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TimoninS, etal. J Epidemiol Community Health 2019;0:1–7. doi:10.1136/jech-2019-213239
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New perspective on geographical mortality divide in
Russia: a district- level cross- sectional analysis,2008–
2012
Sergey Timonin ,1 Domantas Jasilionis,2 Vladimir M. Shkolnikov,1,2 Evgeny Andreev1
To cite: TimoninS,
JasilionisD, ShkolnikovVM,
etal. J Epidemiol Community
Health Epub ahead of print:
[please include Day Month
Year]. doi:10.1136/jech-
2019-213239
►Additional material is
published online only. To view
please visit the journal online
(http:// dx. doi. org/ 10. 1136/
jech- 2019- 213239).
1International Laboratory for
Population and Health, National
Research University Higher
School of Economics, Moscow,
Russia
2Laboratory of Demographic
Data, Max Planck Institute for
Demographic Research, Rostock,
Germany
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.
ABSTRACT
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.
INTRODUCTION
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
countries.
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.
DATA AND METHODS
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.
Methods
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
groups.
RESULTS
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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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
districts
SDR, per
1000
LE at birth,
years
Pop. size,
mln
No of
districts
SDR, per
1000
LE at birth,
years Absolute Relative
Group I
(0–5th)
3.0 12 10.1 71.5 3.4 11 5.9 79.5 8.0 1.11
Group II
(5–10th)
3.1 17 11.4 69.6 3.9 18 6.5 78.4 8.8 1.13
Group III
(10–30th)
12.6 180 15.0 66.0 14.7 155 7.4 76.9 10.9 1.17
Group IV
(30–50th)
12.6 215 16.8 64.1 14.8 204 8.0 75.9 11.8 1.18
Group V
(50–70th)
12.7 445 18.5 62.3 14.5 437 8.7 74.7 12.3 1.20
Group VI
(70–90th)
12.6 744 20.5 60.1 14.6 786 9.6 73.1 13.0 1.22
Group VII
(90–95th)
3.1 271 22.5 58.2 3.7 252 10.6 71.4 13.2 1.23
Group VIII
(95–100th)
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
countries
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.
DISCUSSION
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
Interpretation
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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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/.
ORCID iD
SergeyTimonin http:// orcid. org/ 0000- 0001- 6651- 2023
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