Trends in socioeconomic inequality in mortality during childhood between 1993 and 2021 in India
Bibliographic record
Abstract
INTRODUCTION: In India, most child deaths now occur within the first 28 days of birth. Trends in socioeconomic disparities in death during these early and late neonatal stages over the past few decades have been understudied. This paper elucidates these trends in early neonatal and late neonatal mortality by household wealth and maternal education. We also examined these trends for post neonatal and child mortality, thereby examining the risk of death by socioeconomic status from birth until 59 months. METHODS: Using data from five rounds of India's National Family Health Survey, we examined how the early neonatal, late neonatal, post neonatal and child mortality rates changed between 1993 and 2021 by household wealth and maternal education. We also examined how the absolute (difference in rates) and relative (ratio of rates) inequality between the highest and lowest socioeconomic groups changed for each outcome, and which children are on track to meet the Sustainable Development Goal targets. RESULTS: Despite large absolute reductions in early neonatal, late neonatal, post neonatal and child mortality, India's most vulnerable children remain at the highest risk of death as of 2021. Between 1993 and 2021, the absolute and relative socioeconomic inequality for early neonatal deaths increased. Now, most child deaths are among India's most vulnerable children in terms of household wealth and maternal education, and these children are not on track to meet the Sustainable Development Goal targets for early neonatal and post neonatal mortality. CONCLUSIONS: Our study highlights persistent socioeconomic inequalities in child death, and that these inequalities exist regardless of mortality stage. More pro poor policies and interventions are required to close these gaps. Doing so is essential for India to meet global targets.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".