Adenantera Dwicaksono1,2, Ian Brissette3, Guthrie S Birkhead1, Christine T Bozlak1, Erika G Martin1,4. 1. 1 University at Albany-State University of New York, Albany, NY, USA. 2. 2 Institut Teknologi Bandung, Bandung, Indonesia. 3. 3 New York State Department of Health, Albany, NY, USA. 4. 4 Nelson A. Rockefeller Institute of Government-State University of New York, Albany, NY, USA.
Abstract
OBJECTIVES: One third of school-aged children in New York State (NYS) are overweight or obese, with large geographic disparities across local regions. We used NYS student obesity surveillance data to assess whether these geographical variations are attributable to the built environment. METHOD: We combined NYS Student Weight Status Category Reporting System 2010-2012 data with other government publicly available data. Ordinary least squares regression models identified key determinants of school district-level student obesity rates for elementary and middle/high schools. Geographical weighted regression models explored spatial variations in local coefficients of the built environment predictors. RESULTS: From ordinary least squares models, higher farmers' market density was only significantly associated with lower obesity rates among elementary school students (b = -0.116; p < .01). Higher fast-food restaurant density was significantly associated with higher obesity rates (b = 0.014; p < .05), and higher land use mix was only significantly associated with lower obesity rates (b = -0.054; p < .01) among middle/high school students. In geographical weighted regression analyses, the inverse association between market density and obesity rates among elementary school students was more pronounced in the eastern portion of the state. The relationship between higher fast-food restaurant density and higher obesity rates among middle/high school students was found in the southeastern portion of the state. CONCLUSIONS: Different patterns of food consumption may explain varying determinants of obesity between younger and older students. Regional variations in local associations between the built environment variables and obesity may suggest differences in how healthy food sources are accessed locally.
OBJECTIVES: One third of school-aged children in New York State (NYS) are overweight or obese, with large geographic disparities across local regions. We used NYS student obesity surveillance data to assess whether these geographical variations are attributable to the built environment. METHOD: We combined NYS Student Weight Status Category Reporting System 2010-2012 data with other government publicly available data. Ordinary least squares regression models identified key determinants of school district-level student obesity rates for elementary and middle/high schools. Geographical weighted regression models explored spatial variations in local coefficients of the built environment predictors. RESULTS: From ordinary least squares models, higher farmers' market density was only significantly associated with lower obesity rates among elementary school students (b = -0.116; p < .01). Higher fast-food restaurant density was significantly associated with higher obesity rates (b = 0.014; p < .05), and higher land use mix was only significantly associated with lower obesity rates (b = -0.054; p < .01) among middle/high school students. In geographical weighted regression analyses, the inverse association between market density and obesity rates among elementary school students was more pronounced in the eastern portion of the state. The relationship between higher fast-food restaurant density and higher obesity rates among middle/high school students was found in the southeastern portion of the state. CONCLUSIONS: Different patterns of food consumption may explain varying determinants of obesity between younger and older students. Regional variations in local associations between the built environment variables and obesity may suggest differences in how healthy food sources are accessed locally.
Entities:
Keywords:
built environment; childhood obesity; food environment; geographical weighted regression; health disparity; physical activity environment
Authors: Mika Matsuzaki; Brisa N Sánchez; Maria Elena Acosta; Jillian Botkin; Emma V Sanchez-Vaznaugh Journal: Obes Rev Date: 2020-02-05 Impact factor: 9.213
Authors: Kun Mei; Hong Huang; Fang Xia; Andy Hong; Xiang Chen; Chi Zhang; Ge Qiu; Gang Chen; Zhenfeng Wang; Chongjian Wang; Bo Yang; Qian Xiao; Peng Jia Journal: Obes Rev Date: 2020-07-28 Impact factor: 9.213