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In this note, we report about the results of a statistical investigation into the relation between vaccination against COVID-19 and the mortality rates in the three weeks after vaccination, in the age groups 12-65, 65-80 and 80+ respectively in The Netherlands. We investigate whether the observed mortality during this period can be better explained by the expected mortality as computed similarly to the method of the CBS (Statistics Netherlands), or by a scenario in which vaccination leads to a small increase in the mortality in the three weeks following the vaccination. We found that in the cohort 12-65, the two scenarios predict the actual data roughly equally well, but for the cohort 65-80, the second scenario explains the data much better. In the 80+ cohort, there is a difference between men and women: for men, the second scenario explains the data much better, but for women the contrary is the case. We discuss the interpretation of these results, and conclude that they urgently call for further investigations into the effects of vaccination, and publication of the possible risks of vaccination against COVID-19.
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COVID-19 vaccinations and mortality - a Bayesian analysis
Ronald Meester, Wouter Aukema, and Theo Schetters
Summary
In this note, we report about the results of a statistical investigation into the relation
between vaccination against COVID-19 and the mortality rates in the three weeks
after vaccination, in the age groups 12 - 65, 65 - 80 and 80+ respectively in The
Netherlands. We investigate whether the observed mortality during this period can
be better explained by the expected mortality as computed similarly to the method of
the CBS (Statistics Netherlands), or by a scenario in which vaccination leads to a
small increase in the mortality in the three weeks following the vaccination. We found
that in the cohort 12 - 65, the two scenarios predict the actual data roughly equally
well, but for the cohort 65 - 80, the second scenario explains the data much better. In
the 80+ cohort, there is a difference between men and women: for men, the second
scenario explains the data much better, but for women the contrary is the case. We
discuss the interpretation of these results, and conclude that they urgently call for
further investigations into the effects of vaccination, and publication of the possible
risks of vaccination against COVID-19.
Introduction
It is clear that a statistical investigation into the relation between the mass
vaccination program in The Netherlands and the mortality rate is difficult and subtle.
The Netherlands has seen very high mortality rates in the fall of 20211, but the
reason for this is not so easy to understand. Various explanations could be offered:
COVID-19 mortality, delayed health care, the effect of mass vaccination, aging, and
probably other ones as well. A statistical analysis of these high mortality rates
requires more detailed information on the individual level – information which is not
(yet) available.
1https://www.cbs.nl/nl-nl/reeksen/sterfte-per-week/
There is, however, another relation between vaccination and mortality that can in fact
be statistically investigated without detailed individual information, namely the
mortality rates directly following vaccination. There is detailed information about the
number of vaccinations per week for each age group, and together with the observed
mortality per week per age group, this enables us to statistically investigate the direct
effect of vaccination on the mortality rates, without the results being confounded by
other effects. This analysis is carried out in the present paper.
Methods
Population data for the period 2015 - 2021 was downloaded from Eurostat.2
Mortality data for the period 2015 to 2021 (week 41) was also downloaded from
Eurostat.3Expected mortality was calculated using the same method as described
by the CBS.4Vaccination data was obtained from the RIVM (National Institute for
Public Health and the Environment).5
We performed a Bayesian analysis. In such an analysis, one computes the
probability of the actual outcome under each of the two hypotheses. If one
hypothesis assigns a (much) higher probability to the outcome than the other, then
the first one explains the data (much) better than the second one and is therefore a
(much) better explanation than the other hypothesis. The ratio of the probabilities of
the outcome under each of the scenarios is called the likelihood ratio (LR): it conveys
which of the two hypotheses explains the data best, and by what factor.
Here is a very simple example of using this method that might help understanding
the method. Suppose you are uncertain about the fairness of a coin. You consider
two hypotheses: the first hypothesis is that the coin is fair, the second hypothesis is
that the coin is biased in a 40-60 way. Now we flip the coin 100 times. If 51 heads
5https://www.rivm.nl/covid-19-vaccinatie/cijfers-vaccinatieprogramma. Percentage uptake for
each age-group was equally distributed along the corresponding dimensions for sex,
age-group and time, and recorded as the number of doses administered for dosis one and
two.
4https://www.cbs.nl/en-gb/news/2020/49/higher-mortality-in-week-48/expected-mortality. We
chose not to apply the adjustments CBS made internally since details of how these
adjustments were applied by CBS across the age-group, sexe and time dimension, were not
available.
3https://ec.europa.eu/eurostat/databrowser/view/demo_r_mweek3/default/table?lang=en.
