Multilevel Longitudinal Models and Causal Networks for Childhood Obesity

描述(申请人提供)：1980至2004年间，美国儿童和青少年的肥胖率增加了一倍多。肥胖症让医疗保健提供者感到沮丧，因为它很难逆转。识别肥胖高危儿童一直是个难题，而且很难在任何层面上找出可以采取预防性干预措施的致病因素。我们建议开发创新的统计方法，并将其应用于FELS纵向研究(FLS)数据库，以分析过去30年儿童肥胖率上升的多变量和多水平决定因素。从出生起反复测量身体尺寸和组成，以及数十年来频繁对FLS中的同一个人进行关于生活方式、饮食、锻炼和SES的问卷调查，可以推断出横断面数据无法提供的因果关系。我们计划使用多水平纵向分层模型和S的格兰杰因果关系，这两个模型应该是最容易受到干预的，以预防或推迟儿童肥胖的发生。我们将使用从1929年开始在FLS中收集的2076人的系列数据，来开发多变量和多水平模型和格兰杰网络来推断因果关系。我们将通过Bootstrap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稳健性。我们对肥胖症流行中涉及的因素的多层次建模和格兰杰因果关系网络应该描绘出解释和跟踪该流行病的因素之间合理的路径和相互作用。这些途径和相互作用的发现和验证应该揭示同时进行多水平干预以预防儿童肥胖和/或改变相关因果变量的时间进程的最佳目标。我们认为，在减少社会、经济、饮食和其他环境变量方面，这种多层次干预措施将比目前应用的单层干预措施更成功。这项研究还可以发现和分析1929至2008年间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。我们的新方法应该有助于其他研究人员规划纵向研究和分析纵向数据。

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.