The role of health financing system and pluralism in achieving sustainable development goal health outcomes in India: a multilevel analysis

Abstract

Introduction

The health system in India is pluralistic, with different sources of financing and multiple, overlapping financing systems across states. Existing studies often neglect system-specific factors such as fragmented health insurance schemes and state-level financing mechanisms. This study examines how health insurance pluralism, financing systems, and contextual state factors influence SDG health outcomes, integrating both individual and structural dimensions.

Methods

Data from NFHS-5, RBI database (2019-20), and National Health Accounts (2019–2020) were analysed. States were classified by financing systems (public, prepaid, and out-of-pocket) and categorized into low, medium, and high fragmentation groups using a fragmentation index. Multilevel logistic regression accounted for the hierarchical structure of health data, analysing both individual-level characteristics and state-level contextual factors. The descriptive analysis summarized sample characteristics and variable distributions.

Results

States demonstrated diverse financing systems, with significant fragmentation in out-of-pocket-dominated systems. Public and pre-paid systems had beneficial effects on child mortality, vaccination rates, and WASH access. While fragmentation occasionally enhanced outcomes, it also increased expenditures. Out-of-pocket expenditures persisted as a key driver of inequity and financial risk.

Conclusion

Strengthening India’s health system requires reducing fragmentation, promoting equitable financing, and aligning policies with the objectives of universal health coverage and the Sustainable Development Goals.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12939-026-02888-x.

Introduction

The announcement of Universal Health Coverage (UHC) under the Sustainable Development Goals (SDGs) has brought significant global attention to the relationship between health financing and health outcomes, particularly in developing countries such as India. UHC embodies the aspiration that all individuals within a jurisdiction can access the quality health services they need without experiencing financial hardship, irrespective of their socio-economic conditions [1]. The health financing system plays a pivotal role in achieving UHC, as it influences the availability, affordability, and quality of healthcare services, ultimately shaping population health outcomes.

Progressive health financing mechanisms, such as government and insurance-funded systems, are widely recognized for their potential to improve health outcomes [2–3]. Conversely, out-of-pocket (OOP) expenditure-driven systems are often linked to poorer health outcomes due to the financial barriers they create for healthcare access [4]. Evidence suggests that government-funded health insurance schemes have contributed to reducing OOP expenditures, enhancing healthcare utilization, and achieving better health outcomes among the insured population [3]. Recognizing these benefits, many countries are pursuing health financing reforms to expedite progress toward UHC [5].

In India, however, health financing has historically been regressive and inadequate [6]. OOP expenditures have dominated the health financing landscape, leading to inequities in healthcare access and financial protection. While government initiatives have gradually reduced the share of OOP expenditure [7], the proportion of government spending in total health expenditure has not seen a corresponding significant increase. Furthermore, insurance financing, despite the introduction of multiple government-sponsored schemes and the growth of the private insurance market, has remained relatively stagnant [8]. Adding to these challenges is the complexity of India’s pluralistic health system.

The global empirical literature on pluralistic health financing systems demonstrates that their structural features significantly shape health system performance and outcomes. Fragmentation, arising from pluralistic systems, has been widely identified as a barrier to achieving equitable access and UHC, particularly in low- and middle-income countries [9]. Evidence suggests that these fragmented systems, characterized by multiple risk pools and segmented population coverage, weaken redistributive capacity and contribute to disparities in access, quality, and health outcomes across population groups [10]. Studies such as Gabani et al. [5] demonstrate that transitions toward more unified and publicly financed systems are associated with improved population health outcomes and enhanced financial protection. In this context, India represents a paradigmatic case of a low- and middle-income country with a deeply pluralistic and institutionally fragmented health financing system. The scale and complexity of this fragmentation necessitate a structured analytical lens to examine how these systemic features translate into health outcomes. Accordingly, the following conceptual framework delineates the key dimensions of India’s pluralistic health system.

Conceptual framework

India’s health system is uniquely characterized by its pluralistic nature, encompassing three key dimensions: decentralized healthcare systems, parallel healthcare systems, and multiple private payer systems, as outlined by Drummond et al. [11]. Each dimension reflects distinct structural and functional attributes, collectively contributing to the complexity of health financing and service delivery in the country [11].

Decentralized healthcare systems

In decentralized systems, the responsibility for financing and delivering healthcare is devolved geographically to provinces, states, or regions, as seen in countries like Canada, Italy, and Spain. In India, health is constitutionally designated as a State subject, resulting in significant decentralization. States bear primary responsibility for public health services, although the Central government retains the authority to formulate nationwide health policies. This shared governance creates pluralism in decision-making authority across levels of government, leading to variation in government participation and health financing across states, creating disparities in healthcare access and quality.

Parallel healthcare systems

Parallel systems operate concurrently within overlapping geographic regions but cater to distinct population segments. For example, some nations feature mandatory national health insurance schemes alongside subsidized public programs and private insurance. In India, this parallelism is evident in the coexistence of centrally funded public systems, such as the Pradhan Mantri Jan Arogya Yojana (PM-JAY), state-specific health initiatives, and a vast private healthcare sector. This fragmentation disperses insurance coverage across the population, limiting the reach and effectiveness of any single financing model leading to segmentation of populations across distinct schemes with limited integration.

Multiple private payer systems

India’s health financing system is further characterized by a multiplicity of payers, reflecting fragmentation in financial pooling mechanisms. This includes a growing private health insurance market with numerous insurers, alongside multiple publicly funded insurance schemes operating at both the central and state levels. The coexistence of these diverse payers results in the formation of multiple, often uncoordinated risk pools, limiting the scope for cross-subsidization and efficient resource allocation. Consequently, despite the expansion of insurance coverage, OOP expenditure continues to remain the dominant mode of health financing, indicating persistent gaps in financial risk protection [6].

