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Financial Burden of Transient Morbidity: A Case Study of Slums in Delhi

Financial Burden of Transient Morbidity: A Case Study of Slums in Delhi

Morbidity and its treatment can be potentially burdensome or even catastrophic for poor households. While public policy has shown some response to this phenomenon, there is scope for improvement of the coverage of the programmes. Health insurance schemes like the Rashtriya Swasthya Bima Yojana cover only conditional hospitalisation expenses. This paper argues that treatment cost incurred on ailments not requiring hospitalisation is also a substantial burden on the urban poor. Based on a case study of 150 slum households in south Delhi with a history of treated ailments within a specific recall period, the study estimates the degree and distribution of this burden across socio-economic and disease characteristics in the sample. The paper argues for a more holistic approach in social safety nets like the RSBY, and for explicitly including uncovered healthcare payments in measurement of the poverty lines for a more accurate estimation of the marginalised.


Financial Burden of Transient Morbidity: A Case Study of Slums in Delhi

Samik Chowdhury

Morbidity and its treatment can be potentially burdensome or even catastrophic for poor households. While public policy has shown some response to this phenomenon, there is scope for improvement of the coverage of the programmes. Health insurance schemes like the Rashtriya Swasthya Bima Yojana cover only conditional hospitalisation expenses. This paper argues that treatment cost incurred on ailments not requiring hospitalisation is also a substantial burden on the urban poor. Based on a case study of 150 slum households in south Delhi with a history of treated ailments within a specific recall period, the study estimates the degree and distribution of this burden across socio-economic and disease characteristics in the sample. The paper argues for a more holistic approach in social safety nets like the RSBY, and for explicitly including uncovered healthcare payments in measurement of the poverty lines for a more accurate estimation of the marginalised.

I thank Amitabh Kundu for his comments on an earlier version of the paper. I have also benefited from discussions with Dipendra Nath Das. Finally, I thank the inhabitants of the Coolie Camp and Kusumpur Pahadi for their unconditional cooperation.

Samik Chowdhury ( is at the National Institute of Public Finance and Policy, New Delhi.

ndia spends around 4% of its gross domestic product (GDP) on health. Public spending (central, state and local governments combined) on health, however, accounts for just 1% of the GDP, with the remaining 3% being spent by private and external sources. The share of public expenditure in total health expenditure is around 20% while households account for another 70% of total health spending, almost all of which is in the form of out-ofpocket (OOP) expenses.1 Reimbursement in any form is generally availed of by those employed in the formal sector, which is a m inority in India.2 Such high levels of OOP spending by the households have certain adverse implications. While for some, access to healthcare is reduced considerably,3 others who opt for treatment face the catastrophic burden of healthcare expenditures, and are in consequent danger of becoming impoverished.

In recent years, the prevalence of illness-induced alteration in the standard of living, especially amongst the poor, has been studied extensively. However, the first concrete step in acknowledging and addressing the phenomenon from a policy perspective has been the Rashtriya Swasthya Bima Yojana (RSBY) which was formally launched in October 2007, and was operationalised from April 2008. The scheme envisages to provide smart card based cashless health insurance cover up to Rs 30,000 to all the below poverty line (BPL) households in the unorganised sector in the next five years. The programme intends to cover an estimated six crore BPL workers in all 600 districts in the country in a phased manner. As on 31 May 2011,4 about 2.34 crore smart cards had been issued in 25 states and union territories that have taken initiatives to implement the RSBY.

As is the case with most health insurance schemes, the services under this programme are restricted to hospitalisation episodes within the households. Though hospitalisation entails higher treatment costs than non-hospitalised morbidity, the latter is generally the more prevalent form of indisposition. Also, the l atest NSS round on morbidity (2004) shows that people prefer private service providers for treatment as outpatients since the waiting time in a public source is high. Sixty five per cent of c asual labour households (who might be assumed to be one of the beneficiaries of the proposed scheme) in urban India get themselves treated from a private source in case of non-hospitalised illnesses.5

Against this backdrop, this paper presents findings from a case study of 150 slum households in south Delhi, carried out in May-June 2008. The respondents had a history of at least one treated ailment within a specific recall period. We attempt a disaggregated analysis of the patterns in household health expenditure

Economic & Political Weekly

on non-hospitalised treatment across socio-economic and a ilment categories. The paper also estimates the financial burden imposed by these ailments. This burden is recurrent unlike that of hospitalisation expenses, and is largely unaddressed by schemes such as the RSBY. The paper is organised as follows. S ection 1 presents a review of literature on financial implications of OOP healthcare spending. Section 2 presents a general description of the sample along with the pattern of morbidity and health service utilisation prevailing in the slums. Section 3 briefly outlines the two approaches used in the literature to measure the effects of OOP spending. Section 4 reports the results of the survey using the two approaches, quantifying catastrophic expenditure and “medical poverty” and its variation. Section 5 concludes with a discussion on emerging policy perspectives.

