We all know that numbers and statistics are not everyone's cup of tea.
Many are happy to leave the survey and analysis to experts and trust the results presented.
But you are not always well advised.
For example, in the case of the death figures published by the Federal Statistical
Office during the Corona period.
We all witnessed how the office announced a significant excess mortality rate
after the outbreak of the crisis in 2020.
Fuelled by the media, it was clear to most people that the pandemic was claiming victims and that anyone
who wanted to protect themselves had to be immunized.
The fear generated had an effect.
The situation will be different in 2021 and 2022.
The large-scale campaign had achieved its goal.
The majority of the population was vaccinated.
According to official statements, there were no longer any
irregularities in the mortality rates.
However, as the business information scientist Marcel Barthes discovered, this is an outright lie.
Quote He painstakingly compared raw data and came up with astonishing results.
He discovered that, contrary to official statements, there was no excess mortality in 2020.
In the following two years... ... not only an alarming number of people died,
but also a strikingly high number of men and women of middle and younger age groups.
For the first time, the death rate also showed an unprecedented, completely
atypical course in relation to the seasons.
Marcel Barthes shows the strange mistakes that the authorities make
when dealing with the official figures.
How can it be, the author wonders, that public communication
deviates so far from the raw data?
Watch the video lecture now and spread this important information in your environment.
The death process in the raw data
A video contribution by Marcel Barz (Jüterbog, November 20,
2023) About this video... The official data from the Federal Statistical Office show beyond
doubt that there were thousands too many deaths in Germany in 2021 and 2022.
Worse still, for the first time the conspicuous death rate is also affecting younger age groups.
Why don't we see, hear or read about it in the media?
What deadly danger has befallen our population?
What role does the Federal Statistical Office in Wiesbaden play?
In this presentation I will show the official death figures and the
strange mistakes our authorities make when publishing these figures.
We citizens are not honestly informed about the death process.
Yes, my name is Marcel Barz.
I am 48 years old.
I come from the Berlin area, from the small town of Jüterbock to be precise.
And I am a qualified business IT specialist.
In other words, I deal with data and numbers.
And because I always do it so precisely and do the math again, I've earned myself the title
of bean counter, which I'm quite proud of.
So, the topic is supposed to be the death events in the raw data.
By raw data I mean unaltered data.
So I don't look at ready-made press releases or graphics, but I get data packages, as
detailed as I can get them, and then I start calculating totals and
creating graphics and so on myself.
So that's what I mean by raw data.
And when it comes to dying, that's what it's all about...
The question is, did a lot or a few people die in Germany?
So not only the number of people, but also to assess whether there
were more than usual or fewer?
Nothing more. That is the question.
And because some people are a little irritated when I talk so casually about the dead, I
want to say straight away that I know there is also an emotional level here.
So anyone who has ever lost a relative knows what I mean.
But I'm really only looking at these figures as a scientist.
And today these are just mortality figures and tomorrow they may be physical figures
and data and geographical and so on.
I'm really just a statistician.
Please understand what I am saying here.
A friend recently said to me, why are you actually doing this?
You sit there for weeks and rummage around in the raw data.
This is all confirmed knowledge.
So you just have to google it, then you know exactly how many people
died and whether it was many or few.
There are experts who do nothing else all day, who are
paid to determine this data.
Why should we doubt that something is wrong, that mistakes are being made?
And three or four years ago, I myself would have...
I would have thought and spoken the same way three years ago.
But in the meantime, so many things have simply happened to me that I have to say
today that mistakes are being made here.
And that is actually the central message of my presentation.
The deaths were not depicted as we see them in the raw data.
And I want to prove this and I want to say it right now.
I want to say that right away.
Conclusion of the presentation.
What I see in the raw data, i.e. in the raw data on the number of deaths, is completely
the opposite of what the public thinks.
And this is actually the case, i.e. the death rate in 2020.
