BRIEF RESEARCH REPORT
published: 29 January 2019
Frontiers in Public Health | www.frontiersin.org 1January 2019 | Volume 7 | Article 9
Obinna E. Onwujekwe,
University of Nigeria, Nigeria
Chhabi Lal Ranabhat,
Yonsei University, South Korea
University of Kragujevac, Serbia
This article was submitted to
a section of the journal
Frontiers in Public Health
Received: 05 July 2018
Accepted: 10 January 2019
Published: 29 January 2019
Sangar S, Dutt V and Thakur R (2019)
Comparative Assessment of
Economic Burden of Disease in
Relation to Out of Pocket Expenditure.
Front. Public Health 7:9.
Comparative Assessment of
Economic Burden of Disease in
Relation to Out of Pocket
Shivendra Sangar, Varun Dutt and Ramna Thakur*
School of Humanities and Social Sciences, Indian Institute of Technology Mandi, Mandi, India
Background: The economic costs associated with morbidity pose a great ﬁnancial
risk on the population. Household’s over-dependence on out-of-pocket (OOP) health
expenditure and their inability to cope up with the economic costs associated with illness
often push them into poverty. The current paper aims to measure the economic burden
and resultant impoverishment associated with OOP health expenditure for a diverse set
of ailments in India.
Methods: Cross-sectional data from National Sample Survey Organization (NSSO)
71st Round on “Key Indicators of Social Consumption: Health” has been employed in
the study. Indices, namely the payment headcount, payment gap, concentration index,
poverty headcount and poverty gap, are deﬁned and computed. The measurement of
catastrophic burden of OOP health expenditure is done at 10% threshold level.
Results: Results of the study reveal that collectively non-communicable diseases
(NCDs) have higher economic and catastrophic burden, individually infections rather than
NCDs such as Cardio Vascular Diseases and cancers have a higher catastrophic burden
and resultant impoverishment in India. Ailments such as gastro-intestinal, respiratory,
musco-skeletal, obstetrics, and injuries also have a substantial economic burden on
population and push them below the poverty line. Results also show that despite
the pro-poor concentration of infections, their economic burden is more concentrated
among the wealthier consumption groups.
Conclusion: The study concludes that universal health coverage through adequate
provision of pooled resources for health care and community-based health insurance
is critical to reduce the economic burden and impoverishment related to OOP health
expenditure. Measures should also be instituted to insulate people from economic burden
on morbidity, especially the NCDs.
Keywords: economic burden, impoverishment, ailments, infections, morbidity
Sangar et al. Impoverishment Impact of Ailments
The economic costs associated with morbidity pose a great
ﬁnancial risk on population (1). Direct spending on health
care discourages people from using health care services and
encourages them to postpone their health care needs (2,3). In
the majority of LMICs, limited resources for health care and lack
of protection against catastrophic health spending have led to the
over-reliance on OOP health expenditure (4).
Household’s overly dependence on out-of-pocket (OOP)
health expenditure and their inability to cope up with the
economic costs of illness often push them into poverty (5,6).
Households faced with this situation face enormous ﬁnancial
liability and are devoid of adequate means for other essential
needs such as food and education (7,8). Further, rural dwelling,
low socioeconomic status and outpatient care also contribute to
the increasing economic burden of illness (9,10).
There are few studies which have talked about this issue by
taking into consideration either NCDs or infectious diseases
without analyzing its impact on impoverishment (6,11,12).
There is no study which has analyzed the monetary burden and
impoverishment impact of diﬀerent sets of NCDs and infectious
diseases separately. Therefore, this study aims at ﬁlling this gap
in the literature by analyzing the economic burden and resultant
impoverishment due to OOP health expenditure for a diverse set
of ailments by employing the recent health expenditure survey in
MATERIALS AND METHODS
The current study employed a nationally representative data
from National Sample Survey Organization (NSSO), 71st Round
(2004) on Key indicators of social consumption: Health (13).
The survey comprises 65,932 sample households consisting
a population of 0.33 million persons. The survey adopted a
stratiﬁed multistage sample design, using census villages for the
rural areas and urban blocks for the urban areas as the ﬁrst-
stage units (FSUs), and households as the second-stage units.
The reference period for inpatient and outpatient care is 365
and 15 days, respectively. For the analysis, the OOP health
expenditure for inpatient and outpatient care is converted into
monthly ﬁgures and added together to get the total OOP health
expenditure. In this study, OOP health expenditure is calculated
by deducting the amount of reimbursement from total health
expenditure. Respective sample weights have been applied in the
calculation of the results. For the analysis, we have taken 16
groups of diﬀerent ailments (13). The analysis is done on Stata
Measuring the Economic Burden of Morbidity
The economic burden of morbidity has been calculated in terms
of headcount and payment gap (15). Headcount (HC) measures
the percentage of population incurring OOP health expenditure.
