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Abstract #154541

• Handout (free access)

George Siaway, MSEH¹, John O. Davies-Cole, PhD, MPH², and Gebreyesus Kidane, PhD². (1) Bureau of Epidemiology and Health Risk Assessment, D.C. Department of Health, 825 N. Capitol Street, N.E., #3137, Washington, DC 20002, 202-442-5869, george.siaway@dc.gov, (2) Bureau of Epidemiology & Health Risk Assessment, District of Columbia Department of Health, 825 North Capitol St. N.E., Washington, DC 20002

Introduction: Overweight and obesity and their associated health problems have a significant economic impact on the health care system in the District of Columbia. Quantification of the magnitude of the association between obesity and selected risk factors can help obtain corrected estimates of trends and geographical distributions of obesity.

Objective: This study seeks to quantify the spatial associations between obesity, overweight, poverty, fast food outlets, high blood pressure and physical inactivity.

Methodology: We used spatial statistics to evaluate the magnitude and directional distribution of the association between obesity, overweight and selected behavioral risk factors within the 18-49 and >49 age groups. Autocorrelation denotes a method to study similarity/dissimilarity of the same variables between corresponding intervals, and detect departures from spatial randomness.

Results: Preliminary spatial autocorrelation analysis showed the highest spatial dependency for obesity and poverty (@ 0.28 Moran's I and Z-Score of 78.2 standard deviations). Overweight had a lower dependency than obesity, and dissimilar values of overweight are dispersed in the zip code areas. Obesity and the selected risk factors seem to generally cluster around areas with high poverty, and fast food outlets.

Conclusions: We will show that obesity and selected risk factors exhibit random clusters (regions where adjacent areas have similar values) or spatial outliers (areas distinct from their neighbors). This will help us to better plan intervention measures.

Learning Objectives:

• To identify and quantify risk factors and environmental determinants of overweight and obesity in the District of Columbia.
• To evaluate the use of spatial autocorrelation analysis in identifying environmental determinants of overweight and obesity.
• To understand how spatial statistics can be used by public health practitioners to address public health challenges.

Keywords: Geographic Information Systems, Epidemiology

Presenting author's disclosure statement:

Any relevant financial relationships? No
Any institutionally-contracted trials related to this submission?

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.

• Handout (free access)