2https://ec.europa.eu/eurostat/databrowser/view/DEMO_PJAN/default/table?lang=en.
come up, then this is much more likely under the first than under the second
hypothesis, and hence this is evidence in favor of the first. If 65 heads come up then
this is much more likely under the second than under the first hypothesis, and hence
this is evidence in favor of the second. In the former case, the LR will be larger than
1, in the latter it will be smaller.
The evidence in a Bayesian approach should always be understood in the context.
Without going into the mathematics, strong evidence in favor of a hypothesis does
not necessarily imply that you are convinced that this hypothesis describes the truth
well. An outrageous hypothesis requires more evidence to become believable than a
hypothesis that one deems believable from the outset. We will come back to this in
the discussion.
The hypotheses
Here is the first of our hypotheses:
CBS Hypothesis: Each week, the probability of death for every person in the
population is calculated based on the expected number of deaths similar to the CBS.
You could interpret this as the ‘baseline’ scenario. In our calculations, we distinguish
between the sexes, something which turned out to be relevant only for the 80+
cohort.
The alternative hypothesis states that someone who is vaccinated has an increased
chance of dying in the three weeks after vaccination. We increased the probability for
the three weeks after vaccination by 10% compared to the probability coming from
the CBS hypothesis. Both the period (three weeks) and the extra opportunity (10%)
are of course flexible, and can also be chosen differently. We carried out a sensitivity
analysis for these two choices, and the results were very stable for large ranges of
other possible choices.
Increased Risk Hypothesis: A person who is vaccinated in a certain week has a
mortality probability in the following three weeks that is 10% per week larger than in
the CBS Hypothesis. After that period of three weeks, this person has the CBS
mortality probability again.
Results
Age group 12 - 65: in this age group, there seems to be no discernable direct
mortality effect of vaccination; see Figure 1.
Figure 1. See the text for an explanation.
We explain the figure now. In the first column, we have written the week numbers of
2021. In the rightmost column we have written the number of vaccinations (including
both first and second dose) in this cohort in the specific week, and we have
visualized this with the blue bars. The second and third column contain the LR per
week, both numerically and visualized. A LR larger than 1 favors the increased risk
hypothesis. A red bar indicates that the evidence favors Increased Risk, and a green
bar that it favors the CBS Hypothesis. The length of the bars reflects the logarithm of
the LR. The overall LR for this cohort is around 11, so the two hypotheses explain
the data roughly equally well, with a slight advantage for the Increased Risk
Hypothesis. Note that the red and green bars do not represent numbers of deaths;
they only indicate which hypothesis explains the data best and by what factor. We
have also made computations for both sexes separately, but there were no relevant
differences between these two groups.
Age group 65 - 80: in this age group, the situation is very different than in the
previous one; see Figure 2.
Figure 2. See the text for an explanation.
We see that the Increased Risk Hypothesis explains the data much better than the
CBS Hypothesis. The data is overall much more likely under the Increased Risk
Hypothesis than under the CBS Hypothesis. The overall LR is incredibly large, and
hence the evidence very strongly favors the Increased Risk Hypothesis. We remark
that the scale of the visualization of the LR is different than in Figure 1. The
computed LR’s are much, much larger here.
Age group 80+: in this age group there is a remarkable difference between the
sexes, see Figure 3.
Figure 3. See the text for an explanation.
Discussion
A Bayesian analysis constitutes a comparison between two hypotheses. A large LR
does not necessarily imply that the favored one is likely to be the ‘correct’ one: bóth
hypotheses could be far from the truth. If we compare two false hypotheses, this can
nevertheless result in a high LR favoring one of them, compared to the other.
Furthermore, a LR should be understood in context. A large LR favoring a certain
hypothesis does not make that hypothesis immediately credible. If the hypothesis
was considered very unlikely prior to the evidence, then a LR will typically make it
more credible, but not necessarily to the extent that the hypothesis becomes
believable or likely to be true.
In our situation, the two competing hypotheses only differ around the time of
vaccination of the relevant cohort. Therefore, away from the vaccination period, the
hypotheses predict the same, and the data will not be distinguishing between them.
This is confirmed in all the figures above, where the LR is essentially 1 outside the
vaccination weeks.
Figure 1 shows that the data does not distinguish strongly between the two
hypotheses of interest for the 12 - 65 cohort. This is to be expected, since the
mortality rate in the cohort is low in any case. We find it reassuring that the weekly
LR’s do not consistently point in either direction, and variations can be due to chance
fluctuations. In other words, the CBS Hypothesis seems to be in good agreement
with the data, and the slightly larger mortality rate under the Increased Risk
Hypothesis performs only slightly better.
