Changes in Child Undernutrition and Overweight in India From 2006 to 2021: An Ecological Analysis of 36 States
Bibliographic record
Abstract
OBJECTIVES: We evaluated changes in priority indicators of child growth from 2006 to 2021 and examined the role of human development measures in these changes. METHODS: We estimated cumulative and annualized changes in state- and district-level child growth indicators using 3 rounds of National Family Health Surveys (2005-2006, 2015-2016, 2019-2021) in 36 states. Outcomes included stunting, underweight, wasting, and overweight. Human development was measured using a principal components analysis of 9 ecological indicators. We contrasted expected versus observed changes in district-level growth outcomes between 2016 and 2021 based on changes in development indicators using 2-way Blinder Oaxaca decomposition. RESULTS: From 2006 to 2021, the prevalence of stunting, underweight, and wasting decreased by 12.3, 10.3, and 0.7 percentage points, respectively, while the prevalence of overweight increased by 1.9 percentage points. The annualized rate of within-state change for stunting was lower from 2016 to 2021 compared with the 2006 to 2016 period, while the rate of change in overweight was higher. Simultaneously, all 9 human development indicators improved between 2006 and 2021. A unit increase between 2016 and 2021 in the human development score predicted a -5.1 percentage point (95% confidence interval=-5.8, -4.4) change in stunting, yet observed stunting declined by just -2.5 percentage points. CONCLUSIONS: From 2016 to 2021, population-level reduction in child stunting has slowed and the rise in child overweight has accelerated, relative to the 10 years preceding this period.
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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 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".