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Abstract
This paper analyses the health status, access to health services and the extent of financial risk protection among the urban poor in India. Our analysis is based on data collected from multistage primary survey of 3350 households in Jhuggi Jhopdi (JJ) clusters across all districts of Delhi. Data analysis has been carried out using STATA. Results suggest that prevalence of chronic morbidities and illness was considerably high among urban poor and they relied on private facilities for short term morbidities, where distance also played a major role in deciding. Households spend nearly half their capacity to pay on healthcare mainly financed by income. Significant association between health insurance use during healthcare and its effect on reducing household’s catastrophic expenditure among the urban poor was visible. There is a pressing need to expand social insurance measures, with the State providing the additional budgetary support.
1.Background
The ever growing health programmes and initiatives in India are now increasingly becoming responsive towards evidence-based decision-making, with focus on equity and efficiency. Moreover, India is committed to ensure universal coverage of health services, with its ultimate motive to eliminate inequity in all its manifestations from health systems, by ensuring equitable service access, reducing avoidable and disproportionate risks of ill-health among the vulnerable populations, and extending adequate financial risk protection. (Mazumdar & Mazumdar, 2013). The rhetoric of urban bias in development and better conditions in urban areas vis-à-vis rural areas has masked the real picture of the health conditions of urban poor (Agarwal et al., 2007). Health data commonly available in India provides aggregate figures for rural and urban areas which mask the inequalities existing within urban areas. Given the increasing number of poor urban residents and low income communities in majority of cities, and our limited understanding of the interconnections between urban deprivation and poor health, study of this issue is essential.
Ensuring equity in terms of socio-economic status or the targeted groups is a real challenge to deal with as financial burden of out-of-pocket (OOP) spending increases faster among the disadvantaged groups relative to their counterparts. According to the results based on the three rounds of NSSO (1999-2000, 2004-05 and 2011-12) 20 percent of the poorest households experienced a decline in the proportion of OOP for inpatient care as compared to the top 20 percent, Muslim households saw an increase in the same during 2000-2012 but the poorest 20 percent of households experienced a faster increase in the proportion reporting any OOP for outpatient care than their top 20 percent counterparts (Karan et al., 2014). There is a need to explore the reasons behind the lack of effectiveness of the existing public health financing programmes
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and public sector health services in reaching less-advantaged castes and religious minorities. Health indicators in India have certainly improved but quality and affordability of health care services continue to elude the poor (Acharya & Ranson, 2005). The socio-economic differentials in household’s health expenditure, coupled with the stark inequalities in financing medical care, are profound and have been shown by few recent studies conducted in Delhi (Chowdhury, 2011), Bangalore (Bhojani et al., 2012) and other urban centres (Ergler et al., 2011; Anand et al., 2007). Sherawat & Rao (2012) suggest that insurance schemes which cover only hospital expenses, will fail to adequately protect the poor against impoverishment due to spending on health and issues related to identifying the poor, and their targeting will constrain the scheme’s impact. Inclusion of medicines and outpatient care for the poor and near poor for a broader coverage is necessary to achieve significant protection from impoverishment (Rathi et al., 2012). Schemes should be targeted at assisting the poor in coping with indirect spending to reduce the household impact of high costs (Skordis-Worrall et al., 2011; Selvaraj & Karan, 2009) as lower socio-economic status (SES) is associated with a higher proportion of informal payments.
Though there are many schemes with regard to health financing, it is important that these schemes are managed in such a manner that existing schemes can be beneficial (Devadasan, 2006; Nolan et al., 2014). Devadasan et al., (2013) found that nearly 60 percent of insured patients made OOP payments, so better monitoring of the scheme is required to enhance the effectiveness of financial coverage of the schemes like Rashtriya Swasthya Bima Yojana (RSBY), if the nodal agency at state level would strengthen its stewardship and functions. Estimates given by Garg and Karan (2009) shows that approximately 5 percent of the total household expenditure is on Out-of-Pocket Expenditure (OOPE). They argue that Targeted policies to reduce OOPE in just five poor states could help prevent almost 60 percent of the poverty headcount increase through OOP payments. Scheme like RSBY was planned to be extended
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by the Union government to the unorganised working class poor but the Government of Maharashtra decided to scrap RSBY and replace it with the Rajiv Gandhi Jeevandayee Arogya Yojana. Gothoskar (2014) who analysed the conditions of waste pickers, argued not to pit one type of healthcare against another. This further explains a need to increase the efficiency of the existing schemes as Gupta and Chowdhury (2014) consider subsequent merging of all publicly-financed schemes along with proper administrative and management system to have inclusive health coverage. In India we find high incidence of distressed health care financing, hence, there is a need for social protection policies and improved health care (Joe, 2014). In order to improve efficiency, it is important to improve provider payment mechanisms (Devadasan et al., 2007) such that these health facilities can be utilised and prove to be competent in extending financial risk protections. Insurance schemes targeted at the poor like the RSBY have an important role to play in financially protecting vulnerable households (Rao et al, 2011). Schemes like Central Government Health Scheme provided health services to government employees, pensioners and their dependents and it was the second fastest growing segment but stuck with medical and administrative problems (Grover, 2014). Public private partnership can also help in delivery of healthcare services. Commercial insurance companies have limited interest in awareness generation and enrolment, responsibility for enrolment could be given to independent public agencies or to those who will make sure that these insurance schemes reach the needy (Ghosh, 2014; Das & Leino, 2011; Rajasekhar et al, 2011).
