Health inequalities in Slovakia assessed by the Health Index: Unveiling regional disparities and their impact on the population


195

Introduction

According to the World Health Organization 
(2018), health inequalities refer to differences 
in health status as well as the distribution of 
health resources among different population 
groups. These disparities result from social 
factors, which may include, for example, ac-
cess to education, educational attainment, 
employment status, income level, and gen-
der or ethnicity. As defined by Global Health 
Europe (2009), the terms ‘inequity’ and ‘in-
equality’ are ‘inequity and inequality’: these 
terms are sometimes confused but are not 
interchangeable. ‘Inequity’ refers to avoidable 
inequities resulting from poor governance, 

corruption or cultural exclusion, while ‘in-
equality’ simply refers to the unequal distri-
bution of health or health resources due to 
genetic or other factors or lack of resources. 
‘Inequity’ is often measured in terms of the 
inequality of health or resources, which is 
appropriate where one might reasonably ex-
pect equality. For example, there is no reason 
for differences in access to health resources 
between men and women within a country 
other than cultural prejudice and or a failure 
of governance, basic health services should 
be available to all citizens within a commu-
nity according to need. 

In line with Arcaya, M.C. et al. (2015), we 
can refer to health inequalities as regular 

1 Constantine the Philosopher University in Nitra, Faculty of Natural Sciences and Informatics, Department 
of Geography, Geoinformatics and Regional Development. Trieda Andreja Hlinku 1, 949 01 Nitra, Slovakia, 
E-mails: kvilinova@ukf.sk, kikabullova@gmail.com

Health inequalities in Slovakia assessed by the Health Index:
Unveiling regional disparities and their impact on the population

Katarína VILINOVÁ1 and Kristína BULLOVÁ1

Abstract

Health inequalities represent a significant social problem not only in the world but also in Slovakia. They are 
conditioned by several factors such as socio-economic status, geographic location, age, ethnicity and access to 
health care. Inequalities in the general health status of districts can be assessed using selected determinants. A 
composite indicator (Health Index) was used to quantitatively assess health inequalities in the districts. This 
Health Index consists of 8 assessment domains and 50 indicators at the district level (LAU1) in the Slovak 
Republic. We evaluated the data using the multi-criteria decision-making method (WSA method). The findings 
suggest that when districts are assigned different weights, changes occur in the health index values. Identifying 
problem regions is therefore very important. The health situation in Slovakia is not uniform and the results of 
the research showed differences between the West and the East. The districts located in the southern part of 
Slovakia, which achieved the lowest values of the index, can be included among the areas at risk in the context 
of the Health Index assessment. In order to mitigate them, it is necessary to improve access to health care, invest 
in prevention and improve the economic conditions of the population. It is also essential to propose possible 
solutions. These include improving access to preventive care and health education. The next step is reforms 
in the health system. These aim to reduce inequalities and improve public health.

Keywords: health inequalities, Health Index, WSA method, districts, Slovakia 

Received January 2025, accepted May 2025.

DOI: 10.15201/hungeobull.74.2.5  Hungarian Geographical Bulletin 74 (2025) (2) 195–216.

mailto:kvilinova@ukf.sk


Vilinová, K. and Bullová, K. Hungarian Geographical Bulletin 74 (2025) (2) 195–216.196

disparities that have an impact on the social 
and economic costs not only of the individual 
but also of society. Any measurable aspect 
of health that differs between individuals 
or between socially relevant groups can be 
termed health inequality. This is an unfair 
disparity, since in an ideal world everyone 
should have an equal opportunity to reach 
their full health potential. At the same time, 
no one should be disadvantaged in achiev-
ing it if the disadvantage can be avoided 
(Hübelová, D. et al. 2021a). Health inequali-
ties are systematic differences in health be-
tween groups of people based on their social 
status. However, not every difference in the 
health status of a population automatically 
implies inequality – it becomes inequality 
when it is associated with characteristics that 
make it inequitable. Societies with significant 
health inequalities that affect broad segments 
of the population tend to face wide health in-
equalities. Conversely, if health inequalities 
affect only a small group (for example, ben-
efit recipients, ethnic minorities or migrants), 
overall inequalities within the population 
may be relatively small, even if contrasts be-
tween these groups are stark (McCartney, G.  
et al. 2013). Schoon, P.M. and Krumwiede, 
K. (2022) point out that health inequalities 
that could be prevented by appropriate 
measures are the result of broader social 
inequalities. Given inequalities are shaped 
from birth and are significantly influenced 
by socio-economic factors throughout the life 
course. The conditions in which people are 
born, grow up, live, work and age are fun-
damental to their health. The realities of life 
are largely determined by the way in which 
finance, power and resources are distributed 
at national and local levels. At the same time, 
health inequalities are caused by government 
policies affecting the quantity, quality and 
distribution of determinants (Chiavarini, M. 
et al. 2014). 

One of the key aspects of tracking health in-
equalities is the geographical space in which 
the disparities are analysed. Jutz, R. (2020) 
focuses in his paper on the comparison of 
health inequalities between post-communist 

countries of Eastern Europe and Western 
European countries. The study points out 
that the communist regime laid the founda-
tions for different levels of health inequalities, 
especially in terms of education, in Eastern 
and Western Europe. Past research has shown 
that health inequalities within countries are 
closely related to welfare state systems. The 
structure and institutions of social security 
not only shape the daily lives of the popula-
tion, but also have a major impact on socio-
economic health inequalities. Factors such as 
access to health care, education levels, em-
ployment and living conditions are directly 
influenced by welfare state policies, which 
can either mitigate or exacerbate disparities. 
Chelak, K. and Chakole, S (2023) stress the 
importance of reducing health inequalities, 
with the key to addressing this being the 
elimination of the unequal distribution of 
power, finance and resources. The authors 
also highlight the importance of everyday 
living conditions, which can be influenced 
through the social determinants of health, as 
their impact on health status is considerable. 

Eliminating health inequalities requires ap-
propriate decisions from the economic and 
social policy environment, which influence a 
wide range of factors – employment, educa-
tion, socio-economic status, social support 
networks, health policy and access to health 
care. Targeted interventions in these areas 
can make a significant contribution to im-
proving community health and enhancing 
equity in health care. A wealth of research 
confirms that avoidable systematic health 
inequalities are present not only between 
societies, but also within them, and at all hi-
erarchical levels. This is amply documented 
in the literature on the subject. As examples, 
some of the works of (Graham, H. 2004; 
Ottersen, O.P. et al. 2014; Cabrera-Barona, 
P. et al. 2015; Agenor, M. 2020). 

Theoretical aspects

Population health is closely related to the so-
cio-economic organisation of society, which 



197

forms the basis for effective policies to im-
prove it. While it is important to ensure qual-
ity and efficient health services, health goes 
beyond health care. Government and private 
sector policies at all levels significantly in-
fluence the health status of the population. 
Health policy decisions should be based on 
the best available evidence, as should poli-
cies on the social determinants of health. A 
wide range of factors are addressed, such as 
the impact of early life, social gradients, job 
insecurity, psychosocial environment, trans-
port, social support, food policy, poverty, so-
cial exclusion, ethnic inequalities, housing. 
These factors shape health inequalities and 
understanding them is key to developing ef-
fective strategies to mitigate them (Marmot, 
M. and Wilkinson, R. 2005). According to 
Marmot, M. (2010), a combination of poor 
social conditions, bad government poli-
cies and inequitable distribution of wealth 
in society causes health disparities among 
people. Social and economic disparities 
are an inseparable reality in every country. 
However, these differences should not cause 
disease, misery, poverty and suffering to the 
extent that we are seeing today. It is unjust, 
however, not uncontrollable. And that is the 
essence of health inequalities.

Public health research and action is built 
on a shared understanding of ‘health’ and the 
related concept of ‘health inequalities’. The 
literature has discussed differences in how 
these concepts are understood and defined 
and how this translates into measurement, 
analysis and interpretation. The assumptions, 
emphasis and values underlying the use of 
different approaches are less often explic-
it (Krieger, N. 2011). Weinstein, J.N. et al. 
(2017) concur with the definition of health 
inequalities as they, like others, consider 
them as systematic differences that certain 
population groups must overcome to achieve 
optimal health. This leads to inequitable and 
avoidable disparities in health outcomes. In 
their publication, they explain the intercon-
nectedness between health inequalities, struc-
tural inequalities and social determinants 
of health. The authors state that the social, 

environmental, economic and cultural de-
terminants of health create the conditions in 
which structural inequalities produce health 
inequalities. Thus, the point is that structur-
al inequalities, which represent a variety of 
personal, interpersonal, institutional, and sys-
temic drivers. For example, racism, gender 
discrimination, class, adaptive capacity, etc., 
which are important for the equitable distri-
bution of health opportunities and outcomes. 

Like other authors, Adler, N. et al. (2007) 
confirm that the relationship between health 
and socio-economic resources is complex 
because they influence each other. The 
imaginary rung (the level of our socio-eco-
nomic status) we are on affects our health, 
and our health in turn affects our ability to 
reach higher levels. Regarding perceptions 
of health inequalities in the United States, 
Dickman, S.L. et al. (2017) explain that the 
deepening of economic inequality in the 
US is accompanied by widening health dis-
parities. They also argue that a health care 
system that could reduce health disparities 
often instead exacerbates them. Among the 
key findings, the authors note that the gap 
in life expectancy is widening among pop-
ulations with different incomes, which in 
practice means that the wealthiest residents 
of the United States are living 10 to 15 years 
longer (10.1 years for women, 14.6 years for 
men) than the poorest population. 

The World Health Organization talks 
about the fact that not only poverty itself 
causes health inequalities, but in fact the so-
cial meaning of disadvantage plays a role if 
you are poor, unemployed, socially excluded 
or otherwise stigmatized (Scholz, N. 2020). 
According to Docteur, E. and Berenson, R.A. 
(2014) a report by the European Commission 
identifies five broad challenges that need to 
be addressed in order to minimise health in-
equalities within the member states of the 
European Union. These challenges are (im-
proving the evidence base to assist policy 
making, addressing the social determinants 
of health, ensuring universal access to health 
care, promoting and educating for healthy 
lifestyles, strengthening health governance). 

DOI: 10.15201/hungeobull.74.2.5  Hungarian Geographical Bulletin 74 (2025) (2) 195–216.Vilinová, K. and Bullová, K. Hungarian Geographical Bulletin 74 (2025) (2) 195–216.



Vilinová, K. and Bullová, K. Hungarian Geographical Bulletin 74 (2025) (2) 195–216.198

In their study, Mackenbach, J.P. et al. 
(2018) analyse trends in health inequalities 
in 27 European countries. They explain that 
inequalities in mortality and morbidity are 
a highly persistent phenomenon among so-
cio-economic groups. This is despite the 
fact that they have been the focus of public 
health policy in many countries. They anal-
ysed health trends by education in European 
countries, paying particular attention to the 
possibility of breaking trends that may have 
been affected by the 2008 financial crisis. Their 
research found that in Western Europe, in-
equalities in mortality have decreased due to a 
decline in overall mortality, both among low-
er- and higher-educated populations. Most 
Western European countries have been expe-
riencing such a decline in mortality for sever-
al decades, influenced by steadily improving 
living standards, advances in prevention, 
particularly through changes in health-seek-
ing behaviour, and health care. Advances in 
prevention have also resulted in a more rapid 
decline in mortality from smoking-related dis-
eases and coronary heart disease.  On the oth-
er hand, the high number of healthy life years 
in Malta can be attributed to factors such as 
high life expectancy, a well-functioning health 
care system, a reduction in premature deaths 
(especially from cardiovascular disease and 
cancer), but also to ongoing efforts to address 
public health challenges and an improving 
health system (Azzopardi-Muscat, N. et al. 
2017). Health disparities across Europe be-
tween social groups have also been docu-
mented in another study by Salmi, L.-R. et 
al. (2017). The summary and results of the 
Addressing Inequalities in Regions (AIR) 
project, which identified illustrative inter-
ventions and policies developed in European 
regions aimed at reducing inequalities at the 
primary health care level. 

As with poverty measures, health inequal-
ities can be assessed in absolute or relative 
terms. This may be important when there are 
secular trends in the average health of the 
population (e.g., a downward trend in the 
average may increase relative inequalities 
even if absolute disparities remain stable). 