India’s positioning across these three dimensions reflects a high degree of pluralism within its health system [11]. While decentralization and diverse financing models offer opportunities for tailored healthcare delivery, they also present challenges, such as inefficiencies, inequities, and the persistence of OOP payments as the primary source of healthcare financing [6].

India’s decentralized health system also places the primary responsibility for health financing on individual states, resulting in diverse dominant sources of financing across the country. These sources can be broadly categorized into three types based on their largest share in total health expenditure: government-financed, OOP-financed, and insurance-financed systems [5]. For this study, health financing refers to the dominant source of financing in each state, while pluralism specifically denotes the parallel healthcare system. This system encompasses centrally funded public health assurance programs such as the Employee State Insurance Scheme (ESIS), the Central Government Health Scheme (CGHS), the Rashtriya Swasthya Bima Yojana (RSBY), state-funded health systems like State Health Insurance Scheme (SHIS); and an extensive private sector that includes Community-Based Health Insurance (CBHI), Private Health Insurance (PHI), and other types of coverage [12–13].

This pluralistic financing structure presents both potential and limitations. The coexistence of numerous insurance schemes and payers results in fragmented financial protection and limited pooling of resources, further exacerbating inequities in health outcomes (Fig. 1). Although India has expanded coverage, it still struggles to meet key health targets under Sustainable Development Goal 3 (SDG 3), which aims to ensure healthy lives and promote well-being for all at all ages [14]. The NITI Aayog’s SDG report identifies persistent deficiencies in indicators such as maternal mortality, under-five mortality, and disease burdens. For instance, the maternal mortality ratio remains at 113 per 100,000 live births, well above the global target of 70, despite notable progress in states like Kerala, Maharashtra, Tamil Nadu, Telangana, and Andhra Pradesh [15]. Gaps are also evident in under-five mortality, tuberculosis, non-communicable disease mortality, and deaths from accidents and suicides [16–18].

Fig. 1.

Impact of the pluralistic healthcare system on health outcomes. Source: Authors’ conceptualization based on review of existing literature

The persistence of poor outcomes despite expanding insurance points to structural issues. International experience suggests that centralized, unified systems such as the United Kingdom’s National Health Service [19] or China’s integrated urban-rural coverage can reduce inefficiencies [20]. In contrast, India’s fragmented insurance landscape leads to overlapping coverage, administrative complexities, and inefficient resource allocation. These challenges undermine the potential of insurance to enhance access and financial protection. Furthermore, the interplay among India’s various insurance models has not been systematically assessed in relation to SDG targets. While existing schemes aim to reduce financial barriers, they often fall short in addressing underutilization, limited awareness, and partial coverage. The concept of “progressive universalization,” as proposed by the UNDP, is gaining relevance in low- and middle-income countries as a pathway to equitable health financing. Achieving this requires reducing OOP spending and increasing public and pooled financing [7].

Given this context, it is essential to investigate how India’s complex financing mechanisms, including insurance pluralism, shape health outcomes. These outcomes often result from interactions at various nested levels, such as individual, household, state, and country. Additionally, variations in health outcomes occur even among units situated at the same level. This complexity necessitates the use of multilevel analysis to accurately assess the impact of health insurance and financing systems [21]. Previous studies in India have not accounted for system-specific factors, such as the pluralistic health insurance system and state-level health financing mechanisms, in their analysis of health outcomes [22–26]. The present study makes a novel attempt to fill this gap. It examines the influence of health insurance pluralism, financing systems, and other state-level variables on SDG health outcomes in India, providing a comprehensive understanding of their interactions.

Methods

Data

This study draws on data from the fifth round of the National Family Health Survey (NFHS-5) [27], conducted in 2019–21, which provides comprehensive and nationally representative data on population, health, and nutrition indicators across Indian states and union territories. NFHS-5 follows a stratified two-stage sampling design and covers detailed information on household characteristics, maternal and child health, access to healthcare services, and health insurance coverage. Individual- and household-level data from NFHS-5 were integrated with state-level variables sourced from multiple databases. Data on Net State Domestic Product (NSDP) were obtained from the Reserve Bank of India’s Database on the Indian Economy (DBIE) for the period 2019-20 [28]. Health financing indicators, including per capita health expenditure, the share of government spending in total health expenditure, and the share of prepaid expenditure1, and the share of OOP spending was drawn from the National Health Accounts (NHA) Estimates for 2019-20 [29].

The analysis examines the influence of health insurance coverage, health financing system and insurance fragmentation score on 13 health outcome indicators related to SDG 3 targets. Details of the selected SDG 3 outcomes are presented in Table 1. Due to missing values for certain health financing indicators in some states, the analysis includes only 21 Indian states for which complete data are available. Data were merged using state residency codes to match individual-level records from NFHS 5 with state-level variables. Data were cleaned to address missing or incomplete values and verified for consistency before analysis. Sampling weights provided by NFHS were applied to ensure representativeness, and all statistical analyses were conducted using STATA version 17.0.

Table 1.

