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  • v.11(12); 2021

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Original research

Impact of public-funded health insurances in india on health care utilisation and financial risk protection: a systematic review, bhageerathy reshmi.

1 Department of Health Information Management, Manipal College of Health Professions, Manipal Academy of Higher Education (MAHE), Manipal, Karnataka, India

Bhaskaran Unnikrishnan

2 Department of Community Medicine, Kasturba Medical College, Mangalore, Manipal Academy of Higher Education, Manipal, Karnataka, India

3 Department of Health Information, Public Health Evidence South Asia, Prasanna School of Public Health, Manipal Academy of Higher Education (MAHE), Manipal, Karnataka, India

Shradha S Parsekar

Ratheebhai vijayamma.

4 Manipal Institute of Communication, MAHE, Manipal, Karnataka, India

Bhumika Tumkur Venkatesh

Associated data.


Data are available upon reasonable request. All data relevant to the study are included in the article or uploaded as supplementary information. The datasets used and/or analysed during the current study are available from the corresponding author on request.

Universal Health Coverage aims to address the challenges posed by healthcare inequalities and inequities by increasing the accessibility and affordability of healthcare for the entire population. This review provides information related to impact of public-funded health insurance (PFHI) on financial risk protection and utilisation of healthcare.

Systematic review.

Data sources

Medline (via PubMed, Web of Science), Scopus, Social Science Research Network and 3ie impact evaluation repository were searched from their inception until 15 July 2020, for English-language publications.

Eligibility criteria

Studies giving information about the different PFHI in India, irrespective of population groups (above 18 years), were included. Cross-sectional studies with comparison, impact evaluations, difference-in-difference design based on before and after implementation of the scheme, pre–post, experimental trials and quasi-randomised trials were eligible for inclusion.

Data extraction and synthesis

Data extraction was performed by three reviewers independently. Due to heterogeneity in population and study design, statistical pooling was not possible; therefore, narrative synthesis was performed.

Utilisation of healthcare, willingness-to-pay (WTP), out-of-pocket expenditure (including outpatient and inpatient), catastrophic health expenditure and impoverishment.

The impact of PFHI on financial risk protection reports no conclusive evidence to suggest that the schemes had any impact on financial protection. The impact of PFHIs such as Rashtriya Swasthy Bima Yojana, Vajpayee Arogyashree and Pradhan Mantri Jan Arogya Yojana showed increased access and utilisation of healthcare services. There is a lack of evidence to conclude on WTP an additional amount to the existing monthly financial contribution.

Different central and state PFHIs increased the utilisation of healthcare services by the beneficiaries, but there was no conclusive evidence for reduction in financial risk protection of the beneficiaries.


Not registered.

Strengths and limitations of this study

  • Inclusion of all kinds of empirical evidence to answer the research question about impact of public-funded health insurance (PFHI) schemes in India.
  • This is one of the very few reviews that has used a systematic methodology to provide latest evidence on the impact of the newly launched Pradhan Mantri Jan Arogya Yojana scheme in India.
  • Choice of quality appraisal tool, due to unavailability of other tools for this kind of study, was a limitation.
  • Multiple PFHI (state-specific and central) schemes in India (with different benefit packages) and modifications in the schemes due to changes in central/state governments led to high data heterogeneity.
  • Due to heterogeneity in data, we could not provide the pooled estimate via meta-analysis. However, results were explained via a narrative synthesis.


India has a complex and mixed healthcare framework with presence of parallel public and private healthcare systems. 1 2 There is a stark difference in government spending on both public and private healthcare. 3 Health policies in India have been guided by the principle of equity with prioritising the needs of the poor and underprivileged. 4 Out-of-pocket expenditure (OOPE) for health is one of the important factors while addressing the inequities in healthcare, and in India, it is an important source of healthcare financing. It is estimated that, in India, around 71% of the healthcare spending is met by OOPE. This not only is an immediate financial burden to the poor households but also pushes the households into a never-ending poverty trap. 5 Health-related OOPE poses a threat to the principle of financial risk protection and adds to the unaffordability and inaccessibility of healthcare for the poor. High OOPE also leads to catastrophic health expenditure (CHE), which is the increase in healthcare payment by a household, beyond the threshold, where the threshold is defined as the household’s income or capacity to pay. This is further divided into catastrophe 1, where healthcare OOPE exceeds by 10% of the household’s consumption expenditure, and catastrophe 2, if OOPE exceeds to more than 40% of the household’s non-food expenditure. The increase in OOPE affects the rural population marginally more than the urban population and the effect of OOPE is more pronounced among the people living below the poverty line (BPL) than those above the poverty line (APL), as BPL people are pushed more into poverty than APL, due to the high OOPE, when measured via the increase in poverty head counts. 5

Over the years, government of India has rolled out different initiatives to address the healthcare-related inequities in India. The public healthcare system was revised and reframed as the National Rural Health Mission in 2005, later restructured as National Health Mission in 2014. 5 6 Other initiatives like Janani Suraksha Yojana and the public funded health insurance (PFHI) schemes such as Rashtriya Swasthya Bima Yojana (RSBY) were also introduced to address the health inequalities, improve health outcomes and provide financial risk protection. 6 Many states sponsored health insurance (HI) schemes, viz., the Vajpayee Arogyashree Scheme (VAS) by Karnataka, Comprehensive Health Insurance Scheme (CHIS) by Kerala and Chief Minister Health Insurance Scheme (CMHIS) by Tamil Nadu, which have been introduced for ensuring financial protection of the vulnerable population.

Challenges posed by healthcare inequalities and inequities like OOPE can also be addressed via the Universal Health Coverage (UHC). The UHC, as defined by the WHO, means that all people and communities can use the promotive, preventive, curative, rehabilitative and palliative health services they need, of sufficient quality to be effective, while also ensuring that the use of these services does not expose the user to financial hardship. The UHC aims towards increasing the accessibility and affordability of healthcare for the entire population. The definition of UHC is embodied in its three objectives, that is, equity, quality and financial protection. 7

The twelfth 5-year plan of the government of India acknowledges the importance of UHC as it introduces a work plan for achieving UHC for the 1.3 billion population of the country. The agenda for this plan is based on the principle of providing affordable, accessible and good quality healthcare with financial protection to the people of the country. 8 The provision of UHC has been included in the National Health Policy of India (2017). To achieve the UHC, government of India announced the ‘Ayushman Bharat’ programme in 2018 with two initiatives, that is, (a) Health and Wellness center and (b) National health protection scheme —Pradhan Mantri Jan Arogya Yojana (PMJAY), that is intended to cover around 500 million beneficiaries (from vulnerable families) and is intended to cover up to Indian National Rupees (INR) 500 000 per family, per year, for secondary and tertiary hospitalisation. 9

The addition of PMJAY scheme to the various existing PFHI (central and state) schemes aims to increase the UHC, by increasing the affordability and accessibility of good quality healthcare. It is important to assess whether these schemes (including PMJAY) have been proven to be effective in improving health outcomes and providing financial protection to the vulnerable population. Following the principles of UHC, willingness to pay (WTP) for a particular HI scheme can also be used as an indicator to assess the affordability and effectiveness of a scheme in providing good quality healthcare. Additionally, data on beneficiaries willing to pay more or contribute more for a HI scheme (viz., CGHS) indirectly provide information on their satisfaction with the services provided by the scheme, therefore, making it an indicator to assess effectiveness of the scheme. The previous systematic review 10 on assessing the effectiveness of PFHI schemes in India was conducted before complete rolling out of the PMJAY and, therefore, did not include findings on the effectiveness of the scheme (PMJAY). Also, this review 10 did not provide information on the WTP component of assessing impact of the HI schemes. The present review was, therefore, conducted with an aim to provide information related to effectiveness of the central and state-funded HI schemes (including the PMJAY scheme) via healthcare utilisation, WTP and financial risk protection of the beneficiaries. This review was planned to answer the following research question: (a) What is the impact of PFHI schemes on access and utilisation of healthcare, willingness-to-pay and financial risk protection in India?

This systematic review follows the methodology by Cochrane handbook for systematic review of interventions 11 and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses checklist was used to report the review. 12

Criteria for including studies in the review

  • Population: population group above 18 years of age enrolled in a PFHI scheme in India.
  • Intervention: HI schemes funded by either central or state government, and that covers, range of services such as hospitalisation, out-patient charges, medicine costs, treatment procedures, etc. Different PFHI schemes in India, for example, RSBY, VAS, CMHIS and PMJAY were eligible to be included. Private or community-based HIs were not eligible to be included. Mixture of HIs was excluded provided a study carried out subgroup analysis for PFHIs.
  • Comparison: comparison group comprises of people who did not receive any PFHI services.
  • Outcomes: this review includes the following outcomes: (a) utilisation of healthcare, (b) WTP, (c) financial risk protection measured in terms of OOPE, CHE and impoverishment.
  • Study design: cross-sectional studies with comparison, impact evaluations, difference-in-differences design based on before and after implementation of the scheme, pre–post design, experimental trials and quasi-randomised trials were eligible to be included.

Search methods for identification of studies

Electronic databases such as Medline (via PubMed, Web of Science), SCOPUS, Social Science Research Network and International Initiative for impact evaluation (3ie) repository were searched from their inception until 15 July 2020; however, only English publications, published in the last 10 years were considered. References and forward citations of the included studies were scanned through for any additional eligible studies. Keywords were identified before the initiation of the search. The initial search was carried out in PubMed ( online supplemental file 1 ) and was replicated in other databases. Search was conducted by a designated information scientist.

Supplementary data

Data collection.

Result of search strategy was imported to Endnote V.X7 reference manager software. Duplicates were removed and the unique citations were exported to Microsoft Excel spreadsheet for screening.

Selection of studies

Unique citations were subjected to title and abstract screening independently by two reviewers. Eligible abstracts of all the relevant studies as per the inclusion criteria were included for full-text screening (by BTV, ER and SSP) and relevant ones from these were included for analysis. Before initiating full-text screening, we tried to retrieve the full-text articles by contacting authors of the respective articles and the full texts that were not retrieved were excluded. Disagreements were resolved by discussion or by a third reviewer.

Data extraction

Data extraction was done (by ER, BTV, SSP) using a predesigned data extraction form. Information on variables such as bibliographic details (author names, publication year, journal name); study details (information about the objectives of the study and research question addressed); study setting (name of the state, rural/urban); participant characteristics (age, gender, socioeconomic status, occupation); intervention details (name and type of HI, mode of delivery of the HI, incentives given, healthcare services covered, time duration of seeking HI, any additional HIs); comparison details; outcome details (information about changes in accessibility of healthcare, utilisation of healthcare services, OOPE, WTP, health outcomes like morbidity and mortality, measurement of the outcomes, method used for measurement, time at which the outcome was measured) and study design details (type of study design and analysis) were extracted.

After pilot testing of the data extraction form, it was revised according to the modifications suggested by the team. Disagreements among the reviewers, during data extraction, were resolved by consensus, if still not resolved, third reviewer was approached for resolving the disagreements. Extracted data from all the included studies were cross-checked and independent extraction was done for one-third randomly selected studies.

Methodological quality

The methodological quality of the included studies was assessed using Effective Public Health Practice Project Quality Assessment Tool (EPHPP). 13 This tool assesses methodological quality of the quantitative studies based on questions under the following seven domains, that is, (a) selection bias, (b) study design, (c) confounders, (d) blinding, (e) data collection method, (f) withdrawals and dropouts, (g) intervention integrity and (h) analysis. Quality assessment using this scale was performed independently by reviewers in groups of two. After discussion, global rating for the scale was followed and studies were marked as (1) methodologically strong, if none of the domains had any weak rating, (2) moderate, if at least one domain was marked as weak and (3) weak, if two or more domains were marked as weak. Quality assessment was performed using Microsoft excel spreadsheet.

Data analysis

Due to heterogeneity in data, narrative synthesis was performed to answer the research question. The results are summarised based on outcomes and types of PFHIs. The effect measures of included studies such as mean difference or correlation coefficients with appropriate CI and/or p values are reported.

Public and patient involvement

We did not involve public or patient during the process of this review.

The literature search on electronic databases generated 555 citation yield, out of which 179 were duplicates. Additionally, 17 records were identified from forward and backward reference checking. After title and abstract screening of 393 citations, 157 were included for full-text screening, of which finally 25 articles were included for data synthesis. Schematic representation of the selection process is shown in figure 1 .

