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3
Spatial, Age, and Cause-of-Death Patterns of Mortality in Russia, 1988-1989
Sergei A. Vassin and Christine A. Costello
Introduction
Although the overall mortality level in the former Soviet Union and its republics has been well studied by both Western and Soviet demographers, much less is known about spatial differentials of mortality in these countries. In the European part of Russia (which is relatively homogeneous compared with Russia as a whole), differences in level of life expectancy at birth are still substantial. Even after considerable decline in the range of life expectancy at birth during the 1980s, in 1988 the range was 8.5 years for the rural male population and 4.4 years for the urban male population (Shkolnikov and Vassin, 1994:400-401). If both the European and Asian parts of Russia are considered, the range widens.
The health situation in Russia has been characterized by an unusually long, continuing crisis in adult mortality. Very high death rates from injuries and early cardiovascular disease have had a significant impact on the level of Russian mortality, but should also have a pronounced effect on its age pattern. As shown by Anderson and Silver (in this volume), the age patterns of mortality in Russia and the Baltic states are unusual and different from the widely used neutral West model life table pattern (Coale and Demeny, 1966; Coale et al., 1983). Given the size and diversity of Russia, and in particular the spatial mortality differentials noted above, the questions arise of how many different age patterns of mortality exist in Russia, and how they compare with mortality patterns found elsewhere around the world.
The question of the Russian age pattern of mortality, along with its underlying cause-of-death profile, is relevant in two contexts in this volume: in the use
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of an appropriate standard pattern of mortality to assess data quality and in the use of such a pattern to establish deaths for the purpose of assessing potential years of life lost to premature mortality. Several of the chapters in the first part of this volume address quality-of-data issues. Two of the chapters—those by Kingkade and Arriaga and by Murray and Bobadilla—examine the potential years of life lost to premature mortality. Generally, potential years of life lost is calculated by comparing actual with expected deaths, where expected deaths are based on a standard age pattern of mortality appropriate to a region. To determine health priorities within the different regions of Russia, appropriate standards on which to base expected deaths must be selected, unless an arbitrary age at which all life ceases is chosen.
Previous work has investigated levels of life expectancy, spatial differentials, and age-specific components of life expectancy within European Russia (Shkolnikov and Vassin, 1994). This paper expands that spatial analysis to include both the European and Asian parts of Russia, introduces data on cause of death, and examines differentials not only in the level but also the shape (age pattern) of mortality profiles for the years 1988-1989.
Establishing the underlying age patterns of Russian mortality at the end of the 1980s is particularly pertinent in the context of long-term change within Russia. The election of Gorbachev to the position of General Secretary occurred in March 1985. Less than two months later (May 7, 1985), a resolution for ''actions against drunkenness and alcoholism" was issued. Three weeks later, the anti-alcohol campaign had begun. This campaign, which lasted to the end of 1987 (see Nemcov, 1995), had the effect of reducing regional differences in mortality as well as the characteristically high excess mortality among middle-aged Russian males. The collapse of the political and economic structure of the Soviet Union occurred over the period 1989-1991. Thus the years 1988-1989 serve as a benchmark for mortality patterns in Russia. Moreover, since the subsequent social upheaval has been suspected of disrupting the state statistical system, it is appropriate to use the years 1988-1989 as baseline data against which to measure future change. In addition, the census of 1989 provides the most reliable age structure by province, and the use of two years of death data improves the reliability of the estimates. Finally, this period preceded the accelerated mortality observed during 1992-1993.
The next section of this chapter describes the data sources and methods used for the analysis. This is followed by discussion of the variation in mortality levels in Russia in 1988-1989 by selected causes of death: injuries, cardiovascular disease, and neoplasm. The discussion addresses basic differentials in male-female and rural-urban mortality, regional and provincial variation in mortality levels, and regional variation in cause-specific mortality. Differences in underlying age patterns of mortality are then investigated through the use of cluster analysis for the mathematical grouping of provincial life tables, which results in a set of "typical" age patterns of mortality for males and females for different
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regions of the country. Next, the chapter compares the Russian mortality patterns with Coale-Demeny model life tables, other standard mortality patterns, and other European and U.S. patterns. The chapter ends with a summary and conclusions.
Data Sources and Methods
The basic data for administrative units of Russia used for this analysis are numbers of deaths in 1988 and 1989 in Russia by age, sex, cause of death, urban or rural location, and administrative unit. The data are from statistical reports of the State Committee of Statistics of the former Soviet Union for provinces, special districts, and autonomous republics (respectively, oblast, krai, and autonomous republics, hereafter referred to as provinces). Population data are from the 1989 census. For the analysis, the entire data set consists of 292 observations: for each sex, 146 observations cover the urban population of 73 provinces, including Moscow and Leningrad cities; the rural population of 71 provinces; and the total urban and rural population of all of Russia.
For the analysis of spatial variation in mortality levels, we use life expectancies at birth and cause-specific death rates for three causes of death: injuries, cardiovascular disease, and neoplasm. Death rates are standardized by age to the European standard (Waterhouse et al., 1976) for each of four subpopulations: male urban and rural, and female urban and rural (Table 3-1). The underlying provincial life expectancies and age-standardized cause-specific death rates for three causes of death are included in Annex 3- 1. Percentiles for the life expectancies and cause-specific death rates were calculated and quintiles assigned. Quintiles were used to allow comparisons within a province of the rankings in different causes of death. The lowest quintile, 1, represents a situation of low mortality, while the highest, 5, represents high mortality. Quintiles of life expectancy and cause-specific death rates are also given in the annex.
