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Income inequality in non-communicable diseases mortality among the regions of the Slovak Republic
B. Gavurová, V. Kováč, M. Šoltés, S. Kot, J. Majerník
Jazyk angličtina Země Česko
Typ dokumentu časopisecké články
Digitální knihovna NLK
Zdroj
NLK
Free Medical Journals
od 2004
ProQuest Central
od 2009-03-01 do Před 6 měsíci
Medline Complete (EBSCOhost)
od 2006-03-01 do Před 6 měsíci
Nursing & Allied Health Database (ProQuest)
od 2009-03-01 do Před 6 měsíci
Health & Medicine (ProQuest)
od 2009-03-01 do Před 6 měsíci
Public Health Database (ProQuest)
od 2009-03-01 do Před 6 měsíci
ROAD: Directory of Open Access Scholarly Resources
od 1993
PubMed
29524367
DOI
10.21101/cejph.a4958
Knihovny.cz E-zdroje
- MeSH
- lidé MeSH
- neinfekční nemoci mortalita MeSH
- příjem statistika a číselné údaje MeSH
- sexuální faktory MeSH
- socioekonomické faktory MeSH
- věkové faktory MeSH
- Check Tag
- lidé MeSH
- mužské pohlaví MeSH
- ženské pohlaví MeSH
- Publikační typ
- časopisecké články MeSH
- Geografické názvy
- Slovenská republika epidemiologie MeSH
AIM: A great amount of non-communicable disease deaths poses a threat for all people and therefore represents the challenge for health policy makers, health providers and other health or social policy actors. The aim of this study is to analyse regional differences in non-communicable disease mortality in the Slovak Republic, and to quantify the relationship between mortality and economic indicators of the Slovak regions. METHODS: Standardised mortality rates adjusted for age, sex, region, and period were calculated applying direct standardisation methods with the European standard population covering the time span from 2005 to 2013. The impact of income indicators on standardised mortality rates was calculated using the panel regression models. RESULTS: The Bratislava region reaches the lowest values of standardised mortality rate for non-communicable diseases for both sexes. On the other side, the Nitra region has the highest standardised mortality rate for non-communicable diseases. Income quintile ratio has the highest effect on mortality, however, the expected positive impact is not confirmed. Gini coefficient at the 0.001 significance level and social benefits at the 0.01 significance level look like the most influencing variables on the standardised mortality rate. By addition of one percentage point of Gini coefficient, mortality rate increases by 148.19 units. When a share of population receiving social benefits increases by one percentage point, the standardised mortality rate will increase by 22.36 units. CONCLUSIONS: Non-communicable disease mortality together with income inequalities among the regions of the Slovak Republic highlight the importance of economic impact on population health.
Department of Finance Faculty of Economics Technical University of Košice Košice Slovak Republic
Faculty of Economic and Management Sciences North West University Mafikeng Republic of South Africa
Faculty of Management Czestochowa University of Technology Czestochowa Poland
Citace poskytuje Crossref.org
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- $a AIM: A great amount of non-communicable disease deaths poses a threat for all people and therefore represents the challenge for health policy makers, health providers and other health or social policy actors. The aim of this study is to analyse regional differences in non-communicable disease mortality in the Slovak Republic, and to quantify the relationship between mortality and economic indicators of the Slovak regions. METHODS: Standardised mortality rates adjusted for age, sex, region, and period were calculated applying direct standardisation methods with the European standard population covering the time span from 2005 to 2013. The impact of income indicators on standardised mortality rates was calculated using the panel regression models. RESULTS: The Bratislava region reaches the lowest values of standardised mortality rate for non-communicable diseases for both sexes. On the other side, the Nitra region has the highest standardised mortality rate for non-communicable diseases. Income quintile ratio has the highest effect on mortality, however, the expected positive impact is not confirmed. Gini coefficient at the 0.001 significance level and social benefits at the 0.01 significance level look like the most influencing variables on the standardised mortality rate. By addition of one percentage point of Gini coefficient, mortality rate increases by 148.19 units. When a share of population receiving social benefits increases by one percentage point, the standardised mortality rate will increase by 22.36 units. CONCLUSIONS: Non-communicable disease mortality together with income inequalities among the regions of the Slovak Republic highlight the importance of economic impact on population health.
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