Patricia L. Mabry

Changing the future of obesity: science, policy, and action

The global obesity epidemic has been escalating for four decades, yet sustained prevention efforts have barely begun. An emerging science that uses quantitative models has provided key insights into the dynamics of this epidemic, and enabled researchers to combine evidence and to calculate the effect of behaviours, interventions, and policies at several levels--from individual to population. Forecasts suggest that high rates of obesity will affect future population health and economics. Energy gap models have quantified the association of changes in energy intake and expenditure with weight change, and have documented the effect of higher intake on obesity prevalence. Empirical evidence that shows interventions are effective is limited but expanding. We identify several cost-effective policies that governments should prioritise for implementation. Systems science provides a framework for organising the complexity of forces driving the obesity epidemic and has important implications for policy makers. Many parties (such as governments, international organisations, the private sector, and civil society) need to contribute complementary actions in a coordinated approach. Priority actions include policies to improve the food and built environments, cross-cutting actions (such as leadership, healthy public policies, and monitoring), and much greater funding for prevention programmes. Increased investment in population obesity monitoring would improve the accuracy of forecasts and evaluations. The integration of actions within existing systems into both health and non-health sectors (trade, agriculture, transport, urban planning, and development) can greatly increase the influence and sustainability of policies. We call for a sustained worldwide effort to monitor, prevent, and control obesity.

Simulation models of obesity: a review of the literature and implications for research and policy.

Simulation models (SMs) combine information from a variety of sources to provide a useful tool for examining how the effects of obesity unfold over time and impact population health. SMs can aid in the understanding of the complex interaction of the drivers of diet and activity and their relation to health outcomes. As emphasized in a recently released report of the Institute or Medicine, SMs can be especially useful for considering the potential impact of an array of policies that will be required to tackle the obesity problem. The purpose of this paper is to present an overview of existing SMs for obesity. First, a background section introduces the different types of models, explains how models are constructed, shows the utility of SMs and discusses their strengths and weaknesses. Using these typologies, we then briefly review extant obesity SMs. We categorize these models according to their focus: health and economic outcomes, trends in obesity as a function of past trends, physiologically based behavioural models, environmental contributors to obesity and policy interventions. Finally, we suggest directions for future research.

Systems Science: A Revolution in Public Health Policy Research

The new systems science approaches emerging in public health research are not new at all; they have a track record earned over several decades in other disciplines, such as physics, operations research, economics, engineering, and, more recently, systems biology. At their core, systems science methodologies are designed to generate models, or simplified versions, of reality. By replicating the real world in important ways—simplifying where possible while retaining the critical aspects relevant to the problem under study—we can better understand the structural complexity of real-world problems that results from the interaction of specific phenomena and their environments. Systems science approaches have been used to address wide-ranging topics such as wildfire control, overfishing, decline of ancient civilizations, climate change, and terrorism networks. A major reason for their recent adoption in the public health arena is a growing recognition of their utility for addressing the complex problems rampant in public health generally1 and in specific domains (e.g., neighborhood effects on health and obesity).2,3 A small but growing number of studies have employed systems science methodologies to understand and address public health problems such as pandemic flu,4 HIV/AIDS,5 diabetes prevalence,6 and heroin markets.7 A few researchers have been using systems science to further tobacco control for nearly a decade.8,9

Boosting Population Quits Through Evidence-Based Cessation Treatment and Policy

Only large increases in adult cessation will rapidly reduce population smoking prevalence. Evidence-based smoking-cessation treatments and treatment policies exist but are underutilized. More needs to be done to coordinate the widespread, efficient dissemination and implementation of effective treatments and policies. This paper is the first in a series of three to demonstrate the impact of an integrated, comprehensive systems approach to cessation treatment and policy. This paper provides an analytic framework and selected literature review that guide the two subsequent computer simulation modeling papers to show how critical leverage points may have an impact on reductions in smoking prevalence. Evidence is reviewed from the U.S. Public Health Service 2008 clinical practice guideline and other sources regarding the impact of five cessation treatment policies on quit attempts, use of evidence-based treatment, and quit rates. Cessation treatment policies would: (1) expand cessation treatment coverage and provider reimbursement; (2) mandate adequate funding for the use and promotion of evidence-based state-sponsored telephone quitlines; (3) support healthcare systems changes to prompt, guide, and incentivize tobacco treatment; (4) support and promote evidence-based treatment via the Internet; and (5) improve individually tailored, stepped-care approaches and the long-term effectiveness of evidence-based treatments. This series of papers provides an analytic framework to inform heuristic simulation models in order to take a new look at ways to markedly increase population smoking cessation by implementing a defined set of treatments and treatment-related policies with the potential to improve motivation to quit, evidence-based treatment use, and long-term effectiveness.

Modeling the impact of smoking-cessation treatment policies on quit rates.

