Instituto de Estudios Políticos y Derecho Público "Dr. Humberto J. La Roche"
de la Facultad de Ciencias Jurídicas y Políticas de la Universidad del Zulia
Maracaibo, Venezuela
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Vol. 39, Nº 71 (2021), 986-1007
IEPDP-Facultad de Ciencias Jurídicas y Políticas - LUZ
Recibido el 01/09/2021 Aceptado el 22/11/2021
Natural population movement and
COVID-19: data from Russia
DOI: https://doi.org/10.46398/cuestpol.3971.60
Ilmir Nusratullin *
Igor Drozdov **
Alexei Ermakov ***
Elena Koksharova ****
Maya Mashchenko *****
Abstract
The COVID-19 pandemic is highly infectious, so it paralyzed
the health systems of many countries causing a high mortality
rate. Ocial data on COVID-19 deaths at many sites are
questioned, and the gures are considered several times higher
than ocial data. In this sense, the objective of the study was to
determine the impact of the COVID-19 pandemic on the natural
movement of the population and, in addition, to evaluate the real
mortality rate from COVID-19 in Russia from the construction of predictive
mortality models. The study used data from the World Health Organization
and the Statistical Service of the Federal State of Russia; se used linear and
polynomial models to construct mortality models. The study revealed an
underestimation of the ocial COVID-19 death rate by 2.4 to 6.8 times,
depending on the data source. There was a sharp increase in mortality in
Russia in 2020 among people over 50 years of age, and with the increase
in age, mortality increased. The main reasons for the sharp increase in
mortality were coronary heart disease, cerebrovascular diseases, and
respiratory diseases, among others.
Keywords: COVID-19 pandemic; demographics; vital movement;
mortality, geopolitics.
* PhD in Economics, Associate Professor, Bashkir State University, Ufa, Russia. ORCID ID: https://
orcid.org/0000-0001-7810-2945
** PhD in Psychology, Associate Professor, Far Eastern Federal University, Vladivostok, Russia. ORCID
ID: https://orcid.org/0000-0001-7311-4920
*** PhD in Pedagogy, Associate Professor, Russian State University of Physical Education, Sport, Youth
and Tourism (SCOLIPE), Moscow, Russia. ORCID ID: https://orcid.org/0000-0002-7505-085X
**** PhD in Pedagogy, Associate Professor, Russian State Vocational Pedagogical University, Yekaterinburg,
Russia. ORCID ID: https://orcid.org/0000-0003-4956-5291
***** PhD in Pedagogy, Associate Professor, Russian State Vocational Pedagogical University,
Yekaterinburg, Russia. ORCID ID: https://orcid.org/0000-0002-7071-6376
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CUESTIONES POLÍTICAS
Vol. 39 Nº 71 (2021): 986-1007
Movimiento de población natural y COVID-19:
datos de Rusia
Resumen
La pandemia de COVID-19 es altamente infecciosa, por lo que paralizó
los sistemas de salud de muchos países provocando una alta tasa de
mortalidad. Se cuestionan los datos ociales sobre muertes por COVID-19
en muchos sitios, y las cifras se consideran varias veces más altas que
los datos ociales. En este sentido, el objetivo del estudio fue determinar
el impacto de la pandemia de COVID-19 en el movimiento natural de la
población y, además, evaluar la tasa de mortalidad real por COVID-19 en
Rusia a partir de la construcción de modelos predictivos de mortalidad. El
estudio utilizó datos de la Organización Mundial de la Salud y del Servicio
de Estadísticas del Estado Federal de Rusia; se utilizaron modelos lineales
y polinomiales para construir modelos de mortalidad. El estudio reveló
una subestimación de la tasa ocial de mortalidad por COVID-19 de 2,4
a 6,8 veces, según la fuente de datos. Se produjo un fuerte aumento de la
mortalidad en Rusia en 2020 entre las personas mayores de 50 años y, con
el aumento de la edad, la mortalidad aumentó. Las principales razones del
fuerte aumento de la mortalidad fueron las cardiopatías coronarias, las
enfermedades cerebrovasculares y las enfermedades respiratorias, entre
otras.
