FIRST, DO NO HARM!

For a good decision it’s necessary to be well informed. Therefore I will lead you through some mindsteps in this document which I really find important. Even though this report is about germany you can take this process as a guideline for your own country. At first an individual has to assess the risk of a disease (in this case Covid). Not by media and how big the numbers are or how deeply

!!!RED!!!

the background is priming and framing you with pictures from hundreds of dead people but about rational calculations. As a second step, which is the primary focus in this document, someone has to assess the risk for Adverse Events (AE) especially Severe Adverse Events (SAE) from the medicine (in this case the Covid-Injections). A third step is to weigh the risks against each other to get a benefit-cost-analysis. It is crucial that the individual cohorts are taken into account. There is no “one fits all” solution. One dimensional thinking isn’t appropriate to tackle such a complex problem. Clustering by age alone also can’t fix this problem (https://twitter.com/Thomas_Wilckens/status/1491762388617551876). Even after clustering the “right” vulnerable group a decision must always be well informed and free. First, do no harm!

Risk of Disease

The Population in Germany counts 83.2 million people in 2021. Nearly half of that are womean (50,7%) and men (49,3%) (https://www.destatis.de/DE/Themen/Gesellschaft-Umwelt/Bevoelkerung/Bevoelkerungsstand/_inhalt.html). Over approximately 2 years there were 12,391,463 recognized cases of Covid of which 119,939 died as of 14.02.2022 (https://coronavirus.jhu.edu/data/mortality). Sadly we still don’t know in how many cases Covid was the reason. Maybe we will know in the future (https://www.bild.de/politik/inland/politik-inland/corona-patienten-sollen-endlich-richtig-gezaehlt-werden-neue-datenerhebung-gepla-79099740.bild.html). Acutally we must claim that not all death were due to Covid (https://www.berliner-zeitung.de/news/falsche-daten-viele-corona-patienten-liegen-nicht-wegen-corona-im-krankenhaus-li.208423). But if they all were due to Covid the Case-Fatality Rate is 0.97%. This means that, the risk of an individual to get Covid is (historically) 14.89% and to die from Covid is 0.14%. Digging deeper shows the distribution by age and sex.

Death_Covid_by_Age_and_Sex


Chance of dying with/because of Covid by age group and alternative death ratios:

Death_Covid_by_Age_and_Sex2

Note: Marginal diff. in cases and death to J. Hopkins because of little diff. in Time.

Median and mean Age for Death in correlation with Covid and Historie:

Death_Covid_by_Age_and_Sex2

Long Covid:

An argument which often occurs in this context is long Covid. “I don’t fear to die, I fear to get long Covid”. In this document I will not dig deep into this topic. I think there are a lot of good studies out there.

I just want to share the next illustration. For me it seems like LONG COVID is another instrumentalized topic:

Long Covid Kids: Death_Covid_by_Age_and_Sex2


Risk of Adverse Events

Pfizer

Any medical interventions must first be proven safe. “The vaccines are safe and effective”. Firstly, Pfizer’s own documents don’t seem to conclude that.

Canadian Covid Care Alliance


Efficacy of the mRNA-1273 SARS-CoV-2 Vaccine at Completion of Blinded Phase Efficacy of the mRNA-1273 SARS-CoV-2 Vaccine at Completion of Blinded Phase

VAERS

If you look at the chart above you can see that 2021 is a really big outlier and even 2022 is in mid-february already as high as the expected amount of 2021. Now we have to ask two questions.

  1. Are there really more adverse events in 2021 and 2022 than in 2020 or are they just in this passive surveillance system?
  2. Are there more reports in this system because of the high attention in the public and the higher share of fearful persons?

Even though the passive surveillance systems are highly underreported, as usual in every year, it’s plausible that in this range of underreporting there could be really an increase in reporting for Covid-Injections in the last 2 years. So only a simple comparision over time is not really meaningful. But if there is more attention and more fearful persons who reported Adverse Events we would expect that this increase would fluctuates in ranges over all Covid batches.

