different time to remove any biases caused by different ages, sex, comorbidities, etc. In this case, they compared vaccinated people within 28 days of getting the vaccine with the same vaccinated people more than 28 days after getting the vaccine. The two groups being compared (before 28 days and after 28 days) are the same people, so they are automatically matched in every way – age, ethnicity, health conditions, etc.

The Health Department linked vaccine records with death certificates to count how many vaccinated people died within 28 days of getting the vaccine compared with how many died after 28 days. So, all of their comparisons involved recently- or less-recently-vaccinated people. If the vaccine has deadly side effects which are short-term and not long-term, the death rate should be higher before day 28 than after day 28. This within-person method of comparison makes the assumption that vaccine injuries are more likely to occur immediately rather than later. Many anti-vaxxers would question that assumption, but the results of the study are still interesting.

Because there have been many reports of people developing heart problems after the vaccine, especially myocarditis and pericarditis, the state of Florida checked for two causes of deaths – all-cause deaths and only-cardiac-related deaths. They found no statistically significant increase in all-cause deaths in the 28-day period after the vaccine. But they did find a statistically significant increase in cardiac-related deaths. The increase in heart-related deaths was small, only 7%, in the population as a whole, but was extremely large in young people, especially young men. The most-impacted group was males age 18 to 39 who experienced a 97% increase in heart-related deaths during the month after inoculation. Of course, 18 to 39-year-old men don’t typically die from heart disease, so the actual numbers were all small.

This study was done for vaccinations given in 2021. At the time, the Janssen (Johnson & Johnson) vaccine was still being given in addition to Pfizer and Moderna. Surprisingly, Janssen, which is not an mRNA vaccine, actually showed a statistically significant decrease in cardiac-related deaths. Only the mRNA vaccines increased heart-related deaths.

Why is the state of Florida the only governmental body in the US doing studies like this? Isn’t this the job of the CDC or FDA?

Unlike the US, many other countries are now doing similar investigations. A SCCS study in England at https://www.ahajournals.org/doi/10.1161/CIRCULATIONAHA.122.059970 used medical records from the National Health Service to analyze the entire vaccinated population of the country, about 43M people. They counted hospital admissions and deaths specifically from myocarditis to see if they increased immediately after people were vaccinated. England used mostly Pfizer and Moderna vaccines against Covid, but also some AstraZeneca. The researchers compared the number of myocarditis cases that occurred from 1 to 28 days following the vaccination to the number of cases after 28 days. There was a statistically significant increase in hospitalizations and deaths from myocarditis during the first month following vaccination compared to later. So, this answers the question, do the vaccines cause myocarditis immediately after vaccination? Yes, they do. The increase in myocarditis is large, albeit from a small base. (It’s a very rare disease in the unvaccinated and a somewhat rare disease in the vaccinated.) Whether the vaccines also cause myocarditis more than 28 days after the injection cannot be determined from this or any other SCCS study.

As other studies have found, the results for men were worse than women, and the 2^nd dose was more dangerous than the 1^st. The AstraZeneca vaccine, which does not use mRNA, increased myocarditis hospitalizations and deaths, but only insignificantly. But both mRNA vaccines increased myocarditis significantly. Rather than report all of the numbers, I’ll just note that the 2^nd dose of Pfizer increased cases of myocarditis by 57% and the 2^nd dose of Moderna increased it 1076%. For men under age 40, the increases were 208% and 1583% for the two vaccines.

Their conclusion:

“Overall, the risk of myocarditis is greater after SARS-CoV-2 infection than after COVID-19 vaccination…. However, the risk of myocarditis after vaccination is higher [than after Covid] in younger men, particularly after a second dose of the [Moderna] mRNA-1273 vaccine.”

That is, for most people, they say catching Covid increases the risk of myocarditis more than getting the vaccine. But for men under the age of 40, getting the vaccine increases the risk more than Covid does.

Although much higher after taking the vaccine, the total risk of myocarditis from vaccination was still small. Of the 43M people in England who got the vaccine, only 2861 were hospitalized or died from myocarditis. And only 617 of those cases occurred in the time period from 1 to 28 days after the injection. So, while hospitalization and death rates were higher for the newly vaccinated, they were still very low.

