The emergence of universal health coverage (UHC) as the dominant discourse in global health brought financial protection against direct household payments for health care to centre-stage [1]. The formal definition provided in the glossary of the first monitoring report is elaborate and comprehensive: “Universal health coverage means all people receiving the health services they need, including health initiatives designed to promote better health (such as anti-tobacco policies), prevent illness (such as vaccinations), and provide treatment, rehabilitation and palliative care (such as end-of-life care) of sufficient quality to be effective while at the same time ensuring that the use of these services does not expose the user to financial hardship” [2]. Though financial protection forms a crucial aspect of the definition of UHC, such financial protection need not be equated only with insurance schemes. Tax-financed free care or care subsidized by public health facilities receives either superficial mention or is ignored altogether when discussing financial protection [3], even though it has all the elements of pre-payment and risk-pooling [4].

In the period after 2005, state and central governments in India launched a number of Public Funded Health Insurance (PFHI) programmes. These are not social health insurance as commonly understood. The central government’s Rashtriya Swashtya Bima Yojana (RSBY) programme (translates to National Health Insurance Scheme) is meant to cover all those below the poverty line, but individual families have to pay a token sum each year to enrol or renew membership, and such enrolment and renewal is not mandatory [5]. The insurance agencies provide the enrolment or renewal, and the government pays a premium for each household that is covered. In most of the state schemes, coverage is conferred on those who are eligible based on income criteria that are certified by the public distribution system, which distributes income category cards to enable lower socioeconomic groups to access subsidized food grains. All of these PFHI schemes provide for cashless services at the point of care for a wide list of hospitalizations [6–8]. They do not provide coverage for ambulatory care [9]. In contrast to these post-2005 PFHIs, there are two earlier social insurance schemes that are operational from the 1950s – one for regular government employees and another for organized workers (Employees State Insurance Scheme or ESI), and they cover ambulatory care as well, in which enrolment and contribution are mandatory. The package of services is larger, and the sum assured are also much higher. However, these two schemes account for only approximately 35 million of the 300 million stated as covered under all PFHIs [10]. We have described the key features of the PFHIs in Additional file 1.

In the period leading up to the finalization of the 12th Five Year Plan, there were pressures within the Indian Planning Commission to shift the role of government from a provider to a purchaser of services, including from an integrated network of providers. After a new government came to power in 2014, the Niti Aayog, an institution that supplanted the Planning Commission, continued with this policy thrust. This shift was contested [11]. The recently announced National Health Policy 2017 calls for both strategies and provisions of tax-funded free care by public health hospitals and primary care facilities as well as an expansion of strategic purchasing using PFHIs [12]. However, there are concerns about where the emphasis would be in regard to implementation [13, 14].

This paper aims to discuss a) the coverage and effectiveness of both government purchasing through insurance and government provision of tax-funded free or subsidized care as strategies of financial protection; b) the contribution that PFHI makes to the reduction in catastrophic health expenditures due to hospitalization; and c) the equity dimensions of both financial protection strategies. Since government purchasing of health care is currently limited almost exclusively to hospitalization, we have assessed financial protection strategies only with reference to hospitalization expenses. We also discuss the potential of this National Sample Survey (NSS) health survey and database to be one of the central tools to measure progress and guide policy with regard to charting India’s roadmap towards UHC.

The data for the paper are derived from the unit records of the “Social Consumption: Health” survey (71st round) conducted by the NSS Office, which provides information at national and state (provincial) levels related to morbidity, hospitalization, reported causes for illness and hospitalization, the cost of care, the type of service provider, and care in pregnancy and for the elderly [15].

In India, the NSS Office, a government institution, organizes annual surveys on different areas of consumption, poverty, and employment, and once every 10 years, it undertakes a survey called Social Consumption: Health [15].

This survey uses a two-stage stratified sampling approach, with the first sampling units composed of village and urban blocks and the second stage composed of households. Data collection was performed from January 2014 to June 2014 in two segments of 3 months each. A total of 65,932 households (rural: 36480, urban: 29452) were surveyed for the entire Indian Union, which included a total of 333,104 individuals (rural: 189573, urban: 143531; male: 168697 female: 164407).