1 Literature Review: Implications of Health Expenditure

The existing literature on the financial implications of healthcare has largely used two proxy measures to compute this burden – catastrophic health expenditure and healthcare induced impoverishment. Catastrophic payments represent circumstances when OOP payments cross some threshold share of household e xpenditure, and are a major concern in the health financing system of any country.6 It has been recognised that the choice of this threshold is somewhat arbitrary – 10% of total expenditure has been a common choice (Pradhan and Prescott 2002; Ranson 2002; Wagstaff and Van Doorslaer 2001). The rationale is that this represents an approximate threshold at which the household, is forced to sacrifice other basic needs, sell productive assets, i ncur debt, or be impoverished (Russell 2004). A survey by the World Health Organisation (WHO), using data from 89 countries finds that 3% of households in low-income countries, 1.8% of households in middle-income countries, and 0.6% of households in high-income countries incur catastrophic health expenditures (Xu et al 2007).

Soaring healthcare expenditure often affects the magnitude and pattern of household consumption. When a member falls ill, the household faces several different costs (treatment cost, transportation cost, opportunity cost of care giving, etc) and takes r ecourse to diverse strategies to finance the same. While the OOP expenses set in “real time” deterioration in the standard of living, the coping strategies very often turn out to be potential poverty traps. The chain of events has often been termed as the “poverty ratchet” (Chambers 1983) or the “medical poverty trap” (Whitehead et al 2001). Gertler and Gruber (2002), for instance, studied the impact of health shocks on households’ consumption patterns in Indonesia, providing evidence that illness reduced labour supply and household income. Similarly, Wagstaff (2005) found that health shocks were associated with a reduction in consumption in Vietnam, in particular for the uninsured and better-off households. Dercon and Krishnan (2000) show that in Ethiopia the consumption risks associated with health shocks are not borne equally by all household members. In addition, estimates of the financial burden of illness are available for at least six Latin American countries (Baeza and Packard 2005), China (Lindelow and Wagstaff 2005), Thailand (Limwattananon et al 2007), and 14 Asian countries and territories (Van Doorslaer et al 2007).


Studies on India have found average expenditure on medical care rising invariably with monthly per capita consumer expenditure or income of the household (NSSO 1992, 1998; Visaria and Gumber 1994; Rajarathnam et al 1996). However, medical expenditure as a proportion of total resources at the household’s disposal was much lower for the rich (Krishnan 2000). Based on household data from rural and urban areas of 15 major states and the north-east, Gumber (2002) showed that health expenditure as a percentage of annual income varies from 3% in the richest 20% of the households to 12% in the bottom 20% of the households. The prevalence of illness-induced impoverishment has also been studied in the Indian context. A study of 35 villages in Rajasthan found that health and health expenses were one of the main causes for 85% of all cases of impoverishment (Krishna 2004). One-half to two-thirds of all households falling into poverty mentioned ill-health and health expenses as a contributory cause (ibid). Such impoverishment is of even greater concern given the evidence from another detailed study in Rajasthan that shows healthcare purchased is often of poor quality, even harmful (Banerjee et al 2004). More than 37 million people in India were pushed below the poverty line in 1999-2000 because of OOP e xpenditure, as per the $1 norm of the poverty line (van Doorslaer et al 2005). This is in addition to those who are already below the poverty line and are further pushed into acute poverty because of OOP payments. A more recent study using the NSSO data reports that after adjusting for the sources (borrowings, contributions and sale of assets, etc) of OOP expenditure, 63.22 million individuals or 11.88 million households were impoverished due to healthcare expenditure in 2004 (Berman et al 2010).

2 Sample Description and Study Design

The non-notified jhuggi-jhonpri (slum) colony at Coolie Camp in Vasant Vihar was built on land owned by the Delhi Development Authority. The slum hosted approximately 350 households mostly from the neighbouring states of Uttar Pradesh and Rajasthan. It was located along a nallah (drain) fed by sewage from the nearby commercial and residential establishments. For the entire slum, there were just two taps with a very infrequent supply of water. Supplementary arrangements of water tankers arrived at odd hours when the male members of the household were at work. It was often not possible for women to carry filled jerry-cans of w ater into their jhuggi from the main road where the tanker was parked. Many of the jhuggis were of the unserviceable kutcha v ariety and roughly measured six feet by six feet. There was no toilet and the inhabitants defecated in the forest nearby. The community toilet that had been built ceased to function due to infrequent water supply. The drains inside the slum were open and kutcha. Although there was electricity in all the jhuggis, the slum-dwellers complained of disproportionately high metre (newly installed) readings. The nearest private hospital, doctor or chemist shop was located within a distance of 1.5 km. However, the nearest government hospital or health centre was relatively far from the slum.

