Growth dynamics among adolescent girls in Bangladesh: Evidence from nationally representative data spanning 2011–2014
Bibliographic record
Abstract
BACKGROUND: Adolescence is the last opportunity to reverse any growth faltering accumulated from fetal life through childhood and it is considered a crucial period to optimize human development. In Bangladesh, a growing double burden of underweight and obesity in adolescents is recognized, yet limited data exists on how, when, and where to intervene. This study assesses the dynamics of growth among adolescent girls in Bangladesh, providing insight about critical junctures where faltering occurs and where immediate interventions are warranted. METHODS: We pooled data from Bangladesh's Food Security and Nutrition Surveillance Project collected between 2011 and 2014 to document the age dynamics of weight and linear growth. 20,572 adolescent girls were measured for height and 19,345 for weight. We constructed growth curves for height, weight, stunting, and underweight. We also stratified growth dynamics by wealth quintile to assess socioeconomic inequities in adolescent trajectories. RESULTS: Height-for-age z-score (HAZ) in Bangladeshi girls deteriorates throughout adolescence and especially during the early years. Mean HAZ decreases by 0.20 standard deviations (sd) per year in early adolescence (10-14 years) vs 0.06 sd/year during late adolescence (15-19 years), while stunting increases by 16 percentage points (pp) vs 6.7 pp, respectively. Conversely, BMI-for-age z-score (BAZ) increases by 0.13 sd/year in early adolescence vs 0.02 sd/year in late adolescence, and underweight decreases by 12.8 pp vs 3.2 pp. Adolescent girls in all socioeconomic groups show a similar pattern of HAZ and BAZ dynamics, but the curve for the richest quintile stays above that of the poorest across all ages. CONCLUSIONS: Trends and levels of stunting and underweight among adolescent girls in Bangladesh are worrisome, suggesting substantial linear growth faltering in early adolescence, with improving weight-for-age occurring only as linear growth slows and stops. Given the rising burden of non-communicable diseases (NCDs) in Bangladesh and emerging evidence of the link between stunting and later chronic diseases, greater attention to adolescent growth and development is needed. Our findings suggest that, to address stunting, interventions in early adolescence would have the greatest benefits. School-based interventions could be a way to target this population.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".