11(6) 2020 ITJEMAST Research Articles

Volume 11 Issue 6 (2020)
ISSN 2228-9860
eISSN 1906-9642
http://TuEngr.com
IMPACTS OF FOREIGN DIRECT
INVESTMENT ON INDIAN RETAIL
BAZAAR: AN EMPIRICAL STUDY
ALGORITHMIZATION FOR PROCESSES OF
REGIONAL DIFFERENTIATION AND
CONCENTRATION OF INVESTMENTS IN
RUSSIAN AGRICULTURE
FACTORS AFFECTING ACCEPTANCE AND
THE USE OF TECHNOLOGY IN YEMENI
TELECOM COMPANIES
STUDENTS' FINANCIAL LITERACY AND
POLICIES FOR ITS DEVELOPMENT
3D MODELING IN AUTOCAD? AS A BASIC
COMPONENT OF THE INITIAL TRAINING
OF MECHANICAL ENGINEERS
USE OF FULL-FAT SOY FLOUR IN
COMPOUND FEEDS FOR MEAT CHICKENS
OF THE INITIAL LINES AND BROILER
CHICKENS
A ROBUST VIDEO STABILIZATION
ALGORITHMS FOR GLOBAL MOTION
ESTIMATION USING BLOCK MATCHING
EVALUATION OF ERGONOMIC DESIGN
OF DESK AND CHAIR FOR PRIMARY
SCHOOLS IN ERBIL CITY
MICRO AND MACRO FINANCIAL INCLUSION AND
THEIR IMPACTS ON ECONOMIC GROWTH: EVIDENCE
FROM ASIAN ECONOMIES WITH ALTERNATIVE
APPROACHES
DEVELOPMENT OF TECHNOLOGY FOR THE
PRODUCTION OF MULTICOMPONENT FEED
SUPPLEMENT
HOSPITAL ANXIETY AND DEPRESSION OF PATIENTS
WITH HEART FAILURE IN SOUTH PUNJAB
PAKISTAN: A SECTIONAL SURVEY STUDY
ROLES OF PHYSICAL ENVIRONMENT IN
CHARACTERIZING THE IDENTITY OF MALAY ROYAL
TOWN IN ALOR SETAR, KEDAH
GREEN HUMAN RESOURCE MANAGEMENT
PRACTICES AND ORGANIZATIONAL CITIZENSHIP
BEHAVIOUR FOR ENVIRONMENT: THE INTERACTIVE
EFFECTS OF GREEN PASSION
CHARACTERISTICS OF POST-GLOBALIZATION
APPLICATION OF NUMERICAL MODELING TO
PREDICT AND EVALUATE THE EFFECTS OF
MANAGEMENT SCENARIOS IN GROUNDWATER
RESOURCES
EFFECTS OF IRRIGATION SCHEDULING AT
DIFFERENT MANAGED ALLOWABLE DEPLETION IN
SALINE SOIL ON THREE RICE VARIETIES
DYNAMIC NEXUS AMONG GOLD PRICE, EXCHANGE
RATES, AND EQUITY RETURN OF PAKISTAN
SMALL SPACES NEED SMART SOLUTIONS: IMPACTS
OF SMART INTERIOR DESIGN SOLUTIONS ON
ACHIEVING FLEXIBLE SPACES
STATE AND EFFECTIVENESS OF THE RUSSIAN
ENTERPRISE OF HORTICULTURE PRODUCTION
IMPACTS OF SUBSTANTIAL STRATEGIC ISSUES AND
TRENDS OF FOREIGN DIRECT INVESTMENT ON
INDIAN RETAIL CONSUMER SERVICE INDUSTRY

International Transaction Journal of Engineering,
Management, & Applied Sciences & Technologies
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Professor
Universiti Sains Malaysia,
MALAYSIA
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Assistant Prof.Dr. Rohit Srivastava (Indian Institute of Technology Bombay, INDIA)
Assistant Prof. Dr.Muhammad Yar Khan (COMSATS University, Pakistan)
Assistant Prof. Dr. David Kuria (Kimathi University College of Technology, KENYA )
Dr. Mazran bin Ismail (Universiti Sains Malaysia, MALAYSIA )
Dr. Salahaddin Yasin Baper (Salahaddin University - Hawler, IRAQ )
Dr. Foong Swee Yeok (Universiti Sains Malaysia, MALAYSIA)
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Dr.Yasser Arab (Ittihad Private University, SYRIA)
Dr.Arslan Khalid (Shandong University, CHINA)
©2020 International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies.

i
©2020 International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies.
:: International Transaction Journal of Engineering,
Volume 11 Issue 6 (2020) ISSN 2228-9860
http://TuEngr.com eISSN 1906-9642
FEATURE PEER-REVIEWED ARTICLES
MICRO AND MACRO FINANCIAL INCLUSION AND THEIR IMPACTS ON
ECONOMIC GROWTH: EVIDENCE FROM ASIAN ECONOMIES WITH
ALTERNATIVE APPROACHES
11A06A
DEVELOPMENT OF TECHNOLOGY FOR THE PRODUCTION OF
MULTICOMPONENT FEED SUPPLEMENT 11A06B
HOSPITAL ANXIETY AND DEPRESSION OF PATIENTS WITH HEART
FAILURE IN SOUTH PUNJAB PAKISTAN: A SECTIONAL SURVEY STUDY 11A06C
ROLES OF PHYSICAL ENVIRONMENT IN CHARACTERIZING THE IDENTITY
OF MALAY ROYAL TOWN IN ALOR SETAR, KEDAH 11A06D
GREEN HUMAN RESOURCE MANAGEMENT PRACTICES AND
ORGANIZATIONAL CITIZENSHIP BEHAVIOUR FOR ENVIRONMENT: THE
INTERACTIVE EFFECTS OF GREEN PASSION
11A06E
CHARACTERISTICS OF POST-GLOBALIZATION 11A06F
APPLICATION OF NUMERICAL MODELING TO PREDICT AND EVALUATE
THE EFFECTS OF MANAGEMENT SCENARIOS IN GROUNDWATER
RESOURCES
11A06G
EFFECTS OF IRRIGATION SCHEDULING AT DIFFERENT MANAGED
ALLOWABLE DEPLETION IN SALINE SOIL ON THREE RICE VARIETIES
11A06H
DYNAMIC NEXUS AMONG GOLD PRICE, EXCHANGE RATES, AND EQUITY
RETURN OF PAKISTAN
11A06I
SMALL SPACES NEED SMART SOLUTIONS: IMPACTS OF SMART INTERIOR
DESIGN SOLUTIONS ON ACHIEVING FLEXIBLE SPACES 11A06J
STATE AND EFFECTIVENESS OF THE RUSSIAN ENTERPRISE OF
HORTICULTURE PRODUCTION 11A06K
IMPACTS OF SUBSTANTIAL STRATEGIC ISSUES AND TRENDS OF FOREIGN
DIRECT INVESTMENT ON INDIAN RETAIL CONSUMER SERVICE INDUSTRY:
AN EMPIRICAL INVESTIGATION
11A06L

ii
IMPACTS OF FOREIGN DIRECT INVESTMENT ON INDIAN RETAIL
BAZAAR: AN EMPIRICAL STUDY 11A06M
ALGORITHMIZATION FOR PROCESSES OF REGIONAL DIFFERENTIATION
AND CONCENTRATION OF INVESTMENTS IN RUSSIAN AGRICULTURE 11A06N
FACTORS AFFECTING ACCEPTANCE AND THE USE OF TECHNOLOGY
IN YEMENI TELECOM COMPANIES 11A06O
STUDENTS' FINANCIAL LITERACY AND POLICIES FOR ITS
DEVELOPMENT 11A06P
3D MODELING IN AUTOCAD? AS A BASIC COMPONENT OF THE INITIAL
TRAINING OF MECHANICAL ENGINEERS 11A06Q
USE OF FULL-FAT SOY FLOUR IN COMPOUND FEEDS FOR MEAT
CHICKENS OF THE INITIAL LINES AND BROILER CHICKENS 11A06R
A ROBUST VIDEO STABILIZATION ALGORITHMS FOR GLOBAL MOTION
ESTIMATION USING BLOCK MATCHING
11A06S
EVALUATION OF ERGONOMIC DESIGN OF DESK AND CHAIR FOR
PRIMARY SCHOOLS IN ERBIL CITY
11A06T
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Associate Professor Dr.Boonsap Witchayangkoon (Executive Editor), Thammasat
School of Engineering, THAMMASAT UNIVERSITY, Klong-Luang, Pathumtani, 12120,
THAILAND. Tel: +66-2-5643005 Ext 3101. Fax: +66-2-5643022
DrBoonsap@gmail.com, Editor@TuEngr.com
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Side image is the colorful models of Corona COVID19 virus.

