Patterns in child stunting by age: A cross‐sectional study of 94 low‐ and middle‐income countries
Bibliographic record
Abstract
Child stunting prevalence is primarily used as an indicator of impeded physical growth due to undernutrition and infections, which also increases the risk of mortality, morbidity and cognitive problems, particularly when occurring during the 1000 days from conception to age 2 years. This paper estimated the relationship between stunting prevalence and age for children 0-59 months old in 94 low- and middle-income countries. The overall stunting prevalence was 32%. We found higher stunting prevalence among older children until around 28 months of age-presumably from longer exposure times and accumulation of adverse exposures to undernutrition and infections. In most countries, the stunting prevalence was lower for older children after around 28 months-presumably mostly due to further adverse exposures being less detrimental for older children, and catch-up growth. The age for which stunting prevalence was the highest was fairly consistent across countries. Stunting prevalence and gradient of the rise in stunting prevalence by age varied across world regions, countries, living standards and sex. Poorer countries and households had a higher prevalence at all ages and a sharper positive age gradient before age 2. Boys had higher stunting prevalence but had peak stunting prevalence at lower ages than girls. Stunting prevalence was similar for boys and girls after around age 45 months. These results suggest that programmes to prevent undernutrition and infections should focus on younger children to optimise impact in reducing stunting prevalence. Importantly, however, since some catch-up growth may be achieved after age 2, screening around this time can be beneficial.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".