In the chart above you can see extreme differences in reports relating to Covid batches, between manufacturer and within manufacturer. That’s not what we would expect relating to our first 2 questions. We would expect an increase in reports but a variability which is seen in all batches like the flu-vaccine for example (https://www.bitchute.com/video/8wJYP2NpGwN2/)

Now there could be another reason for this extreme differnces in variability.

  1. Are there a lot of typos in the Database(VAERS)? After correcting for typos, is there a more stable and expected variablilty picture?

Note: All AstraZeneca batches have equal or less than 5 records.

After correcting for typos the picture got a little bit clearer but it keeps the pattern of extreme differences between batches and manufacturer. Before digging deeper into this subject here is the plot for Severe Adverse Events (SAE).

The pattern is pretty the same. This is what must be exptected based on the AE charts. More AE leads to more SAE in absolute numbers (more dosis more damage). To get this into perspective we need now the batch size from the manufacturer to measure ratios. Sadly we don’t have valid data for all batches in history. But what we can measure is the ratio of toxicity. This means we divide SAE by AE per batch/lot. Independent from batch size we expect a stable toxicity range for all batches. The Next Table shows toxicity ranges for 4 different cases (AE > 10, AE > 25, AE > 50 and AE > 100).

##      TOXICITY_10         TOXICITY_25         TOXICITY_50      
##     "Min.   :0.03846  " "Min.   :0.03846  " "Min.   :0.2143  "
##     "1st Qu.:0.51485  " "1st Qu.:0.51282  " "1st Qu.:0.5258  "
##     "Median :0.58261  " "Median :0.57627  " "Median :0.5719  "
##     "Mean   :0.57405  " "Mean   :0.56309  " "Mean   :0.5636  "
##     "3rd Qu.:0.65657  " "3rd Qu.:0.63399  " "3rd Qu.:0.6030  "
##     "Max.   :0.94444  " "Max.   :0.82857  " "Max.   :0.7544  "
## IQR "0.14171"           "0.12117"           "0.07722"         
##      TOXICITY_100     
##     "Min.   :0.3488  "
##     "1st Qu.:0.5203  "
##     "Median :0.5576  "
##     "Mean   :0.5504  "
##     "3rd Qu.:0.5828  "
##     "Max.   :0.6964  "
## IQR "0.06245"

Note: Severity is calculated as: If a person was in hospital, had life threatening, disability, was in emergency room or died. If it matches one criteria it is one severe case.

As you can see in the table above. Batches with < 25 records are, as expected, more sensitive because of the base effect. Here is the plot for severe cases from batches with > 50 records.

That looks pretty stable right? But at first lets make the Y-Axes more clearer:

There are Batches with ~35-47% toxicity and there are batches with ~55-75%. This means a range of 35% to 75% so a difference of up to approximately ~40%. So lets calculate the p-value for the difference in batches between Pfizer and Moderna (I don’t choose Janssen because there is only 1 report per batch > 50 - XE395) based on the first toxicity measure.

We can see in the chart above that there seems to be no significant difference in toxicity (based on this measure) between Pfizer and Moderna. Later in this report I look within the same manufacturer UPDATED: Within manufacturer (Pfizer)).

We can’t reject that batches between Moderna and Pfizer are significantly different based on toxicity ratio right now. But later in this report we have to reject that batches from Pfizer and Moderna are equally within the manufacturer. This reminds me a little bit of (https://en.wikipedia.org/wiki/Simpson%27s_paradox).

Second 56% of all records are severe. This could be because many people report Adverse Events if they are not very mild. For example headache for some hours or short fever or things like that is probably less reported. In reality probably many people just report things when they feel very uncomfortable.

Third by only looking at how many reports are severe we can’t difference how much more severe one case is compared to another. Therefore we have to summarize the multiple severe outcomes (e.g. life threatening, disability and following death is more severe than “only” hospitalized) to compare the level of severity between batches.