The public spending on health services in Delhi has been constantly on the rise in the last few years and is probably the first state in the country to spend almost 10 percent of its budget on health. According to the Ministry of Health & Family Welfare estimates of 2008-09, per capita health expenditure for Delhi stands at Rs.840, which is way above the national average of Rs.503 and ranked ninth among all states and UTs in India. Private expenditure accounts for more than two-third (77
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percent) of the total expenditure borne by households, of which a major proportion is out-of-pocket. For Delhi, (Mazumdar & Mazumdar, 2013) estimated that households spend about 3 percent of their total non-food expenditure and around 2 percent of their total Monthly per Capita Expenditure (MPCE) on medical expenditure according to recent MPCE surveys of the National Sample Survey (NSSO) including 55th (1999-2000), 60th (2004-05) and 66th (2009-10). Further they found that the levels remain virtually unchanged over the five years from 2004-2009. The IHD-SDTT survey found higher expenditure on medical care among the households with higher income and those with higher educational levels in Delhi (IHD, 2012). As far as the source of health financing is concerned, estimates based on NSSO’s 60th round (2005-06) shows that about 90 percent of the expenses incurred by households in Delhi on non-hospitalised and hospitalised ailments were financed out of the household incomes and/or past savings. The very recent perception survey conducted in 2013 for the Delhi Human Development Report also revealed that a small proportion of the households were able to support their medical expenses through alternative means including insurance and risk protection mechanisms, such as employers supported healthcare.
Delhi, being the national capital of India has always been viewed as the center of excellence in terms of availability and accessibility to health facilities (Mazumdar & Mazumdar, 2013). Presence of several general and super-specialist government and non-government hospitals has been serving large population of northern India including Delhi. However, very few attempts have been made to examine the health status, healthcare utilisation and cost of health facility use among the low income communities. Further, this study also provides evidence of sources of health expenses and coverage of financial risk protection among urban poor and its implications for India’s health policy commitment towards ensuring effective risk-protection against the financial impacts arising out of illness and reduction in barriers to financial accessibility among the urban poor.
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2.Study Description
This study is based on a large-scale multistage sample survey conducted in Delhi from March to May 2014. A total of 3350 households, providing a final sample of 18,192 individuals were covered in Jhuggi Jhopdi (JJ) Clusters located across all districts in Delhi. According to the Delhi Urban Shelter Improvement Board (DUSIB), “these settlements considered as sub-standard, slum and squatter settlements rank among the worst and it is the urban poor that live predominantly in such settlements”. The list of all JJ clusters for each district has been collected from the DUSIB. The DUSIB functions under the control of Govt. of NCT of Delhi and is primarily functioning under the purview of the DUSIB Act, 2010. This act empowers the DUSIB to notify certain areas as Slums, where with the passage of time, the buildings have become dilapidated and the basic civic services are missing. Apart from this, DUSIB has been also assigned the role of looking after the JJ Squatter Settlements/Clusters by way of provision of civic amenities and their resettlement too.
In each JJ cluster, at first a house-listing exercise was carried out to identify the sample households. This screening survey was conducted to get the initial glance of the low income community regarding socio-economic composition, hospitalisation, health insurance coverage and its utilisation. Nearly, 10,707 households were screened in 52 JJ clusters. In order to capture the heterogeneity in terms of socio-economic characteristics in the sample, stratified sampling was adopted. A composite index based on three household characteristics namely, (i) highest education level, (ii) Caste, and (iii) household income were generated. The scores were arranged in lowest to highest order and categorised into three SES as: (i) low, (ii) medium, and (iii) high. The entire sample size was thus stratified and the proportion of households
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was selected from each stratum. A summary description of the sample household is provided in Appendix 1.
The study includes selected socio-economic characteristics of the households and individuals which include age, sex, education, caste, MPCE of the household and district of residence. It is imperative to mention here that, although the entire survey was conducted in JJ clusters– predominately poor, variations in consumption expenditure across the households cannot be ignored. Thus, standard procedure of calculating MPCE was conducted based on the detailed consumption expenditure of the household asked during the survey to every household. Along with basic household characteristics a detailed information related to short-term morbidity (defined as any short-term illness or morbidity that did not lead to hospitalisation in last one month like fever, cough & cold, respiratory infections, diarrhoea etc.), and chronic morbidity or illness (including diabetes, heart diseases, hypertension, respiratory infections, cancer etc. for the person 16 years and older in last one year) were obtained. The survey further asked respondents about the type of healthcare visits separately for short-term morbidity and chronic morbidity or illness. Elaborate information related to expenditure on healthcare use including doctor’s fee, diagnostic tests, medicines, vaccinations etc. were collected. The survey also obtained relevant information to assess the financial risk protection and sources of expenses for healthcare among urban poor.
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3.Results
At first, it is essential to assess the present service need of the urban poor in Delhi. A detailed assessment of disease prevalence, type of healthcare use and hospitalisation by selected socio-economic and inter-district is provided.