BACKGROUND Smoking-cessation treatment policies could yield substantial increases in adult quit rates in the U.S. PURPOSE The goals of this paper are to model the effects of individual cessation treatment policies on population quit rates, and to illustrate the potential benefits of combining policies to leverage their synergistic effects. METHODS A mathematical model is updated to examine the impact of five cessation treatment policies on quit attempts, treatment use, and treatment effectiveness. Policies include: (1) expand cessation treatment coverage and provider reimbursement; (2) mandate adequate funding for the use and promotion of evidence-based, state-sponsored telephone quitlines; (3) support healthcare system changes to prompt, guide, and incentivize tobacco treatment; (4) support and promote evidence-based treatment via the Internet; and (5) improve individually tailored, stepped-care approaches and the long-term effectiveness of evidence-based treatments. RESULTS The annual baseline population quit rate is 4.3% of all current smokers. Implementing any policy in isolation is projected to increase the quit rate to between 4.5% and 6%. By implementing all five policies in combination, the quit rate is projected to increase to 10.9%, or 2.5 times the baseline rate. CONCLUSIONS If fully implemented in a coordinated fashion, cessation treatment policies could reduce smoking prevalence from its current rate of 20.5% to 17.2% within 1 year. By modeling the policy impacts on the components of the population quit rate (quit attempts, treatment use, treatment effectiveness), key indicators are identified that need to be analyzed in attempts to improve the effect of cessation treatment policies.

A Call to Address Complexity in Prevention Science Research

The problems targeted by preventive interventions are often complex, embedded in multiple levels of social and environmental context, and span the developmental lifespan. Despite this appreciation for multiple levels and systems of influence, prevention science has yet to apply analytic approaches that can satisfactorily address the complexities with which it is faced. In this article, we introduce a systems science approach to problem solving and methods especially equipped to handle complex relationships and their evolution over time. Progress in prevention science may be significantly enhanced by applying approaches that can examine a wide array of complex systems interactions among biology, behavior, and environment that jointly yield unique combinations of developmental risk and protective factors and outcomes. To illustrate the potential utility of a systems science approach, we present examples of current prevention research challenges, and propose how to complement traditional methods and augment research objectives by applying systems science methodologies.

Reaching Healthy People 2010 by 2013: A SimSmoke Simulation

BACKGROUND Healthy People (HP2010) set as a goal to reduce adult smoking prevalence to 12% by 2010. PURPOSE This paper uses simulation modeling to examine the effects of three tobacco control policies and cessation treatment policies-alone and in conjunction-on population smoking prevalence. METHODS Building on previous versions of the SimSmoke model, the effects of a defined set of policies on quit attempts, treatment use, and treatment effectiveness are estimated as potential levers to reduce smoking prevalence. The analysis considers the effects of (1) price increases through cigarette tax increases, (2) smokefree indoor air laws, (3) mass media/educational policies, and (4) evidence-based and promising cessation treatment policies. RESULTS Evidence-based cessation treatment policies have the strongest effect, boosting the population quit rate by 78.8% in relative terms. Treatment policies are followed by cigarette tax increases (65.9%); smokefree air laws (31.8%); and mass media/educational policies (18.2%). Relative to the status quo in 2020, the model projects that smoking prevalence is reduced by 14.3% through a nationwide tax increase of

Developmental Systems Science: Exploring the Application of Systems Science Methods to Developmental Science Questions

2.00, by 7.2% through smokefree laws, by 4.7% through mass media/educational policies, and by 16.5% through cessation treatment policies alone. Implementing all of the above policies at the same time would increase the quit rate by 296%, such that the HP2010 smoking prevalence goal of 12% is reached by 2013. CONCLUSIONS The impact of a combination of policies led to some surprisingly positive possible futures in lowering smoking prevalence to 12% within just several years. Simulation models can be a useful tool for evaluating complex scenarios in which policies are implemented simultaneously, and for which there are limited data.

Exploring Scenarios to Dramatically Reduce Smoking Prevalence: A Simulation Model of the Three-Part Cessation Process

Developmental science theorists fully acknowledge the wide array of complex interactions among biology, behavior, and environment that together give rise to development. However, despite this conceptual understanding of development as a system, developmental science has not fully applied analytic methods commensurate with this systems perspective. This article provides a brief introduction to systems science, an approach to problem solving that involves the use of methods especially equipped to handle complex relationships and their evolution over time. In addition, a rationale is provided for why and how these methods can serve the needs of the developmental science research community.

Agent-Based Models and Systems Science Approaches to Public Health

OBJECTIVES We used a simulation model to analyze whether the Healthy People 2010 goal of reducing smoking prevalence from the current 19.8% rate to 12% by 2010 could be accomplished by increasing quit attempts, increasing the use of treatments, or increasing the effectiveness of treatment. METHODS We expanded on previous versions of the tobacco control simulation model SimSmoke to assess the effects of an increase in quit attempts, treatment use, and treatment effectiveness to reduce smoking prevalence. In the model, we considered increases in each of these parameters individually and in combination. RESULTS Individually, 100% increases in quit attempts, treatment use, and treatment effectiveness reduced the projected 2020 prevalence to 13.9%, 16.7%, and 15.9%, respectively. With a combined 100% increase in all components, the goal of a 12% adult smoking prevalence could be reached by 2012. CONCLUSIONS If we are to come close to reaching Healthy People 2010 goals in the foreseeable future, we must not only induce quit attempts but also increase treatment use and effectiveness. Simulation models provide a useful tool for evaluating the potential to reach public health targets.