Association between the diagnosis of diet-related non-communicable diseases and the use of nutritional labeling among Mexican, Mexican American, and non-Mexican American adults: a cross-sectional study from the International Food Policy Study 2021–2022
Bibliographic record
Abstract
BACKGROUND: Diet-related non-communicable diseases (NCDs) are major global health concerns. Front-of-package warning labels (WLs) and nutrition facts labels (NFLs) aim to promote healthier food choices, yet their use among individuals with NCDs and across populations remains underexplored. This study uniquely compares two labeling systems (WL and NFL) and examines the association between NCD diagnosis and label use among Mexican, Mexican American, and non-Mexican American adults. METHODS: Cross-sectional data from the 2021–2022 International Food Policy Study were analyzed. Self-reported WL and NFL use and NCD diagnoses (diabetes, hypertension, heart disease, high cholesterol, cancer) were examined using multivariate logistic regression models adjusting for sociodemographic and health-related confounders. RESULTS: Among 23,951 adults, NFL use was highest among non-Mexican Americans (80.1%) and lowest among Mexicans (69.8%, p < 0.01). NFL use was significantly associated with diabetes and multiple NCDs in non-Mexican Americans. Specifically, non-Mexican Americans diagnosed with diabetes were 68% more likely to use the NFL (AOR 1.68, 99% CI: 1.26–2.24) compared to those without an NCD diagnosis. Furthermore, non-Mexican Americans with 3 or more NCDs were 48% more likely to use the NFL (AOR 1.48, 99% CI: 1.07–2.06). In Mexico, WL use (77.4%) exceeded NFL use (69.8%). Mexicans with high cholesterol were more likely to perceive the “Excess Sodium” label as the most useful (AOR 1.89, 99% CI: 1.15–3.12), while those with diabetes found the “Excess Sugar” label particularly helpful (AOR 1.65, 99% CI: 1.07–2.54). CONCLUSIONS: Labeling use varied across populations and NCD status. These findings highlight the potential of interpretive front-of-package labels to support informed food choices, particularly among individuals with NCDs, and to strengthen public health strategies that promote healthier dietary behaviors.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".