Financial Burden, Out-of-Pocket Health Spending, and Household Economic Well-Being in Heart Failure Patients in India: A Multicentre Cross-Sectional Survey

Introduction

Heart failure (HF) affects an estimated 26 million people worldwide, with its burden increasing steadily in low- and middle-income countries (LMICs) (1). In India alone, HF accounts for 1.8 million hospitalisations annually and places a considerable burden on the health system due to prolonged inpatient care and the need for long-term management, straining already limited healthcare resources. Further, due to its debilitating nature, HF is also known for its massive financial burden on patients, caregivers, and society (1).

Despite improved insurance coverage through schemes like Ayushman Bharat, the financial impact of cardiovascular diseases (CVDs), particularly HF, remains substantial for economically vulnerable households in India (2). The Ayushman Bharat scheme provides an annual health insurance coverage of ₹5 lakhs (INT$22K) per family for secondary and tertiary care hospitalisation, focusing mainly on economically disadvantaged populations (3). By extending coverage to a large section of the population, Ayushman Bharat promotes universal health coverage and lowers out-of-pocket (OOP) expenditure. However, gaps remain in awareness, enrolment, and access, particularly for chronic conditions like HF. In a study from Manipal, the average cost per HF hospitalisation was approximately ₹1.34 lakhs (INT$5.9K), of which 62% was OOP expenses (4). Similarly, in a study from Punjab, CVD conditions incurred the highest inpatient and outpatient costs among non-communicable diseases.

HF is associated with high costs due to the need for long-term medications, repeated hospitalisations, and specialised care, especially during acute episodes. These costs often result in financial burden, distress, and toxicity, particularly in the absence of comprehensive insurance. Financial burden refers to the comprehensive economic strain, encompassing both direct medical costs (hospitalisations, medications, devices) and indirect costs (lost productivity, caregiver time), which affects patients, families, and health systems (5). The current understanding of financial burden in chronic disabling conditions, such as HF, in the Indian context remains limited. Most existing data are from small, region-specific studies. There is a lack of national-level evidence on OOP spending, catastrophic health spending (CHS), distress financing (DF), and their contribution to financial burden among HF patients. This study aimed to generate evidence on the financial burden faced by HF patients in India, to inform health policy strategies that strengthen financial protection, advance universal health coverage, and alleviate the economic burden on vulnerable populations.

Methodology

The detailed methodology of our study is published elsewhere (6). In brief, consecutive HF patients were enrolled from 21 tertiary care hospitals in India from September 2019 to December 2022. Hospital sites were selected to represent diverse regions (states) based on the epidemiological transition levels (ETL), defined as the ratio of all-age disability-adjusted life years (DALYs) attributable to communicable, maternal, neonatal, and nutritional diseases (CMNNDs) to those resulting from non-communicable diseases and injuries. States were categorised as having low (0.56–0.75), lower-middle (0.41–0.55), higher-middle (0.31–0.40), or high (<0.31) ETLs. This study combined low and lower-middle ETL states (0.41–0.75) into a single group, while higher-middle and high ETLs were retained as separate categories. Six to eight hospitals were selected from each of the three ETL-based regions.

We included HF patients whose most recent hospitalisation occurred 6–15 months before data collection. Limiting data collection to the period after the acute admission date enabled us to collect inpatient, procedural, and outpatient costs from all study participants. The recruitment target was 1,800 participants, 600 from each of the three ETL-based regions, which ensured 80% power to detect an 8% absolute difference (50% and 58%) in the primary outcome, defined as OOP expenditure as a proportion of total health expenditure, between any two groups.

The Sree Chitra Tirunal Institute for Medical Sciences and Technology, Trivandrum (SCTIMST), coordinated the study across the selected centres. The institutional ethics committee (IEC) of SCTIMST (SCT/IEC/1313.6/DECEMBER-2018) approved the study protocol. Further, we obtained approval from the IECs of individual participating centres. Written informed consent was obtained from all participants before data collection.

Results

We recruited 1,859 participants across three epidemiological transition level (ETL) categories: higher ETL (n = 666), higher-middle ETL (n = 600), and low and lower-middle ETL (n = 593). The mean age of the participants was 55.9 years (SD: 11.3), and 30.2% of the study population were women. The average duration of formal education was 11.3 years (SD: 3.8). Approximately 48.6% of the participants resided in rural areas (Table 1).

