Literature DB >> 18448158

Proximity of fast food restaurants to schools: do neighborhood income and type of school matter?

Paul A Simon1, David Kwan, Aida Angelescu, Margaret Shih, Jonathan E Fielding.   

Abstract

OBJECTIVES: To investigate the proximity of fast food restaurants to public schools and examine proximity by neighborhood income and school level (elementary, middle, or high school).
METHODS: Geocoded school and restaurant databases from 2005 and 2003, respectively, were used to determine the percentage of schools with one or more fast food restaurants within 400 m and 800 m of all public schools in Los Angeles County, California. Single-factor analysis of variance (ANOVA) models were run to examine fast food restaurant proximity to schools by median household income of the surrounding census tract and by school level. Two-factor ANOVA models were run to assess the additional influence of neighborhood level of commercialization.
RESULTS: Overall, 23.3% and 64.8% of schools had one or more fast food restaurants located within 400 m and 800 m, respectively. Fast food restaurant proximity was greater for high schools than for middle and elementary schools, and was inversely related to neighborhood income for schools in the highest commercial areas. No association with income was observed in less commercial areas.
CONCLUSIONS: Fast food restaurants are located in close proximity to many schools in this large metropolitan area, especially high schools and schools located in low income highly commercial neighborhoods. Further research is needed to assess the relationship between fast food proximity and student dietary practices and obesity risk.

Entities:  

Mesh:

Year:  2008        PMID: 18448158     DOI: 10.1016/j.ypmed.2008.02.021

Source DB:  PubMed          Journal:  Prev Med        ISSN: 0091-7435            Impact factor:   4.018


  42 in total

1.  Small area estimates reveal high cigarette smoking prevalence in low-income cities of Los Angeles county.

Authors:  Yan Cui; Susie B Baldwin; Amy S Lightstone; Margaret Shih; Hongjian Yu; Steven Teutsch
Journal:  J Urban Health       Date:  2012-06       Impact factor: 3.671

Review 2.  Food availability/convenience and obesity.

Authors:  Penny Gordon-Larsen
Journal:  Adv Nutr       Date:  2014-11-14       Impact factor: 8.701

3.  Energy and nutrient intake from pizza in the United States.

Authors:  Lisa M Powell; Binh T Nguyen; William H Dietz
Journal:  Pediatrics       Date:  2015-01-19       Impact factor: 7.124

4.  Healthy food availability and participation in WIC (Special Supplemental Nutrition Program for Women, Infants, and Children) in food stores around lower- and higher-income elementary schools.

Authors:  June M Tester; Irene H Yen; Lauren C Pallis; Barbara A Laraia
Journal:  Public Health Nutr       Date:  2010-12-21       Impact factor: 4.022

5.  Changes in Fast Food Outlet Availability Near Schools: Unequal Patterns by Income, Race/Ethnicity, and Urbanicity.

Authors:  Emma V Sanchez-Vaznaugh; Aiko Weverka; Mika Matsuzaki; Brisa N Sánchez
Journal:  Am J Prev Med       Date:  2019-08-01       Impact factor: 5.043

6.  Impact of the new U.S. Department of Agriculture school meal standards on food selection, consumption, and waste.

Authors:  Juliana F W Cohen; Scott Richardson; Ellen Parker; Paul J Catalano; Eric B Rimm
Journal:  Am J Prev Med       Date:  2014-04       Impact factor: 5.043

Review 7.  The geography of Fast Food outlets: a review.

Authors:  Lorna K Fraser; Kimberly L Edwards; Janet Cade; Graham P Clarke
Journal:  Int J Environ Res Public Health       Date:  2010-05-06       Impact factor: 3.390

8.  Environmental metrics for community health improvement.

Authors:  Benjamin Jakubowski; Howard Frumkin
Journal:  Prev Chronic Dis       Date:  2010-06-15       Impact factor: 2.830

9.  School lunch waste among middle school students: nutrients consumed and costs.

Authors:  Juliana F W Cohen; Scott Richardson; S Bryn Austin; Christina D Economos; Eric B Rimm
Journal:  Am J Prev Med       Date:  2013-02       Impact factor: 5.043

10.  Arterial roads and area socioeconomic status are predictors of fast food restaurant density in King County, WA.

Authors:  Philip M Hurvitz; Anne V Moudon; Colin D Rehm; Laura C Streichert; Adam Drewnowski
Journal:  Int J Behav Nutr Phys Act       Date:  2009-07-24       Impact factor: 6.457

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.