Therefore the next 2 Measures are (https://howbad.info/lethal.html):

  1. Lethality = reports that are fatal = deaths/total number of reports
  2. Severity (2.0) = reports that are multiple severe = (deaths + disabilities + life threatening events)/total number of reports

First I will cut out all batches with AE <= 175 to get a more stable view and to avoid high severity and lethality because of batches with low counts of reports (base effect).

Now the chart above isn’t even looking stable any longer. It seems like there are strong differences in severity. Not only there are differences in severity by a factor of ~3 but also differences in lethality by a factor of approximately 5 to 7. Another thing which gets obvious is that some batches are so severe that they are highly unusual in the Population (red line) based on a Bootstrap-Distribution.

Note: The CI in the chart above is calculated with AE > 175 to be very good comparable.

Here is the chart for lethality:

Bootstrap CI

What is the 95% and 99.9% CI for severity in the Population (Germany)

Here you can see the distribution in the Population based on Bootstrap (10,000x) CI with AE > 10.

The 95% CI is therefore:

##      2.5%     97.5% 
## 0.1279992 0.1606015

And the distribution of (mutiple) severity ratio from the sample size is:

##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##  0.0000  0.0750  0.1206  0.1434  0.1824  0.7273

What does that mean? It means that batches with < 12.8% and > 16.1% are very unusual in the Population. Now imagine where batches with 0.08 or 0.2 are in the distribution.

Hint:

Note: The CI is calculated with AE > 10. This is very conservative because of the base effect and the high severity rate in batches with low AE. Calculating with AE > 100 make this picture dramatically worse! E.g the upper 99.9% CI for AE > 175 is: 0.147 (upper red dotted line in the Toxicity 2.0 chart).

Is the Effect (multiple severity) significant?

Between manufacturer

Now we want to know if we just maybe have problems with our eyes and the effect is strongly visible but maybe we have cognitive biases like Müller-Lyer-Illusion or so. Therefore In this chapter I will analyse the significance by testing with permutation. In this chapter I will compare the batches between the three manufacturer (Janssen, Moderna & Pfizer). I will make three permutation tests to test the significance between the 3 pairs (Janssen vs. Moderna, Jannsen vs. Pfizer and Pfizer vs. Moderna).

As we can see in the chart above, even if the distribution is a bit skewed, we can’t reject the H0 (Jannsen & Moderna are equally severe) by the standard significance level (0.95, 0.99, 0.999). Nevertheless as you can see the blue dotted line (difference in mean by these groups) is also not really near the 0. Keep in mind from earlier explanation that 2 groups can reverse an effect within groups.

The comparison between Janssen & Pfizer is really another picture. The chart is a little bit skewed but that is no huge problem. The p-value is highly significant. So here we must reject the H0 (Jannsen & Pfizer are equally (multiple) severe).

Also the comparison between Pfizer and Moderna is highly significant with a p-value of 0.0155. This is really interesting because in the single severity measure there was no significant effect. It seemed like they canceled the effect each other out. Therefore it’s necessary that we will at least drill down to one manufacturer again which happens in the next step.

At this point we have to say that it seems like Pfizer is significantly different in severity from the other manufacturer. This is what we also saw in the Toxicity 2.0 chart. It seems like the following order appears: Janssen < Moderna < Pfizer. But there is something we have to remember. The number of reports are also Jannsen < Moderna < Pfizer. Pfizer was in germany the most dominant injection. Here is the table of distinct batches by manufacturer > 10 AE.

## # A tibble: 3 x 2
##   VAX_MANU           Count
##   <chr>              <int>
## 1 "PFIZER\\BIONTECH"    99
## 2 "MODERNA"             34
## 3 "JANSSEN"             12

So to get rid of these 2 potentially biases the next step is looking at just Pfizer batches alone.

UPDATED: Within manufacturer (Pfizer)

I’m very sorry for the first Version. I calculated the p-value by dividing the Pfizer-Batches into two groups based on the mean. The problem with this process is that you literally get always significant p-values around 0 even if differences between groups are completely marginal. So this process was not suitable to tackle this problem. I changed the process so that now I compare the differences between the Pfizer series as also 4 single batches from the “E” series.

