Causes and Interventions for Childhood Obesity:Innovative Systems Analysis

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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.