Changes in Child Undernutrition and Overweight in India from 2006 to 2019: An Analysis of 22 States
Bibliographic record
Abstract
Abstract Objectives: India has historically displayed high levels of child stunting and low levels of child overweight. Using newly released data, we evaluated changes in priority indicators of child growth from 2006 to 2019 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 three rounds of National Family Health Surveys (2005-06, 2015-16, 2019-20) in 22 states. Outcomes included stunting, underweight, wasting, and overweight. Human development was measured using a principal components analysis of nine survey-based items. We contrasted expected versus observed changes in district-level growth indicators between 2015 and 2019 based on changes in development measures using two-way Blinder Oaxaca decomposition. Results: From 2006 to 2019, the prevalence of stunting and underweight decreased by 10.9 percentage points (pp) and 7.1 pp, respectively, while the prevalence of wasting and overweight increased by 2.8 pp and 2.2 pp, respectively. Annualized rates of change for stunting, wasting, and underweight were lower from 2015 to 2020 compared with the 2006 to 2015 period, while rates of change in overweight were higher. Simultaneously, all nine human development indicators improved between 2006 and 2020. A unit increase between 2015 and 2020 in the human development score predicted a -4.7 pp (95% CI: - 5.7, -3.6) change in stunting, yet stunting declined by just -0.3 pp. Conclusions: Population-level reductions in child undernutrition have stalled and the rise in child overweight has accelerated between 2015 and 2020 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| 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".