In this multilevel analysis of population data from 13 former communist countries, measures of income inequality were not associated with rates of poor self‐rated health. Corruption index and homicide rates were associated with poor health when individual socioeconomic factors were taken into account, but the associations were not statistically significant after additionally controlling for GDP. GDP was also positively associated with good health status, although this association also lost significance when controlled statistically for level of corruption.
We draw attention to a number of potential limitations that are not unique to this study. First, a more objective health outcome would be desirable, mainly because of the potential for reporting bias (less healthy persons may be more likely to spuriously over‐report unfavourable social circumstances). However, such reporting bias is unlikely to be associated with societal characteristics that were taken from external sources, such as GDP, the Gini coefficient, the corruption index or homicide rate.
Second, measurement of macroeconomic indicators may be problematic in countries in transition, where a part of the economy is “shadow” economy. In this context, it is important that the officially estimated Gini coefficients correlated well with the inequality measures derived from the survey data (r
0.71 and 0.55 for the ratios of 80th vs. 20th and 90th vs. 10th percentiles respectively). It is equally encouraging that official estimates of GDP agreed well with the number of items owned by the household (r
Third, more detailed data on individuals' socioeconomic circumstances, were they available, may explain more of the effect of societal characteristics. This is particularly so with income; internationally comparable figures would be preferable to income in local currencies. However, calculating household income adjusted for purchasing power parity is not straightforward, especially since in seven countries household income was reported in bands. On the other hand, household item ownership correlated well with the absolute difference between countries and would probably capture at least some of the absolute differences in material conditions. One additional limitation is related to household income, namely that it was not adjusted for household size since data on household size were available in only six countries. However, it is unlikely that this problem affected the results of our study for three reasons. First, the differences in average household size between these six countries were negligible. Second, the results from analyses restricted to these six countries were virtually identical whether or not household size was included in the analysis. Third, official data show that the differences in household size among the 13 participating countries are small;32
thus, any influence on our results would also be small.
Finally, the analysis of societal effects is based on 13 countries only. Due to the collinearity between societal measures, it is difficult to reliably separate their effects. A larger number of countries would be needed to separate the effects of different societal characteristics.
Despite these limitations, our study also has a number of strengths. It is one of the largest multinational studies including data on both societal and individual characteristics. The variations in health and societal characteristics were much larger than among Western countries. This increases the chances of finding an effect, if it exists. Importantly, the national rates of poor health correlated remarkably well with life expectancy at age 15; this provides further support for self‐rated health as a valid indicator of population health. Overall, the good correlation between official figures and estimates derived from the survey data also suggests that the samples were sufficiently representative for results to be generalisable to whole populations.
Despite the large variation in both health and income inequalities, neither measure of income inequality was associated with health status. The lack of an effect of income inequality is consistent with an earlier study in seven post‐communist countries.22
One reason for the absence of an effect may be the type of study population. In the literature, a link between income inequality and health status seems to be restricted to wealthy countries, among which overall income (GDP) is no longer a predictor of mortality.2
While the former communist countries are not developing countries, most of them fall into the category of “middle‐income” countries, rather than high‐income countries, where most previous studies of income distribution have been conducted. It is possible that the effect of income inequality is not apparent in the presence of pronounced economic hardship, although, given the large variation in income inequality in these data, in countries which used to be egalitarian only 15 years ago, one would think that an effect of income inequality would be detected, if it existed. It is also possible, of course, that such an effect could have been missed due to the relatively low statistical power of the study.
There was, however, a relation between health and three societal characteristics: corruption index, homicide rate and GDP. The associations lost statistical significance after controlling for GDP but the interpretation of the last model is difficult – a multivariable analysis of just 13 points (countries) with mutually correlated variables is not statistically reliable. However, the associations of both corruption index and homicide rate with health status were in the expected direction, and they may reflect the effects on health of social institutions, governance and possibly social cohesion and social capital.25,26,28,29,30
This would be consistent with a number of ecological studies within countries both in CEE/FSU18,19,20,21
Throughout the analyses, GDP was consistently inversely associated with health status. This is not surprising given the grave economic problems of some of the participating countries. It is possible that, at least among the countries with low income, material deprivation is the most important social influence on health. On the other hand, adjustment for individual socioeconomic status led to only a modest reduction in the effect of GDP on health. This suggests that the role of GDP is not exclusively mediated by individual deprivation. In the ecological analyses, GDP correlated strongly with both corruption index and homicide rate. It is likely that, in addition to measuring societal wealth, GDP also reflects the quality of social organisation, which in turn also includes social cohesion.
As mentioned above, it is probably not possible to separate the effects of societal wealth from other societal characteristics in a study involving only 13 countries. However, one would expect that societal influences operating via material deprivation would be stronger in persons with low income. The fact that the effects on health of GDP, corruption and homicide rate were similar in persons below and above median household suggests that these effects do not operate exclusively through material deprivation.
It is plausible that the influence of GDP, corruption and crime reflect the presence and functioning of democratic institutions. A number of studies have suggested that democracy is positively associated with health status;34,35,36
the proposed mechanisms include representation (social and health policies are more a priority for groups who dominate in democracies), accountability (greater attention to social and health issues in democracies) and selection of political leaders (policies are formulated and supported by competent and less corrupt leaders).36
It also appears that the policy response to the social, economic and health crisis in CEE/FSU was more effective in countries with the better established democratic institutions (GA Cornia, 2006, unpublished).
In conclusion, these data confirm the close associations between health and GDP and other societal characteristics. The fact that the association between corruption and homicide rate and health is reduced after controlling statistically for GDP and, conversely, that of GDP to health is reduced when controlling for corruption does not make it straightforward to distinguish causal pathways, especially given the problem of collinearity. It is plausible, however, that the causal connection is from low GDP to lack of social cohesion and poorly functioning democratic institutions. Support for this theory is provided by the fact that the association between low GDP and poor health is as strong in people with higher than median income as in those with lower than median income. This study lends support to the view that social development in these countries, as in others where there have been market failures,37
will require the building of social institutions and well‐functioning communities.
What is already known on this subject
At population level, health is associated with societal characteristics, such as income inequality, social capital and national prosperity. It is not clear, however, if this represents something more than the association of health with individual socioeconomic circumstances.
What this study adds
This is one of the largest multinational studies including data on both societal and individual characteristics. Societal measures of prosperity, crime and corruption, but not income inequality, seem to influence health independently of individual‐level socioeconomic characteristics. The finding that these effects were similar in people with lower and higher income suggests that these factors do not operate exclusively through poverty.
Reducing income inequality alone may not improve population health. However, the results support the view that social development in post‐communist countries, as in others where there have been market failures, will require the building of social institutions and well‐functioning communities.