Consequently, methods for determining 
health inequalities vary depending on which 
inequality is of most interest. Health inequal-
ities persist over time and have been found in 
most countries where they have been stud-
ied (McCartney, G. et al. 2019). According 
to Hübelová, D. et al. (2023), several classi-
fications of the determinants of health in-
equalities and their impact on population 
health are known. As an example, we refer 
to the Conceptual Framework for Action on 
Social Determinants of Health (Solar, O. and 
Irwin, A. 2010). The impact of different fac-
tors on population health has been identified 
as follows: genetic basis accounts for 10–15 
percent, health and health care accounts for 
10–15 percent, environment accounts for 
20 percent and lifestyle factors account for 
50 percent (Marmot, M. and Wilkinson, 
R. 2005). In addition, the County Health 
Ranking Model (UW Population Health 
Institute, 2020) uses the following propor-
tions: health and health care contribute 20 
percent, environment contributes 10 per-
cent, social and economic factors contribute 
40 percent, and lifestyle factors contribute 30 
percent. According to the EURO-HEALTHY 
project, the Population Health Index (PHI) is 
developed for EU countries at NUTS2 level 
(the regional level unit for the application of 
regional policies) and for 10 selected metro-
politan areas (EURO-HEALTHY Consortium 
2017). The results show that systematic spa-
tial inequalities persist in Europe at NUTS2 
level. In a spatial context, a study conducted 
in France (Fayet, Y. et al. 2020) is a geograph-
ic classification of health studies (GeoClasH). 
It is inspiring and stimulating due to its fo-
cus on the municipal scale when consider-
ing variables from the physical environment, 
social characteristics of the population and 
spatial accessibility to health care. 

According to Pearson-Stuttard, J. and 
Davies, S.C. (2025), the recommendation 
for the CHI (Composite Health Index) was 
based on two themes: health as a basic eco-
nomic asset and persistent health inequal-
ities, particularly in terms of healthy life 
expectancy. Both themes have become more 



199

pressing since the COVID-19 pandemic, as 
economic inactivity and health inequalities 
have worsened. All data used in the Health 
Index come from publicly available sourc-
es, usually the Office for National Statistics 
(ONS) or other government departments. 
The purpose of the ONS Health Index is to 
measure the state of health within commu-
nities and provide detailed information us-
ing 56 indicators in three domains: healthy 
places (the wider determinants of health), 
healthy lives (health-related behaviours) and 
healthy people (health outcomes). The ONS 
Health Index revealed substantial differenc-
es in health status over time and geography. 
Although the national score improved from 
2020 to 2021, it remained lower than before 
the pandemic. Health inequalities between 
communities have also deepened. Objective 
identification and monitoring of health in-
equalities is essential at two levels: (National 
Academies of Sciences…, 2016) to improve 
the average quality of health of the popula-
tion and to reduce inequalities in achieving 
good health themselves. Creating a quality 
and sustainable environment and an ade-
quate level of economic and social develop-
ment simultaneously promotes good health 
and social justice (Costa, C. et al. 2019). 

Data and method

To assess health inequalities in Slovakia, 
we used a composite indicator – the Health 
Index. The Health Index includes 47 health 
determinants and indicators. One of the 
key aspects in selecting the indicators was 
the availability of data in public databases 
over time and at the required geographic 
level (79 districts of the Slovak Republic). 
Another crucial aspect was determining the 
scope of available indicators (health determi-
nants, health status, health care, etc. (Brave-
man, P. 1998). The overall Health Index is 
composed of eight areas (1. Economic condi-
tions and social protection, 2. Education, 3. 
Demographic indicators, 4. Environmental 
conditions, 5. Individual living conditions, 

6. Road safety and crime, 7. Health and so-
cial care resources, and 8. Health status). It 
highlights spatial differentiation in health in-
equalities based on a complex set of relevant 
determinants and health indicators. Through 
this index, we can track spatial differentia-
tion using 47 indicators, expressed as a single 
value – the Health Index. The list of indica-
tors is documented in Table 1.

The data were obtained from publicly 
available databases, including the Statistical 
Office of the Slovak Republic, the National 
Health Information Centre, the Ministry 
of Labour, Social Affairs and Family of the 
Slovak Republic, the Ministry of the Interior 
of the Slovak Republic, the Slovak National 
Emission Information System, and the 2021 
Population and Housing Census. The data 
cover the years 2021 and 2022. The Health 
Index is a mathematical combination of vari-
ables reflecting several selected indicators 
(Nardo, M. et al. 2005). The method used to 
calculate the Health Index was a multi-crite-
ria variance evaluation method, specifically 
the Weighted Sum Approach (WSA). The 
WSA method is based on the principle of 
maximizing utility. It also assumes linearity 
and maximization of all partial utility func-
tions, which are obtained by normalizing 
the original input data. The higher the val-
ue of the Health Index, the more favourable 
the situation in the region. The calculations 
were performed using MS Excel, Microsoft 
Corporation, Redmond, DC, USA. We ap-
proached the Health Index values for in-
dividual districts in Slovakia in two ways. 
In the first case, each of the eight areas had 
equal importance with a weight of 1 (WSA 
method without weights). In the second case, 
each of the eight areas had a specific weight 
(WSA method with weights). The weights 
of the areas were adopted according to the 
methodology by (Hübelová, D. et al. 2021b). 
Their methodology explains how to create 
and determine the significance of each area, 
which was based on an interdisciplinary ex-
pert assessment using the Delphi method.

A total of ten independent experts from 
various scientific fields (sociology, demog-

DOI: 10.15201/hungeobull.74.2.5  Hungarian Geographical Bulletin 74 (2025) (2) 195–216.Vilinová, K. and Bullová, K. Hungarian Geographical Bulletin 74 (2025) (2) 195–216.



Vilinová, K. and Bullová, K. Hungarian Geographical Bulletin 74 (2025) (2) 195–216.200
Ta

bl
e 1

. D
es

cr
ip

tio
n 

of
 th

e r
es

ea
rc

h 
gr

ou
p 

ar
ea

 1
–8

A
re

a
Th

em
e

D
es

cr
ip

tio
n

1.
 E

co
no

m
ic

 
co

nd
iti

on
Em

pl
oy

m
en

t 
ra

te

U
ne

m
pl

oy
ed

 p
er

so
ns

 (t
ot

al
): 

nu
m

be
r o

f a
va

ila
bl

e 
jo

bs
ee

ke
rs

 a
ge

d 
15

–6
4 

co
m

pa
re

d 
to

 p
er

so
ns

 o
f t

he
 s

am
e 

ag
e,

 %
 

U
ne

m
pl

oy
ed

 p
er

so
ns

 (m
en

): 
nu

m
be

r o
f a

va
ila

bl
e 

jo
bs

ee
ke

rs
 a

ge
d 

15
–6

4 
co

m
pa

re
d 

to
 p

er
so

ns
 o

f t
he

 s
am

e 
ag

e,
 %

U
ne

m
pl

oy
ed

 p
er

so
ns

 (w
om

en
): 

nu
m

be
r o

f a
va

ila
bl

e 
jo

bs
ee

ke
rs

 a
ge

d 
15

–6
4 

co
m

pa
re

d 
to

 p
er

so
ns

 o
f t

he
 s

am
e 

ag
e,

 %
Jo

bs
ee

ke
rs

 a
ge

d 
50

–6
4:

 n
um

be
r o

f j
ob

se
ek

er
s 

ag
ed

 5
0–

64
 c

om
pa

re
d 

to
 p

er
so

ns
 o

f t
he

 s
am

e 
ag

e,
 %

Jo
bs

ee
ke

rs
 a

ge
d 

15
–2

4:
 n

um
be

r o
f j

ob
se

ek
er

s 
ag

ed
 1

5–
24

 c
om

pa
re

d 
to

 p
er

so
ns

 o
f t

he
 s

am
e 

ag
e,

 %
Jo

bs
ee

ke
rs

 w
ith

 a
n 

un
em

pl
oy

m
en

t d
ur

at
io

n 
of

 1
2 

m
on

th
s o

r m
or

e: 
nu

m
be

r o
f j

ob
se

ek
er

s 
w

ith
 a

n 
un

em
pl

oy
m

en
t d

ur
at

io
n 

of
 1

2 
m

on
th

s 
or

 m
or

e 
co

m
pa

re
d 

to
 th

e 
to

ta
l n

um
be

r o
f j

ob
se

ek
er

s,
 %

Jo
bs

ee
ke

rs
 w

ith
 b

as
ic

 ed
uc

at
io

n:
 n

um
be

r o
f j

ob
se

ek
er

s 
w

ith
 b

as
ic

 e
du

ca
tio

n 
co

m
pa

re
d 

to
 th

e 
to

ta
l n

um
be

r o
f j

ob
se

ek
er

s,
 %

Jo
bs

ee
ke

rs
 w

ith
 se

co
nd

ar
y 

vo
ca

tio
na

l e
du

ca
tio

n 
w

ith
ou

t c
om

pl
et

in
g 

a 
lea

vi
ng

 ex
am

 (i
nc

l. 
ap

pr
en

tic
es

): 
nu

m
be

r o
f j

ob
se

ek
er

s w
ith

 se
co

nd
-

ar
y 

vo
ca

tio
na

l e
du

ca
tio

n 
w

ith
ou

t c
om

pl
et

in
g 

a 
le

av
in

g 
ex

am
 (i

nc
l. 

ap
pr

en
tic

es
 c

om
pa

re
d 

to
 th

e 
to

ta
l n

um
be

r o
f j

ob
se

ek
er

s, 
%

2.
 E

du
ca

tio
n

Ed
uc

at
io

na
l 

st
ru

ct
ur

e

Po
pu

la
tio

n 
w

ith
 b

as
ic

 a
nd

 in
co

m
pl

et
e e

du
ca

tio
n:

 s
ha

re
 o

f p
er

so
ns

 w
ith

 b
as

ic
 a

nd
 in

co
m

pl
et

e 
ed

uc
at

io
n 

ag
ed

 1
5 

an
d 

ov
er

 in
 re

la
tio

n 
to

 p
er

so
ns

 o
f t

he
 s

am
e 

ag
e,

 %
Po

pu
la

tio
n 

w
ith

 u
ni

ve
rs

ity
 ed

uc
at

io
n:

 sh
ar

e 
of

 p
er

so
ns

 w
ith

 u
ni

ve
rs

ity
 e

du
ca

tio
n 

ag
ed

 1
5 

an
d 

ov
er

 in
 re

la
tio

n 
to

 p
er

so
ns

 o
f t

he
 sa

m
e 

ag
e,

 %

3.
 D

em
og

ra
ph

ic
 

si
tu

at
io

n

M
ig

ra
tio

n
Fo

re
ig

ne
rs

 b
y 

th
e m

os
t f

re
qu

en
t c

iti
ze

ns
hi

ps
: s

um
 o

f t
he

 n
um

be
r o

f m
os

t f
re

qu
en

t c
iti

ze
ns

hi
ps

 o
f f

or
ei

gn
er

s 
in

 re
la

tio
n 

to
 th

e 
w

ho
le

 
po

pu
la

tio
n,

 %
A

gi
ng

A
ge

 in
de

x:
 n

um
be

r o
f p

er
so

ns
 a

ge
d 

65
 a

nd
 o

ve
r c

om
pa

re
d 

to
 th

e 
nu

m
be

r o
f p

er
so

ns
 a

ge
d 

0–
14

, %
U

rb
an

iz
at

io
n

Le
ve

l o
f u

rb
an

iz
at

io
n:

 s
ha

re
 o

f p
op

ul
at

io
n 

liv
in

g 
in

 c
iti

es
, %

  
Et

hn
ic

 s
tr

uc
tu

re
Ro

m
a 

po
pu

la
tio

n:
 s

ha
re

 o
f p

op
ul

at
io

n 
w

ith
 R

om
a 

na
tio

na
lit

y,
 %

4.
 E

nv
ir

on
m

en
ta

l 
co

nd
iti

on
s

A
ir

 q
ua

lit
y

A
nn

ua
l a

ve
ra

ge
 co

nc
en

tr
at

io
n 

of
 su

sp
en

de
d 

pa
rt

ic
ul

ar
 m

att
er

 P
M

2,
5;

 in
 μ

g/
m

3

A
nn

ua
l a

ve
ra

ge
 co

nc
en

tr
at

io
n 

of
 su

sp
en

de
d 

pa
rt

ic
ul

ar
 m

att
er

 P
M

10
; i

n 
μg

/m
3

A
nn

ua
l a

ve
ra

ge
 co

nc
en

tr
at

io
n 

of
 b

en
zo

l[a
]p

yr
en

e; 
in

 n
g/

m
3

A
nn

ua
l a

ve
ra

ge
 N

O
2 

co
nc

en
tr

at
io

n;
 in

 μ
g/

m
3

A
nn

ua
l a

ve
ra

ge
 b

en
ze

ne
 co

nc
en

tr
at

io
n;

 in
 μ

g/
m

3

5.
 In

di
vi

du
al

 
liv

in
g 

co
nd

iti
on

s

Li
vi

ng
 

co
nd

iti
on

s
Av

er
ag

e l
iv

in
g 

sp
ac

e p
er

 p
er

so
n;

 m
2

H
ea

tin
g 

m
et

ho
d:

 s
ha

re
 o

f d
w

el
lin

gs
 h

ea
te

d 
by

 e
le

ct
ri

ci
ty

 o
r g

as
 to

 d
w

el
lin

gs
 h

ea
te

d 
by

 s
ol

id
 fu

el
s;

