Finally, the above sheet was sorted by that reader into a smart spreadsheet which allows you to easily observe which codes had the greatest increase in 2021-2022 (e.g. to sort them or create graphs). For those of you who are data inclined, you will likely want to create this sheet yourself, but for everyone else this is an excellent reference to start with). They were also able to use a script to put about one third of the names for the ICD-10 codes, but since there are fifteen thousand of them, it wasn’t practical for us to manually add in the rest and you will frequently need to directly look up the codes themselves (if a group wants to add the other codes in, I will be happy to repost that).

When Lausen presented the data, for each time period (e.g., 2016 quarter 1), he chose to add two different values together (code_20161 and nocode_20161). As best as I can tell from reviewing the FOIA request and the provided data, these categories represent those who also received a vaccine injury code and those who did not (as a result the majority of Germans belong to the “nocode” category).

I believe Lausen’s rationale for presenting the data in this manner was that a large number of vaccine injuries will go unreported and many vaccine injured patients are thus within the “nocode” category. Conversely, the total number of medical conditions observed in the country is not dependent upon accurate recognition of vaccine injuries.

Separating the patients by (the somewhat inaccurately classified) vaccine injury status is nonetheless a helpful means for evaluating vaccine injuries (I saw a variety of interesting trends in my preliminary examination). However, for the reasons outlined in this article, for the initial spreadsheet presented below, those result are combined.

Additionally, some of the extreme outliers exist because new ICD-10 codes are added each year and thus did not exist prior to 2021/2022. Finally, some of the codes you would expect to have large changes may not show in this dataset if they are codes typically used in a hospital setting as this dataset does not include hospital code submissions.

In the coming days, I know many will use this data to verify our work identifying which codes in 2021-2022 had the greatest increase (you can also do that piece by piece with the already available tool), and then cross reference those to the increases reported in VAERS or other datasets. There is an immense amount to be ascertained here, and I believe it represents the credible evidence we have been looking for since the start of the pandemic to have an objective metric for quantifying the impact of vaccine injury. However, it is also critical we determine which of the observed trends are not due to artifacts within the data.

Lausen’s Presentation of the KBV Data

This is probably the most important graph of Lausen’s presentation. We have all heard stories of individuals dying suddenly after vaccination (I’ve even read a report of an individual who appeared to be in good health making a thump in another room and being found dead shortly after by their spouse).

This issue was recently brought to the public’s attention with Died Suddenly, a documentary that effectively brought attention to this issue, but also had factual errors which were counterproductive for persuading the public that this issue is real. However, while some of the proof that Died Suddenly provided to assert the existence of the sudden death phenomenon could not stand up to outside scrutiny, the same cannot be said of the KBV data.

Additionally, one way that individuals have analyzed the unusual changes in health following the vaccination campaigns has been to assess how far they fall outside of the expected range of variation (this was also done for the final spreadsheet). I did a quick calculation for the above graph and found that 2021’s increase from 2016-2020 was 37.7σ, while 2022’s was 41.0σ. This is quite a big deal (the rarity of an event happening by chance increases exponentially as the σ increases). For context, a 7σ event has a 1/390,632,286,180 chance of spontaneously occurring (it is thought to occur once in a billion years), a 10σ event happens spontaneously once every 5.249e+020 years, and a 25σ event happens by chance every 1.309e+135 years (I was not able to find a reference on the probabilities for the even higher σ events observed here).

Given these numbers, it is very difficult to argue that these events were not caused by something. In this regard, we are also quite fortunate that while the vaccines were rushed to the market over a period of time far too short to establish safety, that process still took a year. Because of this lag, it is possible to refute the reflexively cited counterargument that these changes were due to COVID-19 or the lockdowns, as these only occurred in 2020 (the only possible exception I can think of is that Delta emerged near the end of 2020, but the spike started well before Delta became prevalent in Europe later in 2021).

