Retrospective cohort study of the efficiency of Sputnik V and EpiVacCorona vaccines against infections and deaths caused by SARS-CoV-2 Delta variant in Moscow (June- July 2021)

Abstract

Objective
The goal of the study was to evaluate the epidemiological effectiveness of the Sputnik V and EpiVacCorona vaccines against COVID-19.

Method
This work is a retrospective cohort study of COVID-19 patients. The cohort created by the Moscow Health Department included more than 300,000 infected people who sought medical care in June and July 2021.

Results
Analysis of data revealed a tendency for the increase in the Sputnik V vaccine effectiveness (VE) as the severity of the disease increased. Protection was the lowest for mild disease, and it was more pronounced for severe disease. We also observed a decrease in VE with increasing age. For the youngest group (18–50 years old), the estimated VE in preventing death in June 2021 was 95% (95% CI 64–100), and for the older group (50 + years old), it was 74% (95% CI 67–87). The estimated protection against a severe form of the disease in the 18-50-year-old group was above 81% (CI 95% 72–93), and in the 50 + years-old group, it was above 68% (CI 95% 65–82). According to our analysis, EpiVacCorona proved to be an ineffective vaccine and therefore cannot protect against COVID-19.

Full text

Citation: Matveeva, O.; Ershov, A.
Retrospective Cohort Study of the
Effectiveness of the Sputnik V and
EpiVacCorona Vaccines against the
SARS-CoV-2 Delta Variant in
Moscow (June–July 2021). Vaccines
2022,10, 984. https://doi.org/
10.3390/vaccines10070984
Academic Editors: Ralph A. Tripp
and Scott Anthony
Received: 1 June 2022
Accepted: 16 June 2022
Published: 21 June 2022
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Copyright: © 2022 by the authors.
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4.0/).
Article
Retrospective Cohort Study of the Effectiveness of the Sputnik
V and EpiVacCorona Vaccines against the SARS-CoV-2 Delta
Variant in Moscow (June–July 2021)
Olga Matveeva 1, * and Alexander Ershov 2
1Sendai Viralytics LLC, 23 Nylander Way, Acton, MA 01720, USA
2Medusa Project SIA, Krisjana Barona iela 5-2, LV-1050 R¯
ıga, Latvia; anershov@gmail.com
*Correspondence: olga.matveeva@gmail.com
Abstract:
The goal of this study was to evaluate the epidemiological effectiveness of the Sputnik V
and EpiVacCorona vaccines against COVID-19. This work is a retrospective cohort study of COVID-19
patients. The cohort created by the Moscow Health Department included more than 300,000 infected
people who sought medical care in June and July 2021. Analysis of data revealed a tendency for the
increase in the Sputnik V vaccine effectiveness (VE) as the severity of the disease increased. Protection
was the lowest for mild disease, and it was more pronounced for severe disease. We also observed
a decrease in VE with increasing age. For the youngest group (18–50 years old), the estimated VE
in preventing death in June 2021 was 95% (95% CI 64–100), and for the older group (50+ years old),
it was 74% (95% CI 67–87). The estimated protection against a severe form of the disease in the
18–50-year-old group was above 81% (CI 95% 72–93), and in the 50+ years-old group, it was above
68% (CI 95% 65–82). According to our analysis, EpiVacCorona proved to be an ineffective vaccine
and therefore cannot protect against COVID-19.
Keywords:
COVID-19; vaccine effectiveness; SARS-CoV-2; Delta variant; Sputnik V vaccine; EpiVac-
Corona vaccine; medical informatics
1. Introduction
A wide range of effective vaccines for the prevention of COVID-19 are now available
worldwide. The efficacy and effectiveness of these vaccines have been demonstrated in
both clinical and retrospective studies [
1
–
4
]. However, the SARS-CoV-2 viral genomes
continue to evolve, giving the virus an increasing number of advantages in evading the
immune response. In this regard, the effectiveness of vaccines based on antigens of the
ancestral variant needs to be re-evaluated for new viral variants of concern (VOCs) that
can cause new epidemic waves.
There are two vaccines, the effectiveness of which is analyzed in this study. Both have
been approved for use in Russia. One of them is a viral vector vaccine (Sputnik V), and the
other is a peptide-based vaccine (EpiVacCorona).
Sputnik V (Gam-COVID-Vac) is based on a recombinant human replication-defective
adenovirus of two types: Ad26 (serotype 26) and Ad5 (serotype 5). Both Ad26 and Ad5 are
used as vectors for the expression of the SARS-CoV-2 spike protein (S-protein). Immuniza-
tion with Sputnik V occurs with two doses of the vaccine, one of which is a primer (rAd26)
and the other of which is a booster (rAd5).
There is another vaccine that resembles Sputnik V (primer dose), which was developed
by Janssen/Johnson & Johnson. The vaccine Ad26COV2 is based on the rAd26 adenovirus
vector. It encodes a full length, stabilized S protein of SARS-CoV-2 and is used in a one-time,
single-dose immunization regimen [5].
According to a randomized, double-blind, placebo-controlled, multicenter phase 3
trial that took place in Russia, the protective efficacy of the Sputnik V vaccine is 91.6% [
6
].
Vaccines 2022,10, 984. https://doi.org/10.3390/vaccines10070984 https://www.mdpi.com/journal/vaccines