*Corresponding author (Jamshed Ali) Tel: +92-343-5262476 Email: jamshed_ali1991@yahoo.com ©2020 International
Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 11 No.5 ISSN 2228-9860 eISSN
1906-9642 CODEN: ITJEA8 Paper ID:11A06A http://TUENGR.COM/V11/11A06A.pdf DOI: 10.14456/ITJEMAST.2020.101
1
http://TuEngr.com
PAPER ID: 11A06A
MICRO AND MACRO FINANCIAL INCLUSION AND THEIR
IMPACTS ON ECONOMIC GROWTH: EVIDENCE FROM
ASIAN ECONOMIES WITH ALTERNATIVE APPROACHES
Jamshed Ali
1*
, Muhammad Arshad Khan
2
1
Department of Management Science, COMSATS University Islamabad, PAKISTAN.
2
Department of Economics, COMSATS University Islamabad, PAKISTAN.
A R T I C L E I N F O A B S T RA C T
Article history:
Received 19 June 2019
Received in revised form 10
December 2019
Accepted 27 December 2019
Available online 06 January
2020
Keywords:
Micro-FII; Macro-FII;
Financial inclusion
indices; Economic
growth; Dynamic
Factor Model (DFM);
Financial development
indicators; GMM.
This study examines the impacts of financial inclusion on
economic growth in a panel of 20 Asian economies using annual data
over the period 1995-2017. Two separate indices, namely micro-
financial inclusion and macro-financial inclusion were constructed
following Sarma (2008) and the Dynamic Factor Model. The impact of
each index, as well as individual indicators of financial inclusion, is
analyzed to get deeper insights into the impact of financial inclusion on
economic growth in Asian economies. The impact of the micro-
financial inclusion index based on Saram’s (2008) methodology is
found to be significant, while the impact of macro-financial inclusion
index remains insignificant. In contrast, the impact of DFM-based
micro and macro-financial inclusion indices exerts an insignificant
impact on economic growth. The difference in results could be due to
the fact that Sharma’s index measures the level of financial inclusion,
while DFM-based indices reflect the growth of financial inclusion
indicators. With regard to the individual indicators of financial
inclusion, the results show that a number of banks, borrower accounts,
and deposit accounts have a significant positive impact on economic
growth, while financial system deposits and insurance premium exerts
an insignificant impact on economic growth.
Disciplinary: Financial Management and Economic Sciences.
©2020 INT TRANS J ENG MANAG SCI TECH.
1 INTRODUCTION
Financial inclusion is a multi-dimensional term used to describe a financial system that
provides easy access to financial services at affordable costs to households and businesses,
irrespective of their size and market value (Mohan, 2006). It is a process that ensures access to
financial services to every member of society in a transparent manner and at affordable prices
through the proper institutional setup. World Bank (2008, p. 27) defines financial inclusion as “the
©2020 International Transaction Journal of Engineering, Management, & Applied Sciences & Technologies

2 Jamshed Ali, Muhammad Arshad Khan
access to financial services without non-price and price barriers to all members of society”.
Deposits, credit, payments, and insurance are generally considered as the main indicators of
financial inclusion. Allen et al. (2012) argued that bank account increases savings, empowers
women, boost household consumption and raise productive investment, which in turn accelerate
economic growth. Financial inclusion enables institutions to operate under comprehensive
regulations, organizational framework and industry-specific performance standards. It brings
sustainability in the performance of institutions and ensures financial stability through continuity of
funds. It helps in allocating financial risks to those agents who have the capacity to bear it without
hurting their financial position (Demirguc-Kunt et al, 2008). Financial inclusion enhances market
competition through increasing choices among different financial products and affordability for
potential customers. It also augments inclusive growth, economic development, and financial
deepening thereby reduce poverty and income inequality. In contrast, financial exclusion leads to
the emergence of unorganized and exploitative financial markets and financial illiteracy (Sharma,
2016).
Schumpeter (1911) is the pioneer detecting a link between finance and economic growth that
an effective and organized financial system is important for facilitating economic growth through
the channelization of funds. Economic growth can be simulated by having a well-functional
financial system with reasonable financial depth. The deepening of the financial sector increases the
supply of financial services, which in turn stimulates economic growth. In the modern socio-
economic development policy agenda, broader accessibility to various financial products and
services is considered an important enabling factor (Buera et al, 2011). Financial inclusion is vital
for economic development and decreasing poverty (Beck & Torre, 2006).
The available literature on the impact of Asian financial inclusion and economic growth is
scant. Many studies concentrated on specific geographical areas like the Organization of Islamic
Countries (OIC) and the Middle East and North Africa (MENA), while Asian has been largely
neglected. It would be very interesting to examine the impact of financial inclusion on Asian
economic growth. Asia is diverse in terms of culture, political system, economic size and level of
economic development. Asia is the biggest continent on the basis of population as it hosts 60% of
the world’s population and it covers 30% of the land area. The average Asian GDP growth rate was
more than 5% per annum. The Organization for Economic Cooperation and Development (OECD,
2017) projected that the Asia-pacific region is expected to grow at a rate of more than 5% in the
coming years. The growth of China and India is exceptional in the region. The other economies are
also growing at an average rate of 5%. The nominal GDP of Asia was $31.58 trillion; while per
capita income was $7351 (IMF, 2019). Despite the large economic size and exceptional economic
growth, a large number of population has still a very low level of financial access in Asia as
compared to other parts of the world. According to the Global Findex (2017) more than half of the
financially excluded peoples living in China, India, Bangladesh, Pakistan, Indonesia, Nigeria, and
Mexico. This indicates that Asia is also hosting the largest segment of the world’s financially
excluded peoples. Thus, improving financial access can lead to Asia as an important stimulus for
achieving sustainable economic growth.
Several empirical studies (inter alia by Demirguç-Kunt et al, 2013; Efobi et al, 2014; Allen et
al, 2016; Muhammad et al, 2017) focused on individual characteristics of financial inclusion and

3
overlooked the role of a macro-level factor in determining economic growth. Given the importance
of financial inclusion in economic growth, the main objective of this study is to examine the impact
of micro-based and macro-based indicators of financial inclusion on economic growth for a panel of
20 Asian countries for 1995-2017, as no previous studies found. This study uses the Dynamic
Factor Model (DFM), which is a relatively new approach for the construction of micro-based and
macro-based financial inclusion indices. The DFM method is useful to deal with the issues of
multicollinearity and dimensionality. To check the robustness we also construct micro-based and
macro-based financial inclusion indices using Sarma (2008) methodology. The empirical analysis
based on micro and macro-based indices of financial inclusion allows us to understand their relative
importance in the determination of economic growth in the Asian region. This study also
investigates the impact of each component of financial inclusion on economic growth in 20 Asian
countries.
2 LITERATURE REVIEW
Theoretically, Schumpeter (1911) argued that a well-functional financial sector facilitates the
real sector’s growth which enhances the economic growth of an economy. Shaw (1973) and
Mckinnon (1973) asserted that a well-developed financial sector decreases monitoring and
transaction costs and minimizes asymmetric information and thus enhances financial intermediation
in an economy. Financial inclusion received much attention after the late 1990s and the early 2000s.
In 2010, G20 countries accepted financial services accessibility as one of the major pillars of the
world development agenda (Zins & Weill, 2016). Many researches on financial inclusion shown
mixed results on the relationship between finance and economic growth, divided into three groups.
The first group concluded positive relationships between financial inclusion and economic growth,
using a variety of financial inclusion indicators (FII). For example, Wong (2015) found positive
impacts of financial access on economic growth in developing countries. Inoue and Hamori (2016)
detected positive impacts of commercial bank branches on economic growth in 37 Sub-Saharan
African economies. Subrahmanyam & Acharya (2017) demonstrated that financial inclusion as a
component of the supply-leading strategy of the financial development model can clearly create
faster economic growth. Kim et al. (2018) examined the impact of financial access on economic
growth in 55 economies of the OIC region. Their results showed that a high level of financial access
causes faster economic growth. Likewise, Sethi & Acharyaa (2018) concluded that financial
inclusion exerted a positive impact on economic growth in a panel of 31 countries.
At the country level, the positive effects of financial inclusion on economic growth were
reported by (Aduda & Kalunda, 2012; Onalapo, 2015; Babajide et al, 2015; Sharma, 2016) using
different approaches. They concluded that access to finance spurs economic growth in different
countries. For instance, Dev (2006) demonstrated that in India financial inclusion can work as a
strategic tool for improving rural enterprises and livelihood of underprivileged farmers. Mehrotra et
al. (2009) reported that access to financial services enables saving, transferring and investing
money, which faster economic growth. Similarly, Swamy (2010) emphasized the importance of
access to finance for long term inclusive growth. Lenka & Sharman (2017) revealed a positive
association between economic growth and access to finance in India. The second group
demonstrated that financial inclusion has a negative impact on economic growth. For example,

Naceur et al. (2007) explored that in MENA region bank development adversely affected economic
growth, meaning that banking sector development has a negative effect on economic growth. They
also observed that private sector credit exerted an insignificant impact on growth in MENA
countries. Pearce (2011) demonstrated that financial inclusion enhances the economic growth of a
smaller social class, while the disadvantaged groups like underprivileged, elders, women and
uneducated populations cannot get benefits from the financial system. Thus, financial inclusion
does not affect the economic growth of disadvantaged groups. The third group explored the causal
links of financial inclusion and economic growth. For instance, Apergis et al. (2007) found
bidirectional causality between financial inclusion and economic growth in a panel of 65 OECD and
non-OECD countries. Sharma (2016) explored bi-directional causality between financial inclusion
and economic growth. Similarly; Kim et al. (2018) also revealed the presence of a bi-directional
causal relationship between financial inclusion and economic growth in OIC countries.
The conflicting evidence with respect to the link between financial inclusion and economic
growth creates a need for an investigation to gain new insights. It is worthwhile for developing
regions like Asia to examine how micro and macro-level financial inclusion impacts economic
growth. Since financial intermediaries work as catalysts for economic growth in developing
economies like Asia, therefore, economic growth is assumed to be dependent on financial sector
development.
3 MODEL, METHODOLOGY, AND DATA
The present study investigates different dimensions of financial inclusion in 20 Asian countries
in a more comprehensive way. To get deep insights into financial inclusion and economic growth
nexus, we have used different statistical techniques such as Sarma (2008), DFM and dynamic panel
Generalized Moment Method (GMM) estimator.
3.1 FINANCIAL INCLUSION AND ECONOMIC GROWTH
To examine the impact of financial inclusion on economic growth, we have used a two-step
procedure. In the first step, we have examined the impact of financial inclusion on economic growth
in a panel of 20 Asian countries. We have constructed two separate indices for micro and macro-
financial inclusion. Then, we have examined the impact of individual indicators of financial
inclusion on economic growth. Following Kim et al. (2018), we specify the empirical model
itiitit
itititititit
FIIUnemp
TradeSEPOPINFLnGDPPCLnGDPPC
enββ
ββββθβ
++++
+++++= −
65
432110
(1),
where cross-sectional units are represented by i, while t represents the time period, that is, 1995-
2017. The term iv represents country-specific effects, while the error term itε is assumed to be
identically and independently distributed (iid ) over the whole sample period. In Equation (1), one
lag of itLnGDPPC is included to control the endogeneity problem. The dependent variable
LnGDPPC is the logarithmic value of GDP per capita which is used as a proxy of economic
growth, FII is the financial inclusion index. INF is the inflation rate, POP is the population
growth rate, SE indicates school enrolment and Unemp represents the unemployment rate. To
examine the impact of individual indicators of financial inclusion on economic growth, we can
rewrite Equation (1) as:

