MOTS: Modeling Obesity Through Simulation

描述（由申请人提供）：儿童超重和肥胖已经成为一种流行病。2003-2004年，超过33%的美国儿童和青少年超重或肥胖。随着1980年以后出生的人群步入成年和中年，我们将看到糖尿病、心脏病、肾衰竭和相关代谢紊乱的发病率增加。为了确定儿童肥胖的原因，已经花费了大量的研究工作，这是提出潜在的预防策略以减轻或扭转这一日益严重的问题的必要的第一步。然而，到目前为止，只解决了问题的特定子集。一些已确定的与肥胖有关的因素显然是相互关联的。例如，建成环境的特征因种族而异，致肥因素在弱势社区更为普遍；这可能会增加儿童肥胖和低体力活动水平的风险。同样，代谢过程也嵌套在个体内部，个体进一步位于社区内；因此，胰岛素抵抗也可能与接近健康社区及其便利设施有关。解开儿童肥胖的因果网络是一项艰巨的任务，现有的数据和标准的流行病学分析可能不足以捕捉多重和相互作用的因果关系。这个问题并不是肥胖流行病学所特有的；微观结构问题和宏观结构问题是社会科学研究中普遍存在的问题。此外，反馈回路的存在排除了因果关系的标准方法，这些方法基于定向（非反馈）关系、效果的独立性和可识别性假设。针对RFA-HD-08-023 (Innovative Computational and Statistical Methodologies for Design and Analysis of Multilevel Studies on Childhood Obesity)，我们提出了一种新的多水平研究设计和分析方案。我们特别提出了一种基于综合、模拟和操作的策略。我们的方法将集中在一个新的方法技术，基于主体的计算建模。

DESCRIPTION (provided by applicant): Childhood overweight and obesity has emerged as an epidemic. In 2003-2004, over 33 percent of US children and adolescents were overweight or obese. As the cohort born since 1980 moves into adulthood and middle age, we will see increasing incidence of diabetes, heart disease, kidney failure, and related metabolic disorders. Considerable research effort has been expended to identify the causes of childhood obesity, a necessary first step in suggesting potential prevention strategies to mitigate or reverse this growing problem. However, to date only particular subsets of the problem have been addressed. Some of the identified factors related to obesity are clearly nested within others. For example, characteristics of the built environment vary by race-ethnicity, and obesogenic factors are more prevalent in disadvantaged communities; this may increase the risk of obesity and low physical activity levels in children. Likewise, metabolic processes are nested within individuals, who are further located within neighborhoods; as a result, insulin resistance may also follow proximity to healthy neighborhoods and their amenities. Disentangling the web of causation of childhood obesity is a formidable task, and both available data and standard epidemiologic analyses may be inadequate to capture multiple and interacting levels of causation. This problem is not particular to obesity epidemiology; the problems of micro- and macro-structure are common in social sciences. Furthermore, the presence of feedback loops precludes standard approaches to causality, which are based on directed (non-feedback) relationships, independence of effects, and identifiability assumptions. In response to RFA-HD-08-023 (Innovative Computational and Statistical Methodologies for the Design and Analysis of Multilevel Studies on Childhood Obesity), we propose a novel multilevel study design and analysis plan to address this problem. In particular, we propose a strategy based on synthesis, simulation, and manipulation. Our methods will focus on a novel methodological technique, agent-based computational modeling. PUBLIC HEALTH RELEVANCE: Disentangling the web of causation of childhood obesity is a formidable task, and both available data and standard epidemiologic analyses may be inadequate to capture multiple and interacting levels of causation. We propose a novel multilevel study design and analysis plan to address this problem, based on synthesis, simulation, and manipulation. Our methods will focus on a novel methodological technique, agent-based computational modeling.