Public opinion is that we had a conspicuous number of deaths here because of the pandemic.
And I myself was convinced that it was... Is in 2020, and there we
are also talking about people's behavior.
Yes, I myself was afraid of dying, afraid of the virus.
And when I look at the raw data, I see exactly the opposite.
Not only do I not see any anomalies in 2020.
I even see historic lows in the number of deaths in 2020.
And the question is, how can this be so far apart?
What can be seen in the data and what the public thinks about it.
And I will, that is my assertion, but I will also justify
it in detail in the presentation.
And not just that it was like this in 2020.
In 2022, and I'll take this as a second example, it will be exactly the
opposite of what the public thinks.
So while the public opinion is that there are no longer any abnormalities in the number of deaths.
And accordingly, people's behavior is also normal.
Nobody talks about death rates any more.
We are talking about Ukraine, we are talking about Israel.
There are completely different topics.
Corona is so yesterday.
And when I look at the mortality figures, I see these anomalies.
Yes, what many thought they would see in 2020, I now see in the death figures.
And the dramatic part.
I also see these anomalies in the younger age groups, which I have never
seen such patterns in the past.
But now I'm seeing a noticeable increase in the number of young people dying, and that's really dramatic.
And the question is, where does the public come from, or let's say, where
does this raw data actually come from?
What is my source for saying that?
So I'm going to get these.
The raw data from the Federal Statistical Office.
Yes, there are databases where anyone can access and download the data.
And there are mortality tables at the Federal Statistical Office, freely accessible to everyone.
This is my database for the raw data.
And if you ask yourself now, where does public opinion actually come from or where is it made?
Then you think, of course, it's the media reporting what's happening with death.
But the media don't make up the death figures; everything that is
published about deaths is actually what the Federal Statistical Office
- and here we are back to the same authority - tells the public.
So the Office's public communication through press releases and special
evaluations and press conferences is ultimately what the media disseminate.
And what the public thinks.
And the question now is actually, how can it be that the public communication in the
Federal Statistical Office deviates so far from the actual picture in the raw data.
And I know that this is a serious accusation that I am making here.
But I will now explain this in detail.
And secondly.
On the other hand, I will now justify this in detail.
This is the result of two years of intensive research, lengthy exchanges with the responsible
department heads at the Federal Statistical Office, several requests under the Freedom
of Information Act, press inquiries and so on.
So, if you now think that what I am saying here is true and you already knew
that, then there is really no reason to listen to this lecture.
But if you think what I'm saying here is nonsense, if you're outraged by this
claim that I'm making here, then I'd ask you to take
a look at it and refute me.
Do the math, it's easy.
I'll show you where to get the data and how to check it.
Do the math and if I've made a mistake, tell me.
I'll also write my e-mail address under here.
I am open to any discussion.
But if you do the math and realize that what I'm saying here is true, then please
don't write to me, but directly to those who are responsible for it.
Because I believe that the only way to change things here is through public pressure.
Because I have been trying for two years now to bring clarity to the situation
and talk to those responsible.
It doesn't lead anywhere.
The only way is through public pressure.
So please prove me wrong, check and do the math yourself.
And don't let fact-checkers stop you from believing this.
to carry out a review.
I'm sure the fact-checkers will come back and talk everything I say here to pieces.
Check for yourself.
Don't let this stop you from doing the math.
Okay, so that was the indictment.
Now I come to the evidence.
I would like to go through these five points here and start with
the selection of key figures.
So if you are interested in the death rate in Germany, you will automatically end up at the
special evaluations of the Federal Statistical Office and the press releases.
And when we look at the key figures used, we see absolute figures,
the number of deaths per year.
We see percentages for excess mortality values, for a month or for a year.
And if you take a look, you will also find the key figure of life expectancy
and you will find mortality rates.
It is very different.
And the question now is which of these is useful or which is meaningful in order to make
a judgment about the death rate in Germany.
And I'll start with the death figures.
