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Article Not peer-reviewed version
An Update on the Link between
COVID-19 Vaccination and
Mortality
Jarle Aarstad *
Posted Date: 28 August 2023
doi: 10.20944/preprints202308.1433.v2
Keywords: COVID-19; full vaccination; booster vaccination; all-cause mortality; excess mortality
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Article
An Update on the Link between COVID-19
Vaccination and Mortality
Jarle Aarstad
The Mohn Centre for Innovation and Regional Development, Western Norway University of Applied
Sciences; jarle.aarstad@hvl.no
Abstract: This study updates previous research showing that 22 all-cause mortality in 31 European
countries increased over time the higher the 21 COVID-19 full vaccination uptake. The update
illuminates that a one percentage point increase in 21 full vaccination uptake initially decreased all-
cause mortality from Jan to Mar 22 by –0.423 percent (95% CI –0.577, –0.270), but the following 14
months, a one percentage point increase in 21 booster vaccination uptake oppositely increased
mortality by 0.366 percent (95% CI 0.250, 0.482). The findings indicate that full vaccination initially
prevented mortality, but subsequently, booster vaccination, in particular, detrimentally and
consistently induced higher mortality. The effects remained robust when controlling for alternative
explanations. Studies have argued that heat waves caused mortality in the 22 summer and energy
prices caused mortality in the 22-23 winter. However, the update shows that booster vaccination
consistently induced higher mortality when neither heat waves nor energy prices were likely
explanations.
Keywords: COVID-19; full vaccination; booster vaccination; all-cause mortality; excess mortality
1. Introduction
This paper updates previous research by Aarstad and Kvitastein [1], showing that all-cause
mortality in 31 European countries during the nine first months of 22 increased over time the higher
the COVID-19 full vaccination uptake by the end of 21. The following paragraphs address the update’s
contributions.
First, it adds eight monthly observations of country-level all-cause mortality, extending the study
period from Jan 22 to May 23. I.e., it extends the study period from nine to 17 months.
Second, it includes country-level first booster vaccination uptake by the end of 21 as an additional
explanatory variable of mortality in the following months. Booster vaccination in this study implies that
three doses have been administered, and the motive for including the measure is Uversky, et al. [2]
asserting that repeated injections can suppress natural antiviral responses in addition to causing
autoimmune diseases, including myocarditis and promote cancer growth. In line with these statements,
I do not rule out that booster vaccination may affect mortality, which the update investigates.
Third, the update in more detail than the Aarstad and Kvitastein [1] study assesses how the
association between vaccination uptake by the end of 21 and subsequent mortality has changed over
time. They showed that the overall association increased from Jan 22 to Sep 22, which does not rule out
it may have been negative initially, i.e., vaccination having a temporal preventive mortality effect.
Subsequently, the association likely turned positive, which implies that vaccination, in the longer run,
has had a detrimental harmful mortality effect. To gain more detailed and explicit knowledge about
these issues, the update examines the association between vaccination uptake by the end of 21 and
mortality for each of the following 17 months of observation. The approach assesses in which months
during 22 and 23 the mortality may have decreased or increased from vaccination uptake by the end of
21. It further contributes to other research showing that the preventive effect of COVID-19 vaccination
against hospital admission and death waned [3], but to my knowledge, we do not know when the
preventive effect may have gone into negative terrain, causing increased mortality.
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© 2023 by the author(s). Distributed under a Creative Commons CC BY license.
2
Fourth, the update particularly emphasizes the association between COVID-19 vaccination uptake
and mortality in Apr and May, 22 and 23, plus Sep and Oct in 22. The motive for emphasizing those six
months is a recent study arguing that heat waves increased mortality between Jun and Aug in the
summer of 22 [4], and a second study arguing that increased energy prices caused increased mortality
in the following winter between Nov 22 and Mar 23 [5]. Analyzing Apr and May in 22 and 23, plus Sep
and Oct in 22 – during spring and autumn months neither strongly exposed to heat waves nor adverse
effects of increased energy prices – partakes to rule out alternative explanations of mortality as
suggested by the two studies.
