- Open Access
Regional mortality by socioeconomic factors in Slovakia: a comparison of 15 years of changes
© The Author(s). 2016
- Received: 24 March 2016
- Accepted: 11 July 2016
- Published: 19 July 2016
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.
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.
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.
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.
- Material needs
- Regional differences
Most Central and Eastern European countries have passed through a period of turbulent changes affecting the socio-political context which sets the social determinants of health . Some of the EU-member states which entered in 2004 or later, including Slovakia, have experienced a period of economic growth and huge investments of European structural funds. As in most European countries, mortality in Slovakia has shown a declining tendency since the turning point of 1989 . Significant differences exist on the regional level , even though some areas have not improved or have performed even worse. Therefore, it is of interest to study factors that change over time and simultaneously contribute to the standardised mortality rate (SMR) in order to explain differences in the changes in mortality over time.
The associations of income, unemployment and poverty with mortality are seen as very important and have been previously discussed [4–8]. Income is one of the main determinants influencing not only survival but also death . Mortality decreases significantly when income increases , although the opposite effect has also been suggested . The most frequent income-mortality associations are usually studied at a single point in time , though a few papers have investigated this relation as a trend over time [11, 12], both confirming  and contradicting the effect of time .
Unemployed persons have a higher risk of premature death than those who are employed . A time trend study performed in the United States at the national level confirmed that increased unemployment was associated with a substantial increase in mortality . A Swedish time trend study presented contrasting findings showing a correlation between higher unemployment and lower mortality , while another regional study did not find any relation between unemployment and overall mortality .
Poverty is also associated with mortality . The convention is to measure poverty in terms of absolute income . However, this has met with criticism, so also an increasing number of measures based on the construct of ‘material deprivation’  is used. Material deprivation refers to the inability of individuals or households to afford consumer goods and activities that are typical in a society . Benefits regarding material need can be considered as a solid poverty indicator, as such benefits are set to fill the gap between the person’s income and his needs, and anyone whose income is below a minimum level is entitled to receive them . However, studies on the association between poverty defined as the recipients of material need benefits and mortality are scarce [19, 20]. Andrén and Gustafsson  showed that benefit recipients have twice the probability of death as non-recipients. Naper  found that the all-cause mortality of benefit recipients in Norway was considerably higher than that of the general population.
Socioeconomic differences in mortality seem to be gender-specific, although findings contrast with one another [7, 10]. Regarding the association between income and mortality, some studies have shown that the results were the same for both sexes [21, 22]. On the other hand, Fukuda et al.  showed a significant association between higher income and higher mortality in females only and a stronger unemployment-mortality relationship in males. In Slovakia, socioeconomic indicators in regional mortality were found only among males, where unemployment significantly contributed to mortality differences but income did not [23, 24].
Findings on associations between income and regional mortality are scarce, while those between unemployment and regional mortality are contradictory and between recipients of material need benefits and mortality are limited. The pattern between these indicators and mortality seems to differ by gender. Furthermore, Slovakia passed through a period of huge societal changes which might influence regional differences in both socioeconomic indicators and mortality. The aim of the study was to analyse the associations between selected socioeconomic indicators (unemployment, income, recipients of material need benefits) and the SMR in the population aged 20–64 years by gender in the districts of the Slovak Republic in the periods 1997–1998 and 2012–2013.
The study population covers all of the inhabitants of the Slovak Republic aged 20–64 years in two periods: 1997–1998 and 2012–2013. The selected age group is primarily the economically active population integrated into the labour market. This part of the population has the relatively lowest mortality rate by age, has finished the process of education and receives a certain kind of income, either as a salary or as social security benefits.
The average number of inhabitants aged 20–64 years in the Slovak Republic as of July 1st during the period 1997–1998 was 3,185,682 people (49.4 % men); in the period 2012–2013 it was 3,551,193 people (50.0 % men). The total number of deaths among those aged 20–64 years over the two-year period of 1997–1998 was 29,239 (70.8 % men), and in 2012–2013 was 28,817 (71.3 % men). Thus, on average about 14 thousand deaths occurred each year.
