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Abstract

In the absence of a universal health insurance mechanism, the increasing burden of out‐of‐pocket (OOP) health expenditure has become a growing concern in India. To cope with the cost of illness, people use either their savings and income, or they have to rely upon distress means of finance such as depletion of household assets, borrowings from banks and moneylenders, and contributions from family and friends. This paper analyses the changes that have taken place in the incidence and covariates of distress financing in India by using data from National Sample Survey Organisation for the years 2004 and 2014. Results indicate that during this period the incidence of distress sources as a means to finance OOP health expenditure has hovered around 50%. Further, the results reveal a significant socioeconomic gradient in the incidence of distress financing. Socioeconomic and health‐related covariates significantly impact the likelihood of distress financing as a means to cope with OOP health expenditure. The results indicate the need for government action to formulate a comprehensive plan through an increase in public spending on health care that will improve the quantity and quality of the public health‐care system and enhance the scope of health insurance in India.
REGULAR ARTICLE
Distress financing of outofpocket health
expenditure in India
Shivendra Sangar
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Varun Dutt
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Ramna Thakur
School of Humanities and Social
Sciences, Indian Institute of Technology,
Mandi, Kamand (H.P), 175005
Correspondence
Ramna Thakur, School of Humanities and
Social Sciences, Indian Institute of
Technology, Mandi, Kamand (H.P),
175005.
Email: ramna@iitmandi.ac.in
Abstract
In the absence of a universal health insurance mechanism,
the increasing burden of outofpocket (OOP) health
expenditure has become a growing concern in India. To
cope with the cost of illness, people use either their sav-
ings and income, or they have to rely upon distress
means of finance such as depletion of household assets,
borrowings from banks and moneylenders, and contribu-
tions from family and friends. This paper analyses the
changes that have taken place in the incidence and
covariates of distress financing in India by using data
from National Sample Survey Organisation for the years
2004 and 2014. Results indicate that during this period
the incidence of distress sources as a means to finance
OOP health expenditure has hovered around 50%.
Further, the results reveal a significant socioeconomic
gradient in the incidence of distress financing. Socioeco-
nomic and healthrelated covariates significantly impact
the likelihood of distress financing as a means to cope
with OOP health expenditure. The results indicate the
need for government action to formulate a comprehensive
plan through an increase in public spending on health
care that will improve the quantity and quality of the pub-
lic healthcare system and enhance the scope of health
insurance in India.
KEYWORDS
Coping, Incidence, Inequality, source of finance
DOI: 10.1111/rode.12540
Rev Dev Econ. 2018;117. wileyonlinelibrary.com/journal/rode © 2018 John Wiley & Sons Ltd
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INTRODUCTION
The healthcare system in every nation aims to provide inclusive and efficient care which can
maintain and improve the health status of the population (Bredenkamp, Mendola, & Gragnolati,
2010; World Health Organization (WHO), 2010). Countries all over the world design health sys-
tems so as to have a minimal adverse impact on the economic wellbeing of individuals (Bovbjerg,
2001; Ransom, 2002). However, the experience of lowand middleincome countries (LMICs)
such as India reveals a different state altogether (Ramani & Mavalankar, 2006). Although in India
the government's efforts in public health have reduced the infant mortality rate and increasing life
expectancy, these initiatives are still only moderately successful by international standards (Min-
istry of Health and Family Welfare (MOHFW), 2002). On key health indicators, India's healthcare
system ranked 112th among 190 countries in the world (WHO, 2000). To a certain extent, the dis-
mal performance of India's healthcare system can be attributed to the level of government spend-
ing, which is is as low as 1.15% of gross domestic product (GDP) (MOHFW, 2016). In India, the
inadequacy of healthcare spending by the government has resulted in poor healthinsurance cover-
age (Selvaraj & Karan, 2012). Health insurance is an essential tool that safeguards people against
the economic burden associated with the cost of illness (Dilip & Duggal, 2002). But in India only
17% of the total population have healthinsurance coverage, which is abysmally low when com-
pared to other LMICs (Insurance Regulatory and Development Authority of India, 2016). Sections
of the society not covered by regular health insurance face a higher burden of healthcare costs
(Dilip & Duggal, 2002).
Over the years, the private healthcare sector has become an overwhelming choice in India,
especially outpatient visits (Sengupta & Nundy, 2005). The lack of responsiveness of public hospi-
tals to the healthcare needs of people is mainly responsible for the extraordinary growth of private
health care in India (Bajpai, 2014). Thus, the deficiencies in the public healthcare system. on the
one hand. and lack of a universal healthinsurance mechanism, on the other, have resulted in
higher outofpocket (OOP) spending in India (Jayakrishnan, Jeeja, Kuniyil, & Paramasivam,
2016). According to India's National Health Accounts, 201314, OOP spending accounts for
64.2% of total health spending (MOHFW, 2016). The unusually high share of OOP expenditure on
health care could drastically affect the economic condition of households and even push them
below the poverty line (Berman, Ahuja, & Bhandari, 2010; Ghosh, 2010; O'Donnell et al., 2008).
In these situations, people either use their savings and income or they have to rely upon alternative
sources of finance such as depletion of household assets, borrowings from banks and moneylen-
ders, and contributions from family and friends to cope with the cost of illness (Flores, Krishnaku-
mar, O'Donnell, & Van Doorslaer, 2008; Leive & Xu, 2008). Coping strategies aim to avert the
financial hardship associated with the economic burden of illness on households (Sauerborn,
Adams, & Hien, 1996). In LMICs, the financial risks of seeking healthcare services are higher
among the most deprived households in countries with less health insurance (Kruk, Goldmann, &
Galea, 2009). Socially vulnerable sections of society such as Scheduled Castes, Scheduled Tribes
minority religious groups, and females are more likely to use coping strategies to finance health
shocks, leading to greater welfare loss among these groups (Dhanaraj, 2014; Joe, 2014). Coping
strategies such as using savings, borrowing, the sale of assets and transfers finance threefourths of
the cost of inpatient care in rural areas and twothirds of the cost in urban areas in India (Dilip &
Duggal, 2002; Flores et al., 2008).
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SANGAR ET AL.
Earlier studies on coping mechanisms have mainly examined the incidence and correlates of
distress financing at a point in time. However, it is also imperative to analyze the changes that
have taken place over an extended period in the incidence and covariates of coping strategies used
by households. In this paper, our contribution to the literature on coping mechanisms explores the
changes in the incidence of different sources of finance in India for two periods, 2004 and 2014.
Further, we examine the level of inequality in the incidence of different sources of finance in the
period 20042014. Finally, we investigate the impact of various socioeconomic and healthrelated
covariates on the likelihood of using distress financing (borrowings, the sale of assets, and contri-
butions from friends and relatives) to cope with the cost of illness.
The structure of the paper is as follows. In Section 2 we discuss the data and methodology used
in the analysis. In Section 3 we present the results; Sections 3.13.3 show the incidence of house-
holds using different sources of finance as coping mechanism in India for the periods 2004 and
2014, Section 3.4 deals with the level of inequality in the incidence of sources of funding used as
coping mechanism, and Section 3.5 presents the results of a multivariate logistic regression on the
likelihood of using distress financing to cope with the cost of illness. Section 4 presents a
discussion of the results. Section 5 concludes.
