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Economic burden, impoverishment, and coping mechanisms associated with out-of-pocket health expenditure in India: A disaggregated analysis at the state level

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Introduction The high share of out‐of‐pocket (OOP) health expenditure imposes an extreme financial burden on households, and they have to incur a substantial amount of expenditure to avail health care services. This study analyses the inter‐state differentials in the economic burden of OOP health expenditure, resultant impoverishment impact, and sources of finance used as coping mechanisms. Materials and methods The study is based on health expenditure survey, namely the 71st Round on “Key Indicators of Social Consumption in India: Health,” (2014) conducted in India by the National Sample Survey Organisation. The study uses headcount, payment gap, and concentration index to measure the economic burden, impoverishment impact of OOP health expenditure, and the level of inequality. Results On the basis of results, the states can be divided into four distinct categories: (1) States with low economic burden and low poverty impact of OOP health expenditure, (2) low economic burden and high poverty impact of OOP health expenditure, (3) high economic burden and low poverty impact of OOP health expenditure, and (4) high economic burden and high poverty impact of OOP health expenditure. Conclusions Inter‐state differentials in OOP health expenditure and impoverishment need proper attention of the government especially the policy makers.
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RESEARCH ARTICLE
Economic burden, impoverishment, and coping
mechanisms associated with outofpocket health
expenditure in India: A disaggregated analysis at
the state level
Shivendra Sangar |Varun Dutt |Ramna Thakur
School of Humanities and Social Sciences,
Indian Institute of Technology, Mandi, India
Correspondence
Ramna Thakur, Assistant Professor, School of
Humanities and Social Sciences, Indian
Institute of Technology, Mandi, Kamand (H.P),
175005, India.
Email: ramna@iitmandi.ac.in
Summary
Introduction: The high share of outofpocket (OOP)
health expenditure imposes an extreme financial burden
on households, and they have to incur a substantial amount
of expenditure to avail health care services. This study anal-
yses the interstate differentials in the economic burden of
OOP health expenditure, resultant impoverishment impact,
and sources of finance used as coping mechanisms.
Materials and methods: The study is based on health
expenditure survey, namely the 71
st
Round on Key Indica-
tors of Social Consumption in India: Health,(2014)
conducted in India by the National Sample Survey Orga-
nisation. The study uses headcount, payment gap, and
concentration index to measure the economic burden,
impoverishment impact of OOP health expenditure, and
the level of inequality.
Results: On the basis of results, the states can be divided
into four distinct categories: (1) States with low economic
burden and low poverty impact of OOP health expenditure,
(2) low economic burden and high poverty impact of OOP
health expenditure, (3) high economic burden and low
poverty impact of OOP health expenditure, and (4) high
economic burden and high poverty impact of OOP health
expenditure.
Conclusions: Interstate differentials in OOP health
expenditure and impoverishment need proper attention of
the government especially the policy makers.
Received: 8 August 2018 Accepted: 9 August 2018
DOI: 10.1002/hpm.2649
Int J Health Plann Mgmt. 2018;113. © 2018 John Wiley & Sons, Ltd.wileyonlinelibrary.com/journal/hpm 1
KEYWORDS
burden, coping, impoverishment, outofpocket
1|INTRODUCTION
Provision of affordable health care services is the major challenge faced by the majority of low and middleincome
countries in the world.
1,2
Like many other low and middleincome countries, the health care expenditure in India is
financed either through government budget (including centre and state) or private sector. Among these sources of
health financing, private sector contributes around 70% of total health care expenditure, out of which 64.2% comes
from outofpocket (OOP) by the households.
3
During past two decades, public health expenditure as a share of
gross domestic product continues to remain stagnant around 1%.
4
In addition, 86% of rural and 82% of urban
population continue to remain uncovered under any public or private health insurance scheme.
5
All these factors
have an enormous impact on the affordability of health care, which acts as a major deterrent in the access to health
care services in India.
6,7
The high share of OOP health expenditure indicates a lack of financial risk protection for households, that is also
an important goal of universal health coverage.
8,9
As a result, households have to incur a substantial amount of
expenditure to avail health care services.
10
Expenditure on health care imposes an extreme financial burden on
households, and many of them fall below the poverty line.
2,11,12
According to literature, 6% to 8% of the population
in India is impoverished due to OOP health expenditure.
13,14
The impact of such impoverishment is more prominent
among the socially and economically vulnerable sections of society.
15,16
Along with socioeconomic differentials, there is also evidence of wider interstate differentials in the impover-
ishment impact of OOP health expenditure in India. Five states namely, Uttar Pradesh, Maharashtra, West Bengal,
Andhra Pradesh, and Kerala accounted for more than 50% of total impoverishment on account of OOP health expen-
diture.
11-13,17
During the last decade, states like Kerala, Himachal Pradesh, and Uttar Pradesh have witnessed the
highest increase in poverty due to OOP health expenditure.
14
Studies have also found that the poverty deepening
on account of OOP health expenditure is much higher in rural areas of poor states such as Bihar, Madhya Pradesh,
and Odisha.
18
Furthermore, in the absence of formal risk pooling measures, households, generally, use past savings or even bor-
row money extensively to finance the health care.
19-22
Sometimes, poor households have to rely on informal mech-
anisms such as money lenders to cope up with health expenditure. The sale of productive assets and borrowing
impose costs that drive families into deep poverty.
23,24
Although the researchers have examined the interstate variations in the economic burden of OOP health expen-
diture and resultant impoverishment by taking consumer expenditure survey. However, the health surveys predom-
inantly designed to capture the health expenditure have been rarely used.
13,14
Moreover, the researchers have not
examined together the interstate variations in three aspects of OOP health expenditure, ie, the level of economic
burden of OOP health expenditure, resultant impoverishment, and the related coping mechanisms. Therefore, the
current paper addresses this research gap by analysing the interstate variations in the economic burden of OOP
health expenditure, resultant impoverishment impact, and sources of finance used as coping mechanisms by using
the recent health expenditure data.
2|METHODS
2.1 |Data
The present paper is based on health expenditure survey, namely the 71
st
Round on Key Indicators of Social
Consumption in India: Health,(2014) conducted in India by the National Sample Survey Organisation.
5
It comprises
2SANGAR ET AL.
65 932 sample households and a sample population of 0.33 million persons. The survey adopted a stratified multi-
stage 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 recall period for inpatient and outpatient care was 365 and
15 days, respectively. All details regarding consumption expenditure were collected after a recall period of 1 month.