To be able to study regional differences, the study population was analysed at the district level using an ecological study design. The Slovak Republic is divided into 8 regions at the regional level NUTS 3 (Nomenclature of Territorial Units for Statistics) and further into 79 districts at the local level LAU 1 (Local Administrative Units), 5 of which constitute the capital city Bratislava and 4 the second largest city, Košice. The average number of inhabitants aged 20–64 per district in the period 1997–1998 was 40,321 persons, ranging from 7240 to 97,297 inhabitants, and in the period 2012–2013 this was 44,952 persons, ranging from 7668 to 108,967.
The data consist of absolute population numbers and numbers of deaths by gender in the districts of the Slovak Republic in the periods 1997–1998 and 2012–2013 and were obtained from the Statistical Office of the Slovak Republic .
The unemployment rate, income and the proportion of inhabitants in material need in a district were used as economic indicators associated with the mortality rate. All indicators were calculated for each district in the two separate periods of 1997–1998 and 2012–2013.
Basic data for the Slovak population aged 20–64 years – averages for the periods 1997–1998 and 2012–2013
Standardised mortality (per 100,000 inh.)
% in material need
Measures of mortality
Using the regional mortality data, the SMR was calculated. For each region the mortality by 5-year age-groups (20–24, 25–29, 30–34, 35–39, 40–44, 45–49, 50–54, 55–59, 60–64) and the total mortality rate by gender were calculated. Regional mortality rates were standardised by the direct method of standardisation and by age using the Slovak population as the standard. The mortality rate is expressed as the number of deaths per 100,000 inhabitants.
Linear regressions were applied: regional differences in SMR were set as the dependent variable; the unemployment rate, income and the proportion of those in material need were set as independent variables. Initially the crude effect of each factor was analysed separately and then all factors were included into the final model. Both analyses were done separately for both periods and for males and females. The regression models were checked for collinearity. To check changes in the coefficients between two periods the F-test was used.
Analyses were done using SPSS version 20.0.
Maps were constructed using regional SMR and data by socioeconomic indicators. The range of the indicators on the maps was divided into quartiles.
Maps were created using ArcView.
Linear regression between standardised mortality rates of those aged 20–64 years and economic indicators (separately) for the periods 1997–1998 and 2012–2013
Economic indicators (separately)
Standardised Coefficients (Beta)
Standardised Coefficients (Beta)
Proportion of inhabitants in material need
Proportion of inhabitants in material need
Linear regression between standardised mortality rates of those aged 20–64 years and economic indicators (together) for the periods 1997–1998 and 2012–2013
Economic indicators (together)
Standardised Coefficients (Beta)
Standardised Coefficients (Beta)
Proportion of inhabitants in material need
.309 / .281
.117 / .082
Proportion of inhabitants in material need
.283 / .255
.524 / .505
Collinearity and its influence on the model were also tested. The results of collinearity show that the model did not appear to have a substantial problem (maximum condition number 29.8).
The F-test was used to check changes in the coefficients between the two periods. The regression coefficients were found to be identical in females (p-value 5.8 %). In males we found a significant change in the constant, while all other regression coefficients are the same (p-value 55.1 %).
We analysed the associations between selected socioeconomic indicators and SMR in the population aged 20–64 years by gender in the districts of the Slovak Republic in the periods of 1997–1998 and 2012–2013. In the Slovak population excess male mortality was observed in both selected periods, which is a typical phenomenon, however, for the structure of deaths by gender and age in the population of developed countries. Selected indicators were examined in relation to SMR, first separately and then together in the mutual model. A higher proportion of inhabitants in material need, and among males also lower income were significantly associated with higher SMR in both periods. The unemployment rate did not contribute to this prediction in either gender or in either period. We further found in the studied periods an increase of the explained variance of the SMR in females. Despite changes in the analysed variables and their distribution over the districts in the studied periods, we did not show significant changes in regional (i.e. district) mortality differences by the selected socioeconomic factors.
In most studies examining the association between income and mortality, as in our study, a negative relationship was found [11, 12]. It seems that higher income can be used to purchase healthier food, to invest in better health care, housing, schooling and recreation , which might have a distinct effect on mortality . In our study a significant relation was confirmed only among men, which is different from other studies [21, 22]. Income may be a better indicator of men’s material conditions, as their incomes generally comprise the greater part of a household’s purchasing power. It may be also more important for self-esteem and self-respect among men, as work stands for a greater part of their life and they are considered to be the “bread-winners” .
