Analysis of Health Care System Development in the Regions amidst the

Economic Inclusiveness and Social Determinants of Health

NATALIIA LETUNOVSKA1, LIUDMYLA SAHER1, LIUBOV SYHYDA1,

ALONA YEVDOKYMOVA2

1Department of Marketing, Sumy State University, Rymskyi-Korsakiv Street 2, UKRAINE

2Oleg Balatskyi Department of Management, Sumy State University, Rymskyi-Korsakiv Street 2,

UKRAINE

Abstract: - The article proposes a neural network-based Kohonen's self-organized maps cluster analysis of

Ukraine’s health care system at regional level. At analysis, economic patterns and social determinants of health

are considered. The research aims to estimate regional security at the public health level. For that, behavioral

and social patterns determine a regions’ potential resistance to public health risks. The authors identify the

strengths and weaknesses of each region and assess the effectiveness of health care as it is provided.

Interestingly, the clustering algorithm fits multidimensional space design into spaces with a lower dimension.

Additionally, similar vectors in the source space appear closely on the resulting map. The algorithm design,

stages of evaluation, and input groups of indicators by components are described. The data set reflects the 22

regions of Ukraine. The rationing of indicators is calculated to make the data comparable. Data are checked for

quality, sparsity, duplicates, and inconsistencies. Five clusters are generated based on development of patterns

within regions as well as the information value of healthcare-related socio-economic indicators. The residents

of regions that belong to the first cluster systematically assess their health. Demographically, these residents are

more physically active compared with residents in clusters of other regions. Findings also indicate that residents

in the first cluster monitor their nutrition. The second cluster is informative on residents’ behavioral

components. In the third cluster are grouped regions with financially secure residents. The fourth cluster

includes leader regions. The fifth cluster includes outsider regions. The proposed model can easily fit to new

data, to identify new patterns and to graphically represent new results. The model can also analyze

computationally complex approach based on a complete set of multidirectional indicators relating to the

country's medical system at a state of risk. Moreover, this cluster-based approach can identify areas that require

increased attention by state public health agencies.

Key-Words: - Regional health care system, Inclusive health, Public health, Behavioral patterns of health, Social

determinants of health, Kohonen's self-organized maps, Regions' Clustering, Healthy region.

1 Introduction

Challenges to the regional healthcare security

systems are being addressed. Health care protection

and illness prevention have increased since 2020 in

the context of the COVID-19 pandemic.

Reformation of public health systems is a

prerequisite for fighting against the dangerous virus,

which has caused significant human and economic

losses. The number of deaths from COVID-19

worldwide is about 4.5 million people. The number

of failures is 4.4% of the world's gross domestic

product (GDP). In Ukraine, these figures are 58.1

thousand people and 5% of the GDP, respectively,

[10]. Several sources confirm a strong link between

the population's health and the state of the economy.

The study in [8] notes that in 12 countries from

1820 to 2010, there was a significant correlation

between the level of health care system

development, mortality reduction, and GDP per

capita growth. A World Bank study, [55], states that

from 1780 to 1979, 30% of Britain's GDP growth

was due to people's improved health and nutrition.

The World Health Organization (WHO) estimates

that human morbidity increases treatment costs

(20% of costs), and the economy loses the working-

age population (80% of costs). About 6% of

Ukraine's GDP losses are due to premature

mortality. One-third of deaths are due to

cardiovascular disease, the crucial causes of which

are low physical activity and poor nutrition, [8]. At

the same time, the cost of health care is not the main

factor in improving the population's health. For

example, in many countries in the European region,

economic growth associated with increased life

expectancy from 1970 to 2003 was 29-38% of GDP.

Received: August 25, 2022. Revised: November 27, 2022. Accepted: January 19, 2023. Published: February 24, 2023.

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DOI: 10.37394/232015.2023.19.14

Nataliia Letunovska, Liudmyla Saher,

Liubov Syhyda, Alona Yevdokymova

E-ISSN: 2224-3496

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This growth is much higher than health care

spending.