Morbidity as reported by the household is captured in response to three questions: 1) Were you ill in the past 15 days? 2) Were you hospitalized in the past 365 days? 3) If so, what was the cause? The reported cause is then attributed to the nearest fit of one of 60 diagnostic categories on the basis of a medical diagnosis conveyed to the study team by the respondent, or failing this approach, the main symptom was used. This survey also probed whether each household and individual had any insurance coverage, including the type of insurance coverage. For each illness or hospitalization, the study team noted the choice of provider and reported costs of care, disaggregated into medical and non-medical costs by component. The sample consisted of 57,456 hospitalization episodes (55,026 episodes excluding death), of which approximately 10,168 episodes had insurance coverage.

Those with insurance coverage were asked about the type of scheme, and their responses were categorized into four groups. One is the PFHIs, which include both the social insurance schemes that began in the 1950s and the post-2005 government-sponsored schemes that target the poorer socio-economic groups for coverage. The second category is composed of individual households that voluntarily purchase their own insurance coverage from private insurance firms or “private insurance”. The third category is employer-provided insurance coverage for employees, and a fourth category of “others” is associated with special schemes such as Yashwasini, which is privately initiated and linked to cooperatives. Because the last is very small, we have not shown it in the calculations (only 0.1%). Of these four types of insurance, only PFHI and the post-2005 types of schemes focus on providing financial protection for the poor and are being considered for scaling up as the national strategy. Therefore, we have limited most of our analysis to only this first category, and within that, we separately analysed the lower three quintiles since those covered by the two social insurance schemes would largely or entirely belong to the upper two quintiles.

This study examines various dimensions of equity, defined as unequal insurance coverage, hospital rates, and/or financial protection based on sex, social group, economic quintile, or rural/urban residence. This study measures financial protection in terms of seven indicators. These are 1) the mean OOPE per hospitalization episode; 2) the median OOPE per hospitalization episode; 3) the proportion of hospitalization episodes where OOPE was less than Rs 500; 4) the proportion of hospitalization episodes where OOPE was less than Rs 1000; 5) the incidence of households experiencing catastrophic health expenditures, which is the proportion of households whose costs of hospitalization were beyond a threshold, defined as 10% of annual consumption expenditure (CHE-10); 6) the incidence of catastrophic health expenditure, which is the proportion of households whose costs of hospitalization exceeded a threshold, defined as 25% of annual consumption expenditure (CHE-25); and 7) the impoverishment related to hospitalization costs.

OOPE for hospitalization was calculated per episode, including transportation, and reimbursements were subtracted. Only 2% of those hospitalized received reimbursements.

The two indicators of incidence of hospitalization for which OOPE was below Rs 500 or Rs 1000 are introduced since the stated objective of most government-funded insurance schemes is to provide cashless services for hospitalization care, not merely a reduction in OOPE. Since some incidental expenses may be counted, thresholds of Rs 500 and Rs 1000 for OOPE enabled better measurement of the proportion of hospitalizations in which this objective of cashless services was achieved.

The methodology discussed by Wagstaff and Doorslaer [16] to assess catastrophic health expenditure (CHE) for healthcare was applied in this study. A household OOPE on hospitalization in the preceding year is considered an incidence of CHE when the payment exceeds the 10% (CHE-10) and 25% (CHE-25) thresholds of the household annual total household consumption expenditure. This household annual total consumption expenditure represented 12 times the UMPCE (usual monthly per capita consumption expenditure). UMPCE is measured by a consumption survey using a methodology standardized by NSSO and accepted widely in India as a reliable proxy for income levels. We chose to study OOPE and CHE only in regard to hospitalization because this is what approximately all government insurance covers.

To estimate impoverishment due to hospitalization costs, this study used the threshold recommended by the Government of India Planning Commission Report of June 2016 for measuring the poverty line (per capita) as Rs 972 in rural and Rs 1407 in urban areas [17].. Households whose MPCE was initially above this threshold but later fell below it after incurring hospital expenditures were considered to have experienced ‘impoverishment due to hospitalization costs’. Households whose MPCE was initially itself below the poverty line were considered to have experienced ‘deepening of poverty due to hospitalization costs’.

