Causes and Interventions for Childhood Obesity: Innovative Systems Analysis

This study uses innovative, integrated and conceptual frameworks, along with multilevel statistical analysis approaches, to examine the influences and interactions between individual, family and environmental factors on childhood obesity.

Program Background

China offers an unprecedented opportunity for systems-oriented pediatric obesity research considering its large population size, area and regional contextual variation; the rapid economic growth and many social-environmental transformations over the past 2-3 decades, including dramatic changes in its food systems and steep growth of the fast food industry.

Some of the China data sets to be used include millions of school-aged children, a large number of families, as well as rich measures about the communities such as food prices, food stores, and restaurants. Some data collections have been conducted since the 1980s and are still ongoing. The large variance and change in macro-level contextual variables and in health outcomes, including obesity, can help advance understanding of disparities in the U.S. through comparative studies.This will broaden the range of potential environmental and policy intervention options to be tested.

Findings will have many important policy implications for other countries.  For example, findings may prompt other countries to reexamine their national food policies and programs and the potential future options to fight the obesity and chronic disease epidemic. In addition, the insight gained regarding the complex causal loops and systems models will help guide future research in the field.

Project Focus

The main project goals are:

  • To examine the influences and interactions between individual, family and environmental factors on childhood obesity using innovative, integrated conceptual framework and multilevel statistical analysis approaches
  • To test simple rules (e.g., how children may interact with their social and built environments) that help explain individual's EBRB and obesity risk and the changes in population level rates of these outcomes using agent-based models (ABM) 
  • 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.
  • To identify and characterize promising intervention/policy strategies based our results of aims 1-3 and those in the literature.