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Sep 30, 2021 44 tweets 15 min read Read on X
🚨 Does #Covid19 Vaccination lead to less COVID-19-Like Illness (CLI)?

A test-negative design study with 41,552 participants looked CLI diagnosis, symptomatic illness, and test positivity by vaccination status.

⬇️ Megathread below... ⬇️ Like & RT 🚨
#Covid #Corona #Coronavirus Image
Let's start from the beginning!

A recent study by Thompson et al., which was published in the @NEJM, funded and features by @CDCgov in its weekly MMWR series, looked at COVID-19 Like Illness (CLI) hospitalizations for the age group 50+. ImageImage
The study authors identified over 40k hospitalizations.

These were not all-cause hospitalizations, but CLI diagnosed hospitalizations, which is a subset of all hospitalizations. Time frame: 1/1/21 - 6/22/21.

The below diagram tries to visualize the study sample (red). Image
They gathered 103,199 hospitalizations from 7 diff. locations, see map.

They used a test-negative study design, means that they included everyone that matched the criteria (50+ & CLI diagnosis). Image
This is not ideal, since it’s still not all-cause hospitalization by vaccine status, but it’s much better than what we’ve seen before.

And it should be representative within it's sample.
It is noteworthy, that they excluded 61,647 hospitalizations, that did not match their study criteria:
1) <50
2) No vaxx records
3) Repeat admissions
4) No covid19 testing
5) Recently Vaxxed (<14d) Image
Excluding 1-4 makes sense.

Excluding 5, dose1 vaccinated <14 days, does not make any sense to me! Are these hospitalizations in connection with the vaccine?
In the first chart, I have included the fifth category, specifically 1,872 CLI hospitalizations that were excluded by the authors.
Notably here, the authors also made a simple math mistake! Unbelievable!! Image
So the main result of their study was:

"The effectiveness of full messenger RNA (mRNA) vaccination (≥14 days after the second dose) was 89% [...] against laboratory-confirmed SARS-CoV-2 infection leading to hospitalization [...]"

OK, so 89% effective. Great, right? Image
I have calculated Covid19 Hosp. Vaccine Efficacy (VE) based on the raw numbers, and I arrive at 91%. That's close, and the reason why it's off by 2% is the additional statistical corrections the authors did (age, testing bias, etc.). Image
Ok, so what’s the problem with this?

The problem remains to be the highly sensitive PCR test. Confirmed covid in this case is defined as CLI & PCR+. Same as in the vaccine studies, see here Pfizers.

They did not tell us the rate of all-cause sickness or CLI! Image
So if we calculate the vaccine efficacy in the same manner as before, taking into account all the CLI admissions including the excluded 1,872 visits (dose1 <14 day vaxxed), I calculate a VE of -13%! ImageImage
Important note:

All following charts are excluding the 1,872 “patients receiving dose-1 <14 days prior to index hospitalization date”, since they were excluded in the final study sample, and no detailed information is available!
So, based on the study sample of 41,552, what if we only look at how many people actually got sick?
If we actually look at how many people were diagnosed by their physician with CLI, we can see that all three groups were diagnosed with about the same rate... Odd right? Image
Here's the same chart, CLI clinical diagnosis, with the COVID-19 RT-PCR test positivity (blue).
We can observe that while CLI diagnosis remains steady; Covid cases are significantly lower in vaccinated. Why is that? More on that topic later below… Image
First let's look at actual Covid-Like-Illness (CLI) symptoms! This is what IMO should be the relevant metric, as this is a sign that a human is potentially sick FROM a respiratory virus.

We observe the same effect, the rate of symptomatic CLI is very similar! Image
Let's see how these people tested for Covid-19 with the PCR test?

Similar picture as before, while partially vaccinated, and vaccinated are slightly more symptomatic, they test less positive for the SARS-CoV-2 virus. Image
The differences between symptomatic CLI could be by chance, or could it be possible, that they are due to the vaccine itself?

Specifically, partially vaccinated had the most symptoms!

Note, that this does not even include the 1,872 excluded dose1/<14day patients!!! Image
In the same manner as the original authors, we can now calculate the Vaccine Efficacy against symptomatic Covid-Like-Illness (CLI).

Huh?! Negative too. Yep, that's right. Because based on the data, the vaccinated groups actually experience more CLI symptoms. Image
Interesting, right? So this means that despite Covid19 vaccination, people appear to get as sick and hospitalized (if not even more!), as before?!

