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New perspective on geographical mortality divide in Russia: a district-level cross-sectional analysis, 2008–2012


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Background Prior studies on spatial inequalities in mortality in Russia were restricted to the highest level of administrative division, ignoring variations within the regions. Using mortality data for 2239 districts, this study is the first analysis to capture the scale of the mortality divide at a more detailed level. Methods Age-standardised death rates are calculated using aggregated deaths for 2008–2012 and population exposures from the 2010 census. Inequality indices and decomposition are applied to quantify both the total mortality disparities across the districts and the contributions of the variations between and within regions. Results Regional variations in mortality mask one-third (males) and one-half (females) of the inequalities observed at the district level. A comparison of the 5% of individuals residing in the districts with the highest and the lowest mortality shows a gap of 15.5 years for males and 10.3 years for females. The lowest life expectancy levels are in the shrinking areas of the Far East and Northwest of Russia. The highest life expectancy clusters are in the intercity districts of Moscow and Saint Petersburg, and in several science cities. Life expectancy in these best-practice districts is close to the national averages of Poland and Estonia, but is still substantially below the averages in Western countries. Conclusion The large between-regional and within-regional disparities suggest that national-level mortality could be lowered if these disparities are reduced by improving health in the laggard areas. This can be achieved by introducing policies that promote health convergence both within and between the Russian regions.
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TimoninS, etal. J Epidemiol Community Health 2019;0:1–7. doi:10.1136/jech-2019-213239
Research report
New perspective on geographical mortality divide in
Russia: a district- level cross- sectional analysis,2008–
Sergey Timonin ,1 Domantas Jasilionis,2 Vladimir M. Shkolnikov,1,2 Evgeny Andreev1
To cite: TimoninS,
JasilionisD, ShkolnikovVM,
etal. J Epidemiol Community
Health Epub ahead of print:
[please include Day Month
Year]. doi:10.1136/jech-
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published online only. To view
please visit the journal online
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1International Laboratory for
Population and Health, National
Research University Higher
School of Economics, Moscow,
2Laboratory of Demographic
Data, Max Planck Institute for
Demographic Research, Rostock,
Correspondence to
Dr Sergey Timonin, International
Laboratory for Population and
Health, National Research
University Higher School of
Economics, Moscow 10100,
Russia; stimonin@ hse. ru
Received 16 September 2019
Revised 5 October 2019
Accepted 7 October 2019
http:// dx. doi. org/ 10. 1136/
jech- 2019- 213332
© Author(s) (or their
employer(s)) 2019. Re- use
permitted under CC BY- NC. No
commercial re- use. See rights
and permissions. Published
by BMJ.
Background Prior studies on spatial inequalities in
mortality in Russia were restricted to the highest level
of administrative division, ignoring variations within the
regions. Using mortality data for 2239 districts, this study
is the first analysis to capture the scale of the mortality
divide at a more detailed level.
Methods Age- standardised death rates are calculated
using aggregated deaths for 2008–2012 and population
exposures from the 2010 census. Inequality indices
and decomposition are applied to quantify both the
total mortality disparities across the districts and the
contributions of the variations between and within
Results Regional variations in mortality mask one-
third (males) and one- half (females) of the inequalities
observed at the district level. A comparison of the 5%
of individuals residing in the districts with the highest
and the lowest mortality shows a gap of 15.5 years
for males and 10.3 years for females. The lowest life
expectancy levels are in the shrinking areas of the Far
East and Northwest of Russia. The highest life expectancy
clusters are in the intercity districts of Moscow and Saint
Petersburg, and in several science cities. Life expectancy
in these best- practice districts is close to the national
averages of Poland and Estonia, but is still substantially
below the averages in Western countries.
Conclusion The large between- regional and within-
regional disparities suggest that national- level mortality
could be lowered if these disparities are reduced by
improving health in the laggard areas. This can be
achieved by introducing policies that promote health
convergence both within and between the Russian
Concerns have been raised about the persisting
or even increasing health inequalities between
and within countries.1 2 Although a large body of
research has examined socioeconomic disparities
in mortality and health, the spatial dimensions of
these trends are of equal importance. A number of
ecological studies have explored the geographical
patterns and variations in total and cause- specific
mortality at different geographical levels. Most of
these studies relied on the data aggregated to the
highest administrative division within a country,
which inevitably led them to ignore important
disparities at the next spatial scale. More compre-
hensive studies on health inequalities that are based
on data for smaller geographical units, or that use a
multilevel approach (ie, that simultaneously analyse
area- level and individual- level variables), have
emerged relatively recently.3
Several novel studies on long- term trends in
mortality across small areas in the UK, the USA
and New Zealand have provided evidence of
persisting or even increasing geographical dispar-
ities in these countries. An examination of long
time series (1921–2007) in the UK found that the
increasing trend in spatial mortality inequalities
accelerated in the 2000s, reaching the highest levels
since those recorded in the early 1920s.4 In New
Zealand, geographical disparities in health had also
reached historically high levels by the beginning
of the 2000s.5 A study on cross- county mortality
disparities in the USA (1961–1999) found a similar
upsurge in geographical disparities between 1983
and 1999, mainly due to stagnating or increasing
mortality in the laggard populations.6 This evidence
suggests that in recent years, overall life expec-
tancy improvements at the national level have been
masking growing spatial mortality disparities within
Although it has made significant progress in
reducing mortality since the mid- 2000s, Russia
continues to have one of the lowest life expectancy
levels across the developed countries.7 8 Underlying
the overall life expectancy disadvantage in Russia is
substantial diversity in the social, economic, ecolog-
ical and other characteristics of different regions.9 10
Prior studies using regional mortality data—that is,
at the highest level of administrative division—
collected around the 1970 and 1979 censuses
detected a pronounced southwest- to- northeast
geographical mortality gradient.11 12 Later studies
showed that this gradient could be largely explained
by mortality variations from external and alcohol-
related causes of death at the middle ages.10 13
Gorbachev’s antialcohol campaign of the late 1980s
weakened the gradient, and caused the inter-
regional disparities in Russia to fall to their lowest
recorded levels.14 However, as the radical socioeco-
nomic changes of the 1990s had selective effects
on population health across the regions of Russia,
the country’s inter- regional disparities increased,
and its regional mortality pattern changed.15–18 The
evidence obtained for the recent period of health
improvements in Russia (from 2003 onward) shows
very small changes in the aggregated measure of
inequality, which can be attributed to the diverse
effects of mortality convergence at young and
middle ages, and of mortality divergence at older
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Table 1 Selected measures of mortality disparities across regions (n=77) and districts (n=2239), by sex (average for 2008–2012)
Males Females
Districts Regions Districts to regions ratio Districts Regions Districts to regions ratio
Population- unweighted measures
Max–min range 39.2 14.0 2.8 24.6 9.5 2.6
Max–min ratio 5.6 2.3 2.4 5.8 2.5 2.3
Standard deviation (SD) 3.5 2.4 1.5 1.8 1.3 1.4
Population- weighted measures
Inter- quartile range (IQR) 4.1 2.9 1.4 1.7 1.0 1.7
Standard deviation (SD) 3.6 2.8 1.3 1.4 1.0 1.4
Regression- based measures
Slope index of inequality (SII) 12.8 8.2 1.6 6.0 3.6 1.7
Relative index of inequality (RII) 2.1 1.6 1.3 2.1 1.5 1.4
Decomposition of total variance across districts
Between- region variance, % 65.4 52.8
Within- region variance, % 34.6 47.2
Relative measures of inequality are put in italics.
ages. Moscow and Saint Petersburg are currently pioneering
mortality improvements in Russia, and contribute more than
other districts to the inter- regional divergence at older ages.14
One of the major disadvantages of all prior studies on spatial
health inequalities in Russia is that the data they used were from
the highest level of administrative division, and thus masked
large disparities within these territories. This study represents
the first comprehensive mortality analysis conducted across
Russia’s municipal units with the aim of understanding the real
scale and the spatial mortality disparities.