Situated alongside the remnants of the endangered Delhi Ridge Area around Vasant Kunj, Kusumpur Pahadi was a slum cluster more in the form of an urban village. It had a population

August 13, 2011 vol xlvI no 33


of more than 20,000. The inhabitants were more diverse vis-à-vis households. Second, we randomly identified 44 and 40 house
the Coolie Camp, with respect to their places of domicile. There holds from each block of Kusumpur Pahadi and Coolie Camp res
was substantial disparity in access to basic services, especially pectively, which had a case of treated ailment within the speci
water, and the deprivation was along the lines of political inclina fied recall period. Thus in effect, we selected and numbered 300
tion, economic status or even place of domicile. However, a pucca households with ailments, i e, 220 from Kusumpur Pahadi and 80
motorable road within the slum allowed water tankers, among from Coolie Camp. Third, we chose every odd-numbered house
other vehicles, to serve the farthest corner of the colony. A majo hold out of these 300 households for canvassing of the full ques
rity of the houses were of the serviceable kutcha variety but with tionnaire. So finally we had 150 households, 40 from the smaller
out their own toilet. Drainage within the clusters was of open Coolie Camp and 110 from the larger Kusumpur Pahadi.
kutcha type. The slum was self-sufficient as far as services such The households have been living in the selected slums for 18
as provision store, chemist shop, grocery shop, stationery shop, years on an average and a majority (95%) of them had migrated
jewellery shop, tea stalls, etc, were concerned. However, medical from rural areas, mostly from the neighbouring states. The
facility available within the slum was of a rather dubious nature. a verage and modal household size was 5.66 and 5 respectively.
There were a number of shady clinics run by bangali daakters,7 The mean age of the respondents was 23. Almost 3% were infants
who reportedly charged meagre amounts and were not ade (less than or equal to one year of age), 60% were in the age group
quately trained in medicine. However, the dearth of genuine 15-59 years while 4.5% of them were more than 60 years old.
medical facility – public or private – had allowed entry points to Women constituted 48% of the sample population. The married
some non-governmental organisations (NGOs) which were doing accounted for around 41% of the population while 4% were
a commendable job in this area. w idowed or divorced. Nearly 30% were illiterate. A majority of
Table 1: Distribution of the Selected Sample the literate respondents had dropped out after the fifth standard.
Coolie Camp Kusumpur Pahadi All Their economic condition notwithstanding, most of the children
Number of households surveyed 40 110 150 in the school-going age were found to attend schools. Out of the
Number of individuals surveyed 207 664 871 871 individuals surveyed, 303 (around 35%) were currently em-
Number of ailment cases 47 111 158 ployed, 58% of whom worked as daily wage earners. Only 14%
South Delhi hosts two of the largest public health institutions of the working population were salaried employees, and the
in India, the All India Institute of Medical Sciences (AIIMS) and r emaining 28% self-employed. The average monthly expenditure
the Safdarjang Hospital, which cater to patients not only from of the sample households was Rs 4,100. The median of the house-
Delhi and neighbouring areas, but from all over India and even hold expenditure was consistently lower than the average, imply
from abroad. The rationale for the selection of these slums is that ing the presence of outliers at the upper end of the income ladder.
these were situated at a distance of 7-10 kms from these institu- Though just 38% of the individuals in the sample were found to
tions, which could hardly be termed as proximal, especially when be “officially poor”,9 the deplorable status of basic necessities
the case in question is that of a medical emergency. This presum within the slums underscored the limited potency of consump
ably has a bearing on the healthcare utilisation pattern of the tion expenditure levels as a proxy for access to services. A visit
slum-dwellers. Thus, in a way, the selection of the sample itself to these slums and a study of the living standards of the inhabit
brought in an element of randomness in the choice of the medical ants raises serious doubts on official poverty estimates and
provider, which again had a direct bearing on the financial bur their methodology.
den of treatment. The randomness was further enhanced when Fever, gastrointestinal diseases, and respiratory diseases in
we considered some other factors such as the presence of private cluding asthma were the three major ailments, together consti
healthcare institutions in the vicinity and their charges, the quality/ tuting around 60% of all cases. The women accounted for all the
efficacy and quantity of services by provider-type, the general level cases of anaemia and generalised weakness. People displayed a
of health awareness among households, the occupational pattern, marked preference for private sources of treatment. In about 73%
and the presence or absence of any formal health insurance. of the cases, the respondents approached a private doctor for
A questionnaire designed to elicit responses on the type of treatment. The most appalling finding, however, was that almost
morbidity, health service utilisation and the cost of treatment 15% of the ailing sample opted for treatment from an unregis
was canvassed among 150 households with a history of outpa tered and unqualified private practitioner. These were quacks,
tient visit within a brief recall period of 30 days (Table 1). Thus, locally known as bangali daakters, who were quite conspicuous
this was a case of non-probabilistic purposive sampling8 covering within the slums. These shady clinics attracted a lot of patients
only those households with a history of ailment. The following owing to their locational utility and low charges. Despite being
methodology was adopted for the selection of the sample. First, aware of the limited efficacy, and in certain cases even fatality of
we obtained a complete house listing of the slums from the local the treatment offered by these men, people approached them
councillor in the case of Kusumpur Pahadi and from an NGO since the direct cost and opportunity cost incurred on treatment
working on maternal health issues in the case of the Coolie Camp from their formal counterparts was often high and burdensome.
slum. Both the slums were demarcated into blocks (five in case of Another significant observation was that only 12% of the ailing
Kusumpur Pahadi and two in case of Coolie Camp) for adminis individuals opted for public institutions for treatment. Personal
trative purposes. As is often the case, the blocks were different communication with the respondents indicate a variety of r easons
from each other in terms of the places of origin of the residing for this – the opportunity cost in terms of work-hours lost,
Economic & Political Weekly August 13, 2011 vol xlvI no 33 61

