![Vaccine status and mortality relative to the unvaccinated in persons aged 18-39 and COVID cases. Monthly average COVID cases per day, by specimen date, are from [41], but have been shifted forward by one month.](https://www.researchgate.net/publication/369948867/figure/fig1/AS:11431281145232140@1681388463951/accine-status-and-mortality-relative-to-the-unvaccinated-in-persons-aged-18-39-and-COVID_Q320.jpg)
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Article
Effect of Age, Sex, and COVID-19 Vaccination History
on All-Cause Mortality: Unexpected Outcomes in a
Complex Biological and Social System
Rodney P Jones 1,* and Andrey Ponomarenko 2
1 Healthcare Analysis & Forecasting, Wantage, UK OX12 0NE; hcaf_rod@yahoo.co.uk
2 Department of Biophysics, Informatics and Medical Instrumentation, Odessa National
Medical University, Valikhovsky Lane 2, 65082 Odessa, Ukraine; aponom@hotmail.com
* Correspondence: hcaf_rod@yahoo.co.uk.
Abstract: All vaccines exhibit both specific and non-specific effects. The specific
effects are measured by the efficacy against the target pathogen, while the non-
specific effects can be detected by the change in all-cause mortality . All-cause
mortality data (gender, age band, vaccination history, month of death) between
January 2021 and May 2022 was compiled by the Office for National Statistics.
COVID–19 vaccination gave good protection on many occasions but less so for
younger ages. Each gender and age group shows its own unique vaccination ben-
efit/disbenefit time profile. Individuals are free to make vaccination decisions. For
example, women aged 18-39 show a cohort who do not progress beyond the first
or second dose. The all-cause mortality outcomes for the Omicron variant showed
a very poor response to vaccination with 70% of sex/age/vaccination stage/month
combinations increasing all-cause mortality, probably due to unfavorable anti-
genic distance between the first-generation vaccines and this variant, and addi-
tional non-specific effects. The all-cause mortality outcomes of COVID–19 vac-
cination is far more nuanced than have been widely appreciated, and virus vector
appear better than the mRNA vaccines in this specific respect. The latter are seem-
ingly more likely to increase all-cause mortality especially in younger age groups.
An extensive discussion/literature review is included to provide potential expla-
nations for the observed unexpected vaccine effects.
Keywords: COVID–19; vaccination; all-cause mortality ; age; gender; complex
system; pathogen interference; seasonality; miRNAs; nonspecific vaccine effects
1. Introduction
There is increasing awareness that vaccines exhibit both specific and
non-specific effects [1-4].The specific effects are measured by the efficacy
of the vaccine against the targeted pathogen, while the non-specific ef-
fects can be discerned by evaluating the change in all-cause mortality. A
fully efficacious vaccine will reduce deaths arising from the targeted
pathogen and will thereby also reduce all-cause mortality .
There are two examples of the non-specific effects of vaccines during
COVID-19. During the early stages of the pandemic both influenza and
BCG vaccination gave non-specific protective effects against COVID-19
morbidity and mortality [5-8].
The non-specific effects arise from the ability of pathogen antigens
to cause polyclonal immune activation [9,10], immunostimulation [11],
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© 2023 by the author(s). Distributed under a Creative Commons CC BY license.
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antitumor effects [12], and the ability of pathogen antigens to initiate the
mechanisms of pathogen interference, which are mediated by the produc-
tion of small noncoding RNAs (miRNAs) [4]. The small non-coding
RNAs (ncRNAs): miRNA, siRNA etc. [4]. The small ncRNAs then regu-
late gene expression which either enhances or diminishes infection by
other pathogens. Vaccines (as a class of antigens) also stimulate the pro-
duction of miRNAs [4], and hence create sometimes unexpected, non-
specific outcomes like pathogen interference. Vaccination may also in-
duce antibody-dependent enhancement with negative health conse-
quences [13-16].
While it is true that all vaccines in commercial production are effec-
tive against the target pathogen, we have recently demonstrated that in-
fluenza vaccination has powerful non-specific effects against all-cause
winter mortality [17]. Indeed, using a data set of nearly 100 countries over
a 4-decade period no long-term net effect against all-cause winter mortal-
ity could be demonstrated [17,18]. This was because in some years influ-
enza vaccination was associated with benefit, while in others with net
disbenefit [17,18]. The degree of benefit/disbenefit varied each winter (as
does the composition of the vaccine) and between countries. Obesity may
be associated with net disbenefit [18]. Climatic and other variables appear
to explain the different levels of international pathogen circulation and
diversity over the winter or rainy season near to the equator.
Influenza and SARS-CoV-2 are among the class of RNA pathogens
showing high mutation rates [19-22]. Each new clade of antigen muta-
tions leads to a unique age profile for each variant which is also associated
with the generation of specific miRNAs, further nuances of pathogen in-
terference and epigenetic modifications [23]. In the UK, the COVID–19
pandemic commenced somewhere in early 2020 with the first laboratory-
confirmed death occurring on 2 March 2020 [24]. However, COVID–19
testing capacity was very low at that time and earlier deaths are possible.
Research in the USA suggests that COVID–19 deaths may have started in
early January 2020 [25]. Hence, we have the pre-COVID era which ends
in December 2019 through to the ongoing surges as new variants come to
the fore [22,26-29].
As for the strains of COVID–19 the original strain is predominant
during 2020. The Alpha strain (formerly the Kent variant) appears around
December 2020 and predominates from January to June 2021, the Delta
strain (formerly the Indian variant) commences around May 2021 and
predominates from July to December 2021. While Omicron (BA.1) first
emerges in November 2021 but begins to spread in December 2021 and
dominates in 2022 (BA.2 followed by BA.4/5) [22,26-29]. The alpha variant
caused slightly higher mortality than the original strain and will therefore
affect mortality in the winter of 2020/21 [22,26-29]. The Delta variant
which mainly affected the winter of 2021/22 had higher transmission and
a slightly lower or equal mortality risk [22,26-29].
In the UK, COVID-19 vaccines were approved in the following or-
der: Pfizer/BioNTech (2 December 2020 - deployed 8 December 2020),
AstraZeneca (30 December 2020 - deployed 4 January 2021), Moderna (8
January 2021 - deployed 7 April 2021). Data regarding the proportions of
persons vaccinated by age and time with the different manufacturers
(Pfizer/AstraZeneca/Moderna) does not appear to be publicly available.
COVID–19 vaccination began on 8 Dec 2020 for care home residents,
persons aged 80+, and some health care workers, by 18 January 2021 this
included age 70+ and persons with very high clinical risk, by 15 February
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age 65+ and persons with high risk, and by 22 May age 32+ and age 18+
by 18 June 2021 [29-31]. Following reports of a rare type of blood clot in
late March 2021 for the AstraZeneca vaccine, persons under 30 years were
all given the mRNA vaccine from 7 April 2021 onward, and those aged
under 40 from 7 May 2021 onward [32].
Some younger children with high clinical risk were vaccinated
mainly from January 2021 onward [29-32]. Vaccination of persons aged
16 17 years was from July 2021 onward, 12 15 years from September 2021
onward and 5 11 years from February 2022 onwards. The majority aged
12+ were vaccinated during late 2021. All with mRNA as per the age un-
der-40 rule as above. Booster doses began to be delivered from 16 Sep-
tember 2021, February/March 2022, and September 2022 for the winter of
2022/23 respectively. From around spring 2022 onward all persons were
vaccinated (including booster) mainly with the mRNA vaccine.
Healthcare workers in the NHS (who will mostly be under the age
of 65) began to be vaccinated in from 8 December 2020 (initially with
Pfizer/BioNTech) and by March 2021 over 80% of clinical staff had re-
ceived their first dose and over 39% had received their second dose [33].
Table 1 summarizes which vaccines were prevalent in each age band for
vaccination during the three variants.
Table 1. Vaccine type received by most persons in each age band during the pe-
riods when different SARS-CoV-2 variants were prevalent. Dates in brackets are
when deaths due to the variants mostly occurred. Vaccination generally occurs
from oldest to youngest in each age band.
Age band
Alpha
(Jan-Jun 2021)
Delta
(Jul-21 to Feb-22)
Omicron
(Mar-22 onward)
5-11 Not vaccinated
Start Feb-22
mRNA
mRNA
12-15 Not vaccinated
Start Sep-21
mRNA
mRNA
16-17
Not vaccinated
mRNA
mRNA
18-39
Mixed, increasing
mRNA in last 2
months of Alpha
mRNA mRNA
40-49
mixed
mixed
mRNA
50-59
mixed
mixed
mRNA
60-69
mixed
mixed
mRNA
70-79
mixed
mixed
mRNA
80-89
mixed
mixed
mRNA
90+
Mixed but mRNA
rich
mixed mRNA
To vaccinate the most people, the timing of the second dose was de-
layed to 12 weeks [31]. In practice, vaccination schedules showed local
and regional variation. In my family (RPJ) two people in their mid-60’s
received their second dose at 10 and 11 weeks respectively. In order not
to waste vaccines, many centers would send social media messages to-
ward the end of a day for adults of any age to come and be vaccinated.
A somewhat neglected 2010 study suggested that optimum vaccina-
tion outcomes can only be achieved when the timing of vaccination is ad-
justed relative to the target and competing pathogens [34]. The implica-
tion is that sub-optimum outcomes are possible. The timing for the ap-
proval of COVID-19 vaccines (listed above) meant that the English pop-
ulation (mainly oldest first) only began to be vaccinated during an out-
break of the Alpha variant [23], and with first shot still being delivered to
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some people into 2022 during a large outbreak of the Omicron variant
[35]. Ample opportunities for suboptimum time-based outcomes are
therefore present.
While COVID–19 vaccination is clearly effective against COVID–19
mortality per se [36-38] there is a paucity of studies using the ‘gold stand-
ard’ of a reduction in all-cause mortality . This was achieved using a rec-
ord-linked whole population study of COVID–19 vaccination in England
by the Office for National Statistics (ONS) in 2021 and 2022. This study
uses age bands, month of death, and vaccination status (first, second,
third dose at both up to 12 weeks and greater than 12 weeks post vaccina-
tion) [39].
The all-cause mortality data set used in this study is very large and
covers all residents of England who are registered with a GP and were
residents in England at the 2011 census [39]. This allows detailed analysis
of 944 000 deaths over a 24-month period by gender, over 7 age bands,
and by various stages of vaccination split by less than 21 days, and greater
than 21 days post vaccination, and at monthly intervals – which can be
grouped by SARS-CoV-2 variant [32,38]. Since the ONS data is not ad-
justed for clinical risk, we employ a novel method based on the shape of
the profiles of the all-cause mortality rate relative to the unvaccinated.
Such profiles compare the mortality rates by age and gender within a vac-
cination stage or over time. The shape of the profile gives an internal con-
sistency check. We thereby avoid arguments regarding the exact value of
each data point.
2. Materials and Methods
2.1. All-cause mortality by vaccination status in England
Data regarding month of death, vaccination status and age band
comes from a whole population record-linked study by the Office for Na-
tional Statistics (ONS) [38]. This data source has two files. The first file
contains data for the period January 2021 to May 2022. The second file
contains updated data for the period April 2021 to December 2022. The
data is continuously updated implying that the file to December 2022 has
more deaths in 2021 than the file to May 2022. Numerical data in both
files is stored as text which was converted back to numbers using the Mi-
crosoft Excel Data tool, ‘Text to Columns’. In this study data for the
months January to March 2021 was taken from the first file while that
from April 2021 to December 2022 was from the second file.
Both files give the age standardized mortality rate (deaths per 100
000 person years) for several age bands. Confidence intervals are given in
the ONS data file and show the expected variation with number of deaths,
hence, ± 20% of the mortality rate value based on 100 deaths (for example
females aged 18-39, first dose > 21 days ago, for death in April 2021), and
± 3.9% at 2500 deaths, etc. Age standardized mortality is not given for
instances where there are less than 3 deaths within the age band - zero
deaths are reported as zero.
In this case ‘less than 3’ was substituted by 1 death and the resulting
unadjusted mortality rate was calculated. This was compared to the time
series for that age band and low values were assumed to have 2 deaths.
The mortality rate is standardized within each age band. The raw versus
standardized mortality rates by age band were compared and showed
high correlation (R-squared = 0.998).
The raw mortality rate tended to be lower than the standardized rate
and the interquartile range for the difference was -3.6% to +1.1%. The high
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correlation between the two arises from the fact that the age bands are
mostly only 10-years wide, except for ages 18 39 and 90+. Age standard-
ization within such relatively narrow age bands is unable to have a large
impact on the difference between raw and age standardized mortality
rates. The raw mortality rate is only used when an age standardized value
is not available on 29% of occasions.
This approach is needed to produce a continuous time-series for all
gender/age combinations. Even if the smaller death figures have a wide
confidence interval it is important to see the wider time-series. If the
shape of the time-series is consistent, valid conclusions can be drawn.
3. Results
SARS-CoV-2 variants have unique year-of-age profiles for mortality
[23]. Hence it is possible that studies conducted over different time peri-
ods may contain hidden confounding. This possibility will be commented
upon in both the Results and Discussion.
3.1. Effect of time of vaccination, vaccine history, gender and age upon all-
cause mortality
The ‘real-world’ effects can be analyzed in several ways. For in-
stance, the data can be aggregated to give an average over the entire pe-
riod, or the median calculated from the monthly data. This is illustrated
in Figure A1 which shows results for males plus females from January
2021 to May 2022.
In Figure A1 the entire period average will be driven by the months
in which most deaths occur and more importantly by the contribution
from SARS-CoV-2 variants to the efficacy of the first generation (Wuhan)
COVID-19 vaccines [23]. The median is the middle point of the monthly
ranked values. In general, vaccine protection is evident, except for the
median for first dose at >21 days, but the situation is clearly far more dy-
namic than revealed by simple analysis and the dependence on age seems
key. The higher values of the median for first dose >21 days indicates that
the underlying time-based distribution is highly skewed, far more so than
for other stages of the vaccination journey.
Adverse outcomes are evident especially when using the median
value. Such adverse values should become more apparent when using
the median because the distribution is bounded by a 100% reduction in
the mortality rate relative to the unvaccinated as the maximum possible
efficacy. However, the adverse effects of vaccination are unbounded and
can exceed a +100% maximum possible increase in all-cause mortality rel-
ative to the unvaccinated. This will be addressed later.
