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Health index scores and health insurance coverage across India: A state level spatiotemporal analysis

  • Rajesh Kamath
    Correspondence
    Corresponding author. Department of Social and Health Innovation, Prasanna School of Public Health, Manipal Academy of Higher Education, Manipal, India.
    Affiliations
    Department of Social and Health Innovation, Prasanna School of Public Health, Manipal Academy of Higher Education, Manipal, India

    Department of International Health, Care and Public Health Research Institute – CAPHRI, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, the Netherlands
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  • Vani Lakshmi
    Affiliations
    Department of Data Science, Prasanna School of Public Health, Manipal Academy of Higher Education, Manipal, India
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  • Helmut Brand
    Affiliations
    Department of International Health, Care and Public Health Research Institute – CAPHRI, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, the Netherlands

    Prasanna School of Public Health, Manipal Academy of Higher Education, Manipal, India
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Open AccessPublished:November 29, 2022DOI:https://doi.org/10.1016/j.cegh.2022.101185

      Abstract

      Introduction

      Since the Government of India has decided to continue with the publicly funded health insurance (PFHI) strategy, it is now pertinent to attempt to determine the factors that drive health insurance coverage in India. The NITI (National Institute for Transforming India) Aayog (i.e. Commission) is the apex public policy think tank of the Government of India. The NITI Aayog assesses the health status of the states through its acclaimed health index consisting of 24 indicators for health outcomes, governance and infrastructure. All states and Union Territories (UTs) are ranked on the index. This study aims to assess associations between NITI Aayog health index scores and health insurance coverage across India through a state-level lens.

      Methods

      Health insurance coverage data has been extracted from the National Family Health Survey (NFHS) 4 and NFHS-5 data. NFHS-4 was conducted during 2015–16. NFHS-5 was interrupted by the COVID-19 pandemic and conducted in two phases from 2019 to 2021. This change in health insurance coverage is mapped to the NITI Aayog health index scores for the states and UTs. The NITI Aayog has classified states into two categories: Larger states and smaller states. Based on performance in health indices, NITI Aayog also classifies the states and UTs as Aspirants, Achievers and Front runners.

      Results and discussion

      There is a positive linear relationship between the health index scores of front-runners (Pearson's r = 0.6037, p = 0.029) and the total insurance coverage. We observe poor linear relationship between the health index scores of achievers (Pearson's r = 0.2822, p = 0.498) and the total insurance coverage. There is no linear relationship between the NFHS-5 Total Insurance Coverage and the NITI Health Index Scores (Pearson's r = 0.2766, p = 0.125). Also, we observe a moderate positive linear relationship between the health index scores and the total insurance coverage among the Union Territories which is not statistically significant (Pearson's r = 0.4343, p = 0.465). A similar conclusion is made in the context of smaller states (Pearson's r = 0.3692, p = 0.368) and larger states (Pearson's r = 0.2103, p = 0.387). At the same time, we observe a decrease in insurance coverage across NFHS-4 and NFHS-5 in some states and UTs. Further research is needed to identify the determinants of these spatial changes across a span of five years, from a temporal lens.

      Keywords

      1. Introduction

      India has a population of 1.4 billion people with a rural tilt: 65% of the population lives in rural areas. India has 28 states and 8 union territories. Health is a state subject: the responsibility of the state governments. The central government funds several national health programs and also provides policies and guidelines. There is significant interstate variation in the quality and provision of public and private healthcare. 75% of doctors serve the 35% population in the urban areas with only 25% of doctors serving the 65% population in the rural areas. 2 out of 3 healthcare providers in rural areas are not qualified medical service providers. This phenomenon is not limited to rural areas. Delhi, the national capital territory is home to 80,000 quacks.
      Are India's quacks the answer to its shortage of doctors?.
      (see Table 5, Table 6)
      India's public spending on healthcare, i.e. the sum of the central and State government spending is 1.5% of GDP, which is about 30% of the total spending on healthcare in India.
      • Demand for grants 2020-21 analysis
      Health and family welfare.
      70% of national spending on healthcare is Out-Of-Pocket Expenditure (OOPE).
      • NewIndianXpress
      Nearly 70 per cent expenditure on health to come out of patients' pockets: finance commission report.
      The government healthcare system consists of sub-centers, Primary health centers and Community health centers in rural areas with district and sub-district hospitals in urban areas.