Here are some other aspects that I looked at:
1) Age:
Observation: Older age groups have higher vaxx rate, so these charts are in line with expectations. Image
2) Comorbidities
Did not appear to be a driving factor as the difference was within 2-3 percentage points.
3) Hospitalization, that later lead to ICU admission
Between 16-20% of initially as CLI admitted later went to ICU.
There’s a slight difference in favor of vaxxed. However, this diff is rather small in magnitude. Could be due to confounders/chance, physician bias, exclusion... Image
Assuming these were accurate numbers, it would only yield a 20% VE, which would be modest, and way below CDC’s 50% threshold. Image
4) Rank of CLI diagnosis

How they coded the CLI diagnosis, primary vs non-primary.
We can see that the primary CLI diagnosis decr. by about half from unvaxxed to fully vaxx. Could this also be due to physician bias?
Since we know that symptoms are equal between groups. Image
5) Testing Propensity (Bias) between the groups.
Based on the data provided, I find that unvaccinated hospitalized CLI/50+ patients, are 11% more often tested for Covid19, or vaccinated CLI/50+ patients are 12.5% less often tested. Image
Summary/Interpretation:
1) Rate of CLI admission, diagnosis, symptoms/signs similar between unvaccinated vs vaccinated (possibly even higher in vaccinated) → No sign. difference in covid like illness between vaxx groups.
2) Study sample exclusions lead to distortion of data:
such as 1,872 excluded dose1 <14 day vaxxed.
Such as 1,847 potentially excluded vaccinated CLI’s due to testing bias (-12.5%/11%)
3) Rate of Covid19 test positives are significantly lower, but why? See some explanations below:
Possible Explanations/Open Questions:
So now that we have proven, that vaccinated people do not get significantly less sick with CLI and therefore in general, what might actually be happening?
We do not know for certain, as this would require all-cause hospitalization/death data by vaxx status. No country (afaik) publishes this, specifically including # of performed tests by vaxx status.
CDC has acknowledged to me that they have this data, but they do not publish it and have denied my FOIA request (for now)... Why?

docs.google.com/document/d/1EG…
We can only list out some hypothesis:

A) The vaccines are effective in eradicating the virus, at given VE (90%), BUT this does not lead to less sickness (CLI).
(i) Is similar sickness, then caused by other pathogens?
(ii) If the vaccine is eradicating a virus, but people still get sick at the same rate, what's the purpose of mass vaccinations?
B) Is there a difference in testing? We know everyone in the study sample is tested, but are they using the exact same tests (protocols, CT, # of genes, etc.)?
C) Is illness severity decreasing due to the vaccine?
In the data, we can see a little less ICU admissions in the vaccinated groups. However the decrease is only one fifth of the claimed VE.
..C) Hence other factors, such as chance, physician bias, etc. could be at play here. Also we could see that exclusions and testing propensity distort the data towards unvaccinated.
Additional thoughts:
In my opinion this proves that the roughly same amount of people in each group are hospitalized, regardless of the Covid19 vaccination status, and that PCR+ does not seem to lead to less CLI.
We can observe this in the real world. See here for examples for Germany and the US.

Note, that the CLI diagnosis (orange) is not very much correlated to the total hospitalizations. This is likely due to the same effect as described above. ImageImage
Methodology/Sources:

CDC MMWR: cdc.gov/mmwr/volumes/7…
Study: nejm.org/doi/10.1056/NE…
Calculations: docs.google.com/spreadsheets/d…
Most of the data, used for calculations was extracted from this table, of the study appendix.
nejm.org/doi/suppl/10.1… ImageImage
Thanks to:
@shasafaya for pointing me to this study & working with me on this!
@prof_shahar for comment, feedback and advice!
@tlowdon for pointing me to the US hospitalization dashboard.
Everyone for following, liking, sharing and commenting!

Please let me know what you think!
Please let me know if there are any errors/mistakes in this analysis.

@threadreaderapp unroll please
And if you haven't seen my website yet, check out USMortality.com for latest US all-cause (pop/age corrected) mortality data.

Maybe @CDCgov can let us know why we're seeing huge excess deaths again in the US... Is it really all the unvaxxed?
Please just publish the data! Image

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More from @USMortality

Aug 20
💥💥💥 An official CDC FOIA response confirms that the validation of the SARS-CoV-2 genome has not been completed to scientific standards! 💥💥💥

CDC Unable to Scientifically Verify Full SARS-CoV-2 Genome, Leaving Potential for Semi-Random Construct.

🧵 A thread...Image
The response:

CDC has responded to my FOIA request, in which I have asked for records related to these four points:

1. Records on single virion sequencing of SARS-CoV-2 that ensured the virion was physically isolated from any other genetic material before sequencing.
2. Records of a single sequencing (long-)read from the first position [..] to the last position [..] of the genome. [..]
Read 17 tweets
Jun 5
This may as well be part of the script/disinformation campaign after all:

>> This is important to understand <<

Hypothetical Disinformation Campaign Scenario

1. Initial Denial:
• Key Players: Military, secret agencies, health authorities, virologists, philanthropists, etc.
• Action: Strongly deny any allegations of a secret operation involving a lab-manufactured virus leak (commonly referred to as the “Lab Leak Theory”).
• Narrative: Label the lab leak theory as a baseless conspiracy, dismissing it without thorough investigation.