2
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DATA AND METHODS
2.1
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Data
The analysis used the crosssectional data from National Sample Survey Organisation (NSSO),
namely, the 60th round (2004) on Health, Morbidity and Condition of Agedand the 71st round
on Key Indicators of Social Consumption: Health, involving 73,868 and 65,932 households,
respectively (NSSO, 2004, 2014). The surveys adopted a stratified multistage sample design, using
census villages for the rural areas and urban blocks for the urban areas as the firststage units and
households as the secondstage units. The survey periods for 60th and 71st rounds were from Jan-
uary to June 2004 and January to June 2014, respectively. The survey provided information about
various sources of finance used as the coping mechanism for OOP healthcare expenditure. These
consisted of household income/savings, borrowings, sale of physical assets, contributions from
friends and relatives, and other sources. Although the survey provided information for both inpa-
tient and outpatient care, our study is mainly restricted to inpatient care (including childbirth).
Household savings and income mostly finance outpatient care, which limits the scope of further
analysis of distress financing. We have used the household as the unit of study and adjusted all
estimates according to their respective weights.
2.2
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Variables
In our model, the dependent variable is a binary variable which shows the likelihood of a house-
hold using distress financing (borrowings, contributions from friends and relatives, and sale of
assets) as a coping mechanism.
The incidence of distress financing depends on different socioeconomic and healthrelated vari-
ables (Flores et al., 2008; Joe, 2014; Mondal, Lucas, Peters, & Kanjilal, 2014). For analysis, three
prominent socioeconomic variables, namely, place of residence (rural and urban), social category
(SCST and nonSCST), and poverty (poor and nonpoor) have been grouped to form intersectional
variables. Place of residence includes whether the household resides in a rural or urban area. Social
categories include general (forward castes) and Other Backward Classes (OBCs) and Scheduled
SANGAR ET AL.
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3
Tribes (STs), Scheduled Castes (SCs) (socially marginalized as per the Constitution of India). In
this analysis, we have combined SCs and STs as SCSTs and general and OBCs as nonSCSTs.
The poverty line classifies poor households for the period in consideration (Planning Commission,
2014). The intersection of these three variables generates mutually exclusive subgroups consisting
of: (1) ruralSCSTpoor; (2) ruralnonSCSTpoor; (3) ruralSCSTnonpoor; (4) ruralnon
SCSTnonpoor; (5) urbanSCSTpoor; (6) urbannonSCSTpoor; (7) urbanSCSTnonpoor;
and (8) urbannonSCSTnonpoor.
Other socioeconomic variables include religion, which consists of Hinduism (comprising 80%
of total population of India), Islam, Christianity and other religions (Registrar General, 2011). We
have also included household size, which measures the number of people living in the household.
Household size has a significant bearing on the likelihood of using distress financing. The house-
hold type defines the source of earnings and includes casual/agriculture labor, regular/salaried, and
others as defined by the survey. Healthrelated variables can also have a significant influence on
the likelihood of using a particular source of finance. These variables include whether an ailing
household member is using a private healthcare facility and a dichotomous variable for the num-
ber of days of hospitalization (up to or more than 10 days). Furthermore, noncommunicable dis-
eases (NCDs) have high economic costs, and they can impact the likelihood of households using
distress financing to a large extent. In the analysis, we have taken three NCDs, namely, cancer,
cardiovascular diseases (CVDs) and injuries.
2.3
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Methods
We initially analyzed the proportion of households utilizing various sources of finance (to cope
with the cost of illness) in 2004 and 2014.
1
The incidence, I, of households employing multiple
sources of finance is calculated as
I¼1
N
N
i¼1
H;(1)
where Nis the sample size and His the number of households using a source of finance.
In line with the above analysis, the concentration index (CI) and concentration curve (CC) have
been used to determine the socioeconomic inequalities in the use of various sources of finance.
The concentration curve (CC) plots the cumulative proportions of households using a particular
source of financing on the yaxis against the cumulative proportions of the population (ranked by
socioeconomic status) on the xaxis. The CC will coincide with the 45° line of equality if the inci-
dence of a specific source of finance is equally distributed across the socioeconomic groups. If it
is concentrated among higher (lower) consumption groups, then the CC will lie below (above) the
line of equality; and the further the CC from the line of equality, the greater would be the inci-
dence among the poorer households (Wagstaff, O'Donnell, Van Doorslaer, & Lindelow, 2007).
The concentration index (CI) can be derived from the CC. It is defined as twice the area between
the CC and the line of equality. The CI varies between 1 and +1, indicating the direction of the
relationship between various sources of finance and socioeconomic status. A negative value
reflects that the source of finance is more concentrated towards the poor, while a positive value
implies a higher concentration among the rich. The CI can be computed by the following formula
given by Fuller and Lury (1977):
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SANGAR ET AL.
CI ¼ðp1L2p2L1Þþðp2L3p3L2Þþþðpt1LtptLt1Þ;(2)
where p
t
is the cumulative percentage of the sample ranked by household consumption expenditure
in group t, and L
t
is the corresponding health variable (i.e., incidence of different coping
mechanisms).
In order to check the robustness of the results based on equation (2), we use the methodology
given by Erreygers (2009):
TABLE 1 Incidence of Use of Various Sources of Finance as a Coping Mechanism by Households in India:
Inpatient Care
2004 2014
Savings/ Income Borrowings Other Sources Savings/ Income Borrowings Other Sources
Quintiles (MPCE*)
Poorest 80.7
(78.6, 82.8)
52.2
(49.9, 54.3)
28.8
(26.9, 30.9)
77.7
(75.7, 79.7)
43.6
(41.2, 45.9)
22.1
(19.9, 24.3)
Poor 84.7
(83.1, 86.4)
50.6
(48.4, 52.8)
26.6
(24.4, 28.8)
81.4
(79.4, 82.7)
41.7
(39.6, 43.8)
19.7
(18.1, 21.3)
Middle 85.5
(83.7, 87.2)
46.9
(44.6, 49.1)
23.9
(21.9, 25.8)
83.6
(81.9, 85.3)
39.6
(37.2, 42.0)
18.2
(16.5, 19.9)
Rich 89.3
(87.7, 90.8)
38.3
(36.2, 40.8)
23.3
(20.9, 25.6)
85.1
(83.5, 86.6)
34.9
(32.7, 37.1)
18.2
(15.9, 20.4)
Richest 93.3
(92.2, 94.3)
24.3
(22.1, 25.9)
19.4
(17.4, 21.3)
87.9
(86.2, 89.7)
28.2
(25.9, 30.4)
18.5
(16.0, 20.9)
Place of residence
Rural 85.0
(84.0, 86.0)
48.1
(46.9, 49.4)
26.6
(25.4, 27.8)
81.4
(80.4, 82.5)
41.2
(39.9, 42.6)
20.2
(19.0, 21.4)
Urban 90.3
(89.2, 91.4)
29.9
(28.4, 31.5)
19.5
(18.2, 20.8)
86.4
(85.5, 87.4)
30.2
(28.7, 31.6)
17.6
(16.2, 18.9)
Social categories
STs 86.2
(83.4, 89.1)
42.6
(38.6, 46.6)
27.4
(23.3, 31.4)
82.9
(80.9, 85.1)
39.1
(35.3, 42.8)
18.1
(15.7, 20.5)
SCs 82.8
(81.1, 84.6)
51.1
(48.8, 53.4)
26.4
(24.2, 28.5)
81.2
(79.2, 83.1)
44.6
(41.9, 47.3)
18.9
(16.8, 20.9)
OBCs 85.6
(84.2, 86.9)
45.6
(44.0, 47.2)
24.1
(22.7, 25.5)
81.8
(80.5, 83.1)
38.7
(37.2, 40.2)
20.2
(18.7, 21.8)
General 90.1
(89.1, 91.1)
34.1
(32.5, 35.7)
23.21
(21.6, 24.8)
86.2
(85.0, 87.4)
31.1
(29.4, 32.8)
18.6
(17.2, 20.2)
Total 86.7
(85.9, 87.4)
42.4
(41.4, 43.4)
24.4
(23.5, 25.3)
83.1
(82.3, 83.9)
37.6
(36.6, 38.6)
19.3
(18.4, 20.3)
Note. Figures are based on the author's calculations from the NSSO 60th and 71st rounds. Other sources include sale of assets, con-
tributions and others. Values in parentheses are 95% confidence intervals.
