Gary Sacks

The global obesity pandemic: shaped by global drivers and local environments

The simultaneous increases in obesity in almost all countries seem to be driven mainly by changes in the global food system, which is producing more processed, affordable, and effectively marketed food than ever before. This passive overconsumption of energy leading to obesity is a predictable outcome of market economies predicated on consumption-based growth. The global food system drivers interact with local environmental factors to create a wide variation in obesity prevalence between populations. Within populations, the interactions between environmental and individual factors, including genetic makeup, explain variability in body size between individuals. However, even with this individual variation, the epidemic has predictable patterns in subpopulations. In low-income countries, obesity mostly affects middle-aged adults (especially women) from wealthy, urban environments; whereas in high-income countries it affects both sexes and all ages, but is disproportionately greater in disadvantaged groups. Unlike other major causes of preventable death and disability, such as tobacco use, injuries, and infectious diseases, there are no exemplar populations in which the obesity epidemic has been reversed by public health measures. This absence increases the urgency for evidence-creating policy action, with a priority on reduction of the supply-side drivers.

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.

Increased food energy supply is more than sufficient to explain the US epidemic of obesity

BACKGROUND The major drivers of the obesity epidemic are much debated and have considerable policy importance for the population-wide prevention of obesity. OBJECTIVE The objective was to determine the relative contributions of increased energy intake and reduced physical activity to the US obesity epidemic. DESIGN We predicted the changes in weight from the changes in estimated energy intakes in US children and adults between the 1970s and 2000s. The increased US food energy supply (adjusted for wastage and assumed to be proportional to energy intake) was apportioned to children and adults and inserted into equations that relate energy intake to body weight derived from doubly labeled water studies. The weight increases predicted from the equations were compared with weight increases measured in representative US surveys over the same period. RESULTS For children, the measured weight gain was 4.0 kg, and the predicted weight gain for the increased energy intake was identical at 4.0 kg. For adults, the measured weight gain was 8.6 kg, whereas the predicted weight gain was somewhat higher (10.8 kg). CONCLUSIONS Increased energy intake appears to be more than sufficient to explain weight gain in the US population. A reversal of the increase in energy intake of approximately 2000 kJ/d (500 kcal/d) for adults and of 1500 kJ/d (350 kcal/d) for children would be needed for a reversal to the mean body weights of the 1970s. Alternatively, large compensatory increases in physical activity (eg, 110-150 min of walking/d), or a combination of both, would achieve the same outcome. Population approaches to reducing obesity should emphasize a reduction in the drivers of increased energy intake.

Estimating the changes in energy flux that characterize the rise in obesity prevalence

BACKGROUND The daily energy imbalance gap associated with the current population weight gain in the obesity epidemic is relatively small. However, the substantially higher body weights of populations that have accumulated over several years are associated with a substantially higher total energy expenditure (TEE) and total energy intake (TEI), or energy flux (EnFlux = TEE = TEI). OBJECTIVE The objective was to develop an equation relating EnFlux to body weight in adults for estimating the rise in EnFlux associated with the obesity epidemic. DESIGN Multicenter, cross-sectional data for TEE from doubly labeled water studies in 1399 adults aged 5.9 +/- 18.8 y (mean +/- SD) were analyzed in linear regression models with natural log (ln) weight as the dependent variable and ln EnFlux as the independent variable, adjusted for height, age, and sex. These equations were compared with those for children and applied to population trends in weight gain. RESULTS ln EnFlux was positively related to ln weight (beta = 0.71; 95% CI: 0.66, 0.76; R2 = 0.52), adjusted for height, age, and sex. This slope was significantly steeper than that previously described for children (beta = 0.45; 95% CI: 0.38, 0.51). CONCLUSIONS This relation suggests that substantial increases in TEI have driven the increases in body weight over the past 3 decades. Adults have a higher proportional weight gain than children for the same proportional increase in energy intake, mostly because of a higher fat content of the weight being gained. The obesity epidemic will not be reversed without large reductions in energy intake, increases in physical activity, or both.

INFORMAS (International Network for Food and Obesity/non-communicable diseases Research, Monitoring and Action Support): overview and key principles

Non‐communicable diseases (NCDs) dominate disease burdens globally and poor nutrition increasingly contributes to this global burden. Comprehensive monitoring of food environments, and evaluation of the impact of public and private sector policies on food environments is needed to strengthen accountability systems to reduce NCDs. The International Network for Food and Obesity/NCDs Research, Monitoring and Action Support (INFORMAS) is a global network of public‐interest organizations and researchers that aims to monitor, benchmark and support public and private sector actions to create healthy food environments and reduce obesity, NCDs and their related inequalities. The INFORMAS framework includes two ‘process’ modules, that monitor the policies and actions of the public and private sectors, seven ‘impact’ modules that monitor the key characteristics of food environments and three ‘outcome’ modules that monitor dietary quality, risk factors and NCD morbidity and mortality. Monitoring frameworks and indicators have been developed for 10 modules to provide consistency, but allowing for stepwise approaches (‘minimal’, ‘expanded’, ‘optimal’) to data collection and analysis. INFORMAS data will enable benchmarking of food environments between countries, and monitoring of progress over time within countries. Through monitoring and benchmarking, INFORMAS will strengthen the accountability systems needed to help reduce the burden of obesity, NCDs and their related inequalities.