2. Materials and Methods
The 31 countries studied are EU member states, plus Norway, Iceland, Switzerland, and
Liechtenstein. The dependent variable is monthly country-level excess all-cause mortality compared
to average pre-pandemic all-cause mortality in the same months between 16 and 19. Values higher
(lower) than zero indicate positive (negative) excess mortality. E.g., a value of 10 (–10) for a given
month in a given country indicates ten percent higher (lower) mortality than the pre-pandemic
average, i.e., positive (negative) excess mortality. The data were sourced from the Eurostat mortality
database [6]. At the time of data collection, the database included complete mortality data from all 31
countries from Jan 22 to May 23, except for Italy, missing data for May 23. In total, the study includes
526 country-level observations of monthly mortality.
Table 1 reports descriptive statistics concerning the dependent variable, excess mortality, for
different country-level months (on which I will report in further detail shortly). All the study’s
statistics are weighted by the countries’ population size at the beginning of 20 (intuitively except for
minimum and maximum values), as reported in Table 2, sourced from Eurostat [7]. The blue line in
Figure 1 reports weighted average excess mortality between Jan 22 and May 23 for all the 31 countries
(except for missing data from Italy in May 23). The blue line shows positive excess mortality
throughout the study period, except for Feb 23 when it was slightly negative (shortly, I report on the
figure in more detail, including the red and green lines).
As independent explanatory variables, the study includes data on country-level full vaccination
and first booster vaccination uptake by the end of 21. Full vaccination implies the percentage of the
total population in each country having taken the primary course, i.e., two doses, and booster
vaccination implies the percentage of the population having taken a third dose. The data were
accessed from the European Centre for Disease Prevention and Control [8], except for Switzerland
where the they were accessed from Our World in Data [9]. Table 2 reports full vaccination and booster
vaccination uptake for each country.
To account for alternative explanations, the study includes models controlling for the country-
level average 20-21 all-cause mortality relative to the average 16-19 all-cause mortality [10], the life
expectancy in 19 [11], the median age in 18 [12], and the purchasing power adjusted per capita GDP
in 19 [13] where I include the same number for Liechtenstein as reported for Switzerland. Aarstad
and Kvitastein [1] explain the motive for including the two first control variables, but briefly, the
concepts account for mortality and the countries’ health conditions prior to examining the study’s
dependent variable. Concerning the two latter control variables, the median age is an additional
proxy for a country’s health conditions, as people in countries with old populations, ceteris paribus,
are more prone to sickness and death than in countries with young populations. GDP is an indicator
of countries’ financial ability to handle challenges related to health issues.
Table 3 reports descriptive statistics concerning the independent- and control variables, and
Table 4 reports correlations. Concerning correlations taking high absolute values, potentially causing
problems with multicollinearity cf. [14], I shortly address how I deal with the issue.
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Table 1. Descriptive statistics concerning the dependent variable, excess mortality. Mean values and
standard deviations are weighed by countries’ population size.
Months included Min Max Mean St dev Monthly obs
Jan22-May23 –27.9 54.4 8.77 8.66 526
Jan22-Mar22 –4.5 54.4 7.40 7.59 93
Apr22-May23 –27.9 38.2 9.06 8.86 433
Apr22, May22, Sep22, Oct22, Apr23, May23 –27.9 36.8 8.52 6.27 185
Table 2. Full vaccination uptake, booster vaccination uptake, and countries’ population size. The table
is sorted by booster uptake.
Country Full vacc Booster vacc Population
Bulgaria 27.7 4 6951482
Romania 40.8 5.9 19328838
Croatia 53.3 13.2 4058165
Latvia 64.9 13.7 1907675
Poland 55.8 18.4 37958138
Slovakia 48.5 20.2 5457873
Slovenia 55.6 21.5 2095861
Lithuania 65.7 22.6 2794090
Finland 75 23.2 5525292
Estonia 61.3 24.2 1328976
Sweden 69.3 24.3 10327589
Switzerland 67.1 25.2 8606033
Czechia 62.2 27.7 10693939
Netherlands 67.6 27.8 17407585
Norway 73 29 5367580
Spain 74.9 30.2 47332614
Portugal 83.1 30.7 10295909
Liechtenstein 65.4 31.1 38747
Luxembourg 68.2 31.8 626108
Hungary 61 32.4 9769526
Italy 74.9 33.5 59641488
Cyprus 69.4 33.8 888005
Greece 66.3 34.1 10718565
France 73.3 36.1 67320216
Belgium 76.2 37.9 11522440
Germany 72.8 41.4 83166711
Ireland 76.6 43.5 4964440
Malta 82.1 44.5 514564
Austria 70.4 44.7 8901064
Denmark 78.7 46.9 5822763
Iceland 76.4 54.2 364134
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Figure 1. The blue line shows monthly excess mortality between Jan 22 and May 23. For the same
months the red line shows the monthly full vaccination association with excess mortality, and the
green line shows the monthly booster vaccination association with excess mortality.
