Online Program

Using GIS to assess the relationship between the built environment and obesity among adults in los angeles county

Tuesday, November 5, 2013

Diane Tan, MSPH, Department of Health Policy and Management, UCLA Fielding School of Public Health, Los Angeles, CA
Ami Shah, MPH, UCLA Center for Health Policy Research, UCLA, Los Angeles, CA
Joelle Wolstein, MPP, Center for Health Policy Research, Fielding School of Public Health, UCLA, Los Angeles, CA
Susan Babey, PhD, UCLA Center for Health Policy Research, UCLA, Los Angeles, CA
Héctor Alcalá, MPH, PhD, Department of Community Health Sciences, UCLA, Los Angeles, CA
Areas with increased access to “bad” food outlets, such as fast-food restaurants, may be associated with higher rates of obesity. Food outlet addresses collected by the Los Angeles County (LAC) Department of Public Health in 2010 were geocoded and used to calculate the number of fast-food outlets per 100,000 population for each LAC health district (HD) (n=26). To examine the relationship between obesity and food environment, these numbers were plotted over indirect estimates of obesity (BMI>=30 kg/m2 for adults 18 years old and older) from the 2009 California Health Interview Survey using ArcGIS10.1. San Antonio, a predominantly young, Latino district with the highest obesity rate (36%), had one of the most fast-food outlets (86 outlets/100,000 population). Central, an ethnically diverse district with an average obesity rate (28%), had the most fast-food outlets overall (131 outlets/100,000 population), while West, a predominantly White district with the lowest obesity rate (11%), had an average number of fast-food outlets (81 outlets/100,000 population). Additional analyses will consider other area-level factors, such sociodemographic characteristics, another measure of fast-food density (i.e., average distances between outlets relative to each total HD area), and average park space, to further assess this geographic variation in obesity and fast-food outlets. HDs with many fast-food outlets per population tended to have high obesity rates, but the relationship varied across HDs. While no causal inference can be made using cross-sectional data, GIS analyses provide the opportunity to assess factors influencing obesogenic environments in LAC, which may guide local health policies and future research.

Learning Areas:

Assessment of individual and community needs for health education
Public health or related public policy
Public health or related research
Social and behavioral sciences

Learning Objectives:
Demonstrate the use of data from a statewide health survey and local health department. Analyze the relationship between the built environment (local food environment) and obesity prevalence using GIS mapping. Discuss the relationship between the built environment (local food environment) and obesity.

Keyword(s): Geographic Information Systems, Community Health Planning

Presenting author's disclosure statement:

Qualified on the content I am responsible for because: I am a PhD student interested using GIS technique to better understand area-level factors that affect health outcomes like obesity.
Any relevant financial relationships? No

I agree to comply with the American Public Health Association Conflict of Interest and Commercial Support Guidelines, and to disclose to the participants any off-label or experimental uses of a commercial product or service discussed in my presentation.