For the purpose of this analysis, OOP health expenditure for inpatient and outpatient care was converted into
monthly figures and added together. OOP health expenditure was calculated by deducting the amount of reimburse-
ment from total health expenditure. The survey also provided information about various sources of finance used as
cope up with the OOP health expenditure which consist of
1
household income/savings,
2
borrowings,
3
sale of phys-
ical assets,
4
contributions from friends and relatives, and
5
other sources. The present analysis excludes childbirth, and
respective sample weights are applied.
3|METHODOLOGY
3.1 |Measuring the economic burden of OOP health expenditure
The current paper uses the methodology proposed by Wagstaff, Doorslaer.
25
The economic burden of OOP health
expenditure both in terms of incidence and intensity is calculated at three threshold levels, ie, base (Proportion of
population reporting OOP), 10%, and 25%.
3.1.1 |Headcount
Headcount measures the percentage of population that spend more on health care than the threshold (Z). It is a frac-
tion of the sample whose OOP health expenditure as a proportion of total consumption expenditure (TCE) exceeds
the threshold (Z). The headcount HC ¼
1
NN
i¼1Ei:where E
i
is equal to one if T
i
/X
i
> Z and zero otherwise, T
i
is the
OOP health expenditure of person i, X
i
is the consumption expenditure of person i, and N is the sample size.
3.1.2 |Payment gap
The payment gap (G) measures the average degree by which OOP health expenditure as a share of consumption
expenditure exceeded the threshold, Z. The payment gap is given by G¼
1
NN
i¼1Oi, where N is the sample size,
O
i
is the overshoot of person I, O
i
=E
i
((T
i
/X
i
)Z).
3.1.3 |Concentration index
Concentration index (CI) measures the level of inequality in the burden of OOP health expenditure both in terms of
headcount (HC) and payment gap (G). Positive values of the CI indicate a greater tendency for rich to exceed the
threshold, while negative values indicate a greater tendency for poor to exceed the threshold. Concentration index
C
E
and C
O
(for HC and payment gap, respectively) as given by the following formula given by,
26
CI =(p
1
L
2
p
2
L
1
)+(p
2
L
3
p
3
L
2
)++(p
t1
L
t
p
t
L
t1
), where CI is the concentration index, p
t
is the cumulative
percentage of the sample ranked by household consumption expenditure in group t, L
t
is the corresponding health
variable, ie, catastrophic HC and payment gap.
3.2 |Measuring impoverishment impact due to OOP health expenditure
The impoverishment impact of OOP health expenditure is calculated as the difference between prepayment and
postpayment poverty impact in terms of poverty HC, poverty gap, and normalised poverty gap.
SANGAR ET AL.3
3.2.1 |Poverty headcount
It measures the fraction of population falling below the poverty line due to OOP health expenditure. The poverty
headcount impact, PI
HC
=HC
Post
HC
Pre
, where Z
Pre
be the prepayment poverty line. Then, P
Pre
= 1 if x < Z
Pre
HCPre ¼
1
NN
i¼1PPre,P
Pre
is the prepayment poverty headcount, and HC
Post
and HC
Post
are the post and prepayment
poverty headcount.
3.2.2 |Poverty gap
Poverty gap impact measures the average shortfall due to OOP health expenditure from the existing poverty line.
The poverty gap impact PI
G
=G
Post
G
Pre
, where g
Pre
is the prepayment gap, that is equal to x Z
Pre
if x < Z
Pre
,
and zero otherwise;GPre ¼
1
NN
i¼1gPre ,G
Post
and G
Pre
are the post and prepayment poverty gap.
3.2.3 |Normalised poverty gap
The normalised poverty gap impact measures the poverty deepening due to OOP health expenditure and can be cal-
culated by dividing the poverty gap by the existing poverty line. The normalised poverty gap impact
PI
NG
=NG
Post
NG
Pre
, where NG
Post
and NG
Pre
are the post and prepayment normalised poverty gap calculated
as NG
Pre
=G
Pre
/Z
Pre
.
The poverty impact of OOP health expenditure is measured using the official poverty line given by the Planning
Commission.
27
The respective poverty lines for different states for the year 2011 to 2012 were updated for 2014 by
using the consumer price index for agricultural labourers for rural areas (AL) and industrial workers (IW) for urban
areas separately.
3.3 |Measuring the incidence of sources of finance used as coping mechanisms
The incidence reveals the proportion of population utilising different sources of finance, I¼
1
NN
i¼1H, where Nis the
sample size, and His the number of persons using a source of finance. In all the states, sources of finance are more
than one (saving/income, borrowing etc.), ie, some households may not have exclusively used only one source of
finance but have used more than one in different proportion. In National Sample Survey Organisation 71
st
round,
these sources have been termed as first and second major source of finance. For example, households which have
used saving/income and borrowing as first and second major sources of finance have been counted in both the cat-
egories as per the reporting. Therefore, in Table 3, when the incidence of all the sources of finance is added together,
the total incidence is more than 100%.
4|RESULTS
4.1 |Interstate variations in the economic burden of OOP health expenditure in India,
2014
Table 1 reports the interstate variations in the economic burden of OOP health expenditure at different threshold
levels in India. Proportion of population reporting OOP, at 10% and 25% threshold levels, is significantly higher in
Kerala followed by Goa, West Bengal, Andhra Pradesh, Punjab, and Tamil Nadu as compared with Chhattisgarh,
North East region, Assam, Bihar, Haryana, Rajasthan, Jharkhand, and Jammu and Kashmir. The concentration index
(CI
E
) reflects interstate variations in the incidence of OOP health expenditure across the consumption distribution.
Although, in most of the states, the incidence of population reporting OOP is concentrated among richer consump-
tion groups, whereas in states like Assam, Maharashtra, Karnataka, and Kerala it is pro poor. In states such as, Jammu
4SANGAR ET AL.
