Why the UK ONS data shouldn't be used to justify public policy

Here's why I believe the UK Government Office of National Statistics data is unreliable and shouldn't be used by either side. This is a new analysis.

Executive summary

Many people have relied on the UK government data from ONS to prove their points.

I show here that if you follow 100,000 people who are vaccinated vs. unvaccinated for an entire year, it results in a completely nonsensical result where the vaccinated people are nearly immortal (can’t die from most diseases).

Introduction

I was inspired by reading this post from Dr. Tim Ellison entitled “I HOPE there is nothing to see here ...” The post makes a calculation of what the mortality rate is for each age group and determines that no matter how old you are, you are better off avoiding the jab. In this post, I’m going to do a more detailed calculation showing that the simplistic calculation got the right answer.

Here’s Tim’s calculation:

Basically, the UK data shows no matter how old you are, you are an idiot for taking the jab. You are basically allowing your government to increase your chance of death. Nobody’s life is saved here. If you believe the numbers, they are basically trying to kill off people of all ages.

Tim’s chart shows that the vaccines make COVID look like rounding error. At most, COVID is a 20% bump in the death rate, e.g., 1.2X. The vaccine is a 2.4X multiplier from baseline. So it’s not saving any lives. Instead, it is almost tripling the death rate for all ages. It is the biggest killer of all time AFAIK.

But not so fast. This is just a point sample of the statistics in a single month so it’s just an estimate. More importantly, the data itself from the UK government can be suspect.

UK data

The UK death by vaccination status.

The correct way to do the comparison

The more detailed calculation involves comparing 100,000 people starting at Day 0 and comparing two groups: one that ignores government advice, and the other group obeys the government vaccination directives.

My spreadsheet

I created a spreadsheet to do this in Excel using tables and filtering. The state of the spreadsheet has a filter applied for one of the cases so you have to refilter to get the case you want to look at. I only did the young patients and the 50-59 year olds and by then it was clear it was a waste of time.

Government agencies will not do these calculations

As you all know by now, government agencies never do these calculations to compare how deadly the vaccines are because if they did, nobody would take the vaccine.

So the onus falls on the misinformation superspreaders such as myself to inform the public of the truth about the vaccines using the UK government’s own data

The reason we use the UK government data is because there is no US government data available to do the same calculation. This is because the US government is smarter than the UK government and doesn’t release vaccinated vs. unvaccinated data because they know, if they did that, it would expose the fact that Biden is pushing a kill shot. So they keep the data hidden from public view.

The UK government is more transparent with their data and they’ve been hoping nobody is smart enough to do the calculations required to show how deadly the shots are. So their luck just ran out because I’m going to show you what their data shows.

I’m going to follow 100,000 people for 1 year starting on Jun 1, 2021 and ending on May 31, 2022 which is the latest date that statistics are available. You are welcome to do the computation for any other date range; the result will be roughly the same.

For the vaccinated group, we assume the following vaccination schedule:

1. Dose 1 on Jun 1, 2021 (4 week duration)

2. Dose 2 on Jul 1, 2021 (24 week duration)

3. Dose 3 on Jan 1, 2022 (24 week duration)

The results

I only did this for the 18-39 and 50-59 year olds.

The death results after 1 year (100,000 people) were (unvaxxed, vaxxed):

• 18-39: 45, 31

• 50-59: 626, 337

In short, according to the UK numbers, the vaccine resulted in the greatest decrease in all-cause mortality in history AFAIK. The numbers show that we should all get 3 shots and we can avoid dying from almost any disease (since a very large fraction of young deaths are from accidents, if you reduce the ACM this much, it means you don’t die from diseases). It’s the greatest drug ever created!

This is one of the reasons that people who have studied the UK ONS data, such as UK Professor Norman Fenton, call the data complete garbage.

I don’t think I goofed. You can check my work.

Norman wasn’t aware of anyone doing the calculation following people for 1 year as I did. He encouraged me to publish this just to show what happens when you attempt to use their data to do an estimate to compare the all-cause mortality (ACM) of the two groups.

There is an old saying “Garbage in, garbage out.” This appears to be the case here.

So sadly, Tim Ellison wasn’t justified in making his conclusions, even though he’s probably very close to the truth.

Comments

This article generated a lot of excellent comments, too numerous to list here. Here are a few:

1. Paula Fey is a Brit and wrote about the situation there.

2. Analysis by another British group

3. Comment by Adrian Ryan about how the UK numbers showed an even more ridiculous “42 times greater chance of death without the experimental non vax.” The UK government retracted the obvious error, but the BBC didn’t because it would be counter-narrative. Note that it was FORMER ONS employees that challenged the numbers, not CURRENT employees. This is a tacit admission that the people currently working at the ONS are either brain dead or afraid to speak out. Either one should be troubling to anyone who lives in the UK. The UK ONS is just as bad as our CDC and FDA.

I encourage you to look at the other comments.

Summary

The UK ONS data is too unreliable to use. I’ve used it before to make some calculations and I pointed out at the time I did it how nonsensical the data was which is why I carefully selected a set of rows that passed a simple sanity test. But even that is unreliable. I was trying to do the best estimate possible at the time with crappy data.

Note: I put this out quickly due to time constraints and may have made an error. As always, I’ll update this article if I made a mistake. Check back after Aug 25, 2022 to be sure.