3.1 Disease burden
Findings show that about 12 percent of the urban poor suffered from short-term morbidity in the last one month prior to the survey (Table 1). Considerable variations were evident across selected socio-economic characteristics including gender, education and household consumption expenditure. Moreover, the prevalence of short-term morbidity substantially varies across different districts – highest in New Delhi (15 percent) to lowest in West Delhi (6 percent). Results suggest that about 97 percent of the respondents reported any type of short-term morbidity utilised healthcare. However, variations across type of health facility were observed. For instance, nearly two in five utilised private healthcare facility, followed by informal providers (34 percent). Informal health facility is other than public and private like, quacks, medicine
shops, pharmacy, and traditional medical practitioners. Overall, 27 percent of the individuals preferred to use public health facility in case of short-term morbidity. Difference in type of healthcare use by selected socio-economic characteristics of the sample suggested few noticeable patterns, like informal care was higher among older age groups than younger age groups. Of those respondents who had completed higher education and above, higher proportion preferred to visit private health facility (44 percent). It is also vital to observe that about 36 percent of the households who belonged to the poorest of the poor (lowest MPCE
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quintile) utilised private health facility in Delhi. Public health facility for short-term morbidity was least utilised in South Delhi, whereas at 44 percent of the households in Central Delhi utilised the highest. However, public and informal health facilities were utilised maximum in South and West Delhi respectively.
The prevalence of chronic morbidity among urban poor in Delhi was nearly 7 percent in the last one year. Again, huge inter-district differences were evident – highest in South Delhi (10 percent) to lowest in West Delhi (4 percent). Further, considerable gap was observed in the selected background characteristics. For instance, chronic morbidity was higher among females (8 percent) than their male (5 percent) counterparts. About 17 percent of the respondents in the age group 50-65 years, reported incidence of chronic morbidity, lower than the younger age groups. Similarly, chronic morbidity was nearly twice higher among the illiterate than those who had completed higher secondary level of schooling or above. Further, prevalence of chronic morbidity among the top MPCE households was observed about 10 percent which was twice higher than the households belonging to the lowest consumption group (5 percent). Similar as short-term morbidity, over 96 percent people reported chronic morbidity in the last one year and utilised healthcare. About three in every five respondents (57 percent) visited public healthcare provider for chronic illness, followed by private (33 percent) and informal care (9 percent) providers. This study did not find considerable variations in the selected socio-economic characteristics. However, few patterns by MPCE and districts are noticeable. For instance, the public health facility was utilised more by the respondents who belonged to the poorest of the poor household (62 percent), as compared to the other MPCE groups. Further, it is also to be observed that about 31 percent of the poorest of poor households had gone for private healthcare facility in case of chronic illness. The inter-district variations in chronic illness was apparent and the study finds public health facility for chronic morbidity to be the highest utilised in West Delhi (79 percent), and least utilised by South Delhi (51 percent).
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3.2 Hospitalisation
Findings revealed that the rate of hospitalisation among the urban poor in Delhi was unacceptably high at 42 percent (Figure 1). The rate of hospitalisation was based on the hospitalisation information of any member of the household, with any kind of illness/disease/ailment during the last one year
prior to the survey. Moreover, huge inter-district variations were evident. It was highest in North Delhi (53 percent), followed by North-east Delhi (52 percent) and Central Delhi (47 percent). On the other side, the study recorded lowest hospitalisation cases in West Delhi (32 percent), followed by South Delhi (33 percent). The pattern clearly reveals that the hospitalisation among urban poor in Delhi was uniquely distributed and few areas shared higher burden.
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3.3 Health expenditure
Many previous studies across different settings have shown that the Indian healthcare scenario is characterised by private OOPE incurred on treatment and related expenses, with the impact often being catastrophic and leading to impoverishment (Shiva Kumar et. al., 2011), particularly among those with limited means and vulnerable livelihoods– like, the urban poor. In this regard, an attempt is made to provide existing pattern of healthcare expenditure among low income communities. In order to assess the financial burden of healthcare on the household, the overall monthly expenditure on health, monthly OOPE and proportion share of OOPE to the total MPCE of the households (refers as Capacity to Pay) are presented.
The total monthly expenditure on health care was obtained by summing the expenditure for treatment of various illness and morbidities. Household health expenditure is the sum of monthly health expenditure for the individual members. Out-of-pocket health payments referred to the payments made by the household while receiving health care services which includes doctor’s consultation fee, purchase of medicines and hospital bills, but excludes expenditure incurred on transportation, special nutrition and on attendants, net of any insurance reimbursement (Xu, 2005). OOPE for the households was calculated accordingly after deducting transportation costs and other expenses related to expenditure on attendants and food during inpatient stay. The household’s capacity to pay is used to compute the extent of catastrophic expenditure and impoverishments defined as the non-subsistence effective income of the household. According to WHO, catastrophic expenditure occurs when a household’s total out-of-pocket health payments equal or exceed 40 percent of the household’s capacity to pay or non-subsistence spending (Xu, 2005).
Results presented in Table 2 shows mean payment was higher in Central Delhi (Rs.3625/-), followed by North Delhi (Rs.2642/-) and lowest in case of West Delhi (Rs.1564/-). Similarly, in case of mean
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OOPE, expenses were higher in Central and North Delhi region. Further, the mean total health expenditure and OOPE by household’s MPCE was increasing with the increase in MPCE. However, a clearer picture emerges if we examine the average percentage share of the OOPE to the ability or capacity to pay by the households, which is extremely crucial from policy purposes, as by definition, any expenditure, which is in excess of 40 percent of the capacity to pay, is considered as catastrophic. However, the threshold might be different for households with differing economic status, as in absolute monetary terms, the residual amount can vary according to different consumption expenditure classes.
