Early childhood linear growth faltering in low-income and middle-income countries as a whole-population condition: analysis of 179 Demographic and Health Surveys from 64 countries (1993–2015)
Bibliographic record
Abstract
BACKGROUND: The causes of early childhood linear growth faltering (known as stunting) in low-income and middle-income countries remain inadequately understood. We aimed to determine if the progressive postnatal decline in mean height-for-age Z score (HAZ) in low-income and middle-income countries is driven by relatively slow growth of certain high-risk children versus faltering of the entire population. METHODS: Distributions of HAZ (based on WHO growth standards) were analysed in 3-month age intervals from 0 to 36 months of age in 179 Demographic and Health Surveys from 64 low-income and middle-income countries (1993-2015). Mean, standard deviation (SD), fifth percentiles, and 95th percentiles of the HAZ distribution were estimated for each age interval in each survey. Associations between mean HAZ and SD, fifth percentile, and 95th percentile were estimated using multilevel linear models. Stratified analyses were performed in consideration of potential modifiers (world region, national income, sample size, year, or mean HAZ in the 0-3 month age band). We also used Monte Carlo simulations to model the effects of subgroup versus whole-population faltering on the HAZ distribution. FINDINGS: Declines in mean HAZ from birth to 3 years of age were accompanied by declines in both the fifth and 95th percentiles, leading to nearly symmetrical narrowing of the HAZ distributions. Thus, children with relatively low HAZ were not more likely to have faltered than taller same-age peers. Inferences were unchanged in surveys regardless of world region, national income, sample size, year, or mean HAZ in the 0-3 month age band. Simulations showed that the narrowing of the HAZ distribution as mean HAZ declined could not be explained by faltering limited to a growth-restricted subgroup of children. INTERPRETATION: In low-income and middle-income countries, declines in mean HAZ with age are due to a downward shift in the entire HAZ distribution, revealing that children across the HAZ spectrum experience slower growth compared to the international standard. Efforts to mitigate postnatal linear growth faltering in low-income and middle-income countries should prioritise action on community-level determinants of childhood HAZ trajectories. FUNDING: Bill & Melinda Gates Foundation.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".