Description of Health Outcome Indicators

Goal	Target	Indicator	Indicator Used
3.1	Reducing world maternal mortality ratio (MMR) to < 70 / 100,000 live births by 2030.	3.1.1 Skilled Birth Attendance	1. Skilled Birth Attendance
3.2	Neonatal mortality rate (NMR) < 12 / 1,000 live births by 2030. Under five mortality rate (U5MR) < 25 / 1,000 live births by 2030.	3.2.1 Under-five mortality rate 3.2.2 Neonatal mortality rate	2. Under-five deaths 3. Neonatal deaths
3.3	End AIDS, tuberculosis, malaria, and other neglected tropical diseases epidemics, fight hepatitis, water-borne illnesses, and other communicable diseases by 2030.	3.3.2 Tuberculosis incidence per 100,000 populations.	4. Tuberculosis prevalence
3.4	Reducing premature mortality from non-communicable diseases by one-third and promoting mental health and well-being by 2030.	3.4.1 Mortality rate attributed to NCDs	5. Prevalence of NCDs (Diabetes, Heart Disease, Blood pressure, Asthma, Thyroid, and Cancer)
3.5	Strengthen efforts to prevent and treat substance abuse, particularly the harmful use of alcohol and narcotic drugs.	3.5.2 Alcohol consumption per capita (aged 15 years and older)	6. Prevalence of Alcohol Consumption
3.6	Reduce by half the number of individuals killed and injured from road traffic accidents worldwide by 2020.	3.6.1 Death due to injuries/Accidents	7. Death due to injuries/Accidents
3.7	By 2030, ensure universal access to sexual and reproductive health-care services, including for family planning, information and education, and the integration of reproductive health into national strategies and programmes	3.7.1 Proportion of women of reproductive age (15–49 years) who have their need for family planning satisfied with modern methods. 3.7.2 Adolescent birth rate per 1,000 women aged 15–19 years.	8. Access to modern contraceptive methods 9. Prevalence of adolescent births/teenage pregnancy
3.9	Substantially reducing the number of deaths and illnesses from hazardous chemicals and air, water, and soil pollution and contamination by 2030	3.9.1 Mortality rate attributed to unsafe water, unsafe sanitation, and lack of hygiene.	10. Access to Water, Sanitization, and Hygiene (WASH) Facilities 11. Acute Respiratory Illness Prevalence
3.a	Strengthen the implementation of the WHO Framework Convention on Tobacco Control	3.a.1. Age-standardized prevalence of current tobacco use among persons aged 15 years and older.	12. Smoking/Tobacco Prevalence
3.b	Access to vaccines and medicines for communicable and NCDs	3.b.1. Proportion of the population with access to affordable medicines and vaccines on a sustainable basis.	13. Prevalence of full vaccination coverage among children aged 12–23 months.

Methods

A descriptive analysis was conducted to summarise the distribution and key characteristics of the variables. We classified state-level health financing systems into three types: government-financed, prepaid, and OOP-financed, based on the dominant share in total health expenditure, following the approach of Gabani et al. [5]. We further examined insurance pluralism by calculating fragmentation in health insurance coverage across Indian states using the reversed Bice-Boxerman Index (BBI) framework developed by Kern et al. [30]. In this context, the index captures the degree of insurance pluralism and dispersion of coverage across different financing arrangements within a state. Higher values indicate a more fragmented insurance landscape, where beneficiaries are spread across several schemes, while lower values suggest greater concentration of coverage within fewer providers. The index ranges from 0 to 1, where 0 represents complete concentration (all individuals covered by a single provider) and 1 indicates maximum fragmentation (coverage evenly distributed across multiple providers). The study considered insurance coverage providers, with the following major categories used: ESIS, CGHS, SHIS, RSBY, CBHI, PHI, and other types of coverage. This customization allowed the model to capture both dispersion (the spread of the population across multiple coverage providers) and density (the extent to which a single coverage type dominated the others).

The formula for the Fragmentation Index used in this study was:

where:

n = Total population covered by any insurance provider in the state during the period 2019–21.

= Population covered by provider i.

p = Total number of providers (in this case, p = 7).

States were classified into quantiles (7 values per category for 21 states) by first sorting the fragmentation scores in ascending order and then dividing them into three equal groups. These thresholds were selected to reflect varying degrees of fragmentation in the health insurance landscape.

Finally, multilevel logistic regression was applied to examine factors influencing health outcomes (Table 1), accounting for the hierarchical structure of the data. This approach captured both individual- and state-level influences, such as personal attributes and contextual variables like state health expenditure and economic growth, as presented in Table 2, while the units of all variables are detailed in Table A1 in the Appendix [21]. State-level health system capacity was proxied using the classification based on the NITI Aayog Health Index, which categorizes states into “Front Runner,” “Performer,” and “Aspirant” groups. Aspirant states typically reflect relatively weaker health system capacity, while Front Runner states indicate comparatively stronger performance. Accounting for this multilevel structure is essential, as ignoring data nesting could bias estimates due to intra-state correlation. Separate models were estimated for each health outcome. The model specification is outlined below.

Table 2.

Independent Variables

Independent Variable	Values/Measurement	Sources
Main Exposure Variables
Health insurance coverage	1 = Yes; 0 = No	NFHS-5
Dominant health financing system	0 = OOP; 1 = Government; 2 = Insurance	NHA 2019-21
Health insurance fragmentation score	Continuous	NFHS-5 (constructed)
Individual/household-Level Confounders (Level-1 Variables)
Place of residence	0 = Urban; 1 = Rural	NFHS-5
Income group	0 = Rich; 1 = Poor; 2 = Middle income	NFHS-5
Educational attainment	0 = No education; 1 = Educated	NFHS-5
Caste-based social group membership	0 = Other (Non-SCST); 1 = SCST	NFHS-5
Religion	0 = Other (Non-Hindu); 1 = Hindu	NFHS-5
Gender of the covered person	0 = Other; 1 = Female	NFHS-5
State-Level Contextual Variables (Level-2 Variables)
Categorization of states based on NITI Aayog health index	0 = Front Runner; 1 = Aspirant; 2 = Performer	NITI Aayog 2021
Log-NSDP	Continuous (log-transformed)	RBI-DBIE
Log Per Capita Health Expenditure	Continuous (log-transformed)	RBI-DBIE

Source: Compiled by the Authors

Where:

i index individuals/households, and j indexes state (i = 1 to n; j = 1 to 21).

represent binary response variable for health outcomes for individuals/households 𝑖 in state 𝑗 (Table 1).

represent individual/household-level covariates for individuals/households 𝑖 in state 𝑗 (Table 2).

represents state-level predictors in state j, capturing contextual characteristics (Table 2).

