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      Estimating the health impacts of sugar-sweetened beverage tax for informing policy decisions about the obesity burden in Vietnam

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          Abstract

          Background

          Taxation on sugar-sweetened beverages (SSBs) has been adopted in more than 40 countries but remained under discussion in Vietnam. This study aimed to estimate the health impacts of different SSBs tax plans currently under discussion to provide an evidence base to inform decision-making about a SSBs tax policy in Vietnam.

          Method & findings

          Five tax scenarios were modelled, representing three levels of price increase: 5%, 11% and 19–20%. Scenarios of the highest price increase were assessed across three different tax designs: ad valorem, volume-based specific tax & sugar-based specific tax. We modelled SSBs consumption in each tax scenario; how this reduction in consumption translates to a reduction in total energy intake and how this relationship in turn translates to an average change in body weight and obesity status among adults by applying the calorie-to weight conversion factor. Changes in type 2 diabetes burden were then calculated based on the change in average BMI of the modelled cohort. A Monte Carlo simulation approach was applied on the conversion factor of weight change and diabetes risk reduction for the sensitivity analysis. We found that the taxation that involved a 5% price increase gave relatively small impacts while increasing SSBs’ price up to 20% appeared to impact substantially on overweight and obesity rates (reduction of 12.7% and 12.4% respectively) saving 27 million USD for direct medical cost. The greatest reduction was observed for overweight and obesity class I. The decline in overweight and obesity rates was slightly higher for women than men.

          Conclusion

          This study supports the SSB tax policy in pursuit of public health benefits, especially where the tax increase involves around a 20% price increase. The health benefit and revenue gains were evident across all three tax designs with the specific tax based on sugar density achieving greatest effects.

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          Most cited references34

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          Consumption of sugar sweetened beverages, artificially sweetened beverages, and fruit juice and incidence of type 2 diabetes: systematic review, meta-analysis, and estimation of population attributable fraction

          Objectives To examine the prospective associations between consumption of sugar sweetened beverages, artificially sweetened beverages, and fruit juice with type 2 diabetes before and after adjustment for adiposity, and to estimate the population attributable fraction for type 2 diabetes from consumption of sugar sweetened beverages in the United States and United Kingdom. Design Systematic review and meta-analysis. Data sources and eligibility PubMed, Embase, Ovid, and Web of Knowledge for prospective studies of adults without diabetes, published until February 2014. The population attributable fraction was estimated in national surveys in the USA, 2009-10 (n=4729 representing 189.1 million adults without diabetes) and the UK, 2008-12 (n=1932 representing 44.7 million). Synthesis methods Random effects meta-analysis and survey analysis for population attributable fraction associated with consumption of sugar sweetened beverages. Results Prespecified information was extracted from 17 cohorts (38 253 cases/10 126 754 person years). Higher consumption of sugar sweetened beverages was associated with a greater incidence of type 2 diabetes, by 18% per one serving/day (95% confidence interval 9% to 28%, I2 for heterogeneity=89%) and 13% (6% to 21%, I2=79%) before and after adjustment for adiposity; for artificially sweetened beverages, 25% (18% to 33%, I2=70%) and 8% (2% to 15%, I2=64%); and for fruit juice, 5% (−1% to 11%, I2=58%) and 7% (1% to 14%, I2=51%). Potential sources of heterogeneity or bias were not evident for sugar sweetened beverages. For artificially sweetened beverages, publication bias and residual confounding were indicated. For fruit juice the finding was non-significant in studies ascertaining type 2 diabetes objectively (P for heterogeneity=0.008). Under specified assumptions for population attributable fraction, of 20.9 million events of type 2 diabetes predicted to occur over 10 years in the USA (absolute event rate 11.0%), 1.8 million would be attributable to consumption of sugar sweetened beverages (population attributable fraction 8.7%, 95% confidence interval 3.9% to 12.9%); and of 2.6 million events in the UK (absolute event rate 5.8%), 79 000 would be attributable to consumption of sugar sweetened beverages (population attributable fraction 3.6%, 1.7% to 5.6%). Conclusions Habitual consumption of sugar sweetened beverages was associated with a greater incidence of type 2 diabetes, independently of adiposity. Although artificially sweetened beverages and fruit juice also showd positive associations with incidence of type 2 diabetes, the findings were likely to involve bias. None the less, both artificially sweetened beverages and fruit juice were unlikely to be healthy alternatives to sugar sweetened beverages for the prevention of type 2 diabetes. Under assumption of causality, consumption of sugar sweetened beverages over years may be related to a substantial number of cases of new onset diabetes.
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            The global burden of disease attributable to high body mass index in 195 countries and territories, 1990–2017: An analysis of the Global Burden of Disease Study

