Progress towards universal health coverage in South Asia, 2000–2030: an examination of the twin elements of primary healthcare provision and financial protection

Abstract

Background

Universal health coverage (UHC) is crucial for achieving Sustainable Development Goal (SDG) 3: Good Health and Well-being. This study projects UHC progress in seven South Asian countries by 2030, focusing on two crucial underpinning components, primary healthcare (PHC) service coverage and financial risk protection.

Methods

We applied Bayesian random-effects models to project UHC indicators linked to PHC and financial risk protection including the Composite Coverage Index, catastrophic health expenditures (CHE) and impoverishing health expenditures. Data were sourced from the WHO Global Health Observatory, Demographic Health Surveys, National Health Surveys and the WHO Global Health Expenditure database. Projections were made from 2000 to 2030, with PHC and government health expenditures as key predictors.

Results

The analysis showed notable disparities in current and projected service coverage and financial protection across the region. Health service coverage is projected to increase from 53.5% in 2000 to 81.5% by 2030, with Bhutan and Sri Lanka expected to achieve higher levels. However, Afghanistan and Pakistan will likely remain below the SDG target of 80% coverage. Despite progress in coverage, financial protection is a challenge, with CHE expected to rise from 7.2% in 2000 to 18.6% by 2030. Bhutan and Sri Lanka show strong protection measures, while Afghanistan and India will face higher CHE and impoverishment rates due to high out-of-pocket spending.

Conclusion

This is the first study to project trends towards UHC in South Asia using Bayesian estimates of both health coverage and financial risk protection. Progress towards UHC requires advances in both service coverage and health financing reform. Although service coverage is improving, financial protection remains insufficient, particularly in rural and conflict-affected areas. Targeted PHC investment, context-sensitive financing reforms and stronger risk-pooling mechanisms are essential—drawing lessons from stable settings (Bhutan, Sri Lanka) to guide strategies in fragile contexts (Afghanistan, Pakistan).

WHAT IS ALREADY KNOWN?

• Primary healthcare (PHC) and financial risk protection are widely recognised as foundational pillars of universal health coverage (UHC), particularly in low- and middle-income countries. However, limited empirical evidence exists on how PHC financing has shaped UHC progress across South Asia.

• Most existing studies have focused on either health service coverage or financial protection in isolation, with few offering country-specific projections or examining rural–urban disparities within the region.

WHAT ARE THE NEW FINDINGS?

WHAT DO THE NEW FINDINGS IMPLY?

• Progress towards UHC in South Asia cannot rely on service expansion alone. The rising burden of catastrophic health expenditures, especially among rural and underserved populations, exacerbated in conflict-affected countries, calls for urgent policy action to increase PHC financing, expand prepayment and risk-pooling mechanisms and implement equity-focused reforms that protect vulnerable households from financial hardship.

Introduction

Universal health coverage (UHC) is central to attaining Sustainable Development Goal (SDG) 3: Good Health and Well-being, which aims to ensure healthy lives and well-being for all by 2030,¹ as it ensures equitable access to essential health services without financial hardship.¹ To monitor progress towards UHC, the WHO and the World Bank typically focus on two fundamental components: (1) ensuring primary healthcare (PHC) services and (2) complete financial protection from catastrophic health expenditures (CHE) and impoverishment.¹,³

Globally, PHC provision has proven transformative, capable of delivering 90 per cent of essential UHC interventions.⁴,⁷ This is because PHC delivers essential, cost-effective services, including maternal and child healthcare, immunisations and treatment for common illnesses, addressing most health needs while reducing reliance on secondary and tertiary care.^{5 8 9} Investments in PHC from 2020 to 2030 could save 64 million lives, increase global life expectancy by nearly 4 years and provide significant economic benefits, especially for women and children.^{6 10}

Financial protection is especially crucial for achieving UHC in low- and middle-income countries (LMICs), particularly in South Asia, where high out-of-pocket expenditure (OOPE) remains a major barrier to healthcare access.²¹¹,¹³ High OOPE increases the risk of CHE and impoverishment, compelling millions of households to cut spending on essentials like food, education and housing or pushing them into poverty.¹ In 2021, 4.5 billion people lacked access to essential health services, over 1 billion faced CHE and 344 million were pushed into extreme poverty due to OOPE.^{3 6}

Despite the critical role of PHC in advancing UHC, there is limited evidence on the trends and projections of PHC financing and its association with UHC indicators, particularly in LMICs. Based on a systematic research in PubMed, we identified eight studies on PHC financing,^{57 14},¹⁹ yet only two directly examined its relationship with UHC health-related indicators.^{5 19} Furthermore, country-specific predictions of UHC indicators using updated data are scarce, particularly in South Asia, a region home to over 2 billion people where achieving UHC remains a significant challenge.^{14 15} Conflict-affected settings in South Asia, such as Afghanistan and Pakistan, face particularly severe barriers to achieving UHC.^{1112 20},²² For instance, decades of instability in Afghanistan have severely weakened health infrastructure, limited access to essential maternal and child health services and increased reliance on donor-driven financing, leaving healthcare services fragmented and unsustainable. Similarly, regions within Pakistan experience disrupted healthcare delivery due to ongoing conflicts, contributing to persistent inequalities and insufficient health workforce capacity. These disruptions have led to high OOPE, exacerbated financial hardship and significantly hampered progress towards equitable health service coverage and financial risk protection.

