Food Insecurity, Food Environment and Obesity Among Urban School-Aged Children in Queretaro, Mexico
Bibliographic record
Abstract
To evaluate the relationship between food insecurity, food environment and obesity in school-aged children in Queretaro. In this cross-sectional study, weight, height and body fat % (BF%) were measured in 122 school-aged children (8.1 ± 1.5 y) from an urban area in Queretaro, Mexico. Additionally, household food insecurity was assessed using the Latin American and Caribbean Food Security Scale (ELCSA). Geolocation data of both food establishments (FE) and participants’ households (HH) were collected and uploaded into a GIS database. The distance to the closest FE within a 300 m radius from each participant's household was calculated using GIS. FE were categorized as follows: (1) FEPF, which mainly sold processed foods (e.g., convenient stores); and (2) FEnPF, which mainly sold non-processed foods (e.g., fruterías – only fruits and vegetables). Univariate analysis was used to assess the interaction of BF%, food insecurity and FE categories using SPSS v23.0. Almost half of the children showed high BF% (48%), while 43% lived in HH with some degree of food insecurity. Children in moderately/severely food insecure HH and who lived close to FEnPF had significantly lower BF% (18.9 ± 4.7%), compared to children in food secure HH (24.4 ± 2.5%) or HH experiencing mild food insecurity (25.0 ± 2.6%) (P < 0.05). However, children in moderately/severely food insecure HH, who lived close to FEPF had significantly higher BF% (29.4 ± 2.7%), compared to those experiencing mild food insecurity (23.7 ± 1.5%), or who were food secure (22.8 ± 1.1%) (P < 0.05). Children living in moderately/severely food insecure HH showed an opposite BF% pattern when compared to those in food secure or mildly food insecure HH, depending on how close they live to FE that either mainly sold processed foods or unprocessed foods, indicating the potential relationship of food environment to childhood obesity mediated by food insecurity. Partially funded by FOFI, UAQ.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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, unvalidatedMachine predicted; a candidate call from one teacher head, 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".