MOTS: Modeling Obesity Through Simulation

描述(申请人提供)：儿童超重和肥胖已成为一种流行病。2003-2004年间，超过33%的美国儿童和青少年超重或肥胖。随着1980年后出生的人群进入成年和中年，我们将看到糖尿病、心脏病、肾衰竭和相关代谢疾病的发病率不断上升。为了确定儿童肥胖的原因，已经进行了大量的研究工作，这是提出可能的预防战略以缓解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相关的因素显然与其他因素有关联。例如，建筑环境的特征因种族而异，致肥因素在弱势社区中更为普遍；这可能会增加儿童肥胖和低体力活动水平的风险。同样，新陈代谢过程嵌套在个人体内，而个人更靠近社区；因此，胰岛素抵抗也可能发生在接近健康社区及其设施的地方。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都不足以捕捉到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；微观和宏观结构问题在社会科学中很常见。此外，反馈回路的存在排除了因果关系的标准方法，这些方法基于直接(非反馈)关系、影响的独立性和可辨识性假设。为响应RFA-HD-08-023(儿童肥胖多水平研究设计和分析的创新计算和统计方法)，我们提出了一个新的多水平研究设计和分析计划来解决这个问题。特别是，我们提出了一种基于综合、仿真和操纵的策略。我们的方法将专注于一种新的方法学技术-基于代理的计算建模。 公共卫生相关性：理清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都不足以捕捉多种相互作用的因果关系。我们提出了一种新的基于综合、模拟和操作的多水平研究设计和分析计划来解决这个问题。我们的方法将专注于一种新的方法学技术-基于代理的计算建模。

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.