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Research Article

The impact of public health insurance on health care utilisation, financial protection and health status in low- and middle-income countries: A systematic review

Roles Conceptualization, Data curation, Formal analysis, Investigation, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliation Department of Health Sciences, University of York, York, England, United Kingdom

Roles Investigation, Methodology, Supervision, Writing – review & editing

Affiliations Centre of Health Economics, University of York, York, England, United Kingdom, Luxembourg Institute of Socio-economic Research (LISER), Luxembourg

Roles Conceptualization, Methodology, Supervision, Writing – review & editing

Affiliations Department of Health Sciences, University of York, York, England, United Kingdom, Department of Epidemiology and Biostatistics, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada

Roles Conceptualization, Investigation, Supervision, Writing – review & editing

• Darius Erlangga, 
• Marc Suhrcke, 
• Shehzad Ali, 
• Karen Bloor

• Published: August 28, 2019
• https://doi.org/10.1371/journal.pone.0219731
• Reader Comments

7 Nov 2019: Erlangga D, Suhrcke M, Ali S, Bloor K (2019) Correction: The impact of public health insurance on health care utilisation, financial protection and health status in low- and middle-income countries: A systematic review. PLOS ONE 14(11): e0225237. https://doi.org/10.1371/journal.pone.0225237 View correction

Expanding public health insurance seeks to attain several desirable objectives, including increasing access to healthcare services, reducing the risk of catastrophic healthcare expenditures, and improving health outcomes. The extent to which these objectives are met in a real-world policy context remains an empirical question of increasing research and policy interest in recent years.

We reviewed systematically empirical studies published from July 2010 to September 2016 using Medline, Embase, Econlit, CINAHL Plus via EBSCO, and Web of Science and grey literature databases. No language restrictions were applied. Our focus was on both randomised and observational studies, particularly those including explicitly attempts to tackle selection bias in estimating the treatment effect of health insurance. The main outcomes are: (1) utilisation of health services, (2) financial protection for the target population, and (3) changes in health status.

8755 abstracts and 118 full-text articles were assessed. Sixty-eight studies met the inclusion criteria including six randomised studies, reflecting a substantial increase in the quantity and quality of research output compared to the time period before 2010. Overall, health insurance schemes in low- and middle-income countries (LMICs) have been found to improve access to health care as measured by increased utilisation of health care facilities (32 out of 40 studies). There also appeared to be a favourable effect on financial protection (26 out of 46 studies), although several studies indicated otherwise. There is moderate evidence that health insurance schemes improve the health of the insured (9 out of 12 studies).

Interpretation

Increased health insurance coverage generally appears to increase access to health care facilities, improve financial protection and improve health status, although findings are not totally consistent. Understanding the drivers of differences in the outcomes of insurance reforms is critical to inform future implementations of publicly funded health insurance to achieve the broader goal of universal health coverage.

Citation: Erlangga D, Suhrcke M, Ali S, Bloor K (2019) The impact of public health insurance on health care utilisation, financial protection and health status in low- and middle-income countries: A systematic review. PLoS ONE 14(8): e0219731. https://doi.org/10.1371/journal.pone.0219731

Editor: Sandra C. Buttigieg, University of Malta Faculty of Health Sciences, MALTA

Received: March 19, 2018; Accepted: July 2, 2019; Published: August 28, 2019

Copyright: © 2019 Erlangga 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: The search strategy for this review is available in Supporting Information files.

Funding: The authors received no specific funding for this work.

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

Introduction

In recent decades, achieving universal health coverage (UHC) has been a major health policy focus globally.[ 1 – 3 ] UHC entitles all people to access healthcare services through publicly organised risk pooling,[ 4 ] safeguarding against the risk of catastrophic healthcare expenditures.[ 5 ] Low- and middle-income countries (LMICs) face particular challenges in achieving UHC due to particularly limited public resources for health care, inefficient allocation, over-reliance on out-of-pocket payments, and often large population size.[ 5 ] As a result, access to health care and the burden of financial cost in LMICs tends to be worse for the poor, often resulting in forgone care.[ 6 – 8 ]

Introducing and increasing the coverage of publicly organised and financed health insurance is widely seen as the most promising way of achieving UHC,[ 9 , 10 ] since private insurance is mostly unaffordable for the poor.[ 11 ] Historically, social health insurance, tax-based insurance, or a mix of the two have been the dominant health insurance models amongst high income countries and some LMICs, including Brazil, Colombia, Costa Rica, Mexico, and Thailand.[ 12 ] This is partly influenced by the size of the formal sector economy from which taxes and payroll contributions can be collected. In recent decades, community-based health insurance (CBHI) or “mutual health organizations” have become increasingly popular among LMICs, particularly in Sub-Saharan Africa (e.g. Burkina Faso,[ 13 ] Senegal[ 14 ] and Rwanda[ 15 ]) as well as Asia (e.g. China[ 16 ] and India[ 17 ]). CBHI has emerged as an alternative health financing strategy, particularly in cases where the public sector has failed to provide adequate access to health care.[ 18 ]

We searched for existing systematic reviews on health insurance in the Cochrane Database for Systematic Reviews, Medline, Embase, and Econlit. Search terms “health insurance”, “low-middle income countries”, and “utilisation” were used alongside methodological search strategy to locate reviews. Seven systematic reviews were identified of varying levels of quality, [ 19 – 26 ] with Acharya et al.[ 27 ] being the most comprehensive. The majority of existing reviews has suggested that publicly-funded health insurance has typically shown a positive impact on access to care, while the picture for financial protection was mixed, and evidence of the impact on health status was very sparse.

This study reviews systematically the recent fast-growing evidence on the impact of health insurance on health care utilisation, financial protection and health status in LMICs. Since the publication of Acharya et al. (which conducted literature searches in July 2010), the empirical evidence on the impact of health insurance has expanded significantly in terms of quantity and quality, with growing use of sophisticated techniques to account for statistical challenges[ 28 ] (particularly insurance selection bias). This study makes an important contribution towards our understanding of the impact of health insurance in LMICs, taking particular care in appraising the quality of studies. We recognise the heterogeneity of insurance schemes implemented in LMICs and therefore do not attempt to generalise findings, but we aim to explore the pattern emerging from various studies and to extract common factors that may affect the effectiveness of health insurance, that should be the focus of future policy and research. Furthermore, we explore evidence of moral hazard in insurance membership, an aspect that was not addressed in the Acharya et al review.[ 27 ]

This review was planned, conducted, and reported in adherence with PRISMA standards of quality for reporting systematic reviews.[ 29 ]

Participants

Studies focusing on LMICs are included, as measured by per capita gross national income (GNI) estimated using the World Bank Atlas method per July 2016.[ 30 ]

Intervention

Classification of health insurance can be complicated due to the many characteristics defining its structure, including the mode of participation (compulsory or voluntary), benefit entitlement, level of membership (individual or household), methods for raising funds (taxes, flat premium, or income-based premium) and the mechanism and extent of risk pooling [ 31 ]. For the purpose of this review, we included all health insurance schemes organised by government, comprising social health insurance and tax-based health insurance. Private health insurance was excluded from our review, but we recognise the presence of community-based health insurance (CBHI) in many LMICs, especially in Africa and Asia [ 18 ]. We also therefore included CBHI if it was scaled up nationally or was actively promoted by national government. Primary studies that included both public and private health insurance were also considered for inclusion if a clear distinction between the two was made in the primary paper. Studies examining other types of financial incentives to increase the demand for healthcare services, such as voucher schemes or cash transfers, were excluded.

Control group

In order to provide robust evidence on the effect on insurance, it is necessary to compare an insured group with an appropriate control group. In this review, we selected studies that used an uninsured population as the control group. Multiple comparison groups were allowed, but an uninsured group had to be one of them.

Outcome measures

We focus on three main outcomes:

• Utilisation of health care facilities or services (e.g. immunisation coverage, number of visits, rates of hospitalisation).
• Financial protection, as measured by changes in out-of-pocket (OOP) health expenditure at household or individual level, and also catastrophic health expenditure or impoverishment from medical expenses.
• Health status, as measured by morbidity and mortality rates, indicators of risk factors (e.g. nutritional status), and self-reported health status.

The scope of this review is not restricted to any level of healthcare delivery (i.e. primary or secondary care). All types of health services were considered in this review.

Types of studies

The review includes randomized controlled trials, quasi-experimental studies (or “natural experiments”[ 32 ]), and observational studies that account for selection bias due to insurance endogeneity (i.e. bias caused by insurance decisions that are correlated with the expected level of utilisation and/or OOP expenditure). Observational studies that did not take account of selection bias were excluded.

Databases and search terms

A search for relevant articles was conducted on 6 September 2016 using peer-reviewed databases (Medline, Embase, Econlit, CINAHL Plus via EBSCO and Web of Science) and grey literatures (WHO, World Bank, and PAHO). Our search was restricted to studies published since July 2010, immediately after the period covered by the earlier Acharya et al. (2012) review. No language restrictions were applied. Full details of our search strategy are available in the supporting information ( S1 Table ).

Screening and data extraction

Two independent reviewers (DE and MS) screened all titles and abstracts of the initially identified studies to determine whether they satisfied the inclusion criteria. Any disagreement was resolved through mutual consensus. Full texts were retrieved for the studies that met the inclusion criteria. A data collection form was used to extract the relevant information from the included studies.

Assessment of study quality

We used the Grades of Assessment, Development and Evaluation (GRADE) system checklist[ 33 , 34 ] which is commonly used for quality assessment in systematic reviews. However, GRADE does not rate observational studies based on whether they controlled for selection bias. Therefore, we supplemented the GRADE score with the ‘Quality of Effectiveness Estimates from Non-randomised Studies’ (QuEENS) checklist.[ 35 ]

cRandomised studies were considered to have low risk of bias. Non-randomised studies that account for selection on observable variables, such as propensity score matching (PSM), were categorised as high risk of bias unless they provided adequate assumption checks or compared the results to those from other methods, in which case they may be classed as medium risk. Non-randomised studies that account for selection on both observables and unobservables, such as regression with difference-in-differences (DiD) or Heckman sample selection models, were considered to have medium risk of bias–some of these studies were graded as high or low risk depending on sufficiency of assumption checks and comparison with results from other methods.