For the analysis of age patterns of mortality, 2-year multiple decrement life tables for 1988-1989 were calculated. These life tables are based on the above data and were constructed by Chiang's (1978) method. 1Thus, the data set consists of 292 life tables, one for each sex and administrative unit. We examine variation in the age-sex profiles by constructing typical profiles through clustering of the provincial mortality profiles. We use a formal approach based on a generalized concept of profile structure developed a number of years ago (Cronbach and Gleser, 1953), which allows the use of cluster analysis to find mortality curves with identical shape (Wunsch, 1984). This approach is discussed more thoroughly in a later section of the chapter.
Given the data quality issues addressed by many of the authors in this volume, a word about the reliability of the data used for this analysis is in order. Certainly, provincial-level data are subject to greater error than national estimates. We note some of these problems with certain provinces in the course of
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TABLE 3-1 Rural-Urban Variation in Life Expectancy and Mortality Rates from Selected Causes of Death in Russia, 1988-1989
Life Expectancy at Age
Standardized Death Rate per 100,000
Population
0
15
35
60
Neoplasm
Cardiovascular Disease
Injuries
Average of Provincial Levels
Male, rural
61.8
49.1
32.3
14.6
289.4
927.3
266.4
Male, urban
64.4
51.3
33.5
14.7
327.8
882.3
191.8
Female, rural
73.4
60.3
41.4
19.7
114.6
604.3
64.4
Female, urban
74.3
60.8
41.6
19.3
149.2
589.9
50.4
Standard Deviation
Male, rural
1.8
1.7
1.3
1.0
57.8
122.4
55.6
Male, urban
1.2
1.2
0.9
0.8
44.7
100.3
34.7
Female, rural
1.8
1.7
1.6
1.3
27.9
78.7
22.1
Female, urban
1.2
1.1
1.0
0.9
19.0
64.4
11.7
Coefficient of Variation (%)
Male. rural
2.8
3.4
4.2
7.0
20.0
13.2
20.9
Male, urban
1.9
2.3
2.8
5.7
13.6
11.4
18.1
Female, rural
2.5
2.8
3.8
6.4
24.4
13.0
34.3
Female, urban
1.6
1.8
2.5
4.7
12.7
10.9
23.2
NOTE: Standardized death rate determined by direct method of standardization using European standard.
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the discussion. We find extremes in life expectancy levels to be represented by the Northern Caucasus autonomous republics, with high life expectancies, and the more isolated provinces of the Northern, Far Eastern, and Eastern Siberian autonomous republics, with low life expectancies. The age pattern of mortality in these provinces is unusual as compared with other provinces, thus raising the question of whether these unusual patterns are a consequence of unusual life conditions or of doubtful statistics. In general, however, we believe the data for a sufficient number of provinces to be of high enough quality to support the analysis undertaken herein.
Variation in Mortality Levels, 1988-1989
This section examines variation in mortality levels in Russia during 19881989, focusing first on female-male and rural-urban differentials, then on regional and provincial variation, and finally on regional variation in cause-specific mortality.
Female-Male and Rural-Urban Differentials in Mortality
One well-known feature of Russian mortality is the significant difference in life expectancy at birth [e(0)] between males and females. Among rural populations, male life expectancy is 11 years less than female e(0), and among urban populations it is 10 years less, based on the average of provincial life tables (Table 3-1). Much higher death rates among males due to neoplasm, cardiovascular disease, and injuries contribute significantly to these differentials, which are similar across all provinces and administrative units of Russia (see Annex 3-1).
Differentials in rural and urban mortality are also marked in Russia, although to a much lesser extent than those between the sexes. Rural males have a life expectancy at birth 2.6 years less than urban males. The difference in remaining life expectancy at higher ages diminishes with increasing age, but does not disappear until age 60. Rural females also have a lower life expectancy than urban females, although on average the difference is less than 1 year. The difference among females diminishes at younger ages, nearly disappearing by about age 35. At younger adult ages, rural mortality is higher, but crossover effects are somewhat evident in higher urban mortality at older adult ages. (Crossover effects are discussed by Anderson and Silver, in this volume.) Overall, higher mortality in rural areas is found consistently in nearly all provinces, particularly for males, for whom only one province shows a reversal of the differential. For females, only nine provinces show higher urban than rural mortality.
Among rural males, the absolute range in life expectancy at birth across European and Asian provinces is over 11 years; among urban males, the range is 8 years (Annex 3-1). However, the absolute range misrepresents to a certain extent the spatial differentiation in mortality in Russia. Relative measures of
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variation, such as the standard deviation, demonstrate that differences in level of life expectancy are rather moderate (Table 3-1). The standard deviation in provincial levels of life expectancy at birth for both sexes is 1.2 years in urban areas and 1.8 years in rural areas. Thus, the majority of the population live in areas of fairly similar mortality levels, but variation in life expectancy is greater among the rural than the urban population.
Rural areas overwhelmingly and consistently have higher rates of death due to injury than urban areas, by a substantial margin. For males, rural morality rates due to injury are on average 38 percent higher than in urban areas. This differential is particularly marked in several European regions of Russia: the Northern, Northwestern, Central, Volga-Vyatka, Central Blackearth, and Baltic regions. For females, differentials between rural and urban morality rates due to injury are on average smaller—28 percent higher in rural than in urban areas. The differential for females is most marked in the Northwestern, Central, and Volga-Vyatka regions and in part of the Ural region.
The differential between rural and urban areas is much less marked for cardiovascular disease than for injury. Generally, mortality rates due to cardiovascular disease are on average 5 and 3 percent higher in rural than in urban areas for males and females, respectively. For males, the differentials are the greatest in the Northern and Northwestern regions and in parts of the Central Region, where rural mortality from cardiovascular disease is 10 to 20 percent higher than urban. In selected provinces of the Volga, North Caucasus, and Ural regions, the differential reverses, with higher urban than rural rates. For females, only a few provinces in high-mortality regions show high differentials. In the lower-mortality regions (Central Blackearth, Volga, and North Caucasus), urban mortality from cardiovascular disease is higher than rural in many provinces.