District-level data
We performed our analysis based on data for 2239 districts
aggregated into 77 regions representing the top level of admin-
istrative division in Russia (see online supplementary appendix
for more information on the definition of districts). We excluded
130 districts because there were problems with the numerator-
denominator data linkage, or because the districts had a very
small population size (<1000 males and/or females) or were in
the North Caucasus region. As the mortality estimates for the
Caucasus republics display dubious patterns, especially at old
ages, scholars have expressed concerns about their validity.19 The
total excluded population made up 4.9% of the total population.
The data on deaths classified by sex, age and district for
2008–2012 were obtained by aggregating anonymous vital regis-
tration records provided by the Russian State Statistical Service
(Rosstat). The population counts came from the 2010 popula-
tion census. The data on district- level deaths have never been
published, and are used in this study for the first time.
We calculated the sex- specific age- standardised death rates
(SDRs) for each district and region. To ensure that our results are
comparable to the findings from the other countries, we applied
direct standardisation using the 1976 WHO European standard
population.20 To account for random annual fluctuations due to
the small numbers, we aggregated deaths over a 5- year period
around the 2010 census.
Several absolute and relative measures of inequality were tested:
namely, conventional statistical indices (maximum–minimum
range and ratio, standard deviation (SD) and inter- quartile range
(IQR)) and regression- based measures (slope index of inequality
(SII) and relative index of inequality (RII)).21 22 Using the decom-
position technique, we split the total variance in mortality across
districts into between- region and within- region partitions.23
We ranked all the districts by SDRs and assigned them to eight
groups defined by population percentiles: four groups repre-
senting 5% on the tails of the ranking, and four groups in- be-
tween representing 20% of the total population each. We then
calculated the sex- specific life expectancies for each of these
Magnitude of spatial mortality inequalities at the two levels
of geographical division
Table 1 provides several major relative and absolute measures of
inequality in sex- specific, SDRs across the districts and regions
of Russia. As expected, we find that the mortality disparities at
the municipal level are greater than those at the regional level for
both males and females. The absolute gap in the SDR between the
best- performing and the worst- performing district is about 2.5
times bigger than the corresponding difference between the best-
performing and the worst- performing region. Although analyses
of more advanced measures of inequality (IQR, weighted SD,
SII or RII) come to the same overall conclusion, the differences
in inequality levels they found between districts and regions
are smaller, at 1.3 to 1.7- fold, depending on the measure. The
magnitude of the absolute spatial disparities is larger for males,
whereas the relative inequalities are about the same for males
and females at each level of the geographical hierarchy.
Decomposition analysis allows us to quantify the share of
cross- district variations in mortality that cannot be explained
by the differentials observed across the regions. Of the total
interdistrict inequalities in mortality, around one- third for males
and almost one- half for females are due to mortality variations
within the regions.
Ranking and grouping the districts by SDRs
Summary statistics for the eight groups of districts or clusters
arranged from the districts in our data (see the Methods section
above) are presented in table 2. The most important finding is
that the two best- performing groups, constituting 10% of the
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Research report
Table 2 Definition and major characteristics of the eight groups of districts (average for 2008–2012)
Groups of districts (by
population percentiles)
Males Females Gender gap in LE
Pop. size, mln
No of
SDR, per
LE at birth,
Pop. size,
No of
SDR, per
LE at birth,
years Absolute Relative
Group I
3.0 12 10.1 71.5 3.4 11 5.9 79.5 8.0 1.11
Group II
3.1 17 11.4 69.6 3.9 18 6.5 78.4 8.8 1.13
Group III
12.6 180 15.0 66.0 14.7 155 7.4 76.9 10.9 1.17
Group IV
12.6 215 16.8 64.1 14.8 204 8.0 75.9 11.8 1.18
Group V
12.7 445 18.5 62.3 14.5 437 8.7 74.7 12.3 1.20
Group VI
12.6 744 20.5 60.1 14.6 786 9.6 73.1 13.0 1.22
Group VII
3.1 271 22.5 58.2 3.7 252 10.6 71.4 13.2 1.23
Group VIII
3.1 355 25.0 56.1 3.7 376 11.9 69.2 13.1 1.23
Average 17.5 62.9 8.5 74.9 12.0 1.19
Max–min range 14.9 15.5 6.0 10.3 5.1
Max–min ratio 2.5 1.3 2.0 1.1 1.6
LE, life expectancy; SDR, standardised death rate.
Figure 1 Age- standardised death rates (SDRs) in districts and eight
groups of districts, by sex, average for 2008–2012.
total population, are living in only 29 districts; while the corre-
sponding two worst- off groups are living in around 360 districts
(355 for males and 376 for females). Many of the districts with
extremely high mortality are very large in terms of land area, and
most have very small populations.
Figure 1 displays a very steep mortality gradient across both
the districts and the eight groups of districts. It shows that a
substantial share of the total population is living in settings
with exceptionally high mortality: compared with the mortality
levels in the most advanced districts (group VIII vs group I), the
mortality levels in these districts are 2.5 times higher for males
and 2.0 times higher for females (table 2). Another important
observation is that the gender gap increases significantly from
the best- performing to the worst- performing districts, which
suggests a much steeper mortality gradient for males than for
females. The sex- specific rate ratio increases from 1.7 to 2.1
from the best- off to the worst- off groups.
Spatial patterns in mortality
Figure 2 provides evidence on mortality disparities by high-
lighting the exact geographical locations and spatial clustering of
the districts assigned to each of the eight groups. A visual inspec-
tion of the map allows us to identify hot spots of elevated and
low mortality in space, and to see whether the spatial clusters
coincided with or crossed the regional borders.
In general, the existence of a geographical gradient of
increasing mortality from the southwest to the northeast, previ-
ously detected by Andreev11 and Shkolnikov,12 is confirmed
using the district- level data. However, this type of data gives
us the opportunity to track the spatial changes with a clearer
microscopic view. The largest (in terms of land area) territories
of low mortality are located in the southern part of European
Russia, and in the neighbouring Central Black Earth region.
Other low- mortality areas are more compact in size, including
the districts of the Republic of Tatarstan in the Volga region,
the majority of the intercity districts in the metropolitan areas
of Moscow and Saint Petersburg, and the districts in some oil-
drilling and gas- mining areas in West Siberia. Among the other
best- practice districts in Russia are the so- called ‘science’ cities
and cities with a special regime (often related to the defense/
applied industry). Moreover, in the south of the European part
of Russia, resort cities and towns with even lower mortality
levels can be distinguished.
