Exploring racial disparity in obesity: A mediation analysis considering geo-coded environmental factors.
Qingzhao Yu, Richard A Scribner, Claudia Leonardi, Lu Zhang, Chi Park, Liwei Chen, Neal R Simonsen
Author Information
Qingzhao Yu: Biostatistics Program, School of Public Health, Louisiana State University Health Sciences Center, 3rd floor, 2020 Gravier Street, New Orleans, LA 70112, United States. Electronic address: qyu@lsuhsc.edu.
Richard A Scribner: Epidemiology Program, School of Public Health, Louisiana State University Health Sciences Center, New Orleans, LA, United States . Electronic address: rscrib@lsuhsc.edu.
Claudia Leonardi: Epidemiology Program, School of Public Health, Louisiana State University Health Sciences Center, New Orleans, LA, United States . Electronic address: cleon1@lsuhsc.edu.
Lu Zhang: Department of Public Health Sciences, Clemson University, Clemson, SC, United States. Electronic address: lz3@clemson.edu.
Chi Park: Epidemiology Program, School of Public Health, Louisiana State University Health Sciences Center, New Orleans, LA, United States . Electronic address: liz0817@gmail.com.
Liwei Chen: Department of Public Health Sciences, Clemson University, Clemson, SC, United States. Electronic address: liweic@clemson.edu.
Neal R Simonsen: New Orleans, LA, United States . Electronic address: epiman@yahoo.com.
Research shows aconsistent racial disparity in obesity between white and black adults in the United States. Accounting for the disparity is a challenge given the variety of the contributing factors, the nature of the association, and the multilevel relationships among the factors. We used the multivariable mediation analysis (MMA) method to explore the racial disparity in obesity considering not only the individual behavior but also geospatially derived environmental risk factors. Results from generalized linear models (GLM) were compared with those from multiple additive regression trees (MART) which allow for hierarchical data structure, and fitting of nonlinear and complex interactive relationships. As results, both individual and geographically defined factors contributed to the racial disparity in obesity. MART performed better than GLM models in that MART explained a larger proportion of the racial disparity in obesity. However, there remained disparities that cannot be explained by factors collected in this study.
