Last week, the National Institute for Public Health and the Environment (RIVM) published a new report with the jubilant title "Three-quarters less likely to be admitted to hospital after corona repeat vaccination". The timing is no coincidence. The autumn campaign for people aged 60+ to get the umpteenth repeat vaccination against Covid-19 is in full swing, but many older people are not getting the repeat vaccination.
Now the RIVM has a downright abominable track record when it comes to the quality of reports that are released during a vaccination campaign. This time last year, the National Institute for Public Health and the Environment (RIVM) came up with a "report" That should prove that the vaccinations had saved as many as 88,000 hospitalizations. The National Institute for Public Health and the Environment (RIVM) had made a model for the number of expected hospital admissions and compared it with the actual number of admissions. When that was much, much lower, they did not come to the correct conclusion, namely that their model was wrong, but concluded that the vaccinations were insanely successful. How coincidental in the middle of a prick campaign. Maurice de Hond wrote a nice article about.
With this in mind, let's take a look at how RIVM fared this year. Have they produced a scientific report or is it again just a piece of propaganda with fantasized claims that are not supported by the data to convince people to participate in the jab campaign? I will already reveal that it is the latter, but it is of course still interesting how the RIVM has manipulated the figures to arrive at the desired, but incorrect, outcome. In addition, another conclusion can be drawn from the RIVM data that is interesting. Read on for that.
The news release from the RIVM a PDF with more data about the study. In it, we find that hospital admissions were looked at in the period 9 October to 20 November, using data from the NICE COVID-19 database. The report indicates that this data is not complete and that the relative incidences per vaccination status have therefore been examined. In this case, relative means comparing the ratio between the number of hospital admissions of people with and without a repeat vaccination compared to the ratio of those people in the general population. The idea is that if proportionally fewer people with repeat vaccinations are in hospital because of Covid-19 than the ratio in the general population within a certain age category, the repeat vaccination will be effective against Covid-19. So much for the theory.
There are a number of ways to manipulate this type of data:
- Build in a period between getting the repeat vaccination and being counted in the category "has had repeat vaccination"
- Taking a different time to calculate the ratio within the general population than the time of admission of the patient to the hospital
- Mixing patients who are in hospital due to Covid with patients who have tested positive for SARS-COV-2 upon entering for something else
- Assigning patients whose vaccination status is unknown to a specific category
We're going to go through all four of them.
Build in the period between getting the repeat vaccination and counting
In the original Pfizer trials, they saw that participants were actually more susceptible to infection with SARS-COV-2 in the first 14 days than people in the placebo group. Their solution was to count participants as unvaccinated in those first 14 days. If your research period is long enough, it doesn't really matter, but especially with shorter research periods, it makes a huge difference and you can make it appear that an ineffective drug is very effective. Statistics professor Norman Fenton has this in this video made transparent. If this trick was used in this study, we can assume that there were people in hospital who had already received the repeat vaccination, but were still put in the category "no repeat vaccination" in this study. Let's take a look at the report to see if they pulled off this magic trick. From the report:
“The first day of illness for all age groups is estimated at 7 days before hospitalization. Individuals will be classified as having at least one previous vaccination immediately after an initial vaccination and 7 days after the repeat vaccination in the autumn round 2023 as "Received the autumn vaccination 2023"
This means that a person who received the repeat vaccination 13 days before admission to hospital has indeed been placed in the category of vaccinated people who have received the repeat vaccination not have achieved. So the 14-day time-out is also included in this study. This is especially striking when you consider that this study only lasted 6 weeks. In that case, 2 weeks in which someone may have had a repeat vaccination, but is still registered as if that person had not had a repeat vaccination, is a very large share. To express this in figures, they have added 2/6ths of the repeat vaccination group to the group without repeat vaccination, which is therefore 8/6ths large. If the repeat vaccination were a saline solution with 0% effectiveness against COVID-19, it would seem as if this saline solution prevented no less than 50% of all hospital admissions as a result of this trick. This gives a good indication of what the actual purpose of this report was, and it certainly wasn't possible to find out whether the repeat vaccination leads to fewer hospital admissions.
If the repeat vaccination were a saline solution with 0% effectiveness against COVID-19, it seems as if this saline solution prevents no less than 50% of all hospital admissions due to this trick
Taking a different time to calculate the ratio within the general population than the time of admission of the patient to the hospital
One factor that makes this analysis very difficult is that this research is being carried out during the jab campaign. The ratio between people with and without a repeat vaccination in the general population therefore changes during the period studied, and for each person in hospital you will have to look at the admission date (or according to the RIVM 14 days before the admission date) and the ratio in the population at that time. That means that you have to calculate this per patient and start averaging it. That's pretty complex. However, if your goal is to make the repeat vaccination look positive, you simply take the ratio in the general population at the end date of the study. Unfortunately, the report does not say a word about how this calculation was carried out. So we can't check whether the data has been manipulated in a certain direction or not at this point.