            Background Obesity represents an urgent problem that needs to be properly addressed, especially among children. Public and global health policy- and decision-makers need timely, reliable quantitative information to develop effective interventions aimed at counteracting the burden generated by high body mass index (BMI). Few studies have assessed the high-BMI-related burden on a global scale. Methods and findings Following the methodology framework and analytical strategies used in the Global Burden of Disease Study (GBD) 2017, the global deaths and disability-adjusted life years (DALYs) attributable to high BMI were analyzed by age, sex, year, and geographical location and by Socio-demographic Index (SDI). All causes of death and DALYs estimated in GBD 2017 were organized into 4 hierarchical levels: level 1 contained 3 broad cause groupings, level 2 included more specific categories within the level 1 groupings, level 3 comprised more detailed causes within the level 2 categories, and level 4 included sub-causes of some level 3 causes. From 1990 to 2017, the global deaths and DALYs attributable to high BMI have more than doubled for both females and males. However, during the study period, the age-standardized rate of high-BMI-related deaths remained stable for females and only increased by 14.5% for males, and the age-standardized rate of high-BMI-related DALYs only increased by 12.7% for females and 26.8% for males. In 2017, the 6 leading GBD level 3 causes of high-BMI-related DALYs were ischemic heart disease, stroke, diabetes mellitus, chronic kidney disease, hypertensive heart disease, and low back pain. For most GBD level 3 causes of high-BMI-related DALYs, high-income North America had the highest attributable proportions of age-standardized DALYs due to high BMI among the 21 GBD regions in both sexes, whereas the lowest attributable proportions were observed in high-income Asia Pacific for females and in eastern sub-Saharan Africa for males. The association between SDI and high-BMI-related DALYs suggested that the lowest age-standardized DALY rates were found in countries in the low-SDI quintile and high-SDI quintile in 2017, and from 1990 to 2017, the age-standardized DALY rates tended to increase in regions with the lowest SDI, but declined in regions with the highest SDI, with the exception of high-income North America. The study’s main limitations included the use of information collected from some self-reported data, the employment of cutoff values that may not be adequate for all populations and groups at risk, and the use of a metric that cannot distinguish between lean and fat mass. Conclusions In this study, we observed that the number of global deaths and DALYs attributable to high BMI has substantially increased between 1990 and 2017. Successful population-wide initiatives targeting high BMI may mitigate the burden of a wide range of diseases. Given the large variations in high-BMI-related burden of disease by SDI, future strategies to prevent and reduce the burden should be developed and implemented based on country-specific development status.
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              Quantification of the effect of energy imbalance on bodyweight.