Hence, while this view is interesting it is mixing time-dependent
changes. However, note that outcomes >21 days ago are generally worse
than their <21 days counterpart – although highly age dependent, seem-
ingly an outcome of vaccine waning which is especially rapid in the
mRNA vaccines [40]. However, the principle of using shape profiles has
been demonstrated.
A review of pathogen interference strongly implied that the efficacy
of vaccination should generally vary over time, i.e., month of vaccination
[4,33]. This is because COVID-19 infection and COVID-19 vaccination are
competing with other human pathogens whose incidence varies with
time and place, and in response to COVID-19 infection [4,33].
Figure S1(Supplementary material) illustrates how such time-based
analysis is conducted. In Figure S1 this illustrative analysis is restricted to
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males aged 18-39, 60-69 and 90+. The solid lines show the trend in all-
cause mortality for the unvaccinated, while the dashed lines show the
outcomes at monthly intervals for the six different stages in the vaccina-
tion journey, namely, first dose at less than 21 days or equal to or greater
than 21 days, and the same for the second or third/booster doses.
As seen in Figure S1 there are a series of time-based trajectories for
each stage in the vaccination process which go below (protection) or
above (adverse outcomes) the solid lines for all-cause mortality in the un-
vaccinated. The all-cause mortality rate trends downward as may be ex-
pected from ongoing acquisition of naturally acquired immunity and the
different effects of SARS-CoV-2 variants upon mortality [23].
All subsequent analysis then calculates the ratio of age-standardized
all-cause mortality in the vaccinated compared to the unvaccinated. The
proportion of months in which gender/age/vaccine combinations experi-
ence disbenefit is shown in Figure 1a, while Figure 1b shows the trend in
the net effect of vaccination for all ages above 18 years (persons receiving
1 or more doses) compared to the unvaccinated over the interval Apr-21
to Dec-22.
Figure 1. a. Proportion of months (January 2021 to December 2022) when certain
gender/age/vaccine groups experience disbenefit, i.e., all-cause mortality rate in
the vaccinated is higher than the unvaccinated [38]. F = female, M = male.
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
F 18-39
F 40-49
F 50-59
F 60-69
F 70-79
F 80-89
F 90+
M 18-39
M 40-49
M 50-59
M 60-69
M 70-79
M 80-89
M 90+
Proportion with disbenefit
First >21 d First <21 d Second >21 d
Second <21 d Third/booster >21 d Third/booster <21 d
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Figure 1. b. Trend (Apr-21 to Dec-22) for the net age 18+ (age standardized) re-
duction in the all-cause mortality rate relative to the unvaccinated for persons
receiving 1 or more doses of vaccine [38].
In Figure 1a a number below 50% implies a higher proportion of
months in which protection occurs. Note that the rise in all-cause mortal-
ity after January 2022 appears to coincide with both the arrival of Omi-
cron [23] and the introduction of the mRNA vaccine across all ages. It is
impossible to disentangle the two with the ONS data set.
Persons who received only the first dose of the vaccine at >21 days
ago experience higher mortality than the unvaccinated in more than 70%
of months. This percentage rises with age and females tend to have a
lower percentage than males. First dose less than 21 days ago is roughly
50% disbenefit up to age 49, drops to 30% to 40% disbenefit in the age
range 50 69, and then rises to around 60% disbenefit for 70+.
A higher proportion of benefit, i.e., below 50% disbenefit tends to
occur for persons receiving their second dose onward. Highest levels of
proportion of months experiencing benefit occurs for females ages 50 59
(89% of months show benefit) for third dose or booster less than 21 days
ago and for males aged 80 89 and females aged 90+ for third dose or
booster more than 21 days ago (87% of months show benefit).
This mix of benefit/disbenefit translates into a whole population net
benefit from vaccination which is shown in Figure 1b. As can be seen the
net population benefit declines with time with the greatest decline after
the arrival of the Omicron variant, whose deaths start to occur from the
end of Feb-22. Net benefit has dropped to close to zero by around Dec-22
– although the unvaccinated will have progressively gained naturally ac-
quired immunity over this time (discussed later). Note that each SARS-
CoV-2 variant has a distinct age profile for deaths [23].
Figures A2.1 to A2.7 in the Appendix show the full detail of the all-
cause mortality rate relative to the unvaccinated at monthly intervals, for
males and females, by age band, and vaccine history for the 24 months
from January 2021 to December 2022 [38].
While the outcome of vaccination is generally protective, especially
note the rise in all-cause mortality for specific vaccine-time-age-gender
combinations, i.e., protection can rapidly turn to disbenefit under a spe-
cific set of conditions which would be outside the scope of a controlled
-70%
-60%
-50%
-40%
-30%
-20%
-10%
0%
4月-21
5月-21
6月-21
7月-21
8
月
-21
9月-21
10月-…
11
月
-…
12月-…
1月-22
2
月
-22
3月-22
4月-22
5
月
-22
6月-22
7月-22
8
月
-22
9月-22
10月-…
11
月
-…
12月-…
All-
cause mortality rate relative
to unvaccinated
Female
Male
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vaccine trial. Also note that a controlled vaccine trial would usually only
measure:
1. Specific outcomes rather than the non-specific outcomes
which lie hidden in the all-cause mortality approach.
2. Outcomes for the fully vaccinated.
A controlled trial is also unlikely to present results as a monthly
trend.
Figures A2.1 to A2.7 show the months when the benefit/disbenefit
occurs while Figure A3 (Appendix) shows the pattern of benefit/disben-
efit associated with vaccine history, after combining male and female to-
gether to gain the benefit of large sample size, during each of three SARS-
CoV-2 variants [23]. Figure A3 is presented in landscape format to enable
the full detail to be discerned.
Figure A3 amplifies the time patterns shown in Figures A2.1 to A2.7
and reveals important patterns of benefit/disbenefit (specific to all-cause
mortality ) for each of the SARS-CoV-2 variants. As can be seen outcomes
during Omicron (when all persons received mRNA vaccines) were gen-
erally adverse. The y-axis is truncated at + 500% disbenefit, and the cluster
of adverse outcomes for any age receiving their first dose range above
+300%.
As an overall summary, the benefit against all-cause mortality de-
clines from Alpha to Omicron, hence the median value is 60% reduction
in all-cause mortality for Alpha, 33% reduction in all-cause mortality for
Delta and 79% increase in all-cause mortality for Omicron. Best protection
achieved was 86% reduction in all-cause mortality for Alpha (age 80-89,
second dose less than 21 days ago), 80% reduction for Delta (age 50-59,
third dose or booster less than 21 days ago), and only a 43% reduction in
all-cause mortality for Omicron (age 18-39, third dose or booster less than
21 days ago).
In general, almost all ages had an adverse outcome during Delta
with a first dose greater than 21 days ago. Otherwise, the Delta outcomes
were generally positive. During Alpha, ages 18-39 had adverse all-cause
mortality outcomes, all other ages were beneficial. During Omicron, only
6 out of 42 combinations were beneficial. Four of these ranged between
ages 50 to 89 and had benefit for the third dose or booster greater than 21
days ago. The other two groups had benefit at less than 21 days for third
dose or booster, age 50-59 and 18-39. Age 90+ showed a 20% reduction in
all-cause mortality less than 21 days after vaccination and only a 5% re-
duction in all-cause mortality for greater than 21 days post vaccination.
These patterns are further complicated by time lags which are possi-
bly artefacts of the timing of vaccination for the general population as
detailed in the Introduction. Note that persons with high clinical risk and
health care staff (with higher risk due to exposure) are generally vac-
cinated earlier than the general population. Such potential time lags are
illustrated in Figure A4 for unvaccinated persons. In Figure A4 the Feb-
ruary 2021 peak for persons over the age of 69 is the outcome of the Alpha
outbreak which commenced in late 2020. However, deaths per se peak in
February of 2021. The older ages are affected because Alpha targets the
elderly more so than the young [23].
At the other extreme is a trough occurring in April or May of 2021
for age bands below 50 years. The trough represents the non-outbreak tail
end of the Alpha variant [23], followed by the arrival of the Delta variant
which specifically targets the younger ages [23].
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Further undulations reflect the relative impact of outbreaks of the
three different SARS-CoV-2 variants upon different age groups [23]. The
arrival of Delta has a disproportionate impact on the younger age bands
[23]. Likewise, Omicron had a disproportionate effect on the age 90+
group [23]. Hence the overall shapes of the trends are consistent with the
independently characterized effects of the variants upon the year-of-age
age profiles for mortality [23].
3.2. Vaccination during large COVID–19 surges
The final issue to be addressed is the timing of vaccination relative
to large surges in COVID–19 activity, creating the equivalent to super in-
fection. Figure 2 attempts to explore this issue using age 18 39 years as
an example.
Figure 2. Vaccine status and mortality relative to the unvaccinated in persons
aged 18–39 and COVID cases. Monthly average COVID cases per day, by speci-
men date, are from [41], but have been shifted forward by one month.
Interpreting the analysis is complicated by time lags between infec-
tion and ultimate death, and that the month is the month of death. The
time lags arise from the time between infection, serious illness, and ulti-
mate death. These may be age-dependent and probably show highly
skewed time profiles. Figure 3 assumes a 30-day lag hence COVID cases
per day [58] have been shifted forward by 1 month. A further complica-
tion is that the cases (black dashed line) are by specimen date which in-
volves an additional lag from the point of infection. The specific age pro-
file of each variant also leads to time-dependent undulations in the dif-
ferent age bands.
From Figure 2 adverse outcomes (allowing for lags) look to be more
prevalent during periods of high COVID–19 activity. Note that the surges
in activity from July-21 onward involve the high transmission/low mor-
tality Omicron variants [23]. Also note the scale on Figure 3 has been trun-
cated at 400% relative mortality. Hence for second dose more than 21 days
ago for death in Jan-21 the figure is +900% higher relative mortality, while
first and second dose less than 21 days ago for deaths beyond Mar-22 the
relative mortality is above +1000%.
0
40000
80000
120000
-100%
0%
100%
200%
300%
400%
1月-21
2月-21
3月-21
4月-21
5月-21
6月-21
7月-21
8月-21
9月-21
10月-21
11月-21
12月-21
1月-22
2月-22
3月-22
4月-22
5月-22
COVID-19 new cases per day
Mortality rate relative to unvaccinated
First dose >21 days ago First dose <21 days ago
Second dose >21 days ago Second dose <21 days ago
Third dose or booster >21 days ago Third dose or booster <21 days ago
all-age cases per day (previous month)
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The possibility that vaccination during an active infection yields
worse outcomes looks to be real – however will be modified by other fac-
tors which will be covered in the Discussion.
3.3. Range in ‘real world’ vaccine outcomes
To illustrate the ‘real world’ outcomes in all-cause mortality Figure
3 shows the distribution of all possible group outcomes: gender (x2), vac-
cine history (x6), age group (7x), month of death (x24) with data available
for 1747 combinations. These combinations have then been split into the
major variant causing death (Alpha n = 336, Delta n = 596,Omicron n =
815). As can be seen in Figure 3 vaccine effectiveness declines from Alpha
through to Omicron.
Vaccine effectiveness for Omicron is extremely poor such that only
24% of female and 27% of male combinations showed a reduction in the
all-cause mortality rate relative to the unvaccinated. For females the
worst Omicron outcomes exceed a 1000% increase in all-cause mortality
for 19% of combinations, and 17% of combinations for males. All persons
were vaccinated with mRNA vaccine during Omicron.
Figure 3. Distribution of all possible (n = 1188) combinations of gender: age group:
vaccine history: month of death, split into the major variant causing death. Note
the scale on the Y-axis has been truncated at + 300% increase in all-cause mortality
relative to the unvaccinated.
For comparison, during Alpha only 10% of combinations exceed a
73% increase in relative mortality for females and +173% for males. Dur-
ing Delta 10% of combinations exceed a 126% increase for females and
+137% for males.
The best outcome was achieved during Alpha with a 95% reduction
in all-cause mortality relative to the unvaccinated for males aged 70-79
receiving their second dose less than 21 days ago for deaths during Feb-
ruary 2021.
-100%
-50%
0%
50%
100%
150%
200%
250%
300%
0% 20% 40% 60% 80% 100%
All-
cause mortality relative to unvaccinated
Proportion of combinations
Female Alpha
Male Alpha
Female Delta
Male Delta
Female Omicron
Male Omicron
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The best outcome during Delta was a 94% reduction in all-cause
mortality for females aged 70-79 receiving their third dose/booster less
than 21 days ago for deaths during September 2021.
It is assumed that no one would argue regarding the 95% reduction
in all-cause mortality, however, some disquiet may be expressed about
the proportion of combinations showing a net increase in the all-cause
mortality rate relative to the unvaccinated.
We must point out that the net effects of vaccination (as in Figure 1b)
are driven by the groups with the highest number of person years and
that some of the poor outcomes are for groups with low person years –
which account for 28% of combinations. However, this is the range in ‘real
world’ outcomes in a system with exquisite levels of biological and social
complexity.
To provide more detail than in Figure 3, Figure A5.1 to A5.5 (Appen-
dix) investigates the possibility that the switch from viral vector to mRNA
vaccine reveals any insight into the efficacy/safety of the two vaccine
types against all-cause mortality. These also include vaccine outcomes
during the transition period of approximately two months when one var-
iant is replaced by another [23]. As can be seen in Figure A5.1 to A5.5 a
large proportion of vaccine types perform well for Alpha. Performance
decreases slightly during the Alpha to Delta transition period [23], a fur-
ther decrease during Delta followed by large decreases during the Delta
to Omicron period and then poorest performance during Omicron when
the mRNA type is almost universally employed.
Those vaccinated against Omicron (first, second, third or booster
dose all less than 21 days ago) have around 50% of the person years found
in the unvaccinated group. As indicated in Figure A5.1 to A5.5 the out-
comes were mostly poor. Taking all persons vaccinated (all ages, males
and females) the overall all-cause mortality relative to the unvaccinated
was 54% higher. In hindsight and couched in terms of the effect on all-
cause mortality it may have been better not to vaccinate against Omicron,
except perhaps in those over 80 years where this variant caused highest
deaths [23].
Once again, an excellent example of vaccination in the face of scien-
tific uncertainty. The key point being that all-cause mortality reveals the
net balance between the specific effects of COVID-19 vaccines against
COVID-19 per se (protective) and the non-specific effects of the vaccine
(unknown protection or disbenefit).
The all-cause mortality outcomes reported above are significantly
worse than the best specific vaccine efficacy during Delta for Pfizer-Bi-
oNTech of around 67% at 2 to 4 weeks, followed by rapid waning [42].
