99: The Double Threat of Childhood Obesity and Stunting in Rural Indigenous Ecuador
Bibliographic record
Abstract
Overweight and obesity are a major health threat in middle and low-income countries and mark a new evolution in the nutritional situation of impoverished populations. Indigenous populations face additional risk factors for malnutrition such as food insecurity and low food diversity. It is estimated that in Ecuador, the rate of stunting in children <5 years of age is 23%, and 6% are overweight. In our study we present stunting and overweight data in an indigenous population in Ecuador and identify risk factors in this population. To examine anthropomorphic measures in children 0 to 12 years old, household food security, dietary diversity, and demographic variables in a rural community in the Andean mountains of Ecuador. Child nutritional status of children in a single isolated indigenous community were assessed according World Health Organization (WHO) criteria for stunting (low height-for-age), wasting (low weight-for-age) and overweight (BMI >25) and obesity (BMI >30). Escala Latina Americana de Seguridad Alimentaria (ELCSA) survey was used to assess household food security, locally designed dietary diversity score questionnaire measuring the number of food categories consumed by household. Sixty households (63% indigenous) and 88 children were included in our study. Overall, 40 (46%) of children were stunted, 25 (28%) were overweight, and three (3%) were obese. The concurrent prevalence of stunting and overweight or obesity was 17%. Nine (10%) children were wasted or severely wasted. Only two (2%) of households were food secure, with 28 (43%), 34 (39%) and 14(16%) being mild, moderately, and severely food insecure respectively. Indigenous children were more likely to be stunted than non-indigenous (P=0.01), and increasing number of children per household, and absence of primary education correlated with worsening food security (P=0.02, P=0.01). Regression analysis revealed dietary diversity scores correlated with education (P<0.001). We document that concurrent childhood stunting and overweight or obesity is an important health issue for indigenous children in Ecuador. Furthermore, we identify low education and having more than two children per family as risk factors for food insecurity.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".