Neoplasm demonstrates the opposite pattern from injuries and cardiovascular disease, showing a larger rural-urban differential among females than among males. In general, urban rates of mortality from neoplasm are nearly always greater than rural rates—on average 12 percent higher for males and 23 percent higher for females. Within each region, there are single provinces where rural mortality from neoplasm is 30 to 40 percent lower than urban. In the Volga-Vatkya region, the differential among males is fairly large and consistent across provinces, with 20 to 40 percent lower mortality due to neoplasm in rural areas. For females, a larger differential is most frequent in provinces of the low-mortality regions, especially the Central Blackearth and Volga regions. In the high-mortality regions of Siberia and the Far East, the differential is small or reversed.
Regional and Provincial Variation in Mortality
Life expectancies at birth and the relevant quintiles relating to mortality levels of the 1988-1989 provincial life tables are shown for males and females in Annex 3-1 for urban and rural populations. Annex 3-1 also presents age-stan-
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dardized cause-of-death rates and quintiles for injury, cardiovascular disease, and neoplasm within each of the four subpopulations.
In general, the Northern and Northwestern regions in the north of European Russia, the northern part of the Ural region, a large part of Siberia, and the Far East include the territories with the lowest life expectancies. The unusually high variation in life expectancy in an industrialized country such as Russia is due to high mortality in these more remote regions of the country. High mortality is particularly evident in the less-populated regions of the Eastern Siberian and Far Eastern regions, in both rural and urban areas, with life expectancies between 55.7 and 63.7 years for males and 65.1 and 73.5 years for females. Certain provinces in Western Siberia and in the northern part of the Ural region show moderately high mortality at quintile 4.
In most areas of the Northwestern region, mortality is high for males and moderately high for females. The notable exception is exceedingly low mortality for males, but not females, in the city of St. Petersburg. Also in this general geographic zone is the Northern region, which shows a more moderate level of overall mortality for both sexes.
A northeastern to southwestern gradient, moving from higher to lower mortality levels across regions, is evident in the 1988-1989 levels of life expectancy. This gradient is most evident for urban areas and has been described in the literature (Andreev, 1979; Shkolnikov and Vassin, 1994). In general, the high-mortality areas are sparsely populated, but because they are rich in minerals, they are territories of intensive industrial development. (These development areas are classified as urban in the present analysis.) Migrants and prisoners are an important part of the population of these regions. Certainly, the extremely severe climatic conditions, absence of an advanced social infrastructure, and housing shortages that characterize these regions are not attractive to prospective inmigrants. However, the Soviet government stimulated the flow of labor into these areas through a special system of privileges. Although absence of freedom of movement characterized the Soviet Union, migrants who worked for extremely long periods of time in difficult conditions in these areas were granted the privilege to settle in any part of the country.
As a rule, those who went to work in the Northern and Asian parts of Russia had particularly good health. Migrants had to be certified by a special state medical commission as being fit to move. Thus, the migration stream into the Northern and Far Eastern regions of the country consisted of younger, healthy in-migrants, while the return stream consisted of older, less healthy, but wealthier migrants moving to the more prosperous regions of the country. Through this exchange, the poor health of the north was spread over the more favored regions of the south. Of course, there was a prevalence of men among the migrants, and thus the composition of the Russian Northern and Far Eastern populations demonstrates significant sex disproportions.
The most favorable levels of mortality are found in the North Caucasus
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region, where there are four autonomous republics. These favorable levels are questioned by some, who suggest that they are a product of underregistration of deaths rather than good health conditions, but conclusive work on this topic is not yet in the literature.
High levels of life expectancy are also found in the southern areas of the country. The Central Blackearth (Chernozem) region and much of the Volga region have life expectancies between 60.7 and 66.4 years for males and 71.3 and 75.7 years for females. Few areas in these regions have mortality levels at or above the median (with one exception). The Central Blackearth and Volga regions are the main grain regions of Russia, with the best natural and climatic conditions for agriculture (New Russia, 1994). Parts of these regions are frequently called the "granary of Russia," and "Chernozem" soils are considered the embodiment of fruitfulness. Thus, many of the provinces of these rural areas are relatively wealthy and productive.
In the remaining regions, the provinces generally exhibit intermediate levels of mortality, but within each region there are pockets of high mortality. The Central region, spread over a large area, shows numerous pockets of moderately high mortality, particularly in rural areas.
The rural population in the northern and central areas of European Russia suffers particularly high mortality, largely because of the poor living conditions of the Nechernozem zone. The Nechernozem (meaning poor agricultural conditions) zone includes 23 areas and 6 autonomous republics in the Northern, Northwestern, Central, and Volga-Vyatka economic regions; the Baltic region (Kaliningrad); and the Sverdlovsk, Perm, and Udmurt autonomous republics in the Ural region. Agriculture and living conditions in these territories deteriorated greatly during implementation of the program "Prospective Villages," begun in the 1970s at the initiative of the Central Committee of the Communist Party. Under this program, villages were divided into prospective and nonprospective groups. Prospective villages were favored for infrastructure development and were targeted to attract migrants from the nonprospective villages. However, the majority of the prospective villages were never fully developed. Rural migrants moved from both types of villages to urban areas, rather than to the prospective villages. The population left behind tended to be older and less well off. Thus the nonprospective villages were actually doomed to extinction, and the prospective villages failed to thrive. The Nechernozem zone became synonymous with a "dying" countryside and very poor living conditions of the rural population. The last days of one of these "condemned" villages are chronicled by a contemporary author in the novel Farewell to Matyora (Rasputin, 1991).