The effectiveness does vary by Omicron subvariants [43], however, wan-
ing remains a common feature.
The key question is whether the risk of thromboembolism from virus
vector [44] is the same during Alpha, Delta, and Omicron. Also, would
viral vector have performed as well as mRNA during Delta when as-
sessed using all-cause mortality? Such issues require urgent research us-
ing international data.
3.4. Individuals make decisions about their vaccination history
While medical professionals recommend full vaccination, the public
are free to make their own value judgements. For example, numerous
women experienced disruption to their menstrual cycle following
COVID-19 vaccination. Many decided to curtail their vaccination
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journey. This group of women who are sensitive to menstrual disruption
by COVID-19 vaccination then form a cohort which may respond to ex-
posure to different SARS-CoV-2 variants in different ways to the rest of
the population.
Figure 4 gives an example of such decisions across all ages. These
persons accumulate in the greater than 21 days post vaccination group.
The unvaccinated are included for comparison. In December 2022 of the
total persons not progressing beyond their first doses 67% and 70% were
aged 18-39, female and male respectively. Since they are under age 40
many received the mRNA vaccine (under 30 from April 2021 and under
40 from May 2021) – mixed vaccine types prior to this. As can be seen
there is only a slight drop off for the first dose group, more so for the
second dose group.
For comparison, of the unvaccinated 60% (female) and 62% (male)
are aged 18-39. The small drop between 2021 and 2022 in the unvac-
cinated is probably due to death, indicating that this group have fixed
opinions.
However, Figure 4 is a composite of multiple reasons to curtail the
vaccine journey and in the 18-39 age group the risk of COVID-19 death is
very low, which may be a factor involved in the decision to curtail the
vaccine journey.
Figure 4. Males and females (all ages) who do not progress beyond first or second
dose in December 2021 and December 2022.
This is an example of a real-world vaccination outcome which was
unanticipated. The number of ‘stuck’ journeys decreases with age due to
higher overall adherence to full vaccination based on the known higher
risk of death in the elderly. Male ‘stuck’ journeys are lower in the older
age groups presumably due to loss of members due to higher male mor-
tality.
10000
100000
1000000
10000000
First >21 d
First >21 d
Second >21 d
Second >21 d
Unvaccinated
Unvaccinated
Female Male Female Male Female Male
Persons
Dec-21
Dec-22
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Figure 5. Vaccine journey outcomes in persons (male and female) who do not
progress to further vaccination following their first or second dose of COVID-19
vaccine.
Figure 5 shows the effect of the point at which the vaccination jour-
ney is halted and of age upon vaccine outcome. The set of results for ‘third
dose or booster’ are possibly not strictly ‘stuck’ journeys since for Delta
they will be the third dose while for Omicron the booster dose. However,
they are included for comparison. No third/booster doses were delivered
during Alpha.
The first thing to note is that there are a set of nested cycles embed-
ded in the data – which suggests that the data is internally consistent.
While these patterns may not be fully understood it must be recalled that
each variant has its own unique age profile compared to the original Wu-
han strain which formed the basis for the vaccines used prior to the win-
ter of 2022/23 [23].
For persons receiving their first dose >21 days ago the cycle during
Alpha has its best outcome at age 80-89, etc. While for first dose >21 days
there is a further cycle due to the variant where worst outcome peaks for
Delta at age 70-79, etc.
Other examples are persons receiving their first COVID-19 vaccine
dose during the early 2022 Omicron outbreak – who then go on to expe-
rience very high all-cause mortality relative to the unvaccinated across all
age bands. The advice to commence first dose vaccination at this very late
stage was probably well-intended given the highly mutated nature of the
Omicron variant, however, it was also known that Omicron had far lower
clinical risk. This is an example of decision making in the face of scientific
uncertainty and possibly the hidden assumption or general ignorance
that the nonspecific effects of vaccination do exist and may be relevant
when giving advice to individuals who have managed to survive for
nearly two years of the pandemic.
3.5. Vaccination history and the ratio of male to female mortality rate
It is well known that in the unvaccinated males suffer a higher mor-
tality rate than females [45-46]. Figure 6a and 6b confirm this view, how-
ever with added ‘real world’ age band and variant interactions.
-90%
-60%
-30%
0%
30%
60%
90%
120%
150%
180%
210%
18-39 First >21 d
40-49 First >21 d
50-59 First >21 d
60-69 First >21 d
70-79 First >21 d
80-89 First >21 d
90+ First >21 d
18-39 Second >21 d
40-49 Second >21 d
50-59 Second >21 d
60-69 Second >21 d
70-79 Second >21 d
80-89 Second >21 d
90+ Second >21 d
18-39 Third or booster, >21 d
40-49 Third or booster, >21 d
50-59 Third or booster, >21 d
60-69 Third or booster, >21 d
70-79 Third or booster, >21 d
80-89 Third or booster, >21 d
90+ Third or booster, >21 d
All-
cause mortality relative to the
unvaccinated
Alpha
Delta
Omicron
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Figure 6. a. Median value for the ratio of male to female all-cause mortality rela-
tive to the unvaccinated for various stages of vaccination and the unvaccinated
during the Alpha, Delta, and Omicron variants. The median comes from the
ranked values of monthly mortality rates and may not reflect the months with the
highest deaths during the peak of each variant’s outbreak.
Figure 6. b. Average value for the ratio of male to female all-cause mortality rela-
tive to the unvaccinated for various stages of vaccination and the unvaccinated
during the Alpha, Delta, and Omicron variants. The average is calculated from
the sum of deaths and person-years and therefore is shifted to those months with
the highest deaths. Due to low deaths in the first dose less than 21 days group
during Delta the data has been smoothed by shifting percentages between the
first 5 groups.
0%
40%
80%
120%
160%
200%
240%
18-39 Unvaccinated
40-49 Unvaccinated
50-59 Unvaccinated
60-69 Unvaccinated
70-79 Unvaccinated
80-89 Unvaccinated
90+ Unvaccinated
18-39 First <21 d
40-49 First <21 d
50-59 First <21 d
60-69 First <21 d
70-79 First <21 d
80-89 First <21 d
90+ First <21 d
18-39 First >21 d
40-49 First >21 d
50-59 First >21 d
60-69 First >21 d
70-79 First >21 d
80-89 First >21 d
90+ First >21 d
18-39 Second <21 d
40-49 Second <21 d
50-59 Second <21 d
60-69 Second <21 d
70-79 Second <21 d
80-89 Second <21 d
90+ Second <21 d
18-39 Second >21 d
40-49 Second >21 d
50-59 Second >21 d
60-69 Second >21 d
70-79 Second >21 d
80-89 Second >21 d
90+ Second >21 d
18-39 Third/booster <21 d
40-49 Third/booster <21 d
50-59 Third/booster <21 d
60-69 Third/booster <21 d
70-79 Third/booster <21 d
80-89 Third/booster <21 d
90+ Third/booster <21 d
18-39 Third/booster >21 d
40-49 Third/booster >21 d
50-59 Third/booster >21 d
60-69 Third/booster >21 d
70-79 Third/booster >21 d
80-89 Third/booster >21 d
90+ Third/booster >21 d
Male vs female all
-cause mortality rate
Alpha Delta Omicron
-20%
0%
20%
40%
60%
80%
100%
120%
140%
Unvaccinated 18-39
Unvaccinated 40-49
Unvaccinated 50-59
Unvaccinated 60-69
Unvaccinated 70-79
Unvaccinated 80-89
Unvaccinated 90+
First <21 d 18-39
First <21 d 40-49
First <21 d 50-59
First <21 d 60-69
First <21 d 70-79
First <21 d 80-89
First <21 d 90+
First > 21 d 18-39
First > 21 d 40-49
First > 21 d 50-59
First > 21 d 60-69
First > 21 d 70-79
First > 21 d 80-89
First > 21 d 90+
Second <21 d 18-39
Second <21 d 40-49
Second <21 d 50-59
Second <21 d 60-69
Second <21 d 70-79
Second <21 d 80-89
Second <21 d 90+
Second > 21 d 18-39
Second > 21 d 40-49
Second > 21 d 50-59
Second > 21 d 60-69
Second > 21 d 70-79
Second > 21 d 80-89
Second > 21 d 90+
Third /booster <21 d 18-39
Third /booster <21 d 40-49
Third /booster <21 d 50-59
Third /booster <21 d 60-69
Third /booster <21 d 70-79
Third /booster <21 d 80-89
Third /booster <21 d 90+
Third /booster > 21 d 18-39
Third /booster > 21 d 40-49
Third /booster > 21 d 50-59
Third /booster > 21 d 60-69
Third /booster > 21 d 70-79
Third /booster > 21 d 80-89
Third /booster > 21 d 90+
Male vs female all
-cause mortality rate
Alpha
Delta
Omicron
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Figure 6a uses the median value of the male to female relative mor-
tality rates to obtain an estimate for this ratio. The median is said to be a
‘robust’ statistic [47]. Figure 6b uses the average (sum of deaths divided
by sum of person-years) across the entire period of each variant and will
be weighted to those months with highest deaths. This method has the
advantage of substantially higher deaths over the period of each variant,
and hence lower statistical uncertainty. What is most surprising is the
magnitude of the interactions between age band, vaccination status and
variant. As an overall comment Omicron generally shows lower relative
male mortality. The unvaccinated show the lowest divergence with vac-
cine history and the highest agreement between the two methods. In ad-
dition, the ratio can drop below 1, but only with Omicron for age 50-59
third/booster given less than 21 days ago in Figure 6b.
Up to the present no one has had a data set large enough and at
monthly level, for a rapidly mutating pathogen such as influenza or
SARS-CoV-2, to explore such subtle nuances.
4. Discussion
The discussion includes a survey of explanatory literature and will
attempt to present a whole system framework in which to interpret both
the conclusions of this paper and other international studies. It will also
seek to emphasize roles for system complexity [4,17,18], and how this
may contribute to unexpected non-specific vaccine outcomes under spe-
cific conditions. We begin by emphasizing the roles of small noncoding
RNAs in the processes of ‘pathogen interference’ and how these can have
unintended nonspecific consequences affecting vaccine outcomes [4].
4.1. Factors driving complexity in COVID–19 mortality and the vaccine
response
4.1.1. The central role of small non-coding RNAs in gene expression
Some 70 % of the human genome is transcribed to RNA but only 2%
of these are translated into proteins [48]. The other transcripts are defined
as noncoding RNAs (ncRNAs), including long noncoding RNAs
(lncRNAs) and small noncoding RNAs (smRNAs). Small non-coding
RNAs (microRNA, small nuclear RNA, small nucleolar RNA, tRNA de-
rived small RNA and Piwi-interacting RNA) can be considered a rela-
tively new class of molecule that are differentially regulated in many dis-
eases [49]; hence and represent a class of biomarkers to be explored in a
variety of contexts [50]. The terms ‘micro’ and ‘small’ are seemingly used
interchangeably in the literature.
In humans, miRNAs are synthesized from primary miRNAs (pri-
miRNAs) in two stages by the action of two RNase III-type proteins:
Drosha in the nucleus and Dicer in the cytoplasm [48-52]. The mature
miRNAs are then bound by Argonaute (Ago) subfamily proteins. These
miRNAs target mRNAs and thereby function as posttranscriptional reg-
ulators [48-52]. See later regarding the potential effects of mRNA vaccines
binding to miRNAs.
miRNAs are estimated to regulate over 30% of mammalian gene
products [53], and mostly function post-transcriptionally to regulate the
gene expression by hybridizing to mRNAs, but are also involved in pro-
tein translation, RNA splicing, gene activation/silencing, modifications,
and editing [52,54,55]. Some miRNA transcription initiators have been
identified [53] – giving potential insight into why certain miRNAs are
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selected based upon various challenges. Of the 27 500 known human
ncRNAs there are more than 1881 miRNA precursor sequences in the hu-
man genome, allowing the generation of 2588 mature miRNAs of which
200 are associated with at least one mRNA [56].
ncRNAs are expressed in different tissues and cell types that can in-
teract with target mRNAs, through base-pairing, to inhibit their transla-
tion [54-56]. miRNAs are powerful regulators of cellular activities includ-
ing cell growth, differentiation, development, proliferation and death,
apoptosis, fat metabolism, neuronal patterning, hematopoietic differenti-
ation, and in immune function [57]. An altered miRNA expression is as-
sociated with many human diseases and poor immune function [49,50,
52,58,59]. They are also involved in the regulation of autoimmune dis-
eases [60] and oncogenesis [61]. The pathways producing the multiple
types of miRNAs are interconnected and compete among themselves
[59]. ncRNAs are also involved in the processes of epigenetic modifica-
tion [62,63], which adds a further layer of complexity into the issue of
gene expression. The role of miRNAs in viral infections, including the in-
itiation and progression of infectious diseases, and especially respiratory
infections, has been extensively reported [64-67].
A selection of miRNAs are transported through the cell membrane
[68] and activate genes in neighboring and more distant cells [69].
To date, more than 500 miRNAs have been attributed to viral infec-
tions [68], of which at least 200 are from DNA viruses [68], while at least
70 are specific to common bacterial infections [69]. Host and pathogen
miRNAs target both viral and cellular transcripts and are involved in cel-
lular reprogramming to regulate the latent-lytic switch, support viral rep-
lication by promoting cell survival, proliferation, and/or differentiation,
and modulate host immune response. In this way, pathogen miRNAs and
proteins work synergistically, exploiting conserved gene regulatory
mechanisms within the host cell to promote a cellular environment favor-
able to the completion of the viral life cycle [68-72].
Several excellent reviews and papers cover the scope of miRNA,
their mode of action and the ever-expanding range in small RNAs stim-
ulated by (mainly) RNA viruses [72-76]. However, it must be noted that
the entire field of ncRNAs is expanding at a rapid pace with nomencla-
ture and number of entities growing all the time – new techniques show-
ing that many had been missed in the past [72-76]. Viral infections such
as COVID-19 trigger specific miRNAs – see next section. An earlier re-
view [4] also emphasized the role of age in the ncRNA response. In nature
miRNAs show long-term stability but have mechanisms for removal if no
longer required [77].
Genomic distribution analysis reveals the highest density of miRNA
sequences on the X chromosome [78]. This links directly to the lower risk
of female death from COVID–19 infection [79] and to any miRNAs asso-
ciated with chromosome 3 identified as a genetic risk factor (see later).