 c
oe

ffi
ci

en
t

Te
ch

ni
ca

l i
nf

ra
-

st
ru

ct
ur

e
Sh

ar
e o

f m
un

ic
ip

al
iti

es
 in

 th
e d

ist
ric

t w
ith

 co
nn

ec
tio

n 
to

 th
e s

ew
er

ag
e s

ys
te

m
 te

rm
in

at
ed

 b
y 

a 
W

W
TP

; %

6.
 R

oa
d 

sa
fe

ty
 

an
d 

cr
im

e

Tr
affi

c 
ac

ci
de

nt
s

To
ta

l t
ra

ffi
c a

cc
id

en
ts

: t
ot

al
 n

um
be

r o
f t

ra
ffi

c 
ac

ci
de

nt
s 

in
 re

la
tio

n 
to

 th
e 

to
ta

l p
op

ul
at

io
n;

 p
er

 1
00

0 
in

ha
bi

ta
nt

s
To

ta
l t

ra
ffi

c a
cc

id
en

ts
 u

nd
er

 th
e i

nfl
ue

nc
e o

f a
lc

oh
ol

: n
um

be
r o

f t
ra

ffi
c 

ac
ci

de
nt

s 
un

de
r t

he
 in

flu
en

ce
 o

f a
lc

oh
ol

 re
la

tiv
e 

to
 th

e 
to

ta
l 

po
pu

la
tio

n;
 p

er
 1

00
0 

in
ha

bi
ta

nt
s

D
ea

th
s d

ue
 to

 ro
ad

 a
cc

id
en

ts
: n

um
be

r o
f d

ea
th

s 
du

e 
to

 ro
ad

 a
cc

id
en

ts
 re

la
tiv

e 
to

 th
e 

nu
m

be
r o

f i
nh

ab
ita

nt
s;

 p
er

 1
00

,0
00

 in
ha

bi
ta

nt
s

C
ri

m
e

D
ea

th
s d

ue
 to

 a
ss

au
lt 

(a
tta

ck
): 

nu
m

be
r o

f d
ea

th
s 

du
e 

to
 a

ss
au

lt 
(a

tta
ck

) r
el

at
iv

e 
to

 n
um

be
r o

f i
nh

ab
ita

nt
s;

 p
er

 1
00

,0
00

 in
ha

bi
ta

nt
s

Re
gi

st
er

ed
 o

ffe
ns

es
: n

um
be

r o
f r

eg
is

te
re

d 
off

en
se

s 
in

 re
la

tio
n 

to
 th

e 
to

ta
l p

op
ul

at
io

n;
 p

er
 1

00
0 

in
ha

bi
ta

nt
s



201

Ta
bl

e 1
. C

on
tin

ue
d

A
re

a
Th

em
e

D
es

cr
ip

tio
n

7.
 S

ou
rc

es
 o

f 
he

al
th

 a
nd

 s
oc

ia
l 

ca
re

H
ea

lth
 c

ar
e 

ca
pa

ci
tie

s

Ph
ys

ic
ia

ns
 in

 h
ea

lth
ca

re
 fa

ci
lit

ies
: n

um
be

r o
f p

hy
si

ci
an

s 
re

la
tiv

e 
to

 th
e 

to
ta

l p
op

ul
at

io
n;

 p
er

 1
00

0 
in

ha
bi

ta
nt

s
H

os
pi

ta
l b

ed
s: 

nu
m

be
r o

f h
os

pi
ta

l b
ed

s 
in

 re
la

tio
n 

to
 th

e 
to

ta
l p

op
ul

at
io

n;
 p

er
 1

00
0 

in
ha

bi
ta

nt
s

RE
G

IO
N

S 
(id

en
tic

al
 v

al
ue

 o
f t

he
 re

gi
on

 le
ve

l a
ss

ig
ne

d 
to

 th
e 

di
st

ri
ct

s 
of

 th
e 

re
sp

ec
tiv

e 
re

gi
on

)
So

ci
al

 c
ar

e 
ca

pa
ci

tie
s

Pl
ac

e i
n 

so
ci

al
 se

rv
ic

e f
ac

ili
tie

s: 
nu

m
be

r o
f p

la
ce

s 
in

 s
oc

ia
l s

er
vi

ce
s 

fa
ci

lit
ie

s 
re

la
tiv

e 
to

 th
e 

to
ta

l p
op

ul
at

io
n;

 p
er

 1
00

0 
in

ha
bi

ta
nt

s
RE

G
IO

N
S 

(id
en

tic
al

 v
al

ue
 o

f t
he

 re
gi

on
 le

ve
l a

ss
ig

ne
d 

to
 th

e 
di

st
ri

ct
s 

of
 th

e 
re

sp
ec

tiv
e 

re
gi

on
) 

8.
 H

ea
lth

 s
ta

tu
s

Li
fe

 e
xp

ec
ta

nc
y 

an
d 

m
or

ta
lit

y 
st

ru
ct

ur
e

Li
fe 

ex
pe

ct
an

cy
 a

t b
irt

h 
(m

en
); 

ye
ar

Li
fe 

ex
pe

ct
an

cy
 a

t b
irt

h 
(w

om
en

); 
ye

ar
To

ta
l m

or
ta

lit
y:

 to
ta

l n
um

be
r o

f d
ea

th
s 

re
la

tiv
e 

to
 th

e 
to

ta
l p

op
ul

at
io

n;
 p

er
 1

00
,0

00
 in

ha
bi

ta
nt

s
M

al
e m

or
ta

lit
y:

 n
um

be
r o

f m
al

e 
de

at
hs

 re
la

tiv
e 

to
 to

ta
l m

al
e 

nu
m

be
r; 

pe
r 1

00
,0

00
 in

ha
bi

ta
nt

s
In

fa
nt

 m
or

ta
lit

y:
 n

um
be

r o
f d

ea
th

s 
w

ith
in

 1
 y

ea
r r

el
at

iv
e 

to
 to

ta
l n

um
be

r o
f l

iv
e 

bi
rt

hs
; p

er
 1

00
0 

liv
e 

bi
rt

hs
N

eo
na

ta
l m

or
ta

lit
y:

 n
um

be
r o

f d
ea

th
s 

w
ith

in
 2

8 
da

ys
 o

f b
ir

th
 v

er
su

s 
nu

m
be

r o
f l

iv
e 

bi
rt

hs
, p

er
 1

00
0 

liv
e 

bi
rt

hs
D

ea
th

s f
ro

m
 in

fec
tio

us
 a

nd
 p

ar
as

iti
c d

ise
as

e: 
nu

m
be

r o
f d

ea
th

s 
fr

om
 in

fe
ct

io
us

 a
nd

 p
ar

as
iti

c 
di

se
as

e 
re

la
tiv

e 
to

 th
e 

to
ta

l p
op

ul
at

io
n;

 
pe

r 1
00

,0
00

 in
ha

bi
ta

nt
s

D
ea

th
s f

ro
m

 ci
rc

ul
at

or
y 

sy
st

em
 d

ise
as

es
: n

um
be

r o
f d

ea
th

s 
fr

om
 c

ir
cu

la
to

ry
 s

ys
te

m
 d

is
ea

se
s 

re
la

tiv
e 

to
 th

e 
to

ta
l p

op
ul

at
io

n;
 p

er
 

10
0,

00
0 

in
ha

bi
ta

nt
s

D
ea

th
s f

ro
m

 re
sp

ira
to

ry
 d

ise
as

es
: n

um
be

r o
f d

ea
th

s f
ro

m
 re

sp
ir

at
or

y 
di

se
as

es
 re

la
tiv

e 
to

 th
e 

to
ta

l p
op

ul
at

io
n;

 p
er

 1
00

,0
00

 in
ha

bi
ta

nt
s

D
ea

th
s f

ro
m

 m
al

ig
na

nt
 n

eo
pl

as
m

s: 
nu

m
be

r o
f d

ea
th

s f
ro

m
 m

al
ig

na
nt

 n
eo

pl
as

m
s r

el
at

iv
e 

to
 th

e 
to

ta
l p

op
ul

at
io

n;
 p

er
 1

00
,0

00
 in

ha
bi

ta
nt

s
D

ea
th

s f
ro

m
 g

as
tr

oi
nt

es
tin

al
 d

ise
as

es
: n

um
be

r o
f d

ea
th

s 
fr

om
 g

as
tr

oi
nt

es
tin

al
 d

is
ea

se
s 

re
la

tiv
e 

to
 th

e 
to

ta
l p

op
ul

at
io

n;
 p

er
 1

00
,0

00
 

in
ha

bi
ta

nt
s

D
ea

th
s f

ro
m

 o
th

er
 ca

us
es

: n
um

be
r o

f d
ea

th
s 

fr
om

 o
th

er
 c

au
se

s 
re

la
tiv

e 
to

 th
e 

to
ta

l p
op

ul
at

io
n;

 p
er

 1
00

,0
00

 in
ha

bi
ta

nt
s

In
te

nt
io

na
l s

elf
-h

ar
m

 (m
en

): 
nu

m
be

r o
f m

en
 w

ho
 d

ie
d 

as
 a

 re
su

lt 
of

 s
el

f-h
ar

m
 re

la
tiv

e 
to

 th
e 

to
ta

l n
um

be
r o

f m
en

; p
er

 1
00

,0
00

 
in

ha
bi

ta
nt

s
In

te
nt

io
na

l s
elf

-h
ar

m
 (w

om
en

): 
nu

m
be

r o
f w

om
en

 w
ho

 d
ie

d 
as

 a
 re

su
lt 

of
 s

el
f-h

ar
m

 re
la

tiv
e 

to
 th

e 
to

ta
l n

um
be

r o
f w

om
en

; p
er

 
10

0,
00

0 
in

ha
bi

ta
nt

s
D

ea
th

 d
ue

 to
 li

ve
r d

ise
as

e: 
nu

m
be

r o
f d

ea
th

s 
fr

om
 li

ve
r d

is
ea

se
 (a

lc
oh

ol
ic

, t
ox

ic
, c

ir
rh

os
is

, c
hr

on
ic

 a
nd

 o
th

er
 in

fla
m

m
at

io
ns

 a
nd

 
di

se
as

es
) r

el
at

iv
e 

to
 th

e 
to

ta
l p

op
ul

at
io

n;
 p

er
 1

00
,0

00
 in

ha
bi

ta
nt

s
D

ea
th

 d
ue

 to
 sm

ok
in

g 
to

ba
cc

o:
 n

um
be

r o
f d

ea
th

s 
du

e 
to

 s
m

ok
in

g 
to

ba
cc

o 
(m

al
ig

na
nt

 n
eo

pl
as

m
 o

f l
ar

yn
x,

 tr
ac

he
a,

 b
ro

nc
hi

 a
nd

 
lu

ng
s)

 re
la

tiv
e 

to
 th

e 
to

ta
l p

op
ul

at
io

n;
 p

er
 1

00
,0

00
 in

ha
bi

ta
nt

s
D

ia
be

te
s m

ell
itu

s d
ea

th
s: 

nu
m

be
r o

f d
ea

th
s 

du
e 

to
 d

ia
be

te
s 

m
el

lit
us

 re
la

tiv
e 

to
 th

e 
to

ta
l p

op
ul

at
io

n;
 p

er
 1

00
,0

00
 in

ha
bi

ta
nt

s
So

ur
ce

: A
ut

ho
rs

’ o
w

n 
re

se
ar

ch
 a

nd
 p

ro
ce

ss
in

g.

DOI: 10.15201/hungeobull.74.2.5  Hungarian Geographical Bulletin 74 (2025) (2) 195–216.Vilinová, K. and Bullová, K. Hungarian Geographical Bulletin 74 (2025) (2) 195–216.



Vilinová, K. and Bullová, K. Hungarian Geographical Bulletin 74 (2025) (2) 195–216.202

raphy, environmental science, medicine and 
public health, law) and experts from prac-
tice (health policy, public health support, 
preventive medicine) related to population 
health anonymously assigned a weight (sig-
nificance) to each area (Table 2). The areas 
were always evaluated as a whole, meaning 
the weight of individual areas was not influ-
enced by the number of included indicators. 
The weights of the areas were determined 
through a questionnaire, and the experts’ 
opinions were refined through a three-round 
evaluation (Han, H. et al. 2012). The Health 
Index in the Slovak population and its spatial 
differentiation will be evaluated in the light 
of the theoretical background and the appro-
priately chosen methodological approach.

The data used in this study relate to 47 in-
dicators, divided into 8 different areas. Each 
of the examined areas contains between 2 
and 17 indicators (Table 3).

Methodologically, we divided the crea-
tion of the study into three phases. In the 
first phase, we obtained the assessment of 
districts for each area separately (with equal 

weights for indicators within the areas) us-
ing the WSA method. In the second phase, 
the same method was applied to evaluate all 
eight areas together (first with equal weights 
for all eight areas, and then with different 
weights assigned by a group of experts). 
The overall result of our evaluation was the 
creation of the Health Index for individual 
districts in Slovakia. In the third phase, we 
graphically represented the Health Index val-
ues on a map of Slovakia, dividing them into 
clusters and identifying spatial disparities. 
For the evaluation, we used the Weighted 
Sum Approach (WSA), a method based on 
maximizing utility. This method is one of the 
most frequently used in this field. It is based 
on constructing a linear utility function on a 
scale from 0 to 1. The worst variant for a giv-
en indicator will have a utility of zero, while 
the best variant will have a utility of one. 
Other variants will have a utility between 
these two extreme values (Kampf, R. 2002). 