It is measured by HC =1
i=1ni, where N is the sample size,
HC is the headcount and n is number of persons incurring OOP
health expenditure. The payment gap is explained as the share
of OOP health expenditure in total consumption expenditure is
given by G=H
TCE ∗100, S is the payment gap, H is the OOP
health expenditure, TCE is the total consumption expenditure.
Measurement of catastrophic burden of OOP health expenditure
(headcount and payment gap) is done at 10% threshold level of
TCE which has been a standard benchmark in the literature (15,
16). Catastrophic headcount is a fraction of the population whose
OOP health expenditure as a proportion of TCE exceed the given
threshold. Whereas, catastrophic payment gap measures the
average degree by which OOP health expenditure as a proportion
of TCE exceeds the threshold level. The concentration index has
been used to determine whether the poor incur more OOP health
expenditure or the rich (17). Concentration index CEand CO
(for headcount and payment gap, respectively) as given by the
following formula: CI =p1L2−p2L1+p2L3−p3L2+...+
(pt−1Lt−ptLt−1), Where CI is the concentration index, ptis
the cumulative percentage of the population ranked by monthly
consumption expenditure in group t, Ltis the corresponding
Measuring the Impoverishment Impact of Morbidity
Poverty headcount impact measures the fraction of population
falling below the poverty line due to OOP health expenditure.
The poverty impact in terms of headcount is measured as PIHC =
HCPost −HCPre , where PIHC is the poverty headcount impact,
ZPre be the pre-payment poverty line. Then PPre =1 if x <
ZPre HCPre =1
i=1PPre, PPre is the pre-payment poverty
headcount, HCPost and HCPost are the post and pre-payment
poverty headcount. Poverty impact in terms of gap measures
the average shortfall due to OOP health expenditure from the
existing poverty line. It is given as PIG=GPost −GPre , where
PIGis the poverty gap impact, gPre is the pre-payment gap, that
is equal to x—ZPre if x <ZPre, and zero otherwise, GPre =
i=1gPre , GPost and GPre are the post and pre-payment
Economic Burden of Morbidity in India
Table 1 reports the economic and catastrophic burden of
OOP health expenditure incurred on diﬀerent ailments.
Although, collectively NCDs have higher economic burden,
in case of individual ailments a signiﬁcant proportion of
population reported OOP health expenditure in case of
infections followed by respiratory, CVDs, musco-skeletal,
gastro-intestinal, psychiatric, and injuries. Although in lesser
proportions, OOP health expenditure is also reported in other
categories of ailments. Similarly, the payment gap as share of
OOP health expenditure in TCE is also higher among infections
as compared to other ailments. Similar to the economic burden,
the catastrophic burden reported at 10% threshold level is
relatively higher in case of infections than other ailments.
However, collectively NCDs have higher catastrophic impact on
the population. In case of infections, the negative value of C.IE
and the positive value of C.IOreveal that despite its pro-poor
concentration, it is the wealthier consumption groups which
Frontiers in Public Health | www.frontiersin.org 2January 2019 | Volume 7 | Article 9
Sangar et al. Impoverishment Impact of Ailments
TABLE 1 | Economic burden of morbidity in India.
Type of ailments Population
reporting OOP (%)
EPayment gap (%) C.I**
payment gap 10%