While the ability of vaccines to stimulate a large antibody response
is a key part of the specific effects of vaccines, their ability to stimulate the
production of ncRNAs, is a poorly investigated area and is probably cen-
tral to understanding the diversity of vaccine outcomes observed in this
study. Discussed later.
The relevance of this section to this study is that powerful regulatory
mechanisms for non-specific effects from antigen presentation (patho-
gens and vaccines) do indeed exist. However, the underlying mecha-
nisms remain obscure and operate within a complex systems framework.
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Unexpected outcomes arising in certain situations should therefore be ex-
pected, as evidenced by the time-dependent behavior of COVID-19 vac-
cination upon all-cause mortality displayed in Figures A2.1 to A2.7 in the
Appendix.
4.1.2. COVID–19 infection alters the miRNA landscape and ensuing
gene expression.
In the absence of vaccines, infection by pathogens modify infection
by other pathogens via pathogen interference, which has its basis in
miRNA production and subsequent modification of gene expression in-
cluding interferon production [4]. This is especially important in the clin-
ical outcome of all respiratory infections [62].
There are now numerous studies regarding the effects of COVID-19
infection upon miRNA production, both by cells in response to the infec-
tion and by SARS-CoV-2 to promote its own successful infection [80-96,].
These studies include the effects on gene expression, altered biochemical
pathways, interferon signaling, interaction with host mRNAs, and have
demonstrated that various miRNAs appear associated with clinical sever-
ity including inflammatory and cytokine storm mechanisms [85,94-97].
One study has identified both chemokine (CC) CCL20, inflammatory cy-
tokines IL6 and IL10, and miR-451a as key correlates of fatal COVID-19
[98], i.e., miRNAs are part of a wider whole system response.
COVID–19 replication releases sub-genomic RNAs (sgRNAs) some
of which are the precursors of pathogen miRNAs [84,99]. A core sgRNA
repertoire exists which encode functional short peptides. The ratio of
Spike sgRNA to nucleocapsid is highest among β-coronaviruses in
COVID–19, the adjustment of this ratio is by modifications to the viral
RNA replication machinery, representing a form of viral gene regulation
probably involved in generating new variants. COVID–19 sgRNA diver-
sity is further enhanced by bidirectional template switching [84,99].
When reading these studies recall that only selected miRNAs are
transported out of the cell [66,67] and remain stable in blood [49]. Hence
studies on miRNAs in COVID-19 disease severity are measuring a poten-
tially confusing mix of selectively transported miRNAs and miRNAs re-
leased by extensive cell lysis in more severe infections. This probably ex-
plains the confusing variety of miRNAs described in human studies us-
ing blood samples. Alas, further confusion can arise because different
protocols and libraries can miss certain miRNAs [73,74,78]. Computa-
tional methods are also being developed to identify miRNA transcription
start sites (TSS) [53].
Zhang et al [100] propose that that COVID-19 cleverly exploits the
interplay between the miRNAs and other biomolecules to avoid being ef-
fectively recognized and attacked from host immune protection as well
to deactivate functional genes that are crucial for immune function.
Dare we suggest that COVID-19 vaccination has unduly focused on
antibody production which has ignored a vast regulatory machinery of
potentially greater importance. The role of miRNAs in the ‘real world’
success of vaccination has been largely ignored and is discussed later.
4.1.3. Interplay between interferons and miRNAs
Interferons (IFNs) are cytokines that are spontaneously produced in
response to virus infection, i.e., as in pathogen interference [4]. They act
by binding to IFN-receptors (IFN-R), which trigger JAK/STAT cell signal-
ing and the subsequent induction of hundreds of IFN-inducible genes,
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18
including both protein-coding and miRNA genes. IFN-induced genes
then act synergistically to prevent virus replication and create an anti-
viral state. miRNAs are therefore integral to the innate response to virus
infection and are important components of IFN-mediated biology [101-
105].
It is of interest to note that severe COVID-19 disease patients mount
a dysregulated interferon response compared to those with mild disease
[106] and that the Omicron variant is less effective than Delta in antago-
nizing the interferon response in human cells [104]. Treatment with inter-
feron-α, interferon-β, and interferon-γ revealed that the weaker inter-
feron antagonism by Omicron translates into an increased Omicron sen-
sitivity to interferon treatment [105]. These seemingly explain the
reemergence of influenza due to altered pathogen interference upon the
arrival of Omicron [107].
4.1.4. COVID–19 alters coinfection and super infection by other patho-
gens via pathogen interference
Due to widespread lack of awareness to pathogen interference the
imposition of lockdowns and other measures have been incorrectly at-
tributed to changes in pathogen prevalence [4,108,109]. Admissions for
respiratory tract infections declined after the outbreak of COVID–19. The
proportion of other viruses was 14% lower and the proportion of bacte-
rial- and viral co-detections was reduced by half. Streptococcus pneumoniae
in the pre-COVID was largely replaced by Staphylococcus aureus in the
COVID cohort, however Adenovirus, Parainfluenza virus and several
bacteria showed little change in the proportion of detections [110]. Such
selective shifts cannot be explained by the imposition of protective
measures.
More importantly, during COVID–19 in 2020 the genetic diversity of
influenza(s) was dramatically reduced [111] with influenza B/Yamagata
going ‘extinct’ [112]. Such selective ‘extinction’ is not the outcome of lock-
downs. We have demonstrated that such changes are more likely to be
the result of COVID-induced pathogen interference [4].
However, all studies agree that only a small proportion of COVID–
19 compared to influenza patients have a co-infection or super infection
[113,114], and those that do mostly occur among those admitted to inten-
sive care [113,114]. Viral co-infection is even less common than bacterial
co-infection [113]. These facts further confirm that pathogen interference
is the primary cause with lockdown and other measures playing a sec-
ondary role. The most common bacteria are Mycoplasma pneumonia, Pseu-
domonas aeruginosa and Haemophilus influenzae, while Respiratory Syncyt-
ial Virus and influenza A are the most common viruses [113]. Viral coin-
fection is more common in children [115] and a smaller number of fungal
infections have been reported [113].
Wang et al [116] have reviewed the ability of COVID–19 to disrupt
both the respiratory and gut microbiota. This has knock-on effects to the
spectrum of microbiota produced metabolites, immune function and to
long-COVID.
The previous two sections provide a mechanistic basis for the ob-
served ability of COVID–19 to suppress other pathogens via both changes
in host and pathogen-derived miRNAs and far wider metabolic and im-
mune changes.
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19
Hence, vaccination in persons already COVID infected, with its pro-
found effects on ncRNA production, is a candidate for unexpected out-
comes as was demonstrated in Figures A2.1 to A2.7.
4.1.5. Non-specific effects of vaccines
Given the implications of this study to the non-specific effects of vac-
cines via ncRNAs and other heterologous mechanisms it is of interest to
note the reported beneficial effects of prior BCG vaccination against
COVID–19 infection [5,6,117,118]. Both influenzas, diptheria, and tetanus
vaccines have likewise been suggested to reduce serious COVID-19 out-
comes [119]. Our unpublished research suggests that the nonspecific ef-
fects of influenza vaccine disappeared with the arrival of the Omicron
variant. It is probably fair to say that many vaccinologists are unaware
that vaccines alter the miRNA landscape with consequent non-specific
consequences.
In their recent review Diener et al [120] note regarding miRNAs that
“their cellular effects are so numerous that off-target effects can hardly be
avoided”.
Zhang et al [121] likewise point out that “One miRNA generally tar-
gets tens and even hundreds of genes. We named it “too many targets for
miRNA effect” (TMTME). Further, two adverse events from the discon-
tinuation of two miRNA therapeutics were exactly answered by TMTME.
In summary, TMTME is inevitable because of the special complementary
approach between miRNA and its target. It means that miRNA therapeu-
tics would trigger a series of unknown and unpreventable consequences,
which makes it a considerable alternative for application.”
It is at this point that we suggest that the nonspecific effects of vac-
cines may be mediated by their miRNA profiles.
Sufficient studies on the miRNA profiles generated in response to
human vaccines have been published to support the notion that the pro-
files are specific to the vaccine type and its efficacy in individuals [122-
129]. Circulating extracellular vesicles (EVs) deliver miRNAs to myeloid
and lymphoid cells [127]. miR-21 levels in serum EVs also increase with
aging and regulates the expression of IL-12 required for Th1 responses;
therefore, EV miR-21 is expected to regulate vaccine efficacy. miR-451a,
another important miRNA, is abundant in serum EVs and controls the
expression of cytokines, such as type I interferon and IL-6 [127].
In COVID-19 vaccination EV miR-92a-2-5p levels in sera were nega-
tively correlated with degrees of adverse reactions, and EV miR-148a lev-
els were associated with specific antibody titers [129].
A study of influenza vaccination in children up to 12 years of age
revealed that 19 miRNAs were expressed at 21 days after receiving a pan-
demic (H1N1) vaccine. However, several miRNAs were expressed which
were not present in existing RNA sequencing data [126]. This concurs
with studies indicating that better methods and libraries are required to
fully understand the nuanced responses [73,74,78].
As an example of nonspecific effects, in children and adolescents
vaccinated with Pandemrix vaccine (a H1N1 influenza pandemic vac-
cine) the number of narcolepsy cases increased [123].
Our basic conclusion here is that the COVID-19 vaccines based on
the original Wuhan strain was reinforcing both immune and regulatory
miRNA responses which were becoming increasingly unhelpful as
COVID-19 variants emerged. Seemingly sluggish immune and regula-
tory miRNA responses in the elderly blunted the unhelpful nonspecific
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20
effects of this mismatch. This allowed the elderly to benefit from the spe-
cific effects of the vaccines while avoiding most of the nonspecific effects.
Alas, this was not so in younger recipients who then experienced increas-
ingly higher levels of nonspecific effects in the youngest ages.
A recurring emphasis in the above studies are that the vaccine re-
sponse is specific to the individual. Such an individual basis for COVID-
19 risk will now be discussed.
4.1.6. A genetic basis for COVID–19 risk
There is now increasing evidence that the risk of COVID–19 morbid-
ity and mortality has a strong genetic basis centered around chromosome
3 mutations inherited from Neanderthals, hence, higher risk among var-
ious people groups, and certain blood groups [130-139]. Nakanishi et al
[131] showed that chromosome 3 rs10490770 risk allele carriers had a 40%
increased risk of all-cause mortality , 110% increased risk of severe res-
piratory failure, +70% venous thromboembolism, and +50% hepatic in-
jury. Risk allele carriers aged under 60 years had higher odds of death or
severe respiratory failure +170%, compared with +50% in those aged 60+.
Among individuals younger than 60 years who died or experienced se-
vere respiratory failure, 32% were risk-variant carriers [131]. The risk-as-
sociated DNA segment modulates the expression of several chemokine
receptors, among them CCR5, a coreceptor for HIV which is down-regu-
lated in carriers of the risk haplotype who also have a 27% lower risk of
HIV infection [132,133].
The issue of genetic risk also seems to involve mutations and vari-
ants in the ACE2 receptor site [139-142].
An especially important study investigated which genes were in-
volved in adverse outcomes from respiratory infections including influ-
enza and COVID–19 [138]. The authors concluded that: “The 166-gene sig-
nature was surprisingly conserved across all viral pandemics, including
COVID–19, and a subset of 20-genes classified disease severity, inspiring the
nomenclatures ViP and severe-ViP signatures, respectively.” The ViP signa-
tures regulate a lung-epithelial and myeloid cell IL15 cytokine storm, ep-
ithelial and NK cell senescence and apoptosis which determine sever-
ity/fatality [138]. The cytokines IL15/IL15RA were elevated in the lungs
of patients with fatal disease, and plasma levels of the cytokines indicated
disease severity. The 20 ‘severe-ViP’ genes were involved in DNA meth-
ylation and amyloid fiber formation plus other aspects of health [138].
DNA methylation acts to control gene expression while amyloid fiber for-
mation is implicated in Alzheimer’s disease.
The genetic factors imply that repeating the English vaccination
study in another country may yield different outcomes depending on the
constituent people groups. Given the exclusion of persons arriving in
England after 2011 in the ONS data [29] this would imply immigration
from the European Union (EU) countries, especially the Eastern Euro-
pean new EU members, is excluded. As per Table S2 this affects mainly
the younger age groups, although the magnitude of the people group ef-
fect is unknown.
4.1.7. Gene expression varies with season and latitude
Many health conditions, from psychiatric disorders to cardiovascu-
lar disease, show seasonal variation in severity and onset [143]. Goldinger
et al [143] examined seasonal variation in the transcriptome of 606 indi-
viduals. Some 74 transcripts associated with a 12-month seasonal cycle
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were enriched for processes involved in DNA repair and binding. An-
other 94 showed significant seasonal variability that was associated with
blood cell count levels. These transcripts were enriched for immune func-
tion, protein production, and specific cellular markers for lymphocytes.
Cell counts for erythrocytes, platelets, neutrophils, monocytes, and CD19
cells demonstrated a significant 12-month seasonal cycle. Notable
changes in leukocyte counts and genes involved in immune function in-
dicate that immune cell physiology varies in a seasonal manner.
Another study analyzed blood and adipose tissue from 16 000 peo-
ple around the world to show that nearly a quarter of genes differ with
season [144]. This seasonality affects immune cells, the composition of
blood and adipose tissue. The pattern of seasonal activity was not as
strong in Iceland, while in Gambia peak expression occurred in the rainy
season. The ARNTL gene which is most active in summer suppresses in-
flammation. In winter, those at greatest risk will reach the ‘threshold’ at
which the disease becomes a problem more rapidly. A key finding was
that a set of genes associated with the response to vaccination were more
active in winter [144]. This may affect the response to COVID vaccination
depending on latitude and is highly relevant to the month-of-year pat-
terns seen in this study in Figures A2.1 to 2.7.
As to be expected, miRNAs are indeed involved in the expression of
seasonal diseases [145].
It is highly likely that seasonal patterns lie behind Figures A2.1 to
A2.7, however, the different timing for the arrival of new variants and the
timescale of vaccination imposed by the need to vaccinate the whole na-
tion has probably disrupted these patterns and additionally contributed
to a portion of the observed variation.
Once again, the latitude dependance of gene expression implies that
the results from England will show subtle differences to those derived
from other countries.
Hence to summarize, there are ample interlinked mechanisms to ex-
plain the observed variation in Figures A2.1 to A2.7 – however, many of
these mechanisms remain poorly understood in terms of how different
types of COVID-19 vaccination will interact with age, gender, COVID-19
variants, and stage of the vaccine journey.
