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Abstract Background Early diagnosis, control of blood glucose levels and cardiovascular risk factors, and regular screening are essential to prevent or delay complications of diabetes. However, most adults with diabetes do not meet recommended targets, and some populations have disproportionately high rates of potentially preventable diabetes-related hospitalizations. Understanding the factors that contribute to geographic disparities can guide resource allocation and help ensure that future interventions are designed to meet the specific needs of these communities. Therefore, the objectives of this study were (1) to identify determinants of diabetes-related hospitalization rates at the ZIP code tabulation area (ZCTA) level in Florida, and (2) assess if the strengths of these relationships vary by geographic location and at different spatial scales. Methods Diabetes-related hospitalization (DRH) rates were computed at the ZCTA level using data from 2016 to 2019. A global ordinary least squares regression model was fit to identify socioeconomic, demographic, healthcare-related, and built environment characteristics associated with log-transformed DRH rates. A multiscale geographically weighted regression (MGWR) model was then fit to investigate and describe spatial heterogeneity of regression coefficients. Results Populations of ZCTAs with high rates of diabetes-related hospitalizations tended to have higher proportions of older adults (p

Background: Early diagnosis, control of blood glucose levels and cardiovascular risk factors, and regular screening are essential to prevent or delay complications of diabetes. However, most adults with diabetes do not meet recommended targets, and some populations have disproportionately high rates of potentially preventable diabetes-related hospitalizations. Understanding the factors that contribute to geographic disparities can guide resource allocation and help ensure that future interventions are designed to meet the specific needs of these communities. Therefore, the objectives of this study were (1) to identify determinants of diabetes-related hospitalization rates at the ZIP code tabulation area (ZCTA) level in Florida, and (2) assess if the strengths of these relationships vary by geographic location and at different spatial scales. Methods: Diabetes-related hospitalization (DRH) rates were computed at the ZCTA level using data from 2016 to 2019. A global ordinary least squares regression model was fit to identify socioeconomic, demographic, healthcare-related, and built environment characteristics associated with log-transformed DRH rates. A multiscale geographically weighted regression (MGWR) model was then fit to investigate and describe spatial heterogeneity of regression coefficients. Results: Populations of ZCTAs with high rates of diabetes-related hospitalizations tended to have higher proportions of older adults (p < 0.0001) and non-Hispanic Black residents (p = 0.003). In addition, DRH rates were associated with higher levels of unemployment (p = 0.001), uninsurance (p < 0.0001), and lack of access to a vehicle (p = 0.002). Population density and median household income had significant (p < 0.0001) negative associations with DRH rates. Non-stationary variables exhibited spatial heterogeneity at local (percent non-Hispanic Black, educational attainment), regional (age composition, unemployment, health insurance coverage), and statewide scales (population density, income, vehicle access). Conclusions: The findings of this study underscore the importance of socioeconomic resources and rurality in shaping population health. Understanding the spatial context of the observed relationships provides valuable insights to guide needs-based, locally-focused health planning to reduce disparities in the burden of potentially avoidable hospitalizations. Highlights: Diabetes-related hospitalization rates exhibited marked variation at the local level, which may be masked in investigations of larger geographic units. Hospitalization rates can be a useful indicator of diabetes outcomes at the local level, particularly in states or countries without population-level data from disease registries and/or spatially representative health surveys. This is the first study to use multiscale geographically weighted regression (MGWR) to investigate determinants of diabetes-related hospitalization rates at the local level. Strengths of associations between determinants and hospitalization rates varied based on geographic location within the study area. This information is useful to guide targeted resource allocation and needs-based health planning, and MGWR can be employed in other study areas to investigate spatially-variable determinants of diabetes-related outcomes. Associations between the identified determinants and diabetes-related hospitalization rates exhibited spatial heterogeneity at local, regional, and statewide levels. This information can serve policymakers and public health planners by suggesting the spatial scale at which a given intervention strategy should be implemented. Future ecological studies should consider spatial scale as well as geographic location when investigating determinants of diabetes and other chronic disease outcomes. [ABSTRACT FROM AUTHOR]