In Figure 2, for the 65 - 80 cohort, the situation is dramatically different. The
Increased Risk Hypothesis outperforms the CBS Hypothesis by many orders of
magnitude. Clearly, the actual mortality was higher than was predicted by the CBS,
and the Increased Risk Hypothesis explains the data much, much better than the
CBS Hypothesis.
Is there a way to say anything meaningful about the prior belief in the Increased Risk
Hypothesis? We have received reports about adverse events following vaccination,
but to the best of our knowledge, no detailed study of this has been carried out. On
the other hand, Martin Neil, Norman Fenton and their co-authors have reached the
conclusion that there might be a serious direct mortality effect of vaccination.6
Hence, the hypothesis that vaccination comes with a certain risk is not outrageous at
all. The reported overall LR, together with a certain prior plausibility, makes the
Increased Risk Hypothesis rather credible for this cohort. It seems, therefore, that
the public should be informed about the plausibility of this hypothesis, something
which has not been done so far.
We finally mention that in the 65 - 80 cohort, no serious differences were observed
when we distinguished between the sexes.
For the 80+ cohort, things are different. For women, the data strongly favors the CBS
Hypothesis, but for men it favors the Increased Risk Hypothesis. Part of an
explanation could be that over 2021, the mortality rate among women was very low,
possibly partly due to the higher mortality rate during the first waves of the pandemic.
In any case, we do not seem to be able to draw strong conclusions from our
outcomes for this cohort.
We plan to perform a similar analysis to investigate the protection effect of
vaccinations in the near future. Since our population data comes from Eurostat, we
invite our colleagues to perform a similar analysis in other countries as well. Finally,
our spreadsheet is available upon request.
Conclusion
Our results for the 65 - 80 cohort strongly suggest that further investigation into the
direct mortality effects of COVID-19 vaccinations is urgently called for, and that the
current mass vaccination campaign should not be continued without proper public
information about the risks.
6Martin Neil, Norman Fenton, Joel Smalley, Clare Craig, Joshua Guetzkow, Scott McLachlan,
Jonathan Engler and Jessica Rose (2021), “Latest statistics on England mortality data suggest
systematic miscategorisation of vaccine status and uncertain effectiveness of Covid-19 vaccination”,
http://dx.doi.org/10.13140/RG.2.2.14176.20483.
Appendix
All data points were collected into an Excel spreadsheet where we developed a
custom function (using Excel VBA) to calculate probability under the Increased Risk
Hypothesis as follows:
Function pdf(deaths, population, expected, vaccinated, added_risc) As Double
vpdf = 0
If vaccinated = 0 Then
vpdf = WorksheetFunction.Binom_Dist(deaths, population, expected / population,
False)
GoTo EndFunction
End If
If vaccinated >= population Then
vpdf = WorksheetFunction.Binom_Dist(deaths, vaccinated, _
(expected / population) * (1 + added_risc), False)
GoTo EndFunction
End If
For n = 0 To deaths
U = WorksheetFunction.Binom_Dist(deaths - n, population - vaccinated, expected /
population, False) 'Unvaccinated Population
V = WorksheetFunction.Binom_Dist(n, vaccinated, (expected / population) _
* (1 + added_risc), False) 'Vaccinated Population
vpdf = vpdf + V * U
Next n
EndFunction:
pdf = vpdf
End Function
... Excess mortality for non-COVID reasons has increased in several highly vaccinated jurisdictions, not clearly due to overwhelmed healthcare capacity. 14,15 Myocarditis in young adult males is caused by mRNA vaccination, much more than from COVID-19 infection itself, and may be why EMS calls in Israel for cardiac arrest and acute coronary syndrome increased by 25% in young adults during vaccination rollout [and not during previous COVID-19 waves]. [16][17][18][19] ...
Preprint
We aim to use a recently published research study as an example in order to demonstrate how data can be misinterpreted and result in deriving misleading policy implications. Bagshaw et al wrote that unvaccinated patients with COVID-19 in Alberta, Canada “had substantially greater rates of ICU admissions, ICU bed days, and ICU related costs than vaccinated patients did. This increased resource use would have been potentially avoidable had these unvaccinated patients been vaccinated.” The authors in Bagshaw et al then concluded that their findings “have important implications for discourse on the relative balance of increasingly stringent public health protection (restrictions), including mandatory vaccination policies, and the sustainability and function of health system infrastructure and capacity during the ongoing COVID-19 pandemic.” Here we show the following. First, the effect of vaccination on intensive care admissions were grossly over-estimated. Second, an effect of vaccination on access to acute care and on all-cause excess deaths was grossly over-stated. Third, policy implications were overstated and at best unclear. Overall, the data cannot support what Bagshaw et al called “increasingly stringent public health protection (restrictions), including mandatory vaccination policies”.
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