New UK Government Data Shows the Covid Vaccines Kill More People Than They Save

Overview

New UK government data allows us to analyze the data in a way we couldn’t before. This new analysis shows clearly that the COVID vaccines kill more people than they save for all age groups. In other words, they shouldn’t be used by anyone. The younger you are, the less sense it makes.

Anyone can validate the data and methodology. The results make it clear that the COVID vaccines should be halted immediately.

Not a single public health authority in any country will have a conversation with us on the record to justify their vaccine recommendations or explain how this analysis is wrong. I wonder why?

What the data shows

Here’s the result of the analysis comparing unvaxxed vs. 2 doses given at least 6 months ago. I believe this analysis is conservative and the actual numbers are worse than this due to the seasonal variation of the all-cause mortality.

Figure 1. Risk/benefit determination from the UK data shows that for all ages, the vaccines kill more people than they save. A value of 15 means we kill 15 people from the vaccine to save 1 life from COVID. This is from the Exec Summary tab of the spreadsheet.

What this means is that if you are 25 years old, the vaccine kills 15 people for every person it saves from dying from COVID. Below 80, the younger you are, the more nonsensical vaccination is. The cells with * means that the vaccine actually caused more COVID cases to happen than the unvaccinated.

Above 80, the UK data was too confounded to be useful. Until we have that data, it’s irresponsible to make a recommendation.

I describe below how you can compute this yourself from the UK data.

Please share this result on all your social media platforms. One user got 10,000 likes in less than 24 hours on Twitter and he had only 2,000 followers. So Twitter permanently suspended his account. So probably not a good idea to share on Twitter. According to Twitter, “health officials consider the COVID-19 vaccines safe for most people” and therefore any UK government data that shows that they are lying is a violation of Twitter Community Standards.

Introduction

One of my friends recently sent me a link to the mortality data from the UK government Office of National Statistics from January 1, 2021 to January 31, 2022. I had not seen this data before so I analyzed it.

What I found was absolutely stunning because it was consistent with the VAERS risk-benefit analysis by age that I had done in November, 2021.

Where to get the UK government source data

The government data is archived here. You want to open the spreadsheet, and look at the spreadsheet tab labeled Table 6.

You can also access the original source at: https://www.ons.gov.uk/peoplepopulationandcommunity/birthsdeathsandmarriages/deaths/datasets/deathsbyvaccinationstatusengland which you can see at the top of the page.

In either case, you click the green button labeled “xlsx” to get the spreadsheet, then go to tab “Table 6”:

To visualize it, see this tweet.

Note: The data is from England only, not all of the UK.

Where to get my analysis of the data

I annotated the UK source data and you can download it here. This makes it easier to see what is going on. You can see all the original data and my formulas for calculating the ACM ratios and risk benefit analysis on the Table 6 tab.

It is all in plain sight for everyone to see. I then copied values to the Summary and Exec Summary tabs.

Methodology

I compared the all-cause mortality (ACM) for people who got 2 shots at least 6 months ago with the unvaccinated. The 6-month time frame provides a minimum reasonable “runway” to observe the outcomes for the typical “fully vaccinated” person.

Sadly, the data provided by the UK isn’t usable to do a proper risk-benefit analysis. A proper analysis compares two equal groups where the only difference is that one group got the intervention and the other group didn’t. Every person in each group should start being tracked on Jan 1 and end being tracked a year later.

The UK data is completely inadequate for this purpose because each of the rows captures data from different people in different months starting at different times. This is a subtle but extremely important point.

Thus, our goal in this analysis is to try to extract data from this dataset to arrive at the most reasonable best estimate at the true number.

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