If you only look at the number of cases, i.e. the number of coffins, if you
like, then you can easily be misled.
So if the population is constantly growing or if the group that is growing, the group of
older people, the over-80s, is growing strongly and you only look at the number of deaths,
then you can't assess the death rate or you get a distortion and perhaps have
a completely wrong picture.
That's why I wouldn't favor the key figure.
And let's take a look at excess mortality.
To do this, we need to take a look and understand this concept.
So excess mortality, that's a very good question.
Excess mortality simply means that more people have died in a given period than expected.
If I do the math for our town here, for example, I expect 100 deaths in Jüterburg this
year and at the end of the year there were 102, I have two more deaths than expected, so I
have two percent excess mortality.
So it's a pretty simple concept.
The excess mortality in percent simply indicates this delta, so to speak, between
the expectation and the reality.
And what is the expectation, i.e. this predicted value?
I'd say that's something of a crystal ball.
This prediction for Jüterburg, for example, was 100 dead and the
reality is the number of coffins.
And excess mortality is, so to speak, the deviation of the crystal ball from reality.
That is the concept.
And crystal ball does not mean arbitrariness or hocus-pocus, but crystal ball in the sense of, yes, these
are mathematical models that have become established and that look at
the historical mortality figures.
But it is important to know that there is not just one glass
ball, but several glass balls.
And this one, for example, is the WHO's crystal ball.
And the next one here is the Euromomo crystal ball.
This is a different crystal ball, which produces different expected values and therefore
different excess mortality values.
This sphere here, for example, is, let's say, the one from the actuarial associations.
And the one at the back here is perhaps the one from the Federal Statistical Office.
And to show how such a crystal ball works, I'll take this crystal ball from the Federal
Statistical Office as an example and simplify the principle so that
everyone can understand it.
So the crystal ball from the Federal Statistical Office, I have one here, it's
called the median method and it works as follows.
There is now an employee at the Federal Statistical Office who is talking
to a crystal ball and saying, tell me what you expect for Jüterburg.
We are now in the 40th calendar week.
Crystal ball, what is your expected value for this week?
And then the crystal ball says, I might have to say, that's what the Federal Statistical Office does.
So there's an employee, we'll call him Felix for now, and he has this conversation with the crystal
ball and says, what's your expected value?
And the crystal ball says, Felix, why don't you go to the actuarial associations, filing cabinet
and get last year's folder for Jüterburg and see how many died in the 40th calendar week.
And then Felix does that and says, eleven, okay, says the crystal ball, go back to the cupboard
and get the folder from the year before last.
How many were there in the 40th calendar week?
Okay, nine dead.
You do that again from three years ago the folder and from four years ago, okay, eight and thirteen.
And now, says the crystal ball, find the smallest number.
Okay, that's the eight and you delete it now.
Okay, and you delete the biggest one too, that's thirteen.
And then the numbers nine and eleven remain.
And the value that is exactly in the middle between nine and eleven, i.e. ten, is the
expected value for Jüterburg for the 40th calendar week calculated by the crystal ball.
Yes, this is how the median method actually works in civil society.
It's a pretty simple principle.
And now Felix is exactly the person at the Federal Statistical Office who gets the
actual death figures on the table.
So the reality, yes, the number of coffins is counted.
And now it turns out there are eleven.
So the crystal ball said ten and the reality was eleven.
And now Felix says, oh, here's a deviation.
The crystal ball said ten and the reality was eleven.
The crystal ball is always right.
Something must be going on in reality.
Who knows what it is?
Is it the hole in the ozone layer, climate change?
He doesn't know what's going on.
He calls the RKI. Ah, Corona.
Okay, then we know we have an increased mortality rate, says the crystal ball.
And it's because of Corona.
Now he does the math, one more death for every ten.
So instead of ten, there are eleven.
So he has ten percent excess mortality.
And that is an alarming figure for Felix.
And now he's doing what he has to do.
