# UK Data Shows No All-Cause Mortality Benefit for COVID-19 Vaccines

by Mathew Crawford via Substack

#### The Vaccine Wars Part I

The original title of this article was, “Did Alex Berenson Fall for a Simpson’s Paradox?” I’m rewriting the title and introduction to this article after having finished an analysis that demonstrates **no all-cause mortality benefit to the vaccinated in the UK**. You’ll have to read through the story to understand why.

Are the vaccinated dying at two-to-three times the rate of the unvaccinated in the UK as Alex Berenson’s recent article suggests?

Let’s be clear: Alex Berenson has done an excellent and courageous job during the pandemic. I follow his work and recommend that you do as well. He definitely digs into data better than most journalists, though we should not expect for him to do everything and be everything. **He doesn’t have to be perfect and you don’t have to agree with every take to understand how important his work is**. *I am writing this before I analyze the data*. However, I will note that my first instinct is to think that the [twice] Vaccinated cohort *has an older age profile* than the Unvaccinated cohort. Thus, we *should* expect to see greater all cause mortality in that group. This means what we are seeing is an illusion of data aggregation often called a Simpson’s paradox, something I’ve written about several times before (here and here).

**If you don’t want to read through my discussion of this particular statistical illusion, skip down to the next section to read the conclusion.**

Let’s be clear: I’ve seen statisticians and Wall Street quants at the highest levels fall for a Simpson’s paradox here or there. Recognizing the way an aggregate *can* or *should* be disentangled is a different job than *just doing the statistics*. It often takes *domain knowledge* (and humility in reining in expectations) in addition to statistical awareness to spot a Simpson’s paradox. Reversed trends in aggregates have probably fooled every human that walked the planet at one point or another, assuming they were numerate enough to view any data at all.

When I teach my Combinatorics, Probability, and Statistics courses, I stop and spend 10% of every course at every level on Simpson’s paradox’s and similar conditional data twists alone, giving students a chance to noodle over the strange “contradictions” in the results. This is from curriculum I’ve used with students ages 10-70:

This story is based on circumstances similar to those I’ve personally witnessed in baseball statistics. Here is the punch line: Peakoles is clearly the superior hitter (at least so far as batting average is concerned):

I spend so much time in class focusing on this kind of paradox and conditionality in probability and statistics for several reasons:

- I want for students to understand that problems aren’t always straight-forward.
- Understanding these problems
*is*the difference between an elite professional data geek and somebody*with enough knowledge to be dangerous*(which appears to be the substantial majority of the medical profession and all but the most elite epidemiologists during this pandemic).

We may reach a point at which having more problem solvers trained in teasing apart this kind of data results in millions of lives saved and hundreds of billions or even trillions of dollars saved annually in recovered lost productivity associated with poor management and governance decisions.

**Modeling the UK Data Shows No All-Cause Mortality Benefit**

I wrote the first half of this article prior to modeling the data.

I took the period mortality tables from the UK’s Office of National Statistics (ONS). Next, I painstakingly (yuck) estimated every relevant point on the cumulative vaccinated chart by age from the UK’s vaccination surveillance report, using some reasonable interpolations along the way. Then I realized that I needed population proportions for the subgroups of the age 10-59 cohort (Can we get a little granularity, please? Or just *open source data*?). Now, I can put together a projected mortality profile for each group during an ordinary year using weighted averages (where each age group’s projected mortality gets multiplied by its *proportion* within each cohort, and the results are summed) for each week.

I then took the raw data Berenson pointed to out to one more decimal place, and plotted the actual 2021 all cause mortality data versus the expected all cause mortality data. As we can see, the Vaccinated cohort, due to having a generally older age profile, was *expected* to have higher all cause mortality. (Lighter hues are projected, heavier hues are what actually happened.)

So, Alex was wrong to suggest that the data showed *prima facie* higher mortality in the Vaccinated cohort *due to the vaccines*. However, this result is quite interesting! It’s hard to look at these graphs and easily determine which cohort has suffered more excess mortality during the middle months of 2021! So, I took the excess mortality from each cohort for each week, and also cumulatively, and plotted them:

The cumulative trends go back-and-forth, and it seems reasonable to dismiss any difference as statistical noise. But when we *do* compute the *tiny* overall observed benefit at the end of the 28 week stretch to the vaccinated group, it amounts to a mere 5 deaths per million doses (at over $6 million per life saved).

I wonder *who could have predicted all this*?

* Fair use excerpt. Read the whole article here*.