Table 3. Descriptive statistics concerning the independent- and control variables. The number of
countries included is 31. Mean values and standard deviations are weighed by countries’ population
size.
Variable Min Max Mean St dev
Full vacc by the end of 21 27.7 83.1 68.7 10.5
Booster vacc by the end of 21 4 54.2 31.2 9.88
Avg 2020-2021 mort rel to 16-19 101.4 126.1 112.5 5.67
Life expectancy 19 75.1 84.3 81.5 2.51
Median age 18 36.5 47.1 43.3 2.45
Per capita GDP 19 53 254 101.6 24.5
N=31.
Table 4. Correlations concerning the independent- and control variables. The number of countries
included is 31. Correlations are weighed by countries’ population size.
Full vacc by the end of 21 (1) 1 2 3 4 5
Booster vacc by the end of 21 (2) 0.827***
Avg. 20-21 mort rel to 16-19 (3) –0.742*** –0.750***
Life expectancy 19 (4) 0.857*** 0.629*** –0.620***
Median age 18 (5) 0.316† 0.488** –0.308† 0.195
Per capita GDP 19 0.544** 0.629*** –0.740*** 0.515** 0.168
Two-tailed tests of significance. † p < 0.10; * p < 0.05; ** p < 0.01; *** p < 0.001. N=31.
3. Results
Initially, I carried out explorative analyses, which showed that the independent variables,
country-level full- and booster vaccination uptake by the end of 21, respectively, were negatively
associated with the dependent variable, excess all-cause mortality, Jan, Feb, and Mar 22. The
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following 14 months, i.e., from Apr 22 to May 23, the association turned positive. (Shortly, I will
report on and explain these findings in detail.)
Based on the negative associations between the independent and dependent variables from Jan
22 to Mar 22, Models 1-3 (Table 5) conducted multilevel mixed effects random intercept linear
regressions for this period. The dependent variable observations of monthly excess mortality were
nested at the country level [for further readings, see 15,16]. The regressions were weighted by the
countries’ population size (cf. Table 2) and report robust standard errors concerning the fixed effects
(intercepts and regressors). Model 1 includes full vaccination uptake only as an independent variable.
It shows that between Jan 22 and Mar 22 full vaccination uptake was negatively and significantly
associated with all-cause mortality. Specifically, a one percentage point increase in country-level full
vaccination uptake decreased all-cause mortality by –0.423 percent (95% CI –0.577, –0.270). Model 2
includes booster vaccination uptake only as an independent variable and also shows a significant
negative association with the dependent variable, but the effect is weaker than in the previous model.
Model 3 includes both the independent variables and indicates that full vaccination uptake had a
genuine negative significant association with the dependent variable, while booster vaccination
uptake had a non-significant negative association. Having said that, as the correlation between the
two independent variables is high, taking a value of 0.827 (cf. Table 4) and reflected in a high variance
inflation factor, I do not rule out multicollinearity in Model 3. Therefore, I conclude that both the
independent variables had a strong negative association with the dependent variable between Jan 22
and Mar 22, but full vaccination uptake probably had a more dominant and genuine effect on excess
mortality than booster vaccination uptake. Thus, full vaccination uptake by the end of 21 seems to
have reduced excess mortality the three first months of the following year. In other words, the
findings indicate that full vaccination prevented mortality during this period.