It is significant to restructure the health care

system of territorial units in the country while

considering each region's problems, needs, and

potential. The analysis of development indicators

makes it possible to identify different approaches to

the health care support of the internal population. In

times of global stress on the health sector, local

health management strategies must rely on an

innovative interpretation of the causal links between

the environment (social and physical) and public

health. It becomes possible thanks to the study of

the determinants of regional health by groups of

parameters that derive from the territory's

demographic, social, economic, and environmental

parameters. This work aimed to identify different

components of regional health: the current state of

health, the level of disease prevention, aspects

related to public information, provision of medical

services, financial aspects of the latest reforms, and

the behavioral component of life in each area. The

development of effective measures to preserve and

strengthen regional health is the output of such an

analysis, which makes it possible to identify the

strengths and weaknesses of the region and carefully

assess the feasibility and capacity (financial,

managerial, or time) to improve. This set of tools

aims to overcome the negative trend of life quality

deteriorating, in particular the health of the people.

According to [12], people in Ukraine spend 39

billion UAH annually on pharmacy purchases

(excluding reimbursement volumes).

The authors decided to generalize different

indicators of the health system in Ukraine and

define the level of health care in regions with their

further typology.

2 Problem Formulation

2.1 Literature Review

The formation of a theoretical approach to defining

goals and essence, a "fair evaluation" in the

healthcare sector, [53], creates an opportunity to

methodologically assess the state of a healthcare

system of a particular region or of multiple regions

with a clear understanding of the tasks while

mitigating potential mistakes. Assessment of the

relationships between stakeholders and their costs

for healthcare are analyzed in [16].

Some systems generalize specific indices of

regional development. They are presented in [7]. In

[31], the authors proposed an algorithm for solving

the problem of development indicators at the

enterprise level. It could help analyze health care

system development, as it allows to take into

account various impact aspects. Many scientists

include assessing the health care system indicators

to evaluate the territory's competitiveness or

determine the interdependence between public

health and other macroeconomic parameters, [4],

[5], [7], [13], [23], [24], [25], [28], [29], [35], [49].

Measuring public healthcare delivery efficiency

from a regional perspective by applying conditional

nonparametric models is discussed in the following

studies, [1], [15]. The researchers used data

development analysis (DEA) and the free disposal

hull (FGH) method to determine the impact of GDP

level on regional healthcare delivery. In the article

[3]. DEA is described as a tool for analysis of health

delivery with ranking, but it is discouraged due to

lack of discrimination and comparability. The

researchers proposed a DEA-based model, which is

improved.

Applying cluster factor analysis, [51], allowed a

cross-sector analysis of secondary data for mapping

and scheduling based on ISR in health regions.

Using the example of Brazil, researchers have

identified the regions with the highest and lowest

rates of health system development, depending on

the level of socio-economic development.

Correlation and regression analysis was performed

in [46], [48] to estimate the relationship between

healthcare and economic indicators.

The study of various aspects of the Covid-19

pandemic, including the implementation of

clustering of regions based on Boxplots and Pearson

correlations that determine relationships between

outcomes, clusters, and contextual factors, [9].

Another method is spatial modeling of disease

transmission [32] by using Global Ordinary Least

Squares (OLS), Geographically Weighted

Regression (GWR), and multiscale geographically

weighted regression MGWR. The researchers

studied the involvement degree in solving different

problems of key stakeholders ([42], [48]) for

assessing critical changes in health care under the

influence of health threats ([43], [44]) and

identification the socioecological-economic

vulnerability parameters of regions for adjusting

state and regional programs concerning the

mobilization of economic and healthcare systems,

[26].

The study of essence of inclusive development as

a part of achieving sustainable development goals is

in [14], [19]. Consideration of the role of

stakeholders in this process of essence is in [6],

[20], [29], [30], [38] that allowed to define the goals

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DOI: 10.37394/232015.2023.19.14

Nataliia Letunovska, Liudmyla Saher,

Liubov Syhyda, Alona Yevdokymova

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of health care development as one of the critical

areas ensuring the well-being of individual regions

and the country.