The coefficients and represent the fixed effects (individual/household and state, respectively), indicating the change in the log-odds of the outcome associated with a one-unit change in the corresponding predictor.

The term denotes the overall intercept.

is the random intercept capturing unobserved heterogeneity across states.

Additionally, the Intraclass Correlation Coefficient (ICC) was estimated for each model to assess the proportion of variance attributable to state-level differences. For multilevel logistic models, ICC was calculated as σ²_u / (σ²_u + 3.29), where σ²_u represents the variance of the random intercept. A detailed description of all independent variables used in the multilevel models is provided in Table 2. The main exposure variables include insurance coverage, health financing system, and insurance pluralism indicators, while the models additionally control for relevant individual/household-level and state-level confounding variables.

For the multilevel logit model estimation, the ‘melogit’ command in Stata, which employs maximum likelihood estimation with adaptive quadrature for fitting multilevel models, was used. The random effects were modelled at the state level to capture intra-state correlation. The number of integration points was set to 7 to ensure convergence and computational efficiency. Multivariate adaptive Hermite quadrature was used for approximating the likelihood. This is particularly useful when fitting multilevel or hierarchical models with non-linear responses. Model diagnostics were conducted to assess fit and address convergence issues. Multicollinearity was assessed using the variance inflation factor (VIF), and variables with VIFs greater than 5 were dropped from the respective models. In particular, the variables religion and gender were removed from certain models due to their high multicollinearity.

Results

Descriptive analysis

State-level health financing profile

Table 3 presents state-wise data on the health financing indicators and NSDP. It reveals significant regional variations in health financing across Indian states, with health insurance coverage ranging from 13.84% in Jammu and Kashmir to 87.84% in Rajasthan. Per capita health expenditure varies widely, from Rs. 1,588 in Bihar to Rs. 10,607 in Kerala. On average, government expenditure accounts for 39.44% of total health spending, while out-of-pocket expenditure remains high at 51.2%. Prepaid health expenditure averages at 9.35%.

Table 3.

State-wise health indicators

States	Health Insurance Coverage (%)	Per Capita Health Expenditure (in Rs)	Government health expenditure as a share of total (%)	OOP health expenditure as a share of total (%)	Pre-paid health expenditure as a share of total (%)	NSDP (’000)
Andhra Pradesh	80.18	5114	33.2	63.6	3.2	114,324
Assam	66.68	2863	57.9	34.9	7.2	57,227
Bihar	17.4	1588	44.1	54.3	1.6	28,127
Chhattisgarh	71.35	3416	52.4	36.7	10.9	72,236
Gujarat	44.41	4130	45.1	40.8	14.1	160,321
Haryana	25.65	5178	40.7	45.5	13.8	165,617
Himachal Pradesh	38.89	7386	51.8	46	2.2	133,079
Jammu and Kashmir	13.84	3109	50	46.6	3.4	65,172
Jharkhand	50.31	3089	32.8	64.7	2.5	51,365
Karnataka	31.81	5418	30.5	31.8	37.7	154,123
Kerala	57.79	10,607	24.4	67.9	7.7	134,878
Madhya Pradesh	38.05	2831	44.1	53	2.9	58,334
Maharashtra	22.39	6301	26.6	44.1	29.3	133,356
Odisha	47.94	3603	41.5	53.4	5.1	76,564
Punjab	25.16	5118	30.1	64.7	5.2	118,487
Rajasthan	87.84	3916	42.4	47.4	10.2	76,882
Tamil Nadu	66.48	4605	44.3	44.2	11.5	144,845
Uttar Pradesh	15.87	3721	25.6	71.8	2.6	43,053
Uttarakhand	62.49	3678	61.8	35.8	2.4	148,303
West Bengal	33.68	5103	26.4	67.1	6.5	71,719
Telangana	17.07	5110	44.3	41.6	14.1	153,360
Mean (SD)	0.43 (0.49)	4172.74 (1571.37)	39.44 (10.31)	51.20(12.40)	9.35(9.29)	96,746.65 (35,335.57)

Source: Authors’ calculations based on data from the National Family Health Survey (NFHS-5), the Database on Indian Economy (DBIE) maintained by the Reserve Bank of India, and the National Health Accounts (NHA) Estimates 2019–20. Health insurance coverage (%) was derived from NFHS-5 household data using appropriate sample weights. Per capita health expenditure and the shares of government, out-of-pocket (OOP), and prepaid expenditure in total health expenditure were obtained from NHA 2019–20. Net State Domestic Product (NSDP) figures were sourced from DBIE

Figure 2 shows the contribution of each coverage type to the total coverage in India. As it is shown state health insurance scheme has the highest contribution, followed by other forms of coverage.

Fig. 2.