He must inform the public.
Yes, that means he's now doing press releases, writing the special evaluation, press conferences and
saying ten percent excess mortality in Jüterburg in the 40th calendar week.
And now come the journalists.
And say, oh, what a story.
Ten percent in Jüterburg in just one week.
And then there are headlines.
If this goes on for ten weeks, the whole of Jüterburg will be extinct.
Or is the AfD to blame?
Lockdown for Jüterburg.
We have to deploy the Bundeswehr internally and so on.
You can imagine the headlines.
But it's actually all because the crystal ball said ten.
And if Felix had used a different crystal ball, he might even have measured under-mortality
in Jüterburg, depending on his choice.
And if he had then done the same, informed the public, then the
media representatives, then the headlines might have been.
Under-mortality in Jüterburg is due to the great policies of the SPD or the Greens.
You can imagine how the media representatives will react.
And it all depends on which crystal ball is selected.
And once you understand that I can turn the value of immortality in any direction by selecting
the sphere, you realize that this concept doesn't actually go in that direction.
Is not suitable.
At least not when I have such a selection of balls.
Just to say that there have been sophisticated methods for doing this in actuarial
mathematics for 200 years.
And that's what the Aktualvereinigung's crystal ball is for.
But if the big institutions don't adhere to it or don't use it, but introduce something like
this, this media method here, and a certain arbitrariness arises as a result,
then this concept is simply of no use at this point, it doesn't help.
And that is precisely why I consider this concept of excess mortality to be unsuitable
for assessing the death rate in Germany.
What about life expectancy?
I think that life expectancy is an excellent indicator
for assessing mortality.
But it is a little more difficult to understand, a little more difficult to calculate.
So this is a key figure for professionals.
Yes, I think that we ordinary people should work with the mortality rate.
What is the mortality rate?
Quite simply, it only indicates what percentage of people who have lived have died.
So if you have 100 people and two of them die, then we have a
mortality rate of two percent.
It's a very simple concept.
So, the selection of key figures has now fallen on the mortality rate.
That would be my recommendation, because it is so easy to calculate and
because it is so simple to understand.
Okay. The Federal Statistical Office hides important raw data, I have called it.
So, having just understood that the mortality rate is really
good for judging mortality.
So, having just understood that the mortality rate is really
good for judging mortality.
I had a look at what the Federal Statistical Office is doing.
And I have seen that they use mortality rates in their specialist publications,
for example in this one.
Yes, and the important thing about the mortality rate is that we need it down to the week.
Yes, just like here, one value every week, 52 values per year.
And not across all people living in the country, but across age groups. Yes.
because everyone has a different mortality rate.
So younger people have a lower mortality rate because they die less often and
older people have a higher mortality rate.
And that's what the Federal Statistical Office does.
For example in this publication here I was very pleased.
And then I wanted to look at the mortality rates and went to the database of the Federal Statistical
Office, the Genesis database, and saw, oh, there are no weekly mortality rates for
individual years of age there at all.
Okay, then I asked the Federal Statistical Office directly, so I made inquiries, freedom
of information requests, and said, please send me the mortality rates for the last few years.
And the answer was then, after consultation within the company, we answer you thus,
the information you requested is not available to us, so that
Your application is to be filed.
I was wondering, because you have the data.
You use them yourself in your publications.
Why can't I get them?
And then I saw that you can derive the mortality data, i.e. the data
we have here, the weekly mortality rates, from other tables yourself.
So there are the mortality tables at the Federal Statistical Office and
there is the federal health reporting.
So you can derive the mortality rates from other tables.
I am also happy to make the mortality rates available for download here.
Anyone can take a look.
What we need are the weekly, age-specific mortality rates for Germany.
That is the exciting key figure.
Okay, so now we've reached the point where we need to take a look at the mortality rates.
And we don't even start calculating, as we did with the crystal
ball earlier, we just record it.
We simply have Excel create graphs to show the mortality rates.