An external file that holds a picture, illustration, etc.
Object name is bmjopen-2021-050077f01.jpg

PRISMA flow diagram. PFHI, public-funded health insurance; PRISMA, Preferred Reporting Items for Systematic Reviews and Meta-Analyses.

Characteristics of included studies

The summary of study characteristics is given in table 1 and the detailed characteristics of included studies are given in online supplemental file 1 .

Summary characteristics of included studies

Serial numberStudy characteristicSummary
1.Geographical locationOut of the 25 included studies, 10 studies were conducted nationally, and one was conducted in 12 cities—Bhubaneshwar, Thiruvananthapuram, Ahmedabad, Chandigarh, Meerut, Patna, Jabalpur, Lucknow, Hyderabad, Kolkata, Mumbai and Delhi. Other studies were conducted in different states. Studies covering northern region of India were conducted in Uttar Pradesh, Haryana and Punjab. Studies covering southern region of India were undertaken in Karnataka, Andhra Pradesh, Kerala and Tamil Nadu. Remaining studies were carried out in eastern region, ., Jharkhand, Bihar, Chhattisgarh and western region, ., Maharashtra.
2.PopulationPopulation among the included studies differed in characteristics. General population was included in nine studies. Around seven studies comprised of below poverty line (BPL) households. A mixed population from rural and urban households was considered in three studies. One study comprised of patients selected from Rashtriya Swasthya Bima Yojana (RSBY) empaneled hospitals and key stakeholders. One study included Self-help group members or head of the households. One study comprised of socially excluded households focusing on Scheduled Castes, Muslims and upper caste poor. Two studies comprised of a mix population of BPL and above poverty line households. One study comprised of Central Government Health Scheme (CGHS) and Ex-servicemen Contributory Health Scheme (ECHS) principal beneficiaries, empaneled private healthcare providers and officials of the schemes across 12 Indian cities.
3.Type of InsuranceCentral government-funded health insurance (HI): about 14 studies were conducted on central government-funded HI schemes, that is, RSBY. One study was conducted on Pradhan Mantri Jan Arogya Yojana (PMJAY). Three studies were conducted on CGHS. Two studies were conducted on Employee State Insurance Scheme. State government funded HI: three studies each were conducted on Vajpayee Arogyashree Scheme (VAS) in Karnataka and Rajiv Arogya Shree (RAS) in Andhra Pradesh. One study each reported on CHIS (Philip ) and ECHS. Any government-funded HI: remaining other studies were generally all PFHI.
4.Study designImpact evaluation including quasi-randomised designs was used in eight studies. Observational study design was used in five studies. Secondary data analysis was performed in 11 studies. Mixed method approach was used in one study.
5.Outcomes The impact of different PFHI schemes (including state insurance schemes) on financial risk protection were reported by 13 studies. Impact of RAS was assessed by single study. Five studies assessed the impact of CHIS on utilisation of healthcare. One study evaluated the impact of PMJAY on healthcare utilisation. Hospitalisation rate was reported in two studies with the implementation of RAS. Two studies reported hospital utilisation rate with implementation of VAS.
6.Methodological qualityOut of 25 studies, 3 were of moderate quality, 2 weak methodological quality and remaining others were of high quality.

Impact of PFHI on financial risk protection, utilisation of healthcare and WTP

This systematic review provides evidence on the impact of different PFHI schemes that have been operational in India. These schemes are funded by the central government, viz., RSBY, CGHS, Employee State Insurance Scheme, Swavlamban, Nirmaya-Disability Health Insurance Scheme and PMJAY and by the state governments like VAS (Karnataka), Rajiv Arogya Shree (Andhra Pradesh) and CHIS (Tamil Nadu). The eligibility criteria and benefits offered under each scheme vary according to different state governments. More information on these PFHI schemes is given in box 1 .

Central and state-sponsored PFHI schemes in India

Central-funded health insurance schemes

  • Rashtriya Swasthya Bima Yojana—RSBY (2008) is a central-funded health insurance scheme in which 75% of the annual premium is provided by the central government and rest 25% by the state governments. In-patient expenditure of upto Indian National Rupees (INR) 30 000 per family per annum is insured for below poverty line families. Unorganised sector is also covered under this scheme.
  • Prime Minister’s Jan Arogya Yojana—PMJAY (2018) is a fully government sponsored scheme, which provides a cover of INR 500 000 per family per year in government empanelled public and private hospitals of India, for secondary and tertiary-level hospitalisation. Vulnerable and below the poverty line (BPL) families are eligible to avail the services under this scheme.
  • Central Government Health Scheme (1954) is eligible for central government employees and pensioners enrolled under the scheme. According to this scheme, inpatient services at the government empanelled hospitals, outpatient services including medicines, consultation by experts, maternity and child health services (family welfare) and medical consultation for alternative system of medicines are covered.
  • Swavlamban (2015), this is a central-funded health insurance scheme for people with disabilities. Eligible population includes BPL and differently abled people with blindness, hearing impairment, leprosy-cured, locomotor disability, mental illness, etc. A sum of INR 200 000 per annum is covered and treatment of pre-existing illness is covered under the scheme.
  • Nirmaya-Disability Health Insurance Scheme (2008), this central-funded health insurance scheme is specifically for people with Cerebral Palsy, autism, multiple disabilities and mental retardation. Services of upto INR 100 000 are covered under this scheme.
  • Employee State insurance Scheme—Employee State Insurance Scheme(1952), this scheme is funded by the employers and staff contributions and is applicable to employees of factories and establishments drawing wages upto INR 15 000 a month. Under this scheme, a number of benefits to protect the employees or workers from illness, disability and death are paid to the beneficiaries. Benefits such as sickness benefit (70% of wages), temporary disablement benefit (90% of last wage), permanent disability benefit (90% of wage), maternity benefit (100% of wage), dependent benefit (90% of wage), INR 10000 to dependents for funeral expenses in case of death of the employees and other benefits like vocational and physical rehabilitation are given to the beneficiaries.

State government-funded health insurance schemes

  • Aarogyasri Scheme (2007), this scheme is by the Telangana state and BPL families belonging to the state are eligible. Benefits include cashless transactions for treatment of extreme illness, for up to INR 200 000 per year, covered under the scheme.
  • Ayushman Bharat—Mahatma Gandhi Rajasthan Swasthya Bima Yojana (2019), this scheme is by the government of Rajasthan and is formed by merging PMJAY scheme and Bhamashah Swasthya Bima Yojana. All the Rajasthani families belonging to BPL category are covered under this scheme. Under this scheme, an insured amount of INR 50 000and INR 450 000 are provided for secondary and tertiary illness, respectively.
  • Chief Minister’s Comprehensive Health Insurance Scheme (2012), this is a state-funded HI scheme by government of Tamil Nadu. People belonging to families of less than INR 72 000 are annual earning or less and members of unorganised labour welfare boards, including their families are eligible. Services and benefits of up to INR 500 000 per family per year are covered under the scheme.
  • Deen Dayal Swasthaya Seva Yojana (2016), by Goa government, for residents of Goa (residing for at least 5 years), central and state government employees already covered under other government health insurance benefits are eligible. Benefits include cashless inpatient services under government empanelled services. Annual coverage of upto INR 250 000 for a family of three and INR 400 000 for a family of four or more is given. Beneficiaries have to provide an annual premium of INR 200–300 to avail the benefits of the scheme.
  • Dr YSR Aarogyasri Scheme (Formerly called Rajiv Arogyasri Community Health Insurance Scheme)−2007, by the Andhra Pradesh government, this scheme covers BPL families from Andhra Pradesh. Under this scheme, free end-to-end cashless services are provided for patients undergoing treatment for therapies listed by the network hospitals. Free outpatient assessments are done for patients not undergoing treatment under the sited therapies.
  • Vajpayee Arogaya Shree (2009), this scheme is funded by the government of Karnataka and is applicable for BPL families from rural and urban areas of Karnataka. A total of INR 150 000 is reimbursed for services provided to five members of the beneficiary family, an extra sum of INR 50 000 per annum is provided in case-to-case basis.
  • West Bengal Health for All Employees and Pensioners Cashless Medical Treatment Scheme (2014), previously known as ‘West Bengal Health Scheme’, by the government of West Bengal, this scheme is for West Bengal government employees, pensioners and their family members. Benefits include reimbursement for in-patient services in the state empaneled hospitals and outpatient services for 15 diseases mentioned in the scheme. Cashless medical treatment for up to INR 100 000 is provided for inpatient treatment.
  • Yeshasvini co-operative farmer’s healthcare scheme (2003), by government of Karnataka, this scheme is for farmers who are members of the cooperative societies. According to this scheme, beneficiaries from the rural areas have to contribute INR 250 (for general category) and INR 50 (for SC/ST families) per annum. Beneficiaries from the urban areas have to contribute INR 710 (for general category) and INR 110 (for SC/ST) per annum. Benefits include inpatient services, discount rates for lab investigations, tests, outpatient services and medical emergency services due to mishaps during farming or any other agriculture related work.

Summary of the impact findings of RSBY and other PFHIs is given in tables 2 and 3 , respectively, and the detailed synthesis is provided in online supplemental file 1 .

Impact of RSBY on financial risk protection and healthcare utilisation

) The per-capita inpatient expenditure for RSBY-treated households, decreased in both rural and urban areas. The impact of RSBY on inpatient expenditure was reduced for unmatched and matched samples, when RSBY was implemented for a minimum of 2 months duration. After removing Uttar Pradesh (UP) and Haryana from the analysis, the triple difference findings (ie, with a second control of non-BPL households) showed a reduction in inpatient expenditure, but the double difference analysis showed an increase in inpatient expenditure due to RSBY. However, none of these findings was statistically significant. Both the studies included NSSO data from Andhra Pradesh, Karnataka and Tamil Nadu and used matching and DID methodology for analysis. Sabharwal , used PSM impact analysis to report that average annual household expenditure on inpatient care was significantly less for RSBY beneficiary households when compared with non-beneficiary households. This study also reported that average annual household expenditure spent on inpatient was higher for RSBY beneficiaries who used the smart card for inpatient expenses than the RSBY beneficiaries who did not use the RSBY smart card. However, a low methodological study reported a significant increase in inpatient expenditure for both public and private healthcare, in the state of Maharashtra. This difference was calculated using DID method for the years 2004 and 2012 (after implementation of RSBY in the state). The scheme did not have a significant effect on the OOPE expenditure for inpatient visits. A good methodological study applied the coarsened exact matching and linear and logit regression to report the impact of RSBY on OOPE for inpatient visits, among insured households. No statistically significant difference was reported between RSBY-insured and uninsured households. Another good methodological study, applied PSM and DID approach, to find the impact of RSBY on inpatient OOPE in total household expenditure, by dividing treatment districts into Treatment 1 (TT1), ie, March 2010 and Treatment 2 (TT2) group, ie, April 2010 to March 2012. No impact of RSBY on the inpatient OOPE as share of total household expenditure was observed. The probability of incurring 0 OOPE inpatient expenditure was not significantly different for RSBY and non-RSBY families. RSBY increased the probability of incurring inpatient OOPE by 22% (TT1) and 28% (TT2), respectively. However, these findings were not significant. provided inconclusive information on the effect of RSBY on outpatient OOPE. RSBY had a negative impact on the outpatient expenditure. According to Azam, implementation of RSBY reduced the per capita outpatient expenditure for both rural and urban areas. The outpatient expenditure reduced for RSBY households for the overall matched sample and for the matched sample minus UP and Haryana. There was no statistically significant difference between RSBY-insured and uninsured households in terms of OOPE on outpatient visits. RSBY increased the probability of incurring outpatient OOPE for households participating in RSBY before March 2010, by 23%; however, there was no significant effect on the scheme on outpatient OOPE for the RSBY households between April 2010 and March 2012. RSBY resulted in reduction of total OOPE of the households. The findings of these studies were mostly not significant. Two studies used matching and DID for analysis and two used matching and regression.
)Four studies provided information on the effect of RSBY on CHE, the RSBY households were less likely to incur CHE for outpatient care, inpatient care and overall CHE. It was observed that beneficiaries of the scheme reported a reduction in CHE; however, one study reported that there was no effect of RSBY on CHE. According to Azam, the effect was same for both rural and urban households. RSBY increased the likelihood of CHE 25. All these findings about the impact of RSBY on CHE were not significant. However, incidence of CHE was significantly reduced for RSBY households with childbirth in last 1 year of data collection. Two studies performed matching and analysed using DID analysis, and other studies performed matching and linear and logistic regression. The cost of medicines was significantly reduced by 22 INR for RSBY households in the rural areas; however, it increased for the urban households by 28 INR, but this result was not significant.
The effect of RSBY on impoverishment was not clear. One study reported that RSBY had no effect on impoverishment due to OOP on inpatient care and on the total overall probability of impoverishment. However, in another study among RSBY enrolled APL households, the incidence of health expenditure induced poverty was significantly increased, that is, APL households were pushed to BPL because of healthcare expenditure. Both the studies performed matching and used regression analysis, linear and logistic regression.
Around eight studies looked at the impact of RSBY on healthcare utilisation. The outcomes assessed by these studies include reporting of illness, hospitalisation rate, outpatient care and inpatient care utilisation and utilisation of hospital services. The impact of RSBY on hospitalisation was assessed by six studies ; all the studies showed increase in the hospitalisation, of which three studies showed significant increase in hospitalisation among female heads, scheduled tribes and for poorest. For women seeking treatment in obstetrics department. The studies suggested increase in both, inpatient and outpatient services. However, the results were significant for inpatient care for one of the studies. A study assessed the impact of health insurance on reporting morbidity and seeking treatment for illness in both rural and urban areas. The ATT analysis suggested increase in reporting of morbidity, seeking treatment for short-term and long-term illnesses and long-term morbidity in rural India compared with urban India. The increased value ranges from 0.7% to 3.2%. In urban India, the increase in reporting illness by RSBY holders varied from 2.3% to 2.4%, which was not statistically significant.