5
}{ itiitititititit
i
i
ititititittiit
LnPCLnFSDLnIPLnBALnDALnBanks
UnempTradeSEPOPINFLnGDPPCLnGDPPC
enγ
βββββθβ
+++
++++++=
∑=
−
,,,,,,
6
1
5432110
(2),
where DA, BA , IP , FSDand PC are the deposit accounts, borrower accounts, insurance
premium relative to GDP, total deposits in the financial system and private sector credit relative to
GDP, respectively. The description of variables and data sources is given in Table 1.
Table 1: Definition of variables and data source.
Variable Symbol Definition of Variable Data Source
Real GDP per capita
itGDPPC Real GDP per capita is obtained as gross
domestic product adjusted for inflation dividing
by the population of each country.
World Bank
Population growth
itPOP The population growth rate for the year t is the
growth rate of the midyear population from
1−t to t .
World Bank
Unemployment rate
itUnemp The unemployment rate measures the number of
people actively looking for jobs as a percentage
of the total labor force.
International Labour
Organization
Inflation rate
itINF Inflation is calculated as changes in Consumer
Price Index (CPI) with base year 2010=100.
World Bank/ International
Monetary Fund (IMF)
School enrolment
itSE Net primary enrollment is obtained as the ratio
of school age who are enrolled in school and
population of official school age.
UNESCO Institute of
Statistics
Trade Openness
itTrade Sum of exports and imports relative to GDP. World Bank/IMF
Deposit accounts
itDA A number of deposit accounts with commercial
banks per 100000 of the population.
IMF/World Bank’s World
Development Indicators
and respective country
central bank and statistics
department
Borrower accounts
itBA A number of borrower accounts with
commercial banks per 100000 of the population.
No of banks
itBanks Number of commercial bank per 100000 of
population
Private sector credit
relative to GDP
itPC Total credit distributed to the private sector as a
percentage of GDP.
World Bank/IMF
Financial system
deposits relative to
GDP
itFSD Total demand, time and saving deposits with
banks and other financial institutions as a
percentage of GDP.
World Bank/ IFS
Insurance premium
relative to GDP
itIP Total life and non-life insurance premium as a
percentage of GDP.
World Bank
3.2 DATA
This study main object is to explore the impact of financial inclusion on economic growth in 20
Asian economies. The available 1995-2017 data on various variables shown in Table 1 are
extracted from the World Development Indicators (WDI), IMF’s World Governance Indicators,
United Nations Education, Scientific, and Cultural Organization (UNESCO), United Nation
Development Programme (UNDP), International Financial Statistics (IFS), Oxford University
Database, Central Banks, and Statistical Ministries of respective countries.
3.3 METHODOLOGY
One objective of this study is to construct a financial inclusion index to overcome the issues of
multi-dimensionality and multicollinearity among the different indicators of financial inclusion. To
this end, we have used Sarma’s (2008) methodology and DFM advanced by Stock and Watson
(2002) for the construction of micro-based and macro-based financial inclusion indices.

3.3.1 SHARMA’S METHODOLOGY
Following Sarma (2008), we have constructed a micro-based financial inclusion index by
considering the following indicators (justification given in Pina (2018)):
o A number of branches of commercial banks per 100,000 adults.
o A number of commercial banks deposit accounts per 100,000 adults.
o A number of borrowers from commercial banks per 100,000 adults.
For the construction of macro-financial inclusion index, we consider the following indicators:
o Private credit as a percentage of GDP.
o Insurance premium as a percentage of GDP.
o Deposits in the financial system as a percentage to GDP.
The average of each indicator for the selected economies is computed first and then we
calculated the index of each indicator following Sarma (2008) as
jj
jj
i
mM
mA
D
−
−
= (3),
where jA is the actual value of the indicator j , jm is the minimum value of indicator i and jM is
the maximum value of the indicator j . Financial inclusion index for the country I is measured by
the normalized inverse of Euclidean distance of point id computed in Equation (3) from the ideal
point which is considered as 1. We used the following formula to compute financial inclusion index
( FII ):
n
ddd
FII n
i
22
2
2
1 )1(................)1()1(
1
−++−+−
−= (4),
where numerator of the second term in Equation (4) is the Euclidean distance from an ideal point
which is assumed to be 1, it is normalized by the square root of the number of observations and
subtracting id from ideal value 1, giving the inverse normalized distance. All indicators are
normalized for making it standardized and its value lies between 0 and 1, where 1 shows the highest
level of financial inclusion and 0 reflects the lowest level of financial inclusion.
It can be argued that Saram’s (2008) methodology is based on static factor models that
involved the issue of multicollinearity. It also reflects the level of financial inclusion regardless of
growth in FII. To overcome this issue, we have used the DFM technique to construct the financial
inclusion index that is a relatively new methodology.
3.3.2 DYNAMIC FACTOR MODEL
The DFM has the ability to overcome problems of high dimensionality and multicollinearity.
The DFM method can decompose each variable into common and idiosyncratic components which
make the variables different. Following Stock & Watson (2002) we have constructed micro-based
and macro-based financial inclusion indices using the DFM methodology.
3.4 TRENDS OF FINANCIAL INCLUSION INDICES
The trends of financial inclusion index based on Sarma’s (2008) methodology are depicted in
Figure 1. In order to get deeper insights into the accessibility and usage of financial services, we
have constructed two different indices, the micro-FII that reflects the micro-side of financial
inclusion. Micro-FII is constructed using the information on number of bank branches per 100,000

7
adults, number of deposit accounts per 100,000 adults and number of saving accounts per 100.000
adults, while macro-FII deals with macro-side of financial inclusion, which is constructed using the
financial development indicators such as insurance premium relative to GDP, private sector credit
relative to GDP and total deposits in financial system of economy. The score of FII lies between 0
and 1, 0 indicates a minimal level of financial inclusion; while 1 reflects the highest level of
financial inclusion.
Figure 1: Trends of Macro and Micro financial inclusion indices (1995-2017)
(Sources: Authors calculations)
Figure 1, Japan has the highest score in micro-FII and macro-FII, followed by Indonesia, South
Korea, and Singapore. The relatively high discrepancy between micro and macro-financial
inclusion indices in some countries could be due to the difference between the level of financial
accessibility and financial sector development. For example, Thailand has quite a good financial
development level as compared to the level of financial accessibility. We have ranked countries
based on the level of financial inclusion. Table 2 depicts macro-FII and micro-FII ranking.
Table 2: Ranking of Asian Countries by Micro-FII and Macro-FII (1995-2017).
(Sources: Authors calculation).
Rank Country Micro-FII Rank Country Macro-FII
1 Japan 0.601 1 Japan 0.735
2 South Korea 0.356 2 South Korea 0.591
3 Singapore 0.325 3 Thailand 0.421
4 Brunei-DS 0.317 4 China 0.277
5 Magnolia 0.256 5 Singapore 0.276
6 Malaysia 0.206 6 Malaysia 0.265
7 Uzbekistan 0.182 7 Kyrgyz rep 0.234
8 India 0.146 8 India 0.205
9 Thailand 0.145 9 Brunei-DS 0.174
10 Indonesia 0.116 10 Magnolia 0.171
11 Sri Lanka 0.104 11 Vietnam 0.135
12 Khazikistan 0.088 12 Indonesia 0.133
13 Azerbaijan 0.085 13 Sri Lanka 0.114
14 China 0.074 14 Philippine 0.109
15 Bangladesh 0.070 15 Bangladesh 0.109
16 Pakistan 0.070 16 Khazikistan 0.084
17 Philippine 0.063 17 Pakistan 0.046
18 Tajikistan 0.042 18 Azerbaijan 0.043
19 Kyrgyz Rep 0.039 19 Tajikistan 0.037
20 Vietnam 0.038 20 Uzbekistan 0.035
Table 2, on the basis of micro-FII, Japan has the highest level of financial inclusion, followed

