An analytical approach towards attaining leave no one behind using patterns and distributions of inequalities in antenatal and facility delivery coverage in Uttar Pradesh, India

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dc.contributor.authorNamasivayam, Vasanthakumar
dc.contributor.authorPrakash, Ravi
dc.contributor.authorDehury, Bidyadhar
dc.contributor.authorIsac, Shajy
dc.contributor.authorWehrmeister, Fernando C.
dc.contributor.authorBecker, Marissa
dc.contributor.authorBlanchard, James
dc.contributor.authorBoerma, Ties
dc.date.accessioned2025-03-05T21:59:48Z
dc.date.available2025-03-05T21:59:48Z
dc.date.issued2025-02-25
dc.date.updated2025-03-01T04:36:24Z
dc.description.abstractAbstract Background Leave No One Behind (LNOB) is a central, transformative promise of the 2030 Agenda for Sustainable Development Goals. To attain LNOB, systematic analysis of patterns and distributions of inequalities in coverage of health outcomes on a continuous basis at different program delivery layers is required to design tailored health interventions. We analysed the patterns of change and geographic distribution of inequalities in coverage of antenatal care and facility-based delivery in Uttar Pradesh (UP), India and developed a framework to guide health programmers to understand inequalities better, to accelerate progress by reaching those left behind. Methods Data from five-rounds of National Family Health Survey (1992–2021) and two-rounds of Community Behaviour Tracking Survey (2014–2018) is used. Education and wealth have been used as stratifiers. Three measures of inequality- mean difference from mean, slope index of inequality, and inequality pattern index are used to depict the state, district and sub-district level inequalities. Results UP observed a substantial reduction in the education-related inequality in ANC and facility-delivery during 1992–2021. The slope index of inequality declined from 65.3 [95%CI:60.0-70.6] to 9.3 [95%CI:7.8–10.8] for ANC and from 44.7 [95%CI:38.5–50.9] to 29.9 [95%CI:27.8–32.0] for facility-delivery during 1992–2021. The inequality pattern index showed that, with improved reach of interventions, many districts moved towards bottom inequality from top inequality for any ANC while fewer districts for facility-delivery. Even in districts with high coverage and low inequality, sub-district level(blocks) inequality persisted. Similarly, in blocks with high coverage and low inequality, Accredited Social Health Activist (ASHA) level inequality persisted. Interestingly, for the same ASHA area, the patterns of inequality differed for any ANC and facility delivery; in some districts, inequality direction changed based on the stratifier chosen. Conclusions The proposed health equity framework suggests that to achieve LNOB status, understanding inequality with the coverage status is important. If coverage is high and inequality persists, identify the program layer at which maximum inequality persists to identify the left behinds. Whereas, if coverage is poor, programs are required to improve coverage first. Findings also call for a systematic way of collecting and organizing granular data to understand inequality and identify the left-behinds.
dc.identifier.citationInternational Journal for Equity in Health. 2025 Feb 25;24(1):55
dc.identifier.doi10.1186/s12939-025-02411-8
dc.identifier.urihttp://hdl.handle.net/1993/38912
dc.language.isoeng
dc.language.rfc3066en
dc.publisherBMC
dc.rights.holderThe Author(s)
dc.subjectLeave no one behind
dc.subjectHealth Equity Framework for Programs (HEFP)
dc.subjectBottom inequality
dc.subjectInequality Pattern Index
dc.subjectUttar Pradesh
dc.titleAn analytical approach towards attaining leave no one behind using patterns and distributions of inequalities in antenatal and facility delivery coverage in Uttar Pradesh, India
dc.typeJournal Article
local.author.affiliationRady Faculty of Health Sciences::Max Rady College of Medicine::Department of Community Health Sciences
oaire.citation.issue55
oaire.citation.titleInternational Journal for Equity in Health
oaire.citation.volume24
project.funder.identifierhttp://dx.doi.org/10.13039/100000865
project.funder.nameBill & Melinda Gates Foundation
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