2. Diversion:
• Media Strategy: Shift the focus of the media to alternative explanations, such as the “Zoonosis Theory” (natural transmission from animals to humans).
• Examples: Highlight potential sources such as bats and pangolins to distract and redirect public attention.
• Impact: This redirection aims to convince the majority of the population (~70%) to believe in a "viral spillover", thus novelty of the virus.

3. False Confirmation:
• Controlled Leaks: Release unverifiable “evidence” that appears to confirm the lab leak theory through credible sources.
• Staged Reports: Media outlets present findings like the Furin Cleavage Site or HIV inserts as proof of the lab-manufactured origin.
• Public Reaction: Skeptics (~25%) quickly adopt this narrative, now able to direct their frustration towards those seemingly responsible.

4. Framing:
• Agenda Alignment: Shape the lab leak confirmation to justify specific actions or policies that stakeholders wish to implement.
• Justifications: Use this narrative to defend the necessity of questionable virological surveillance, mass testing, lockdowns, masking, and mass vaccinations as preventive measures.

5. Public Manipulation:
• Perceived Investigation: Convince the public that the origin of the virus has been thoroughly investigated and validated, creating a false sense of certainty.
• Acceptance: The public now either believes in the perpetual risk of natural spillover or lab leak pandemics, leading to widespread acceptance of continuous countermeasures.
• Focus Shift: Rather than calling for the cessation of Gain-of-Function (GoF) research, the narrative shifts to the inevitability of such research due to its international nature, emphasizing the need for ongoing measures like viral surveillance, mass testing and vaccinations.

Summary: Stakeholders deny the “Lab Leak Theory,” redirect media to natural origins, then release false evidence supporting the lab leak to win over skeptics. This frames their original techniques and countermeasures as necessary, manipulating the public into accepting any future measures.Image
Instead, people like Dr. Binder have pointed out since 2020, that the use of mass PCR testing, is entirely responsible for this phenomenon:
In addition, Dr. Rancourt has shown strong epidemiological evidence, that the mass casualties that were observed in some regions cannot be caused by a novel risk-additive pathogen:
Read 4 tweets
Apr 25
There are several problems with the reference genome (b) published by Wu et al. 2020 (a):

1. The sequenced patient sample contained genetic material from different sources: human, bacterial, viral, etc. Although known sequences were filtered out after sequencing, there is no guarantee that all non-novel-viral sequences were actually removed.

2. The patient's human genome was not sequenced for control.

3. Reassembly of the dataset published by Wu using Megahit does not provide the exact or complete sequence as published.

4. Trinity, the second program used for de novo sequencing, is unable to generate the identical contig.

5. When using untrimmed or protocol-trimmed reads (Takara), no reads are found that perfectly match both ends of the genome. This is unusual because, according to a theoretical simulation, several ends should be found in the sample. (c)

6. It has not yet been proven that the entire sequence (~30 KB) actually occurs in this form in the samples, e.g. by agarose gel electrophoresis or (Sanger/whole genome) sequencing.

7. Wu et al. published three versions of the reference genome, the first of which contained known sequences from the human reference genome. The fact that the first version contained human sequences suggests possible problems with sequencing or analysis.

8. The amplicons, i.e. the sequences of the ends found using RACE, have not been published. The non-publication of the amplicon sequences raises questions about the transparency and reproducibility of the study.

9. The only non-Chinese author of this paper, Eddie Holmes, confirmed to me by email that he had no detailed knowledge of these issues. There was silence from the Chinese side, although questions were asked via Holmes. (d)

These clear scientific problems therefore clearly call into question the validity of the SARS-CoV-2 sequence.

(a)
(b)
(c)
(d) ncbi.nlm.nih.gov/pmc/articles/P…
ncbi.nlm.nih.gov/labs/virus/vss…
usmortality.substack.com/p/why-the-ends…
usmortality.substack.com/p/why-do-wu-et…
10. Wu et al., only published a single run, which is supposed to prove the sequence.
11. Wu claims they found a complete sequence before they actually knew its true length - they were just missing the ends, which they then added manually via RACE.
That's a fallacy - because how can one determine the length of a new sequence without first finding the ends, and thus the true length?
Read 7 tweets
Mar 17
There's a large pharma funded Measles Scare Campaign ongoing.
The actual data doesn't support this.Image
Read 8 tweets
Mar 7
‼️ Phantom Vaccine Efficacy!

A list of statistical tricks, that can be used to calculate an illusion of vaccine efficacy with a placebo alone.

For this exercise, I have used a sine wave to simulate weekly deaths:Image
... and a logistic growth function to simulate placebo vaccination from 0 to 75% of the population.
By the green/red dots, we can see no difference/effect, as no statistical tricks are applied yet. Image
Trick 1: Unknown Vaccination Status --> Unvaccinated.

If 50% of Unknown vaccination status is treated as unvaccinated, almost 3x higher mortality rates appear for unvaccinated. This is entirely an illusion. Image
Read 6 tweets

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