Individual<b>-</b>, maternal- and household-level factors associated with stunting among children aged 0–23 months in Bangladesh
Bibliographic record
Abstract
OBJECTIVE: Childhood stunting remains a major public health concern in Bangladesh. To accelerate the reduction rate of stunting, special focus is required during the first 23 months of a child's life when the bulk of growth takes place. Therefore the present study explored individual-, maternal- and household-level factors associated with stunting among children under 2 years of age in Bangladesh. DESIGN: Data were collected through a nationwide cross-sectional survey conducted between October 2015 and January 2016. A two-stage cluster random sampling procedure was applied to select 11 428 households. In the first stage, 210 enumerations areas (EA) were selected with probability proportional to EA size (180 EA from rural areas, thirty EA from urban slums). In the second stage, an average of fifty-four households were selected from each EA through systematic random sampling. SETTING: Rural areas and urban slums of Bangladesh.ParticipantsA total of 6539 children aged 0-23 months. RESULTS: Overall, 29·9 % of the children were stunted. After adjusting for all potential confounders in the modified Poisson regression model, child's gender, birth weight (individual level), maternal education, age at first pregnancy, nutrition (maternal level), administrative division, place of residence, socio-economic status, food security status, access to sanitary latrine and toilet hygiene condition (household level) were significantly associated with stunting. CONCLUSIONS: The study identified a number of potentially addressable multilevel risk factors for stunting among young children in Bangladesh that should be addressed through comprehensive multicomponent interventions.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".