The extent of absolute financial burden on the households on account of OOPE on healthcare can be determined by studying the proportional share of these expenses to the total MPCE of the households. For the study population as a whole, we find that on an average about 16 percent of the total MPCE expenditure (52 percent of the capacity to pay), is spent for healthcare services, which is quite substantial as a lesser amount is left for other expenditures (Table 2). Moreover, the burden is significantly high among the poorest in the consumption expenditure quintiles, who spend nearly 21 percent (77 percent of their capacity to pay) on healthcare as compared to the upper most quintile. Thus, the average household from the low income settings across Delhi, clearly face financial catastrophe as a consequence of out-of-pocket payments for health care. Further, the inter-district variations are substantial than the average.
Health spending is regarded as catastrophic when a household has to reduce its basic expenditure over a period of time to cope with the health costs. Evidence indicates that in developing countries, catastrophic health care spending for a household is not usually the result of a single disastrous event, but rather a series of events and is related more to “every-day illnesses” than to major illness episodes like injuries or cardiovascular ailments (Garg & Karan 2005; Thuan et al., 2006). WHO has suggested taking up a cut-off of 40 percent of the capacity
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to pay as the threshold level of measuring catastrophic expenditure (Xu et al., 2003, Xu 2005). However, previous studies have employed different cut-off points to examine the proportion of households facing financial catastrophe (Berki 1986; Su et al., 2006). In this study, we have calculated the prevalence of catastrophic expenditure among households at three thresholds or cut-off levels – equal to or greater than 20, 30 and 40 percent of capacity to pay (Table 3). A considerably large proportion of households, 35-54 percent in our study area had catastrophic health expenditures.
Catastrophic expenditures at all the threshold levels were the highest in North Delhi and lowest in South Delhi. This can be possibly explained by higher hospitalisation rates in North Delhi than the South Delhi. Catastrophic expenditure also follows an economic gradient while considering the prevalence across the consumption expenditure quintiles. With respect to the highest threshold of 40 percent of the capacity to pay (CTP), catastrophic expenditure steadily declined, with 42 percent of households facing calamity in the poorest quintile as compared to 29 percent in the richest quintile. Similar pattern was evident when 20 and 30 percent thresholds were considered. Hence, there is equivocal evidence that the poorer households are facing catastrophic expenditure to a greater extent compared to the richer households. Similar pattern is visible across education, where considering 40 percent of threshold level illiterates were facing higher catastrophic expenditure (38 percent) than those who had completed higher education and above (32 percent).
3.4 Sources of health expenditure
One of the important aspects of the health expenditure and healthcare financing is to examine how people manage their healthcare expenditure. In a setting where coverage of health insurance or any other risk-pooling mechanism is lower and most of the health spending is out-of-pocket, financing health expenditures largely hinges on the access to resources
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and the ability to pay. It has been widely observed that the well-off households can manage to pay for health expenditures through their income or savings, however the poorer households, often facing even modest expenditures fail to pay for it out of own resources, and are left to borrow, which in most of the cases initiates a long cycle of indebtedness. This leads to a double burden—the one caused by the illness itself, and the other, more far-reaching consequences leading to indebtedness of the poor families. The source of expenses can substantially vary by type of morbidity/illness. For instance, expenditure on short term morbidities could easily be met with the household savings, whereas chronic diseases or to that matter sudden accidents and injuries require considerable amount that could be a huge burden on the poor households. In this section we examined the potential sources of healthcare expenses on selected morbidities and illness.
Findings presented in Table 4 suggest that a majority of households depend on their own savings to manage any kind of healthcare expenses – either short-term or chronic morbidity. For short-term morbidity, 79 percent of the expenses were met with household income, but in nearly 15 percent of the cases, the households had to resort to borrowings from friends/neighbours and relatives. About 3 percent of the households were forced to borrow from money lenders to meet the expenses incurred for treatment of the family members, while 2 percent of the expenditure was financed through employers. The larger proportion of self-income as the major source of financing healthcare expenditure for less serious morbidities conforms to the lower average expenses for these types of ailments. Being of less severe nature, smaller amounts were required to finance the treatment related expenses, which could have been met through self-income, and occasional borrowings from neighbours and relatives.
The sources of financing expenditure for chronic morbidities, however, present a similar picture. About 70 percent of the total expenses could be financed through self-savings of household. The second most
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preferred sources were borrowing money from friends/neighbours or relatives (20 percent). However, it is important to note that during our field survey we observed that in many cases the borrowing from friends/neighbours or relatives were not accepted for any interest. About 5 percent of the household resorting took loans from moneylenders, often at an exorbitant rate of interest. These households can hardly come out of the vicious cycle of poverty which engulfs them, almost in perpetuity. Only 2 percent of the households utilised health insurance towards meeting the expenses of chronic illness.
3.5 Coverage of formal health insurance
Our study also assessed formal means of financial risk protection (FRP) among low income households, the results are presented in Table 5 which reveal that only 7 percent of the households are covered under the formal health insurance scheme, mainly RSBY. However, this study further shows unequal pattern across districts and other socio-economic groups. For instance, the coverage of formal health insurance was higher in West Delhi (16 percent), followed by South Delhi (12 percent). On the other hand, households from the North and Central Delhi were the least covered by any kind of formal health insurance benefits. Household education played important role in getting enrolled under formal health insurance scheme among low income communities in Delhi. Further, study shows lower coverage of any formal health benefit scheme among lowest consumption household (6 percent) than their counterparts.
