Causes and Interventions for Childhood Obesity:Innovative Systems Analysis

描述（由申请人提供）：儿童肥胖的原因和干预措施：创新系统分析肥胖已成为美国的公共卫生危机。肥胖被认为是生物、行为、文化、社会、环境和经济动态在多个层面上复杂相互作用的结果。使用传统的分析方法来研究如此复杂的动力学是一个挑战。我们提出的项目旨在满足该领域的几个迫切需求，包括实证结果和方法问题。我们的项目将解决最近几个NIH RFA和PAR中指定的几个目标，包括PAR-08-224“使用系统科学方法来保护和改善人口健康（R21）”。我们的中心假设是，个体能量平衡相关行为（EBRB）和体重结果的决定因素涉及复杂的动态过程，包括多层次因素之间的各种反馈回路。我们有四个具体目标（目标1中的分析为目标2-3做准备；目标2-3中开发的系统模型将用于目标4）：目标1：使用创新的、综合的概念框架和多层次统计分析方法，研究个人、家庭和环境因素对儿童肥胖的影响和相互作用。目标2：使用基于主体的模型（ABM）来测试简单的规则（例如，儿童如何与他们的社会和建筑环境相互作用），这些规则有助于解释个人的EBRB和肥胖风险以及这些结果在人口水平上的变化。目标3：在人口水平上确定儿童肥胖流行的主要背景驱动因素（即时间趋势），使用系统分析方法和与背景措施相关联的具有全国代表性的数据集的新组合。这将有助于开发和校准系统动力学模型（SDM），该模型可以复制肥胖流行的时间过程，并有助于预测未来肥胖趋势和干预方案的影响。目标4：根据我们的目标1-3和文献中的结果，考虑到非线性、反馈回路和递归因果关系，确定和描述有希望的干预/政策策略；并使用目标2-3中开发和校准的SDM和ABM模型来预测/模拟这些策略对肥胖率的影响。我们将根据不同规格的模型进行敏感性分析。我们的系统分析将使用一套创新的、复杂的方法进行，包括多层次模型（MLM）和系统分析模型，用于分析经验和模拟数据。将使用来自国家调查的数据，包括与来自其他数据源的背景测量相关联的队列研究。我们的多学科团队拥有丰富的相关经验。我们的方法论产品将有利于未来的研究，我们的实证研究结果将有助于澄清围绕儿童肥胖流行原因的几个争议，并有助于指导未来的干预措施。

DESCRIPTION (provided by applicant): Causes and Interventions for Childhood Obesity: Innovative Systems Analysis Obesity has become a public health crisis in the United States. Obesity is believed as the result of a complex interplay between biological, behavioral, cultural, social, environmental and economic dynamics operating at multiple levels. Studying such complex dynamics is a challenge using traditional analytical approaches. Our proposed project aimed to meet several urgent needs in the field, including both empirical results and methodological issues. Our project will address several of the objectives specified in several recent NIH RFA and PAR including PAR-08-224 "Using Systems Science Methodologies to Protect and Improve Population Health (R21)." Our central hypothesis is that the determinants of individuals' energy balance related behaviors (EBRB) and body weight outcomes involve complex, dynamic processes including various feedback loops across multi-level factors. We have four specific aims (analysis in Aim 1 prepares for Aims 2-3; and system models developed in Aims 2-3 will be used in Aim 4): Aim 1: Using innovative, integrated conceptual framework and multilevel statistical analysis approaches, to examine the influences and interactions between individual, family and environmental factors on childhood obesity. Aim 2: Using agent-based models (ABM) to test simple rules (e.g., how children may interact with their social and built environments) that help explain individuals' EBRB and obesity risk and the changes in population level rates of these outcomes. Aim 3: To determine the key contextual drivers of the childhood obesity epidemic at the population level (i.e., time trends), using a novel combination of systems analysis methods and nationally representative data sets linked with contextual measures. This will help develop and calibrate systems dynamics models (SDM) that can replicate the time-course of the obesity epidemic and help project future obesity trends and impact of intervention options. Aim 4: To identify and characterize promising intervention/policy strategies based our results of aims 1-3 and those in the literature, taking into account non- linearities, feedback loops and recursive causal relations; and to project/simulate impacts of these strategies on obesity rates using SDM and ABM models developed and calibrated in Aims 2-3. We will conduct sensitivity analyses based on various specifications of models. Our systematic analysis will be conducted using a set of innovative, sophisticated methods including multilevel models (MLM) and systems analysis models for analyses of empirical and simulation data. Data from national surveys including cohort studies linked with contextual measures from other data sources will be used. Our multidisciplinary team has extensive related experiences. Our methodological products will benefit future studies, and our empirical findings will help clarify several controversies surrounding the causes of childhood obesity epidemic and help guide future interventions. PUBLIC HEALTH RELEVANCE: Using integrated conceptual framework, innovative statistical analysis approaches including systems analysis models, national survey data sets and contextual measures we will study the influences and interactions between individual and environmental factors on childhood obesity, the key drivers of the US childhood obesity epidemic. We will also project the impacts of several childhood obesity intervention strategies.