Heterogeneity of health insurance programmes across countries and variability in empirical methods used across studies precluded a formal meta-analysis. We therefore conducted a narrative synthesis of the literature and did not report the effect size. Throughout this review, we only considered three possible effects: positive outcome, negative outcome, or no statistically significant effect (here defined as p-value > 0.1).

Results of the search

Our database search identified 8,755 studies. Five additional studies were retrieved from grey literature. After screening of titles and abstracts, 118 studies were identified as potentially relevant. After reviewing the full-texts, 68 studies were included in the systematic review (see Fig 1 for the PRISMA diagram). A full description of the included studies is presented in the supporting information ( S2 Table ). Of the 68 included studies, 40 studies examined the effect on utilisation, 46 studies on financial protection, and only 12 studies on health status (see Table 1 ).

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https://doi.org/10.1371/journal.pone.0219731.g001

https://doi.org/10.1371/journal.pone.0219731.t001

Utilisation of health care

Table 2 collates evidence on the effects of health insurance on utilisation of healthcare services. Three main findings were observed:

• Evidence on utilisation of curative care generally suggested a positive effect, with 30 out of 38 studies reporting a statistically significant positive effect.
• Evidence on preventive care is less clear with 4 out of 7 studies reporting a positive effect, two studies finding a negative effect and one study reporting no effect.
• Among the higher quality studies, i.e. those that suitably controlled for selection bias reflected by moderate or low GRADE score and low risk of bias (score = 3) on QuEENS, seven studies reported a positive relationship between insurance and utilisation. One study[ 36 ] reported no statistically significant effect, and another study found a statistically significant negative effect.[ 37 ]

https://doi.org/10.1371/journal.pone.0219731.t002

Financial protection

Overall, evidence on the impact of health insurance on financial protection is less clear than that for utilisation (see Table 3 ). 34 of the 46 studies reported the impact of health insurance on the level of out-of-pocket health expenditure. Among those 34 studies, 17 found a positive effect (i.e. a reduction in out-of-pocket expenditure), 15 studies found no statistically significant effect, and two studies–from Indonesia[ 59 ] and Peru[ 62 ]–reported a negative effect (i.e. an increase in out-of-pocket expenditure).

https://doi.org/10.1371/journal.pone.0219731.t003

Another financial protection measure is the probability of incurring catastrophic health expenditure defined as OOP exceeding a certain threshold percentage of total expenditure or income. Of the 14 studies reporting this measure, nine reported reduction in the risk of catastrophic expenditure, three found no statistically significant difference, and two found a negative effect of health insurance. Only four studies reported sensitivity analysis varying changes in the threshold level,[ 59 , 62 , 75 , 76 ] though this did not materially affect the findings.

• Two studies used a different measure of financial protection, the probability of impoverishment due to catastrophic health expenditure, reporting conflicting findings.[ 77 , 78 ] Finally, four studies evaluated the effect on financial protection by assessing the impact of insurance on non-healthcare consumption or saving behaviour, such as non-medical related consumption[ 79 ], probability of financing medical bills via asset sales or borrowing[ 40 ], and household saving[ 80 ]. No clear pattern can be observed from those four studies.

Health status

Improving health is one of the main objectives of health insurance, yet very few studies thus far have attempted to evaluate health outcomes. We identified 12 studies, with considerable variation in the precise health measure considered (see Table 4 ). There was some evidence of positive impact on health status: nine studies found a positive effect, one study reported a negative effect, and two studies reported no effect.

https://doi.org/10.1371/journal.pone.0219731.t004

Type of insurance and countries

Considering the heterogeneity of insurance schemes among different countries, we attempted to explore the aggregate results by the type of insurance scheme and by country. Table 5 provides a summary of results classified by three type of insurance scheme: community-based health insurance, voluntary health insurance (non-CBHI), and compulsory health insurance. This division is based on the mode of participation (compulsory vs voluntary), which may affect the presence of adverse selection and moral hazard. Premiums are typically community-rated in CBHI, risk-rated in voluntary schemes and income-rated in compulsory schemes.

https://doi.org/10.1371/journal.pone.0219731.t005

In principle, CBHI is also considered a voluntary scheme, but we separated it to explore whether the larger size of pooling from non-CBHI schemes may affect the outcomes. Social health insurance is theoretically a mandatory scheme that requires contribution from the enrolees. However, in the context of LMICs, the mandatory element is hard to enforce, and in practice the scheme adopts a voluntary enrolment. Additionally, the government may also want to subsidise the premium for poor people. Therefore, in this review SHI schemes can fall into either the voluntary health insurance (non-CBHI) or compulsory health insurance (non-CBHI), depending on the target population defined in the evaluation study. Lastly, we chose studies with high quality/low risk only to provide more robust results.

Based on the summary in Table 5 , the effect on utilisation overall does not differ based on type of insurance, with most evidence suggesting an overall increase in utilisation by the insured. The two studies showing no effect or reduced consumption of care were conducted in two different areas of India, which may–somewhat tentatively–suggest a common factor unique to India’s health system that may compromise the effectiveness of health insurance in increasing utilisation.

Regarding financial protection, the evidence for both CBHI and non-CBHI voluntary health insurance is inconclusive. Furthermore, there is an indication of heterogeneity by supply side factors captured by proximity to health facilities. Evidence from studies exploring subsidised schemes suggests no effect on financial protection, even a negative effect among the insured in Peru.

Lastly, evidence for health status may be influenced by how health outcomes are measured. Studies exploring specific health status, (examples included health indexes, wasting, C-reactive protein, and low birth weight), show a positive effect, whereas studies using mortality rates tends to show no effect or even negative effects. Studies exploring CBHI scheme did not find any evidence of positive effect on health status, as measured either by mortality rate or specific health status.

This review synthesises the recent, burgeoning empirical literature on the impact of health insurance in LMICs. We identified a total of 68 eligible studies over a period of six years–double the amount identified by the previous review by Acharya et al. over an approximately 60-year time horizon (1950—July 2010). We used two quality assessment checklists to scrutinise the study methodology, taking more explicit account of the methodological robustness of non-experimental designs.

Programme evaluation has been of interest to many researchers for reporting on the effectiveness of a public policy to policymakers. In theory, the gold standard for a programme evaluation is the randomised control trial, in which the treatment is randomly assigned to the participants. The treatment assignment process has to be exogenous to ensure that any observed effect between the treated and control groups can only be caused by the difference in the treatment assignment. Unfortunately, this ideal scenario is often not feasible in a public policy setting. Our findings showed that only three papers between 2010 and 2016 were able to conduct a randomised study to evaluate the impact of health insurance programmes in developing countries, particularly CBHI [ 38 , 75 , 103 ]. Policymakers may believe in the value of an intervention regardless of its actual evidence base, or they may believe that the intervention is beneficial and that no one in need should be denied it. In addition, policymakers are inclined to demonstrate the effectiveness of an intervention that they want implemented in the most promising contexts, as opposed to random allocation [ 104 ].

Consequently, programme evaluators often have to deal with a non-randomised treatment assignment which may result in selection bias problems. Selection bias is defined as a spurious relationship between the treatment and the outcome of interest due to the systematic differences between the treated and the control groups [ 105 ]. In the case of health insurance, an individual who chooses to enrol in the scheme may have different characteristics to an individual who chooses not to enrol. When those important characteristics are unobservable, the analyst needs to apply more advanced techniques and, sometimes, stronger assumptions. Based on our findings, we noted several popular methods, including propensity score matching (N = 8), difference-in-difference (N = 10), fixed or random effects of panel data (N = 6), instrumental variables (N = 12) and regression discontinuity (N = 6). Those methods have varying degree of success in controlling the unobserved selection bias and analysts should explore the robustness of their findings by comparing initial findings with other methods by testing important assumptions. We noted some papers combining two common methods, such as difference-in-difference with propensity score matching (N = 10) and fixed effects with instrumental variables (N = 8), in order to obtain more robust results.

Overall effect

Compared with the earlier review, our study has found stronger and more consistent evidence of positive effects of health insurance on health care utilisation, but less clear evidence on financial protection. Restricting the evidence base to the small subset of randomised studies, the effects on financial protection appear more consistently positive, i.e. three cluster randomised studies[ 39 , 75 , 76 ] showed a decline in OOP expenditure and one randomised study[ 36 ] found no significant effect.

Besides the impact on utilisation and financial protection, this review identified a number of good quality studies measuring the impact of health insurance on health outcomes. Twelve studies were identified (i.e. twice as many as those published before 2010), nine of which showed a beneficial health effect. This holds for the subset of papers with stronger methodology for tackling selection bias.[ 39 , 49 , 89 , 103 ] In cases where a health insurance programme does not have a positive effect on either utilisation, financial protection, and health status, it is particularly important to understand the underlying reasons.

Possible explanation of heterogeneity

Payment system..

Heterogeneity of the impact of health insurance may be explained by differences in health systems and/or health insurance programmes. Robyn et al. (2012) and Fink et al (2013) argued that the lack of significant effect of insurance in Burkina Faso may have been partially influenced by the capitation payment system. As the health workers relied heavily on user fees for their income, the change of payment system from fee-for-services to capitation may have discouraged provision of high quality services. If enrolees perceive the quality of contracted providers as bad, they might delay seeking treatment, which in turn could impact negatively on health.