Vaccines 2022,10, 984 2 of 15
In Hungary, a large retrospective cohort study was carried out to compare the effectiveness
of vector-based and mRNA-based vaccines. In this study, almost all the data were gath-
ered before the spread of the Delta VOC. Hungarian researchers estimated the protective
effectiveness of Sputnik V in preventing symptomatic infection at 86% and its ability to
prevent deaths associated with COVID-19 at 97% [
7
]. Analysis of data from retrospective
case-control studies conducted in Bahrain shows that the ratio of the risks of death in the
vaccinated and unvaccinated groups was 1/15. Consequently, the vaccine effectiveness in
preventing deaths exceeds 90% [
8
]. Unfortunately, the publication indicated that during
the time when the study was conducted in the country, the dominant variants of the virus
changed, so it is not possible to link the estimated vaccine effectiveness to a specific variant
of SARS-CoV-2.
Russia has also developed another variant of the Sputnik V vaccine, called Sputnik-
Light. This is a single-dose version of Sputnik V, which includes only a primer dose for
immunization. The effectiveness of the similar vaccine Ad26COV2. (Janssen/Johnson &
Johnson) against COVID-19 infection, based on the results of phase 3 clinical trials, is like
Sputnik Light at 67% (95% CI 59–73). The effectiveness of the vaccine against severe COVID-
19 is slightly higher at 85% (95% CI 54–97) [
5
]. However, these trials were conducted before
the Delta VOC became dominant. It can be assumed that in a situation with the dominant
viral variant Delta or Omicron, the result would be different.
The vaccine representing the second platform in Russia is the peptide vaccine Epi-
VacCorona. It consists of three peptides of SARS-CoV-2 S-protein conjugated to a carrier
protein. The carrier in EpiVacCorona is a two-part chimeric protein, one of which is a
viral nucleocapsid protein (N-protein), and the other a bacterial maltose-binding protein.
The carrier protein is expressed in E. coli, and peptides are also expressed in E. coli or
chemically synthesized. The three peptides of the vaccine have the following amino acid
sequences: CRLFRKSNLKPFERDISTEIYQAGS, CKEIDRLNEVAKNLNESLIDLQE, and
CKNLNESLIDLQELGKYEQYIK [
9
]. It is worth noting that these three peptides do not
overlap with the mapped antigenic linear epitopes of the S-protein of SARS-CoV-2 [
10
–
15
].
EpiVacCorona received a registration certificate on 14 October 2020 [
16
]. In Russia,
the certificate provides emergency use authorization. By the time large-scale immunization
with this vaccine began (11 December 2020), even a Phase I clinical trial had not been
completed. The representative of the State Research Center “Vector” told the reporter of
RIA-Novosti (Russian state media outlet) on 22 January 2021: “Clinical trials of Phases I-II
have not yet been completed. There are only intermediate results” [
17
]. Later, preclinical,
and clinical phase I/II trial results were published in Russian journals that are not referenced
in PubMed [9,18].
However, these publications and the design of the vaccine itself have come under
serious criticism from the scientific community. For example, the lack of important controls
in published experiments was noted. In addition, the vaccine has been criticized for the lack
of overlap between the three peptides and the experimentally determined linear antigenic
epitopes of SARS-CoV-2 S-protein reactive B-cells [19].
After an injection of EpiVacCorona, a vaccinated person can develop antibodies not
only to the S-protein peptides of the coronavirus, the protective function of which has not
been established, but also to the chimeric protein antigens present in the vaccine to the
viral N-protein and bacterial maltose-binding protein. The antiviral immune protective
function of the latter has not been demonstrated either.
Independent studies, the results of which were presented on the preprint server in
Russia, showed the absence of neutralizing antibodies in the plasma of those vaccinated
with EpiVacCorona [20].
Only 3000 participants were enrolled in the EpiVacCorona Phase III clinical trial [
21
].
It was planned that 25% (750 of 3000) of the participants would receive a placebo. The total
number of participants was very small. Therefore, it is difficult to imagine that any
statistically significant information can be extracted from the trial data. The trial was
registered with ClinicalTrial.gov on 3 March 2021.

