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).

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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

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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

MPCE

Household

Expenditure Net

of OOP Payment H

Poverty

\

Line

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

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

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

4 Methodology-wise Survey Results

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

Anaemia and generalised weakness 52.9 52.9 0 128 151 23

Cardiological 31.4 51.4 20.0 7 40 33

Fever and ENT infection 31.3 36.5 5.2 26 60 34

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

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.

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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

5 Conclusions

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.