      Health care in rural areas. Press Information Bureau. (n.d.). Retrieved from https://pib.gov.in/PressReleasePage.aspx?PRID=1777642.

      Doctor absenteeism, overcrowding, lack of hygiene, stock-outs of medicines and essential supplies, sub-standard medicines and supplies, lack of or malfunctioning equipment, understaffing, lack of ownership, lack of accountability and lack of quality care are accepted challenges in the Indian public healthcare system. No data is available on quality audits of the 150,000 sub-centers, 25,000 primary health centers and 6000 community health centers in India. It is the same scenario in the private sector as well. Quality and accreditation in the healthcare space in India is synonymous with the National Accreditation Board for Hospitals and Healthcare providers (NABH), a quasi-governmental body that hands out various levels of accreditation to hospitals. NABH is entirely voluntary, which is probably one reason why only about 1% of Indian hospitals have applied for accreditation. With concerns over the profitability of Publicly Funded Health Insurance (PFHIs) packages to hospitals and the low penetration of private insurance, hospitals seem happy to cater to the substantial portion of the patient pool that pays out of pocket.

      Revised rates of Ayushman Bharat insufficient for private sector: IMA. Down To Earth. Retrieved from https://www.downtoearth.org.in/news/health/revised-rates-of-ayushman-bharat-insufficient-for-private-sector-ima-66998.

