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      Predicting obesity reduction after implementing warning labels in Mexico: A modeling study

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          Abstract

          Background

          In October 2019, Mexico approved a law to establish that nonalcoholic beverages and packaged foods that exceed a threshold for added calories, sugars, fats, trans fat, or sodium should have an “excess of” warning label. We aimed to estimate the expected reduction in the obesity prevalence and obesity costs in Mexico by introducing warning labels, over 5 years, among adults under 60 years of age.

          Methods and findings

          Baseline intakes of beverages and snacks were obtained from the 2016 Mexican National Health and Nutrition Survey. The expected impact of labels on caloric intake was obtained from an experimental study, with a 10.5% caloric reduction for beverages and 3.0% caloric reduction for snacks. The caloric reduction was introduced into a dynamic model to estimate weight change. The model output was then used to estimate the expected changes in the prevalence of obesity and overweight. To predict obesity costs, we used the Health Ministry report of the impact of overweight and obesity in Mexico 1999–2023. We estimated a mean caloric reduction of 36.8 kcal/day/person (23.2 kcal/day from beverages and 13.6 kcal/day from snacks). Five years after implementation, this caloric reduction could reduce 1.68 kg and 4.98 percentage points (pp) in obesity (14.7%, with respect to baseline), which translates into a reduction of 1.3 million cases of obesity and a reduction of US$1.8 billion in direct and indirect costs. Our estimate is based on experimental evidence derived from warning labels as proposed in Canada, which include a single label and less restrictive limits to sugar, sodium, and saturated fats. Our estimates depend on various assumptions, such as the transportability of effect estimates from the experimental study to the Mexican population and that other factors that could influence weight and food and beverage consumption remain unchanged. Our results will need to be corroborated by future observational studies through the analysis of changes in sales, consumption, and body weight.

          Conclusions

          In this study, we estimated that warning labels may effectively reduce obesity and obesity-related costs. Mexico is following Chile, Peru, and Uruguay in implementing warning labels to processed foods, but other countries could benefit from this intervention.

          Abstract

          Tonatiuh Barrientos-Gutierrez and colleagues model the weight loss benefits from using warning labels on beverages and packaged foods.

          Author summary

          Why was this study done?
          • In October 2019, the Mexican government approved new warning labels for packaged food and nonalcoholic beverages.

          • Products that exceed a certain limit of calories, sugars, fats, trans fats, or sodium will now have a black octagon with an “excess of” label.

          • Estimating the impact of these labels is necessary to translate this effort into expected reductions in intake and potential changes in body weight, obesity prevalence, and healthcare cost reductions.

          What did the researchers do and found?
          • Adults in Mexico consume approximately 31% of their total energy intake from beverages and snacks. This study found that warning labels could reduce on average 36.8 kcal/person/day (23.2 kcal from beverages and 13.6 kcal from snacks).

          • Using a mathematical model, we translated the expected caloric change into expected changes in body weight and obesity prevalence. Five years after the warning label implementation, obesity prevalence could be reduced by 14.7%, with respect to baseline, translating into 1.30 million cases of obesity reduced.

          • Larger effects will be expected among males, young adults, and the middle and high socioeconomic status (SES) group.

          • After five years, warning labels could save an estimated US$1.8 billion on obesity costs.

          • Our model has some important limitations, such as using a Canadian estimate of the effect of warning labels. To capture a wider scope of effects, we used studies from Chile and Uruguay, which produced larger effects than the Canadian-based scenario.

          What do these findings mean?
          • Warning labels have the potential to reduce the intake of nonessential high caloric food, reduce obesity, and lead to healthcare cost savings in Mexico.

          • Warning labels could be considered by other countries with similar conditions, as part of their obesity control packages.

          • These projections will need to be confirmed by future studies analyzing the change in food and beverage consumption and body weight after the implementation of the new warning labels.

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          Most cited references15

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          Cost-Effectiveness in Health and Medicine

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            Impact of front-of-pack 'traffic-light' nutrition labelling on consumer food purchases in the UK.

            Front-of-pack 'traffic-light' nutrition labelling has been widely proposed as a tool to improve public health nutrition. This study examined changes to consumer food purchases after the introduction of traffic-light labels with the aim of assessing the impact of the labels on the 'healthiness' of foods purchased. The study examined sales data from a major UK retailer in 2007. We analysed products in two categories ('ready meals' and sandwiches), investigating the percentage change in sales 4 weeks before and after traffic-light labels were introduced, and taking into account seasonality, product promotions and product life-cycle. We investigated whether changes in sales were related to the healthiness of products. All products that were not new and not on promotion immediately before or after the introduction of traffic-light labels were selected for the analysis (n = 6 for ready meals and n = 12 for sandwiches). For the selected ready-meals, sales increased (by 2.4% of category sales) in the 4 weeks after the introduction of traffic-light labels, whereas sales of the selected sandwiches did not change significantly. Critically, there was no association between changes in product sales and the healthiness of the products. This short-term study based on a small number of ready meals and sandwiches found that the introduction of a system of four traffic-light labels had no discernable effect on the relative healthiness of consumer purchases. Further research on the influence of nutrition signposting will be needed before this labelling format can be considered a promising public health intervention.
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              'Traffic-light' nutrition labelling and 'junk-food' tax: a modelled comparison of cost-effectiveness for obesity prevention.