*Monthly per capita consumption expenditure.
SANGAR ET AL.
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5
TABLE 2 Incidence of Use of Various Sources of Finance as a Coping Mechanism by Households in Rural and Urban India: Inpatient Care
2004 2014
Rural Urban Rural Urban
Savings/
Income Borrowings
Other
Sources
Savings/
Income Borrowings
Other
Sources Savings/Income Borrowings
Other
Sources
Savings/
Income Borrowings
Other
Sources
Quintiles (MPCE*)
Poorest 81.0
(78.2, 83.7)
53.9
(51.2, 56.5)
27.3
(25.0, 29.6)
84.2
(81.6, 86.8)
41.6
(38.3, 44.8)
24.3
(21.5, 27.2)
78.0
(75.8, 80.3)
45.4
(42.4, 48.4)
21.8
(19.3, 24.3)
79.8
(77.3, 82.2)
38.5
(35.7, 41.3)
21.3
(18.6, 24.1)
Poor 81.9
(79.6, 84.2)
51.1
(48.4, 53.9)
26.4
(23.8, 28.9)
87.4
(83.7, 91.1)
39.3
(35.3, 43.2)
19.8
(17.1, 22.5)
78.7
(76.0, 81.4)
41.5
(38.8, 44.3)
20.8
(18.3, 23.2)
85.3
(83.4, 87.2)
34.5
(31.4, 37.7)
18.5
(16.1, 20.8)
Middle 86.2
(84.5, 87.9)
52.5
(49.9, 55.1)
22.4
(20.3, 24.6)
91.6
(89.8, 93.5)
33.9
(29.8, 38.0)
17.5
(15.0, 20.1)
82.3
(80.2, 84.5)
44.6
(41.6, 47.6)
18.5
(16.4, 20.6)
85.7
(83.7, 87.5)
33.3
(30.1, 36.5)
17.3
(13.9, 20.7)
Rich 86.4
(84.4, 88.1)
44.2
(41.9, 47.4)
22.6
(20.6, 25.2)
92.3
(90.4, 94.1)
21.0
(18.1, 23.8)
16.4
(13.6, 19.3)
83.5
(81.2, 85.8)
38.4
(35.3, 41.6)
19.2
(16.2, 22.2)
88.6
(86.0, 91.1)
26.9
(23.4, 30.5)
17.9
(14.3, 21.4)
Richest 89.9
(87.9, 92.0)
37.4
(34.4, 40.4)
23.3
(20.4, 26.3)
96.1
(94.9, 97.3)
14.1
(12.1, 16.2)
14.7
(11.9, 17.5)
84.6
(82.1, 87.0)
36.2
(33.3, 39.2)
20.8
(17.7, 23.9)
93.0
(91.6, 94.5)
17.6
(14.7, 20.4)
12.7
(10.8, 14.6)
Social Categories
STs 85.2
(82.0, 88.5)
45.6
(40.1, 49.0)
25.2
(20.9, 29.4)
92.2
(87.8, 96.6)
30.6
(22.2, 39.1)
22.4
(15.9, 28.9)
82.0
(79.6, 84.3)
39.2
(35.3, 43.0)
18.3
(15.6, 21.0)
89.0
(85.2, 92.0)
38.4
(25.8, 51.0)
16.9
(11.9, 21.9)
SCs 80.9
(78.7, 83.0)
55.0
(52.4, 57.7)
27.0
(24.5, 29.4)
88.4
(85.8, 90.9)
39.7
(35.1, 44.4)
17.5
(14.5, 20.5)
81.0
(78.7, 83.3)
47.9
(44.6, 51.2)
18.5
(16.0, 21.0)
81.7
(78.0, 85.3)
34.7
(31.3, 38.2)
20.1
(16.5, 23.8)
OBCs 84.3
(82.6, 86.0)
49.8
(47.9, 51.7)
23.9
(22.3, 25.5)
88.9
(86.6, 91.1)
34.8
(32.0, 37.6)
18.5
(16.4, 20.7)
80.4
(78.6, 82.2)
41.0
(39.0, 43.0)
21.5
(19.5, 23.6)
84.7
(83.3, 86.1)
34.0
(31.8, 36.3)
17.6
(15.6, 19.6)
General 88.7
(87.3, 90.2)
41.8
(39.5, 44.1)
23.5
(21.3, 25.7)
92.0
(90.7, 93.4)
22.8
(20.8, 24.7)
18.7
(16.9, 20.5)
83.3
(81.6, 85.2)
36.7
(34.3, 39.2)
20.1
(18.1, 22.0)
90.0
(88.8, 91.2)
23.3
(21.2, 25.4)
16.5
(14.5, 18.6)
Ailments
(Continues)
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SANGAR ET AL.
TABLE 2 (Continued)
2004 2014
Rural Urban Rural Urban
Savings/
Income Borrowings
Other
Sources
Savings/
Income Borrowings
Other
Sources Savings/Income Borrowings
Other
Sources
Savings/
Income Borrowings
Other
Sources
CVDs 85.4
(81.2, 89.6)
52.0
(46.0, 58.0)
31.1
(24.4, 37.9)
92.3
(89.5, 95.1)
26.0
(21.9, 30.1)
20.1
(16.0, 24.2)
86.5
(82.1, 90.8)
40.7
(35.2, 46.2)
26.8
(19.9, 33.6)
89.9
(87.4, 92.3)
30.8
(26.0, 35.6)
19.6
(16.1, 23.2)
Injuries 83.8
(81.1, 86.5)
50.1
(46.4, 53.9)
30.3
(26.7, 33.9)
85.0
(77.9, 92.1)
37.9
(31.3, 44.5)
23.8
(18.4, 29.3)
86.1
(82.9, 89.3)
45.6
(41.3, 49.9)
20.9
(17.3, 24.4)
89.6
(87.7, 91.4)
34.2
(29.9, 38.5)
20.1
(15.2, 25.0)
Cancer 87.7
(83.3, 92.0)
56.1
(48.7, 63.7)
33.7
(26.6, 40.8)
86.8
(80.1, 93.5)
36.1
(26.7, 45.6)
18.0
(18.0, 33.9)
84.4
(79.0, 89.8)
61.1
(52.2, 69.9)
28.9
(21.5, 36.2)
88.2
(83.7, 92.6)
40.1
(32.8, 47.5)
30.4
(23.2, 37.6)
Total 85.1
(84.1, 85.9)
48.1
(46.9, 49.4)
26.6
(25.4, 27.8)
90.3
(89.3, 91.4)
29.9
(28.4, 31.5)
19.5
(18.2, 20.8)
81.4
(80.4, 82.5)
41.2
(39.9, 42.6)
20.2
(19.0, 21.4)
86.4
(85.5, 87.4)
30.2
(28.7, 31.6)
17.5
(16.3, 18.9)
Note. Figures are based on the author's calculations from the NSSO 60th and 71st rounds. Other sources include sale of assets, contributions and others. Values in parentheses are 95% confidence
intervals.