p rocedural complications, failure to precisely communicate with the doctor, dependence on private sources anyway for medicines, travel expenses, and informal payments sought by hospital staff.

3 Effects of OOP Health Spending: Two Approaches

Catastrophic Impact of OOP Health Expenditure: The methodology applied for computing the extent and depth of catastrophic expenditure and impoverishment is based on Xu et al (2003) and Wagstaff and Doorslaer (2003). The treatment cost components10 were added to arrive at the total health expenditure. Reimbursement, if any, was deducted to get the net OOP health payments made by the household (say P). Suppose X represents the total consumption expenditure of the household. Health expenditure is assumed to be catastrophic when P/X > Z (a fraction). Catastrophic payment headcount or CPH = 1/N ¦ E, where E = 1 for all P/X > Z and N stands for total population/ sample. Catastrophic payment gap captures the average degree by which payments as a proportion of income exceeds/overshoots the threshold, Z. The catastrophic payment gap (G) is given by G = 1/N ¦ Oi, where Oi = Ei ((Pi/ Xi)-Z). The rationale for this measure is that spending a substantial proportion of the

Table 2: Average Cost of Treatment as Outpatient (in Rs) Figure 1: Diagrammatic Exposition of Illness-Induced Impoverishment



Expenditure Net

of OOP Payment H




A B C Household Expenditure Gross of OOP Payment

Hgross Hnet

Cumulative Proportion Source: World Bank (2008). of Population Ranked by Household MPCE

considered as non-poor in spite of their MPCE net of OOP healthcare payments being below the poverty line. The rationale for this measure is that standard measures of poverty that compare total household expenditure with a stipulated poverty line, fail to adequately reflect healthcare needs that are highly stochastic. Households might be classified as non-poor just because higher health spending on critical healthcare raises their total spending above the poverty line levels, while spending on other nondiscretionary items is below the

Slum Medical Expenditure Associated Expenditure Total Expenditure

subsistence level.

Min Max Med Avg Min Max Med Avg Min Max Med Avg

For computational convenience,

Coolie Camp 0 3,000 300 490 0 500 0 43 0 3,500 350 533 Kusumpur Pahadi 0 4,300 300 608 0 500 0 42 0 4,800 300 651 we exclude four households that re-

All 0 4,300 300 573 0 500 0 43 0 4,800 305 615 ported more than one (two) illness

  • (i) Associated Expenditure includes transport, charges paid to the escort and others.
  • (ii) Min – minimum, Max – maximum, Med – median, Avg – average. Source: Computed from data collected during the case study.
  • household budget on medical treatment is by and large at the expense of the consumption of other essential goods and services. The affected household has to bear this opportunity cost in the short-term or in the future, depending on whether healthcare is financed by cutting down current consumption or through s avings, sale of assets or credit.

    Impoverishing Effects of OOP Health Expenditure: The methodology is an adaptation of Wagstaff and Doorslaer’s (2003) attempt to estimate the impact of OOP payments for healthcare on the two fundamental measures of poverty – the headcount and the poverty gap11 – for Vietnam in 1993 and 1998. Figure 1 plots the household monthly per capita expenditure (MPCE) pre- and post-payment (OOP payment) on the Y-axis against households ranked by pre-payment MPCE on the X-axis. The case displayed in the figure makes an implicit assumption that the relative position of households in the gross and net of OOP expenditure distribution does not change. In the standard case (pre- payment), headcount is Hgross and poverty gap is equal to the area “A”. In the special case (post-payment), poverty headcount increases to Hnet and the gap is now given by the sum of “A”, “B” and “C”. Area “B” represents the increase in the intensity of poverty due to healthcare payments, for those households who were already poor on the b asis of pre-payment MPCE.

