Atomsk's Sanakan Profile picture
Mar 27, 2021 48 tweets 42 min read Read on X
1/J

John Ioannidis published an article defending his low estimate of COVID-19's fatality rate.

It contains so many distortions that I'll try something I've never done on Twitter for a paper:

Go thru distortions page-by-page.

This will take awhile. 😑

onlinelibrary.wiley.com/doi/10.1111/ec… Image
2/J

Some context:

Infection fatality rate, or IFR, is the proportion of people infected with the virus SARS-CoV-2 who die of the disease COVID-19.

There are many IFR estimates, including some from Ioannidis.



institutefordiseasemodeling.github.io/nCoV-public/an… Image
3/J

Seroprevalence studies (serosurveys) measure antibody levels to estimate the number of infected people.

Dividing COVID-19 deaths by that number of infected people gives a seroprevalence-based IFR.



who.int/bulletin/volum… Image
4/J

One can over-estimate seroprevalence (+ thus under-estimate IFR) by measuring seroprevalence in a sample that does not represent the general population, and then extrapolating that sample to the general population.

Ioannidis does this.

5/J

Ioannidis defends his use of non-representative samples.

But his defense fails. For example:

- non-representative samples are still unreliable
- he uses non-representative samples even in hard hit areas



who.int/bulletin/volum… Image
6/J

- he could just wait for representative sampling in less hard hit areas
- areas often looked less hard hit because they under-estimated COVID-19 deaths, so including them under-estimates IFR

etc.

bmj.com/content/370/bm…



medrxiv.org/content/10.110… Image
7/J

So in this thread, *keep this in mind*:

Ioannidis has to keep non-representative samples in, because representative samples show an IFR incompatible with his position.

That's his main game, + what he often distracts from



Image
8/J

With that framework in place, let's start with the page-by-page review of Ioannidis' paper:

Ioannidis excludes @GidMK + @BillHanage's paper Levin et al., because it focused on specific countries.



link.springer.com/article/10.100…

onlinelibrary.wiley.com/doi/10.1111/ec… Image
9/J

Ioannidis' exclusion fits with him under-estimating IFR by using non-representative samples in areas that under-estimate COVID deaths.

The WHO + the USA's CDC know better, and so rely on Levin et al.:

web.archive.org/web/2021032419…


link.springer.com/article/10.100… Image
10/J

Seroprevalence-based IFR was ~0.76% in @LeaMerone + @GidMK's paper, when they focused on seroprevalence studies with a low risk of bias.

Ioannidis conveniently leaves that out.

sciencedirect.com/science/articl…


onlinelibrary.wiley.com/doi/10.1111/ec… Image
11/J

Ioannidis co-authored 2009 PRISMA guidelines that stated one should competently assess studies for risk of bias.

@LeaMerone + @GidMK did that.
Ioannidis didn't, letting in non-representative samples

bmj.com/content/339/bm…



Image
12/J

The "low IFR" Ioannidis references is one he inferred from a Los Angeles County study.

That IFR is impossible since it requires more people are infected than actually exist.

jamanetwork.com/journals/jama/…


onlinelibrary.wiley.com/doi/10.1111/ec… Image
13/J

The "IFR = 0.31%" study Ioannidis mentioned is below.

@LeaMerone + @GidMK excluded it because "did not allow for an estimate of confidence bounds"
sciencedirect.com/science/articl…

"to estimate an overall IFR for the United States of 0.863 percent"
papers.ssrn.com/sol3/papers.cf… Image
14/J

It's 0.31% IFR is unreliable anyway since, for example, the studies for Santa Clara, New York (both), + Chelsea used non-representative sampling. Miami-Dade was wrong.

ncbi.nlm.nih.gov/pmc/articles/P…



16/J

- Kenya used non-representative sampling on blood donors
science.sciencemag.org/content/371/65…


- Due to co-linearity, the nationwide study ICCRT cites supplants Rio Grande do Sul
nature.com/articles/s4159…



imperial.ac.uk/media/imperial… Image
17/J

Taking a break for a bit.

The thread so far covers *less than a page* of the distortions + misleading statements in Ioannidis' paper.

I hope people understand why many experts in this field no longer invest time in addressing his nonsensical under-estimating of IFR. 🤦‍♂️
19/J

- the New York sample under-estimated IFR (see 18/J)

- low response rate biases seroprevalence up, under-estimating IFR


- the IFR in Italy was likely over-estimated, due to lower sensitivity of the Abbott assay

onlinelibrary.wiley.com/doi/10.1111/ec… Image
20/J

His adjustment makes no sense since it's already implicit in test adjustments for sensitivity.

And IgA assessment isn't required, given IgG.
thelancet.com/action/showPdf… (table 2)
ncbi.nlm.nih.gov/pmc/articles/P…
bmj.com/content/370/bm…



onlinelibrary.wiley.com/doi/10.1111/ec… Image
21/J

I'll leave to others (maybe @GidMK?) to discuss the meta-analysis details.