2.2. Materials and Methods

The article generalizes the level of health care in the

country's regions and provide the typology of the

parts. This is performed by cluster analysis with the

goal to identify issues that need urgent solutions as

well as promising areas of development of the

health care system. The information base of the

study consists of open Internet sources, data from

reports of international and national health

organizations and organizations in related industries,

statistical yearbooks and thematic collections, and

sociological surveys.

The selected cluster technique is non-traditional

Kohonen’s self-organizing maps (Kohonen’s

network), a type of neural network algorithm. It is a

method for fitting multidimensional space design

into spaces with a lower dimension. Importantly,

similar vectors in the source space appear closely on

the resulting map.

The selected clustering method addresses various

scientific problems. Here, it is used to stratify

regions of Ukraine depending on the level of health

care. The dividing multiplicity of the areas into

clusters will help to identify internal patterns in

groups and understand the informativeness of the

socio-economic properties of these areas. As a

result, this could enable the development of local

and national level health care strategies. The

Deductor Studio Academic 5.3 software is

employed to create Kohonen’s maps. This software

has several advantages, such as accessibility to a

wide range of users in the field of neural network

computing, significant analytical power, and

graphical and statistical capabilities with a user-

friendly process of interactive research analysis.

3 Problem Solution

To conduct the study, the authors have selected

input indicators that contain various aspects of

health care in the regions: the current state of health,

the level of preventive work, coordination of

information work (information influence), support

for health services, financial aspects considering

reforms in the health care system, and the behavior

of the population of the region. The following

indicators are involved in certain groups (we

indicate them together with the sources of

information):

1. The current state of health:

 average life expectancy after 60 (retirement

age), [39], [47];

 the mortality rate in the age group of 35-44

years (based on WHO classification of young

people and number of employees, [27]);

 the number of deaths from illnesses of the

circulatory system (incidences of cardiovascular

disease are the leading cause of death in Ukraine at

a four times higher rate compared with EU countries

[36], [18]);

 the number of deaths from malignant

neoplasms-cancer (Between 2015-2020, cancer

followed cardiovascular diseases as the largest cause

of death among Ukrainians [54]);

 self-assessment of health at the levels of

"good" or better among the population (results of

primary research are informative and representative

sources of information in health care systems, [41]);

2. Level of preventive work:

 vaccination of neonates against

tuberculosis, [18];

 percentage of people vaccinated against

COVID-19, [52];

 AIDS incidence (timely detection of human

immunodeficiency virus (HIV) infection does not

always lead to AIDS. Prevention screening helps to

early detect HIV and support people living with

HIV), [18];

3. Provision of medical services:

 number of hospitals, [21], [34];

 ratio of doctors to people, [45];

 population per health worker, [45].

4. Financial component:

 payments under the contracts of the Medical

Guarantees Program. Since 2020, there have been

systemic changes in the medical sector of Ukraine.

In particular, the National Health Service provided

the new Medical Guarantees Program. According to

this program, all medical service providers receive

funds under the contract for specific medical

services, [2], [50];

 percentage of declarations submitted to

primary care physicians, [2].

5. Information influence:

 level of trust in Ukrainian television.

According to results of all-Ukrainian polls,

television remains in the first place among all

media, from which the country's residents most

often receive information about current events

affairs in Ukraine and around the world, [22]. Thus,

it is appropriate to use television to disseminate

information on various aspects of disease control

and prevention and the operations of the national

health system. The effectiveness of this information

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DOI: 10.37394/232015.2023.19.14

Nataliia Letunovska, Liudmyla Saher,

Liubov Syhyda, Alona Yevdokymova

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depends on the level of trust in media in different

regions of the country, [33];

 number of Internet users. Currently, the

Internet is the second source of information after

television. People often search the Internet for

information about social events in the health care

sphere. The Internet can be used as an effective

communication tool for dissemination of

information. However, this is conditioned to the

proportion of residents with stable access to the

Internet in all regions and rural areas, [37];

 count of Ukrainian-language and Russian-

language search queries of "disease prevention"

according to Google Trends. There is a hypothesis

that people's willingness to influence information

can be checked by the activity of search queries on

disease prevention, [57]:

 knowledge about the symptoms of a stroke.