Several studies from China found the utilisation of expensive treatment and higher-level health care facilities to have increased following the introduction of the insurance scheme.[ 41 , 44 , 45 , 88 ] A fee-for-service payment system may have incentivised providers to include more expensive treatments.[ 43 , 83 , 88 ] Recent systematic reviews suggested that payment systems might play a key role in determining the success of insurance schemes,[ 23 , 106 ] but this evidence is still weak, as most of the included studies were observational studies that did not control sufficiently for selection bias.

Uncovered essential items.

Sood et al. (2014) found no statistically significant effect of community-based health insurance on utilisation in India. They argued that this could be caused by their inability to specify the medical conditions covered by the insurance, causing dilution of a potential true effect. In other countries, transportation costs[ 69 ] and treatments that were not covered by the insurance[ 59 , 60 ] may explain the absence of a reduction in out-of-pocket health expenditures.

Methodological differences.

Two studies in Georgia evaluated the same programme but with different conclusions.[ 50 , 51 ] This discrepancy may be explained by the difference in the estimated treatment effect: one used average treatment effect (ATE), finding no effect, and another used average treatment effect on the treated (ATT), reporting a positive effect. ATE is of prime interest when policymakers are interested in scaling up the programme, whereas ATT is useful to measure the effect on people who were actually exposed to insurance.[ 107 ]

Duration of health insurance.

We also found that the longer an insurance programme has been in place prior to the timing of the evaluation, the higher the odds of improved health outcomes. It is plausible that health insurance would not change the health status of population instantly upon implementation.[ 21 ] While there may be an appetite among policymakers to obtain favourable short term assessments, it is important to compare the impact over time, where feasible.

Moral hazard.

Acharya et al (2012) raised an important question about the possibility of a moral hazard effect as an unintended consequence of introducing (or expanding) health insurance in LMICs. We found seven studies exploring ex-ante moral hazard by estimating the effect on preventive care. If uninsured individuals expect to be covered in the future, they may reduce the consumption of preventive care or invest less in healthy behaviours.[ 108 , 109 ] Current overall evidence cannot suggest a definite conclusion considering the heterogeneity in chosen outcomes. One study found that the use of a self-treated bed nets to prevent malaria declined among the insured group in Ghana[ 54 ] while two studies reported an increase in vaccination rates[ 62 ] and the number of prenatal care visits[ 55 , 62 ]among the insured group. Another study reported no evidence that health insurance encouraged unhealthy behaviour or reduction of preventive efforts in Thailand.[ 66 ]

Two studies from Colombia found that the insured group is more likely to increase their demand for preventive treatment.[ 47 , 49 ] As preventive treatment is free for all, both authors attributed this increased demand to the scheme’s capitation system, incentivising providers to promote preventive care to avoid future costly treatments.[ 110 ] Another study of a different health insurance programme in Colombia found an opposite effect.[ 48 ]

Study limitations.

This review includes a large variety of study designs and indicators for assessing the multiple potential impacts of health insurance, making it hard to directly compare and aggregate findings. For those studies that used a control group, the use of self-selected controls in many cases creates potential bias. Studies of the effect of CBHI are often better at establishing the counterfactual by allowing the use of randomisation in a small area, whereas government schemes or social health insurance covering larger populations have limited opportunity to use randomisation. Non-randomised studies are more susceptible to confounding factors unobserved by the analysts. For a better understanding of the links between health insurance and relevant outcomes, there is also a need to go beyond quantitative evidence alone and combine the quantitative findings with qualitative insights. This is particularly important when trying to interpret some of the counterintuitive results encountered in some studies.

The impact of different health insurance schemes in many countries on utilisation generally shows a positive effect. This is aligned with the supply-demand theory in whichhealth insurance decreases the price of health care services resulting in increased demand. It is difficult to draw an overall conclusion about the impact of health insurance on financial protection, most likely because of differences in health insurance programmes. The impact of health insurance on health status suggests a promising positive effect, but more studies from different countries is required.

The interest in achieving UHC via publicly funded health insurance is likely to increase even further in the coming years, and it is one of the United Nation’s Sustainable Development Goals (SDGs) for 2030[ 111 ]. As public health insurance is still being widely implemented in many LMICs, the findings from this review should be of interest to health experts and policy-makers at the national and the international level.

Supporting information

S1 table. search strategies..

https://doi.org/10.1371/journal.pone.0219731.s001

S2 Table. Study characteristic and reported effect from the included studies (N = 68).

https://doi.org/10.1371/journal.pone.0219731.s002

S3 Table. PRISMA 2009 checklist.

https://doi.org/10.1371/journal.pone.0219731.s003

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The Value of Health Insurance during a Crisis: Effects of Medicaid Implementation on Pandemic Influenza Mortality

This paper studies how better access to public health insurance affects infant mortality during pandemics. Our analysis combines cross-state variation in mandated eligibility for Medicaid with two influenza pandemics – the 1957-58 “Asian Flu” Pandemic and the 1968-69 “Hong Kong Flu” Pandemic – that arrived shortly before and after the program's introduction. Exploiting heterogeneity in the underlying severity of these two shocks across counties, we find no relationship between Medicaid eligibility and pandemic infant mortality during the 1957-58 outbreak. In contrast during the 1968-1969 pandemic, which occurred after Medicaid implementation, we find that better access to insurance in high-eligibility states substantially reduced infant mortality. The reductions in pandemic infant mortality are too large to be attributable solely to new Medicaid recipients, suggesting that the expansion in health insurance coverage mitigated disease transmission among the broader population.

We thank Lowell Taylor, Maureen Cropper, Tatyana Deryugina, Teevrat Garg, Raphael Godefroy, and Nick Kuminoff for insightful suggestions, and seminar participants at Carnegie Mellon University, McGill University, and Université de Montréal, and conference participants at the 66th Annual North American Meetings of the Regional Science Association International, the ASSA meetings, and the Southern Economics Association meeting for valuable comments. The authors gratefully acknowledge financial support from the Center for Electricity Industry Studies, Heinz College, and the Berkman fund at Carnegie Mellon University, from the National Science Foundation Grant SES-1627432, and from the Université de Montréal. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.

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• May 5, 2020

Non-Technical Summaries

• Pandemic Influenza Mortality Reduced by Medicaid Coverage Author(s): Karen Clay Joshua A. Lewis Edson R. Severnini Xiao Wang What role can health insurance play in ameliorating the health effects of a pandemic? In The Value of Health Insurance during a...

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Health insurance sector in India: an analysis of its performance

Vilakshan - XIMB Journal of Management

ISSN : 0973-1954

Article publication date: 30 November 2020

Issue publication date: 16 December 2020

Health insurance is one of the major contributors of growth of general insurance industry in India. It alone accounts for around 29% of total general insurance premium income earned in India. The growth of this sector is important from the perspective of overall growth of general insurance Industry. At the same time, problems in this sector are also many which are affecting its performance.

Design/methodology/approach

The paper provides an understanding on performance of health insurance sector in India. This study attempts to find out how much claims and commission and management expenses it has to incur to earn certain amount of premium. Methodology used for the study is regression analysis to establish relationship between dependent variable (Profit/Loss) and independent variable (Health Insurance Premium earned).

Findings of the study indicate that there is significant relationship between earned premium and underwriting loss. There has been increase of premium earnings which instead of increasing profit for the sector in fact has increased underwriting loss over the years. The earnings of the sector is growing at compounded annual growth rate of 27% still it is unable to earn underwriting profit.

Originality/value

This study is self-driven based on secondary data obtained from insurance regulatory and development authority site.

• Health insurance premium
• Management expenses
• Insurance regulatory and development authority
• Underwriting loss
• Compound annual growth rate

Dutta, M.M. (2020), "Health insurance sector in India: an analysis of its performance", Vilakshan - XIMB Journal of Management , Vol. 17 No. 1/2, pp. 97-109. https://doi.org/10.1108/XJM-07-2020-0021

Emerald Publishing Limited

Copyright © 2020, Madan Mohan Dutta.

Published in Vilakshan - XIMB Journal of Management . Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence maybe seen at http://creativecommons.org/licences/by/4.0/legalcode

1. Introduction

1.1 meaning of insurance.

Insurance is a contract between two parties where by one party agrees to undertake the risk of the other in exchange for consideration known as premium and promises to indemnify the party on happening of an uncertain event. The great advantage of insurance is that it spreads the risk of a few people over a large group of people exposed to risk of similar type.

Insurance has been identified as a sunrise sector by the financial planners of India. The insurance industry has lot of potential to grow, penetrate and service the masses of India. Insurance is all about protection. An insured needs two types of protection life and non-life. General insurance industry deals with non-life protection of the insured of which health insurance is a part.

1.2 Meaning of health insurance

Health insurance is a part of general insurance which contributes about 29% of premium amongst all other sectors of general insurance. But problems in this sector are many which is the driving force behind this study. This study will help the insurance companies to understand their performance and the quantum of losses that this sector is making over the years.

A plan that covers or shares the expenses associated with health care can be described as health insurance. These plans fall into commercial health insurance, which is provided by government, private and stand-alone health insurance companies.

Health insurance in India typically pays for only inpatient hospitalization and for treatment at hospitals in India. Outpatient services are not payable under health policies in India. The first health policy in India was Mediclaim Policy. In 2000, the Government of India liberalized insurance and allowed private players into the insurance sector. The advent of private insurers in India saw the introduction of many innovative products like family floater plans, critical illness plans, hospital cash and top-up policies.

Health insurance in India is an emerging insurance sector after life and automobile insurance sector. Rise in middle class, higher hospitalization cost, expensive health care, digitization and increase in awareness level are some important drivers for the growth of health insurance market in India.

Lifestyle diseases are on the rise. A sedentary lifestyle has pervaded our being. There is lower physical labour today than earlier and there is no reason why this would not be the trend going forward. The implication is the advent of lifestyle chronic diseases such as cardiac problems and diabetes.

