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.

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.

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.

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