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
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.
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 | 0.001 | 0.004 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".