Thoughts on Easter, the risk-benefit ratio of Covid-19 vaccination and key sources of information
No matter how you feel about Easter, whether you actively celebrate it as a Christian, with nostalgic memories of hunting for Easter eggs as a child, or whether you are a modern, science-oriented person who thinks Easter beliefs are superstitions: It is a holiday and should be celebrated. The arrival of spring was already celebrated in pre-Christian times. Christianity has given these ancient feasts a new face with the celebration of the resurrection of Jesus. What was ever truly alive – that is, standing in the fullness of God – does not die. At best, it is transformed. Or rather, it is resurrected into new life. That is Easter, and that is what is to be celebrated.
We have received a small Easter present, I think, in that the German Bundestag has rejected compulsory vaccination by a large majority. You can find out here how the parliamentary groups voted: the majority of SPD and Greens voted in favour of compulsory vaccination, most representatives of CDU, AfD, FDP and Die Linke voted against it. I suggest you write to your MPs, either thanking them, or admonishing and reminding them again. You can filter the results to see the MPs who represent your constituency and how they voted, and then write an email. Maybe our MWGFD action helped; because we sent our exit strategy to all MPs. You can download it there and send it again to the MPs who voted for compulsory vaccination, perhaps with a few more personal words.
The benefits of vaccination
Perhaps the following arguments will also help. If one looks at the current data from the RKI, which summarizes the cases of Covid-19 sufferers (as defined by the RKI and when clinical information was available) and hospitalized (downloaded on April 15th 2022), then one finds the following correlations (I have added up the columns of the tables for this purpose and the age categories as well; since the categories “basic vaccination” and “booster vaccination” are mutually exclusive, I have also added these up)
|Covid-19 contracted per 100,000
|Hospitalised per 100,000
The excess of cases among the unvaccinated is mainly due to the category of younger people. If we leave the age distribution given by the RKI, we get the following table (the persons with basic immunization and booster are summarized under “vaccinated”):
|Covid-19 per 100. 000
|Hospitalised per 100,000
It can be seen that the benefit of vaccination is very age specific. People in the age category of 18 to 59 years are less likely to get Covid-19 if they are unvaccinated. In younger people, for whom the disease is actually of little risk, hospitalization rates are relatively high among the unvaccinated. Whether this is really a clinical reality or rather an artefact of the testing and reporting strategy remains to be seen here. Among the hospitalized, the effect of vaccination is clear, as in Table 1. Here it is particularly visible among the elderly, over 60 years of age. Actually, this data series should also be kept for the deceased, and I find it strange that this is not done.
Overall, this data seems to speak very strongly for the effect of vaccination to begin with. Two things must now be taken into account here.
- The effect size of the vaccination, i.e. the effect put in relation to all vaccinations and non-vaccinations.
- The risk associated with the vaccinations, considering side effects and associated deaths.
Let’s proceed in order.
The effect size of vaccination
We now use the figures from Table 1 as estimates for the absolute effect size of vaccination, and specifically for absolute risk reduction, not relative effectiveness. To obtain this effect size, we calculate the
Absolute Risk Reduction (ARR)
which is defined as
ARR = number of cases in the control group of unvaccinated/all unvaccinated – number of cases in the treatment group of vaccinated/all vaccinated
ARR = 1,270/100,000 – 371.6/100,000 = 0.00899
or about 9 per mille.
So the risk of being hospitalised is reduced by about 9 per mille by vaccination.
We see: the relative benefit, as shown in Table 1, is relatively impressive, but the absolute benefit, considering the total number of all who receive the intervention, is less impressive. That this is so can be seen from the Number Needed to Vaccinate, that is, the number of people who need to be vaccinated to prevent one death or hospitalisation.
Number Needed to Vaccinate
If you want to calculate the Number Needed to Vaccinate (NNV) to prevent one case of hospitalisation due to Covid-19, you have to divide 1 by this absolute risk reduction calculated above and you get
NNVHospitalisation= 1/0.00899 = 111.23
That means we have to vaccinate 111 people to prevent one case of hospitalisation. Deaths are not included there because we don’t have that data.