*Monthly per capita consumption expenditure.
SANGAR ET AL.
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7
CI ¼2
N2μh
n
i¼1
zihi;zi¼Nþ1
2ri;(3)
where Nis the sample size, his the particular source of finance, μhis its mean, r
i
is the socioeco-
nomic rank of the household, with the most welloff household ranked first and the least welloff
ranked last.
Next, the association between socioeconomic covariates and probabilities of using distress
financing as a coping mechanism has been modeled using multivariate logistic regression. The
mathematical form of the multivariate logistic regression is given by
TABLE 3 Incidence of Use of Various Sources of Finance as a Coping Mechanism by Households in India:
Outpatient Care
2004 2014
Savings/income Borrowings Other sources Savings/income Borrowings Other sources
Quintiles (MPCE*)
Poorest 93.2
(91.9, 94.5)
11.6
(10.1, 13.1)
3.9
(3.1, 4.5)
97.5
(96.5, 98.6)
10.9
(8.7, 13.3)
6.6
(4.9, 8.3)
Poor 93.1
(91.9, 94.3)
10.2
(8.8, 11.6)
2.9
(2.2, 3.8)
97.1
(96.0, 98.3)
7.3
(5.5, 9.1)
6.3
(4.5, 8.1)
Middle 94.3
(93.3, 95.3)
8.2
(6.9, 9.3)
2.6
(1.8, 3.4)
98.6
(98.0, 99.1)
6.3
(4.6, 7.9)
2.9
(2.1, 3.6)
Rich 95.7
(94.8, 96.6)
6.8
(5.5, 8.0)
1.8
(0.9, 2.6)
97.7
(96.7, 98.6)
4.4
(3.1, 5.7)
4.1
(2.9, 5.4)
Richest 96.3v
(95.4, 97.2)
3.7
(2.9, 4.5)
1.8
(1.2, 2.4)
97.9
(97.2, 98.7)
3.8
(2.5, 5.2)
3.9
(2.9, 4.9)
Place of residence
Rural 93.3
(93.1, 94.3)
9.7
(8.9, 10.4)
2.9
(2.4, 3.3)
97.5
(96.9, 98.1)
7.8
(6.7, 8.9)
5.4
(4.5, 6.3)
Urban 96.4
(95.7, 97.2)
4.2
(3.5, 4.9)
1.9
(1.3, 2.7)
98.2
(97.8, 98.7)
4.3
(3.4, 5.2)
3.5
(2.9, 4.2)
Social categories
STs 95.6
(93.7, 97.6)
7.7
(5.3, 10.2)
3.7
(2.1, 5.3)
97.7
(96.3, 99.0)
8.9
(5.7, 12.1)
5.4
(3.0, 7.7)
SCs 93.7
(92.5, 94.8)
10.9
(9.4, 12.3)
2.7
(1.9, 3.5)
97.2
(95.9, 98.4)
7.5
(5.5, 9.4)
4.3
(3.0, 5.5)
OBCs 94.0
(93.1, 94.8)
8.8
(7.9, 8.8)
2.7
(2.1, 3.2)
97.6
(96.9, 98.2)
7.1
(5.9, 8.3)
4.1
(3.1, 4.9)
General 95.4
(94.7, 96.1)
5.9
(5.1, 6.7)
2.4
(1.7, 3.0)
98.4
(97.7, 99.0)
4.8
(3.7, 5.9)
5.8
(4.6, 6.9)
Total 94.5
(93.8, 96.2)
8.1
(7.1, 9.6)
2.6
(1.8, 3.1)
97.8
(97.3, 98.2)
6.6
(5.8, 7.3)
3.7
(2.9, 4.1)
Note. Figures are based on the author's calculations from the NSSO 60th and 71st rounds. Other sources include sale of assets,
contributions and others. Values in parentheses are 95% confidence intervals.
*Monthly per capita consumption expenditure.
8
|
SANGAR ET AL.
P
1P¼B0þB1X1þB2X2þB3X3þB4X4þB5X5þB6X6þB7X7
þB8X8þB9X9þui
(4)
where Pis the probability of using distress sources as a coping mechanism, 1 Pis the probabil-
ity of not using distress sources as a coping mechanism, X
1
denotes intersectional variables, X
2
denotes religion, X
3
is household size, X
4
is household type, X
5
denotes a household member using
private healthcare facility, X
6
the number of days hospitalized, X
7
a household member suffering
from CVDs, X
8
a household member suffering from cancer, X
9
a household member suffering
from injuries, u
i
is the random disturbance term, and B
0
,,B
9
are the parameters to be estimated.
A more meaningful interpretation of the results is through the odds ratio. The odds ratio is
obtained by taking the antilog of various slope coefficients. Equation (4) can be written in odds
ratio form as:
P
1P¼1þez
1þez¼ez
;(5)
where P/(1 P) is the odds ratio for using a source of finance, Z=B
0
+B
1
X
1
++B
9
X
9
, and
e
Z
is the antilog of Z. By taking the natural log of Equation (5) we obtain the logit function, writ-
ten as
L¼In P
1P

¼Z¼B0þB1X1þ...þB9X9:(6)
3
|
RESULTS
3.1
|
Incidence of use of various sources of finance as a coping mechanism
by households in India: Inpatient care
Table 1 shows that in 2004, 86.7%, 42.4% and 24.4% of households used savings/income, borrow-
ings, and other sources (sale of assets and contributions from relatives and friends), respectively, to
finance health care. In 2014, the incidence was 83.1%, 37.6%, and 19.3% respectively. A signifi-
cant socioeconomic gradient is also present, whereby the incidence of distress sources of financing
such as borrowings and other sources is concentrated among poorer quintiles, while savings/in-
come is concentrated towards the more affluent households. The proclivity for borrowings and
other distress sources keeps the poor perpetually bound to the vicious circle of poverty. Further,
with regard to place of residence, the incidence of borrowings in rural areas (48.1% and 41.2% in
2004 and 2014, respectively) is very high in comparison to urban areas (29.9% and 30.2% in 2004
and 2014, respectively). This shows that borrowings play a critical role in rural areas in coping
with the cost of illness. Finally, with regard to different social categories, the incidence of borrow-
ings is unusually high among socially vulnerable sections (SCs and STs). For instance, the inci-
dence of borrowings among SCs is 51.1% and 44.6%, respectively, for 2004 and 2014. Distress
financing is highly pervasive among socially vulnerable sections such as SCs and STs and poses a
higher risk of healthrelated financial indebtedness among them. Overall, the trend shows that
despite the higher incidence of savings/income, a significant proportion of households have to
resort to debt or even sell their assets to finance healthrelated expenditure.
SANGAR ET AL.
|
9
3.2
|
Incidence of use of various sources of finance as a coping mechanism
by households in rural and urban India: Inpatient care
An analysis of rural households vis-à-vis urban households is done to reveal more insights into the
incidence of various sources of finance used as a coping mechanism in inpatient care (Table 2).