But I can say Ioannidis under-estimates seroprevalence-based IFR in southeast Asian countries such as Japan + South Korea.




onlinelibrary.wiley.com/doi/10.1111/ec… Image
22/J

There are at least three approaches to dealing with areas lacking representative samples:

1) exclude those areas + wait for data
2) use regions with representative samples to extrapolate over
3) include non-representative samples from those areas

onlinelibrary.wiley.com/doi/10.1111/ec… Image
23/J

#3 is worst because it extrapolates from inaccurate samples, under-estimating IFR. Yet that's what Ioannidis chooses to do + uses Bobrovitz for.

#1 makes sense; that's what "Meyerowitz-Katz" (@GidMK) did. But if you must have data for policy or planning, #2 can work.
24/J

And now in his discussion section, Ioannidis turns to the core point.

I'll spend a few tweets on this because this is *the* central pillar of his position, and is how he's been misleading millions of people for over a year.



onlinelibrary.wiley.com/doi/10.1111/ec… Image
25/J

Suppose you want to know what proportion of people in a city like dogs.

You could survey people in 1 building.

By luck the percentage you get might match the percentage you would get for the city overall. But you didn't design the survey to make that more likely.
26/J

The same point applies to seroprevalence studies.

Non-representative sampling might *luckily* get results that match the overall population. But representative sampling is *designed* to be more likely to match the population.

academic.oup.com/cid/advance-ar… Image
27/J

Scientists know methods that get representative samples that are more likely to match the general population; they applied them to diseases before COVID-19.

Ioannidis discards those methods, + relies on non-representative sampling luckily matching.

Image
28/J

So his "[n]o consensus" claim is misleading. There's an evidence-based consensus (outside of Ioannidis) that those samples could *luckily* match, but are not designed to + are thus less likely to.

Covered in another thread:



onlinelibrary.wiley.com/doi/10.1111/ec… Image
29/J

And "non-participating invitees" are less likely to be infected, so Ioannidis was wrong. We don't the response rate for his Santa Clara study, since he has no targeted sample.


medrxiv.org/content/10.110…



medrxiv.org/content/10.110… Image
30/J

Most infected people increase antibody levels. In the general population that antibody increase persists for ≥6 months in most people, besides with some assays like Abbott.





onlinelibrary.wiley.com/doi/10.1111/ec… Image
32/J

- Germany's excess deaths for the 1st wave were low in simple comparisons to previous years
archive.is/0oQH8

- scientists make the necessary adjustments already


- the Germany study Ioannidis cites undermines his claim
ncbi.nlm.nih.gov/pmc/articles/P… Image
33/J

Levin et al. focused on studies where death numbers were not accelerating after the seroprevalence study. That helped mitigate over-estimation of deaths
link.springer.com/article/10.100…

Developing countries can have reporting delays
thelancet.com/journals/lance…

onlinelibrary.wiley.com/doi/10.1111/ec… Image
34/J

The Brazil "1%" IFR study is peer-reviewed, as are others
thelancet.com/journals/langl…


#12 overlaps with 0% seroprevalence 🤦‍♂️
nature.com/articles/s4159…

#54 is non-representative sampling of blood donors
science.sciencemag.org/content/371/65…
Image
35/J

IFR with representative sampling is higher in Japan:


With the possible exception of #58, his cited studies use non-representative sampling:


#58 contradicts his low IFR:


onlinelibrary.wiley.com/doi/10.1111/ec… Image
36/J

He complains about having to follow the PRISMA guidelines he co-authored 🙄

IFR increased with less risk of bias (as expected when non-representative sampling skews IFR down):
sciencedirect.com/science/articl…




onlinelibrary.wiley.com/doi/10.1111/ec… Image
37/J

It's telling how Ioannidis twists O'Driscoll et al. to support a lower IFR that contradicts their paper:



Same for Ioannidis returning to abusing non-representative samples:


onlinelibrary.wiley.com/doi/10.1111/ec… Image
39/J

Now, some people might wonder why I spent so much time explaining factual errors in this paper.

Well, it's wrong and needs correction. But there's another reason:

It's the most unprofessional + disingenuous peer-reviewed paper I've ever read.
40/J

Re: "It's the most unprofessional + disingenuous peer-reviewed paper I've ever read."

If you don't believe me on that, then read Appendix 1 on page 38, and ask yourself if you've *ever* seen this in a scientific paper from a competent scientist:

onlinelibrary.wiley.com/doi/10.1111/ec… Image
41/J

I have seen scientists address criticism of their work from Twitter, usually by responding to the general criticism without mentioning the tweets.

I have *never* seen a scientist evade criticisms of their work, while naming Twitter accounts, their number of tweets, etc.
42/J

In doing this, Ioannidis has tacitly admitted he can't rebut criticism of his work.

Are any of the people who whined about negative tone of Ioannidis' critics going to call him out for this?