And then we see if we can see anything in it.
As I said, the mortality rates of young people are lower than the mortality rates of old people.
My mortality rate, for example, is 0 , 2.
This simply means that the mortality rate of 48-year-olds is 0.2.
That means 0.2 percent of 48-year-olds, all 48-year-olds in Germany, die every year.
That's just the way it is.
And if you look at the 70-year-olds now, we're already at a 2 percent mortality rate.
And 5 percent of 80-year-olds.
And for 90-year-olds, we would probably be at 20 percent.
If we had 180-year-olds sitting here now, so to speak, 5 percent would be gone in a year.
Next year, there would be 5 percent fewer people sitting there, so to speak.
And you may know this from class reunions.
So the longer it's been since school, the fewer people come, and then to the class reunion.
That's how it is.
And if I now want to show this graphically, the mortality rate of an age cohort, for
example the 82-year-olds here, then we can see that it is now around 5 percent.
Conclusion, minus, approximately.
But we can also see that it has changed over time.
So 30 years ago, the mortality rate for 82-year-olds was much higher.
It was once at 7 percent.
At 10 percent and even higher.
So the mortality rate has continued to fall over time.
And not just for 82-year-olds, but for all age groups.
And we see a minimum somewhere around the year 2000.
There was a low point and then the mortality rate went up again.
I always say they're one of those field hockey sticks.
So mortality rates.
So not everyone looks like this, but many age groups look like this,
they are field hockey sticks.
So now the question was, how can you, there are 100 of these field hockey sticks, so to speak,
because we have 100 age groups, how can you represent them in a graphic?
And the idea was to take a table and write the years at the bottom and the age
cohorts on the side, on the Y-axis.
And now for my age group, for the 48-year-olds, I have written in
the mortality rates for each year.
And if you fill out the entire table, it's almost impossible to keep track of.
Then there are so many numbers with five decimal places.
But now you can display it in color.
You can tell the computer to display the highest number in dark blue and the lowest death
rate per age cohort in light blue.
And if we do this for all age groups and for years, then the graph looks like this.
So we no longer have to talk about individual figures here, we are
now looking at color samples.
We look where there are stains.
And you can see immediately with the naked eye, so on the left, 2010, 11, 12, it was very dark, which
means that the Grim Reaper was really active.
And if you look at 2019, 20, it was bright.
In other words, the Grim Reaper was on vacation.
That's all it means here.
And what surprised me a little is that it also affects the younger age groups.
So here, too, we have these falling mortality rates among the younger age groups.
And when I think now about how I behaved in 2010, for example, when the Grim Reaper was
very active, even in my age group, I didn't think about dying at all,
but instead drove down the highway at 150 km/h with a coffee in my hand.
And here, when the Grim Reaper was on vacation in 2020, I sat in front of the TV and almost
chewed my fingers off for fear of dying because I saw the pictures from Bergamo.
In other words, this behavior that we have is actually
related to media coverage.
And I see that they obviously, or here you can just see how much the reporting deviates
from what you see in the raw data.
Because it has to be said, these are not calculated values or theoretical values,
we are talking about facts here.
That is absolutely objective.
The mortality rates are objective. Objective values. Okay.
Now the idea was, how could you combine this, or the question, how could you combine this complex
graphic here with these color spots in a diagram?
So how can you turn 100 mortality rates back into one?
And there is the method of age standardization, i.e. established, you can google
how it works, I won't go into it now.
But this makes it possible to merge 100 different mortality rates back into one.
And it looks like this.
This is the diagram here.
And the thing is, if we have 100 field hockey sticks and bring them together, then this
graphic is of course another field hockey stick.
So again you can see in the left-hand section, we come from way up with the mortality rates,
the mortality rate has fallen, there was a minimum and then it rose again.
And you may have noticed that earlier we always had annual mortality rates, so in this big
blue, colorful graphic there, or not colorful, but with this spot graphic, this color
chart, every single field was always a year.