              Obesity interventions can result in weight loss, but accurate prediction of the bodyweight time course requires properly accounting for dynamic energy imbalances. In this report, we describe a mathematical modelling approach to adult human metabolism that simulates energy expenditure adaptations during weight loss. We also present a web-based simulator for prediction of weight change dynamics. We show that the bodyweight response to a change of energy intake is slow, with half times of about 1 year. Furthermore, adults with greater adiposity have a larger expected weight loss for the same change of energy intake, and to reach their steady-state weight will take longer than it would for those with less initial body fat. Using a population-averaged model, we calculated the energy-balance dynamics corresponding to the development of the US adult obesity epidemic. A small persistent average daily energy imbalance gap between intake and expenditure of about 30 kJ per day underlies the observed average weight gain. However, energy intake must have risen to keep pace with increased expenditure associated with increased weight. The average increase of energy intake needed to sustain the increased weight (the maintenance energy gap) has amounted to about 0·9 MJ per day and quantifies the public health challenge to reverse the obesity epidemic. Copyright © 2011 Elsevier Ltd. All rights reserved.
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                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Funding acquisitionRole: MethodologyRole: Project administrationRole: ResourcesRole: SoftwareRole: SupervisionRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Funding acquisitionRole: MethodologyRole: Project administrationRole: SupervisionRole: ValidationRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: Funding acquisitionRole: MethodologyRole: ResourcesRole: ValidationRole: Writing – review & editing
                Role: ConceptualizationRole: Formal analysisRole: Funding acquisitionRole: MethodologyRole: Project administrationRole: ValidationRole: Writing – review & editing
                Role: ConceptualizationRole: MethodologyRole: ResourcesRole: Writing – review & editing
                Role: ConceptualizationRole: Formal analysisRole: MethodologyRole: SupervisionRole: ValidationRole: Writing – review & editing
                Role: Data curationRole: MethodologyRole: SupervisionRole: ValidationRole: Writing – review & editing
                Role: ConceptualizationRole: MethodologyRole: Project administrationRole: ValidationRole: Writing – review & editing
                Role: Formal analysisRole: MethodologyRole: ResourcesRole: ValidationRole: Writing – review & editing
                Role: Formal analysisRole: MethodologyRole: ResourcesRole: Writing – review & editing
                Role: Formal analysisRole: MethodologyRole: ResourcesRole: Writing – review & editing
                Role: Data curationRole: ResourcesRole: ValidationRole: Writing – review & editing
                Role: Data curationRole: MethodologyRole: SoftwareRole: SupervisionRole: ValidationRole: VisualizationRole: Writing – original draft
                Role: Editor
                Journal
                PLoS One
                PLoS One
                plos
                PLOS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                4 April 2023
                2023
                : 18
                : 4
                : e0274928
                Affiliations
                [1 ] Center for Population Health Sciences, Hanoi University of Public Health, Hanoi, Vietnam
                [2 ] School of Medicine, Dentistry & Biomedical Sciences, Queen’s University Belfast, Belfast, United Kingdom
                [3 ] Department of Economics, Thuong Mai University, Hanoi, Vietnam
                [4 ] UNICEF Vietnam, Hanoi, Vietnam
                [5 ] UNICEF United States of American, New York, New York, United States of America
                [6 ] Nuffield Department of Population Health, University of Oxford, Oxford, United Kingdom
                [7 ] Health Bridge Foundation of Canada, Hanoi, Vietnam
                [8 ] General Department of Preventive Medicine, Ministry of Health, Hanoi, Vietnam
                Federal University of Minas Gerais: Universidade Federal de Minas Gerais, BRAZIL
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Author information
                https://orcid.org/0000-0001-6692-2110
                https://orcid.org/0000-0001-8334-9985
                Article
                PONE-D-22-24726
                10.1371/journal.pone.0274928
                10072454
                37014838
                02ff6cc1-c213-4f74-82bc-7bc389d2587b
                © 2023 Nguyen 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
                : 5 September 2022
                : 6 February 2023
                Page count
                Figures: 1, Tables: 5, Pages: 16
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/100006641, UNICEF;
                Award ID: Contract no. PO 43331943
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100006641, UNICEF;
                Award ID: Contract no. PO 43331943
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/100006641, UNICEF;
                Award ID: Contract no. PO 43331943
                Award Recipient : Son The Dao
                DTN, HVM & SD received funding from UNICEF Vietnam to conduct this study under contract no. PO 43331943. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. PHD, QDN, JJ & MM were contributed to this manuscript by their personal capacity and do not represent the view of their affiliation.
                Categories
                Research Article
                Social Sciences
                Political Science
                Public Policy
                Taxes
                Biology and Life Sciences
                Physiology
                Physiological Parameters
                Body Weight
                Obesity
                Medicine and Health Sciences
                Endocrinology
                Endocrine Disorders
                Diabetes Mellitus
                Medicine and Health Sciences
                Medical Conditions
                Metabolic Disorders
                Diabetes Mellitus
                Biology and Life Sciences
                Physiology
                Physiological Parameters
                Body Weight
                Biology and Life Sciences
                Physiology
                Physiological Parameters
                Body Weight
                Overweight
                Biology and Life Sciences
                Physiology
                Physiological Parameters
                Body Weight
                Body Mass Index
                Biology and Life Sciences
                Physiology
                Physiological Parameters
                Body Weight
                Obesity
                Childhood Obesity
                Medicine and Health Sciences
                Endocrinology
                Endocrine Disorders
                Diabetes Mellitus
                Type 2 Diabetes
                Medicine and Health Sciences
                Medical Conditions
                Metabolic Disorders
                Diabetes Mellitus
                Type 2 Diabetes
                Custom metadata
                The data underlying the results presented in the study are available from Vietnam Ministry of Health and WHO Office in Vietnam on request by contacting NCD Surveillance Team, World Health Organization ( ncdmonitoring@ 123456who.int ) or visiting http://www.who.int/ncds/surveillance/steps/en/. We confirm that the authors did not have any special access or request privileges that others would not have. Others would be able to access or request these data in the same manner as the authors.

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