TABLE 1 Interstate variations in the economic burden of OOP health expenditure, India, 2014
States
Population Reporting OOP 10% 25%
HC
a
(%) CI Gap (%) CI HC (%) CI Gap (%) CI HC (%) CI Gap (%) CI
Jammu &
Kashmir
32.5
(26.538.5)
0.004
(0.0950.103)
8.9
(6.711.1)
0.067
(0.0230.157)
22.9
(17.528.3)
0.010
(0.1360.114)
6.2
(4.48.1)
0.072
(0.0280.172)
13.6
(9.417.9)
0.042
(0.2060.121)
3.8
(2.55.1)
0.082
(0.0300.195)
Himachal
Pradesh
38.3
(32.344.3)
0.061
(0.0310.153)
10.0
(7.712.3)
0.181
(0.0830.277)
21.3
(16.725.9)
0.032
(0.1620.098)
6.8
(4.88.8)
0.215
(0.1060.324)
11.9
(8.515.2)
0.064
(0.2080.080)
4.5
(3.06.1)
0.269
(0.1460.392)
Punjab 58.1
(52.163.5)
0.030
(0.0200.081)
12.7
(10.914.4)
0.171
(0.1020.239)
32.3
(27.437.2)
0.044
(0.1240.036)
8.2
(6.89.6)
0.181
(0.1040.257)
18.1
(14.521.6)
0.088
(0.1920.016)
4.7
(3.75.6)
0.214
(0.1250.303)
Uttarakhand 41.0
(32.549.5)
0.011
(0.0970.120)
11.3
(7.115.5)
0.345
(0.2220.467)
23.8
(16.331.4)
0.070
(0.2210.082)
8.2
(4.511.9)
0.369
(0.2270.511)
12.7
(7.418.0)
0.054
(0.2510.141)
5.7
(2.78.7)
0.409
(0.2430.575)
Haryana 34.4
(29.339.5)
0.071
(0.0070.149)
7.7
(5.89.6)
0.217
(0.1400.294)
17.2
(13.720.7)
0.143
(0.0170.270)
5.2
(3.76.7)
0.234
(0.1440.324)
10.2
(7.213.1)
0.151
(0.0390.341)
3.2
(2.14.2)
0.130
(0.0710.187)
Rajasthan 32.7
(29.436.0)
0.011
(0.0430.066)
7.9
(6.59.4)
0.243
(0.1850.301)
17.3
(14.620.0)
0.019
(0.0960.058)
5.5
(4.36.7)
0.267
(0.1990.334)
10.1
(7.912.2)
0.035
(0.1360.064)
3.5
(2.54.4)
0.299
(0.2170.381)
Uttar
Pradesh
39.6
(37.341.9)
0.041
(0.0100.072)
13.3
(11.814.8)
0.287
(0.2560.318)
23.8
(21.925.7)
0.032
(0.0100.074)
9.9
(8.511.3)
0.310
(0.2760.345)
13.8
(12.315.2)
0.108
(0.0470.169)
6.8
(5.67.9)
0.348
(0.3080.388)
Bihar 33.4
(29.537.3)
0.009
(0.0540.072)
10.6
(7.313.8)
0.279
(0.2240.334)
17.8
(14.720.9)
0.034
(0.1280.059)
8.0
(5.010.9)
0.309
(0.2470.372)
11.6
(8.814.4)
0.010
(0.1260.146)
5.8
(3.38.2)
0.349
(0.2770.421)
North East
(Excl.
Assam)
22.4
(21.125.4)
0.001
(0.0020.000)
5.9
(3.28.6)
0.003
(0.0040.002)
11.9
(10.113.6)
0.002
(0.003‐‐0.001)
4.2
(1.86.7)
0.003
(0.004‐‐0.002)
6.3
(5.07.6)
0.003
(0.004‐‐
0.002)
2.9
(0.84.9)
0.004
(0.005‐‐0.003)
Assam 23.2
(19.625.4)
0.015
(0.1200.090)
5.9
(3.78.1)
0.279
(0.1980.361)
11.2
(9.113.3)
0.264
(0.361‐‐0.166)
4.3
(2.36.3)
0.325
(0.2240.425)
6.2
(4.57.8)
0.265
(0.396‐‐0.133)
2.9
(1.24.6)
0.007
(0.0040.009)
West
Bengal
54.0
(51.057.1)
0.069
(0.0400.099)
14.6
(13.016.3)
0.243
(0.2060.280)
32.8
(30.035.6)
0.045
(0.0010.090)
10.1
(8.611.6)
0.278
(0.2400.316)
16.9
(14.818.9)
0.029
(0.0190.027)
6.3
(5.17.5)
0.302
(0.2490.354)
Jharkhand 29.9
(24.735.1)
0.107
(0.0070.207)
8.5
(3.1.13.9)
0.340
(0.2540.426)
15.3
(11.519.1)
0.132
(0.0220.287)
6.0
(1.110.9)
0.367
(0.2640.469)
7.2
(4.69.8)
0.067
(0.1670.300)
4.0
(0.17.8)
0.415
(0.2870.543)
Odisha 43.7
(39.847.6)
0.078
(0.0300.126)
16.3
(13.019.6)
0.257
(0.2060.309)
31.3
(27.834.8)
0.088
(0.0260.150)
12.3
(9.315.2)
0.282
(0.2230.341)
16.8
(14.119.4)
0.120
(0.0340.207)
8.4
(5.910.9)
0.310
(0.2410.379)
Chhattisgarh 21.8
(16.527.0)
0.048
(0.0630.160)
9.9
(3.816.0)
0.391
(0.2960.485)
11.9
(8.215.5)
0.072
(0.0080.124)
7.7
(2.213.3)
0.434
(0.3220.545)
5.1
(3.36.9)
0.238
(0.0110.464)
5.7
(1.110.3)
0.485
(0.3550.615)
Madhya
Pradesh
34.0
(30.837.2)
0.097
(0.0440.150)
10.4
(8.911.8)
0.280
(0.2310.328)
19.3
(16.621.9)
0.057
(0.0180.132)
7.6
(6.38.8)
0.299
(0.2440.355)
11.5
(9.413.6)
0.072
(0.0290.175)
5.1
(4.16.1)
0.334
(0.2690.399)
Gujarat 36.0
(32.639.2)
0.024
(0.0250.074)
6.6
(5.67.7)
0.208
(0.1510.264)
16.5
(14.318.8)
0.007
(0.0690.084)
4.1
(3.25.0)
0.256
(0.1850.327)
6.2
(5.07.4)
0.098
(0.0190.216)
2.4
(1.73.1)
0.319
(0.2270.412)
Maharashtra 37.1
(34.539.6)
0.015
(0.0500.023)
8.9
(7.810.0)
0.158
(0.1180.197)
21.8
(19.723.9)
0.038
(0.0890.012)
6.3
(5.37.2)
0.180
(0.1350.224)
11.3
(9.712.9)
0.006
(0.0720.084)
4.1
(3.34.8)
0.226
(0.1740.278)
(Continues)
SANGAR ET AL.5
TABLE 1 (Continued)
States
Population Reporting OOP 10% 25%
HC
a
(%) CI Gap (%) CI HC (%) CI Gap (%) CI HC (%) CI Gap (%) CI
Andhra
Pradesh
54.6
(50.159.1)
0.071
(0.0310.113)
13.4
(10.915.8)
0.259
(0.2070.311)
28.6
(24.932.2)
0.044
(0.0210.111)
9.2
(7.011.4)
0.284
(0.2250.344)
15.4
(12.618.2)
0.103
(0.0030.203)
5.7
(4.07.5)
0.326
(0.2530.399)
Karnataka 42.4
(38.746.3)
0.031
(0.0790.017)
10.8
(9.412.1)
0.211
(01.600.262)
27.5
(24.230.7)
0.093
(0.1520.033)
7.4
(6.38.5)
0.235
(0.1780.292)
14.2
(11.816.5)
0.096
(0.1680.024)
4.7
(3.95.5)
0.283
(0.2170.350)
Goa 66.0
(50.981.1)
0.023
(0.0910.136)
13.8
(7.520.2)
0.139
(0.0540.333)
33.2
(19.147.3)
0.084
(0.1770.346)
8.8
(3.714.0)
0.174
(0.0650.413)
15.3
(4.326.3)
0.341
(0.2540.935)
4.4
(1.47.4)
0.224
(0.0800.154)
Kerala 76.1
(73.179.2)
0.012
(0.0340.010)
17.0
(14.719.3)
0.144
(0.0990.188)
42.9
(39.646.2)
0.034
(0.0750.007)
11.3
(9.213.4)
0.175
(0.1190.230)
18.8
(16.521.1)
0.034
(0.0320.101)
6.8
(5.28.4)
0.230
(0.1570.302)
Tamil
Nadu
48.9
(45.552.3)
0.042
(0.0010.082)
11.4
(9.912.8)
0.240
(0.1920.286)
24.1
(21.526.7)
0.001
(0.0970.095)
7.7
(6.59.0)
0.281
(0.2270.333)
12.6
(10.814.5)
0.098
(.0110.186)
4.8
(3.85.8)
0.332
(0.2700.393)
Telangana 38.9
(33.644.2)
0.003
(0.0710.078)
