RESUMEN
BACKGROUND: Like most Central European countries Slovakia has experienced a period of socioeconomic changes and at the same time a decline in the mortality rate. Therefore, the aim is to study socioeconomic factors that changed over time and simultaneously contributed to regional differences in mortality. METHODS: The associations between selected socioeconomic indicators and the standardised mortality rate in the population aged 20-64 years in the districts of the Slovak Republic in the periods 1997-1998 and 2012-2013 were analysed using linear regression models. RESULTS: A higher proportion of inhabitants in material need, and among males also lower income, significantly contributed to higher standardised mortality in both periods. The unemployment rate did not contribute to this prediction. Between the two periods no significant changes in regional mortality differences by the selected socioeconomic factors were found. CONCLUSIONS: Despite the fact that economic growth combined with investments of European structural funds contributed to the improvement of the socioeconomic situation in many districts of Slovakia, there are still districts which remain "poor" and which maintain regional mortality differences.
Asunto(s)
Estado de Salud , Mortalidad/tendencias , Pobreza/estadística & datos numéricos , Adulto , Anciano , Etnicidad , Femenino , Humanos , Modelos Lineales , Masculino , Persona de Mediana Edad , Eslovaquia/epidemiología , Clase Social , Factores Socioeconómicos , Desempleo/estadística & datos numéricos , Adulto JovenRESUMEN
BACKGROUND: The socioeconomic and ethnic composition of urban neighbourhoods may affect mortality, but evidence on Central European cities is lacking. The aim of this study was to assess the associations between socioeconomic and ethnic neighbourhood indicators and the mortality of individuals aged 20-64 years old in the two biggest cities of the Slovak Republic. METHODS: We obtained data on the characteristics of neighbourhoods and districts (educational level, unemployment, income and share of Roma) and on individual mortality of residents aged 20-64 years old, for the two largest cities in the Slovak Republic (Bratislava and Kosice) in the period 2003-2005. We performed multilevel Poisson regression analyses adjusted for age and gender on the individual (mortality), neighbourhood (education level and share of Roma in population) and district levels (unemployment and income). RESULTS: The proportions of Roma and of low-educated residents were associated with mortality at the neighbourhood level in both cities. Mutually adjusted, only the association with the proportion of Roma remained in the model (risk ratio 1.02; 95 % confidence interval 1.01-1.04). The area indicators - high education, income and unemployment - were not associated with mortality. CONCLUSION: The proportion of Roma is associated with early mortality in the two biggest cities in the Slovak Republic.
Asunto(s)
Disparidades en Atención de Salud/estadística & datos numéricos , Mortalidad/etnología , Características de la Residencia/estadística & datos numéricos , Romaní/estadística & datos numéricos , Factores Socioeconómicos , Adulto , Ciudades/epidemiología , Ciudades/etnología , Femenino , Disparidades en Atención de Salud/etnología , Humanos , Masculino , Persona de Mediana Edad , Análisis Multinivel , Factores de Riesgo , Eslovaquia/epidemiología , Eslovaquia/etnologíaRESUMEN
OBJECTIVES: Large socioeconomic inequalities in health are still present in the Central Europe. The aim was to explore socioeconomic inequalities in mortality in Visegrad countries-the Czech Republic, Hungary, Poland and Slovakia (V4), by three different socioeconomic indicators (unemployment, risk of poverty/social exclusion, education). The study was conducted within the H2020 Euro-Healthy project. METHODS: The associations between selected socioeconomic indicators and the standardised mortality rates by four main causes (mortality related to cancer, circulatory, respiratory and digestive system) in the economically active population aged 20-64 years in the 35 NUTS 2 level regions of the V4 in the period 2011-2013 were explored, using linear regression models. RESULTS: Lower education level was the most significant predictor of mortality in the V4. The lowest mortality rates by all causes of death were found in the regions of the Czech Republic, the highest in regions of Hungary. CONCLUSIONS: Despite the common origin, the pathways of the V4 countries in employment, poverty and education seem to be different, also having impact on health equity. Therefore, where you live in the V4 can significantly influence your health.
Asunto(s)
Causas de Muerte , Mortalidad/tendencias , Pobreza/estadística & datos numéricos , Discriminación Social , Factores Socioeconómicos , Desempleo/estadística & datos numéricos , Adulto , República Checa/epidemiología , Femenino , Predicción , Humanos , Hungría/epidemiología , Modelos Lineales , Masculino , Persona de Mediana Edad , Polonia/epidemiología , Eslovaquia/epidemiología , Adulto JovenRESUMEN
OBJECTIVE: Exploring the associations of regional differences in infant mortality with selected socioeconomic indicators and ethnicity could offer important clues for designing public health policy measures. METHODS: Data included perinatal and infant mortality in the 79 districts of the Slovak population in 2004. Linear regression was used to analyse the contribution of education, unemployment, income and proportion of Roma population on regional differences in perinatal and infant mortality rates. RESULTS: All the explored socioeconomic indicators and ethnicity individually contributed significantly to both perinatal and infant mortality, with the exception of income. In the model exploring the influence of all these variables together on perinatal and infant mortality, only the effect of the proportion of Roma population remained significant. This model explained 34.9% of the variance for perinatal and 36.4% of the variance for infant mortality. CONCLUSIONS: Living in Roma settlements indicates an accumulation of socioeconomic disadvantage. Health literacy, health-related behaviour and many other factors might contribute to the explanation of the differences in infant mortality, and a better understanding of these processes might help us to design tailored interventions.
Asunto(s)
Etnicidad , Mortalidad Infantil/tendencias , Romaní , Clase Social , Escolaridad , Femenino , Humanos , Renta/estadística & datos numéricos , Lactante , Mortalidad Infantil/etnología , Recién Nacido , Masculino , Romaní/estadística & datos numéricos , Eslovaquia/epidemiología , Desempleo/estadística & datos numéricosRESUMEN
Regional differences in alcohol-related mortality might reflect strong socioeconomic differences between regions. The present study examines the contribution of education, unemployment, income and minority proportion on regional differences in alcohol-related mortality for inhabitants aged 20-64 years. Linear regression analysis and a non-parametric regression tree analysis were used separately for males and females. The unemployment rate and low education appeared as important determinants of regional alcohol-related mortality, while the proportion of Roma and income were not significantly associated with alcohol-related mortality among males in Slovak districts. A district's unemployment rate was assumed to be the strongest predictor of the outcome measure.
Asunto(s)
Alcoholismo/etnología , Alcoholismo/mortalidad , Árboles de Decisión , Factores Socioeconómicos , Adulto , Alcoholismo/epidemiología , Bases de Datos Factuales , Femenino , Humanos , Masculino , Persona de Mediana Edad , Eslovaquia/epidemiología , Adulto JovenRESUMEN
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.