Among different quintiles (based on MPCE), the incidence of distress financing (borrowings and
other sources) is higher among poorer households, while richer households rely more on savings/
income. A ruralurban differential is visible as the incidence of distress financing is significantly
higher among the rural poor than the urban poor. For instance, in 2014, more than 65% of rural
poor used distress financing, while less than 60% of urban poor did so. Among different social cat-
egories, the SCs and STs are more dependent upon borrowings and other sources than are house-
holds belonging to the general category. Here also there is a noticeable ruralurban differential
among different social categories. The incidence of distress financing is higher among the SCs,
STs, and OBCs residing in rural areas than in urban areas. For instance, in 2014, 47.9% and
34.7% of SCs in rural and urban areas respectively used borrowings as a coping mechanism. The
incidence of distress financing is also quite high while seeking inpatient care for CVDs, cancer,
and injuries. For instance, in 2004 and 2014, 56.1% and 40.1% rural households respectively used
borrowings as a coping strategy. In the case of NCDs (CVDs and cancer) and injuries, the inci-
dence of borrowings and other sources is significantly higher in rural areas than urban areas. In
both periods, distress financing as a coping mechanism is more prevalent among poor and
marginalized households living in rural areas which could further worsen their condition. More-
over, NCDs such as cancer and CVDs could have a substantial economic impact on the overall
welfare of the households living in rural areas.
3.3
|
Incidence of use of various sources of finance as a coping mechanism
by households in India: Outpatient care
Compared to inpatient care, outpatient care is overwhelmingly dependent upon household savings
and income (Table 3). The incidence of savings/income is 94.5% and 97.8% for 2004 and 2014,
respectively. The incidence of distress sources (borrowings and other sources) in outpatient care is
around 10% for both 2004 and 2014. As in the case of inpatient care, a significant socioeconomic
gradient is present in the incidence of borrowings and other sources. Higher proportions of poor
households rely upon distress financing than richer ones. Further, households residing in rural
areas are more dependent upon distress financing than their urban counterparts. Among socially
vulnerable sections such as SCs and STs, the incidence of borrowings in higher than for the gen-
eral category. But the incidence of other sources is higher for the general category than for other
social categories.
Therefore, based on the above discussion, incidence of distress financing is higher in case of
inpatient care than outpatient care.
3.4
|
Level of inequality in the incidence of sources of finance used as
coping mechanism
The analysis so far has revealed the incidence of various sources of finance used as a coping
mechanism. It is imperative to examine the level of inequality among rich and poor in the inci-
dence of these sources of finance. We have used the concentration index and the concentration
curve to measure the level of inequality. The values of CI based on both the FullerLury and
10
|
SANGAR ET AL.
Erreygers methods (the latter in parentheses in this paragraph) indicate that while the incidence of
savings/income is higher among more affluent households, borrowings and other sources are more
concentrated in poorer households (Table 4). For instance, in 2014, the value of the CI is 0.088
(0.118) and 0.035 (0.032) for borrowings and other sources, respectively. Between 2004 and
2014, the incidence of savings/income remained concentrated among more affluent households,
while the level of inequality with regard to distress sources (borrowings and other sources)
decreased in 2014. For example, the value of the CI for borrowings is 0.135 (0.152) and 0.088
(0.118) for 2004 and 2014, respectively. Limited sources of income compel the poor to depend
upon loans or even sell their assets to cope with healthrelated expenditure.
Furthermore, the concentration curves for savings/income, borrowings, and other sources show
the level of inequality among different quintiles (Figure 1). The curves also reveal a higher con-
centration of savings/income among wealthier households. On the other hand, the incidence of bor-
rowings and other sources is higher among the poorer households. This shows that the incidence
of distress financing follows a socioeconomic gradient where poorer households have a higher risk
of healthrelated financial hardship. However, if we compare the time periods 2004 and 2014, then
the slope of the CC shows that the incidence of saving/income continues to be prorich. Mean-
while, over the period 20042014 the slope of the CC for borrowings has moved closer toward
the line of equality, indicating that the incidence is less concentrated among poor households in
2014 than in 2004.
3.5
|
Multivariate logistic regression of the likelihood of using distress
financing as coping mechanism
The above analysis reveals the higher incidence of distress financing in India. However, it does
not show how various socioeconomic and healthrelated covariates influence the likelihood of
using distress financing as a coping mechanism. For this purpose, we have used multivariate logis-
tic regression (for the years 2004 and 2014) to show the odds of using distress financing as a cop-
ing mechanism (Table 5). With regard to intersectional variables, results show that in both 2004
and 2014 the ruralSCSTpoor households are more likely to use distress financing than other
intersectional variables. For instance, in 2004 and 2014, ruralnonSCSTnonpoor households are
50% (odds ratio (OR): 0.50) and 41% (OR: 0.59) respectively less likely to use distress financing
than ruralSCSTpoor households. Similarly, urbanSCSTpoor households are 0.72 (2004) and
0.70 (2014) times less likely to rely upon distress financing than their rural counterparts. The ORs
of other intersectional variables reveal that socioeconomically vulnerable sections (poor, SCST)
have a higher likelihood of using distress financing than socioeconomically advantaged sections
TABLE 4 Concentration Index for Different Sources of Finance in India, 20042014
Year
CI (Fuller and Lury, 1977) CI (Erreygers, 2009)
Savings/
Income Borrowings Other Sources
Savings/
Income Borrowings Other Sources
2004 0.028
(0.024, 0.032)
0.135
(0.149, 0.120)
0.075
(0.097, 0.052)
0.022
(0.019, 0.026)
0.152
(0.165, 0.133)
0.077
(0.098, 0.053)
2014 0.024
(0.020, 0.029)
0.088
(0.104, 0.070)
0.035
(0.064, 0.005)
0.020
(0.017, 0.026)
0.118
(0.134, 0.101)
0.032
(0.062, 0.004)
Note. Figures are based on the author's calculations from the NSSO 60th and 71st rounds. Other sources include sale of assets,
contributions and others. Values in parentheses are 95% confidence intervals.
SANGAR ET AL.
|
11
(nonpoor, nonSCST). Among different religions, minority groups such as Muslims and Christians
have a higher likelihood of using distress financing than Hindus. For example, households practic-
ing Islam are 1.32 and 1.25 times more likely to use distress financing than those practicing Hin-
duism in 2004 and 2014, respectively. Next, household size shows that households with more than
five members are less likely to use distress financing than the households with less than five mem-
bers. Smaller households may have lower capacity to save than larger households. As a result,
smaller families might have to rely on distress financing. Further, households with regular/salaried
members have lower odds of using distress financing, while both other categories of household
type have very high odds. For instance, in 2014 the casual/agricultural labor household type is
1.84 times more likely to use distress financing. With low earning capacity, casual and agricultural
labor households have to rely upon borrowings and contributions from others to finance the cost
of illness.