The levels of financial protection are measured and presented for four situations: a) care is from the private sector, and there is no insurance coverage; b) care is from the private sector, and there is PFHI coverage; c) care is from a tax-funded public provider, which affords financial protection through subsidies, but there is no insurance coverage; and d) care is from the tax-funded public provider and is complemented by PFHI coverage. This allows us to compare the reduction in OOPE and the protection against CHE that are provided in each of these contexts.

Other than the above, another indicator of financial hardship available from this survey is the source of financing. Households were asked for the source of financing for hospitalization, and responses were categorized into following options: 1) household income or savings; 2) borrowings; 3) sale of physical asset; 4) contributions from friends and relatives and 5) other. There could be multiple responses. Having to borrow or sell physical assets as one of the sources of financing indicates financial hardship.

We know that enrolment in PFHI is not random, and it is correlated with the OOPE and CHE; those who were enrolled in PFHI may differ from those who were not in some systematic way. In this situation, reduction in OOPE and CHE could be under-estimated because of confounding factors, such as economic quintile, type of provider, social group, education level, sex and disease category for which treatment was sought.

We used propensity score matching (PSM) to match for these characteristics across the households with PFHI coverage to those without any insurance coverage to estimate the contribution that PFHI makes to reducing the incidence of CHE due to hospitalization expenses. In cross-sectional data, PSM establishes that an intervention of interest (in this case PFHI) contributes to an outcome of interest (in this case household CHE incidence). This method ensures that other observed background characteristics or variables are matched in intervention and non-intervention groups so that their influence can be controlled. Using a counterfactual model, we have estimated the average outcome of the treated households (which is the incidence of CHE in households with insurance coverage in this study) and the average outcome that the treated households would have obtained in the absence of PFHI, which is unobserved. The average treatment on treated (ATT), which measures the average difference in CHE incidence that PFHI affords to households with PFHI coverage, is a measure of the effectiveness of the PFHI [18].

We matched for the following six variables: sex, social group (caste), education of head of household, economic quintile, choice of public or private provider and disease category. The main causes of hospitalization were categorized into 12 groups: infections, cancers, metabolic and blood diseases, mental and neurological illness, eye, cardiovascular, respiratory, gastro-intestinal, musculo-skeletal and genito-urinary, obstetric (including childbirth), injuries and others. The nearest neighbour matching method with replacement was used in combination with a logit model. To satisfy the balancing property on all of the background characteristics, a ‘hit’ or ‘miss’ approach was used.

The category of PFHI includes a variety of schemes (see Additional file 1), including those that provide insurance coverage for government employees. To study the contribution of the subset of PFHIs that are designed to prevent CHE among the poorer socio-economic groups of the society, we have conducted the PSM in two stages. First, PSM was applied to the total population (all five income quintiles), and later, it was applied only for the bottom three quintiles. We conducted PSM for all hospitalizations, irrespective of quintile groups, then conducted PSM again separately for the bottom three quintiles.

This study also examined whether PFHI coverage is associated with a change in the choice of provider since PFHIs were expected to overcome financial barriers to access of care in the private sector. Furthermore, to also study whether PFHI coverage led to increased utilization of hospitalization services, a multiple logit model was constructed, with the likelihood of hospitalization as the dependent variable and with publicly funded insurance coverage, education, age, economic quintile, sex, urban residence and social status as independent variables. Analysis was conducted using STATA software (version 13).

Across nations, the emergence of universal health coverage as the dominant discourse on health policy has led to a renewed attention towards financial protection. In India as in many other developing nations, in the post-colonial period, the provision of free or subsidized care through government hospitals and health care facilities was the main form in which this was sought to be achieved. This changed in the 1990s with the introduction of health sector reforms linked to structural adjustment programmes. These reforms focused on limiting public services to a few select services that were chosen using the logic of least “dollar spent per DALY saved” and proposed leaving the rest of health care to the market [19]. Structural adjustment also resulted in a reduction in subsidies, introduction of user fees for cost recovery and restrictions on public sector employment, including that of nurses and doctors. These policies adversely affected the performance of health systems and equity in access to healthcare [20]. User fees are now accepted as highly exclusionary [21, 22], but in the 1990s, this was one of the main tenets of reform, and it still persists.