One of the prominent objective of any health insurance scheme is to minimise the household health expenditure along with providing timely and appropriate care for those who are in need. In this regard, this study also attempted to examine the effect of financial risk protection on household health expenditure after adjusting for other socio-economic variables. For this purpose we applied multivariate logit model for household reported hospitalisation in the last one year prior to the survey
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(Table 6). Findings suggest that after adjusting selected variables in the model, the effect of financial risk protection was not significant in the case of overall health expenditure and the share of OOPE to the household overall consumption expenditure. However, the likelihood of catastrophic health expenditure was lower (β=-0.40; 95 percent CI -0.68; -0.11) among households that utilised any healthcare scheme for the hospitalisation in last one year as compared to those households who neither availed any formal health insurance nor used hospitalisation. Health insurance significantly leads to reduction of overall OOP among the urban poor, although the strength of association was modest (β=-0.30; 95 percent CI -0.60; -0.02). This clearly suggests the net effect of FRP in reducing household’s catastrophic expenditure among the urban poor in Delhi. The model also shows that household MPCE and district significantly determines all the four health expenditures of the households among poor localities in Delhi.
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4.Conclusion
The analysis of the survey data points towards some major concerns with regard to the prevalence of both short-term and chronic morbidity among poor income settings across Delhi, but also in improving access to healthcare and providing financial protection. First, findings revealed that the prevalence of morbidities and illness was considerably higher among urban poor. About 12 percent of the individuals in the study sample reported short-term morbidity in the last one month. Similarly, the prevalence of chronic morbidities like, diabetes, heart diseases, hypertension etc. which are conventionally believed to be lower among poor as compared to the general population is noticeably higher at 7 percent. Morbidity prevalence – both short-term and chronic, follows socio-economic and regional gradients in our study sample. In particular, chronic morbidity considerably varies across gender, age, education and household consumptions which support the notion that social determinants of health are apparent in low income settings of urban areas. This pattern suggests that contrary to common perceptions, most of the chronic, so-called ‘lifestyle diseases’ not only afflict the rich, but rather the poor also face disproportionate risks of exposure to such diseases (Kar et al., 2010; Jeemon and Reddy, 2010; Misra et al., 2001; Rastogi et al., 2004). It is thus likely that the urban poor are a large contributor to the growing burden of chronic diseases in Delhi. The higher burden of diseases or ill-health can have a cascading effect on the household’s economic condition, making it difficult for the poor to come out of the financial implications of the illness episode, particularly among those with poorer ability to pay.
Healthcare utilisation for both morbidities are universal, however our analysis does find variation in the type of providers – public, private and informal. The higher use of private and informal care for short-term morbidity among poor households could be linked with the assumption that people generally consider short-term morbidities such as, cough-
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cold, loose motions, ear infection, or gastric disorder, as a minor health concern and tend to rely on most accessible and cost effective (includes waiting time) healthcare provider. More than half of the respondents told distance or convenience as the foremost reasons that determine healthcare service for short-term morbidity. However, in case of chronic morbidity, poor households rely more on public health facilities. Moreover, the use of informal healthcare was quite low in case of chronic morbidity. This suggests that considering the high cost of treatment in case of chronic morbidity and ailments, the households behave in a protected manner. Majority of respondents said that cost of treatment affects more in deciding the type of healthcare for chronic morbidity. Further, along with cost implications, usual choice of healthcare preferences matters even in the case of chronic morbidity. This strengthens the arguments that even among the poor income communities, people tend to rely on their past experiences or on providers who are satisfied the most.
The rate of hospitalisation evident in this study is higher as compared to the previous studies conducted in other parts of urban settings in India (Bhojani et. al., 2012; Dror et. al., 2008). Moreover, we did not find any promising pattern in hospitalisation across socio-economic groups. However, in few districts hospitalisation was substantially higher, which requires further exploration. A study based on National Sample Survey (2004-05) suggests considerable variations in hospitalisation across states. Moreover, the rate of hospitalisation was substantially higher among poorest households in the states of Kerala and Tamil Nadu, which are more developed in terms of urbanisation, income and other socio-economic parameters (Prinja et al., 2013; Ghosh & Arokiasamy, 2010). Higher rates of hospitalisation among poor communities could be explained in terms of growing expansion of hospital beds in Delhi during the last 15 years, from 24,025 beds in 1997 to 44,019 beds in 2013, with 13 new hospitals added in the last 10 years (DHS, 2013). Further, about 44 private hospitals (built on land made available to these agencies at concessional rates by the State Government) are required to provide free treatment to patients from the economically
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weaker sections (EWS). These hospitals are mandated by legislation to reserve at least 25 percent of their out-patient consultations and 10 percent of their in-patient admissions for the EWS category, as also approximately 650 free beds and 100 critical care beds (Mazumdar & Mazumdar, 2013). However, there is a need for comprehensive auditing of these services, particularly in assessing the extent of free provision of drugs and diagnostics. It has been highlighted in studies that the high prices and poor availability of medicines, and low affordability amongst the patients are some of the key barriers to access medical treatment (Cameron, et al., 2011). Further, OOPE on medicines are often responsible for increasing health expenditures and consequently impoverishment among the consumers of healthcare services (Selvaraj and Karan, 2009; Garg and Karan, 2009). The existing higher level of healthcare expenditure among poor in Delhi, revealed by this study, is one of the major concerns. Overall, households spend nearly half their capacity to pay on healthcare. However, it is apparent that relative financial burden is much higher in the poorer households than their relatively well-off counterparts. Using different threshold levels of household’s capacity to pay, a considerably large proportion of households, were found to incur catastrophic expenditure. At the 40 percent threshold, indicating the sharpest intensity of catastrophe, 41 percent of households in the poorest and 29 percent in the top quintile faced catastrophic expenditure. A study conducted in 2011 in Delhi have shown OOP health expenditures highly regressive in nature, accounting for 20 percent of the total consumption expenditure for households belonging to the poorest quintile. Further, study suggests that about half of the households spent more than 10 percent of their resources on health. However, the study was based on case studies of 150 slum households and restricted to out-patient care only, so it could have underestimated the healthcare expenses (Chowdhury, 2011).
