文章基本信息

标题：Modeling US Adult Obesity Trends: A System Dynamics Model for Estimating Energy Imbalance Gap
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作者：Saeideh Fallah-Fini ; Hazhir Rahmandad ; Terry T.-K. Huang 等
期刊名称：American journal of public health
印刷版ISSN：0090-0036
出版年度：2014
卷号：104
期号：7
页码：1230-1239
DOI：10.2105/AJPH.2014.301882
语种：English
出版社：American Public Health Association
摘要：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. The energy imbalance gap (EIG) is an important factor in the development of obesity and a key target of public health interventions to reduce obesity. 1 The EIG captures the average daily excess energy intake, defined as total energy intake minus total energy expenditure for some unit of time, and is a critical control parameter in the energy system; it governs the speed of change in body mass. 2 A related concept, maintenance energy gap (MEG), captures the increased energy intake needed to maintain higher average body weights compared with an initial (e.g., the early 1970s) distribution of body weight (i.e., heavier individuals expend more energy as a result of their body mass and hence need higher energy intake to keep their weight in equilibrium). 3,4 The MEG captures the extent of change in energy intake that is needed to turn back the obesity epidemic, and as such relates to the long-term accumulation of energy imbalance in the body mass index (BMI, defined as weight in kilograms divided by the square of height in meters) distribution and is often larger than the EIG. 3 Previous studies have pointed to the importance of quantifying both the EIG and MEG to explain the magnitude of changes required to reverse the obesity epidemic, provide intervention targets, and estimate the contribution of different drivers of obesity, 3–6 but concerns have also been raised about the effectiveness of using overly simplified models of the EIG as tools to design obesity interventions. 1 Estimating the EIG at the population level requires the use of models that can capture the feedback relationships between body weight and different body tissues that store and expend energy (e.g., fat mass vs fat-free mass) as well as nonlinear changes over time. 7 For example, the models must account for differential mortality rates by weight class to avoid underestimation of the EIG because of higher mortality among the very obese. To date, the literature has focused on estimates of the EIG and MEG for entire populations averaged over long time horizons. 3–6,8,9 These estimates lack detail on changes in the EIG and MEG over time and across subpopulations and weight groups. Correct specification of these variations is essential because people of different gender and racial/ethnic subpopulations or BMI classes may be affected differentially by the environment and may respond differently to interventions. 7 There is also evidence that secular trends may be diverging among demographic subpopulations in the United States. 10 As such, there is a clear public health need for models that are able to distinguish finer trends and provide more nuanced EIG and MEG estimations to develop and test targeted interventions. We used system dynamics modeling to address the limitations of previous EIG models and leveraged those estimates to also calculate MEG trends for different subpopulations. Although system dynamics methodology is increasingly used in public health research to explain the complex etiology of health and disease 11–16 and to test intervention effectiveness, 17–20 we provide one of the first applications of system dynamics to the population dynamics of EIG and MEG over time as an important first step for the design of obesity prevention interventions targeting specific subpopulations. Many system dynamics applications have been based solely on simulated agents or artificial populations. To inform public health practice, models can be strengthened by connecting what we know about the biology of obesity from clinical and lab-based studies to population dynamics in a way that is explicitly linked to existing empirical data. We used an innovative method 21 to connect a validated individual-level model of weight dynamics 22 to population-level obesity dynamics and estimate the EIG associated with different gender and race/ethnicity subpopulations, without the need to simulate a large number of individuals explicitly. 16 Finally, we calculated the MEG values by using the EIG and population BMI profile dynamics. This allowed us to address 3 key questions: (1) How can the dynamics of the average EIG help explain observed changes in the prevalence of obesity in the US adult population in the past 4 decades?; (2) How do these dynamics differ across different gender, race/ethnicity, and BMI groups?; and (3) How have MEG values changed over the past 4 decades across different subpopulations?