MétaCan
Menu
Back to cohort
Record W4402917648 · doi:10.1371/journal.pmed.1004463

Decreases in purchases of energy, sodium, sugar, and saturated fat 3 years after implementation of the Chilean food labeling and marketing law: An interrupted time series analysis

2024· article· en· W4402917648 on OpenAlexfundno aff
Lindsey Smith Taillie, Maxime Bercholz, Barry M. Popkin, Natalia Rebolledo, Marcela Reyes, Camila Corvalán

Bibliographic record

VenuePLoS Medicine · 2024
Typearticle
Languageen
FieldMedicine
TopicConsumer Attitudes and Food Labeling
Canadian institutionsnot available
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesInternational Development Research CentreBloomberg Philanthropies
KeywordsAdded sugarCounterfactual thinkingSaturated fatSugarNutrientCalorieFood scienceBusinessAgricultural economicsEnvironmental healthFood marketingFood policyAdvertisingEconomicsMedicineGeographyAgriculturePsychologyFood securityBiology

Abstract

fetched live from OpenAlex

BACKGROUND: In 2016, Chile implemented a multiphase set of policies that mandated warning labels, restricted food marketing to children, and banned school sales of foods and beverages high in nutrients of concern ("high-in" foods). 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 high-in, 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 high-in 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 interrupted time series 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 25, 2019. Nutrition facts panel data from food and beverage packages were linked at the product level and reviewed by nutritionists. Products were considered "high-in" if they contained added sugar, sodium, or saturated fat and exceeded nutrient or calorie thresholds. 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 trends during a 36-month pre-policy timeframe. Compared to the counterfactual, we observed significant decreases in high-in purchases of foods and beverages during Phase 2, including a relative 36.8% reduction in sugar (-30.4 calories/capita/day, 95% CI -34.5, -26.3), a 23.0% relative reduction in energy (-51.6 calories/capita/day, 95% CI -60.7, -42.6), a 21.9% relative reduction in sodium (-85.8 mg/capita/day, 95% CI -105.0, -66.7), and a 15.7% relative reduction in saturated fat (-6.4 calories/capita/day, 95% CI -8.4, -4.3), while purchases of not-high-in foods and drinks increased. Reductions in sugar and energy purchases were driven by beverage purchases, whereas reductions in sodium and saturated fat were driven by foods. Compared to the counterfactual, changes in both high-in purchases and not high-in purchases observed in Phase 2 tended to be larger than changes observed in Phase 1. The pattern of changes in purchases was similar for households of lower versus higher socioeconomic status. A limitation of this study is that some results were sensitive to the use of shorter pre-policy time frames. CONCLUSIONS: Compared to a counterfactual based on a 36-month pre-policy timeframe, Chilean policies on food labeling, marketing, and school food sales led to declines in nutrients of concern during Phase 2 of implementation, particularly from foods and drinks high in nutrients of concern. These declines were sustained or even increased over phases of policy implementation.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.125
Threshold uncertainty score0.314

Codex and Gemma teacher scores by category

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

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 head0.014
GPT teacher head0.276
Teacher spread0.263 · 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 teacher head, 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

Citations64
Published2024
Admission routes1
Has abstractyes

Explore more

Same venuePLoS MedicineSame topicConsumer Attitudes and Food LabelingFrench-language works237,207