Stunting incidence and reversal as metrics of postnatal linear growth faltering in low- and middle-income countries: a critical appraisal and simulation study
Bibliographic record
Abstract
OBJECTIVES: Length-for-age z-scores (LAZ) and stunting prevalence (%LAZ <-2) are commonly used to quantify linear growth faltering in young children in low- and middle-income countries (LMICs). The Healthy Birth, Growth and Development knowledge integration (HBGDki) consortium described postnatal linear growth faltering using LAZ-by-age trajectory modelling and child-level LAZ threshold-crossing events, including incident stunting onset (first occurrence of LAZ <-2) and stunting reversal (LAZ rising from <-2 to ≥-2). Using simulations, we assessed the suitability of these LAZ threshold-crossing metrics for characterising linear growth faltering in LMICs. METHODS: We simulated a synthetic cohort with a harmonically downward-shifting LAZ trajectory from birth to 24 months of age, with mean LAZs similar to the HBGDki pooled South Asian cohorts, and without any input parameters intended to differentially affect individuals' growth across the height distribution or at different ages. We compared HBGDki empirical estimates of age interval-specific frequencies of incident stunting onset and stunting reversal with those from the synthetic cohort. Using synthetic cohorts, we examined how estimates of incident onset and reversal were affected by missing data, magnitude of the whole-population shift in the LAZ distribution and strength of the between-time-point correlation. We also compared the 3-24 month pattern of linear growth faltering expressed as age-related trajectories of average growth delay (chronological age minus height-age), mean LAZ or stunting prevalence. RESULTS: Empirical estimates of age interval-specific incident stunting onset and stunting reversal in the HBGDki cohorts were similar to those observed in a synthetic cohort. Variability in LAZ threshold-crossing event rates is explained by starting LAZ, between-time-point correlation and the magnitude of the whole-population shift in the LAZ distribution. Incident stunting onset is also affected by missing data in preceding intervals. Stunting reversal occurs due to within-child variability (ie, imperfect between-time-point correlation) in the absence of any other phenomena that cause stunted children to become non-stunted at a later age. The linear growth faltering pattern based on growth delay differed from corresponding age-related trajectories of mean LAZ or stunting prevalence. CONCLUSIONS: In longitudinal studies of linear growth faltering in LMICs, LAZ threshold-crossing indicators are byproducts of whole-population shifts in LAZ and within-child variability and should be interpreted accordingly. Reporting incident stunting onset and reversal rates, or analyses in which children are grouped by the timing of LAZ threshold-crossing events, may detract from efforts to understand when and why nearly all children in LMICs grow more slowly than expected for their age. Since mean LAZ and stunting prevalence are unsuitable for quantifying the rate and timing of population-average postnatal linear growth faltering, growth delay is recommended for consideration as a preferred metric.
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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 | 0.017 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".