Palabras clave: Pandemia COVID-19; demografía; movimiento vital;
mortalidad, geopolítica.
Introduction
In December 2019, the world learned about the emergence of
COVID-19, a new infectious disease, or Severe Acute Respiratory Syndrome
Coronavirus 2 (SARS-CoV-2) (Huang et al., 2020). The epidemic that
began in December 2019 has now spread to all continents and countries of
the world. As of July 15, 2021, there were 188,128,952 conrmed cases of
COVID-19, including 4,059,339 deaths (World Health Organization, 2021).
The mortality rate from COVID-19 is dierent in dierent countries, and
there are countries with a high mortality rate (for example, Peru – 590.22
deaths per 100.000 population, Hungary – 307.21, Bosnia and Herzegovina
-294.56), as well as with a low mortality rate (for example, Tanzania – 0.04
deaths per 100.000 population, Lao – 0.04, Burundi – 0.07) (World Health
Organization, 2021).
Today, some countries have managed to cope with cases of the disease
with a high degree of recovery, and they have developed sustainable
988
Ilmir Nusratullin, Igor Drozdov, Alexei Ermakov, Elena Koksharova y Maya Mashchenko
Natural population movement and COVID-19: data from Russia
methods of treatment (Are and Ekum, 2020). At the end of 2020 and at
the beginning of 2021, vaccines against COVID-19 were developed, which
are now being actively used for vaccination all over the world. As of July
15, 2021, 3,402,275,866 doses of vaccine have been given (World Health
Organization, 2021). New coronavirus strains are of particular concern
today (Bollinger and Ray, 2021), however, at the moment they do not
fundamentally aect the overall strategy for overcoming the crisis caused
by the pandemic.
The COVID-19 pandemic has had a great impact on public relations, the
economy, and the nancial system of the countries of the world (Nusratullin
et al., 2021). However, the most negative consequence of the pandemic is
an increase in the death rate of the population. The increase in mortality
was not only due to the disease itself, but due to the lack of bed capacity,
equipment, insucient nancing of the health care system, and inability to
provide planned medical care in the existing situation. (Gerli et al., 2020).
The pandemic also had a negative impact on the birth rate, as due to stress
in 2020, there was a decrease in marriage and birth rates. In addition, the
closure of borders and tightening of the rules for crossing them led to a
decrease in migration (Ryazantsev et al., 2021).
If we talk about mortality from COVID-19 in Russia, then the ocial
data are as follows. In Russian Federation, from 3 January 2020 to 15 July
2021, there have been 5 882 295 conrmed cases of COVID-19 with 146 069
deaths. As of 12 July 2021, a total of 47 572 228 vaccine doses have been
administered (World Health Organization, 2021). These data indicate that
the COVID-19 pandemic has greatly aected the demographic situation in
Russia.
However, it should be noted that the ocial data on deaths from
COVID-19 cannot explain the real numbers of depopulation. The natural
population decline within the year in Russia in 2020 increased by 2.2 times
compared to 2019 and amounted to 702,072 people, which is 0.5% of the
population of all of Russia (Federal State Statistics Service of Russia, 2021).
In this regard, the problem of determining the true impact of the COVID-19
pandemic on mortality in Russia has become topical.
It is quite dicult to assess the direct impact of coronavirus on mortality
in a particular country since dierent countries apply dierent standards
for the causes of death partition (Middelburg and Rosendaal, 2020). In this
study, we will assess mortality from COVID-19 by constructing predictive
mortality models based on data for 2011-2019 and forecasting for 2020
within the established trend. And then we will compare the results obtained
and the ocial data on mortality from COVID-19.
