Introduction

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The scale of the health crisis facing the Russian people in recent years is now well recognised. After a period of steady improvement after the second world war, life expectancy at birth began to lag behind that in the West in the mid-1960s. A substantial improvement in 1985, coinciding with a major campaign to reduce alcohol consumption, ^{1 2} was rapidly reversed and has fallen even further since the collapse of the Soviet Union,³ with life expectancy at birth falling by over 5 years between 1990 and 1994. We have previously shown that these changes cannot be attributed to artefact.⁴

The decline in Russians' life expectancy in the 1990s is clearly driven by profound economic, political, and social changes. There is also considerable evidence that alcohol has played a major part.⁴ The nature of these relations, however, remains unclear. In particular, the relative importance of impoverishment and of the effects of rapid social and economic transition requires elucidation, as some argue that little can be done in the absence of policies to deal with the economic decline that has occurred in the 1990s whereas others have suggested that the effect of rapid change is more important.⁵

The nature of the social and economic transition in Russia has not been uniform, with some regions affected much more than others. There are also large differences in mortality within the regions of Russia.⁶ We explored the nature of the links between economic factors and mortality by taking advantage of this regional diversity. In particular, we examined whether it was possible to distinguish the effects of impoverishment from those resulting from the pace of transition. If the increase in mortality has been a result of impoverishment we would expect the greatest falls in life expectancy in regions experiencing the largest falls in average income; if it has been a result of the rapid pace of transition we would expect the greatest falls in regions experiencing the greatest disruption in patterns of employment.

	Methods

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All data were obtained from Goskomstat, the Russian state statistics committee, which collects data disaggregated to each of the 71 regions and two major cities (Moscow and St Petersburg) existing before 1992 in the Russian Federation (subsequently some regions were split to produce a total of 88 regions, but the earlier boundaries have been used to maintain consistency). Data on deaths by age and cause are compiled from returns by registration offices (ZAGs) in each region. Data on population have been derived by Goskomstat from returns in the 1989 census with allowance for demographic change and estimated migration. Numbers of deaths in each region were obtained by sex, by 5 year age band (except for those aged under 5, for whom numbers of deaths by individual year were available), and by cause of death (with the Soviet classification that consists of 175 primary diagnostic categories). Causes of death were aggregated into nine broad categories (table 1). Corresponding mid-year population estimates were used as denominators.

We sought data that would capture four broad categories: change in income; equity; transition; and social cohesion. These were derived from a regular series of surveys undertaken by Goskomstat, with a random sample of about 50 000 households drawn from all regions. Trends in average household income were adjusted for changes in the consumer price index, which was available for each region. Equity was measured by the Robin Hood index.⁷ This approximates to the share of total income that would have to be transferred from those with above average income to those below average to achieve an equal distribution of income. We chose this index rather than the more usual Gini coefficient because the Gini coefficient is more susceptible to the quality of data at the extremes of the distribution. The two measures were, however, highly correlated (r=0.915). Data on distribution of income were available only for 1994. 

Drawing on work by Cornia, we calculated an explanatory variable"labour turnover"which is the sum of job gains and losses in medium and large enterprises in 1993 and 1994, the years for which data were available, as a percentage of the average employment in these enterprises. This variable can be considered as a measure of "transition" or "labour market shock." Earlier work showed that this variable was strongly associated with increases in mortality in Russian regions (G A Cornia, UNU/WIDER project meeting on economic shocks, social stress, and the demographic impact, Helsinki, April 1997).⁸

Trends in reported crimes were used as an indirect measure of civic cohesion; we assumed that high levels of crime suggest lack of cohesion. This is consistent with a growing body of evidence from elsewhere suggesting that crime can be a proxy measure of social cohesion or social capital.⁹ Ideally, we would have included data on alcohol consumption. Unfortunately, although data on sales are available, they are known to be unreliable, especially since 1987 when illicit production increased greatly.¹⁰

For the purposes of the analysis, data from Siberia and from four predominantly Muslim regions in southern Russia were excluded, leaving the 52 more homogenous regions of European Russia. Siberia was excluded because there are major distortions in the local economy to take account of the much higher cost of living. For example, the median monthly household income in European Russia in 1990 was 194 roubles (£180) compared with 221 in Siberia (Mann-Whitney P=0.0009). More importantly, there were substantial subsidies that changed rapidly during this period, the effects of which are difficult to identify. Data from the excluded regions in southern Russia are also problematic for several reasons. There are few data from Chechenia because of the war, and the neighbouring regions have been affected substantially by refugee movements. In addition, historically these regions exhibit a pattern of health that is much more like that seen in the countries of Transcaucasia (Armenia, Georgia, and Azerbaijan) than in Russia. These regions are also believed to underregister deaths in elderly people.

