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Record W4388893393 · doi:10.1101/2023.11.21.23298789

Decreases in purchases of energy, sodium, sugar, and saturated fat three years after implementation of the Chilean Food Labelling and Marketing Law

2023· preprint· en· W4388893393 on OpenAlexfundno aff
Lindsey Smith Taillie, Maxime Bercholz, Barry M. Popkin, Natalia Rebolledo, Marcela Reyes, M. Camila Corvalán

Bibliographic record

VenuemedRxiv · 2023
Typepreprint
Languageen
FieldMedicine
TopicConsumer Attitudes and Food Labeling
Canadian institutionsnot available
FundersComisión Nacional de Investigación Científica y TecnológicaInternational Development Research CentreBloomberg Philanthropies
KeywordsCounterfactual thinkingAdded sugarSugarSaturated fatBusinessAdvertisingNutrientCalorieFood policyAgricultural economicsTrans fatMarketingFood scienceAgricultural scienceEconomicsEnvironmental scienceGeographyMedicinePsychologyChemistryFood securityAgriculture

Abstract

fetched live from OpenAlex

Abstract Background In 2016, Chile implemented a multi-phase set of policies that mandated warning labels, restricted food marketing to children, and banned school sales of unhealthy foods and beverages. Chile’s law, particularly the warning label component, set the precedent for a rapid global proliferation of similar policies. While our initial evaluation showed policy-linked decreases in purchases of products carrying the warning label, a longer-term evaluation is needed, particularly as later phases of Chile’s law included stricter nutrient thresholds and introduced a daytime ban on advertising of labeled foods for all audiences. The objective is to evaluate changes in purchases of energy, sugar, sodium, and saturated fat purchased after Phase 2 implementation of the Chilean policies. Methods and Findings This before- and after-study used longitudinal data on monthly food and beverage purchases from 2,844 Chilean households (138,391 household-months) from July 1, 2013 until June 30, 2019. Nutrition facts panel data from food and beverage packages were linked at the product level and reviewed by nutritionists. Products were considered to carry the warning label if they contained added sugar, sodium, or saturated fat, and exceeded the final phase nutrient or calorie thresholds (thus would carry the warning label). Using correlated random-effects models and an interrupted time series design, we estimated the nutrient content of food and beverage purchases associated with Phase 1 and Phase 2 compared to a counterfactual scenario based on pre-policy trends. Compared to the counterfactual, we observed significant decreases in purchases of foods and beverages carrying the warning label during Phase 2, including a relative 36.8% reduction in sugar (−30.3 calories, 95% CI −34.5, −26.3), a 23.0% relative reduction in energy (−51.6 calories, 95% CI −60.7, −42.6), a 21.9% relative reduction in sodium (−85.8 mg, 95% CI −105.0, −66.7) and a 15.7% relative reduction in saturated fat (−6.4 calories, 95% CI −8.4, −4.3). Decreases were partially offset by increases in non-labeled purchases, but the net effect shows a significant decrease in total nutrients of concern purchased during Phase 2. Reductions in sugar and energy were driven by beverage purchases, whereas reductions in sodium and saturated fat were driven by foods. The pattern of declines in purchases was similar for households of lower vs. higher socioeconomic status. A key limitation of this study is that the data include only a portion of what Chilean households purchase that, while including important categories impacted by the law, do not cover an entire diet. Conclusions The Chilean policies on food labeling, marketing, and school food sales led to declines in nutrients of concern during a more complete phase of implementation, particularly from foods and drinks carrying the warning label.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.086
Threshold uncertainty score0.171

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.027
GPT teacher head0.281
Teacher spread0.254 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2023
Admission routes1
Has abstractyes

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