The purpose of this study is to determine the impact of the COVID-19
pandemic on the natural population movement and to assess the real
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CUESTIONES POLÍTICAS
Vol. 39 Nº 71 (2021): 986-1007
mortality from COVID-19 in Russia based on the construction of predictive
mortality models. To achieve this goal, it is necessary to solve the following
tasks:
1) To analysed data on the natural population movement in Russia.
2) To build predictive models of mortality in Russia as a whole and for
causes of death in particular.
3) to compare the results obtained with ocial data and draw
conclusions.
1. Literature Review
With the spread of COVID-19 since the end of 2019, the rst studies
have focused primarily on the spread and dynamics of the spread of the
virus. The main epidemiological, clinical and laboratory characteristics
of COVID-19 disease, as well as treatment data and clinical outcomes of
patients, are disclosed in the work of Huang et al. (2020), Liu et al. (2020),
Ferguson et al. (2020).
The rst predictions of the spread of the new coronavirus were made in
the studies of Read et al. (2020), Zhao et al. (2020), Li et al., (2020), and
in addition, they draw attention to the seriousness of the problem of the
rapid spread of COVID-19. The rst assessment of the impact of the new
epidemic on the health systems of countries was carried out in the works of
Tang et al. (2020), Yang et al. (2020).
Further research was aimed at nding ways to reduce the incidence of
new COVID-19 cases, as well as the causes of asymptomatic cases. A study
by Wu and McGoogan (2020) and Oran and Topol (2020) conrmed that
people with asymptomatic COVID-19 disease are carriers of the disease and
can actively infect people around them. Adeniyi et al. (2020) conrm that
compliance with hygiene rules and rules of conduct in the conditions of the
spread of infectious diseases reduces the rate of spread of a new coronavirus
infection.
Today, there are many studies on the consequences of the COVID-19
epidemic on various areas of human activity. For example, the United
Nations (2020) report shows that in 2020, the world gross domestic product
declined by an estimated 4.3%, and in developed countries, it dropped by
5.6%. 420 million jobs were lost in the last two quarters of 2020. This is
considerably superior to the global recession in 2009, when production
went down by only 1.7%.
The economic impact of COVID-19 is assessed by Chudik et al. (2020).
The results of the analysis show that the global recession will be prolonged
990
Ilmir Nusratullin, Igor Drozdov, Alexei Ermakov, Elena Koksharova y Maya Mashchenko
Natural population movement and COVID-19: data from Russia
and no country will escape its consequences, regardless of the strategies
to mitigate the consequences of COVID-19. Iluno et al. (2021) found that
there is a non-linear relationship between mortality from COVID-19 and
economic well-being, with mortality from COVID-19 negatively aecting
well-being.
You can also highlight a separate block of research related to the
interpretation, analysis and modelling of data on mortality from COVID-19.
In their study, Sornette et al. (2020) have analysed the statistics of mortality
from the epidemic of a new coronavirus infection in a number of countries.
According to the data obtained, it has been revealed that the highest
mortality rate per million inhabitants is observed in Western countries. The
main reason for the relatively more severe COVID-19 epidemic in Western
countries is the large number of older people, with the exception of Norway
and Japan where other factors predominate.
Ivanaj and Oukhallou (2020) have analysed the economic and
institutional determinants of COVID-19 mortality in their study. As a
result, it was found that economic variables do not have a direct impact
on COVID-19 mortality, while institutional variables such as the quality of
regulation, government eectiveness and control of corruption, etc. have a
signicant and consistent downward correlation with COVID-19 mortality
in dierent countries. These results support the claim that investing in
institutions enhancing helps reduce mortality from infectious diseases.
Analyzing the scientic works of Aronov et al. (2020), Drapkina et
al. (2020), Ferraro et al. (2020) regarding the modelling of COVID-19
morbidity and mortality from it, we should note that preliminary prognoses
regarding ocial data are overestimated. This could be due to the more
ecient operation of the health care system, or due to the underestimation
of the ocial death rate from COVID-19. Further research has shown that
the second reason is much more common.