by South Korea, Singapore, and Brunei Darussalam, while Vietnam and the Kyrgyz Republic have
the lowest level. Pakistan is in the 16th position; it means that access to financial services in
Pakistan is better than Philippine, Tajikistan, Kyrgyz Republic and Vietnam.
Macro-FII reflects the level of financial sector development. Again, Japan has the highest
score, followed by South Korea, Thailand, China, Singapore, and Malaysia, while Uzbekistan has
the lowest score. Pakistan is in the 17th position based on the level of financial sector development.
3.5 DYNAMIC FACTOR MODEL-BASED FINANCIAL INCLUSION INDICES
The micro-FII and macro-FII based on the DFM approach are depicted in Figure 2.
Figure 2: Trends of Macro-FII and Micro-FII based on DFM Approach (1995-2017)
(Sources: authors’ calculations).
The Micro-FII depicted in Figure 2 shows higher variation across countries. This means that
countries with a high level of financial inclusion have a higher score based on the DFM approach.
However, macro-FII indicates relatively less variation as compared to the fluctuation associated
with micro-FII. This clearly indicates that micro-FII is more volatile than macro-FII and in terms of
access to finance because Asian economies are more heterogeneous.
4 RESULT
4.1 DESCRIPTIVE STATISTICS
In Table 3, the mean values of all variables are positive. More importantly, positive mean
values of Micro-FII and Macro-FII indicates an important role of access to financial services and
financial development in economic growth in selected Asian countries. The mean value of micro-
level financial inclusion, such as the number of bank branches, the number of deposit accounts and
a number of borrower accounts is positive. Among the micro-level FII, the mean value of a number
of deposit accounts is highest (112865.6), followed by a number of borrower accounts (23755.96),
showing the key role of these variables in economic growth. Similarly, the large difference between
maximum and minimum values for all variables indicating that the selected sample is
heterogeneous with large and small countries. On the other hand, the mean value of macro-level FII
is also positive with a mean value of private sector credit is 53.40, followed by financial system
deposits relative to GDP (49.46). The standard deviation of all variables is positive. However, the
standard deviation of deposit accounts (DA) and borrower accounts (BA) has the highest variation
among all the variables, showing large volatility in these variables. The standard deviation of

9
Sarma’s micro and macro-financial inclusion indices is lowest because of the standardization of
indices values between 0 and 1.
Table 3: Descriptive statistics.
Variable Mean SD Min Max
itLnGDPPC 8.93 1.15 6.65 11.45
itFIIMacroDFM −: 0.37 0.99 -2.51 6.66
itFIIMicroDFM −: 1.58 2.55 -5.02 8.10
itFIIMacroSarma −: 0.21 0.18 0.01 0.79
itFIIMicroSarma −: 0.17 0.15 0.01 0.67
itSE 91.08 8.91 42.23 99.92
itTrade 95.73 74.67 16.68 437.3
itUnemp 5.15 3.10 0.39 16.5
itPOP 1.30 0.79 -1.75 5.32
itINF 9.49 32.38 -8.52 512.3
itBanks 13.55 13.38 0.081 71.61
itDA 112865.6 153097.5 462.79 726909
itBA 23755.96 26136.8 207.42 123300
itPC 53.40 48.40 0.94 192.46
itFSD 49.46 48.58 1.42 221.13
itIP 2.33 2.83 0.02 13.18
To examine the possibility of multicollinearity among the variables under consideration, we
performed Pearson’s correlation analysis and the results are reported in Table 4.
Table 4: Correlation analysis.
Ln (GDPPC) SE Trade Unemp POP INF Banks DA BA PC FSD IP
Ln (GDPPC) 1.00
SE 0.44 1.00
Trade 0.38 0.32 1.00
Unemp -0.23 0.02 0.01 1.00
POP -0.12 -0.29 0.27 -0.12 1.00
INF -0.24 -0.13 0.02 0.27 -0.01 1.00
Banks 0.41 0.22 0.01 -0.09 -0.02 -0.10 1.00
DA 0.62 0.35 0.01 -0.17 -0.35 -0.13 0.39 1.00
BA 0.81 0.37 0.39 -0.21 -0.13 -0.16 0.33 0.77 1.00
PC 0.38 0.45 0.01 -0.33 -0.38 -0.16 0.34 0.55 0.37 1.00
FSD 0.44 0.34 -0.09 -0.19 -0.39 -0.15 0.19 0.84 0.61 0.64 1.00
IP 0.51 0.36 0.16 -0.28 -0.33 -0.13 0.09 0.79 0.68 0.50 0.74 1.00
The correlation coefficients reported in Table 4 show that unemployment, population growth,
and inflation rate are negatively correlated with real GDP per capita. The reason for the negative
correlation of these variables with real GDP per capita could be the adverse effect of macro and
micro variables on economic growth. Population growth without a proper plan and economic
resources leads to serious socioeconomic problems; the main issue is that it can cause
unemployment. An increase in unemployment may decreases productivity and reflects the
underutilization of human resource. Similarly, inflation adversely affects a household’s
consumption and savings, which in turn have a negative impact on real GDP per capita growth.
Among the financial inclusion indicators, BA and DA have a positive correlation with real GDP per
capita with correlation coefficients are 0.62 and 0.81 respectively. It reflects that an increase in
borrower accounts and deposit accounts causes to increase in real GDP per capita in Asian

countries. The positive correlation could be due to the fact that it facilitates a pro-growth
environment that enables inclusive growth.
4.2 IMPACT OF MICRO FINANCIAL INCLUSION ON ECONOMIC GROWTH
Using a dynamic GMM estimator, we have estimated five models by considering micro-FII and
individual components of financial inclusion. The main aim of using individual indicators of
financial inclusion is to get deeper insights regarding the impact of each indicator on economic
growth. Table 5 presents the estimated result when micro-level FII is considered.
Table 5: Estimates of micro-FII and economic growth. (Dependent variable: itLnGDPPC ).
Variable Model (A) Model (B) Model (C) Model (D) Model (E)
1−itLnGDPPC 0.787***
(7.05)
0.90***
(12.13)
0.085***
(11.75)
0.81***
(7.02)
0.76***
(9.73)
itSE 0.0024
(0.53)
0.0043
(0.63)
0.0008
(1.55)
0.0032
(0.36)
0.002**
(2.26)
itTrade 0.0015**
(2.05)
0.0013*
(1.89)
0.0003**
(2.05)
0.0022***
(3.07)
0.0012*
(1.82)
itINF 0.00007
(0.09)
-0.0008
(-1.64)
-0.010***
(-3.75)
0.00023
(0.26)
-0.052**
(-2.05)
itPOP -0.16
(-1.10)
-0.094*
(-1.99)
-0.024**
(-2.22)
-0.11
(-1.48)
-0.028
(-0.76)
itUnemp -0.041**
(-2.11)
-0.03**
(-2.18)
-0.0023
(-0.74)
-0.042***
(-3.53)
-0.038***
(-2.91)
itFIIMicroSarma −: 0.022**
(2.19)
- - - -
itFIIMicroDFM −: - -0.007
(-1.15)
- - -
itLnBanks - - 0.004**
(2.15)
- -
itLnDA - - - 0.024**
(2.21)
-
itLnBA - - - - 0.029**
(2.14)
Observations 460 460 460 460 460
AR(2) 0.27 0.13 0.21 0.19 0.52
Hansen test 0.63 0.68 0.64 0.55 0.71
F-state 230.23***
435.16***
683.4***
110.86**
184.01***
Note: Values in parentheses are the t-stat. *, **, *** respectively indicate significance at the 10%, 5% and 1% level.
The results associated with Model (A) indicate the impact of the financial inclusion index
based on Sarma’s (2008) method. The finding shows that financial inclusion based on micro
indicators exerts a significant positive impact on economic growth. This implies that an increase in
the accessibility of financial services plays an important role in enhancing economic growth in the
Asian region. Furthermore, one period lagged GDPPC, trade openness and unemployment has a
statistically significant impact on economic growth. Concerning the control variables, the result
reveals that trade openness exerts a significant positive impact on economic growth as it improves
the usage of financial services and also increases the circulation of capital. The impact of the
unemployment rate on economic growth is negative and significant. It could be due to the fact that
when unemployment increases, economic activities slowed down, which in turn deteriorates
economic growth. Other control variables such as school enrolment, inflation rate, and population
growth exert an insignificant impact on economic growth. Model (B) shows the impact of DFM-
based micro-FII on economic growth. The result indicates that DFM-based micro-FII exerts an
insignificant negative impact on economic growth in Asian countries. Other variables such as
lagged GDPPC, trade openness, population growth and unemployment rate have a significant

