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      Priority-setting for obesity prevention—The Assessing Cost-Effectiveness of obesity prevention policies in Australia (ACE-Obesity Policy) study

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          Abstract

          The aim of the ACE-Obesity Policy study was to assess the economic credentials of a suite of obesity prevention policies across multiple sectors and areas of governance for the Australian setting. The study aimed to place the cost-effectiveness results within a broad decision-making context by providing an assessment of the key considerations for policy implementation. The Assessing Cost-Effectiveness (ACE) approach to priority-setting was used. Systematic literature reviews were undertaken to assess the evidence of intervention effectiveness on body mass index and/or physical activity for selected interventions. A standardised evaluation framework was used to assess the cost-effectiveness of each intervention compared to a ‘no intervention’ comparator, from a limited societal perspective. A multi-state life table Markov cohort model was used to estimate the long-term health impacts (quantified as health adjusted life years (HALYs)) and health care cost-savings resulting from each intervention. In addition to the technical cost-effectiveness results, qualitative assessments of implementation considerations were undertaken. All 16 interventions evaluated were found to be cost-effective (using a willingness-to-pay threshold of AUD50,000 per HALY gained). Eleven interventions were dominant (health promoting and cost-saving). The incremental cost-effectiveness ratio for the non-dominant interventions ranged from AUD1,728 to 28,703 per HALY gained. Regulatory interventions tended to rank higher on their cost-effectiveness results, driven by lower implementation costs. However, the program-based policy interventions were generally based on higher quality evidence of intervention effectiveness. This comparative analysis of the economic credentials of obesity prevention policies for Australia indicates that there are a broad range of policies that are likely to be cost-effective, although policy options vary in strength of evidence for effectiveness, affordability, feasibility, acceptability to stakeholders, equity impact and sustainability. Implementation of these policies will require sustained co-ordination across jurisdictions and multiple government sectors in order to generate the predicted health benefits for the Australian population.

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          Worldwide trends in body-mass index, underweight, overweight, and obesity from 1975 to 2016: a pooled analysis of 2416 population-based measurement studies in 128·9 million children, adolescents, and adults

          Leandra Abarca-Gómez, Ziad A Abdeen, Zargar Abdul Hamid … (2019)
          Summary Background Underweight, overweight, and obesity in childhood and adolescence are associated with adverse health consequences throughout the life-course. Our aim was to estimate worldwide trends in mean body-mass index (BMI) and a comprehensive set of BMI categories that cover underweight to obesity in children and adolescents, and to compare trends with those of adults. Methods We pooled 2416 population-based studies with measurements of height and weight on 128·9 million participants aged 5 years and older, including 31·5 million aged 5–19 years. We used a Bayesian hierarchical model to estimate trends from 1975 to 2016 in 200 countries for mean BMI and for prevalence of BMI in the following categories for children and adolescents aged 5–19 years: more than 2 SD below the median of the WHO growth reference for children and adolescents (referred to as moderate and severe underweight hereafter), 2 SD to more than 1 SD below the median (mild underweight), 1 SD below the median to 1 SD above the median (healthy weight), more than 1 SD to 2 SD above the median (overweight but not obese), and more than 2 SD above the median (obesity). Findings Regional change in age-standardised mean BMI in girls from 1975 to 2016 ranged from virtually no change (−0·01 kg/m2 per decade; 95% credible interval −0·42 to 0·39, posterior probability [PP] of the observed decrease being a true decrease=0·5098) in eastern Europe to an increase of 1·00 kg/m2 per decade (0·69–1·35, PP>0·9999) in central Latin America and an increase of 0·95 kg/m2 per decade (0·64–1·25, PP>0·9999) in Polynesia and Micronesia. The range for boys was from a non-significant increase of 0·09 kg/m2 per decade (−0·33 to 0·49, PP=0·6926) in eastern Europe to an increase of 0·77 kg/m2 per decade (0·50–1·06, PP>0·9999) in Polynesia and Micronesia. Trends in mean BMI have recently flattened in northwestern Europe and the high-income English-speaking and Asia-Pacific regions for both sexes, southwestern Europe for boys, and central and Andean Latin America for girls. By contrast, the rise in BMI has accelerated in east and south Asia for both sexes, and southeast Asia for boys. Global age-standardised prevalence of obesity increased from 0·7% (0·4–1·2) in 1975 to 5·6% (4·8–6·5) in 2016 in girls, and from 0·9% (0·5–1·3) in 1975 to 7·8% (6·7–9·1) in 2016 in boys; the prevalence of moderate and severe underweight decreased from 9·2% (6·0–12·9) in 1975 to 8·4% (6·8–10·1) in 2016 in girls and from 14·8% (10·4–19·5) in 1975 to 12·4% (10·3–14·5) in 2016 in boys. Prevalence of moderate and severe underweight was highest in India, at 22·7% (16·7–29·6) among girls and 30·7% (23·5–38·0) among boys. Prevalence of obesity was more than 30% in girls in Nauru, the Cook Islands, and Palau; and boys in the Cook Islands, Nauru, Palau, Niue, and American Samoa in 2016. Prevalence of obesity was about 20% or more in several countries in Polynesia and Micronesia, the Middle East and north Africa, the Caribbean, and the USA. In 2016, 75 (44–117) million girls and 117 (70–178) million boys worldwide were moderately or severely underweight. In the same year, 50 (24–89) million girls and 74 (39–125) million boys worldwide were obese. Interpretation The rising trends in children's and adolescents' BMI have plateaued in many high-income countries, albeit at high levels, but have accelerated in parts of Asia, with trends no longer correlated with those of adults. Funding Wellcome Trust, AstraZeneca Young Health Programme.
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            2011 Compendium of Physical Activities: a second update of codes and MET values.