By contrast, a huge belt of very high mortality levels can
be detected in the Far East and East Siberia. These are mostly
depopulating areas with very severe environmental conditions.
The settlements in these areas are usually located very far from
the main cities, where the country’s human and economic
resources tend to accumulate. Moreover, the Far East and East
Siberia are the main regions where the indigenous northern
populations, who have long had extremely high levels of adult
mortality, live.24 Another cluster of elevated mortality consists
of economically disadvantaged areas of the Northwest of Euro-
pean Russia, located between Moscow, Saint Petersburg, and the
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Figure 2 Geographical patterns of mortality in Russia, average for 2008–2012 (males—upper panel, females—lower panel). LE, life expectancy.
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Figure 3 Life expectancy at birth in eight groups of Russian districts and in selected countries, by sex. Source: table 2 for Russia as a whole and
eight groups of districts (average for 2008-2012); UN world population prospects, the 2019 revision for india and south africa (estimates for 2010-
2015); human mortality database for other countries (estimates for 2010).
bordering Baltic countries and Finland. Deindustrialised urban
and rural districts in the European North form the third- most
important cluster of high mortality.
It is quite surprising that no big differences between males and
females can be seen in these geographical patterns. Male and
female mortalities across the districts show high and statistically
significant levels of correlation, with Pearson’s coefficient equal
to 0.79 (p<0.001).
Comparisons of clusters to national averages in other
Figure 3, which was produced in a manner similar to that of
figure 1, compares the inequalities in sex- specific life expec-
tancies for the same eight groups of Russian districts with the
national estimates for Russia as a whole and selected devel-
oped and developing countries. The figure shows the best, the
average and the lowest life expectancy levels.25 26 These results
clearly illustrate the striking life expectancy divide between the
best- performing and the worst- performing groups of districts
in Russia. Even at this aggregated level, the life expectancy gap
ranges from 10.3 years for females to 15.5 years for males. Inter-
estingly, the life expectancy of the worst- off females is almost
equal to that of the best- off males.
The international comparison confirms that Russia occupies
an unfavourable position, as even the best- performing districts
in Russia have substantial life expectancy disadvantages relative
to the life expectancy levels observed in Western Europe, Japan,
and the US. However, these best- practice districts in Russia have
life expectancy levels that are similar to the national averages
in the best- performing countries in Central and Eastern Europe
(Poland, Estonia, Lithuania and Latvia). At the same time,
the males and females in the worst- performing districts have
extremely large disadvantages with the life expectancy levels
close to those in South Africa and India (females), respectively.
Summary of the main findings
Our analysis using district- level mortality data provides new
evidence of the strikingly large size of the geographical mortality
divide in Russia. We have shown that the previous studies on
this topic, which relied on data for the highest level of admin-
istrative division in Russia, tended to mask important variations
in mortality within the regions. Depending on the metrics of
mortality disparity applied, the cross- district mortality inequali-
ties were found to be 1.3–2.6 times larger than the cross- regional
mortality inequalities. Of the total cross- district variance in
mortality, the inter- regional variation explained only one- third
for males and one- half for females. Even more striking is the
finding that there was a huge life expectancy divide (almost 16
years for males and more than 10 years for females) between the
best- performing and the worst- performing groups of districts,
each of which accounted for 5% of the total population. The
results clearly show that the disproportionately low life expec-
tancy levels observed in the worst- performing groups of districts
were comparable to or even worse than the levels in some
developing countries, such as India or South Africa; and can
be considered largely responsible for the persisting overall life
expectancy disadvantage of Russia in the international context.
The findings also indicate that Russia’s longevity vanguards,
who live primarily in Moscow, Saint Petersburg and some other
cities, had life expectancy levels similar to the levels observed
in successful Central and Eastern European countries, such as
Poland and Estonia. Nonetheless, given the economic wealth of
Russia, and of its metropolitan cities in particular, the expected
length of life in the country could be substantially higher.27
The current study has provided only indirect evidence regarding
the mechanisms that underlie the small- area health disparities
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What is already known on this subject
Based on the data at the highest (ie, regional) level of
administrative division, there is evidence of a persisting
‘Southwest to Northeast’ mortality gradient in Russia.
Regional mortality inequalities had reached their highest
levels by the mid- 2000s.
Moscow and Saint Petersburg are currently pioneering
mortality improvements in Russia. Because of their rapid
progress in reducing mortality at older ages, these cities are
contributing more than other territories to the inter- regional
mortality disparities in Russia.
What this study adds
Analysing mortality data at the level of larger administrative
areas (regions) leads to a substantial underestimation of the
full extent of the mortality inequalities in Russia.
This study has found a marked life expectancy divide
between the best- performing and the worst- performing
groups of districts. The findings indicate that the
disproportionately low life expectancy levels in the worst-
off groups of districts are responsible for Russia’s persisting
overall disadvantage in an international context, while the
relatively high life expectancy levels in the best- practice
districts suggest that there is a huge potential for further
reducing excess mortality at the national level.
The largest share of the spatial inequalities within the
regions is usually determined by a strong polarisation of life
expectancy levels between the administrative centres of the
districts (‘cores’) and the remaining peripheral areas.
This study points to the failures of previous policies to ensure
sustainable and equitable socioeconomic and health progress
across and within the regions of Russia. At the same time, the
results clearly show that district- level mortality can be used
for monitoring and addressing public health issues at the
national and the local level.
we observed in Russia. The persisting disparities found across
the districts suggest that the public health losses in some of
these areas are avoidable, and should be addressed through
appropriate policies aimed at promoting the convergence of
the economic, social, and health conditions within the regions
of Russia. Such policies can be planned and implemented by
central and local governments, and should be based on reliable
population- level evidence and data collected through timely
monitoring. Achieving further sustained reductions in excess
mortality and significant longevity gains at the national level in
Russia will be very difficult if the existing cross- district mortality
disparities remain at the same level or increase.
