Multilevel population and socioeconomic variation in health insurance coverage in India

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.

Socioeconomic and geographic variation in coverage of health insurance across India

Introduction

In India, regular monitoring of health insurance at district levels (the most essential administrative unit) is important for its effective uptake to contain the high out of pocket health expenditures. Given that the last individual data on health insurance coverage at district levels in India was in 2016, we update the evidence using the latest round of the National Family Health Survey conducted in 2019-2021.

Methods

We use the unit records of households from the latest round (2021) of the nationally representative National Family Health Survey to calculate the weighted percentage (and 95% CI) of households with at least one member covered by any form of health insurance and its types across socio-economic characteristics and geographies of India. Further, we used a random intercept logistic regression to measure the variation in coverage across communities, district and state. Such household level study of coverage is helpful as it represents awareness and outreach for at least one member, which can percolate easily to the entire household with further interventions.

Results

We found that only 2/5^th of households in India had insurance coverage for at least one of its members, with vast geographic variation emphasizing need for aggressive expansion. About 15.5% were covered by national schemes, 47.1% by state health scheme, 13.2% by employer provided health insurance, 3.3% had purchased health insurance privately and 25.6% were covered by other health insurance schemes (not covered above). About 30.5% of the total variation in coverage was attributable to state, 2.7% to districts and 9.5% to clusters. Household size, gender, marital status and education of household head show weak gradient for coverage under "any" insurance.

Discussion

Despite substantial increase in population eligible for state sponsored health insurance and rise in private health insurance companies, nearly 60% of families do not have a single person covered under any health insurance scheme. Further, the existing coverage is fragmented, with significant rural/urban and geographic variation within districts. It is essential to consider these disparities and adopt rigorous place-based interventions for improving health insurance coverage.

Socio-economic variations of breast cancer treatment and discontinuation: a study from a public tertiary cancer hospital in Mumbai, India

BACKGROUND

The study examined the socio-economic variation of breast cancer treatment and treatment discontinuation due to deaths and financial crisis.

METHODS

We used primary data of 500 patients with breast cancer sought treatment at India's one of the largest cancer hospital in Mumbai, between June 2019 and March 2022. This study is registered on the Clinical Trial Registry of India (CTRI/2019/07/020142). Kaplan-Meier method and Cox-hazard regression model were used to calculate the probability of treatment discontinuation.

RESULTS

Of the 500 patients, three-fifths were under 50 years, with the median age being 46 years. More than half of the patients were from outside of the state and had travelled an average distance of 1,044 kms to get treatment. The majority of the patients were poor with an average household income of INR15,551. A total of 71 (14%) patients out of 500 had discontinued their treatment. About 5.2% of the patients died and 4.8% of them discontinued treatment due to financial crisis. Over one-fourth of all deaths were reported among stage IV patients (25%). Patients who did not have any health insurance, never attended school, cancer stage IV had a higher percentage of treatment discontinuation due to financial crisis. Hazard of discontinuation was lower for patients with secondary (HR:0.48; 95% CI: 0.27-0.84) and higher secondary education (HR: 0.42; 95% CI: 0.19-0.92), patients from rural area (HR: 0.79; 95% CI: 0.42-1.50), treated under general or non-chargeable category (HR: 0.60; 95% CI:0.22-1.60) while it was higher for the stage IV patients (HR: 3.61; 95% CI: 1.58-8.29).

CONCLUSION

Integrating breast cancer screening in maternal and child health programme can reduce delay in diagnosis and premature mortality. Provisioning of free treatment for poor patients may reduce discontinuation of treatment.

Multimorbidity and out-of-pocket expenditure for medicines in China and India

Introduction

Using nationally representative survey data from China and India, this study examined (1) the distribution and patterns of multimorbidity in relation to socioeconomic status and (2) association between multimorbidity and out-of-pocket expenditure (OOPE) for medicines by socioeconomic groups.

Methods

Secondary data analysis of adult population aged 45 years and older from WHO Study on Global Ageing and Adult Health (SAGE) India 2015 (n=7397) and China Health and Retirement Longitudinal Study (CHARLS) 2015 (n=11 570). Log-linear, two-parts, zero-inflated and quantile regression models were performed to assess the association between multimorbidity and OOPE for medicines in both countries. Quantile regression was adopted to assess the observed relationship across OOPE distributions.