Vaccines 2022,10, 984 3 of 15
At the time of this writing (May 2022), the results of Phase III clinical trials showing
the epidemiological effectiveness of the vaccine have not been published. To the best of our
knowledge, the first data on the effectiveness of vaccines are presented below.
Delta (B.1.617.2) VOCs were first detected in patient samples from India but quickly
spread and became dominant in other countries [
22
]. This viral variant can circulate effi-
ciently at current vaccination levels in most countries [
23
–
25
]. In addition, the effectiveness
of vaccines against the disease caused by the SARS-CoV-2 Delta variant was shown to be
reduced [26–28].
In Moscow, the Delta variant replaced all other variants and became dominant in the
summer of 2021 [
26
,
29
,
30
]. In June, it averaged more than half of COVID-19 infections, and
in July, its share was already more than 90%. However, most of the cases in June corre-
sponded to the second half of the month, when the Delta VOC became dominant. Among
the variants of B.1.617.2, the following representatives, AY.4, AY.5, AY.6, AY.10, AY.12, AY.20,
AY.23, and AY.24, were encountered in Moscow. However, two variants prevailed: B.1.617.2
and AY.12 [
29
,
31
]. The genomes of these variants have several characteristic mutations in
the S-protein, which significantly reduce the neutralizing potential of antiviral antibodies
directed to this protein [32].
It is of interest to conduct studies that can reveal vaccine effectiveness against different
dominant virus variants in Russia. Performing such studies is a new challenge at present
since vaccines were developed against one (Wuhan) ancestral variant of SARS-CoV-2,
whereas they should protect against other rapidly appearing variants with different anti-
genic properties. The effectiveness of vaccines registered for use in Russia, especially those
with no published results from Phase III clinical trials, such as EpiVacCorona, needs to be
evaluated.
One method that can help answer the question of whether a vaccine against the
new dominant variant of SARS-CoV-2 is effective is a retrospective cohort data analysis.
We performed such a study based on data collected in June and July 2021 in Moscow,
Russia. During this period, the viral Delta variant dominated [26,29,30].
2. Materials and Methods
2.1. Dataset of COVID-19-Infected Individuals
We conducted our analysis using the dataset created by the Moscow Health Depart-
ment that included people who sought medical care for COVID-19 in Moscow, Russian
Federation, in June and July 2021. The dataset was published in the Telegram group
“COVID-19 Vaccine News: 2021” and is in the public domain [33].
In this dataset, the number of COVID-19 cases is divided into categories according
to patient age and vaccination status. All vaccinated cases were further divided into
subcategories according to the type of vaccine (Sputnik V, EpiVacCorona, and CoviVac)
and the number of doses (one or two) patients received. In addition, COVID-19 cases are
categorized according to the severity of patients’ illnesses or deaths. The severe form of the
disease patient group includes those with severe pneumonia, in which more than 75% of
the lungs are affected. The moderately severe patient group included those who had less
lung damage but still needed oxygen support. Hospitalized patients not requiring oxygen
supplementation or outpatients constitute the mild disease group, and PCR+ patients
without COVID-19 symptoms constitute the asymptomatic group.
In June, Moscow residents were mainly immunized with Sputnik V. At the end of
the month, the number of those immunized with other vaccines was less than 3% of the
total number. In July, there were more of those immunized with EpiVacCorona or Covivac.
In our work, only those vaccinated with two doses of each vaccine were counted.
A less detailed analysis of Sputnik V data, as well as a comparison of its effectiveness
with EpiVacCorona vaccine was carried out for data obtained in July. At the same time,
a more detailed analysis of the VE of Sputnik V was carried out using the data obtained in
June. We were unable to perform a more detailed analysis of VE values for July data since

Vaccines 2022,10, 984 4 of 15
the number of people vaccinated and diagnosed with COVID-19 for July was available in
the database only for the severe COVID-19 case category.
Russian vaccination protocols do not recommend mixing vaccines. Therefore, the en-
tire vaccinated population received only one type of vaccine.
In our work, a few methods were used to count the number of people in a control
group. From the total number of city residents in each age group, we subtracted (1) the
number of vaccinated individuals or (2) the number of seropositive individuals. The first
control group was formed by subtracting vaccinated city residents from the total population
of the same age group. The second control group was formed by subtracting seropositive
city residents from the total population of the same age group.
To calculate the number of those fully vaccinated by the time the number of cases was
estimated, we used data from the Ministry of Health registry created for the registration
and issuance of vaccine certificates in Russia [
34
]. A representative sample of the registry
contents was generated by computer polling of vaccine certificate issuance service URL
addresses from the space of all possible unique registry record numbers. Data from this
registry on those who received Sputnik V or EpiVacCorona vaccines were grouped into the
same age categories used to estimate COVID-19 cases.
The 2021 demographics were used to normalize the data and estimate the total number
of Moscow residents in different control age groups [
35
]. The study also used estimates of
the total number of vaccinated and COVID-19-infected city residents at various dates in
June and July 2021 [36,37].
We calculated the number of seropositive city residents in each age group based on the
Moscow Department of Health data in the published preprint [
29
]. The details are shown
below in the following section.
2.2. Estimation of the Percentage of Moscow Residents with Antibodies
The monitoring of a representative sample of Moscow residents demonstrated that
just under half of them had antibodies to SARS-CoV-2 by the end of June 2021. This number
is based on the results of continuous serological studies. For this seroprevalence study,
samples were from patients who were admitted to the hospital for routine treatment for a
disease not related to COVID-19. The average number of patients tested for SARS-CoV-2
antibodies was 10,000 per week. The presence of IgG antibodies in the venous blood
serum was evaluated in all patients using a Mindray Medical International Limited (China)
immunochemiluminescent analyzer. The results of sero-monitoring were made public in
the preprint publication [
29
]. This estimation was used by us to form a second control
group by subtracting seropositive city residents from the total population.
2.3. Method of VE Calculation
The calculations were based on published algorithms [
38
,
39
]. The significance values
for VE estimations were calculated using the chi-square method [
40
]. If the outcome in
the study population is rare, as with COVID-19 cases among vaccinated or unvaccinated
individuals, the odds ratio obtained accurately estimates the risk ratio (RR).
The odds ratio (OR) was calculated as follows:
OR =a×d
b×c
95% OR =exp(ln (OR)±1.96 ×r1
a+1
b+1
c+1
d!
considering:
a. number of COVID-19 cases among vaccinated people in Moscow
b. number of vaccinated in Moscow
c. number of COVID-19 cases among unvaccinated people in Moscow

