*Purely Speculative calculation – take it with a grain of salt!*

by *Igor Chudov*

Summary: This post is my attempt to find the **relative risk of mortality by vaccination status** based on *two completely different data sets *involving tens or hundreds of millions of people that give *surprisingly similar estimates of relative risk*.

(For those who like to ask, “what is the bottom line,” I calculated the **relative risk of vaccinated mortality as 40% greater than unvaccinated** – but read on, please.)

WARNING: This post brings forth a VERY PRELIMINARY conclusion. I would prefer that you consider my calculation to be “*food for thought*” as opposed to “firm proof.” I am bringing up a hypothesis to be discussed and nothing more. Even the calculation itself may be mistaken. I selected the title and subtitle accordingly so as not to be unnecessarily alarmist and limit the spread of this post.

Before we get into numbers:

- Many countries are experiencing excess mortality
- There are some indications that excess mortality is related to the Covid vaccination program.
*Various countries*have very different RATES of Covid vaccination among their populations – and their mortality statistics are available.- In the UK, the population is split into
*deprivation quintiles*with varying vaccination levels, with excess mortality also known.

I analyzed excess mortality by country as well as excess mortality in the UK by deprivation quintile:

By country:

By UK deprivation quintile:

**Is UK’s Depopulation Caused by Covid Vaccines?**

This gives us two sources to correlate excess mortality with vaccination rates: *by country* (first article) and *by deprivation quintile* (second article).

These two methods involve completely different data sets — *and yet they produce amazingly similar results*!!!

Here are the two respective linear regressions:

Both regressions contain the “equation,” that is, the line that best fits the data provided in the inputs. Y is the excess mortality, and X is the vaccination rate.

Equation for *Regression by country*:

`ExcessMortality = 0.34*VaxRate - 0.1445`

Equation for *Regression by deprivation quintile*:

`ExcessMortality = 0.36*VaxRate - 0.2118`

Let’s plot both equations together:

I am quite struck by how similar the two straight lines are, despite having been obtained from completely different data sets! The slopes are extremely similar, and so are the Y-intercepts (-0.14 and -0.21).

By the way, the *confidence intervals* for both slopes, as well as Y-intercepts, *intersect*, further buttressing a conclusion that we are not just seeing random data flukes.

### The Y-Intercept is the “Pull-Forward Effect”

A sad outcome of the first two years of the Covid pandemic is that Covid killed persons with “comorbidities,” very old people, and so on. Had the pandemic not happened, the mortality would be the same yearly. However, Covid caused the unfortunate *premature* demise of people who were likely to die in the next few years.

So, if the pandemic stopped and vaccines were not affecting mortality, you would expect NEGATIVE excess mortality — simply because people who were likely to die in 2022 already died in 2020. The negative Y-intercept shows this **pull-forward effect**, discussed in detail by the Ethical Skeptic.

## Estimating Relative Risk of Mortality

Let me mention again that the calculation of relative risk is extremely speculative and is only meant as food for thought. Nevertheless, I consider this to be a very important topic for discussion.

Encouraged by how similar the two linear fits for completely different data sets are, I will pick the by-country fit for the discussion because the confidence intervals for it are smaller.

`Excess Mortality = 0.34*Vaccination Rate - 0.1445`

Imagine a country, let’s name it *Unvaxedland*, that lies perfectly on this line and has zero vaccination rate.

*Unvaxedland’s* mortality would be 1-0.1445 =85.55% of expected.

Now let’s imagine another country, let’s name it *Vaxedland*, that is 100% vaccinated and also lies perfectly on the above-mentioned linear graph. *Vaxedland*’s mortality would be:

*Vaxedland* mortality = 1 – 0.1445+0.34 = 119.55% of expected.

Now we have **Relative Risk** = Vaxedland Mortality/Unvaxedland mortality = 119.55/85.55 = 1.397.

So, under our assumptions, **residents of ****Vaxedland**** would have a 40% higher risk of dying than residents of *** Unvaxedland*.

## Disclaimers and Confounders

Let me mention some confounding factors. Things are not so simple, and the picture is complex, as you would expect – with **conflicting considerations**.

- Some highly vaccinated countries, like New Zealand, had lower mortality in the first two years of the pandemic. Their higher excess mortality in 2022 may be partly due to survivorship bias.
- The above bullet point is directly contradicted by the analysis of the UK
*by deprivation quintile*. All UK deprivation quintiles reside in one country — yet show a similar relationship between vaccination rates and excess mortality*by quintile*as exists when analyzed*by country*. - Covid contributed to excess mortality but was not the overwhelming factor.
- It is possible that the current high excess mortality is temporary and “everything will return to normal.” This is what I want!
- My analysis may be based on incorrect assumptions or calculations.

Again, this calculation is of speculative and imprecise nature. I hope that other people can criticize my methods and come up with better approaches.

In the ideal world, we would have a demographics institute of a large country set up comparable cohorts of vaccinated and unvaccinated people, with several million people in each cohort, and follow them for a few months to compare mortality. I do not own such a demographics institute, so I use the data I can find.

What do you think?

Will excess mortality get better or worse over time?