## [1] "P-Values"
## [1] 0.503 0.047 0.007 0.010 0.051 0.000

You can see above the p-values for the pairs (AE > 35): 1 vs. E, 1 vs. F, 1 vs. S, E vs. F, E vs. S and F vs S.

We have to reject all Nullhypotheses (batches with different series are same severe) except for the Series 1 vs. E.

Note: Same Analysis with different types of AE (10, 25, 50, 100, 175) come to similar p-values.

## [1] "EJ6796" "EM0477" "EX3510" "EX3599"
## [1] "P-Values"
## [1] 0.739 0.000 0.001 0.000 0.001 0.016

As you can see above all 4 Batches from the “E” series have > 300 AE are in an age range of 46 - 51 and are very different in severity (as also lethality). You can see from the p-values above, that only EJ6796 vs. EM0477 can’t reject the Nullhypothesis. EJ6796 vs. EX3510, EJ6796 vs. EX3599, EM0477 vs. EX3510, EM0477 vs. EX3599 and also EX3510 vs. EX3599 must reject the Hypothesis that these batches are equally same severe by a strong significance level of >98%.

Conclusion: Empirical testing has to reject the null-hypothesis (H0: All batches are the same) and it seems that the theoretical explanations with variation of active mRNA could be the reason why (https://www.bitchute.com/video/8tbOPuO0jyuV/).

Correlation isn’t causation

So there is a correlation between batches and severity. Now we all know that correlation isn’t causation. But we know that correlation is necessary for causation. I don’t want to dig deeper into this topic but I just want to point out to the Bradford-Hill Criteria:

  1. Strength: The bigger the effect the more likely that it’s causal
  2. Consistency: If different observers in different places (countries) with different samples come to similar conclusions a causation is more likely (https://www.transparenztest.de/post/pei-bericht-244576-covid-impf-nebenwirkungen-und-2255-todesfaelle, https://twitter.com/theotherphilipp/status/1486030456810356737, https://covid-crime.org/ema-numbers-12-21/)
  3. Specificity: Causation is likely if there is a very specific population at a specific site and disease with no other likely explanation
  4. Temporality: The effect has to occur after the cause - e.g. if it is more likely due to covid why is the correlation not in 2020?
  5. Biological gradient: Greater exposure should generally lead to greater incidence of the effect (https://howbad.info/sweden.html)
  6. Plausibility: A plausible mechanism between cause and effect is helpful (https://www.nejm.org/doi/suppl/10.1056/NEJMoa2113017/suppl_file/nejmoa2113017_appendix.pdf)

7.Coherence: Coherence between epidemiological and laboratory findings increases the likelihood of an effect

8.Experiment: Occasionally it’s possible to appeal to experimental evidence FDA

  1. Analogy: The effect of similar factors may be considered FDA


Is there a correlation between age and severity?

So maybe the difference in severity is not because of difference in active mRNA (toxicity) but because of age-dependence. One could argue that some batches were just given to specific age-groups for example in care centres. So if this would be the case we should at least need to see a correlation between severity and the average age per batch. At first I want to give you the summary of the average age which reported adverse events (AE > 10).

##     AVG_AGE     
##  Min.   :26.05  
##  1st Qu.:43.05  
##  Median :49.09  
##  Mean   :49.46  
##  3rd Qu.:56.00  
##  Max.   :75.84

As we can see in the plots above. In the upper two plots there is a relatively low correlation (0.279 - 0.311) between average age and severity. In the more robust plots beneath is no to very low correlation (0.155 - 0.152). This low to no correlation seems to be a pretty bad predictor for severity and a pretty bad explanation for causality. Further more we can see the following:

Note: As you can see in the table on the beginning of this chapter mean and median is approximately the same value for the age variable so there is not a bias because of less robustness against outlier by mean.