Health insurance coverage. Source: Author’s calculations based on data from the National Family Health Survey (NFHS-5). Health insurance coverage was estimated using household-level data with appropriate sample weights

Table 4 presents the population covered under various health insurance schemes across different states in India. Among the states, Andhra Pradesh has the highest coverage under SHIS (23,530), while Kerala stands out with the highest coverage under CGHS (437) and the highest under RSBY (9,423). Maharashtra, Tamil Nadu, and Rajasthan also report high coverage figures, particularly under schemes like SHIS and RSBY. On a national scale, India has a total coverage of 115,179 under SHIS, 39,686 under RSBY, and a considerable number under other schemes like PHI (8,598) and ESIS (7,471).

Table 4.

Population covered under insurance schemes

Table 5 categorizes Indian states into three groups based on health insurance fragmentation scores: low (T1: 0.06–0.32), medium (T2: 0.34–0.63), and high (T3: 0.65–0.78). T1 states like Assam and Andhra Pradesh show low fragmentation. T2 states such as Uttar Pradesh and Kerala exhibit moderate fragmentation with multiple but not dominant schemes. T3 states, including Maharashtra and Punjab, reflect high fragmentation. The national average fragmentation score is 0.44.

Table 5.

Classification of states into fragmentation tertiles

Fragmentation Tertile	States	Health Insurance Fragmentation	Average Fragmentation	Fragmentation Range
Low Fragmentation (T1)	Assam	0.06	0.17	0.06–0.32
	Andhra Pradesh	0.10
	Rajasthan	0.11
	Telangana	0.13
	Jharkhand	0.20
	Tamil Nadu	0.25
	Uttarakhand	0.32
Medium Fragmentation (T2)	Uttar Pradesh	0.34	0.47	0.34–0.63
	Kerala	0.37
	Chhattisgarh	0.39
	Odisha	0.43
	Bihar	0.46
	Madhya Pradesh	0.53
	Gujarat	0.59
	Karnataka	0.63
High Fragmentation (T3)	Jammu and Kashmir	0.65	0.72	0.65–0.78
	West Bengal	0.68
	Himachal Pradesh	0.69
	Haryana	0.73
	Maharashtra	0.76
	Punjab	0.78
Total	India	0.70	0.44 (All state)	0.06–0.78 (All states)

Source: Authors’ calculations based on unit-level data from the National Family Health Survey (NFHS-5). The health insurance fragmentation index was computed using the reversed Bice–Boxerman Index, based on the distribution of the insured households across major insurance providers (ESIS, CGHS, SHIS, RSBY, CBHI, PHI, and others) for each state

Table 6 categorizes Indian states by the predominant sources of health expenditure, classifying them into three health financing systems: Government Financed Health Systems, Pre-Paid Financed Health Systems, and OOP Financed Health Systems. This classification is based on the relative shares of government expenditure, OOP expenditure, and prepaid expenditure in total health expenditure (as presented in Table 3), with each state assigned to the category corresponding to the highest contributing share. Government-financed systems are observed in states such as Assam, Chhattisgarh, Gujarat, Himachal Pradesh, Jammu and Kashmir, Tamil Nadu, Uttarakhand, and Telangana. Karnataka is categorized under the pre-paid financed health system. OOP-financed systems are observed in Andhra Pradesh, Bihar, Haryana, Jharkhand, Kerala, Madhya Pradesh, Maharashtra, Odisha, Punjab, Rajasthan, Uttar Pradesh, and West Bengal.

Table 6.

Classification of states based on dominant health financing systems

Health System Type	States
Government-Financed Health System	Assam, Chhattisgarh, Gujarat, Himachal Pradesh, Jammu and Kashmir, Tamil Nadu, Uttarakhand, Telangana
Pre-Paid Financed Health System	Karnataka
OOP Financed System	Andhra Pradesh, Bihar, Haryana, Jharkhand, Kerala, Madhya Pradesh, Maharashtra, Odisha, Punjab, Rajasthan, Uttar Pradesh, West Bengal

Source: Author’s classification based on data from the National Health Accounts (NHA) Estimates 2019–20

Figure 3 illustrates the distribution of states across different fragmentation tertiles based on various health expenditure indicators, highlighting their relative membership and expenditure patterns. States categorized under T3 represent the highest levels of health insurance fragmentation and include several of the top states in per capita health expenditure. States classified under Government-Financed Health Systems are primarily concentrated in T1 and T2 fragmentation categories. A similar distribution is observed for states with pre-paid financing systems, where most states are located within T1 and T2. States dominated by OOP financing are predominantly concentrated in the T2 category.

Fig. 3.

Fragmentation tertile membership of top 10 spender states in health expenditure indicators. Source: Authors’ classifications based on data from the National Family Health Survey (NFHS-5) and National Health Accounts (NHA) Estimates 2019–20. Fragmentation tertiles were derived using the reversed Bice–Boxerman Index computed from NFHS-5 insurance coverage data, while health expenditure indicators used to identify the top 10 spending states were obtained from NHA

Distribution of SDG health outcomes

Table 7 presents the prevalence of various health outcomes across Indian states, distinguishing between positive indicators (higher values highlighted in green) and negative indicators (higher values highlighted in red). Kerala demonstrates the highest positive outcomes, with near-perfect skilled birth attendance and high vaccination coverage, while states like Assam show lower levels of skilled birth attendance and contraceptive access. Negative health indicators such as NCDs and alcohol consumption are more prevalent in states like Andhra Pradesh, West Bengal, and Odisha.

Table 7.

Prevalence of health outcomes across states (in %)

Multilevel regression analysis

Table 8 shows the results from separate multilevel logistic regression models for each health outcome. It reveals the influence of the health financing system and pluralism on health outcomes. The models utilize log-odds coefficients to quantify associations, accounting for hierarchical clustering effects inherent in the data structure.