Regional Variation in Cause-Specific Mortality
In the Far Eastern region, cause-specific mortality rates are generally high from injury, cardiovascular disease, and neoplasm. However, rural males do not
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exhibit as extreme rates as the other three subpopulations (urban males and rural and urban females).
In Eastern Siberia, one sees fairly high rates2 of injury at quintiles 4 or 5 among males and females in most rural and urban areas. Neoplasm rates are generally high for females and more moderate for males. Low rates of mortality from cardiovascular disease are evident among males in particular, with rates at quintiles 1 or 2 for the male-urban and male-rural populations. Rates are more moderate for the female-urban population; rural females in Eastern Siberia, however, have moderately high levels of cardiovascular disease.
Western Siberia has high rates of injury for urban females, and moderate to high neoplasm rates in more than half of the areas for all four subpopulations. In contrast, in three provinces of the Ural region, there are high levels of injury among all four subpopulations, but generally lower rates of cardiovascular disease and neoplasm.
In the Northern region, high rates of cardiovascular disease are found for all subpopulations, but injury is not predominant. In the Northwestern area, there are generally moderately high mortality rates from all three causes, with the noted exception among males in Leningrad.
In the Central Blackearth and Volga regions, there are generally low rates from injury, cardiovascular disease, and neoplasm, with selected provinces as exceptions.
In general, cause-specific mortality rates from injury, cardiovascular disease, and neoplasm vary consistently with the overall level of mortality in four regions, but the relative importance of these causes varies in the remaining regions. Correlations between life expectancy and injury levels are -.63 (urban male) and -.88 (rural male), with rural and urban females at -.74. For cardiovascular disease, the correlations with e(0) are -.45, -.61, -.70, and -.72 for urban and rural males and urban and rural females, respectively. For neoplasm, the equivalent correlations are -.57,-.45, -.62, and -.72, respectively. Correlations between causes of death are also fairly high. The relation between mortality rates from injury and cardiovascular disease is on the order of 0.45 for three of the four subpopulations studied, although for urban males, the correlation between those rates is only 0.16.
Yet given the regional variations in mortality patterns documented in this chapter, it is not sufficient to pay particular attention to areas of high mortality and assume a similar underlying cause-of-death structure. Rather, particular causes of death contributing to high mortality in a region need to be identified before regional health issues can be characterized.
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Russian Age Patterns of Mortality
General Approach to Classifying of the Shape of the Mortality Curve
Many mortality studies have demonstrated that the level is the major component of "explained" variation in mortality curves (Ledermann and Breas, 1959; Bourgeois-Pichat, 1962; Messinger, 1980). Its impact is so considerable that it prevents closer inspection of weaker, but not less informative and important, differences in the shape of mortality curves. In particular, the shape of the mortality curve, or the age pattern of mortality, may reflect specific conditions of life among a population better than does the level. For example, elevated child mortality relative to other ages is a feature of the Coale-Demeny South model life table that reflects increased risk of intestinal infections produced by climatic, sanitary, dietetic, cultural, and behavioral features of the lifestyle of Southern Europeans at the end of the nineteenth and first half of the twentieth centuries (Coale and Demeny, 1966; Coale et al., 1983). Social class groupings are distinguished not only by level, but also by the shape of the mortality curve, which is thus useful for the study of social inequality in mortality (Anson, 1994). The shape of the mortality curve can also reflect peculiarities of the process of the epidemiological transition (Vassin, 1994).
The shape of the mortality curve is supposed to be more stable than the level, for even when the level varies considerably, a relative shape is maintained (Valaouras, 1974). This adherence to an underlying shape enables the construction of regional model life tables, and also emphasizes that the shape is more strongly connected to the specific character of a social situation than the level.
To analyze mortality profiles by the shape of the curve, it is necessary to eliminate differences in level and to identify the underlying typical patterns. There are different approaches to classifying life tables according to mortality profiles. A major classification effort was carried out by Coale and Demeny (1966; Coale et al., 1983), resulting in the widely used four regional families of model life tables. To find typical mortality patterns, Coale and Demeny visually analyzed several hundred mortality patterns. In this chapter, we employ a more formal approach based on a generalized concept of profile structure developed a number of years ago by Cronbach and Gleser (1953).3 This concept allows the use of cluster analysis to find mortality curves with identical shapes. According to this concept, any profile consists of three components: elevation, scatter, and shape. The level is equivalent to an average of the profile (expressed, e.g., as a simple or geometric average). Scatter is a measure of variation (like variance), whereas shape is something that remains in the profile after the first two components have been removed, similar to the product-moment of correlation between profiles. In demographic practice, the shape of a profile is understood to be all that remains after elimination of differences in level only. We have not departed
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from this usual practice and have accepted the concept of shape as that which incorporates two components—scatter and shape.
To be certain that cluster analysis would be reliable for grouping of life tables, we tested the approach on the entire range of Coale-Demeny and U.N. model life tables.4 Variation in the level of mortality in such a sample of model life tables is enormously high (life expectancy varies from 20 to 80 years, with a standard deviation of 18 years and a coefficient of variation of 36 percent). If, in spite of this variation, the method manages to classify all model life tables appropriately into their own families, this means the method is able to ignore differences in the level and to classify mortality curves effectively and properly according to their shape. Results of this test were the correct classification of the entire set of male life tables and the misclassification of only 2 among 279 female life tables, proving that this method is suitable for the classification of life tables by the shape of the mortality curve.