And now we have here, we are already a little broader and now have four per year, we are now
going here on a quarterly basis, so to speak.
We zoom in, so to speak, we actually zoom in, from annual values to quarterly values and then we
zoom in even further and go to weekly values.
But here, too, you can now see where the minimum was.
So the minimum was somewhere between 2019 and 2020.
As a historian, I would also describe the year 2020 as the historical minimum.
19 and 20 are the minima. Or is the turning point in mortality rates.
Because what you also have to know is that the further I go to the left,
which is now cut off here, the higher the mortality rate climbs.
So there is a historic low and we had that in 2019 and 2020.
Okay. I have changed the map.
I took the liberty of posting this graphic on Twitter or X recently and wrote in
it, here, up there, we celebrated life and became European soccer world champions.
And there was actually an enormously high mortality rate, definitely much higher than we had
in 2020, when we were sitting in front of the TV in fear and in lockdown.
Yes, fear of the death rate.
Although the mortality rate was at a minimum then.
And now that mortality rates have gone up again, we are back to normal
and no longer think about dying.
So that's what I mean by complete
A mirror image of how we behave and how
the public thinks about death.
This can finally be shown well in this graphic.
Well, and I said we'd zoom in further, because this graph we have here, these
annual values, are of course far too rough.
It gets interesting when we go to a weekly presentation, because then
you can see seasonal fluctuations.
And that's what I've done here for the years 2016 to 2020.
This spaghetti graphic is a bit confusing.
But we see the weeks from 1 to 52 below.
It is actually the same graphic that was shown earlier in the technical article
from the Federal Statistical Office.
Except that I'm taking them all over here now.
And that I'm laying them all on top of each other here, the years.
So below are the weeks.
And we have the mortality rates again.
And only for the age group.
I can't read it here now.
I think I had taken 80 to 85-year-olds.
Just for this one age group.
And of course we see the outliers.
We see mountains here, for example.
You can see this red mountain, for example.
Or the blue mountain at the back.
But if you look at the legend below, this is not the year 2020.
So the year 2020, when we were in lockdown, when we had Bergamo, when
we saw lots of pictures of coffins on TV.
And, as I said, I was very anxious myself.
We were in this blue curve here, in the 14th, 15th, 16th calendar week.
And if you take a look now, what anomalies are there?
So I don't see any.
In fact, the blue curve and the orange curve here represent the historic
low in mortality rates in 2019.
So if I were to draw in all the years here, the spaghetti
would keep moving upwards.
The lowest curves at the bottom are the orange and light blue curves.
So the overall level for the year is at the minimum, so to speak.
But even the weekly swings are not worth mentioning.
I find all the outliers in other years, but not in 2020.
So even at the weekly level, I can't find any anomalies at all in 2020.
So, to point 4.
And we are now looking at the mortality rates in 2021 and 2022.
And that is also the most important point for me.
We have already seen in this chart that mortality rates are rising again in both years.
Yes, the field hockey stick so to speak, now it's time to go up again.
And I'm not going to make any assumptions as to why that is.
I just want to make it clear that there is something going on in the mortality rates.
We can see it very clearly.
And it is interesting if we now zoom in at weekly level.
And that is precisely this graphic.
And now I haven't put the spaghetti on top of each other, so to speak, but one behind the other.
So from 2016 to 2023, so to speak.
And I have it even further back.
But I cut that off first so that you can see something.
And these blue zigzag lines are now the mortality rate.
And only in the 45 to 50 age group.
And we see a fairly constant pattern.
Yes, it sometimes breaks out at the top and sometimes at the bottom, so to speak.
But it behaves relatively evenly.
Yes, always the same pattern.
In winter they are just a little higher.
And in summer it goes down a bit again.
Up again next winter.
And if we were to extend this to the left, it would always
result in this pattern.
And now comes the worst pandemic of all time.