A similar study was done in France at https://www.nature.com/articles/s41467-022-31401-5 . It used nationwide medical records to analyze all cases of hospitalizations for myocarditis and pericarditis, focusing especially on young people. They analyzed the medical history of all 24M French citizens aged 12 to 50 who had been vaccinated. They found an enormous increase in the risk of hospitalization during the first week following vaccination, especially for men and especially after the 2^nd vaccine dose.

For myocarditis, hospitalizations increased in the week after Pfizer dose 2 by 8-fold, and after Moderna dose 2 by 30-fold. For pericarditis, hospitalizations increased in the week after Pfizer dose 2 by 3-fold, and after Moderna dose 2 by 5-fold.

Among young men, the increases were astronomical: For males aged 18 to 24, myocarditis hospitalizations increased after Pfizer dose 2 by 13-fold, and after Moderna dose 2 by 44-fold. For males aged 18 to 24, pericarditis hospitalizations increased after Pfizer dose 2 by 6-fold, and after Moderna dose 2 by 11-fold.

Their summary:

“We find that vaccination with both mRNA vaccines was associated with an increased risk of myocarditis and pericarditis within the first week after vaccination. The associations were particularly pronounced after the second dose, and were evident in both males and females. We found a trend of increased risks towards younger age groups but a significant risk was also found in males over 30 years to develop myocarditis and in females over 30 years to develop pericarditis after vaccination.”

Although this study found a large statistically significant increase in myocarditis and pericarditis in recently vaccinated young people, the absolute numbers were low. Among the 24M who got vaccinated, there were only 1612 cases of myocarditis and 1613 cases of pericarditis requiring hospitalization.

There is a similar study of myocarditis and pericarditis that covered Denmark, Finland, Norway, and Sweden at https://jamanetwork.com/journals/jamacardiology/fullarticle/2791253 . They examined the hospital records of 23M residents of the 4 Nordic countries to compare the rates of myocarditis and pericarditis that people experienced during the month after vaccination to an unvaccinated population matched by age, sex, calendar period, health care worker status, whether in a nursing home, previous Covid infection, and 5 comorbidities (pulmonary disease, kidney disease, autoimmune disease, cardiovascular disease or diabetes, and cancer).

For the entire population, risk of hospitalization for myocarditis or pericarditis increased after the 2^nd dose of Pfizer by 65% and after the 2^nd dose of Moderna by 363%.

For males age 16 to 24 years old, risk of hospitalization increased 4-fold after the 2^nd dose of Pfizer and 11-fold after the 2^nd dose of Moderna.

There was no statistically significant increase in the risk of myocarditis or pericarditis after taking the non-mRNA AstraZeneca vaccine.

Their conclusion:

“In this cohort study of 23 million Nordic residents aged 12 years or older, the risk of myocarditis was higher within 28 days of vaccination with both BNT162b2 [Pfizer] and mRNA-1273 [Moderna] compared with being unvaccinated, and higher after the second dose of vaccine than the first dose.… The risk was highest among males aged 16 to 24 years.”

But again, although the risk went up a lot after vaccination, it was still small. Among the 23M Nordic residents, only 1077 were hospitalized for myocarditis and 1149 for pericarditis.

Here’s a similar study from Israel: https://www.nejm.org/doi/full/10.1056/NEJMoa2109730 . The Israeli Ministry of Health reviewed all hospital cases of myocarditis and compared those occurring in the 30 days immediately following the injection to two baselines: what they expected the number of myocarditis cases to be based on the pre-pandemic years, and the current number of myocarditis cases among unvaccinated people.

Israel only used the Pfizer vaccine. 5M Israelis were vaccinated with 2 doses of Pfizer. Here are the increases in rates of myocarditis within 30 days of getting the shot for all people and for just young men:

Compared to historical data from the pre-pandemic period of 2017 through 2019, vaccination increased myocarditis 5-fold overall. For males aged 16 to 19, it increased 14-fold.

Compared to the rates among unvaccinated Israelis in 2021, vaccination increased myocarditis 2-fold overall. For males aged 16 to 19, it increased 9-fold.