      Steps like making NABH accreditation mandatory for cashless facilities and PFHIs paying an additional 10–15% on the package rates to NABH accredited hospitals have so far not made a significant impact on the NABH accreditation application rates of hospitals. Depending on the definition used, the prevalence of catastrophic OOPE may vary, but there is a general consensus that it is a significant concern.
      • Kastor A.
      • Mohanty S.K.
      Disease-specific out-of-pocket and catastrophic health expenditure on hospitalization in India: do Indian households face distress health financing?.
      With this background, the Indian central government in 2008 launched a PFHI, the Rashtriya Swasthya Bima Yojana (RSBY) (national health insurance program) that targeted Below Poverty Line (BPL) families.
      • Karan A.
      • Yip W.
      • Mahal A.
      Extending health insurance to the poor in India: an impact evaluation of Rashtriya Swasthya Bima Yojana on out of pocket spending for healthcare.
      Depending on the BPL definitions in government calculations one looked at, the%age of BPL families varied between 20% and 30%.
      • Kamath S.
      Kamath RShortcomings in India's first national attempt at universal healthcare through publicly funded health insurance.
      The government national gross enrolment numbers for RSBY were healthy. The financial cover under RSBY was Rs. 30,000 per family.
      • Ecks S.
      "When the Government Changes, the Card Will Also Change": Questioning Identity in Biometrie Smartcards for National Health Insurance (RSBY) in India.
      The Ayushman Bharat Pradhan Mantri Jan Arogya Yojana (ABPMJAY) increased the financial cover by 16 times to Rs. 500,000 per family.
      • Angell B.J.
      • Prinja S.
      • Gupt A.
      • Jha V.
      • Jan S.
      The Ayushman Bharat Pradhan Mantri Jan Arogya Yojana and the path to universal health coverage in India: overcoming the challenges of stewardship and governance.
      ABPMJAY is India's biggest attempt at a national PFHI. It targets the bottom 40% of the population.
      • Bhaduri S.D.
      Ayushman Bharat and universal health coverage in India: is our approach ethical?.
      Like in the case of RSBY, government national gross enrolment numbers are healthy. But these numbers, along with the financial risk protection afforded by these schemes in the form of reduction or elimination of OOPE must be double-checked through community surveys. There is evidence to suggest that PFHIs can increase health service utilization significantly.
      • Mahapatro S.R.
      • Singh P.
      • Singh Y.
      How effective health insurance schemes are in tackling economic burden of healthcare in India.
      • Ranjan A.
      • Dixit P.
      • Mukhopadhyay I.
      • et al.
      Effectiveness of government strategies for financial protection against costs of hospitalization care in India.
      • Sriram S.
      • Khan M.M.
      Effect of health insurance program for the poor on out-of-pocket inpatient care cost in India: evidence from a nationally representative cross-sectional survey.
      • Nandi S.
      • Schneider H.
      • Dixit P.
      Hospital utilization and out of pocket expenditure in public and private sectors under the universal government health insurance scheme in Chhattisgarh state, India: lessons for universal health coverage.
      • Katyal A.
      • Singh P.V.
      • Bergkvist S.
      • et al.
      Private sector participation in delivering tertiary health care: a dichotomy of access and affordability across two Indian states.
      • Philip N.E.
      • Kannan S.
      • Sarma S.P.
      Utilization of comprehensive health insurance scheme, Kerala: a comparative study of insured and uninsured Below-Poverty-Line households.
      • Sood N.
      • Bendavid E.
      • Mukherji A.
      • et al.
      Government health insurance for people below poverty line in India: quasi-experimental evaluation of insurance and health outcomes.
      • Sood N.
      • Wagner Z.
      Impact of health insurance for tertiary care on postoperative outcomes and seeking care for symptoms: quasi-experimental evidence from Karnataka, India.
      There is very little evidence to suggest that PFHIs can be relied upon, as a means to reduce OOPE significantly.
      • Rao M.
      • Katyal A.
      • Singh P.V.
      • et al.
      Changes in addressing inequalities in access to hospital care in Andhra Pradesh and Maharashtra states of India: a difference-in-differences study using repeated cross-sectional surveys.
      • Katyal A.
      • Singh P.V.
      • Bergkvist S.
      • et al.
      Private sector participation in delivering tertiary health care: a dichotomy of access and affordability across two Indian states.
      • Garg S.
      • Chowdhury S.
      • Sundararaman T.
      Utilisation and financial protection for hospital care under publicly funded health insurance in three states in southern India.
      • Philip N.E.
      • Kannan S.
      • Sarma S.P.
      Utilization of comprehensive health insurance scheme, Kerala: a comparative study of insured and uninsured Below-Poverty-Line households.

      Garg S, Bebarta KK, Tripathi N. Performance of India's National Publicly Funded Health Insurance Scheme, Pradhan Mantri Jan Arogaya Yojana (PMJAY), in Improving Access and Financial Protection for Hospital Care: Findings from Household Surveys in Chhattisgarh State.

      Since the Government of India has decided to continue with the PFHI strategy, it is now pertinent to attempt to determine the factors that drive health insurance coverage in India. The NITI Aayog is the apex public policy think tank of the Indian government.
      • Patnaik P.
      From the planning commission to the NITI Aayog.
      The NITI Aayog health index has 24 indicators for health outcomes, governance and infrastructure.

      Health performance. NITI Aayog, National Institution for Transforming India, Government of India. Retrieved from http://social.niti.gov.in/hlt-ranking/group-info/1.

      All states and UTs are ranked on the index. This study aims to assess whether there is an association between NITI Aayog health index scores and health insurance coverage, based on state-level data in India.