Vaccines 2022,10, 984 5 of 15
d.
number of Moscow residents in the control group. To calculate the number of
individuals in the control group, two variants of numerical estimation were used
for each age group: (1) the number of unvaccinated Moscow residents and (2) the
number of seronegative city residents.
Vaccine effectiveness (VE) % was calculated as follows:
VE = 100 ×(1 −OR)
The data for the calculations were obtained from the following sources:
The total number of Moscow residents in each age group was calculated based on the
city’s 2021 demographics [35].
a and c—[33] and Supplementary Table S1.
b.
register of vaccinations in Moscow, with data aggregated for the same age groups [
34
]
and [36].
d.
A numerical estimate of the number of individuals in each age group of the control
group was made by subtracting the number of vaccinated or seropositive citizens
from the number of Moscow residents in each age group.
2.4. Confidence Intervals for Vaccine Effectiveness Estimates
In this paper, we estimate vaccine effectiveness using two types of assumptions about
the control group. Because of this, we can only estimate a vaccine’s effectiveness value
as a value within a confidence interval whose width is determined by our assumptions.
Thus, the lower end of the interval for estimating effectiveness can be used as the minimum
among all estimates derived from assumptions about the size of the control group, and the
upper end can be used as the maximum. The confidence intervals for the effectiveness of
the Sputnik V vaccine in preventing severe disease, calculated using this algorithm, are
shown in the last column of Table 1.
Table 1.
Efficiency of the Sputnik V vaccine to prevent severe forms of COVID-19 with confidence
intervals. Data obtained in Moscow in July 2021.
Age, Years
Control Group—Unvaccinated Control Group—Seronegative CI 95% of Both
Control Groups
VE% CI 95% VE% CI 95%
Lower Upper Lower Upper Lower Upper
18–50 89 81 94 93 88 96 81 96
51–70 76 70 81 84 80 87 70 87
70+ 42 32 51 63 56 69 32 69
VE, vaccine effectiveness; CI, confidence interval. The EpiVacCorona vaccine is not effective.
3. Results
3.1. The Effectiveness of Sputnik V and EpiVacCorona against Severe COVID-19 and Death
The results of the VE analysis of the Sputnik V and EpiVacCorona vaccines against
severe COVID-19 are shown in Figure 1a. A comparison of the results in the upper and
lower bar charts of Figure 1a suggests that, regardless of the definition of the control
group, the estimated VE of Sputnik V to prevent severe COVID-19 is high, and its value is
statistically significant. The bar graphs also show the presence of an age-related decrease in
vaccine efficiency. The older the person is, the lower the estimated VE is.

Vaccines 2022,10, 984 6 of 15
Vaccines 2022, 10, x FOR PEER REVIEW 6 of 16
Figure 1. Characterization of immune protection by COVID-19 vaccines. Data obtained in Mos-
cow for the summer of 2021. (a) The estimated effectiveness values of the Sputnik V and EpiVac-
Corona vaccines in preventing severe forms of COVID-19. The analysis is based on data from
July 2021. The upper chart shows the results of the analysis performed with a control group of
unvaccinated Moscow residents, and the lower chart corresponds to the data analysis performed
with a control group of seronegative individuals. The VE estimates for the Sputnik V vaccine
were positive and highly significant (p < 0.001) by the chi-square test for both age groups. The
VE values for EpiVacCorona vaccines were negative and nonsignificant (p > 0.05) for the 18–50
age group and negative and significant for the 50+ age group (p < 0.001). (b) The panel shows the
number of deaths and severe cases of COVID-19 among fully vaccinated or seronegative Mos-
cow residents, normalized per 10,000 person-years.
Estimates of the number of deaths and severe COVID-19 among vaccinated or
seronegative people in Moscow are plotted on the bar graphs shown in Figure 1b. We
clearly see that the risk of either outcome is drastically reduced in individuals vac-
cinated with Sputnik V. Among those who received the vaccine but were older than
51, the number of deaths and serious illnesses was significantly higher compared to
those who were younger than 50.
We also created a graphical illustration showing the Sputnik V VE in preventing
COVID-19-related deaths (Figure 2, left panel). A comparison of the upper and lower
charts in the left panel of Figure 2 shows that the estimate of VE in preventing COVID-
19 deaths is almost independent of how the control group is defined.
Figure 1.
Characterization of immune protection by COVID-19 vaccines. Data obtained in Moscow
for the summer of 2021. (
a
) The estimated effectiveness values of the Sputnik V and EpiVacCorona
vaccines in preventing severe forms of COVID-19. The analysis is based on data from July 2021.
The upper chart shows the results of the analysis performed with a control group of unvaccinated
Moscow residents, and the lower chart corresponds to the data analysis performed with a control
group of seronegative individuals. The VE estimates for the Sputnik V vaccine were positive and
highly significant (p< 0.001) by the chi-square test for both age groups. The VE values for EpiVac-
Corona vaccines were negative and nonsignificant (p> 0.05) for the 18–50 age group and negative
and significant for the 50+ age group (p< 0.001). (
b
) The panel shows the number of deaths and
severe cases of COVID-19 among fully vaccinated or seronegative Moscow residents, normalized per
10,000 person-years.
Estimates of the number of deaths and severe COVID-19 among vaccinated or seroneg-
ative people in Moscow are plotted on the bar graphs shown in Figure 1b. We clearly see
that the risk of either outcome is drastically reduced in individuals vaccinated with Sputnik
V. Among those who received the vaccine but were older than 51, the number of deaths and
serious illnesses was significantly higher compared to those who were younger than 50.
We also created a graphical illustration showing the Sputnik V VE in preventing
COVID-19-related deaths (Figure 2, left panel). A comparison of the upper and lower charts
in the left panel of Figure 2shows that the estimate of VE in preventing COVID-19 deaths
is almost independent of how the control group is defined.