APL, Above poverty line; ATT, Average Treatment Effect on Treated; DID, Difference in Differences; NSSO, National Sample Survey Office; OOPE, out-of-pocket expenditure; PSM, Propensity Score Matching; RSBY, Rashtriya Swasthya Bima Yojana.

Impact of other public-funded health insurance (PFHI) schemes on financial risk protection and healthcare utilisation

)The PFHI households were less likely to entail OOPE and there was a significant reduction in OOP for these households. All the studies used regression analysis, linear and logit model for analysis. However, using Tobit regression, it was found that there was no effect of PFHI schemes on OOPE of the households. For Vajpayee Arogyashree Scheme (VAS), the OOPE was less for the insured households, when compared with uninsured households; however, the two-stage least squares (2sls) regression model reported no association between VAS enrolment and size of OOPE. According to Barnes , reduction in OOPE increased with increase in quantiles of spending. At the 75th quantile, the significant reduction in OOPE for VAS households was Indian National Rupees (INR) 4485, and at 95th quantile, it was INR 23548.19. There was no association between RAS (Andhra Pradesh- AP) enrolment and size of OOPE, by using 2sls regression model. By using difference-in-differences (DID), among phase 1 (2007), for Arogyashree enrolled households (AP), significant reduction in per-capital monthly OOP inpatient expenditure and inpatient drug expenditure was observed ; and an increase in inpatient expenditure for RAS households. For Rajiv Arogya Shree (RAS) (AP), Katyal reported a significant increase in both public and private inpatient expenditure, when calculated for the year 2004 and 2012 via DID analysis. Enrolment in CHIS of Tamil Nadu was not significantly associated with size of OOPE. For the CHIS operational in Kerala, the mean OOP expenses for inpatient services among insured participants (INR 448.95) was significantly higher than that of the uninsured households (INR 159.93), using Mann-Whitney U test. There was one study that reported findings on the effect of Pradhan Mantri Jan Arogya Yojana (PMJAY) on OOPE and CHE. It was reported that enrolment in PMJAY did not decrease the OOPE or CHE. There was statistically insignificant more reduction in OOPE for PMJAY enrolled households than other PFHI enrolled households. Statistical significant reduction in log of OOPE was marginally more for PMJAY-enrolled households than other PFHIs. OLS model was used for calculation of the abovementioned continuous outcome variable. As per the Probit model, there was a significant increase in CHE25 and CHE40 of PMJAY-enrolled households. But not for Propensity Score Matching (PSM) model, wherein reduction in OOPE for PMJAY and other PFHI was significant and CHE10 was not associated with PMJAY and PFHI enrolment according to any of the models. The naïve OLS model showed no association between the size of OOPE and enrolment under PMJAY or any PFHI schemes, these findings did not change under propensity score matching and Instrumental Variable (IV) models.
)Six studies reported the effect of PFHI schemes on CHE. The PFHIs led to reduction in CHE; however, the effect was very small. With PSM, the PFHI-enrolled households were 13% less likely to experience CHE10% and 6% less likely to experience CHE25. For the lowest three quintiles, this effect was even less pronounced as only 0.4% of PFHI households and 1% of PFHI households were likely to experience CHE10 and CHE25. There was a consistent increase in the catastrophic headcount threshold 40% of non-food expenditure for treatment, outpatient, inpatient and drugs. This increase was even reported in a long-term sample, that is, households that have been enrolled in the PFHI schemes for a year. Two studies used DID for analysis, whereas another used logistic regression for analysis. The VAS scheme had a limited effect on CHE; there was no association between enrolment in VAS and CHE25, CHE40 and CHE10, using two-step IV Probit model. In another study, the percentage of VAS households borrowing money for health reasons in the past 1 year was significantly lower than non-VAS households. According to Barnes , there was a marginal reduction in % of CHE (both as % of non-food expenditure and total expenditure) for VAS households than non-VAS households. This finding consists of both non-significant and significant results; however, reduction for 40% and 80% of CHE of the total non-food expenditure and 40% of CHE of the total expenditure was a significant finding. Additionally, money spent by VAS households on CHE was significantly lesser than non-VAS households. For RAS in Andhra Pradesh, there was no association between RAS enrolment and CHE25, CHE40, CHE10, by using two-step IV Probit model. There was no clear effect of Arogyashree enrolment on CHE. Enrolment in CHIS of Tamil Nadu was not significantly associated with CHE25, CHE40 and CHE10.
The PFHIs had a marginal effect on the reduction of impoverishment of households. For the overall sample, the PFHIs led to marginal reduction in overall impoverishment and OOP impoverishment, for both short-term and long-term samples (more than a year). However, in the state fixed effect model for overall impoverishment, it was reported that the PFHI schemes had no effect on impoverishment. The state-fixed effect model was used because of the assumption that presence of different state health insurance (HI) schemes alter the findings, and this was analysed using regression analysis. There was no significant difference seen among Arogyashree-enrolled households in AP, compared with south India and all India sample on impoverishment and impoverishment due to OOPE.
Two studies exclusively assessed impact of VAS on hospital utilisation rate. There was significant increase in utilisation of healthcare for all tertiary care facilities. The quasi-randomised study suggested significant increase in healthcare utilisation with respect to accessing healthcare for any symptoms with adjusted difference of 4.96%. The increase in rate of hospitalisation in primary and tertiary care varied from 4.3% to 12.3%, showing the significant change in healthcare utilisation after the implementation of VAS. The quasi-randomised study found significant increase in treatment-seeking behaviour for symptoms associated with cardiac conditions than for non-cardiac symptoms. Eligible households for VAS were 4.4% more likely to seek treatment than non-eligible households. The RAS was assessed by Katyal . The DID analysis suggested increase in healthcare utilisation in Andhra Pradesh and hospitalisation. The five studies assessed the impact of CHIS and other PFHIs and suggested an increase in inpatient and outpatient services. The matched cross-sectional study suggests significant increase in overall utilisation of inpatient services and non-significant results with respect to outpatient services among CHIS insured compared with uninsured. The multivariate analysis showed increased hospitalisation, hospitalisation for chronic conditions, hospitalisation among all age groups for PFHI households. It was also observed via Tobit regression model, being enrolled in PFHI was not significantly associated with length of stay during hospitalisation, contradictory to people with chronic illness. Though the association of HI with healthcare utilisation was high, inequality in accessing healthcare was higher among the higher economic people. The naive profit model analysis that assessed VAS, RAS and CHIS suggested significant increase in hospitalisation in Karnataka after the implementation of VAS. The only study that evaluated PMJAY; the data analysis from NSS data based on PSM and naive models on the hospitalisation did not show any significant difference in hospital care utilisation among both enrolled and non-enrolled population for insurance.

OLS, Ordinary Least Squares.

Financial risk protection

Twenty-one studies measured financial risk protection, of which 17 were of strong methodological quality, 14–30 3 of moderate methodological quality 31–33 and 1 weak methodological quality. 34 Nine studies 14 16 18 19 23 25 30 32 34 reported the impact of RSBY alone on financial protection. Thirteen studies 15 17 20–22 24 26–29 31–33 provided information on the effect of different PFHI schemes (including state insurance schemes) on financial risk protection.

Three high methodological quality studies reported a reduction in in-patient OOPE for RSBY households; 14 18 30 however, the findings were not significant. One low methodological study stated that after implementation of RSBY in Maharashtra state, there was a significant increase in in-patient expenditure for both public and private healthcare. 32 RSBY did not have a significant effect on in-patient OOPE as a share of total health expenditure, this was reported by two good methodological studies. 16 19 The findings for the impact of RSBY on outpatient OOPE were mixed as out of five good methodological quality studies, two studies mentioned that RSBY led to a reduction in outpatient OOPE, 14 18 two studies reported that RSBY did not have any impact on the outpatient OOPE 16 30 and one study reported that the probability of incurring increased after implementation of RSBY. 19 It was reported that the RSBY households were less likely to incur CHE for outpatient care, in-patient care and overall CHE; 14 16 19 however, one high methodological quality study reported that there was no impact of RSBY on CHE. 25 All these findings were non-significant. The effect of RSBY on impoverishment was not clear as one study reported that RSBY had no effect on impoversihment, 16 whereas another study reported an increase in impoverishment among the Above Poverty Line (APL) housholds. 25

For other PFHI schemes, the findings for effect of HI schemes on financial risk protection were mixed. Three studies reported a reduction in OOPE for insured households, 20 21 26 whereas another study reported no effect on OOPE. 24 For households insured under VAS and RAS, no effect of these schemes was seen on OOPE. 17 One study reported a reduction in in-patient drug expenditure for RAS households; 15 however, other studies reported an increase in-patient household expenditure. 27 32 For CHIS in Tamil Nadu, one study reported no association of CHIS with size of OOPE 17 and another study reported an increase in OOPE in-patient expenditure. 33 It was reported that CHE was reduced for households enrolled under different PFHI schemes, 21 28 however, specifically for VAS, one study reported reduction in CHE, 31 and another study reported no association between CHE and insurance. 17 For CHIS and RAS, no association was reported for CHE and insurance schemes. 15 17 Enrolment in PMJAY did not decrease the OOPE or CHE of the enrolled households. 29

Due to mixed evidence reported for the impact of PFHI schemes on different financial risk protection parameters, it is not possible to conclude whether these schemes have proven to be beneficial in reducing financial risk of the beneficiaries. A summary of these findings is given in tables 2 and 3 .

Access and utilisation of health services

Overall, 16 studies assessed the impact of PFHI on access and utilisation of health services ( tables 2 and 3 ). The HI programmes were RSBY, 14 16 23 26 27 30 32 35 VAS 36 37 RAS, 17 27 32 CHIS 20 21 24 26 33 and PMJAY. 29 Of the 16 studies, 13 studies 14 16 17 20 21 23 24 26 27 29 30 36 37 were assessed to be of strong methodological quality, 32 33 2 were assessed as of moderate quality and 35 1 was rated as weak quality. The analysis that was carried out majorly to look at the impact was logistic regression, profit models and other types. The outcomes that were reported include reporting of illness or morbidity, hospitalisation rate, outpatient care and in-patient care utilisation, duration of hospitalisation and utilisation of hospital services. Findings demonstrated increased access, utilisation of healthcare (both in rural and urban areas) and hospitalisation for RSBY. 14 16 23 26 27 30 32 35 For other PFHI schemes like VAS, RAS and CHIS, an increase in utilisation of healthcare and in-patient outpatient services was reported. 20 21 24 26 32 33 36 37 No significant difference in healthcare utilisation was reported for PMJAY beneficiaries. 29


A high methodological study 38 reported WTP for the insurance scheme. A majority (71 per cent) of CGHS beneficiaries considered that their current contribution was low and were willing to contribute more. Only 28 per cent Ex-servicemen Contributory Health Scheme beneficiaries were willing to pay an additional monthly financial contribution for better quality healthcare under the schemes. In comparison to higher employment grade beneficiaries, the CGHS beneficiaries from low employment grade were more willing to pay an additional amount to the existing monthly financial contribution.