Gerli et al. (2020) have assessed the spread of the COVID-19 virus,
described its trends in the 27 countries of the European Union, Switzerland,
and Italy, and have made predictions of mortality from it. Shojaee et al.
(2020) have estimated the number of deaths in Italy, Iran and South Korea
from COVID-19. Al-Raeei (2020) has calculated the COVID-19 pandemic
mortality rates for China, the United States, Russia, and the Syrian Arab
Republic. Sebastiani et al. (2020), Chintalapudi et al. (2020), Onder et al.
(2020) have done a great job assessing the spread of the new coronavirus,
described the trends in the spread of Covid-19 in Italy and have made the
rst mortality prognoses from it. Anastassopoulou et al. (2020) и Gao et
al. (2020) have built the rst projections of the number of deaths from
COVID-19 in China. Sánchez-Villegas and Daponte Codina (2020) have
studied the COVID-19 epidemic and gave mortality prognoses in Spain.
Semenova et al. (2020) have predicted the number of deaths from COVID-19
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CUESTIONES POLÍTICAS
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in Kazakhstan. Vandoros (2020) has studied the impact of COVID-19 on
mortality in England and Wales.
As part of this study, we will build predictive models of mortality in
Russia as a whole and then compare them with actual data for their reasons
and draw conclusions about the true scale of mortality from COVID-19.
2. Methodology
The study used data from the World Health Organization, 2021 and the
Federal State Statistics Service of Russia, 2021 for 2011-2020. The work
used data on the number of deaths, births, data on the natural movement
of the population, fertility and mortality rates including by age groups, data
on causes of death, and data on life expectancy.
To build a mortality model for Russia as a whole, a polynomial model
of the second degree was used. Let y be the dependent variable and x the
independent variable, polynomial regression is a special case of multiple
regression with one independent variable x. A one-parameter polynomial
regression model with the k order can be expressed as:
yi=β0+β1xi+β2xi
2+⋯+βkxik+ei, i=1,2,⋯, n; (1)
where k is the order of the polynomial, the βs are the unknown
parameters to be estimated, and e is the error term.
If k = 1, then equation (1) becomes:
yi=β0+β1xi+ei;i=1,2,⋯,n. (2)
Equation (2) is a simple linear model.
If k = 2, then equation (1) becomes:
yi=β0+β1xi+β2xi
2+ei;i=1,2,⋯,n. (3)
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Ilmir Nusratullin, Igor Drozdov, Alexei Ermakov, Elena Koksharova y Maya Mashchenko
Natural population movement and COVID-19: data from Russia
To build models of mortality due to their causes, a polynomial model
of the rst degree, or simply a linear model, was used (2). The choice of
models for making a prognosis was carried out in the framework of their
reliability.
3. Results and Discussion
Analysing the natural movement of the population in Russia over the
past 10 years, it should be noted that it has a negative trend and can be
characterised as depopulation. In society, the institution of the family is
being transformed and value attitudes towards children are changing,
women begin to give birth to fewer children and do it much later (Ryazantsev
et al., 2021). In Russia, simple reproduction of the population is not ensured
(2.14-2.15 children per woman of the reproductive age). The total fertility
rate in Russia from 2011 to 2020 is shown in Figure 1.
Figure 1. Total fertility rate in Russia from 2011 to 2020
Source: Federal State Statistics Service of Russia, 2021.
The total fertility rate in Russia from 2011 to 2015 grew steadily from
1.58 to 1.78, but since 2016 it has seen a sharp drop to 1.51 in 2020. The
decline in the birth rate also aects the natural movement of the population
in Russia. But in 2020, a new factor is added to the negative trend of
declining fertility, the COVID-19 pandemic. Table 1 shows data on the
number of deaths and births in Russia over the past 10 years.