When they repeated the analysis but comparing the vaccinated during only the first 7 days after the vaccine to unvaccinated controls, males aged 16 to 19 suffered a massive 32-fold increase in hospitalizations for myocarditis.

Their conclusion:

“The incidence of myocarditis after two doses of the BNT162b2 [Pfizer] mRNA vaccine was low but higher than the incidence among unvaccinated persons and among historical controls. The risk of myocarditis was driven primarily by the increased incidence after the second dose of vaccine and in young male recipients.”

As in other countries, the increase in relative risk after vaccination was huge, but the actual number of cases was miniscule. 5M Israelis took 2 doses of Pfizer. But there were a total of only 304 hospitalizations for myocarditis in that vaccinated population.

I found one similar study from the US, done by a nationwide network of Kaiser Permanente and associated hospitals, at https://www.cdc.gov/vaccines/acip/meetings/downloads/slides-2021-10-20-21/08-COVID-Klein-508.pdf . They compared the number of cases of myocarditis and pericarditis in Kaiser Permanente patients 0 to 7 days after receiving the vaccine compared to the cases 22 to 42 days after being vaccinated. So, their “healthy baseline” was vaccinated patients who had received the vaccine about 1 month ago. This seems like a pretty hard baseline to exceed, since if the vaccines cause problems a week after vaccination, they probably also cause problems a month after vaccination. Nevertheless, they did find dramatically higher rates of myocarditis and pericarditis one week after the shot than one month afterwards.

Kaiser Permanente compared the incidence of these two heart conditions within 7 days following vaccination with what they call “vaccinated concurrent comparators”. “Vaccinated” means they compared them to patients who had been vaccinated one month earlier, “concurrent” means they compared events on the same day to eliminate seasonality, and “comparators” were patients of the same age, sex, race, and geographic region. They reported the increase in myocarditis and pericarditis for three age groups: 12 to 39-year-olds and its subgroups 12 to 17-year-olds and 18 to 39-year-olds. It turns out the 12 to 17-year-olds suffered much worse side effects than the 18 to 39-year-olds, so the age subgroups were important.

For the whole 12 to 39 year age group, dose 1 of the Pfizer vaccine raised the rate of myocarditis and pericarditis 8-fold, and dose 2 raised it 30-fold. Dose 1 of Moderna raised it 10-fold, and the increase for dose 2 was too high to calculate, with an upper bound of infinity.

The results for the 12 to 17 age subgroup were even worse. All increases in the rate of these heart conditions – for the Pfizer shot, the Moderna shot, dose 1, and dose 2 – among 12 to 17-year-olds were incalculable. All Confidence Intervals had upper bounds of infinity. The reason is that 12 to 17-year-olds normally have zero cases of myocarditis and pericarditis, but after taking the vaccine a few of them do get these problems, so the ratio of vaccine cases to non-vaccine cases was infinite.

Here is the relevant line from the table on page 18 of their presentation comparing the dozens of 12 to 17-year-olds who developed myocarditis or pericarditis immediately after vaccination to the exactly zero 12 to 17-year-olds who got those heart conditions outside of the week-long risk interval. “Events” are hospitalizations:

Instead of computing the Rate Ratio as “Infinity”, they call it “Very High”. You don’t say. All of the childhood cases of myocarditis and pericarditis that they found occurred within one week of vaccination, none at other times. There are no cases without the Covid vaccine. The occurrence of myocarditis and pericarditis among children is an entirely vaccine-produced problem.

So, definitely get your vaccine, kids!

As ridiculous as this is, it’s also sick: Take perfectly healthy teenagers with zero heart problems. Give them a shot that immediately gives some of them heart problems. Send them to the hospital for care. Call it medicine!

If these results seem terrible, fortunately there is a newer version of this same report available at https://www.cdc.gov/vaccines/acip/meetings/downloads/slides-2022-02-04/10-COVID-Klein-508.pdf which makes these problems go away by simply not reporting them. This newer paper no longer shows any results for the 12 to 17-year-old age group or even for the 12 to 39-year-old age group. They only report on 18 to 39-year-olds. These results, comparing myocarditis and pericarditis occurring 0 to 7 days after the vaccine to the rates occurring 22 to 42 days after, are less alarming in that age group: For 18 to 39-year-olds, the 1^st dose of Pfizer raised the rate of myocarditis and pericarditis 3-fold, and the 2^nd dose 14-fold. The 1^st dose of Moderna raised the incidence 3-fold, and the 2^nd dose 18-fold.