In the context of the Indian health insurance industry, one could look at it both ways. Mired by low penetration and negative consumer perception about its utility are affecting the prospect of this industry. The flipside though is that we have hardly scratched the surface of the opportunity that lies in the future. It is as if the glass is half full. Much remains to be conquered and even more remains to be accomplished.

Health insurance companies needs to be optimistic and have courage to bring in innovation in the areas of product, services and distribution system. Bring it to the fold as the safety net that smartly covers and craft a health insurance plan befitting the need of the customers.

1.3 Background of health insurance sector in India

India’s tryst with health insurance programme goes back to the late 1940s and early 1950s when the civil servants (Central Government Health Scheme) and formal sector workers (Employees’ State Insurance Scheme) were enrolled into a contributory but heavily subsidized health insurance programmes. As a consequence of liberalization of the economy since the early 1990s, the government opened up private sector (including health insurance) in 1999. This development threw open the possibility for higher income groups to access quality care from private tertiary care facilities. However, India in the past five years (since 2007) has witnessed a plethora of new initiatives, both by the central government and a host of state governments also entering the bandwagon of health insurance. One of the reasons for initiating such programs may be traced to the commitment of the governments in India to scale up public spending in health care.

1.4 The need for health insurance in India

1.4.1 lifestyles have changed..

Indians today suffer from high levels of stress. Long hours at work, little exercise, disregard for a healthy balanced diet and a consequent dependence on junk food have weakened our immune systems and put us at an increased risk of contracting illnesses.

1.4.2 Rare non-communicable diseases are now common.

Obesity, high blood pressure, strokes and heart attacks, which were earlier considered rare, now affect an increasing number of urban Indians.

1.4.3 Medical care is unbelievably expensive.

Medical breakthroughs have resulted in cures for dreaded diseases. These cures however are available only to a select few. This is because of high operating and treatment expenses.

1.4.4 Indirect costs add to the financial burden.

Indirect sources of expense like travel, boarding and lodging, and even temporary loss of income account for as much as 35% of the overall cost of treatment. These facts are overlooked when planning for medical expenses.

1.4.5 Incomplete financial planning.

Most of us have insured our home, vehicle, child’s education and even our retirement years. Ironically however we have not insured our health. We ignore the fact that illnesses strike without warning and seriously impact our finances and eat into our savings in the absence of a good health insurance or medical insurance plan.

1.5 Classification of health insurance plans in India

Health insurance plans in India today can be broadly classified into the following categories:

1.5.1 Hospitalization.

Hospitalization plans are indemnity plans that pay cost of hospitalization and medical costs of the insured subject to the sum insured. There is another type of hospitalization policy called a top-up policy . Top-up policies have a high deductible typically set a level of existing cover.

1.5.2 Family floater health insurance.

Family health insurance plan covers entire family in one health insurance plan. It works under assumption that not all member of a family will suffer from illness in one time.

1.5.3 Pre-existing disease cover plans.

It offers covers against disease that policyholder had before buying health policy. Pre-existing disease cover plans offers cover against pre-existing disease, e.g. diabetes, kidney failure and many more. After waiting for two to four years, it gives covers to the insured.

1.5.4 Senior citizen health insurance.

This type of health insurance plan is for older people in the family. It provides covers and protection from health issues during old age.

1.5.5 Maternity Health insurance.

Maternity health insurance ensures coverage for maternity and other additional expenses.

1.5.6 Hospital daily cash benefit plans.

Daily cash benefits are a defined benefit policy that pays a defined sum of money for every day of hospitalization.

1.5.7 Critical illness plans.

These are benefit-based policies which pay a lump sum amount on certain critical illnesses, e.g. heart attack, cancer and stroke.

1.5.8 Disease-specific special plans.

Some companies offer specially designed disease-specific plans such as Dengue Care and Corona Kavach policy.

1.6 Strength, weakness, opportunity and threat analysis of health insurance sector (SWOT analysis)

The strengths, weaknesses, opportunities and threats (SWOT) is a study undertaken to identify internal strengths and weaknesses as well as external opportunities and threats of the health insurance sector.

1.6.1 Strengths.

The growth trend of the health insurance sector is likely to be high due to rise in per capita income and emerging middle-income group in India. New products are being launched in this sector by different insurance companies which will help to satisfy customers need. Customers will be hugely benefited when cash less facility will be provided to all across the country by all the insurance companies.

1.6.2 Weaknesses.

The financial condition of this sector is weak due to low investment in this sector. The public sector insurance companies are still dominating this industry due to their greater infrastructure facilities. This sector is prone to high claim ratio and many false claims are also made.

1.6.3 Opportunities.

The possibility of future growth of this sector is high, as penetration in the rural sector is low. The improvement of technology and the use of internet facility are helping this sector to grow in magnitude and move towards environment-friendly paperless regime.

1.6.4 Threats.

The biggest threat of this sector lies in the change in the government regulations. The profitability of this sector is affected due to increasing expenses and claims. The economic slowdown and recession in the economy can affect growth of this sector adversely. The increasing losses and need for insurance might reach a point of no return where insurance companies may be compelled to decline an insurance policy.

1.7 Political economic socio cultural and technological analysis of health insurance sector (PEST analysis)

This analysis describes a framework of macro-environmental factors used as strategic tool for understanding business position, growth potential and direction for operations.

1.7.1 Political factors.

Service tax on premium on insurance policies is being increased by the government for past few years during budget. Government monopoly in this sector came to an end after insurance companies were opened up for private participation in the year 2000. Foreign players were allowed to enter into joint venture with their Indian counterpart with 26% holding and which was further increased to 49% in the year 2015.

1.7.2 Economic factors.

The gross savings of people in India have increased significantly thereby encouraging people to buy insurance policy to cover their risks. Insurance companies are fast becoming prominent players in the security market. As these companies have huge disposable income which they are investing in the security market.

1.7.3 Socio-cultural factors.

Increase in insurance knowledge is helping people to increase their awareness about the risk to be covered through insurance. Change in lifestyle is leading to increase in risk thereby giving an opportunity to insurance companies to innovate newer products. Societal benefit is derived by transfer of risk through insurance due to improved socio-cultural environment.

1.7.4 Technological factors.

Insurance companies deals in large database and maintaining it by the application of latest technology is huge gain for this sector. Technological advancement has helped insurance companies to sale their products through their electronic portals. This has made their task of providing service to the customers easier and faster.

2. Review of literature

After opening up of the insurance industry health insurance sector has become significant both from economic and social point of view and researchers have explored and probed these aspects.

Ellis et al. (2000) reviewed a variety of health insurance systems in India. It was revealed that there is a need for a competitive environment which can only happen with the opening up of the insurance sector. Aubu (2014) conducted a comparative study on public and private companies towards marketing of health insurance policies. Study revealed that private sector services evoked better response than that of public sector because of new strategies and technologies adopted by them. Nair (2019) has made a comparative study of the satisfaction level of health insurance claimants of public and private sector general insurance companies. It was revealed that majority of the respondents had claim of reimbursement nature through third party administrator. Satisfaction with respect to settlement of claim was found relatively higher for public sector than private sector. Devadasan et al. (2004) studied community health insurance to be an important intermediate step in the evolution of an equitable health financing mechanism in Europe and Japan. It was concluded that community health insurance programmes in India offer valuable lessons for its policy makers. Kumar (2009) examined the role of insurance in financing health care in India. It was found that insurance can be an important means of mobilizing resources, providing risk protection and health insurance facilities. But for this to happen, it will require systemic reforms of this sector from the end of the Government of India. Dror et al. (2006) studied about willingness among rural and poor persons in India to pay for their health insurance. Study revealed that insured persons were more willing to pay for their insurance than the uninsured persons. Jayaprakash (2007) examined to understand the hurdles preventing the people to purchase health insurance policies in the country and methods to reduce claims ratio in this sector. Yadav and Sudhakar (2017) studied personal factors influencing purchase decision of health insurance policies in India. It was found that factors such as awareness, tax benefit, financial security and risk coverage has significant influence on purchase decision of health insurance policy holders. Thomas (2017) examined health insurance in India from the perspective of consumer insights. It was found that consumers consider various aspects before choosing a health insurer like presence of a good hospital network, policy coverage and firm with wide product choice and responsive employees. Savita (2014) studied the reason for the decline of membership of micro health insurance in Karnataka. Major reason for this decline was lack of money, lack of clarity on the scheme and intra house-hold factors. However designing the scheme according to the need of the customer is the main challenge of the micro insurance sector. Shah (2017) analysed health insurance sector post liberalization in India. It was found that significant relationship exists between premiums collected and claims paid and demographic variables impacted policy holding status of the respondents. Binny and Gupta (2017) examined opportunities and challenges of health insurance in India. These opportunities are facilitating market players to expand their business and competitiveness in the market. But there are some structural problems faced by the companies such as high claim ratio and changing need of the customers which entails companies to innovate products for the satisfaction of the customers. Chatterjee et al. (2018) have studied health insurance sector in India. The premise of this paper was to study the current situation of the health-care insurance industry in India. It was observed that India is focusing more on short-term care of its citizens and must move from short-term to long-term care. Gambhir et al. (2019) studied out-patient coverage of private sector insurance in India. It was revealed that the share of the private health insurance companies has increased considerably, despite of the fact that health insurance is not a good deal. Chauhan (2019) examined medical underwriting and rating modalities in health insurance sector. It was revealed that while underwriting a health policy one has to keep in mind the various aspects of insured including lifestyle, occupation, health condition and habits. There have been substantial studies on health insurance done in India and abroad. But there has not been any work on performance of health insurance sector based on underwriting profit or loss.

3. Research gap

After extensive review of literature it is understood that there has not been substantial study on the performance of health insurance sector taking underwriting profit or loss into consideration. In spite of high rate of growth of earned premium, this sector is unable to make underwriting profit. This is mainly because growth of premium is more than compensated by claims incurred and commission and other expenses paid. Thereby leading to growth of underwriting loss over the years across the different insurance companies covered under both public and private sector. This unique feature of negative performance of this sector has not been studied so far in India.

