Front-of-pack labeling is a cost-effective strategy to decrease population consumption of sodium, sugar, saturated fat, total fat, and trans-fatty acids, considered critical nutrients for chronic disease. Our main objective was to explore the subjective understanding of labels that are currently used internationally among low- and middle-income Mexican consumers. We performed two phases of 10 focus groups with adolescents (13–15 y), young adults (21–23 y), mothers of children 3–12 y, fathers of children 3–12 y and older adults (55–70 y). Seven FOPL were evaluated: Guideline Daily Amounts, Multiple Traffic Light, Chilean Warning labels, Warning labels in Red, 5-Color Nutrition Label, Health Star Rating, and Healthy Choice label. Data was analyzed with a triangulation of researchers using a content analysis, based on three codes: 1) awareness and use of the Guideline Daily Amounts, 2) acceptability, and 3) subjective understanding of labels. Most participants were aware of the Guideline Daily Amounts, however they rarely used it because interpreting the information displayed on the label was too complicated. Health Star Rating, Warning labels, Multiple Traffic Light and the Healthy Choice logo labels were the most understandable, however the acceptability of the The Healthy Choice logo decreased as it did not provide information on specific ingredients. The Warning labels was the only label able to warn about critical nutrients that could represent a health risk. The Warning labels in red was more accepted compared to Warning labels in black. Results show that directive and semi-directive labels, such as Warning labels, Health Star Rating or Multiple Traffic Light, may be better at helping population of low- and middle income make healthier food choices, than non-directive FOPL such as the Guideline Daily Amounts implemented in México. The study results highlight the potential of Warning labels to support decreases in the consumption of critical ingredients in low- and middle-income Mexican consumers.

Background: Front-of-pack (FOP) nutrition labeling is a cost-effective strategy to help consumers make informed and healthier food choices. We aimed to investigate the effect of the FOP labels used in the Latin American region on consumers' shopping intentions when prompted to make their choices with specific nutrients-to-limit in mind among low-and middle-income Mexican adults (> 18 y). Methods: In this experimental study of an online simulated shopping situation participants (n = 2194) were randomly assigned to one of three labeling conditions: Guideline Daily Amounts (GDA), Multiple Traffic Lights (MTL), or red Warning Labels (WL). Participants were required to view a video explaining how to correctly interpret the assigned label. Primary outcomes were the overall nutritional quality (estimated using the Nutrient Profiling Scoring Criterion [NPSC] and NPSC baseline score) and mean energy and nutrient content of purchases. Secondary outcomes included shopping time variables. We also evaluated the impact of the labels across food categories (ready-made foods, dairy beverages, non-dairy beverages, salty snacks, and breakfast cereals) and sociodemographic subgroups.Results: The MTL and the WL led to a better overall nutritional quality of the shopping cart compared to the GDA (p < 0.05). According to the NPSC score, the WL led to a better nutritional quality across breakfast cereals and salty snacks compared to the GDA (p < 0.05); a similar effect was observed for the MTL among non-dairy beverages (p < 0.05). The MTL and the WL required shorter shopping times compared to GDA (p < 0.05). Across all labeling conditions, the nutritional quality of the shopping cart tended to be lower among those with low income, education and nutrition knowledge levels.Conclusion: WL and MTL may foster healthier food choices in a faster way among low-and middle-income groups in Mexico. To produce an equitable impact among consumers of all socioeconomic strata, efforts beyond simply the inclusion of a communication campaign on how to use and interpret FOP labels will be required.(Continued on next page)

BackgroundFront-of-package labelling is a cost-effective strategy to help consumers make healthier choices and informed food purchases. The effect of labels is mediated by consumer understanding and acceptability of the label. We compared the acceptability and understanding of labels used in Latin-America among low- and middle-income Mexican adults.MethodsParticipants (n = 2105) were randomly assigned to one of three labels: Mexican Guideline Daily Allowances (GDA), Ecuador’s Multiple Traffic Lights (MTL), or Chile’s Warning Labels (WL) in red. Label acceptability was evaluated through items regarding likeability, attractiveness and perceived cognitive workload. Objective understanding was evaluated by asking participants to select the product with the lowest nutritional quality among three products. We measured the time participants took to choose the product. Differences in label acceptability, understanding and time required to choose a product across labels were tested.ResultsCompared to the GDA, a higher proportion of participants liked the MTL and WL, considered them attractive, and with a lower perceived cognitive workload (p < 0.05). Participants had 4.00 (2.86–5.59) times the odds of correctly identifying the product with the lowest nutritional quality when using the MTL label and 4.52 (3.24–6.29) times the odds when using the WL, in comparison to the GDA. Time required to choose the product was lower for the MTL (Median: 11.25 s; IQR = 8.00–16.09) and the WL (Median = 11.94 s, IQR = 8.56–16.52) compared to the GDA (Median: 15.31 s; IQR = 10.81–20.21; p < 0.05). No differences were observed between the MTL and the WL.ConclusionsGDA had the lowest acceptability and understanding among the labels tested. The MTL and the WL were more accepted and understood, and allowed low- and middle-income consumers to make nutrition-quality related decisions more quickly. WL or MTL may foster healthier food choices in the most vulnerable groups in Mexico compared to the current labelling format.

