Causes and Interventions for Childhood Obesity:Innovative Systems Analysis

儿童肥胖的原因和干预措施：创新系统分析

基本信息

批准号：
8150352
负责人：
YOUFA WANG
金额：
$ 37.14万
依托单位：
JOHNS HOPKINS UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2010
资助国家：
美国
起止时间：
2010-09-29 至 2014-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8150352
关键词：
Abbreviations Accounting Address Adopted Affect Age Behavior Behavioral Biological Biological Models Biometry Body Weight Centers for Disease Control and Prevention (U.S.)Child Chronic Disease Cohort Studies Communities Complex Computer software Data Data Set Data Sources Dependence Development Dietary intake Discipline Eating Economics Engineering Environment Environmental Risk Factor Epidemic Epidemiology Ethnic Origin Family Feedback Food Food Services Future Glass Human Biology Individual Intervention Learning Linear Models Link Literature Measures Mediating Methodology Methods Modeling Neighborhoods Obesity Outcome Physical activity Policies Population Prevalence Prevention Research Price Process Public Health Relative (related person)Request for Applications Research Design Risk Role Sampling Schools Science Simulate Social Sciences Source Specific qualifier value Surveys System Systems Analysis Techniques Testing Time United States United States National Institutes of Health Weight Weight Gain base cohort disorder prevention energy balance experience fruits and vegetables improved innovation meetings multidisciplinary multilevel analysis novel nutrition obesity in children obesity risk peer influence population health public health relevance sex simulation social trend

项目摘要

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.

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。使用传统分析方法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紧迫需求，包括实证结果和方法论问题。我们的项目将解决最近的 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，包括跨多层次因素的各种反馈循环。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) 测试简单的规则（例如，儿童如何与其社交和建筑环境互动），帮助解释个人的 EBRB 和肥胖风险以及这些结果的人口水平变化。目标 3：使用系统分析方法和与背景测量相关的全国代表性数据集的新颖组合，确定人口层面儿童肥胖流行的关键背景驱动因素（即时间趋势）。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，并帮助预测未来的肥胖趋势和干预方案的影响。目标 4：根据目标 1-3 的结果和文献中的结果，识别和描述有前景的干预/政策策略，同时考虑非线性、反馈循环和递归因果关系；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。我们会根据不同规格的型号进行敏感性分析。我们的系统分析将使用一套创新、复杂的方法进行，包括用于分析经验和模拟数据的多级模型（MLM）和系统分析模型。将使用来自全国调查的数据，包括与其他数据源的背景测量相关的队列研究。我们的多学科团队拥有丰富的相关经验。我们的方法学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一些争议，并有助于指导未来的干预措施。公共卫生相关性：利用综合概念框架、创新的统计分析方法（包括系统分析模型、国家调查数据集和背景测量），我们将研究个人和环境因素对儿童肥胖的影响和相互作用，而儿童肥胖是美国儿童肥胖流行的关键驱动因素。我们还将预测几种儿童肥胖干预策略的影响。