Among healthrelated variables, if an ailing household member is using a private healthcare
facility then they are 78% (OR 1.78) and 67% (OR 1.67) more likely to use distress financing for
the years 2004 and 2014, respectively. Further, a household member spending more than 10 days
in hospital has a higher likelihood of using distress financing. This happens because the longer
duration of stay in hospital increases the cost of illness and puts extra pressure on the entire
0
20
40
60
80
100
0 20406080100
Cumulave proporon of households using
other sources
Cumulave proporon of households ranked on MPCE
Other Sources (2004) Other Sources (2014)
0
20
40
60
80
100
0 20406080100
Cumulave proporon of households
ulising savings/income
Cumulave proporon of households ranked on mpce
Savings (2004) Savings (2014)
0
20
40
60
80
100
020406080100
Cumulave proporon of households
ulising borrowings
Cumulave proporon of households ranked on mpce
Borrowings (2004) Borrowings (2014)
FIGURE 1 Concentration curves for savings, borrowings and other sources at all India level.
Note: The figures are based on author's calculations from NSSO 60
th
and 71
st
Round. Other sources include sale of
assets, contributions and others
12
|
SANGAR ET AL.
TABLE 5 Multivariate Logistic Regression of the Likelihood of Using Distress Financing as a Coping
Mechanism
Variables
2004 2014
Odds ratio 95% CI Odds ratio 95% CI
Intersectional variables
RuralSCSTPoor 1.00 1.00
RuralNonSCSTPoor 0.79 (0.04) (0.71, 0.88) 0.84 (0.05) (0.75, 0.95)
RuralSCSTNonPoor 0.68 (0.04) (0.61, 0.77) 0.74 (0.04) (0.67, 0.84)
RuralNonSCSTNonPoor 0.50 (0.02) (0.45, 0.55) 0.59 (0.03) (0.53, 0.65)
UrbanSCSTPoor 0.72 (0.07) (0.60, 0.86) 0.70 (0.06) (0.60, 0.82)
UrbanNonSCSTPoor 0.55 (0.04) (0.48, 0.63) 0.64 (0.04) (0.56, 0.72)
UrbanSCSTNonPoor 0.33 (0.02) (0.28, 0.38) 0.48 (0.03) (0.43, 0.54)
UrbanNonSCSTNonPoor 0.24 (0.01) (0.21, 0.26) 0.33 (0.02) (0.30, 0.37)
Religion
Hinduism (Ref.) 1.00 1.00
Islam 1.32 (0.05) (1.22, 1.44) 1.25 (0.04) (1.18, 1.33)
Christianity 1.01 (0.06) *** (0.90, 1.33) 1.26 (0.06) (1.15, 1.39)
Other religions
a
0.67 (0.05) (0.58, 0.77) 0.95 (0.05) *** (0.85, 1.07)
Household size
< 5 Members (Ref.) 1.00 1.00
> 5 Members 0.75 (0.02) (0.71, 0.80) 0.87 (0.02) (0.83, 0.91)
Household type
Regular/salaried (Ref.) 1.00 1.00
Casual/agricultural 2.25 (0.09) (2.05, 2.46) 1.84 (0.07) (1.71, 1.99)
Others
b
1.18 (0.04) (1.10, 1.26) 1.23 (0.03) (1.16, 1.29)
Household member using private health facility
No (Ref.) 1.00 1.00
Yes 1.78 (0.05) (1.68, 1.87) 1.67 (0.04) (1.59, 1.74)
No. of days household member hospitalized
10 days 1.00 1.00
> 10 days 2.68 (0.08) (2.54, 2.83) 2.12 (0.05) (2.02, 2.23)
Household member suffering from CVDs
No (Ref.) 1.00 1.00
Yes 1.12 (0.05) 1.04, 1.22) 1.04 (0.07) ** (0.98, 1.11)
Household member suffering from cancer
No (Ref.) 1.00 1.00
Yes 1.62 (0.12) (1.40, 1.87) 1.39 (0.03) (1.26, 1.54)
Household member suffering from injuries
No (Ref.) 1.00 1.00
Yes 1.35 (0.05) (1.25, 1.45) 1.14 (0.03) (1.07, 1.20)
Note. Figures are based on author's calculations from the NSSO 60th and 71st rounds. Values in parentheses show standard errors.
a
The others category includes Sikhism, Buddhism, Jainism, Zoroastrianism and other religions,
b
The others category includes self
employed and those household types not included in the above categories.
**Significant at 10% level.
***Not significant.
SANGAR ET AL.
|
13
household. In case of noncommunicable diseases (CVDs and cancer) and injuries, the likelihood
of distress financing is higher than for a household member not suffering from any of these.
Among these, cancer has a higher incidence of distress financing than CVDs and injuries. For
instance, for the years 2004 and 2014 respectively, the odds of using distress financing are 1.62
and 1.39 times for a household member who has cancer than for a household member not suffer-
ing from the same. The inference drawn from these results is that households in rural areas,
belonging to minorities and socially and economically vulnerable sections of society have a higher
incidence of distress means to finance their healthcare expenditure. Healthrelated variables such
as duration of stay in hospitals and suffering from NCDs also have a significant influence on
householdsdependence on borrowings, the sale of assets and contributions from relatives and
friends.
4
|
DISCUSSION
The results of this study indicate that households in India have to rely upon borrowings or sell
assets to cope with the cost of illness. Between 2004 and 2014, the incidence of borrowings,
the sale of assets, and contributions from family and friends as a means to finance healthcare
payments hovered around 50%. In a lowincome and highly populated country like India, the
high dependence on distress means of financing as a coping mechanism is along expected lines.
The incidence of distress means such as borrowings is highly concentrated among poor house-
holds, whereas more affluent households mainly pay through their savings and income. It is a
double whammy for poor households as, on the one hand, they are already suffering from the
physical burden of illness and, on the other hand, the burden of debt further worsens their
situation.
Among different socioeconomic variables, the households belonging to rural areas make more
use of distress means of financing than their urban counterparts. With limited sources of income
and lack of saving habits, rural households rely more on borrowings and even sell their assets to
cope with the cost of illness (Joe, 2014; Singh, 2012). The socioeconomically vulnerable sections
(poor and SCST) are in a highly disadvantageous position as they are dependent upon distress
means to finance healthrelated expenditure. This can bind them into the vicious circle of poverty
and indebtedness (Deshpande, 2000; Joe, 2014). Also, minority religious groups such as those
practicing Islam rely more on distress financing than households practicing Hinduism. Further,
households earning their living through casual/agricultural labor do not have a regular source of
income, as a result of which they are heavily reliant on distress financing. Results also reveal that
small families have to rely upon distress financing, whereas large families bank on their savings/in-
come as a coping mechanism. With regard to various healthrelated variables, the household mem-
ber using private healthcare facilities puts pressure on the entire household because private health
care facilities are costlier than public facilities (Bhat, 1996). As a result, households are more
dependent upon distress financing. It has also been found that if an ailing household member
spends more days as an inpatient then the household's likelihood of using distress financing
increases due to the escalation in the cost of illness. In the case of NCDs (CVDs and cancer) and
injuries to a household member, the entire household suffers the economic burden of illness. They
often incur debt, sell their assets or rely upon contributions from others to cope with the burden of
disease (Dilip & Duggal, 2002).
