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Burden of out-of-pocket health expenditure and its impoverishment impact in India: Evidence from National Sample Survey

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In low- and middle-income countries like India, financing of health-care expenditure is predominantly characterized by out-of-pocket (OOP) spending. Given this context, the current study examined the economic burden of OOP health expenditure and resultant impoverishment in India. The study employed nationally representative survey on ‘Health and Morbidity’ conducted by National Sample Survey Organization (NSSO) in 2014 in India. Standard catastrophic, inequality and impoverishment measures were used to analyse the burden and impact of OOP health expenditure. Findings revealed that although the overall incidence and intensity of OOP health expenditure was concentrated among the richer consumption groups, in-depth study of the same in terms of inpatient and outpatient care showed that the incidence of outpatient care was highly concentrated towards the poorer consumption groups. Study also revealed that around 8% of the population fell below the poverty line due to OOP health expenditure in which outpatient care was the main contributing factor (5.8%). Among different socio-economic covariates, rural population, Muslims, Scheduled Castes and casual/agriculture labour were most affected and had higher impoverishment impact. Our findings suggest that there is a need to revisit the approach towards health-care financing in India. Abbreviations: OOP: Out-of-pocket; MPCE: Monthly per capita consumption expenditure; SCs: Scheduled Castes; STs: Scheduled Tribes; OBCs: Other Backward Castes; TCE: Total consumption expenditure; PFHI: Publicly financed health insurance; RSBY: Rashtriya Swasthya Bima Yojana; CMCHIS: Chief Minister’s Comprehensive Health Insurance Scheme; UHC: Universal health coverage
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Burden of out-of-pocket health expenditure and its impoverishment impact
in India: Evidence from National Sample Survey
Shivendra Sangar1, Varun Dutt2, Ramna Thakur3*
* Correspondence: ramna@iitmandi.ac.in
1 PhD Scholar, School of Humanities and Social Sciences, Indian Institute of Technology
Mandi, Kamand (Mandi), 175005, Himachal Pradesh, India.
shivendrasangar2009@gmail.com, shivendra_sangar@students.iitmandi.ac.in
2 Assistant Professor, School of Humanities and Social Sciences, Indian Institute of
Technology Mandi, Kamand (Mandi), 175005, Himachal Pradesh, India.
varun@iitmandi.ac.in
3Assistant Professor, School of Humanities and Social Sciences, Indian Institute of
Technology Mandi, Kamand (Mandi), 175005, Himachal Pradesh, India.
ramna@iitmandi.ac.in
Word Count - 7639
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Abstract
In low and middle-income countries like India, financing of health care expenditure is
predominantly characterised by out-of-pocket (OOP) spending. Given this context, the
current study examined the economic burden of OOP health expenditure and resultant
impoverishment in India. The study employed nationally representative survey on ‘Health
and Morbidity’ conducted by National Sample Survey Organisation (NSSO) in 2014 in India.
Standard catastrophic, inequality and impoverishment measures were used to analyse the
burden and impact of OOP health expenditure. Findings revealed that although the overall
incidence and intensity of OOP health expenditure was concentrated among the richer
consumption groups, in-depth study of the same in terms of inpatient and outpatient care
showed that the incidence of outpatient care was highly concentrated towards the poorer
consumption groups. Study also revealed that around 8% of the population fell below the
poverty line due to OOP health expenditure in which outpatient care was the main
contributing factor (5.8%). Among different socio-economic covariates, rural population,
Muslims, Scheduled Castes and casual/agriculture labour were most affected and had higher
impoverishment impact. Our findings suggest that there is a need to revisit the approach
towards health care financing in India.
Keywords: Out-of-Pocket; catastrophic; impoverishment; inequality; incidence; intensity
Introduction
Health care systems all over the world aim to ensure that necessary services are accessible to
people at affordable prices (WHO, 2010). Though, all countries face the issues related to
affordability of health care, but it is more pronounced in low- and middle-income countries
(LMICs) (Niens, 2014). Most of the high-income countries have health insurance systems
which deal with the issues fairly equitably and provide affordable health care to the majority
of the population (Thomson, Foubister, & Mossialos, 2009). Health care financing in many
LMICs including India is heavily relied upon out-of-pocket (OOP) payments of the
individuals (Garg & Karan, 2008; Ramani & Mavalankar, 2006). In India, this fact is further
supported by National Health Accounts which reveal that the share of OOP health
expenditure is 64.2% (NHA, 2016).
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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. Consequently,
households have to cut down on necessities such as food and clothing and compromise with
the education of their children (Van Minh, Phuong, Saksena, James, & Xu, 2013). Further, in
the absence of universal health coverage (UHC), the catastrophic health expenditure is likely
to push a considerable proportion of the population into poverty (Garg & Karan, 2008;
Ladusingh & Pandey, 2013).
Background
Studies in LMICs reveal that there is heavy reliance on OOP as its share in total health
spending constitutes three-fifths in Bangladesh and China, three-fourth in Vietnam and Nepal
and more than one half in Ghana and Kenya (Akazili et al., 2017; Chuma & Maina, 2012;
Van Doorslaer et al., 2007). Higher levels of catastrophic payments are mainly attributed to
the costly nature of health services, low capacity to pay, and the lack of health insurance
(Onwujekwe, Hanson, & Uzochukwu, 2012; Xu et al., 2003). Though private health care
facilities have higher catastrophic burden and poverty impact than public (Basar, Brown, &
Hole, 2012; Beogo, Huang, Gagnon, & Amendah, 2016; Limwattananon,
Tangcharoensathien, & Prakongsai, 2007) but in certain countries public health care facilities
also led to catastrophic health expenditure (Onwujekwe et al., 2012). Barring few studies like
(Limwattananon et al., 2007; Rahman, Gilmour, Saito, Sultana, & Shibuya, 2013), the
catastrophic burden and resultant impoverishment is higher in outpatient care than inpatient
care (Chuma & Maina, 2012; Onwujekwe et al., 2012).
