Trends in low birth weight across 36 states and union territories in India, 1993-2021
Bibliographic record
Abstract
INTRODUCTION: Low birth weight is an important measure of the health of pregnant women and newborns. We investigated the prevalence of low birth weights in India over nearly three decades to assess trends and convergence across states. METHODS: Data came from five waves of the National Family Health Survey (1992-93 to 2019-21). The prevalence of low birth weight was estimated. To explore the sensitivity of our results to missing birth weight data-since the completeness of birth weight information has changed drastically-we also estimated prevalence from multiple imputation models, Heckman selection models, and by reweighting the data so that socioeconomic characteristics of children with birth weight data matched across surveys. RESULTS: The overall prevalence of low birth weight in India declined from 26% to 18% during the period. The 2019-21 survey revealed that four states, Uttar Pradesh, Bihar, Maharashtra, and West Bengal accounted for almost half of all low-birth-weight births in India. The Pearson's correlation between the prevalence of low birth weight in 1992-93 and percentage point change across the period was -0.85, suggesting convergence between states, where states with greater prevalence in 1992-93 had faster declines. Convergence was robust across sensitivity specifications. CONCLUSION: State-level convergence indicates a potential 'catch-up' phenomenon, where states with initially higher prevalence have experienced greater declines. This finding suggested a possible impact of interventions prompted by dire figures in the earliest surveys, yet also stresses the necessity for continued interventions across all states to maintain and further progress. Our analysis, however, warrants a cautious interpretation due to data limitations. However, we observed convergence in the prevalence of low birth weight across states in all sensitivity specifications.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".