According to  Friebelová, J.  and 
Klicnarová, J. (2007), the ideal variant H 
with evaluation (h1, h2, ..., hn) and the base-

line variant D with evaluation (d1, 
d2, …, dn) must first be determined. 
The utility of the ideal variant is 1, 
and the baseline variant is 0. The 
resulting utilities for specific vari-
ants range between these values. 
Furthermore, a standardized ma-
trix R is created, whose elements 
are obtained using the formula:

where rij represents the standard-
ized value of the i-th variant and 
the j-th indicator.

For each variant, the overall util-
ity of the i-th variant, u(yi), is calcu-
lated as a weighted sum of partial 
utilities and their corresponding 
weights, where vj is the weight of 
the j-th indicator:

Table 2. Areas assessed in the Health Index and their associated 
weightings

No Area Weight
1
2
3
4
5
6
7
8

Economic conditions and social protection 
Education
Demographic indicators 
Environmental conditions 
Individual living conditions 
Road safety and crime 
Health and social care resources
Health status 

0.19
0.18
0.08
0.14
0.09
0.04
0.10
0.20

Source: Authors’ own research and processing.

Table 3. Basic description of compared areas

No Area Number 
of criteria

1
2
3
4
5
6
7
8

Economic conditions and social protection 
Education
Demographic indicators 
Environmental conditions 
Individual living conditions 
Road safety and crime 
Health and social care resources
Health status 

8
2
4
5
3
5
3
1

Source: Authors’ own research and processing.

(1)

(2)



203

In accordance with Alinezhad, A. and 
Khalili, J. (2019), each indicator fj (denoted 
as Aj

+) represents the highest value of the in-
dicator, Aj

+ = max yij, and Aj⁻  represent the 
lowest value of the indicator, Aj⁻  = min yij. 
Based on the data yij, for each alternative (in 
our case, district) ai and each indicator fj, we 
calculate the standardized value rij:

The final ranking is based on utility – the 
higher the value, the better it is:

Results

In this section, we will describe the steps 
we followed in calculating the utility in our 
study. As an example, we will use the Area 2 
(Education), which consists of two indicators 
(criteria): 2_1 Population with basic educa-
tion (%) and 2_2 Population with higher edu-
cation (%). The evaluation using WSA starts 
with the data matrix Y (Table 4), where the 
lowest (minimum) and highest (maximum) 
values are found. The use of formula A2 ap-
plies to the first criterion, which we want to 
minimize, and the use of formula A1 applies 
to the second criterion, which we want to 
maximize. Next, the matrix is standardized 
according to the formula rij. For example, 
for the district Bratislava I, the standardized 
value for the indicator 2_1 is calculated as 
(40.23–2.82) / 37.41 = 1.000. The best district 
has a value of 1 (in the case of indicators 
2_1 and 2_2, this is the district Bratislava I, 
as shown in Table 5). Next, an equal weight 
(in this case, ½) is assigned to each indica-
tor, and a weighted matrix is calculated (see 
Table 5). Finally, the overall utility for each 
district is computed as the sum of the val-

ues in the row of Table 5 (Table 6). This result 
from the first phase is used as input for the 
second phase, where the same steps are car-
ried out for the eight areas (treated as indica-
tors). Subsequently, we are able to determine 
the Health Index value for each district of 
Slovakia.

The Health Index will be spatially analysed 
at the level of districts of Slovakia. The spatial 
breakdown of Slovakia is shown in Figure 1.

One important step was to identify the key 
determinants and indicators of health posi-
tions that underlie health inequalities. We 

(formula A1)

(formula A2)

(3)

Table 4. Data for Area 2, selected districts – Education
Data (Y matrix)

No. District Crit. 2_a Crit. 2_b
1
2
3
4
5

…
50
51
…
78
79

Bratislava I
Bratislava II
Bratislava III
Bratislava IV
Bratislava V
…
Lučenec
Poltár
…
Spišská Nová Ves
Trebišov

2.82
7.80
5.92
5.83
7.13

15.78
19.65
26.52
17.19
16.08
18.19

55.41
30.78
35.96
37.52
31.43
16.73
10.81
7.07

11.88
11.16
9.02

– Crit. type min max
–
–
–
–

minimum Aj-
maximum Aj+
max-min
crit. weights vj

2.82
40.23
37.41
0.50

5.90
55.41
49.51
0.50

Source: Authors’ own research and processing.

Table 5. Normalized matrix for Area 2 – selected districts 

Normalized matrix R (rij values)
No. District Crit. 2_a Crit. 2_b

1
2
3
4
5

…
50
51
…
78
79

Bratislava I
Bratislava II
Bratislava III
Bratislava IV
Bratislava V
…
Lučenec
Poltár
…
Spišská Nová Ves
Trebišov

1.0000
0.8670
0.9172
0.8848
0.88.48

…
0.5502
0.3665

…
0.6457
0.5891

1.0000
0.5025
0.6072
0.6386
0.5158

…
0.0992
0.0237

…
0.1064
0.0631

Source: Authors’ own research and processing.

DOI: 10.15201/hungeobull.74.2.5  Hungarian Geographical Bulletin 74 (2025) (2) 195–216.Vilinová, K. and Bullová, K. Hungarian Geographical Bulletin 74 (2025) (2) 195–216.



Vilinová, K. and Bullová, K. Hungarian Geographical Bulletin 74 (2025) (2) 195–216.204

analysed the districts that we deliberately se-
lected based on the highest and lowest health 
attainment values calculated by the WSA 
method (Table 7). We used a decomposition 
of the Health Index into domains to identify 
key determinants and indicators (outcomes) 
of community health that reflect positive 
and negative inequalities (Table 8). A more 
detailed analysis of the results was carried 
out for the districts, which were assigned 
different weights. Within the domains, we 
specified sub-indicators. Decomposing first, 
we present a comparison of the results of all 

domains by districts with a high 
value of the Health Index WSA 
calculated with weights (dis-
tricts – Bratislava I, Bratislava 
IV, Senec, Bratislava V, Košice I). 

The main contributors to the 
positive results for these dis-
tricts include Area 2 (education; 
weight 0.18) and Area 8 (health; 
weight 0.20). In Area 2, educa-
tion is characterised by an above 
average proportion of people 
with a university degree and a 
low proportion of people with 
incomplete or primary educa-
tion. In Area 8, health condi-

tions are associated with above-average life 
expectancy and below-average overall stand-
ardized mortality, as well as below-average 
mortality by underlying causes of death, 
including deaths caused by tobacco smok-
ing and diabetes mellitus. In Senec district, 
the results are also favourable in Domain  
3 (demographic conditions; weight 0.08) and 
Domain 8 (health status; weight 0.8). 

In Domain 1, the districts of Bratislava I, 
Bratislava IV and Bratislava V scored particu-
larly favourably on the economic conditions 
and social protection index (Figure 2). These 

Fig. 1. Regional division of Slovakia. Source: Authors’ own processing.

Table 6. Results of WSA for Area 2 (weighted matrix and utility of 
selected districts)

Weighted matrix
No. District Crit. 2_a Crit. 2_b Utility u(ai)

1
2
3
4
5
…
50
51
…
78
79

Bratislava I
Bratislava II
Bratislava III
Bratislava IV
Bratislava V
…
Lučenec
Poltár
…
Spišská Nová Ves
Trebišov

0.5000
0.4335
0.4586
0.4424
0.3399

…
0.2581
0.2800

…
0.2247
0.2179

0.5000
0.2513
0.3036
0.2579
0.0780

…
0.0137
0.0316

…
0.0172
0.0063

1.0000
0.6848
0.7622
0.7791
0.4178

…
0.3028
0.3116

…
0.2419
0.2243

Source: Authors’ own research and processing.



205

districts benefited from the dynamic eco-
nomic environment of the capital city, which 
is characterised by a high concentration of 
investment, a well-developed business sec-
tor and a wide range of employment oppor-
tunities. The average unemployment rate in 
these districts was significantly lower than 
the national average, reflecting the stable eco-
nomic base and high level of employment. 
In the capital Bratislava and in the Košice I 

district, we observe unfavour-
able results in area 3, which 
includes demographic indi-
cators (with a weight of 0.08). 
This negative trend is due 
to the current demographic 
situation, characterised by de-
clining birth rates, an ageing 
population and an increasing 
dependency index, which 
points to a growing propor-
tion of economically inactive 
residents. On the contrary, 
Senec district maintains a 
favourable position in this 

area. This development is mainly the result 
of above-average birth rates and high immi-
gration rates. The inflow of new inhabitants 
is closely linked to the strong suburbanisa-
tion process that has been observed in the 
region for a long time. Senec benefits from its 
proximity to Bratislava, while the attractive-
ness of the district is enhanced by the avail-
ability of housing, quality infrastructure and 
favourable conditions for family life. 

Table 7. WSA (Weight 1) and WSA (different weightings)

No.
WSA (Weight 1) WSA (different weightings)

District Health 
Index District Health 

Index
1
2
3
4
5

75
76
77
78
79

Senec
Bratislava I
Bratislava V
Košice I
Košice IV
Trebišov
Sobrance
Rožňava
Rimavská Sobota
Revúca

0.71
0.70
0.68
0.67
0.67
0.41
0.40
0.38
0.35
0.32

Bratislava I
Bratislava IV
Senec
Bratislava V.
Košice I
Trebišov
Medzilaborce
Rožňava
Rimavská Sobota
Revúca

0.82
0.74
0.73
0.73
0.72
0.38
0.37
0.35
0.31
0.30

Source: Authors’ own research and processing.

Fig. 2. Spatial differentiation based on the calculation of the Health Index values using equal for the different 
areas – WSA method (2021–2022). The higher Health Index values are indicated by the darker colours of the 

districts. Source: Authors’ own research and processing.

DOI: 10.15201/hungeobull.74.2.5  Hungarian Geographical Bulletin 74 (2025) (2) 195–216.Vilinová, K. and Bullová, K. Hungarian Geographical Bulletin 74 (2025) (2) 195–216.Vilinová, K. and Bullová, K. Hungarian Geographical Bulletin 74 (2025) (2) 195–216.



Vilinová, K. and Bullová, K. Hungarian Geographical Bulletin 74 (2025) (2) 195–216.206

The Bratislava I district shows 
unfavourable results in area 6, 
which includes road safety and 
crime index. This negative trend 
is primarily influenced by an 
above-average number of traf-
fic accidents, which are a conse-
quence of high traffic intensity 
and heavy traffic in the city centre. 
At the same time, there is an in-
crease in the number of registered 
crimes, with property crime, pick-
pocketing and vandalism being 
among the most common, which 
are typical of busy urban areas. 

Domain which focuses on in-
dividual living conditions (with 
a weight of 0.09), shows a similar 
regional distribution as the other 
socio-economic indicators. The 
districts of Slovakia’s two larg-
est cities, Bratislava I and Košice 
I, continue to maintain the best 
scores. Their favourable position 
is the result of a higher standard 
of living, the availability of qual-
ity housing, good civic amenities 
and a wide range of services in 
health care, education and cul-
ture. However, despite these pos-
itives, certain challenges remain in 
these areas, such as the high cost 
of housing, differences in income 
levels of residents. On the other 
hand, in Domain 7, which includes 
health and social care resources 
(with a weight of 0.10), Bratislava 
IV and Bratislava V districts per-
form less favourably compared to 
other Bratislava districts. The main 
reason for their weaker position is 
the low bed capacity of hospitals, 
which is insufficient to cover the 
needs of the growing population 
in these areas. 