4.1.8. Different immune responses between males and females
This study has established that males and females show different all-
cause mortality outcomes both in the unvaccinated and in the vaccinated,
and that these responses are different between SARS-CoV-2 variants. This
observation is unsurprising since sex is a biological variable that affects
the functions of the innate and adaptive immune system. In their com-
prehensive review Klein & Flanagan [146] have demonstrated how the
differing immune system responses change with age and are influenced
by the reproductive status of the individual. Both sex chromosome genes
and sex hormones differentially regulate immune responses. Environ-
mental factors, including nutrition status and the composition of the mi-
crobiome, also alter the development and functioning of the immune sys-
tem differently in males and females. Sex differences in immune re-
sponses result in differential susceptibility of males and females to infec-
tious diseases, as well as affecting the outcome of vaccination [146].
A study regarding COVID-19 infection showed that male patients
had higher levels of innate immune cytokines such as IL-8 and IL-18
along with a stronger induction of non-classical monocytes. Female
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patients had stronger T cell activation. A poor T cell response negatively
correlated with patients' age and was associated with worse disease out-
come in male patients. In females, higher levels of innate immune cyto-
kines were associated with worse disease progression [147].
Another study showed that showed that the concentration of IgG
antibody in mild, and recovering patients showed no difference between
males and females. However, in severe status, there were more female
patients having a relatively high concentration of serum SARS-CoV-2 IgG
antibody. The generation of IgG antibody in female patients was stronger
than male patients in the early disease phase [148].
A Hungarian study showed that the ratio of male to female deaths
changed between the Alpha and Delta waves. They observed statistically
higher excess female deaths aged 55-64 during the Delta outbreak [149].
Inspection of their data indicates that the ratio of male to female deaths
probably changed by age band in both the Alpha and Delta outbreaks
which is consistent with our findings. Vaccination rates in Hungary dur-
ing Delta were lower than in England indicating that the effect is proba-
bly dominated by the year of age profile of the variant [23] with possible
additional vaccine interactions.
Our previous study on the year of age profiles for COVID-19 vari-
ants likewise highlighted the role of gender in the specific outcomes at
different ages and for different birth cohorts [23].
4.1.9. Age and the risks/rewards of COVID–19 vaccination
Our previous studies regarding influenza vaccination highlighted is-
sues surrounding the age at which ‘healthy’ individuals should be vac-
cinated [4,13,14]. This issue is linked to the single-year-of-age profile risk
of death for different SARS-CoV-2 variants [23], and the observed single-
year-of-age efficacy of influenza vaccines [4]. This whole area is poorly
studied since most vaccine trials or vaccine effectiveness (VE) estimates
do not have enough participants to detect the full nuances of age [4,13].
The processes of aging are evident in human miRNA production.
For example, miRNA 92a declines with age in CD3+CD8+CD62L+ cells
and CD8+ T-lymphocytes. This suggests that the age-related attrition of
human naïve T cells could be connected to a reduced miRNA-92a in T-
lymphocytes and downregulation of the miRNA-92a level might indicate
exhaustion of naïve T-cells due to alteration of the immunologic condi-
tion with aging, and hence in vaccine response [150].
Bera [151] reports that in Italy for children and adolescents receiving
mRNA vaccine the risk of myocarditis and severe adverse events is much
higher than the risk of COVID-induced admission to critical care.
The study of Nakanishi et al [131] noted that in those aged below 60
years the prediction of death or severe respiratory failure improved when
including the rs10490770 risk allele (AUC 0·82 vs 0·84, p=0·016) and that
the prediction ability of risk allele was similar or better than, most estab-
lished clinical risk factors.
As noted in the Introduction, healthy children aged 5 11 only begin
to be vaccinated from February 2022 onward, i.e., during Omicron. Chil-
dren with high clinical risk are vaccinated across the entire time range.
Unfortunately, the detailed ONS data does not go below age 18 39. How-
ever, Table 6 in the ONS data [38] gives entire period data using 5-year
age bands (male plus female) which can be aggregated at the level of any
vaccine dose (first or more) to give enough deaths in the younger age
bands. This has been reported separately and appears to show adverse
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all-cause mortality vaccine outcomes for children aged 10-14 and 15-19 –
all having received mRNA vaccine [152]. This approach has limitations
and wider international studies were recommended to fully disentangle
age and vaccine type effects in the younger ages.
A Hungarian study likewise indicated age/sex interactions in all-
cause mortality following COVID-19 vaccination [149].
Dinetz [153] notes that as more of the younger population (under 40)
are getting vaccinated, based on vaccine safety approvals, the real-world
safety reporting data on adverse events have yet had time to catch up. He
details three distinct neurological events that occurred after the Pfizer
mRNA vaccine, without identifiable alternate etiologies, in patients with
an average age of 36 years, all within eight weeks of one another. The
cases occurred within hours of the second dose and, in one case, after the
third booster dose of the vaccine.
These cases illustrate rising concerns of risks in widely recognized
very low-risk age categories as shown in Figure 7. These concerns are es-
pecially relevant given studies indicating that some types of COVID-19
vaccines may increase rather than decrease all-cause mortality. In such
cases the real-world safety reporting process will be missing a range of
highly nuanced causes of death which are directly linked to vaccination.
As we have noted a sluggish immune and regulatory response in the
elderly seems to favor COVID-19 vaccine effectiveness.
4.2. Simultaneous benefit/disbenefit
Figures A5.1 to A5.5 showed a continuous gradient against all-cause
mortality ranging from benefit through to disbenefit. This suggests that
the effects of vaccination may be the net effect of benefit and disbenefit.
A recent study suggests that the same may occur for the nonspecific
effects of influenza vaccination [4]. Hence at a theoretical 100% vaccina-
tion rate in persons aged 65+, influenza vaccination was associated with
outcomes ranging from a 6% reduction in all-cause winter mortality in
2003/04, no effect in 2009/10, and to an increase of 7.5% in 2014/15 [4].
There was no apparent correlation between the specific measure of Vac-
cine Effectiveness (VE) and the non-specific effect against all-cause mor-
tality.
One of the nonspecific effects of influenza vaccination emerges in
children and the elderly by which influenza infection is diminished by
vaccination, however, influenza is simply replaced by alternative patho-
gens [4]. The resulting all-cause mortality effect then depends on which
other pathogens are most prevalent in that winter and location [4]. A sim-
ilar effect may occur after COVID-19 vaccination but has not yet been in-
vestigated.
These suggest that the outcomes are a balance between the propor-
tion of individuals who experience benefit against those who experience
disbenefit – perhaps due to genetic and other factors. In this respect all
the results shown in this, and other studies, are ‘average’ outcomes from
population-wide studies.
4.3. Other studies employing all-cause mortality and COVID-19 vaccination
It is important to corroborate that the general outcomes of this study
are also seen in other studies.
4.3.1. General studies
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A Swedish study involving nursing home residents and the oldest
old showed that a fourth dose of mRNA vaccine was effective in lowering
all-cause mortality in this specific group [154]. This confirms the results
of this study for the elderly.
Benn et al [155] appraised the randomized control trials (RCTs) of
mRNA and adenovirus-vector vaccines reporting overall mortality, in-
cluding COVID–19 deaths, accident deaths, cardiovascular deaths and
other non-COVID–19 deaths. For overall mortality, with 74,193 partici-
pants and 61 deaths (mRNA:31; placebo:30), the relative risk (RR) for the
two mRNA vaccines compared with placebo was 1.03 (95% CI=0.63-1.71).
In the adenovirus-vector vaccines there were 122,164 participants and 46
deaths (vaccine:16; controls:30). The RR for adenovirus-vector vaccines
versus placebo/control vaccine was 0.37 (0.19-0.70). The adenovirus-vec-
tor vaccines were associated with protection against COVID–19 deaths
(RR=0.11 (0.02-0.87)) and non-accident, non-COVID–19 deaths (RR=0.38
(0.17-0.88)). They argue for performing RCTs of mRNA and adeno-vec-
tored vaccines head-to-head comparing long-term effects on overall mor-
tality [151]. The study of Ben et al [155] confirms the results of the much
larger Hungarian study which contained over 6 million participants after
exclusion of partly vaccinated individuals [156].
Several other studies have implicated COVID-19 vaccination in in-
creased all-cause mortality. These studies have used different methods,
countries, age groups, and time periods covering different COVID-19
variants, different vaccine histories, and time following vaccination [157-
163]. A common theme is the involvement of mRNA vaccines, and poor
outcomes in children and young adults. While some of these studies may
be flawed, the point is that they cannot all be wrong, and that they
broadly confirm that such a possibility exists, as noted in this study.
In view of the possibility that mRNA vaccines may be associated
with adverse all-cause mortality a review of potential adverse effects
from mRNA vaccination raised several issues which could impact long-,
medium- and short-term all-cause mortality [164]. Other studies have
raised concerns around neurological side-effects, reverse transcription,
and toxicity of the naked spike protein [153,165-171].
Given the emphasis on the regulatory role of miRNAs in this study
the possibility has been raised that the mRNA from the vaccine will bind
to cellular miRNAs thereby interfering in unexpected ways with cell reg-
ulation [172]. A comprehensive review of the potential immunological
and biochemical effects of mRNA vaccines against innate and other im-
munity, and miRNA regulation, identified potential disturbances in reg-
ulatory control of protein synthesis and cancer surveillance with a possi-
ble causal link to neurodegenerative disease, myocarditis, immune
thrombocytopenia, Bell's palsy, liver disease, impaired adaptive immun-
ity, impaired DNA damage response and tumorigenesis [173]. While
some of these concerns may be proved to be unwarranted, they neverthe-
less may provide further explanations for some of the adverse effects seen
in this study.
The next section explores all-cause mortality differences between
different types of COVID-19 vaccines.
4.3.2. Differences between vaccines
Given the transition away from the AstraZeneca (virus vector) vac-
cine in the UK, especially among young adults (moved to mRNA), it is of
interest to see if this may have influenced the results of this study. Several
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studies are available which address the issue of COVID–19 vaccine type
upon all-cause mortality.
Firstly, a Hungarian study with comprehensive risk adjustment
showed that the magnitude and post vaccination trajectory of all-cause
survival after COVID–19 vaccination varied markedly between COVID
vaccine manufacturers [156].
Taking survival at 21 days during the epidemic period (April to June
2021), which is the break point in the ONS vaccination data for England
[43], all vaccines deliver protection, however all-cause survival is highest
for Janssen (viral vector) followed equally by Sputnik and AstraZeneca
(both viral vector). Next is Sinopharm (inactivated whole virus) and then
lowest protection equally by Moderna and Pfizer (both mRNA) [156]
However, 80-day survival during the epidemic period) was highest
for Janssen (viral vector), Sputnik (viral vector) and AstraZeneca (viral
vector). Moderna (mRNA) had a worse all-cause mortality outcome than
the unvaccinated, while Pfizer (mRNA) was equal to the unvaccinated,
while Sinopharm (inactivated whole virus) was slightly better than the
unvaccinated [156]. Survival for the Moderna vaccine had dropped below
the unvaccinated around day 65, while that for Pfizer had fallen to that
for the unvaccinated at day 80. During the non-epidemic period (55-day
survival), Moderna was once again worse than the unvaccinated, while
Pfizer was very close to the unvaccinated. Sputnik had by far the highest
survival, then followed by AstraZeneca and Janssen. Sinopharm was
once again intermediate [156].
Hence all viral vector vaccines gave highest long-term all-cause sur-
vival while mRNA vaccines gave no better or worse than the unvac-
cinated. Inactivated whole virus was intermediate.
4.3.3. Specific and nonspecific effects of vaccine waning
Waning is a part of the real-world effects of both influenza [174,] and
COVID-19 vaccination [175-180]. The waning of efficacy after COVID–19
vaccination will mostly affect the >21 days after vaccination group in Fig-
ures A2.1 to A2.7. Such a process is indeed observed in these Figures, with
higher reduction in the all-cause rate relative to the unvaccinated gener-
ally, but not always, occurring in this group.
A large population study in Israel demonstrated that rates of rein-
fection were highest following mRNA (Pfizer) in the two-dose cohort at
6 to 7 months after vaccination (88 infections per 100 000 person days),
but only 15 infections per 100 000) for the unvaccinated or one dose/re-
covered hybrid group after 6 to 7 months, 10 for the recovered/one dose
hybrid group [178]. At >12 months after infection the recovered/unvac-
cinated cohort were still only showing 30 infections per 100 000 person
days. At up to 1 month the three-dose cohort had higher than 2-times the
infection rate of the recovered/one dose cohort [178]. Seemingly far better
protection is afforded in the hybrid group and vaccine waning is steep
for the vaccine-only group which confirms the results reported in Hun-
gary [156].
Another UK population-based study investigating COVID-19 re-
lated hospitalization or death (not all-cause mortality ) found that follow-
ing doses 1 and 2 of the AstraZeneca vector and dose 1 of the Pfizer
mRNA the outcomes reached zero protection by approximately days 60–
80 and then went negative. By Day 70, VE/rVE was –25% and 10% for
doses 1 and 2 of AstraZeneca, respectively, and 42% and 53% for Doses 1
and 2 of Pfizer respectively. rVE for dose 2 of Pfizer remained above zero
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throughout and reached 46% after 98 days of follow-up [146]. This study
broadly confirms potential negative vaccine effectiveness; however, it
only covers COVID-19 confirmed hospitalization or death, i.e., not all-
cause mortality [179] .
A study in Hungary during the Delta outbreak (Sep-21 to Dec-21)
showed that vaccine waning occurred after the primary vaccine dose (de-
livered before Delta) such that Vaccine Effectiveness (VE) for COVID-19
death at >240 days had fallen to between 80% (Sputnik, Moderna) and
50% (Sinopharm, Astra Zeneca) [175]. Hence the specific effects of
COVID-19 vaccines show no evidence of a transition to negative VE.
On the other hand, during the earlier Alpha outbreak in Hungary
the profiles regarding all-cause mortality were vastly different. During
the Alpha epidemic period (Apr-21 to Jun-21) both Pfizer and Moderna
(mRNA vaccines) led to increased all-cause mortality (negative VE) be-
yond 65 days (Moderna) and 80 days (Pfizer). Vaccination during the
non-epidemic period (Jun-21 to Aug-21) showed far greater waning with
Moderna offering no protection up to 27 days and increased all-cause
mortality beyond 27 days. Pfizer was slightly better up to 60 days post
vaccination after which negative VE was likely [156]. None of the other
vaccines (Sputnik, Astra Zeneca, Sinopharm, Janssen) showed any evi-
dence for long-term decay into negative VE. Our study appears to con-
firm these results.
