Public health insurance coverage in India before and after PM-JAY: repeated cross-sectional analysis of nationally representative survey data

Identification of high-risk imaging features in hypertrophic cardiomyopathy using electrocardiography: A deep-learning approach

BACKGROUND

Patients with hypertrophic cardiomyopathy (HCM) are at risk of sudden death, and individuals with ≥1 major risk markers are considered for primary prevention implantable cardioverter-defibrillators. Guidelines recommend cardiac magnetic resonance (CMR) imaging to identify high-risk imaging features. However, CMR imaging is resource intensive and is not widely accessible worldwide.

OBJECTIVE

The purpose of this study was to develop electrocardiogram (ECG) deep-learning (DL) models for the identification of patients with HCM and high-risk imaging features.

METHODS

Patients with HCM evaluated at Tufts Medical Center (N = 1930; Boston, MA) were used to develop ECG-DL models for the prediction of high-risk imaging features: systolic dysfunction, massive hypertrophy (≥30 mm), apical aneurysm, and extensive late gadolinium enhancement. ECG-DL models were externally validated in a cohort of patients with HCM from the Amrita Hospital HCM Center (N = 233; Kochi, India).

RESULTS

ECG-DL models reliably identified high-risk features (systolic dysfunction, massive hypertrophy, apical aneurysm, and extensive late gadolinium enhancement) during holdout testing (c-statistic 0.72, 0.83, 0.93, and 0.76) and external validation (c-statistic 0.71, 0.76, 0.91, and 0.68). A hypothetical screening strategy using echocardiography combined with ECG-DL-guided selective CMR use demonstrated a sensitivity of 97% for identifying patients with high-risk features while reducing the number of recommended CMRs by 61%. The negative predictive value with this screening strategy for the absence of high-risk features in patients without ECG-DL recommendation for CMR was 99.5%.

CONCLUSION

In HCM, novel ECG-DL models reliably identified patients with high-risk imaging features while offering the potential to reduce CMR testing requirements in underresourced areas.

Catastrophic health expenditure and distress financing of breast cancer treatment in India: evidence from a longitudinal cohort study

OBJECTIVE

To estimate the catastrophic health expenditure and distress financing of breast cancer treatment in India.

METHODS

The unit data from a longitudinal survey that followed 500 breast cancer patients treated at Tata Memorial Centre (TMC), Mumbai from June 2019 to March 2022 were used. The catastrophic health expenditure (CHE) was estimated using households' capacity to pay and distress financing as selling assets or borrowing loans to meet cost of treatment. Bivariate and logistic regression models were used for analysis.

FINDINGS

The CHE of breast cancer was estimated at 84.2% (95% CI: 80.8,87.9%) and distress financing at 72.4% (95% CI: 67.8,76.6%). Higher prevalence of CHE and distress financing was found among rural, poor, agriculture dependent households and among patients from outside of Maharashtra. About 75% of breast cancer patients had some form of reimbursement but it reduced the incidence of catastrophic health expenditure by only 14%. Nearly 80% of the patients utilised multiple financing sources to meet the cost of treatment. The significant predictors of distress financing were catastrophic health expenditure, type of patient, educational attainment, main income source, health insurance, and state of residence.

CONCLUSION

In India, the CHE and distress financing of breast cancer treatment is very high. Most of the patients who had CHE were more likely to incur distress financing. Inclusion of direct non-medical cost such as accommodation, food and travel of patients and accompanying person in the ambit of reimbursement of breast cancer treatment can reduce the CHE. We suggest that city specific cancer care centre need to be strengthened under the aegis of PM-JAY to cater quality cancer care in their own states of residence.

TRIAL REGISTRATION

CTRI/2019/07/020142 on 10/07/2019.