12.8
(10.015.5)
0.225
(0.1510.297)
28.6
(23.833.4)
0.038
(0.1260.049)
9.4
(6.911.8)
0.255
(0.1740.336)
14.7
(11.417.9)
0.048
(0.1590.063)
6.2
(4.28.2)
0.313
(0.2210.407)
India 40.5
(39.641.6)
0.064
(0.0530.075)
11.0
(10.511.5)
0.251
(0.2390.262)
23.7
(22.924.4)
0.033
(0.0180.049)
7.8
(7.38.2)
0.271
(0.2580.284)
12.4
(11.912.9)
0.045
(0.0220.068)
5.0
(4.75.4)
0.306
(0.2900.321)
Note: The figures are based on author's calculations from NSSO 71
st
Round. Values in parentheses are 95% confidence interval. INR, Indian National Rupee. Pop. Reporting OOP is Pop-
ulation reporting OOP. The calculations exclude childbirth.
a
HC is headcount.
6SANGAR ET AL.
and Kashmir, Himachal Pradesh, Punjab, Uttarakhand, Rajasthan, and Telangana, the incidence of OOP health
expenditure is pro poor at 10% and 25% threshold levels.
The intensity of OOP health expenditure as a share of TCE is significantly higher in states such as Kerala, Odisha,
West Bengal, Punjab, Goa, Andhra Pradesh, and Uttar Pradesh. On the other hand, Assam, North East region,
Jharkhand, Chhattisgarh, Rajasthan, Gujarat. Haryana, and Jammu and Kashmir have a considerably lower intensity
of OOP health expenditure. The concentration index (CI
O
) shows that the intensity of OOP health expenditure is
concentrated among the richer consumption groups. Even if the threshold level is raised to 10% and 25%, the
intensity of OOP health expenditure remains pro rich with higher degree of interstate variations.
4.2 |Interstate variations in the impoverishment impact of OOP health expenditure in
India, 2014
Table 2 presents the poverty impact of OOP health expenditure among different states in India. The incidence of
poverty due to OOP health expenditure is more than national average (8%) in Andhra Pradesh, Kerala, West Bengal,
Odisha, Uttarakhand, Jammu and Kashmir, Himachal Pradesh, Karnataka, and Uttar Pradesh. On the contrary,
Chhattisgarh, Goa, Assam, and North East region reported the poverty impact of less than 5%. Other states like
Haryana, Bihar, Gujarat, Rajasthan, Madhya Pradesh, Maharashtra, and Jharkhand also have lesser poverty impact
than at the national average. Significant interstate variations are also reported in the intensity of impoverishment
as measured by poverty gap. However, the poverty gap comparisons are more useful if they are normalised by the
poverty line. Poverty deepening due to OOP health expenditure is higher in states such as Odisha, Uttarakhand,
West Bengal, Bihar, and Uttar Pradesh. On the other hand, Gujarat, Assam, Goa, Maharashtra, and North East region
reported lower level of poverty deepening due to OOP health expenditure.
4.3 |Interstate variations in the incidence of various sources of finance used as coping
mechanism in India, 2014
In the absence of adequate risk pooling measures, people have to use different sources of finance such as savings,
borrowings, sale of assets, and contributions from friends and relatives to cope up with the OOP health expenditure.
Table 3 reports interstate variations in the incidence of various sources of finance used as coping mechanisms for
inpatient and outpatient care. In case of inpatient care, savings is the primary source of finance to cope up with
the OOP health expenditure in all the states. In many states like Andhra Pradesh, Odisha, Karnataka, Telangana,
Bihar, Tamil Nadu, and Assam, the incidence of borrowings is also significantly high. While, states such as
Chhattisgarh, Jharkhand, Bihar, Punjab, and Gujarat also rely heavily on contributions from friends and relatives as
a coping mechanism. People also rely upon sale of assets and other sources, but their incidence is relatively lower
than savings and borrowings. Similar to inpatient care, in outpatient care, the share of savings/income is also signif-
icantly higher than borrowings and the other sources. However, barring few states such as, Bihar, Odisha, Assam, and
Chhattisgarh, the incidence of all other sources is much lower in outpatient care. As compared with outpatient care,
the inpatient care is substantially financed by distress sources such as borrowings, sale of assets, and contributions
from friends and relatives.
5|DISCUSSION
Results of the study reveal wide variations among Indian states in the economic burden, resultant impoverishment,
and coping mechanisms used to finance OOP health expenditure. On the basis of results, the states can be divided
into four distinct categories (see Table 4): (1) states with low economic burden and low poverty impact of OOP health
expenditure (North East, Assam, Gujarat, Haryana, Rajasthan, Chhattisgarh, Madhya Pradesh, Bihar, Jharkhand, and
Maharashtra), (2) states with low economic burden and high poverty impact of OOP health expenditure
SANGAR ET AL.7
(Jammu and Kashmir, Himachal Pradesh, and Uttar Pradesh), (3) states with high economic burden and low poverty
impact of OOP health expenditure (Goa), and (4) states with high economic burden and high poverty impact of OOP
health expenditure (Karnataka, Uttarakhand, Tamil Nadu, Punjab, Andhra Pradesh, West Bengal, Orissa, and Kerala).
