The COVID vaccine: all risk, no benefit

Czech Republic age-standardardized mortality rate (ASMR) was 30% higher for Moderna than Pfizer. It's highly statistically significant (the 95% CI were VERY narrow).

Executive summary

There are literally hundreds of “black swan” data points consistent with the hypothesis that the COVID vaccines killed more people than they saved.

In this article, I will summarize for you that the COVID vaccines:

1. Made it more likely you’d get a COVID infection

2. Didn’t reduce your risk of hospitalization

3. Didn’t reduce your risk of dying from COVID

4. Made you more likely to die of all causes (brand comparison in Czech Republic).

More likely to get a COVID infection

The shots actually increased your risk of getting COVID. We know that from the Cleveland Clinic study.

Didn’t reduce risk of hospitalization

The shots did nothing to reduce your risk of hospitalization. We know that from the data revealed in a study published in JAMA by a top epidemiologist at the VA.

Didn’t reduce the risk of death from COVID

If the COVID vaccine saved lives, the case fatality rate (CFR) should have dropped after the vaccine rolled out. It didn’t. It went up. So there was NO BENEFIT.

We can see this in:

1. The official aggregate US COVID statistics published in OWID. See More evidence that the COVID shots did NOT reduce mortality

2. The US Nursing home data. See The COVID vaccines did NOTHING to reduce COVID mortality. ZERO.

These are both ground level gold standard datasets. It doesn’t get any more fundamental than that.

Increased your all-cause mortality risk… new analysis

See this article for a summary of the Czech Republic data.

To date, no epidemiologist in the world has produced an analysis showing that both vaccines are safe.

I’ve updated the github with a new ASMR analysis by dose. This shows that the Moderna vaccine had a 30% higher 1-year from the shot age-standardized mortality rate (ASMR) than Pfizer. So even if Pfizer was perfectly 100% safe, Moderna should be immediately pulled from the market. By doing a brand comparison, we mostly neutralize any confounder that can bias the result.

The M/P column gives the mortality rate ratio (MRR) defined by the Moderna MR/Pfizer MR for that age range. As you can see, for every 5 year age group, the MRR is >1 meaning Moderna kills more people. What kind of confounder can explain this consistency? Nobody can identify one.

The summary tab of the ASMR spreadsheet shows the ratio of the ASMR for Moderna/Pfizer: Moderna had a 30% higher ASMR than Pfizer for the 1-year mortality after doses 1 and 2. That’s a train wreck. As you can see, the numbers were high so the confidence interval of the odds ratio was narrow, i.e., the result is highly statistically significant. Barring exculpatory evidence, Moderna should be pulled from the market by any honest health authority.

I also did two other studies:

1. If you restrict people to dying in months of no COVID, the mortality of the Moderna shots was still high in all age groups. This means that the mortality difference wasn’t because Moderna saved any lives from COVID. During months of no COVID, there should be no mortality difference. And yet, there it was. You can see the details in the analysis tab of vax1.xlsx spreadsheet which shows the mortality rates by month of vaccination of people who died in no COVID months. If the vaccines are safe and were randomly distributed by brand, there should be no difference in the mortality rate by brand. And yet we find that for ages 60-69, for every month of vaccination without exception, the mortality was significantly higher in non-COVID months for Moderna.

2. In order to show the mortality difference wasn’t due to some unknown systemic or systematic bias, I did another analysis looking at mortality rate differences by sex. If there are differences in the mortality rate ratio (MRR) calculated for males vs. females for a given 5 year age bracket, then this wasn’t selection bias; it has to be the drug doing that (i.e., the drug had a differential effect on females in certain age groups vs. males). If it was selection bias, it would vary by brand, not by sex.
   
   You can see the details in the MRR by sex and age tab of the vax2.xlsx spreadsheet.

   I also did a 95% confidence band calculation just to make sure the differences by sex were statistically significant. It’s wide, but still statistically significant: 95% Confidence interval for 1.5: 1.08 to 2.16

For example, large scale studies show impossibly large increases in cardiovascular morbidity and mortality that is impossible to explain from any other cause

This analysis of the UK data is hard to explain. There are so many other examples.

Here’s another study showing things got much worse post vaccine when they should have gotten better:

Hard to explain anecdotes

Personal stories are my favorite because they cannot be gamed, they can be verified directly by third parties, and they are often so statistically significant that a single anecdote is sufficient to prove the point. Taken together, they simply cannot happen if the vaccines are not killing massive numbers of people.

For example:

1. 6 members of FDNY died within 3 months of the COVID vaccine rollout. This is unprecedented. Normally, they might see 1 and at most 2 deaths in a year for active duty firemen. If you run the numbers, the chance of this being a “coincidence” is 2.7e-7. In short, something killed a whole lot of really healthy young people and if it wasn’t the vaccine, what was it? It wasn’t COVID and it wasn’t bad luck.

2. Harry Fisher saw 20 vaccine deaths as a paramedic. Assuming he’s the largest cherry in the probability distribution (at the very tail end of the Poisson distribution of 200,000 people), the mean value would be about 6. There are over 100,000 paramedics in the US. 6*100,000 is a big number. See what I mean about one anecdote? Even after assuming he’s on the far end of the Poisson distribution, this is devastating. And this is just one of hundreds of such stories.

3. And more including:

   1. Observations from doctors, nurses, and paramedics in the US. The comments are stunning.

   2. A police officer in a large US metro who reports that “natural deaths” went up by more than a factor of 2 after the shots rolled out. I’m trying to validate those statistics independently now.

4. Jay Bonnar lost 15 friends unexpectedly since the shots rolled out. Four of them died on the same day as their shot and 3 of the 4 were 30 or younger. Before the shots, his death count was 0. This is statistically impossible (5e-22) and his story is verifiable.

More evidence that the COVID vaccine made people more likely to get COVID and die from COVID

Here are some examples:

1. Apple Valley Village: COVID mortality pre-vax: 0 in 28 cases. Post-vax: 28 deaths in 90 cases. This is statistically impossible if the vaccines are safe since it was the same variant. This is in the Medicare database.

2. FDNY captain Dan Smith reported after the jabs rolled out to firemen in NYC: “everyone was sick with COVID”

Summary

It’s unlikely the COVID vaccine was safe. There are too many “coincidences” that cannot be explained away. In this article, I tried to summarize it down to the essence:

1. There was no COVID benefit because the case fatality rate (CFR) didn’t go down after vaccine rollout (OWID data and Medicare nursing home data). FULL STOP. We’re done. YOU SHOULDN’T DEPLOY A VACCINE WHERE THERE IS NO MORTALITY BENEFIT (unless it stops transmission which this one doesn’t do). This data has been in plain sight for years and NOBODY has noticed this except for a few misinformation superspreaders ;).

2. The Czech data clearly showed a 1-year mortality increase for Moderna over Pfizer. The magnitude was huge: an ASMR increase of 30% that was highly statistically significant. This effect cannot be explained by selection bias. And 8 other methods confirmed the observation of increased mortality. The only explanation consistent with the observations is that the vaccines increased all-cause mortality.

3. The other 3 points above are supportive of the two key points about.