Area 4, which focuses on ecolog-
ical conditions (with a weight of 
0.14), shows rather unfavourable 
results for this group of districts, 

Ta
bl

e 8
. B

as
ic

 d
es

cr
ip

tio
n 

of
 co

m
pa

re
d 

ar
ea

s b
y 

H
ea

lth
 In

de
x 

W
SA

 D
iff

er
en

t W
eig

ht
in

gs
W

SA
 (D

iff
er

en
t W

ei
gh

tin
gs

) D
et

er
m

in
an

ts
 o

f H
ea

lth
   

   
   

   
   

   
   

   
   

   
   

   
   

   
   

   
   

   
   

   
   

   
   

   
   

   
   

   
   

   
   

   
   

   
   

   
   

   
   

   
   

   
   

   
   

   
   

   
   

   
 H

ea
lth

 In
di

ca
to

rs
A

re
a 

1
A

re
a 

2
A

re
a 

3
A

re
a 

4
A

re
a 

5
A

re
a 

6
A

re
a 

7
A

re
a 

8

H
ea

lth
 In

de
x

Ec
on

om
ic

 
co

nd
iti

on
s 

an
d 

so
ci

al
 

pr
ot

ec
tio

n

Ed
uc

at
io

n
D

em
og

ra
ph

ic
in

di
ca

to
rs

En
vi

ro
nm

en
ta

l
co

nd
iti

on
s

In
di

vi
du

al
 

liv
in

g 
co

nd
iti

on
s

Ro
ad

 s
af

et
y 

an
d 

cr
im

e

H
ea

lth
 a

nd
 

so
ci

al
 c

ar
e 

re
so

ur
ce

s

H
ea

lth
 

st
at

us

D
is

tr
ic

t
Ra

nk
In

de
x 

va
lu

e
Ra

nk
In

de
x 

va
lu

e
Ra

nk
In

de
x 

va
lu

e
Ra

nk
In

de
x 

va
lu

e
Ra

nk
In

de
x 

va
lu

e
Ra

nk
In

de
x 

va
lu

e
Ra

nk
In

de
x 

va
lu

e
Ra

nk
In

de
x 

va
lu

e
Ra

nk
In

de
x 

va
lu

e
Br

at
is

la
va

 I
Br

at
is

la
va

 IV
Se

ne
c

Br
at

is
la

va
 V

K
oš

ic
e 

I
Po

ltá
r

K
om

ár
no

Tr
eb

iš
ov

M
ed

zi
la

bo
rc

e
Ro

žň
av

a
Ri

m
av

sk
á 

So
bo

ta
Re

vú
ca

1 2 3 4 5 73 74 75 76 77 78 79

0.
82

2
0.

74
1

0.
73

6
0.

73
5

0.
72

5
0.

39
9

0.
38

3
0.

38
0

0.
37

0
0.

35
1

0.
30

6
0.

30
2

1 5 9 2 24 66 45 72 73 76 79 78

0.
94

1
0.

91
1

0.
89

6
0.

92
7

0.
80

4
0.

42
4

0.
68

7
0.

35
4

0.
33

0
0.

27
7

0.
17

8
0.

08
6

1 2 14 4 5 76 75 40 53 37 62 59

0.
95

4
0.

77
9

0.
48

2
0.

70
3

0.
68

8
0.

19
5

0.
22

1
0.

32
6

0.
28

6
0.

34
2

0.
26

3
0.

27
6

54 77 1 75 76 16 58 34 73 38 51 59

0.
49

1
0.

32
4

0.
94

8
0.

38
1

0.
34

7
0.

59
4

0.
47

9
0.

53
7

0.
42

8
0.

52
6

0.
49

4
0.

47
8

39 48 11 23 8 18 62 29 12 37 43 51

0.
98

9
0.

98
1

0.
99

6
0.

99
3

0.
99

7
0.

99
5

0.
96

3
0.

99
2

0.
99

6
0.

98
9

0.
98

5
0.

97
7

3 7 24 1 4 72 74 75 59 68 77 71

0.
78

1
0.

56
9

0.
41

0
0.

89
5

0.
64

6
0.

16
6

0.
15

2
0.

13
7

0.
24

4
0.

21
0

0.
11

8
0.

17
4

79 23 16 40 68 3 11 37 36 60 47 73

0.
46

1
0.

83
1

0.
86

0
0.

78
1

0.
62

9
0.

94
4

0.
90

2
0.

78
3

0.
78

7
0.

68
4

0.
74

2
0.

58
7

3 73 79 48 1 68 58 41 2 15 36 54

0.
44

5
0.

15
3

0.
07

3
0.

25
6

0.
72

3
0.

19
1

0.
21

8
0.

26
2

0.
58

3
0.

32
6

0.
27

1
0.

23
2

4 2 1 8 13 67 78 73 79 77 71 74

0.
78

4
0.

83
8

0.
87

4
0.

73
7

0.
70

8
0.

51
4

0.
36

2
0.

47
2

0.
34

9
0.

41
3

0.
49

2
0.

46
3

So
ur

ce
: A

ut
ho

rs
’ o

w
n 

re
se

ar
ch

 a
nd

 p
ro

ce
ss

in
g.



207

mainly due to above-average air pollution lev-
els. This negative trend is due to high urban-
isation rates, dense traffic, industrial activity 
and increased emissions, which affect air qual-
ity and the overall environment (scorecard). 

The same methods of decomposing and 
comparing the results of individual areas 
were also applied to the districts with low 
Health Index scores, which include Revúca, 
Rimavská Sobota, Rožňava, Medzilaborce 
and Trebišov. The analysis was again con-
ducted using the WSA method with assigned 
weights, which allowed for a more accurate 
assessment of the factors influencing the 
health status of the population in these re-
gions. This group of districts is associated 
with unfavourable results of the Health Index 
assessment. The most pronounced negative 
impacts are seen in Domain 1 (economic con-
ditions and social protection; weight 0.19), 
Domain 8 (health; weight 0.20) and Domain 
2 (education; weight 0.18). These districts are 
among the weakest economically in the coun-
try, characterised by high unemployment 
rates and low average wages, which limit 
the living conditions of their inhabitants. 
The low level of education is also a significant 
problem, with a high proportion of residents 
having only primary education. This trend 
is largely influenced by the socio-economic  
situation, as well as by the higher represen-
tation of the Roma national minority. The 
health situation in these regions is also unfa-
vourable, with above-average mortality rates 
and some causes of death, such as diseases of 
the circulatory system. This situation is exac-
erbated by the lack of access to healthcare, 
the limited number of doctors and healthcare 
facilities, and the low level of preventive care. 

Other domains, namely Domain 3 (demo-
graphic indicators; weight 0.08), Domain 4 
(environmental conditions), Domain 6 (road 
safety and crime index) and Domain 7 (health 
and social care resources), could not be clear-
ly assessed in the interpretation of the results 
for the identified group of districts with the 
lowest Health Index values. For example, in 
Domain 3 (demographic indicators), the in-
dex is characterised by a wide range of val-

ues, with some districts, such as Trebišov and 
Rožňava, achieving higher values due to a 
younger or average age structure of the po- 
pulation. On the contrary, the Medzilaborce 
district shows a low value of the demograph-
ic index, which is due to an above-average 
age index and a significant migration loss, as 
the younger population often leaves for better 
economic opportunities in other regions or 
abroad. These differences suggest that demo-
graphic factors have a different impact on the 
overall Health Index in different districts (see 
Figure 2). 

The WSA assessment method with equal 
weights spatially identifies the districts of 
Slovakia that achieved the highest Health 
Index values, which include Bratislava I, 
Bratislava IV, Bratislava V, Senec, and Košice 
I. In contrast, the districts located in the 
southern part of Slovakia – Rožňava, Revúca, 
and Rimavská Sobota – showed the lowest 
Health Index values Higher Health Index 
values are indicated by darker shading, re-
flecting a more favourable situation. Regions 
with a high Health Index are characterized 
by positive regional differences, such as a 
high proportion of university graduates, pos-
itive net migration, and low unemployment 
rates. Conversely, regions with a low Health 
Index display negative regional disparities, 
including high unemployment, a low share 
of university-educated residents, negative 
net migration, and high infant mortality. 
These regions also report the presence of so-
cially excluded communities with an ethnic 
minority (Roma) and a higher proportion of 
residents with a lower socio-economic status. 

A very similar situation is also manifested 
in area 4 (environmental conditions; weight 
0.14), where, however, the districts of Revúca 
and Medzilaborce show significantly differ-
ent values. While Revúca scores above aver-
age on the pollution index, Medzilaborce, on 
the other hand, shows favourable environ-
mental conditions. In the case of Revúca, the 
unfavourable environmental quality is main-
ly influenced by industrial activity, the his-
torical burden of metallurgy and mining, as 
well as the high production of emissions from 

DOI: 10.15201/hungeobull.74.2.5  Hungarian Geographical Bulletin 74 (2025) (2) 195–216.Vilinová, K. and Bullová, K. Hungarian Geographical Bulletin 74 (2025) (2) 195–216.



Vilinová, K. and Bullová, K. Hungarian Geographical Bulletin 74 (2025) (2) 195–216.208

local industrial enterprises. On the contrary, 
the favourable situation in Medzilaborce is 
the result of several factors such as the low 
level of industrial activity, lower population 
density and extensive forest cover in the vi-
cinity, which contribute to better air quality. 

The results in Domain 5 (individual living 
conditions; weight 0.09) show a similar trend, 
with a low index of living conditions in these 
districts, which is mainly the result of several 
factors. One of the main reasons for this is the 
low proportion of households heating their 
homes with electricity or gas, leading to a great-
er reliance on solid fuels such as wood or coal. 

In Domain 7 (health and social care; weight 
0.10) we observe a favourable situation in 
Medzilaborce district, where the index val-
ues benefit significantly from the good avail-
ability of social services. This positive devel-
opment is mainly due to the relatively high 
number of places in social service facilities 
available to the population, which improves 
the quality of life of the elderly and vulnera-
ble groups. These factors, together with the 
relatively low population density and less 
pressure on local health and social institu-
tions, allow for more efficient and individ-
ualised care. In Slovakia, the Health Index 
shows regional variations, with an east-west 
gradient. Although the lowest values of the 
Health Index were recorded mainly in the 
districts of southern Slovakia, the spatial 
pattern of the east-west gradient remains an 
important geographical phenomenon. 

The WSA assessment method with equal 
weights, spatially identifies the districts of 
Slovakia that achieved the best Health Index 
values, which included the districts of Senec, 
Bratislava I, Bratislava V, Bratislava V, and 
Košice I. In the southern part of Slovakia 
there are districts Revúca, Rimavská Sobota, 
and Rožňava, where we recorded the lowest 
values of the Health Index (Figure 3).

Discussion

According to Rosenkötter, N. et al. (2015), 
health inequalities have not been a major pol-

icy priority in the context of the development 
of a sustainable health information infra-
structure in Europe. However, a significant 
shift has been taking place in recent years. 
The debate on the importance of health in-
formation infrastructure and the steps to fur-
ther develop it has intensified considerably. 
This development is probably related to the 
increasing demands for health information, 
which serves as a basis for the formulation 
of country-specific recommendations. Moni-
toring WHO and European Union policy is 
therefore crucial, as both institutions place 
emphasis on the development of quality in-
formation. Experts involved in health data 
monitoring and reporting in Europe stress 
the need for a sustainable health informa-
tion infrastructure and an appropriate legal 
framework. This phenomenon requires sys-
tematic monitoring and analysis, especially 
in terms of morbidity and health inequalities.

For this reason, it is also important to ex-
amine health inequalities at the regional lev-
el, which allows for a more precise identifica-
tion of spatial disparities and their causes. In 
this paper, the territorial level of districts of 
Slovakia (LAU1) was therefore deliberately 
chosen. The spatial differentiation of health 
status and its determinants at this level pro-
vides a more detailed view compared to the 
national or regional NUTS2 level. Such anal-
yses are not only crucial from the perspective 
of international statistics and projects, but 
play an important part in effective measures 
to reduce inequalities.

A variety of methods have been used to 
assess health inequalities, including the de-
velopment of indices (composite indicators) 
at international and national level, as report-
ed by Freitas, A. et al. (2018), Fernandez-
Crehuet, J.M. (2019), and Pearson-Stuttard, 
J. et al. (2019). One of the significant factors 
was the presence of COVID-19. The pan-
demic further deepened existing health in-
equalities, highlighting disparities in access 
to healthcare and overall population health 
(Bambra, C. et al. 2020; Kerschbaumer, L. 
et al. 2024). In Slovakia, the topic of health 
inequalities comes to the fore only sporadi-



209

cally and mostly remains in the background 
of expert analyses and statistical surveys. 
Most discussions on the health situation in 
the country focus on selected indicators such 
as life expectancy, incidence of civilisation 
diseases or access to health care. 

What is missing, however, is a broader so-
cietal discussion that highlights how health 
inequalities are linked to socio-economic 
factors, education, employment or living 
environment. While statistics and analytical 
outputs provide valuable information, they 
often do not reveal the complex causes and 
consequences of health inequalities. For ex-
ample, health disparities between different 
regions of Slovakia are not only reflected in 
figures on hospital admissions or mortality 
rates, but are deeply rooted in the availabil-
ity of quality housing, healthy lifestyles and 
healthcare infrastructure. However, these 
links are only minimally discussed publicly 
(Sopóci, J. and Hrabovská, A. 2015). 

A systematic comparison of the findings 
of research on various aspects of health in-

equalities carried out in Western and Central 
European countries has made it possible to 
confirm these conclusions and to identify 
some basic trends in this area. For example, 
research findings in post-socialist countries 
have also confirmed the existence of a signif-
icant relationship between socio-economic 
status and health. The increasing economic 
and social differentiation in post-socialist 
countries has been accompanied by growing 
health inequalities between different social 
classes. These states also have higher levels 
of health inequalities than Western European 
states (Džambazovič, R. and Gerbery, D. 
2014). Meanwhile, the changes undergone by 
the Central and Eastern European states have 
had the most negative consequences regard-
ing health inequalities on populations with 
lower socio-economic status. In the Slovak 
Republic, for example, Roma in particular 
have been affected (Ginter, E. et al. 2001; 
Rosicova, K. et al. 2011). 