Our findings on the association between unemployment and SMR in districts were not consistent, which is in accordance with study of Svensson . This is in contrast to Brenner , who shows a clear relationship between the unemployment rate and mortality. The lack of an association between the unemployment rate and SMR in Slovak districts in the adjusted regression model might be caused by the fact that unemployed people in Slovakia are at the same time the recipients of material need benefits, which might mask the contribution of unemployment. However, we did not find indications for multicollinearity.
The high mortality rate among recipients of material need benefits may be determined by a general susceptibility leading to higher mortality from all causes, so there is an overall health risk associated with being a benefits recipient . Moreover, poverty is closely linked to a variety of behaviours that impact mortality (smoking, alcohol abuse, physical inactivity, etc.)  and above all, benefits recipients seem to be more frequently exposed to violence than others . Consequently, it seems that receiving a social benefit does not serve as a (sufficient) protective factor with regard to health as intended, as it is a risk factor regarding regional differences in SMR.
Despite the substantial economic growth between the periods 1997–1998 and 2012–2013 combined with the investments from European structural funds to the Central European countries, there are regions which remained “untouched” and “unimproved” in terms of poverty expressed in the proportion of inhabitants in material need, which significantly contributed to the regional differences in SMR. Moreover, there is a high probability that similar findings might also be found in other Central European countries.
Strengths and limitations of the study
The strength of our study is the combination of the area-based design, age specification of the population and the over-time perspective. An ecological study uses data that generally already exist and is a quick and cost-efficient approach compared with individual level studies. It is also particularly valuable when an individual level association is evident and an ecological level association is assessed to determine its public health impact. The deficient databases regarding income (average monthly gross payment), which is available only for companies with 20 or more employees at the district level in Slovakia, is one of the main limitations of our study. The proportion of such enterprises is about 60 %, although the total number of enterprises cannot be determined due to the lack of available data. It would be very interesting to identify income as a variable which also includes data from small enterprises and to use it in a similar analysis on mortality rate, as is done in this paper.
The results of our study indicate that a higher proportion of inhabitants in material need, and among males also lower income significantly contributed to higher standardised mortality in both periods. The main contribution of this paper is focusing of attention on decreasing the proportion of inhabitants in material need in Slovakia with the aim of reducing mortality rates. Our findings can be used in the development of social policies which should preferably increase employment in the regions with the highest proportion of inhabitants in material need, since probably only surveys, suitable policies and interventions will be able to “revitalise” regions at risk. Such a redistribution of parts of the government over the country could contribute to the reduction of the proportion of inhabitants in material need and thus reduce the SMR in such regions. In particular, these are the districts of the southern and eastern regions of the Slovak Republic, which had most of the unemployed, infrastructure lag and also deformed age structure reflecting unfavourable population development .
Factors that were analysed in this paper still create a prerequisite for further exploration of this field, where in addition to examining the overall level of income it would be appropriate to consider income inequality within a region and through the Gini coefficient, similarly as in studies carried out previously [34, 35].
In conclusion, the proportion of inhabitants in material need, and among males also lower income seems to be the strongest predictors of standardised mortality in the population aged 20–64 years in the districts of the Slovak Republic in the periods 1997–1998 and 2012–2013. 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 remained “poor”, what maintained the regional mortality differences.
SMR, standardised mortality rate; NUTS, Nomenclature of Territorial Units for Statistics; LAU, Local Administrative Units
This work was partially funded within the framework of the project “Social determinants of health in socially and physically disadvantaged and other groups of population” (CZ.1.07/2.3.00/20.0063) and has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 643398 (Euro-Healthy project).
The funders had no role in the study design, the data collection or the analysis, or in the decision to publish or preparation of the manuscript.
Availability of data and materials
The datasets supporting the conclusions of this article are available in the Statistical Office of the Slovak Republic repository https://slovak.statistics.sk/ and Centre of Labour, Social Affairs and Family of the Slovak Republic repository http://www.upsvar.sk/statistiky.html?page_id=1247.