Based on the positive associations between the independent and dependent variables from Apr
22 to May 23 in the explorative analyses referred to above (and which I will report on in more detail
shortly), Models 4-6 (Table 5) conducted multilevel mixed effects random intercept linear regressions
for this period similarly as reported above. Model 4 includes full vaccination uptake only as an
independent variable. It shows that between Apr 22 and May 23 full vaccination uptake was
positively and significantly associated with all-cause mortality. Model 5 includes booster vaccination
uptake only as an independent variable and also shows a significant positive association with the
dependent variable, and the effect is stronger than in the previous model. Specifically, a one
percentage point increase in country-level booster vaccination uptake increased country-level all-
cause mortality by 0.366 percent (95% CI 0.250, 0.482). Model 6 includes both the independent
variables and indicates that booster vaccination uptake had a genuine positive significant association
with the dependent variable, while full vaccination uptake had a non-significant positive association.
Again, multicollinearity may be an issue when including both independent variables in the same
model (cf. my discussion above). Therefore, I conclude that both the independent variables had a
strong positive association with the dependent variable between Apr 22 and May 23, but booster
vaccination uptake probably had a more dominant and genuine effect on excess mortality than full
vaccination uptake. Thus, booster vaccination uptake by the end of 21 seems to have increased excess
mortality from Apr 22 to May 23. In other words, the findings indicate that booster vaccination had
a detrimental effect leading to higher mortality during this period.
Table 5. Multilevel mixed-effects random intercept linear regressions with robust standard errors and
monthly all-cause mortality as the dependent variable.
Model 1 Model 2 Model 3 Model 4 Model 5 Model 6
Months included in the analysis
J
an22-Mar22 Jan22-Mar22
J
an22-Mar22 Apr22-May23 Apr22-
May23
Apr22-
May23
FIXED EFFECTS
Intercept 36.5*** 19.9*** 34.4*** -12.5*** -2.37 -6.07
(5.36) (2.95) (6.30) (3.94) (1.89) (3.72)
Full vacc by the end of 21 –0.423*** –0.351* 0.314*** 0.090
(0.078) (0.138) (0.061) (0.090)
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Model 1 Model 2 Model 3 Model 4 Model 5 Model 6
Booster vacc by the end of 21 –0.400*** –0.092 0.366*** 0.288*
(0.085) (0.161) (0.059) (0.113)
RANDOM EFFECTS
Residual 29.8 29.8 29.8 61.9 61.9 61.9
(11.3) (11.3) (11.3) (6.22) (6.21) (6.21)
Country effect 8.30 12.2 8.05 5.76 3.57 3.29
(5.76) (7.81) (5.89) (2.34) (2.64) (2.31)
Wald χ2 29.3*** 22.0*** 28.5*** 26.2*** 38.6*** 44.4***
Log pseudo-likelihood –4.46e9 –4.50e9 –4.45e9 –2.25e10 –2.24e10 –2.24e10
Monthly observations 93 93 93 433 433 433
Variance inflation factor 3.16 3.17
Estimates are weighted by countries’ population size, and I report robust standard errors in parentheses
concerning the fixed effects. For fixed effects, I also report conservative two-tailed tests of significance. † p < 0.10;
* p < 0.05; ** p < 0.01; *** p< 0.001.
To account for alternative explanations concerning the negative association between full
vaccination uptake and excess mortality between Jan 22 and Mar 22, Table 6 includes the control
variables without altering any statistical conclusion. Similarly, Table 7 includes the control variables
to account for eventual alternative explanations concerning the positive association between booster
vaccination uptake and excess mortality between Apr 22 and May 23, but without altering any
statistical conclusion.
Table 6. Multilevel mixed-effects random intercept linear regressions with robust standard errors and
monthly all-cause mortality as the dependent variable. Months included are Jan 22 to Mar 22.