In the context of the selected Health Index 
indicators, a considerable number of expert 

Fig. 3. Spatial differentiation based on the calculation of the Health Index values using different weightings – 
WSA method (2021–2022). The higher Health Index values are indicated by the darker colours of the district. 

Source: Authors’ own research and processing.

DOI: 10.15201/hungeobull.74.2.5  Hungarian Geographical Bulletin 74 (2025) (2) 195–216.Vilinová, K. and Bullová, K. Hungarian Geographical Bulletin 74 (2025) (2) 195–216.



Vilinová, K. and Bullová, K. Hungarian Geographical Bulletin 74 (2025) (2) 195–216.210

studies and papers have been produced in 
Slovakia. Regarding the mortality indicator, 
this issue has been addressed, for example, 
by Mészáros, J. (2008), and Šprocha, B. et al. 
(2015). From the demographic point of view, 
health inequalities have been analysed by 
Káčerová, M. et al. (2014), while the environ-
mental aspect has been elaborated in detail in 
Rapant, S. et al. (2010, 2013). A comprehen-
sive analysis of the health status of the popu-
lation in Slovakia was provided by Vilinová, 
K. (2012). Together, these studies offer a com-
prehensive view of the factors influencing 
the health situation in the country. Sopóci, J. 
et al. (2015) argue that in the long and short 
term we observe deepening socio-economic 
disparities between regions and their posi-
tion within the Slovak Republic is changing 
based on their economic and social devel-
opment. The most significant consequence 
of this development is the concentration of 
social and economic problems in certain re-
gions. The most developed region in Slovakia 
is the Bratislava Region, but even this region 
is not a homogeneous territorial unit. Here, 
too, there is a visible differentiation between 
Bratislava and other districts of the Bratislava 
Region. It cannot be denied that within the 
Slovak Republic, the Bratislava Region has a 
specific position in terms of material and so-
cio-economic conditions, demographic char-
acteristics and also in terms of health care. 

The Slovak Republic, as one of the V4 
countries, is very often characterised and 
compared with countries in this area in terms 
of aspects of health inequalities. Poland, for 
example, has recently stepped up health pro-
motion in an effort to increase healthy life ex-
pectancy and reduce health inequalities. As 
in other countries, Poland has a high preva-
lence of health problems determined primar-
ily by lifestyle-related factors. Karasiewicz, 
M. et al. (2021) in their study point to the 
need to intensify health promotion in ru-
ral, remote and disadvantaged populations. 
From their findings, they model the conclu-
sion that despite the efforts of policy mak-
ers, there is still a high risk of unmet health 
needs in deprived areas. According to Sowa-

Kofka, A. (2018), the health care system in 
Poland faces various challenges in ensuring 
equal access to services. The level of public 
spending on healthcare is one of the lowest 
in the European Union. Insufficient funding 
affects the quality of health services offered, 
increasing waiting times, resulting in an in-
crease in inequalities. Rój, J. and Jankowiak, 
M. (2021) report that based on the distri-
bution of socio-economic determinants of 
health, they identified inequalities among 
geographically defined populations. They 
show that in Poland, due to their geograph-
ic location, the population does not have the 
same opportunity to reach their full health 
potential. The results of their research con-
firmed that voivodeships are considerably 
heterogeneous in terms of the distribution 
of socio-economic determinants of health. 
Kobza, J. and Geremek, M. (2015) report 
that the reduction in mortality from cardio-
vascular diseases, as well as changes in diet 
quality or the impact of economic conditions 
on health outcomes, also played a significant 
role in the longer survival years in the health 
of the Polish population. 

According to Hübelová, D. et al. (2023), 
spatial health inequalities persist in the 
Czech Republic, influenced by econom-
ic, social, demographic and environmen-
tal factors, as well as local access to health 
care. This is despite the fact that the Czech 
Republic is a relatively demographically, 
socially, economically and ethnically homo-
geneous country with a low proportion of 
socially excluded individuals or those living 
below the poverty line. However, regional or 
micro-regional health inequalities have per-
sisted for a long time. The study shows that 
both the inner and outer peripheries exhibit 
poor health outcomes, challenging the as-
sumption that urban areas are better off. The 
causes of inequalities in the rural periphery 
stem primarily from demographic and in-
stitutional factors and an inadequate labour 
market. As far as reducing the intensity of 
health inequalities in the Czech Republic is 
concerned, the study shows that the success 
rate is not great. It cites a combination of 



211

poverty and other vulnerability indicators 
such as age (children, elderly), disability or 
minority origin as a cause that exacerbates 
these inequalities. 

Hübelová, D. et al. (2021c) point to a very 
favourable situation in the Czech districts 
of Prague-East and Prague-West, thanks in 
particular to a high proportion of university 
graduates, low unemployment, low ageing 
index, low infant mortality, low abortion 
rate as well as affordable housing subsidies. 
It can be stated that such a favourable situ-
ation of the districts in question is due to 
the immediate proximity of the district of 
Prague – capital city. On the contrary, the 
unfavourable situation in the districts of 
Chomutov, Teplice and Most (all districts 
belong to the Ústí nad Labem Region locat-
ed in the north-west of the Czech Republic), 
compared to the districts of Prague-West 
and Prague-East, is characterised by dif-
ferences such as high housing subsidies, 
high unemployment rate, low proportion 
of university graduates, negative migration 
balance or high infant mortality and abor-
tion rates. On the basis of such results, it 
was possible to specify regional disparities 
in demographic and socio-economic indi-
cators that cause health inequalities, either 
negatively or positively. 

As far as the Czech health system is con-
cerned, the Ministry of Health plays both 
a regulatory and a strategic role. Both the 
Czech Republic and Slovakia have a public 
health insurance system that is largely regu-
lated by the government. Health insurance is 
compulsory and access to healthcare is prac-
tically universal. Vrabcová, J. et al. (2017) ar-
gue that factors influencing years of healthy 
life in the Czech Republic include improve-
ments in living conditions, public health 
interventions and advances in medical care. 
These improvements have contributed to an 
increase in the number of healthy life years, 
which is an important indicator of potential 
demand for both health and long-term care 
services, especially for the elderly.

Uzzoli, A. et al. (2020) explain that the 
general health status of the Hungarian pop-

ulation is worse than justified by the level 
of economic development. The deterioration 
in health status that had been ongoing since 
the mid-1960s turned into an epidemiologi-
cal crisis in the early 1990s and affected the 
entire adult population. Since the second half 
of the 1990s, Hungary has faced significant 
improvements in many health outcomes, but 
the country still lags behind many more de-
veloped countries. Most of the main health 
indicators are worse than the OECD average, 
indicating that Hungary belongs to the mid-
dle tier of countries in the world in terms of 
the overall health of its population. 

The poorer health outcomes are related to 
significant regional disparities in the country. 
Relatively, the greatest spatial inequalities 
are observed especially between the western 
and eastern parts of Hungary. The disparity 
between the west and the east of Hungary is 
also confirmed by the geographical distribu-
tion of health services, where we can observe 
significant differences, especially in special-
ised care. The disparity in public funding of 
outpatient capacity means that waiting times 
for diagnosis are prolonged, as doctors can 
only examine a certain number of patients for 
a selected paid time. In practice, this means 
that residents who have sufficient finances 
often use private services to reduce waiting 
times for examinations or to ensure access 
to better quality services (Albert, F. 2018). 

Overall inequality can also be seen in life 
expectancy in Hungary, especially between 
the highest and lowest income groups. The 
latter could be reduced by as much as half, by 
reducing avoidable causes of death to the lev-
els seen in Hungary’s wealthiest settlements. 
The evidence on the role of avoidable deaths 
suggests that there is considerable scope for 
policy makers to increase the life expectan-
cy of individuals in poorer areas as well as 
to reduce existing inequalities. Specifically, 
these include incentives to improve diets and 
reduce smoking, reduce solid fuel heating to 
improve air quality, provide better access to 
health care, and help poorer people receive 
standard health check-ups (Bíró, A. et al. 
2021).

DOI: 10.15201/hungeobull.74.2.5  Hungarian Geographical Bulletin 74 (2025) (2) 195–216.Vilinová, K. and Bullová, K. Hungarian Geographical Bulletin 74 (2025) (2) 195–216.



Vilinová, K. and Bullová, K. Hungarian Geographical Bulletin 74 (2025) (2) 195–216.212

Conclusions

According to Hübelová, D. et al. (2021a) since 
2009, the European Union has made reduc-
ing health inequalities a priority among its 
activities, with the support of the Commis-
sion’s Communication Solidarity in Health 
in the form of the Communication ‘Reducing 
Health Inequalities in the European Union’. 
Our analysis provides new information in 
several ways. In one place, we provide a com-
prehensive assessment of population health 
indicators using combined data from different 
databases. We work with data at a detailed 
spatial (district) resolution, allowing target-
ed action to reduce health inequalities at the 
local level. This assessment approach has not 
yet been applied in Slovakia. It is important 
to continue research on this issue. Research 
could focus on the districts that perform worst 
in terms of the Health Index and on possible 
explanatory factors at the individual level. 

Using the weighted sum method, we have 
obtained aggregate Health Index values. We 
approached this index in two ways. In the 
first case, each of the given eight domains 
had equal importance with a weight of 1 
(WSA method without weights). In the sec-
ond case, each of the eight domains had a 
specific weight (WSA method with weights). 
On the basis of calculations, graphical and 
cartographic processing, we found that in 
both cases the districts with higher, more 
favourable values of the Health Index are 
mainly located in the western part of Slovakia 
(Bratislava I, Bratislava IV and Senec). On the 
contrary, districts with lower, more unfa-
vourable values are mostly located located in 
the southern and eastern part of the country 
(Revúca, Rimavská Sobota, Rožňava). 

The Health Index is a comprehensive in-
dicator that reflects the health status of a 
population based on a number of factors. In 
Slovakia, the index varies according to geo-
graphical location, with a strong east-west 
gradient. Western Slovakia, especially the 
Bratislava and Trnava regions, is character-
ised by a better health status of the popula-
tion. Eastern Slovakia, especially the Prešov 

and Košice regions, joined by the Banská 
Bystrica Region, shows worse results. The 
differences between these regions are condi-
tioned by several factors. Western Slovakia 
has better access to healthcare, which means 
a higher concentration of hospitals, special-
ised medical facilities and doctors. The eco-
nomic situation in these regions is more fa-
vourable, which allows for a higher standard 
of living, better nutrition, healthier lifestyles 
and a better level of prevention. In addition, 
there is a higher level of education, which 
contributes to a better awareness of healthy 
lifestyles and disease prevention. 

In contrast, eastern and southern Slovakia 
face a number of challenges that negatively 
affect the health status of the population. The 
availability of healthcare is worse in these 
regions, with fewer hospitals and special-
ised doctors. Lower economic levels, higher 
unemployment rates and lower average in-
comes make access to healthcare more diffi-
cult and affect lifestyles. In addition to these 
factors, migration also plays an important 
role. Young and educated people often leave 
eastern and southern Slovakia for the west 
in search of better conditions, thus, deep-
ening regional disparities. Infrastructure is 
also an important aspect, affecting access to 
healthcare and overall living standards in 
individual regions. 

The health situation in Slovakia is not 
uniform and the differences between the re-
gions are marked. In order to mitigate them, 
it is necessary to improve access to health-
care in the regions of eastern and southern 
Slovakia, invest in prevention and increase 
economic opportunities for the population. 
Closing these gaps is key to improving the 
overall health status of Slovaks and im-
proving the quality of life across the coun-
try. The COVID-19 pandemic has exposed 
and exacerbated existing health inequalities 
and socio-economic conditions in Slovakia 
as well. Although the virus affected all seg-
ments of society, its impact was not evenly 
distributed. Vulnerable groups such as the 
elderly, economically weaker families, mar-
ginalised communities and the disabled were 



213

the most affected. The pandemic has also ex-
posed problems in the Slovak health sector, 
such as undersized hospitals, shortages of 
medical staff and ineffective health care man-
agement. Measures such as lockdowns and 
restrictions on healthcare for other diseases 
have caused the deterioration of the health 
status of many patients. 

It is important to note that local govern-
ments have an important role to play in 
promoting health and addressing health in-
equalities. Municipalities, cities and coun-
ties have competence in a number of areas 
related to the determinants of health (e.g., 
housing, social care, environment, spatial 
planning, etc.). Through their decisions, 
they can largely influence the factors that 
affect the health of the population. One of 
the key roles of local governments is to be 
able to bring together a wide range of ac-
tors at the local level to create the condi-
tions for interdisciplinary cooperation that 
would lead to the development and later 
implementation of policies, programmes 
and activities to promote health. Within 
Slovakia, the Government of the Slovak 
Republic has approved the National Health 
Promotion Programme for 2021–2030. At 
this level, there are projects such as Healthy 
Communities, whose main activity is the im-
plementation of community health promo-
tion. Having access to a wide range of data 
and information on key health indicators 
is essential for successful interventions to 
tackle health inequalities. This is where our 
paper could find its future application, as it 
contains a wealth of data and information 
that could be implemented in the design of 
programmes or activities to promote health 
in the districts of Slovakia.