In many media sources, the signs of the stroke are

called indicative. It claims the success or

ineffectiveness of information work among people

of all ages, [17].

6. Behavioral component:

 consumption of milk and dairy products.

There is a hypothesis that the consumption of

healthy foods, such as milk, and dairy products.

First, this is considered a personal choice. Second, it

is a crucial component of human health, disease

prevention and disease control, [4];

 consumption of fruits for example, berries,

and grapes, [4];

 consumption of fish and fish products, [4];

 consumption of sugar. This component

gauges population health nutrition trends. Research

finds show that consumption of sugar replaces the

consumption of fruits such as a fresh fruits or dried

fruits, [4];

 number of people engaged in physical

activities such as sports (this indicates people's

lifestyle and personal health care), [11];

 number of smokers, [40];

 population that is obese, [40].

The listed set of indicators differs in dimension

and indicators' direction. For some indicators, a

higher value is the best; for others – a lower value is

the best. We use two formulas to bring all the

indicators into a single dimension to facilitate our

cluster analysis:

М = 𝐾𝑚𝑎𝑥−𝐾𝑖𝑗

𝐾𝑚𝑎𝑥−𝐾𝑚𝑖𝑛

(1)

where Kmax, Kmin – are the maximum and

minimum values of indicators, respectively; Kij – the

value of the i-th indicator of the j-th region for the

analyzed case.

This formula is used to evaluate indicators whose

growth is positive: average life expectancy; self-

assessment of health at the levels not lower than

"good" among the population of the region;

vaccination coverage against tuberculosis; the

percentage of people vaccinated against COVID-19;

the number of hospitals; the ratio of doctors;

payments under the contracts of the Medical

Guarantee Program; the percentage of declarations

submitted to primary care physicians; level of trust

in Ukrainian television; the number of Internet

users; count of Ukrainian-language search queries

"disease prevention", count of Russian-language

search queries "disease prevention"; knowledge

about the symptoms of a stroke; consumption of

milk and dairy products; consumption of fruits;

consumption of fish and fish products; number of

people engaged in physical activities.

М = 𝐾𝑖𝑗−𝐾𝑚𝑖𝑛

𝐾𝑚𝑎𝑥−𝐾𝑚𝑖𝑛

(2)

This formula is used to evaluate indicators whose

growth has a negative effect. All other indicators not

listed above belong to this group.

Table 1 presents the initial values of indicators

for the six components of the primary integrated

assessment of the health care system in the regions

of Ukraine. Table 2 shows the results of data

generalization of the rest of the indicators on the

information influence and behavioral components.

Tables 3-4 present the values of equal indicators

after their rationing. The authors didn't consider the

Luhansk and Donetsk regions due to missing or

incomplete indicators. After preliminary data

preparation, an analysis of the level of health care in

the regions of Ukraine was performed using

Kohonen's self-organized maps.

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DOI: 10.37394/232015.2023.19.14

Nataliia Letunovska, Liudmyla Saher,

Liubov Syhyda, Alona Yevdokymova

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Table 1. Indicators for integrated assessment of the

health care system of the regions of Ukraine

(first part)

Software-wise, the following options were

considered to construct the Kohonen's self-

organized maps:

1) for all variables, the initial purpose "Inbox"

is set. For the variable "Region," the goal is

"Information";

2) extension 16:12 was chosen as a parameter

of the map. It was enough to identify a set of

regions' clusters;

3) the number of epochs is equal to 500, and

the level of error for recognition is less than 0.05;

4) to determine the initial weights of neurons,

the method "From eigenvectors" was chosen. This

method allows to initialize the initial weights of

neurons with the values of a subset of the

hyperplane through which two eigenvectors of the

covariance matrix of input sample values pass, [56];

5) as a function of the neighborhood, step one

is chosen.