The previous findings on this topic suggest that regional
mortality inequalities in Russia were increasing through the end
of the 2000s, when they reached their peak levels. Since then,
these inequalities have remained at the same level or have even
slightly increased.14 It is, therefore, very likely that that there
has also been a lack of progress in reducing mortality inequal-
ities across the districts. This can be seen as a worrying sign,
as it suggests that the marked improvements in life expectancy
recently observed at the national level have been spread very
unevenly across the regions and districts. Several observations
can be made about the regions with particularly large cross-
district inequalities in mortality. In all of these regions, the
mortality levels in the peripheral areas were found to be 1.7–3.0
times higher than the mortality levels in the administrative
centres (cities). This finding supports the idea that there is a
strong polarisation between the healthier and wealthier ‘core’
and the ailing and impoverished ‘periphery’.28 29 Our results are
further supported by the findings of another important study for
Russia, which showed that life expectancy differences have been
increasing between the largest cities (with a population of more
than one million people, n=13) and the rest of the country.30
The advantages of the populations of the largest cities relative
the populations living in the rest of the country can be explained
by several fundamental compositional factors. Compared with
their counterparts living elsewhere, these urban residents have
better education, lower unemployment, higher income and
better access to healthcare services.31–34 Other best- practice
districts in Russia found to have very high life expectancy levels
are science cities and cities with a special regime (often related
to the defense/applied industries). The people living in these
settlements can be considered an ‘urban elite,’ as they have high
concentrations of intellectual, scientific, and technical skill levels,
and often enjoy economic and administrative privileges.35 36
To ensure the sustainable spatial development of Russia and
to reduce the inter- regional and within- regional differences in
socioeconomic development levels, the Russian government has
launched a project called the ‘Spatial Development Strategy until
2025’.37 Among the main principles of this initiative is to increase
access to social and healthcare services for populations living in
different geographical areas. The national project ‘Healthcare’,
which has been allotted an additional budget of around US$25
billion for 2019–2024, also aims to improve the health of the
Russian people by ensuring that they have optimal access to
healthcare services and adequate healthcare resources.38
Strengths and limitations
To the best of our knowledge, this study is the first systematic
analysis of the geographical mortality inequalities conducted at
such a detailed level of administrative division. Our study has
shed light on the real scale and the demographic costs of the
spatial mortality disparities in 2239 districts across 77 regions
of Russia. By offering some broad initial insights into the small-
area geographical mortality inequalities in Russia, our findings
provide useful directions for future research on the changes in
and the determinants of mortality inequalities in this vast and
geographically diverse country.
This study inevitably has a number of limitations. An important
limitation is that we were unable to explore the temporal changes
in spatial inequalities because age- specific data are not routinely
collected and published at the district level. Technically, deaths
can be tabulated by district only from 2000 onwards, when the
electronic system of vital registration was first introduced in
Russia. Moreover, the intercensus estimates of the population
age structure in the small areas seem to be rather inconsistent,
mainly due to the difficulties involved in accounting for migra-
tion. Another important point is that the definition of what
constitues a district has changed several times, which makes
conducting comparable long- term analysis of trends in health
disparities almost impossible.
Acknowledgements We thank Svetlana Nikitina from the Russian State Statistical
Service (Rosstat) for providing the microdata on all deaths that occurred between 1
January 2008 and 31 December 2012.
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Research report
Contributors ST, VS and DJ conceived the research question and designed the
study. ST and EA collected the mortality data. ST conducted the statistical analysis,
interpreted the results and wrote the first and subsequent drafts of the manuscript.
ST, DJ and VS contributed to the interpretation of the data and to revisions of the
manuscript. All authors approved the final version of the submitted manuscript.
Funding The paper was prepared within the framework of the Basic Research
Program at HSE University, and was funded by the Russian Academic Excellence
Project ’5-100’. The work of ST on GIS mapping was partly supported by the Russian
Foundation for Basic Research (research project no 18-05-60037).
Competing interests None declared.
Patient consent for publication Not required.
Provenance and peer review Not commissioned; internally peer reviewed.
Data availability statement Data can be obtained upon reasonable request.
Open access This is an open access article distributed in accordance with the
Creative Commons Attribution Non Commercial (CC BY- NC 4.0) license, which
permits others to distribute, remix, adapt, build upon this work non- commercially,
and license their derivative works on different terms, provided the original work is
properly cited, appropriate credit is given, any changes made indicated, and the use
is non- commercial. See:http:// creativecommons. org/ licenses/ by- nc/ 4. 0/.
SergeyTimonin http:// orcid. org/ 0000- 0001- 6651- 2023
1 Mackenbach JP, Kulhánová I, Menvielle G, etal. Trends in inequalities in premature
mortality: a study of 3.2 million deaths in 13 European countries. J Epidemiol
Community Health 2015;69:207–17.
2 Marmot M. The health gap: the challenge of an unequal world. 400. London:
Bloomsbury, 2015.
3 Subramanian SV, Jones K, Duncan C. Multilevel methods for public health research.
In: Kawachi I, Berkman F, eds. Neighborhoods and health. Oxford University Press,
2003: 65–111.
4 Thomas B, Dorling D, Smith GD. Inequalities in premature mortality in Britain:
observational study from 1921 to 2007. BMJ 2010;341:c3639.
5 Pearce J, Dorling D. Increasing geographical inequalities in health in New Zealand,
1980–2001. Int J Epidemiol 2006;35:597–603.
6 Ezzati M, Friedman AB, Kulkarni SC, etal. The reversal of fortunes: trends in County
mortality and cross- county mortality disparities in the United States. PLoS Med
7 Shkolnikov VM, Andreev EM, McKee M, etal. Components and possible determinants
of decrease in Russian mortality in 2004-2010. Demogr Res 2013;28:917–50.
8 Grigoriev P, Meslé F, Shkolnikov VM, etal. The recent mortality decline in Russia:
beginning of the cardiovascular revolution? Popul Dev Rev 2014;40:107–29.
9 Shkolnikov V, Vassin S. Spatial differences in life expectancy in European Russia in the
1980s. In: Lutz W, Scherbov S, Volkov A, eds. Demographic trends and patterns in the
Soviet Union before 1991. IIASA: Routledge, 1994: 379–402.
10 Vallin J, Andreev E, Meslé F, etal. Geographical diversity of cause- of- death patterns
and trends in Russia. Demogr Res 2005;12:323–80.
11 Andreev E. Length of life in the USSR: a differential analysis (in Russian). In: Andreev
E A, Vishnevsky A, eds. Length of life: analysis and modeling. Moscow: Statistika,
1979: 7–30.
12 Shkolnikov VM. Geographical factors of length of life (in Russian). Izvestiya AN SSSR.
Geographical Series 1987;3:35–44.
13 Vassin SA. Spatial CCA age, and cause- of- death patterns of mortality in Russia,
1988–1989. In: Bobadilla JL, Costello CA, Mitchell F, eds. Premature death in the new
independent states. Washington, DC: National Academy Press, 1997: 184–214.
14 Timonin S, Danilova I, Andreev E, etal. Recent mortality trend reversal in Russia: are
regions following the same tempo? Eur J Popul 2017;33:733–63.
15 Walberg P, McKee M, Shkolnikov V, etal. Economic change, crime, and mortality crisis
in Russia: regional analysis. BMJ 1998;317:312–8.
16 Cornia GA, Pannicci R. The transition mortality crisis: evidence, interpretation, and
policy responses. In: Cornia GA, Panicci R, eds. The mortality crisis in transition
economies. New York: Oxford University Press, 2000: 3–37.
17 Shkolnikov VM, Cornia GA. Population crisis and rising mortality in transitional Russia.
In: Cornia GA, Panicci R, eds. The mortality crisis in transition economies. New York:
Oxford University Press, 2000: 253–79.
18 Treisman D. Death and prices: the political economy of Russia’s alcohol crisis.
Economics of Transition 2010;18:281–331.
19 Andreev E. On accuracy of Russia population censuses results and level of confidence
in different sources of information (in Russian). Statistical Issues 2012;11:21–35.
20 Waterhouse JAH, Muir CS, Correa P. Cancer incidence in five continents. Lyon: IARC,
1976: 3. 456.