Nutrient profiling systems (NPS) are used around the world. In some countries, the food industry participates in the design of these systems. We aimed to compare the ability of various NPS to identify processed and ultra-processed Mexican products containing excessive amounts of critical nutrients. A sample of 2544 foods and beverages available in the Mexican market were classified as compliant and non-compliant according to seven NPS: the Pan American Health Organization (PAHO) model, which served as our reference, the Nutrient Profiling Scoring Criterion (NPSC), the Mexican Committee of Nutrition Experts (MCNE), the Health Star Rating (HSR), the Mexican Nutritional Seal (MNS), the Chilean Warning Octagons (CWO) 2016, 2018 and 2019 criteria, and Ecuador’s Multiple Traffic Light (MTL). Overall, the proportion of foods classified as compliant by the HSR, MTL and MCNE models was similar to the PAHO model. In contrast, the NPSC, the MNS and the CWO-2016 classified a higher amount of foods as compliant. Larger differences between NPS classification were observed across food categories. Results support the notion that models developed with the involvement of food manufacturers are more permissive than those based on scientific evidence. Results highlight the importance of thoroughly evaluating the underlying criteria of a model.

Background Different nutrient profiles (NPs) have been developed in Latin America to assess the nutritional quality of packaged food products. Recently, the Mexican NP was developed as part of the new warning label regulation implemented in 2020, considering 5 warning octagons (calories, sugar, sodium, saturated fats, and trans fats) and 2 warning rectangles (caffeine and non-nutritive sweeteners). The objective of this cross-sectional study was to evaluate the Mexican NP and other NPs proposed or used in Latin America against the Pan American Health Organization (PAHO) model. Methods and findings Nutrition content data of 38,872 packaged food products available in the Mexican market were collected in 2016 and 2017. The evaluation of the Mexican NP, including its 3 implementation phases of increasing stringency (2020, 2023, and 2025), was conducted by comparing the percentage of products classified as “healthy” (without warnings) or “less healthy” (with 1 or more warnings), as well as the number and type of warnings assigned to food products, against the PAHO NP. Using the calibration method, we compared the classifications produced by the PAHO model against those produced by the NP models of Ecuador, Chile (3 phases), Peru (2 phases), Uruguay, and Brazil. Kappa coefficients and Pearson correlations were estimated, and proportion tests were performed. We found that the 3 implementation phases of the Mexican NP had near to perfect agreement in the classification of healthy foods (Mexico NP models: 19.1% to 23.8%; PAHO model: 19.7%) and a strong correlation (>91.9%) with the PAHO model. Other NPs with high agreement with the PAHO model were the Ecuador (89.8%), Uruguay (82.5%), Chile Phase 3 (82.3%), and Peru Phase 2 (84.2%) NPs. In contrast, the Peru Phase 1, Brazil, and Chile Phase 1 NP models had the highest percentage of foods classified as healthy (49.2%, 47.1%, and 46.5%, respectively) and the lowest agreement with the PAHO model (69.9%, 69.3%, and 73%, respectively). Study limitations include that warnings considered by the Mexican NP models were evaluated as if all the warnings were octagon seals, while 2 out of the 7 were rectangular warnings (caffeine and non-nutritive sweeteners), and that our data are limited by the quality of the information reported in the list of ingredients and the nutrition facts table of the products. Conclusions The 3 implementation phases of the Mexican NP were useful to identify healthy food products. In contrast, the Peru Phase 1, Brazil, and Chile Phase 1 NP models may have limited usefulness for the classification of foods according to the content of ingredients of concern. The results of this study may inform countries seeking to adapt and evaluate existing NP models for use in population-specific applications.