Table 2. Indicators for integrated assessment of the

health care system of the regions of Ukraine

(second part)

Table 3. Normalized values of health assessment

indicators in the regions of Ukraine (first part)

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Table 4. Normalized values of health assessment

indicators in the regions of Ukraine (second part)

It is possible to generate five clusters. The results

are presented in Figure 1.

Fig. 1: Kohonen’s maps after going through the

steps of construction in a software environment

According to the Kohonen’s self-organized

maps, the regions were divided into five clusters

(Table 5).

Table 5. Clusters of regions of Ukraine according to

the integrated level of health care

Cluster

Regions included in

the cluster

Characteristic

1 (blue and

dark blue))

Vinnytsia, Volyn,

Zakarpattia,

Kirovohrad,

Mykolaiv, Kherson,

Cherkasy

These are regions whose

residents tend to re-evaluate

their health compared to the

other areas, as a high self-

assessment of health quality

is not supported by the actual

parameters of the health care

system. However, the

population here is quite

physically active. Another

advantage of the regions of

this cluster is the monitoring

of the diet among the people.

As for sugar consumption,

this cluster leads negatively

among others.

2 (azure)

Lviv, Rivne,

Khmelnytskyi

The health care system in this

group of regions is

characterized by a high level

of individual indicators

related to information work

and behavioral components.

According to other indicators,

the regions of this group are

at the average level,

significantly not lagging

behind other regions.

3 (green)

Dnipropetrovsk,

Zaporizhzhia, Odesa,

Poltava, Sumy,

Kharkiv

These are more financially

secure regions in the health

care system compared to

other territorial units, but this

is where their benefits end

(excluding the lowest sugar

consumption). Regarding all

other components, these

regions are at an average level

and below average.

4 (yellow)

Ivano-Frankivsk,

Ternopil, Chernivtsi

It is a group of leading

regions in almost all

components with a limitation

on some indicators. These are

regions of the exclusively

western part of Ukraine.

5 (red)

Zhytomyr, Kyiv,

Chernihiv

It is a group of outsider

regions in almost all

components, except financial

and information, wherein they

even lead among other

regions.

* – formulated based on the calculations in Table of

Appendix 1

4 Discussion

By changing the values of the input indicators, it is

possible to assess the feasibility of

recommendations for improving the health care

system in the regions either individually or in

selected groups. For example, after improving one

of the components, the researcher could check

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changes in the system and modifications that affect

the ratio of components. If the indicator improves,

then the region can be transferred to another cluster.

Conversely, this is considered for the deterioration

of the parameters that recognize the successful

functioning of the medical care system [58]. Also, it

is possible to assess the regions' ability to be moved

to a related cluster here, of a different color.

Additionally, it is probable to improve the

position of an entire cluster by developing the

performance of a particular region in the group.

Several indicators tend to change over time for

instance, percentage of people vaccinated against

COVID-19, percentage of declarations submitted to

primary care physicians, advancement of positions

of regions. A permanent monitoring of selected

indicators helps to identify problematic issues

related to regions' development. As a result, it

allows adjusting mentioned issues while managing

and implementing action strategies. Under

optimistic scenarios for improved regional health

care systems in Ukraine, it is possible to reduce the

number of clusters for example, 3-4 by increasing

the values of indicators.

5 Conclusion

This paper presents analysis on the further

development of the state health care system in the

regions of Ukraine. The study identified groups of

regions that are leading or underdeveloped in

specific healthcare-related components. The

significant difference of this study from the existing

ones is in applying a complex approach using the

complete set of multidirectional indicators relating

to the quality of the country's medical system in

functioning in a state of risk. Additionally, the study

identified areas that require additional attention

from the relevant government agencies involved in

public health. For the analysis, the Kohonen’s self-

organized maps were used. This model allowed to

visualize clusters of regions and analyze the

obtained profiles according to the input indicators.

Five clusters were obtained. In each group, regions

have a similar level of health care system

development. This study could be helpful for the

improvement of medium- and long-term programs

for the development of health care systems at the

national level. Future research will focus on creating

recommendations for each cluster. It will allow the

development of specific models for improving

existing health care systems at the local level.