              Cost-effectiveness analyses are important tools in efforts to prioritise interventions for obesity prevention. Modelling facilitates evaluation of multiple scenarios with varying assumptions. This study compares the cost-effectiveness of conservative scenarios for two commonly proposed policy-based interventions: front-of-pack 'traffic-light' nutrition labelling (traffic-light labelling) and a tax on unhealthy foods ('junk-food' tax). For traffic-light labelling, estimates of changes in energy intake were based on an assumed 10% shift in consumption towards healthier options in four food categories (breakfast cereals, pastries, sausages and preprepared meals) in 10% of adults. For the 'junk-food' tax, price elasticities were used to estimate a change in energy intake in response to a 10% price increase in seven food categories (including soft drinks, confectionery and snack foods). Changes in population weight and body mass index by sex were then estimated based on these changes in population energy intake, along with subsequent impacts on disability-adjusted life years (DALYs). Associated resource use was measured and costed using pathway analysis, based on a health sector perspective (with some industry costs included). Costs and health outcomes were discounted at 3%. The cost-effectiveness of each intervention was modelled for the 2003 Australian adult population. Both interventions resulted in reduced mean weight (traffic-light labelling: 1.3 kg (95% uncertainty interval (UI): 1.2; 1.4); 'junk-food' tax: 1.6 kg (95% UI: 1.5; 1.7)); and DALYs averted (traffic-light labelling: 45,100 (95% UI: 37,700; 60,100); 'junk-food' tax: 559,000 (95% UI: 459,500; 676,000)). Cost outlays were AUD81 million (95% UI: 44.7; 108.0) for traffic-light labelling and AUD18 million (95% UI: 14.4; 21.6) for 'junk-food' tax. Cost-effectiveness analysis showed both interventions were 'dominant' (effective and cost-saving). Policy-based population-wide interventions such as traffic-light nutrition labelling and taxes on unhealthy foods are likely to offer excellent 'value for money' as obesity prevention measures.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Formal analysisRole: InvestigationRole: MethodologyRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: Writing – original draftRole: Writing – review & editing
                Role: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: Writing – original draftRole: Writing – review & editing
                Role: InvestigationRole: MethodologyRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: InvestigationRole: MethodologyRole: VisualizationRole: Writing – review & editing
                Role: ConceptualizationRole: InvestigationRole: MethodologyRole: SupervisionRole: Writing – review & editing
                Role: ConceptualizationRole: Funding acquisitionRole: InvestigationRole: SupervisionRole: VisualizationRole: Writing – review & editing
                Role: ConceptualizationRole: InvestigationRole: Project administrationRole: ResourcesRole: SupervisionRole: Writing – review & editing
                Role: ConceptualizationRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Project administrationRole: SupervisionRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: Academic Editor
                Journal
                PLoS Med
                PLoS Med
                plos
                plosmed
                PLoS Medicine
                Public Library of Science (San Francisco, CA USA )
                1549-1277
                1549-1676
                28 July 2020
                July 2020
                : 17
                : 7
                : e1003221
                Affiliations
                [1 ] Center for Population Health Research, National Institute of Public Health, Cuernavaca, Mexico
                [2 ] Center for Health Systems Research, National Institute of Public Health, Cuernavaca, Mexico
                [3 ] Center for Nutrition and Health Research, National Institute of Public Health, Cuernavaca, Mexico
                [4 ] National Institute of Public Health, Cuernavaca, Mexico
                INSERM, U-872, Nutriomique (team 7), FRANCE
                Author notes

                The authors have declared that no competing interests exist.

                Author information
                http://orcid.org/0000-0003-1600-4797
                http://orcid.org/0000-0003-3646-7975
                http://orcid.org/0000-0001-6423-180X
                http://orcid.org/0000-0003-0083-1536
                http://orcid.org/0000-0002-4891-7120
                http://orcid.org/0000-0003-1854-4615
                http://orcid.org/0000-0002-0826-9106
                Article
                PMEDICINE-D-20-00367
                10.1371/journal.pmed.1003221
                7386611
                32722682
                05e5cc64-9e24-4729-9571-724d6934078c
                © 2020 Basto-Abreu et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 7 February 2020
                : 24 June 2020
                Page count
                Figures: 4, Tables: 3, Pages: 14
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/100007500, Bloomberg Family Foundation;
                Award Recipient :
                This project was funded by Bloomberg Philanthropies ( https://www.bloomberg.org/; JAR received the grant). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Research Article
                Biology and Life Sciences
                Physiology
                Physiological Parameters
                Body Weight
                Obesity
                Biology and Life Sciences
                Nutrition
                Diet
                Beverages
                Medicine and Health Sciences
                Nutrition
                Diet
                Beverages
                Biology and Life Sciences
                Nutrition
                Diet
                Food
                Medicine and Health Sciences
                Nutrition
                Diet
                Food
                Biology and Life Sciences
                Physiology
                Physiological Parameters
                Body Weight
                People and places
                Geographical locations
                North America
                Mexico
                People and places
                Population groupings
                Ethnicities
                Latin American people
                Mexican People
                Biology and Life Sciences
                Biochemistry
                Lipids
                Fats
                Biology and Life Sciences
                Physiology
                Physiological Parameters
                Body Weight
                Body Mass Index
                Custom metadata
                Baseline characteristics of the Mexican population are available at http://ensanut.insp.mx/. The final dataset after running our model is available at the open science framework, osf.io/5j4va (doi: 10.17605/OSF.IO/5J4VA). The mathematical model used to estimate the impact on body weight is freely available in the github within an R package named "bw" in https://github.com/INSP-RH/bw.

                Medicine
                Medicine

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