            BARBARA E. AINSWORTH, WILLIAM L. HASKELL, STEPHEN HERRMANN … (2011)
            The Compendium of Physical Activities was developed to enhance the comparability of results across studies using self-report physical activity (PA) and is used to quantify the energy cost of a wide variety of PA. We provide the second update of the Compendium, called the 2011 Compendium. The 2011 Compendium retains the previous coding scheme to identify the major category headings and specific PA by their rate of energy expenditure in MET. Modifications in the 2011 Compendium include cataloging measured MET values and their source references, when available; addition of new codes and specific activities; an update of the Compendium tracking guide that links information in the 1993, 2000, and 2011 compendia versions; and the creation of a Web site to facilitate easy access and downloading of Compendium documents. Measured MET values were obtained from a systematic search of databases using defined key words. The 2011 Compendium contains 821 codes for specific activities. Two hundred seventeen new codes were added, 68% (561/821) of which have measured MET values. Approximately half (317/604) of the codes from the 2000 Compendium were modified to improve the definitions and/or to consolidate specific activities and to update estimated MET values where measured values did not exist. Updated MET values accounted for 73% of all code changes. The Compendium is used globally to quantify the energy cost of PA in adults for surveillance activities, research studies, and, in clinical settings, to write PA recommendations and to assess energy expenditure in individuals. The 2011 Compendium is an update of a system for quantifying the energy cost of adult human PA and is a living document that is moving in the direction of being 100% evidence based.
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              Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990–2015: a systematic analysis for the Global Burden of Disease Study 2015

