Prevalence of severe and moderate anthropometric failure among children in India, 1993–2021
Bibliographic record
Abstract
Though child anthropometric failure (CAF) is a persistent problem in India, previous studies have often neglected state-level variance and aggregated moderate and severe CAF categories. This study addresses this gap by examining moderate and severe malnutrition across India's states and union territories (UTs) from 1993 to 2021. Data of children under 2 years old from five waves of National Family Health Surveys, a representative cross-sectional survey of Indian households, were analysed. Outcomes included prevalence of moderate and severe stunting, underweight and wasting, as per the 2006 World Health Organization growth standards. Percentage prevalence and standardized absolute change (SAC) were calculated nationally and by region for each wave. From 1993 to 2021, there was a notable reduction in the nationwide prevalence of moderate stunting, underweight and wasting, with rates dropping from 20% to 16%, 23% to 18%, and 15% to 12%, respectively. Severe stunting and underweight declined considerably from 23% to 16% and 18% to 11%, respectively; severe wasting marginally increased from 8% to 9%. From 2016 to 2021 moderate underweight was noted to have the highest SAC across all regions, although 15 regions saw an increase in the prevalence of moderate underweight. In the 2016-2021 period, severe wasting has increased in 13 of the 36 regions. While there has been a nationwide reduction in most indicators of CAF since 1993, the rate and direction of change vary widely among states and UTs and between moderate and severe categories within each of the states and UTs. Understanding these patterns of change can direct context-specific interventions for improving child nutrition and health. A greater focus on reducing severe wasting, which has increased since 1993, is also crucial.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".