Apart from studies in LMICs, there are a number of studies in India which have also analysed
the economic burden and resultant impoverishment impact of OOP health expenditure.
Studies have shown that in India, the incidence of households falling below the poverty line
due to OOP health expenditure varies between 3% to 7% (Berman, Ahuja, & Bhandari, 2010;
Chuma & Maina, 2012; Ghosh, 2010; Kumar et al., 2015). The incidence of poverty in India
due to OOP health expenditure is higher in rural areas and poorer states compared to urban
areas and richer states (Garg & Karan, 2008; Ghosh, 2010; Ladusingh & Pandey, 2013;
Sangar, Dutt, & Thakur, 2018b). Apart from rural-urban differentials, the financial burden
associated with OOP health expenditure and resultant impoverishment has increased among
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the disadvantaged sections of the society as compared to more advantaged sections
(Mukherjee, Haddad, & Narayana, 2011), especially in outpatient care, as poor sections of
society forego inpatient care because of the expensive treatment (Berman et al., 2010; Karan
et al., 2014). Studies have also reported the effects of different socio-economic covariates
such as religion, social category and household type on the OOP health expenditure and its
impoverishment impact in India (Bonu, Bhushan, Rani, & Anderson, 2009; Chowdhury,
2015).
Research gaps and contribution
Although, the above studies have focused on the catastrophic burden of OOP health
expenditure in India in one or the other way, there are specific essential areas in which this
study has contributed to the existing literature, such as; this study used the National Sample
Survey Organisation (NSSO) survey on ‘health and morbidity’ which is not as frequently
used as ‘consumption expenditure survey’ (CES). The NSSO health rounds capture the health
expenditure in a more extensive
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way than the NSSO CES rounds which mainly focus on
consumption expenditure. Further, the earlier studies based on health rounds, did not analyse
the catastrophic burden and impoverishment impact of OOP health expenditure together and
for inpatient and outpatient care intensively by taking different socio-economic covariates.
The current study fills this gap by measuring the economic burden and impoverishment
impact of OOP health expenditure across different socio-economic covariates such as:
religion, area of residence, social categories, household type and level of living. These
variables are extremely important in Indian context. In addition, this study has gone beyond
the calculations of the incidence and intensity of OOP health expenditure by calculating the
weighted indices at a wider range of thresholds. This is important for the checking the
robustness of the results and has rarely been done in the previous literature on India. Also, the
study has extensively analysed the share of various components (medicines, other medical
expenditure, and non-medical expenditure) of OOP health expenditure separately for
inpatient and outpatient care.
The structure of the paper is as follows: The next section discussed the data and methodology
used in the analysis. This was followed by the results of the study. The last section comprised
the discussion and conclusion of the study.
Materials and Methods
Data
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The present paper employed nationally representative survey; 71st Round on ‘Social
Consumption: Health’ of National Sample Survey Organisation (NSSO) comprising a sample
population of 0.33 million people (NSSO, 2014). In this round, a stratified multistage sample
design was adopted, 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 survey
period for 71st Round was from January to June 2014. The recall period for inpatient and
outpatient expenditure was 365 and 15 days respectively. All details regarding consumption
expenditure were collected on a recall period of one month. The analysis was done separately
for inpatient and outpatient care and total (inpatient and outpatient care). OOP health
expenditure was calculated by deducting the amount of reimbursement from total health
expenditure. The present analysis excluded childbirth and respective sample weights were
applied. Description of the analytic sample for 2014 in India is given below. The findings of
this study are extremely relevant despite the fact that the data for the NSSO 71st round was
collected in 2014, as this is the health round in India which is the most extensive and contains
a broader set of questions on morbidity and health expenditure.
Insert Table 1 here
Methodology
Measuring catastrophic expenditure
The current paper used the methodology proposed by Wagstaff and Doorslaer (2003). The
economic burden of OOP health expenditure was measured in terms of its incidence, intensity
and level of inequality. The intensity of OOP health expenditure was calculated both as a
share of total consumption expenditure (TCE) and average per capita amount. We used
household usual consumption expenditure as a denominator to calculate the economic burden
of OOP health expenditure.
Headcount - Headcount (HCat) measured the percentage of households that spent more on
health care than the threshold. It was a fraction of the sample whose OOP health expenditure
as a proportion of total consumption expenditure exceeds the threshold (Z). Since there were
no universally accepted threshold levels, the economic burden of OOP health expenditure
was calculated at three threshold levels i.e., base (Proportion of population reporting OOP),
10% and 25%. The economic burden of OOP health expenditure was also measured at the
reporting level as we believed that for poor people whose consumption expenditure was very
low, any spending on healthcare was catastrophic as it worsened their condition. The value of
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catastrophic headcount was given as,
 where N is the sample size, Ei is
equal to one if Ti/Xi > Z and zero otherwise, Ti is the OOP health expenditure of person i, Xi
is the consumption expenditure of person I, Z is the threshold level.
Payment gap - The payment gap (GCat) captured the average degree by which OOP health
expenditure as a proportion of consumption expenditure exceeded the threshold, Z. The
catastrophic payment gap was given as, 
 where N is the sample size, Oi is
the overshoot of person i, Oi = Ei ((Ti/Xi) Z). Catastrophic payment gap revealed the
intensity of OOP health expenditure in terms of its share in total consumption expenditure.
However, it was also imperative to measure it in terms of average per capita amount. Average
per capita OOP health expenditure was calculated by dividing OOP health expenditure by
total population.
