Individual and Ecological Variation in Child Undernutrition in India
Bibliographic record
Abstract
Despite the substantial burden of child undernutrition in South Asia, little is known on the relative importance and contribution of individual and micro/macro environments in shaping variation in child undernutrition. Using measures of anthropometry, we decompose the variation in child undernutrition in India to the levels of child, communities and states, quantifying the extent to which variation at each of these levels can be explained by known proximal and distal risk factors, measured at the individual (child/household) level. Data are from under-five singleton children participating in the 2005–2006 National Family Health Survey (NFHS-3). The outcome variables were: height-for-age z-score (HAZ), weight-for-age z-score (WAZ) and weight-for-height z-score (WHZ), as well as their associated measures of anthropometric failure: stunting, underweight and wasting, defined as more than two standard deviations below the median of the referred z-scores, respectively. We also considered the composite index of anthropometric failure (CIAF), defined by combinations of child anthropometric failure. After accounting for risk factors, of the total variation in HAZ, 93.2 per cent, 4.9 per cent and 1.9 per cent were attributable to the individual, community and state levels, respectively. The observed risk factors explained 6.3 per cent and 46.9 per cent of the variation at the individual and community level, respectively; however, between-state variation was not explained by these risk factors. Variability in other measures of anthropometry and anthropometric failure largely followed this pattern. Additionally, there were also considerable differences in the amount of variation at the individual and community levels among different states. Hence, there is a substantial variability at the community level compared to the state level, suggesting the presence of micro-geographies of undernutrition. Additionally, while a substantial majority of the variation in child undernutrition is at the individual level, our ability to explain variability in undernutrition at the individual-level risk factors is extremely limited. Further research is needed to explore community level or environmental factors affecting child undernutrition, generating evidence for policies to target these determinants.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".