Jay Maddock1. 1. University of Hawaii at Manoa, Honolulu, Hawaii 96822, USA. jmaddock@hawaii.edu
Abstract
PURPOSE: Obesity accounts for approximately 300,000 deaths a year in the United States, and prevalence rates have been increasing over the past decade. The nutrition environment may be contributing to this epidemic. This study examined the relationship between fast food restaurants and obesity on a state-wide basis. DESIGN: A one-time cross-sectional analysis of secondary data was used for this study. SETTING: The setting for this study was the United States. SUBJECTS: State-level data were used as the unit of analysis. Alaska was excluded as an outlier, and the District of Columbia was added (N = 50). MEASURES: Measures included aggregate state-level means for square miles per fast food restaurant, population per fast food restaurant, population density, ethnicity, age, gender, physical inactivity, fruit and vegetable intake, and obesity rates. Data were obtained from the 2002 Behavioral Risk Factor and Surveillance Survey, the 2000 U.S. Census, and the 2002 U.S. Yellow Pages. RESULTS: Multiple hierarchal regressions revealed that square miles per fast food restaurants and residents per restaurant accounted for 6% of the variance in state obesity rates after controlling for population density, ethnicity, age, gender, physical inactivity, and fruit and vegetable intake. The entire model explained 70% of the total variance in state obesity rates. CONCLUSIONS: These results indicate a correlational relationship between both the number of residents per fast food restaurant and the square miles per fast food restaurants with state-level obesity prevalence. Limitations include the use of correlational aggregate data.
PURPOSE:Obesity accounts for approximately 300,000 deaths a year in the United States, and prevalence rates have been increasing over the past decade. The nutrition environment may be contributing to this epidemic. This study examined the relationship between fast food restaurants and obesity on a state-wide basis. DESIGN: A one-time cross-sectional analysis of secondary data was used for this study. SETTING: The setting for this study was the United States. SUBJECTS: State-level data were used as the unit of analysis. Alaska was excluded as an outlier, and the District of Columbia was added (N = 50). MEASURES: Measures included aggregate state-level means for square miles per fast food restaurant, population per fast food restaurant, population density, ethnicity, age, gender, physical inactivity, fruit and vegetable intake, and obesity rates. Data were obtained from the 2002 Behavioral Risk Factor and Surveillance Survey, the 2000 U.S. Census, and the 2002 U.S. Yellow Pages. RESULTS: Multiple hierarchal regressions revealed that square miles per fast food restaurants and residents per restaurant accounted for 6% of the variance in state obesity rates after controlling for population density, ethnicity, age, gender, physical inactivity, and fruit and vegetable intake. The entire model explained 70% of the total variance in state obesity rates. CONCLUSIONS: These results indicate a correlational relationship between both the number of residents per fast food restaurant and the square miles per fast food restaurants with state-level obesity prevalence. Limitations include the use of correlational aggregate data.
Authors: Sara N Bleich; Jesse C Jones-Smith; Marian P Jarlenski; Julia A Wolfson; Johannah M Frelier; Huiru Tao; Yuchen Hu; Anna Zink; Caroline G Dunn; Mark J Soto; Bradley J Herring Journal: J Gen Intern Med Date: 2020-02-14 Impact factor: 5.128