But I can’t emphasize this enough: there is no mention in the updated presentation (same title, same authors, only the date is different) of any medical problems occurring in 12 to 17-year-olds. There is no mention of 12 to 17-year-olds at all anymore.

But again, although the increase in risk is large (or even infinite), the actual number of cases of myocarditis and pericarditis is small – hundreds of cases out of millions of patients.

In fact, let’s take a closer look at the numbers in this last, sanitized report: During the 13 months of the study, 2.3M people age 18 to 39 in the Kaiser Permanente hospital system got fully vaccinated with 2 doses of mRNA. In this time period, they found 297 cases of myocarditis or pericarditis. After adjudication, they reduced this number to 213 confirmed cases. 63 of these cases occurred in the especially dangerous “risk interval” of 0 to 7 days after dose 2 of either mRNA vaccine.

So, the study covered 2.3M people * 56 weeks = 130M person-weeks. The overall rate of myocarditis and pericarditis in this entire population was 213 cases / 130M person-weeks = 0.0000016 cases / person-week. That is, the normal myocarditis and pericarditis rate for one person is 0.0000016 cases / week.

During this 13-month period, these 2.3M patients received 2 doses of either Pfizer or Moderna. The worst risk interval was days 0 to 7 after the 2^nd shot, so the total risk interval was 2.3M people * 1 week = 2.3M person-weeks. The rate of myocarditis and pericarditis in the recently vaccinated population was 63 cases / 2.3M person-weeks = 0.000027 cases / person-week. So, the myocarditis and pericarditis rate for someone in the most dangerous period after vaccination is 0.000027 cases / week.

Thus, getting vaccinated increased the myocarditis and pericarditis risk from 0.0000016 cases / week to 0.000027 cases / week. This is a 16-fold increase.

Two things jump out from these numbers:

1. The risk of myocarditis and pericarditis increased 16-fold after receiving the 2^nd dose of the vaccine (14-fold after Pfizer and 18-fold after Moderna), which is a lot.
2. The risk of myocarditis and pericarditis, even after vaccination, is infinitesimally small.

In fact, these results could be spun as actually good news for the vaccinated. What this study found, similar to the other SCCS-type studies presented earlier, was that the risk of these heart conditions was 16 times higher during the week after vaccination compared to the month after vaccination, and this difference in risk was strongly statistically significant. But that’s equivalent to saying that the risk of these heart diseases was 16 times lower the month after vaccination compared to the week after vaccination, and that’s equally statistically significant. Making the reasonable assumption that this temporal pattern holds longer term – that the risk is also lower the year after vaccination compared to the month after, and the decade after vaccination compared to the year after – this means that if you got vaccinated and you didn’t suffer from myocarditis or pericarditis during the “risk interval” of the first week, then you’re almost certainly not going to get them after that. You might not survive the week-long risk interval, but if you do, you really have nothing more to worry about. The risk interval is statistically significantly more dangerous. But the post-risk-interval – which is the rest of your life – is statistically significantly less dangerous. It’s kind of like one-shot Russian roulette. If it doesn’t kill you instantly, you don’t have to worry about it killing you later. Getting boosted is like three- or four-shot Russian roulette. I suppose you could keep playing Russian roulette with the vaccine until you’re seriously hurt, but I wouldn’t recommend it.

So, for those people who got vaccinated or boosted last week – good luck. But for all those people who took the vaccine more than one week ago – you’re safe, at least from these heart problems.

