OBJECTIVES: Regional differences in mortality might reflect socioeconomic and ethnic differences between regions. The present study examines the relationship between education, unemployment, income, Roma population and regional mortality in the Slovak Republic. METHODS: Separately for males and females, data on standardised mortality in the Slovak population aged 20-64 years in the year 2002 were calculated for each of the 79 districts. Similarly the proportions of respondents with tertiary education, unemployed status, Roma ethnicity and income data were calculated per district. A linear regression model was used to analyse the data. RESULTS: Socioeconomic differences in regional mortality were found among males, but not among females. While education and unemployment rate significantly contributed to mortality differences between regions, income and the proportion of Roma population did not. The model explained 32.9% of the variance in standardised mortality rate among districts for males and 7.6% for females. CONCLUSION: Low education and high unemployment rate seems to be an indicator of regions with high mortality of male and therefore should be targeted by policy measures aimed at decreasing mortality in productive age.
OBJECTIVES: Regional differences in mortality might reflect socioeconomic and ethnic differences between regions. The present study examines the relationship between education, unemployment, income, Roma population and regional mortality in the Slovak Republic. METHODS: Separately for males and females, data on standardised mortality in the Slovak population aged 20-64 years in the year 2002 were calculated for each of the 79 districts. Similarly the proportions of respondents with tertiary education, unemployed status, Roma ethnicity and income data were calculated per district. A linear regression model was used to analyse the data. RESULTS: Socioeconomic differences in regional mortality were found among males, but not among females. While education and unemployment rate significantly contributed to mortality differences between regions, income and the proportion of Roma population did not. The model explained 32.9% of the variance in standardised mortality rate among districts for males and 7.6% for females. CONCLUSION: Low education and high unemployment rate seems to be an indicator of regions with high mortality of male and therefore should be targeted by policy measures aimed at decreasing mortality in productive age.
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