For batches with more than 175 reports the ones with the highest severity rate are almost younger than older. So high severity has nothing to do with age. We also can’t see that on the one hand only younger people (around 50 years old) are always high in severity or always low in severity and on the other hand the same seems to be for the older people (around 70). It really seems to be mixed so like I said, age is a pretty bad preditor and has low correlation and therefore has low potential for causality.

Is AE a good predictor for severity?

As seen in the chart AE is pretty bad predictor for severity. So we can’t say: Batches with high Adverse Event reports a more severe. There is no correlation. Therefore I advise to focus generally more on severity than AE when looking at a batch.

Differences between Countries

This report was focused on germany but there are differences also between countries (https://howbad.info/international.html).

VAERS is a public passive surveillance system

I often read that VAERS is not a valid Database because theoretically every private person could make a report. But after studying the records and after some Text-Mining-Analysis I have to say that at least 80% of all records are highly valid by physicans, healthcare workes or similiar persons. Also I pointed out in number 2 of the Bradford-Hill Criteria VAERS isn’t the only database which seems to have similar patterns. PEI (Paul-Ehrlich-Institut), EMA (European Medicines Agency) via EudraVigilance, WHO via Vigiaccess, InEK (Institut für das Entgeltsystem im Krankenhaus) and DoD (Department of Defense) all seems to have similiar pattern about strongly rising Adverse Events more than what should be expected. I think it’s absolutely necessary to investigate further more! Especially with not only passive surveillance systems but also with active ones.

Conclusion

Risk is everywhere. Everyday we are confronted with different types of risks. Risks to fall down the stairs, risk to have a car accident, risk of getting robbed, risks of a getting sick by a respiratory virus and risk of getting adverse events from an injection. There is no one fits all strategy for everyone. Everyone has to choose his own risk appetite. But what is necessary is that everyone is as good informed as he/she can. Only then it’s possible to make a rational decision. This means not that the decision will alaways have the wished outcome, but in the long run the probability is on your side. What we are seeing with Covid is multidimensional. Some got pretty sick and some will maybe become pretty sick in the future. But what we can see is that most people with good immunity system have no big risks to take especially the more younger you are. If you are younger than 30 it’s more likely that you will die from a hornet, wasp or bee sting than from Covid. If you are younger than 70 there is nearly the same chance by dying from drowning than from Covid and even if you are 80+ the chance for dying with Covid is just a little bit higher than from dying in a motor-vehicle-crash. Now you know some of your risk profile. Does it mean that children shouldn’t play in the garden or are you afraid to go swimming in the holidays or do you stay at home instead of driving to your familiy on christmas? In particular with the Covid-injections there seem many things really strange. Normally a Vaccine needs approx. 10 years to be ready to vaccinate many people. The mRNA gen-therapies just needed 1! It seems that they had to cut corners to be so fast. And this is something about some people blows the whistle (e.g. https://twitter.com/IamBrookJackson/with_replies). As you saw Pfizer also not reached the endpoint on the clinicial trial phase 3 (more people died in the mRNA group than in the placebo) and tried to publish their data over a timeframe of 75 years. I think this isn’t something trustworthy. Further more there are strange things happening in the insurance and funeral companies (https://gettr.com/post/pu46g39a32) as also the things Project Veritas is uncovering (https://www.youtube.com/watch?v=6nSXHrmOy8o&t=103s). On the other hand I showed you some analysis of the Adverse Events which occurs in germany but got reported to VAERS. It’s pretty clear that something is strange about the high differences between/within manufacturer and batches. We can’t say the vaccine are the causality for this at this moment. We just can say there are hints for that and that this must be investigated.

“Don’t risk something you have and need for something you don’t have and don’t need!”

Lot/Batches Searching for Germany (AE > 10):

Sum overall (AE > 10)

##       AE HOSPITAL L_THREAT  DISABLE     DIED   SEVERE 
##    17628     8950     1121      258      821     9892

Note: > 0.5 Sex per Batch means higher percentage of females et vice versa.