              Mohammad Forouzanfar, Ashkan Afshin, Lily T Alexander … (2017)
              Summary Background The Global Burden of Diseases, Injuries, and Risk Factors Study 2015 provides an up-to-date synthesis of the evidence for risk factor exposure and the attributable burden of disease. By providing national and subnational assessments spanning the past 25 years, this study can inform debates on the importance of addressing risks in context. Methods We used the comparative risk assessment framework developed for previous iterations of the Global Burden of Disease Study to estimate attributable deaths, disability-adjusted life-years (DALYs), and trends in exposure by age group, sex, year, and geography for 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks from 1990 to 2015. This study included 388 risk-outcome pairs that met World Cancer Research Fund-defined criteria for convincing or probable evidence. We extracted relative risk and exposure estimates from randomised controlled trials, cohorts, pooled cohorts, household surveys, census data, satellite data, and other sources. We used statistical models to pool data, adjust for bias, and incorporate covariates. We developed a metric that allows comparisons of exposure across risk factors—the summary exposure value. Using the counterfactual scenario of theoretical minimum risk level, we estimated the portion of deaths and DALYs that could be attributed to a given risk. We decomposed trends in attributable burden into contributions from population growth, population age structure, risk exposure, and risk-deleted cause-specific DALY rates. We characterised risk exposure in relation to a Socio-demographic Index (SDI). Findings Between 1990 and 2015, global exposure to unsafe sanitation, household air pollution, childhood underweight, childhood stunting, and smoking each decreased by more than 25%. Global exposure for several occupational risks, high body-mass index (BMI), and drug use increased by more than 25% over the same period. All risks jointly evaluated in 2015 accounted for 57·8% (95% CI 56·6–58·8) of global deaths and 41·2% (39·8–42·8) of DALYs. In 2015, the ten largest contributors to global DALYs among Level 3 risks were high systolic blood pressure (211·8 million [192·7 million to 231·1 million] global DALYs), smoking (148·6 million [134·2 million to 163·1 million]), high fasting plasma glucose (143·1 million [125·1 million to 163·5 million]), high BMI (120·1 million [83·8 million to 158·4 million]), childhood undernutrition (113·3 million [103·9 million to 123·4 million]), ambient particulate matter (103·1 million [90·8 million to 115·1 million]), high total cholesterol (88·7 million [74·6 million to 105·7 million]), household air pollution (85·6 million [66·7 million to 106·1 million]), alcohol use (85·0 million [77·2 million to 93·0 million]), and diets high in sodium (83·0 million [49·3 million to 127·5 million]). From 1990 to 2015, attributable DALYs declined for micronutrient deficiencies, childhood undernutrition, unsafe sanitation and water, and household air pollution; reductions in risk-deleted DALY rates rather than reductions in exposure drove these declines. Rising exposure contributed to notable increases in attributable DALYs from high BMI, high fasting plasma glucose, occupational carcinogens, and drug use. Environmental risks and childhood undernutrition declined steadily with SDI; low physical activity, high BMI, and high fasting plasma glucose increased with SDI. In 119 countries, metabolic risks, such as high BMI and fasting plasma glucose, contributed the most attributable DALYs in 2015. Regionally, smoking still ranked among the leading five risk factors for attributable DALYs in 109 countries; childhood underweight and unsafe sex remained primary drivers of early death and disability in much of sub-Saharan Africa. Interpretation Declines in some key environmental risks have contributed to declines in critical infectious diseases. Some risks appear to be invariant to SDI. Increasing risks, including high BMI, high fasting plasma glucose, drug use, and some occupational exposures, contribute to rising burden from some conditions, but also provide opportunities for intervention. Some highly preventable risks, such as smoking, remain major causes of attributable DALYs, even as exposure is declining. Public policy makers need to pay attention to the risks that are increasingly major contributors to global burden. Funding Bill & Melinda Gates Foundation.
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                Author and article information

                Contributors
                Jaithri Ananthapavan:
                ORCID: http://orcid.org/0000-0002-5957-6931
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: MethodologyRole: Project administrationRole: SupervisionRole: ValidationRole: Writing – original draftRole: Writing – review & editing
                Gary Sacks: Role: ConceptualizationRole: Formal analysisRole: InvestigationRole: MethodologyRole: Project administrationRole: SupervisionRole: ValidationRole: Writing – original draftRole: Writing – review & editing
                Vicki Brown: Role: Formal analysisRole: InvestigationRole: MethodologyRole: ValidationRole: Writing – original draftRole: Writing – review & editing
                Marj Moodie: Role: ConceptualizationRole: Formal analysisRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Project administrationRole: SupervisionRole: ValidationRole: Writing – original draftRole: Writing – review & editing
                Phuong Nguyen: Role: Data curationRole: Formal analysisRole: ValidationRole: Writing – original draftRole: Writing – review & editing
                Lennert Veerman:
                ORCID: http://orcid.org/0000-0002-3206-8232
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: SupervisionRole: ValidationRole: Writing – original draftRole: Writing – review & editing
                Ana Maria Mantilla Herrera: Role: Data curationRole: Formal analysisRole: MethodologyRole: ValidationRole: Writing – original draftRole: Writing – review & editing
                Anita Lal: Role: Formal analysisRole: ValidationRole: Writing – original draftRole: Writing – review & editing
                Anna Peeters: Role: ConceptualizationRole: SupervisionRole: ValidationRole: Writing – original draftRole: Writing – review & editing
                Rob Carter: Role: ConceptualizationRole: Funding acquisitionRole: InvestigationRole: MethodologyRole: Project administrationRole: SupervisionRole: ValidationRole: Writing – original draftRole: Writing – review & editing
                Stefan K. Lhachimi: Role: Editor
                Journal
                Journal ID (nlm-ta): PLoS One
                Journal ID (iso-abbrev): PLoS ONE
                Journal ID (publisher-id): plos
                Journal ID (pmc): plosone
                Title: PLoS ONE
                Publisher: Public Library of Science (San Francisco, CA USA )
                ISSN (Electronic): 1932-6203
                Publication date (Electronic): 19 June 2020
                Publication date Collection: 2020
                Volume: 15
                Issue: 6
                Electronic Location Identifier: e0234804
                Affiliations
                [1 ] Deakin Health Economics, School of Health and Social Development, Institute for Health Transformation, Deakin University, Geelong, Australia
                [2 ] Global Obesity Centre, School of Health and Social Development, Institute for Health Transformation, Deakin University, Geelong, Australia
                [3 ] School of Medicine, Griffith University, Gold Coast, Australia
                [4 ] Queensland Centre for Mental Health Research, Brisbane, Australia
                [5 ] School of Public Health, The University of Queensland, Brisbane, Australia
                [6 ] Institute for Health Metrics and Evaluation, University of Washington, Seattle, Washington, United States of America
                Universitat Bremen, GERMANY
                Author notes

