Effect of correcting for gestational age at birth on population prevalence of early childhood undernutrition
Bibliographic record
Abstract
Abstract Background Postmenstrual and/or gestational age-corrected age (CA) is required to apply child growth standards to children born preterm (< 37 weeks gestational age). Yet, CA is rarely used in epidemiologic studies in low- and middle-income countries (LMICs), which may bias population estimates of childhood undernutrition. To evaluate the effect of accounting for GA in the application of growth standards, we used GA-specific standards at birth (INTERGROWTH-21st newborn size standards) in conjunction with CA for preterm-born children in the application of World Health Organization Child Growth Standards postnatally (referred to as ‘CA’ strategy) versus postnatal age for all children, to estimate mean length-for-age (LAZ) and weight-for-age (WAZ) z scores at 0, 3, 12, 24, and 48-months of age in the 2004 Pelotas (Brazil) Birth Cohort. Results At birth (n = 4066), mean LAZ was higher and the prevalence of stunting (LAZ < −2) was lower using CA versus postnatal age (mean ± SD): − 0.36 ± 1.19 versus − 0.67 ± 1.32; and 8.3 versus 11.6%, respectively. Odds ratio (OR) and population attributable risk (PAR) of stunting due to preterm birth were attenuated and changed inferences using CA versus postnatal age at birth [OR, 95% confidence interval (CI): 1.32 (95% CI 0.95, 1.82) vs 14.7 (95% CI 11.7, 18.4); PAR 3.1 vs 42.9%]; differences in inferences persisted at 3-months. At 12, 24, and 48-months, preterm birth was associated with stunting, but ORs/PARs remained attenuated using CA compared to postnatal age. Findings were similar for weight-for-age z scores. Conclusions Population-based epidemiologic studies in LMICs in which GA is unused or unavailable may overestimate the prevalence of early childhood undernutrition and inflate the fraction of undernutrition attributable to preterm birth.
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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 | 0.002 |
| 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".