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APHA Scientific Session and Event Listing

Tammie M. Johnson, MPH, Diabetes Prevention and Control Program, Florida Department of Health, 4052 Bald Cypress Way, Bin A18, Tallahassee, FL 32399, 850-245-4330, tammie_johnson@doh.state.fl.us and Gebre-Egziabher Kiros, PhD, College of Pharmacy and Pharmaceutical Sciences/Institute of Public Health, Florida A&M University, Science Research Center, Tallahassee, FL 32307.

In recent years, researchers have been interested in investigating whether or not area-level indicators of behavioral factors and socioeconomic status can be used as predictors of health outcomes. Diabetes-related non-traumatic lower extremity amputations are a tremendous socioeconomic burden in Florida. In Florida's 67 counties, the age-adjusted lower extremity amputation rates per 100,000 population range from 12.9 to 58.7, with a mean of 27.6. In this study we posit that identifying population-based, county-level predictors of amputation rates could potentially enable counties to identify factors contributing to these rates and address the problem. The specific purpose of this analysis is to determine if county-level census, socioeconomic, vital statistics, behavior, and hospitalization data from various data sources can be used as predictors of non-traumatic lower extremity amputation rates among Florida county residents with diabetes. The dataset used for this analysis was constructed using data from several sources: hospital discharge, vital statistics, United States Census Bureau, and the Florida county-level Behavioral Risk Factor Surveillance Survey. SAS was used for the analyses. Results from multivariate regression models identified four key predictive variables. Our results show that increases in diabetes-related hospitalization rates, proportion of adults without high school diplomas, and proportion of the county residents that are minority are positively and significantly related to lower extremity amputation rates. On the other hand, the proportion of adults who are sedentary is negatively and significantly related to lower extremity amputation rates. The implications of our findings as well as their limitations are discussed in detail.

Learning Objectives:

• Describe age-adjusted lower extremity amputation rates in Florida by county.
• Identify factors that predict lower extremity amputation rates in Florida counties.
• Articulate the proceedure for developing and diagnosing the linear regression model developed.

Keywords: Diabetes, Community Health

Presenting author's disclosure statement:

Not Answered