21 Pamuk ER. Social class inequality in mortality from 1921 to 1972 in England and
Wales. Popul Stud 1985;39:17–31.
22 Anand S, Diderechsen F, Evans T, etal. Measuring disparities in health: methods and
indicators. In: Evans T, Whitehead M, Diderichsen F, etal, ed s. Challenging inequalities
in health: from ethics to action. New York: Oxford University Press, 2001: 48–67.
23 Timonin S, Shkolnikov VM, Jasilionis D, etal. Disparities in length of life across
developed countries: measuring and decomposing changes over time within and
between country groups. Popul Health Metr 2016;14:29.
24 Bogoyavlenskiy D. Russia’s Indigenous peoples of the North: a demographic portrait
at the beginning of the twenty- first century. Sibirica 2010;9:91–114.
25 Human Mortality Database. University of California, Berkeley (USA), and Max
Planck Institute for Demographic Research (Germany). Available: www. mortality. org
[Accessed 08 Aug 2019].
26 United Nations, Department of Economic and Social Affairs, Population Division.
World population prospects 2019, online edition 2019.
27 Shkolnikov VM, Andreev EM, Tursun- Zade R, etal. Patterns in the relationship
between life expectancy and gross domestic product in Russia in 2005-15: a cross-
sectional analysis. Lancet Public Health 2019;4:e181–8.
28 Gritsai OV, Joffe GV, Treivish AI. Centre and periphery in regional development (in
Russian). 168. Moscow: Nauka, 1991.
29 Karachurina LB, Mkrtchyan NV. Population dynamics in the municipal units of Russia
as the reflection of the center- periphery framework in the spatial development (in
Russian). Regional Studies 2010;3:69–83.
30 Shchur AE. Cities of over a million people on the mortality map of Russia (in Russian).
Demographic Review 2018;5:66–91.
31 Zubarevich NV. Cities as centers for the modernization of the economy and human
capital (in Russian). Social Sciences and the Contemporary World 2010;5:5–19.
32 Shelton N, Birkin M, Dorling D. Where not to live: geographic inequalities in mortality
in Britain,1981–2000. Health and Place 2007;12:557–69.
33 Eikemo TA, Hoffmann R, Kulik MC, etal. How can inequalities in mortality be
reduced? A quantitative analysis of 6 risk factors in 21 European populations. PLoS
One 2014;9:e110952.
34 Timonin S, Kontsevaya A, McKee M, etal. Reducing geographic inequalities in access
times for acute treatment of myocardial infarction in a large country: the example of
Russia. Int J Epidemiol 2018;47:1594–602.
35 Lappo GM, Polyan PM. Closed cities of Russia (in Russian). Population and Society
36 Lappo GM, Polyan PM. Science cities of Russia: yesterday’s forbidden and
semi- forbidden cities are today’s growth points (in Russian). Universe of Russia
37 Spatial development strategy of the Russian Federation until 2025. Available: http://
government. ru/ docs/ 35733/ [Accessed 08 Aug 2019].
38 Passport of the National Project ’Healthcare’. Available: http:// government. ru/ info/
35561/ [Accessed 08 Aug 2019].
copyright. on November 1, 2019 by guest. Protected by Epidemiol Community Health: first published as 10.1136/jech-2019-213239 on 1 November 2019. Downloaded from
... In the literature, there is support for these hypotheses. For example, across all districts in Russia there is evidence of improvements in lowering the infant mortality rate (Timonin et al. 2020). Middle age groups for males are uncertain as the inclusion of the 1990s when their mortality rose could cause divergence towards higher levels of mortality than the Soviet era while middle age females are hypothesized to diverge lower. ...
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North Ossetia-Alania (NOA) regional mortality over thirty years of transition is described in detail for the first time. Though NOA and other Caucasus regions are perceived to have higher life expectancy than Russia overall, we find that, like the rest of Russia, men live much shorter lives in both rural and urban areas. Urban mortality is lower for most causes of death than rural mortality with women in particular showing improvements in lowering mortality and higher life expectancy at birth. Male mortality across all ages is better in the 21st century than the 1990s but has not improved recently relative to the early 2000s. Sigma and beta mortality divergence reveal little improvement in male mortality relative to female mortality with the gender gap unchanged since the Soviet era which is unusual at the regional level. Nostalgia for Soviet era health systems is misplaced as health has improved since then especially like other regions in the area of infant mortality and deaths before the age of 5. Policy recommendations include moving some health care out of the capital of Vladikavkaz to take advantage of relatively lower rural mortality as well as promoting more rural economic development.
... At the same time, due to limited data, the study of geographic inequality in mortality in Russia until very recently concerned only the highest level of the administrative-territorial division. As a consequence, much of the centerperipheral differences in mortality rates remained hidden from researchers (Timonin et al. 2020). ...
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Elevated mortality (compared with the West) and significant spatial differences in life expectancy are serious challenges facing Russia. The goals of improving Russians' health and increasing their life expectancy by reducing inequality in mortality between regions and settlements are closely intertwined with the goals of spatial development of Russia, aimed at reducing interregional differences in the quality of life. This paper presents an assessment of the scope and dynamics of changes in mortality differences between the ‘center’ and the ‘periphery’ in 67 regions of Russia, which are home to three-quarters of the country's population. The selected research period - 2003-2018 - is characterized by a steady increase in life expectancy at birth (LE) in Russia. Using unpublished data from Rosstat for cities, we estimated life expectancy at birth in 67 regional centers and in the rest of the regions (‘periphery’). Depending on the magnitude of the differences in LE and the dynamics, we identified 6 types of regions. For those regions with a LE gap between center and periphery larger than the average, the decomposition method was applied, which made it possible to determine the key age groups and causes of death responsible for such high differences. In 36 regions of Russia classified as types I-III, the center-peripheral gap exceeded the average Russian level, while only in six regions in 2003-18 was there a tendency towards a reduction in the size of this gap. The decomposition results showed that elevated mortality of males in the periphery is due to a higher mortality rate at working age from external causes of death, especially from traffic accidents, homicides and suicides, as well as from ‘alcoholic’ causes of death; females in the periphery suffer from higher mortality rate at older ages from chronic non-communicable diseases. Despite the seemingly ‘objective’ nature of the mortality differences between the center and the periphery (the advantage of the former being due to the socio-demographic characteristics of its residents and the educational structure of the population, as well as to selective migration), the positive experience of other countries shows that effective public health policies can substantially reduce the degree of spatial inequality in mortality even if significant heterogeneity in the level of socio-economic development remains.
... The reason for this discrepancy may be linked to intraregional inequality. Spatial inequalities in health have also been confirmed by previous studies [67,68,71,72]. There is no doubt that for countries as geographically expansive as Russia, inequality is determined not only by economic factors or healthcare costs, but also by climatic, ethnic and sociocultural diversity [73]. ...
