The Role of Public Health Services in Reducing Maternal and Newborn Health Inequalities in Urban India: A Survey Analysis of 200,000 Births Over Two Decades
Bibliographic record
Abstract
India has experienced rapid urbanisation, straining the healthcare system. The National Urban Health Mission was launched in 2013 to improve access to public healthcare, particularly among socio-economically disadvantaged urban populations. This study aimed to assess whether inequalities in maternal and newborn health (MNH) service coverage and outcomes between richer and poorer groups have improved at public and private sources across urban India in the last two decades. We used pooled data from four national cross-sectional surveys, the District Level Household Surveys from 2002 to 2008 and National Family Health Surveys from 2015 to 2021, covering 94,826 and 108,152 births in urban India, respectively. We analysed trends in coverage of antenatal, delivery, and postnatal care services and neonatal mortality by source across wealth deciles, and summarised inequalities using the slope index of inequality, concentration index, and inequality pattern index. The study found that coverage of all MNH services, and to a lesser extent neonatal survival, increased substantially between 2002-2008 and 2015-2021 in urban India. Improvements were steeper among the poorest groups. Coverage by public health facilities notably increased, and neonatal mortality rates were lower at public than private facilities, particularly among the poorest. However, the poorest decile remained well behind all other groups, reflecting bottom inequalities. Rapid improvements with reduced inequalities in MNH service coverage appear to be driven by increased access to public sector services in urban India. It remains critical for the public healthcare system to understand and address the particular needs of the poorest groups to reduce ongoing bottom MNH inequalities in urban India.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".