Finally, I’ll end with one funny study. This should put everyone at ease about vaccine safety. A brave researcher has finally figured out why so many people suffer deadly side effects from the Covid vaccine. It turns out it’s not the vaccine at all. It’s misinformation from anti-vaxxers, which causes people getting the vaccine to be afraid, and this anxiety causes side effects like heart attacks:

“Misinformation perpetuated by the anti-vaccination movement may be causing more deaths and side effects from [the] vaccine.… If subjects are panicked, concerned, stressed or scared of the vaccination, their arteries will constrict and become smaller in and around the time of receiving the vaccine. This biological mechanism (the constriction of veins, arteries and vessels under mental stress) is the most likely cause [of] blood clots, strokes, heart attacks … after vaccine administration.”

This appears to be a real study, not a joke. The author is an absurdly shady character who sells cannabis-based nutritional supplements. (Where can I get some of those?) But the article was published in a legitimate, peer-reviewed, medical journal:

https://www.biomedicinej.com/biomedicine/vol12/iss3/1/

It’s also available on Pubmed:

https://pubmed.ncbi.nlm.nih.gov/36381188/

Maybe he was just trolling them, or maybe they’re that shameless.

5. Conclusions

My view on vaccine safety has changed somewhat over the past 3 years:

In 2021, I thought: mRNA is a new technology. Its track record has been terrible. For 20 years, pharmaceutical companies tried to use it against cancer, but the side effects were so severe that the FDA considered mRNA treatments too dangerous to use even on terminal cancer patients. To this day, Moderna (previously written ModeRNA to emphasize its mRNA specialty) has only one FDA-approved product on the market – the one rushed through at “Warp Speed”. The red flags were so obvious, you’d have to be crazy to volunteer to be first in line to get this vaccine. I decided to wait a year and see what happened to the guinea pigs who took it first.

In 2022, I thought: There’s still no clear evidence one way or the other about vaccine safety that I can tell. The authorities say it’s safe, but you can’t trust them. The crackpots say it’s dangerous, but you can’t trust them. (Or maybe you can. As Senator Kennedy likes to say, “We’re going to have to get some new conspiracy theories, because all of the old ones turned out to be true.”) So, I still don’t know whether the vaccine is really bad. But, for me personally, since I’ve already gotten Omicron so now have natural immunity which is better than vaccine immunity, avoiding the vaccine is a no-brainer now.

In 2023, I think: The evidence is finally clear to me. The vaccine is not safe. Good call to not take it.

This is not that different than how Scott Adams, the Dilbert comic, describes his change of mind:

In 2021, Scott Adams said: I’ve read the government studies, and the balance of the evidence suggests that the vaccines are safe and effective. So, I’ll take them.

In 2023, Scott Adams said: I was wrong. The vaccines are not safe or effective. It turns out that the simple heuristic of “everything the government says is a lie, so do the opposite of what they tell you to do” was right in this case. It’s not always correct, but actually the government does lie a lot, so you sometimes can make good decisions just by doing the opposite of what they say. With the vaccines, using that dumb anti-government heuristic ended up working better than what I did, which was to gather the data, weigh the evidence, and calculate the odds.

That’s basically where I am except that, in 2021, I was already following the anti-government heuristic.

My conclusions from the research above are:

1. The vaccines are harmful. Obviously, the FDA should remove them from the market as they would normally do with any product that was even a fraction as dangerous. People in government and pharma need to go to jail for the deaths they caused.

2. However, this doesn’t mean that every vaccinated individual was severely harmed, or even harmed at all. For young people, Covid itself causes almost no injuries or deaths, and the vaccines probably cause few. For the elderly, Covid sometimes leads to pneumonia and death, but in most cases it is just a very bad cold. The vaccines are probably worse than that – in some cases deadly, but in most cases not. The vaccines are bad, but not apocalyptic.

3. Of course, you can’t directly compare getting Covid with getting vaccinated because you can’t choose between them. If the vaccines worked perfectly, and vaccinated people never caught Covid, then you could choose one and not the other, although even then being unvaccinated and catching Covid is probably safer than getting vaccinated and preventing Covid. But that’s not how the vaccines work in real life. The vaccines make you more likely to catch Covid, not less likely (the subject of my other paper). So, the choice people actually face is a) remain unvaccinated and suffer a chance of catching Covid, or b) get vaccinated, possibly be harmed by the vaccine, and then suffer a greater chance of catching Covid, which can also hurt you. The risks are not separable, they are additive. This is not a hard choice.