Out-of-Pocket Expenditure for Selected Surgeries in the Cardiology Department for Ayushman Bharat Pradhan Mantri Jan Arogya Yojana (AB-PMJAY), Private Health Insurance, and Uninsured Patients in a Tertiary Care Teaching Hospital in Karnataka, India

INTRODUCTION

Cardiovascular diseases are a major public health issue and the leading cause of mortality globally. The global economic burden of out-of-pocket expenditure (OOPE) for cardiovascular surgeries and procedures is substantial, with average costs being significantly higher than other treatments. This imposes a heavy economic burden. Government insurance schemes like Ayushman Bharat Pradhan Mantri Jan Arogya Yojana (AB-PMJAY) aim to enhance affordability and access to cardiac care.

METHODOLOGY

This retrospective study analyzed OOPE incurred for top cardiac surgeries under AB-PMJAY, private insurance, and uninsured patients at a tertiary care teaching hospital in Karnataka. Data of 1021 patients undergoing common cardiac procedures from January to July 2023 were analyzed using descriptive statistics (mean, median) and the Shapiro-Wilk test for normality. The study aims to evaluate financial risk protection offered by AB-PMJAY compared to private plans and inform effective policy-making in reducing the OOPE burden for surgeries in India.

RESULTS

The study analyzed OOPE across 1021 patients undergoing any of four surgeries at a tertiary care teaching hospital in Karnataka. AB-PMJAY patients incurred zero OOPE across all surgeries. Uninsured patients faced the highest median OOPE, ranging from ₹1,15,292 (1390.57 USD) to ₹1,72,490 (2080.45 USD) depending on surgery type. Despite the presence of private insurance, the median out-of-pocket expenditure ranged from ₹1,689 (20.38 USD) to ₹68,788 (829.67 USD). Significant variations in OOPE were observed within different payment groups. Private insurance in comparison with AB-PMJAY had limitations like co-payments, deductibles, and limited coverage resulting in higher OOPE for patients.

DISCUSSION

The results illustrate the efficacy of AB-PMJAY in reducing the financial burden and improving the affordability of cardiac procedures compared to private insurance. This emphasizes the significance of programmmes funded by the government in reducing the OOPE burden and ensuring equitable healthcare access. The comprehensive and particular estimates of OOPE for different surgical procedures, categorized by payment methods provide valuable information to guide the development of policies that aim to reduce OOPE and progress toward universal health coverage in India.

Out-of-pocket payment and financial risk protection for breast cancer treatment: a prospective study from India

Background

Available data on cost of cancer treatment, out-of-pocket payment and reimbursement are limited in India. We estimated the treatment costs, out-of-pocket payment, and reimbursement in a cohort of breast cancer patients who sought treatment at a publicly funded tertiary cancer care hospital in India.

Methods

A prospective longitudinal study was conducted from June 2019 to March 2022 at Tata Memorial Centre (TMC), Mumbai. Data on expenditure during each visit of treatment was collected by a team of trained medical social workers. The primary outcome variables were total cost (TC) of treatment, out-of-pocket payment (OOP), and reimbursement. TC included cost incurred by breast cancer patients during treatment at TMC. OOP was defined as the total cost incurred at TMC less of reimbursement. Reimbursement was any form of financial assistance (cashless or repayment), including social health insurance, private health insurance, employee health schemes, and assistance from charitable trusts, received by the patients for breast cancer treatment.

Findings

Of the 500 patients included in the study, 45 discontinued treatment (due to financial or other reasons) and 26 died during treatment. The mean TC of breast cancer treatment was ₹258,095/US$3531 (95% CI: 238,225, 277,934). Direct medical cost (MC) accounted for 56.3% of the TC. Systemic therapy costs (₹50,869/US$696) were higher than radiotherapy (₹33,483/US$458) and surgery costs (₹25,075/US$343). About 74.4% patients availed some form of financial assistance at TMC; 8% patients received full reimbursement. The mean OOP for breast cancer treatment was ₹186,461/US$2551 (95% CI: 167,666, 205,257), accounting for 72.2% of the TC. Social health insurance (SHI) had a reasonable coverage (33.1%), followed by charitable trusts (29.6%), employee health insurance (5.1%), private health insurance (4.4%) and 25.6% had no reimbursement. But SHI covered only 40.1% of the TC of treatment compared to private health insurance that covered as much as 57.1% of it. Both TC and OOP were higher for patients who were younger, belonged to rural areas, had a comorbidity, were diagnosed at an advanced stage, and were from outside Maharashtra.