      2. Methodology

      This study aims to assess whether there is an association between health index scores and health insurance coverage across India. Health insurance coverage data has been extracted from two comprehensive nationwide surveys. The National Family Health Survey (NFHS) is a large-scale, multi-round survey conducted on a representative sample of households throughout India.
      National Family Health Survey
      NFHS-4 was conducted in 2015–16. NFHS-5 was interrupted by the COVID-19 pandemic and conducted in 2 phases from 2019 to 2021.
      This change in health insurance coverage is mapped to the NITI Aayog health index scores for the states and UTs. The NITI Aayog has classified states into two categories based on their size: larger states and smaller states. The NITI Health index indicators for larger states, smaller states and UTs are outlined in Table 1, Table 2, Table 3. The states and UTs are classified into Aspirants, Achievers and Front runners based on the scores obtained in the Health Index in Table 1:
      Table 1NITI Aayog classification of states and UTs.
      Larger statesSmaller statesUTs
      Aspirant0–480–500–45
      Achiever48–6250–6245–55
      Front runner>62>62>55
      Table 2NITI Aayog health index indicators.
      1.Neonatal mortality rate
      2.Under-five Mortality Rate
      3.Total Fertility Rate
      4.Proportion of Low Birth Weight among infants
      5.Sex Ratio at Birth
      6.Full immunization coverage
      7.Proportion of institutional deliveries
      8.Total Case Notification Rate of TB
      9.Treatment success rate of new micro biologically confirmed tuberculosis (TB) cases
      10.Proportion of People Living with HIV (PLHIV) on Anti-Retroviral Therapy (ART)
      11.Data Integrity Measure: a. Institutional deliveries b. ANC registered within first trimester
      12.Average occupancy of an officer (in months), combined for following three posts at State level for last three years 1. Principal Secretary 2. Mission Director - National Health Mission (NHM) 3. Director- Health Services
      13.Average occupancy of a full-time officer (in months) in last three years for all Districts- District Chief Medical Officers (CMOs) or equivalent post (Heading District Health Services)
      14.Proportion of vacant health care provider positions (regular + contractual) in public health facilities
      15.Proportion of total staff (regular + contractual) for whom an e-pay slip can be generated in the IT enabled Human Resource Management Information System (HRMIS).
      16.Proportion of specified type of facilities functional as First Referral Units (FRUs)
      17.Proportion of functional 24 × 7 PHCs
      18.Proportion of Districts with Functional Cardiac Care Units (CCUs)
      19.Proportion of ANC registered within first trimester against total registrations
      20.Level of registration of births (%)
      21.Completeness of IDSP reporting of P and L form (%)
      22.Proportion of CHCs with grading of 4 points or above
      23.Proportion of public health facilities with accreditation certificates by a standard quality assurance programme: National Quality Assurance Standards (NQAS)/National Accreditation Board for Hospitals & Healthcare Providers (NABH)/International Organization for Standardization (ISO)/Association of Healthcare Providers (India) AHPI)
      24.Average number of days for transfer of Central NHM fund from State Treasury to implementation agency (Department/Society) based on all tranches of the last financial year
      Table 3NITI Aayog Health Index.
      NITI Aayog health index (Larger states)
      Aspirant (0–48)Assam, Jharkhand, Odisha, Uttarakhand, Rajasthan, Madhya Pradesh, Bihar, Uttar Pradesh,
      Achiever (48–62)Punjab, Karnataka, Chattisgarh, Haryana
      Front runner (>62)Kerala, Tamil Nadu, Telangana, Andhra Pradesh, Maharashtra, Gujarat and Himachal Pradesh
      NITI Aayog health index (Smaller states)
      Aspirant (0–50)Meghalaya, Manipur, Arunachal Pradesh, Nagaland
      Achiever (50–62)Sikkim, Goa
      Front runner (>62)Mizoram, Tripura
      NITI Aayog health index (Union territories)
      Aspirant (0–45)Andaman and Nicobar islands
      Achiever (45–55)Lakshadweep, Puducherry, Delhi, Jammu and Kashmir
      Front runner (>55)Dadra Nagar Haveli and Daman and Diu, Chandigarh
      The indicators that comprise the NITI Aayog health index for larger states, smaller states and UTs are listed in Table 2, Table 3

