Modeling US adult obesity trends: a system dynamics model for estimating energy imbalance gap.

OBJECTIVES We present a system dynamics model that quantifies the energy imbalance gap responsible for the US adult obesity epidemic among gender and racial subpopulations. METHODS We divided the adult population into gender-race/ethnicity subpopulations and body mass index (BMI) classes. We defined transition rates between classes as a function of metabolic dynamics of individuals within each class. We estimated energy intake in each BMI class within the past 4 decades as a multiplication of the equilibrium energy intake of individuals in that class. Through calibration, we estimated the energy gap multiplier for each gender-race-BMI group by matching simulated BMI distributions for each subpopulation against national data with maximum likelihood estimation. RESULTS No subpopulation showed a negative or zero energy gap, suggesting that the obesity epidemic continues to worsen, albeit at a slower rate. In the past decade the epidemic has slowed for non-Hispanic Whites, is starting to slow for non-Hispanic Blacks, but continues to accelerate among Mexican Americans. CONCLUSIONS The differential energy balance gap across subpopulations and over time suggests that interventions should be tailored to subpopulations' needs.

[1]  Carson C. Chow,et al.  Quantification of the effect of energy imbalance on bodyweight , 2011, The Lancet.

[2]  Bobby Milstein,et al.  Charting Plausible Futures for Diabetes Prevalence in the United States: A Role for System Dynamics Simulation Modeling , 2007, Preventing chronic disease.

[3]  J. Homer,et al.  Models for collaboration: how system dynamics helped a community organize cost‐effective care for chronic illness , 2004 .

[4]  Achieving energy balance at the population level through increases in physical activity. , 2007, American journal of public health.

[5]  T. Vanitallie Prevalence of obesity. , 1996, Endocrinology and metabolism clinics of North America.

[6]  Jennifer L Baker,et al.  The levelling off of the obesity epidemic since the year 1999 – a review of evidence and perspectives , 2010, Obesity reviews : an official journal of the International Association for the Study of Obesity.

[7]  David T Levy,et al.  Simulation modeling and tobacco control: creating more robust public health policies. , 2006, American journal of public health.

[8]  Katherine M Flegal,et al.  Prevalence of obesity in the United States, 2009-2010. , 2012, NCHS data brief.

[9]  E. Ravussin,et al.  Estimating the changes in energy flux that characterize the rise in obesity prevalence. , 2009, The American journal of clinical nutrition.

[10]  J. Homer,et al.  System dynamics modeling for public health: background and opportunities. , 2006, American journal of public health.

[11]  Karen M. Kuntz,et al.  Estimating the Energy Gap Among US Children: A Counterfactual Approach , 2006, Pediatrics.

[12]  James O. Hill,et al.  Obesity and the Environment: Where Do We Go from Here? , 2003, Science.

[13]  J. Homer A system dynamics model of national cocaine prevalence , 1993 .

[14]  B. Swinburn,et al.  The global obesity pandemic: shaped by global drivers and local environments , 2011, The Lancet.

[15]  C. Ogden,et al.  Prevalence of Overweight, Obesity, and Extreme Obesity Among Adults: United States, Trends 1960-1962 Through 2009-2010 , 2012 .

[16]  Nutrition Board,et al.  Bridging the Evidence Gap in Obesity Prevention: A Framework to Inform Decision Making , 2010 .

[17]  J. Homer,et al.  A system dynamics model for planning cardiovascular disease interventions. , 2010, American journal of public health.

[18]  Hazhir Rahmandad,et al.  Connecting micro dynamics and population distributions in system dynamics models. , 2013, System dynamics review.

[19]  D. Luke,et al.  Systems science methods in public health: dynamics, networks, and agents. , 2012, Annual review of public health.

[20]  Kevin D Hall,et al.  Energy balance and its components: implications for body weight regulation. , 2012, The American journal of clinical nutrition.

[21]  Hazhir Rahmandad,et al.  Reporting guidelines for simulation‐based research in social sciences , 2012 .

[22]  D. S. Gray Diagnosis and prevalence of obesity. , 1989, The Medical clinics of North America.

[23]  S. Zubrick,et al.  Tackling overweight and obesity: does the public health message match the science? , 2013, BMC Medicine.

[24]  J. Homer,et al.  Toward a dynamic theory of antibiotic resistance , 2000 .

[25]  Kevin D. Hall,et al.  The Progressive Increase of Food Waste in America and Its Environmental Impact , 2009, PloS one.

[26]  Katherine M Flegal,et al.  Prevalence of obesity and trends in the distribution of body mass index among US adults, 1999-2010. , 2012, JAMA.

[27]  Nathaniel D. Osgood,et al.  Using System Dynamics tools to gain insight into intervention options related to the interaction between tobacco and tuberculosis , 2010, Global health promotion.

[28]  B. Swinburn,et al.  Dynamics of childhood growth and obesity: development and validation of a quantitative mathematical model. , 2013, The lancet. Diabetes & endocrinology.

[29]  J. Homer,et al.  Understanding diabetes population dynamics through simulation modeling and experimentation. , 2006, American journal of public health.