Household Food Insecurity is Associated with Obesity in Mexican Children
Bibliographic record
Abstract
Food insecurity has been found to be prevalent in obese individuals in different populations, but the information has been inconsistent. The aim of this cross‐sectional study was to assess the association between household food insecurity and mother's food insecurity during her childhood with the presence of obesity in school‐aged children living in a rural community of Queretaro, Mexico. Weight, height and body composition (DEXA) were measured in 300 children (8.4 y ±1.5). Food insecurity was measured with a modified 17‐item version of the Latin American and Caribbean Food Security Scale (ELCSA) and a 6‐item maternal past food insecurity scale. Prevalence and OR from a multinomial regression model were estimated. Overall, 60% of the children lived in a household with some degree of food insecurity and 90% of the mothers had past food insecurity during her childhood. Prevalence of severe food insecurity was significantly higher in children with obesity (20%), compared with children with overweight (6%) or normal weight (5%) (p<0.05). Children with food insecurity were four times more at risk of obesity than secure children (p<0.05). In this population, no association was found between maternal past food insecurity and childhood obesity (p=0.930). In conclusion, household food insecurity, but not mother's past food insecurity, is associated with obesity in Mexican children. This study contributes to a better understanding of the impact food insecurity might have in the health and nutritional status of children, which is essential for policy makers and program designers attending vulnerable populations.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".