Vaccines 2022,10, 984 7 of 15
Vaccines 2022, 10, x FOR PEER REVIEW 7 of 16
Figure 2. Protective effectiveness of the Sputnik V vaccine against COVID-19 disease of varying
severity or death (%). The upper bar charts represent calculations with control group 1, and the
lower bar charts represent calculations with control group 2. All positive VE estimates in all
charts are highly significant according to the chi-square test, p < 0.001. Data obtained in Moscow
in June 2021.
More detailed information on the age-related VE of Sputnik V is presented in Ta-
ble 1. Because any control group definition is not completely satisfactory in all neces-
sary aspects of VE estimation, it may be appropriate to use a combination of calculated
95% confidence intervals to determine the upper and lower limits of VE. The combina-
tion obtained from the two control groups is presented in the last column under the
title “CI 95% of both control groups” in Table 1. Based on this 95% CI combination, we
can say that the vaccine protects against severe forms of the disease with an effective-
ness of over 81% in the group under 50 years of age and over 32% in the group over 70
years of age.
Table 1. Efficiency of the Sputnik V vaccine to prevent severe forms of COVID-19 with confi-
dence intervals. Data obtained in Moscow in July 2021.
Age, Years
Control Group—Unvaccinated Control Group—Seronegative CI 95% of Both
Control Groups
VE% CI 95% VE% CI 95%
Lower Upper Lower Upper Lower Upper
18–50 89 81 94 93 88 96 81 96
51–70 76 70 81 84 80 87 70 87
70+ 42 32 51 63 56 69 32 69
VE, vaccine effectiveness; CI, confidence interval. The EpiVacCorona vaccine is not effective.
In Figure 1a, instead of positive estimates of EpiVacCorona vaccine effectiveness,
negative estimates are shown. Despite the wide confidence intervals of these negative
Figure 2.
Protective effectiveness of the Sputnik V vaccine against COVID-19 disease of varying
severity or death (%). The upper bar charts represent calculations with control group 1, and the lower
bar charts represent calculations with control group 2. All positive VE estimates in all charts are
highly significant according to the chi-square test, p< 0.001. Data obtained in Moscow in June 2021.
More detailed information on the age-related VE of Sputnik V is presented in Table 1.
Because any control group definition is not completely satisfactory in all necessary aspects
of VE estimation, it may be appropriate to use a combination of calculated 95% confidence
intervals to determine the upper and lower limits of VE. The combination obtained from
the two control groups is presented in the last column under the title “CI 95% of both
control groups” in Table 1. Based on this 95% CI combination, we can say that the vaccine
protects against severe forms of the disease with an effectiveness of over 81% in the group
under 50 years of age and over 32% in the group over 70 years of age.
In Figure 1a, instead of positive estimates of EpiVacCorona vaccine effectiveness,
negative estimates are shown. Despite the wide confidence intervals of these negative
values, we can confidently state that the EpiVacCorona vaccine cannot protect against
severe COVID-19.
3.2. Comparative Effectiveness of the Sputnik V Vaccine in Preventing COVID-19 of Varying
Severity in Different Age Groups
Table 2demonstrates the calculated odds ratios of COVID-19 outcomes among vacci-
nated or seronegative individuals. Although all calculations were based on data collected
in one month (June or July), we introduced a time parameter into our estimates to compare
our results with those of other studies that use time normalization.

