Association between Household Livestock Ownership and Childhood Stunting in Bangladesh – A Spatial Analysis
Bibliographic record
Abstract
Livestock is an integrated part of agriculture, yet the relationship between household livestock ownership and child nutrition is a significant knowledge gap. The present study aimed to assess the association between household livestock ownership and childhood stunting and to explore the geospatial variations at district level in Bangladesh. A complete data of 19 295 children aged below 5 years were extracted from the latest Bangladesh Multiple Indicator Cluster Survey 2012-13. The tropical livestock unit (TLU) score calculated as a weighted value for each livestock and categorized as low, medium, and high using tertile. A hierarchical Bayesian spatial logistic model was used to assess the association between TLU and childhood stunting. Children from the household with high TLU were 10% less likely to be stunted (adjusted posterior odds ratio: 0.90, 95% credible interval: 0.84-0.97) after controlling for demographic, socioeconomic, morbidity, place of residence and spatial effects. There was also a substantial spatial variation in childhood stunting across districts in Bangladesh with the highest burden in the Northern and North-Eastern regions. The positive effect of household livestock ownership on reducing child stunting suggests that, in addition to nutritional intervention in Bangladesh, efforts to strengthen livestock production would be beneficial for improving child nutrition status. However, a small effect size may be owing to the lack of dietary diversity, livestock health and productivity data as well as the complexity of the relationship, requiring further study. Furthermore, a significant regional disparity in stunting highlighted the importance of spatial targeting during the design of interventions and implementation.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".