This is a similar graph to the previous one, but include sudden cardiac death, which as many of you know also “unexpectedly” increased. Many authoritative sources have argued Lausen made a mistake to correlate vaccine injuries with the spike in sudden death because very few vaccines were given at the start of 2021 and thus if a correlation was there, it should have been not emerged until the second quarter.

For context, this was the rate of COVID vaccination in Germany:

As you can see, many vaccines were given in the first quarter of 2021.

COVID-19 is not the only vaccination regularly received. For example in Germany in 2019, it was estimated that 39% of those 65 and older received an influenza vaccination. However, unlike previous vaccines, the introduction of the COVID-19 vaccine caused far more people to require medical care for a vaccine side effect.

Given that Germany has a longstanding practice of evaluating vaccine injuries, this graph makes a very important point. An actual increase in vaccine injuries is occurring and it is not a result of a bias leading to over-reporting; it is a result of the vaccines being dangerous and patients needing medical care for the injuries.

Additionally, an outside team which looked at this data concluded approximately 5% of vaccine recipients subsequently required medical care, which is in line with the 7.7% discovered in V-Safe’s data and required a court order to be released as the CDC understandably did not wish to disclose this information. Note: I believe this discrepancy could be partially explained by the undercounting of vaccine codes highlighted in this article.

The general correlation between these two datasets is important. V-safe monitored 10 million vaccine recipients for a few specific things and was arguably the best surveillance system in place for tracking the side effects of these vaccines as it had a large but defined sample who were provided an easy way to report the chosen side effects. Since one of its key metrics matches the KBV data, this argues that at least some of the KBV data is valid.

Note: I am not sure if this specific dataset is referring to the total number of patients who sought care or the total number of times codes were submitted for vaccine injuries (which would mean a smaller number in total were injured).

Given that there are thousands of ICD codes that I could search the database for (many other increases, such as those of certain cancers, were highlighted in Lausen’s presentation), I had to put some thought into which of those many increases would be the best to show for this article (there were a lot of compelling candidates).

Previously, I proposed a model for the unusual fibrous clots observed in Died Suddenly that revolved around spike proteins causing protein misfolding. In support of this model, I highlighted an observed increase of an extremely rare protein misfolding disease which continues to be reported in VAERS.

Creutzfeldt-Jakob disease typically develop over years and occurs in approximately one in a million people annually, making its occurrence immediately after vaccination rare to the point that suggests causation (and as Jessica Rose noted, new reports are continuing to arrive in VAERS). The increase I proposed was a key point of contention for those who did not agree with my misfolded clot hypothesis, so I was eager to see if a current dataset could evaluate what was occurring.

This increase is also quite large, and for all practical purposes impossible to have occurred by chance (although I will mention the authors who published the original case series linking COVID vaccination to 26 cases of CJD also determined that Delta appeared to have an increased capacity to trigger protein misfolding but I do not believe that can explain the above trend).

Since this article was published, one reader has now attempted to present a longer analysis of this data which shows multiple interesting trends (e.g., many of the side effects commonly attributed to COVID vaccination appeared to have increased) along with raising additional questions about this data. It is my hope others will also do so!

Finally, the presentation on the KBV data proposes a fatality rate for the COVID vaccines. This chart was compiled by Lausen from the officially reported adverse events to the vaccines and likely are significantly undercounting the vaccine fatality rate.

Is This Data Valid?

Following the AFD’s press conference, the leading medical research institute in Germany, ZI, acted as a third party to present a rebuttal of how AFD interpreted KBV’s data. I did not agree with their argument (that there results were an artifact of AFD also requesting for everyone who specifically died in 2021), but did note that their response acknowledged the authenticity of this data.

The primary argument presented by ZI was that since the FOIA request selected for all patients who were vaccine injured in 2021-2022, the rise in deaths observed in 2021-2022 was simply due to the fact anyone who was vaccinated in 2021-2022 could not have died prior to 2021-2022 and thus the increase in deaths observed in 2021-2022 compared to what occurred prior to this time was due to the cohort effect.