Vaccines 2022,10, 984 8 of 15
Table 2. Estimation of deaths and severe COVID-19 cases. Data obtained in Moscow in June 2021.
Age
Normalized Data (10,000 Persons per Year)
Odds Ratio
95% CI
Deaths Severe Form
of COVID-19 Deaths Severe Form
of COVID-19
Unvac. Vac. Unvac. Vac. Deaths Severe Form Lower Upper Lower Upper
18–50 6.4 0.17 40 4 0.03 0.1 0 0.21 0.07 0.15
51+ 78 13 255 51 0.17 0.2 0.13 0.22 0.18 0.23
Figures 1and 2show a tendency for the calculated VE values to decrease as age
increases. This trend was independent of which control group was used for the analysis.
According to this estimation, breakthrough infections occur more frequently in the older
age group.
In addition, Figure 2also revealed the following trend: the more severe the disease,
the better the vaccine protection observed. We noticed a tendency for vaccine effectiveness
to increase as the severity of the disease increases, so that the vaccine protects particularly
well against the most severe form of the disease or even death.
4. Discussion
4.1. Limitations of Our Study
Our retrospective cohort study has several limitations. We must deal with a dataset
that lacks important information about those who received COVID-19. We do not know
the demographics, except for the age of COVID-19 patients. We do not know the timing
of vaccination, and we cannot connect each vaccinated and unvaccinated person so that
their characteristics match each other. Commercial test systems that have been used to
determine the presence of antibodies in the Moscow population have not been able to
distinguish antibodies acquired through natural infection from those acquired through
vaccination against SARS-CoV-2. Therefore, evidence of prior infection was not available
for the studied population. However, estimates of the proportion of the Moscow population
with antibodies capable of recognizing viral proteins were available (at the end of June).
Nevertheless, it was unclear whether these antibodies recognized viral N-protein, which
arise because of natural infection, or S-protein, which can also arise as a result of vaccination.
4.2. EpiVacCorona Is an Inefficient Vaccine
To date, the developers of EpiVacCorona have not published the results of their phase
III study. The results of this analysis, along with an earlier analysis [
41
], indicate a lack of
protective VE. Our study showed the negative efficacy of EpiVacCorona. What could this
be due to? We assume that the active or even aggressive advertising campaign in Moscow
for this particular vaccine as the most sparing for health, i.e., the vaccine with the least
number of side effects, led to a greater number of people with morbid conditions choosing
this particular vaccine. Thus, it is highly likely that there were many more chronically ill
people in the vaccination group who were prone to more severe COVID-19. As a result of
this bias, the vaccinated group has more incidents of severe disease and deaths compared
to unvaccinated groups.
Of course, one can also try to explain the negative effectiveness of EpiVacCorona
by biological factors. For example, immune imprinting may be to blame. The vaccine
contains a large amount of viral N-protein. Thus, when those who have been immunized
with EpiVacCorona encounter a real virus, their memory B-cell clones produce mainly IgG
antibodies targeting the viral N-protein. These antibodies cannot bind the virus, since only
antibodies targeting the viral S-protein are able to do that and induce immune protection.
Therefore, the immunized person’s body produces the wrong ineffective antibodies after
SARS-CoV-2 invasion.
The developers of EpiVacCorona tried, but failed, to equip the vaccine with S-protein
fragments (in the form of three peptides), which can trigger an antibody response to the

Vaccines 2022,10, 984 9 of 15
virus. This failure is explained by a lack of overlap between the three protein fragments
they chose as vaccine peptides and the experimentally mapped linear antigenic epitopes of
the S-protein of SARS-CoV-2 [10–15].
4.3. Perhaps There Are More People with Comorbid Chronic Conditions in the Vaccinated
Elderly Groups
It may be assumed that the vaccinated elderly groups had many more people with
comorbid chronic conditions than the other groups, simply because people with chronic
conditions were more motivated to get vaccinated and received the vaccine more often.
As a result, despite vaccination, groups of older people with more comorbid conditions
had more infections than they would have had if people with chronic conditions were
not there. This effect can significantly decrease the VE in certain age groups of people or
even make it negative. Perhaps for this reason, and because there are more people with
comorbid chronic diseases in the vaccinated elderly groups than in other groups, morbidity
after EpiVacCorona is somewhat more common among vaccinated individuals and results
in negative vaccine efficiency values. It is possible that this may also explain the low or
even negative efficiency values of Sputnik V for preventing non severe forms of COVID-19
disease in the older age groups.
4.4. Biological Factors May Contribute to the Decrease in VE in Older Age Groups
In addition to social and behavioral factors, features of the immune system of the
elderly may contribute to the age-related decline in VE. Below is a detailed description of
the specific functioning of the immune system in the elderly.
4.4.1. Protection against SARS-CoV-2 Reinfection Is Lower in the Older Age Group
Interestingly, the ability of the human immune system to protect against COVID-19
reinfection also appears to depend on age. For example, a study of 4 million RT-PCR-
positive cases in the first and second wave of infections in Denmark showed that protection
against reinfection was stronger in the younger generation: 81% in the under-65 group
versus 47% in the over-65 group [42].
4.4.2. The Rate of Decrease in the Level of Antibodies Is Higher in the Older Age Group
Our retrospective cohort analysis revealed a consistent decline in the estimated vaccine
effectiveness with age. Although we attribute this decline to the assumption that there are
more people with comorbidities among the elderly vaccinated compared to the unvacci-
nated of the same age, we do not rule out that in the elderly, vaccine-induced protective
immunity can weaken more quickly because their antibody levels might decrease faster,
as shown in a number of publications for different vaccines [
43
–
46
]. After vaccination with
BNT162b2, the level of antiviral IgG antibodies decreased faster in older vaccine recipients
(
≥
50). Interestingly, no such effect was observed after mRNA-1273 vaccination for six
months [47].
4.5. Comparison with Retrospective Studies of Vaccine Effectiveness Estimates in
Different Countries
In general, our VE estimate of the Sputnik V vaccine in preventing COVID-19-related
deaths or severe disease is comparable to the VE estimates of vaccines from other developers.
However, the VE that we estimated decreased as the age of the individual increased.
The reasons for this trend may be related to deficiencies in the cohort study design that does
not consider large differences in the characteristics of people in the observation groups.
In addition, differences in the immunological and behavioral characteristics of people in
different age cohorts may contribute to this effect. Does the same tendency appear in the
studies of other authors? As seen in Table 3, the results are variable but most frequently are
consistent with our own.