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CUESTIONES POLÍTICAS
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Table 1. The number of births, deaths and natural increase in
Russia for the period of 2011-2020
2011 2012 2013 2014 2015
Number of births within
a year 1796629 1902084 1895822 1942683 1940579
Number of deaths within
a year 1925720 1906335 1871809 1912347 1908541
Natural increase within a
year -129091 -4251 24013 30336 32038
2016 2017 2018 2019 2020
Number of births within
a year 1888729 1690307 1604344 1481074 1436514
Number of deaths within
a year 1891015 1826125 1828910 1798307 2138586
Natural increase within a
year -2286 -135818 -224566 -317233 -702072
Source: Federal State Statistics Service of Russia, 2021.
Table 1 clearly shows that from 2011 to 2015, there is a clear trend
towards an increase in the number of births from 1.80 million to 1.94
million, but in 2016 this trend was reversed and in 2020 the number of
births was 1.44 million. As for the number of deaths, its trend is clearer and
there is a gradual decrease in mortality from 1.93 million to 1.80 million,
but only in 2020, there is a sharp jump to 2.14 million. These trends can be
traced more clearly in Figure 2.
Figure 2. The number of births, deaths and natural increase in
Russia for the period of 2011-2020
Source: Federal State Statistics Service of Russia, 2021
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Ilmir Nusratullin, Igor Drozdov, Alexei Ermakov, Elena Koksharova y Maya Mashchenko
Natural population movement and COVID-19: data from Russia
The presented dynamics of fertility and mortality in Russia led to the
fact that positive population growth in 2013-2015 was replaced by negative
population growth since 2016 and sharply increased in 2020 and amounted
to a natural population decline of 0.7 million people in a year.
The decline in the number of births has a long-term trend that has been
observed since 2016. The decrease in the total number of births is mainly
explained by the decrease in the number of women giving birth at an early
age (15-24 years) and mean age of childbearing (from 25-34 years). It
should be noted that in recent years there has been an established trend
in the number of women giving birth at the age of 35 and older (Table 2,
Figure 3).
Table 2. Age-specic fertility rates (the average number of
births per 1000 women aged per year, years)
2011 2012 2013 2014 2015 2016 2017 2018 2019 2020
15-19 years 26.70 27.30 26.60 26 24 21.50 18.39 16.10 14.60 14.10
20-24 years 87.50 91.30 89.90 89.80 90 87.20 81.20 78.40 74.80 73.59
25-29 years 99.80 106.60 107.60 110.20 112.60 111.50 100.10 96.50 91.20 92.60
30-34 years 68.20 74.30 76.20 79.80 83 84.40 77.20 76.09 71.59 70.80
35-39 years 31.40 34.90 36.79 39 39.79 41 39.20 39.70 38.70 39.20
40-44 years 6.30 7 7.40 8.10 8.30 8.80 8.69 8.90 8.90 9.19
45-49 years 0.30 0.30 0.30 0.40 0.40 0.50 0.50 0.50 0.50 0.60
50-54 years 0 0 0 0 0 0 0 0 0.10 0.10
Table 2 and Figure 3 show the demographic problem of the Russian
society, when women are giving birth less and less, and the number of
children in families is decreasing, of which there are mainly 1-2, rarely 3
or more. The decline in the number of births from year to year in Russia
is a long-term trend and is more associated with economic problems
(Nusratullin et al., 2020), with changes in society and the psychology of
people.
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CUESTIONES POLÍTICAS
Vol. 39 Nº 71 (2021): 986-1007
Figure 3. Age-specic fertility rates (the average number of
births per 1000 women aged per year, years)
Source: Federal State Statistics Service of Russia, 2021
However, as mentioned above, the increase in mortality in 2020
to 2.13 million people compared with 1.79 million people in 2019 is not
a consequence of the current trend, but it is more associated with the
COVID-19 factor. But if we turn to ocial statistics, in 2020, 59,019
people died from COVID-19 in Russia (Starostina and Tkachev, 2021).
This suggests a conclusion either about the presence of another factor in
the increase in mortality, or about the underestimation of ocial data on
mortality from the pandemic.