11
impact on economic growth, while school enrolment and inflation rate are insignificant.
With regard to the individual indicators of financial inclusion, in the model (C) a number of
bank branches are positively related to economic growth. This implies that an increase in the
number of bank branches providing access to financial services, which in turn exerts a positive
impact on economic growth. The positive association shows that the increase in banking penetration
is a key factor in deriving economic growth. Furthermore, the impact of lagged GDPPC, trade
openness, inflation rate and unemployment rate are statistically significant. Model (D) indicates that
the impact of deposit accounts on economic growth is positive and statistically significant. This
reveals that an increase in the number of deposit accounts enhances economic growth in Asian
countries. This finding is important in the sense that an increase in the number of deposit accounts
is a very basic step towards achieving financial inclusion. Additionally, lagged GDPPC, trade
openness, inflation rate and population growth have a significant impact on economic growth.
Finally, in Model (E) the impact of borrower accounts on economic growth is positive and
significant, implying that an increase in loan accounts leads to enhances economic growth in Asian
countries. Other variables entered in the Model (E) with a statistically significant impact on
economic growth. Overall, the results reported in Models (C) to (E) reveal a positive and significant
relationship of a number of banks, deposits accounts and borrower’s accounts with economic
growth which supports the financial inclusion-growth nexus in selected Asian countries.
4.3 IMPACT OF MACRO FINANCIAL INCLUSION ON ECONOMIC GROWTH
Table 6 estimates the impact of macro-financial inclusion index as well as individual macro-
indicators of financial inclusion on economic growth. It gives useful insights into the impact of
financial deepening on economic growth in Asian countries.
Table 6: Estimations of macro-FII and economic growth in Asian countries
(Dependent variable: itLnGDPPC ).
Variable Model (F) Model (G) Model (H) Model (I) Model (J)
1−itLnGDPPC 0.877***
(10.08)
0.867***
(8.24)
0.961***
(13.05)
0.864***
(11.74)
0.910***
(13.34)
itSE 0.006
(0.45)
0.011
(1.08)
-0.022**
(-2.20)
0.0027
(0.20)
0.0032
(0.51)
itTrade 0.001*
(1.94)
0.001
(1.33)
0.002***
(3.07)
0.001**
(2.28)
0.001***
(3.24)
itINF -0.0004
(-0.20)
0.001
(1.13)
0.001
(-1.35)
0.0007
(0.32)
-0.0001
(-0.04)
itPOP -0.160
(-1.41)
-0.21
(-1.34)
-0.054*
(-1.86)
-0.13
(-1.02)
-0.190
(-1.37)
itUnemp -0.043*
(-1.88)
-0.470**
(-2.15)
-0.037***
(-2.77)
-0.052**
(-2.22)
-0.046
(-1.39)
itFIIMacroSarma −: -0.007
(-0.56)
- - - -
itFIIMacroDFM −: - -0.010
(-0.58)
- - -
itLnPC - - 0.003*
(1.73)
- -
itLnFSD - - - 0.003**
(2.06)
-
itLnIP - - - - -0.029
(-1.18)
Observations 460 460 460 460 460
AR(2) 0.21 0.20 0.89 0.14 0.23
Hansen test 0.80 0.39 60.4 0.45 0.77
F-stat 309.05***
340.35***
88.41***
141.01***
429.2***
Note: 1) Values in parentheses are the t-stat. *, **, *** respectively indicate significance at the 10%, 5% and 1% level.

It is evident from Table 6 (Model (F)) that the impact of macro-financial inclusion index
(macro-FII) due to Sarma’s (2008) on economic growth is insignificant. This means that the
financial deepening-side of financial inclusion does not influence economic growth significantly in
selected countries of the Asian region. In addition, lagged GDPPC, trade openness and the
unemployment rate has a statistically significant impact on economic growth. Similarly, in Model
(G) the impact of macro-financial inclusion index based on DFM (DFM-macro-FII) on economic
growth has also insignificant. However, lagged GDPC, trade openness, and the unemployment rate
has a significant impact on economic growth in selected Asian countries. This implies that the
growth of the financial sector does not produce any significant impact on economic growth in Asian
countries.
With respect to individual macro components of financial inclusion, Model (H) reflects that
private sector credit relative to GDP has a significant positive impact on economic growth. This
implies that an increase in private sector credit exerts a positive impact on economic growth,
although the impact of this variable on economic growth is too small. The impact of total deposit
with the financial sector ( itLnFSD ) on economic growth in Model (I) is positive and statistically
significant. This also implies that the growth of total deposits of the financial system has a positive
impact on economic growth. However, the coefficient of this variable is also too small. On the other
hand, in Model (J) the effect of insurance premium ( itLnIP ) on economic growth is found to be
negative but statistically insignificant. This means the insurance sector does not play any role in
economic growth in Asian countries. The impact of lagged GDPPC and other control variables,
such as trade openness, population growth, and the unemployment rate is statistically significant in
Model(s) (H) through (J), suggesting the key role of these macroeconomic variables in the growth
process in Asian countries.
Overall, this study result is consistent with the existing literature. For example, Kendall et al.
(2010) revealed that economic development is associated with bank branches, deposit accounts,
loan accounts and ATM’s availability. Andrianaivo & Kpodar (2011) found a positive and
statistically significant effect of financial inclusion on economic growth. Similarly, Demirguc &
Klapper (2013) observed that those economies where the commercial bank branches and the
volume of deposits are high, the income level of households in those areas also increases at a faster
pace. Wong (2015) revealed that growth in developing countries was positively affected by access
to finance. Likewise, Inoue & Hamori (2016) detected the positive impact of financial inclusion on
the economic growth of selected countries. More recently Kim et al. (2018) found that a high level
of financial access causes faster economic growth in OIC economies. They also detected the
significant positive impact of ATMs, bank branches, borrower accounts, deposit accounts and
private sector credit was significant on economic growth, while the role of insurance premium on
economic growth was insignificant. Likewise, Sethi & Acharyaa (2018) concluded that economic
growth in OIC economies was positively affected by financial inclusion. They reported bi-
directional causality between financial inclusion and economic growth. Thus, the above-cited
literature confirms that financial access is among the main drivers which cause economic
growth in various part of the world.

13
5 CONCLUSION
To measure financial inclusion, we have constructed two separate indices, namely micro and
macro-financial inclusion, using Sarma (2008) and Stock & Watson’s (2002) Dynamic Factor
Model. The result of micro-financial inclusion based on Sarma’s (2008) method reveals a positive
association of financial inclusion and economic growth, while the impact of DFM-based financial
inclusion index on economic growth is insignificant. The difference between the results could be
due to the fact that Sarma’s index measures the level of financial inclusion, while DFM considers
the growth of FII. This suggests that the level of financial inclusion contributes to economic growth
rather than the growth of financial inclusion. Furthermore, we have also examined the impact
individual indicator of micro-level financial inclusion on economic growth and the results show that
the number of banks, number of borrower accounts and number of deposit accounts have a
statistically significant positive impact on economic growth in selected Asian countries. The impact
of macro-financial inclusion indices based on Sarma and DFM methods on economic growth was
found to be insignificant. Additionally, the impact of individual indicators of financial deepening-
side of financial inclusion is also examined. We find that the impact of private credit relative to
GDP and financial system deposits relative to GDP has a significant impact on economic growth,
while the impact of insurance premium on economic growth remains insignificant.
The results show that micro-level financial inclusion index and indicators of financial inclusion
such as the number of bank branches, number of borrower accounts and number of deposit accounts
have a significant positive impact on economic growth in Asian countries. Therefore, to stimulate
economic growth in Asian economies, policymakers may strengthen financial services accessibility.
It can be argued that improved accessibility of financial services works as a stimulus of capital
accumulation and financial resources mobilization, which in turn gradually promotes economic
growth and social welfare. Therefore, Asian countries may increase the penetration of financial
services and eliminate barriers to financial access in order to stimulate economic growth in the
region. Furthermore, the expansion of the financial sector can also contribute to economic growth in
these economies.
6 DATA AND MATERIAL AVAILABILITY
Information regarding this study is available by contacting the corresponding author.
7 ACKNOWLEDGEMENT
The authors acknowledge the financial support provided by the Higher Education Commission
of Pakistan for the first author’s PhD study. The authors acknowledge the contribution of Mr.
Abdur Rehman Rana, a PhD scholar at Department of Management Sciences COMSATS
University Islamabad in construction of the financial inclusion indices via DFM for this study.
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Jamshed Ali is a PhD scholar at the Department of Management Sciences, COMSATS University Islamabad,
Pakistan. He got his Master’s degree in Finance, from SZABIST Islamabad, Pakitan. His research interests include
Financial Development, Financial Inclusion, Economic Growth, Poverty, Indices Construction, Sovereign Rating and
Dynamics of Financial Markets Cointegration.
Dr. Muhammad Arshad Khand is an Associate Professor and Chairman, Department of Economics, COMSATS
University Islamabad, Pakistan. Dr. Khan holds a PhD in Economics from the Pakistan Institute of Development
Economics (PIDE), Islamabad. He is interested in Financial Engineering & Financial Dynamics, International
Economics, Econometrics, International Money and Finance, Environmental Economics, Agricultural and Resource
Economics and Data Science.

*Corresponding author (Sergei Dotsenko). Tel: +7-989-99-87887. Email: lyudmila0511@mail.ru ©2020 International
1906-9642 CODEN: ITJEA8 Paper ID:11A06B http://TUENGR.COM/V10A/10A06B.pdf DOI: 10.14456/ITJEMAST.2020.102
1
http://TuEngr.com
PAPER ID: 11A06B
DEVELOPMENT OF TECHNOLOGY FOR THE PRODUCTION
OF MULTICOMPONENT FEED SUPPLEMENT
Sergei Dotsenko a*
, Lyudmila Kryuchkova b
, Andrei Burmaga
c
a
Department of Building Production and Engineering Structures, Far Eastern State Agrarian
University, RUSSIA.
b
Department of Higher Mathematics, Far Eastern State Agrarian University, RUSSIA.
c
Department of Transport and Energy Means and Mechanization of Agribusiness, Far Eastern State
Agrarian University, RUSSIA.
Article history:
Received 06 April 2019
December 2019
2020
Keywords:
Feed technology;
Insoluble pulp residue;
Calcium supplement
feed; Protein
supplement; β-carotene
supplement; Energy
intensity; Feed
production efficiency.
This article presents the results of the development of a new highly
effective feed additive based on the use of secondary fractions in the
production of a substitute for whole milk. The resulting additive is
characterized by high calcium content (10.8 times), as well as a content
of β-carotene for 2 mg/100g of the product, which provides it with high
biological and nutritional value, as well as antioxidant activity. The
developed technology for energy intensity is more than 3 times lower
than the base one recommended for small and medium capacity farms.
The implementation of the developed technology, as well as the set of
equipment adopted for it, makes it possible to increase the efficiency of
the functioning of the mechanized system of feeding animals on small
and medium-sized farms by reducing the cost of energy, labor, and
money.
Disciplinary: Animal Sciences (Animal Nutrition and Feed Technology).
1. INTRODUCTION
An analysis of the data characterizing the condition of the feed base at small and medium-sized
enterprises for the production of livestock and poultry products shows that currently there is a
shortage of protein, vitamins, macro and micronutrients in feed rations. So, in particular, the use of
low-fat soybean meal does not allow to have vitamin E in the diets, and the absence of a root
component in the diets, in the form of pumpkin or carrot - β–carotene (Tutelian, 2004, Dotsenko et al.,
2020).
When developing substitutes for milk-containing feeds, still few include vegetable protein and
vitamin substances, and in particular, soy, root, vegetable, and others. Low energy-intensive
technologies and technical means for extracting proteins and vitamins from soy grain and root crops