This gap underscores the importance of longitudinal, country-specific analyses to inform PHC investments and health financing reform, particularly in protecting populations from financial risk and advance UHC goals in South Asia. With the countdown to assessing progress on the SDGs underway, there is an urgent need for research on health financing reforms and PHC financing to understand their implications for UHC attainment. The aim of this study is to report modelled projections of UHC in seven countries in South Asia to 2030 by predicting progress on the twin underpinning elements of PHC service coverage and financial risk protection. A further aim of the paper is to shed additional understanding on the policy levers to advance UHC. We do so by first modelling the impact of levels of financing on PHC across the seven countries. Second, we report on the basis of a systematic review of the literature the impact of health financing reform on PHC service coverage and financial risk protection from CHE and impoverishment. Both the modelled projections of progress of UHC indicators to 2030 and the analysis of the impact of investment levels and health service reforms will provide critical evidence to guide policy decisions and investments in strengthening PHC systems and addressing financial protection gaps in South Asia.

Methods

Measures

Health service coverage can be measured using various metrics, such as UHC Service Coverage Index (SCI),² composite promotion index, composite treatment index or composite coverage index (CCI).^{12 23} Additionally, single or multiple indicators can be used to track health service coverage in UHC assessments.^{23 24} In this study, we selected CCI as the primary metric for assessing essential PHC service coverage, particularly reproductive, maternal, newborn and child health (RMNCH) interventions. Introduced by Countdown to 2030 in 2008,^{25 26} CCI consolidates key services delivered through PHC systems, including antenatal care, family planning, child immunisations, skilled birth attendance and care-seeking for childhood illnesses. Unlike broader indices, CCI focuses specifically on PHC-based services, providing a clear measure of PHC performance and its contribution to UHC service coverage. To evaluate financial risk protection, this study used two key indicators: CHE, defined as spending exceeding 10% of total household consumption and impoverishing health expenditures, measured against the $3.10 per day poverty line.^{2 3} Together, CCI and these two financial risk indicators offer a comprehensive view of progress toward UHC.

Data

We used the WHO Global Health Observatory (GHO) database to extract data on the CCI, CHE and impoverishment across national, urban and rural levels.²⁷ The 2022 Demographic Health Surveys (DHS) for Bangladesh and Nepal, along with the 2022–2023 Multiple Indicator Cluster Survey (MICS) for Afghanistan, were used to estimate recent CCI at national, urban and rural level. Additionally, micro data from the Bangladesh Household Income and Expenditure Survey (2022) and the Bhutan National Health Survey (2023) were used to estimate CHE and impoverishment. The estimated CCI, CHE and impoverishment data were merged with the GHO data file to create a master dataset for analysis. The detailed data sources and available data points for each country’s CCI, CHE and impoverishment are presented in the online supplemental appendix table 1,2. PHC financing data were obtained from the WHO Global Health Expenditure database,¹³ while the sociodemographic index (SDI) was extracted from the Global Burden of Diseases database. Further data on gross domestic product (GDP), health expenditure, life expectancy, total fertility rate and other demographic statistics were collected from World Bank and WHO GHO.

Statistical analysis

We employed Bayesian random-intercept linear regression models to estimate trends in UHC indicators, including the CCI, CHE and impoverishment, across time, countries, region and areas of residence. The Bayesian approach offers several advantages: it integrates prior knowledge with new data to improve parameter estimation, reducing biases from small sample sizes and model misspecifications.²⁸ Additionally, Bayesian models facilitate clearer interpretation of estimates and uncertainties by presenting credible intervals (CrIs) rather than confidence intervals (CIs). Our model predicted the posterior distribution of logit-transformed UHC indicators and calculated the probability of achieving the UHC target by 2030. The logit transformation of the outcome variable ensured that predicted values remained within the probability range of 0% to 100%.

First, we performed a Bayesian random-effects model to estimate trends and project the CCI up to 2030 at regional, national, urban and rural levels, using SDI and PHC expenditure per capita as predictor variables to capture the impact of key socioeconomic and health system factors on PHC service coverage outcomes. Second, to analyse trends and projections of CHE and impoverishment from 2000 to 2030 at regional, national, urban and rural levels, we applied a Bayesian random-effects regression model, similar to the CCI model. In this model, we replaced PHC expenditure per capita with government general health expenditure per capita (GGHE) as a percentage of GDP as the predictor variable, alongside SDI, to assess the impact of socioeconomic and health financing factors on financial risk protection. Finally, we estimated national and regional trends in PHC financing from 2016 to 2030 using a Bayesian random effects model, treating countries as random intercepts, with year as the sole predictor variable. Outcome variables included log-transformed PHC expenditure per capita (US$), PHC GGHE (US$), PHC expenditure as a percentage of current health expenditure and PHC expenditure as a percentage of GGHE. Missing PHC expenditure values for UHC indicators projection were imputed using 2016 values for 2000–2015 and assuming 2021/2022 values remained constant through 2030. We acknowledge that projecting CCI using Bayesian models with limited country-specific data points, such as the two data points available for Afghanistan, introduces uncertainty. The Bayesian framework mitigates some bias from sparse data by leveraging regional trends and prior knowledge from neighbouring contexts. However, projections based on limited data should be interpreted cautiously. Specifically, projections for countries like Afghanistan rely significantly on broader regional trends, PHC expenditure patterns and socioeconomic indicators (eg, SDI), which might influence national-level projections away from short-term observed trends.

The model was fitted using Gibbs sampling with Markov Chain Monte Carlo in Just Another Gibbs Sampler (JAGS), employing three chains, 10 000 burn-in iterations, 50 000 sampling iterations and thinning of 20 to ensure stability and robust parameter estimation. Model validation included out-of-sample testing and posterior predictive checks, comparing observed data to simulated data from the posterior distribution to assess the goodness of fit. Bayesian model diagnostics were assessed using the Potential Scale Reduction Factor, with values below 1.1 considered indicative of good convergence. Data management was conducted in R and Stata V.17.1/MP, while Bayesian models were developed in JAGS and implemented in R V.4.2.0. Full details of the Bayesian models, cross-validation and diagnostics are provided in the online supplemental appendix eMethod 1.