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Background The capacity for health comparisons, including the accurate comparison of indicators, is necessary for a comprehensive evaluation of well-being in places where people live. An important issue is the assessment of within-country heterogeneity for geographically extensive countries. The aim of this study was to assess the spatial and temporal changes in health status in Russia and to compare these regional changes with global trends. Methods The index, which considers the infant mortality rate and the male and female life expectancy at birth, was used for this purpose. Homogeneous territorial groups were identified using principal component analysis and multivariate ranking procedures. Trend analysis of individual indicators included in the index was also performed to assess the changes over the past 20 years (1990–2017). Results The study indicated a trend towards convergence in health indicators worldwide, which is largely due to changes in infant mortality. It also revealed that the trend of increasing life expectancy in many regions of Russia is not statistically significant. Significant interregional heterogeneity of health status in Russia was identified according to the application of typological ranking. The regions were characterized by similar index values until the mid-1990s. Conclusions The strong spatial inequality in health of population was found in Russia. While many regions of Russia were comparable to the countries in the high-income group in terms of GDP, the progress in health was less pronounced. Perhaps this can be explained by intraregional inequality, expressed by significant fluctuations in income levels. Trial registration Not applicable.
... Различия в продолжительности жизни социально-экономических групп давно стали предметом пристального интереса демографов. Люди живут дольше или короче, в зависимости от страны или даже региона проживания (в России разница ожидаемой продолжительности жизни между регионами достигает почти 16 лет для мужчин и 10 лет для женщин) (Bessudnov, McKee, Stuckler, 2011;Timonin et al., 2017Timonin et al., , 2020, в зависимости от экономического положения, но особенно от уровня образования (Пьянкова, Фаттахов, 2017; Харькова, Никитина, Андреев, 2017). Исследования показывают, что даже в Скандинавских странах, где уровень неравенства относительно низок, высокообразованные люди живут дольше низкообразованных, причем даже в тех случаях, когда их экономическое благосостояние идентично. ...
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nequality is amongst the most fundamental and formidable social problems of modernity. It thus does not come as a surprise that it is a major theme that cuts through multiple areas of sociology. However, the major limitation of most social research on inequality is that its focus is often limited to the redistribution of resources, be them material or symbolic. Indeed, some of the most important effects of inequality, ones that make inequality so crucial to studies of modernity, go far beyond unfair access to certain goods. These reveal themselves in elements of social disintegration, lack of social cohesion, multidimensional social exclusion, marginalization of large social groups and gaps between their basic frames of references, which can reach the level of mutual impenetrability. To achieve an integrated perspective, this study follows the lead of quickly developing frontier approaches in the cultural sociology of inequality, and, following its leaders’ appeals, focuses on another dimension of inequality that is complementary to distribution — a social recognition of personal and social group identities. This allows us to assess how economic forces associated with inequality interact with cultural patterns and cognitive processes which persist in the behaviors of both individuals and social groups. Following this line of inquiry, this study focuses on cultural and emotional mechanisms of recognition and how it shapes people’s identity and dignity, and tries to tie these mechanisms to cognitive processes, which shape people’s aspirations and can “ignite” their actions. This study is ultimately intended as a kind of “manifesto” for the sociology of culture and inequality, and thus includes calls for wider intra- and inter-disciplinary input and collaboration in these areas.
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The Central Federal District (CFD) is the most populous district in Russia, as of the beginning of 2021, 27% of the Russian population lived out there. However, the CFD mortality rate is characterized by a significant variation across different territories of the District. The purpose of this study is to assess the CFD territorial differentiation of mortality rate based on life expectancy with a breakdown on municipalities in 2010-2019. Life expectancy by CFD municipality and urban district was based on information about population size of the CDF municipalities and urban districts, obtained from the database on municipalities for the period 2010-2020 and individual non-personalized Rosstat (Federal State Statistics Service) data on cause-of-death mortality. A total of 326 municipalities and 63 urban districts were included in the analysis. A total of 11.5 thousand life tables were developed. The analysis shows a significant differentiation of mortality rate at the municipal level. Even though the CFD life expectancy is above the Russian average mainly due to high levels of life expectancy in Moscow, the vast majority of the CFD municipalities report a life expectancy below the Russian average. The worst situation with mortality in the CFD is developed in municipalities with a population of less than 10 thousand people. The main factor for a high gap in life expectancy across territories and gender is a high male mortality in working ages. There are significant reserves of for life expectancy growth within the CFD. These reserves are concentrated in the regional capitals with adjacent municipalities, as well as other large municipalities with a population over 100 thousand people.
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The article compares the demographic situation and related processes in Kyzyl and Elista, the capitals of two of Russia’s constituent republics — Tuva and Kalmykia, respectively. The authors have focused on the changes in three major demographic processes — fertility, mortality and migration — as well as demographic structures and behaviours. The authors have also calculated birth rates, migration indices (including age-specific rates) and examined the age and gender structure of the demographics, drawing up the demographic balance for both Kyzyl and Elista. Despite their evident similarity due to comparable population size and its share in the grand total for the republic, both of which have complex ethnoconfessional structure, demographic processes at Kyzyl and Elista have their own specific features. In Kyzyl, a combination of natural population increase and migration-related decrease leads to an overall population rise. In Elista, on the contrary, the rise has recently notably slowed down, largely due to a much older demographics, with low birth rates and high mortality rates. Applying the natural population movement rates (which are independent of age structure distortions) to the situation in each of the cities shows that the birth and mortality rates are higher in Kyzyl. In Elista both men and women, as well as the population in total, on average live longer. Migration patterns in both cities are similar, but Kyzyl features a more intense migration among the females, as well as higher departure rates among elder citizens, which helps optimize the population’s age structure. Since access to statistical data on the municipal level has been limited until recently, and the methodology of analyzing these data has been underdeveloped, many of the methods and indicators have been for the first time apllied in this study to demographic processes in Kyzyl and Elista.
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В статье проводится сравнительный анализ демографической ситуации и происходящих процессов Кызыла и Элисты, столиц Тувы и Калмыкии (Россия). Проанализирована специфика изменений основных демографических процессов (рождаемости, смертности и миграции), демографических структур и поведения. Авторами рассчитаны специальные коэффициенты рождаемости, показатели миграции (включая возрастные коэффициенты), а также изучены особенности возрастно-половой структуры и составлен демографический баланс Кызыла и Элисты. Несмотря на кажущуюся схожесть (сопоставимая численность и доля в общем населении своих республик, этноконфессиональный состав), у Кызыла и Элисты есть свои особые черты развития демографических процессов. Сочетание естественного прироста и миграционный убыли в Кызыле приводит к общему приросту и, соответственно, росту численности населения. В Элисте в последние годы наблюдается заметное замедление роста. Во многом это вызвано более старой возрастной структурой населения Элисты, способствующей низким значениям числа родившихся и высоким числа умерших. Применение характеристик естественного движения населения, не зависящих от искажений возрастной структуры, показывает, что и рождаемость, и смертность выше в Кызыле. А в Элисте все население, а также мужчины и женщины по отдельности живут, в среднем, дольше. Миграционные процессы в изучаемых городах схожи, но для Кызыла более характерны повышенная миграционная активность женщин, а также выбытие людей в старших возрастах, что способствует оптимизации возрастной структуры населения. В силу ограниченности доступа до недавнего времени к статистическим данным на муниципальном уровне, а также недостаточно развитой методологии работы с ними, многие используемые методы и показатели для изучения демографических процессов Кызыла и Элисты применены впервые.