4. Objectives

review health insurance scenario in India; and

study the performance of health insurance sector in India with respect to underwriting profit or loss by the application of regression analysis.

5. Research methodology

The study is based on secondary data sourced from the annual reports of Insurance Regulatory Development Authority (IRDA), various journals, research articles and websites. An attempt has been made to evaluate the performance of the health insurance sector in India. Appropriate research tools have been used as per the need and type of the study. The information so collected has been classified, tabulated and analysed as per the objectives of the study.

The data is based on a time period of 12 years ranging from 2006–2007 to 2018–2019.

Secondary data analysis has been done using regression of the form: Y =   a   +   b X

The research has used SPSS statistics software package for carrying out regression and for the various graphs Microsoft Excel software has been used.

5.1 The problem statement

It is taken to be a general assumption that whenever the premium increases the profit also increases. This determines that profits are actually dependent on the premium income. Hence, whenever the premium tends to increase, the profit made also supposed to increase.

The aim of the study is to find out whether the underwriting profit of the health insurance sector is increasing or there is an underwriting loss.

The problem statement is resolved by applying regression analysis between the premium earned and underwriting profit or loss incurred. It is assumed that if the underwriting profit increases along with the premium received, then the pattern forms a normal distribution and alternate hypothesis can be accepted and if this pattern of dependability is not found then the null hypothesis will be accepted stating that there is no relation between the premium and the underwriting loss or the underwriting profit by the sector. But what is happening in this sector is the increase in premium is leading to increase in underwriting loss. So premium is negatively impacting underwriting profit which is astonishing thing to happen and is the crux of the problem of this sector.

5.1.1 Underwriting profit/loss = net premium earned – (claim settled + commission and management expenses incurred).

Underwriting profit is a term used in the insurance industry to indicate earned premium remaining after claims have been settled and commission and administrative expenses have been paid. It excludes income from investment earned on premium held by the company. It is the profit generated by the insurance company in the normal course of its business.

5.2 Data analysis

Table 1 shows that health insurance premium increased from Rs.1910 crores in 2006–2007 to Rs. 33011 crores in 2018–2019. But claims incurred together with commission and management expenses have grown from Rs. 3349 crores to Rs. 40076 crores during the same period. So the claims and management expenses incurred together is more than the health insurance premium earned in all the years of our study thereby leading to underwriting loss.

Claim incurred shown above is the outcome of the risk covered against which premium is received and commission and management expenses are incurred to obtain contract of insurance. Both these expenses are important for insurance companies to generate new business as stiff competition exists in this sector since it was opened up in the year 2000.

Figure 1 depicts the relationship between health insurance premium earned and claims and management expenses incurred by the insurance companies of the health insurance sector for the period 2006–2007 to 2018–2019.

Bar chart between premiums earned and claims and management expenses incurred show that claims and management expenses together is higher than premium earned in all the years of the study thereby leading to losses. Claims, commission and management expenses are important factors leading to the sale of insurance policies thereby earning revenue for the insurance companies in the form of premium. But proper management of claims and commission and management expenses will help this sector to improve its performance.

Table 2 provides insight into the performance of health insurance sector in India. The growth of health insurance in India has been from Rs.1909 crores for the financial year 2006–2007 to Rs. 33011crores for the financial year 2018–2019. The growth percentage is 1629% i.e. growing at an average rate of 135% per annum. Compounded Annual Growth Rate (CAGR) is working out to be 27%.

From the same table, it can be inferred that health insurance sector is making underwriting loss in all the financial years. There is no specific trend can be seen, it has increased in some years and decreased in some other years. Here underwriting loss is calculated by deducting claims and commission and management expenses incurred from health insurance premium earned during these periods.

With every unit of increase in premium income the claims incurred together with commission and management expenses paid increased more than a unit. Thereby up setting the bottom line. So instead of earning profit due to better business through higher premium income, it has incurred losses.

Underwriting principles needs to be streamlined so that proper scrutiny of each policy is carried out so that performance of this sector improves.

It is seen from Figure 2 that there is stiff rise in premium earned over the years but claims and commission and management expenses incurred have also grown equally and together surpassed earned premium. So the net impact resulted in loss to this sector which can also be seen in the figure. It is also seen that loss is increasing over the years. So, increase in earnings of revenue in the form of premium is leading to increase in losses in this sector which is normally not seen in any other sectors.

But a time will come when commission and management expenses will stabilize through market forces to minimize underwriting losses. On the other hand, it will also require proper management of claims so that health insurance sector can come of this unprofitable period.

5.3 Interpretation of regression analysis

5.3.1 regression model..

Where Y = Dependent variable

X = Independent variablea = Intercept of the lineb = Slope of the line

5.3.2 Regression fit.

Here, Y is dependent variable (Underwriting Profit or Loss) which is to be predicted, X is the known independent variable (Health Insurance Premium earned) on which predictions are to be based and a and b are parameters, the value of which are to be determined ( Table 3 ). Y =   − 1028.737 − 0.226   X

5.3.3 Predictive ability of the model.

The value of R 2 = 0.866 which explains 86.6% relationship between health insurance premium earned and loss made by this sector ( Table 4 ). In other words, 13.4% of the total variation of the relationship has remained unexplained.

4.1 Regression coefficients ( Table 5 ).

H1.1 : β = 0 (No influence of Health Insurance Premium earned on Underwriting Profit or Loss made)

5.4.1.2 Alternative hypothesis.

H1.2 : β ≠ 0 (Health Insurance Premium earned influences underwriting Profit or Loss made by this sector)

The computed p -value at 95% confidence level is 0.000 which is less than 0.05. This is the confidence with which the alternative hypothesis is accepted and the null hypothesis is rejected. Thus regression equation shows that there is influence of health insurance premium earned on loss incurred by this sector.

The outcome obtained in this analysis is not what happens normally in the industry. With the increase of revenue income in the form of premium, it may lead to either profit or loss. But what is happening surprisingly here is that increase of revenue income is leading to increase of losses. So growth of premium income instead of influencing profit is actually influencing growth of losses.

6.1 Findings

The finding from the analysis is listed below:

The average growth of net premium for the health insurance has been around 135% per annum even then this sector is unable to earn underwriting profit.

The CAGR works out to around 27%. CAGR of 27% for insurance sector is considered to be very good rate of growth by any standard.

Along with high growth of premium, claims and commission and management expenses incurred in this sector have also grown substantially and together it surpassed in all the years of the study.

Thus, growth of claims and commission and management expenses incurred has more than compensated high rate of growth of health insurance premium earned. This resulted into underwriting loss that this sector is consistently making.

Astonishing findings has been higher rate of increase of premium earnings leading to higher rate of underwriting loss incurred over the years. Even though the sector is showing promise in terms of its revenue collection, but it is not enough to earn underwriting profit.

6.2 Recommendations

COVID 19 outbreak in India has led to a spike in health-care costs in the country. So, upward revision of premium charges must be considered to see bottom line improvement in this sector.

Immediate investigation of the claim is required. This will enable the insurers to curb unfair practice and dishonest means of making a claim which is rampant in this sector.

Health insurance market is not able to attract younger generation of the society. So entry age-based pricing might attract this group of customers. An individual insured at the age 30 and after 10 years of continuous coverage the premium will be less than the other individual buying a policy at the age of 40 for the first time.

6.3 Limitations and scope of future studies

The analysis of performance of health insurance sector in India taking underwriting profit into consideration is the only study of its kind in this sector. As a result, adequate literature on the subject was not available.

Health insurance and health care are part of medical care industry and are inter dependent with each other. So performance of health insurance sector can be better understood by taking health-care industry into consideration which is beyond the scope of the study.

This sector is consistently incurring losses. So, new ideas need to be incorporated to reduce losses if not making profits.

Opportunity of the insurance companies in this sector lies in establishing innovative product, services and distribution channels. So, continuous modification by the application of research is required to be undertaken.

Health insurance sector will take a massive hit, as tax benefit is going to be optional from this financial year. This can be a subject of study for the future.

6.4 Conclusion

This sector is prone to claims and its bottom line is always under tremendous pressure. In recent times, IRDA has taken bold step by increasing the premium rate of health insurance products. This will help in the growth of this sector.

With better technological expertise coming in from the foreign partners and involvement by the IRDA the health insurance sector in India must turn around and start to earn profit.

The COVID-19 pandemic is a challenge for the health insurance industry on various fronts at the same time it provides an opportunity to the insurers to fetch in new customers.

The main reason for high commission and management expense being cut-throat competition brought in after opening up of the insurance sector in the year 2000. So, new companies are offering higher incentives to the agents and brokers to penetrate into the market. This trend needs to be arrested as indirectly it is affecting profitability of this sector.

The study will richly contribute to the existing literature and help insurance companies to know about their performance and take necessary measures to rectify the situation.

Chart on health insurance premium earned and claims and management expenses paid

Chart on performance of health insurance sector in India

Data showing health insurance premium earned and claims and management expenses paid

. Dependent variable: Underwriting profit or loss;

. Predictors: (Constant), Health insurance premium earned

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Further reading

Beri , G.C. ( 2010 ), Marketing Research , TATA McGraw Hill Education Private , New Delhi, ND .

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Pai , V.A. and Diwan , M.G. ( 2001 ), “ Practice of general insurance ”, Insurance Institute of India , Mumbai, MM .

Shahi , A.K. and Gill , H.S. ( 2013 ), “ Origin, growth, pattern and trends: a study of Indian health insurance sector ”, IOSR Journal of Humanities and Social Science , Vol. 12 , pp. 1 - 9 .

