Demographic losses related to socio-economic inequalities in adult mortality in Lithuania: evidence from census-linked longitudinal data

Introduction: Health status and mortality indirectly measure wellbeing in a society and also point to the fairness of distribution of wealth across socioeconomic groups and individuals. Increasing attention of scientists and policy makers for health inequality issues is related to both ethical dimensions of inequality and social and economic consequences of health inequalities. According to the recent study by Mackenbach, Meerding and Kunst (2011), at the EU level, elimination of health inequalities would allow to save about 15 – 20% of the health and social security budgets. According to their estimates, overall loss of GDP due to health inequalities may reach as much as about 1.4% (Mackenbach, Meerding ir Kunst, 2011). It is obvious that in countries with higher mortality and health inequalities (such as Lithuania) demographic and economic burden of inequality may be even higher. If compared to other members of European Union, Lithuania show a very unfavourable situation with male life expectancy at birth. Despite very recent significant improvements, the country is classified as the worst performing EU country according to this criterion. Previous research suggests that one of the most important determinants of such unfavourable situation in life expectancy in Lithuania concerns striking mortality differences by socio-demographic group. Data and methods, The current study presents the comprehensive analysis of socio-economic differences in adult mortality in Lithuania using high quality census-linked longitudinal data covering entire adult population for the period 2001 – 2005. The study simultaneously uses three socioeconomic dimensions: education, economic activity status, and occupation. Socio-economic mortality differentials are assessed using both traditional range-type measures (Poisson regression mortality rate ratios) and advanced Gini-type measures accounting for both mortality in all socio-economic groups and group-specific population weights. The public health burden attributable to socioeconomic mortality differentials is measured using the method of cause-specific population attributable fractions. Results: The study found that distinctive features of the Lithuanian pattern of socioeconomic mortality differentials concern higher magnitude of mortality differentials and strikingly unfavourable health situation of farmers and farm workers. Differently from the ‘old’ EU member states, this group in Lithuania showed the worst mortality indicators if compared to other occupational groups. For the majority of causes of death, both relative and absolute mortality inequalities were higher than they are in the ‘old’ EU member states. The highest absolute demographic losses related to socioeconomic differences in adult mortality are attributable to excess mortality due to cardiovascular system diseases, external causes of death, digestive system diseases, and alcohol-related deaths. This suggests the striking prevalence of unhealthy life styles (smoking and alcohol consumption), poor psychosocial conditions, and lack of access to modern medical treatment and prevention among the lower socioeconomic groups at working ages in Lithuania. Conclusion: These new findings on socio-economic differentials in adult mortality in Lithuania will allow to perform more objective judgements about the effectiveness of the existing public health policies and to create preconditions for designing and implementation of more effective programs devoted to reduction of demographic burden of inequalities.