Browsing by Author "O'Donnell, Owen"

Now showing 1 - 2 of 2
  • Thumbnail Image
    Healthcare inequity arising from unequal response to need in the older (45+ years) population of India: Analysis of nationally representative data
    Mohanty, Sanjay K.; Khan, Junaid; Maiti, Suraj; Kämpfen, Fabrice; Maurer, Jürgen; O'Donnell, Owen (Elsevier, 2024-11-20)
    Given the large and growing number of older (45+ years) people in India, inequitable access to healthcare in this population would slow global progress toward universal health coverage. We used a 2017-18 nationally representative sample of this population (n = 53,687) to estimate healthcare inequality and inequity by economic status. We used an extensive battery of indicators in nine health domains, plus age and sex, to adjust for need. We measured economic status by monthly per capita consumption expenditure and used a concentration index to measure inequalities in probabilities of any doctor visit and any hospitalisation within 12 months. We decomposed inequality with a regression method that allowed for economic and urban/rural heterogeneity in partial associations between healthcare and both need and non-need covariates. We used the associations achieved by the richest fifth of urban dwellers to predict a need-justified distribution of healthcare and compared the actual distribution with that benchmark to identify inequity. We found pro-rich inequalities in doctor visits and hospitalisations, which were driven by use of private healthcare. Adjustment for the greater need of poorer individuals revealed pro-rich inequity that exceeded inequality by about 65% and 39% for doctor visits and hospitalisations, respectively. These adjustments would have been substantially smaller, and inequity underestimated, without allowing for use-need heterogeneity, which accounted for 11% of the inequity in doctor visits and was 373% of inequity in hospitalisations. Targeting service coverage on poorer and rural groups, and increasing their access to private providers, would both reduce inequity and raise average coverage in the older population of India.
  • Thumbnail Image
    Public health insurance coverage in India before and after PM-JAY: Repeated cross-sectional analysis of nationally representative survey data
    Mohanty, Sanjay K.; Upadhyay, Ashish Kumar; Maiti, Suraj; Mishra, Radhe Shyam; Kämpfen, Fabrice; Maurer, Jürgen; O'Donnell, Owen (BMJ, 2023-08-28)
    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.