KR drafted the manuscript, analysed the statistical data and prepared outputs of the analysis. LB participated in the design of the study and participated in its design and coordination and helped to draft the manuscript. AMG participated and helped to draft the manuscript. MR participated in the design of the study from side of socioeconomic indicators. MA participated and helped to draft the manuscript. IZ carried out the statistical analysis. JWG participated in the design of the study. JPvD conceived the study, and participated in its design and coordination and helped to draft the manuscript. All authors read and approved the final manuscript.
The authors declare that they have no competing interests.
Consent for publication
Ethics approval and consent to participate
The study was approved by the Ethics Committee of the Faculty of Medicine at Safarik University in Kosice under no. 104/2011.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
- Solar O, Irwin A. A conceptual framework for action on the social determinants of health. Discussion paper for the Commission on Social Determinants of Health. Geneva: Commission on Social Determinants of Health, World Health Organization; 2007.Google Scholar
- Vojtekova M. Využitie Brassovho modelu pri vyhodnotení vývoja úmrtnosti v SR (Use of Brass model for the assessment of trends in mortality in Slovakia). Forum Statisticum Slovacum. 2007;6:262–5.Google Scholar
- Meszaros J. Atlas úmrtnosti Slovenska 1993-2007 (Atlas of mortality in the Slovak Republik 1993-2007). Bratislava: INFOSTAT; 2008.Google Scholar
- Galea S, Tracy M, Hoggatt KJ, Dimaggio C, Karpati A. Estimated deaths attributable to social factors in the United States. Am J Public Health. 2011;101:1456–65.View ArticlePubMedPubMed CentralGoogle Scholar
- Jusot F. Revenu et mortalité: Analyse économique des inégalités sociales de santé en France (Income and mortality: Economic analysis of health inequalities in France). 2003. These de doctorat en sciences economiques de l’Ecole des Hautes Etudes en Sciences Sociales.Google Scholar
- Preston SH. The changing relation between mortality and level of economic development. Int J Epidemiol. 2007;36:484–90.View ArticlePubMedGoogle Scholar
- Roelfs DJ, Shor E, Davidson KW, Schwartz JE. Losing life and livelihood: a systematic review and meta-analysis of unemployment and allcause mortality. Soc Sci Med. 2011;72:840–54.View ArticlePubMedPubMed CentralGoogle Scholar
- Sen A. Mortality as an indicator of economic success and failure. Econ J. 1998;108:1–25.View ArticleGoogle Scholar
- Gerdtham UG, Johannesson M. Business cycles and mortality: results from Swedish microdata. Soc Sci Med. 2005;60:205–18.View ArticlePubMedGoogle Scholar
- Fukuda Y, Nakamura K, Takano T. Wide range of socioeconomic factors associated with mortality among cities in Japan. Health Promot Int. 2004;19:177–87.View ArticlePubMedGoogle Scholar
- Deaton A, Paxson C. Mortality, Income, and Income Inequality over Time in Britain and the United States. In: Wise DA, editor. Perspectives on the Economics of Aging. Chicago: Chicago University Press; 2004. p. 247–85.View ArticleGoogle Scholar
- Dowd JB, Albright J, Raghunathan TE, Schoeni RF, LeClere F, Kaplan GA. Deeper and wider: income and mortality in the USA over three decades. Int J Epidemiol. 2011;40:183–8.View ArticlePubMedGoogle Scholar
- Brenner MH. Commentary: economic growth is the basis of mortality rate decline in the 20th century-experience of the United States 1901–2000. Int J Epidemiol. 2005;34:1214–21.View ArticlePubMedGoogle Scholar
- Granados JAT, Ionides EL. Mortality and macroeconomic fluctuations in contemporary Sweden. Eur J Popul. 2011;27:157–84.View ArticleGoogle Scholar
- Svensson M. Economic upturns are good for your health but watch out for accidents: a study on Swedish regional data 1976–2005. Appl Econ. 2010;42:615–25.View ArticleGoogle Scholar
- Vlacuha R, Velcicka J. Poverty in Slovakia in terms of material deprivation – analysis of data from EU SILC 2005-2008. Forum Statisticum Slovacum. 2009;5:126–31.Google Scholar
- The Organisation for Economic Co-operation and Development [Internet]. Glossary of Statistical Terms - Material Deprivation. 2007. Available from: http://stats.oecd.org/glossary/detail.asp?ID=7326.