A further interesting observation regarding the specific effects of
vaccination in the Hungarian study was that protection against COVID-
19 infection showed very high waning after the primary dose going neg-
ative at around 180 days for Sinopharm, 220 days for Astra Zeneca, 240
days for Sputnik, and at some point >240 days for Janssen, Moderna and
Pfizer. Waning against hospitalization lay between that seen for infection
and death [175]. Which leads to the interesting question as to why waning
shows different trajectories for COVID-19 infection, hospitalization, and
death and for all-cause mortality. Multiple layers of complexity appear to
be involved.
4.4. Other relevant immune studies
Here we explore if other immune studies implicate effects specific to
mRNA vaccines.
4.4.1. Reevaluation of the study of Rinchai et al [181] in relation to the
effects of mRNA vaccination
An increase in the concentration of erythroid cells in peripheral
blood means activation (mobilization) of bone marrow hematopoietic
stem cells (HSCs), this manifests itself as increase in division rate of the
HSCs and start of their differentiation in erythroid lineage. This is a pos-
itive event in terms of stimulation of regeneration, but it may have nega-
tive consequences because of possible fusion of the progenitor cells with
adult somatic cells. Such fusion provides somatic cell increased replica-
tion potential and increased resistance to apoptosis. Both consequences
are good for normal, healthy cells, but potentially can activate oncogene-
sis, generate tumor-initiating-like cells, and increase resistance of tumor
cells to chemotherapy [182].
It is somewhat unfortunate that basic issues such as the above were
not investigates prior to the wider release of the mRNA vaccines. Such
issues are illustrated in the somewhat belated study of Rinchai et al [181].
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In their study the data presented in Fig. 1B demonstrated up to 4-
fold (-2 log2) decrease in blood concentration of IgM and total IgG anti-
bodies specific to all studied antigens of SARS-CoV-2 at day 7 in all
COVID-19 naïve persons after their vaccination with the mRNA vaccines,
i.e., the negative seroconversion. This can be considered as an increase of
susceptibility to Sars-CoV-2 infection of all previously uninfected persons
up to day 7 after vaccination. See Supplementary material S3 for a possi-
ble similar phenomenon. The decreased levels of the specific IgM anti-
bodies look to be especially strange because IgM antibodies should first
react to an antigenic challenge, but not in this case of mRNA vaccine ap-
plication.
The biological response to the second dose of the mRNA vaccine
summarized in Fig.9 of Rinchai et al [181] is strikingly resembles data ob-
tained during an investigation of the mechanisms of side effects after
transplantation of autologous (syngeneic) embryonic cells into adult mice
[201]. Both observe the picture of an inflammatory response in the pres-
ence of unusually high concentrations of progenitor cells (in case mRNA
second dose – erythroid cells) which are genetically identical to the host
organism, but significantly differ from adult cells in the expression profile
of some potent bioactive substances, including cytokines: LIF, GM-CSF,
VEGF, IL-6, TNF and TGF [184-187].
Some of these cytokines: IL-6 and TNF-alpha – are well known pro-
inflammatory agents. What is especially noteworthy in Zaporozhan et al
[188] is that to describe the side effects of the authologous embryonic cell
transplantation they introduced the term “Cytokine toxicosis” and men-
tioned that the complex morphological and immunological alterations re-
semble that in psoriasis patients: exactly like the consequences of the sec-
ond dose of the mRNA vaccination when compared to Rinchai et al. [181,
pp 9].
Based on the data from Fig.9. of Rinchai et al [181] we suggest the
following possible mechanism of the inflammatory response to the sec-
ond dose mRNA vaccine. It is evident that the second dose initiates rapid
enrichment of blood with Erythroid progenitor cells. Such an event
bears a hidden threat because in natural physiological conditions such
mobilization and release of progenitor cells from their niche in bone mar-
row to peripheral blood stream is accompanied (prepared) by specific al-
leviation (restraint) of the person’s immunity by a specific population of
T lymphocytes: the Tregs (CD25+ CD4+ cells) [188]. Stem and progenitor
cells are bearing on their surface some residual embryonic antigens which
in the case of contact with peripheral blood can elicit auto-immune reac-
tions. Besides, progenitor cells produce proinflammatory cytokines. To
prevent autoimmune and inflammatory reactions related to migration of
the progenitor cells to peripheral blood, an organism (under physiologi-
cal conditions) restrains its immunity using increasing concentration of
the Tregs [188]. We suggest that the second mRNA dose causes rapid re-
lease of the erythroid progenitor cells without precautionary release (ac-
tivation?) of the Tregs.
This possibly causes auto-immune reaction against the erythroid
cells and their destruction at days 1 – 4 after the second mRNA dose.
As mentioned above, stem and progenitor cells have peculiar gene
expression profiles, which differ from that of adult differentiated cells.
Consequently, the progenitor cells produce much higher concentrations
of the proinflammatory cytokines in comparison with normal adult cells.
Therefore, acute enrichment of peripheral blood with erythroid
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progenitors in persons receiving the second dose of the mRNA vaccine
inadvertently cause significant release of the proinflammatory cytokines
in tissues and blood stream. In transplantation of autologous embryonic
cells to adult mice this caused significant inflammatory response with
specific alterations in kidney, liver, skin and some generalized reactions
[201]. The proposed mechanism of possible side effects after the second
dose of mRNA vaccine explains the more harmful consequences, demon-
strated in this study, of the vaccination with mRNA vaccines for young
and middle-aged people. The amount of adult stem and progenitor cells
(and their activity) in elderly is negligibly small in comparison with
young persons, therefore possibility (chances) of their mobilization to pe-
ripheral blood with secretion of proinflammatory cytokines is also signif-
icantly smaller than in young and middle-aged people.
From a positive point of view, we could expect some rejuvenating
effect from the second dose of the mRNA vaccines (because as shown in
Rinchai e al [181] activation of the progenitor cells is an analog of “regen-
eration therapy”. But to achieve this rejuvenation and to decrease the side
effects of the mRNA vaccination we should arrange simultaneous with
the second vaccine dose downregulation (restrain) of the vaccinee’s im-
munity possibly by corticosteroids or other immunosuppressants (?).
Next the gene expression profiling data presented in Rinchai et al.,
[181], witnesses that from an immunological point of view the first dose
of the mRNA vaccine cause sensibilization effect: It looks like the encoded
by the vaccine’s mRNA polypeptide or some vaccine constituents acts
(behave) as a potent allergen. Rinchai e al [181] use the terminology:
“training of the innate immune response by the first vaccine dose”.
That is why the second dose of the mRNA causes an allergic reaction with
potent inflammation response. The acuity of the inflammatory responses
usually decreasing with age, therefore unwanted side effects (all-cause
mortality) after the mRNA vaccination most evident in persons below age
50.
Finally in Table 1 of Rinchai et al [181] it is shown that the most se-
vere side effects were developed in a youngest (among participants) pre-
viously infected with SARS-CoV-2, notably woman. This fact questioned
the safety of vaccination of such persons with mRNA vaccines. Such ef-
fects were demonstrated in this study.
4.4.2. Original antigenic sin
Although the "original antigenic sin" effects have been predicted
against new variants of SARS-CoV-2 [23], limited evidence are available
regarding its impact on the safety and effectiveness of COVID-19 vac-
cines. Castillo-Aleman et al [189] report a case of a 39-year-old male who
complained about pruritus and discomfort around the injection site of
inactivated SARS-CoV-2 vaccines administrated 18, 17, and 13 months
earlier. Those symptoms resembled the side effects previously experi-
enced with one of the booster doses, and a sole erythematous papule was
also documented. The patient was diagnosed with COVID-19 one or two
days after noticing these local signs and symptoms, and high serum titers
of immunoglobulin M (IgM) and immunoglobulin E (IgE) were found
five weeks after the onset, along with SARS-CoV-2-specific immuno-
globulin G (IgG) antibodies. Therefore, the OAS might be a plausible phe-
nomenon to consider in individuals immunized with inactivated vaccines
and exposed secondarily to a wild virus with antigenic variations [189].
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We expect that such effects are likely to be SARS-CoV-2 variant depend-
ent [23].
4.5. Issues relating to the Office for National Statistics data set
4.5.1. The unvaccinated as a reference group
The number of persons in the unvaccinated group diminishes over
time (commencing at 2.7-million-person-years in January 2021) and had
fallen to 12% of this total by November 2021. However, beyond Novem-
ber 2021 there is an asymptote at around 11.5% between March and May
of 2021. During January to May 2022 the 18-39 group has the highest un-
vaccinated person years at 179 500, with 55 100 at 40-49, down to 1100 at
age 90+ [38]. The unvaccinated therefore represent a large reference
group. However, one of the potential limitations of this study is the use
of the unvaccinated as an unbiased reference group.
There are so many potential factors from certain medical conditions
through to occupational exposure, racial, genetic, frailty functional status
factors [190-200]. A combined score based on genetic and clinical risk
gave enhanced prediction of severe COVID-19 infection [200]. However,
Figure 4 strongly suggests that the unvaccinated remained a stable group
and were not easily dissuaded from their position.
A further factor is the acquisition of natural immunity. An Israeli
study on unadjusted data for people over 60 years of age who were not
infected (and therefore not protected by natural infection before the 2nd
or 3rd dose), the risk is 18-times higher to have a severe COVID-19 after
the 3rd dose compared to people protected by natural infection and 118-
times higher with only 2 doses, again compared to non-vaccinated people
protected by natural infection [178].
The UK study of Agrawal et al [197] noted that persons with a pre-
vious COVID–19 infection were at reduced risk when infected ≥9 months
before booster dose vs no previous infection; aRR 0·41 [95% CI 0·29–0·58].
The unvaccinated are therefore acquiring protection via natural im-
munity and therefore become a ‘real-world’ part of the issue of the base-
line, which is leading to declining all-cause mortality in the unvaccinated.
It is unknown if the unvaccinated acquire natural immunity faster than
the vaccinated.
Given the ambiguity regarding which risk adjustment method to use
we believe that ‘shape of the trend’ method used in this study allows for
ready identification of the key issues without falling into the trap of spec-
ulating about the exact value of the individual points.
4.5.2. The 21-day break point to characterize vaccine time related effects
All vaccines have time related effects including the build-up of anti-
bodies and subsequent vaccine waning. Regarding the latter, the vaccine
effectiveness (VE) for the 2021/22 flu vaccine declined to zero in the inter-
val 120-149 days after vaccination [174]. Antibody levels are said to reach
their maximum at around 21 days after vaccination and spike antibody
levels start waning after around 6 weeks [201].
The ONS 21-day break point is therefore a pragmatic choice. How-
ever, the effects against all-cause mortality up to 21 days are more com-
plex and are discussed in greater detail in the Supplementary Material S3
where it is highlighted that COVID-19 deaths in those aged over 69 years
reaches a maximum relative to non-COVID-19 deaths at the 21-day break
point. The effect of waning in the >21-day group will therefore depend on
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30
the point at which individuals chose to curtail their overall vaccine jour-
ney.
In their detailed analysis of the ONS vaccination data, after correct-
ing for factors believed to be subject to misclassification in the ONS data,
Neil et al [202] nevertheless concluded that “the vaccines …… rather pro-
duce genuine spikes in all-cause mortality shortly after vaccination”. This con-
clusion excluded analysis of the 15–59 age group. However, this conclu-
sion is broadly supported by Figures A2.1 to A2.7, although modified by
the month in which the individual is vaccinated.
4.6. Limitations of the study
The study based on ONS reported vaccine status in England is com-
prehensive [38] but of necessity uses age groups and lacks risk adjust-
ment.
The study excludes people arriving in England after the census in
2011 (mainly from Eastern Europe) but is nevertheless a consistent study
population.
The outcome is based on the degree to which the unvaccinated
group may have a higher/lower proportion of high-risk individuals. If the
proportion is higher, then the vaccine benefit will be slightly over-esti-
mated or in the case of disbenefit this will be under-estimated.
It has been claimed that the ONS data for the unvaccinated contains
persons who have been vaccinated due to misclassification errors
[39,202]. However, the data is continually updated, and such errors
should be minimized in the second file ending December 2022. Also note
that claims of misclassification could be based on the perception that
COVID-19 vaccination should always be beneficial against all-cause mor-
tality. The objections seem based on the incorrect premise that:
1. COVID-19 vaccines are supposed to behave in a way which
does not involve nonspecific effects.
2. That the year-of-age behavior of the different SARS-CoV-2
variants is roughly similar [32].
We have demonstrated that neither of these assumptions is valid.
However, in most cases of disbenefit the values are so high that dis-
benefit can be assumed to be real – although there may be arguments re-
garding the ‘exact’ value.
Finally, we have conducted internal validation between the two
ONS data sets, i.e., the one concluding May-2022 versus that concluding
December 2022, and conclude that the differences between the two make
no material difference to this study.
It is our opinion that the unvaccinated baseline is of sufficient accu-
racy for the purpose of this study – especially given our emphasis upon
the shape of the time profile rather than focusing on absolute values.
4.7. Individual versus population risk
Healthy individuals can have an inherent genetic risk of an adverse
COVID-19 outcome. This inherent genetic risk is not included in current
risk-adjustment models. Dite et al [200] noted that a combined clinical
and genetic risk profile gave better results in predicting COVID-19 out-
comes. This is especially the case for persons under the age of 40 – as in
Figure 1a, etc. Individuals with other risk factors such as occupational
exposure, immune suppressing drugs, obesity, kidney disease, Vitamin
D deficiency, frailty etc. [190-199], are advised to be vaccinated or else
take other protective measures. As mentioned earlier specific diseases are
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31
associated with altered miRNA profiles. Alas there will always be an ele-
ment of the unknown as COVID–19 continues to mutate, and different
year of age risk profiles for new variants emerge [23].
We have emphasized the importance of human immune diversity in
relation to the mechanisms behind ‘pathogen interference’ [4]. Regarding
the role of age in COVID-19 disease and vaccination Cevigel et al [203]
have identified nine immunotypes that displayed different aging-associ-
ated immune signatures. These immunotypes explained inter-individual
variation better than age. We point to the need to better understand the
behavior in the 90+ age group observed in this study, especially markedly
increased mortality relative to other ages during Omicron [23].
4.8. The non-specific effects of COVID-19 vaccines upon longevity and
morbidity
It should be clear that any agent increasing all-cause mortality in per-
sons under the age of 40 will have a significant impact upon longevity.