Interpretation

In India, the cost and OOP for breast cancer treatment are high and reimbursement for the treatment flows from multiple sources. Though many of the patients receive some form of reimbursement, it is insufficient to prevent high OOP. Hence both wider insurance coverage as well as higher cap of the insurance packages in the health insurance schemes is suggested. Allowing for the automatic inclusion of cancer treatment in SHI can mitigate the financial burden of cancer patients in India.

Funding

This work was funded by an extramural grant from the Women's Cancer Initiative and the Nag Foundation and an intramural grant from the International Institute of Population Sciences, Mumbai.

Health and health system effects on poverty: A narrative review of global evidence

Ill-health causes poverty. The effect runs through multiple mechanisms that span lifetimes and cross generations. Health systems can reduce poverty by improving health and weakening links from ill-health to poverty. This paper maps routes through which ill-health can cause poverty and identifies those that are potentially amenable to health policy. The review confirms that ill-health is an important contributor to poverty and it finds that the effect through health-related loss of earnings is often larger than that through medical expenses. Both effects are smaller in countries that are closer to universal health coverage and have higher social safety nets. The paper also reviews evidence from low- and middle-income countries (LMICs) and the United States (US) on the poverty-reduction effectiveness of public health insurance (PubHI) for low-income households. This reveals that PubHI does not always deliver financial protection to its targeted population in LMICs. Countries that have succeeded in achieving this goal often combine extension of coverage with supply-side interventions to build capacity and avoid perverse provider incentives in response to insurance. In the US, PubHI is effective in reducing poverty by shielding low-income households with children from healthcare costs and, consequently, generating long-run improvements in health that increase lifetime earnings. Poverty reduction is a potentially important co-benefit of health systems.

Public health insurance coverage in India before and after PM-JAY: repeated cross-sectional analysis of nationally representative survey data

INTRODUCTION

The provision of non-contributory public health insurance (NPHI) to marginalised populations is a critical step along the path to universal health coverage. We aimed to assess the extent to which Ayushman Bharat-Pradhan Mantri Jan Arogya Yojana (PM-JAY)-potentially, the world's largest NPHI programme-has succeeded in raising health insurance coverage of the poorest two-fifths of the population of India.

METHODS

We used nationally representative data from the National Family Health Survey on 633 699 and 601 509 households in 2015-2016 (pre-PM-JAY) and 2019-2021 (mostly, post PM-JAY), respectively. We stratified by urban/rural and estimated NPHI coverage nationally, and by state, district and socioeconomic categories. We decomposed coverage variance between states, districts, and households and measured socioeconomic inequality in coverage. For Uttar Pradesh, we tested whether coverage increased most in districts where PM-JAY had been implemented before the second survey and whether coverage increased most for targeted poorer households in these districts.

RESULTS

We estimated that NPHI coverage increased by 11.7 percentage points (pp) (95% CI 11.0% to 12.4%) and 8.0 pp (95% CI 7.3% to 8.7%) in rural and urban India, respectively. In rural areas, coverage increased most for targeted households and pro-rich inequality decreased. Geographical inequalities in coverage narrowed. Coverage did not increase more in states that implemented PM-JAY. In Uttar Pradesh, the coverage increase was larger by 3.4 pp (95% CI 0.9% to 6.0%) and 4.2 pp (95% CI 1.2% to 7.1%) in rural and urban areas, respectively, in districts exposed to PM-JAY and the increase was 3.5 pp (95% CI 0.9% to 6.1%) larger for targeted households in these districts.

CONCLUSION

The introduction of PM-JAY coincided with increased public health insurance coverage and decreased inequality in coverage. But the gains cannot all be plausibly attributed to PM-JAY, and they are insufficient to reach the goal of universal coverage of the poor.