The above results have significant policy implications, and the Indian government must play an
active role in addressing these issues. One of the reasons behind the dependence on distress
14
|
SANGAR ET AL.
sources of financing as a coping mechanism is the low healthcare spending by the government. In
India, the government spends only 1.15% of GDP on health care, whereas the empirical evidence
shows it must spend at least 56% of GDP to meet the primary healthcare needs (Xu et al.,
2010). Lower spending on health care has led to the proliferation of private healthcare facilities
(Mills, 2014). The report shows that a considerable proportion of households prefer private health
care facilities over public hospitals because of a lack of faith, long waiting and poor quality of
available resources (NSSO, 2014). Evidence from the NSSO (2014) shows that more than 60% of
the population is dependent upon private hospitals for inpatient care. Heavy dependence on private
healthcare facilities further drives households, especially the poor, into financial indebtedness
(Dilip & Duggal, 2002). The situation becomes more complicated if a household member suffers
from expensive NCDs such as CVDs and cancer. The government has to address the issue of
increasing burden of NCDs in India as these are putting an extra financial burden on households
(Mahal, Karan, Fan, & Engelgau, 2013; Rao, Bhatnagar, & Murphy, 2011). Further, the lower
incidence of health insurance in India has led to a higher proportion of OOP expenditure on health
care, which in turn compels households to use debt and sell assets to finance the same. Although,
as part of universal health coverage, governments both at the national and state level have intro-
duced publicly funded health insurance schemes such as the Aarogyashri Scheme and Rashtriya
Swasthya Bima Yojana to protect households from incurring OOP health expenditure, they seem
ineffective in reducing the burden of OOP expenditure on households, especially the poor (Karan,
Yip, & Mahal, 2017; Prinja, Chauhan, Karan, Kaur, & Kumar, 2017). It is further evident from
the fact that only 12% and 13% of the population are covered through publicly financed health
insurance schemes in rural and urban areas, respectively (NSSO, 2014). With recently announced
National Health Policy 2017, the government intends to introduce a health insurance scheme for
the entire population, but this has yet to be implemented (MOHFW, 2017).
The limitations of the analysis in this paper are mainly concerned with data. One of these is
that in the NSSO (2014) data, OOP health expenditure is not segregated for different sources of
finance used as coping mechanisms. This restricts the analysis on the relative cost of a specific
source of finance.
5
|
CONCLUSION
We conclude that dependence on distress means of financing such as borrowing money, selling
household assets, and contributions from friends and relatives is one of the main concerns of
healthcare financing in India. The situation becomes worse when socially and economically vul-
nerable sections of society have to rely upon distress financing to cope with the cost of illness.
The results indicate the need for government action to formulate a comprehensive plan to prevent
the situation from further deterioration in the coming years. The government must increase public
spending on health care, improve the quantity and quality of the public healthcare system, and
enhance the scope of health insurance in India.
CONFLICT OF INTEREST
Authors have no conflict of interest.
ENDNOTE
SANGAR ET AL.
|
15
1
As there is more than one source of finance are more than one (saving/income, borrowing, etc.), some households
may have used more than one source in different proportions. In the NSSO 71st round, these sources have been
termed the first and second major source of finance. For example, households which have used saving/income and
borrowing as first and second major source of finance have been counted in both the categories as per the report-
ing. Therefore, in Tables 13, when the incidences of all sources of finance are added together, the total is more
than 100%.
ORCID
Ramna Thakur http://orcid.org/0000-0002-7253-1033
REFERENCES
Bajpai, V. (2014). The challenges confronting public hospitals in India, their origins, and possible solutions.
Advances in Public Health,2014, 898502.
Berman, P., Ahuja, R., & Bhandari, L. (2010). The impoverishing effect of healthcare payments in India: new
methodology and findings. Economic and Political Weekly,45(16), 6571.
Bhat, R. (1996). Regulation of the private health sector in India. International Journal of Health Planning and Man-
agement,11(3), 253274.
Bovbjerg, R. R. (2001). Covering catastrophic health care and containing costs: Preliminary lessons for policy from
the US experience (World Bank LCSHD Paper No. 66). Washington, DC: World Bank.
Bredenkamp, C., Mendola, M., & Gragnolati, M. (2010). Catastrophic and impoverishing effects of health expendi-
ture: New evidence from the western Balkans. Health Policy and Planning,26(4), 349356.
Deshpande, A. (2000). Does caste still define disparity? A look at inequality in Kerala, India. American Economic
Review,90(2), 322325.
Dhanaraj, S. (2014). Health shocks and coping strategies (WIDER Working Paper 2014/003). Helsinki: World Insti-
tute for Development Economics Research, United Nations University.
Dilip, T., & Duggal, R. (2002). Incidence of non-fatal health outcomes and debt in urban India. Mumbai: CEHAT.
Erreygers, G. (2009). Correcting the concentration index. Journal of Health Economics,28(2), 504515.
Flores, G., Krishnakumar, J., O'Donnell, O., & Van Doorslaer, E. (2008). Coping with healthcare costs: Implica-
tions for the measurement of catastrophic expenditures and poverty. Health Economics,17(12), 13931412.
Fuller, M. F., & Lury, D. A. (1977). Statistics workbook for social science students. Deddington: P. Allan.
Ghosh, S. (2010). Catastrophic payments and Impoverishment due to Out-of-Pocket health spending: The effects of
recent health sector reforms in India.
Insurance Regulatory and Development Authority of India (2016). Annual Report 201516. Hyderabad: IRDAI.
Jayakrishnan, T., Jeeja, M., Kuniyil, V., & Paramasivam, S. (2016). Increasing outofpocket health care expenditure
in Indiadue to supply or demand? Pharmacoeconomics,1(1), 1000105.
Joe, W. (2014). Distressed financing of household outofpocket health care payments in India: Incidence and corre-
lates. Health Policy and Planning,30(6), 728741.
Karan, A., Yip, W., & Mahal, A. (2017). Extending health insurance to the poor in India: An impact evaluation of
Rashtriya Swasthya Bima Yojana on out of pocket spending for healthcare. Social Science & Medicine,181,83
92.
Kruk, M. E., Goldmann, E., & Galea, S. (2009). Borrowing and selling to pay for health care in lowand middle
income countries. Health Affairs,28(4), 10561066.
Leive, A., & Xu, K. (2008). Coping with outofpocket health payments: Empirical evidence from 15 African coun-
tries. Bulletin of the World Health Organization,86(11), 849856C.
Mahal, A., Karan, A., Fan, V. Y., & Engelgau, M. (2013). The economic burden of cancers on Indian households.
PloS ONE,8(8), e71853.
Mills, A. (2014). Health care systems in lowand middleincome countries. New England Journal of Medicine,370
(6), 552557.
Ministry of Health and Family Welfare (2002). National Health Policy. New Delhi: MOHFW.
16
|
SANGAR ET AL.
Ministry of Health and Family Welfare (2016). National Health Accounts 201314. New Delhi: MOHFW.
Ministry of Health and Family Welfare (2017). National Health Policy 2017. New Delhi: MOHFW.
Mondal, S., Lucas, H., Peters, D., & Kanjilal, B. (2014). Catastrophic outofpocket payment for healthcare and
implications for household coping strategies: Evidence from West Bengal, India. Economics Bulletin,34(2),
13031316.
NSSO (2004). Health, morbidity and condition of aged: NSSO 60th Round (JanuaryJune 2004). New Delhi: Min-
istry of Statistics and Programme Implementaion, Government of India.
NSSO (2014). Key indicators of social consumption in India: Health, NSS 71st Round (JanuaryJune 2014). New
Delhi: Ministry of Statistics and Programme Implementation, Government of India.