Application of Cluster Analysis to Russian Provincial Life Tables
To determine whether there were natural clusters of age patterns of mortality in Russia, and the number of such clusters, we first used the same method of classification as that used in the test on model life tables. This searching for natural clusters showed that for males and females, there exist only two natural clusters: one urban and one rural. This means that despite its vast territory, Russia is relatively homogeneous in the shape of its mortality curves and that there are two salient patterns of mortality: rural and urban. However, moderate differences in the shape of Russian regional curves can still be reasonably important within the country. To investigate within-country differences in mortality, we took further steps to break the urban and rural patterns down into more detail by using the Ward method of cluster analysis (Wunsch, 1984). This method has two features that should be noted. First, since the method is vulnerable to outliers, we excluded 15 of the most unusual mortality curves from the analysis to get more reliable results. Second, with this method, the researcher defines an arbitrary final number of clusters. We set the number of clusters equal to six both for males and females. However, as will be shown below, the proper number of clusters is less than six.
The typical age patterns of mortality resulting from the cluster analysis are shown graphically in Figures 3-1a for males and 3-1b for females. The cluster profiles shown are the average of all members of each cluster.5 Scores of the double standardized logits of the probability of death are shown as dashed lines on the right y-axis. 6 The deviations of scores specify whether mortality is higher or lower for each cluster relative to the average profile of the whole set of logit scores of q(x). Negative deviations indicate that mortality is below average, and positive, that it is higher. The sum of deviations from the average is equal to zero.7
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Males - Rural Areas
Province
e(0)
Q
Injury
Q
CVD
Q
Neopl.
Q
Northern Region
Arhangelsk
60.9
4
273.3
3
1090.6
5
314.6
4
Karelia
61.8
3
251.2
3
1115.4
5
353.4
5
KOMI
60.8
4
294.9
4
1079.4
5
277.5
2
Murmansk
Vologda
62.1
3
227.3
1
1057.7
5
290.2
3
Northwestern Region
Lenin. Obl.
62.0
3
265.4
3
1031.6
5
351.8
5
Leningrad
Novgorod
57.8
5
361.3
5
1116.6
5
345.4
5
Pskov
60.2
5
297.8
4
1058.8
5
292.3
3
Central Region
Bryansk
62.1
3
244.3
2
938.8
3
261.7
2
Ivanovo
60.5
5
265.5
3
1126.2
5
320.4
4
Jaroslav
60.1
5
298.8
4
1033.2
5
297.0
3
Kalinin
58.6
5
361.1
5
1144.9
5
299.2
4
Kaluga
59.8
5
289.5
4
1004.7
4
316.7
4
Kostroma
61.2
4
274.2
3
1088.5
5
298.3
4
Moscow Obl.
62.8
2
267.3
3
955.7
4
361.2
5
Moscow
Orlovskay
61.5
4
298.8
4
892.4
3
269.1
2
Ryazan
61.1
4
309.9
5
938.7
3
290.3
3
Smolensk
60.6
4
289.6
4
988.0
4
299.2
4
Vladimir
61.6
3
245.0
2
1027.7
4
337.9
5
Volga-Vyatka Region
Chuvashia
62.4
2
320.6
5
773.5
1
172.7
1
Gorkovskaya
61.7
3
258.2
3
920.1
3
273.4
2
Kirovskay
61.3
4
304.4
5
893.7
3
253.4
2
Maryiskay
60.7
4
345.5
5
870.0
2
185.1
1
Mordva
63.7
1
204.8
1
902.6
3
234.3
1
Central Blackearth Region
Belgorod
63.8
1
228.1
1
841.0
2
219.6
1
Kurskay
61.7
3
265.3
3
946.8
4
254.4
2
Lipezk
62.2
2
270.4
3
938.7
3
286.2
3
Tambov
61.1
4
274.8
4
911.3
3
296.0
3
Voronej
63.3
1
226.1
1
806.5
1
239.9
1
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Males - Urban Areas
Province
e(0)
Q
Injury
Q
CVD
Q
Neopl.
Q
Volga Region
Astrahan
63.8
4
210.8
4
924.0
4
351.7
4
Kalmyikia
61.2
5
233.1
5
873.3
3
314.9
2
Kuibyishevsk
64.9
2
169.2
2
855.5
2
347.7
4
Penza
65.2
2
184.1
3
861.8
3
315.4
2
Saratov
64.5
3
175.5
2
917.7
4
333.0
3
Tataria
65.6
1
183.1
3
832.4
2
288.1
1
Ulyanovsk
65.2
2
175.1
2
936.8
4
326.5
3
Volgograd
65.4
1
171.6
2
800.1
1
338.5
3
North Caucasus Region
Chechny
64.9
2
125.9
1
820.3
1
249.3
1
Dagestan
68.0
1
110.6
1
583.5
1
212.9
1
Kabarda
65.8
1
136.5
1
766.8
1
242.8
1
Krasnodar
64.9
2
185.6
3
851.5
2
283.7
1
Osetia
66.6
1
144.7
1
821.2
1
228.6
1
Rostov
65.0
2
156.0
1
855.9
2
281.4
1
Stavropol
65.9
1
162.3
2
813.9
1
287.2
1
Ural Region
Bashkiria
65.1
2
178.2
2
839.8
280.2
1
Chelyabinsk
64.9
2
183.0
3
781.7
1
345.5
4
Kurganskay
64.4
3
200.7
4
812.0
1
362.8
5
Orenburg
64.9
2
190.7
3
833.6
2
330.1
3
Perm
64.2
4
212.8
5
912.4
4
309.5
2
Sverdlovsk
64.2
4
200.9
4
879.4
3
327.0
3
Udmurtia
64.0
4
223.9
5
885.8
4
269.3
1
Western Siberia Region
Altai
63.8
4
221.5
5
792.9
1
361.6
5
Kemerovo
63.1
5
256.4
5
893.5
4
310.2
2
Novosibirsk
64.0
4
182.9
3
873.2
3
342.5
4
Omsk
64.7
3
195.7
3
782.7
1
374.6
5
Tomsk
64.4
3
173.9
2
863.8
3
349.1
4
Tumen
64.9
2
195.9
3
825.7
2
292.6
2
Eastern Siberia Region
Buryatia
63.0
5
226.5
5
795.8
1
347.5
4
Chita
63.4
5
229.4
5
788.4
1
296.4
2
Irkutskay
62.8
5
244.6
5
850.2
2
326.8
3
Jakutia
63.0
5
63.5
1
904.5
4
374.5
5
Krasnoyarsk
63.2
5
204.1
4
830.3
2
337.5
3
Tuva
59.8
5
344.9
5
756.5
1
381.9
5
OCR for page 111
-->
Males - Rural Areas
Province
e(0)
Q
Injury
Q
CVD
Q
Neopl.