Model 1 Model 2 Model 3 Model 4
FIXED EFFECTS
Intercept –3.05 –13.7 50.0** 37.7***
(26.6) (40.0) (15.9) (5.02)
Full vacc by the end of 2021 –0.310** –0.576*** –0.397*** –0.316***
(0.098) (0.138) (0.079) (0.084)
Avg. 2020-2021 mort rel to 2016-2019 0.282
(0.187)
Life expectancy 2019 0.745
(0.585)
Median age 2018 –0.356
(0.380)
Per capita GDP 2019 –0.085*
(0.037)
RANDOM EFFECTS
Residual 29.8 29.8 29.8 29.8
(11.3) (11.3) (11.3) (11.3)
Country effect 7.19 7.40 7.64 5.39
(6.20) (5.46) (6.11) (4.88)
Wald χ2 38.9*** 35.2*** 29.5*** 38.9***
Log pseudo-likelihood –4.44e9 –4.44e9 –4.459 –4.42e9
Variance inflation factor 2.23 3.76 1.11 1.42
Estimates are weighted by country size in population by Jan 1, 20, and I report robust standard errors in
parentheses concerning the fixed effects. For fixed effects, I also report conservative two-tailed tests of
significance. † p < 0.10; * p < 0.05; ** p < 0.01; *** p< 0.001. The number of monthly observations is 93.
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Table 7. Multilevel mixed-effects random intercept linear regressions with robust standard errors and
monthly all-cause mortality as the dependent variable. Weighted by countries’ population size.
Months included are Apr 22 to May 23.
Model 1 Model 2 Model 3 Model 4
FIXED EFFECTS
Intercept 7.10 –28.6 –12.7 –4.63*
(19.5) (25.6) (8.83) (2.28)
Booster vacc by the end of 21 0.334*** 0.311** 0.334*** 0.300***
(0.092) (0.094) (0.063) (0.078)
Avg. 2020-2021 mort rel to 16-19 –0.075
(0.152)
Life expectancy 19 0.343
(0.339)
Median age 18 0.262
(0.220)
Per capita GDP 19 0.043
(0.027)
RANDOM EFFECTS
Residual 61.9 61.9 61.9 61.9
(6.21) (6.22) (6.22) (6.21)
Country effect 3.49 3.12 3.25 2.94
(2.58) (2.04) (2.68) (2.27)
Wald χ2 36.1*** 39.4*** 45.6*** 41.9***
Log pseudo-likelihood –2.24e10 –2.24e10 –2.24e10 –2.24e10
Variance inflation factor 2.30 1.66 1.31 1.66
Estimates are weighted by countries’ population size by Jan 1, 20, and I report robust standard errors in
parentheses concerning the fixed effects. For fixed effects, I also report conservative two-tailed tests of
significance. † p < 0.10; * p < 0.05; ** p < 0.01; *** p< 0.001. The number of monthly observations is 433.
Graph A in Figure 2, based on Model 1 in Table 5, shows how the country-level mortality
decreased between Jan 22 and Mar 22 as a function of country-level full vaccination uptake. Similarly,
Graph B, based on Model 5 in Table 5, shows how the country-level mortality between Apr 22 and
May 23 increased as a function of country-level booster vaccination uptake. The graphs report 95%
confidential intervals (CIs) for minimum, weighted mean, and maximum vaccination values. Graphs
C and D are based on regressions that add interaction terms between month dummies and
vaccination uptake. Consistent with Graph A, Graph C shows how the mortality decreased as a
function of full vaccination uptake, but in addition, it illustrates the association for each month
between Jan 22 and Mar 22. Similarly, and consistent with Graph B, Graph D shows how the mortality
increased as a function of booster uptake, but in addition, it illustrates the association for each month
between Apr 22 and May 23.
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Figure 2. Graph A shows country-level variation in excess mortality between Jan 22 and Mar 22 as a
function of country-level full vaccination by the end of 2021 (based on Model 1, Table 5). Graph B
shows country-level variation excess mortality between Apr 22 and May 23 as a function of country-
level booster vaccination by the end of 21 (based on Model 5, Table 5). Graphs C and D show monthly
country-level variations in excess mortality. Minimum, weighted mean, and maximum values are
reported with 95% confidential intervals.
