Infections and nutrient deficiencies during infancy predict impaired growth at 5 years: Findings from the MAL-ED study in Pakistan
Bibliographic record
Abstract
Background Socio-economic, nutritional, and infectious factors have been associated with impaired infant growth, but how the presence of these factors during infancy affects growth around 5 years is not well understood. Methods This secondary analysis of the MAL-ED cohort included 277 children from Pakistan for whom socio-demographic, breastfeeding, complementary foods, illness, nutritional biomarkers, stool pathogens and environmental enteropathy indicators between 0 and 11 months were recorded. We used linear regression models to analyze associations of these indicators with height-for-age (HAZ), weight-for-age (WAZ) and weight-for-height (WLZ) at 54–66 months (~5 years), and Poisson regression with robust standard errors to estimate risk ratios for stunting and underweight ~5 years, controlling for gender, first available weight, and income. Results Among the 237 infants followed longitudinally and evaluated at about 5 years of age, exclusive breastfeeding was short (median = 14 days). Complementary feeding started before 6 months with rice, bread, noodles, or sugary foods. Roots, dairy products, fruits/vegetables, and animal-source foods were provided later than recommended (9–12 months). Anemia (70.9%), deficiencies in iron (22.0%), zinc (80.0%), vitamin A (53.4%) and iodine (13.3%) were common. Most infants (>90%) presented with diarrhea and respiratory infections in their first year. At ~5 years, low WAZ (mean-1.91 ± 0.06) and LAZ (−2.11 ± 0.06) resulted in high prevalence of stunting (55.5%) and underweight (44.4%) but a relatively low rate of wasting (5.5%). While 3.4% had concurrent stunting and wasting ~5 years, 37.8% of children had coexisting stunting and underweight. A higher income and receiving formula or dairy products during infancy were associated with a higher LAZ ~5 years, but infant’s history of hospitalizations and more respiratory infections were associated with lower LAZ and higher risk of stunting ~5 years. Infants’ intake of commercial baby foods and higher serum-transferrin receptors were associated with higher WAZ and lower risk of underweight ~5 years. Presence of Campylobacter and fecal neopterin >6.8 nmol/L in the first year were associated with increased risk of underweight ~5 years. Conclusion Growth indicators ~5 years were associated with poverty, inappropriate complementary feeding, and infections during the first year of life, which supports the early start of public health interventions for preventing growth delay ~5 years.
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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 | 0.000 | 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 | 0.000 |
| 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".