Q
Volga Region
Astrahan
63.8
1
209.4
1
940.7
3
346.2
5
Kalmyikia
61.8
3
234.4
2
841.2
2
268.5
2
Kuibyishevsk
62.8
2
241.1
2
903.8
3
282.8
3
Penza
63.0
2
250.6
2
876.9
2
274.2
2
Saratov
62.1
3
239.0
2
907.1
3
322.4
4
Tataria
63.8
1
233.3
2
844.1
2
222.5
1
Ulyanovsk
63.0
2
228.9
1
1008.6
4
275.4
2
Volgograd
63.3
1
232.8
2
813.9
1
327.1
5
North Caucasus Region
Chechny
64.6
1
128.9
1
666.1
1
230.8
1
Dagestan
67.1
1
120.0
1
561.5
1
147.4
1
Kabarda
65.5
1
152.0
1
711.6
1
216.8
1
Krasnodar
62.9
2
253.3
3
862.6
2
266.7
2
Osetia
64.1
1
193.1
1
860.0
2
224.0
1
Rostov
63.6
1
216.0
1
844.0
2
256.8
2
Stavropol
63.8
1
207.3
1
882.4
2
260.8
2
Ural Region
Bashkiria
63.3
1
249.5
2
816.9
1
218.6
1
Chelyabinsk
62.9
2
217.8
1
832.8
1
333.1
5
Kurganskay
62.6
2
250.3
2
813.0
1
304.8
4
Orenburg
64.5
1
195.3
1
853.1
2
254.9
2
Perm
60.1
5
322.7
5
975.5
4
251.4
1
Sverdlovsk
60.7
4
288.4
4
904.1
3
297.0
3
Udmurtia
61.3
4
325.2
5
809.1
1
221.3
1
Western Siberia Region
Altai
62.2
2
253.3
3
825.3
1
297.6
4
Kemerovo
60.4
5
324.0
5
946.4
4
281.7
3
Novosibirsk
62.3
2
235.1
2
885.0
3
298.4
4
Omsk
62.8
2
235.0
2
869.0
2
318.2
4
Tomsk
61.6
3
252.7
3
942.4
4
326.3
4
Tumen
61.9
3
277.0
4
860.0
2
232.2
1
Eastern Siberia region
Buryatia
61.5
4
277.1
4
827.3
1
291.4
3
Chita
62.0
3
283.3
4
830.3
1
286.1
3
Irkutskay
59.1
5
332.3
5
880.7
2
295.7
3
Jakutia
61.1
4
263.7
3
799.1
1
379.8
5
Krasnoyarsk
60.5
5
287.2
4
853.3
2
281.7
3
Tuva
55.7
5
455.8
5
951.7
4
357.7
5
OCR for page 112
-->
Males - Urban Areas
Province
e(0)
Q
Injury
Q
CVD
Q
Neopl.
Q
Far Eastern Region
Amurskay
63.7
4
216.6
5
889.3
4
311.6
2
Habarovsk
62.2
5
233.2
5
1010.3
5
370.7
5
Kamchatka
62.7
5
218.6
5
1376.2
5
433.8
5
Magadan
63.1
5
157.6
1
1119.1
5
496.0
5
Primorski
63.4
5
227.5
5
941.6
5
332.5
3
Sahalin
62.5
5
233.9
5
1072.2
5
377.5
5
Baltic Region
Kaliningrad
65.3
1
192.9
3
876.5
3
346.8
4
Summary Statistics
Mean
62.7
183.1
870.2
323.3
Std Deviation
10.5
48.8
142.5
58.2
OCR for page 113
-->
Males - Rural Areas
Province
e(0)
Q
Injury
Q
CVD
Q
Neopl.
Q
Far Eastern Region
Amurskay
62.1
3
239.3
2
954.8
4
271.4
2
Habarovsk
60.1
5
293.6
4
1009.8
4
349.4
5
Kamchatka
60.2
5
249.8
2
1304.9
5
323.6
4
Magadan
60.9
4
403.1
5
1105.2
5
552.0
5
Primorski
60.9
4
287.7
4
1001.7
4
293.1
3
Sahalin
61.9
3
276.8
4
1124.8
5
369.0
5
Baltic Region
Kaliningrad
59.4
5
352.3
5
990.0
4
369.2
5
Summary Statistics
Mean
61.8
266.4
927.3
289.4
Std Deviation
1.8
55.2
121.5
57.3
OCR for page 114
-->
Females - Urban Areas
Province
e(0)
Q
Injury
Q
CVD
Q
Neopl.
Q
Northern Region
Arhangelsk
74.7
3
45.9
3
619.3
4
145.5
3
Karelia
74.0
4
52.6
3
667.6
5
151.6
4
KOMI
73.1
5
62.5
5
667.5
5
151.9
4
Murmansk
74.6
3
41.6
2
656.6
5
140.2
2
Vologda
74.6
3
40.4
2
625.9
5
139.9
2
Northwestern Region
Lenin. Obl.