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Нехватка данных до недавнего времени определяла отсутствие работ, рассматривающих демографические показатели на уровне малых территорий для всех регионов Российской Федерации. Представленная статья посвящена анализу рождаемости в 2304 ячейках муниципального уровня субъектов РФ. Исследование базируется на данных муниципальной статистики Росстата о численности женщин репродуктивного возраста по пятилетним возрастным группам и числе родившихся. Для устранения влияния возрастной структуры на локальном (муниципальном) уровне были рассчитаны стандартизованные коэффициенты рождаемости в среднем за 2017–2019 гг. Расчёты базировались на методе косвенной стандартизации, в качестве стандарта выбрано население России. Различия в уровне рождаемости сокращаются как на региональном, так и на муниципальном уровне за счёт продолжающегося снижения рождаемости для территорий, где её уровень по-прежнему высок. Однако расчёты показывают, что отношение коэффициента вариации на муниципальном уровне к аналогичному показателю для регионов возрастает: увеличивается роль местных факторов. При анализе динамики числа родившихся было обнаружено, что происходит стремительная концентрация рождений в региональных центрах (рост с 36,5 до 42,8% в 2011–2019 гг.). В то же время около половины прироста доли родившихся в центрах субъектов РФ обеспечено изменением правил регистрации рождений. Соотношение общего и стандартизованного коэффициента призвано оценить вклад возрастной структуры женщин в уровень рождаемости. Из-за особенностей возрастного состава населения максимальная недооценка уровня рождаемости наблюдается в малых сельских муниципалитетах Европейского Севера и Поволжья. Различия оценочного коэффициента суммарной рождаемости (КСР) на муниципальном уровне позволяют более подробно описать распределение территорий по уровню воспроизводства населения. Только 10,6% населения России проживает на территориях, где оценка КСР выше уровня простого воспроизводства (2,15). Демографическая модернизация почти не оставила значительных (по численности населения) ареалов высокой рождаемости даже в регионах, где её уровень традиционно велик (Чечня, Дагестан, Ингушетия, Тува, Алтай).
To solve demographic problems and, in particular, to increase the life expectancy in Arctic regions, analysis of mortality in small territories is of particular relevance. The object of this study is to analyze the intraregional differentiation of mortality based on age-standardized indicator of lost years of life caused by premature mortality in the context of main classes of death’s causes for the period 2010- 2019 in Arkhangelsk region. The standardized indicator of lost years of life has decreased for all ages and all classes of death’s causes in Arkhangelsk region. Dynamic and level of the standardized indicator of lost years of life have big differentiation at municipal level. For example, in urban districts and adjacent areas the reduction in mortality was bigger than in border and peripheral areas. Our study shows that in the structure of lost years of life the first place is occupied by diseases of circulatory system (50.0%), the second – by external causes (19.7%), and the third – by neoplasms (14.3%). The most difficult situation is in the peripheral areas of the region due to higher losses from external causes of death at working age.
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Высокий в сравнении со странами Запада уровень смертности и значительные пространственные различия в ожидаемой продолжительности жизни – серьезные вызовы, стоящие перед Россией. Решение задач повышения продолжительности жизни и улучшения здоровья россиян, в том числе за счет уменьшения неравенства в смертности как между субъектами РФ, так и на внутрирегиональном уровне, тесным образом переплетаются с целями пространственного развития России, направленными на сокращение межрегиональных различий в уровне и качестве жизни людей. В статье представлена оценка различий в смертности между «центром» и «периферией» регионов и динамика этих различий в 67 регионах России, в которых проживает три четверти населения страны. Период исследования – 2003-2018 гг. – характеризуется устойчивым ростом ожидаемой продолжительности жизни при рождении в России. Используя данные Росстата по городам, мы оценили ожидаемую продолжительность жизни при рождении для 67 региональных центров и для остальной территории регионов («периферии»). В зависимости от величины различий и динамики их изменений были выделены 6 типов регионов (I-VI). Для регионов, в которых наблюдается наибольший центр-периферийный разрыв в ожидаемой продолжительности жизни, применен метод декомпозиции, позволивший определить ключевые возрастные группы и причины смерти, ответственные за столь высокие различия. В 36 регионах России, отнесенных к I-III типам, центр-периферийный разрыв превышал среднероссийский уровень, при этом лишь в 6 из них в 2003-2018 гг. отмечалась тенденция к сокращению разрыва. Анализ результатов декомпозиции различий в продолжительности жизни для первых трех типов регионов показал, что у мужчин отставание периферии от центров обусловлено различиями в смертности в трудоспособном возрасте от внешних причин смерти, особенно от ДТП, убийств и самоубийств, а также от «алкогольных» причин смерти, у женщин – различиями в смертности в пожилом возрасте от хронических неинфекционных заболеваний. Несмотря на, казалось бы, «объективный» характер центр-периферийных различий в уровне смертности в России (преимущество центров в продолжительности жизни в первую очередь зависит от социально-демографических характеристик жителей, образовательной структуры населения, а также селективной миграции, направленной из периферии в центры), положительный опыт других стран показывает, что эффективная политика в области общественного здоровья может значительно сократить размах пространственного неравенства в уровне смертности даже при сохранении существенной гетерогенности в уровне социально-экономического развития территорий.
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The problem of excess mortality in Russia has not lost its relevance. The situation is complicated by the high level of spatial inequality in health, which is usually measured at the regional level in our country. This work is one of the first attempts to look at the dynamics and extent of spatial inequality in health in Russia at the sub-regional level, by contrasting the "center/core" (in our case, represented by the largest Russian cities) with the "periphery" (the rest of the country). Cities with a population of over a million people were chosen based on the spatial hierarchy that exists in Russia, according to which the highest level of social and economic development is concentrated in the largest cities. As a rule, a higher level of development of human capital corresponds to lower mortality. Using data provided by Rosstat, we calculated life expectancy at birth for Russian cities with a population of over a million people in 1989-2016. The results fully coincided with our expectations: the polarization in the health levels between the largest Russian cities and the rest of the country has significantly increased in the last twenty-five years, which is a reflection of those centripetal processes that have been taking place in our country during this period. Russian cities with a population of over a million people are attractive destinations for both internal and external migrants, and thus acquire, among other things, a much more educated population. Since people with higher education take better care of their health, having a more educated population is undoubtedly an essential advantage of bigger cities over the periphery when it comes to the overall health level. Without solving the structural problems that restrain social and economic development outside the largest agglomerations, convergence in mortality rates between cities with a population of over a million people and the surrounding territories is hardly possible.