Due to the relationship between morbidity and mortality every extra
death implies even higher levels of morbidity and related hospital admis-
sions [204], which may at first seem totally unrelated to vaccination.
Figure 1b is profoundly relevant to this issue in that as of December
2022 the net all-age and all-cause mortality benefit from mRNA vaccina-
tion had declined to close to zero and could possibly go negative.
Hence from the beginning of 2023 the cost and risk of further mRNA
vaccination could be counterproductive in terms of the net mortality ben-
efit.
5. Study summary
This study is not in any way suggesting that COVID-19 vaccination
does not provide a measure of protection against COVID-19 disease per
se. We do however question its ability to reduce all-cause mortality in a
consistent way – which is the ‘gold standard’ for vaccine efficacy. The
interaction between individual health status (age, immunotypes, etc.),
COVID–19 infection, SARS-CoV-2 variant, vaccination, and the environ-
ment represent a case of exquisite complexity. This is made more so by
the decision of individuals to terminate their vaccine journey amid the
unexpected consequences of such decisions against all-cause mortality.
The necessary urgency to develop and implement vaccines has inad-
vertently interacted with this complexity in sometimes adverse ways. To
deny that such complexity exists is unhelpful and hinders efforts to im-
plement safe vaccination development and implementation. The move to
discontinue the use of viral vector vaccines in favor of mRNA vaccines
due to increased risk of thromboembolic events is an example of a poten-
tially incorrect decision, since the available evidence supports the notion
that the viral vector vaccines have overall lower all-cause mortality.
This study confirms that COVID vaccination of the elderly has gen-
erally been a success – within the limits of certain optimum gen-
der/time/age combinations – and less so during the Omicron period.
However, all-cause mortality outcomes for mRNA vaccines during
the Omicron variant (the UK had switched to almost exclusive mRNA
vaccination prior to Omicron) were especially poor with most age/gen-
der/vaccine stage/time combinations showing higher all-cause mortality
in the vaccinated compared to the unvaccinated.
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32
Generally worse all-cause mortality outcomes after COVID vaccina-
tion among persons aged below 40 years are a common theme among this
and other studies.
6. Conclusions
Our overall conclusion is that the COVID-19 vaccines employed in
England during 2021 and 2022 led to unintended selective all-cause mor-
tality harm under specific combinations of age, sex, vaccine history,
COVID-19 variant, and time (season, outbreak or non-outbreak months).
The question is which of the three vaccines employed during 2021 and
the two employed in 2022 led to the most all-cause mortality harm.
Human vaccines must be open to scientific scrutiny, and it is only in
such an open framework that vaccines can be continuously improved. It
is our observation that researchers questioning aspects of COVID-19 vac-
cination are not ‘anti-vaxxers’ but are seeking genuine answers to seem-
ing ‘anomalies’ in the data. Indeed, such anomalies can be hidden by the
application of certain types of analysis, especially after age-standardiza-
tion across all age groups.
There is no such thing as ‘perfect’ protection, only a balance between
the risks and rewards, which this study demonstrates are far more com-
plex than has been appreciated. We highlight that a ‘good’ vaccine should
not disadvantage those (via increased all-cause mortality ) who choose to
halt their vaccine journey. The high rate of waning in mRNA vaccines
leading to eventual higher mortality in the vaccinated compared to the
unvaccinated remains a concern. A very recent study has confirmed that
age-based selection occurs between persons receiving the second and
third dose of the mRNA vaccine [205].
Hence, our main conclusion is that COVID-19 vaccines seem to per-
form better for the elderly. Their sluggish immune and miRNA regula-
tory processes allow them to enjoy the benefits of the vaccines but to
avoid the nonspecific disbenefits. Alas the young suffer increasing non-
specific disbenefits which worsen as the COVID-19 variants progres-
sively mutate.
It would also appear that the process for reporting/detecting adverse
events following vaccination is not achieving its intended purpose since
subtle changes in all-cause mortality are going undetected in both influ-
enza [4-6] and COVID-19 vaccination. The latter has been elegantly illus-
trated by the analysis of Seneff et al [173] and inferred by Pantazatos and
Seligmann [158].
We also recommend that long term surveillance and re-analysis of
all-cause disease outcomes following COVID–19 vaccination is actively
pursued. Vaccination of the young must be reconsidered based on the
comprehensive evaluation of the all-cause mortality vaccination out-
comes [152].
It is strongly recommended that international studies are conducted
to determine the exact contribution of the different types of COVID-19
vaccines against all-cause mortality. The expressed opinions that mRNA
vaccines may have long-term adverse effects cannot be ignored.
Reporting of outcomes by month as used in the ONS study is the
recommended approach as it allows both seasonal, and epidemic and
non-epidemic effects to be disentangled along with the effects of different
variants. Studies which report outcomes using time (days) after vaccina-
tion as a continuous variable are also recommended. We suggest that in-
fluenza vaccines be subject to the same all-cause mortality scrutiny [4].
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Indeed, it is entirely possible that the optimum vaccine choice is both
gender, age, and context specific (timing, latitude, mix of circulating path-
ogens, ethnicity, and personal risk factors).
The era of assuming that vaccines should only be assessed by their
specific effects is hopefully ending.
Supplementary Materials: Section S1: An illustration of how the raw data has
been analyzed; Section S2: Estimated proportion of reported deaths in England
which are missing from the ONS vaccination study, by age band; Section S3: Role
of time after vaccination on the ratio of COVID-19 to non-COVID-19 deaths.
Author Contributions: Conceptualization, R.P.J. and Y.Y.; methodology, R.P.J.;
validation, R.P.J.; formal analysis, R.P.J.; investigation, R.P.J.; resources, R.P.J.;
data curation, R.P.J.; writing—original draft preparation, R.P.J and A.P. (two sec-
tions).; writing—review and editing, R.P.J. and A.P.; visualization, R.P.J.; super-
vision, R.P.J.; project administration, R.P.J. All authors have read and agreed to
the published version of the manuscript.
Funding: This research received no external funding
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: All data are publicly available, and sources are
listed in the Materials and Methods section.
Acknowledgments: Thanks is expressed to Stephen Andrews for sending details
of several relevant papers.
Conflicts of Interest: The authors declare no conflict of interest.
Appendix A: Figures A1 to AX.
Figure A1. Entire period average or median (male plus female, January 2021
to May 2022) for all-cause mortality rate relative to the unvaccinated for different
age bands. A negative value implies protection by the vaccine.
Figures A2.1 to A2.8. Female and male vaccinated age standardized
mortality rate relative to the unvaccinated. Each chart is for a different
age band from youngest to oldest. The month on the x-axis is the month
of death. The y-axis has been truncated. Some high values are above the
-80%
-40%
0%
40%
80%
120%
160%
First <21d 18-39
First <21d 50-59
First <21d 70-79
First <21d 90+
First >21d 18-39
First >21d 50-59
First >21d 70-79
First >21d 90+
Second <21d 18-39
Second <21d 50-59
Second <21d 70-79
Second <21d 90+
Second >21d 18-39
Second >21d 50-59
Second >21d 70-79
Second >21d 90+
Third/booster <21d 18-39
Third/booster <21d 50-59
Third/booster <21d 70-79
Third/booster <21d 90+
Third/booster >21d 18-39
Third/booster >21d 50-59
Third/booster >21d 70-79
Third/booster >21d 90+
All-
cause mortality relative to
unvaccinated
Median
Whole period
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truncated limit. Note that there is a time progression of SARS-CoV-2 var-
iants from Alpha through to Delta and then to Omicron [J&P 2023].
-100%
0%
100%
200%
300%
400%
500%
All-
cause mortality relative to the
unvaccinated
18-39 F First >21 d 18-39 F First <21 d 18-39 F Second >21 d
18-39 F Second <21 d 18-39 F Third/booster >21 d 18-39 F Third/booster <21 d
18-39 M First >21 d 18-39 M First <21 d 18-39 M Second >21 d
18-39 M Second <21 d 18-39 M Third/booster >21 d 18-39 M Third/booster <21 d
-100%
0%
100%
200%
300%
400%
500%
1月-21
2月-21
3月-21
4月-21
5月-21
6月-21
7月-21
8月-21
9月-21
10月-21
11月-21
12月-21
1月-22
2月-22
3月-22
4月-22
5月-22
6月-22
7月-22
8月-22
9月-22
10月-22
11月-22
12月-22
40-49 F First >21 d 40-49 F First <21 d 40-49 F Second >21 d
40-49 F Second <21 d 40-49 F Third/booster >21 d 40-49 F Third/booster <21 d
40-49 M First >21 d 40-49 M First <21 d 40-49 M Second >21 d
40-49 M Second <21 d 40-49 M Third/booster >21 d 40-49 M Third/booster <21 d
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-100%
0%
100%
200%
300%
400%
500%
1月-21
2月-21
3月-21
4月-21
5月-21
6月-21
7月-21
8月-21
9月-21
10月-21
11月-21
12月-21
1月-22
2月-22
3月-22
4月-22
5月-22
6月-22
7月-22
8月-22
9月-22
10月-22
11月-22
12月-22
50-59 F First >21 d 50-59 F First <21 d 50-59 F Second >21 d
50-59 F Second <21 d 50-59 F Third/booster >21 d 50-59 F Third/booster <21 d
50-59 M First >21 d 50-59 M First <21 d 50-59 M Second >21 d
50-59 M Second <21 d 50-59 M Third/booster >21 d 50-59 M Third/booster <21 d
-100%
0%
100%
200%
300%
400%
500%
1月-21
2月-21
3月-21
4月-21
5月-21
6月-21
7月-21
8月-21
9月-21
10月-21
11月-21
12月-21
1月-22
2月-22
3月-22
4月-22
5月-22
6月-22
7月-22
8月-22
9月-22
10月-22
11月-22
12月-22
60-69 F First >21 d 60-69 F First <21 d 60-69 F Second >21 d
60-69 F Second <21 d 60-69 F Third/booster >21 d 60-69 F Third/booster <21 d
60-69 M First >21 d 60-69 M First <21 d 60-69 M Second >21 d
60-69 M Second <21 d 60-69 M Third/booster >21 d 60-69 M Third/booster <21 d
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-100%
0%
100%
200%
300%
400%
500%
1月-21
2月-21
3月-21
4月-21
5月-21
6月-21
7月-21
8月-21
9月-21
10月-21
11月-21
12月-21
1月-22
2月-22
3月-22
4月-22
5月-22
6月-22
7月-22
8月-22
9月-22
10月-22
11月-22
12月-22
70-79 F First >21 d 70-79 F First <21 d 70-79 F Second >21 d
70-79 F Second <21 d 70-79 F Third/booster >21 d 70-79 F Third/booster <21 d
70-79 M First >21 d 70-79 M First <21 d 70-79 M Second >21 d
70-79 M Second <21 d 70-79 M Third/booster >21 d 70-79 M Third/booster <21 d
-100%
0%
100%
200%
300%
400%
500%
1月-21
2月-21
3月-21
4月-21
5月-21
6月-21
7月-21
8月-21
9月-21
10月-21
11月-21
12月-21
1月-22
2月-22
3月-22
4月-22
5月-22
6月-22
7月-22
8月-22
9月-22
10月-22
11月-22
12月-22
80-89 F First >21 d 80-89 F First <21 d 80-89 F Second >21 d
80-89 F Second <21 d 80-89 F Third/booster >21 d 80-89 F Third/booster <21 d
80-89 M First >21 d 80-89 M First <21 d 80-89 M Second >21 d
80-89 M Second <21 d 80-89 M Third/booster >21 d 80-89 M Third/booster <21 d
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-100%
0%
100%
200%
300%
400%
500%
1月-21
2月-21
3月-21
4月-21
5月-21
6月-21
7月-21
8月-21
9月-21
10月-21
11月-21
12月-21
1月-22
2月-22
3月-22
4月-22
5月-22
6月-22
7月-22
8月-22
9月-22
10月-22
11月-22
12月-22
90+ F First >21 d 90+ F First <21 d 90+ F Second >21 d
90+ F Second <21 d 90+ F Third/booster >21 d 90+ F Third/booster <21 d
90+ M First >21 d 90+ M First <21 d 90+ M Second >21 d
90+ M Second <21 d 90+ M Third/booster >21 d 90+ M Third/booster <21 d
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Figure A3. All-cause mortality in males plus females relative to the unvaccinated, by vaccination history, and SARS-CoV-2 variant at the time of death. Data
is from the first ONS file ending May 2022.
-100%
-50%
0%
50%
100%
150%
200%
18-39 First <21 d
40-49 First <21 d
50-59 First <21 d
60-69 First <21 d
70-79 First <21 d
80-89 First <21 d
90+ First <21 d
18-39 First >21 d
40-49 First >21 d
50-59 First >21 d
60-69 First >21 d
70-79 First >21 d
80-89 First >21 d
90+ First >21 d
18-39 Second <21 d
40-49 Second <21 d
50-59 Second <21 d
60-69 Second <21 d
70-79 Second <21 d
80-89 Second <21 d
90+ Second <21 d
18-39 Second >21 d
40-49 Second >21 d
50-59 Second >21 d
60-69 Second >21 d
70-79 Second >21 d
80-89 Second >21 d
90+ Second >21 d
18-39 Third or booster, <21 d
40-49 Third or booster, <21 d
50-59 Third or booster, <21 d
60-69 Third or booster, <21 d
70-79 Third or booster, <21 d
80-89 Third or booster, <21 d
90+ Third or booster, <21 d
18-39 Third or booster, >21 d
40-49 Third or booster, >21 d
50-59 Third or booster, >21 d
60-69 Third or booster, >21 d
70-79 Third or booster, >21 d
80-89 Third or booster, >21 d
90+ Third or booster, >21 d
All-
cause mortality relative to unvaccinated
Alpha Delta Omicron
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Figure A4. Trend in all-cause mortality rate in the unvaccinated between January 2021 and
December 2022 in England. Data for the first three months is from the ONS file concluding May-22
while subsequent data is from the file concluding Dec-22 [38]. Note that the mortality rate will in-
crease during large outbreaks of the various SARS-CoV-2 variants, each of which has a particular
year of age profile for deaths [23]. For example, the Alpha variant concludes around May-21 while
Delta commences around June-21. Because this variant unduly affects younger people the shape of
the profiles is shifted in the two youngest age groups. Note that Delta appears to affect younger
males more so than females. Recall that deaths always lag infections and that the time to death after
infection is likely to be age dependent.