73 .8
4
61.0
5
636.3
5
173 .4
5
Leningrad
74.1
4
53.2
3
577.7
3
197.2
5
Novgorod
74.1
4
53.9
4
613.5
4
146.2
3
Pskov
74.2
4
47.2
3
632.5
5
154.4
4
Central Region
Bryansk
75.3
1
35.8
1
586.1
3
141.6
2
Ivanovo
74.1
4
41.6
2
656.6
5
139.2
2
Jaroslav
75.0
2
20.3
1
579.3
3
147.6
3
Kalinin
74.7
3
48.8
3
598.8
4
138.6
2
Kaluga
74.8
2
38.0
1
577.5
3
150.7
4
Kostroma
74.4
3
47.4
3
637.1
5
147.8
3
Moscow Obl.
74.6
3
43.2
2
594.7
4
165.4
5
Moscow
74.2
4
43.7
2
556.1
2
188.6
5
Orlovskay
75.4
1
45.3
3
533.1
1
140.4
2
Ryazan
75.5
1
39.1
1
529.1
1
145.4
3
Smolensk
75.0
2
39.4
2
565.9
2
156.7
4
Vladimir
75.0
2
37.2
1
597.4
4
142.3
2
Volga-Vyatka Region
Chuvashia
75.6
1
60.6
5
514.2
1
121.2
1
Gorkovskaya
74.8
2
39.7
2
587.6
3
149.3
3
Kirovskay
74.7
3
57.5
4
598.5
4
115.0
1
Maryiskay
75.1
2
56.7
4
529.6
1
124.4
1
Mordva
75.7
1
39.2
1
541.7
1
130.5
1
Central Blackearth Region
Belgorod
74.9
2
36.5
1
557.9
2
143.3
2
Kurskay
74.9
2
41.4
2
569.9
2
135.1
1
Lipezk
75.4
1
37.8
1
564.6
2
143.7
2
Tambov
74.9
2
38.6
1
556.9
2
147.9
3
Voronej
75.7
1
20.4
1
529.0
1
128.4
1
OCR for page 115
-->
Females - Rural Areas
Province
e(0)
Q
Injury
Q
CVD
Q
Neopl.
Q
Northern Region
Arhangelsk
73.5
3
59.1
3
661.7
5
106.0
3
Karelia
73.2
4
54.5
2
763.9
5
115.7
3
KOMI
72.1
4
75.9
4
738.0
5
94.2
1
Murmansk
73.7
3
51.2
2
762.6
5
115.7
3
Vologda
74.5
2
48.7
1
627.6
4
100.4
2
Northwestern Region
Lenin. Obl.
74.0
3
69.7
4
595.1
3
136.1
5
Leningrad
Novgorod
72.9
4
79.1
4
637.2
4
114.0
3
Pskov
72.5
4
81.0
5
642.3
4
116.6
4
Central Region
Bryansk
74.7
1
48.3
1
569.3
2
91.5
1
Ivanovo
73.5
3
53.9
2
670.9
5
106.0
3
Jaroslav
74.3
2
61.8
3
581.4
3
112.7
3
Kalinin
72.4
4
82.5
5
665.2
5
104.5
2
Kaluga
72.7
4
62.0
4
632.9
4
118.9
4
Kostroma
73.4
4
54.9
2
686.8
5
111.2
3
Moscow Obl.
74.4
2
54.9
2
612.1
3
144.3
5
Moscow
Orlovskay
74.1
3
60.7
3
572.8
2
99.0
2
Ryazan
74.3
2
61.1
3
563.2
2
105.8
2
Smolensk
73.4
4
64.5
4
616.6
4
115.9
4
Vladimir
73.6
3
56.6
3
639.9
4
119.4
4
Volga-Vyatka Region
Chuvashia
73.1
4
115.7
5
549.6
1
76.2
1
Gorkovskaya
74.6
2
50.0
2
570.2
2
102.5
2
Kirovskay
73.3
4
90.8
5
562.4
2
92.9
1
Maryiskay
71.3
5
133.3
5
612.8
4
80.8
1
Mordva
75.5
1
49.6
2
538.5
1
82.91
Central Blackearth Region
Belgorod
75.7
1
42.7
1
537.1
1
84.2
1
Kurskay
74.3
2
54.2
2
587.7
3
95.4
2
Lipezk
75.1
1
60.9
3
550.8
1
92.4
1
Tambov
74.6
2
51.3
2
556.2
2
104.8
2
Voronej
75.0
1
42.4
1
527.5
1
96.4
2
OCR for page 116
-->
Females - Urban Areas
Province
e(0)
Q
Injury
Q
CVD
Q
Neopl.
