Version 1
: Received: 8 September 2023 / Approved: 11 September 2023 / Online: 11 September 2023 (11:31:34 CEST)
Version 2
: Received: 13 September 2023 / Approved: 14 September 2023 / Online: 15 September 2023 (05:26:17 CEST)
How to cite:
Hegarty, P. ExcessMortality Data and the Effect of the COVID-19 Vaccines Part 1: European Data. Preprints2023, 2023090674. https://doi.org/10.20944/preprints202309.0674.v1
Hegarty, P. ExcessMortality Data and the Effect of the COVID-19 Vaccines Part 1: European Data. Preprints 2023, 2023090674. https://doi.org/10.20944/preprints202309.0674.v1
Hegarty, P. ExcessMortality Data and the Effect of the COVID-19 Vaccines Part 1: European Data. Preprints2023, 2023090674. https://doi.org/10.20944/preprints202309.0674.v1
APA Style
Hegarty, P. (2023). ExcessMortality Data and the Effect of the COVID-19 Vaccines Part 1: European Data. Preprints. https://doi.org/10.20944/preprints202309.0674.v1
Chicago/Turabian Style
Hegarty, P. 2023 "ExcessMortality Data and the Effect of the COVID-19 Vaccines Part 1: European Data" Preprints. https://doi.org/10.20944/preprints202309.0674.v1
Abstract
Using publicly available data for 28 EU/EES countries from Eurostat and Our World in Data, we investigate how the current rate of Covid vaccination in a country compares to its average rate of excess mortality (EM) in the pandemic to date. We find that, in the linear regression, the correlation between average EM and vaccination rate is strongly negative, a priori evidence to support the claim that the Covid vaccines have saved many lives. However, a closer analysis of the timeline suggests otherwise. The correlation was already strongly negative before the vaccines were rolled out and is only weakly negative thereafter. In theory, survivor bias could still explain this shift, especially since waves of EM closely align with Covid waves. However, we find in addition that about half of our 28 countries experienced higher EM in 2022 than in 2021, and all that did so have higher than average vaccination rates. This is something which survivor bias cannot explain and raises the real possibility that the vaccines have not just failed to save many lives, but may have already caused net harm. Moreover, any such harm may be ongoing since we find that EM and vaccination rates have been consistently positively correlated since April 2022. We show that all these findings are robust to several different ways of measuring EM and/or vaccination rates. Finally, using public data from Worldometers, we show that the correlation over time of official Covid mortality rates with current vaccination rates closely tracks that of EM rates, even as Covid mortality has waxed and waned and even in the post-omicron period.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
The commenter has declared there is no conflict of interests.
Comment:
You are doing a linear regression and list the two parameters of the regression but you are talking about correlation. Regression and correlation are different things. Have you calculated the correlation coefficient? Otherwise, the negative trend is obvious.
Commenter's Conflict of Interests:
I am one of the author
Comment:
@Lyudmil Antonov: I prepared an analog of Figure 2, plotting instead the (Pearson) correlation coefficient in the y-direction. I can't see how to attach figures to comments here though. The "shape" of it is the same as that of the figures in the paper, in particular the coefficient is positive for each month from April 2022 onwards. The lowest value in that period is 0.2677 (August 2022), the highest is 0.6835 (June 2022) and the average is 0.4953. I wanted to do linear regression, obviously to also get an estimate of "how much" vaccination rates affect excess mortality, but yes I perhaps should have been more careful with the terminology.
The commenter has declared there is no conflict of interests.
Comment:
The correlation coefficient for a regression is one number and it is calculated as follows:
For simple linear regression, the correlation coefficient can be calculated from the regression parameters using the following formula:
$$correlation coefficient = sign(slope) \times \sqrt{\frac{SS(regression)}{SS(Total)}$$ (you can see the formula more clearly in a TeX or LaTex editor)
where:
- SS(regression) is the sum of squares due to regression,
- SS(Total) is the total sum of squares, which equals SS(regression) + SS(Error),
- sign(slope) is 1 if slope > 0, -1 if slope < 0, and 0 if slope = 0.
The graph for correlation usually looks like this
where in your case X can be vaccination rate and Y can be excess mortality rate.
I put the image in the comment with the icon "Insert image"
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Commenter:
The commenter has declared there is no conflict of interests.
Commenter:
Commenter's Conflict of Interests: I am one of the author
Commenter:
The commenter has declared there is no conflict of interests.
For simple linear regression, the correlation coefficient can be calculated from the regression parameters using the following formula:
$$correlation coefficient = sign(slope) \times \sqrt{\frac{SS(regression)}{SS(Total)}$$ (you can see the formula more clearly in a TeX or LaTex editor)
where:
- SS(regression) is the sum of squares due to regression,
- SS(Total) is the total sum of squares, which equals SS(regression) + SS(Error),
- sign(slope) is 1 if slope > 0, -1 if slope < 0, and 0 if slope = 0.
The graph for correlation usually looks like this
where in your case X can be vaccination rate and Y can be excess mortality rate.
I put the image in the comment with the icon "Insert image"