These variations are mainly attributed to the underlying socioeconomic and health related variables.
Among the states with low economic burden and low poverty impact of OOP health expenditure, Rajasthan,
Chhattisgarh, and Maharashtra have relatively reported good coverage under Publicly Financed Health Insurance
(PFHI) schemes while communitybased health insurance schemes in Gujarat provide protection against OOP health
expenditure.
28,29
Studies have also shown that states like Madhya Pradesh, parts of North East, Bihar, Jharkhand,
Rajasthan, parts of North East and Assam are in early to middle stages of health transition, when people spend less
on health care due to low income and have limited access to health care services.
30
The health care utilisation is
lower than national average in states like Bihar, North East, Assam, Jharkhand, and Chhattisgarh which reduces
the proportion of those incurring OOP health expenditure.
5
Among states with high economic burden and high poverty impact of OOP health expenditure, Kerala, Tamil
Nadu, Punjab, and Karnataka have relatively higher development indicators and are also in the higher stages of health
transition. As a result, the burden of noncommunicable diseases is much higher than communicable diseases, which
reveals a high OOP share and resultant impoverishment in these states.
18,30
Burden of OOP health expenditure is
also high in these states because they utilise private health care facilities in higher proportions which are much cost-
lier than the public health care facilities.
5
Further, Andhra Pradesh and Tamil Nadu have their own PFHI schemes, ie,
TABLE 2 Interstate variations in impoverishment impact of OOP health expenditure in India, 2014
States Headcount, % Poverty Gap, INR Normalised Poverty Gap, %
Jammu and Kashmir 9.3 (5.513.1) 63.9(45.782.0) 5.9
Himachal Pradesh 9.2(6.312.1) 60.9(43.478.4) 5.3
Punjab 8.3(6.110.4) 77.1(61.592.7) 5.9
Uttarakhand 10.0(4.715.4) 81.5(49.0113.9) 7.4
Haryana 7.4(4.99.9) 48.0(33.462.5) 3.8
Rajasthan 6.5(4.88.2) 45.5(36.154.8) 4.0
Uttar Pradesh 9.1(7.910.2) 65.3(58.072.5) 6.6
Bihar 7.3(5.29.5) 66.8(47.685.9) 6.7
North East (Excl. Assam) 4.5(3.35.6) 35.6(28.942.2) 3.3
Assam 4.1(2.65.6) 28.2(19.337.2) 2.8
West Bengal 11.5(9.813.2) 71.7(61.981.5) 7.0
Jharkhand 5.3(3.17.6) 36.9(17.955.8) 3.8
Odisha 10.7(8.612.7) 81.6(68.394.7) 9.3
Chhattisgarh 3.3(2.04.8) 34.4(23.245.7) 3.7
Madhya Pradesh 6.5(5.07.9) 56.5(45.167.9) 5.8
Gujarat 6.6(5.08.2) 33.2(26.040.3) 2.7
Maharashtra 5.9(4.47.4) 39.6(30.249.0) 3.2
Andhra Pradesh (incl. Telangana)
a
12.6(10.514.7) 63.0(52.273.7) 5.7
Karnataka 9.3(7.710.9) 80.2(69.690.7) 6.7
Goa 3.4(1.14.9) 42.6(21.563.2) 3.2
Kerala 11.9(9.813.9) 71.3(58.284.4) 5.8
Tamil Nadu 8.7(7.110.3) 47.6(39.855.3) 4.3
All India 8.0(7.68.4) 63.1(60.166.2) 5.7
Note: The figures are based on author's calculations from NSSO 71
st
Round. Values in parentheses are 95% confidence
interval. INR: Indian National Rupee. The calculations exclude childbirth.
a
In 2014, Telangana was part of Andhra Pradesh, there was no specific poverty line for Telangana.
8SANGAR ET AL.
TABLE 3 Interstate variations in various sources of finance used as coping mechanisms, India, 2014
Inpatient Care Outpatient Care
Savings Borrowings Sale of Assets Contributions Other Sources Savings Borrowings Sale of Assets Contributions Other Sources
Jammu and Kashmir 97.4
(94.999.9)
20.7
(14.127.2)
2.4
(0.25.1)
10.4
(6.114.7)
10.6
(5.715.4)
95.8
(89.2100.2)
2.6
(0.44.9)
0.02
(0.020.06)
5.7
(0.912.4)
18.7
(8.628.8)
Himachal Pradesh 97.2
(95.598.9)
17.6
(12.622.6)
0.4
(0.31.0)
15.4
(8.422.4)
4.7
(1.611.0)
99.7
(99.4100.4)
7.2
(0.515.0)
0.0 2.2
(0.75.2)
0.05
(0.050.2)
Punjab 92.3
(89.994.7)
22.3
(17.027.7)
0.3
(0.10.6)
20.5
(14.426.5)
1.3
(0.42.1)
97.0
(94.799.1)
1.2
(0.22.3)
0.0 4.6
(1.77.5)
2.8
(0.54.9)
Uttarakhand 94.7
(91.398.1)
24.3
(15.033.4)
0.3
(0.30.9)
16.6
(5.827.3)
1.3
(0.53.0)
99.1
(96.8102.4)
0.03
(0.080.02)
0.08
(0.080.2)
2.9
(0.046.1)
0.2
(0.20.5)
Haryana 92.5
(89.795.2)
24.2
(19.229.3)
0.7
(0.31.6)
9.7
(6.912.6)
8.5
(2.619.6)
99.4
(98.8100.0)
1.8
(0.043.5)
0.01
(0.010.02)
0.6
(0.021.2)
0.9
(0.11.6)
Rajasthan 91.0
(88.993.1)
33.1
(28.437.8)
0.8
(0.31.2)
10.7
(8.312.9)
1.1
(0.22.0)
97.6
(95.899.4)
6.4
(2.510.3)
0.0 2.0
(0.83.1)
0.1
(0.040.2)
Uttar Pradesh 91.6
(89.293.4)
38.1
(34.641.7)
0.6
(0.30.8)
18.6