This review identified and provided information on the impact of different PFHI schemes (operational in India) on healthcare utilisation, WTP and financial risk protection of the beneficiaries. It was observed that although the utilisation of healthcare services via in-patient and outpatient visits increased for insured beneficiaries, there was inconclusive evidence on the impact of different PFHII schemes on financial risk protection.

Our findings report that there is no conclusive evidence to suggest that RSBY reduced the OOPE and CHE or had an impact on financial risk protection. For other PFHIs including the state-sponsored PFHIs, viz., RAS, VAS and CHIS, the findings suggest a mixed impact of these schemes on OOPE, CHE and impoverishment, leading to inconclusive evidence for financial risk protection. Our findings are similar to another systematic review, 10 which reported lack of substantial evidence for reduction in OOPE or improvement in financial risk protection by PFHI schemes in India.

For financial risk protection, varying results, from different studies for the same PFHI scheme, resulted in mixed findings for this outcome. Therefore, it was a challenge to pool evidence together and conclude on the impact of PFHI schemes on financial risk protection. One of the plausible reasons for this can be the different study designs and analysis methods used by different studies to assess the impact of financial risk protection. Also, difference in benefits packages and implementation of the scheme by various successive governments might have resulted in these mixed findings for this outcome.

One of the reasons for studies reporting no substantial impact of RSBY on financial risk protection can be the limited insurance cover, for example, INR 30 000 annually under RSBY. As the utilisation of healthcare and hospitalisation under RSBY has increased over the years, 10 it is possible that beneficiaries would have been hospitalised for hospital services of more than INR 30 000, leading to additional OOP payment. Hospitalisation for services not offered by the RSBY package and denial of hospitalisation by the empaneled hospitals has also led to an increase in OOPE. 39 Another reason for the negligible impact of RSBY in reducing OOPE, as reported in some of the studies, can be the operational or functional error of the scheme. An important component of the scheme is the insurance companies, which are responsible for enrolling beneficiaries, empaneling hospitals, processing claims and reimbursing money. Delayed reimbursement from the insurance companies leads to hospitals asking beneficiaries to buy medicines and other consumables from outside, which results in high OOPE. Additionally, as there is no incentive for the insurance companies to keep a check on the OOPE payments, hospitals might charge patients or deny reimbursement of money on trivial grounds, leading to high OOPE. 39 Another reason could be (which is based on personal experience of authors) to get an appointment for the surgery in empenelled hospitals, beneficiaries of the PFHIs usually wait for a longer period of time. Therefore, to avoid the delay in treatment, beneficiaries have to resort to OOP.

The impact of PFHIs (other than RSBY) including the state-sponsored schemes was reported to be mixed and inconclusive, similar to another systematic review that reported lack of substantial evidence of impact on OOPE for PFHI operational in low and middle-income countries (LMICs). 40 Additionally, as the functioning of any PFHI scheme depends on the governance, different governance structures and demographic profiles of the states would have led to heterogeneity in results. Poor impact of different PFHIs on financial risk protection (reported in some of the studies) can be attributed to similar factors that affect RSBY, that is, low coverage or benefits offered by the schemes leading to OOPE and CHE even for insured beneficiaries and interference or reimbursement issues due to functioning of insurance companies or ‘trusts’.

This systematic review is the first one that has focused on the impact of PMJAY. Our findings suggest that there is a lack of evidence related to the impact of PMJAY, as only one study reported the poor impact of PMJAY on reduction in OOPE and financial risk protection. The reasons for poor impact can be similar as experienced by the earlier PFHIs schemes that is, problem of ‘double billing’, private providers monopoly and administrative problems. As PMJAY is a relatively new scheme, more evidence is needed to conclude on its impact. Additionally, as the only study included in the review was specifically for the state of Chhattisgarh, availability of evidence from other states is needed to summarise the impact of this scheme.

According to our review, there was an increase in incidence of outpatient and in-patient visits and the utilisation of medical services, however, the healthcare utilisation rate differed between states. The utilisation rate increased both among rural and urban areas for the RSBY and VAS. However, there was one study that assessed healthcare utilisation for PMJAY, and the results reported no significant increase in utilisation of healthcare by the PMJAY enrolees. One plausible reason for these results could be the lack of awareness regarding PMJAY, as it is a relatively new scheme. It is not justified to conclude based on a single study, and at the same time, it is important to look into various other aspects, due to which the results of the PMJAY are insignificant in increasing healthcare utilisation. The healthcare utilisation rate was assessed in terms of reporting morbidity, hospitalisation, utilisation of inpatient and outpatient services.

Overall, majority of the evidence suggests that implementation of PFHI has increased hospitalisation and the utilisation of outpatient care. Our findings are consistent with other systematic reviews, 10 40 that is, PFHIs had a positive influence on utilisation of healthcare and hospitalisation in India and other LMICs. Although there is substantial evidence on the impact of PFHI on healthcare utilisation, more rigorous evaluation studies are required to evaluate the impact of health insurance schemes and especially the newly launched PMJAY.

It was reported that although the participants were willing to pay more, the findings for WTP are inconclusive, because the evidence is generated from a single study and the focus of the insurance was limited.

Strengths and limitations

Our review is the first comprehensive review, which has summarised the impact of PFHI schemes in India (including the new scheme of PMJAY under the Ayushman Bharat) on utilisation of healthcare and financial risk protection. One of the limitations of the review is the choice of quality assessment tool used for critical appraisal of included studies due to absence of any other valid tool for secondary data analysis. Responses to some of the questions and individual domain ratings for the EPHPP tool were subjective, although, before finalising the rating, we had a substantial discussion on every domain rating score. Additionally, the tool is used to assess quality of all the quantitative studies, which makes it very vague. Also, due to heterogeneity in methods, population and types of insurances, we could not perform meta-analysis.

Implications of practice and research

Our systematic review has vast policy and practice implications. Since UHC is one of the important components to achieve the sustainable development goals, the role of PFHI becomes even more important in providing equitable and affordable healthcare access to everyone. Financial risk protection is one of the key components of any PFHI scheme that ensures affordable healthcare for everyone. Poor impact of PFHIs on financial risk protection also indicates failure of the PFHI schemes. More research on PFHIs, especially PMJAY and its effect on financial risk protection and healthcare utilisation, are needed as this scheme is an important component of the Ayushman Bharat scheme under the UHC. Similarly, future studies can consider studying the effect of some of the state-funded insurances such as by the government of Goa and West Bengal, which also includes APL households, for which, currently, there is no evidence.

State and central governments could consider including APL households, especially middle-income group under the purview of PMJAY. There should be mechanisms to check corruption in the process of PFHI enrolment and focus could be provided to ease out the administrative difficulties faced by people at the time of claiming insurance. Future research in form of rigorous qualitative research, formative evaluations and process evaluations should be directed towards the reasons for the failure of different PFHIs in improving financial risk protection of the beneficiaries and demand-side and supply-side barriers to implementation and uptake of PFHI. Research reporting reasons for failure of the PFHIs, in improving financial protection, will help in revising and modifying the functioning and implementation of the PFHI schemes for benefit of the consumers.

PFHI schemes, viz, RSBY, VAS, RAS and CHIS have been operational in India since 2008. These schemes have been impactful in increasing healthcare utilisation in terms of outpatient and in-patient care in both rural and urban areas. However, evidence related to financial risk protection was mixed and inconclusive. The new scheme of Pradhan Mantri Jan Arogya Yojana or PMJAY has incorporated administrative and strategic changes, which were based on the shortcomings of earlier PFHIs, viz., provision of a 24-hour inquiry helpline and increased coverage of healthcare services and benefit package. However, limited evidence available on the impact of PMJAY suggests no improvement in healthcare utilisation and financial risk protection of the beneficiaries. Future research on the impact of PMJAY and reasons for failure of other PFHIs on financial risk protection need to be explored.

Supplementary Material


We acknowledge PHRI-RESEARCH grant by Public Health Foundation of India, with the financial support of Department of Science and Technology to partially support authors to carry out this research. We would like to acknowledge the technical support provided by Public Health Evidence South Asia (PHESA), Prasanna School of Public Health (PSPH), Manipal Academy of Higher Education (MAHE), Manipal. We would like to thank Dr. Jisha B Krishnan, Research Assistant, PHESA, PSPH, MAHE, Manipal for supporting us in the title/abstract screening and quality assessment of the included studies and Dr. Vijay Shree Dhyani, Research Assistant, PHESA, PSPH, MAHE, Manipal, for supporting us in title abstract screening.

Twitter: @ParsekarShrads

Contributors: RB is the guarantor of the review. BTV, ER, RB and SSP conceptualised the topic. RV developed search strategy and conducted the search. SSP carried out title/abstract screening and BTV, ER, SSP carried out full text screening. BTV, ER and SSP extracted first round of data extraction, analysed and synthesised the data for the review. Extracted data from all the included studies was cross-checked and independent extraction was done for one third randomly selected studies by BTV, ER, SSP. Quality assessment was performed by BTV, ER, SSP. BTV, ER, SSP drafted the first version of report, which was further edited by RB, BTV, ER, RV, BU and SSP. All the authors read, provided feedback and approved the final report.

Funding: The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

Competing interests: None declared.

Provenance and peer review: Not commissioned; externally peer reviewed.

Supplemental material: This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

Data availability statement

Ethics statements, patient consent for publication.

Not applicable.

Ethics approval

This study does not involve human participants.

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Research Article

Impact of Publicly Financed Health Insurance Schemes on Healthcare Utilization and Financial Risk Protection in India: A Systematic Review

* E-mail: [email protected]

Affiliation School of Public Health, Post Graduate Institute of Medical Education and Research, Chandigarh, India

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Affiliation Indian Institute of Public Health, Delhi, Public Health Foundation of India, Delhi NCR, India

  • Shankar Prinja, 
  • Akashdeep Singh Chauhan, 
  • Anup Karan, 
  • Gunjeet Kaur, 
  • Rajesh Kumar


  • Published: February 2, 2017
  • https://doi.org/10.1371/journal.pone.0170996
  • Reader Comments

Fig 1

Several publicly financed health insurance schemes have been launched in India with the aim of providing universalizing health coverage (UHC). In this paper, we report the impact of publicly financed health insurance schemes on health service utilization, out-of-pocket (OOP) expenditure, financial risk protection and health status. Empirical research studies focussing on the impact or evaluation of publicly financed health insurance schemes in India were searched on PubMed, Google scholar, Ovid, Scopus, Embase and relevant websites. The studies were selected based on two stage screening PRISMA guidelines in which two researchers independently assessed the suitability and quality of the studies. The studies included in the review were divided into two groups i.e., with and without a comparison group. To assess the impact on utilization, OOP expenditure and health indicators, only the studies with a comparison group were reviewed. Out of 1265 articles screened after initial search, 43 studies were found eligible and reviewed in full text, finally yielding 14 studies which had a comparator group in their evaluation design. All the studies (n-7) focussing on utilization showed a positive effect in terms of increase in the consumption of health services with introduction of health insurance. About 70% studies (n-5) studies with a strong design and assessing financial risk protection showed no impact in reduction of OOP expenditures, while remaining 30% of evaluations (n-2), which particularly evaluated state sponsored health insurance schemes, reported a decline in OOP expenditure among the enrolled households. One study which evaluated impact on health outcome showed reduction in mortality among enrolled as compared to non-enrolled households, from conditions covered by the insurance scheme. While utilization of healthcare did improve among those enrolled in the scheme, there is no clear evidence yet to suggest that these have resulted in reduced OOP expenditures or higher financial risk protection.

Citation: Prinja S, Chauhan AS, Karan A, Kaur G, Kumar R (2017) Impact of Publicly Financed Health Insurance Schemes on Healthcare Utilization and Financial Risk Protection in India: A Systematic Review. PLoS ONE 12(2): e0170996. https://doi.org/10.1371/journal.pone.0170996

Editor: Cheng-Yi Xia, Tianjin University of Technology, CHINA

Received: June 30, 2016; Accepted: January 13, 2017; Published: February 2, 2017

Copyright: © 2017 Prinja et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper.