The risk-benefit ratio of deaths we recently calculated – I had already written about this – by using Pfizer’s 6-month study, which gives us a slightly more detailed look . This data allows an estimate of one NNV to prevent one death, and this is around 22,000. Incidentally, on the Pfizer 6-month study, colleagues recently published a critical commentary. They did the math and found that the number of deaths reported by the Pfizer study cannot be correct. Because if you take normal mortality statistics into account, the mortality data in the placebo group reported by Thomas’s study  is too low by at least 25%, in other words, 25% more deaths should actually have been reported. Put another way, the Pfizer study is most likely not reliable and thus our estimate of NNV of 22,000 to prevent one death is probably still too low. But let’s leave it at that and use that estimate.
The side-effect statistics of the Paul Ehrlich Institute
The current Paul Ehrlich Institute safety report (the one I use is available here and goes to the end of December 2021, thus covering one year) gives a total of 2,255 deaths, or 2 suspected deaths per 100,000 vaccinations, and 29,786 suspected cases of serious adverse reactions, or 164 such serious cases per 100,000 vaccinations.
In plain English, if we vaccinate 100,000 people, we save 5 lives, maximum, probably more like less, and prevent about 900 hospitalisations (because we have to vaccinate 111 people to prevent one hospitalisation; so with 100,000 vaccinations we prevent 100,000/111 = 900.9 hospitalisations). We buy this with a presumed 2 deaths and almost 165 serious side effects. Actually, the ratios are even worse because the data above in Tables 1 and 2 are for individuals and each person receives an average of 2.5 vaccinations. So you would have to multiply the side effect data by a factor of 2.5 and would then have to reckon with 412 serious side effects. This means that the number of hospitalisations prevented is in relation to the side effects to be accepted by a ratio of 2:1 and the number of deaths prevented is in relation to the deaths that might be accepted by a ratio of about 1:1.
Are 900 hospitalisations prevented worth putting up with 412 serious adverse events? Are 5, rather fewer, prevented deaths worth at least 2, rather 5 people dying from the vaccines, perhaps more?
The problem is: the side effect data is not reliable. According to a new study they underestimate true case numbers by at least 70%. This is why a whole range of initiatives have formed, e.g. those here of “Pflege für Aufklärung”, which collect cases from people who have experienced side effects but have great problems getting them recognized as such. We would have to introduce a new billable category for doctors to be rewarded for filling in side effect reports, which certainly take about 20-30 minutes, then we would have a better overview.
The risk-benefit ratio of the Covid-19 vaccine is conceivably poor
If we add 75% to the 2 documented suspected deaths per 100,000 vaccinations, we are very close to the number of 5 deaths that are maximally prevented by vaccination. If we add another 75% to the 412 serious side effects, then we are close to 721 serious side effects per 100,000 people vaccinated (I extrapolated to 2.5 vaccinations per person for these calculations). In plain language: There is no vaccination on God’s wide earth that has such a poor risk-benefit profile as this one. Suspected deaths are 560 times higher than for all other vaccines combined (see our MWGFD phase-out concept; the graph is shown there).
There is no vaccination that would have been approved in the first place under normal circumstances with such a profile. From my point of view, this is a case for the public prosecutor because the Paul Ehrlich Institute has neglected its duty of supervision, or, in case of doubt, the Federal Minister of Health.
Those who wish to send these figures to your MPs are welcome to do so; anything I write here is freely available, as long as the source is attributed. It must be clear to everyone by now: What is happening here has nothing to do with medicine and health care. At best, it is a money printing machine for the industry. At its worst, it is an agenda to introduce new procedures – mRNA vaccination technology, vaccination passports.
That there is a clear agenda behind this vaccination campaign can be seen from the fact that all simple and quickly effective treatments have been systematically death-talked. There is a very interesting US initiative by high profile scientists who have compiled meta-analyses of the effect of all sorts of interventions to treat Covid-19 in a series of meta-analyses, for example, the supposedly ineffective Ivermectin and many other interventions, such as vitamin D, vitamin A, zinc and others. One thing that stands out is that the interventions recommended as effective by the CDC and NIH, such as remdesivir, are less effective than most others. The really effective ones are not recommended except in trials. This is very strange in my view. It can easily be explained by a small conspiracy, which brings us back to the themes of my last two blogs: Namely, if there is no really effective treatment against a disease, then one can apply for emergency approval of, say, a new vaccine, even if it is not well studied yet. And the fact that authorities such as the CDC and the NIH are, of their own volition, manipulating the data in such a way that this is precisely the result, even though the real data situation is completely different, cannot be explained by stupidity – given the cumulative competence of these authorities – thus only by a clear political intent.
