Back to Annual Meeting Page		American Public Health Association 133rd Annual Meeting & Exposition December 10-14, 2005 Philadelphia, PA

Abstract #107159

Russ Lopez, MCRP, DSc, Department of Environmental Health, Boston University, 715 Albany Street, Talbot 2E, Boston, MA 02118, N/A, RPTLOPEZ@bu.edu

This study included 18289 respondents in 326 zip codes in the greater Boston metropolitan area. Data source was the Behavioral Risk Factor Surveillance System from the years 1998 to 2002. As part of its state added optional questions, Massachusetts asked respondents to report their zip code during these years.

Zip code level variables were derived from the 2000 Census and the 2001 County Business Pattern data on employment and establishments. Zip code level variables assessed included social, demographic, employment and establishment measures. Individual control variables included age, education, income, race, smoking and sex. Hierarchical linear modeling was used to assess the risk of being obese (BMI >30) versus not being obese.

Many of the zip code level variables performed poorly and were dropped from the final model. Remaining variables were:

Percent Black OR: 1.732 , 95% CI: 1.042, 2.88 * Median Income (thousands) OR: .990 95% CI: .987, .993 ** Population density (thousands persons per square mile) OR: .972, 95% CI: .959, .986 ** Presence of grocery stores OR: .800, 95% CI: .666, .963 *

* significant at the 05 level ** significant at the .01 level

Density of fast food establishments was not significant. OR:1.002, 95% CI: .996, 1.007.

Increased population density, income and access to grocery stores appear to reduce obesity risk. Higher black population appears to increase obesity risk. Perhaps the ubiquity of fast food access has limited its impact on obesity rates.

This study may provide evidence that the neighborhood environment may affect obesity risk.

Learning Objectives:

• Describe the distribution of obesity across a metropolitan area.
• Understand some of the neighborhood social-, economic-, and built– environment factors that contribute to obesity risk.
• Describe how these factors that could be contributing to disparities in obesity risk.

Keywords: Obesity, Environment

Presenting author's disclosure statement:

I wish to disclose that I have NO financial interests or other relationship with the manufactures of commercial products, suppliers of commercial services or commercial supporters.