    Similarly, area “C” stands for the addition to the poverty gap due to new entrants into poverty after paying for healthcare. The value of (Hnet - Hgross) corresponds to the fraction of households episodes within the r eference

    period. This also allows us a mapping (one-to-one) of the burden across two very important individual characteristics, viz, nature of disease and source of treatment. If we retain a household with multiple cases of treated ailment, and that household was found to experience economic burden of illness by the current definition, it would not be possible to isolate the particular episode (nature) of ailment, or the type of service utilisation12 that constituted the burden on the household. This is a departure from earlier studies on economic burden of illness, with a distinct possibility of a more holistic understanding of the phenomenon of financial ramifications of OOP expenses.

    4 Methodology-wise Survey Results
    4.1 Catastrophic Impact of OOP Healthcare Payments

    The monthly average and median expenditure on treatment for the entire sample was Rs 615 and Rs 305 respectively. The average associated expenditure incurred, mostly on account of transport, amounted to Rs 43 per capita per month (Table 2).

    The median and mean of the share of OOP health expenditure in total income was 10% and 15% respectively. Table 3 presents the aggregated results of the analysis. As many as 39% of the

    Table 3: Catastrophic Impact of OOP Payments within the Sample Households

    Catastrophic Threshold (more than)

    10% (Median) 15% (Mean) 20% 40%

    Headcount (%) 50.0 38.7 26.6 6.5

    Mean gap (%) 7.9 5.6 3.9 0.9

    Source: Same as Table 2.

    August 13, 2011 vol xlvI no 33


    households spent more than 15% of their household expenditure on healthcare, which also happens to be the mean for the entire sample of households with at least one treated ailment. The a verage overshoot amounted to 8% of total income, which means that the households spending more than a tenth of their income on healthcare exceeded the threshold by 8% on an average. What is alarming is that there are households which have spent 40% or even 50% of their monthly income on non-hospitalised treatment.

    Table 4 presents the distribution of the burden of illness across expenditure quintiles and occupation of the main earner of the household. The average expenditure demonstrated a slightly p ositive income gradient. As we move up the expenditure quintile, the catastrophic headcount declines. Thus a poorer person bears a disproportionately higher burden of treatment cost.

    A disease-specific summary of treatment cost (Table 5) shows that persons with orthopaedic ailments incurred the highest a verage expenditure followed by gastro-intestinal and cardiological ailments. The most common ailments, i e, fever and ENT infection, accounted for an average cost of Rs 252. The fact that a visit to a quack (“private unregistered” formally) costs around Rs 80 explains why the urban poor opt for treatment of such dubious quality, in spite of being aware of the often limited efficacy of the medicines sold by these units. The corresponding costs for the registered private and even the public counterparts are much higher. Table 5 also shows the incidence and intensity of burden across ailment categories and type of service provider.

    Treatment of “others”, which included accidents and injuries among other problems, required the highest share of household resources (OOP share). Individuals suffering

    Table 4: Distribution of Burden of Illness across Expenditure Quintiles and Occupation of the Main Earner of the Householdfrom these ailments had to spend around 19.2%

    Medical Associated Total Average Catastrophic of their total monthly household expenditure
    Expenditure (Rs) Mean Median Expenditure (Rs) Mean Median Expenditure (Rs) Mean Median OOP Share (%) Impact – 10%Head-Gap (%) on treatment. This was closely followed by
    count (%) g ynaecological and gastrointestinal diseases.
    Expenditure Quintile This is not surprising since prenatal and post
    Poorest 526 300 41 0 567 300 20.3 79.2 11.2 natal check-ups involve expensive and unavoid
    Lower middle Middle 656 384 375 250 38 34 0 0 693 417 425 300 20.2 13 66.7 43.8 11.86.4 able diagnostic tests and prolonged medication. More than half the individuals with cases of tuber
    Upper middle Richest 706 669 250 450 67 39 0 0 773 708 300 450 15.4 10.1 38.1 31 8.73.7 culosis, respiratory diseases including asthma,
    Occupation of the main earner Salaried 804 555 31 0 869 585 13.2 40.7 6.2 gastrointestinal diseases and others spent more than 10% of their household expenditure on

    Casual and contractual labour 644 500 49 0 698 550 16.3 53.2 8.8treatment. The average intensity of fi nancial

