Covid-19

Courts, Governments, Railway Board, Listen Up Everyone:

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Wearing masks is harmful to health – a new meta-analysis of a total of 37 studies proves this

Just in time for Christmas, the working group led by Kai Kisielinski and Andreas Sönnichsen has made a meta-analysis available on the preprint server Research Square [1] that clearly proves that mask-wearing has harmful health effects. You should take this into account, dear judges, dear members of governments, regulatory authorities, school administrators, responsible persons at the railways, if you continue to make the wearing of masks compulsory. Because you make yourself liable to prosecution for bodily harm. The meta-analysis shows: in all studied parameters, which are physiological indicators of health exposure, the wearing of face masks leads to relatively large, significant and harmful effects.

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Dangerous Rituals: Face Masks – More Harm Than Good

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This is a slightly modified version of an article that first appeared in Nexus magazine and the accompanying text to my contribution at the “Long Night of Masks”, the MWGFD Mask Symposium. 

Face masks are nocebos

Face masks are extremely powerful, ubiquitous nocebos. Nocebos are psychological stimuli that cause harm via psychological, neurological or immunological processes.

Face masks, in fact, trigger fear. This is because their presence is coupled with the message: “A powerful killer virus threatens us always and everywhere! We must all protect ourselves!” This message is a message of fear. For the very first public-media response to the pandemic was to spread fear [1]. Once fear is firmly installed, it is very quickly evoked again and again. The face masks are a visible symbol of the pandemic and psychologically conditioned stimuli that induce fear. And they do so extremely quickly and without our being able to resist them – because the emotional evaluation of sensory stimuli always occurs temporally before the conscious semantic analysis. This is because all sensory channels have an anatomically direct pathway to the amygdalae (sing. Amygdala), the small paired brain centres in the diencephalon responsible for threat evaluation. When “threat” is registered there, the entire mental apparatus is primed to perceive, act and explore behavioural alternatives accordingly. Worse still, involuntary physiology, autonomic processes such as blood pressure regulation, heartbeat and immune modulation, is also affected accordingly.

How quickly and unconsciously this can happen is shown to us by a now famous psychological experiment conducted some years ago:

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The World Economic Forum (WEF) and the “measures” (NPIs) During the Pandemic

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Our new study is published

For some time now, I have been conducting expert interviews documenting how various experts from science, the media, politics and civil society assess the pandemic, what factors they see at work, what their views are. Every now and then the idea comes up, that the World Economic Forum (WEF), which Prof. Klaus Schwab set up many years ago, could play a role.

I thought for a while about whether and how this theory could be tested. In the end, I came up with the idea that one could use the number of Young Global Leaders (YGL) that the WEF has trained over the years as a parameter and relate it to the intensity of “non-pharmaceutical interventions” (NPI), popularly and hereafter referred to as “measures”. This is what we, my colleague Johannes Klement and I, then did, at two points in time: at the beginning of the corona crisis, i.e. in March 2020, and at the second peak in the winter of 2020/2021. The study is now published in the peer-reviewed online journal “Cureus” and can be freely downloaded [1]. (Cureus is an interesting journal, by the way; our immunology survey was already published there [2]. It is a journal based in California and started by physicians who proceed without “conflict of interest” and very openly. It is peer-reviewed, usually with 3, at least 2 reviews.)

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Covid-19 Vaccinations Do More Harm Than Good

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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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Modelling and Model Building

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…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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