This paper examines the impact of India’s National Publicly Funded Health Assurance Scheme, Ayushman Bharat Pradhan Mantri Jan Aarogya Yojana (PM-JAY), in Haryana on out-of-pocket (OOP) expenses and catastrophic health expenditure (CHE).

We conducted a case-control study using a stratified random sampling approach.

Six districts in Haryana, based on utilisation, were selected: Mewat, Faridabad, Sonipat, Ambala, Kurukshetra and Karnal.

A total sample size of 772 individuals, that is, 386 PM-JAY beneficiaries (cases) and non-beneficiaries (controls) each.

Data were collected using a semistructured questionnaire covering household demographics and expenditure details. The interview gathered information on hospitalisation within the past year, types of ailments, the type of empanelled facility visited, expenditure details and borrowing/selling of assets for treatment.

Mean OOP expenditure was calculated for beneficiaries and non-beneficiaries based on the type of healthcare provider. The impact of PM-JAY on OOP expenditure was analysed using a generalised linear model controlling for religion, caste, type of house, type of family, morbidity patterns, type of disease, type of health facility, hospital stay duration, average distance to the facility and travel time. CHE was defined as OOP payments ≥30% of household income. Logistic regression was used to assess the determinants of CHE.

We found that direct medical expenses incurred for hospitalisations were 65% lower for beneficiaries (11 131 rupees) compared with non-beneficiaries (31 675 rupees). While OOP expenditures are similar for both groups in public empanelled hospitals, non-beneficiaries incur OOP costs three times higher than PM-JAY beneficiaries in private empanelled hospitals. Factors, including the disease type, average distance from home to the facility, average travel time and type of hospital, significantly influence these expenses. Furthermore, the prevalence of CHE is significantly lower among PM-JAY beneficiaries (13.3%) compared with non-beneficiaries (45.9%), with an OR of 7.15 (95% CI: 4.74 to 10.80; p

Our analysis shows the scheme’s impact on decreasing OOP expenditure and CHE. To enhance the scheme’s effectiveness, the study highlights the necessity of addressing non-medical expenses and expanding coverage for indirect costs, such as food, accommodation and transportation. Additionally, strengthening the supply side through improved drug availability at healthcare facilities is crucial for enhancing financial protection and access to care.

This study aimed to evaluate the cost-effectiveness of integrating nutritional support into India’s National Tuberculosis Elimination Programme (NTEP) using the MUKTI initiative.

Economic evaluation.

Primary data on the cost of delivering healthcare services, out-of-pocket expenditure and health-related quality of life among patients with tuberculosis (TB) were collected from Dhar district of Madhya Pradesh, India.

Integration of nutritional support (MUKTI initiative) into the NTEP of India.

Routine standard of care in the NTEP of India.

Incremental cost per quality-adjusted life year (QALY) gained.

A mathematical model, combining a Markov model and a compartmental susceptible–infected–recovered model, was used to simulate outcomes for patients with pulmonary TB under NTEP and MUKTI protocols. Primary data collected from 2615 patients with TB, supplemented with estimates from published literature, were used to model progression of disease, treatment outcomes and community transmission dynamics over a 2-year time horizon. Health-related quality of life was assessed using the EuroQol 5-Dimension 5-Level scale. Costs to the health system and out-of-pocket expenditures were included. A multivariable probabilistic sensitivity analysis was undertaken to estimate the effect of joint parameter uncertainty. A scenario analysis explored outcomes without considering community transmission. Results are presented based on health-system and abridged societal perspectives.

Over 2 years, patients in the NTEP plus MUKTI programme had higher life years (1.693 vs 1.622) and QALYs (1.357 vs 1.294) than those in NTEP alone, with increased health system costs (11 538 vs 6807 (US$139 vs US$82)). Incremental cost per life year gained and QALY gained were 67 164 (US$809) and 76 306 (US$919), respectively. At the per capita gross domestic product threshold of 161 500 (US$1946) for India, the MUKTI programme had a 99.9% probability of being cost-effective but exceeded the threshold when excluding community transmission.

The findings highlight the potential benefits of a cost-effective, holistic approach that addresses socio-economic determinants such as nutrition. Reduction in community transmission is the driver of cost-effectiveness of nutritional interventions in patients with TB.

Glaucoma is a major cause of irreversible blindness in India; however, if detected early, its progression can be either prevented or stabilised through appropriate medical or surgical treatment. We aim to evaluate the cost–utility of various models for population-based glaucoma screening at primary health centres in India. We also assess the potential impact of the implementation of a population-based screening programme on overall costs of care for glaucoma.

Cost–utility analysis using a mathematical model comprising a decision tree and Markov model was conducted to simulate relevant costs and health outcomes over a lifetime horizon.

Screening services were assumed to be delivered at primary health centres in India.

A hypothetical cohort of different target population groups in terms of age groups and risk of glaucoma (age group 40–75 years, 50–75 years, 40–75 years age group at high risk of glaucoma, 50–75 years age group at high risk of glaucoma) were included in comparative screening strategies.

The exclusive intervention scenarios were 12 screening strategies based on different target population groups (age group 40–75 years, 50–75 years, 40–75 years age group at high risk of glaucoma, 50–75 years age group at high risk of glaucoma), screening methods (face-to-face screening and artificial intelligence-supported face-to-face screening) and screening frequencies for 40–75 years aged population (annual vs once every 5 years screening), in comparison to usual care scenario. The usual care scenario (current practice) implied opportunistic diagnosis by the ophthalmologists at higher levels of care.

The primary outcome was the incremental cost–utility ratio for each of the screening strategies in comparison to usual care. The secondary outcomes were per person lifetime costs, lifetime out-of-pocket expenditures, life years and quality-adjusted life-years (QALYs) in all screening scenarios and usual care.

Depending on the type of screening strategy, the gain in QALY per person ranged from 0.006 to 0.046 relative to usual care. However, the screening strategies, whether adjusted for specific age groups, patient risk profiles, screening methods or frequency, were not found to be cost-effective. Nonetheless, annual face-to-face screening strategies for individuals aged 40–75 years could become cost-effective in a scenario of strengthened public financing and provisioning, such that at least 67% of those seeking care for confirmatory diagnosis and treatment use government-funded facilities, in conjunction with 60% availability of medications at government hospitals.

Enhancing continuity of care following screening through either strengthening of public provisioning or strategic purchasing of care could make glaucoma screening interventions not only cost-effective, but also potentially cost-saving.