REFERENCES

Adler, N., Stewart, J., Cohen, S., Cullen, M., 
Diez-Roux, A., Dow, W., Evans, G., Kawachi, I., 
Marmot, M., Matthews, K., McEven, B., Schwartz, 
J., Seeman, T. and Williams, D. 2007. Reaching 
for a Healthier Life: Facts on Socio-economic Status 
and Health in the US. Chicago, The John D. and 

Catherine T. MacArthur Foundation Research 
Network. https://scholar.harvard.edu/davidrwil-
liams/reports/reaching-healthier-life

Agenor, M. 2020. Future directions for incorporating 
intersectionality into quantitative population health 
research. American Journal of Public Health 110. (6): 
803–806. https://doi.org/10.2105/AJPH.2020.305610

Albert, F. 2018. ESPN Thematic Report on Inequalities 
in Access to Healthcare: Hungary. Thematic Report. 
Directorate General for Employment, Social Affairs 
and Inclusion. Brussels, European Commission. 
https://ec.europa.eu/social/BlobServlet?docId=20
362&langId=en

Alinezhad, A. and Khalili, J. 2019. New Methods and 
Applications in Multiple Attribute Decision Making 
(MADM). Cham, Switzerland, Springer Nature. 
https://doi.org/10.1007/978-3-030-15009-9

Arcaya, M.C., Arcaya, A.L. and Subranamian, S.V. 
2015. Inequalities in health: definitions, concepts, 
and theories. Global Health Action 8. (1): 103402. 
https://doi.org/10.3402/gha.v8.27106 

Azzopardi-Muscat, N., Calleja, N., Calleja, A. 
and Cylus, J. 2017. Malta: Health system review. 
Health Systems in Transition 19. (1): 1–137. https://
eurohealthobservatory.who.int/publications/i/
malta-health-system-review-2017

Bambra, C., Riordan, R., Ford, J. and Matthews, 
F. 2020. The COVID-19 pandemic and health in-
equalities. Journal of Epidemiology and Community 
Health 74. (11): 964–968. https://doi.org/10.1136/
jech-2020-214401

Bíró, A., Hajdu, T., Kertesi, G. and Prinz, D. 2021. 
Life expectancy inequalities in Hungary over 25 
years: The role of avoidable deaths. Population 
Studies 75. (3): 443–455. https://doi.org/10.1080/00
324728.2021.1877332

Braveman, P. 1998. Monitoring Inequities in Health: 
A Policy-Oriented Approach in Low- and Middle-
Income Countries. Division of Analysis, Research, and 
Assessment. Geneva, Switzerland, World Health 
Organization.

Cabrera-Barona, P., Murphy, T., Kienberger, S. and 
Blaschke, T. 2015. A multi-criteria spatial depriva-
tion index to support health inequality analyses. 
International Journal of Health Geographics 14. (11): 
https://doi.org/10.1186/s12942-015-0004-x

Chelak, K. and Chakole, S. 2023. The role of social de-
terminants of health in promoting health equality: 
A narrative review. Cureus 15. (1): e33425. https://
doi.org/10.7759/cureus.33425 

Chiavarini, M., Minelli, L., Pieroni, L. and Salmasi, 
L. 2014. Decomposition of health inequalities 
at birth: A Shapley Value approach. European 
Journal of Public Health 24. (2): 162–065. https://doi.
org/10.1093/eurpub/cku162.065

Costa, C., Santana, P., Dimitroulopoulou, S., 
Burström, B., Borell, C., Schweikart, J., Dzúrová, 
D., Zangarini, N., Katsouyanni, K., Deboosere, P., 

DOI: 10.15201/hungeobull.74.2.5  Hungarian Geographical Bulletin 74 (2025) (2) 195–216.Vilinová, K. and Bullová, K. Hungarian Geographical Bulletin 74 (2025) (2) 195–216.

https://scholar.harvard.edu/davidrwilliams/reports/reaching-healthier-life
https://scholar.harvard.edu/davidrwilliams/reports/reaching-healthier-life
https://doi.org/10.2105/AJPH.2020.305610
https://ec.europa.eu/social/BlobServlet?docId=20362&langId=en
https://ec.europa.eu/social/BlobServlet?docId=20362&langId=en
https://doi.org/10.1007/978-3-030-15009-9
https://doi.org/10.3402/gha.v8.27106
https://eurohealthobservatory.who.int/publications/i/malta-health-system-review-2017
https://eurohealthobservatory.who.int/publications/i/malta-health-system-review-2017
https://eurohealthobservatory.who.int/publications/i/malta-health-system-review-2017
https://doi.org/10.1136/jech-2020-214401
https://doi.org/10.1136/jech-2020-214401
https://doi.org/10.1080/00324728.2021.1877332
https://doi.org/10.1080/00324728.2021.1877332
https://doi.org/10.1186/s12942-015-0004-x
https://doi.org/10.7759/cureus.33425
https://doi.org/10.7759/cureus.33425
https://doi.org/10.1093/eurpub/cku162.065
https://doi.org/10.1093/eurpub/cku162.065


Vilinová, K. and Bullová, K. Hungarian Geographical Bulletin 74 (2025) (2) 195–216.214

Freitas, Â., Mitsakou, C., Samoli, E., Vardoulakis, 
S., Dell’Olmo, M.M., Gotsens, M., Lustigova, 
M., Corman, D. and Costa, G. 2019. Population 
health inequalities across and within European 
metropolitan areas through the lens of the EURO-
HEALTHY Population Health Index. International 
Journal of Environmental Research and Public Health 
16. (5): 836. https://doi.org/10.3390/ijerph16050836 

Dickman, S.L., Himmelstein, D.U. and Woolhandler, 
S. 2017. Inequality and the health-care system in the 
USA. The Lancet 389. (10077): 1431–1441. https://doi.
org/10.1016/S0140-6736(17)30398-7

Docteur, E. and Berenson, R.A. 2014. In Pursuit of 
Health Equity: Comparing U.S. and EU Approaches to 
Eliminating Disparities. Timely Analysis of Immediate 
Health Policy Issues. New Jersey, Robert Wood 
Johnson Foundation – Washington D.C, Urban 
Institute. https://doi.org/10.2139/ssrn.2462922

Džambazovič, R. and Gerbery, D. 2014. Sociálno-
ekonomické nerovnosti v zdraví: Sociálnoekonomický 
status ako determinant zdravia (Socio-economic 
inequalities in health: Socio-economic status as a 
determinant of health). Sociológia 46. (2): 194–219. 
https://www.sav.sk/journals/uploads/04300827D-
zambazovic%20-%20OK.pdf

EURO-HEALTHY Consortium 2017. EURO-
HEALTHY Population Health Index. The healthyre-
gionseurope webgis. Coimbra, Portugal, University 
of Coimbra. https://healthyregionseurope.uc.pt 

Fayet, Y., Praud, D., Fervers, B., Ray-Coquard, I., 
Blay, J-Y., Ducimetiere, F., Fagherazzi, G. and 
Faure, E. 2020. Beyond the map: Evidencing 
the spatial dimension of health inequalities. 
International Journal of Health Geographics 19. (46): 
https://doi.org/10.1186/s12942-020-00242-0

Fernandez-Crehuet, J.M., Rosales-Salas, J. and de 
Ramos, S. 2019. State of health in the European 
Union: A European Health Index. Journal of 
Healthcare Quality Research 34. 308–313. https://doi.
org/10.1016/j.jhqr.2019.07.001 

Freitas, Â., Santana, P., Oliveira, M.D., Almendra, 
R., Bana Ecosta, J.C. and Bana Ecosta, C.A. 2018. 
Indicators for evaluating European population 
health: A Delphi selection process. BMC Public 
Health 18. 557. https://doi.org/10.1186/s12889-018-
5463-0

Friebelová, J. and Klicnarová, J. 2007. Rozhodovací 
modely pro ekonomy (Decision models for econo-
mists). České Budějovice, Czech republic, Jihočeská 
univerzita v Českých Budějovicích.

Ginter, E., Krajcovicova-Kudlackova, M., Kacala, 
O., Kovacic, V. and Valachovicova, M. 2001. 
Health status of Romanies (Gypsies) in the Slovak 
Republic and in the neighbouring countries. 
Bratislavské lekárske listy 102. (10): 479–484.

Global Health Europe 2009. Inequity and Inequality 
in Health. Platform for European Engagement 
in Global Health. Maastricht, NL, Maastricht 

University. https://globalhealtheurope.org/values/
inequity-and-inequality-in-health

Graham, H. 2004. Social determinants and their 
unequal distribution: Clarifying policy understand-
ings. The Milbank Quarterly 82. (1): 101–124. https://
doi.org/10.1111/j.0887-378X.2004.00303.x

Han, H., Ahn, D.H., Song, J., Hwang, T.Y. and Roh, 
S. 2012. Development of mental health indicators 
in Korea. Psychiatry Investigation 4. 311–318. https://
doi.org/10.4306/pi.2012.9.4.311

Hübelová, D., Chromková Manea, B.-E. and 
Kozumplíková, A. 2021a. Územní diferenciace 
nerovností ve zdraví v České republice (Territorial 
differentiation of health inequalities in the Czech 
republic). Praha, Grada Publishing a.s.

Hübelová, D., Kuncová, M., Vojáčková, H., 
Coufalová, J., Kozumplíková, A., Lategan, S.F. 
and Chromková Manea, B.-E. 2021b. Inequalities in 
health: Methodological approaches to spatial differ-
entiation. The International Journal of Environmental 
Research and Public Health 18. (23): 1–21. https://doi.
org/10.3390/ijerph182312275

Hübelová, D., Ptáček, P. and Šlechtová, T. 2021c. 
Demographic and socio-economic factors influencing 
health inequalities in the Czech Republic. GeoScape 
15. (1): 53–65. https://doi.org/10.2478/geosc-2021-0005

Hübelová ,  D., Caha, J . ,  Janošíková, L. and 
Kozumplíková, A. 2023. A holistic model of health in-
equalities for health policy and state administration: 
A case study in the regions of the Czech Republic. 
International Journal for Equity in Health 22. (183): 1–18. 
https://doi.org/10.1186/s12939-023-01996-2

Jutz, R. 2020. Health inequalities in Eastern Europe. 
Does the role of the welfare regime differ from 
Western Europe? Social Science & Medicine 267. 113357. 
https://doi.org/10.1016/j.socscimed.2020.113357

Káčerová, M., Ondačková, J. and Mládek, J. 2014. 
Time-space differences of population ageing in 
Europe. Hungarian Geographical Bulletin 63. (2): 
177–199. https://doi.org/10.15201/hungeobull.63.2.4

Kampf, R. 2002. Vícekriteriální rozhodovaní – metoda 
WSA (Multi-criteria decision making – WSA meth-
od). Scientific Papers of the University of Pardubice. 
Series B, The Jan Perner Transport Faculty 8. 39–48.  
https://hdl.handle.net/10195/32111

Karasiewicz, M., Chawłowska, E., Lipiak, A. and 
Więckowska, B. 2021. A step towards understand-
ing and tackling health inequalities: The use of sec-
ondary prevention services and the need for health 
promotion in a rural setting. The International Journal 
of Environmental Research and Public Health 18. (16): 
1–18. https://doi.org/10.3390/ijerph18168492

Kerschbaumer, L., Crossett, L., Holaus, M. and 
Costa, U. 2024. COVID-19 and health inequalities: 
The impact of social determinants of health on 
individuals affected by poverty. Health Policy and 
Technology 13. (1): 100803. https://doi.org/10.1016/j.
hlpt.2023.100803

https://pubmed.ncbi.nlm.nih.gov/?term=%22Freitas%20%C3%82%22%5BAuthor%5D
https://doi.org/10.3390/ijerph16050836
https://doi.org/10.1016/S0140-6736(17)30398-7
https://doi.org/10.1016/S0140-6736(17)30398-7
https://doi.org/10.2139/ssrn.2462922
https://www.sav.sk/journals/uploads/04300827Dzambazovic%20-%20OK.pdf
https://www.sav.sk/journals/uploads/04300827Dzambazovic%20-%20OK.pdf
https://healthyregionseurope.uc.pt
https://doi.org/10.1186/s12942-020-00242-0
https://doi.org/10.1016/j.jhqr.2019.07.001
https://doi.org/10.1016/j.jhqr.2019.07.001
https://doi.org/10.1186/s12889-018-5463-0
https://doi.org/10.1186/s12889-018-5463-0
https://globalhealtheurope.org/values/inequity-and-inequality-in-health
https://globalhealtheurope.org/values/inequity-and-inequality-in-health
https://doi.org/10.1111/j.0887-378X.2004.00303.x
https://doi.org/10.1111/j.0887-378X.2004.00303.x
https://doi.org/10.4306/pi.2012.9.4.311
https://doi.org/10.4306/pi.2012.9.4.311
https://doi.org/10.3390/ijerph182312275
https://doi.org/10.3390/ijerph182312275
https://doi.org/10.2478/geosc-2021-0005
https://doi.org/10.1186/s12939-023-01996-2
https://doi.org/10.1016/j.socscimed.2020.113357
https://doi.org/10.15201/hungeobull.63.2.4
https://hdl.handle.net/10195/32111
https://doi.org/10.3390/ijerph18168492
https://doi.org/10.1016/j.hlpt.2023.100803
https://doi.org/10.1016/j.hlpt.2023.100803


