Yu Qingzhao, Scribner Richard A, Leonardi Claudia, Zhang Lu, Park Chi, Chen Liwei, Simonsen Neal R
Biostatistics Program, School of Public Health, Louisiana State University Health Sciences Center, 3rd floor, 2020 Gravier Street, New Orleans, LA 70112, United States.
Epidemiology Program, School of Public Health, Louisiana State University Health Sciences Center, New Orleans, LA, United States .
Spat Spatiotemporal Epidemiol. 2017 Jun;21:13-23. doi: 10.1016/j.sste.2017.02.001. Epub 2017 Feb 17.
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.
研究表明，美国成年白人和黑人在肥胖问题上存在持续的种族差异。鉴于多种促成因素、关联的性质以及这些因素之间的多层次关系，解释这种差异是一项挑战。我们使用多变量中介分析（MMA）方法来探讨肥胖方面的种族差异，不仅考虑个体行为，还考虑地理空间衍生的环境风险因素。将广义线性模型（GLM）的结果与多重加法回归树（MART）的结果进行比较，MART允许分层数据结构，并能拟合非线性和复杂的交互关系。结果显示，个体因素和地理因素都导致了肥胖方面的种族差异。MART在解释肥胖方面的种族差异比例上比GLM模型表现更好。然而，仍存在一些差异无法用本研究收集的因素来解释。
