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

基本信息

批准号：
7742757
负责人：
SHUMEI S SUN
金额：
$ 36.32万
依托单位：
VIRGINIA COMMONWEALTH UNIVERSITY
依托单位国家：
美国
项目类别：
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/7742757
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 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 depression 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

项目摘要

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）：美国儿童和青少年的肥胖症患病率在1980年至2004年之间增加了一倍以上。肥胖使医疗保健提供者感到沮丧，因为它难以逆转。识别高肥胖风险的儿童是有问题的，很难在任何水平上识别可进行预防干预措施的因素。我们建议将创新的统计方法开发和应用于FELS纵向研究（FLS）数据库，以分析过去三十年来儿童肥胖症患病率增加的多元和多层次决定因素。重复测量从出生和频繁地给出有关佛罗里达州同一个人的生活方式，饮食，运动和SES的体型和成分的重复测量，几十年来，在同一个人身上允许推断因果关系，而横截面数据无法提供因果关系。我们计划使用多级纵向分层模型和Granger因果关系，这些因果关系最容易受到干预措施，以防止或延迟童年时期肥胖症的发作。从1929年开始，我们将使用FLS中2076个个人收集的序列数据来开发多元和多级模型以及Granger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拟FLS数据库的模拟数据来验证这些模型和网络的鲁棒性。我们的多层次建模和肥胖流行因素的Granger因果关系网络应描绘出可话的途径以及解释和跟踪流行病的因素之间的合理途径和相互作用。这些途径和相互作用的发现和验证应揭示同时多级干预措施的最佳目标，以防止童年和/或改变相关因果变量的时间过程。我们认为，这种多层次干预措施将比当前在还原，经济，饮食和其他环境变量中应用的单级干预措施更成功。该研究还允许发现和分析1929年至2008年社会环境变化的队列影响。尽管将对肥胖风险因素进行研究，但这些方法将适用于其他变量集。我们的新方法应协助其他研究人员计划纵向研究并分析纵向数据。