Covid-19 Vaccinations Do More Harm Than Good

Now We Have It in Black And White

In July, Mörl, Günther and Rockenfeller published a high-profile paper in the peer-reviewed online journal Frontiers in Medicine [1]. They compared the number of adverse events in the five pivotal trials of the Covid-19 vaccine with the number of adverse events in the control groups, as well as the number of severe Covid-19 cases in both groups, and calculated a harm-benefit ratio. If this is less than 1, then the vaccines do more good than harm. If it is greater than 1, they do more harm than good. Only two studies had a harm-benefit ratio smaller than 1, but very close to 1 (0.9 and 0.6). The authors point out that it would probably be reasonable to expect a harm-benefit ratio much smaller than 0.1, that is, ten more severe courses among control cases than among vaccinated people.

Clearly, this is not the case. In the BioNTech study, the ratio is actually very large at 25. This means that 25 times more serious side effects are registered in the vaccination group than in the control group. In the Moderna study, the ratio of 1.1 is about the same, but also far from favourable. They do not interpret the Sputnik pivotal study because the ratio there is negative, which is hardly credible.

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Why People in Germany Get Vaccinated

Our vaccination motivation study is now published in BMJ Open

On January 6th, 2022, I had referred to the preprint of our study “Why do people consent to receiving SARS-CoV2 vaccinations – A representative survey in Germany” and discussed it in detail there [1].

Now it has been published in British Medical Journal Open (BMJ Open) [2]. The content has not changed from the preprint, so I won’t go into detail about the study again now. Only one additional analysis was added at the request of a reviewer.

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Without Vaccination 18 Million More Deaths Worldwide? – Really?

People like me are often accused of not taking note of the high-ranking published data on the danger of SARS-CoV2 and the effectiveness of vaccination. I do take note, and I want to take this opportunity to say a few words about it.

A recent publication in Lancet Infectious Diseases [1] estimates that Covid-19 vaccinations have prevented 18 million Covid-19 deaths. This is a steep claim given that 6.4 million deaths involving or caused by Covid-19 have been reported worldwide to date (https://ourworldindata.org/covid-deaths, accessed Aug. 2nd, 22; see Fig. 1)

Figure 1 – Cumulative number of Covid-19 deaths, https://ourworldindata.org/covid-deaths, accessed Aug. 2nd, 22  
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Modelling And Model Building

…using the example of our study: “Identification of different factors associated with Covid-19 deaths in Europe during the first pandemic wave”

A large group of statistical techniques designed to explain past data and also to predict future data is statistical modelling. This means that for a given data set with very different variables, one finds a mathematical structure that represents this data set as well as possible, firstly in a purely formal way. This procedure can be used to examine the influence of different variables on an outcome variable. In the language of modelling, the variable that one wants to explain is the dependent variable or criterion or outcome variable, and the different variables that are supposed to contribute to the clarification of this one variable are several independent variables resp. predictors.

I use our recently published modelling study [1] as a concrete example. It was conceived by me, I calculated the first analyses, then my colleague Rainer J. Klement got involved, who as a physicist is much more nimble in dealing with such models than I am.

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Meta-Review: The Backbone of Evidence Based Medicine Is Weak

Only about 6 percent of all interventions used in medicine have sufficiently good data and are effective

Our new meta-review shows: The backbone of Evidence Based Medicine is weak

Regular readers of my texts know that I am very sceptical about the postmodern redemption narrative of modern medicine that proclaims: We live so long and do so well because modern medicine has made such tremendous advances. Therefore, everything that modern pharmacology brings us is good, welcome and worthy of support (and should be funded by the public).

Even the legendary social physician Thomas McKeown from Birmingham pointed out in the 1970s that this widespread popular opinion is most likely wrong and said in the introduction to his still very readable work “The Role of Medicine: Dream, Mirage, or Nemesis?” [1,2]: If he were St. Peter, he would only allow two types of doctors into heaven, namely trauma surgeons and dentists. Because those would be the only ones who really contributed to a reduction of suffering. The real progress and thus the extension of life span and the improvement of quality of life would not be due to medicine, but to socio-political progress, better nutrition, hygiene and living conditions without constant fear of hardship and death.

Well, that was in the 70s. Maybe it’s different today? We did a very large meta-study to answer the question of how good the data is for medical interventions in general. It has now been published in the Journal of Clinical Epidemiology [3]. I discuss the study and its findings in a little more detail in this blog. For those in a hurry: the data has not changed much. A maximum of 6 percent of all interventions used in medicine, no matter where, are covered by good data.

Only for a limited time (until 08-22-2022) the meta-review is freely accessible at: https://authors.elsevier.com/c/1fIHz3BcJQAobl

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