Changes in food purchases after the Chilean policies on food labelling, marketing, and sales in schools: a before and after study
Bibliographic record
Abstract
BACKGROUND: In 2016, Chile implemented a unique law mandating front-of-package warning labels, restricting marketing, and banning school sales for products high in calories, sodium, sugar, or saturated fat. We aimed to examine changes in the calorie, sugar, sodium, and saturated fat content of food and beverage purchases after the first phase of implementation of this law. METHODS: This before and after study used longitudinal data on food and beverage purchases from 2381 Chilean households from Jan 1, 2015, to Dec 31, 2017. Nutrition facts panel data from food and beverage packages were linked to household purchases at the product level using barcode, brand name, and product description. Nutritionists reviewed each product for nutritional accuracy and categorised it as high-in if it contained added sugar, sodium, or saturated fat and exceeded phase 1 nutrient or calorie thresholds, and thus was subject to the labelling, marketing, and school regulations. Using fixed-effects models, we examined the mean nutrient content (overall calories, sugar, saturated fat, and sodium) of purchases in the post-policy period compared to a counterfactual scenario based on pre-policy trends. FINDINGS: Compared with the counterfactual scenario, overall calories purchased declined by 16·4 kcal/capita/day (95% CI -27·3 to -5·6; p=0·0031) or 3·5%. Overall sugar declined by 11·5 kcal/capita/day (-14·6 to -8·4; p<0·0001) or 10·2%, and saturated fat declined by 2·2 kcal/capita/day (-3·8 to -0·5; p=0·0097) or 3·9%. The sodium content of overall purchases declined by 27·7 mg/capita/day (-46·3 to -9·1; p=0·0035) or 4·7%. Declines from high-in purchases drove these results with some offset by increases in not-high-in purchases. Among high-in purchases, relative to the counterfactual scenario, there were notable declines of 23·8% in calories purchased (-49·4 kcal/capita/day, 95% CI -55·1 to -43·7; p<0·0001), 36·7% in sodium purchased (-96·6 mg/capita/day,-105·3 to -87·8; p<0·0001), and 26·7% in sugar purchased (-20·7 kcal/capita/day, -23·4 to -18·1; p<0·0001). INTERPRETATION: The Chilean phase 1 law of food labelling and advertising policies were associated with reduced high-in purchases, leading to declines in purchased nutrients of concern. Greater changes might reasonably be anticipated after the implementation of phases 2 and 3. FUNDING: Bloomberg Philanthropies, International Development Research Center, and Eunice Kennedy Shriver National Institute of Child Health and Human Development of the National Institutes of Health.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".