Funding: This research was supported by USAID India grant AID-386-A-14-00006. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors declared that no competing interests exist.


Achieving Universal Health Coverage (UHC) is a major policy goal in health sector globally. [ 1 , 2 ] Despite the acceptance of UHC at policy level in India, around three-quarters of healthcare spending is borne by households. [ 3 ] The recent National Sample Survey (NSS) report reveals that only 12% of the urban and 13% of the rural population is under any kind of health protection coverage. [ 4 ] Not surprisingly, nearly 26% of the total health spending by rural households is sourced from either borrowings or selling of assets. [ 4 ] Further, OOP spending pushes approximately 3.5% to 6.2% of the India’s population below the poverty line every year. [ 5 – 7 ]

Traditionally, health care financing in India had been mostly restricted to the supply side, focussing on the strengthening of infrastructure and human resource. The advent of National Rural Health Mission (NRHM) in 2005 also served as an instrument of strengthening the supply-side infrastructure. [ 8 ] The Planning Commission’s High Level Expert Group (HLEG) proposed a model to achieve UHC under which citizens would have full access to free healthcare from a combination of public and private facilities. [ 9 ] This shifted government’s attention from its prior focus on supply side to demand side financing models in the form of publicly sponsored health insurance schemes.

Since 2007, several publicly financed health insurance schemes have been launched in India both at the state level such as Rajiv Aarogyasri Health Insurance Scheme (RAS) in Andhra Pradesh [ 10 ], Rajiv Gandhi Jeevandayee Arogya Yojana (RGJAY) in Maharashtra [ 11 ], Chief Minister’s Comprehensive Health Insurance scheme (CMCHIS) in Tamil Nadu [ 12 ], and Rashtriya Swasthya Bima Yojana (RSBY) at the Central level. [ 13 ] These demand-side financing mechanisms entitle poor and other vulnerable households to choose cashless healthcare from a pool of empanelled private or public providers. While the RSBY scheme was designed and implemented by the Ministry of Labour and Employment (MOLE), the implementation role for RSBY–now called Rashtriya Swasthya Suraksha Yojana (RSSY, however we refer to as RSBY in the entire paper), has been recently transferred to Ministry of Health and Family Welfare in 2015. [ 14 ]

In the last 7–8 years, a large amount of government’s money has been invested in the implementation of these health insurance schemes. A total of INR 370 billion (USD 587 million) tax money has been allocated for RSBY since its launch in 2008–09. [ 15 ] If the budgets of state sponsored schemes are also pooled, it amounts to a significant amount of public exchequer’s money, thereby justifying a need to determine whether these schemes are achieving their desired objectives.

In line with this policy need for an appraisal, the Government of India constituted a task force on costing of health services. One of the terms of reference for this Task Force included an assessment of RSBY. [ 16 ] Also, several State Governments have set up independent commissions to determine the best way forward to achieve universal health coverage. [ 17 , 18 ] As a result, there is a need to systematically review evidence in terms of whether these schemes have been able to achieve the objectives of universalizing health care for which they were launched. Two reviews have been published earlier, both of which measured the impact of health insurance in low and middle income countries as a whole without a specific focus on India. [ 19 , 20 ] Specific characteristics of the scheme implementation and contextual differences in various countries support a case for a systematic review with a national focus. Further, one of these review focussed on only social and community based health insurance schemes. [ 20 ] However, much of the current interest is on determining success or failure of tax-funded health insurance schemes which cover nearly 14% out of the total 15% population who have any form of health care insurance.

As a result, we conducted a systematic review to primarily assess the impact of publicly financed health insurance schemes on utilization of health care services, out of pocket expenditure, financial risk protection and on the health of population in India. Secondly, we also summarise the findings of various process evaluations, which have assessed the performance of these schemes in terms of extent of community awareness, determinants of enrolment and utilization, accessibility and utilization of different services across states in India.


Search strategy.

A comprehensive computerised search was conducted to search for empirical studies focussing on the impact or evaluation of publicly sponsored health insurance schemes in India. PubMed, Google scholar, Ovid, Scopus and Embase databases were searched to identify eligible studies published till September 2015. Official websites of various health insurance schemes ( www.rsby.gov.in , www.aarogyasri.telangana.gov.in , www.sast.gov.in/home/VAS.html , http://www.cmchistn.com and / www.chiak.org ) were also searched. The review used the search strategy consisting of following key words:

(((((((((((Publicly sponsored health insurance) OR government sponsored health insurance) OR Rashtriya Swasthya Bima Yojana) OR RSBY) OR rajiv arogyasree health insurance scheme) OR rajiv aarogyasri community health insurance scheme) OR vajpayee arogyasri) OR vajpayee arogyasri yojana) OR chief minister kalaignar insurance scheme) OR rajiv gandhi jeevandayee arogya yojana) OR comprehensive health insurance scheme)”.

The search strategy was defined by reviewing the previously done systematic reviews and in consultation with the research staff from the Advanced Centre for Evidence-Based Child Health and the library staff of the Post-Graduate Institute of Medical Education and Research, Chandigarh. The key words were checked for controlled vocabulary under Medical Subject Headings (MeSH) of PubMed. Two investigators (ASC and GK) had access to abstract and full text of the paper to decide on its inclusion. Discrepancies between the two investigators were solved by discussion with the third investigator (SP). Two authors of this review are familiar with the methods of systematic review (SP and AK), two are experts in health economics with strong interest and familiarity with the health financing policies (SP and AK), while another author is a senior public health expert (RK).

Inclusion criteria and study selection

The review included peer-reviewed articles, government reports and working papers that were reported in the English language and excludes abstracts, expert opinions, narrative reviews, commentaries, case reports and conference papers.

The studies were selected based on a two stage screening process as per PRISMA guidelines [ 21 ] ( S1 Table ). The first step comprised of searching for studies based on the search strategy from the selected databases and websites. Following this, duplicates were removed and the remaining studies were then screened by applying inclusion criteria to the titles and abstracts. Based on the screening of titles and abstracts, potentially relevant articles were selected for further review, which involved examining the content of their full text. After reviewing full text, only empirical research studies were considered eligible while others were excluded. At this stage, a bibliographic search of the selected studies was also carried out to identify additional relevant articles. The search was continued until no new article was found ( Fig 1 ).


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Data extraction and quality

A standardised data extraction form was developed to collect information from the selected studies on the relevant impact outcomes, besides the general and methodological aspects. The latter included information on year of publication, funding agency, study design or type of study (experimental and observational), description of intervention and control group, duration and location of the study, sample size, type of outcome assessed, etc. Two researchers (ASC and GK) independently extracted the data and assessed the quality of the studies.

The studies selected in the review were divided into two groups i.e., with a comparison or control group (against which the insured group was measured) and without a control group (descriptive in nature). To assess the impact on utilization, OOP expenditure and health indicators, studies with a comparison group alone were reviewed. Process level indicators were assessed based on the findings of studies from both the groups, i.e. with and without control group. Further, quality of these studies was assessed by Effective Public Health Practice Project (EPHPP) quality assessment tool for quantitative studies. [ 22 ] The components of quality assessment in the EPHPP tool include type of study, presence of any kind of selection bias, consideration to blinding and confounders, validity and reliability of the data collection tools and consideration to withdrawals and loss to follow ups, if any. We also categorised the studies (having a control group) based on their analytical approach–i.e. Intention to Treat (ITT) and Average Treatment effect on the Treated (ATT) analysis. [ 23 ] Basically, ITT measures impact on the eligible population irrespective of getting enrolled or utilising the services while ATT measures impact on those who are enrolled in the scheme.

A total of 1265 articles were identified from databases (n = 1244), websites (n = 18) and bibliographic search (n = 3) as shown in Fig 1 . After removing duplicates, the remaining 814 articles were screened by applying inclusion criteria to the titles and abstracts. A total of 671 articles were excluded in the 1 st stage screening and 143 studies were identified as eligible for 2 nd screening. Full text papers of these 143 studies were reviewed. Ultimately, 43 articles were found eligible for this systematic review. Out of this, 14 studies had a comparison group [ 24 – 37 ] and the remaining 29 were without a comparison group [ 38 – 66 ].

General characteristics of selected studies

Out of the 14 studies with a comparison group, 7 were cross-sectional studies with data collected from intervention and control group, while 6 studies were quasi experimental in nature adopting a pre and post design. Out of these 6 studies, 2 studies evaluated the impact based on difference in difference analysis and one study followed geographic discontinuity design ( Table 1 ). Most of these studies (n = 8) were published in peer reviewed journals while the remaining were reports (n = 3) and working papers (n = 3). Around half of the studies (n = 6) evaluated RSBY scheme, followed by studies on RAS (n = 3), Vajpayee Aarogyashri Scheme (VAS) (n = 1) and Comprehensive Health Insurance Scheme in Kerala (n = 1). Further, focus of the remaining 3 studies was on both RSBY and RAS. Twelve studies evaluated the health insurance scheme within 3 years of their implementation while the remaining 2 studies evaluated the scheme following 3 years of implementation.



With regards to studies without a comparison group (n = 29), majority of them (59%, n = 17) were published in peer reviewed journals, 28% (n = 8) were working papers and the remaining were reports (13%) ( Table 1 ). All the studies had a cross sectional study design, out of which 8 studies were based on secondary data and 4 had a regression model based analysis. Nearly 83% (n = 24) of the studies evaluated RSBY, followed by 10% studies (n = 3) on RAS. More than half (56%, n = 16) of these studies were done within 3 years of the implementation of the scheme, followed by 31% (n = 9), assessing the scheme following 3 years of implementation. For the rest, 13% of the studies duration between implementation of the scheme and evaluation of the study was not clearly stated in the article.

Impact assessment

Table 2 summarises the impact of various publicly financed health insurance schemes reported in the selected 14 studies with a comparison group. Nine of these studies were based on ATT analysis approach [ 26 – 29 , 31 , 34 – 37 ], while remaining 5 studies were ITT in nature. [ 24 , 25 , 30 , 32 , 33 ]



Among these, 7 studies (50%) assessed financial risk protection only, one study measured utilization alone, while remaining 5 studies (36%) evaluated both utilization and financial risk protection. Only one study included all the impact outcomes including the impact of insurance on the health of the population.

Financial risk protection.

Out of the 13 studies assessing financial risk protection [ 24 – 36 ], 9 (69%) reported no reduction in OOP expenditure among enrolled households after implementation of health insurance schemes. [ 24 – 27 , 30 – 32 , 34 , 35 ] In terms of quality, 7 studies had a strong methodological design [ 24 , 30 – 33 , 35 , 36 ], out of which 5 reported increase in the OOP expenses. [ 24 , 30 – 32 , 35 ] The remaining 2 studies, which evaluated state sponsored insurance schemes of Andhra Pradesh and Karnataka, showed a decline in OOP expenses. [ 33 , 36 ] Out of the five strong quality studies showing increase in OOP expenditure, 3 studies were based on the same data and methodology but had measured varied outcomes in terms of financial protection. [ 24 , 30 , 32 ] Specifically, among studies measuring catastrophic health expenditure as a measure of financial protection, 3/4 th showed increase in the incidence of catastrophic health count. [ 24 – 26 ] Only a single high quality study, which evaluated Andhra Pradesh’s RAS scheme showed a reduction in incidence catastrophic head count after implementation of the scheme. [ 33 ]

The studies (n = 5) which measured the impact of RSBY only, were either of a low or moderate quality and among these, 2 studies reported a reduction in OOP expenses [ 28 , 29 ], but none showed any decrease in incidence of catastrophic health expenditure. Among the 4 studies which evaluated state sponsored schemes [ 33 – 36 ], 2 reported reduction in OOP expenses [ 33 , 36 ], and one study showed decrease in number of catastrophic head count [ 33 ]. One study which considered all the publicly sponsored health insurance schemes together as one, reported that all these were associated with rise in OOP expenditure and catastrophic health expenditure. [ 25 ]

Three studies, which were based on similar data and methodology, compared the impact of RAS in Andhra Pradesh with that of RSBY in Maharashtra. [ 24 , 30 , 32 ] One of these studies showed that in both the states, schemes were associated with increase in OOP expenditure and catastrophic health expenditure, with higher increase in the state of Maharashtra. [ 24 ] Other study showed that this increase in expenditure was observed among both the household groups who accessed care in public or private health facilities. [ 32 ] The latter finding implied some protective effect of RAS in Andhra Pradesh, relative to RSBY in Maharashtra. However, independently, RAS did not result in a reduction in OOP expenses among insured. Another study inferred that this relative reduction in OOP expenditure and catastrophic health expenditure in Andhra Pradesh (compared to Maharashtra) was concentrated more among the richest 60%, implying an inequitable effect. [ 30 ]