Table 8.

Results from the multilevel model

Health insurance coverage and SDG health outcomes

Health insurance coverage demonstrates statistically significant positive associations with several SDG health outcomes.

Maternal and child health outcomes

Insurance coverage is positively associated with skilled birth attendance (β = 0.1298, p < 0.05), access to modern contraceptives (β = 0.3545, p < 0.05), and vaccination coverage (β = 0.2956, p < 0.05). Positive associations are also observed for under-five mortality (β = 0.0754, p < 0.05), neonatal mortality (β = 0.0533, p < 0.05), and adolescent births (β = 0.1386, p < 0.05).

Preventive and public health outcomes

Insured households are more likely to report improved access to WASH facilities (β = 0.2331, p < 0.05).

Disease outcomes

Health insurance coverage is positively associated with reported tuberculosis (β = 0.1622, p < 0.05), asthma (β = 0.2006, p < 0.05), and NCDs (β = 0.2389, p < 0.05).

Behavioural and injury-related outcomes

Insurance coverage is positively associated with smoking behaviour (β = 0.0687, p < 0.05), while no statistically significant association is observed for alcohol consumption or deaths due to accidents and injuries.

Dominant health financing systems and SDG outcomes

The associations between dominant health financing systems and SDG health outcomes were assessed using OOP-financed systems as the reference category.

Maternal and child health outcomes

Government-financed health systems are positively associated with vaccination coverage (β = 0.3941, p < 0.05) and negatively associated with under-five mortality (β = -0.4793, p < 0.05) and neonatal mortality (β = -0.4868, p < 0.05). Similarly, insurance-financed systems are positively associated with access to modern contraceptives (β = 0.3757, p < 0.05) and negatively associated with under-five mortality (β = -0.5499, p < 0.05) and neonatal mortality (β = -0.5525, p < 0.05).

Preventive and public health outcomes

Government-financed systems demonstrate a positive association with WASH access (β = 0.3335, p < 0.05), whereas insurance-financed systems do not show statistically significant associations with WASH outcomes.

Disease outcomes

Government-financed systems are negatively associated with asthma prevalence (β = -0.3270, p < 0.05). No statistically significant associations are observed between insurance-financed systems and disease-related outcomes.

Behavioural and injury-related outcomes

Neither government-financed nor insurance-financed systems demonstrate statistically significant associations with behavioural outcomes or deaths due to accidents and injuries.

Health financing fragmentation and SDG outcomes

Health financing fragmentation, reflecting pluralistic financing arrangements within the health system, demonstrates significant associations with several SDG health outcomes.

Maternal and child health outcomes

Health financing fragmentation is positively associated with access to modern contraceptives (β = 0.5056, p < 0.05) and vaccination coverage (β = 1.2319, p < 0.05). In contrast, it is negatively associated with under-five mortality (β = -0.5681, p < 0.05) and neonatal mortality (β = -0.5623, p < 0.05).

Preventive and public health outcomes

No statistically significant association is observed between fragmentation and WASH outcomes.

Disease outcomes

A positive association is observed between fragmentation and reported non-communicable diseases (β = 0.7466, p < 0.05).

Behavioural and injury-related outcomes

Health financing fragmentation is negatively associated with alcohol consumption (β = -1.4187, p < 0.05). No statistically significant associations are observed for smoking or deaths due to accidents and injuries.

Effect of confounding variables

Several individual-, household-, and state-level confounding variables also demonstrate significant associations with SDG health outcomes. Higher per capita public health expenditure is positively associated with improved WASH access (β = 1.52, p < 0.05) and higher reported prevalence of tuberculosis (β = 2.0153, p < 0.05) and NCDs (β = 1.3476, p < 0.05), while showing negative associations with under-five mortality (β = -1.7847, p < 0.05) and neonatal mortality (β = -1.9368, p < 0.05).

State developmental performance, measured through the NITI Aayog classification, also exhibits significant associations with selected health outcomes. Compared to Front Runner states, Aspirant and Performer states generally report lower levels of skilled birth attendance, contraceptive access, vaccination coverage, and asthma-related outcomes. Higher NSDP is positively associated with access to modern contraceptives (β = 0.7152, p < 0.05), but negatively associated with WASH access (β = -0.7881, p < 0.05) and tuberculosis prevalence (β = -1.5638, p < 0.05).

Household and individual-level characteristics further influence health outcomes. Rural residence, lower socioeconomic status, and SC/ST affiliation are generally associated with poorer health outcomes across several indicators. In contrast, education demonstrates positive associations with healthcare utilization and reduced risky health behaviours. Detailed estimates for all confounding variables are presented in Table 8.

Random effects and intraclass correlation coefficients (ICC)

The random intercept variance across models indicates significant between-group variability in health outcomes, justifying the use of multilevel modelling. Notable variance is observed in models for skilled birth attendance (variance = 0.7255), accidental deaths (variance = 0.3247), alcohol consumption (variance = 0.3366), and NCDs (variance = 0.1003), suggesting considerable clustering effects at the state level. Lower variance in models such as contraceptive access (variance = 0.0373) and WASH (variance = 0.0380) implies relatively less between-group heterogeneity for these outcomes.

In addition, the ICC was estimated to assess the extent of state-level variation in health outcomes. The ICC values indicate that a modest proportion of the total variance is attributable to differences across states. For skilled birth attendance (ICC = 0.18), a relatively higher share of variation is explained at the state level. In contrast, most other outcomes exhibit relatively low ICC values (ranging between 0.01 and 0.04). Moderate state-level variation is observed for behavioural outcomes such as alcohol consumption and smoking (ICC ≈ 0.09).

