Do Early Infant Feeding Practices and Modifiable Household Behaviors Contribute to Age-Specific Interindividual Variations in Infant Linear Growth? Evidence from a Birth Cohort in Dhaka, Bangladesh
Bibliographic record
Abstract
BACKGROUND: Causes of infant linear growth faltering in low-income settings remain poorly understood. Identifying age-specific risk factors in observational studies might be influenced by statistical model selection. OBJECTIVES: -scores (LAZs) or attained LAZ, using 5 statistical approaches. METHODS: = 1157) were analyzed. Multivariable-adjusted associations of infant feeding patterns or household factors with conditional LAZ (cLAZ) were estimated for 5 intervals in infancy. Two alternative approaches were used to estimate differences in interval changes in LAZ, and differences in end-interval attained LAZ and RRs of stunting (LAZ < -2) were estimated. RESULTS: LAZ was symmetrically distributed with mean ± SD = -0.95 ± 1.02 at birth and -1.00 ± 1.04 at 12 mo. Compared with exclusively breastfed infants, partial breastfeeding (difference in cLAZ: -0.11; 95% CI: -0.20, -0.02) or no breastfeeding (-0.30; 95% CI: -0.54, -0.07) were associated with slower growth from 0 to 3 mo. However, associations were not sustained beyond 6 mo. Modifiable household factors (smoking, water treatment, soap at handwashing station) were not associated with infant growth, attained size, or stunting. Alternative statistical approaches yielded mostly similar results as conditional growth models. CONCLUSIONS: The entire infant LAZ distribution was shifted down, indicating that length deficits were mostly caused by ubiquitous or community-level factors. Early-infant feeding practices explained minimal variation in early growth, and associations were not sustained to 12 mo of age. Statistical model choice did not substantially alter the conclusions. Modifications of household hygiene, smoking, or early infant feeding practices would be unlikely to improve infant linear growth in Bangladesh or other settings where growth faltering is widespread.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".