Mean positive gap - The incidence and intensity of OOP health expenditure are related
through mean positive gap (MPG). It measured the intensity of OOP health expenditure in the
excess of the threshold, averaged over all individuals exceeding that threshold.

Concentration index - In order to determine whether poor incurred more catastrophic
payments than rich, the concentration index (CI) had to be calculated (Fuller & Lury, 1977).
Positive values of the concentration index indicated a greater tendency for rich to exceed the
threshold, while negative values indicated a greater tendency for poor to exceed the
threshold.
Rank weighted measures - There was a possibility that the headcount E and concentration
index for headcount (CE) could move in opposite directions over time. In order to trade off
these differences, we constructed a weighted version of the headcount that considered
whether it was mostly poor people who exceeded the threshold or better-off people. This was
done by weighting the variable indicating whether the person had exceeded the threshold, Ei,
by the individual's rank in the distribution of consumption expenditure. Let Ri denote person
is absolute rank. This was equal to 1 for person 1, 2 for person 2, and N for person N. Then,
 
. Thus, Wi was equal to 2 for the most disadvantaged person, declines by 2/N
for each one-person step up through the income distribution, and reaches 2/N for the least
disadvantaged person. If we weight the Ei by the Wi, we get 
 where N is
the sample size, 
is the weighted index for headcount. Similarly, the weighted index for
the gap could be calculated.
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Measuring impoverishment due to OOP health expenditure
The poverty impact due to OOP health expenditure was calculated by differentiating between
pre-payment and post-payment impoverishment in terms of poverty headcount, poverty gap
and normalised poverty gap.
Poverty headcount measured the proportion of population falling below the poverty line due
to OOP health expenditure. The poverty headcount was measured as,  
, where PIHC is the poverty headcount impact, ZPre be the pre-payment poverty line.
Then PPre = 1 if x < ZPre 

 , PPre is the pre-payment poverty headcount,
HCPost and HCPost are the post and pre-payment poverty headcount.
The next measure, poverty gap measured the average amount by which individuals fell short
of the existing poverty line. It was given by expression,    where PIG is
the poverty gap impact, gPre is the pre-payment gap, that is equal to x ZPre if x < ZPre, and
zero otherwise 

 , GPost and GPre are the post and pre-payment poverty
gap.
After measuring the poverty gap, it was necessary to normalise the same by the poverty line.
The normalised poverty gap impact measured the poverty deepening due to OOP health
expenditure and was calculated by dividing the poverty gap by the existing poverty line.
The poverty impact of OOP health expenditure was measured using the official poverty line
as suggested by Tendulkar committee and adopted by the Planning Commission, Government
of India (Commission, 2013). The poverty line for the year 2011-12 was updated for 2014
after adjusting to consumer price index (CPI) as agricultural labourers for rural areas (AL)
and industrial workers (IW) for urban areas separately.
Multivariate logistic regression
The association between socio-economic covariates and probabilities of incurring
catastrophic OOP health expenditure and its impoverishment impact was modelled using
multivariate logistic regressions. The mathematical form of multivariate logistic regression
could be written as,
       ,
where P is the probability of catastrophic burden and impoverishment, 1-P is the probability of
no catastrophic burden and impoverishment. X1 = Area of residence, X2 = Religion, X3 = Social
group, X4 = Household type, X5 = Household size, X6 = MPCE quintiles, ui is the random
disturbance term, B0 to B6 are the parameters to be estimated. A more meaningful interpretation
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of the results was done through odds ratio. Odds ratio was obtained by taking antilog of various
slope coefficients as,
  
  where
 = is the odds ratio of using a source
of finance, Z = B0 + B1X1 ……………. + B6X6, eZ is the antilog of Z. Then, by taking the
natural log of above equation we obtained the logit function, written as,   
  
      where L is the log of the odds ratio. L is the logit model.
Covariates of logistic regression
The catastrophic OOP health expenditure and its impoverishment impact depended on
various socio-economic covariates. Area of residence
2
consisted of rural and urban areas.
Religion
3
was divided into Hinduism, Islam and other religions (Christianity, Sikhism,
Buddhism, Zoroastrianism and others). Social categories
4
were divided into Scheduled Tribes
(STs), Scheduled Castes (SCs), Other Backward Castes (OBCs) and general. The household
type was defined on the basis of source of earning a livelihood and included regular/salaried,
casual/agricultural, self-employed and others. Household size
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was divided into two parts i.e.,
less than and equal to 4 and greater than 4 members. Due to unavailability of reliable income
data, the monthly per capita consumption expenditure (MPCE) was used as a proxy and
population was divided into five quintiles based on MPCE.
Results
Share of various components of OOP health expenditure in India, inpatient and outpatient
care
OOP health expenditure has several components such as package component, doctor fee,
medicines, diagnostic tests, bed charges, other medical expenses, transport for the patient and
other non-medical expenditure. The share of various components of OOP health expenditure
was analysed separately for inpatient and outpatient care in order to examine the relative
burden of each component of OOP health expenditure. Figure 1A and 1B revealed the
contribution of these components of OOP health expenditure for inpatient and outpatient care
in India. In inpatient care, package component had the highest share, which was closely
followed by medicine and doctor fee. The share of other components such as diagnostic tests,
bed charges and other medical expenditure was relatively less. On the other hand, in
outpatient care, the share of medicines was substantially higher than other components of
OOP health expenditure. Though, the share of transportation was relatively less, it was an
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extremely significant component of OOP health expenditure. Due to poor access in rural and
far-off areas, patients had to travel to big cities for treatment which increased the overall
burden of medical care.
