1. Today’s antivax propaganda comes from a….vaccine manufacturer?

Unfortunately, yes. The manufacturer of the Sputnik V vaccine is tweeting absolutely nonsense statistics in an effort to question the safety record of its competitors. Image
2. Their unfounded claim is that we are observing higher death rates among Pfizer recipients.

This is rubbish. In our book, we address the way in which people will try to bamboozle you with the unwarranted authority of numbers by throwing lots of stats at you.
3. But statistics (1) are only as good as the methods used to derive them, and (2) are only useful when they allow you to make fair and meaningful comparisons.

The Sputnik V numbers fail spectacularly on both accounts.
4. Here are a few of the things wrong with these figures.

The numbers in the column at left represent country-level averages, unweighted by number of recipients. No one with an iota of quantitative understanding would do this, unless trying to deceive.
5. Taking the straight average of the death rate in Norway, which has vaccinated 1.2 million people, and the US, which has vaccinated 140 million people (not all with Pfizer, in either case), is bizarre.

Why even show these numbers? Because they make Pfizer look bad. Image
6. In the right column, we are given weighted averages. But these are nonsense as well, because the data are not collected in a way that allow for meaningful comparisons.

7. Given that vaccine-associated deaths would be very low even if there *were* a problem, we would expect any causal differences by vaccine type to be swamped my demographic differences in who is receiving in the vaccine.
8. The countries in question differ in factors including

Age distribution of those vaccinated
COVID incidence and mortality rate
Background mortality rate
Accuracy of vaccine logging and death reporting
9. The age distribution can make a huge difference. If, for example. Pfizer is more often preferentially given to the elderly than is Sputnik, we would expect much higher overall death rates among those vaccinated with Pfizer compared to Sputnik.
10. Similarly, the reporting—both of deaths and of who has been vaccinated—can matter a great deal. How confident are you in the numbers coming out of those countries that have most heavily used Sputnik?
11. One straight-up falsehood on the Sputnik slide is that these numbers are being presented as death *rates*.

Rates involve count per time.

These don't take time into consideration. And that matters a LOT because some vaccines have been available longer than others.
12. In the United States, for example, they compare 10.4 deaths (from all causes!!) per million Pfizer vaccine recipients with 7.5 per million J&J recipients.

Pfizer were approved in December. J&J in February. Pfizer recipients have had a lot more time to die!
13. Another example of the sorts of confounds in Sputnik's propaganda. (I have not independently verified this)

14. All in all, these are absolutely useless numbers, compiled in a transparently inappropriate fashion, and coupled with a message promoting fear around the leading vaccine candidates while making the manufacturer's own vaccine look good.

It's awful propaganda from Sputnik.

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More from @CT_Bergstrom

13 May
When you extrapolate from data about within-group values to the existence of between-group differences.

Via @MikeTaddow Image
(To explain a bit more, the people ranking BBQ joints in Seattle are not the same people ranking them in Brownsville, TX. These data tell us that Seattlites are nice when they rank things and/or have low standards for BBQ, not that we are a contender on the national stage.)
I now really want to see the rankings for best pizza, using the same absurd metric.
Read 4 tweets
12 May
Before anyone panics, note (1) the selection bias arising because this is exampled picked out of the various outbreak case reports as being "worrisome", (2) the small sample size, and (3) these numbers still give you a point estimate of 84% effectiveness against infection.
In a bit more detail: As small case clusters arise and are reported worldwide, we expect to see a distribution of effectiveness estimates. Some will have more vaccinated cases by chance, some fewer. The smaller the clusters, the wider the distribution.
Singling out a small cluster that yields a low effectiveness estimate for some variant of concern—and ignoring all the other data on that variant of concern everywhere else in the world—is reckless, and, odds are, misleading.
Read 6 tweets
4 May
In today's much-discussed @nytimes story from @apoorva_nyc (nytimes.com/2021/05/03/hea…) there is a graph that I find quite problematic. It purports to show county-level data about vaccine hesitancy. Image
But look at how sharp those state boundaries are.

One of the key insights from our @callin_bull course is that in the real world, data are messy. And if they come out too clean, something is wrong.

This one screams that something is wrong. Image
So what's going on here?

The @nytimes graphic appears to come from this HHS/CDC report.

aspe.hhs.gov/pdf-report/vac… Image
Read 11 tweets
30 Apr
Today a story has been going around about a cluster of B.1.617 cases in Israel. This is the India-associated strain.

Unfortunately, this is in some places being spun as a possible example of vaccine escape. But the numbers suggest exactly the opposite!

Here are the numbers.

24 with recent travel history.
17 with no travel history
5 children
4 vaccinated

Approximately 85% of the adult population in Israel has been fully vaccinated. So what does this tell us about vaccine effectiveness against B.1.617 in adults?
I'll just do point estimates.

Assume the 5 children were <16 and thus unvaccinated.

That gives us 32 cases among unvaccinated adults, and 4 cases among vaccinated adults.

The basic calculation for effectiveness then gives us a remarkable 98% against B.1.617.
Read 6 tweets
27 Apr
Osprey and dinner
Crows arrive on the scene.

"Wait, how much will you give me if I ride him?
The approach.
Read 7 tweets
24 Apr
Genomics and the poetry of racist injustice:

Let's start with the poetry, because if you read that, it doesn't matter one iota whether you make the connection to genomics.

Please, please take a moment and read this. Slowly, aloud, and more than once.

What does this have to do with genomics?

To pack a huge amount of information into very small genomes, viruses make use of overlapping reading frames. From Bergstrom and Dugatkin (2016), the HBV genome:
We present an extremely stupid example of what this would look like using three-letter English words instead of codon triplets.
Read 5 tweets

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