Returning to Figure 1, its red line illustrates the association between full vaccination uptake by
the end of 21 and mortality the following 17 months, and its green line similarly illustrates the
association between booster vaccination uptake and mortality. The lines show that the associations
from Jan 22 to Mar 22 were negative, which aligns with the results I have reported above. However,
from Apr 22 the associations turned positive and remained so for the rest of the study period till May
23, which also aligns with the results I have reported above. In addition, the red and green lines show
that the associations had increasing trends at least the first half of 22, which aligns with Aarstad and
Kvitastein [1] also showing that the association between vaccination uptake and excess mortality
increased over time. Roughly between May 22 and Jul 22 the positive association between full
vaccination uptake and mortality was stronger than between booster vaccination and mortality, but
from Sep 22 the positive association between booster vaccination and mortality was stronger than
between full vaccination and mortality, except for Feb 23. The results indicate that booster vaccination
over time appears to have a stronger effect on mortality than full vaccination uptake, and they align
with findings reported above. Finally, it is worth mentioning that particularly from Apr 22, a change
in the association between booster vaccination and mortality was strongly correlated with a change in
mortality (correlation coefficient is 0.851), which is also possible to observe from the blue and green
lines in Figure 1. It indicates that months with increasing mortality mainly occurred in countries with
high booster vaccination uptakes.
Table 8 reports statistical details concerning the associations between full- and booster
vaccination uptake by the end of 21, respectively, and mortality in the following 17 months. It shows
that the monthly associations were statistically significant, i.e., p<0.05, except for a few months where
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they either were non-significant (Jan 22 for both vaccination types and Sep 22 for full vaccination) or
borderline-significant (Sep 22 for booster vaccination and Jan 23 for full vaccination), i.e., p<0.10.
Table 8. The monthly associations between 21 full- and booster vaccination uptake, respectively, and
excess mortality. .
Yea
r
Month Full vacc assoc with mort Booster vacc assoc with mort
22
Jan –0.324 (0.214) [–0.761, 0.113] –0.315 (0.220) [–0.764, 0.135]
Feb –0.702*** (0.106) [–0.919, –0.484] –0.712*** (0.127) [–0.973, –0.452]
Mar –0.244*** (0.061) [–0.369, –0.119] –0.172* (0.069) [–0.314, –0.031]
Apr 0.136* (0.058) [0.018, 0.254] 0.218** (0.061) [0.094, 0.341]
May 0.277*** (0.057) [0.159, 0.394] 0.209** (0.067) [0.071, 0.346]
Jun 0.383*** (0.091) [0.196, 0.569] 0.311** (0.101) [0.104, 0.519]
Jul 0.585** (0.153) [0.273, 0.897] 0.371* (0.158) [0.047, 0.694]
Aug 0.141* (0.051) [0.036, 0.246] 0.159** (0.047) [0.063, 0.254]
Sep 0.105 (0.075) [–0.048, 0.259] 0.172† (0.100) [–0.033, 0.376]
Oct 0.317* (0.141) [0.030, 0.604] 0.527** (0.160) [0.200, 0.855]
Nov 0.341** (0.091) [0.154, 0.527] 0.437*** (0.081) [0.272, 0.602]
Dec 0.607* (0.247) [0.101, 1.11] 0.907*** (0.226) [0.444, 1.369]
23
Jan 0.145† (0.145) [–0.035, 0.557] 0.450** (0.138) [0.168, 0.731]
Feb 0.354*** (0.065) [0.220, 0.488] 0.282** (0.079) [0.120, 0.445]
Mar 0.265** (0.074) [0.114, 0.416] 0.309*** (0.059) [0.188, 0.430]
Apr 0.280*** (0.069) [0.138, 0.422] 0.388*** (0.056) [0.273, 0.502]
May 0.321*** (0.073) [0.172, 0.470] 0.372*** (0.058) [0.254, 0.491]
The estimates are weighted by the countries’ population size. I report robust standard errors in parentheses and
95% CIs in brackets. Also, I report conservative two-tailed tests of significance. † p < 0.10; * p < 0.05; ** p < 0.01;
*** p< 0.001. N=31, except for May 23 when N=30 (due to missing data from Italy that month). .
Figure 2 shows that the associations between booster vaccination uptake and mortality were
positive in Apr, May 22, and 23, and Sep and Oct in 22. Table 8 additionally reveals that the positive
associations were statistically significant in all those months except for Sep 22, which was borderline
significant. Multilevel mixed effects random intercept linear regression moreover shows an overall
positive significant association between booster vaccination uptake and excess mortality when
observations for these six months are included as the dependent variable (Table 9). I conclude from
the results that booster vaccination uptake consistently induced higher mortality in months when, as
suggested by other studies [4,5], neither heat waves nor energy prices were likely explanations.