Systematic review

We conducted a systematic review to assess the impact of health financing policy reforms on UHC indicators such as financial protection and PHC service coverage for the seven countries. Searches were conducted in PubMed, Web of Science, Google, the WHO website and national statistical office portals from inception to 26 February 2025, using search terms targeting financial protection, catastrophic expenditures, impoverishment, health service coverage and health insurance policies, including MeSH terms such as (“insurance, health”[MeSH] OR “prepaid health plans”[MeSH]). Eligibility criteria included studies reporting on UHC indicators related to health insurance policies, as well as observational, intervention and quantitative studies conducted in seven listed South Asian countries. Studies were excluded if they focused on irrelevant regions, case studies, correspondence, systematic reviews or lacked clear data. We explicitly defined ‘conflict-affected’ settings using the World Bank and WHO classifications, referring to countries experiencing persistent armed conflicts, widespread violence or prolonged political instability (eg, Afghanistan, regions within Pakistan), significantly disrupting healthcare delivery systems. To mitigate potential selection bias, two independent reviewers screened titles and abstracts, with full-text reviews subsequently conducted independently, resolving discrepancies through discussion and consensus. Additionally, data extraction and narrative evidence synthesis were initially performed by a single reviewer and cross-checked by another reviewer for consistency and accuracy. The method of narrative evidence synthesis of UHC indicators in relation to health insurance policies was based on our recent publication.²⁹ Any disagreements were resolved through discussion. The systematic review protocol was registered in PROSPERO (ID:CRD420251000616), and a brief review protocol is provided in the online supplemental appendix eMethod 2. The detailed search strategy and searched results are presented in the online supplemental appendix table 3-6. Study selection was performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flowchart (online supplemental appendix figure 1).

Patient and public involvement

As this study is intended for policymakers and programme managers, utilising WHO GHO data and country-specific secondary datasets designed explicitly for secondary analyses, patients and the public were not involved in the design, conduct, reporting or dissemination of this research.

Results

Health expenditure, health status and socioeconomic indicators in seven South Asian countries

Health financing, health indicators and health status vary significantly across South Asian countries, reflecting stark disparities in resource allocation and health outcomes (table 1). Health expenditure per capita for 2020–2022 highlights this gap, with Bhutan ($154.49) and Sri Lanka ($145.56) leading, while Pakistan ($38.77) and Bangladesh ($61.09) report the lowest spending. Similarly, general government health expenditure (GGHE) per capita shows Bhutan ($75.64) and Sri Lanka ($58.62) investing significantly more than Afghanistan ($0.63) and Bangladesh ($3.96), indicating uneven government commitment to health financing. PHC expenditure further underscores this divide, with Bhutan ($66.00) and Sri Lanka ($59.52) prioritising PHC, while Pakistan ($20.72) and India ($28.93) lag behind. Notably, Bhutan allocates $45.00 per capita of its GGHE to PHC, in contrast to Afghanistan’s $0.41, the lowest in the region. These financial disparities translate into significant differences in health outcomes. Sri Lanka excels with the highest life expectancy for women (80.26 years), the lowest under-5 mortality rate (U5MR) of 7 and a maternal mortality ratio (MMR) of 36 per 100 000 live births. In contrast, Pakistan records the highest U5MR (65.0), while Afghanistan faces the region’s worst maternal health outcomes, with an MMR of 620. Sri Lanka, Bhutan and Bangladesh lead in male life expectancy (all higher than 70 years), while Afghanistan falls especially behind at 63.38 years.

Table 1. Health expenditure, health status and socioeconomic indicators in seven South Asian countries.

Indicators	AFG	BGD	BTN	IND	NPL	PAK	LKA
Health expenditure (US$)
HE per capita in 2022	80.6	61.1	154.5	79.5	88.3	38.8	145.6
GGHE per capita in 2022	0.63	3.96	75.6	31.1	27.9	14.8	58.6
PHC EXP per capita 2022	46.4	34.2	66.0	28.9	44.4	20.7	59.5
PHC GGHE per capita 2022	0.41	6.1	45.00	12.1	16.7	7.2	15.8
Health status indicators
Life expectancy in 2019 (yrs)
Men	63.4	71.0	71.0	68.5	69.3	66.3	73.6
Women	66.4	74.6	72.0	71.0	72.0	68.0	80.3
Average live births per woman, 2022	4.0	2.0	1.9	2.2	1.8	3.3	2.1
NMR per 1000 live births, 2020	35.2	18.0	15.0	20.3	16.9	40.0	4.0
IMR per 1000 live births, 2022	46.0	22.6	20.5	27.7	24.3	56.9	6.7
U5MR per 1000 live births, 2020	58.0	29.0	28.0	33.0	28.0	65.0	7.0
MMR per 100 000 live births, 2020	620	123.0	60.0	103.0	151.0	154.0	36.0
Socioeconomic indicators
Total population in 2022 (millions)	40.5	167.6	0.78	1404·7	30.1	228.7	21.6
GDP in 2022 (US$ billion)	21.4	400.0	2.5	3250·1	30.9	292.0	83.0
GDP per person 2022 (US$)	575	1750	350	1850	920	1250	430
Literacy rate in 2016–2022 (%)
Women	29.8	96.00	57.1	70.3	59.7	46.5	91.6
Men	55.5	78.00	75.0	84.7	78.6	58.0	93.0
Age dependency ratio in 2020 (%)	80.0	47.02	45.1	48.7	8.9	57.2	53.7
Poverty 2016–2022 (% of population)	97.0	20.50	38.9	20.8	25.2	37.9	11.7