To answer this question, let us rst nd the number of “excess deaths”
in 2020, and for this we will build a predictive mortality model based on
data for 2011-2019. To build the trend line of the time series, the following
models were tested: exponential, linear, logarithmic, and polynomial. The
resulting models and the degree of their reliability are presented in Table 3.
Table 3. Mortality models in Russia and the degree
of their reliability
Model type Model Degree of the reli-
ability, R2
Exponential y = 9 224 452 819 288.89e-0.01x R² = 0.73
Linear y = -14 243.78x + 30 575 568.86 R² = 0.73
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Ilmir Nusratullin, Igor Drozdov, Alexei Ermakov, Elena Koksharova y Maya Mashchenko
Natural population movement and COVID-19: data from Russia
Logarithmic y = -28 695 655.05ln(x) + 220 201 611.33 R² = 0.73
Second-order
polynomial y = -17 655.77x2 + 71 109 098.34x - 71 596
504 001.33 R² = 0.96
Source: calculated by the authors.
As can be seen from the table, the most reliable mortality model in
Russia is polynomial. We will build it and predict mortality in Russia in
2020 according to the data of 2011-2019.
Figure 4. Polynomial mortality model in Russia according to
2011-2019 data
Source: calculated by the authors.
According to the model obtained, the number of deaths in 2020 was
expected in the amount of 1,751,773 deaths, but according to the actual
data, the number of deaths in Russia was 2,138,586 deaths. That is, the
number of excess deaths in Russia was 386,813 deaths. These gures are in
no way combined with the data on the number of deaths from COVID-19
which according to ocial data in 2020 amounted to 57,019 (Starostina and
Tkachev, 2021). To clarify the reasons for the sharp increase in mortality,
let us further consider the age at which the increase in mortality occurred
and their causes.
Age-specic death rates are calculated as the ratio of the deceased people
of the corresponding sex and age during the calendar time to the average
annual number of people of this age per 1000 people. This indicator shows
how often people of a certain age die. Usually, the older the population
group, the higher the mortality rate. Let`s consider this indicator in Table
4 and Figure 5.
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Table 4. Age-specic death rates
2011 2012 2013 2014 2015 2016 2017 2018 2019 2020
1-4 years 0.50 0.50 0.40 0.40 0.40 0.40 0.30 0.30 0.30 0.30
5-9 years 0.30 0.30 0.20 0.20 0.20 0.20 0.20 0.20 0.20 0.20
10-14
years 0.30 0.30 0.30 0.30 0.30 0.30 0.30 0.20 0.20 0.20
15-19
years 0.80 0.80 0.80 0.80 0.70 0.70 0.60 0.60 0.60 0.60
20-24
years 1.60 1.50 1.50 1.40 1.30 1.10 1 1 0.90 1
25-29
years 2.70 2.50 2.40 2.29 2 1.80 1.60 1.50 1.40 1.40
30-34
years 4.09 4 3.90 3.70 3.40 3.10 2.70 2.50 2.40 2.50
35-39
years 4.90 4.80 4.80 5 4.80 4.50 4.09 4 3.80 4
40-44
years 5.90 5.60 5.60 5.70 5.70 5.50 5.09 5.20 5.20 5.70
45-49
years 87.50 7.30 7.30 7.10 6.80 6.30 6.40 6.40 7.30
50-54
years 10.90 10.30 9.90 9.80 9.60 9.40 8.60 8.69 8.50 9.69
55-59
years 15.50 14.70 14 13.90 13.50 13.20 12.40 12.30 12.10 13.90
60-64
years 21.80 20.80 20.10 19.80 19.50 19.10 18 18 17.60 20.60
65-69
years 28.60 27 26 26.20 26.20 26.40 25.10 25.30 24.40 29.60
70-74
years 41.50 41.20 40.10 39.10 38.40 36.70 34.29 34 34.10 42.60
75-79
years 64.40 61.80 58.40 58.20 58 57.10 56 55.70 53.40 64
80-84
years 102 101.70 98.90 96.70 95.20 92.20 87.10 84 82.70 100.70
85 years
older 174.40 173.70 171.80 171.50 172.50 171.20 168.90 168.50 163.69 190.20
Source: Federal State Statistics Service of Russia, 2021.