Steve Kirsch, a US entrepreneur and IT specialist, co-inventor of the optical mouse and philanthropist, maintains a very busy blog (which I recommend). He has offered one million US dollars to members of the Licensing Commission to debate publicly with him the benefits of the Covid-19 vaccine and have it documented – regardless of the outcome, by the way. No one took that bet. Professors he has even offered to name their price. One million dollars is still up for grabs for anyone who can prove that the US CDC is correct in its figures on death rates from Covid-19 and vaccinations.
So far, no one has bitten. Why do you think no one has come forward yet, if it is so easy to prove the benefits of the Covid-19 vaccine? No politician, no NIH official, no Harvard or whatever professor, no public health specialist, no talk show host?
Now the data is piling up that shows: This emergency approval is anything but factually justified. The risk-benefit profile is conceivably poor, not to say miserable. We are talking about a vaccination against a disease that is actually controllable and in no way poses a threat to the public because, I repeat myself, most people probably already carry a T-cell immunity triggered by other coronaviruses, which makes very severe courses unlikely. There is always a residual risk. But we will never get a handle on that. Maybe we should just accept that once and for all. Because if you try to get every risk under control, then the dialectic strikes, and we create a risk that is no longer under control.
It remains for me to point out a very nice site, the “Covidwegweiser”. This is a site that brings together all sorts of information and websites that are critical of Covid-19 and current policy. This gives a very good overview of the initiatives and groups that are active in one way or another to contribute to the information. Many civil society groups are active and dissatisfied with the official Corona management. Wouldn’t it be high time – Easter – to try something new? For example, stop the know-it-all attitude on the part of the government. Or get everyone who has something to say, even if it’s not what you want to hear, around the same table. It should be big.
A new religion? Or Easter?
I’m afraid that’s probably not going to happen. Because basically the Covid-19 crisis is neither about science nor about truth, but about ideology and money, or more precisely about a new religion, the religion of science-believing transhumanism. This religion does not care about data or people. It is not interested in knowledge or truth, but instrumentalizes science. It has an agenda, namely to impose a naturalistic-materialistic world view and to stage the necessary instruments for this. The mRNA technology is one of them. You can do a lot with it in the future once it is accepted. The digital vaccination certificate is another instrument. In the future, it will enable control and access restrictions, and by extension, rewards for good behaviour and punishments for bad behaviour. China sends its regards.
The sad thing is that the very representatives of the Christian religion, the churches, are snoozers and completely sleep through this development. Instead, they make themselves accomplices of state coercion strategies. Perhaps that is why, as was the case about 2000 years ago, individuals must once again be the bearers of the Easter message. Back then, it was first the women, then a few crazy fishermen. For if there is one thing to be learned from this crisis: Institutions are bankrupt. Those who wait for them are waiting for Godot. As I wrote in my Treblinka blog: We have to reinvent everything. It’s a cipher for Easter.
Literature and comments
- Walach H, Klement RJ, Aukema W. The risk-benefit ratio of Covid-19 vaccines: publication policy by retraction does nothing to improve it. Clinical and Translational Discovery. 2022;2(1):e35. doi: 10.1002/ctd2.35.
- Thomas SJ, Moreira ED, Kitchin N, Absalon J, Gurtman A, Lockhart S, et al. Safety and Efficacy of the BNT162b2 mRNA Covid-19 Vaccine through 6 Months. New England Journal of Medicine. 2021;385(19):1761-73. doi: 10.1056/NEJMoa2110345. PubMed PMID: 34525277.
I would like to thank my colleague Rainer J. Klement for a few helpful critical, constructive comments that saved me from making a mistake.