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Health Literacy: the Path to Better Health Outcomes

This essay about health literacy emphasizes its critical role in healthcare, encompassing skills to understand and utilize health information effectively. It discusses the evolution of health literacy, including the need for analytical thinking, numeracy, and communication skills. The essay highlights the impact of inadequate health literacy, such as medication errors and disparities in healthcare access. It also suggests strategies for promoting health literacy, including clear communication by healthcare providers and targeted public health initiatives. Overall, the essay underscores the importance of health literacy in improving healthcare outcomes and achieving equity in healthcare access.

How it works

Health literacy, an indispensable yet frequently underestimated facet of healthcare, delineates the proficiency to acquire, comprehend, and employ health information to render enlightened determinations concerning one’s well-being. This concept transcends mere literacy; it encompasses a plethora of competencies enabling individuals to navigate the intricacies of healthcare systems, assimilate medical directives, and render determinations that positively influence their welfare. Grasping the significance of health literacy is pivotal for refining healthcare delivery and attaining impartial health outcomes.

The definition of health literacy has undergone metamorphosis over time to mirror the burgeoning complexity of healthcare.

Initially construed as the ability to peruse health-related information, it now enshrines proficiencies such as analytical thinking and numeracy. These aptitudes aid individuals in deciphering data, adhering to directives from healthcare providers, and assessing the credibility of diverse health resources. Health literacy also entails efficacious dialogue with healthcare professionals, ensuring that patients can articulate their symptoms, pose pertinent queries, and advocate for their requisites.

A noteworthy constituent of health literacy is the prowess to access pertinent information. In today’s digital epoch, where medical information is prolific yet at times unreliable, discerning accurate and dependable sources is imperative. This acumen assumes even greater significance when pondering how misinformation can sway determinations regarding vaccines, medications, and treatment regimens. Hence, a well-informed individual should possess the acumen to discriminate between credible, evidence-based sources and dubious content that may engender unfavorable health determinations.

Numeracy constitutes another pivotal facet of health literacy, aiding individuals in deciphering statistics and measurements employed in medical contexts. For instance, comprehending dosage instructions, monitoring blood pressure levels, and discerning the ramifications of diagnostic test outcomes necessitate rudimentary numeracy skills. Patients grappling with these competencies may encounter challenges adhering to their treatment regimens, potentially resulting in medication errors or ineffectual disease management.

Communication skills also wield considerable sway in health literacy. Patients must adeptly convey their symptoms and apprehensions to healthcare providers, who, reciprocally, should furnish lucid explanations and counsel. This reciprocal interaction ensures that patients are empowered to render well-informed determinations about their care and adhere to prescribed treatment strategies. Addressing barriers stemming from limited language proficiency or cultural disparities is imperative in fostering equitable health literacy.

The repercussions of inadequate health literacy are extensive. Individuals with deficient health literacy may misconstrue medical directives, culminating in elevated rates of hospitalization, medication errors, and preventable complications. This, in turn, augments healthcare expenditures and engenders heightened strain on healthcare systems. Furthermore, restricted health literacy disproportionately impacts marginalized communities, exacerbating health disparities and impeding endeavors to attain impartial health outcomes.

To confront these challenges, healthcare systems and professionals must actively promote health literacy. Providers should accord priority to lucid communication, employing plain language, visual aids, and teach-back methodologies to affirm patient comprehension. Additionally, public health initiatives can aid by crafting educational materials that are accessible and culturally attuned, targeting populations most susceptible to substandard health literacy. Schools and community organizations can also contribute by nurturing health literacy competencies through educational initiatives and outreach endeavors.

The digital metamorphosis of healthcare unveils novel prospects and challenges in this sphere. Digital tools such as patient portals and mobile health applications can enhance information accessibility and facilitate communication between patients and providers. Nonetheless, ensuring these tools are user-friendly and inclusive is pivotal to forestall exacerbating the digital chasm.

In culmination, health literacy constitutes a multifaceted concept encapsulating an array of competencies essential for traversing the healthcare panorama. By championing health literacy, we endow individuals with the ability to render enlightened determinations, adhere to treatment strategies, and ultimately ameliorate their health outcomes. This is indispensable in realizing a more impartial and efficacious healthcare system wherein every individual can access the care and information requisite for leading healthier lives.

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• Published: 14 May 2024

Using a customer discovery process to enhance the potential dissemination and scalability of a family healthy weight program for rural communities and small towns

• Gwenndolyn C. Porter   ORCID: orcid.org/0000-0002-6592-9537 1 ,
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• Kate A. Heelan 3 ,
• R. Todd Bartee 4 ,
• Caitlin A. Golden 2 ,
• Ali Malmkar 3 ,
• Bryce A. Abbey 3 &
• Paul A. Estabrooks 5  

International Journal of Behavioral Nutrition and Physical Activity volume  21 , Article number:  57 ( 2024 ) Cite this article

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Customer discovery, an entrepreneurial and iterative process to understand the context and needs of potential adoption agencies, may be an innovative strategy to improve broader dissemination of evidence-based interventions. This paper describes the customer discovery process for the Building Healthy Families (BHF) Online Training Resources and Program Package (BHF Resource Package) to support rural community adoption of an evidence-based, family healthy weight program.

The customer discovery process was completed as part of a SPeeding Research-tested INTerventions (SPRINT) training supported by the U.S. Centers for Disease Control and Prevention. Customer discovery interviews ( n =47) were conducted with people that could be potential resource users, economic buyers, and BHF adoption influencers to capture multiple contextual and needs-based factors related to adopting new evidence-based interventions. Qualitative analyses were completed in an iterative fashion as each interview was completed.

The BHF Resource Package was designed to be accessible to a variety of implementation organizations. However, due to different resources being available in different rural communities, customer discovery interviews suggested that focusing on rural health departments may be a consistent setting for intervention adoption. We found that local health departments prioritize childhood obesity but lacked the training and resources necessary to implement effective programming. Several intervention funding approaches were also identified including (1) program grants from local and national foundations, (2) healthcare community benefit initiatives, and (3) regional employer groups. Payment plans recommended in the customer discovery interviews included a mix of licensing and technical support fees for BHF delivery organizations, potential insurance reimbursement, and family fees based on ability to pay. Marketing a range of BHF non-weight related outcomes was also recommended during the customer discovery process to increase the likelihood of BHF scale-up and sustainability.

Conclusions

Engaging in customer discovery provided practical directions for the potential adoption, implementation, and sustainability of the BHF Resource Package. However, the inconsistent finding that health departments are both the ideal implementation organization, but also see childhood obesity treatment as a clinical service, is concerning.

The field of dissemination and implementation science has made pronounced strides in understanding barriers and facilitators related to translating efficacious interventions into practice [ 3 ]. Unfortunately, long lag times between the demonstration of intervention efficacy and use in community, public health, or healthcare settings persist [ 16 ]. Studying implementation processes and gaining an understanding of barriers to widespread uptake is a necessary, yet underutilized, tool in the pathway of moving interventions with demonstrated effectiveness into a position for broad adoption [ 23 ]. Further, the degree to which evidence-based interventions are moved into micropolitan (cities with <50,000 residents) and rural communities is an under-studied and high need area of research due to the confluence of lower socioeconomic status and health disparities [ 12 ].

Approaches such as user-centered design and community-academic partnerships attempt to speed the uptake of evidence-based interventions and close the research-practice gap [ 4 , 6 ]. Within these approaches, designing for dissemination, equity, and sustainability at the beginning of the intervention development process is hypothesized to significantly increase the likelihood that the intervention will be successfully adopted, implemented with fidelity, and sustained [ 3 , 5 , 17 ] by considering the facilitators and barriers relevant to the intended implementation setting, delivery agent, and audience intended to benefit from an evidence-based intervention. User-centered design and partnerships approaches often use backward design processes to increase the likelihood that a given intervention fits the system characteristics based on the goal of adoption and sustainability [ 24 ]. This process includes identifying key outcomes that may result from the intervention, considering user groups’ needs, the selection of required content, and, finally, the mapping of intervention content and processes to key outcomes [ 5 ].

One strategy to improve broader dissemination of evidence-based interventions, that have similar underlying principles to user-centered design and participatory approaches, is the use of a customer discovery process [ 25 ]. Customer discovery focuses on generating motivational data from a range of key people or organizations that can help to refine intervention design and dissemination strategies, while providing insight on the potential for scalability. Funding organizations have applied customer discovery processes to support scientists that have developed effective interventions to determine a product-market fit for their work [ 7 , 19 ]. Scientists involved in this process report seeing value in understanding customer segments and identifying value propositions that can guide dissemination efforts [ 7 , 19 ]. This approach has also been successful in clarifying the product-market fit and in generating intervention adaptations to better serve relevant audiences [ 25 ]. Investigative teams also improved their familiarity and comfort with the market aspects of intervention delivery, resulting in a shift of focus towards implementation, pursuing connections with small businesses, establishing companies for intervention delivery, or, in some cases, redesigning interventions [ 19 ].

The Centers for Disease Control and Prevention (CDC) and the American Academy of Pediatrics (AAP) introduced an opportunity for research teams funded through the Childhood Obesity Research Demonstration (CORD) 3.0 project to participate in a facilitated customer discovery process. This process was adapted from the National Cancer Institute’s SPeeding Research-tested INTerventions (SPRINT) program [ 18 ] and was co-facilitated by the CDC and the AAP. All CORD 3.0 award recipients received training to facilitate the scale-up and acceptability of evidence-based, family healthy weight programs (FHWPs) with a goal to begin investigating sustainable funding and/or reimbursement structures. Participating research teams were encouraged to consider multiple customer segments – from families that could benefit from a FHWP, to community or clinical organizations that could contribute to implementation, and to agencies that could provide funding support.

