Geospatial inequalities and determinants of nutritional status among women and children in Afghanistan: an observational study
Bibliographic record
Abstract
BACKGROUND: Undernutrition is a pervasive condition in Afghanistan, and prevalence is among the highest in the world. We aimed to comprehensively assess district-level geographical disparities and determinants of nutritional status (stunting, wasting, or underweight) among women and children in Afghanistan. METHODS: The study used individualised data from the recent Afghanistan National Nutrition Survey 2013. Outcome variables were based on growth and weight anthropometry data, which we analysed linearly as Z scores and as dichotomous categories. We analysed data from a total of almost 14 000 index mother-child pairs using Bayesian spatial and generalised least squares regression models accounting for the complex survey design. FINDINGS: We noted that childhood stunting, underweight, and combined stunting and wasting were consistently highest in districts in Farah, Nangarhar, Nuristan, Kunar, Paktia, and Badakhshan provinces. District prevalence ranged from 4% to 84% for childhood stunting and 5% to 66% for underweight. Child wasting exceeded 20% in central and high-conflict regions that bordered Pakistan including east, southeast, and south. Among mothers, dual burden of underweight and overweight or obesity existed in districts of north, northeast, central, and central highlands (prevalence of 15-20%). Linear growth and weight of children were independently associated with household wealth, maternal literacy, maternal anthropometry, child age, food security, geography, and improved hygiene and sanitation conditions. The mother's body-mass index was determined by many of the same factors, in addition to ethnolinguistic status and parity. Younger mothers (<20 years old) were more underweight and shorter than older mothers (aged 20-49 years). INTERPRETATION: Afghanistan's rapidly changing political, socioeconomic, and insecurity landscape has both direct and indirect implications on population nutrition. Novel evidence from our study can be used to understand these multifactorial determinants and to identify granular disparities for local level tracking, planning, and implementation of nutritional interventions. FUNDING: None.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.000 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".