Study also confirms that household health expenditure was largely financed through income, followed by resorting to borrowings
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from friends/family/neighbours. For financing treatment of chronic morbidity, nearly 5 percent forced to resort to borrowings from moneylenders, sometimes at usurious rates. As far as the coverage of any formal risk protection (FRP) is concerned, less than 10 percent of the poor household in Delhi avail any facility. Even those who avail health insurance are largely covered under RSBY1. Findings also demonstrated significant association between health insurance use during healthcare and its effect on reducing household’s catastrophic expenditure among the urban poor. Results of the latest Human Development Report of Delhi (2013) shows that only a small proportion of the households were able to support their medical expenses via alternative insurance and risk protection mechanisms (such as employer supported healthcare). In the case of major illnesses, the proportion of households tapping into their savings, followed by social network-based informal risk-sharing through financial help received from friends and neighbours.
This study clearly points towards a predominance of out-of-pocket spending on medical services by households, along with inter-district and socio-economic variations in household budgetary outlays on medical care. Although, these findings could be comparable to other Indian cities, but what makes the case of Delhi different is that it exhibits one of the highest (higher than the national average) levels of public expenditures on healthcare and medical services (Mazumdar & Mazumdar, 2013). Nevertheless, the fact that a considerable proportion of household’s spend significant part of their incomes on availing treatment for their ailing family members, raises important issues with regard to financial risk protection, universalisation of coverage and insuring against unanticipated health shocks.
The State Government has also been proactively pursuing the agenda of financial risk protection through a number of equity-sensitive programmes and interventions. These include the twin schemes- Delhi Arogya Nidhi (DAN, the State Illness Assistance Fund) and the Delhi Arogya Kosh (DAK), apart from schemes such as the Delhi Government
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Employees Health Scheme (DGEHS), and other national health insurance programmes such as the Employee’s State Health Insurance Scheme (ESIC) and the Rashtriya Swasthya Bima Yojana (RSBY). However, study did not find effective reach of these initiatives among those who are most in need – the urban poor in low income areas. Several barriers exist, hampering adequate financial risk protection for those in need of such safety nets. Low awareness amongst the poor, weak efforts by the government to reduce information gaps, stringent eligibility criteria and long-drawn out processes for availing of the benefits often leave the poor with inadequate and ineffective financial coverage. There is a pressing need to expand social insurance measures such as the RSBY by extending their coverage to outpatient services as well, with the State providing the additional budgetary support. The government needs to recognise that universal coverage calls not for multiple, overlapping schemes, but for a single, integrated and effective financial risk protection measure that can be availed by the poor, without any barriers.
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29
Appendix 1. Summary of the Study Population, Delhi
Background Characteristics % Sample
SexMale 53.1 9,661Female 46.9 8,531Age 0-5 9.9 1,8026-14 20.8 3,77815-49 58.4 10,61750-65 9.6 1,74365+ 1.4 252Highest Education Level in the HouseholdIlliterate/never gone to school 31.3 5,317Up to class 5 26.1 4,423Up toclass 8 20.3 3,440Completed class 10 12.7 2,150Completed class 12 & above 9.7 1,651CasteGeneral 19.4 650OBCs 36.5 1,221SCs/STs 44.2 1,479MPCELowest 20.0 6702nd 20.0 6703rd 20.0 6704th 20.0 670Highest 20.0 670DistrictCentral 1.4 47North 1.46 49South 23.88 800West 4.36 146New Delhi 2.96 99North-East 26.9 901South-West 9.91 332North-West 29.13 976Number of Individuals 18192Number of Households 3350
30
Tab
le 1
. P
reva
len
ce o
f S
hor
t-te
rm a
nd
Ch
ron
ic M
orbi
dit
y an
d T
ype
of H
ealt
h S
ervi
ce U
tili
sati
on a
mon
g U
rban
Poo
r, D
elh
i
Bac
kgro
un
d
Ch
arac
teri
stic
sS
hor
t-te
rm
Mor
bid
ity
Typ
e of
P
rovi
der
for
Sh
ort-
term
M
orbi
dit
yC
hro
nic
M
orbi
dit
y
Typ
e of
Pro
vid
er f
or C
hro
nic
M
orb
idit
y
P
ubl
ic
Pri
vate
Info
rmal
P
ub
lic
Pri
vate
Sex
Mal
e11
.126
.238
.035
.85.
359
.233
.1
Fem
ale
13.2
28.4
39.4
32.2
8.0
55.9
33.6
Age
Gro
up
0-5
17.4
21.9
43.2
34.8
NA
NA
NA
6-14
9.5
28.8
42.5
28.8
0.7
78.3
21.7
15-4
911
.127
.538
.034
.57.
856
.134
.250
-65
17.3
30.7
34.5
34.8
17.3
56.2
33.9
65+
17.5
27.3
25.0
47.7
15.9
72.5
25.0
Hig
hes
t ed
uca
tion
L
evel
in H
ouse
hol
d
Illit
erat
e/ne
ver
gone
to
scho
ol15
.029
.035
.335
.811
.358
.532
.4
Upt
o cl
ass
510
.127
.541
.531
.04.
