The association between household food insecurity and obesity in Mexico: a cross-sectional study of ENSANUT MC 2016
Bibliographic record
Abstract
OBJECTIVE: To examine the association between household food insecurity and overweight, obesity and abdominal obesity in Mexican adults. DESIGN: Cross-sectional study. SETTING: We analysed data from the Mexican Halfway National Health and Nutrition Survey 2016, a nationally representative survey that accounted for rural and urban areas in four regions of Mexico: North, Centre, Mexico City and South. PARTICIPANTS: Adults from 20 to 59 years old (n 5456, which represents 45 804 210 individuals at the national level). RESULTS: 70·8 % of the Mexican adults had some degree of household food insecurity. This situation showed larger proportions (P < 0·05) among indigenous people, those living in a rural area, in the Southern region or the lowest socio-economic quintiles. The prevalence of obesity and abdominal obesity was higher in female adults (P < 0·001), with the highest proportions occurring among those experiencing severe household food insecurity. Among women, mean BMI and waist circumference were higher as household food insecurity levels increased (P < 0·001). According to multivariate logistic regression models, severe household food insecurity showed to be positively associated with obesity (OR: 2·36; P = 0·001) in Mexican adult females. CONCLUSIONS: Our findings confirm the association between household food insecurity and obesity among Mexican women. Given the socio-demographic characteristics of the food-insecure population, it is alarming that prevailing socio-economic inequalities in the country might also be contributing to the likelihood of obesity. Therefore, it is crucial to maintain and bolster surveillance systems to track both problems and implement adequate policies and interventions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".