Insert Figure 1A and 1B here
Economic burden of OOP health expenditure in India, inpatient and outpatient care
OOP health expenditure can impose a severe financial burden on the population. To know
about the burden of OOP health expenditure, we used different catastrophic measures for
inpatient care and outpatient care in India (Table 2). The incidence of OOP health
expenditure as measured by catastrophic headcount (HCat) was 14.6% and 32.6% respectively
for inpatient care and outpatient care. In order to know about the concentration of the above
percentage of population we used the concentration index (CE). Overall (inpatient and
outpatient together) positive value of CE indicated that the incidence of OOP health
expenditure was concentrated more towards the rich. While in outpatient care the negative
value of CE at different threshold levels indicated that the incidence was more concentrated
towards the poor. This fact was also overstated by the higher value of rank weighted
headcount 
as compared to the value of catastrophic headcount (HCat).
Next, the intensity of OOP health expenditure was measured both in terms of percentage
share of consumption expenditure and average per capita expenditure. The intensity of OOP
health expenditure (GCat) was 11.0% of total consumption expenditure (TCE) equivalent to
INR 182.5 ($ 2.89). In this, the GCat for outpatient care (7.5%) was almost double to that of
inpatient care (3.5%). Next, the value of mean positive gap (MPG) for inpatient and
outpatient care was 24.0% and 23.0% respectively. The value of MPG increased with
increase in threshold level. Further, both in case of inpatient and outpatient care, the value of
concentration index increased with increase in threshold level, indicating the progressivity of
OOP health expenditure. Since the concentration indices both in terms of share of TCE and
average per capita basis were positive and the value of rank weighted gap 
was smaller
than the catastrophic payment gap GCat which indicated that the OOP health expenditure was
pro rich.
Insert Table 2 here
Multivariate logistic regression of the likelihood of incurring catastrophic OOP health
expenditure
Various socio-economic covariates could influence the likelihood of incurring catastrophic
OOP health expenditure (Table 3). In case of area of residence, urban areas were less likely to
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be affected by the catastrophic OOP health expenditure than rural areas. For instance, at 10%
threshold level, urban areas were 15% (OR: 0.85) less likely to incur catastrophic OOP health
expenditure as compared to their rural counterparts. On the basis of religion, those belonging
to Islam had higher likelihood of catastrophic burden than Hindus and other religions. It was
more in case of outpatient care that inpatient care. Based on social categories, SCs and OBCs
had greater likelihood of catastrophic burden than general category. On the other hand, STs
had less likelihood to incur catastrophic OOP health expenditure. For instance, the STs were
30% (OR: 0.70) less likely to be affected by catastrophic burden of OOP health expenditure.
The analysis at higher threshold levels could not be explained due to insignificant values.
Size of the number of members in the household also matter as the families with less than 4
members had greater likelihood to incur catastrophic health expenditure than larger families.
Next, on the basis of household type, casual/agriculture labour and self-employed had greater
probability of catastrophic burden than the regular/salaried ones. The probability further
increased at higher threshold levels. Last, by MPCE quintile, the odds of catastrophic burden
due to OOP health expenditure increased with increase in MPCE. The richest and rich
quintiles were more likely to incur catastrophic OOP health expenditure than the poor and
poorest quintiles. However, differences existed in inpatient care and outpatient care and
different threshold levels.
Insert Table 3 here
Impoverishment impact of OOP health expenditure in India, inpatient and outpatient care
Catastrophic expenditure on health care could result in extreme financial implications on
people and push them below the poverty line. Table 4 presented the impoverishment impact
of OOP health expenditure for inpatient and outpatient care in India. 8% population in India
comprising 2.2% in inpatient care and 5.8% in outpatient care were impoverished due to OOP
health expenditure. The overall intensity of impoverishment as measured by the poverty gap
was INR 63.1 and for outpatient and inpatient care it was INR 42.9 and INR 14.0
respectively. Overall poverty deepening or normalised poverty gap was 5.7% (3.9% in
outpatient care and 1.3% in inpatient care).
Insert Table 4 here
Multivariate logistic regression of the likelihood of moving into poverty due to OOP health
expenditure
The odds of moving into poverty due to OOP health expenditure among different socio-
economic covariates were explained in Table 5. Based on the area of residence, the likelihood
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of falling below the poverty line were greater in rural areas than urban areas. For instance,
urban areas were 26% (OR: 0.74) less likely to fall below the poverty line than rural areas.
Among different religions, Muslims were more likely to be impoverished than Hindus in
outpatient care. In inpatient care the Muslims were 11% (OR: 0.89) less likely to be
impoverished than Hindus. People belonging to other religions had lesser probability of
falling below the poverty line than Hindus. Among social groups, SCs and OBCs had greater
likelihood of getting impoverished than general category. For instance, SCs 11% (OR: 1.11)
and OBCs 8% (OR: 1.08) were more likely to get impoverished due to OOP health
expenditure. Whereas, STs had lesser odds of getting impoverished than general category.
According to the size of the family, the likelihood of falling below the poverty line was 49%
(OR: 1.49) higher in small families than families with more than four members. It was more
visible in outpatient care than inpatient care. Based on household type, people dependent
upon casual/agricultural labour and self-employed, respectively were 42% (OR: 1.42) and
34% (OR: 1.34) were more likely to fall below the poverty line than the regular/salaried
people. The likelihood was higher in inpatient care than outpatient care. On the basis of
MPCE quintiles, the middle and poor group had the greater likelihood of falling below the
poverty line than the richest groups. It is obvious that the poorest consumption group did not
record any poverty impact because they were already below the poverty line.
