252588 Spatial Disparities: Energy Balance-Related Behaviors and Risk of Prevalent Diabetes Mellitus across 64 Counties of Pennsylvania

Monday, October 31, 2011

Zuolu Liu, Medical Student , Class of 2014, Temple University School of Medicine, Philadelphia, PA
Objectives: The study aims to test an innovative hypothesis that spatial-poverty disparities are significantly associated with diabetes mellitus (DM) and energy balance-related behaviors (EBRB).

Methods: Data from 2007 and2009 Behavior Risk Factor Surveillance System surveys among participants aged>=18 years old (n=22,029) living in 64 counties of Pennsylvania were analyzed. Pre-DM and DM were defined by participants' self-reports of physicians' diagnosis of disease. A sum EBRB score was created according to participants' vegetable and/or fruit intake and physical activity status. EBRB score was then grouped as low, moderate and high risk groups. Associations of pre-DM and DM with EBRB score (individual level), and spatial-poverty rates (county-level) were analyzed using multilevel regression analysis techniques.

Results: Subjects in high-risk EBRB had almost 20% higher risk of having pre-DM (Odd ratio, 95%CI: 1.19, 0.96-1.49), and 40% higher risk of DM [1.39 (1.25-1.55)] than those in the low risk EBRB. Counties with higher poverty rates had higher proportions of those with high risk EBRB, and higher prevalence of DM. Odds ratios (95%CI) of spatial-poverty for DM were 1.09 (0.90-1.32), 1.13 (0.93-1.35), and 1.25 (1.05-1.47) for counties with poverty rates of 10.4% - 12.6%, 12.7%-14.5%, and >=14.5% as compared to counties classified being the lowest poverty rates (<10.4%), respectively. Conclusion: Health disparities of diabetes are not only explained by individual health behaviors, but also by spatial-poverty levels. Health policies and disease prevention strategies should be made with taking into considerations of spatial-poverty disparities across counties.

Learning Areas:
Public health or related public policy
Social and behavioral sciences
Systems thinking models (conceptual and theoretical models), applications related to public health

Learning Objectives:
1. to highlight risk of diabetes across geographic areas. 2. to examine associations between health behaviors and DM across multiple counties.

Keywords: Health Behavior, Geocoding

Presenting author's disclosure statement:

Qualified on the content I am responsible for because: becuase I did the analysis.
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.