153
.835
.8
Upt
o cl
ass
89.
528
.835
.335
.95.
259
.431
.2
Com
plet
ed c
lass
10
10.5
25.9
41.4
32.7
6.1
55.8
34.9
Com
plet
ed c
lass
12
&
abov
e10
.723
.144
.432
.55.
953
.737
.9
31
Cas
te
Gen
eral
12.3
26.3
43.2
30.5
6.2
61.2
33.2
OB
Cs
11.9
30.5
34.3
35.2
6.9
58.6
31.1
SCs/
STs
12.2
25.2
40.4
34.5
6.5
54.5
35.6
MP
CE
Low
est
9.4
33.7
35.6
30.7
4.7
61.9
30.7
2nd10
.830
.235
.534
.35.
759
.432
.4
3rd12
.323
.538
.837
.76.
760
.830
.4
4th13
.825
.040
.234
.87.
355
.635
.0
Hig
hest
16.1
24.8
43.3
31.9
10.0
50.6
37.6
Dis
tric
t
Cen
tral
12.3
43.8
28.1
28.1
6.1
75.0
25.0
Nor
th14
.130
.838
.530
.87.
661
.923
.8
Sout
h13
.318
.243
.838
.06.
151
.039
.2
Wes
t6.
429
.821
.348
.94.
179
.317
.2
New
Del
hi15
.135
.735
.728
.69.
265
.925
.0
Nor
th E
ast
12.7
24.9
36.8
38.3
6.7
60.2
32.8
Sout
h W
est
14.0
36.8
41.7
21.5
10.1
58.3
31.9
Nor
th W
est
10.2
33.5
37.1
29.4
5.8
54.1
33.9
Tot
al12
.127
.338
.733
.96
.657
.333
.4
32
Table 2. Health Expenditure among Urban Poor, Delhi
Background Characteristics
Mean OOPE (in Rs)
Mean monthly OOP Expenditure
(in Rs)
Proportion of OOP Health
Exp. to Monthly HH*
Exp. (in %)
Mean OOPE
as a % of Capacity
to Pay
Highest education level in hhIlliterate/never gone to school
1774.2 1120.3 15.7 51.6
Up to class 5 1856.8 1151.9 16.4 60.1Up to class 8 2225.1 1356.0 16.4 54.4Completed class 10 2115.6 1311.1 15.9 49.2Completed class 12 & above
2146.1 1387.9 14.4 44.1
CasteGeneral 1927.5 1220.9 15.9 48.4OBCs 2155.3 1277.2 15.1 54.6SCs/STs 2026.7 1319.0 16.3 50.8MPCELowest 1460.0 1016.1 21.3 76.62nd 1703.9 1112.3 16.5 56.5
3rd 1866.7 1054.5 13.8 46.7
4th 2094.2 1344.1 14.6 44.7Highest 3160.0 1901.1 12.6 33.7DistrictCentral 2625.4 1472.3 19.1 54.5North 2642.2 1297.0 18.5 52.5South 1761.9 1096.7 10.5 31.2West 1563.6 710.7 10.0 30.2New Delhi 1883.0 1328.0 18.7 70.9North-East 1921.7 1212.4 14.1 53.3South-West 2562.7 1472.8 16.5 48.1North -West 2183.5 1411.8 19.4 63.6
Total 2053.8 1283.8 15.8 51.7
33
Table 2. Health Expenditure among Urban Poor, Delhi Table 3. Catastrophic Expenditure by Selected Background Characteristics among Urban Poor, Delhi
Background Characteristics
>= 20% of CTP
>= 30% of CTP
>= 40% of CTP
Highest Education Level in HH*Illiterate/never gone to school 56.0 45.0 38.3Up to class 5 55.5 43.4 35.6Up to class 8 55.1 42.9 34.6Completed class 10 54.7 43.1 35.4Completed class 12 & above 50.7 40.0 31.8CasteGeneral 51.6 39.9 35.0OBCs 55.1 43.7 35.4SCs/STs 54.7 43.1 34.3MPCELowest 61.2 48.7 41.92nd 54.7 41.9 34.3
3rd 58.1 44.7 34.3
4th 51.5 42.4 34.9Highest 45.5 35.4 28.7Region/DistrictCentral 42.1 36.8 36.8North 73.9 60.9 45.7South 45.7 35.5 26.6West 46.0 36.0 28.0New Delhi 63.6 50.0 40.9North-East 58.3 45.3 36.8South-West 59.8 46.2 39.9
North-West 55.1 44.2 37.7
Total 54.3 42.7 34.9
34
Table 4. Source of Expenses met on Healthcare by Urban Poor, Delhi
Sources Short-term morbidityChronic
morbiditySelf-savings 79.3 69.2
Friends/neighbours/relatives 15.3 19.6
Employer paid 2.0 3.8
Health insurance 0.6 2.4
Borrow from money lenders 2.5 4.5
Selling of assets 0.3 0.4
Table 5. Coverage of Financial Risk Protection among Urban Poor, Delhi
Background CharacteristicsCoverage of Financial Risk
ProtectionHighest education level in HH*Illiterate/never gone to school 5.5Up to class 5 5.4Up to class 8 6.3Completed class 10 6.7Completed class 12 & above 10.0CasteGeneral 8.2OBCs 6.9SCs/STs 6.4MPCELowest 5.72nd 6.63rd 7.04th 8.4Highest 7.0DistrictCentral 2.1North 2.0South 11.5West 15.8New Delhi 7.1North-East 2.4South-West 3.0North-West 7.8
Total 6.9
35
Tab
le 6
. Est
imat
ed C
o-ef
fici
ent
in O
LS
Reg
ress
ion
Mod
els
for
Ove
rall
Hea
lth
Exp
end
itu
re, O
ut
of p
ocke
t E
xpen
dit
ure
, OO
P t
o P
er C
apit
a H
ouse
hol
d (
HH
) E
xpen
dit
ure
an
d O
ut
of P
ocke
t to
Cap
acit
y to
Pay
(N
on-
food
Mon
thly
HH
Exp
end
itu
re)
Bac
kgro
un
d v
aria
bles
Tot
al h
ealt
h
exp
end
itu
reO
OP
hea
lth
ex
pen
dit
ure
Sh
are
of O
OP
hea
lth
ex
p. t
o to
tal m
onth
ly
exp
.C
apac
ity
to P
ay
Coe
f.9
5% C
IC
oef.