(15.921.3)
2.6
(5.44.6)
98.3
(97.299.3)
4.4
(3.05.9)
0.01
(0.010.04)
3.0
(1.64.4)
1.4
(0.52.2)
Bihar 90.7
(87.693.8)
46.9
(40.993.1)
1.1
(0.41.8)
20.9
(13.928.0)
2.7
(0.84.6)
96.6
(93.699.6)
18.8
(11.925.8)
0.0 5.6
(1.49.7)
1.1
(0.72.8)
North East
(Excl. Assam)
96.8
(95.698.0)
22.1
(19.724.4)
1.4
(0.62.1)
21.3
(18.823.7)
9.7
(7.811.5)
98.7
(96.5100.8)
10.7
(5.915.5)
1.1
(1.03.3)
12.6
(7.417.8)
9.8
(0.519.1)
Assam 96.4
(94.598.4)
41.4
(35.247.5)
2.2
(0.73.6)
8.5
(6.310.6)
7.9
(3.112.7)
99.5
(99.399.8)
13.8
(4.822.6)
0.0 8.1
(0.615.6)
2.3
(0.034.9)
West Bengal 93.1
(91.894.5)
37.9
(34.041.8)
1.6
(1.02.3)
14.2
(11.816.6)
2.7
(1.73.7)
98.4
(97.299.7)
9.9
(7.112.6)
0.1
(0.010.2)
6.4
(4.28.6)
1.6
(0.52.7)
Jharkhand 89.2
(82.196.4)
34.9
(25.644.2)
2.7
(1.14.3)
28.1
(19.836.3)
0.6
(.021.2)
99.7
(99.599.9)
6.7
(1.414.9)
0.0 4.2
(0.080.7)
0.01
(0.010.03)
Odisha 87.5
(84.590.5)
54.4
(49.959.0)
4.5
(2.07.0)
8.8
(6.710.7)
1.2
(0.61.9)
97.7
(95.999.5)
15.0
(10.319.8)
0.0 3.5
(1.06.0)
0.09
(0.081.9)
Chhattisgarh 85.5
(72.498.7)
29.3
(17.241.4)
0.9
(0.011.9)
36.9
(19.154.7)
2.4
(1.03.7)
98.6
(97.499.8)
10.3
(2.318.3)
0.2
(0.030.7)
5.4
(2.112.5)
3.5
(1.98.8)
Madhya Pradesh 91.3
(89.493.2)
30.3
(26.134.5)
1.0
(0.41.6)
17.9
(14.421.4)
1.1
(0.61.6)
98.9
(98.499.5)
5.0
(2.87.2)
0.01
(0.010.03)
4.6
(1.97.4)
0.8
(0.021.8)
(Continues)
SANGAR ET AL.9
TABLE 3 (Continued)
Inpatient Care Outpatient Care
Savings Borrowings Sale of Assets Contributions Other Sources Savings Borrowings Sale of Assets Contributions Other Sources
Gujarat 96.0
(94.597.4)
11.5
(8.714.3)
0.7
(0.11.3)
20.4
(17.323.4)
2.0
(1.32.7)
97.9
(96.399.6)
1.2
(0.42.8)
0.1
(0.13.6)
1.2
(0.32.2)
1.1
(0.42.5)
Maharashtra 93.5
(92.494.7)
31.0
(28.233.8)
1.0
(0.51.6)
17.6
(15.319.9)
1.7
(1.12.2)
98.3
(97.399.3)
3.9
(2.45.4)
0.2
(0.10.5)
3.6
(1.65.6)
1.0
(0.052.0)
Andhra Pradesh 72.6
(67.178.2)
56.3
(51.161.5)
3.9
(0.37.5)
5.8
(3.97.6)
1.4
(0.52.3)
96.6
(94.698.5)
5.1
(2.47.7)
0.05
(0.020.1)
1.9
(0.63.1)
0.5
(0.011.0)
Karnataka 85.1
(82.687.6)
51.5
(48.055.0)
0.9
(0.51.3)
11.8
(9.514.2)
2.4
(1.33.5)
97.6
(95.999.3)
5.4
(3.47.3)
0.0 2.9
(1.04.8)
2.1
(0.044.1)
Goa 95.3
(91.199.4)
11.3
(4.318.5)
0.0 12.1
(5.918.3)
2.4
(1.15.9)
99.7
(99.1100.1)
0.0 0.0 0.3
(0.30.9)
0.2
(0.20.5)
Kerala 90.5
(88.092.9)
31.1
(27.534.7)
0.7
(0.21.2)
16.4
(13.319.6)
1.5
(0.72.2)
97.1
(95.898.3)
2.0
(1.12.9)
0.02
(0.020.6)
4.0
(2.55.5)
0.3
(0.40.7)
Tamil Nadu 77.4
(74.380.6)
45.2
(41.748.6)
0.7
(0.31.1)
11.9
(8.814.9)
0.6
(0.20.9)
97.0
(95.598.4)
6.7
(4.39.1)
0.0 1.5
(0.82.3)
0.06
(0.021.4)
Telangana 85.4
(81.689.1)
50.8
(42.858.8)
1.0
(0.21.6)
7.3
(4.510.1)
1.0
(0.31.8)
96.1
(92.499.9)
7.5
(2.612.4)
0.0 3.4
(0.26.9)
0.2
(0.010.4)
India 88.9
(86.391.6)
37.3
(35.838.8)
1.2
(1.01.5)
15.3
(14.316.4)
2.1
(1.72.5)
97.8
(97.498.2)
6.6
(5.87.3)
0.05
(0.020.08)
3.5
(3.04.1)
1.2
(0.91.5)
Note: The figures are based on author's calculations from NSSO 71
st
Round. Values in parentheses are 95% confidence interval. The calculations exclude childbirth.
10 SANGAR ET AL.
Rajiv Aarogyasri Scheme and Chief Minister's Comprehensive Health Insurance Scheme, respectively. Literature
shows that these schemes have increased health care utilisation in these states, which has contributed to the
increase in OOP health expenditure.
31,32
The states such as Uttarakhand have low coverage under PFHI schemes
which could have resulted in higher burden of OOP health expenditure and impoverishment.
5
Further, Orissa and
West Bengal have larger proportion of population just above the poverty line; a small increase in OOP health expen-
diture pushes a higher proportion of population below the poverty line.
Normally, a high share of OOP health expenditure in TCE would imply a high poverty impact and vice versa.
However, states like Jammu and Kashmir, Himachal Pradesh, Uttar Pradesh, and Goa are the exceptions. Despite a
low proportion of OOP in TCE, Jammu and Kashmir, Himachal Pradesh, and Uttar Pradesh witnessed a higher poverty
impact. In these states, the proportion of public health care facilities is higher in case of inpatient care whereas, in
outpatient care, private health facilities are utilised in higher proportions.
5
The impoverishment impact of outpatient
care is relatively higher than inpatient care due to the higher utilisation of private health care facilities in the for-
mer.