Analysis
The analysis focused on change in male life expectancy at birth between 1990 (the first year for which detailed regional data were available to us) and 1994 (the year when it reached its lowest level in nearly all regions). Male life expectancy was used as it has shown the greatest fall. Across regions, however, both the absolute levels and the subsequent relative deterioration in life expectancy at birth for men and women are highly correlated (r=0.796 and r=0.892, respectively).

	Results

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The definitions of the variables analysed are in table 3. Table 4 shows that the fall in life expectancy was most closely correlated with labour turnover (figure 1), followed by income in 1990 (with the wealthiest regions experiencing the greatest falls in life expectancy), and then by the increase in crime. The other significant correlations indicate that the fall in life expectancy was also greatest in regions where crime levels were highest in 1990, which experienced the smallest reductions in income, and which in 1994 were the most unequal. The matrix also suggests that the increase in crime was greatest in regions that were the most unequal.

View larger version (21K): [in this window] [in a new window]   Fig 1.   Association between labour turnover in 1993-4 and fall in life expectancy in 1990-4

The results of the multivariate analysis are shown in table 5. Labour turnover, percentage increase in crime, and mean household income were included in the model, together explaining almost 56% of the observed regional variation in the change in life expectancy. The regression coefficients, adjusted for the other variables in the model, were about two thirds those in the univariate analyses for labour turnover and crime increase but about half of that for income.

As it seemed plausible that some of the factors affecting health might also be affecting crime rates, the regression was repeated without data on crime. The results are shown in table 6. In this case, the Robin Hood index was included in the model.

Turning to the second part of the analysis, between 1990 and 1994 life expectancy in men dropped by 8.6 years in the highest fourth and by 4.97 years in the lowest fourth. The corresponding figures for women were, as expected, rather lower at 4.12 and 2.55 years, respectively. It should be noted that in 1995 nearly all Russian regions experienced a slight improvement, although a few in Siberia have experienced further falls. There was a clear geographical pattern, with the greatest falls in the north and the smallest in the south (figure 2). Many of the regions experiencing the greatest falls were also the most urbanised. There were also several regions in other parts of Russia that experienced particularly small declines. These included some, such as Tatarstan, with large Muslim populations.

View larger version (40K): [in this window] [in a new window]   Fig 2.   European Russia, showing regional pattern of change in male life expectancy at birth between 1990 and 1994

View larger version (24K): [in this window] [in a new window]   Fig 3.   Contribution of deaths in each age band to fall in male life expectancy at birth in highest and lowest fourths of distribution of percentage fall in life expectancy

For both sexes and in all regions deaths in the 30 to 60 year age group contributed most to the fall in life expectancy; these deaths were also the most important contributors to the worse performance of regions in the highest fourth (figure 3). Among men in the highest fourth, deaths between 40 and 54 years accounted for 40% of the total decline in life expectancy. Deaths among children of both sexes made little contribution to the change, nor did deaths among elderly men, although the proportion of those surviving to old age was relatively small because of the high level of premature mortality. Deaths among elderly women, especially in the lowest fourth, made a greater relative contribution.

Examination by cause of death (table 7) showed that for men the 3.6 year difference between the fourths was explained largely by accidents (1.67 years), alcohol related causes (0.84 years), and cardiovascular disease (0.52 years). There were smaller contributions from respiratory and cerebrovascular diseases, and deaths from cancer made a negligible contribution to both the overall fall and the difference between fourths.

Table 8 shows that for women the results were similar, with the 1.57 year difference largely accounted for by deaths from accidents (0.65 years), alcohol related causes (0.41 years), and cardiovascular disease (0.37 years).