Note: Literacy rate percentage of adults ages 15 and above. Age dependency based on working-age population. Poverty headcount ratio at the national poverty line

Note: PHC EXP for AFG in 2022, BGD in 2020, BTN in 2020, IND in 2019, NPL in 2021, PAK in 2022 and LKA in 2018

Sources: Data are from the World Bank, the World Health Organization and the World Population Prospects; PHC data from WHO

AFG, Afghanistan; BGD, Bangladesh; BTN, Bhutan; EXP, Expenditure; GGHE, general government health expenditure; HE, health expenditure; IMR, infant mortality rate; IND, India; LKA, Sri Lanka; MMR, maternal mortality ratio; NMR, neonatal mortality rate; NPL, Nepal; PAK, Pakistan; PHC, primary healthcare; U5MR, under-5 mortality rate.

Predicting UHC: primary health service coverage as measured through CCI

Our model predicted that the South Asia region studied will exceed the UHC goal of 80% CCI coverage by 2030. The regional CCI has shown a steady upward trajectory, rising from 53.5% in 2000 to a predicted 81.5% by 2030 (figure 1A, online supplemental appendix table 7). Sri Lanka and Bhutan lead the region, both surpassing the 80% threshold by 2020 and projected to exceed 90% by 2030. Sri Lanka, in particular, demonstrates remarkable progress, with its CCI climbing from 77.9% in 2000 to 91.6% by 2030, while Bhutan follows closely, advancing from 67.1% to 89.2% over the same period. Bangladesh, though starting from a lower baseline of 52.5% in 2000, is on track to reach the 80% target by 2030, while India and Nepal are also projected to surpass this benchmark, signalling positive strides in expanding health service coverage. In contrast, Afghanistan, despite improving from 22.5% in 2000 to 66.9% by 2030, remains the only country unlikely to achieve the 80% target, highlighting persistent shortfalls associated with conflict-related disruptions. The urban–rural divide in CCI was pronounced, with urban areas consistently projected to outperform rural regions by 2030. By 2030, rural CCI in Afghanistan, Bangladesh and Pakistan is projected to remain notably lower compared with urban areas, reflecting persistent geographic disparities.

Figure 1. Trend and projection UHC indicators in seven South Asian countries, 2000–2030 CCI, Composite Coverage Index; CHE, catastrophic health expenditure; UHC, universal health coverage.

Predicting UHC: financial risk protection indicators

However, juxtaposed against the overall predicted rise in PHC across the region, the CHE incidence is projected to more than double, rising from 7.2% in 2000 to 18.6% by 2030, reflecting an increasing financial burden on households (figure 1B). Afghanistan demonstrated the highest projected increase (from 4.4% to 48.8%), highlighting severe financial vulnerabilities associated with minimal PHC financing and ongoing conflict. Nepal and India are also projected to experience substantial increases, reaching 20.1% and 26.3%, respectively, by 2030. Similarly, Bangladesh, already burdened by a higher baseline of 14.3% in 2000, is expected to climb steadily to 23.8%, while Pakistan exhibits a more modest predicted rise to 5.5%. The outliers Sri Lanka and Bhutan stand out as success stories—Sri Lanka’s CHE incidence is predicted to decline from 6.4% to 5.0%, and Bhutan maintains consistently low rates, rising only modestly from 3.5% to 5.3%.

As above in relation to CHE, the urban–rural divide further amplifies disparities in healthcare affordability (figure 1, online supplemental appendix table 8). Rural households consistently face higher CHE rates than their urban counterparts, with Afghanistan showing the most striking gap—rural CHE is projected to rise from 4.8% in 2000 to 50.9% by 2030, compared with 3.5% to 43.1% in urban areas. Similar disparities are evident in Bangladesh and India, where rural CHE rates are expected to exceed urban rates by 5.5 and 6.0 percentage points, respectively, by 2030. Nepal is also predicted to experience a widening gap of 4.9 points, while Bhutan and Pakistan are expected to maintain smaller differences of under 2 percentage points. Notably, Sri Lanka presents an exception—initially showing higher urban CHE in 2000, the trend is predicted to reverse by 2030, with rural rates slightly surpassing urban ones.

We also examined an additional measure of financial risk protection, namely impoverishment due to OOPE across South Asia (online supplemental appendix table 9). Our model predicted a positive but modest decline from 2.9% in 2000 to 2.0% by 2030. Consistent with our CHE model, Sri Lanka and Bhutan stand out as success stories, consistently expecting to see the lowest impoverishment rates—0.5% and 0.3%, respectively—throughout the period, reflecting effective health financing policies that protect households from falling below the poverty line due to healthcare costs. India is also predicted to demonstrate progress, with impoverishment declining from 3.5% to 2.6%, while Pakistan, we predict, will remain stable at around 3%. Again, consistently the rural–urban divide remains stark, with Afghanistan and Bangladesh exhibiting the largest gaps—1.3 and 1.7 percentage points, respectively, by 2030. In contrast, Bhutan and Sri Lanka report minimal rural–urban differences and are expected to consistently retain gaps below 0.5%, demonstrating an anticipated success in achieving equitable financial protection across populations.