13,33
However, the reason behind higher economic burden and lower poverty impact of OOP health expenditure
in case of Goa could be an anomaly which needs to be studied.
As majority of the population in India do not have any risk pooling measures such as health insurance mechanism,
they have to rely upon alternative strategies to cope up with the OOP health expenditure.
19,20
Although among
various sources of finance, savings/income remain the first option, the dependence on distress sources such as
borrowings, sale of assets, and contributions from friends and relatives in case of inpatient care is also high among
many states. A high dependence on distress financing among relatively poorer states like Bihar, Assam, Jharkhand,
Odisha, and Chhattisgarh is no surprise, but comparatively richer states like Karnataka, Tamil Nadu, and Andhra
Pradesh are also dependent upon borrowings as a coping mechanism mainly due to higher OOP health expenditure.
Unlike inpatient care, household's own saving and income finance the outpatient care. Generally, a significant
proportion of the population may be spending on outpatient visits by their ability to pay
20
whereas, in inpatient care
people have to use distress means if they lack enough savings/income due to the severity of disease.
Interstate differentials in the OOP health expenditure and resultant impoverishment need proper attention of
the government especially the policy makers. The lower share of OOP health expenditure and impoverishment
especially in poor states could be attributed to lower health access and poor infrastructure facilities. The lower values
of Human Development Index also reveals a sorry state of overall development in these states. Whereas, the higher
proportion of OOP health expenditure and impoverishment in richer states is mainly due to the higher levels of
development and better access to health care facilities. Higher prevalence of noncommunicable diseases and other
lifestyle diseases also increases the burden of OOP and impoverishment.
18
Although, there are mixed views on the overall success of PFHI schemes, states like Chhattisgarh, Rajasthan, and
Gujarat are some of the better performers in the coverage of health insurance in India.
28,29
On the contrary, despite
of their own PFHI schemes, Andhra Pradesh and Tamil Nadu are unable to reduce OOP health expenditure and
impoverishment in their respective states.
34
There is a need, not only to increase the coverage of health insurance
in different parts of the country, but it is also imperative that it reduces the burden of OOP health expenditure.
TABLE 4 Categorisation of states
Categories States
Low economic burden low poverty impact of OOP
health expenditure
North East, Assam, Gujarat, Haryana, Rajasthan, Chhattisgarh,
Madhya Pradesh, Bihar, Jharkhand, and Maharashtra
Low economic burden high poverty impact of OOP
health expenditure
Jammu and Kashmir, Himachal Pradesh, and Uttar Pradesh
High economic burden low poverty impact of OOP
health expenditure
Goa
High economic burden high poverty impact of OOP
health expenditure
Karnataka, Uttarakhand, Tamil Nadu, Punjab, Andhra Pradesh,
West Bengal, Orissa, and Kerala
Note: The figures are based on author's calculations from NSSO 71
st
Round.
SANGAR ET AL.11
At present, public spending on health care is very low as central government spends around 1% of gross
domestic product on health care,
4
and at the state level, the situation is no different. According to the Constitution
of India, health is a state subject. It is the prerogative of state governments to provide quality and affordable care to
the people, which can be achieved to a greater extent by increasing the public spending on health care as a
percentage of gross state domestic product. Wide differences exist among states as richer states like Kerala,
Himachal Pradesh and Andhra Pradesh spend higher proportion of gross state domestic product on health care as
compared with the poorer states like Bihar, Jharkhand, Madhya Pradesh, and Uttar Pradesh.
35,36
It needs to be mentioned here that the Government of India has launched the National Health Policy in 2017
which aims to improve the health and wellbeing of the masses by providing universal access to quality health care
and lowering the financial hardships associated with the cost of illness.
37
The just launched Ayushman Bharat
Scheme envisages to provide health insurance cover to 10 crore poor and vulnerable families in the country. Once
implemented, it may reduce the overall burden and resultant impoverishment due to OOP health expenditure.
CONFLICT OF INTEREST
Authors have no conflict of interest.
FUNDING
None
COMPLIANCE WITH ANIMAL/HUMAN ETHICAL GUIDELINES
The study does not require any human/animal subjects to acquire such approval.
ORCID
Ramna Thakur http://orcid.org/0000-0002-7253-1033
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How to cite this article: Sangar S, Dutt V, Thakur R. Economic burden, impoverishment, and coping mecha-
nisms associated with outofpocket health expenditure in India: A disaggregated analysis at state level in
India. Int J Health Plann Mgmt. 2018;113. https://doi.org/10.1002/hpm.2649
SANGAR ET AL.13
... For example, for a hysterectomy under state-level insurance schemes, Tamil Nadu allows for a charge of ₹34,000 and Maharashtra ₹35,000 for the same procedure whereas, in Telangana and Karnataka charges are ₹62,000 and ₹50,000, respectively. 3 Irrespective of the variation in charges, private health services are generally costly (Sangar et al., 2019). Private health providers are already knocking on government doors to complain about the rates at which they are being asked to provide services under NHPS. ...
... In this study, we are not going into quantifying the impoverishment led by healthcare cost and its effects. A humongous volume of literature already exists on the issue of catastrophic health expenditure and impoverishments due to health payments (Garg & Karan, 2009;Pandey et al., 2018;Sangar et al., 2019Sangar et al., , 2022Shahrawat & Rao, 2011) for India. Instead, we are trying to analyse the underlying reasons that are contributing to high cost of healthcare in the private sector from the supply side statistics. ...
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Indian healthcare system is dominated by private sector; its importance is growing with implementation of ‘Ayushman Bharat’, flagship programme of Indian government. Though 62% and 75% of inpatient and outpatient cases in India are treated in private sector, the information about the economy of private healthcare providers is very limited. To the author’s best knowledge, this is the first attempt to address the issue with empirical evidence for the private healthcare providers from a nationally representative survey data for India. Private healthcare sector is estimated to provide employment to 2.34 million persons annually and generate gross value added (GVA) of ₹473.3 billion. Treatment cost on an average is much high in private sector as compared to the public sector. But supply-side data show that average annual receipt per annum is six times higher than average operating cost per annum for unincorporated private healthcare providers in India, indicating underlying profit motive. Analysis of factor payments shows that 55% of GVA of unincorporated private hospitals is gross operating surplus (or profit), followed by emoluments paid to employees and workers (42%). These factors potentially cause over-charging in private sector. Context-specific and appropriate regulatory mechanisms are very much needed to ensure quality of services and control medical inflation.
... Eighty-six percent of rural population and 82% of urban population are not covered under any scheme of health expenditure support. [5] India is often touted as the country with the highest out-of-pocket expense (60.6%). [4] Due to this devastating cost to individuals, about 6%-8% population is pushed below the poverty threshold every year. ...