Low public funding has an adverse impact on both access to care and financial protection. Though lack of financial protection is a major cause for concern, the ideas that this requires a purchaser-provider split and that financial protection is most efficiently delivered through purchasing mechanisms need to be examined in context. Current evidence from India on the effectiveness of PFHIs in providing financial protection, even for hospitalization, to which they are currently largely limited, indicates a limited effectiveness of such programmes. Our results convey a new body of evidence to clearly demonstrate this point. Earlier studies by Karan and Selvaraj (2012) [23], Fan et al. (2011) [24] and Selvaraj et al. (2014) [25] that have used other data sets and different methods have also shown the limited effectiveness of PFHIs in providing financial protection.

In pluralistic health systems similar to those in India, it is essential for policy makers to be guided by periodic measurements of the levels of financial protection achieved and the contributions that different government interventions make towards achieving the criteria. We have used seven indicators that are relatively easy to generate from a household survey to measure the extent of financial protection or its converse, the extent of financial hardship suffered. These include the mean and median OOPE per hospitalization episode, the incidence of catastrophic health expenditures at 10% and 25% of annual consumption expenditure thresholds, the proportion of impoverishment due to health care costs and the proportion of hospitalization episodes where out-of-pocket medical expenditures were less than Rs 500 or less than Rs 1000. These seven indicators could easily be extended to measure financial protection for ambulatory care. The only reason that we did not do so was because we were measuring the effect of PFHIs in India, which in most schemes do not cover ambulatory care.

To guide policy choices, there is a need to develop a framework of measurement and analysis that is not only able to measure financial protection but also the level of equity with which such coverage is offered. Additionally, this framework should further attribute the effectiveness of such coverage or the lack of it to policy choices, particularly to the government purchasing care from private providers through insurance mechanisms, or the government providing access to free or subsidized care in government facilities and various combinations of the two [26].

All these seven indicators show that from the viewpoint of the care-seeker, insurance coverage by itself makes minimal difference to financial protection, and subsidized care from the public provider is more effective in reducing financial hardship. The median OOPE for hospitalization with a tax-funded public provider, even without PFHI coverage, is Rs 1451 (USD 24) and the incidence of CHE-10 is 16.1%. This is far lower than the median OOPE for hospitalization with a private provider in a household having PFHI coverage (Rs 10,120 or USD 169) and the incidence of CHE (60.0%). However, we note that when PFHI coverage complements the utilization of tax-funded public hospitalization, this allows for a further advantage in financial protection with median OOPE decreasing to Rs 950 (USD 16) per hospitalization and the incidence of CHE decreasing to 14.8%. In the lowest three quintiles, after matching for different potential confounders, the contribution that PFHIs make to the reduction in CHE-10 and CHE-25 measured by the PSM method is statistically insignificant.

We also note that utilization rates of hospital services, a proxy to access, also do not change with insurance once we adjust for other factors contributing to the levels of consumption of health care such as urban residence, higher income quintile, higher social group and higher educational status.

What is also a matter of concern is that unlike public provisioning, which was accessed more among lower income quintiles, more PFHI coverage occurs in higher quintiles, although by definition, this is a redistributive measure meant to provide increased access and financial protection for the poorest. The major reasons for the lower coverage of the poor and vulnerable population under PFHI compared to official projections have been attributed to exclusions at various levels, design failures and lack of awareness among the population [27]. Further higher PFHI coverage in the higher quintiles could be a reflection of the earlier social insurance schemes that cover government employees and organized workers.

However, all is not well with public provisioning. If nearly 32.3% of the poorest quintile in rural areas and 48.4% of the poorest in urban areas choose a private provider despite the high OOPE and CHE, then there are clearly other factors at work. This could be explained by unsatisfactory quality (42.7%), long waiting times (27.4%), distant facilities (11.6%), unavailable services (10.3%) and other causes (8.1%) [28, 29]. All of these are reflective of underperforming public health systems, which have been attributed to very low public health spending as well as many issues of design and implementation [30].