Table 9. Multilevel mixed-effects random intercept linear regressions with robust standard errors and
monthly all-cause mortality as the dependent variable. Months included are Apr and May in 22 and
23, plus Sep and Oct in 22.
FIXED EFFECTS
Intercept -1.35
(1.96)
Booster vacc by the end of 21 0.316***
(0.061)
RANDOM EFFECTS
Residual 25.6
(2.93)
Country effect 3.84
(2.52)
Wald χ2 26.6***
Log pseudo-likelihood –8.39e9
Estimates are weighted the countries’ population size by Jan 1, 20, and I report robust standard errors in
parentheses concerning the fixed effects. For fixed effects, I also report conservative two-tailed tests of
significance. † p < 0.10; * p < 0.05; ** p < 0.01; *** p< 0.001. The number of monthly observations is 185.
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4. Discussion
This study updates previous research by Aarstad and Kvitastein [1] showing that 22 all-cause
mortality in 31 European countries increased over time the higher the 21 COVID-19 full vaccination
uptake. The update illuminates that a one percentage point increase in 21 full vaccination uptake
initially decreased all-cause mortality from Jan 22 to Mar 22 by –0.423 percent (95% CI –0.577, –0.270),
but the following 14 months, a one percentage point increase in 21 booster vaccination uptake
oppositely increased mortality by 0.366 percent (95% CI 0.250, 0.482). The findings indicate that full
vaccination initially prevented mortality, but subsequently, booster vaccination, in particular,
detrimentally and consistently induced higher mortality. Studies have argued that heat waves caused
mortality in the 22 summer [4] and energy prices caused mortality in the 22-23 winter [5]. However,
the update shows that booster vaccination uptake consistently induced higher mortality in months
when neither heat waves nor energy prices were likely explanations.
Fortunately, the excess mortality has been reduced from its peak on Dec 22. However, as the
weighted average excess mortality in the 31 countries was positive in 20, 21 [1], and 22, one should
expect that it would be markedly negative at the beginning of 23, but this is not the case. Instead, the
country-level mortality is higher the higher the country-level booster vaccination uptake in
particular, and the association remains positive even when the time asymmetry between the
determination of the independent and dependent variable has increased. In other words, the more
months between the determination of the independent and the dependent variable there is a
significant association, the more likely a genuine causal relationship. Moreover, the initial negative
association between full vaccination uptake and excess mortality Jan 22 - Mar 22, and later the
extended positive association between booster vaccination uptake and excess mortality Apr 22 - May
23, remained statistically significant when controlling for alternative explanations [for a discussion
about how time asymmetry and accounting for alternative explanations partake to assess causality,
please see Stuart Mill cited in Cook and Campbell, 17, pp. 18-20]. Adding to this, Aarstad and
Kvitastein [1] discussed how their study addressed the issue of ecological fallacy [18], e.g., the
classical Robinson’s paradox [19,20] and the Simpson’s paradox [21–24], which also applies to this
updated version.
However, despite addressing the issues of causality and ecological fallacy, a limitation of my
paper is nonetheless that it researches country-level data only. Therefore, I encourage future research
to also investigate person-level data by, for instance, emphasizing different types of vaccination, the
timing, and the number of doses given to males and females in different age cohorts as potential
carriers of long-term mortality. Similarly, I suggest using person-level chronic morbidity as a
dependent variable. Person-level data, too, can have limitations, which the literature has labeled
individualistic fallacy [20,24], but additional research addressing COVID-19 vaccination uptakes as
potential causes of excess mortality and chronic morbidity – important research questions, in my
opinion – at different levels of analysis using different methodologies is warranted, I argue.
Funding: This research has no external funding.
Data Availability Statement: This study uses publicly available data only.
Conflicts of Interest: The author declares no conflict of interest.
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