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

                Author information
                http://orcid.org/0000-0002-5957-6931
                http://orcid.org/0000-0002-3206-8232
                Article
                Publisher ID: PONE-D-20-02180
                DOI: 10.1371/journal.pone.0234804
                PMC ID: 7304600
                PubMed ID: 32559212
                SO-VID: fe351134-6113-46cd-bfb6-c6045e5a6073
                Copyright © © 2020 Ananthapavan et al
                License:

                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
                Date received : 23 January 2020
                Date accepted : 2 June 2020
                Page count
                Figures: 1, Tables: 4, Pages: 19
                Funding
                Funded by: National Health and Medical Research Council
                Award ID: APP1041020
                Award Recipient :
                This research activity was supported by the National Health and Medical Research Council (NHMRC) funded Centre of Research Excellence in Obesity Policy and Food Systems (APP1041020). GS was supported by an Australian Research Council Discovery Early Career Researcher Award (DE160100307) and a National Heart Foundation of Australia Future Leader Fellowship (102035). VB and AL were supported by a Deakin University Postgraduate Research Scholarship. AL was supported by a Deakin University Dean’s Postdoctoral Research Fellowship. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Subject: Research Article
                Subject: Biology and Life Sciences
                Subject: Physiology
                Subject: Physiological Parameters
                Subject: Body Weight
                Subject: Obesity
                Subject: Medicine and Health Sciences
                Subject: Physiology
                Subject: Physiological Parameters
                Subject: Body Weight
                Subject: Obesity
                Subject: Biology and Life Sciences
                Subject: Nutrition
                Subject: Diet
                Subject: Beverages
                Subject: Medicine and Health Sciences
                Subject: Nutrition
                Subject: Diet
                Subject: Beverages
                Subject: Social Sciences
                Subject: Economics
                Subject: Economic Analysis
                Subject: Cost-Effectiveness Analysis
                Subject: Medicine and Health Sciences
                Subject: Public and Occupational Health
                Subject: Physical Activity
                Subject: Social Sciences
                Subject: Political Science
                Subject: Public Policy
                Subject: Taxes
                Subject: Social Sciences
                Subject: Economics
                Subject: Health Economics
                Subject: Medicine and Health Sciences
                Subject: Health Care
                Subject: Health Economics
                Subject: Social Sciences
                Subject: Political Science
                Subject: Governments
                Subject: Biology and Life Sciences
                Subject: Physiology
                Subject: Physiological Parameters
                Subject: Body Weight
                Subject: Body Mass Index
                Subject: Medicine and Health Sciences
                Subject: Physiology
                Subject: Physiological Parameters
                Subject: Body Weight
                Subject: Body Mass Index
                Custom metadata
                Data Availability All relevant data are within the manuscript and its Supporting Information files.

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