In addition, null (intercept-only) models were estimated separately for each outcome variable without including explanatory variables or predictors, incorporating only the outcome variable and the state-level random intercept to assess baseline between-state variation. The null model estimates are presented in Appendix Table A2.

Sensitivity analysis

To assess the robustness of the results, additional sensitivity analyses were conducted using alternative specifications of the fragmentation variable and by excluding states with extreme values in key health financing indicators (Appendix Table A3). The overall results remained largely consistent. For vaccination coverage, the insurance-financed system showed a positive and statistically significant association in the sensitivity analysis. In contrast, for access to modern contraceptives, the fragmentation variable lost its statistical significance, while rural residence emerged as a positive and significant predictor. For adolescent births, the main results remained unchanged, although log-transformed per capita health expenditure became statistically insignificant. These minor variations do not materially alter the overall conclusions of the study, thereby confirming the robustness of the findings.

Discussion

This study employed multilevel logistic regression models to examine the association between health financing systems, insurance pluralism, and SDG-related health outcomes across Indian states while accounting for both individual- and state-level heterogeneity. The findings demonstrate that health financing arrangements are significantly associated with multiple dimensions of health outcomes, including maternal and child health, WASH access, communicable and non-communicable diseases, and behavioural outcomes. The random intercept variance and ICC obtained from the multilevel models indicate meaningful between-state variation across several outcomes, thereby supporting the appropriateness of the multilevel modelling framework.

Among the principal exposure variables, health insurance coverage was positively associated with several favourable health outcomes, including skilled birth attendance, access to modern contraceptives, WASH access, and vaccination coverage. These findings suggest that insurance coverage may improve healthcare utilization and access to preventive services. However, insurance coverage was also positively associated with tuberculosis, asthma, NCD prevalence, adolescent births, and under-five and neonatal mortality. The positive association with disease outcomes may partly reflect improved detection, diagnosis, and reporting among insured populations due to greater healthcare utilization and contact with health systems. Existing studies support this interpretation. For instance, Jain and Alan [31, 34] argue that health insurance coverage enhances disease surveillance and increases the likelihood of disease reporting.

The findings further indicate that the dominant health financing system plays an important role in shaping SDG health outcomes. Compared with OOP-dominated systems, government-financed systems were associated with lower under-five and neonatal mortality, improved vaccination coverage, and better WASH access. Similarly, insurance-financed systems demonstrated favourable associations with contraceptive access and reductions in child mortality indicators. These findings suggest that prepaid and publicly financed systems may offer greater financial protection and facilitate improved access to essential healthcare services relative to OOP-dominated systems. While individual-level insurance coverage did not consistently improve mortality outcomes, state-level financing structures based on government and prepaid financing demonstrated stronger protective effects, indicating the importance of broader system-level financing arrangements in improving population health outcomes.

The study also examined the role of fragmentation within the health insurance system, measured through the distribution of the insured population across multiple schemes. Fragmentation is commonly associated with inefficiency, duplication, and weaker coordination within healthcare systems [32–37]. However, the findings present a more nuanced picture. Higher fragmentation was positively associated with access to modern contraceptives and vaccination coverage and negatively associated with under-five and neonatal mortality. Fragmentation was also associated with higher reported NCD prevalence and lower alcohol consumption. These findings suggest that, within India’s highly pluralistic financing environment, the presence of multiple schemes may expand pathways for healthcare access and utilization. At the same time, fragmented systems may also generate administrative inefficiencies, duplication of resources, and coordination challenges that can constrain system performance.

The findings may be interpreted in relation to broader health financing models. High-fragmentation states reflect decentralized insurance arrangements that diverge from the Bismarck model, which is based on contributory social insurance and integrated risk pooling mechanisms aimed at achieving broad population coverage. In contrast, states with lower fragmentation exhibit characteristics closer to the Beveridge model, where healthcare financing is predominantly tax-funded and coordinated through publicly financed service delivery systems such as Ayushman Bharat [31, 35]. Nonetheless, India’s health system continues to remain substantially dependent on OOP expenditure, which weakens financial protection and limits equitable access to healthcare [32, 36]. Limited government expenditure and low insurance penetration further constrain the effectiveness of both financing models [33, 37].

The results also suggest that the effectiveness of dominant financing systems may be conditioned by the degree of fragmentation. Even where government or insurance financing mechanisms are present, fragmentation may dilute the benefits of integrated risk pooling and coordinated service delivery. Conversely, lower fragmentation may strengthen administrative efficiency and improve the functioning of financing systems. These findings therefore support the importance of expanding prepaid and publicly financed healthcare while simultaneously improving coordination across insurance schemes. A more integrated social health insurance framework may help reduce administrative inefficiencies, streamline resource allocation, and improve financial protection, particularly in highly pluralistic LMIC settings [38–39].

Several individual-, household-, and state-level covariates were also significantly associated with SDG health outcomes. Rural residence, lower socioeconomic status, and SC/ST affiliation were generally associated with poorer health outcomes, whereas education was associated with improved healthcare utilization and lower behavioural risk. An exception is observed in modern contraceptive use. Females from rural areas and with lower education levels are more likely to use modern contraceptives. This unusual pattern supports the findings by Pradhan and Dwivedi [40]. They suggest it is due to the high prevalence of sterilization among less-educated rural females. To test this, a multinomial logistic regression was conducted on the contraceptive method mix. The results confirmed their hypothesis. State-level economic development and public health expenditure were also associated with selected outcomes, particularly WASH access and child mortality, highlighting the continuing importance of socioeconomic and fiscal capacity in shaping population health outcomes.