As can be seen from the table and the gure, the greatest increase in
mortality by age group occurred among the population over 50, and the
older is the age group, the higher the rate of increase in mortality is. In the
population aged 41 to 49, the mortality rate increased insignicantly, but in
the population under the age of 41, it practically did not change.
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Ilmir Nusratullin, Igor Drozdov, Alexei Ermakov, Elena Koksharova y Maya Mashchenko
Natural population movement and COVID-19: data from Russia
Figure 5. Age-specic death rates
Source: Federal State Statistics Service of Russia, 2021.
Mortality data by age group indirectly suggests that their causes are
associated with the COVID-19 pandemic, since the main blow falls on older
people (Polidori et al,. 2021), when the cause of death is not only the virus
itself, but also its complications. Let us consider further for what main
groups of causes there was an increase in mortality (Table 5, Figure 6).
Table 5. The number of deaths by main classes and
individual death causes per year
2011 2012 2013 2014 2015 2016 2017 2018 2019 2020
Infectious and
parasitic diseases 33672 32084 31808 32103 34372 35335 35045 34626 32918 30173
Coronary heart
disease 568182 562957 529824 492303 494638 481780 461786 453306 442328 508657
Cerebrovascular
diseases 332804 323003 310531 295602 290300 279818 264468 263573 260594 278618
Respiratory
diseases 74219 70793 74068 78312 75813 70332 62032 61150 59188 96539
Diseases of the
digestive system 88910 88867 88431 96689 101956 98215 92989 95430 98271 107399
External causes 199358 193774 185353 186779 177590 167543 152741 144612 137633 139583
Alcohol
poisoning 16288 15226 14549 15400 15242 14021 12276 11045 9876 10206
Suicide 31144 29735 28779 26606 25476 23119 20278 18206 17192 16546
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Murder 16795 15408 14427 12921 11984 10569 9048 7986 7302 6859
Malignant
neoplasms 289535 287789 288636 286900 296476 295729 290662 293704 294400 291461
Blastemas 292445 290880 291775 290400 300232 299652 294587 297996 298699 295910
Circulatory
diseases 107645810555921001799 940489 930102 904055 862895 856127 841207 938536
All types of
transport
accidents
29658 30203 29191 28829 24821 21610 20161 19092 17787 17041
Source: Federal State Statistics Service of Russia, 2021.
Based on the data in Table 5 and Figure 6, by groups of causes of death
such as infectious and parasitic diseases, diseases of the digestive system,
external causes, cases of alcohol poisoning, suicide, murder, malignant
neoplasms, blastema’s, all types of transport accidents, the change in
mortality occurred within the established trends, and the calculations
carried out conrmed this hypothesis. However, according to the groups
of causes of death such as coronary heart disease, cerebrovascular diseases,
respiratory diseases, diseases of the circulatory system, the situation is
radically dierent. There is an abnormal increase in mortality for these
reasons.
Figure 6. The number of deaths by main classes and
individual death causes per year
Source: Federal State Statistics Service of Russia, 2021.
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Ilmir Nusratullin, Igor Drozdov, Alexei Ermakov, Elena Koksharova y Maya Mashchenko
Natural population movement and COVID-19: data from Russia
To calculate the “excess mortality” due to the indicated reasons in 2020,
we will build reliable mortality rate models based on the data of 2011-2019
(Table 7). To do this, we will build polynomial models of the rst degree, or
simply linear models.
Figure 7. Polynomial models of the rst degree of mortality in
Russia by their causes according to 2011-2019 data
Source: calculated by the authors.
Based on the models obtained in Figure 7, we will calculate the predicted
values of mortality by cause and nd the number of “excess deaths” (Table
6).