Insert Table 5 here
Discussion
Findings of the study revealed the catastrophic burden of OOP health expenditure and its
resultant impoverishment impact on the population. Results showed that although the overall
incidence and intensity of OOP health expenditure was concentrated among the richer
consumption groups, but in-depth study of the same in terms of inpatient and outpatient care
showed that the incidence of outpatient care was highly concentrated towards the poorer
consumption groups. The pro rich incidence of inpatient care could be attributed to the higher
costs of hospitalisation (inpatient care) which poor do not effort to use. Further, the intensity
of OOP health expenditure and average per capita OOP health expenditure was highly
concentrated among the richer consumption groups. Thus, the difference in incidence and
intensity, especially in outpatient care revealed that the lack of resources in the hands of poor
did not allow them spend beyond a certain level of their consumption expenditure even if
they have ailment.
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Findings also showed that in addition to the catastrophic burden, around 8% (89 million) of
population in India fell below the poverty line because of OOP health expenditure. Similar to
the economic burden of OOP health expenditure, the impoverishment impact and poverty
deepening was higher in outpatient care than the inpatient care. Reason for the above could
be the higher utilisation of outpatient care than inpatient care.
Among different socio-economic covariates, the population residing in rural areas was
catastrophically more affected and impoverished by the OOP health expenditure than their
urban counterparts. Among different religions, Muslims had greater likelihood of incurring
catastrophic expenditure and falling below the poverty line than Hindus and other religious
groups. Based on the social categorisation, SCs and OBCs had greater probability of getting
impoverished due to OOP health expenditure than general category. However, STs were less
likely to incur catastrophic expenditure and fall below the poverty line than general category.
One of the reasons could be the lesser proportion of population reporting OOP health
expenditure among STs (NSSO, 2014). Size of the family also had important impact on
economic burden and resultant impoverishment due to OOP health expenditure. Smaller
families due to less earning capacity were more likely to incur catastrophic health
expenditure than larger families. Further, based on the livelihood, people earning their living
through casual/agricultural labour and self-employment were more likely to incur OOP health
expenditure and fell below the poverty line than the regular/salaried. The regular/salaried
people are generally covered under reimbursement measures due to which they incur less
OOP health expenditure and are insulated from its burden. Last, among MPCE quintiles, the
middle consumption (near poverty line) group was affected most by impoverishment due to
OOP health expenditure. However, not only the middle consumption groups, wealthier
groups also fell below the poverty line due to higher OOP health expenditure.
Our findings have important policy implications. Despite the incredible turnaround in the
economy during past two decades, India witnessed massive shortfall in public health care
financing. The government spending (1.15%) on health care was low which resulted in
compromise with both the quantity and quality of health care facilities (NHA, 2016). In
comparison, not only the developed countries but countries economically less developed than
India spent a more significant proportion of their GDP on healthcare (WHO, 2015). This
static nature of government spending on health care led to the deterioration of public health
care system, which gave rise to the booming private health care sector in India (NHA, 2016).
In outpatient cases, more than 70% utilized private healthcare facilities. Out of which, nearly
45% individuals were not satisfied with the quality of care, 27% complained about the long
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waiting time, and about 10% believe that there was an unavailability of government health
care facility (NSSO, 2014). A recent study showed that the average expenditure at a private
hospital was four times higher than at a public hospital (Jayakrishnan, Jeeja, Kuniyil, &
Paramasivam, 2016).
Further, medicines accounted for a considerable proportion of OOP health expenditure, and
in outpatient care, the share was more than 50%. NSS data also showed that the poor people
spent a more significant proportion of OOP health expenditure on medicines (NSSO, 2014).
One of the reasons was the irrational use of medicines by poor households which often
increased its share in total OOP expenditure (Garg & Karan, 2008). Studies showed that poor
had limited access towards the specialized health care services and they usually opted for
self-medication which further escalated the amount spent on medicines (Garg & Karan, 2008;
Ghosh, 2010). Moreover, the irregular supply of essential drugs in the health care facilities
also compelled the poor to buy them in the open market which further escalated their health
expenditure (Maiti, Bhatia, Padhy, & Hota, 2015). Certain essential drugs were not included
under the price control policy by the Drug Price Control Order (DPCO) which had negatively
influenced public health especially in case of NCDs (Ahmad, Khan, & Patel, 2015; NPPA,
2014).
The other reason for such high OOP expenditure on health care was the poor coverage of
health insurance in the country (IRDA, 2016). Only 12% of the population received any
health insurance coverage through publicly financed health insurance (PFHI) schemes
(NSSO, 2014). Although, there were various publicly funded health insurance schemes in
various states in India such as Rajiv Aarogyasri Health Insurance Scheme (RAS), Chief
Minister’s Comprehensive Health Insurance Scheme (CMCHIS) and Rashtriya Swasthya
Bima Yojana (RSBY) that had led to the rise in consumption of health care services (Prinja,
Chauhan, Karan, Kaur, & Kumar, 2017). However, evidence showed that these state-
sponsored schemes had not been able to reduce the burden of OOP expenditure on health care
in India (Karan, Yip, & Mahal, 2017). Further, RSBY was a flagship scheme of the
government that provided health insurance coverage to the poor, but it did not include
outpatient care that consisted of 60% of total medical expenditure; as a result, its scope in
covering the cost of illness was limited
6
(Ghosh, 2014; Kurian, 2015).
During the last few years central government has taken some important initiatives to
rejuvenate the ailing health care sector in India. The announcement of National Health Policy
(NHP) 2017 is an important step in this direction (MOHFW, 2017). Further, the government
of India has recently taken an important initiative in the form of Ayushman Bharat
14
Scheme
7
which envisages providing health insurance coverage to 100 million poor families in
the country. The annual insurance cover is INR 5,00,000 (equivalent to $ 7,500) with no
restrictions on family size (Bakshi, Sharma, & Kumar, 2018; Pareek, 2018). Thus, through
Ayushman Bharat Scheme the central government has removed two critical drawbacks of
RSBY, i.e., insured amount and cap on family size. The implementation of Ayushman Bharat
Yojana is a welcome step towards the goal of UHC and if implemented efficiently it will
improve the situation of health care financing in India.