Vaccines 2022,10, 984 10 of 15
Table 3. Studies demonstrating the negative correlation between VE and age.
Country of
Data Origin
Type of
Research Vaccines Outcomes of
COVID-19
Age
Dependence
of VE
Virus Variant Type of
Publication Ref.
Scotland retrospective
case control
BNT162b2,
ChAdOx1
nCoV-19
death not detected mainly Delta article [48]
Sweden retrospective
cohort
BNT162b2,
ChAdOx1
nCoV-19,
mRNA-1273
death,
hospitalizations,
symptomatic and
asymptomatic
detected not specified article [49]
Hungary retrospective
cohort
BNT162b2,
mRNA-1273,
Sputnik V,
Sinofarm,
ChAdOx1
nCoV-19
infection not detected not specified article [7]
UK case-control
study
BNT162b2,
ChAdOx1
nCoV-19
mild and severe detected Delta article [50]
UK case-control
study
BNT162b2,
ChAdOx1
nCoV-20
PCR-positive detected Delta article [51]
USA
retrospective
cohort,
controlled by
those who got
flu vaccine
is not specified
death, ICU
hospitalizations,
and many other
outcomes
detected not specified article [52]
USA retrospective
cohort BNT162b2 PCR-positive and
hospitalization
detected, but
minor Delta article [53]
Israel case-negative
control BNT162b2
PCR-positive or
severe form of
COVID-19
not detected for
PCR+ and
detected for
severe COVID-19
not specified article [54]
Israel case-negative
control
BNT162b2,
mRNA-1273 infection detected but
positive Delta article [55]
Qatar case-negative
control
BNT162b2,
mRNA-1273
symptomatic or
asymptomatic
infection
detected Delta article [56]
Russia retrospective
cohort Sputnik Light symptomatic
infection detected Delta preprint [29]
4.6. Perspectives. How Long Does Vaccine Protection Last?
How long can Sputnik V protect against severe COVID-19 and death? When should
the boosting dose be given? There are no open databases that can be analyzed to answer
these questions. However, studies have been performed with other vaccines.
An interesting retrospective cohort study of the duration of vaccine protection against
infection was conducted in Sweden [
49
]. It was found that the effectiveness of the vaccine in
protecting against COVID-19 infection decreased faster among men and the elderly. The ob-
servations of this study, in terms of decreasing vaccine efficacy with age, are consistent
with our findings.
In the Swedish study, however, special emphasis was placed on examining the dura-
tion of the protective effect of the vaccine. It was shown that, on average, over a period
slightly longer than six months, vaccine efficiency in preventing symptomatic COVID-19 for
the BNT162b2 vaccine dropped from 92% to zero. Additionally, over an even shorter period
(four months), the efficiency of the ChAdOx1 nCoV-19 vaccine dropped from 66% to zero.
Slightly more stable was the efficiency of the mRNA-1273 vaccine (Moderna). It dropped
from 96% to 59% in half a year [
49
]. However, the average efficiency of all vaccines to
prevent symptomatic COVID-19 for those under age 50 was still positive, although not high

Vaccines 2022,10, 984 11 of 15
(~34%) even after half a year. In contrast, for those over 50 years of age, vaccine efficiency
in just over half a year waned to zero [49].
Interestingly, in a retrospective cohort study in the United States, the results showed a
slightly slower decline in the effectiveness of the BNT162b2 vaccine compared to the decline
found by scientists in Sweden. The protective effectiveness (against infection with the Delta
variant of SARS-CoV-2) in the United States fell from 93% to 53% in five months [
53
] and
not to zero as in Sweden [
49
]. However, the VE against Delta-related hospitalizations was
overall high at 93% and did not decline for at least six months. VE was age dependent and
was higher for younger adults [53].
Similar results were obtained in the UK in a case-control study. VE against symp-
tomatic disease caused by the Delta viral variant dropped to 70% for BNT162b24 and to 47%
for ChAdOx1 nCoV-19 over 20 weeks but did not fall as much for hospitalizations. The de-
cline in the efficiency of these vaccines was more noticeable in people over 65. The authors
conclude that the VE declines much faster in older people than in younger people [50].
British researchers performed a retrospective cohort design study [
52
] and presented
an analysis of a large database obtained mainly from the United States. In this database,
the researchers identified more than 10,000 vaccine breakthrough COVID-19 cases, which
were matched with those of unvaccinated controls. The work showed that the vaccine
protected those under 60 years of age from death and intensive care unit hospitalization
significantly better than those who were older. In fact, the authors of the study did not
detect any positive effects of vaccination for those over 60 years of age [
52
]. Unfortunately,
the type of vaccine was not specified in the study.
Finally, an Israeli study demonstrated that immunity against SARS-CoV-2 infection
waned in all age groups a few months after vaccination. However, after the same period
following the second dose of the vaccine, severe COVID-19 was more common among
those over 60 years of age [54].
In summary, it is unlikely that the Sputnik V vaccine protects longer than the vaccines
mentioned above, so a boosting dose can be useful six months after the first vaccination.
This recommendation is most applicable to the elderly.
5. Concluding Remarks
Our retrospective study shows that those vaccinated with EpiVacCorona have no
advantage over unvaccinated or seronegative individuals in their chances of contracting
COVID-19. The vaccine was introduced into civilian circulation without sufficient testing,
has not proven itself, and should therefore be withdrawn from production and distribution.
At the same time, all those immunized with this poorly tested vaccine preparation should
be given the opportunity to be vaccinated with a modern, effective vaccine.
We find that the estimated VE of the Sputnik V vaccine to prevent deaths and severe
forms of disease caused by the Delta variant of SARS-CoV-2 is comparable to the estimated
VE of this vaccine against COVID-19 caused by other viral variants that have previously
appeared in circulation. Our observations are consistent with other studies that have
evaluated the VE of Sputnik V in clinical trials [
6
] or case-control studies [
41
]. However,
due to several factors, and primarily due to the limitations of the database with which
we worked, it was impossible to directly compare the estimated VE values in our work
with VE estimates from published papers. Our database does not allow us to normal-
ize the analyzed groups by the number of people with chronic diseases, as well as by
other important demographic characteristics. If there are more people with comorbidities
among the vaccinated elderly than among the unvaccinated, then the VE calculations will
underestimate the effectiveness of the vaccine.
We found that the estimated VE of the Sputnik V vaccine decreases with age and
reaches a minimum in the age cohort over age 70. We do not know the exact reasons for
this decrease in effectiveness, and several hypotheses have been discussed above. Among
them are those based on (a) limitations in the design of our database, (b) biological factors,
and (c) social factors.