Results

Based on 14 (11 self-reported) and 9 (8 self-reported) long-term conditions in the CHARLS and SAGE datasets, respectively, the prevalence of multimorbidity in the adult population aged 45 and older was found to be 63.4% in China and 42.2% in India. Of those with any long-term health condition, 38.6% in China and 20.9% in India had complex multimorbidity. Multimorbidity was significantly associated with higher OOPE for medicines in both countries (p<0.05); an additional physical long-term condition was associated with a 18.8% increase in OOPE for medicine in China (p<0.05) and a 20.9% increase in India (p<0.05). Liver disease was associated with highest increase in OOPE for medicines in China (61.6%) and stroke in India (131.6%). Diabetes had the second largest increase (China: 58.4%, India: 91.6%) in OOPE for medicines in both countries.

Conclusion

Multimorbidity was associated with substantially higher OOPE for medicines in China and India compared with those without multimorbidity. Our findings provide supporting evidence of the need to improve financial protection for populations with an increased burden of chronic diseases in low-income and middle-income countries.

Old-age dependency and catastrophic health expenditure: Evidence from Longitudinal Ageing Study in India

BACKGROUND

Out-of-pocket (OOP) payments and catastrophic health expenditure (CHE) have a strong age gradient. Though studies have examined the socio-demographic and geographic inequality in OOP payments and CHE in India, the role of old-age dependency in financial catastrophe remains unclear. Disaggregated estimates of CHE by the level of old-age dependency of households may help identify the most vulnerable sub-group and provide evidence for specific policies for the financial protection and health care of the elderly. The present study aims to estimate the incidence and intensity of CHE by the old-age dependency of households among middle-aged adults and the elderly in India.

METHODS

A total of 42,949 households from the Longitudinal Aging Study in India (LASI), 2017-18, covering households with at least one-member aged 45+ years, were included in the analysis. Households were classified into three mutually exclusive groups: no old-age dependency, low old-age dependency, and high old-age dependency. The incidence and intensity of CHE were estimated using the capacity-to-pay (CTP) approach. Concentration indices and concentration curves examine the extent of socioeconomic inequality in CHE. Binary logistic regression helps to understand the potential predictors of CHE across each type of old-age-dependent household.

RESULTS

We estimated the overall incidence of CHE at 24.6% (95% CI: 23.3-25.8) among middle-aged adults and the elderly in India. The incidence was 33.2% (95% CI: 31.4-35.1) among households with high old-age dependency, 23.1% (95% CI: 20.8-25.5) among those with low old-age dependency, and 20.4% (95% CI: 19.0-21.7) among no old-age dependency households. CHE intensity was highest among households with low old-age dependency compared to those no old-age dependents. Catastrophic health expenditure was higher among the poorer households in each type of old-age dependency. Among all households, the odds of incurring CHE were higher among households with high old-age dependency (AOR: 1.52; 95% CI: 1.36-1.69) than those with no old-age dependency. Lower-income households, households with pensions as the main source of income, households belonging to scheduled castes, and households residing in rural areas had higher odds of incurring CHE. The co-variates of CHE varied significantly across the type of old-age dependency households. A household's enrolment into a health insurance scheme did not necessarily lower its CHE.

CONCLUSION

Households with high old-age dependency had a higher probability of incurring CHE in India. Providing preventive and curative geriatric care in primary health centres (PHC) is recommended.

Exploring Barriers to, and Enablers of, Evidence-Informed Hip Fracture Care in Five Low- Middle-Income Countries: China, India, Thailand, the Philippines and Vietnam

Globally, populations are ageing and the estimated number of hip fractures will increase from 1.7 million in 1990 to more than 6 million in 2050. The greatest increase in hip fractures is predicted in Low- and Middle‑Income Countries (LMICs), largely in the Asia-Pacific region where direct costs are expected to exceed $US15 billion by 2050. The aims of this qualitative study are to identify barriers to, and enablers of, evidence informed hip fracture care in LMICs, and to determine if the Blue Book standards, developed by the British Orthopaedic Association and British Geriatrics Society to facilitate evidence informed care of patients with fragility fractures, are applicable to these settings. This study utilised semi-structured interviews with clinical and administrative hospital staff to explore current hip fracture care in LMICs. Transcribed interviews were imported into NVivo 12 and analysed thematically. Interviews were conducted with 35 participants from eleven hospitals in five countries. We identified five themes-costs of care and the capacity of patients to pay, timely hospital presentation, competing demands on limited resources, delegation and defined responsibility, and utilisation of available data-and within each theme, barriers and enablers were distinguished. We found a mismatch between patient needs and provision of recommended hip fracture care, which in LMICs must commence at the time of injury. This study describes clinician and administrator perspectives of the barriers to, and enablers of, high quality hip fracture care in LMICs; results indicate that initiatives to overcome barriers (in particular, delays to definitive treatment) are required. While the Blue Book offers a starting point for clinicians and administrators looking to provide high quality hip fracture care to older people in LMICs, locally developed interventions are likely to provide the most successful solutions to improving hip fracture care.