And you can already see that.
It looks like this.
And at first glance you say, it's actually the same as always.
The black line is the seasonal trend, so to speak.
And we are moving in the same direction in 2020 as in previous years.
We even had one downward outlier, which is historic, so to speak.
But I don't see any noticeable pattern.
And now the years 2021 and 2022 have been added.
And that is a pattern that has never been seen in this
age group in all the years before.
So we can clearly see that the blue line remains far above the black line.
The black line barely penetrates and large areas are created.
And that simply means that a lot of young people have died here, from some kind of danger that's
out there that needs to be investigated now.
We have never had this mortality rate in this age group before.
A very clear alarm signal.
And I'm surprised that nothing is happening, because this pattern didn't exist before.
And it only points in one direction, namely upwards.
And let me take another age group.
Let's take a look at the 50- to 55-year-olds.
And we actually see the same picture.
Always similar spikes in the mortality rate.
And now comes the pandemic, 2020.
And we actually see a below-average mortality rate in
the 50 to 55 age group.
So other years were much better.
And now I'm showing the years 2021 and 2022.
And we see the same as in the other age group.
A very clear pattern, which is far above the seasonal trend.
In other words, thousands of people have died here in the young age groups who would
not actually be statistically eligible.
It is a very clear alarm signal.
And I don't hear anything about it.
And the problem is that if you only look at the population as a whole, this alarm signal
is masked by the deaths of older people, because the mortality rates there are simply much higher.
Then you can't see it at all.
You have to look at mortality rates and you have to look at them week by
week and you have to look at them per age group.
You have to look at the vintage or small age group picture.
And then this signal cannot be overlooked.
And since the Federal Statistical Office does this, looking at mortality rates week by week,
as I have shown in the publication, I am surprised that this alarm signal is not made public
and that it is concealed from the public.
I now come to the conclusion.
I think I have been able to show why mortality rates are suitable for assessing the incidence
of death, because they are easy to understand for everyone and are therefore
so well suited for public communication.
Because we have a feeling for what percentages mean.
Two percent, five percent, we know that from everyday life.
And that's why I think we should look at mortality rates.
Mortality rates are objective data.
These are facts, so to speak, that you can't...
There is no calculation, there is no model behind it, but this is how we measured
it, this is how we observed it.
And mortality rates are available from the Federal Statistical Office.
And just because they are not published does not mean that they are not there.
So we should demand the mortality rates from the Federal Statistical Office.
And the reason why the public had the wrong picture of the death rate in 2020 is
simply because the Federal Statistical Office chose the wrong key figures for
public communication, namely death rates and excess mortality rates.
I think I explained quite well that death rates distort the picture when the
population changes, i.e. when the population grows larger and when the population gets older.
And with excess mortality values, if they are calculated using a questionable method
such as this median method, then they also result in inflated
values for an ageing population.
And if we only ever communicate these two key figures, which is what the Federal
Statistical Office does, then it's no wonder that a false impression is created.
And that alone is actually a scandal, that an entire country is being thrown into panic without any
basis, without any foundation in the raw data.
But what we are now experiencing in 2022, that we are not hearing this conspicuous pattern
among the young cohorts at all from the Federal Statistical Office, I think that is almost criminal.
Because when I see these anomalies here, the Federal Statistical Office also sees them.
And the fact that you don't hear anything at all raises a lot of questions.
Because if the Federal Statistical Office were to say, hello, there's a clear anomaly here,
that would have an impact on policy.
And I don't understand why you don't hear anything.
Because the publicity that the office generated in 2020 with press conferences and so on, and all
this alarmism, would now have a real basis in the data and you hear nothing
from the Federal Statistical Office.
So there is an urgent need for clarification here.
And as I said, I would ask for support to ensure that those responsible are called to account and
that we see the mortality rates of young people.
Good. That concludes my remarks.
Thank you for your attention and I wish you all the best.
Take care, Marcel Barth.