      3. Results

      A scatter plot was constructed to explore the relationship between insurance coverage (NFHS-5) and the NITI Health Index scores. As per the definition of the components of the Health Index, better health insurance coverage is an indicator of better health index and vice-versa since better health infrastructure and governance lead to an improved implementation of government policies which will, in turn pave the way for better health outcomes.
      Fig. 1 presents a scatter plot to assess the relationship between the NITI Aayog Health Index and the NFHS-5- Insurance Coverage (in %). A linear relationship between the NFHS-5 Total Insurance Coverage and the NITI Health Index Scores is suspected. However, the association is neither strong nor statistically significant (Pearson's r = 0.2766, p = 0.125). Additionally, the scatter plot also indicates presence of clusters (or groups) in the data. Subsequently, we pursued the assessment of linear relationships as per the state-level classifications (in terms of size of the states and the health index status) as per NITI Aayog.
      Fig. 1
      Fig. 1Relationship between NITI Health Index and NFHS-5 Total insurance coverage.
      Through Fig. 2a and b, we further explore the relationship keeping in mind the size of the state and through the lens of achievers, aspirants and front-runners in the context of the NITI Aayog based performance of states. The scatter plot reveals strong linear association between health index and insurance coverage among front runners (Pearson's r = 0.6037, p = 0.029) and aspirants (Pearson's r = 0.5225, p = 0.099). However, this phenomenon is not observed among achievers (Pearson's r = 0.2822, p = 0.498).
      Fig. 2
      Fig. 2a-2b. Relationship between NITI Health Index scores across the three groups (front-runners, achievers and aspirants) and total insurance coverage.
      Additionally, the overlapping ellipses in Fig. 2b indicates that the size of the state: larger state (Pearson's r = 0.2103, p = 0.387), smaller state (Pearson's r = 0.3692, p = 0.368) and UTs (Pearson's r = 0.4343, p = 0.465) does not really have an effect on the associations. Apart from UTs, there is poor positive linear relationship between health insurance coverage and NITI health index scores among smaller and larger states.
      Subsequently, we explore trends in health insurance coverage across NFHS-4 and NFHS-5 surveys across rural, urban and overall states/UTs (see Fig. 3).
      Fig. 3
      Fig. 3Trends in Total Insurance Coverage_Overall (Rural + Urban): A comparison of NFHS-5 and NFHS-4.
      As seen in Table 4, majority of the larger states (16 out of 19), smaller states (5 out of 8) and UTs (3 out of 5) have witnessed an increase in the health insurance coverage between NFHS-4 and NFHS-5. Similarly, it is observed that majority of the achievers (7 out of 8), aspirants (9 out of 11) and front-runners (8 out of 13) have improved upon their health insurance coverage between NFHS-4 and NFHS-5. This could possibly be attributed to the nationwide implementation of Ayushman Bharat Scheme (see Fig. 4).
      Table 4Frequencies of comparison.
      NITI Classification
      ComparisonLarger StateSmaller StateUnion Territory
      Decrease332
      Increase1653
      NITI Health status
      ComparisonAchieverAspirantFront-runner
      Decrease125
      Increase798
      Table 5Frequencies of comparison.
      NITI Classification
      ComparisonLarger StateSmaller StateUnion Territory
      Decrease431
      Increase1553
      NITI Health status
      ComparisonAchieverAspirantFront-runner
      Decrease035
      Increase887
      Table 6Frequencies of comparison.
      NITI Classification
      ComparisonLarger StateSmaller StateUnion Territory
      Decrease142
      Increase1843
      NITI Health status
      ComparisonAchieverAspirantFront-runner
      Decrease124
      Increase799
      Fig. 4
      Fig. 4Trends in Rural Insurance Coverage: A comparison of NFHS-5 and NFHS-4.
      Focussing on the rural trends in all the states/UTs, we observe that majority of the larger states (15 out of 19), smaller states (5 out of 8) and UTs (3 out of 4) have witnessed an increase in the health insurance coverage between NFHS-4 and NFHS-5. Similarly, it is observed that all the achievers, majority of the aspirants (8 out of 11) and front-runners (7 out of 12) have improved upon their health insurance coverage between NFHS-4 and NFHS-5. This could possibly be attributed to the nationwide implementation of Ayushman Bharat Scheme (see Fig. 5)
      Fig. 5
      Fig. 5Trends in Urban Insurance Coverage: A comparison of NFHS-5 and NFHS-4.
      Focussing on the urban trends in all the states/UTs, we observe that majority of the larger states (18 out of 19), UTs (3 out of 5) and 50% (4 out of 8) of the smaller states have witnessed an increase in the health insurance coverage between NFHS-4 and NFHS-5. Similarly, it is observed that majority of the achievers (7 out of 8), aspirants (9 out of 11) and front-runners (9 out of 13) have improved upon their health insurance coverage between NFHS-4 and NFHS-5. This could possibly be attributed to the nationwide implementation of Ayushman Bharat Scheme.