2 Sergei Dotsenko, Lyudmila Kryuchkova, Andrei Burmaga
have not been developed. Today, neither small nor medium producers have special small-sized
modular-block technology for these purposes (Tutelian, 2005).
Protein-vitamin supplements, protein-vitamin calcium supplements, and others are
homogeneous mixtures of high-protein fodder products and micro additives, crushed to the required
size, used for the preparation of animal feed based on grain fodder.
Obtaining soy protein feed products of high nutritional and biological value is a promising
direction in the development of complete feeds. Get fodder products from soy flour and carrot, or
beetroot, or pumpkin pastes or their compositions, or their combinations based on the principle of
averaging dry matter content under mild conditions and a shorter cooking time with a higher protein
and fat content, the presence of biologically active substances in them - carotene and vitamins C and
R (Kochetkova, 1999).
At the same time, non-fat-free flour is prepared on the basis of soybean seeds, which contains
vitamin E and more than 40% protein, balanced for essential amino acids.
In this regard, obtaining protein-vitamin products based on these raw materials, for example, in
granular physical form, is an urgent task.
Deficiency of proteins and calcium can be eliminated in diets by using meat or fishmeal meals,
but the cost of their production is very high (Petibskaya, 2012).
A theoretical approach to achieving the goal is that upon receipt of soy protein feed products,
including mixing, molding and heat treatment of a mixture of soy protein and carbohydrate-vitamin
components, soy protein-free flour containing dry matter 88-92 is used as a soy protein component %,
and carbohydrate and vitamin - carrot, beetroot or pumpkin paste or their composition, or
combinations thereof with a solids content of 8-12% (Dotsenko et al., 2018).
At the same time, the production technology of so-called “soy milk” based on soy-pumpkin or
soy-carrot compositions, a waste fraction in which soy-pumpkin or soy-carrot pulp residue is known
(Dotsenko, et al, 2016).
Using this waste product in combination with chalk in powder form, you can get granular feed
protein-vitamin-calcium supplement (Ostroumov, 1998).
This study develops a technological scheme for producing protein-vitamin-calcium supplements
and justifies the technology using soya-pumpkin and soya-carrot pulp residue in the technology for
producing granular protein-vitamin-calcium supplements with less labor and money.
2. MATERIALS AND METHODS
Studies have established that pulp obtained as a waste in the production of soya-pumpkin or
soya-carrot product in the form of a substitute for whole milk contains 60-70% water, δ = 10%
protein, β-carotene on average up to 10 mg/kg, however, it has a limited shelf life [1, 2].
According to the proposed technology (Figure 1), on the basis of pre-soaked soybean seeds and
crushed root crops (carrots and pumpkins), with the help of a grinder-extractor -1 (Figure 2), so-called
"soy milk" and the waste fraction - insoluble soybean - are obtained pulp root crop, in which in a
weight ratio of 1: 1, is mixed with chalk, transformed into flour form using a mixer -3. At the same
time, chalk is fed from the feeder - 2.
The moisture content due to its diffuse transition into the chalk component of the composition.
A composition with average humidity is formed into granules with a diameter of 2-3 mm using a

3
screw - 4 and a matrix - 5. The granules are accumulated in a mesh tray - 6 and dried in a cabinet - 7
with active ventilation.
3. RESULT AND DISCUSSION
The energy intensity of traditional and innovative technologies is determined by the formula.
100
l r
N
E
Q P
×
=
×
(1)
where N – energy consumption for producing granulate, kW;
Ql – line productivity kg/h;
Pr – pellet strength, %.
The installed power of electric motors in the set of equipment K7 - FKE is Nt = 254.5 kW, with a
pellet strength of Pr = 93% and a productivity of Ql = 100 kg / h, which gives an indicator value
100 254.5
2.736
100 93 %
t
kWh
E
kg
×
= =
×
.
Figure 1: The technological scheme of the process of obtaining feed products based on soy-pumpkin
and soy-carrot compositions.
For an innovative technology variant, Ei is
100 83.4
0.877
100 95 %
i
kWh
E
kg
×
= =
×
.
Comparative characteristics of the energy intensity of production and nutritional value of feed
additives are given in Table 1. An analysis of the data given in Table 1 shows that the energy
intensity by innovative technology is 3.119 times lower compared to the basic version. Comparative
characteristics of the energy intensity of production and nutritional value of additives.

4 Sergei Dotsenko, Lyudmila Kryuchkova, Andrei Burmaga
Figure 2: Structural-technological (hardware) scheme of the line for producing karate-calcium
additives for farm animals and poultry.
Table 1: The results of evaluating the effectiveness of the developed technology
Product
Electricity costs,
%
kWh
kg
Content
Strength,
%
Calcium,
g/100 g
β-carotene
mg/kg
The product according to a known method (K7 - FKE) 2,736 1,8 - 93,0
Protein-carotene-calcium supplement 0,877 19,5 2,0 95,0
Moreover, in an innovative product, the calcium content is 10.8 times higher. At the same time,
the presence of β-carotene in the additive in an amount of 2 mg / 100 g provides it with high
biological and nutritional value, as well as antioxidant activity.
The developed non-waste technology, with the specified values of the modes and parameters,
allows obtaining granules with a diameter of 2-3 mm and a length of 2-3 cm (see Figure 3). The
strength of the granules was 93-95% and moisture content 92%.
Figure 3: Multicomponent feed supplement.

5
4. CONCLUSION
Based on the adopted innovative approaches, the possibility and feasibility of obtaining an
effective additive using soya-pumpkin and soya-carrot pulp residue, which is a waste fraction in the
production of a substitute for whole milk using soya-pumpkin and soya-carrot compositions, are
substantiated.
The implementation of the developed technology, as well as the set of equipment adopted for it,
makes it possible to increase the efficiency of the functioning of the mechanized system of feeding
animals on small and medium-sized farms by reducing the cost of energy, labor, and money.
5. AVAILABILITY OF DATA AND MATERIAL
Data can be made available by contacting the corresponding authors
6. REFERENCES
Dotsenko, S.M., Goncharuk, O.V., Makarenko, V.V., et al. (2016). Monograph. Scientific and practical
aspects of creating food products of a given composition and properties using soy-plant compositions.
Blagoveshchensk. 355.
Dotsenko, S.M. (2018). A unit for continuous preparation of a substitute for whole milk and animal feed. etc.
RF patent No. 2663610 Published in B.I. No.22.
Dotsenko, S., Kryuchkova, L., Samuylo, V. (2020). Development of Technology for Production of
Protein-Vitamin Granulate. International Transaction Journal of Engineering, Management, &
Applied Sciences & Technologies. 11(5), 11A05M: 1-6.
Tutelian, V.A. (2004). Nutrition and Health. Food Industry. 5, 5-6.
Tutelian, V. A., Poznyakovsky, V.M. (2002). The policy of healthy eating. Federal and regional levels.
Novosibirsk Siberian University Publishing House, 144.
Kochetkova, A. A. (1999). Functional products in the concept of a healthy diet. Food industry. 4, 4.
Tutelian, V.A. (2005). Health is in your hands. Food industry. 4, 6-8.
Petibskaya, V.S. (2012). Soya: chemical composition and use. Maykop, 432.
Stepanova, L.I. (1999). Handbook of a dairy production technologist. Technology and recipes. SPb. GIOGD,
190.
Ostroumov, L.K. (1998). Combined dairy protein products using plant materials. Storage and processing of
agricultural raw materials. 38, 28 - 30.
Ginzburg A.S. (1976). The technology of drying food. Food Industry, 248.
Professor Dr.Sergei Dotsenko is Professor, Far Eastern State Agrarian University. He holds a Doctor of
Technical Sciences degree. His research includes Animal Nutrition, Production Line, Feed Technology,
Binder, Vitamins, Protein Substances, Biological Value.
Lyudmila Kryuchkova, is an Associate Professor, Department of Higher Mathematics, Far Eastern State
Agrarian University. She is a Candidate of Technical Sciences. Her research involves Applications of
Math.
Professor Dr.Andrei Burmaga is Professor, Department of Transport and Energy Facilities and
Mechanization of Agriculture, Far Eastern State Agrarian University. He holds a Doctor of Technical
Sciences. His researches are Feed Technology, Production Line, Feed Technology, Binder, Vitamins,
Protein Substances, Biological Value.

