Date of Award


Document Type


Degree Name

Doctor of Philosophy (PhD)

Legacy Department

Food Technology

Committee Member

Dr. E. Jeffrey Rhodehamel, Committee Chair

Committee Member

Dr. William Bridges

Committee Member

Dr. Cheryl Dye

Committee Member

Dr. Vivian Haley-Zitlin


Malnutrition is a prevalent, serious, and often unrecognized health threat for older adults in the United States. The prevalence estimates of malnutrition in the elderly are highly variable as methods for detection are not standardized. It is dependent on the use of the different available tools, the population being studied, and the different settings (living at home, institutionalized, or hospitalized) The purpose of this study was to determine the spatial variation of the nutritional risk of Area Agencies on Aging (AAA) service recipients in South Carolina (SC) using factors that influence the nutritional status of older adults included in the DETERMINE Your Nutritional Health nutrition screening checklist. In addition, the relationship between neighborhood socioeconomic factors and the participation rates in services provided by the AAA by zip codes was examined. A cross-sectional secondary analysis of data collected by the OAA in SC was conducted. Local Moran's I and the Getis-Ord Gi* statistic were used to identify clusters of high nutritional risk scores by zip code. Principal component analysis was conducted using neighborhood socioeconomic characteristic variables collected from the 2010 Census and the 2014 American Community Survey 5-year estimates. Regression analysis, using participation rates as the dependent variable, was computed with simultaneous entry of the factor scores that resulted from the principal component analysis. Results showed clusters of higher nutritional risk were observed along South Carolina's I-95 corridor and SC Promise Zone. Three factors were retained from the principal component analysis: economic disadvantage, family structure and housing instability. Neighborhood economic disadvantage increased participation rates while housing instability decreased these rates. The use of spatial analysis to identify clusters of nutritional risk among AAA nutritional services participants can serve as a visual link to engage program leaders in developing strategies to better understand this variability and develop strategies to reduce disparities in nutritional risk.



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