Vaccines 2022,10, 984 12 of 15
Biological hypotheses relate to the immune characteristics of the elderly. These charac-
teristics may manifest as lower antibody levels that appear after vaccination in the elderly
and/or a more rapid decline in antibody levels. The protective effect of the vaccine may
thus be shorter. These hypotheses are partially supported by the literature [43–45,57].
A hypothesis based on social factors links the decrease in vaccine effectiveness to
behaviors leading to additional risks of COVID-19 infection predominantly in the elderly
group. Members of this group might feel more protected and spend more time in public
places where the risk of infection is higher, such as on public transportation. The opposite
hypothesis can also be considered. It may be that young people, because of their busier
social lives compared to older people, are more likely to be infected with COVID-19 in an
asymptomatic form, and after vaccination they develop hybrid immunity, which protects
better than just vaccine immunity in older people. Hybrid immunity forms in those who
have had the disease and have been vaccinated [51].
In any case, we and others have found a pattern of decreasing estimated VE with
increasing age. Similar observations for other vaccines have been made in various coun-
tries, including Sweden [
49
], the UK [
50
,
51
,
58
], the United States [
52
,
53
], Israel [
54
], and
Qatar [
56
]. Regardless of how the nature of the detected effect is explained, it might indicate
that elderly people get sick more often than younger people, which means that they need
to protect themselves to a greater extent, e.g., by booster doses of vaccines and, in addition,
by masks, social distance, and all other means.
6. Conclusions
In summary, we can state the following:
1. The EpiVacCorona vaccine does not protect against COVID-19.
2.
The failure of EpiVacCorona to protect those who have been vaccinated demonstrates
that no vaccine should be introduced to the public until strong evidence shows that it
is safe and effective.
3.
The Sputnik V vaccine appears to confer high (significant) protection against the Delta
variant.
4.
The more severe the COVID-19 disease, the better the Sputnik V vaccine protects
against it.
5. The estimated VE of Sputnik V was lower in the elderly than in the young.
Supplementary Materials:
The following supporting information can be downloaded at: https:
//www.mdpi.com/article/10.3390/vaccines10070984/s1, Table S1: COVID-19 cases in Moscow
categorized according to age and vaccination status of patients (June–July 2021).
Author Contributions:
O.M. and A.E. participated in data analysis, writing, and editing. All authors
have read and agreed to the published version of the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement:
The study does not fall within the regulatory definition
of research involving human subjects. Current research involves only secondary data analysis of
already publicly available datasets, which contain no information that can identify subjects, directly
or through identifiers linked to the subjects.
Informed Consent Statement: Not applicable.
Data Availability Statement:
The data used for the calculations made in this work are available
in Supplementary Table S1. In addition, all input data, calculations and the code are available in
GitHub [
59
]. The content of the manuscript has previously appeared online in ResearchSquare
preprint [60].
Acknowledgments:
We express deep gratitude to the organizers of social networks: “Project V1V2.ru,
chat systems about vaccinations” and to particularly Max Popov for pointing out the data that became
the basis for the analysis in this work. We are also very grateful to Andrei Krinitsky, the administrator
of the Telegram chat room, who organized the COVID-19 Grais “epivakorona.com” civic vaccination

Vaccines 2022,10, 984 13 of 15
group, and the members of this group. One member of the group, who is a medical informatics
specialist and who preferred to remain anonymous, helped a great deal with the algorithmic part of
the study and the verification of all the calculations. His help was extremely important and greatly
accelerated our work. Special thanks to Alexander Chepurnov for the experimental data that inspired
the study and to Alena Makarova for our multiple and highly valuable discussions. We also greatly
appreciate the input of Anatoly Alstein, Mikhail Gelfand, Ilya Yasny, Anton Chugunov, Andrey
Panov, Dmitry Kuznets, Denis Lagutkin and Margarita Romanenko for their help in formulating
the ideas that provided the basis for this article. It is important to mention that the “COVID-19
Watch” social network, created, and moderated on Facebook by data analyst Alexei Kouprianov,
has become a useful forum for regular additional discussion and critical assessment of our research
findings, a discussion that significantly speeded up our work. Finally, thanks to Alexander Ljubimov
for careful proofreading of the manuscript, as well as to Eugene Koonin for motivating us to publish
the study.
Conflicts of Interest: The authors declare no conflict of interest.
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