Ischemic heart disease was the predominant aetiology, observed in 74.3% of participants. Dilated cardiomyopathy and rheumatic heart disease were present in 17.1% and 3.4% of participants, respectively. Comorbid diabetes mellitus was reported by 37.0% of participants, while 41.9% reported a history of hypertension. Tobacco use was prevalent in 16.9% of the study population (Table 1). Nearly two-fifths (39.5%) of participants obtained their medications from government-run pharmacies.

The average self-reported monthly individual income was INR 7,866.2 (INT$347.6). Similarly, the average household monthly income was INR 27,805.0 (INT$1,228.7). A reduction in monthly income following HF diagnosis was reported by 32.3% (30.2, 34.4) of individuals and 36.2% (34.0, 38.4) of households (Table 2).

The median length of hospital stay for the last hospitalisation was 5.0 days (IQR 3.0–7.0). The average hospitalisation cost was INR 1,18,954.21 (INT$5,256.5). It was not different in men (INR 1,18,042.5) and women (INR 1,21,063.7).

The average out-of-pocket health spending (OOPS) was INR 1,06,566.9 (INT$4,709). OOP health expenditure accounted for 92.6% (95% CI: 92.5–92.8) of the total health spending. The reported sources of payment for OOPS included personal savings in 67.7% of participants (95% CI: 65.6–69.9), financial support from the family in 54.4% (95% CI: 52.1–56.7), and borrowing from family and friends in 14.6% (95% CI: 13.0–16.2).

CHS was reported by 700 participants (37.7%; 95% CI: 35.5–39.9). The prevalence of CHS was higher among females (39.8%), individuals living in rural areas (45.6%), unemployed individuals (43.5%), and participants with less than secondary education (42.4%) (Table 3 and Figure 1). DF was reported by 17.7% (16.0–19.4) of participants. Individuals living in rural areas (21.3%) and unemployed individuals (20.4%) reported relatively higher DF (Figure 1). Similarly, individuals with lower educational attainment (less than secondary school) experienced a relatively higher prevalence of DF (20.6%) (Figure 1).

Figure 1

Among participants without health insurance, OOPS accounted for 97.9% (95% CI: 97.8–98.0) of total health expenditure (Table 4). In comparison, the proportion of OOPS was lower among those covered by social insurance (73.8%) and private insurance (58.5%). The prevalence of CHS was 40.3% (37.6–43.0) among uninsured participants, whereas it was lower at 30.8% (26.2–35.4) among participants with social insurance (Table 4).

In both the mixed-effects logistic regression and the cluster-adjusted GEE analyses, the absence of insurance was associated with higher CHS than with insurance. The mixed-effects model showed an aOR of 1.28 (95% CI: 1.01–1.62; p = 0.045), whereas the GEE model showed a relatively larger effect size (aOR: 1.38; 95% CI: 1.12–1.71; p = 0.003).CHS was substantially higher in the high ETL group than in the low, middle, and higher-middle ETL groups (Figure 2). In the cluster-adjusted GEE model for DF (Figure 3), the absence of insurance was associated with an aOR of 1.22 (95% CI: 0.93–1.60; p = 0.146), whereas the mixed-effects logistic regression model yielded an aOR of 1.03 (95% CI: 0.72–1.48; p = 0.844).

Figure 2

Figure 3

Discussion

We conducted this multicentre study across 21 institutions selected from different regions of India to assess the economic impact of HF at the individual and household levels. The study population, with a mean age of 55 years, was at least 10–15 years younger than the Western (10, 11) or Chinese HF registries (12), highlighting the impact of the disease on people in their most productive years. OOP spending accounted for over 90% of total health expenditure, and 38% of participants experienced CHS, and 18% reported DF. Lower socio-economic status, rural residence, lack of insurance, and multi-morbidity were key predictors of financial hardship. Our findings highlight the significant economic and societal burden of HF in India.

One out of three individuals and households reported a decline in annual income following an HF diagnosis in our study. Similar income reductions have been observed in economic assessments of other chronic conditions (13). This decline may result from the patient’s inability to continue working due to illness, or from a family member being forced to cut short working hours or stop working to care of the patient. Evidence from multiple countries suggests that the rising burden of chronic diseases, such as CVD, diabetes, and mental health disorders, negatively affects labour force participation (13). Reduced income, in turn, can limit access to treatment, worsen clinical outcomes, and diminish overall quality of life.