Table 6. The number of “excess deaths” due to their
causes in 2020.
2020
(actual
facts)
2020
(prognosis)
The number of
“excess deaths” due
to their causes
Coronary heart disease 508 657 416 995 91 662
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Cerebrovascular diseases 278 618 243 268 35 350
Respiratory diseases 96 539 594 45, 37 094
Circulatory diseases 938 536 786 492 152 044
Total х х 316 150
Thus, in 2020, there were 91,662 “excess deaths” due to coronary
heart disease, 35,350 – due to cerebrovascular diseases, 37,094 – due to
respiratory diseases, 152,044 – due to diseases of the circulatory system, and
total 316,150 “excess deaths”. These data also indirectly indicate that they
are associated with the COVID-19 pandemic, since the main complications
in COVID-19 disease are these reasons (Polidori et al., 2021).
Again, the results obtained are in no way comparable with the ocial
data, according to which 59,019 people died from COVID-19 in Russia in
2020 (Starostina and Tkachev, 2021). They are more comparable with the
results we obtained earlier, namely, 386,813 “excess deaths”.
Our data on the real number of deaths in Russia from COVID-19 is
also comparable with the ndings of other scientists. The Ryazantsev et
al. (2021) study also noted the excess mortality in the amount of 324,000
people in Russia in 2020. According to the authors, more than a third of
these losses are associated with coronavirus infection, directly or indirectly.
The Lifshits and Neklyudova (2020) econometric analysis showed that in
Russia the real mortality rates were underestimated by more than 2 times,
and as new data became available, the results were conrmed.
According to Lifshits and Neklyudova (2020) real indicators began to
be underestimated in May 2020, both in the number of cases and in the
number of deaths. Kobak (2021) argues that data on additional deaths in
Russia in 2020 paint a much darker picture of the death toll from Covid-19
than the ocial daily updated gures. Analysis of excess mortality in Russia
from April to November yielded a dismal 264,100 additional deaths from
COVID-19 in Russia.
It should be noted that at the beginning of 2021 Federal State Statistics
Service of Russia published statistics according to which the number
of deaths from COVID-19 itself was 57,019, deaths associated with the
consequences of COVID-19 amounted to 103,968 deaths, which totally
works out 162,249 deaths (Starostina and Tkachev, 2021). However, these
data are also not comparable with the results obtained by us and other
scientists.
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Natural population movement and COVID-19: data from Russia
Conclusions
The natural population movement in Russia over the past 10 years has a
negative trend and can be characterised as depopulation. The total fertility
rate in Russia from 2011 to 2015 grew steadily from 1.58 to 1.78, but since
2016 it has seen a sharp drop to 1.51 in 2020. From 2011 to 2015, there is a
clear trend towards an increase in the number of births from 1.80 million to
1.94 million, but in 2016 this trend is reversed and in 2020 the number of
births was 1.44 million. As for the number of deaths, its trend is clearer and
there is a gradual decrease in mortality from 1.93 million to 1.80 million in
2019, but only in 2020, there is a sharp spike to 2.14 million.
According to ocial data, the number of deaths from COVID-19 itself was
57,019 deaths, and those associated with the consequences of COVID-19 are
103,968 deaths, which is a total of 162,249 deaths. Within the framework
of our study, from 316,150 to 386,813 “excess deaths” from COVID-19 were
identied by constructing predictive models of mortality in general and
for their reasons. Thus, an underestimation of the ocial mortality rate
from COVID-19 was revealed from 2.4 to 6.8 times, depending on the data
source.
A sharp spike in mortality in Russia in 2020 occurred among people over
50, and, with increasing age, mortality increased. The main reasons for the
sharp increase in mortality were coronary heart disease, cerebrovascular
diseases, respiratory diseases, and diseases of the circulatory systems.
Understanding the true catastrophe of COVID-19 in Russia will allow us to
critically evaluate the actions of state and municipal authorities, as well as
draw the right conclusions on how to get out of this catastrophic situation.
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