- O’Reilly D. Standard indicators of deprivation: do they disadvantage older people? Age Aging. 2002;31:197–202.View ArticleGoogle Scholar
- Andrén T, Gustafsson B. Patterns of social assistance receipt in Sweden. Int J Soc Welf. 2004;13:55–68.View ArticleGoogle Scholar
- Naper SO. All-cause and cause-specific mortality of social assistance recipients in Norway: a register-based follow-up study. Scand J Public Health. 2009;37:820–5.View ArticlePubMedGoogle Scholar
- Lantz PM, House JS, Lepkowski JM, Williams DR, Mero RP, Chen J. Socioeconomic factors, health behaviors and mortality. J Amer Med Assoc. 1998;279:1703–8.View ArticleGoogle Scholar
- Mackenbach JP, Kunst AE, Cavelaars AE, Groenhof F, Geurts JJ. Socioeconomic inequalities in morbidity and mortality in western Europe. Lancet. 1997;349:1655–9.View ArticlePubMedGoogle Scholar
- Rosicova K, Madarasova Geckova A, van Dijk JP, Rosic M, Zezula I, Groothoff JW. Socioeconomic indicators and ethnicity as determinants of regional mortality rates in Slovakia. Int J Public Health. 2009;54:274–83.View ArticlePubMedGoogle Scholar
- Rosicova K, Madarasova Geckova A, Rosic M, Speybroeck N, Groothoff JW, van Dijk J. Socioeconomic factors, ethnicity and alcohol-related mortality in regions in Slovakia. What might a tree analysis add to our understanding? Health Place. 2011;17:701–9.View ArticlePubMedGoogle Scholar
- Statistical Office of the Slovak Republic. Mortality by age in 1997-1998 and 2012-2013 - special data selection of Statistical Office of the Slovak Republic. Bratislava: Statistical Office of the Slovak Republic; 2014.Google Scholar
- Centre of Labour, Social Affairs and Family of the Slovak Republic. Monthly Statistics of unemployment. Bratislava: Centre of Labour, Social Affairs and Family of the Slovak Republic; 2014.Google Scholar
- Statistical Office of the Slovak Republic. Employees and income in 1997-1998 - special data selection of Statistical Office of the Slovak Republic. Bratislava: Statistical Office of the Slovak Republic; 2012.Google Scholar
- Statistical Office of the Slovak Republic. The Regional Statistical Yearbook of Slovakia 2014. Bratislava: Statistical Office of the Slovak Republic; 2015.Google Scholar
- Centre of Labour, Social Affairs and Family of the Slovak Republic. Social Affairs - Statistics. Bratislava: Centre of Labour, Social Affairs and Family of the Slovak Republic; 2014.Google Scholar
- Adler NE, Newman K. Socioeconomic disparities in health: pathways and policies. Inequality in education, income, and occupation exacerbates the gaps between the health “haves” and “have-nots”. Health Affair. 2002;21:60–76.View ArticleGoogle Scholar
- Jusot F. The shape of the relationship between mortality and income in France. Annales d’Economie et de Statistique. 2006;83-84:89–122.View ArticleGoogle Scholar
- Torssander J, Erikson R. Stratification and mortality – A comparison of education, class, status and income. Eur Sociol Rev. 2007;26:465–74.View ArticleGoogle Scholar
- Faltan L, Pašiak J. Regionálny rozvoj Slovenska. Východiska a súčasný stav. (Regional development in Slovakia. Background and current status). Bratislava: Sociologický ústav SAV; 2004.Google Scholar
- Leigh A, Jencks C. Inequality and mortality: Long-run evidence from a panel of countries. J Health Econ. 2007;26:1–24.View ArticlePubMedGoogle Scholar
- Thorbecke E, Charumilind C. Economic inequality and its socioeconomic impact. World Dev. 2002;30:1477–95.View ArticleGoogle Scholar