... [4] Due to this devastating cost to individuals, about 6%-8% population is pushed below the poverty threshold every year. [5] Following in at the heels of developed countries, as more and more of the healthcare deliveries got privatized in India, AHE became popular. ...
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Much has been debated about routine periodic health screening in recent years. Those in favor of such screening of laboratory investigations point to the economic and social advantages of early detection and better diagnosis of disease to an individual and the community. The opponents of this type of blanket screening point to the dubious diagnostic value of many of the abnormal results found and the impracticability of making laboratory screening generally available even if it was shown to be of definite value. Despite contrary evidence, most primary care providers believe that an annual physical examination detects subclinical illness. This is partly driven and shaped by factors such as apparent perception of benefit, patient expectation, employer requirement, and insurance industry protocols. Healthcare institutions in India often offer structured health checkup “packages” for routine screening of common diseases. Some tests included within their ambit are in keeping with international and Indian recommendations; however, many are entirely unwarranted. Unnecessary and inappropriate screening tests cause financial and resource burden. Furthermore, there may be overdiagnosis and overtreatment, psychological distress due to false-positive test results, harm from invasive follow-up tests, and false reassurance due to false-negative test results. Evidence suggests that only certain diseases are amenable for screening in an asymptomatic adult. It is recommended that physicians should abandon recommending these general panels in favor of a more selective approach to prevent health problems individualized to every unique patient.
... On the one hand, the supply-side factors, such as government actions, human resource management and reception of patients, could help change the out-of-pocket health spending [19,25]. On the other hand, Sanger et al. [26] investigated the effect of economic burden on the formal payment for healthcare. Therefore, exploring intermediate effects of external condition could highlight the transferred channel between the business cycle and health financing. ...
... It is widely accepted that health financing is heavily associated with the effectiveness of healthcare systems. Indeed, the high share of OPE means higher financial risk protection for households, this is also crucial for assessing the effectiveness of the healthcare system in countries [26]. If the OPE is a high percentage of total health expenditure, this would generally suggest limited financial protection, as a result of a lower effectiveness of healthcare system. ...
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... At a broader contextual level, income has risen, millions have been lifted out of poverty, the country is urbanizing rapidly, and the population is aging (Desai et al., 2010). While continuing to grapple with the prevention and control of communicable diseases, staggered reduction of maternal and child mortality, and the burden of non-communicable illnesses and substance abuse (Al Kibria, Swasey, Hasan, Sharmeen, & Day, 2019;Hasan, Cohen, et al., 2020;Zodpey & Farooqui, 2018), the Indian health sector is facing a growing challenge of rising healthcare expenditure (Sangar, Dutt, & Thakur, 2019). At the national level, 59% of the total healthcare expenditure is financed by households' out-of-pocket contributions (National Health Accounts Technical Secretariat National Health Systems Resource Centre & Ministry of Health and Family Welfare, 2019). ...
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... The higher proportion of OOP health expenditure is placing a significant financial burden on the population and becoming catastrophic when it crosses a certain threshold of total consumption expenditure (Ghosh, 2010;Karan, Selvaraj, & Mahal, 2014;Sangar, Dutt, & Thakur, 2018a;Wagstaff & Doorslaer, 2003). This high level of OOP health expenditure impends a household's capacity to maintain a basic standard of living. ...
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... The higher proportion of OOP health expenditure is placing a significant financial burden on the population and becoming catastrophic when it crosses a certain threshold of total consumption expenditure (Ghosh, 2010;Karan, Selvaraj, & Mahal, 2014;Sangar, Dutt, & Thakur, 2018a;Wagstaff & Doorslaer, 2003). This high level of OOP health expenditure impends a household's capacity to maintain a basic standard of living. ...
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India launched the ‘Rashtriya Swasthya Bima Yojana’ (RSBY) health insurance scheme for the poor in 2008. Utilising 3 waves (1999-2000, 2004-05 and 2011-12) of household level data from nationally representative surveys of the National Sample Survey Organisation (NSSO) (N=346,615) and district level RSBY administrative data on enrolment, we estimated causal effects of RSBY on out-of-pocket expenditure. Using ‘difference-in-differences’ methods on households in matched districts we find that RSBY did not affect the likelihood of inpatient out-of-pocket spending, the level of inpatient out of pocket spending or catastrophic inpatient spending. We also do not find any statistically significant effect of RSBY on the level of outpatient out-of-pocket expenditure and the probability of incurring outpatient expenditure. In contrast, the likelihood of incurring any out of pocket spending (inpatient and outpatient) rose by 30% due to RSBY and was statistically significant. Although out of pocket spending levels did not change, RSBY raised household non-medical spending by 5%. Overall, the results suggest that RSBY has been ineffective in reducing the burden of out-of-pocket spending on poor households.
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Health care can be expensive for the un-insured, often constituting a potential poverty trap. Urban India is particularly vulnerable to this possibility given the greater demand for health, absence of a structured health care system, overburdened public institutions, ubiquitous, and unregulated private health care market and the generic paucity of public funds. Using nationally representative household level data for two points of time and a suitable alteration of an existing methodology, this article computes the degree and depth of impoverishment from out of pocket medical expenses, and its variation across states and select socioeconomic characteristics. Roughly 6 percent of the urban population or about 18 million people face impoverishment entirely due to out of pocket medical expenses in India. There are substantial inter-state and inter-group variations in the incidence of this burden. The findings are potentially crucial as India prepares to embark on its journey toward universal health coverage.
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Out-of-pocket (OOP) health care payments financed through borrowings or sale of household assets are referred to as distressed health care financing. This article expands this concept (to include contributions from friends or relatives) and examines the incidence and correlates of distressed health care financing in India. The analysis finds a decisive influence of distressed financing in India as over 60 and 40% of hospitalization cases from rural and urban areas, respectively, report use of such coping strategies. Altogether, sources such as borrowings, sale of household assets and contributions from friends and relatives account for 58 and 42% share in total OOP payments for inpatient care in rural and urban India, respectively. Further, the results show significant socioeconomic gradient in the distribution of distressed financing with huge disadvantages for marginalized sections, particularly females, elderly and backward social groups. Multivariate logistic regression informs that households are at an elevated risk of indebtedness while seeking treatment for non-communicable diseases, particularly cancer. Evidence based on intersectional framework reveals that, despite similar socioeconomic background, males are more likely to use borrowings for health care financing than females. In conclusion, the need for social protection policies and improved health care coverage is emphasized to curtail the incidence of distressed health care financing in India.