To prevent one COVID19 case we're sending vaccinated people to hospital (VAERS)
Heard or seen more ambulances than usual lately?
Photo by camilo jimenez on Unsplash
What is a serious adverse event?
VAERS is the Vaccine Adverse Event Reporting System. As well as recording less serious events like injection site pain, it provides fields to record more serious events, but what are these? According to the FDA, a Serious Adverse Event (SAE) as being when the patient suffers from the following:
Requires hospitalization (initial or prolonged)
Results in disability or permanent damage
Causes congenital anomaly or a birth defect
Required intervention to prevent permanent impairment or damage
Following on from my last article on COVID19 vaccines killing younger people sooner, I take a look at some of these SAEs and see what VAERS tells us by age group and whether there’s a pattern or finding we need to understand further.
The small print: For transparency, the VAERS data used in this analysis was from 1st January 2020 to 23rd November 2021. Data was filtered to COVID19 vaccines and the DATA and VAX tables were joined using the last vaccine date to eliminate multiple entries per VAERS_ID. The SYMPTOM table was not used.
Death is perhaps the least contentious item on this list you would think. I reported VAERS deaths by age group in my last article. But even though the effect of death isn’t disputed (when you’re dead, you’re dead right?), it’s causality with respect to COVID19 vaccines is very much disputed.
For example in the UK, the Office of National Statistics (ONS), recently published this article in October 2021which sought to outline how many deaths were due to the vaccine. It said that the 1,645 current deaths reported on the weekly Yellow Card (the UK’s - VAERS ‘like’ - pharmacovigilance system) could not all be attributed to vaccination, which is true.
This [1,645] is the number of deaths reported as possibly linked to a vaccine, however they will not have been fully investigated at the time of reporting and a report is not proof of causation …
However, as I will go on to write in another article, the Yellow Card underreports serious adverse events (but not by as much as VAERS - see here, here and here). And, whilst statistics on burial and cremation are difficult to obtain, this poll suggests 58% prefer cremation. So as ~6/10 of those who have died are likely to have been cremated, it is unlikely that the cause of death will ever be ‘fully investigated.
Despite underreporting, VAERS is still a far more useful data set than the UK’s Yellow Card. And although there is an FDA requirement to record all deaths in VAERS where a vaccine has been administered, it is obvious to anyone who has analyzed the data that the temporal link between vaccination and death is evident - especially for younger people with most of their lives still ahead of them.
This is a slightly different graph to the one reported in my last article as it reports deaths by vaccine dose as well as age, so is a good departure point for other SAEs and is the format I’ll use mostly throughout this article.
Dose 1 is on the left and dose 2 is on the right. Age is plotted along the x-axis with the number of days to death up the y-axis. Each age group’s corresponding count of patients who died for a given number of days after their last vaccine is plotted vertically. A blue linear regression line is also shown. You can see that whilst there are almost equivalent numbers of recorded deaths by dose 1 and 2, both graphs show that death occurred sooner the younger the patient was.
VAERS has a field to record life-threatening adverse events. So although causality and the degree of whether a condition is life-threatening are inherently subjective, the FDA provides guidance. They state it must be reported:
… if suspected that the patient was at substantial risk of dying at the time of the adverse event, or use or continued use of the device or other medical product might have resulted in the death of the patient
It’s worth remembering that SAEs are not mutually exclusive in VAERS. For example, some Hospitalizations might be Life-Threatening and eventually lead to Death. This would lead to a patient appearing in multiple graphs. I have not ‘wrangled’ the data to adjust for this combinatorial event and you should note it’s presence.
Of note is the range of life-threatening median time to onset which when both doses were combined was 5 days (interquartile range 1-20 days) and was reasonably consistent across all age groups (see below).
Perhaps the most worrying conclusion from these graphs is that the number of people under 50 who have been admitted to hospital with what is considered a life-threatening condition. All the more convincing, is that the injection was cause of the SAE by the median time to onset for this cohort, which ranged from 2.5 days for cases aged 0-9 (please don’t inject your kids) to 4 days for people in the 30-39 and 40-49 year old categories.
Events requiring hospitalization
Over 30,000 records in VAERS required hospitalization after their last shot. Considering these are ‘serious’ events, I’m going to apply an under reporting factor to get closer to where the truth may be. So how do we calculate underreporting?
In Steve Kirsch’s paper on estimating the true number of injection related deaths in the US, he outlined a methodology to calculate the VAERS Under Reporting Factor (URF) based on a study by Blumenthal et al which looked at the number of anaphalyxis reactions reported after an mRNA injection in a clinical setting. Kirsch took this number (247/M) as the background rate, divided it by the number of reported rate in VAERS (5.97/M) and arrived at a rounded figure of 41x that he used to estimate that over 150,000 US citizens could have died following the vaccine.
Kirsch also backed up his numbers by offering a $1M Research Grant to anyone who could improve the adjusted deaths estimate by a factor of four or more in either direction (note - this offer may have timed out by the time you are reading this). To date there have been no takers.
Lets do some 'back of the envelope’ math. If we start with the 31,694 hospitalizations above and adjust it by the URF we get.
31,694 x 41 (URF) = ~1.3M hospitalizations.
As of 23 Nov 2020 there were ~214M people over the age of 5 who had at least one dose in the US. So that would mean there was 1 hospitalization for every 165 injections given, i.e. 214M/1.3M.
Hold that thought for a minute.
First, do no harm
Thinking about vaccine related hospitalizations and whether the damage to people’s health is worth it, now consider how many people we need to vaccinate to prevent the spread of the virus.
Clinical trial data showed that the Number Needed to Vaccinate (NNV) was 119 for the Pfizer injectable product. That meant to prevent 1 case of SARS-CoV-2, we need to inject 119 people (it was 84 for J&J and 81 for Moderna).
1 hospitalization for every 165 injections
1 case of SARS-CoV-2 prevented for every 119 injections
If we divide 119/165, we get 0.72.
Or put another way, to prevent 100 cases of SARS-CoV-2, 72 people go to hospital. That sounds unbelievable! Perhaps…
So using a lower figure for the URF that the CDC hinted at in this article produces a figure of 6.5x. In which case the sums look like this:
31,694 x 6.5 (URF) = 206k or 0.206M hospitalizations;
214M/0.206M = 1 hospitalization every 1039 injections;
119/1039 = 0.11
Put another way, to prevent 100 cases of SARS-CoV-2, 11 people go to hospital.
Sound better? …
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This article was top of Google’s search in the UK with this query (“does the covid vaccine cause death”).
214M = 69.5% * (331-23), Where 331 = US citizens in 2021 & 23 = 5 years olds in the US
Looking forward to see what independent analyses you come up with. Naturally, correlation is not causation. I feel there must be a novel way to use VAERS to come up with something solid. I did have one idea, but I don't think it is that useful:
1) Look at all AE death reports that DO NOT have vaccination listed on it.
2) Plot them against time
3) See if spikes correlate with vaccine rollout
The logic behind that would be that if the report does not list vaccines, then the clinician had no suspicion of it, and therefore there are no reporting biases relating to vaccination. If spikes are observed, then no one could say "oh they just reported that vaccine-associated death because they had to".
I am not sure how much more useful this is than just looking at an all-cause mortality curve and correlating with vaccine rollout, which has not seemed to be particularly helpful in picking up the signal.
Does that make sense to you?
Amazing work as always. Thank you