This article reports on the application of a customer discovery process intended to improve future adoption, implementation, and sustainability of the Building Healthy Families (BHF) Online Training Resources and Program Package (BHF Resource Package; [ 10 ], the only CORD 3.0 project explicitly created to improve the uptake and delivery of a FHWP in rural and micropolitan areas [ 10 , 11 ]. The BHF Resource Package was developed collaboratively by (1) researchers with expertise in community-engagement, family healthy weight program development, and implementation science, (2) Building Healthy Families (BHF) program developers and implementers, and (3) a community advisory board with representation from community, public health, and healthcare organizations. During this process, intended users, various user types, product needs, and anticipated problems were discussed and used to inform the BHF Resource Package development [ 10 ]. It was hypothesized that participation in the SPRINT training would generate innovative ideas for scaling up use of the BHF Resource Package. In this paper, we report on the processes and outcomes of a customer discovery process intended to document the unique characteristics of the BHF Resource Package marketplace in micropolitan and rural community settings, strategies to enhance adoption by community organizations, potential adaptations needed to improve the context-intervention fit, and potential sustainable funding models.

Beginning in April through June of 2021, BHF team members ( n =7) participated in a dissemination and implementation accelerator program, SPRINT, to improve the uptake of evidence based FHWPs. Our process focused on the BHF Resource Package which includes, but goes beyond the evidence-based BHF FHWP program, and has components focused on the development of sustainable program recruitment strategies and channels, training on general and session specific content for program coordinators, and an integrated data portal that includes knowledge checks, fidelity assessments, and parent and child behavioral and weight outcomes (all of which are used to generate community and family reports to demonstrate progress) [ 10 , 11 ]. BHF SPRINT team members attended 10 meetings over the course of seven weeks and completed training in customer discovery, business modeling, interview techniques, and developing a translation/commercialization plan for the BHF Resource Package including dedicated modules on program revenue, costs, and market economics [ 18 ]. Each meeting included hands-on instruction on transforming evidence-based FHWPs into market-ready products and services [ 7 ]. All sessions were held synchronously using video conferencing to facilitate participation of CORD 3.0 research teams. This model allowed for peer sharing and learning across the other participating SPRINT research teams focused on translating FHWPs into sustained practice. SPRINT facilitators with expertise in customer discovery and business model planning held team-specific office hours biweekly to further develop and refine business model hypotheses. SPRINT teams generated business model hypotheses (e.g., how the BHF Resource Package would likely be disseminated and sustained in rural communities) to be tested through customer discovery interviews, which inform the creation of an operational business model – the SPRINT program’s final product. Hypotheses covered various segments of a business model, including key partners, key activities, key resources, value propositions, customer relations, communication channels, customer segments, cost structure, and revenue streams [ 20 ]. Details of the customer discovery interviews are outlined below.

The BHF resource package

The BHF Resource Package was designed using participatory methods to address the variability in available organizations and personnel that could deliver BHF, an evidence-based FHWP that focuses on supporting family changes in behavioral skills, dietary changes, and physical activity promotion in micropolitan and rural areas [ 10 ]. The BHF Resource Package development included the identification of primary (i.e., facilitators to be trained to deliver BHF), secondary (i.e., researchers/evaluators interested in tracking outcomes and implementation quality), tertiary (i.e., organizations/individuals who would support/sponsor BHF), and terminal (i.e., BHF family participants) end users. Across each of these users groups the Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) Framework [ 8 ] was used, in a backwards-design process, to describe relevant outcomes. The resultant BHF Resource Package included (1) program materials, lesson plans, PowerPoint slides, and implementation checklists, (2) overarching and session specific training modules for program coordinators and facilitators [ 10 ], (3) knowledge checks to ensure coordinator and facilitator competence, (4) fidelity assessments to track implementation quality, (5) a recruitment module to support family engagement, and (6) a data portal to track program effectiveness and family progress.

Participants

Community organizations, broadly defined as those with priorities related to childhood obesity and personnel with expertise or interest in family-based health promotion, were identified as the primary customer segment for the BHF Resource Package. This allowed for the consideration of community implementation team members from a broad range of community organizations (i.e., primary end users). Building from our primary customer segment, we created an eco-system map (Fig. 1 ) of rural FHWP. We used convenience sampling from existing professional networks to identify participants for our initial round of interviews. The eco-system map was generated to graphically represent the context of rural FHWP delivery and identify the relationships of individuals who would be involved in all aspects of the BHF Resource Package adoption, implementation, sustainment as well as those who would ultimately benefit from BHF program participation. The eco-system map identified potential customers and delivery channels (i.e., those who would support or influence BHF Resource Package implementation). The customer discovery process included interviews with individuals ( n =47) representing different aspects of the eco-system map that could have an influence on, or interest in, the BHF Resource Package adoption and implementation in rural and micropolitan areas (Table 1 ). Interview findings are presented in aggregate (i.e., not separated by stakeholder group) as the formation of an operational business model requires generalizable fit across multiple stakeholder groups within a given eco-system.

Ecosystem map for BHF - a family healthy weight program targeting rural and micropolitan communities

A broad approach with semi-structured, open-ended questions was taken with the interviews to explore and learn from potential BHF Resource Package adopters or influencers about their experiences related to addressing childhood obesity in their organization, identifying potential partners and competitors, and gaining insight about potential scale-up of BHF. The interviews were guided by an initial value proposition that was intended to incorporate key informant perceptions around things they would like to eliminate, things they would like to add, and considerations of the outcomes related to potential adoption of family healthy weight programs [ 20 ]. All participants were asked about how their organization prioritized or contributed to addressing childhood obesity, what other health promotion resources are available in the community, and community needs for addressing childhood obesity. Additional questions regarding program fit, such as, “If a childhood obesity program were to be offered by your organization, what would it look like?,” and, “What things would make it easier for your organization to offer this type of programming?” were asked during interviews. Discussions surrounding costs and revenue were central to all customer discovery interviews as the BHF SPRINT team sought to understand how best to create a sustainable pathway for the use of the BHF Resources Package and delivery of the FHWP in rural and micropolitan communities. Finally, our initial value proposition can be described as micropolitan and rural community organizations need and want to address childhood obesity, but currently do not have an easy way to do it. As such, we hypothesized that the BHF Resource Package would provide a valued, and relatively easy, turn-key approach to address childhood obesity locally.

The interview process followed an iterative approach – business model hypotheses were either confirmed – allowing testing of subsequent hypotheses – or refuted. When interview data suggested a hypothesis was incorrect or not feasible, then the business model hypotheses were revised to more closely align with the goal of creating a practical, robust, and compelling business model [ 19 ]. Of note, at the time of the interviews, the BHF Resource Package was being tested in seven micropolitan communities (and surrounding rural areas) in Nebraska with funding for program resources ($5,500/community) provided to participating communities through grant funding [ 9 ].

As the customer discovery interviews were considered activities with individuals talking about their profession and the items were limited to questions about how the BHF Resources would fit within their work or community activities, rather than about the individual characteristics of the interviewee, this work was not considered human subjects research by the University of Nebraska Medical Center IRB. However, while informed consent was not required; the research team asked all interviewees for permission to record the interview so that an accurate analysis of interview responses mapped to business model hypotheses could be completed. Interviews were conducted using phone or video conferencing and recorded using a password-protected, data-driven product management software platform that allowed for research team members to review interviews and feedback, track new discoveries, and validate (or refute) business model hypotheses. Upon completion of each interview, participants were asked for contact information of others in their network that could provide insight on their experiences addressing childhood obesity and the organizational decision-making process around adopting new evidence-based interventions.

Data analysis

A unique aspect of our approach to customer discovery interviews was the use of a rapid qualitative reduction process to analyze the research team members’ notes from each interview [ 26 ]. During this process, the research team member entered major themes from the interview into an online database and reviewed the existing hypotheses to code the interview as confirming, leaning confirming, neutral, leaning disconfirming, disconfirming, or not applicable to each hypothesis. Codes were discussed by research team members to ensure consensus. For example, if an interview yielded a major theme of fostering better clinic-community partnerships to allow for more referrals to effective FHWPs, the research team member would code that interview as “confirming” for the hypothesis that community communication and marketing resources would be a direct benefit to the customer (value proposition). Similarly, the same interview may have revealed that community health improvement plans are a contributing factor, but not the sole driver of program adoption decisions, and therefore the interview would be coded as “neutral” for the hypothesis, community health improvement plan goals are a primary driver of resource adoption (value proposition). The protected time provided by the research team’s involvement in the SPRINT program allowed for this more thorough data analysis process than what may be employed in more traditional customer discovery activities [ 19 ]. Codes for each hypothesis were quantified and used to rank order the value propositions.

Throughout the customer discovery interviews and expert consultation with the SPRINT facilitation team, we made changes to our hypotheses across all elements of our business model. If participant responses did not align with existing hypotheses, new hypotheses were created in the business model and added to subsequent interview guides. The research team met weekly to review interview progress, refine our target audience, and to discuss business model hypotheses. Hypotheses were edited, created, or removed (an activity prescribed by the customer discovery process) to reflect findings from the interviews and better capture the organizational decision-making process around adopting new evidence-based interventions. Across interviews and team discussions, emergent themes that could inform future scale up were recorded.

Customer discovery

Participants in the customer discovery interviews and the expert SPRINT facilitators indicated that identifying a specific type of community organization that was likely available across a wide variety of micropolitan and rural communities may be necessary to improve marketing and dissemination strategies for BHF program facilitators – the primary end users of the BHF Resources Package. Through the interviews, the consistent type of community organization mentioned was local health departments, and therefore potential BHF program facilitators were indicated to be local health department directors who have decision-making authority over local programming within the broader context of community health priorities. In contrast to identifying a potential single community organization for BHF implementation, interview participants identified a wide range of secondary end users (i.e., those who would support or provide funding for communities to use the BHF Resource Package). The potential supporters or funders included local employers, hospitals, and nonprofit organizations that could provide financial support for local health departments interested in adopting, implementing, and sustaining BHF.