      4. Discussion

      India has been trying to achieve universal health coverage through PFHIs at a national level since 2008 with the RSBY. It is now 14 years since RSBY was launched. There have been PFHIs before RSBY but nowhere close to its scale. Ayushman Bharat is a natural extension and RSBY on a larger scale to encompass the length and breadth of the country. With regard to funding, PFHIs have been of 2 main types: Central government-sponsored and State government-sponsored. PFHIs in India cover 50% of the population i.e. 700 million people. Social health insurance and private voluntary health insurance cover 20% of the population i.e. 250 million people. Therefore, the NITI Aayog claims, an estimated 70% of the population is covered under some form of health insurance and 30% of the population is not covered under any form of health insurance.
      • Kumar Anurag
      • Rakesh Sarwal
      Health Insurance for India's Missing Middle.
      However, NFHS-5 data for India for the indicator “Households with any usual member covered under a health insurance/financing scheme (%)" is 41%. NFHS has a disclaimer: “Readers should be cautious while interpreting and comparing the trends as some States/UTs may have smaller sample sizes. Moreover, at the time of the survey, Ayushman Bharat (AB-PMJAY) and Pradhan Mantri Surakshit Matritva Abhiyan (PMSMA) were not fully rolled out and hence, their coverage may not have been factored in the results of indicator 12 (percentage of households with any usual member covered under a health insurance/financing scheme) and indicator 41 (percentage of mothers who received 4 or more antenatal care check-ups)." The time interval between NFHS-5 and the NITI Aayog report is approximately one year. It seems improbable that national health insurance coverage could increase from 41% to 70% over one year. Further research is needed to ascertain the reason for this gap.
      While the Health Index

      Health Index. NITI Aayog, National Institution for Transforming India, Government of India. Retrieved from http://social.niti.gov.in/uploads/sample/Guidebook_SHI_Round4.pdf.

      of the NITI Aayog does not include health insurance coverage, one would assume that states and union territories with higher health index scores (which indicates better health outcomes, governance, and infrastructure) would also have higher health insurance coverage. However findings from correlation analysis do not comprehensively supplement this assumption. In addition, it is pertinent to shift focus on the states which have witnessed a decrease in insurance coverage. Further research is needed to identify the determinants of these geography-specific decreases across a span of 5 years, which is the time gap between NFHS-4 and NFHS-5.

      Funding

      This study did not receive any funding

      Declaration of competing interest

      There is no conflict of interest.

      Acknowledgements

      We would like to express our gratitude to NITI Aayog, India and DHS for enabling us to have access to the publicly available NFHS (NFHS-5, NFHS-4) state-level factsheet and the NITI Aayog Health Index data.

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