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
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".