    Others 481 280 78 0 559 400 13.1 50 6.3 burden was the highest for patients with uncate-All 573 300 43 0 615 305 15 50 7.9

    gorised ailments (others) which included acci-

    Source: Same as Table 2.

    dents and injuries. It might be noted that acci-

    The intensity of burden presents a more or less similar picture. dents or injuries have a more acute manifestation and therefore The average as well as the median expenditure was higher for the treatment expenses are often high and inflexible. However, the the households whose main earner was salaried. However, the issue of major concern is that even the most common and apparaverage share of OOP health expenses in total expenditure was ently inexpensive diseases such as fever and diarrhoea are imposthe highest among households whose main earner was a casual ing a major financial burden on the lives of the urban poor. labourer. They were also found to bear a disproportionate eco-The average OOP shares across treatment sources exhibit wide nomic burden of illness, both in terms of headcount as well as gap. disparity. The share of health expenditure in the household

    Table 5: Distribution of Burden of Illness across Disease Categories and Source of Treatment

    Medical Expenditure (Rs) Associated Expenditure (Rs) Total Expenditure (Rs) Average OOP Catastrophic Impact – 10 %
    Mean Median Mean Median Mean Median Share (%) Headcount (%) Gap (%)
    Ailment type
    Anaemia and generalised weakness 404 465 0 0 404 465 8.3 28.6 2
    Cardiological 697 500 3 0 700 500 12.2 28.6 3.6
    Fever and ENT infection 243 198 10 0 252 198 6 22.5 0.9
    Gastro-intestinal 887 450 69 0 956 500 17.3 51.4 9.9
    Gynaecological and obstetric 612 300 40 0 652 300 17.5 40 9.5
    Nervous system 517 500 115 75 632 550 16.2 33.3 8.6
    Orthopaedic 960 260 75 100 1,035 460 13.7 44.4 6.6
    Respiratory including asthma 446 500 41 0 486 500 13.3 58.8 5.6
    Skin disease and infection 308 200 40 50 348 300 5.7 0 0
    Tuberculosis 400 500 133 100 533 700 11.5 66.7 3.2
    Others 551 425 40 0 591 475 19.2 50 11.6
    Source of treatment
    Public 174 200 88 75 262 245 6.3 15 0.7
    Private registered 741 500 43 0 785 500 15.3 72.2 7.4
    Private unregistered 78 80 0 0 78 80 2.2 0 0
    All 573 300 43 0 615 305 15 50 7.9
    Source: Same as Table 2.
    Economic Political Weekly August 13, 2011 vol xlvI no 33 63

    budget was the highest for people who opted for treatment from a registered private source. The average share was more than double that of those who opted for a public mode of treatment, i e, a government hospital or dispensary. Those who were treated by the quacks within the slum presumably incurred the lowest OOP share. In terms of extent and depth of the catastrophic burden too, those who chose private medical treatment had to bear a relatively greater economic burden of illness. Ailing persons treated by unqualified medical practitioners were not found to experience economic burden of illness as per our definition. This only goes to show that the possibility of a financial burden might be playing a role in the decision of the urban poor to opt for treatment of inferior quality, which might eventually have an impact on their physical constitution and future earning potential.

    4.2 OOP Health Expenses and Impoverishment

    The OOP expenditure on health raised poverty levels within the sample by around 13 percentage points. The gap also went up by Rs 51 per capita per month (Table 6). Female-headed households

    Table 6: Increase in Poverty Due to Ill Health-Related Expenditure by Sex of Household Head and Occupation of the Main Earner

    Headcount (%) Gap (Rs) Pre-Pay Post-Pay Difference Pre-Pay Post-Pay Difference

    Sex of household head Male 36.3 48.4 12.1 43 94 51

    Female 59.3 76.5 17.2 98 144 46

    Occupation of the main earner Permanent employee 46.1 58.5 12.3 54 117 63

    Casual and contractual labour 42.5 59.3 16.8 83 125 42

    Others 9.0 19.8 10.8 2 16 14

    All 38.4 50.9 12.6 48 98 51

    Source: Same as Table 2.

    and the contractual labour households were the most vulnerable in terms of the number of individuals in the respective group who were impoverished due to health payment. The poverty p atients who were poor remained unchanged (no new entrant into poverty due to treatment cost). However, the net income (income net of treatment cost) of the poor anaemia patients was lower with respect to the poverty line. Hence the post-payment gap was more than the pre-payment gap. Individuals suffering from tuberculosis were the worst affected in terms of the impoverishing impact of healthcare payment due to the high cost of treatment associated with the disease. It seems little has changed in terms of the burden of the disease despite the conscious effort of the government to allocate resources and raise public awareness towards its eradication. The other burdensome ailments within the slums were gynaecological, orthopaedic, cardiological and gastro-intestinal in nature.

