Assessing associational strength of 23 correlates of child anthropometric failure: An econometric analysis of the 2015-2016 National Family Health Survey, India
Bibliographic record
Abstract
Despite the broad consensus that investments in nutrition-sensitive programmes are required to reduce child undernutrition, in practice empirical studies and interventions tend to focus on few nutrition-specific risk factors in isolation. The 2015-16 National Family Health Survey provides the first opportunity in more than a decade to conduct an up-to-date comprehensive evaluation of the relative importance of various maternal and child health and nutrition (MCHN) factors in respect to child anthropometric failures in India. The primary analysis included 140,444 children aged 6-59 months with complete data on 20 MCHN factors, and the secondary analysis included a subset of 25,603 children with additional paternal data. Outcome variables were stunting, underweight and wasting. We conducted logistic regression models to first evaluate each correlate separately in age- and sex-adjusted models, and then jointly in a mutually adjusted model. For all anthropometric failures, indicators of past and present socioeconomic conditions showed the most robust associations. The strongest correlates for stunting were short maternal stature (OR: 4.39; 95%CI: 4.00, 4.81), lack of maternal education (OR: 1.74; 95%CI: 1.60, 1.89), low maternal BMI (OR: 1.64; 95%CI: 1.54, 1.75), poor household wealth (OR: 1.25; 95%CI: 1.15, 1.35) and poor household air quality (OR: 1.22; 95%CI: 1.16, 1.29). Weaker associations were found for other correlates, including dietary diversity, vitamin A supplementation and breastfeeding initiation. Paternal factors were also important predictors of anthropometric failures, but to a lesser degree than maternal factors. The results remained consistent when stratified by children's age (6-23 vs 24-59 months) and sex (girls vs boys), and when low birth weight was additionally considered. Our findings indicate the limitation of nutrition-specific interventions. Breaking multi-generational poverty and improving environmental factors are promising investments to prevent anthropometric failures in early childhood.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.016 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".