The findings of this study both align with and extend the existing empirical literature on health system fragmentation in low- and middle-income countries. Prior studies have consistently identified fragmentation as a structural barrier to equitable access and effective health system performance, primarily due to the presence of multiple risk pools and segmented coverage that weaken redistributive capacity [9–10]. Similarly, cross-country evidence suggests that more unified and publicly financed systems tend to achieve better health outcomes and stronger financial protection [5]. However, the results of this study present a more nuanced picture. While fragmentation is conventionally associated with inefficiencies, the empirical findings of the study suggest that, in the Indian context, fragmentation may also reflect increased availability and diversification of coverage options. This contrasts with the dominant narrative in the literature and indicates that fragmentation, when accompanied by expanded coverage, may enhance service access in certain domains. At the same time, the observed association of fragmentation with higher health expenditure and mixed outcomes reinforces concerns highlighted in the literature regarding these systemic constraints. The study therefore contributes to the literature by demonstrating that fragmentation in highly pluralistic LMIC settings may simultaneously generate inefficiencies and expand service access, depending on the dimension of health system performance being examined.

In this context, the results highlight the need for India to address its fragmented insurance landscape by integrating the strengths of both the Bismarck model of contributory social insurance and the Beveridge model of tax-funded public healthcare, while reducing OOP expenditures. Strengthening financial protection mechanisms, enhancing public–private coordination, and ensuring equitable access remain critical for achieving efficient and sustainable health financing.

Despite these important findings and policy implications, the study has certain limitations that should be considered while interpreting the results. It examines multiple health outcomes simultaneously, which may increase the likelihood of Type I error due to multiple hypothesis testing. Therefore, individual statistically significant associations should be interpreted with caution. Further, while individual-level access factors such as distance to health facilities are important determinants of healthcare utilization, the NFHS-5 geospatial data are intentionally displaced to preserve respondent confidentiality, which limits the precision of distance-based measures. Accordingly, proxy variables such as place of residence and state-level health system capacity were used to capture access-related variations. In addition, the cross-sectional nature of the data constrains the ability to draw causal inferences from the observed associations. The analysis also does not explicitly incorporate interrelationships between certain health outcomes and behavioural risk factors, such as the link between tuberculosis and tobacco or alcohol use. Finally, although the study accounts for multiple dimensions of health financing and pluralism, some unobserved heterogeneity at the individual and state levels may remain.

Conclusion

The findings highlight the complexities and challenges associated with India’s fragmented health financing landscape and its implications for SDG health outcomes. Fragmentation within the health insurance system increases health expenditure, inefficiencies, and administrative challenges, yet also improves access to diverse services, resulting in positive outcomes such as higher vaccination rates and greater use of modern contraceptives. However, the prevalence of OOP expenditures across most states undermines equity, increases financial risk for households, and limits the effectiveness of health coverage in achieving UHC goals. Government and pre-paid health financing systems have demonstrated significant positive impacts on health outcomes, including lower child mortality, improved vaccination coverage, and enhanced access to WASH facilities. These findings highlight the need to reduce fragmentation in the health system by adopting a nationally pooled insurance framework. Fragmentation, especially in decentralized states, raises administrative costs and inefficiencies, while states with lower fragmentation achieve better coordination and financial protection but face limited funding and high OOP spending. To achieve UHC and SDG health targets, a standardized framework, such as SHI, is essential to improve efficiency, equity, and systemic performance.

Supplementary Information

Below is the link to the electronic supplementary material.

Abbreviations

SDG

Sustainable Development Goal

UHC

Universal Health Coverage

NCD

Non-Communicable Disease

NFHS

National Family Health Survey

OOP

Out-of-pocket

SHI

Social Health Insurance

SHIS

State Health Insurance Scheme

ESIS

Employee State Insurance Scheme

CGHS

Central Government Health Scheme

RSBY

Rashtriya Swasthya Bima Yojana

PHI

Private Health Insurance

CBHI

Community-Based Health Insurance

AB-PMJAY

Ayushman Bharat- Pradhan Mantri Jan Arogya Yojana

WASH

Water, Sanitization, and Hygiene

SC/ST

Scheduled Caste/Scheduled Tribe

BBI

Bice-Boxerman Index

VIF

Variance Inflation Factor

ICC

Intraclass Correlation Coefficient

Author contributions

PMS developed the conceptual framework, conducted the literature review, performed the data analysis, interpreted the findings, and prepared the final manuscript. HSR contributed to the conceptual framework, provided overall guidance, and reviewed the manuscript. Both the authors read and approved the final version.

Funding

This research received no specific grant from any funding agency, commercial entity or not-for-profit organization.

Data availability

The datasets generated and/or analysed during the current study are available in the International Institute for Population Sciences website, [https://www.iipsindia.ac.in/content/nfhs-projects], Researve Bank of India [https://data.rbi.org.in/DBIE/#/dbie/home], and National Health Systems Resource Centre [https://nhsrcindia.org/sites/default/files/2023-04/National%20Health%20Accounts-2019-20.pdf].

Declarations

Ethics and consent to participate

As the analysis does not involve interaction with human subjects or the collection of identifiable information, separate ethics approval and participant consent were not required. The NFHS datasets are de-identified and ethically cleared by the Institutional Review Board of the agencies responsible for survey implementation, with informed consent obtained from all original participants by the survey administrators. Access to the datasets was granted through the DHS Program data request system.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.