Understanding policy levers: PHC financing and health service coverage

Figure 2 presents the relationship between PHC financing and health service coverage, measured by the CCI, across South Asian countries for 2016 and CCI 2020 (figure 2A,B). A positive correlation was observed between PHC expenditure per capita and CCI (r=0.49, p=0.07), indicating that higher investments in PHC are associated with improved health coverage. Bhutan and Sri Lanka, with the highest PHC spending per capita, achieved CCI scores exceeding 85% in 2020, demonstrating the impact of sustained investment in PHC. India also showed progress, with its CCI increasing from 72% in 2016 to 76% in 2020, reflecting improvements aligned with increased health financing. In contrast, Afghanistan and Pakistan, where PHC spending remains below $30 per capita, continue to lag behind. Afghanistan’s CCI improved only slightly from 45% to 51%, while Pakistan’s rose from 62% to 66% over the same period, underscoring the challenges faced by countries with limited health financing. Furthermore, GGHE per capita on PHC also shows a significant positive correlation with CCI (r=0.62, p=0.02), with Bhutan and Sri Lanka again leading the region. Interestingly, Bangladesh and Nepal achieved relatively higher CCI scores (70%–80%) despite having lower GGHE per capita on PHC, comparable to Afghanistan and Pakistan, suggesting that efficient resource allocation and broader health system strengthening can enhance service coverage even in resource-constrained settings.

Figure 2. PHC financing and UHC indicators in seven South Asia countries in 2016 and 2020 AFG, Afghanistan; BD, Bangladesh; BTN, Bhutan; IND, India; NPL, Nepal; PAK, Pakistan; LKA, Sri Lanka 42. CHE, catastrophic health expenditure; EXP, expenditure; GGHE, general government health expenditure; PHC, primary healthcare; UHC, universal health coverage.

Understanding policy levers: PHC financing and financial risk protection

Though not statistically significant, the data indicate a potential trend where higher PHC investments contribute to lower CHE (figure 2C, r=−0.30, p=0.30). This trend is reinforced when we also examine the relationship with PHC GGHE per capita and CHE. The data show stronger negative correlation with CHE (figure 2D, r=−0.52, p=0.057), suggesting that prioritising PHC in government budgets may enhance financial protection. Among countries with high PHC financing and low CHE, Bhutan and Sri Lanka stand out, demonstrating the benefits of sustained investments in PHC. Countries with moderate PHC financing but low CHE, such as Nepal, also show relatively strong financial risk protection despite moderate spending. Bangladesh and India, which exhibit low PHC financing and high CHE, highlight the financial vulnerability associated with inadequate PHC investment. Afghanistan’s particularly sharp rise in CHE incidence aligns with persistently low PHC investments and governance challenges related to prolonged conflict.

However, PHC investment is predicted to remain slow across seven South Asian countries from 2016 to 2030 (online supplemental appendix figure 2, table 10). South Asia demonstrates steady growth in PHC expenditure, yet significant disparities persist across the region during 2016 to 2030. For instance, Bhutan and India emerge as leaders, with Bhutan not only allocating the highest proportion of its health budget to PHC but also achieving the most substantial growth in per capita spending. India follows closely, reflecting its efforts to scale up PHC investments. Sri Lanka, Nepal and Pakistan also show notable progress, though their PHC spending remains modest compared with regional leaders. In contrast, Bangladesh and Afghanistan lag behind, with minimal increases in PHC expenditure, highlighting stark inequities in health system priorities.

Model validation

Posterior predictive checks (PPC) indicate that all PHC models provide an acceptable fit, with p values near 0.5, demonstrating good alignment between simulated and observed data (online supplemental appendix figure 3). The service coverage model shows good fit with a PPC p value of 0.891, effectively capturing variability without significant discrepancies (online supplemental appendix figure 4). Similarly, PPC results for CHE (0.502) and impoverishment (0.51) confirm accurate representation of observed data (online supplemental appendix figure 5). Among PHC financing models, PHC as a percentage of CHE achieves the highest predictive accuracy, while PHC GGHE per capita shows comparatively lower performance but remains acceptable (online supplemental appendix table 11). The PSFR Bayesian model validation demonstrated that the upper limit of the CrIs was consistently close to 1, indicating a strong and reliable model fit across all models (online supplemental appendix table 12).

Health financing reforms and impact on UHC indicators

Health financing reforms in South Asia present a diverse landscape, with countries at different stages of achieving UHC. Table 2 maps for each of the seven South Asian countries three dimensions of coverage achieved in current health insurance reforms, namely who is covered; which health services are covered; and how much is (not) covered or remaining levels of OOPE. Afghanistan and Bangladesh remain in the early stages, with OOPE expenditure dominating healthcare funding—74.8% in Afghanistan and 74.0% in Bangladesh (2020). In contrast, Bhutan, India, Pakistan, Sri Lanka and Nepal have made notable progress by implementing mixed models of public and private insurance. Bhutan provides free universal healthcare, integrating traditional and modern medicine through a three-tier system, with 90% of the population living within 2 hours of a health facility. To sustain healthcare financing, the Bhutan Health Trust Fund finances essential drugs and vaccines, while the Royal Insurance Corporation of Bhutan covers hospitalisation and travel costs for medical treatment in India. However, rural OOP expenses remain a concern, affecting 2.55% of the population (2017). Limited evidence is available on the effectiveness of Bhutan’s financing model, particularly concerning rural populations, highlighting a critical gap for future research.

Table 2. Three dimensions of coverage in seven South Asian countries’ major health insurance reforms.

