Date

12-16-2025

Department

School of Health Sciences

Degree

Doctor of Philosophy in Health Sciences (PhD)

Chair

Orchid George

Keywords

Diabetes, diabetes expenditures, homogenous communities, population measures

Disciplines

Medicine and Health Sciences

Abstract

The United States healthcare sector has encountered an increasing burden from diabetes health conditions and diabetes expenditures. Vulnerable populations are disproportionately affected, resulting in increased health mortality and morbidity. This quantitative retrospective, observational, cross-sectional cohort study identified the relationship between the average principal diabetes cost and population measures for Alabama Medicare beneficiaries living in homogeneous communities diagnosed with the principal diagnosis of diabetes. The theoretical framework guiding the study was the socio-ecological theory. Purposive sampling was applied to the secondary Mapping Medicare Disparities data population comprising 57 United States territories and 3,243 counties. Specifically, Medicare beneficiary participants diagnosed with the principal diagnosis of diabetes in 2022 from the state of Alabama, in 67 counties, were selected. Analysis of variance revealed a statistically significant (p < .05) relationship between the average principal diabetes cost and population measures of emergency room visits, hospitalization, and prevention quality indicators. However, there were contradictory results in the factorial analysis of variance showing population measures having a significant change in the level of statistical significance, an indication of interconnected factors among the population measures. Results can be used by healthcare professionals to implement effective management care interventions and value-based healthcare and enhance health policies and interventions with the potential to reduce expenditures while improving the quality of care and motivating patients to manage diabetes. A key recommendation for future studies is to strengthen the generalization by including non-Medicare data and incorporating the full Mapping Medicare Disparities data file.

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