Conclusions
We conclude that the burden of out-of-pocket health expenditure is having catastrophic
implications and has resulted in the impoverishment of millions of people. Our findings
suggest that there is a need to revisit the approach towards healthcare financing in India.
First, the government should increase its share in health care spending. It will improve the
quality and quantity of public health care system in the country, thereby, reducing the
dependence on expensive private healthcare facilities. Increase in the scope of health
insurance coverage will hopefully minimise the catastrophic burden of OOP health
expenditure of all sections of the country.
List of abbreviations
OOP - Out-of-pocket
MPCE Monthly per capita consumption expenditure
SCs Scheduled Castes
STs Scheduled Tribes
OBCs Other Backward Castes
TCE Total consumption expenditure
PFHI Publicly financed health insurance
RSBY Rashtriya Swasthya Bima Yojana
CMCHIS - Chief Minister’s Comprehensive Health Insurance Scheme
UHC Universal health coverage
Declarations
15
Funding - This research received no grant from any funding agency
in the public, commercial or not-for-profit sectors
Conflict of interest The authors declare that they have no conflict of interest
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 http://www.mospi.gov.in/
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Table 1 Description of the analytical sample in India
Variables
Villages/blocks surveyed
Sample households
Sample persons
Estimated persons (00)
Average household size
Average monthly per capita consumption expenditure (INR)
Average monthly per capita OOP health expenditure (INR)
18
INR: Indian National Rupee. Values in parentheses are 95% confidence interval.
19
Table 2 Economic burden of OOP health expenditure
Type of care
Inpatient Care
Outpatient Care
Total
Threshold Levels
Pop.
Reporting
OOP
10%
25%
Pop.
Reporting
OOP
10%
25%
Pop.
Reporting
OOP
10%
25%
Incidence
Catastrophic Headcount (%)
14.6
(14.2 14.9)
(0.202)
6.9
(6.7 7.2)
(0.131)
3.3
(3.2 3.5)
(0.090)
32.6
(31.7 33.4)
(0.430)
17.9
(17.2 18.6)
(0.346)
9.0
(8.4 9.5)
(0.262)
40.5
(39.6 41.6)
(0.442)
23.7
(22.9 24.4)
(0.359)
12.4
(11.9 12.9)
(0.274)
Concentration Index
0.130
(0.115 0.145)
(0.007)
0.139
(0.118 0.160)
(0.011)
0.187
(0.153 0.220)
(0.017)
0.070
(0.056 0.084)
(0.007)
0.011
(-0.009 0.030)
(0.010)
-0.003
(-0.033 0.027)
(0.015)
0.064
(0.053 0.075)
(0.006)
0.033
(0.018 0.049)
(0.008)
0.045
(0.022 0.068)
(0.011)
Rank Weighted Headcount
12.7
5.9
2.7
30.3
17.7
8.9
37.9
22.9
11.8
Intensity
Catastrophic Payment Gap
(%)
3.5
(3.3 3.7)
(0.111)
2.4
(2.2 2.5)
(0.095)
1.5
(1.3 1.6)
(0.073)
7.5
(7.1 7.9)
(0.210)
4.9
(4.6 5.3)
(0.185)
3.0
(2.7 3.3)
(0.149)
11.0
(10.5 11.5)
(0.240)
7.8
(7.3 8.2)
(0.212)
5.0
(4.7 5.4)
(0.172)
Mean Positive Gap (%)
24.0
34.8
45.4
23.0
27.4
33.3
27.1
32.9
40.3
Concentration Index
0.267
(0.253 0.281)
(0.007)
0.295
(0.278 0.312)
(0.009)
0.337
(0.315 0.358)
(0.011)
0.230
(0.216 0.244)
(0.007)
0.230
(0.213 0.247)
(0.009)
0.251
(0.228 0.273)
(0.011)
0.251
(0.239 0.262)
(0.006)
0.271
(0.258 0.284)
(0.007)
0.306
(0.290 0.321)
(0.008)
Rank Weighted Gap
2.6
1.7
1.0
5.8
3.8
2.2
8.2
5.7
3.5
Average per capita OOP health expenditure
Average OOP (INR)
58.4
(54.7 62.1)
(1.89)
39.0
(35.9 42.2)
(1.61)
24.0
(21.6 26.4)
(1.24)
124.0
(117.0 131.1)
(3.61)
81.5
(75.3 87.6)
(3.14)
49.1
(44.2 54.0)
(2.51)
182.5
(174.2 190.7)
(4.20)
128.5
(121.3 135.6)
(3.67)
83.2
(77.4 89.0)
(2.94)
Concentration Index
0.564
(0.506-0.624)
(0.028)
0.611
(0.535-0.688)
(0.039)
0.668
(0.571-0.764)
(0.049)
0.406
(0.356-0.456)
(0.031)
0.420
(0.372-0.499)
(0.034)
0.455
(0.385-0.525)
(0.045)
0.457
(0.417-0.497)
(0.021)
0.485
(0.436-0.536)
(0.025)
0.531
(0.468-0.594)
(0.032)
Rank Weighted Gap
25.5
15.2
8.0
73.7
47.3
26.8
99.1
66.2
39.0
Note: The figures are based on authors calculations from NSSO 71st Round. Values in parentheses are 95% confidence interval and standard errors. INR: Indian National Rupee. Pop. Reporting
OOP is Population reporting OOP. The calculations exclude childbirth.