Infections 14.1 (13.5 to 14.7) −0.034 (−0.055 to −0.014) 2.2 (2.1 to 2.4) 0.058 (0.039 to 0.076) 6.6 (6.2 to 7.0) 1.2 (1.0 to 1.3)
Cancers 0.4 (0.3 to 0.5) 0.282 (0.243 to 0.322) 0.4 (0.3 to 0.5) 0.441 (0.410 to 0.472) 0.3 (0.2 to 0.4) 0.3 (0.2 to 0.4)
CVDs 5.3 (5.0 to 5.7) 0.339 (0.224 to 0.452) 1.7 (1.5 to 1.9) 0.524 (0.455 to 0.593) 2.8 (2.5 to 3.0) 1.0 (0.9 to 1.1)
Injuries 2.3 (2.1 to 2.4) 0.124 (0.081 to 0.166) 0.8 (0.7 to 0.9) 0.293 (0.260 to 0.325) 1.3 (1.2 to 1.4) 0.6 (0.5 to 0.7)
Respiratory 6.0 (5.6 to 6.5) 0.027 ( to 0.010 to 0.065) 1.0 (0.8 to 1.3) 0.130 (0.100 to 0.159) 2.4 (2.1 to 2.7) 0.6 (0.4 to 0.7)
Gastro–intestinal 4.2 (4.0 to 4.5) 0.055 (0.014 to 0.095) 1.0 (0.8 to 1.1) 0.201 (0.173 to 0.229) 2.3 (2.1 to 2.6) 0.7 (0.5 to 0.8)
Blood Disorders 0.7 (0.5 to 0.8) 0.113 (−0.014 to 0.239) 0.2 (0.1 to 0.3) 0.239 (0.166 to 0.311) 0.4 (0.3 to 0.5) 0.1 (0.08 to 0.2)
Endocrine 0.7 (0.5 to 0.8) 0.113 (−0.009 to 0.233) 0.2 (0.1 to 0.3) 0.239 (0.166 to 0.312) 0.4 (0.3 to 0.5) 0.1 (0.08 to 0.2)
Psychiatric 2.7 (2.4 to 3.0) 0.075 (0.026 to 0.124) 0.8 (0.7 to 0.9) 0.267 (0.230 to 0.303) 1.4 (1.3 to 1.6) 0.5 (0.4 to 0.6)
Eye 1.1 (1.0 to 1.3) 0.088 (0.015 to 0.159) 0.2 (0.1 to 0.3) 0.201 (0.147 to 0.256) 0.5 (0.4 to 0.6) 0.1 (0.06 to 0.02)
Ear 0.3 (0.2 to 0.4) 0.167 (0.035 to 0.298) 0.1 (0.02 to 1.7) 0.237 (0.125 to 0.349) 0.2 (0.1 to 0.3) 0.1 (0.01 to 1.9)
Skin 1.1 (1.0 to 1.2) 0.028 (−0.060 to 0.116) 0.2 (0.1 to .0.3) 0.124 (0.058 to 0.190) 0.5 (0.4 to 0.6) 0.1 (0.08 to 1.1)
Musco–skeletal 4.6 (4.3 to 5.0) 0.010 (0.005 to 0.016) 1.0 (0.09 to 1.1) 0.265 (0.230 to 0.299) 2.4 (2.1 to 2.7) 0.6 (0.5 to 0.7)
Genito–urinary 1.6 (1.4 to 1.8) 0.214 (0.153 to 0.274) 0.7 (0.6 to 0.8) 0.293 (0.256 to 0.332) 1.1 (1.0 to 1.2) 0.4 (0.3 to 0.5)
Obstetrics 1.1 (1.0 to 1.2) −0.003 (−0.004 to −0.002) 0.2 (0.1 to 0.3) 0.120 (0.069 to 0.171) 0.5 (0.4 to 0.6) 0.1 (0.04 to 1.9)
Others 1.1 (1.0 to 1.3) 0.195 (0.118 to 0.0.271) 0.3 (0.2 to 0.4) 0.352 (0.292 to 0.412) 0.5 (0.4 to 0.6) 0.2 (0.1 to 0.3)
Total 47.3 (46.4 to 48.4) 0.064 (0.053 to 0.075) 11.0 (10.5 to 11.5) 0.251 (0.239 to 0.262) 23.7 (22.9 to 24.4) 7.8 (7.3 to 8.2)
The ﬁgures are based on author’s calculations from NSSO 71st Round. Values in parentheses are 95% conﬁdence interval.
*C.IEis the concentration index for headcount.
**C.IOis the concentration index for payment gap.
TABLE 2 | Poverty impact of morbidity in India.
Poverty impact gap
Infections 2.1 (1.9–2.3) 23,543,134 14.9 (0.18) (13.6–16.3)
Cancers 0.2 (0.1–0.3) 2,242,203 1.2 (0.01) (0.8–1.5)
CVDs 0.8 (0.7–0.9) 8,968,813 5.0 (0.06) (4.4–5.6)
Injuries 0.5 (0.4–0.7) 5,605,508 3.9 (0.05) (3.4–4.5)
Respiratory 0.6 (0.5–0.8) 6,726,610 5.5 (0.07) (4.1–6.9)
0.8 (0.7–0.9) 8,968,813 5.9 (0.07) (5.0–6.9)
Blood Disorders 0.1 (0.07–1.3) 1,121,102 1.0 (0.01) (0.6–1.3)
Endocrine 0.5 (0.4–0.6) 5,605,508 2.6 (0.03) (2.1–3.1)
Psychiatric 0.5 (0.4–0.6) 5,605,508 3.8 (0.05) (3.3–4.3)
Eye 0.2 (0.09–0.3) 2,242,203 0.8 (0.01) (0.5–1.1)
Ear 0.1 (0.02–0.07) 1,121,102 0.3 (0.01) (0.1–0.5)
Skin 0.1 (0.01–0.3) 1,121,102 1.0 (0.01) (0.8–1.3)
Musco–skeletal 0.7 (0.6–0.9) 7,847,711 5.6 (0.07) (4.5–7.0)
Genito 0.4 (0.3–0.5) 4,484,406 2.8 (0.03) (2.1–3.4)
Obstetrics 0.2 (0.1–0.3) 2,242,203 1.1 (0.01) (0.7–1.5)
Others 0.2 (0.1–0.3) 2,242,203 1.3 (0.02) (0.9–1.7)
Total 8.0 (7.6–8.4) 89,688,129 56.7 (0.70) (53.8–59.9)
The ﬁgures are based on author’s calculations from NSSO 71st Round. Values in
parentheses are 95% conﬁdence interval. INR, Indian National Rupee. INR has been
converted into Euro for the year 2014.