But I do not buy the strange claims made by people who believe the vaccine was created by depopulationists to kill everybody who took it. It’s obvious that they are wrong because their claims follow the same trajectory as all false claims: first they said the vaccines will kill you immediately, but that didn’t happen; now they say the vaccines will kill you in 5 or 10 years, but we can be pretty sure (based on my mortality regressions in section 1 of this report) that this won’t happen either; so then they will say the vaccines will kill you in 20 or 30 years. But the years have a bad habit of passing all too quickly, and these extreme predictions will never come true. This is the way false claims die: eventually the forecasts extend so far out into the future that they become unfalsifiable. From Christian End Times to Climate Change Catastrophe, prophets have been foretelling the end of the world forever. When their predictions don’t come true, they never admit that they were wrong. They just keep changing the timing.

The depopulationist theory also has another problem: how exactly was the evildoers’ plan supposed to work? In the US, and much of the Western world, the vaccine was mostly taken by Elites/Liberals/Democrats, and mostly refused by Deplorables/Conservatives/Republicans. So, what was the evil globalists’ master plan exactly – to kill off the gullible, easily-frightened snowflakes, and hand America over to the monster-truck-driving, gun-toting badasses? That doesn’t seem like something Klaus Schwab and Bill Gates would want to do.

Finally, I have to say something about the bizarre media landscape in which the vaccine controversy has played out. The print and TV media have maintained a 100% blockade on any information contradicting the approved vaccine narrative of “safe and effective”. Social media companies also enforced a 100% blockade on dissenting views, with anyone questioning the narrative immediately banned. 100% blockades are not supposed to be possible in a competitive marketplace, but this did happen, which means the US media is not a free market but a controlled oligopoly. The total news blackout has so far ended at only one company, Twitter, and only after Elon Musk bought it, so maybe it’s a 95% media blackout now. Of course, there are plenty of websites, such as substack, that are not controlled by corporate behemoths like Google and Facebook, so anyone can write anything they want there, but the corporate press and social media remain by far the main sources of news for most people. Yet in spite of suppressing all dissent, the public now appears to know that the vaccines are dangerous.

Last year, there were near-constant reports of healthy young athletes dropping dead on the playing field of heart attacks. None of the news reports ever mentioned the vaccine – as if they were forbidden by some Central News Bureau – but they didn’t have to. That’s what everyone was thinking when they saw these freak events. I don’t think it’s just my imagination that this happened a lot last year. One study ( https://goodsciencing.com/covid/athletes-suffer-cardiac-arrest-die-after-covid-shot/ ) found that, based on sports news, the incidence of professional athletes dying while playing increased 19-fold in 2021 and 2022 over the previous 40 years. I can’t vouch for whether that statistic is true, but my point is that this perception has become widespread in spite of total media gaslighting. The only reason the sports media don’t hide the athletes collapsing on the field in the middle of the game is that they can’t when people watch sports live.

And it’s not my imagination that people are connecting the dots in spite of the unceasing censorship. One poll ( https://www.rasmussenreports.com/public_content/politics/public_surveys/died_suddenly_more_than_1_in_4_think_someone_they_know_died_from_covid_19_vaccines ) found that 28% of Americans say they know someone who died from the Covid vaccine. That’s almost as high as the number of people who know someone who died from Covid itself. Again, I’m not saying that they’re right, but I do think that’s what many people believe now. The word has gotten out, in spite of the best efforts of all media and internet gatekeepers. I just don’t know how it got out exactly. It’s not like a lot of people read anti-vaxxers on substack. I guess if you have healthy friends who die suddenly and inexplicably right after getting the vaccine, “you don’t need a weatherman to know which way the wind blows.”

Eugene Kusmiak was a Red Diaper Baby and a Harvard graduate. After nearly two decades spent developing video games in Silicon Valley, Gene shifted coasts and professions. He retired from a 20-year career as a Portfolio Manager at a quantitative hedge fund in Manhattan in 2022. He survived being surrounded by liberals and progressives in Massachusetts, California, and New York and now enjoys living in rural red-state Ohio. These are his first articles for The Unz Review.