Country and financing scheme	Year of reform	Revenue generation (sources of revenue ordered by proportion of contribution)	Who is covered?		Which services are covered?	How much is covered? (OOPE as % of THE)
			Population(s) targeted by health insurance	Population enrolled (% of total)	Scope of services
Afghanistan (BPHS/EPHS)	2005	Donor funding	Poor population	0.8%	Maternal and child health, control of communicable diseases, mental health, disability and essential drugs	74.8% (2020)
Bangladesh (CHIS)	2012	Formal-sector payroll contributions	Public sector employees and their family	NA	Comprehensive healthcare (inpatient and maternity), life and accident-related disability	74.0% (2020)
Bhutan (Tax-based financing)	2013	General government revenues, trust fund and payroll contributions	All citizens	100.0%	Inpatient and outpatient services	18.8% (2020)
India (RSBY)	2008	Central (75%) and state government (25%) cover premium, beneficiaries pay rupees 30 as a registration fee	Families living BPL	36 million families (by 2014)	Inpatient care, secondary care focus, maternity care	54.8% (2019)
India (AB-PMJAY)	2018	Fully funded by central and state governments (no registration fee for beneficiaries)	Bottom 40% of the population (~55 crore individuals)	55 crore beneficiaries (40%)	Covers secondary and tertiary hospitalisation, diagnostics and follow-up care
Nepal (SHIP)	2016	General government revenues, voluntary household premiums	Formal and informal sectors	11.0%	Promotive, preventive, curative and rehabilitative care, ambulance services	57.9% (2019)
Pakistan (SSP)	2015	General government revenues	Families living BPL	27 million families	Inpatient services, maternity care and post-hospital treatment, cover treatment for seven prioritised illnesses	53.8% (2019)
Sri Lanka (Agrahara)	2016	General and payroll contributions	Public sector employee	13.0%	Inpatient care, including accidents and death claims	45.6% (2019)

AB-PMJAY, Ayushman Bharat - Pradhan Mantri Jan Arogya Yojana; BPHS, basic package of health services; CHIS, Compulsory Health Insurance scheme; EPHS, essential package of hospital services; OOPE, out-of-pocket expenditure; RICB, Royal Insurance Corporation of Bhutan; RLIS, rural life insurance scheme; RSBY, Rashtriya Swasthya Bima Yojana; SHIP, Social Health Insurance Program; SSP, Sehat Sahulat Program; THE, total health expenditure.

India’s national health insurance system evolved from Rashtriya Swasthya Bima Yojana (RSBY) in 2008 to Ayushman Bharat - Pradhan Mantri Jan Arogya Yojana (AB-PMJAY) in 2018, expanding financial protection. Under AB-PMJAY, around 550 million people benefit from nationwide portability and government funding. Sri Lanka prioritises free universal healthcare. The Agrahara health insurance scheme (2016) provides inpatient care, accident and death benefits for public sector employees, but OOP expenditure remains high at 45.6% (2019). Pakistan’s Sehat Sahulat Program (SSP), initiated in 2015 and expanded in 2019, is a government-funded health insurance initiative providing low-income families. By 2022, over 27 million families had enrolled with 3.2 million hospital visits recorded. However, key challenges remain, such as delayed reimbursements, insufficient healthcare provider capacity, limited public awareness and concerns regarding long-term financial sustainability. These factors significantly affect programme effectiveness and underscore the need for systematic policy evaluation. The detailed evidence of health financing for each country is presented in the online supplemental appendix tables 13-19.

Based on our evidence synthesis of 84 studies (see PRISMA flowchart of included and excluded studies in online supplemental appendix figure 1, table 3 presents a synthesis of the impact of types of health insurance on UHC indicators. Detailed country-specific information in the online supplemental appendix tables 20,21. The data, categorised by positive effect, negative effect and no effect or insufficient evidence, reveal mixed effects of health insurance policies on PHC coverage and financial risk protection across South Asia. Overall, the available evidence suggests that public health insurance emerged as particularly impactful, especially in India and Nepal. In India, public insurance significantly increased PHC coverage (11 studies) and reduced financial risk (30 of 53 studies), with private, micro-financing and public-private schemes also improving financial protection (five of six studies). Community-based and public-private insurance had no significant impact on PHC coverage (two of two studies). In Nepal, public insurance enhanced both service coverage (two studies) and financial protection (one study), while in Sri Lanka, public insurance significantly improved financial protection (one study).

Table 3. Narrative summary of UHC indicators change following the implementation of different health insurance policies (n=84 studies).

Country	Types of health insurance	Outcome*
		PHC service coverage			Financial risk protection
		Positive effect	Negative effect	No effect	Positive effect	Negative effect	No effect
Afghanistan	No study	0	0	0	0	0	0
Bangladesh	Public	1	0	0	0	0	0
	Private	0	0	0	1	0	0
	Community-based	1	0	0	10	0	0
	Micro-financing	1	0	0	1	0	1
Bhutan	No study	0	0	0	0	0	0
India	Public	11	0	0	30	4	18
	Community-based	0	0	1	2	0	1
	Microfinancing	0	0	0	2	0	1
	Public and private	0	0	1	3	0	0
Nepal	Public	2	0	0	1	0	0
Pakistan	Health insurance	1	0	0	0	0	0
Sri Lanka	Public	0	0	0	1	0	0

The number of studies is indicated in each circle. Positive effect of the health insurance policy—that is, a statistically significant increase or decrease in the targeted outcome favouring the policy. Negative effect of the health insurance policy—that is, a statistically significant increase or decrease in the targeted outcome favouring the control. No effect of the health insurance policy—that is, a statistically insignificant increase or decrease in the targeted outcome.

^*Financial risk includes the burden of out-of-pocket expenditures (OOPE), catastrophic health expenditures, and impoverishment due to healthcare costs. Health service coverage encompasses key indicators such as family planning, antenatal care, delivery care, immunisation, skilled birth attendance, care-seeking for pneumonia and diarrhoea, healthcare utilisation, as well as composite measures like the Composite Coverage Index (CCI), prevention coverage, treatment coverage and the universal health coverage (UHC) index.