Among 7 studies with a quasi-experimental design, 5 showed that the insurance schemes were associated with a rise in OOP expenditure. [ 24 , 30 – 32 , 35 ] Similarly, among the 3 studies based on DID analysis, 2 reported showed rise in OOP expenditure. [ 24 , 25 ] Among 6 cross sectional studies, a study reported similar [ 27 ] amount of OOP expenditures among enrolled and non-enrolled group and 2 studies reported reduction in incurring of OOP expenses. [ 28 , 29 ]

Out of the 7 studies with a strong methodological design, 4 were done within 3 years of the implementation of the schemes, of which 2 studies reported reduction in OOP expenditure [ 33 , 36 ] and a study showed reduction in catastrophic health expenditure. [ 33 ] Studies done at and after 3 year of implementation showed, that schemes were associated with increase in OOP expenses and number of catastrophic head count. [ 24 , 28 , 30 , 32 ]


Overall 7 articles assessed the impact of health insurance on utilization of health services and the findings of all these studies showed that these insurance schemes were associated with increase in consumption of health care services. In terms of quality, 5 studies were of strong methodological rigour [ 24 , 30 , 32 , 35 , 36 ] and the remaining 2 had a moderate or weak quality. [ 26 , 37 ] The increase in utilization among these studies varied from 12.3% to 244% among the insured as compared to non-insured households. The studies based on ATT analysis showed that this increase was in in the range of 12.3%-244%, [ 26 , 35 – 37 ] whereas studies based on ITT analysis showed the increase in the range of 22%-56% among the enrolled households. [ 24 ]

Among the studies which evaluated RSBY alone (n = 2), increase in utilization varied from 15.3% in Maharashtra [ 37 ] to 244% in Karnataka. [ 26 ] For the state-specific insurance schemes, increase in consumption of health care varied from 12.3% in Karnataka’s VAS [ 36 ] to 35.4% for Comprehensive Health Insurance Scheme of Kerala. [ 35 ]

One out of the 3 studies which were based on same data and methodology, comparing the impact of RAS in Andhra Pradesh with that of RSBY in Maharashtra, showed an increase in utilization in post insurance period in both states with higher increase in the state of Andhra Pradesh. [ 24 ] Another study showed that this significant positive growth in the utilization was more among both the poor and better-off households in Andhra Pradesh as compared to Maharashtra. Further, it also showed that the increase in utilization of simpler conditions such as fever was more among poor while the rich reported more consumption of services required for the management of chronic conditions such as kidney problems. [ 30 ] The third study showed that in the post insurance period utilization of services in private hospitals increased in Andhra Pradesh and decreased in Maharashtra. On the other hand, utilization in public facilities reduced in both the states with more decrease seen in the state of Andhra Pradesh. [ 32 ]

Increase in the utilization rate in early years of implementation was much higher (12.3% to 244%) [ 26 , 35 , 36 ], than the increase in utilization reported (15%) when the scheme was evaluated after 5 years of its implementation. [ 37 ]

Impact on health.

A single study assessed the impact of health insurance on the improvement of health among those enrolled in the scheme. It reported that the mortality rate from conditions covered by the scheme was less in eligible households as compared to ineligible households (0.32% vs 0.90%). [ 36 ] While about half (52%) of deaths among enrolled households were among people aged <60 years, this rose to more than three-fourths (76%) among those not enrolled. The study also showed that impact of the scheme in reducing mortality was more pronounced among poor in the treatment areas and not among population above poverty line.

Process evaluation

Out of the 29 studies without a control group, 77% of them (n = 24) were on RSBY only and the remaining studies either assessed state sponsored health insurance scheme only or compared it with RSBY. The process indicators included in these studies were level of awareness, determinants of enrolment and utilisation and accessibility to hospitals.

Eight studies done across states in India measured the awareness level of various attributes related to the health insurances schemes. [ 26 , 29 , 38 , 41 , 43 , 44 , 53 , 63 ] Further, 10 studies also assessed the source of awareness about these schemes across various states in India. [ 26 , 29 , 38 , 41 , 43 , 44 , 53 , 57 , 63 , 66 ] Furthermore, 6 studies evaluated the role of determinants for enrolment. [ 37 , 42 , 46 , 47 , 49 , 50 ] Similarly, 8 studies measured the association of factors influencing utilization of health services, among the enrolled households. [ 24 , 33 , 37 , 40 , 47 – 49 , 51 ]

Awareness levels of various attributes related the insurance schemes were reported to be in the range of 13.6% to 90% as shown in Table 3 . Awareness was highest for information on BPL status and 5 member per household as the eligibility criteria and relatively lowest for transport allowances and diseases/conditions covered under the insurance schemes. Specifically, information on eligibility condition of 5 members per household varied from 31% in Chhattisgarh to around 63% in Haryana. Further, awareness level ranged from 32% in Gujarat to 65% in Himachal Pradesh regarding information on free treatment being given under the scheme. Similarly regarding knowledge of transport allowance, information levels ranged from 13.6% in Haryana to 43% in Uttar Pradesh. Panchayats (median: 61%) and friends/ neighbours (median: 44.5%) were the most common source of awareness. In around 60% and 43% of the reported studies, panchayat and friends/neighbour respectively were stated as the source of awareness in more than 60% of the studied population. Less than 15% of the population stated the contribution of health care workers for awareness generation ( Table 4 ).





Determinants of enrolment.

The studies selected in the review showed that enrolment was inversely associated with administrative areas having a larger geographic size [ 42 , 49 ] and families belonging to socially disadvantaged communities [ 42 , 46 , 50 ] ( Table 5 ). Further, 2 studies also reported that low enrolment was related to the poverty status of the households. [ 46 , 47 ] On the contrary, higher enrolment was associated with households headed by a female. [ 37 , 46 ] Further, districts with good development indicators in terms of better business index [ 49 ], low corruption index [ 46 ], higher coverage of preventive health services such as DPT immunization [ 50 ] and better accessibility to commercial banks or nearby town [ 50 ] were also positively associated with high enrolment rates. None of the selected studies identified ‘self-selection’ while analysing the determinants of enrolment although one study mentioned that there is less likelihood of self-selection in RSBY as the scheme is open only for poor. [ 50 ]



Determinants of utilization.

Higher the number of empanelled hospitals and proportion of private hospitals in a district, higher were the rates of hospitalization [ 47 – 49 , 51 ] ( Table 6 ). Less advantaged castes were associated with lowest utilization rates. [ 24 , 33 , 37 , 40 ] In contrast to trends in enrolment, districts with better indicators of economic development such as access to educational, commercial, hospitals and transportation institutions and better coverage of preventive or primary health services (such as DPT3 immunization rate) were linked with low utilization rates. [ 48 , 49 ] RSBY scheme was mostly utilized for gynaecological procedures (5–20%), urogenital (33.4%), gastrointestinal (11%) and ophthalmic (6%) conditions ( Fig 2 ). On the contrary, state sponsored health insurance schemes catered mainly to tertiary care needs for injuries (21–27%), oncology (6–17%) and cardiovascular/respiratory/nephrology conditions (9–10%). RSBY scheme was used predominantly for medical as compared to surgical procedures.





Private facilities were observed as the preferred ones by the beneficiaries of both RSBY and state level health insurance schemes. Findings from the states of Gujarat [ 40 ], Uttar Pradesh [ 29 ] and Haryana [ 66 ], showed private facilities to be most commonly utilized (73%, 87% and 67% respectively) under RSBY. Three-quarters of all claims under RSBY in India were reported to have utilized care in private facilities, with Bihar, Madhya Pradesh, and Rajasthan reporting 100% of claims from private facilities. [ 51 ] Over time, claims in Chattisgarh increased by 266% (INR 38436 to 140900) in private hospitals, as compared to 204% increase in public facilities (INR 30525 to 92905). [ 45 ] Considering, state sponsored scheme of Andhra Pradesh, number of surgeries performed in private hospitals were 2.85 times higher than in public facilities. [ 60 ]

It could be assumed that large percentage of empanelled private providers is the reason for high utilization of these facilities under RSBY. The states of Haryana, West Bengal and Bihar, where proportion of private empanelled hospitals was around 90%, the proportion of overall claims in these facilities was more than 95% in each of these states. ( Fig 3 ), [ 67 ] Similarly, in Tripura, Himachal Pradesh and Assam where proportion of private facilities was less than 20%, the proportion of claims in these facilities was less than 30%. Districts such as Kanpur Nagar from UP, Dangs from Gujarat and Karnal from Haryana, having more than 90% of total empanelled hospitals as private had highest hospitalisation rate across the state. [ 47 ]



Even states with lower private sector empanelment, also continue to show higher share of private sector utilization. Private sector contributed 65% and 25% of the total empanelled facilities in the states of Madhya Pradesh and Rajasthan, while 100% of the claims were from private sector in these states ( Fig 3 ). Similarly, Uttar Pradesh and Jharkhand having 98% of claims from private facilities had 62% and 54% of the total empanelled facilities as private respectively. Kerala and Assam were the outliers, where the despite a proportion of private empanelled hospitals of around 50%, the utilization of these facilities was below 30%.

Uniformity and accessibility of hospitals.

Hospitalisation rates under RSBY scheme fell steadily with distance of home from health facility. [ 40 ] Those who lived more than 30 km had a lower inpatient rates as compared to those who lived within 30 km. Likewise, for Andhra Pradesh’s RAS scheme, as distance from the nearest treatment facility increased, the utilization rates declined. [ 58 ] Density of the empanelled hospitals was significantly and positively correlated with the utilization rate. [ 47 , 48 ]

Historically, the health system in India has had a maternal and child health (MCH) centric approach, both in financing and delivery of health services. [ 68 ] Low public spending on health care shifted the burden of seeking care on households by paying out of pocket expenditures. [ 9 ] This led to either a barrier in accessing health services, or catastrophic outcomes for those who sought care. [ 4 , 5 , 7 ] Further, low capacity of public health system has resulted in rapid development of private health care delivery system, as well as a push towards various demand-side financing mechanisms. [ 69 , 70 ] The recent policy thrust on UHC has shifted attention towards a broader focus on health system to meet all the needed preventive as well as curative health care needs of the population.

It is in this contextual framework that various publicly financed health insurance schemes evolved in India. At a time when the debate of ‘how’ to achieve universal health care is raging wide discussions, our paper attempts at summarizing the existing evidence. Our review is the first comprehensive systematic review which focuses on Indian publicly financed health insurance schemes. We find that there is positive evidence that the utilization of hospital services increased after introduction of these insurance schemes. Moreover, this increase in utilization has sustained over time and across regions. However, commensurate with an increase in utilization of services, so far we do not find substantial evidence on reduction of out-of-pocket expenditures or improvement of financial risk protection. In fact, 5 out of 8 studies actually reported either no impact or an increase in OOP expenditures. Finally, although one study does point to some beneficial effect on health of population, there is dearth of robust evidence on the impact of these schemes on the health of the population.

Although our review finds a general increase in utilization of hospitalization services, there are still several unanswered questions. This increase in utilization of hospitalizations could be attributed to 3 reasons: firstly, it could be a result of a pent-up demand on account of previously present barriers to access. However, this could explain the increase in hospitalization during early years of the implementation of health insurance schemes. Persistence of increased utilization over the last 7–8 years rules out this reason. Secondly, it could be attributed to either genuine reduction of financial barriers to access or a supplier induced demand. Given the available evidence, it is difficult to single out the reason from amongst the latter two. Examination of presence and extent of supplier-induced demand is certainly an important future area of research for health economists, although establishing a causal link is fraught with several methodological issues and problems with data availability. It can also be seen that the positive impact on utilization of services which we find in most existing studies could be an underestimate of the true effect considering low awareness level among the enrolled population. As time passes and awareness level improve, this could lead to further increase in utilization of health services [ 71 – 73 ]. Moreover, our review also shows that this increase in utilization is more concentrated in private sector hospitals. Together these two findings imply that it is not only likely to impose fiscal constraints on the government for sustainability of these schemes, but also expected to divert large amount of tax based public money towards private sector.