We note that the wide differential in OOPE between the poorest and richest quintile, even in the public sector, could indicate that there is an under-consumption of health care in the poorest quintiles. Utilization rates for hospitalization (used as a proxy for access) are also lower in poorer quintiles. It was in part to address these problems of access that PFHI were introduced. The fact that coverage with a PFHI, although it increased hospitalization rates, did not change the pattern of choice of provider indicates the need to look beyond financial barriers and quality of care for explaining the choice of provider.

All of this has policy implications for the choice between expanding the insurance schemes to cover a larger proportion of the population with a deeper benefit package, strengthening the provisioning of a more comprehensive package of free or subsidized care in public hospitals or a judicious combination of the two. It is true that unlike for the private sector, OOPE does not represent the entire costs of care of the public hospital since there is already a considerable amount invested by the government on the supply side. However, it does not follow that the costs of purchasing from the private sector are the same or lower than purchasing from the public sector. There is some evidence from state and national health accounts that the full costs of care are lower in a public hospital [31]. Moreover, the public hospital has a number of public functions to perform. The relative efficiencies of care provided versus care that is purchased would have to be studied separately. Our study indicates that PFHIs could play a useful source of additional financing for public hospitals that improves the effectiveness of financial protection in a manner more responsive to caseloads than that achieved by budget line item based financing. Potentially, such additional financing could be used to improve quality of care in public hospitals. However, whether the better strategic choice is to merely increase public financing of the public hospitals or not remains an open question.

The study also questions the effectiveness of currently existing models of PFHI in delivering financial protection. Admittedly, since current investment in PFHIs is small, population level effects on financial protection would be limited. However, our study is focused on hospitalisation episodes which is the mandate of PFHIs. Our concern is that if we are unable to see any effect on financial protection among those who have PFHI coverage and have been hospitalised, then we need to explore the reasons for this before we consider further expansion of purchasing from the private sector on such terms. This could be due to the lack of an appropriate regulatory regime and poor monitoring and governance leading to different forms of co-payment and manipulation, commonly referred to as ‘gaming the system’. If CHE rates due to hospitalization remain similar across income quintiles despite PFHI coverage (see Table 2), further research would be needed to explore the cause of this, including the possibility that the extent of co-payments is influenced by information asymmetries linked to the patient’s ability to pay and the provider’s ability to charge, rather than real needs and costs.

Our findings are in consonance with earlier studies from many nations, such as China, India and nations in Latin America [23, 32–34], and taken together, these results should provide caution to making road maps to universal health coverage that rely mainly on insurance schemes or purchasing from the private sector [21]. In the Indian context (and in many developing nations), regulation of the private sector is very weak, and there are high degrees of irrational care and conflict of interest situations [23, 24]. The institutional capacity for purchasing of care and contracting is also limited. Expanding to include ambulatory care into purchasing on such terms would have even higher risks. However, in parallel, there must be much more investment and effort made in strengthening public providers that are defined more broadly to include not-for-profit providers, as a vehicle not only for access but also for financial protection. This form of purchasing care could help in this regard.

This study has certain limitations. This survey collected information on all publicly funded health insurance schemes from various states and centres as grouped under one category, and two of these were earlier social insurance schemes that did not address the poor. We have addressed this limitation to an extent by analysing the poorer quintiles, but it would have been more robust if the questionnaire had been more specific regarding the type of PFHI. There are also inherent limitations in using only a cross-sectional study to comment on the impact of any intervention, although propensity score matching offers a more robust method to indicate the effect of an intervention in a cross-sectional study. Additionally, we note that although OOPE is a good measure of financial protection from the viewpoint of the beneficiary, to comment on the relative cost efficiency of subsidized public provision and PFHIs, we will also need to account for the entire costs of the respective strategies, which is beyond the scope of this paper.

With respect to the problematic nature of measuring progress towards UHC, this paper illustrated the usefulness of this unique cross-sectional household survey to comment on the differential contribution that the main government strategies of purchasing and providing care make towards achieving this objective. India is a pluralistic health care system, and accurately determining the mix of purchasing and provisioning will remain one of the key issues that policy makers will face for some time to come.