PHC, Primary healthcare.

However, significant evidence gaps persist, especially for conflict-affected countries such as Afghanistan, where no studies evaluating health insurance impacts were identified. Similarly, Bhutan lacks empirical evaluations, and evidence from Bangladesh, Nepal, Pakistan and Sri Lanka remains sparse, affecting the robustness and generalisability of findings. In Bangladesh, pilot-tested public, community-based insurance and micro-health insurance improved service coverage (one study each), while private and community-based insurance reduced financial risk (one study each). Microfinancing insurance showed reduced financial risk (one of two studies). This variability highlights critical research gaps, particularly concerning the contextual factors and implementation challenges that influence the success of insurance schemes in diverse settings across the region.

Discussion

This study provides the first comprehensive evaluation of progress toward UHC across seven South Asian countries. By analysing trends in the PHC service coverage and financial risk protection indicators from 2000 to 2030, we identified and predicted both progress and persistent challenges in equitable access to healthcare.

Progress and disparities in service coverage

Our findings predict a steady improvement in health service coverage across the region, increasing from 53.5% in 2000 to a projected 81.5% by 2030. Notable successes lie in Bhutan and Sri Lanka, where health service coverage is expected to reach 90% by 2030, underscoring the impact of strong public healthcare systems and strategic investments. Bhutan’s government-funded healthcare model³⁰ and Sri Lanka’s ‘Healthcare Delivery for Universal Health Coverage’ policy^{7 31 32} demonstrate the transformative potential of PHC-oriented health systems. The narrower urban–rural gap could be due to smaller land areas and limited inhabited spaces in Bhutan and Sri Lanka, which may make infrastructure and service delivery more manageable and accessible. However, countries like Afghanistan and Pakistan remain below the 80% target, reflecting systemic barriers such as inadequate PHC investment and reliance on OOP expenditures. Urban areas consistently outperform rural ones across the region, with significant rural-urban disparities persisting in health service coverage and financial risk protection indicators.

Challenges in financial risk protection

Despite improvements in service coverage, the threat of financial risk undermines this progress towards UHC in the region. The incidence of CHE is projected to rise from 7.2% in 2000 to 18.6% by 2030 across the region, with Afghanistan and India experiencing the highest increases. While regional impoverishment due to OOPE is projected to decline modestly (from 2.9% to 2.0%), disparities persist across South Asia. Sri Lanka and Bhutan lead with consistently low rates of 0.5% and 0.3%, respectively, reflecting strong financial protection measures such as free healthcare at the point of use and higher PHC spending.^{30 33} India, we predict, will show gradual improvement, reducing its rate from 3.5% to 2.6%, while Pakistan’s rate is predicted to remain stagnant at 3.0%, underscoring the need for targeted reforms to protect vulnerable households. In addition, the rural–urban divide remains stark, with Afghanistan and Bangladesh facing the largest gaps—1.3 and 1.7 percentage points, respectively, by 2030. In contrast, Bhutan and Sri Lanka maintain minimal rural-urban disparities (below 0.5%), demonstrating their success in achieving equitable financial protection. These trends underscore the fragility of health financing systems in much of the region and the importance of financial risk pooling mechanisms to reduce reliance on OOPE and protect vulnerable populations.

Role of PHC financing and health financing reforms

Our analysis shows a significant positive correlation between PHC financing and UHC indicators-PHC service coverage and financial risk protection indicators. Bhutan, Sri Lanka and India demonstrate how sustained investments in PHC can drive improvements in PHC service coverage, yet PHC financing remains inadequate across most of the region. Per capita spending in 2022 ranges from $0.41 in Afghanistan to $45 in Bhutan, highlighting the urgent need to expand PHC investments is to address rural-urban disparities, improve health outcomes and reduce financial vulnerability. Our projections suggest that countries with stronger improvements in PHC tend to experience more favourable trends in reducing CHE by 2030. However, in some settings, increases in PHC service coverage may be accompanied by modest rises in CHE, particularly where expanded service coverage is not matched with adequate financial protection measures. This indicates that while PHC strengthening supports progress in coverage, its impact on CHE depends on concurrent efforts to improve health financing and reduce OOPE.

Achieving SDG targets for UHC in South Asia requires not only scaling up PHC to expand access to quality services but also implementing health financing reforms to improve financial protection.¹⁴ Expanding risk-pooling mechanisms, increasing government spending on PHC and strengthening rural health infrastructure are critical steps. Our systematic review highlights public health insurance as one of the most evidence-based mechanisms for advancing UHC, particularly with reference to India and Nepal. In India, public-funded health insurance has significantly increased PHC service coverage and reduced financial risk,³⁴ while in Nepal, it has improved both PHC coverage and financial protection.^{35 36} Similarly, Bhutan and Sri Lanka exemplify the importance of aligning health financing policies with equity goals. Bhutan offers free universal healthcare funded by general taxation,³⁰ and Sri Lanka’s has achieved success through long-standing PHC investments, decentralised service delivery and a skilled community health workforce.^{7 31 32} However, determining the most effective financing model—insurance-based or tax-funded—depends on national contexts, balancing sustainability with inclusivity.

Enhancing policy relevance through stakeholder perspectives and conflict-affected settings

Tailored strategies, such as community health worker programmes and targeted subsidies for vulnerable populations, can further enhance progress. Global examples, such as Thailand’s and Brazil’s successful health financing reforms,³⁷,³⁹ underscore the effectiveness of equitable financing and public or community-based models in bridging gaps in access and affordability. However, the variability in outcomes across South Asia underscores the need for context-specific adaptations and further research to strengthen the evidence base for health insurance schemes in the region.