On the surface this seems like a credible way to dismiss anyone who would make such an elementary mistake and believe in this data. However, the German authorities have a long track record of attempting to cover up evidence of COVID vaccine harm (in addition to the points discussed above, the German government has been perpetually delaying releasing the death statistics for 2021), so these arguments require a critical evaluation. In turn, there is a few major issue with it:

First, the “nocode” group should not suffer from the cohort effect and it was this group that comprised the majority of the increase in sudden deaths (review the wording of the FOIA request shown above). If the “code” group were to be removed (which potentially suffers from the cohort effect), an almost identical trend would still be present. Lausen presented his data by merging the code and nocode groups together which invalidated this counterargument, and I cannot see how a “cohort effect” is present in the combined data unless KBV failed to fulfill the FIOA in the manner that was requested (Lausen also subsequently provided an interview addressing the government criticisms of his analysis).

An outside analyst also looked at this data and demonstrated that other fatal conditions (which should be vulnerable to same the cohort effect ZI is asserting) did not have the same 2021 spike:

Second, the large σ found for many, (but by no means all) non-fatal conditions in the dataset indicates that something besides artifacts relating to time of death is causing the changes observed. I acknowledge that it is very possible some of the discrepancies present are due to not yet identified artifacts within the data, but at this point in time I have not been able to identify them. I believe that since KBV was focused on debunking the rise in sudden death codes, they did not focus on the rise in other codes for conditions associated with COVID vaccine injuries that were also observed. However, while this was not their focus, this point must nonetheless be considered since it does negate their counterargument.

Additionally, The death argument ZI made was also inconsistent with the death codes in question nonetheless being reported prior to 2021, which they attributed to “coding errors or unaddressed billing fraud.” To some extent this is hand-waving that many others have contested (and something any type of auditing algorithm should have caught years before), but I do not believe it is as important as the first two points.

Conversely, the strongest argument ZI put forward to establish that a cohort effect was occurring for the reported deaths was this spreadsheet. I have not yet been able to discern how the data in it was derived as it does not match the other things I looked at, so I cannot comment on if this is correct (an independent analyst arrived at the same conclusion I did). This spreadsheet is the one source of data that could refute AFD’s argument so I would greatly appreciate any additional thoughts on this one). However, I must also note that if this data is actually correct, it still does not negate the non-fatal complications of vaccination being observed.

A German rebuttal of ZI’s arguments was posted here. AFD also discussed the above rebuttal in a thread here stating:

“Hello all. The death-numbers that we have published are being hotly debated right now. Now the Central Institute for the KBV has joined in has said that the data we presented were quite easy to explain: ‘The data is only for people who have accessed a medical service in 2021 and only such people had been billed and therefore would be in the data and everything in the years before are statistical runaways. There are also different causes of death that were significantly higher in the years before such as I46.9 (heart attack without successful reanimation). ‘ All together 104 000 people have been coded as deaths in the years 2016-2020 of whom the Central Institute of the KBV now says that they were billed medical services in 2021. Now we have a question: If this is really true, we demand an explanation from the Central Institute of the KBV how 104 000 persons that have died in the years 2016-2020 have been billed medical services in 2021.” [Translated courtesy of a reader]

The following was also written in the tweet: “Allegedly, the figures from the KBV are only for patients with health insurance, for whom services were billed in 2021. However, the causes of death were coded for 104,000 patients in previous years. Do we have a data scandal or a billing scandal?”

This table also refutes ZI’s argument that only those who were able to see a doctor and thus were alive in 2021 comprised the cohort of the insurance data.

Many of the German commentators I saw online were also skeptical of the official rebuttals to this data. I was recently sent a detailed summary of the events after the press conference which demonstrated that the rationale for debunking the data changed as time went forward. As best as I can tell, no clear reason was presented for why Lausen’s analysis was flawed given. Instead it was insinuated either that Lausen incorrectly filtered the data (my team and others however arrived at the same results Lausen did) or that there was a data transmission error from the KBV (which is possible but would have had to have been deliberate or inconceivable incompetence).