10
100
1000
10000
100000
1月-21
2月-21
3月-21
4月-21
5月-21
6月-21
7月-21
8月-21
9月-21
10月-21
11月-21
12月-21
1月-22
2月-22
3月-22
4月-22
5月-22
6月-22
7月-22
8月-22
9月-22
10月-22
11月-22
12月-22
Mortality rate in the unvaccinated (deaths per 100 000 person years)
Female 18-39 Female 40-49 Female 50-59 Female 60-69
Female 70-79 Female 80-89 Female 90+ Male 18-39
Male 40-49 Male 50-59 Male 60-69 Male 70-79
Male 80-89 Male 90+
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Figures A5.1 to A5.5 Relative rate of age-standardized all-cause mortality in the vaccinated versus the unvaccinated for those
vaccinated during the Alpha, Delta, and Omicron variant periods (see below). Charts are generally truncated at +200% all-cause
mortality relative to the unvaccinated. Higher values lie above this truncation limit.
-100%
-50%
0%
50%
100%
150%
80-89 Male Second, <21 days
80-89 Female Second, <21 days
90+ Male Second, <21 days
80-89 Male Second, >21 days
70-79 Female Second, <21 days
80-89 Female Second, >21 days
70-79 Male Second, <21 days
90+ Female Second, <21 days
90+ Male Second, >21 days
60-69 Female Second, <21 days
80-89 Male First, <21 days
60-69 Male Second, <21 days
90+ Female Second, >21 days
80-89 Female First, <21 days
70-79 Female Second, >21 days
70-79 Male First, <21 days
70-79 Female First, <21 days
60-69 Female First, <21 days
70-79 Male Second, >21 days
90+ Male First, <21 days
80-89 Male First, >21 days
50-59 Female Second, <21 days
60-69 Male First, <21 days
50-59 Female First, <21 days
80-89 Female First, >21 days
50-59 Male First, <21 days
90+ Female First, <21 days
90+ Male First, >21 days
50-59 Male Second, <21 days
40-49 Female First, <21 days
90+ Female First, >21 days
70-79 Male First, >21 days
70-79 Female First, >21 days
60-69 Female Second, >21 days
60-69 Female First, >21 days
60-69 Male First, >21 days
50-59 Female Second, >21 days
40-49 Male First, <21 days
40-49 Female Second, <21 days
60-69 Male Second, >21 days
50-59 Female First, >21 days
50-59 Male First, >21 days
40-49 Female Second, >21 days
40-49 Male Second, <21 days
50-59 Male Second, >21 days
18-39 Female First, <21 days
40-49 Female First, >21 days
18-39 Female Second, <21 days
40-49 Male Second, >21 days
40-49 Male First, >21 days
18-39 Female Second, >21 days
18-39 Male Second, <21 days
18-39 Male First, <21 days
18-39 Male Second, >21 days
18-39 Female First, >21 days
18-39 Male First, >21 days
All-cause mortality relative to unvaccinated
Alpha variant
-100%
-50%
0%
50%
100%
150%
200%
50-59 Female Second, <21 days
50-59 Male Second, <21 days
40-49 Male Second, <21 days
40-49 Female Second, <21 days
60-69 Male Second, <21 days
60-69 Female Second, <21 days
18-39 Female Second, <21 days
40-49 Male First, <21 days
18-39 Female First, <21 days
50-59 Female Second, >21 days
50-59 Male First, <21 days
60-69 Female Second, >21 days
90+ Male First, <21 days
40-49 Male First, >21 days
70-79 Female Second, >21 days
50-59 Male Second, >21 days
60-69 Male Second, >21 days
40-49 Female First, <21 days
18-39 Male First, <21 days
60-69 Female First, <21 days
80-89 Female First, <21 days
18-39 Male Second, <21 days
70-79 Male Second, >21 days
50-59 Female First, <21 days
60-69 Male First, <21 days
80-89 Female Second, >21 days
80-89 Male Second, >21 days
40-49 Female First, >21 days
90+ Female First, <21 days
40-49 Male Second, >21 days
90+ Female Second, >21 days
40-49 Female Second, >21 days
90+ Male Second, >21 days
70-79 Male First, <21 days
90+ Female Second, <21 days
80-89 Male Second, <21 days
18-39 Male First, >21 days
70-79 Female First, <21 days
80-89 Male First, <21 days
50-59 Male First, >21 days
18-39 Female First, >21 days
70-79 Female Second, <21 days
80-89 Female Second, <21 days
50-59 Female First, >21 days
70-79 Male Second, <21 days
90+ Male Second, <21 days
18-39 Female Second, >21 days
18-39 Male Second, >21 days
90+ Female First, >21 days
60-69 Male First, >21 days
90+ Male First, >21 days
60-69 Female First, >21 days
80-89 Female First, >21 days
80-89 Male First, >21 days
70-79 Female First, >21 days
70-79 Male First, >21 days
All-
cause mortality relative to unvaccinated
Mixed Alpha/Delta
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41
-100%
-50%
0%
50%
100%
150%
200%
50-59 Female Third or booster, <21 days
18-39 Female Second, <21 days
60-69 Female Third or booster, <21 days
70-79 Female Third or booster, <21 days
50-59 Male Third or booster, <21 days
60-69 Male Third or booster, <21 days
70-79 Male Third or booster, <21 days
18-39 Male Second, <21 days
40-49 Male Third or booster, <21 days
40-49 Male Second, <21 days
80-89 Male Third or booster, <21 days
40-49 Female Third or booster, <21 days
80-89 Female Third or booster, <21 days
18-39 Male First, <21 days
70-79 Female Third or booster, >21 days
60-69 Female Third or booster, >21 days
50-59 Female Third or booster, >21 days
40-49 Female Second, <21 days
70-79 Male Third or booster, >21 days
80-89 Female Third or booster, >21 days
60-69 Male Third or booster, >21 days
80-89 Male Third or booster, >21 days
50-59 Female Second, <21 days
50-59 Male First, <21 days
18-39 Female Third or booster, <21 days
50-59 Male Third or booster, >21 days
60-69 Male First, <21 days
50-59 Female Second, >21 days
18-39 Female First, <21 days
50-59 Female First, <21 days
50-59 Male Second, >21 days
60-69 Female Second, >21 days
90+ Female Third or booster, <21 days
90+ Male Third or booster, <21 days
60-69 Female Second, <21 days
60-69 Male Second, >21 days
60-69 Male Second, <21 days
40-49 Female Second, >21 days
40-49 Male Second, >21 days
70-79 Female Second, >21 days
50-59 Male Second, <21 days
40-49 Female Third or booster, >21 days
18-39 Male Second, >21 days
70-79 Male Second, >21 days
18-39 Male Third or booster, <21 days
90+ Female Third or booster, >21 days
80-89 Female Second, >21 days
90+ Male Third or booster, >21 days
18-39 Female Second, >21 days
90+ Female Second, <21 days
80-89 Male Second, >21 days
80-89 Female Second, <21 days
18-39 Male First, >21 days
18-39 Female First, >21 days
90+ Female Second, >21 days
90+ Male Second, <21 days
70-79 Female Second, <21 days
60-69 Female First, <21 days
40-49 Male First, <21 days
80-89 Male Second, <21 days
40-49 Male Third or booster, >21 days
70-79 Female First, <21 days
80-89 Male First, <21 days
80-89 Female First, <21 days
40-49 Female First, <21 days
90+ Male Second, >21 days
70-79 Male Second, <21 days
18-39 Female Third or booster, >21 days
18-39 Male Third or booster, >21 days
70-79 Male First, <21 days
40-49 Female First, >21 days
90+ Female First, >21 days
40-49 Male First, >21 days
50-59 Female First, >21 days
90+ Female First, <21 days
90+ Male First, <21 days
50-59 Male First, >21 days
80-89 Female First, >21 days
90+ Male First, >21 days
60-69 Female First, >21 days
70-79 Female First, >21 days
60-69 Male First, >21 days
80-89 Male First, >21 days
70-79 Male First, >21 days
All-cause mortality relative to unvaccinated
Delta variant
-100%
-50%
0%
50%
100%
150%
200%
40-49 Female Third or booster, <21 days
18-39 Female Third or booster, <21 days
50-59 Female Third or booster, <21 days
40-49 Male Third or booster, <21 days
50-59 Male Third or booster, <21 days
50-59 Female Third or booster, >21 days
18-39 Male Third or booster, <21 days
60-69 Female Third or booster, >21 days
70-79 Female Third or booster, >21 days
60-69 Male Third or booster, >21 days
50-59 Male Third or booster, >21 days
70-79 Male Third or booster, >21 days
80-89 Female Third or booster, >21 days
80-89 Male Third or booster, >21 days
40-49 Male Third or booster, >21 days
50-59 Female Second, <21 days
60-69 Male Third or booster, <21 days
40-49 Female Third or booster, >21 days
60-69 Female Third or booster, <21 days
90+ Female Third or booster, >21 days
90+ Female Third or booster, <21 days
18-39 Male Second, <21 days
90+ Male Third or booster, >21 days
90+ Male Second, <21 days
18-39 Female Third or booster, >21 days
80-89 Male Second, <21 days
60-69 Male First, <21 days
18-39 Male Second, >21 days
50-59 Female First, <21 days
18-39 Female Second, <21 days
18-39 Male Third or booster, >21 days
40-49 Male Second, <21 days
50-59 Male First, <21 days
18-39 Female First, >21 days
70-79 Female Third or booster, <21 days
60-69 Female First, <21 days
18-39 Female Second, >21 days
70-79 Female First, <21 days
50-59 Male Second, <21 days
40-49 Male Second, >21 days
80-89 Female Third or booster, <21 days
80-89 Female Second, <21 days
40-49 Female Second, <21 days
18-39 Male First, <21 days
90+ Female Second, <21 days
40-49 Female Second, >21 days
70-79 Female Second, <21 days
50-59 Female First, >21 days
90+ Male Third or booster, <21 days
60-69 Male Second, <21 days
18-39 Male First, >21 days
90+ Female First, >21 days
18-39 Female First, <21 days
80-89 Male Third or booster, <21 days
70-79 Male Third or booster, <21 days
50-59 Male Second, >21 days
50-59 Female Second, >21 days
70-79 Male Second, <21 days
60-69 Female Second, <21 days
90+ Female Second, >21 days
90+ Male First, >21 days
80-89 Female First, <21 days
60-69 Male Second, >21 days
40-49 Male First, <21 days
80-89 Female First, >21 days
60-69 Male First, >21 days
40-49 Female First, <21 days
80-89 Male First, >21 days
70-79 Female First, >21 days
40-49 Male First, >21 days
60-69 Female Second, >21 days
80-89 Female Second, >21 days
60-69 Female First, >21 days
80-89 Male First, <21 days
40-49 Female First, >21 days
50-59 Male First, >21 days
70-79 Male First, >21 days
90+ Female First, <21 days
90+ Male Second, >21 days
70-79 Female Second, >21 days
80-89 Male Second, >21 days
70-79 Male Second, >21 days
70-79 Male First, <21 days
90+ Male First, <21 days
All-
cause mortality relative to unvaccinated
Mixed Delta/Omicron
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42
Figures A5.1 to A5.5. Relative rate of age-standardized all-cause mortality in the vaccinated
versus the unvaccinated for those vaccinated during Alpha, Delta and Omicron variants. The Y-axis
has been truncated at + 200% higher all-cause mortality .
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-100%
-50%
0%
50%
100%
150%
200%
50-59 Male Third or booster, <21 days
70-79 Female Third or booster, >21 days
50-59 Female Third or booster, >21 days
60-69 Female Third or booster, >21 days
60-69 Male Third or booster, >21 days
80-89 Female Third or booster, >21 days
50-59 Male Third or booster, >21 days
50-59 Female Third or booster, <21 days
80-89 Male Third or booster, >21 days
70-79 Male Third or booster, >21 days
40-49 Female Third or booster, >21 days
90+ Female Second, <21 days
18-39 Male Second, >21 days
90+ Female Third or booster, >21 days
80-89 Female Second, <21 days
18-39 Female Second, >21 days
40-49 Female Third or booster, <21 days
40-49 Male Third or booster, >21 days
90+ Female Third or booster, <21 days
18-39 Male Third or booster, <21 days
90+ Male Third or booster, >21 days
18-39 Female Third or booster, <21 days
18-39 Male Third or booster, >21 days
40-49 Male Third or booster, <21 days
18-39 Male First, >21 days
18-39 Female Third or booster, >21 days
60-69 Female Third or booster, <21 days
90+ Male Third or booster, <21 days
40-49 Female Second, >21 days
50-59 Female Second, >21 days
70-79 Female First, >21 days
60-69 Male Third or booster, <21 days
90+ Male First, >21 days
40-49 Male Second, >21 days
80-89 Female Third or booster, <21 days
50-59 Male Second, >21 days
80-89 Male Third or booster, <21 days
90+ Female First, >21 days
90+ Female Second, >21 days
60-69 Male Second, >21 days
40-49 Female First, >21 days
80-89 Female First, >21 days
80-89 Female Second, >21 days
70-79 Female Third or booster, <21 days
60-69 Female Second, >21 days
90+ Male Second, >21 days
70-79 Male Second, <21 days
80-89 Male Second, >21 days
50-59 Male First, >21 days
70-79 Female Second, >21 days
60-69 Male Second, <21 days
80-89 Male First, >21 days
18-39 Female First, >21 days
60-69 Female First, >21 days
60-69 Male First, >21 days
80-89 Female First, <21 days
50-59 Female First, >21 days
90+ Male Second, <21 days
80-89 Male Second, <21 days
70-79 Male Third or booster, <21 days
60-69 Female Second, <21 days
50-59 Male Second, <21 days
70-79 Male Second, >21 days
50-59 Female Second, <21 days
70-79 Male First, >21 days
18-39 Male Second, <21 days
18-39 Female Second, <21 days
70-79 Female Second, <21 days
40-49 Male First, >21 days
90+ Male First, <21 days
90+ Female First, <21 days
40-49 Female Second, <21 days
40-49 Male Second, <21 days
70-79 Female First, <21 days
70-79 Male First, <21 days
60-69 Male First, <21 days
18-39 Male First, <21 days
80-89 Male First, <21 days
50-59 Male First, <21 days
18-39 Female First, <21 days
60-69 Female First, <21 days
50-59 Female First, <21 days
40-49 Female First, <21 days
40-49 Male First, <21 days
All-cause mortality relative to unvaccinated
Omicron variant
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