Insights from stakeholder engagements in Nepal indicate that incorporating local government authorities and community health workers significantly enhances policy implementation and service uptake, thereby improving PHC service coverage and reducing financial risks at the grassroots level.⁴⁰ In India, feedback from beneficiaries of AB-PMJAY emphasises simplified claim processes and improved health providers’ accountability as critical factors in maximising health insurance effectiveness.⁴¹ These stakeholder insights underscore the importance of participatory governance and tailored communication strategies to enhance UHC reforms.

In conflict-affected settings like Afghanistan and parts of Pakistan, stakeholder consultations reveal severe disruptions in service delivery caused by instability and weakened governance, highlighting the critical need for context-specific adaptations. Case examples from Afghanistan indicate that community-based health initiatives funded through international partnerships have shown resilience and improved service coverage despite instability.²² This suggests potential for policy strategies leveraging community engagement and international collaboration to mitigate conflict-related barriers. However, comprehensive financial risk protection remains elusive, indicating the urgent need for enhanced international funding, stronger governance frameworks and innovative risk-pooling mechanisms designed specifically for fragile contexts.

Strengths and limitations

Our study has several strengths. This is the first study to estimate trends in UHC coverage based on an analysis and projections for both health coverage (PHC) and financial risk protection across seven South Asian countries while also assessing progress in health financing reforms in relation to health insurance. UHC progress was evaluated using the PHC service coverage measured by CCI and two financial risk protection indicators at regional, national, urban and rural levels, providing a comprehensive understanding of disparities. The CCI effectively reflects RMNCH indicators, focusing on key PHC-delivered interventions. Further, it relies on widely available, comparable data, ensuring consistency and ease of adoption for tracking UHC progress. By integrating updated datasets (DHS, MICS, HIES and Bhutan NHS) with WHO GHO data, we ensured accuracy and relevance. The Bayesian random-effects model improved the reliability of trends and projections, while the inclusion of treatment and prevention indicators offered a holistic perspective. Emphasising equity in UHC discussions highlighted the critical gaps across and within countries for rural and low-income populations most impacted by poorer service coverage and OOPE. Despite these strengths, the study has limitations. The analysis included only a subset of South Asian countries, excluding the Maldives due to insufficient data availability, which limits the regional generalisability of findings. The focus on PHC-related service coverage meant that some broader health indicators were not included, restricting the comprehensiveness of the service coverage analysis. A specific limitation relates to projections made for countries with sparse data, notably Afghanistan. Despite observed declines in recent national and rural CCI data, the Bayesian projections indicate an increasing trend, which appears contradictory. This discrepancy arises because Bayesian models integrate limited country-specific data with regional trends and imputed PHC expenditure values, pulling projections towards regional norms. We recommend cautious interpretation of projections in sparse-data contexts and emphasise the necessity of strengthening national data collection and reporting infrastructures to improve future model accuracy and policymaking. Additionally, we were unable to assess the quality or effectiveness of services within countries, which is a critical dimension of UHC. Data availability and quality issues further led to the exclusion of certain countries or indicators from parts of the analysis. These constraints may reduce the applicability of our findings to other regions, such as low-, lower-middle- and upper-middle-income countries outside South Asia. Finally, while the inclusion of diverse data points enhanced our analyses, comparability across time and regions remains a challenge, particularly given variations in data collection methodologies. By acknowledging these limitations and building on the strengths of our approach, this study underscores the importance of reliable data, equity-focused evaluations and tailored interventions to drive progress toward UHC.

Conclusion

This study highlights the crucial link between financial risk protection and health coverage in garnering progress on the SDG targets of UHC in South Asia. Although there have been improvements in health service coverage, financial risk protection remains fragile, particularly for rural and low-income populations. The results emphasise the need for increased investment in PHC, expanded risk-pooling mechanisms and health financing reforms, such as public health insurance, to enhance both service coverage and financial protection.

To bridge the gap between academic frameworks and field-level implementation, our evidence strongly supports practical, context-sensitive policy actions based on successful regional experiences. For example, scaling up India’s AB-PMJAY model, which has effectively improved healthcare access for millions through nationwide portability and simplified beneficiary processes, would significantly strengthen financial protection. Nepal’s community-involved national health insurance initiative provides another actionable blueprint, demonstrating improved PHC coverage outcomes through active stakeholder engagement. Meanwhile, Bhutan and Sri Lanka offer proven tax-funded universal healthcare models that significantly reduce out-of-pocket spending and ensure equitable access. Drawing on these insights, we propose the following concrete and operational recommendations to accelerate UHC progress by 2030:

• Prioritise substantial increases in government PHC expenditure, specifically targeting historically underfunded and conflict-affected countries (eg, Afghanistan, Pakistan).

• Scale up successful public health insurance programmes such as India’s AB-PMJAY, emphasising streamlined administrative processes, accountability frameworks and broad beneficiary engagement.

• Implement robust, general taxation-based financing models, following Bhutan’s and Sri Lanka’s approaches, to universally provide equitable health services with minimal financial barriers.

• Enhance rural health infrastructure and workforce capacity through targeted funding and local governance participation, involving community health workers to ensure effective delivery and local acceptance of healthcare services.

• Introduce tailored community-based PHC initiatives in conflict-affected settings, modelled on resilient programmes observed in Afghanistan, supported through strategic international partnerships and donor coordination to mitigate the impact of instability on health service delivery.

These specific policy actions, grounded in regional best practices and stakeholder consultations, represent a practical pathway from research insights to tangible improvements in health outcomes and equitable UHC implementation across South Asia.

Data availability statement

Data are available upon reasonable request. All data relevant to the study are included in the article or uploaded as supplementary information.