    Private sources of treatment contributed largely to the impoverishing effects of OOP payments for healthcare. The worst condition was probably of those who were impoverished after treatment from an unqualified private source. Apart from the adverse financial implications of the health shock, the quality of treatment meted out to them made them more susceptible to subsequent episodes of illness. Poverty headcount increased by around 16% for individuals who availed of a private source for treatment of their ailments. The corresponding figures for the private unregistered source and the public source were 3.6% and 0% respectively. One interpretation of this result is that preference for the public source was largely prevalent among those who were already poor and therefore there were no new entrants into poverty on account of treatment cost incurred. However once we consider the indirect cost of such treatment in terms of workdays lost, they might ultimately prove to be more burdened. On the other hand, individuals who opted for a private registered source were predominantly above the poverty line. Given the higher expenditure incurred in case of treatment from a private source, there were more cases of treatment cost induced poverty within this group. Individuals who could not protect their living standards (with respect to the poverty line)

    gap, however, was higher for the male-headed

    Table 7: Increase in Poverty Due to Ill Health-Related Expenditure across Ailment Categories households and for those whose main earner and Source of Treatment

    was a permanent employee. A similar analysis Headcount (%) Gap (Rs) Pre-Pay Post-Pay Difference Pre-Pay Post-Pay Difference

    across ailment cate gories and source of treat-

    Ailment categories

    ment makes for some interesting observations.

    Anaemia and generalised weakness 52.9 52.9 0 128 151 23

    For individuals suffering from gynaecolo

    Cardiological 31.4 51.4 20.0 7 40 33

    gical ailments, the pre-payment headcount

    Fever and ENT infection 31.3 36.5 5.2 26 60 34

    ratio of 62.9% changes to 100% post-payment,

    Gastro-intestinal 42.6 62.2 19.6 57 123 66 implying that while 62.9% of the individuals Gynaecological and obstetric 62.9 100.0 37.1 57 169 112

    who had this ailment were poor even before Nervous system 17.9 17.9 0 2 59 57
    payment, all of them were impoverished post Orthopaedic 25.0 59.1 34.1 51 114 63
    payment (Table 7). Although the headcount Respiratory including asthma 56.6 70.7 14.1 62 136 74
    remained unchanged for individuals suffering Skin disease and infection 18.7 18.7 0 13 22 9
    from certain kind of ailments, poverty gap in Tuberculosis 44.4 83.3 38.9 50 125 75
    creased post-payment for all the ailment cate Others 37.2 37.2 0 83 130 47
    gories. For example, in the case of those suffer- Source of treatment Public 38.5 38.5 0 53 79 26
    ing from anaemia, 52.9% of individuals suffer Private registered 39.9 55.8 15.9 50 111 61
    ing from the ailment were poor even before in Private unregistered 29.1 32.7 3.6 28 40 12
    curring the treatment cost (i e, on the basis of All 38.4 50.9 12.6 48 98 51

    their consumption expenditure). After paying Based on poverty line for urban Delhi (Rs 612.91) according to the press release by the Perspective Planning Division, Planning Commission of India, March 2007.

    for treatment the absolute number of anaemia Source: Same as Table 1.

    August 13, 2011 vol xlvI no 33


    after visiting an u nqualified medical practitioner for treatment gastrointestinal and gynaecological ailments were found to be of their ailments actually formed the marginal cases. They were more prone to the burden. A household facing a health shock neither able to bear the direct costs of a qualified private source o ften does not have the resources to seek formal sources of nor the indirect costs associated with a public source. Again, in treatment and falls in the hands of unqualified medical practispite of being marginally above the poverty line, the relatively tioners who charge less but provide services of dubious quality. lower expenditure they incurred on treatment from an unquali-This seems to be one of the alarming findings of the study fied source could not prevent 3.6% of this category from falling especially since none of these households were found to experiinto poverty. ence the burden of illness according to our definition. The rela