'Traffic-light' nutrition labelling and 'junk-food' tax: a modelled comparison of cost-effectiveness for obesity prevention.

Introduction: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).Methods: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.Results: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).Conclusion: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.

Obesity Policy Action framework and analysis grids for a comprehensive policy approach to reducing obesity.

A comprehensive policy approach is needed to control the growing obesity epidemic. This paper proposes the Obesity Policy Action (OPA) framework, modified from the World Health Organization framework for the implementation of the Global Strategy on Diet, Physical Activity and Health, to provide specific guidance for governments to systematically identify areas for obesity policy action. The proposed framework incorporates three different public health approaches to addressing obesity: (i) ‘upstream’ policies influence either the broad social and economic conditions of society (e.g. taxation, education, social security) or the food and physical activity environments to make healthy eating and physical activity choices easier; (ii) ‘midstream’ policies are aimed at directly influencing population behaviours; and (iii) ‘downstream’ policies support health services and clinical interventions. A set of grids for analysing potential policies to support obesity prevention and management is presented. The general pattern that emerges from populating the analysis grids as they relate to the Australian context is that all sectors and levels of government, non‐governmental organizations and private businesses have multiple opportunities to contribute to reducing obesity. The proposed framework and analysis grids provide a comprehensive approach to mapping the policy environment related to obesity, and a tool for identifying policy gaps, barriers and opportunities.

Strengthening of accountability systems to create healthy food environments and reduce global obesity

To achieve WHOs target to halt the rise in obesity and diabetes, dramatic actions are needed to improve the healthiness of food environments. Substantial debate surrounds who is responsible for delivering effective actions and what, specifically, these actions should entail. Arguments are often reduced to a debate between individual and collective responsibilities, and between hard regulatory or fiscal interventions and soft voluntary, education-based approaches. Genuine progress lies beyond the impasse of these entrenched dichotomies. We argue for a strengthening of accountability systems across all actors to substantially improve performance on obesity reduction. In view of the industry opposition and government reluctance to regulate for healthier food environments, quasiregulatory approaches might achieve progress. A four step accountability framework (take the account, share the account, hold to account, and respond to the account) is proposed. The framework identifies multiple levers for change, including quasiregulatory and other approaches that involve government-specified and government-monitored progress of private sector performance, government procurement mechanisms, improved transparency, monitoring of actions, and management of conflicts of interest. Strengthened accountability systems would support government leadership and stewardship, constrain the influence of private sector actors with major conflicts of interest on public policy development, and reinforce the engagement of civil society in creating demand for healthy food environments and in monitoring progress towards obesity action objectives.

A systematic policy approach to changing the food system and physical activity environments to prevent obesity.

As obesity prevention becomes an increasing health priority in many countries, including Australia and New Zealand, the challenge that governments are now facing is how to adopt a systematic policy approach to increase healthy eating and regular physical activity. This article sets out a structure for systematically identifying areas for obesity prevention policy action across the food system and full range of physical activity environments. Areas amenable to policy intervention can be systematically identified by considering policy opportunities for each level of governance (local, state, national, international and organisational) in each sector of the food system (primary production, food processing, distribution, marketing, retail, catering and food service) and each sector that influences physical activity environments (infrastructure and planning, education, employment, transport, sport and recreation). Analysis grids are used to illustrate, in a structured fashion, the broad array of areas amenable to legal and regulatory intervention across all levels of governance and all relevant sectors. In the Australian context, potential regulatory policy intervention areas are widespread throughout the food system, e.g., land-use zoning (primary production within local government), food safety (food processing within state government), food labelling (retail within national government). Policy areas for influencing physical activity are predominantly local and state government responsibilities including, for example, walking and cycling environments (infrastructure and planning sector) and physical activity education in schools (education sector). The analysis structure presented in this article provides a tool to systematically identify policy gaps, barriers and opportunities for obesity prevention, as part of the process of developing and implementing a comprehensive obesity prevention strategy. It also serves to highlight the need for a coordinated approach to policy development and implementation across all levels of government in order to ensure complementary policy action.

Impact of ‘traffic‐light’ nutrition information on online food purchases in Australia

Objective:‘Traffic‐light’ nutrition labelling has been proposed as a potential tool for improving the diet of the population, yet there has been little published research on the impact of traffic‐light nutrition labelling on purchases in a supermarket environment. This study examined changes to online consumer food purchases in response to the introduction of traffic‐light nutrition information (TLNI).