*Corresponding author (S.Bibi, shaguftamalik409@yahoo.com; A.Khalid arslankhalid1989@yahoo.com) ©2020 International
Transaction Journal of Engineering, Management, & Applied Sciences & Technologies. Volume 11 No.6 ISSN2228-9860
eISSN1906-9642 CODEN: ITJEA8 Paper ID:11A06C http://TUENGR.COM/V10A/11A06C.pdf DOI: 10.14456/ITJEMAST.2020.103
1
http://TuEngr.com
PAPER ID: 11A06C
HOSPITAL ANXIETY AND DEPRESSION OF PATIENTS WITH
HEART FAILURE IN SOUTH PUNJAB PAKISTAN: A
SECTIONAL SURVEY STUDY
Abdul Sattar Ghaffari
1
, Ruqia Safdar Bajwa
2
, Mureed Hussain
3
,
Muhammad Tahir
4
, Shagufta Bibi
5*
, Arslan Khalid
6*
1
Zhongtai Securities, Institute for Financial Studies, School of Mathematics, Shandong University, Jinan,
CHINA.
2
Department of Applied Psychology, Bahauddin Zakariya University, Multan, PAKISTAN.
3
Department of Psychology, International Islamic University, Islamabad, PAKISTAN.
4
School of Medicine, Shandong University, Jinan, CHINA.
5
School of Psychology, Shaanxi Normal University, Xi’an, CHINA
6
Department of Health Psychology, School of Nursing, Shandong University, Jinan, CHINA.
Article history:
Received 15 July 2019
December 2019
2020
Keywords:
Heart failure patients;
Hospitalized Patients;
Borderline of anxiety and
depression; Hospital
Anxiety and Depression
Scale (HADS); Heart
failure (HF).
Depression and anxiety are highly prevalent in Heart failure
patients. The main objective of the study is to check the prevalence of
anxiety and depression and associated factors of Heart Failure Patients of
South Punjab Pakistan. A sample of 192 Hospital admitted patients
whose age range was between 27-71 years were selected from various
cardiac hospitals of south Punjab Pakistan for January-July 2017 through
a purposive sampling technique. The Hospital Anxiety and Depression
Scale (HADS) was used to assess the level of depression and anxiety
among patients. The social and psychological parameters including
family care, gender, and relevant support were identified. Findings
revealed a highly significant correlation between the level of anxiety and
depression among Hospitalized heart failure patients. According to
results anxiety and depression is higher among unmarried and patients
with low family care and support. In addition, data has indicated that
economic factors associated with poverty mediate the frequency of
anxiety and depression. It was determined that approximately 50% and
54% of peoples were involved in anxiety and depression respectively,
however, 31% of people were regarded in the borderline of anxiety and
depression and most patients were accompanied by heart disease.
Furthermore, marital status, the difference in income level also promotes
anxiety and depression in patients associated with heart disease.
Disciplinary: Health Sciences (Health Management), Psychological
Sciences.

2 A.S. Ghaffari, R.S.Bajwa, M. Hussain, M. Tahir, S. Bibi, A. Khalid
1. INTRODUCTION
Heart failure (HF) is a serious chronic progressive disease with significant worldwide prevalence
and mortality [1]. The clinical picture of HF patients manifested by a group of symptoms including
pleurisy, difficult breathing, wheezing cough, generalized edema, decrease appetite, tiredness,
fatigue, malaise, rapid heartbeat, sleep apnea, confused thinking, loss of memory, poor social and
psychological functioning due to resultant depression, anxiety and stress [2].
The leading pathological factors of HF are hypertension, cardiomyopathy, congenital heart
disease, lung disorder, diabetes and obesity [3]. Anxiety and depression are common among the
patients of HF. It may worsen their symptoms requiring hospitalization for intensive emergency
care[4]
. Such malign psychological disorders eventually distort patients' physical health.
Research also highlights that cardiologists only focus on the clinical pictures of HF by ignoring
the resultants psychological stressors i.e. anxiety and depression, which further deteriorate the HF
patient's health. Depression plays a vital role to cause high blood pressure and many other cardiac
ailments including CVD and CAD [5, 6].
Several studies revealed that anxiety and depression and CAD are correlated with one another[7]
.
Recently research revealed that depressive patient with MI has a high mortality rate whereas during
hospitalization the depressive state of patient leads to CVD [8,9]. The hospitalized HF patients who
seldom visit by their family or those who are unmarried often seek a high level of anxiety and
depression which may cause impairments of their routine activities [10, 11]. Several types of research
show that there is an association between anxiety, depression and hospitalized HF patients[9].
Although it is highly critical to surviving with advanced heart failure because of the sudden
worsening situation the patient has to be readmitted in the hospital.
In the modern developed countries, health care teams adopted various strategies with the aim to
improve quality of life, enhancing patient endurance along with reduced hospital stay. Such action
may play a vital role to curtail the rate of anxiety and depression among HF patients. Patients with
heart failure admitted to the hospital have a high incident rate (>50%), have a 10 to 15% mortality rate
whereas it is up to 30 to 40 % in case of re-hospitalization within 6 months duration of discharge
[12,13]. Studies revealed that approximately less than 1/3 hospitalized HF patients were assessed by a
cardiac health professional in the early 90 days after discharged from hospital [13, 14]. To reduce the
re-hospitalization rate it is necessary to adopt new strategies including family care at home, long-term
hospital care programs involving palliative care treatment [15].
Another study [16] shows that depression expressed by physical complaints or hospitalization
the non-serious attitude by a cardiologist, nursing staff, and attendants to ignoring the magnitude of
depression ultimately damage the HF patient's health and quality of life. Early understanding,
delectation, and curative treatment of anxiety and depression certainly improve the HF patient’s
health and quality of life[9]. During hospitalization HF patients generally observe anxiety and
depression which can intensify clinical physical ailments, along with sluggishness and social
isolation [10]. Unfortunately, even after coronary artery bypass grafting (CABG), depression remains
untreated then certainly the morbidity rate and mortality will be high [17].
Heart failure patients with depression remain anxious to readmit in the hospital also have a high
risk of mortality [14]. Numerous studies show that HF patients living style and standards associated
with anxiety and depression such as poor diet, alcohol consumption, smoking/tobacco habits, lack of
exercise and social support will interfere with treatment and allied healthcare support [18]. The

3
cardiac patient plus generalized anxiety disorder has a high risk of severe morbidity as compared to
those who not suffered from anxiety or generalized anxiety disorder (GAD) [19]. Social and family
environment also affects cardiac events. The studies found that there is a positive correlation between
the socioeconomic statuses, family psychosocial environment and cardiovascular diseases [20].
The literature clearly indicates a high level of prevalence of depression and anxiety among heart
failure patients in different regions of the world. As such study has not conducted in Pakistan, so it
was planned to check the prevalence of hospital anxiety and depression and related social and
psychological factors in the south part of Pakistan.
2. METHODS
For this study, the samples were collected from 192 Hospitalized Heart failure (HF) patients
were chosen from Nishtar Hospital, Multan, Pakistan and Chaudhry Pervaiz Elahi Institute of
Cardiology, Multan, Pakistan (78 and 114 respectively) from January 2017-to- July 2017 by using the
method of purposive sampling. Most of HF patients were re-hospitalized. The demographic variables
were age, education, residential area, gender, marital status, and family care and support. To collect
the detailed information and observing basic parameters including anxiety and depression among
hospitalized heart failure patients. Purposely, heart failure patients with the phenomena of anxiety
and depression issues were targeted. The questionnaire was filled completely by the consent of all
participants. All the instructions and relevant assistance were provided from the hospital team to fill
this Performa honestly. The same methodology was applied to observe the differences among people
with low and high income and support from the family
Ethics and Consent: Ethical approval was taken from Shandong University China to conduct the
study and verbal consent was taken from patients after briefing them about the purpose of the study.
The Hospital Anxiety and Depression Scale (1983).The Hospital Anxiety and Depression Scale
(HADS) developed by Zigmond and Snaith[21]
to gauge anxiety and depression in a general
therapeutic populace of patients. The scale consisted of 14 items with responses being scored on a
scale of 0-3 where a higher score reflects frequencies of symptoms. In spite of the fact that the tension
and despondency questions are blended inside the survey, it is indispensable that these are scored
independently. Cut-off scores are accessible for evaluation, for instance, a score of at least 8 for
uneasiness has a specificity of 0.78 and affectability of 0.9, and for gloom a specificity of 0.79 and an
affectability of 0.83.
Before starting, patients were made a request to give permission that they have complete
knowledge about this study and willing to take an interest in this study. Instructions were provided to
all candidates to complete the questionnaire with honesty to minimize any chances of error. The
demographic sheet and Hospital Anxiety and Depression Scale were provided to all candidates to
examine the anxiety and depression index in these patients. Purposive sampling method was applied
to collect the sample from these enrolled patients associated with heart failure disease. Subjects were
also given the assurance that their information will not be disclosed. Participated patients were
approached at hospitals to administer the study instruments and help to complete the questionnaire.
The questionnaire took 2 to 5 minutes to complete. To determine the scoring scale of HADS, the data
were analyzed by using SPSS 21. Pearson correlation coefficients tool was used to finding the linkage
of all the variable parameters with each other. Comparisons between marital status for depression and