Mixing patients who are in hospital due to Covid with patients who have tested positive for SARS-COV-2 upon entering for something else
In the period studied, 1,563 individuals were admitted to hospital with COVID-19. Of these, 1,403 were 60 years of age or older. So 160 were under the age of 60. Are these persons included on account of COVID-19 or did a swab end up their nose upon entry for something else that happened to yield a positive result for SARS-COV-2? With COVID-19, in other words. Since January 2022, NICE has been keeping track of whether an admission was with or before COVID-19. So can we assume that all patients included in this study were there because of COVID-19? Well, no. From the report:
“Since 25 January 2022, when registering in NICE, the patient's reason for admission has been asked. Patients indicated to have been admitted for a reason other than COVID-19 were excluded from this analysis. However, the reason for withdrawal is unknown for 52% of admissions since September 25, 2023. These recordings were included in the analysis. It is therefore likely that a number of patients who tested positive for SARS-CoV-2 but were hospitalized for another reason influenced the RRV estimates.“
For 52% or 813 of the patients studied, it is therefore not known whether they were admitted because of or with COVID-19, but they were included in this study. Historical data from last year shows that most patients who receive a COVID-19 label are admitted met COVID-19 and not by COVID-19. Thus, it is very likely that most of these 813 patients were admitted met COVID-19 and it is therefore possible that of those 140 patients under the age of 60, for example, not a single one has been admitted because of COVID-19. Of course, that doesn't bode well for data quality. If you only want to look at the relative incidence anyway, you might as well have left those 813 patients out of the study. That would have improved the data quality. It is unclear why the RIVM researchers did not choose to do so. It is possible that including these 813 patients led to an outcome that better matched the desired outcome.
Assigning patients whose vaccination status is unknown to a specific category
The vaccination status of not all Dutch people is known. This may be due to problems in the data collection at the time of the vaccination campaigns or because they have indicated that their status may not be registered. By assigning these patients to a specific category, you artificially increase the number of patients in that category. Have they done sitting? From the report:
"Another limitation of the NICE data enriched with vaccination data from CIMS is that CIMS only contains information from people who have consented to the inclusion of their vaccination data in this register. As a result, a number of patients without known vaccination data in CIMS have indeed been vaccinated, while they are categorized as unvaccinated in this analysis."
They therefore did not use these people to artificially increase the proportion of "Received an autumn jab 2023" compared to "At least one previous vaccination", but to increase the proportion of "Not vaccinated" compared to the others.
If we look at the graph with this knowledge that is supposed to prove that the repeat vaccination is very effective, we see the following:
The graph on the right shows the difference between hospital admissions of people who have had the last repeat vaccination and people who have been vaccinated but have not had the last repeat vaccination. At least, according to the definition of the RIVM, which we have already seen was deliberately chosen to make the repeat vaccination look more positive than it actually is. If we want to correct for the 14-day trick, we need to reduce the values of the "At least one previous vaccination" group by 25% and increase those of the "Receive an autumn vaccination 2023" group by 50%. It is not possible to say anything about a correction for a repeat vaccination rate in the general population that is too rosy, because these data are missing from the report. What we already see after correction for the 14-day trick is that for all groups except the 80-89 year group, differences between the groups with and without a repeat vaccination are completely within the margin of uncertainty. The small difference for the group of 80-89-year-olds can probably be explained by a rosily chosen repeat vaccination rate. On the basis of these figures, only one conclusion can be justified: It cannot be concluded that the autumn vaccination leads to a lower number of hospital admissions.
On the basis of these figures, it cannot be concluded that the autumn vaccination actually leads to a lower number of hospital admissions.
Is this study totally uninteresting? No, she's not. In the report, we see three groups and of one of those groups, we can safely say that it has remained stable during the entire study period: The group unvaccinated. In addition, due to the RIVM's attempt to push the repeat vaccination, they have now used almost all the tricks that are normally used to artificially boost the numbers for the Corona vaccines compared to unvaccinated people to make the effectiveness of the repeat vaccination appear more positive than it actually is. Only assigning patients with unknown status to the group of unvaccinated people has been used to artificially increase that group.
But even with this trick, we see that the share of unvaccinated in the total number of hospitalizations is between 10% and 13% for the different age groups over 60 years old. This is fairly similar to the share of unvaccinated people in the general population in those age categories. We already knew from studies abroad that the Corona vaccinations increase the risk of reinfection with SARS-COV-2, but this is the first time that RIVM shows data showing that the Corona vaccinations have no demonstrable effectiveness against hospital admissions.
In conclusion, the figures shown by RIVM in this report do not determine the effectiveness of the repeat vaccination. Even more interesting is that any effectiveness of the Corona vaccinations compared to unvaccinated people cannot be established at all. This report did not look at people who ended up in hospital (or died) as a result of the repeat vaccination, but we have seen from previous studies that this number is unfortunately not zero. Based on these figures, we can therefore only advise against the repeat vaccination. Also for the elderly population. The risk of serious side effects of this jab is offset by an immeasurable effectiveness against hospitalisation with or because of COVID-19.
This weekend there will be a follow-up article in response to this report with, among other things, a graphical representation of the vaccine figures as reported by the RIVM, compared with a graph of an effective vaccine – and what that means for the unexplained excess mortality. "Stay tuned" 🙂