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Liubov Syhyda, Alona Yevdokymova

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Contribution of Individual Authors to the

Creation of a Scientific Article (Ghostwriting

Policy)

Nataliia Letunovska has designed the methodology.

Liudmyla Saher has created model.

Liubov Syhyda was responsible for the statistical

data.

Alona Yevdokymova has compiled the literature

review.

Sources of Funding for Research Presented in a

Scientific Article or Scientific Article Itself

This research was conducted within the theme

"Economic and mathematical modeling and

forecasting, development of methodological and

methodological principles of creating a roadmap for

reforming the health care system in Ukraine taking

into account behavioral, social, economic and legal

determinants" (basic funding of the Ministry of

Education and Science, decree from 16 April 2021

№434, Agreement BF/24-2021).

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Conflict of Interest

The authors have no conflicts of interest to declare

that are relevant to the content of this article.

Creative Commons Attribution License 4.0

(Attribution 4.0 International, CC BY 4.0)

This article is published under the terms of the

Creative Commons Attribution License 4.0

https://creativecommons.org/licenses/by/4.0/deed.en

_US

Appendix 1. Average values of indicators in clusters

Indicator

1st

cluster

2nd

cluster

3rd

cluster

4th cluster

5th cluster

Average life expectancy at the age of 60,

years

17,77

17,99

17,81

18,021

17,262

Mortality rate in the age category of 35-44

years, per 100 thousand people of the

appropriate age

702,9

733,4

805,4

526

949,8

The number of deaths from diseases of the

circulatory system, per 100 thousand people

1053

945,1

1133,2

1023,6

1290,4

The number of deaths from malignant

neoplasms per 100 thousand population

189,4

180,3

231

171

206,1

Self-assessment of health at the levels not

lower than "good" , % of respondents

58,4

42,3

42,7

38,3

Coverage of vaccination against tuberculosis

in newborns, %

92,3

87,3

75,7

Percentage of people vaccinated against

COVID-19, %

14,1

14,2

15,3

12,7

17,2

Incidence of AIDS, persons per 10 thousand

population

4,8

14,5

2,6

10,9

The number of hospitals , units per 100

thousand population

4,3

4,7

4,6

5,9

4,3

Ratio of doctors per 10 thousand population

32,9

40,6

39,8

51,1

33,1

Population per average health worker, persons

111,7

98,3

110,3

97,7

104

Payments under the contracts of the Medical

Guarantee Program, UAH billion

0,8

1,21

1,42

0,8

0,88

The percentage of declarations submitted to

primary care physicians, %

87,7

82,7

85,3

Level of trust in Ukrainian television, in %

88,2

81,3

The number of Internet subscribers per

1000 population

0,08

0,07

0,15

0,16

0,08

Popularity of search queries "disease

prevention", in points

58,6

73,7

54,5

44,3

Knowledge about the symptoms of a stroke, %

of respondents

50,9

51,8

26,3

Consumption of milk and dairy products per 1

person per year, kg

207,9

205,1

164,8

252,5

210,2

Consumption of fruits, berries, and grapes per

1 person per year, kg

55,9

57,1

59,5

Consumption of fish and fish products per 1

person per year, kg

10,1

12,2

9,7

14,8

Sugar consumption, per 1 person per year, kg

31,5

29,6

28,1

29,9

30,1

The number of people engaged in physical

culture and sports, per 1 thousand people

259

96,6

158,5

76,6

86,9

Number of smokers per 1,000 people

179,8

151,3

189

153,7

152,8

Population with obesity,%

17,7

14,2

16,6

16,3

20,7

1 – the best value of the indicator among other groups of regions;

2 – the worst value of the indicator among other regions

WSEAS TRANSACTIONS on ENVIRONMENT and DEVELOPMENT

DOI: 10.37394/232015.2023.19.14

Nataliia Letunovska, Liudmyla Saher,

Liubov Syhyda, Alona Yevdokymova

E-ISSN: 2224-3496

173

Volume 19, 2023