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.
The prevalence of obesity among US childrenand adolescents more than doubled between 1980 and 2004 and now stands at 16%. Once established, obesity is difficult to reverse. However, identifying children at high risk of obesity has been problematical, and it has been 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.
|Lu, Juan; Shin, Yongyun; Yen, Miao-Shan et al. (2014) Peak Bone Mass and Patterns of Change in Total Bone Mineral Density and Bone Mineral Contents From Childhood Into Young Adulthood. J Clin Densitom :|
|Chen, Wenan; Chen, Xiangning; Archer, Kellie J et al. (2013) A rapid association test procedure robust under different genetic models accounting for population stratification. Hum Hered 75:23-33|
|Shin, Yongyun; Raudenbush, Stephen W (2013) Efficient analysis of Q-level nested hierarchical general linear models given ignorable missing data. Int J Biostat 9:|
|Carrico, Robert J; Sun, Shumei S; Sima, Adam P et al. (2013) The predictive value of childhood blood pressure values for adult elevated blood pressure. Open J Pediatr 3:116-126|
|Sun, Shumei S; Deng, Xiaoyan; Sabo, Roy et al. (2012) Secular trends in body composition for children and young adults: the Fels Longitudinal Study. Am J Hum Biol 24:506-14|
|Sabo, Roy Travis; Lu, Zheng; Daniels, Stephen et al. (2010) Relationships between serial childhood adiposity measures and adult blood pressure: The Fels longitudinal study. Am J Hum Biol 22:830-5|