A second point of concern which points to inefficiency is the presence of conditions such as gynaecological problems, deliveries, cataract etc. among some of leading conditions for which hospitalizations are done. [ 40 , 49 ] This is a pointer to inefficient allocation of resources since while on one hand the Government is already allocating significant supply-side resources through flagship health programs on strengthening public sector facilities for providing universal access to these conditions [ 74 ]; on other hand these conditions continue to be major sources of utilization in the demand-side financing schemes. Considering that much of this utilization in these demand-side financing schemes happens in the private sector, it is inefficient as it leads to double allocation for meeting the same demand. Moreover, this also points to a possible gaming by providers [ 75 , 76 ], where dual practice could possibly result in siphoning off of public sector demand to private sector for provisioning under these schemes.

Contradicting findings in terms of increase in utilization and lack of significant improvement in financial risk protection needs careful examination. This could be explained based on several possible reasons, Firstly, the height of benefit package under existing schemes such as RSBY is inadequate. With a cover of INR 30,000 per year per household, several high cost illnesses leave the individuals at risk of impoverishment. Secondly, the depth of coverage could possibly be inadequate. RSBY and other state health insurance schemes primarily cover the services requiring hospitalization, while nearly 70% of overall health expenditure is on account of outpatient care which is not covered. [ 77 ] So, even enrolled households continue to pay for outpatient care. Thirdly, there is a possibility that even the private empanelled hospitals are charging the patients who pay the same out-of-pocket. [ 40 ] Finally, and importantly, it is possible that the bulk of private empanelled providers which exist in the urban areas remain elusive to the vast rural population which continues to face geographic barriers to accessing care. [ 78 ] This possibility is also substantiated by the finding that the benefits are mostly gained by the richer quintiles and urban population. In view of limitations of existing evidence, a conclusive statement will require further research which examines these possible explanations. Important policy inferences emerge from the latter point–firstly, that no such demand-side health financing scheme can succeed in providing financial risk protection in the absence of a strong primary health infrastructure. Secondly, this primary health infrastructure needs to be equitably distributed and utilized. Finally, since the rural and disadvantaged areas have not seen the growth of private sector, there is significant merit in the role of investing to strengthen public sector infrastructure.

An important finding from the process evaluation reports is the inequitable nature of the enrolment and utilization. This point towards inefficient targeting towards those who need the services most. Several reasons could be considered to explain this finding. Firstly, insurance companies have an incentive to enrol less than the maximum number of 5 household members, because the premium payment is linked to the number of households enrolled, rather than members. Moreover, villages with higher proportion of BPL population have poorer enrolment. This could be a result of systematic attempt to enrol the better-offs rather than worse offs. Average family size reported in India is 4.8. However studies from the review shows average family size of households under RSBY in the range of 1.46–3.77. [ 27 , 29 , 48 , 50 ]. This points to the need for comparing the characteristics of family member enrolled in RSBY against those who are left out. This would help ascertain whether there is any cream skimming by insurance companies. Secondly, it could be seen that in more backward villages, due to paucity of means, poorer households are not able to get a BPL card. And since the means test to identify a poor household is the BPL card, hence the very poor are unable to enrol in the scheme. [ 42 , 47 ] This in turn could lead to poor targeting under the scheme as most needy and poor are unable to obtain BPL card. Another reason which could contribute to poor enrolment among the poorest could be low level of awareness regarding the means to get an insurance card. This also correlates with the finding of low awareness about publicly sponsored health insurance schemes among the target population. [ 26 , 29 , 38 , 43 , 53 ]

We would like to acknowledge that impact evaluation was the primary objective of the present paper, and as a result we might have missed out on some studies which were purely describing the processes. Secondly, we are also likely to miss qualitative narrative of the implementation of these insurance programs, and which do provide important insights. This also explains our reporting of impact assessment results first, followed by process evaluation. However, it is also important to understand that the process evaluations in literature are not as standardized as the impact evaluations, which makes it difficult to systematically report. Not every process evaluation reported findings on the same set of indicators. This is an important gap in literature and needs to be bridged in future studies.

Given the current policy directions for universal health care, publicly financed health insurance schemes are likely to stay. Hence there is a need to design the schemes and implement safeguards so that the benefits of the risk pooling can be maximized. Firstly, benefits of these demand-side financing mechanisms will be not reaped unless the basic health care infrastructure for delivery of primary health services is strong. This primary health care infrastructure will be necessary to provide basic health services, besides serving as gatekeeping for specialist services. Examples from Thailand, United Kingdom and Mexico substantiate this claim. [ 79 ] Secondly, the public sector needs to be strengthened and incentivized to compete for provision of services. This will generate much needed extra revenue for the public health system, which can in turn be used to strengthen provision of health services. The public sector has demonstrated that it can provide universal access for health care services, which are delivered efficiently and utilized equitably, the only condition being that enough resources are spent. Various interventions for improving access to maternal health care services and institutional delivery in public sector illustrates this point. [ 80 – 82 ] Thirdly, there is a need to invest in systems to monitor and evaluate implementation of health insurance schemes. This is also essential in view of large private sector presence, which has perverse incentives to induce demand; and the intermediary purchaser/ insurer, who has perverse incentive to reduce utilization through cream-skimming. Overall, publicly financed health insurance schemes are not the panacea to achieve UHC in India. Instead, these schemes need to be aligned with proper strengthening of the public sector for provision of comprehensive primary health care. Secondly, presence of health insurance schemes could be used as an opportunity to reform the tenets of the health sector which are beyond the routine regulatory frameworks.

Supporting Information

S1 table. prisma checklist..



We are grateful to the assistance provided by the Mrs Neelima Chadha from the library of Post Graduate Institute of Medical Education and Research (PGIMER) Chandigarh.

Author Contributions

  • Conceptualization: SP RK AK.
  • Data curation: ASC GK.
  • Formal analysis: ASC SP.
  • Funding acquisition: AK SP.
  • Methodology: SP RK.
  • Validation: RK AK.
  • Writing – original draft: ASC SP.
  • Writing – review & editing: SP ASC AK GK RK.
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An analytical study on Indian health insurance sector and its sustainability

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Health insurance sector in India is going through a drastic transformation with the advent of new players into the market and has become quite competitive with rising awareness among the insured. This research paper intends to highlight the performance of health insurance sector during the last seven years. The basic objective of the research paper is thus to offer an insight into the growth pattern and overall scenario of the health insurance prevailing in the country. Research is based on secondary data published by Insurance Information Bureau for the period 2003-2010. Premium collection has grown by 42% on year on year basis. Compounded annual growth rate for no of policies is 20.35%. No of members has grown by 36.84% percent. Results are analyzed using t statistics. Results of t statistics prove that more than 40% growth in premium and more than 20#% growth in no. of policies are statistically significant and 25% growth rate in number of members is not statistically significant...

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For Enterprise and Economy to prosper, Insurance is more of a &quot; partnership that allows individuals and businesses to spread their wings and go where they might otherwise would not have dared to go &quot;. In this context, Micro insurance, relatively a new term, is aimed at poor sections of the population and is a fast – growing sector providing innovative tools for risk protection of low income people. It has become an integral part of poverty eradication programs to strengthen the development of both the financial, health and social security systems. The occurrence of any emergency or risk has the tendency to easily erode the hard earned incomes of the poor households. This is particularly the case when households have no formal cover over them .Micro-insurance serves as a shock reliever to major risks and ensure that the household involved can continue on their route to escaping poverty. The present study is an attempt to analyse the current status of micro insurance in Indi...

IAEME Publication

Against the backdrop of high risky nature of the insurance industry and the growing scepticism regarding the working of companies in this sector, it becomes immensely critical to evaluate and compare financial performance of public and private non life insurance companies operating in India. In this paper a set of ratios have been presented and discussed to lend a hand in the analysis of a non-life insurer’s financial and statistical returns. Three parameters taken from CARAMEL model have been used to analyse and evaluate the financial performance of selected public and private non-life insurers. The first indicator is “Earnings and Profitability” under which five ratios, i.e., Claim Ratio, Expenses Ratio, Combined Ratio, Investment Income Ratio and ROE Ratio have been analysed. The second indicator is “Management Soundness” under which ratio of operational expenditure to gross premium has been analysed. The third and the last indicator is “Liquidity” under which ratio of quick assets to current liabilities has been statistically analysed. For measuring the performance of selected sample companies on the basis of these financial indicators, the present study employs ratio analysis. Various statistical tools like mean, standard deviation and F-test have been used to test the CARAMEL parameters statistically. The analysis of overall underwriting performance reveals that every rupee of earned premium is being drained away in the form of claims and costs more specifically by private insurers which speaks of their improper risk selection and mismanaged expenditure policy. In terms of management soundness, although both set of companies seem to have breached the standard benchmark of 20 percent (ratio of management INTERNATIONAL JOURNAL OF MANAGEMENT (IJM) ISSN 0976-6502 (Print) ISSN 0976-6510 (Online) Volume 6, Issue 1, January (2015), pp. 507-526 © IAEME: http://www.iaeme.com/IJM.asp Journal Impact Factor (2014): 7.2230 (Calculated by GISI)   IJM © I A E M E International Journal of Management (IJM), ISSN 0976 – 6502(Print), ISSN 0976 - 6510(Online), Volume 6, Issue 1, January (2015), pp. 507-526 © IAEME 508 Showket Ahmad Dar & Ishfaq Ahmad Thaku, “A Comparative Analysis of Financial Performance of Public and Private Non Life Insurers in India” – (ICAM 2015)    expenses to premium), but at the same time both public and private insurers managed to control the management expenses to a significant level. The statistical analysis of liquidity ratio reveals that both public and private insurers lack high degree of liquidity and none of the insurers under study seem to have followed the benchmark of 100 percent liquidity ratio.