KBV also issued an astonishing statement refuting AFD’s presentation:

The KBV board clarifies: Based on the billing data transmitted by the KBV to the AfD or. ICD-10 codes cannot be used to establish causal relationships between COVID-19 vaccinations and deaths. From the KBV’s point of view, the increase in deaths shown in quarters I-IV 2021 and quarter I 2022 is largely pandemic-related mortality. This once again illustrates the importance of COVID 19 vaccination as an effective measure to prevent serious forms of progress up to deaths. Without the vaccination, mortality would probably have been much higher.

This statement also cited the previously referenced ZI letter and another one which noted:

•The codes in this database do not include codes entered on death certificates and thus cannot be assigned as the cause of death [however all of these codes cannot be entered unless the patient died; also as the statement above shows KBV is admitting an increase in deaths did occur].

•The codes in this database cannot be correlated with vaccination status because many people received the COVID vaccines in settings that did not result in codes being submitted and coding for COVID vaccination has not yet been included in the dataset due to special regulations [I agree with this point, but it fails to refute this dataset since Lausen chose to combine the code and nocode groups; instead, it simply argues that vaccine injuries are underreported in this dataset].

•This database was not created for the purpose of conducting medical research and therefore no conclusion can be drawn from it [I don’t believe this is a valid argument; a lot can be inferred from it and it is the best available database we have, so until the government chooses to make a better database available, it is “the evidence.” Additionally, this is a very similar argument to what is said for VAERS, but unlike VAERS there is not an over or underreporting issue present with this dataset].

I do not believe any of the above points refute the sudden increases in submitted billing codes (hypothesized to correlate with vaccine injuries) that occurred a year after the pandemic started at the exact same time the vaccination campaign began. However, I also believe some type of not yet identified artifact could account for at least some of what was observed and I have spent the last week revising this article to account for the additional information I come across. If anyone can provider a stronger refutation of the data presented here (preferably, at the pinned comment), I would greatly appreciate it. We need to help each other stay honest and I will gladly retract this article if a critical mistake was made.

Note: An independent analyst who reviewed the entire dataset found that it showed many other signs of being plausible.

Conclusion

Given the extremely concerning implications of the German data, it is not surprising that governments around the world and healthcare systems or insurance providers have been reluctant to release their own data. It is my sincere hope that this release will open the flood gates to additional disclosures and I am in complete agreement with the conclusion of this presentation:

I wish we had an American political party stating the same. There are signs of hope however; today Ron DeSantis did something incredible and requested a grand jury against Pfizer and Moderna, an essential step a few leaders in our movement have been working to lay the groundwork for over the last two years. I am also hopeful that this grand jury will compel the state of Florida to release similar data that can be used to assess the safety of these vaccines.

I strongly encourage those of you who who are able to begin looking through the KBV dataset and identifying important trends that can be correlated to other observations we have made over the last two years. I believe there are many excellent articles that could be written on them. I sincerely thank all of you for your continued support!

My primary goal is to draw attention to this data so numerous independent parties can objectively analyze it and independently verify if the trends it shows correlate to “controversial” increases observed in other datasets like VAERS. This data is extremely important as it is the only access we have ever been given to observe the changes in illness that follow the COVID vaccination campaigns. I also suspect the most important use of this data will be to establish causality for specific vaccine injuries.

This matters because typically when someone suffers a pharmaceutical injury, it is not acknowledged by the the government and the courts because “there is no evidence the product is associated with that injury,” and as you might expect, the pharmaceutical industry work tirelessly to make sure the evidence that could implicate their product never emerges.

At this point I’ve lost track of how many sad instances I’ve seen where this happened to a medically injured patient (in some cases to the point the gaslighted victim gives up and ends their lives), so I greatly support having an independent means to assess causality for vaccine injuries. Those injured by the COVID-19 vaccines are profoundly suffering and they really need help (on the bright side however, recently Senator Johnson and shortly after Governor DeSantis gave a voice to these victims).

Postscript: It appears a similar rise in unexplained deaths is occurring in Canada.