Changes in the Use of Non-nutritive Sweeteners in the Chilean Food and Beverage Supply After the Implementation of the Food Labeling and Advertising Law
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Bibliographic record
Abstract
Reductions on the sugars content of the food supply have been described after the initial implementation Chilean Labeling Law, but it is unclear if sugars were replaced by non-caloric sweeteners (NNS). We evaluated changes in the NNSs use in foods and beverages after the initial implementation of the labeling law. We used longitudinal data on packaged foods and beverages collected in six major supermarkets and three candy distributors in Santiago, Chile, in January–February 2015/2016 and in January–February 2017. We included in the analysis beverages, dairy-based beverages, yogurts, breakfast cereals, desserts and ice creams, candies and sweet confectioneries, and sweet spreads with a market share ≥1% of their food groups ( n = 999). We compared the use of any NNS, the number of different NNSs used, and the combined use of NNSs and ingredients adding sugars using non-parametric tests for matched samples. We evaluated the association between a reduction in sugars and starting NNS use in the post-implementation period using negative binomial regression. The use of any NNS increased from 37.9 to 43.6% ( p < 0.001) after the law's implementation, NNSs increased among beverages, dairy-based beverages, yogurts, and desserts and ice creams ( p < 0.05), driven mostly by increases in sucralose and stevia use ( p < 0.05). We found that reformulated products that reduced the amount of sugars below the law's cutoff were more likely to start using an NNS in the post-implementation period (prevalence ratio: 12.1; 95%CI: 7.2–20.2; p < 0.001). Our results suggest that NNSs likely replaced sugars after the initial implementation of the law. Further analyses should explore how these changes may impact NNS consumption.
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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 | 0.000 | 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 it