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judicial academy, guwahati 9, national law university, delhi 37, national law university, odisha 16, national museum institute of hisotry of art conservation and musicology 2, national sanskrit university 47, navrachana university 25, nehru gram bharati university 157, netaji subhas open university 1, netaji subhas university of technology 9, niilm university 4, niit university 15, nims university rajasthan 359, nirma university 393, nirwan university jaipur 7, nitte university 94, noida international university 87, noorul islam centre for higher education 351, north-eastern hill university 2754, north eastern regional institute of science and technology (nerist) 101, odisha university of agriculture and technology 160, om sterling global university 114, o.p. jindal university 9, opjs university 21, oriental university 92, osmania university 1683, pacific university 2318, padmashree dr. d.y. patil vidyapeeth, navi mumbai 79, pandit deendayal petroleum university 180, pandit s. n. shukla university, shahdol 10, parul university 195, patna university 341, peoples university, bhopal 7, periyar maniammai university 157, periyar university 3301, pes university 24, p.k. university 28, pondicherry university 2509, poornima university 76, potti sreeramulu telugu university 1, p p savani university 3, pravara institute of medical sciences 47, presidency university 115, presidency university, karnataka 167, prist university 86, pt. ravishankar shukla university 2934, pt. sundarlal sharma open university bilaspur 250, punjab engineering college (deemed to be university) 60, punjabi university 3222, rabindra bharati university 421, rabindranath tagore university, bhopal 332, raffles university 16, raiganj university 185, rai university 308, rajasthan technical university, kota 149, rajiv gandhi institute of petroleum technology 37, rajiv gandhi national institute of youth development 7, rajiv gandhi national university of law punjab 32, rajiv gandhi proudyogiki vishwavidyalaya 645, rajiv gandhi university 661, rama devi womens university 13, ramakrishna mission vivekananda educational and research institute 222, ramakrishna mission vivekananda university 1, rama university, uttar pradesh 78, ramchandra chandravansi university 11, ranchi university 110, rani channamma university 122, rani durgavati vishwavidyalaya 356, rashtrasant tukadoji maharaj nagpur university 313, rashtriya raksha university 19, ravenshaw university 486, rayat bahra university, mohali 16, regional centre for biotechnology 1, renaissance university 26, reva university 219, rimt university 38, rkdf university 130, rkdf university ranchi 2, rk university 294, rnb global university 5, sabarmati university 1, sage university, indore 82, sai nath university 138, sambalpur university 1814, sam higginbottom institute of agriculture, technology and sciences 482, sampurnanand sanskrit vishwavidhyalaya 3, sanchi university of buddhist-indic studies, bhopal 6, sandip university 46, sangam university 63, sanjay gandhi post graduate institute of medical sciences, lucknow 12, sanjay ghodawat university 2, sankalchand patel university 65, sanskriti university 49, sant baba bhag singh university 38, sant gadge baba amravati university 3038, sant longowal institute of engineering and technology 315, santosh deemed to be university 104, sardarkrushinagar dantiwada agricultural university 0, sardar patel university 3396, sardar vallabhbhai national institute of technology surat 411, sarvepalli radhakrishnan university 145, sastra university 553, satavahana university 9, sathyabama institute of science and technology 422, saurashtra university 3283, saveetha university 837, school of planning and architecture, bhopal 22, school of planning and architecture, new delhi 49, school of planning and architecture vijayawada 10, seacom skills university 111, sgt university 105, sharda university 215, sher-e-kashmir university of agricultural sciences and technology of jammu 145, shivaji university 4947, shiv nadar university 183, shobhit university, gangoh 29, shobhit university, meerut 166, shoolini university of biotechnology and management sciences 515, shree somnath sanskrit university 110, shri govind guru university 81, shri guru ram rai university 75, shri jagdishprasad jhabarmal tibarewala university 3665, shri khushal das university 143, shri lal bahadur shastri national sanskrit university 545, shri mata vaishno devi university 232, shri ramswaroop memorial university 153, shri rawatpura sarkar university 1, shri vaishnav vidyapeeth vishwavidyalaya 47, shri venkateshwara university, uttar pradesh 2, shyam university 14, sidho kanho birsha university 132, sido kanhu murmu university 21, sikkim manipal university 93, sikkim university 228, siksha "o" anusandhan university 568, singhania university 23, sir padampat singhania university 77, sndt womens university 1263, solapur university 270, south asian university 2, s. p. jain institute of management and research 5, sree sankaracharya university of sanskrit 723, sri balaji university, pune 4, sri balaji vidyapeeth 53, sri chandrasekharendra saraswathi viswa mahavidyalaya 417, sri devaraj urs academy of higher education and research 44, sri guru granth sahib world university 85, sri guru ram das university of health sciences 9, sri krishnadevaraya university 3434, srinivas university 20, sri padmavathi womens university 247, sri ramachandra institute of higher education and research 374, sri sai university 2, sri sathya sai institute of higher learning 110, sri satya sai university of technology & medical sciences 80, sri siddhartha academy of higher education 69, sri sri university 17, sri venkateswara institute of medical sciences 24, sri venkateswara university 5461, srm institute of science and technology 1594, srm university- ap 21, srm university, delhi-ncr, sonepat 83, starex university 16, st. joseph university, dimapur 18, st. peter’s institute of higher education and research 401, st. xaviers university, kolkata 1, sumandeep vidyapeeth deemed to be university 49, sunrise university 5, suresh gyan vihar university 387, sushant university (earlier ansal university) 59, swami rama himalayan university 22, swami ramanand teerth marathwada university 5648, swami vivekanad subharti university 224, swami vivekananda yoga anusandhana sansthana 145, swami vivekanand university 80, swarnim gujarat sports university 7, symbiosis international university 525, tamil nadu agricultural university 1419, tamil nadu dr. ambedkar law university 59, tamil nadu open university 51, tamilnadu physical education and sports university 259, tamil nadu teachers education university, chennai 203, tamil nadu veterinary and animal sciences university 79, tamil university 169, tantia university 253, tata institute of fundamental research 1034, tata institute of social sciences 599, techno india university 4, teerthanker mahaveer university 93, teri school of advanced studies 138, tezpur university 939, thapar institute of engineering and technology 1203, the assam kaziranga university 24, the assam royal global university 15, the charutar vidya mandal cvm university 5, the english & foreign languages university, hyderabad 814, the gandhigram rural institute 1396, the icfai university, dehradun 48, the iihmr university, jaipur 35, the indian law institute, new delhi 19, the lnm institute of information technology 34, the national academy of legal studies and research (nalsar) university of law 50, the national university of advanced legal studies 4, the neotia university 4, the northcap university 109, the tamil nadu dr. m.g.r. medical university 466, the university of burdwan 3079, the west bengal national university of juridical sciences 39, thiruvalluvar university 289, thunchath ezhuthachan malayalam university 16, tilak maharashtra vidyapeeth 915, tilka manjhi bhagalpur university 269, tripura university 476, tumkur university 299, uka tarsadia university 121, university of agricultural sciences, bangalore 458, university of agricultural sciences, dharwad 286, university of agricultural sciences, raichur 63, university of allahabad 2611, university of calicut 2279, university of delhi 5326, university of engineering and management, kolkata 17, university of gour banga 12, university of hyderabad 2774, university of jammu 1237, university of kalyani 2898, university of kashmir 1971, university of kerala 7426, university of kota 139, university of lucknow 6983, university of mysore 4387, university of north bengal 2072, university of patanjali 33, university of petroleum and energy studies (upes) 404, university of rajasthan 1810, university of science and technology, meghalaya 101, u.p. pt. deen dayal upadhyaya pashu chikitsa vigyan vishwavidhyalaya evam go anusandha sansthan 46, u p rajarshi tondon open university 336, usha martin university 21, utkal university 5691, uttarakhand open university 19, uttarakhand sanskrit university 57, uttarakhand technical university 279, uttaranchal university 74, vardhaman mahaveer open university, kota 23, v. b. s. purvanchal university 9194, veer kunwar singh university, arrah 32, veer narmad south gujarat university 3393, veer surendra sai university of technology 189, vellore institute of technology bhopal 8, vellore institute of technology, vellore 2280, vellore institute of technology (vit-ap) 77, vels university 1006, vel tech rangarajan dr. sagunthala r&d institute of science and technology 250, vidyasagar university 900, vignans foundation for science technology and research 214, vijayanagara sri krishnadevaraya university, bellary 131, vikram university 95, vinayaka missions research foundation 393, vinoba bhave university 270, vishwakarma university 27, visva-bharati 1664, visvesvaraya national institute of technology 293, visvesvaraya technological university, belagavi 1118, vivekananda global university 74, william carey university 3, world university of design 1, xim university 26, yashwantrao chavan maharashtra open university 330, ybn university 47, yenepoya (deemed to be university) 135, yogi vemana university 92, about shodhshuddhi.

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Synopses/mrps/pdfs/fellowships, universities contributing, universities+cftis/inis signed mou, prof devika p madalli, sh. manoj kumar k, general / technical query, antiplagiarism query.


The Shodhganga@INFLIBNET Centre provides a platform for research students to deposit their Ph.D. theses and make it available to the entire scholarly community in open access. The repository has the ability to capture, index, store, disseminate and preserve ETDs submitted by the researchers. [Read l]

phd thesis on health insurance in india

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  1. Shodhganga@INFLIBNET: Health Insurance in India: strategies for synergy

    Shodhganga. The Shodhganga@INFLIBNET Centre provides a platform for research students to deposit their Ph.D. theses and make it available to the entire scholarly community in open access. Shodhganga@INFLIBNET. Indian Institute of Foreign Trade. Department of Commerce.

  2. Shodhganga@INFLIBNET: Health economics impact of health insurance on

    Shodhganga. The Shodhganga@INFLIBNET Centre provides a platform for research students to deposit their Ph.D. theses and make it available to the entire scholarly community in open access. Shodhganga@INFLIBNET. University of Mysore.

  3. Shodhganga@INFLIBNET: A Study of Health Insurance Schemes in India with

    Shodhganga : a reservoir of Indian theses @ INFLIBNET The Shodhganga@INFLIBNET Centre provides a platform for research students to deposit their Ph.D. theses and make it available to the entire scholarly community in open access.

  4. Impact of public-funded health insurances in India on health care

    Inclusion of all kinds of empirical evidence to answer the research question about impact of public-funded health insurance (PFHI) schemes in India. This is one of the very few reviews that has used a systematic methodology to provide latest evidence on the impact of the newly launched Pradhan Mantri Jan Arogya Yojana scheme in India.

  5. Health Insurance as a Healthcare Financing Mechanism in India: Key

    Abstract This study provides strategic insights and a business model perspective on health insurance as a vehicle for financing healthcare. It uses both primary (expert interview) and secondary data to investigate the overall disease burden and healthcare industry trends and track healthcare financing through the health insurance mechanism in India. To identify the critical success factors and ...

  6. Determinants of Health Insurance Penetration in India: An Empirical

    Insufficient participation of the government in the provision of health care services to the people puts tremendous pressure on households financing their health care expenditure from private sources. Health insurance can be seen as an effective measure to circumvent this problem. However, penetration of health insurance in India is very low.

  7. Health insurance sector in India: an analysis of its performance

    The objective of this study has been to: review health insurance scenario in India; and study the performance of health insurance sector in India with respect to underwriting pro t or loss by the application of regression analysis.

  8. Health insurance in India: what do we know and why is ethnographic

    What forces have brought health insurance to the attention of Indian policy-makers? In sum, they include (1) high burden of ill health; (2) low public spending on healthcare, (3) high private (especially OOP) healthcare expenditure; and (4) limited coverage by the existing health insurance schemes. India is currently going through a stage of health tran-sition characterized by high burdens of ...

  9. PDF A Study of Health Insurance in India

    health insurance in India. Health insurance can become an alternate tool to finance health care.

  10. Impact of Publicly Financed Health Insurance Schemes on ...

    Several publicly financed health insurance schemes have been launched in India with the aim of providing universalizing health coverage (UHC). In this paper, we report the impact of publicly financed health insurance schemes on health service utilization, out-of-pocket (OOP) expenditure, financial risk protection and health status. Empirical research studies focussing on the impact or ...

  11. PDF Health Insurance Sector in India: Growth & Future Prospects

    Introduction India's Insurance sector has been growing in recent years, its share in the Global Insurance market remains low. India's Insurance sector underlines issues such as inadequate investment in Insurance products, low penetration, density rates, deteriorating financial Health of public-sector players and dominant position.

  12. Shodhganga@INFLIBNET: Health Care Strategy And Its Impact On Quality Of

    Shodhganga : a reservoir of Indian theses @ INFLIBNET The Shodhganga@INFLIBNET Centre provides a platform for research students to deposit their Ph.D. theses and make it available to the entire scholarly community in open access.

  13. A Study of Health Insurance in India

    The present paper is an attempt to outline the current picture of health insurance sector in India which is a part and parcel of general insurance.

  14. PDF Impact of Health Applications on The Indian Health Insurance companies

    The units of analysis within this study are from some random Health Insurance companies in India and the way these health insurance companies can make use of mobile health applications for business model innovation and creating customer value.

  15. Phd Thesis on Health Insurance in India

    Phd Thesis on Health Insurance in India - Free download as PDF File (.pdf), Text File (.txt) or read online for free.

  16. An analytical study on Indian health insurance sector and its

    Health insurance sector in India is going through a drastic transformation with the advent of new players into the market and has become quite competitive with rising awareness among the insured.

  17. PDF IIT Kanpur

    IIT Kanpur

  18. (PDF) A Study on Awareness and Purchasing Pattern of Health Insurance

    The study aims to find out the awareness level of health insurance among the people in Ernakulam district and their pattern of subscription of health insurance policies.

  19. An analytical study on Indian health insurance sector and its

    Health insurance sector in India is going through a drastic transformation with the advent of new players into the market and has become quite competitive with rising awareness among the insured. This research paper intends to highlight the

  20. Shodhganga : a reservoir of Indian theses @ INFLIBNET

    Shodhganga : a reservoir of Indian theses @ INFLIBNET The Shodhganga@INFLIBNET Centre provides a platform for research students to deposit their Ph.D. theses and make it available to the entire scholarly community in open access.

  21. Dissertations / Theses: 'National Health Insurance Scheme (NHIS

    List of dissertations / theses on the topic 'National Health Insurance Scheme (NHIS)'. Scholarly publications with full text pdf download. Related research topic ideas.

  22. Shodhganga : a reservoir of Indian theses @ INFLIBNET

    A reservoir of Indian Theses The Shodhganga@INFLIBNET Centre provides a platform for research students to deposit their Ph.D. theses and make it available to the entire scholarly community in open access. The repository has the ability to capture, index, store, disseminate and preserve ETDs submitted by the researchers. [Read More]

  23. Dissertations / Theses: 'Health insurance'

    List of dissertations / theses on the topic 'Health insurance'. Scholarly publications with full text pdf download. Related research topic ideas.