Q
Volga Region
Astrahan
74.4
3
48.2
3
593.0
3
158.8
4
Kalmyikia
71.3
5
61.1
5
597.4
4
137.4
2
Kuibyishevsk
74.6
3
44.2
2
569.8
2
158.4
4
Penza
75.5
1
45.3
3
561.2
2
134.6
1
Saratov
74.6
3
43.6
2
602.8
4
149.0
3
Tataria
75.6
1
45.4
3
534.3
1
128.8
1
Ulyanovsk
75.1
2
44.2
2
572.3
3
138.3
2
Volgograd
75.1
2
43.0
2
544.6
1
162.4
5
North Caucasus Region
Chechny
74.1
4
36.8
1
551.6
2
135.5
1
Dagestan
77.5
1
31.0
1
383.6
1
104.9
1
Kabarda
76.1
1
35.7
1
494.3
1
125.2
1
Krasnodar
74.4
3
47.7
3
595.6
4
147.9
3
Osetia
75.9
1
36.3
1
531.4
1
133.0
1
Rostov
74.2
4
41.2
2
612.3
4
145.5
3
Stavropol
75.3
1
37.5
1
557.8
2
147.0
3
Ural Region
Bashkiria
74.8
2
51.8
3
546.9
1
130.0
1
Chelyabinsk
74.6
3
51.3
3
544.0
1
152.4
4
Kurganskay
74.9
2
54.1
4
533.8
1
152.9
4
Orenburg
74.8
2
42.3
2
575.8
3
146.1
3
Perm
73.9
4
62.1
5
611.0
4
136.5
2
Sverdlovsk
74.1
4
58.1
4
604.1
4
143.8
3
Udmurtia
74.4
3
63.3
5
587.5
3
119.1
1
Western Siberia Region
Altai
74.2
4
62.8
5
559.8
2
162.3
5
Kemerovo
73.2
5
78.1
5
620.7
5
142.1
2
Novosibirsk
74.0
4
53.9
4
577.0
3
154.9
4
Omsk
74.8
2
56.4
4
518.9
1
177.1
5
Tomsk
73.7
4
57.9
4
572.9
3
168.5
5
Tumen
74.6
3
57.9
4
582.4
3
130.1
1
Eastern Siberia Region
Buryatia
73.5
5
54.6
4
561.5
2
166.9
5
Chita
73.4
5
54.8
4
580.1
3
149.9
3
Irkutskay
73.2
5
63.6
5
584.4
3
164.4
5
Jakutia
72.2
5
45.3
3
619.7
4
183.9
5
Krasnoyarsk
73.5
5
55.9
4
570.8
2
158.2
4
Tuva
70.0
5
96.8
5
603.6
4
193.7
5
OCR for page 117
-->
Females - Rural Areas
Province
e(0)
Q
Injury
Q
CVD
Q
Neopl.
Q
Volga Region
Astrahan
73.9
3
56.0
3
602.7
3
135.2
5
Kalmyikia
72.6
4
49.2
1
581.2
2
103.7
2
Kuibyishevsk
74.3
2
55.9
2
589.3
3
108.5
3
Penza
75.2
1
50.8
2
536.5
1
93.0
1
Saratov
74.5
2
51.6
2
559.9
2
111.4
3
Tataria
75.8
1
46.7
1
504.0
1
85.0
1
Ulyanovsk
74.3
2
53.1
2
616.7
4
102.1
2
Volgograd
74.3
2
52.8
2
562.5
2
127.4
5
North Caucasus Region
Chechny
75.5
1
27.8
1
426.5
1
94.8
2
Dagestan
76.3
1
26.8
1
355.8
1
58.5
1
Kabarda
76.1
1
29.1
1
469.4
1
93.5
1
Krasnodar
74.2
2
56.1
3
590.5
3
122.0
4
Osetia
76.2
1
30.9
1
502.7
1
113.4
3
Rostov
74.8
1
45.4
1
580.7
2
107.2
3
Stavropol
74.1
3
46.1
1
603.3
3
122.4
4
Ural Region
Bashkiria
75.0
1
56.4
3
522.9
1
83.5
1
Chelyabinsk
73.7
3
57.6
3
543.0
1
125.0
4
Kurganskay
74.5
2
59.8
3
540.8
1
122.0
4
Orenburg
74.9
1
49.0
1
554.8
2
101.1
2
Perm
71.7
5
88.7
5
679.1
5
103.1
2
Sverdlovsk
72.6
4
84.1
5
600.1
3
116.1
4
Udmurtia
73.4
4
87.4
5
594.9
3
85.0
1
Western Siberia Region
Altai
73.4
4
63.9
4
575.2
2
124.4
4
Kemerovo
71.8
5
99.4
5
644.6
4
109.4
3
Novosibirsk
73.8
3
58.8
3
580.0
2
113.0
3
Omsk
73.3
4
56.1
3
604.8
3
125.2
4
Tomsk
71.9
5
67.2
4
659.4
5
142.2
5
Tumen
73.7
3
75.3
4
569.7
2
98.7
2
Eastern Siberia Region
Buryatia
71.4
5
69.0
4
614.3
4
151.1
5
Chita
71.3
5
63.3
4
642.6
4
159.7
5
Irkutskay
71.2
5
79.7
4
623.6
4
127.1
5
Jakutia
69.6
5
47.4
1
627.8
4
229.3
5
Krasnoyarsk
72.3
4
76.2
4
584.6
3
115.6
3
Tuva
65.1
5
138.4
5
764.5
5
212.2
5
OCR for page 118
-->
Females - Urban Areas
Province
e(0)
Q
Injury
Q
CVD
Q
Neopl.
Q
Far Eastern Region
Amurskay
73.5
5
54.8
4
657.6
5
139.8
2
Habarovsk
72.8
5
57.5
4
681.6
5
161.1
5
Kamchatka
71.9
5
66.1
5
850.3
5
166.5
5
Magadan
71.6
5
70.5
5
784.6
5
216.0
5
Primorski
73.2
5
64.1
5
671.6
5
157.2
4
Sahalin
72.4
5
64.2
5
755.5
5
156.5
4
Baltic Region
Kaliningrad
74. 3
3
62.4
5
557.9
2
160.8
4
Summary Statistics
Mean
74.3
49.6
589.9
149.2
Std Deviation
1.2
12.4
63.9
18.9
OCR for page 119
-->
Females - Rural Areas
Province
e(0)
Q
Injury
Q
CVD
Q
Neopl.
Q
Far Eastern Region
Amurskay
71.3
5
70.5
4
718.9
5
116.8
4
Habarovsk
70.8
5
73.8
4
763.5
5
151.1
5
Kamchatka
69.8
5
83.7
5
818.0
5
186.3
5
Magadan
Primorski
71.7
5
83.7
5
719.5
5
125.6
4
Sahalin
72.2
4
80.6
5
638.4
4
135.5
5
Baltic Region
Kaliningrad
71.3
5
122.2
5
596.7
3
131.7
5
Summary Statistics
Mean
73.4
64.4
604.3
114.6
Std Deviation
1.8
21.9
78.2
27.7
Representative terms from entire chapter:
cardiovascular disease
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