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

被引:6
|
作者
Mohanty, Sanjay K. [1 ]
Upadhyay, Ashish Kumar [2 ]
Maiti, Suraj [2 ]
Mishra, Radhe Shyam [2 ]
Kaempfen, Fabrice [3 ]
Maurer, Jurgen [4 ,5 ]
O'Donnell, Owen [6 ]
机构
[1] Int Inst Populat Sci, Dept Populat & Dev, Mumbai, Maharashtra, India
[2] Int Inst Populat Sci, Mumbai, Maharashtra, India
[3] Univ Coll Dublin, Dublin, Ireland
[4] Univ Lausanne, Fac Business & Econ HEC, Dept Econ, CH-1015 Lausanne, Switzerland
[5] Univ Lausanne, Fac Business & Econ HEC, Lausanne Ctr Hlth Econ Behav & Policy, Lausanne, Switzerland
[6] Erasmus Univ, Rotterdam, Netherlands
来源
BMJ GLOBAL HEALTH | 2023年 / 8卷 / 08期
基金
瑞士国家科学基金会;
关键词
health insurance; CARE ACCESS; IMPACT; MORTALITY; RISK;
D O I
10.1136/bmjgh-2023-012725
中图分类号
R1 [预防医学、卫生学];
学科分类号
1004 ; 120402 ;
摘要
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.
引用
收藏
页数:12
相关论文
共 50 条
  • [41] Socio-demographic determinants of intimate partner violence in Angola: a cross-sectional study of nationally representative survey data
    Skandro, Simona
    Abio, Anne
    Baernighausen, Till
    Wilson, Michael Lowery
    [J]. ARCHIVES OF WOMENS MENTAL HEALTH, 2024, 27 (01) : 21 - 33
  • [42] Risk factor contributions to socioeconomic inequality in cardiovascular risk in the Philippines: a cross-sectional study of nationally representative survey data
    Brindley, Callum
    Van Ourti, Tom
    Capuno, Joseph
    Kraft, Aleli
    Kudymowa, Jenny
    O'Donnell, Owen
    [J]. BMC PUBLIC HEALTH, 2023, 23 (01)
  • [43] Socio-demographic determinants of intimate partner violence in Angola: a cross-sectional study of nationally representative survey data
    Simona Skandro
    Anne Abio
    Till Baernighausen
    Michael Lowery Wilson
    [J]. Archives of Women's Mental Health, 2024, 27 : 21 - 33
  • [44] Prevalence and Determinants of Low Birth Weight in India: Findings From a Nationally Representative Cross-Sectional Survey (2019-21)
    Girotra, Siaa
    Mohan, Neha
    Malik, Mansi
    Roy, Shubhanjali
    Basu, Saurav
    [J]. CUREUS JOURNAL OF MEDICAL SCIENCE, 2023, 15 (03)
  • [45] Engagement in sun-protective practices based on health insurance coverage: A cross-sectional analysis
    Patel, Shiv
    Patel, Shrey
    Shah, Rohan M.
    Shah, Sareena
    Doshi, Sahil
    Lio, Peter A.
    [J]. JOURNAL OF THE AMERICAN ACADEMY OF DERMATOLOGY, 2022, 87 (06) : 1453 - 1455
  • [46] Child Marriage and Later-Life Risk of Obesity in Women: A Cohort Analysis Using Nationally Representative Repeated Cross-Sectional Data from Tajikistan
    Datta, Biplab
    Tiwari, Ashwini
    Attari, Sara
    [J]. WOMEN, 2023, 3 (01): : 53 - 70
  • [47] Perceived challenges to achieving universal health coverage: a cross-sectional survey of social health insurance managers/administrators in China
    Shan, Linghan
    Wu, Qunhong
    Liu, Chaojie
    Li, Ye
    Cui, Yu
    Liang, Zi
    Hao, Yanhua
    Liang, Libo
    Ning, Ning
    Ding, Ding
    Pan, Qingxia
    Han, Liyuan
    [J]. BMJ OPEN, 2017, 7 (05):
  • [48] Household smoke-exposure risk from cooking fuels and cooking locations in Tanzania: a cross-sectional analysis of nationally representative survey data
    Ahamad, Mazbahul
    Tanin, Fahian
    [J]. LANCET GLOBAL HEALTH, 2020, 8 : 30 - 30
  • [49] Geospatial clustering and correlates of deaths during the Ebola outbreak in Liberia: a Bayesian geoadditive semiparametric analysis of nationally representative cross-sectional survey data
    Johnson, Fiifi Amoako
    Sakyi, Barbara
    [J]. BMJ OPEN, 2022, 12 (06):
  • [50] Comparative analysis of infertility healthcare utilization before and after insurance coverage of assisted reproductive technology: A cross-sectional study using National Patient Sample data
    Lee, Han-Sol
    Lim, Yu-Cheol
    Kim, Dong-Il
    Park, Kyoung-Sun
    Lee, Yoon Jae
    Ha, In-Hyuk
    Lee, Ye-Seul
    [J]. PLOS ONE, 2023, 18 (11):