	Discussion

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Limitations
Before we can discuss the implications of this study it is necessary to explore its limitations. We could not undertake a detailed nationwide validation of the quality of data on either death registration or the explanatory variables used. The quality of recording deaths in Russia has, however, been examined in detail by using standard demographic techniques. ^{4 12 13} These analyses can be summarised as showing that the quality of data is generally good, although there are some concerns about deaths among infants, reflecting the definition of infant deaths used in Russia, and among elderly people. In the present context such concerns are less relevant as deaths in these age groups have had a relatively small role in the observed changes. There is inevitably rather more concern about the quality of the explanatory variables examined, reflecting both the culture of distortion of certain statistics at local level during the Soviet period and the increased difficulty of collecting such information in the more liberal situation pertaining since 1991.¹⁴ In particular, sampling methods may not produce entirely representative samples, missing the very poor and very rich. For example, the Gini coefficients, which are likely to be most affected by such problems, seemed surprisingly small in view of the obvious inequality seen in major Russian cities, hence our choice of the Robin Hood index. The figures for labour turnover are limited to large and medium enterprises and fail to capture job creation in small businesses or the informal economy. It would be surprising if the figures for recorded crime were not an underestimate of the true rates, although such problems are not confined to Russia. Although the data obtained for this study are much more representative and comprehensive than those available previously, the regression results must, none the less, be treated with some caution.

A possible model?
With these caveats, the analyses do shed some light on the factors underlying the decrease in life expectancy in Russia since 1990, which is still poorly understood and highly controversial. Those regions of European Russia that have suffered most since 1990 are the urban areas that have been exposed most to social and economic transition and which, while experiencing the greatest increases in average income, have become the most unequal and have also had the greatest increases in crime.

View larger version (13K): [in this window] [in a new window]   Fig 4.   Putative model of associations among transition, cohesion, and health

Other explanations
These analyses also provide an opportunity to test some of the other hypotheses that have been proposed to explain the decline in health in eastern Europe.¹⁷ These include impoverishment, environmental pollution, deterioration in health services, and psychosocial stress arising from the pace of change.

Environment and health services Neither the causes of death contributing to the decline nor the geographical distribution are consistent with a causal role for environmental pollution, and this has been declining anyway because of the collapse of much heavy industry. Although there can be little doubt that the health service has suffered in many ways since the break up of the Soviet Union,¹⁸ the age groups affected most are those with least contact with health services, with people dying of causes that are relatively insensitive to medical care.

Pace of change The many benefits that were associated with work in certain enterprises in the Soviet bloc, such as access to better health care or housing, means that employment has a rather different social meaning from that in the West.¹⁹ Consequently, change of employment in Russia could be expected to be especially traumatic. This analysis supports the argument that the rapid pace of change has been an important factor in increasing mortality, with those of working age facing the greatest psychological pressures during the transition to a market economy. Although the youngest and the oldest may have suffered more from material deprivation, they may have been less affected psychologically.

Alcohol Although this analysis focuses primarily on the underlying rather than the proximal reasons for the decline in life expectancy, it is important to note that the analyses by cause of death strongly support the argument that alcohol has played a major part in the decline in life expectancy in Russia⁴ and show that conditions associated with alcohol consumption, such as accidents, have been even more important in explaining regional differences. The link between accidents and alcohol is well established in many countries but it is also important to note that, in the context of the very high levels of alcohol consumption seen in Russia,²⁰ there is now an emerging body of evidence that a substantial proportion of cardiovascular deaths, especially among young men, are due to acute effects of binge drinking. ^{21 22} This major role for alcohol is not surprising given the position of alcohol in Russian society. There is a long tradition of heavy drinking, encouraged by governments before and after the revolution pursuing policies to recirculate savings by sales of one of the few goods for which supply could meet demand.²⁰ Since the late 1980s the situation has been exacerbated by extensive illicit trade and prices that have fallen in comparison with those of many other goods.²³

Summary and implications
These findings add to the growing literature on the complex relation between wealth, inequalities, and social cohesion, ^{9 24} but, unlike much other work that examines differences between populations at a point in time, this focuses on change, taking advantage of the unique changes in social, economic, and health indicators that occurred in Russia after the break up of the Soviet Union. The health effects of social and economic transition require considerably more research as, although the scale of the transition in Russia is extreme, it is not unique and there is now considerable evidence that the groups affected most in Russia, particularly young men and men in early middle age, have also suffered from the effects of transition elsewhere.²⁵ Such effects may also be detectable, albeit on a smaller scale, in other settings, such as parts of western Europe that are becoming de-industrialised. This would be consistent with the literature on the health effects of unemployment and fear of unemployment.²⁶