20
Table 3 Multivariate logistic regression of the likelihood of incurring catastrophic OOP health expenditure
Type of Care
Inpatient Care
Outpatient Care
Total
Threshold Levels
Pop.
Reporting
OOP
10%
25%
Pop.
Reporting
OOP
10%
25%
Pop.
Reporting
OOP
10%
25%
Area of Residence
Rural
1.00
1.00
1.00
Urban
0.91
(0.88 0.94)
0.78
(0.75 0.91)
0.73
(0.69 0.77)
0.97*
(0.93 1.00)
0.90
(0.86 0.94)
0.77
(0.72 0.81)
0.93
(0.90 0.97)
0.85
(0.82 0.88)
0.76
(0.73 0.80)
Religion
Hinduism
1.00
1.00
1.00
Islam
1.03**
(0.98 1.08)
0.90
(0.85 0.95)
0.90
(0.84 0.97)
1.14
(1.08 1.19)
1.20
(1.13 1.26)
1.21
(1.13 1.30)
1.02**
(0.98 1.08)
1.02**
(0.97 1.06)
1.05*
(0.99 1.11)
Others1
0.91
(0.86 0.97)
0.82
(0.76 0.88)
0.77
(0.69 0.84)
1.04**
(1.01 1.14)
1.10*
(1.02 1.18)
1.05**
(0.95 1.16)
0.95*
(0.90 0.99)
0.92*
(0.86 0.98)
0.88
(0.82 0.95)
Social Group
General
1.00
1.00
1.00
STs
0.87
(0.83 0.93)
0.59
(0.55 0.64)
0.54
(0.49 0.60)
0.50
(0.47 0.54)
0.53
(0.49 0.57)
0.58
(0.52 0.64)
0.70
(0.66 0.74)
0.53
(0.51 0.57)
0.52
(0.48 0.56)
SCs
1.06*
(1.01 1.11)
0.98**
(0.92 1.03)
0.98**
(0.91 1.05)
1.07
(1.01 1.12)
1.03**
(0.97 1.10)
1.07**
(0.99 1.16)
1.09
(1.03 1.15)
1.01**
(0.96 1.06)
0.97**
(0.92 1.03)
OBCs
1.03**
(0.99 1.06)
1.04*
(1.00 1.09)
1.06*
(1.02 1.14)
1.09
(1.05 1.13)
1.06
(1.02 1.11)
1.07*
(1.01 1.14)
1.06*
(1.02 1.10)
1.04*
(1.00 1.08)
1.04*
(1.00 1.09)
Household Type
Regular/Salaried
1.00
1.00
1.00
Casual/Agricultural
1.16
(1.10 1.22)
1.25
(1.18 1.34)
1.50
(1.37 1.62)
1.22
(1.15 1.30)
1.21
(1.13 1.29)
1.33
(1.21 1.45)
1.19
(1.13 1.26)
1.23
(1.16 1.30)
1.40
(1.31 1.49)
Self Employed
1.16
(1.12 1.21)
1.31
(1.25 1.38)
1.43
(1.34 1.52)
1.04*
(0.99 1.08)
1.11
(1.05 1.17)
1.16
(1.08 1.24)
1.17
(1.12 1.22)
1.22
(1.17 1.27)
1.28
(1.22 1.35)
Others
1.20
(1.14 1.27)
1.35
(1.27 1.44)
1.58
(1.45 1.71)
1.31
(1.24 1.39)
1.34
(1.26 1.44)
1.42
(1.30 1.55)
1.31
(1.24 1.39)
1.40
(1.32 1.47)
1.50
(1.41 1.60)
Household Size
Household Size > 4
1.00
1.00
1.00
Household Size 4
1.29
1.02
0.94*
1.61
1.25
1.07
1.49
1.15
1.06
21
(1.25 1.33)
(0.99 1.06)
(0.90 0.99)
(1.56 1.67)
(1.20 1.30)
(1.02 1.13)
(1.44 1.54)
(1.11 1.19)
(1.02 1.10)
MPCE Quintiles
Richest
1.00
1.00
1.00
Rich
0.83
(0.79 0.87)
0.77
(0.73 0.81)
0.61
(0.57 0.65)
0.79
(0.75 0.83)
0.84
(0.79 0.89)
0.73
(0.68 0.79)
0.82
(0.77 0.87)
0.86
(0.82 0.91)
0.76
(0.71 0.80)
Middle
0.70
(0.67 0.74)
0.64
(0.60 0.68)
0.48
(0.44 0.51)
0.63
(0.60 0.67)
0.73
(0.68 0.78)
0.64
(0.59 0.70)
0.64
(0.61 0.68)
0.71
(0.67 0.75)
0.61
(0.57 0.64)
Poor
0.58
(0.55 0.62)
0.58
(0.55 0.62)
0.44
(0.41 0.48)
0.55
(0.52 0.59)
0.66
(0.62 0.70)
0.60
(0.55 0.65)
0.53
(0.50 0.56)
0.63
(0.60 0.66)
0.55
(0.52 0.59)
Poorest
0.47
(0.45 0.50)
0.53
(0.50 0.57)
0.42
(0.39 0.46)
0.51
(0.48 0.54)
0.68
(0.64 0.73)
0.65
(0.60 0.71)
0.42
(0.40 0.45)
0.61
(0.58 0.65)
0.56
(0.53 0.60)
Note: The figures are based on authors calculations from NSSO 71st Round. Values in parentheses are 95% confidence interval. 1Others include Christianity, Sikhism, Buddhism,
Zoroastrianism and others. Pop. Reporting OOP is Population reporting OOP. The calculations exclude childbirth. *significant at 90% CI level **not significant. All other values are significant
at 95% CI level.
