Changes in the critical nutrient content of packaged foods and beverages after the full implementation of the Chilean Food Labelling and Advertising Law: a repeated cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Chile's Food Labelling Law was implemented in three phases with increasingly stricter limits. After initial implementation, sugars and sodium decreased in packaged foods, with no significant changes for saturated fats. It is unclear whether full implementation is linked with further reformulation or if producers reversed changes due to consumers' preferences. This study examines changes in the proportion of "high in" products and the nutrient content of packaged foods during the Law's three phases. METHODS: This repeated cross-sectional study included the best-selling packaged foods and beverages during 2015-2020. We analyzed the proportion of products classified as "high in" critical nutrients using the final phase cutoffs and examined changes in the content of calories, sugars, sodium, and saturated fats in the three phases. To assess the changes in proportions, we used Firth's bias-reduced logistic regression models and the Cochran-Armitage test for trends. Quantile regression was used to evaluate changes in nutrient content. RESULTS: The proportion of "high in" products decreased from 70.8 to 52.5% after the final phase (p < 0.001). The proportion of "high in" sugars products decreased across all sweet food and beverage groups (p < 0.001), except for candies (- 4.5 percentage points (pp), p = 0.09). The largest reductions occurred in sweet spreads and breakfast cereals (- 44.3 and - 40.4 pp, respectively, p < 0.001). For the proportion of "high in" sodium, reductions occurred in all savory food groups (p < 0.001), except cheeses and ready-to-eat meals (p < 0.24), with the largest decreases in savory baked products and non-sausage meat products (- 40.4 and - 38.9 pp, respectively, p < 0.001). Reductions in "high in" saturated fats and energy were less consistent, with the largest decreases in nuts and snacks and savory spreads (- 22.2 and - 20.0 pp, respectively, p < 0.001) and savory baked products and breakfast cereals (- 32.8 and - 25.7 pp, respectively, p < 0.001), respectively. After full implementation, most sweet categories showed left shifts in sugars distribution, except for candies. Similarly, most savory categories showed left shifts for sodium, except savory spreads and ready-to-eat meals. Changes increased as regulation limits tightened (p for trend < 0.001). CONCLUSIONS: After fully implementing Chile's law, the proportion of "high in" products and the content of critical nutrients decreased in all food and beverage categories. The largest changes occurred for sodium in savory foods and sugars in sweet foods/beverages. Stricter regulatory limits were associated with decreases in critical nutrient content over time.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".