O'Donnell, O., Van Doorslaer, E., Rannan-Eliya, R. P., Somanathan, A., Adhikari, S. R., Akkazieva, B., Herrin,
A. N. (2008). Who pays for health care in Asia? Journal of Health Economics,27(2), 460475.
Planning Commission (2014). Report of the expert group to review the methodology for measurement of poverty.
New Delhi: Government of India.
Prinja, S., Chauhan, A. S., Karan, A., Kaur, G., & Kumar, R. (2017). Impact of publicly financed health insurance
schemes on healthcare utilization and financial risk protection in India: A systematic review. PloS ONE,12(2),
e0170996.
Ramani, K., & Mavalankar, D. (2006). Health system in India: Opportunities and challenges for improvements.
Journal of Health Organization and Management,20(6), 560572.
Ransom, M. K. (2002). Reduction of catastrophic health care expenditures by a communitybased health insurance
scheme in Gujarat, India: Current experiences and challenges. Bulletin of the World Health Organization,80(8),
613.
Rao, K. D., Bhatnagar, A., & Murphy, A. (2011). Socioeconomic inequalities in the financing of cardiovascular &
diabetes inpatient treatment in India. Indian Journal of Medical Research,133(1), 57.
Registrar General (2011). Census of India 2011: Provisional population totals: India data sheet. Office of the Regis-
trar General Census Commissioner, India, Indian Census Bureau. http://censusindia.gov.in/2011-prov-results/
prov_results_paper1_india.html
Sauerborn, R., Adams, A., & Hien, M. (1996). Household strategies to cope with the economic costs of illness.
Social Science & Medicine,43(3), 291301.
Selvaraj, S., & Karan, A. K. (2012). Why publiclyfinanced health insurance schemes are ineffective in providing
financial risk protection. Economic & Political Weekly,47(11), 6168.
Sengupta, A., & Nundy, S. (2005). The private health sector in India. British Medical Journal,331(7526), 1157
1158.
Singh, E. N. (2012). Rural savings and its investment in Manipur: A case study of formal finance visàvis Marups.
Management Convergence,2(2), 1030.
Wagstaff, A., O'Donnell, O., Van Doorslaer, E., & Lindelow, M. (2007). Analyzing health equity using household
survey data: A guide to techniques and their implementation: Washington, DC: World Bank Publications.
World Health Organization (2000). World Health Report 2000: Health systems: Improving performance. Geneva:
WHO.
World Health Organization (2010). World Health Report, 2010: Health systems financing: The path to universal
coverage. Geneva: WHO.
Xu, K., Saksena, P., Jowett, M., Indikadahena, C., Kutzin, J., & Evans, D. B. (2010). Exploring the thresholds of
health expenditure for protection against financial risk. World Health Report (2010). Backgroup Paper 19.
Geneva: WHO.
How to cite this article: Sangar S, Dutt V, Thakur R. Distress financing of outofpocket
health expenditure in India. Rev Dev Econ. 2018;00:117. https://doi.org/10.1111/rode.12540
SANGAR ET AL.
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... Subsequently, analysis was conducted to determine the prevalence of distress financing among different household locations and socio-economic statuses. The prevalence of households having distress financing was measured by dividing the total number of households with distress financing by the total number of households [48]. ...
... Borrowing is a much more common source of healthcare financing among low-and middle-income countries. However, the prevalence shown in this study was relatively low compared to other countries such as India and Cambodia, of which the prevalence was around 42.2% and 22.5% respectively [19,48]. The low prevalence of distress financing in Malaysia is aligned with the healthcare financing system in Malaysia, which is mainly tax-based. ...
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Background Out-of-pocket (OOP) payments for healthcare services potentially have severe consequences on households, especially among the poor. Under certain circumstances, healthcare payments are financed through selling household assets, or borrowings. This certainly could influence households’ decision, which likely resorts to forgoing healthcare services. Thus, the focal point of this study is aimed to identify the inequalities and determinants of distress financing among households in Malaysia. Methods This study used secondary data from the National Health and Morbidity Survey (NHMS) 2019, a national cross-sectional household survey that used a two-stage stratified random sampling design involving 5,146 households. The concentration curve and concentration index were used to determine the economic inequalities in distress financing. Whereas, the determinants of distress financing were identified using the modified Poisson regression model. Results The prevalence of borrowing without interest was the highest (13.86%), followed by borrowing with interest (1.03%) while selling off assets was the lowest (0.87%). Borrowing without interest was highest among rural (16.21%) and poor economic status (23.34%). The distribution of distress financing was higher among the poor, with a concentration index of -0.245. The modified Poisson regression analysis revealed that the poor, middle, rich, and richest had 0.57, 0.58, 0.40 and 0.36 times the risk to develop distress financing than the poorest socio-economic group. Whereas, the presence of one and two or more elderly were associated with a 1.94 and 1.59 times risk of experiencing distress financing than households with no elderly members. The risk of developing distress financing was also 1.28 and 1.58 times higher among households with one and two members receiving inpatient care in the past 12 months compared to none. Conclusions The findings implied that the improvement of health coverage should be emphasized to curtail the prevalence of distress financing, especially among those caring for the elderly, requiring admission to hospitals, and poor socio-economic groups. This study could be of interest to policymakers to help achieve and sustain health coverage for all.
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Unlike other low- and middle-income countries, infectious diseases are still predominant, and non-communicable diseases (NCDs) are emerging without replacing the burden of infectious diseases in India, where it is imposing a double burden of diseases on households in the country. This study aimed to analyse the socio-economic and demographic differentials in the magnitude of economic burden and coping strategies associated with health expenditure on infectious diseases in India. National Sample Survey Organization (NSSO) data on “Key Indicators of Social Consumption in India: Health, (2017–18)” have been employed in this study. The findings of the study revealed that more than 33% of the individuals are still suffering from infectious diseases out of the total ailing population in India. Based on the various socio-economic and demographic covariates, infectious diseases are highly prevalent among individuals with marginalized characteristics, such as individuals residing in rural areas, females, 0–14 age groups, Muslims, illiterates, scheduled tribes (STs), and scheduled castes (SCs), large family households, and economically poor people in the country. The per capita out-of-pocket (OOP) expenditure on infectious diseases is INR 7.28 and INR 29.38 in inpatient and outpatient care, respectively. Whereas, monthly per patient OOP expenditure on infectious diseases by infection-affected populations is INR 881.56 and INR 1,156.34 in inpatient and outpatient care in India. The study found that people residing in rural areas, SCs followed by other backward classes (OBCs), illiterates, poor, and very poor are more dependent on borrowings, sale of assets, and other distressed sources of financing. However, under National Health Policy 2017, many initiatives, such as “Ayushman Bharat,” PM-JAY, and National Digital Health Mission (NDHM) in 2021, have been launched by the government of India in the recent years. These initiatives are holistically launched for ensuring better health facilities, but it is early to make any prediction regarding its outcomes; hopefully, the time will define it over the passing of a few more years. Finally, the study proposed the need for proper implementations of policy initiatives, awareness against unhygienic conditions and contamination of illnesses, immunisations/vaccination campaigns, subsidized medical facilities, and the country's expansion of quality primary health-care facilities.
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... This leads to high expenditure on the patient's household. In India, most of the families rely upon borrowings and sale of assets to deal with the spending as destress means of financing (Sangar et al., 2019). The high out-of-pocket health care expenditures push households into deeper poverty and indebtedness (Deshpande, 2000;Joe, 2014). ...
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