spends more on the treatment. However, in case of NCDs,
especially CVDs and cancer, the positive values of CIEand CIO
reveal a pro-rich concentration of headcount as well as OOP
Poverty Impact of Morbidity in India
Table 2 presents the poverty impact of OOP health expenditure
incurred on diﬀerent ailments in terms of headcount and
payment gap. Poverty impact in terms of headcount is highest
in case of infections, followed by CVDs and gastro-intestinal,
musco-skeletal, respiratory, and injuries. Similarly, the poverty
impact in terms of payment gap is also signiﬁcantly higher in
infections. It shows that the average shortfall from the poverty
line is higher in case of infections than other ailments. Ailments
consisting of gastro, musco-skeletal, respiratory, CVDs, and
injuries also have higher poverty gap impact. Some other ailments
such as skin, blood disorders, eye and ear also marginally
contribute toward impoverishment.
Overall, the results of the study reveal that NCDs such as CVDs,
cancers, etc. have the higher catastrophic burden and resultant
impoverishment in India. Although individually CVDs have a
signiﬁcant economic burden and high poverty impact, it is less
than infections. Ailments such as gastro-intestinal, respiratory,
musco-skeletal, obstetrics and injuries also have a substantial
economic burden on population and push them below the
poverty line. Infections have higher poverty impact because the
population aﬀected with the same is more concentrated around
the poverty line. A smaller increase in OOP health expenditure
pushes the larger proportion of population below the poverty
Although it is true that the burden of NCDs is increasing
in India and cumulatively they have higher catastrophic burden
but it is the infectious diseases which push more people into
Frontiers in Public Health | www.frontiersin.org 3January 2019 | Volume 7 | Article 9
Sangar et al. Impoverishment Impact of Ailments
the quagmire of poverty (18). Studies from other LMICs reveal
that the economic burden and resultant impoverishment due
to OOP health expenditure has been relatively high in case of
NCDs such as CVDs, cancer, diabetes and stroke (19,20). Further,
many countries in Africa have higher incidence of catastrophic
health expenditure due to infectious disease like Malaria and
Tuberculosis (21,22). In LMICs, lack of access to health
services, poor quality of care and high user charges contribute
to higher OOP health expenditure (12). Further, inadequate
public spending on health care and poor implementation of
publicly ﬁnanced health insurance schemes (PFHIs) have further
accentuated the problem of health care ﬁnancing in India
The high catastrophic burden and resultant impoverishment
associated with morbidity highlight the need for a better ﬁnancial
protection mechanism in India, particularly for the poor and
vulnerable. Universal health coverage (UHC) is regarded as a
critical path for improving the health outcomes and providing
ﬁnancial protection against the catastrophic health expenditure
(25). It is a comprehensive health system approach that helps
to provide improved access to health care services which
signiﬁcantly improves the health outcomes (26). UHC can be
achieved through a matured health system that can provide
suﬃcient and continuous pooled resources for health (27). Apart
from improving access to health care services, policy makers
must focus on extending quality care, especially to poor families
(28). LMICs can implement community-based health insurance
(CBHI) which can go a long way in achieving UHC (29).
The future plan of the research will be to measure the
economic burden of ailments at diﬀerent threshold levels. Along
with-it diﬀerent sources of ﬁnance used to cope up with OOP
health expenditure for diﬀerent ailments will also be studied.
A comparative analysis with previous data rounds may also be
DATA AVAILABILITY STATEMENT
This paper is based on anonymized survey data collected by
the National Sample Survey organization (NSSO), a department
of the Ministry of Statistics and Programme Implementation,
Government of India. Data is available in the public domain.
The data is already available in publicly available repositories
to individuals both at national and international level through
RT and SS conceived the idea with inputs from VD. SS
performed the statistical analysis and prepared the initial
draft of the manuscript. RT and VD assisted in the revision
of the manuscript. All authors read and approved the ﬁnal
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Conﬂict of Interest Statement: The authors declare that the research was
conducted in the absence of any commercial or ﬁnancial relationships that could
be construed as a potential conﬂict of interest.
Copyright © 2019 Sangar, Dutt and Thakur. Thisis an open-access article distributed
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author(s) and the copyright owner(s) are credited and that the original publication
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