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Background: Since 2005, Russia has made substantial progress, experiencing an almost doubling of per-capita gross domestic product by purchasing power parity (GDP [PPP]) to US$24 800 and witnessing a 6-year increase in life expectancy, reaching 71·4 years by 2015. Even greater gains in GDP (PPP) were seen for Moscow, the Russian capital, reaching $43 000 in 2015 and with a life expectancy of 75·5 years. We aimed to investigate whether mortality levels now seen in Russia are consistent with what would be expected given this new level of per-capita wealth. Methods: We used per-capita GDP (PPP) and life expectancy from 61 countries in 2014-15, plus those of Russia as a whole and its capital Moscow, to construct a Preston curve expressing the relationship between mortality and national wealth and to examine the positions of Russia and other populations relative to this curve. We adjusted life expectancy values for Moscow for underestimation of mortality at older ages. For comparison, we constructed another Preston curve based on the same set of countries for the year 2005. We used the stepwise replacement algorithm to decompose mortality differences between Russia or Moscow and comparator countries with similar incomes into age and cause-of-death components. Findings: Life expectancy in 2015 for both Russia and Moscow lay below the Preston-curve-based expectations by 6·5 years and 4·9 years, respectively. In 2015, Russia had a lower per-capita income than 36 of the comparator countries but lower life expectancy than 60 comparator countries. However, the gaps between the observed and the Preston-expected life expectancy values for Russia have diminished by about 25% since 2005, when the life expectancy gap was 8·9 years for Russia and 6·6 years for Moscow. When compared with countries with similar level of income, the largest part of the life expectancy deficit was produced by working-age mortality from external causes for Russia and cardiovascular disease at older ages for Moscow. Interpretation: Given the economic wealth of Russia, its life expectancy could be substantially higher. Sustaining the progress seen over the past decade depends on the ability of the Russian Government and society to devote adequate resources to people's health. Funding: This work was partly funded through the International Project on Cardiovascular Disease in Russia supported by a Wellcome Trust Strategic Award (100217) and was supported by the Russian Academic Excellence Project 5-100.
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Background: Russia has the largest area of any country in the world and has one of the highest cardiovascular mortality rates. Over the past decade, the number of facilities able to perform percutaneous coronary interventions (PCIs) has increased substantially. We quantify the extent to which the constraints of geography make equitable access to this effective technology difficult to achieve. Methods: Hospitals performing PCIs in 2010 and 2015 were identified and combined with data on the population of districts throughout the country. A network analysis tool was used to calculate road-travel times to the nearest PCI facility for those aged 40+ years. Results: The number of PCI facilities increased from 144 to 260 between 2010 and 2015. Overall, the median travel time to the closest PCI facility was 48 minutes in 2015, down from 73 minutes in 2010. Two-thirds of the urban population were within 60 minutes' travel time to a PCI facility in 2015, but only one-fifth of the rural population. Creating 67 new PCI facilities in currently underserved urban districts would increase the population share within 60 minutes' travel to 62% of the population, benefiting an additional 5.7 million people currently lacking adequate access. Conclusions: There have been considerable but uneven improvements in timely access to PCI facilities in Russia between 2010 and 2015. Russia has not achieved the level of access seen in other large countries with dispersed populations, such as Australian and Canada. However, creating a relatively small number of further PCI facilities could improve access substantially, thereby reducing inequality.
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After several decades of negative trends and short-term fluctuations, life expectancy has been increasing in Russia since 2004. Between 2003 and 2014, the length of life rose by 6.6 years among males and by 4.6 years among females. While positive trends in life expectancy are observed in all regions of Russia, these trends are unfolding differently in different regions. First, regions entered the phase of life expectancy growth at different points in time. Second, the age- and cause-specific components of the gains in life expectancy and the number of years added vary noticeably. In this paper, we apply decomposition techniques—specifically, the stepwise replacement algorithm—to examine the age- and cause-specific components of the changes in inter-regional disparities during the current period of health improvement. The absolute inter-regional disparities in length of life, measured by the population-weighted standard deviation, decreased slightly between 2003 and 2014, from 3.3 to 3.2 years for males, and from 2.0 to 1.8 years for females. The decomposition of these small changes by ages and causes of death shows that these shifts were the result of diverse effects of mortality convergence at young and middle ages, and of mortality divergence at older ages. With respect to causes of death, the convergence is mainly attributable to external causes, while the inter-regional divergence of trends is largely determined by cardiovascular diseases. The two major cities, Moscow and Saint Petersburg, are currently pioneering mortality improvements in Russia and are making the largest contributions to the inter-regional divergence.
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Background: Over the past half century the global tendency for improvements in longevity has been uneven across countries. This has resulted in widening of inter-country disparities in life expectancy. Moreover, the pattern of divergence appears to be driven in part by processes at the level of country groupings defined in geopolitical terms. A systematic quantitative analysis of this phenomenon has not been possible using demographic decomposition approaches as these have not been suitably adapted for this purpose. In this paper we present an elaboration of conventional decomposition techniques to provide a toolkit for analysis of the inter-country variance, and illustrate its use by analyzing trends in life expectancy in developed countries over a 40-year period. Methods: We analyze trends in the population-weighted variance of life expectancy at birth across 36 developed countries and three country groups over the period 1970–2010. We have modified existing decomposition approaches using the stepwise replacement algorithm to compute age components of changes in the total variance as well as variance between and within groups of Established Market Economies (EME), Central and Eastern Europe (CEE), and the Former Soviet Union (FSU). The method is generally applicable to the decomposition of temporal changes in any aggregate index based on a set of populations. Results: The divergence in life expectancy between developed countries has generally increased over the study period. This tendency dominated from the beginning of 1970s to the early 2000s, and reversed only after 2005. From 1970 to 2010, the total standard deviation of life expectancy increased from 2.0 to 5.6 years among men and from 1.0 to 3.6 years among women. This was determined by the between-group effects due to polarization between the EME and the FSU. The latter contrast was largely fueled by the long-term health crisis in Russia. With respect to age, the increase in the overall divergence was attributable to between-country differences in mortality changes at ages 15–64 years compared to those aged 65 and older. The within-group variance increased, especially among women. This change was mostly produced by growing mortality differences at ages 65 and older. Conclusions: From the early 1970s to the mid-2000s, the strong divergence in life expectancy across developed countries was largely determined by the between-group variance and mortality polarization linked to the East–West geopolitical division.
Objective: To identify which aspects of socioeconomic change were associated with the steep decline in life expectancy in Russia between 1990 and 1994. Design: Regression analysis of regional data, with percentage fall in male life expectancy as dependent variable and a range of socioeconomic measures reflecting transition, change in income, inequity, and social cohesion as independent variables. Determination of contribution of deaths from major causes and in each age group to changes in both male and female life expectancy at birth in regions with the smallest and largest declines. Setting: Regions (oblasts) of European Russia (excluding Siberia and those in the Caucasus affected by the Chechen war). Subjects: The population of European Russia. Results: The fall in life expectancy at birth varied widely between regions, with declines for men and women highly correlated. The regions with the largest falls were predominantly urban, with high rates of labour turnover, large increases in recorded crime, and a higher average but unequal distribution of household income. For both men and women increasing rates of death between the ages of 30 and 60 years accounted for most of the fall in life expectancy, with the greatest contributions being from conditions directly or indirectly associated with heavy alcohol consumption. Conclusions: The decline in life expectancy in Russia in the 1990s cannot be attributed simply to impoverishment Instead, the impact of social and economic transition, exacerbated by a lack of social cohesion, seems to have played a major part, The evidence that alcohol is an important proximate cause of premature death in Russia is strengthened.