In our study, two of five households experienced CHS, and one in six reported DF, indicating a substantial financial burden due to HF. Such expenditures often force families to reduce spending on essential needs, particularly among those with lower incomes, where even modest healthcare costs can be unaffordable. Heart failure, as a chronic and progressively worsening condition, requires frequent hospitalisations and continuous medical care, contributing to high OOP expenses (12). This aligns with prior evidence showing that the risk of CHS is closely tied to the share of OOP spending (4). Multiple factors contribute to financial burden among individuals with HF, with a decline in household income being a key driver. As noted earlier, nearly one-third of our participants experienced reduced income, which may have exacerbated their financial vulnerability.

Despite relatively low coverage, health insurance emerged as an important financial protection mechanism for people living with HF. Only about one-third of participants reported any form of insurance. Insurance coverage, particularly through schemes such as PMJAY and State Health Insurance, was associated with lower OOP expenditure and reduced CHS and DF. However, most existing insurance schemes are largely limited to inpatient care, leaving outpatient visits and long-term medication costs uncovered (14). This structural gap contributes substantially to financial burden, reflected in poor medication adherence and persistent economic strain after hospital discharge. Given that HF is a progressive condition requiring lifelong treatment, regular follow-up, and repeat hospitalisation events (15), the financial burden continues even among insured individuals, while those without insurance face a disproportionate risk of financial distress.

Expanding national insurance schemes to cover outpatient care and essential medications could substantially reduce the financial burden. Strengthening publicly funded insurance, improving access to free medicines through primary care, and including all guideline-directed HF therapies in the Essential Medicines List would further enhance affordability, reduce OOP expenditure, and promote equity in long-term HF care (16, 17).

The primary drivers of CHS include rising healthcare costs and inadequate insurance coverage, both of which increase OOP payments (20, 21). OOP health expenditures are a well-documented cause of financial impoverishment, pushing households across all income levels below the poverty line (18). Low socioeconomic status is a major predictor of CHS; individuals from these groups often have limited access to timely care and face worse health outcomes, including premature mortality (19, 20). Our findings reinforce existing literature linking low income and rural residence to higher CHS risk (11). Notably, participants from rural areas reported substantially greater financial hardship than their urban counterparts.

Strengths and limitations

We embedded this study within the National HF Registry (NHFR), one of the largest HF registries in India, with robust representation from all geographic regions (21). Using a standardised methodology for expenditure data collection across the study population enhanced internal validity. Conducting data collection during the same period at all sites facilitated meaningful comparisons and reliable pooling of data. The large-scale economic impact assessment, coupled with the inclusion of HF patients from across the country, strengthened the external validity of our findings. Furthermore, including both public and private healthcare centres in the sample provided a balanced perspective and further enhanced the generalisability of the results.

Our study is subject to several important limitations. Our findings may be influenced by the non-representativeness of the sample, as we did not account for individuals who forewent medical treatment entirely due to financial constraints or those who could not attend hospital follow-up due to the severity of their illness. Survival bias is another limitation, as the analysis includes only participants who returned for follow-up. Furthermore, estimates of OOP and CHS relied on self-reported expenditure data, which is susceptible to recall bias. Methodologically, the lack of adjustment for clustering (e.g., at the hospital or regional level) may have affected the precision of our estimates. Finally, as expenditure was assessed only immediately after a hospital admission for HF, this represents a limited assessment of financial burden and may not capture the cumulative, long-term economic burden experienced by patients and their families.

Conclusion

In India, seven out of 10 patients with HF lack any form of financial health protection. With OOP expenses comprising over 90% of total health spending, the economic burden on HF patients is substantial, leading to financial burden in more than one-third of cases. This economic strain has profound implications for individual well-being and adversely affects treatment adherence, clinical outcomes, and population-level health equity. Addressing CHS and associated DF in chronic, debilitating conditions such as HF constitutes a critical public health priority, necessitating policy interventions that enhance financial risk protection and ensure equitable access to affordable, guideline-directed care. Future research should explore sustainable financing models and evaluate the impact of expanded insurance coverage and community-based care strategies on reducing financial burden in HF patients.

Competing Interests

PJ received research grants from the Australian National Health and Medical Research Council (1160283 and 1169766), the World Diabetes Federation (WDF-15–959), the National Institute for Health and Care Research, UK (204871), and the Indian Council of Medical Research.