215

Kobza, J. and Geremek, M. 2015. Exploring the 
life expectancy increase in Poland in the con-
text of CVD mortality fall: The risk assessment 
bottom-up approach, from health outcome to 
policies. INQUIRY: The Journal of Health Care 
Organization, Provision, and Financing 52. https://doi.
org/10.1177/0046958015613091 

Krieger, N. 2011. Epidemiology and the People’s 
Health: Theory and Context. New York, Oxford 
University Press. https://doi.org/10.1093/acprof:o
so/9780195383874.001.0001

Mackenbach, J.P., Valverde, J.R., Artnik, B., Bopp, M., 
Brønnum-Hansen, H. Deboosere, P., Kalediene, 
R., Kovács, K., Leinsalu, M., Martikainen, 
P., Menvielle, G., Regidor, E., Rychtaříková, 
J., Rodriguez-Sanz, M., Vineis, P., White, Ch., 
Wojtyniak, B., Hu, Y. and Nusselder, W.J. 2018. 
Trends in health inequalities in 27 European coun-
tries. Proceedings of the National Academy of Sciences 
(PNAS) 115. (25): 6440–6445. https://doi.org/10.1073/
pnas.1800028115 

Marmot, M. and Wilkinson, R. 2005. Social 
Determinants of Health. 2nd edition. Oxford, Oxford 
University Press. https://doi.org/10.1093/acprof:o
so/9780198565895.001.0001

Marmot, M. 2010. Fair Society, Healthy Lives: Strategic 
Review of Health Inequalities in England post 2010. 
London, The Marmot Review. 

McCartney, G., Collins, C. and Mackenzie, M. 2013. 
What (or who) causes health inequalities: theories, 
evidence and implications? Health Policy 113. (3): 221–
227.  https://doi.org/10.1016/j.healthpol.2013.05.021

McCartney, G., Bartley, M., Dundas, R., Katikireddi, 
S.V., Mitchell, R., Popham, F. and Walsh, D. 2019. 
Theorising social class and its application to the study 
of health inequalities. SSM – Population Health 7. 
1003152 https://doi.org/10.1016/j.ssmph.2018.10.015

Mészáros, J. 2008. Atlas of Mortality in Slovakia 1993–
2007. Bratislava, Inštitút Informatiky a Štatistiky.

Nardo, M., Sausana, M., Saltelli, A. and Tarantola, 
S. 2005. Tools for Composite Indicators Building. 
EUR 21682 EN. JRC31473. Brussels, European 
Commission, Joint Research Centre, JRC Publications 
Repository.

National Academies of Sciences, Engineering and 
Medicine 2016. Metrics that Matter for Population 
Health Action: Workshop Summary. Washington DC, 
The National Academies Press. 

Ottersen, O.P., Dasgupta, J., Blouin, Ch., Buss, P.M., 
Chongsuvivatwong, V., Frenk, J., Fukuda-Parr, S., 
Gawanas, B.P., Giacaman, R., Gyapong, J., Leaning, 
J., Marmot, M.G., Mcneill, D., Mongella, G.I., 
Moyo, N., Møgedal, S., Ntsaluba, A., Ooms, G., 
Bjertness, E., Lie, A-L., Moon, S., Roalkvam, S., 
Sandberg, K.I. and Scheel, I. 2014. The political 
origins of health inequity: Prospects for change. The 
Lancet 383. (9917): 630–667. https://doi.org/10.1016/
S0140-6736(13)62407-1 

Pearson-Stuttard, J., Murphy, O. and Davies, S.C. 
2019. A new Health Index for England: The Chief 
Medical Officer’s 2018 annual report. The Lancet 
393. (10166): 10–11. https://doi.org/10.1016/S0140-
6736(18)33210-0

Pearson-Stuttard, J. and Davies, S.C. 2025. The 
Health Index: A framework to guide health-driven 
prosperity. The Lancet 405. (10481): 777. https://doi.
org/10.1016/S0140-6736(25)00010-8

Rapant, S., Letkovičová, M., Cvečková, V., Fajčíková, 
K., Galbavý, J. and Letkovič, M. 2010. Environmental 
and Health Indicators of Slovak Republic. Bratislava, 
Štátny geologický ústav Dionýza Štúra.

Rapant, S., Cvečková, V., Dietzová, Z., Fajčíková, 
K., Hiller, E., Finkelman, R.B. and Škultétyová, 
S. 2013. The potential impact of geological envi-
ronment on health status of residents of the Slovak 
Republic. Environ Geochem Health 36. (3): 543–561. 
https://doi.org/10.1007/s10653-013-9580-5

Rój, J. and Jankowiak, M. 2021. Socio-economic de-
terminants of health and their unequal distribution 
in Poland. The International Journal of Environmental 
Research and Public Health 18. (20): 1–20. https://doi.
org/10.3390/ijerph182010856  

Rosenkötter, N. and van Bon-Martens, M. 
2015. Public health monitoring and reporting: 
Maintaining and improving the evidence-base. 
The European Journal of Public Health 24. (2): 17–20. 
https://doi.org/10.1093/eurpub/cku152.005

Rosicova, K., Madarasova Geckova, A., Dijk, J.P., 
Kollarova, J., Rosic, M. and Groothoff, J.W. 2011. 
Regional socio-economic indicators and ethnicity 
as predictors of regional infant mortality rate in 
Slovakia. International Journal of Public Health 56. (5): 
523–531. https://doi.org/10.1007/s00038-010-0199-3

Salmi, L.-R., Barsanti, S., Bourgueil, Y., Daponte, A., 
Piznal, E., Ménival, S. and AIR Research Group 
2017. Interventions addressing health inequali-
ties in European regions: The AIR project. Health 
Promotion International 32. (3): 430–441. https://doi.
org/10.1093/heapro/dav101

Scholz, N. 2020. Addressing Health Inequalities in the 
European Union. Brussels, European Parliamentary 
Research Service.

Schoon, P.M. and Krumwiede, K. 2022. A holistic 
health determinants model for public health nurs-
ing education and practice. Public Health Nursing 39. 
(5): 1070–1077. https://doi.org/10.1111/phn.13063 

Solar, O. and Irwin, A. 2010. A conceptual framework 
for action on the social determinants of health. Social 
Determinants of Health Discussion Paper 2 (Policy 
and Practice). Geneva, World Health Organization. 
https://iris.who.int/bitstream/handle/10665/44489/ 
9789241500852_eng.pdf?sequence=1&isAllowed=y 

Sopóci, J. and Hrabovská, A. 2015. Kultúrno-behaviorálna 
podmienenosť nerovností v oblasti zdravia na 
Slovensku (The cultural and behavioural determinants 
of health inequalities in Slovakia). Sociologický časopis 

DOI: 10.15201/hungeobull.74.2.5  Hungarian Geographical Bulletin 74 (2025) (2) 195–216.Vilinová, K. and Bullová, K. Hungarian Geographical Bulletin 74 (2025) (2) 195–216.

https://doi.org/10.1177/0046958015613091
https://doi.org/10.1177/0046958015613091
https://doi.org/10.1093/acprof:oso/9780195383874.001.0001
https://doi.org/10.1093/acprof:oso/9780195383874.001.0001
https://doi.org/10.1073/pnas.1800028115
https://doi.org/10.1073/pnas.1800028115
https://doi.org/10.1093/acprof:oso/9780198565895.001.0001
https://doi.org/10.1093/acprof:oso/9780198565895.001.0001
https://doi.org/10.1016/j.healthpol.2013.05.021
https://doi.org/10.1016/j.ssmph.2018.10.015
https://doi.org/10.1016/S0140-6736(13)62407-1
https://doi.org/10.1016/S0140-6736(13)62407-1
https://doi.org/10.1016/S0140-6736(18)33210-0
https://doi.org/10.1016/S0140-6736(18)33210-0
https://doi.org/10.1016/S0140-6736(25)00010-8
https://doi.org/10.1016/S0140-6736(25)00010-8
https://doi.org/10.1007/s10653-013-9580-5
https://doi.org/10.3390/ijerph182010856
https://doi.org/10.3390/ijerph182010856
https://doi.org/10.1093/eurpub/cku152.005
https://doi.org/10.1007/s00038-010-0199-3
https://doi.org/10.1093/heapro/dav101
https://doi.org/10.1093/heapro/dav101
https://doi.org/10.1111/phn.13063
https://iris.who.int/bitstream/handle/10665/44489/9789241500852_eng.pdf?sequence=1&isAllowed=y%20
https://iris.who.int/bitstream/handle/10665/44489/9789241500852_eng.pdf?sequence=1&isAllowed=y%20


Vilinová, K. and Bullová, K. Hungarian Geographical Bulletin 74 (2025) (2) 195–216.216

/Czech Sociological Review 51. (4): 667–699. https://doi.
org/10.13060/00380288.2015.51.4.211

Sopóci, J., Džambazovič, R. and Gerbery, D. 2015. 
Zdravie a zdravotná starostlivosť na Slovensku – nerov-
nosti v zdraví (Health and medical care is Slovakia 
– health inequalities). Bratislava, STIMUL. 

Sowa-Kofka, A. 2018. ESPN Thematic Report on 
Inequalities in Access to Healthcare: Poland. Thematic 
report. Directorate General for Employment, 
Social Affairs and Inclusion. Brussels, European 
Commission.  https://ec.europa.eu/social/
BlobServlet?docId=20352&langId=en

Šprocha, B., Šídlo, L. and Burcin, B. 2015. Úroveň 
úmrtnosti na Slovensku a v Česku v Európskom 
pohľade (Mortality levels in Slovakia and Czechia 
in European comparative perspective). Geografický 
časopis 67. (1): 25–43.

UW Population Health Institute 2020. Ten-Year 
Reflections on the County Health Rankings & 
Roadmaps. University of Wisconsin Population 
Health Institute, Madison, University of Wisconsin-
Madison. https://www.countyhealthrankings.org/
sites/default/files/media/document/CHRR10_year_
reflections_report.pdf

Uzzoli, A., Bán, A., Beke, S., Pál, V. and Vitrai, J. 
2020. Health inequality and its regional disparities 
in Hungary. Current Politics and Economics of Russia, 
Eastern and Central Europe 35. (1): 1–48.

Vilinová, K. 2012. Zdravotný stav obyvateľstva Slovensk 
(Health status of the Slovak population). Nitra, 
Univerzita Konštantína Filozofa v Nitre.

Vrabcová, J., Daňková, Š. and Faltysová, K. 2017. 
Healthy life years in the Czech Republic: Different 
data sources, different figures. Demografie 59. (4): 315–
331. https://csu.gov.cz/docs/107508/618ee1b8-ecaa-
03a2-dde4-f4b5d4387786/vrabcova.pdf?version=1.0

Weinstein, J.N., Geller, A., Negussie, Y. and Baciu, 
A. 2017. Communities in Action: Pathways to Health 
Equity. Washington DC, The National Academies 
Press. https://doi.org/10.17226/24624

WHO 2018. Health Inequities and Their Causes. Geneva, 
CH, World Health Organization. https://www.
who.int/news-room/facts-in-pictures/detail/health-
inequities-and-their-causes

https://doi.org/10.13060/00380288.2015.51.4.211
https://doi.org/10.13060/00380288.2015.51.4.211
https://ec.europa.eu/social/BlobServlet?docId=20352&langId=en
https://ec.europa.eu/social/BlobServlet?docId=20352&langId=en
https://www.countyhealthrankings.org/sites/default/files/media/document/CHRR10_year_reflections_report.pdf
https://www.countyhealthrankings.org/sites/default/files/media/document/CHRR10_year_reflections_report.pdf
https://www.countyhealthrankings.org/sites/default/files/media/document/CHRR10_year_reflections_report.pdf
https://csu.gov.cz/docs/107508/618ee1b8-ecaa-03a2-dde4-f4b5d4387786/vrabcova.pdf?version=1.0
https://csu.gov.cz/docs/107508/618ee1b8-ecaa-03a2-dde4-f4b5d4387786/vrabcova.pdf?version=1.0
https://doi.org/10.17226/24624
https://www.who.int/news-room/facts-in-pictures/detail/health-inequities-and-their-causes
https://www.who.int/news-room/facts-in-pictures/detail/health-inequities-and-their-causes
https://www.who.int/news-room/facts-in-pictures/detail/health-inequities-and-their-causes

	Katarína VILINOVÁ - Kristína BULLOVÁ: Health inequalities in Slovakia assessed by the Health Index: Unveiling regional disparities and their impact on the population. Hungarian Geographical Bulletin Vol 74 Issue 2 195-216