anxiety were analyzed via an independent samples t-test with corrections made for assumed variance
equality. We adopted the same methodology to find the difference among participants with less
family support and care with participants with more family support and care. One way ANOVA and
post hoc tests were used to analyze the differences between depression and anxiety among different
income levels. A P-value of less than 0.05 was considered significant.
3. RESULTS
Table 1 shows descriptive statistics of socio-demographic variables for this study. From overall
192 patients, most patients 122(63.5%) are between the age of 31 to 45 years, 112 (58%) male and
119 (62%) are single. Furthermore, 104 (54%) of the respondents who give their response as low
family care. Similarly, 51 (27%) of the patients have less than 15000 PKR, 50 (26%) belong to
30000-45000 PKR of the patients’ monthly income level.
Table 1: Descriptive Statistics of sociodemographic variables of the participants (n=192)
Individual factors Category Frequency (%)
Age
<31 Years 33(17.2)
31-45 Years 122(63.5)
45-60 Years 33(17.2)
> 60 Years 4(2.1)
Gender
Male 112 (58.0)
Female 80 (42.0)
Marital Status
Single 119 (62.0)
Married 73 (38.0)
Family Care and Support
Low 104 (54.0)
High 88 (46.0)
Monthly Income (Rupees)
<15000 51 (27.0)
15000-3000 36 (19.0)
30000-45000 50 (26.0)
45000-60000 31 (16.0)
>60000 24 (12.0)
Table 2: Categories of Anxiety and Depression on the Basis of Scores
Categories Anxiety Frequency (%) Depression Frequency (%)
Normal 36 (19.0) 28 (15.0)
Borderline Abnormal 60 (31.0) 59 (31.0)
Abnormal 96 (50.0) 105 (54.0)
Table 2 and diagram 1 shows the prevalence of hospital anxiety and depression level in the
sampled patients. There are 60 (31%) and 96 (50%) of the patients who have a borderline abnormal
and abnormal level of anxiety respectively. Similarly, 59 (31%) and 105 (54%) of the heart failure
patients show borderline abnormal and abnormal depression level respectively.
Table 3: Pearson Correlation between anxiety and depression (n=192).
Depression
Anxiety 0.736***
(***p<0.001)
Table 3 explores the correlation between anxiety and depression in patients associated with heart
failure. The value of the correlation coefficient shows that anxiety and depression are significantly
positively correlated with each other.

5
Table 4: Mean, Standard Deviation and t- value for the score of single (n=119) and married (n=73)
on the scale of anxiety and depression.
Variable Marital Status N M SD T P
Anxiety
Single 119 10.85 2.91
2.856 <.003Married 73 9.64 2.72
Depression
Single 119 11.26 2.73
3.357 <.001Married 73 9.90 2.70
Table 4 depicts the frequency of depression and anxiety for the single and married hospitalized
heart failure patients. The results show that the level of anxiety and depression is higher in single as
compared to married patients.
Table 5: Mean, SD and t-value for the score of low family care and support (n=104) and high family
care and support (n=88) on the scale of anxiety and depression.
Variable Family Care and Support N M SD T P
Anxiety
Low 104 10.85 2.94
2.404 <.009High 88 9.85 2.75
Depression
Low 104 11.20 2.81
2.502 <.007High 88 10.24 2.68
Table 6: One Way ANOVA: Anxiety and depression level among hospitalized heart failure patients
with variation in income.
Variable Sum of Squares Df Mean Square F Sig.
Anxiety
Between Groups 106.952 4 26.738 3.359 <.011
Within Groups 1488.752 187 7.961
Total 1595.703 191
Depression
Between Groups 147.364 4 36.841 5.145 <.001
Within Groups 1339.131 187 7.161
Total 1486.495 191
Table 7: Post Hoc Tests Multiple Comparisons.
Dependent
Variable
(I) Income
Status
(J) Income
Status
Mean
Difference
(I-J)
Std. Error Sig.
95% Confidence Interval
Lower
Bound
Upper
Bound
Anxiety
Up to 15000
15000-30000 .312 0.614 0.612 -0.900 1.524
30000-45000 .518 0.562 0.358 -0.590 1.625
45000-60000 1.956*
0.643 0.003 0.689 3.224
Above 60000 1.743*
0.698 0.013 0.365 3.121
15000-30000
30000-45000 .2056 0.617 0.739 -1.011 1.422
45000-60000 1.644*
0.691 0.018 0.280 3.008
Above 60000 1.431 0.744 0.056 -0.036 2.897
30000-45000
45000-60000 1.439*
0.645 0.027 0.166 2.711
Above 60000 1.225 0.701 0.082 -0.157 2.607
45000-60000 Above 60000 -.214 0.767 0.781 -1.727 1.300
Depression
Up to 15000
15000-30000 .444 0.583 0.446 -0.705 1.594
30000-45000 .767 0.533 0.152 -0.284 1.817
45000-60000 2.151*
0.609 0.001 0.948 3.353
Above 60000 2.333*
0.662 0.001 1.027 3.640
15000-30000
30000-45000 .322 0.585 0.582 -0.832 1.476
45000-60000 1.707*
0.656 0.010 0.413 3.000
Above 60000 1.889*
0.705 0.008 0.498 3.280
30000-45000
45000-60000 1.384*
0.612 0.025 0.177 2.591
Above 60000 1.567*
0.665 0.019 0.256 2.878
45000-60000 Above 60000 -.183 0.728 0.802 -1.618 1.253
Table 5 explores that the index of anxiety and depression was higher in patients with less care

and support from the family compared to the patients having more family care and support.
The significant difference in anxiety and depression among different income levels patients was
observed in Tables 6 and 7 that the p-value for anxiety and depression are 0.011*
and 0.001**
respectively, which show that there is a highly significant difference in depression and anxiety among
different income status.
Furthermore, from the Post Hoc test, we can also conclude that the maximum difference in the
level of anxiety is between income group “up to 15000” and “45000-60000”. Similarly, depression
is much higher in the patients belongs to the “up to 15000” income group also the group belongs to
“above 60000 income group”.
Table 8: Regression Analysis: Anxiety and Depression on Age, Gender, Marital Status, Family
Care and Support and Income Level.
Dependent Predictor Β SE (β) T P
95% Confidence Interval for β
Lower Bound Upper Bound
Anxiety
Age 0.025 0.023 1.065 <.288 -0.021 0.071
Gender 1.211 0.390 3.102 <.002 0.441 1.981
Marital Status -1.207 0.400 -3.018 <.003 -1.996 -0.418
Family Care and Support -0.693 0.392 -1.769 .079 -1.466 0.080
Income level -0.527 0.144 -3.652 <.001 -0.812 -0.242
Depression
Age 0.023 0.022 1.034 .302 -0.021 0.067
Gender 0.158 0.375 0.421 .674 -0.581 0.897
Marital Status -1.364 0.384 -3.555 <.001 -2.122 -0.607
Family Care and Support -0.765 0.376 -2.035 <.043 -1.507 -0.024
Income level -0.616 0.139 -4.446 <.001 -0.889 -0.343
Results Table 8 shows that gender, marital status, and income level have a significant effect on
anxiety while no significant effect seen on the basis of age and support and care from family.
Similarly marital status, support, and care from family and income level have significant on
depression which there is no significant effect of age and gender on depression.
4. DISCUSSION
The heart failure patients generally experience frequent hospitalization within 3 to 6 months due
to their sudden health situation. About one-half of heart failure patients have to experience
re-hospitalization. In this way, the mortality rate among heart failure patients is higher generally.
Hospitalization incurs depression and anxiety among heart failure patients which further deteriorates
the status of the heart. The study revealed that epidemiology of anxiety and depression is mainly due
to the poor prognosis of heart failure and longer hospitalization. Results revealed a high level of
abnormal depression and anxiety in the majority of the candidates associated with a heart problem.
Upon extensive review, it has been found that several patients associated with heart problems
were involved in anxiety and depression during the course of their disease [22, 23]. Almost 15-20
percent of hospitalized patients with heart failure is diagnosed with major depressive disorder [24,
25]. A systematic review revealed that Anxiety is a big predictor of hospitalization in heart failure
patients[26]. A meta-analysis [27] reported the incidence and frequency of anxiety disorders in heart
failure patients.
A highly significant positive correlation was found between anxiety and depression among
hospitalized heart failure patients. According to existing literature anxiety and depression are highly

7
prevalent among heart failure patients [28, 29, 30, 31].
It was hypothesized in this study that single patients who don’t have life partners will have more
anxiety and depression as compared to married counterparts. The results supported the hypotheses
and found a significant difference in both married and unmarried groups. The frequency of depression
and anxiety was higher among young unmarried patients. Previous literature also supports this
finding that elderly unmarried patients have depressive symptoms and it had a strong impact on their
recovery as well[32, 33, 34]
. Another remarkable finding of this study discusses that lack of family care
and support also triggers the anxiety and depression among hospitalized heart failure patients. Studies
have enormously focused on the role of social support, it has been found as a very protective factor
against unpleasant effects [35,36]. Re-hospitalization has a strong association with anxiety and
depression [37].
Further, the study revealed a highly significant difference in anxiety and depression among
patients with different income status [38]. Depression epidemiology among HF hospitalized patients
is higher and associated with fear of readmission. Re-hospitalization ultimately exerts a havoc blow
on the economic status of HF patients [39]. This triggers the level of anxiety and depression among
hospitalized HF patients.
5. CONCLUSION
In conclusion, anxiety and depression among hospitalized heart failure patients are certainly
significant. The level of the psychological clinical picture entirely depends upon the state of HF and
admission or readmission in the hospital. Acknowledgment of this causative factor and their
outcomes is essential for future studies to curtail the barrier to curative effective HF treatment. The
cardiologists should try their best to adopt certain advance approaches to reduce the hospitalization
rate among HF patients. The psychologist should also visit HF patients to reduce the level of
depression.
6. AVAILABILITY OF DATA AND MATERIAL
Data can be made available by contacting the corresponding author.
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