95%
CI
Coe
f.9
5% C
IC
oef.
95%
CI
RS
BY
car
d u
seN
o (R
ef.)
Yes
0.22
[-0.
09; 0
.52]
-0.3
0*[-
0.60
; -0.
02]
-0.2
9[-
0.57
; 0.0
5]-0
.40*
*[-
0.68
; -0.
11]
Hig
hes
t ed
uca
tion
le
vel i
nH
H
Illit
erat
e/ne
ver
gone
to
scho
olU
p to
cla
ss 5
0.04
[-0.
16; 0
.240
]0.
06[-
0.13
; 0.2
6]0.
05[-
0.14
; 0.2
4]-0
.01
[-0.
19; 0
.18]
Up
to c
lass
80.
11[-
0.07
; 0.3
1]0.
12[-
0.07
; 0.3
0]0.
05[-
0.13
; 0.2
2]-0
.03
[-0.
20; 0
.15]
Com
plet
ed c
lass
10
0.09
[-0.
11: 0
.29]
0.17
[-0.
02; 0
.36]
0.14
[-0.
05; 0
.32]
0.01
[-0.
17; 0
.20]
Com
plet
ed c
lass
12
&
abov
e0.
10[-
0.10
; 0.3
0]0.
13[-
0.06
; 0.3
2]0.
02[-
0.17
; 0.2
0]-0
.13
[-0.
31; 0
.05]
Cas
teG
ener
al (R
ef.)
OB
Cs
0.08
[-0.
09; 0
.24]
0.01
[-0.
15; 0
.17]
0.02
[-0.
13; 0
.17]
0.03
[-0.
12; 0
.18]
SCs/
STs
0.05
[-0.
11; 0
.20]
-0.0
1[-
0.16
; 0.1
4]0.
00[-
0.14
; 0.1
5]-0
.02
[-0.
17; 0
.12]
MP
CE
Low
est (
Ref
.)2nd
0.14
[-0.
05; 0
.32]
0.06
[-0.
12; 0
.23]
-0.3
0***
[-0.
47; -
0.13
]-0
.20*
[-0.
37; -
0.04
]3rd
0.44
***
[0.2
6; 0
.62]
0.34
***
[0.1
7; 0
.51]
-0.1
9*[-
0.35
; -0.
02]
-0.1
4[-
0.31
; 0.0
3]4th
0.45
***
[0.2
7; 0
.63]
0.42
***
[0.2
5; ,0
.60]
-0.2
8***
[-0.
45; -
0.11
]-0
.27*
*[-
0.43
; -0.
10]
36
Hig
hest
0.58
***
[0.3
9; 0
.76]
0.46
***
[0.2
8; 0
.63]
-0.4
9***
[-0.
66; -
0.32
]-0
.59*
**[-
0.76
; -0.
42]
Reg
ion
/Dis
tric
tC
entr
al (R
ef.)
Nor
th0.
32[-
0.35
; 0.9
9]0.
15[-
0.51
; 0.8
0]0.
01[-
0.62
; 0.6
3]0.
13[-
0.49
; 0.7
6]So
uth
-0.5
3[-
1.04
; -0.
02]
-0.5
3*[-
1.0;
-0.0
5]-0
.73*
*[-
1.20
; -0.
27]
-0.5
3*[-
1.00
;-0.
08]
Wes
t-0
.81*
*[-
1.39
; -0.
22]
-0.7
0*[-
1.26
; -0.
15]
-0.7
9**
[-1.
32; -
0.25
]-0
.62*
[-1.
15; -
0.08
]N
ew D
elhi
-0.2
1[-
0.81
; 0.3
9]-0
.06
[-0.
62; 0
.51]
-0.2
0[-
0.75
; 0.3
4]0.
13[-
0.42
; 0.6
8]N
orth
-Eas
t-0
.24
[-0.
75; 0
.27]
-0.2
9[-
0.77
; 0.2
0]-0
.50*
[-0.
96; -
0.03
]-0
.17
[-0.
64; 0
.31]
Sout
h-W
est
-0.2
2[-
0.75
; 0.3
1]-0
.22
[-0.
72; 0
.28]
-0.3
4[-
0.83
; 0.1
4]-0
.15
[-0.
64; 0
.34]
Nor
th-W
est
-0.3
2[-
0.83
; 0.1
9]-0
.26
[-0.
74; 0
.23]
-0.3
6[-
0.82
; 0.1
0]-0
.19
[-0.
66; 0
.28]
Tot
al
2875
27
77
2774
27
73
R-s
qu
are
d
0.03
21
0.02
88
0.03
34
0.04
4