Value propositions

Over the course of the customer discovery process, we identified nine components of value propositions related to why a customer would adopt the BHF product, how the resource would be a direct benefit to the customer, or what is unique about our solution. These are rank ordered in Table 2 based upon the degree to which they were confirmed across interviews. Specifically, based on confirming and leaning confirming responses, the most consistently confirmed value proposition across interviews was the hypothesis that communities will value the communication materials and marketing resources included in the BHF Resource Package. By rank order of confirmation, the next three value propositions all addressed the alignment of the BHF Resource Package with community priorities and need. Interviewees valued the focus on interorganizational collaboration promoted by the BHF Resource Package. While confirmed, our initial value hypothesis – that communities would value the user-friendly, comprehensive training and resource package – was rank ordered sixth in the nine value proposition hypotheses. Finally, our value hypothesis that the BHF Resource Package would be valued because it would support facilitator work by aligning with current job responsibilities was not confirmed.

Emergent themes from interviews

Findings from our customer discovery interviews illuminated insights beyond identifying local health departments as our primary customer. These themes were present across our business model hypotheses and included: 1) perceptions of responsibility among clinical and community organizations; 2) payer opportunities; and 3) marketing the benefits of the BHF Resource Package beyond child weight change.

Perceptions of responsibility among clinical and community organizations

We interviewed medical providers and clinical staff ( n =10) as well as health practitioners within the community (i.e., health department staff; n =13). What emerged from these discussions was that lifestyle programming for childhood obesity falls into a “grey area” between clinic and community systems. This also provided the context for the finding that there was a lack of alignment between the responsibilities of implementing BHF and current job descriptions of those working in micropolitan and rural communities. Specifically, several of the interview participants discussed the gap in rural communities – between which FHWPs fall – where local health departments focus on obesity prevention and rural health centers do not have the capacity to deliver FHWPs. Thus, FWHP implementation was not a consistent or specific job responsibility in rural communities. Within the potential BHF Resource Package user group, health department interviewees indicated that childhood obesity represented a clinical concern and that approaching families about childhood obesity treatment falls outside of the perceived scope of community health professionals. In contrast, healthcare professionals who were interviewed highlighted the constraints in clinical time and resources needed to deliver recommended FHWPs. Of note, interviewees suggested that there may be the potential for the public health and healthcare systems to complement one another to address childhood obesity by leveraging the patient-provider relationship to refer patients to efficacious community-based programming.

Cost, revenue, and payer opportunities

A common theme during the customer discovery interviews was the cost of providing the program and possible funding and reimbursement structures to support the sustainability of a FHWP. Interviewees identified potential grant funding or sponsorship from local or national foundations, local healthcare community benefit dollars, and employers as the most likely sources of sustainable funding for BHF. Additionally, licensing fees for adopting organizations or advertisement opportunities to offset costs, and a tiered system of customer fees (i.e., participating families) were proposed by interviewees as potential funding mechanisms. A consistent scenario was described that included a combination of a licensing fee for community organization use of the BHF Resources Package, additional fees for technical support or other methods to facilitate community organization adoption and implementation, and a small fee for participating families based on ability to pay. Finally, interviewees also discussed the potential of insurance reimbursement for family participation in BHF, similar to the reimbursement structure in place for the CDC National Diabetes Prevention Program.

Marketing the benefits of BHF beyond child weight change

As the BHF Resources Package is a FHWP, we designed the marketing and recruitment materials to highlight the health promotion aspects of BHF, specifically the focus on behavior change strategies that promote a healthy lifestyle and weight management. However, during our interviews, participants consistently emphasized the health promotion benefits of the BHF program that extend beyond weight management. Additional benefits included, but moved beyond, changes in healthful eating and physical activity to the constructive use of family time, social and emotional well-being, and quality of life. Participants explained that adopting a program like the BHF Resources Package would be more feasible if it could easily be identified as meeting several organizational health goals within nutrition, physical activity, and social and emotional well-being domains. Responses from health department professionals specifically referred to BHF aligning with Community Health Improvement Plans as being an important consideration. Finally, some interviewees highlighted the benefits of providing BHF in workplaces to promote employee well-being and reduce turnover.

Using a customer discovery process provided several critical areas for consideration when addressing the potential adoption, implementation, and sustainability of FHWPs in micropolitan and rural communities. Specifically, our findings suggested that there is a need to not only focus, but expand on the characteristics of an innovation related to designing for dissemination and scalability. Further, we identified the value held by potential delivery organizations in the resources that address issues related to reaching those in need of FHWPs and support for interorganizational collaboration. Finally, we found there is a need to address the ongoing challenges of the grey area of implementation responsibility for FHWPs and how that influences the availability of potentially sustainable funding models.

The BHF Resources Package, was designed for dissemination using a participatory and user-centered approach to create a resource that included all the program materials, training guides, and evaluation materials that micropolitan, rural, and/or under resourced communities need to implement BHF [ 10 ]. As such, we anticipated that the customer discover process would underscore that potential users of the BHF Resource Package would highly value the user-friendly comprehensive training and resource package features. This is consistent with much of the reporting on previous research teams that have used the customer discovery process that emphasizes the fit between the characteristics of the evidence-based intervention and delivery setting [ 3 ]. Our customer discovery process identified this as a value to micropolitan and rural communities, however, the relative rank of our value proposition related to designing for dissemination was rated sixth out of our nine proposed value statements. This may indicate that the user-friendly, comprehensive BHF Resource Package may be a necessary, but insufficient characteristic that communities consider in the adoption decision making process. Specifically, factors that are related to, but distinct from the characteristics of BHF Resource Package are the degree to which the goal of FHWPs aligns with community priorities, organizational missions, and degree of inter-organizational collaboration.

There are two areas in our findings that have not been explicitly addressed in other reports from the customer discovery process within the SPRINT process. First, the highest value that interviewees found in the BHF Resource Package was the inclusion of communication materials and marketing resources intended to increase the reach of the FHWP. Second, we found that interviewees also valued support for interorganizational collaboration. These findings may be due to the unique focus on micropolitan and rural communities, and align with previous research examining FHWPs in micropolitan and rural areas, where members of a community advisory board highlighted the development of sustainable referral protocols that relied on engagement across community organizations [ 1 ].

Perhaps the most daunting challenge identified through the customer discovery process is the grey area of responsibility when it comes to implementing FHWPs and the seemingly conflicting feedback we received that local health departments should be the focus of adoption efforts, but that health departments also prioritize obesity prevention over obesity treatment. Unfortunately, this is not new to our research team and is the underlying rationale for why we approached the development of the BHF Resources Package with a goal to allow for delivery by different organizations based on local systems that prioritized family healthy weights [ 11 ]. Still, focusing on local health departments served as a compass for steering our discussions with prospective partners who were also included in our customer discovery interviews and led to an approach that still valued flexibility in a local implementer while including local health departments as the primary starting place. In addition, after our completion of the customer discovery process, the CDC High Obesity Program included the implementation of FHWPs through state health departments [ 2 ] which should improve the market pull for the BHF Resource Package.

Moving forward with the intention to scale up the BHF Resource Package has been substantially informed by the findings from our customer discovery interviews and SPRINT training. Specifically, this process led to the development of more tailored descriptions of the BHF Resource Package that align with the values highlighted by our interviewees. A primary adaptation to the BHF Resource Package as a result of both the customer discover interviews and our piloting of the BHF Resource Package, was to enhance the recruitment module, introduce training for a recruitment coordinator, and provide a protocol to develop a local recruitment team. We anticipate this adaptation will be well received by communities. Yet, like other health promotion strategies intended for community, identifying potential payers for community-based lifestyle programming and moving towards commercialization is an ongoing and evolving challenge [ 15 , 19 ]. At the completion of this project, the goal of the research team was to develop a non-profit business, embedded within a host university, to provide the ability to receive payment from communities interested in using the BHF Resource Package. Challenges with this process included the time necessary to complete the process within the context of maintaining team project management, research, teaching, and service responsibilities [ 19 ]. Further, as we have moved forward, we have found that understanding the actual costs to provide the BHF Resource Package –from initiating a contract with a delivery organization to providing ongoing technical support—to be a challenge. As such, we recommend that others who are examining the potential to scale interventions like the BHF Resource Package consider using simple contractual agreements with a small number of implementation sites to better understand the costs prior to initiating commercialization processes—nonprofit or otherwise.

This project is limited by the focus specifically on micropolitan and rural communities primarily in Nebraska and the surrounding states. The findings may not be generalizable to other populations or systems outside of this region. For example, in our region, systems such as cooperative extension do not focus on childhood obesity treatment, though in other regions they do [ 13 , 14 , 21 , 22 ] and may be another potential primary user of the BHF Resource Package (or other FHWPs). This underscores the potential need to consider the variability of organizations and resources to deliver evidence-based FHWPs if a broad public health impact in rural areas is to be achieved. Finally, when contrasted with other projects that have used customer discovery processes with the SPRINT model, it appears that there are unique issues related to promoting family healthy weights in micropolitan and rural communities.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Abbreviations

American Academy of Pediatrics

Building Healthy Families

Centers of Disease Control and Prevention

Family Healthy Weight Program

Reach, Effectiveness, Adoption, Implementation, and Maintenance Framework

SPeeding Research-tested INTerventions

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Porter, G.C., Hill, J.L., Heelan, K.A. et al. Using a customer discovery process to enhance the potential dissemination and scalability of a family healthy weight program for rural communities and small towns. Int J Behav Nutr Phys Act 21 , 57 (2024). https://doi.org/10.1186/s12966-024-01605-7

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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.

bmjopen-2021-050077supp001.pdf

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.

Registration

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.

Introduction

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 .

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

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

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

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

Willingness-to-pay

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

Acknowledgments.

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.

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Ethics approval

This study does not involve human participants.