    tively lower financial burden associated with unqualified

    5 Conclusions

    sources of treatment may further dictate the treatment seeking In this paper, we have provided a detailed account of the finan-behaviour among the slum-dwellers. So in a way, the cost of cial burden of treatment cost due to non-hospitalised ailments by service determines the choice of provider. Although public applying two measures largely used in health expenditure analy-sources of treatment cost less, the poor by their own admission sis – catastrophic burden of OOP health expenses and impover-have been found to avoid them due to reasons ranging from ishment effect of healthcare payments – to the data collected lengthy, time- consuming procedures to informal payments to from a case study of selected urban slums in Delhi. OOP health hospital staff. expenditures are found to be highly regressive in nature, ac-Although observations from a single case study cannot be the counting for 20% of the total consumption expenditure for basis for broader policy perspectives, one can safely argue that households (with cases of treated ailments) belonging to the the time has come to target the reasons for impoverishment poorest quintile. Half of the sample households spent more rather than the poor per se. While social security programmes than 10% of their resources on health. Though the possibility of like the RSBY make an attempt in this direction, this study shows, sampling biases in the current case study cannot be undermined, albeit in a restrictive domain, that there is room for reformulait seems extremely unlikely that the spending proportion of the tion of the scheme to include outpatient episodes also, which are urban poor households on healthcare can be different, especially highly debilitating for the households, notwithstanding the relain view of the clear preference for the private sources of treat-tively lower cost of treatment vis-à-vis inpatient cases. Even in ment.13 The female-headed and casual labour households within the context of counting the poor, there is a need to explicitly the sample were disproportionately burdened by the financial i ncorporate OOP health expenses in deciding upon the poverty ramifications of OOP expenses. lines for a more accurate representation of the marginalised

    The paper introduces two new aspects in assessing the finan-s ections of the society. This is particularly true in view of the cial burden of treatment – type of ailment and nature of service gradual withdrawal of the State from its role as a provider of provider. Individuals suffering from tuberculosis, respiratory, health services in the recent years.

    Notes case) quickly and where sampling for proportion-E ffects of Health Shocks in Latin America (World ality is not the primary concern. With a purposive Bank: Stanford University Press).

    1 “National Health Accounts India (2004-05)”, sample, one is likely to get the response of the Banerjee, A, A Deaton and E Duflo (2004): “Health-NHA Cell, Ministry of Health and Family Welfare.

    target population with the associated danger of care Delivery in Rural Rajasthan”, Economic 2 Unorganised workers constitute about 92%, while overweighting subgroups in the population that Political Weekly, Vol 39, No 9, pp 944-49.

    unorganised non-agricultural workers constitute

    are more readily accessible. Berki, S (1986): “A Look at Catastrophic Medical around 72% of the total workforce in India 9 Poverty line for urban Delhi was Rs 612.91 (Plan-E xpenses and the Poor”, Health Affairs: 138-45.

    (Report of the National Commission for Enterning Commission of India, March 2007). Berman, Peter, R Ahuja and L Bhandari (2010): “The

    prises in the Unorganised Sector 2007). 10 Information was collected on doctor’s fee, medi-Impoverishing Effect of Healthcare Payments in

    3 Three consecutive National Sample Survey cines, diagnostic tests, physiotherapy, personal India: New Methodology and Findings”, Economic (NSS) Rounds (42nd, 52nd and 60th) on morbidity medical appliances, food, ambulance services Political Weekly, Vol XLV, No 16.

    and healthcare have shown that financial difand transport, and miscellaneous. Chambers, R (1983): Rural Development: Putting the ficulties are one of the most oft-cited reasons for Last First (London: Longman).

    11 The headcount measures the number of individushowing a rising trend.

    not treating ailments and the phenomenon is als or households living below the poverty line as Commission on Macroeconomics and Health (2001): a percentage of the total population/households. “Macroeconomics and Health: Investing in 4 Press Information Bureau (PIB) release, GoI, The poverty gap measures the total amount of Health for Economic Development”, World Health 29 December 2010.

    i ncome transfer that is needed to lift all the poor Organisation, Geneva.

    5 Computed from NSS 60th round unit record data

    households out of poverty. Dercon, S and P Krishnan (2000): “In Sickness and in on Morbidity and Treatment of Ailments (2004).

    Health: Risk-sharing within Households in Rural 12 In case they were different for each episode with6 Several scholars have worked on this subject. See in the household.

    Berki (1986), CMH (2001), Kawabata and Xu 13 The NSS consumption expenditure data regularly (2002), Meesen and Zang (2003), OECD and WHO puts the average health share in the household (2003), Pradhan and Prescott (2002), Wyszewianbudget of urban India at a meagre 5%. Even on ski (1986), Whitehead et al (2001), Wagstaff and the basis of the NSS 60th round data on morbidi-available at Van Doorslaer (2003); Xu et al (2003).

    ty, the health share in the household budget of 7 Self-identified medical practitioners. Personal urban Delhi for the lowest expenditure quintile Life Book House communication with the respondents reveal the was found to be around 1.2% that happens to be

    general belief that Bengalis make good doctors Shop No 7, Masjid Betul

    the lowest in the country.

    and hence the name bangali daakter.

    Mukarram Subji Mandi Road

    8 The reason was that Delhi displayed a very low inci-

    Bhopal 462 001

    dence of morbidity (around 1.6%) as per the 60th


    round of the NSS. Purposive sampling is generally Madhya Pradesh

    applied in situations where we need to reach a tar-Baeza, C and T G Packard (2005): Beyond Survival:

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