22
Table 4 Impoverishment impact of OOP health expenditure
Poverty measures
Inpatient Care
Outpatient Care
Total
Pre-
payment
Post-
payment
Poverty
impact
Pre-
payment
Post-
payment
Poverty
impact
Pre-
payment
Post-
payment
Poverty
impact
Poverty Headcounts (%)
37.2
(36.3-38.1)
(0.467)
39.4
(38.5-40.3)
(0.465)
2.2
(2.1-2.3)
(0.067)
37.2
(36.3-38.1)
(0.467)
43.0
(42.0-43.9)
(0.469)
5.8
(5.4-6.2)
(0.211)
37.2
(36.3-38.1)
(0.467)
45.2
(44.3-46.2)
(0.467)
8.0
(7.6-8.4)
(0.221)
Poverty Gap (INR)
104.0
(100.5107.4)
(1.76)
118.0
(114.5-121.5)
(1.79)
14.0
(13.3-14.8)
(0.511)
104.0
(100.5107.4)
(1.76)
146.9
(142.3-151.4)
(2.32)
42.9
(40.0-45.7)
(0.902)
104.0
(100.5107.4)
(1.76)
167.1
(162.4-171.7)
(2.37)
63.1
(60.1-66.2)
(1.15)
Normalised Poverty Gap (%)
9.3
10.6
1.3
9.3
13.2
3.9
9.3
15.0
5.7
Note: The figures are based on authors calculations from NSSO 71st Round. Values in parentheses are 95% confidence interval and standard errors. INR: Indian National Rupee. The
calculations exclude childbirth.
23
Table 5 Multivariate logistic regression of the likelihood of becoming poor due to OOP
health expenditure
Type of Care
Inpatient
Outpatient
Total
Sector
Rural
1.00
1.00
1.00
Urban
0.85
(0.79 0.90)
0.82
(0.76 0.88)
0.74
(0.70 0.78)
Religion
Hinduism
1.00
1.00
1.00
Islam
0.89
(0.81 0.97)
1.17
(1.07 1.28)
1.01**
(0.95 1.08)
Others1
0.70
(0.61 0.79)
0.90**
(0.79 1.02)
0.78
(0.71 0.86)
Social Group
General
1.00
1.00
1.00
STs
0.66
(0.59 0.75)
0.70
(0.61 0.80)
0.65
(0.59 0.71)
SCs
1.06**
(0.97 1.16)
1.16
(1.05 1.29)
1.11
(1.03 1.19)
OBCs
1.04**
(0.97 1.11)
1.07**
(0.99 1.16)
1.08
(1.03 1.14)
Household Type
Regular/Salaried
1.00
1.00
1.00
Casual/Agricultural
1.38
(1.24 1.53)
1.32
(1.18 1.48)
1.42
(1.31 1.53)
Self Employed
1.39
(1.28 1.51)
1.16
(1.06 1.26)
1.34
(1.26 1.42)
Others
1.40
(1.26 1.55)
1.29
(1.15 1.44)
1.45
(1.34 1.57)
Household Size
Household Size > 4
1.00
1.00
1.00
Household Size 4
1.06
(0.99 1.12)
1.25
(1.16 1.33)
1.13
(1.08 1.19)
MPCE Quintiles
Richest
1.00
1.00
1.00
Rich
1.03**
(0.93 1.14)
1.32
(1.18 1.48)
0.90
(0.84 0.96)
Middle
2.07
(1.89 2.27)
2.81
(2.52 3.13)
1.62
(1.51 1.73)
Poor
1.83
(1.66 2.01)
2.09
(1.86 2.33)
1.10
(1.03 1.18)
Poorest
0
0
0
Note: The figures are based on author’s calculations from NSSO 71st Round. Values in parentheses are 95% confidence
interval. 1Others include Christianity, Sikhism, Buddhism, Zoroastrianism and others. Pop. Reporting OOP is Population
reporting OOP. The calculations exclude childbirth. *significant at 90% CI level **not significant. All other values are
significant at 95% CI level.
24
Note: The figures are based on authors calculations from NSSO 71st Round.
Figure 1A and 1B
Percentage share of various components of OOP health expenditure in India, inpatient care
and outpatient care
12.8
60.4
9.5
4.2 7.7 5.5
Doctor's fee Medicines
Diagnostic tests Other medical exp.
Transport for patient Other non medical exp.
27.4
15.4
21.7
8.6
8.6
6.9
3.8 7.5
Package component Doctor's fee
Medicines Diagnostic tests
Bed charges Other medical exp.
Transport for patient Other non medical exp.
25
Endnotes
1
In NSSO health rounds, data is collected ailment-wise which consists of a broader set of questions pertaining
to OOP health expenditure.
2 Rural-urban disparities are important to study as around 70% of the population in India resides in rural areas.
3 India is a multi-religion country where around 80% of the population follow Hinduism, 14.2% adheres to
Islam and remaining 6% follow other religions.
4 The Constitution of India has classified the indigenous population into STs, SCs and OBCs and accorded them
special protection. STs and SCs are regarded as socially disadvantaged groups and OBCs are the backward
classes.
5
Household size consists of the number of family members in the household.
6
A family of maximum five members covered under the scheme was entitled to receive a sum of INR 30,000 (equivalent to
$500)
7
RSBY was subsumed into this scheme.
... High out-of-pocket payment for medical treatment often leads to financial hardship and impoverishment in low-and middleincome countries (Aryeetey et al., 2016;Sangar et al., 2022;Van Doorslaer et al., 2006;Van Minh et al., 2013). Sustainable Development Goal3 designed by UNDP targets to achieve universal health coverage with access to good-quality essential healthcare services and financial risk protection. ...
... 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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