Multilevel Longitudinal Models and Causal Networks for Childhood Obesity

儿童肥胖的多层次纵向模型和因果网络

• 批准号: 8129658
• 负责人: SHUMEI S SUN
• 金额: $ 35.56万
• 依托单位: VIRGINIA COMMONWEALTH UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-09-15 至 2014-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8129658
• 关键词: Adolescent; Affect; Age; Automobiles; Birth; Birth Weight; Blood Pressure; Body Composition; Body Size; Body fat; Characteristics; Child; Childhood; Code; Cohort Effect; Collection; Computer Architectures; Computers; Crime; Data; Data Set; Databases; Development; Drug Formulations; Economics; Education; Environment; Epidemic; Etiology; Exposure to; Family; Fructose; Gender; Genes; Genetic; Gestational Age; Growth; Health Personnel; Heritability; Hour; Income; Individual; Infant; Intervention; Investigation; Life; Life Style; Linear Regressions; Longitudinal Studies; Measurement; Mental Depression; Methodology; Methods; Minority; Modeling; Multivariate Analysis; Nature; Neighborhoods; Nuclear Family; Obesity; Outcome; Ownership; Participant; Pathway interactions; Pattern; Personal Computers; Physical activity; Population; Prevalence; Preventive Intervention; Procedures; Property; Questionnaires; Research Personnel; Restaurants; Risk Factors; Schools; Seasonal Variations; Siblings; Signal Transduction; Simulate; Skin; Social Environment; Statistical Methods; Statistical Models; Stochastic Processes; Students; Supermarket; Television; Time; Validation; War; World War II; base; cigarette smoking; cohort; diet and exercise; environmental change; experience; fast food; feeding; high risk; improved; innovation; multilevel analysis; network models; obesity in children; parental influence; prevent; sex; simulation; social; socioeconomics; suburb; trait; tv watching; validation studies; waist circumference

DESCRIPTION (provided by applicant): The prevalence of obesity among US children and adolescents more than doubled between 1980 and 2004. Obesity frustrates health care providers because of its difficulty to reverse. Identifying children at high risk of obesity has been problematic, and it is difficult to identify causative factors at any level that are amenable to preventive interventions. We propose to develop and apply innovative statistical methods to the Fels Longitudinal Study (FLS) database to analyze multivariate and multilevel determinants of the increase in prevalence of childhood obesity over the past three decades. Repeated measurements of body size and composition from birth and frequent administration of questionnaires on lifestyle, diet, exercise and SES on the same individuals in the FLS over decades permits inferences of causality that cannot be supplied by cross-sectional data. We plan to use the multilevel longitudinal hierarchical models and Granger causality s that should be most susceptible to interventions to prevent or delay the onset of obesity in childhood. We will use serial data collected in 2076 individuals in the FLS, beginning in 1929, to develop multivariate and multilevel models and Granger networks to infer causality. We will validate these models and networks for their robustness by bootstrap methods and cross-validation and by using simulated data that mimics the FLS database. Our multilevel modeling and Granger causality networks of factors involved in the obesity epidemic should delineate plausible pathways and interactions among factors that explain and track the epidemic. Discovery and validation of these pathways and interactions should reveal optimal targets for simultaneous multilevel interventions to prevent obesity in childhood and/or to alter the time course of relevant causal variables. We assume that such multilevel interventions will be more successful than currently applied single level interventions in reducsocial, economic, dietary and other environmental variables. The study also permits discovery and analysis of cohort effects of social-environmental changes from 1929 through 2008. While investigations will be performed on risk factors for obesity, these methods will be applicable to other sets of variables. Our new methods should assist other investigators in planning longitudinal studies and in analyzing longitudinal data.

描述（由申请人提供）：1980年至2004年，美国儿童和青少年的肥胖患病率增加了一倍多。肥胖使医疗保健提供者感到沮丧，因为它很难扭转。确定儿童肥胖的高风险一直是个问题，很难确定在任何水平上的致病因素，可以采取预防性干预措施。我们建议开发和应用创新的统计方法，以费尔斯纵向研究（FLS）数据库分析在过去的三十年中，儿童肥胖患病率增加的多变量和多层次的决定因素。从出生开始重复测量身体尺寸和组成，并在FLS中对同一个体进行数十年的生活方式，饮食，运动和SES的问卷调查，可以推断出无法通过横断面数据提供的因果关系。我们计划使用多水平纵向分层模型和格兰杰因果关系，应该是最容易干预，以防止或延迟儿童肥胖的发病。我们将使用从1929年开始在FLS中收集的2076个个体的系列数据来开发多变量和多水平模型以及格兰杰网络来推断因果关系。我们将通过引导方法和交叉验证以及使用模仿FLS数据库的模拟数据来验证这些模型和网络的鲁棒性。我们的多层次模型和格兰杰因果关系网络的因素参与肥胖流行病应该描绘合理的途径和相互作用的因素，解释和跟踪流行病。这些途径和相互作用的发现和验证应该揭示同时多层次干预的最佳目标，以预防儿童肥胖和/或改变相关因果变量的时间过程。我们认为，这种多层次的干预将比目前应用的单一层次的干预在减少社会，经济，饮食和其他环境变量更成功。该研究还允许发现和分析从1929年到2008年社会环境变化的队列效应。虽然将对肥胖的风险因素进行调查，但这些方法将适用于其他变量集。我们的新方法将有助于其他研究者规划纵向研究和分析纵向数据。