Of course not. 🙂



onlinelibrary.wiley.com/doi/10.1111/ec… Image
43/J

And unsurprisingly, Ioannidis extends his evidence-free ideologically-motivated smears to Ferguson et al.'s team at Imperial College, without ever admitting they did not over-estimate IFR.



onlinelibrary.wiley.com/doi/10.1111/ec… Image
44/J

So for Ioannidis' defenders, that's what you're left with:

An ideologue who makes mathematically impossible claims that suit his ideology, and names Twitter accounts in his paper since he can't rebut their critiques.

🤷‍♂️



archive.is/dT97F#selectio… Image
45/J

And there's Ioannidis' not so subtle attempt to encourage his fans to dox me.
en.wikipedia.org/wiki/Doxing

Are any of the people who incorrectly whined about Ioannidis (supposedly) being "silenced", going to call him on that?

Of course not. 😑

onlinelibrary.wiley.com/doi/epdf/10.11… Image
46/J

Also, this was not a one-time lapse in judgment on Ioannidis' part. He's done this before in the same journal.

So Ioannidis was editor-in-chief at European Journal of Clinical Investigation for a decade, and it's now his venue for attacking people

Image
48/J

Found the PubPeer thread on Ioannidis' paper, for those who want to defend it or critique it (I don't post on PubPeer, nor do I have an account there):

pubpeer.com/publications/9… Image

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More from @AtomsksSanakan

Feb 23
71/J

I recently got a copy of Dr. Judith Curry's book without buying it myself.

Looking over it confirmed to me that it's largely misinformation.

I'll illustrate that by assessing its claims on COVID-19.



"11.3.1 COVID-19"

amazon.com/Climate-Uncert…
Image
72/J

To reiterate: Curry draws parallels between COVID-19 + climate change.

But some of the sources she cites suggest an ideologically convenient narrative misinformed her.

That becomes clearer when assessing her claims.




Image
73/J

No mention of the misinformation she + other contrarians promoted, and which conflicted with knowledge advances by experts.

(8/J - 12/J, 32J - 36/J, 44/J, 45/J, 63/J, etc.)








Image
Read 31 tweets
Feb 17
1/J

Dr. Judith Curry recommends people read at least the 45-page preview of her new book.

I did.

It's bad enough I wouldn't recommend buying the book.
It's largely contrarian conspiracist misinformation.




amazon.com/Climate-Uncert…
Image
Read 72 tweets
Aug 30, 2023
PapersOfTheDay

"Executive Summary to the Royal Society report “COVID-19: examining the effectiveness of non-pharmaceutical interventions”"


"Effectiveness of face masks for reducing transmission of SARS-CoV-2: [...]"
royalsocietypublishing.org/doi/10.1098/rs…
royalsocietypublishing.org/doi/10.1098/rs…
Jefferson + Heneghan don't like the papers.

Makes sense they wouldn't given their track record, especially Jefferson on the Cochrane mask review he led.







brownstone.org/articles/royal…



cochrane.org/news/statement…
Image
Read 5 tweets
Mar 13, 2023
69/E

A reminder, since there's a resurgence in Musk + right-wing politicians trying to score political points by saying they want Fauci prosecuted:

Musk's dislike of Fauci drove him to post an easily debunked lie (57/E, 56/, 41/)


Image
70/E

Still no apology from Musk for falsely smearing Grady based on untrue things he was told, or that he made up.

"Elon Musk calls British diver in Thai cave rescue 'pedo' in baseless attack"
theguardian.com/technology/201…



thedailybeast.com/elon-musk-mock… Image
71/E

Another good example of the willful ignorance + baseless paranoia underlying Musk's lab leak conspiracism and his criticisms of Fauci.




archive.is/GZ6er#selectio…
archive.is/ughZK#selectio…
archive.is/WWKtc#selectio… ImageImageImage
Read 11 tweets
Dec 12, 2022
1/E

Some illustrations of the pseudoskepticism that overtakes many crypto / tech bros, using the example of Elon Musk's COVID-19 claims.

"My pronouns are Prosecute/Fauci"


onlinelibrary.wiley.com/doi/full/10.11… Image
2/E

No, neither chloroquine nor hydroxychloroquine worked for SARS-CoV-2.

Fortunately, Fauci recommended neither in March 2020.

9:12 - 14:41 :



Image
Read 29 tweets
Jun 8, 2022
1/B

Thread on a myth Jay Bhattacharya (@DrJBhattacharya) continues to peddle to undermine confidence in public health agencies and to suit his policy agenda.

The myth may undermine responses to future public health emergencies.




stanfordreview.org/the-review-int…
Image
2/B

Some background:

The infection fatality rate (IFR) states the proportion of *SARS-CoV-2-infected* people who die of the disease COVID-19.

The case fatality rate (CFR) states the proportion of *reported cases* who die of COVID-19.

institutefordiseasemodeling.github.io/nCoV-public/an…
Image
3/B

Reporting systems are not perfect, so they sometimes miss infected people. That makes reported cases less than total infections, and thus CFR is higher than IFR.

The WHO was open about this since the early stages of the pandemic:

March 17, 2020:
web.archive.org/web/2020102205…
Image
Read 26 tweets

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