Geographic disparities, determinants, and temporal changes in the prevalence of pre-diabetes in Florida

Abstract

Background Left unchecked, pre-diabetes progresses to diabetes and its complications that are important health burdens in the United States. There is evidence of geographic disparities in the condition with some areas having a significantly high risks of the condition and its risk factors. Identifying these disparities, their determinants, and changes in burden are useful for guiding control programs and stopping the progression of pre-diabetes to diabetes. Therefore, the objectives of this study were to investigate geographic disparities of pre-diabetes prevalence in Florida, identify predictors of the observed spatial patterns, as well as changes in disease burden between 2013 and 2016. Methods The 2013 and 2016 Behavioral Risk Factor Surveillance System data were obtained from the Florida Department of Health. Counties with significant changes in the prevalence of the condition between 2013 and 2016 were identified using tests for equality of proportions adjusted for multiple comparisons using the Simes method. Flexible scan statistics were used to identify significant high prevalence geographic clusters. Multivariable regression models were used to identify determinants of county-level pre-diabetes prevalence. Results The state-wide age-adjusted prevalence of pre-diabetes increased significantly (p ≤ 0.05) from 8.0% in 2013 to 10.5% in 2016 with 72% (48/67) of the counties reporting statistically significant increases. Significant local geographic hotspots were identified. High prevalence of pre-diabetes tended to occur in counties with high proportions of non-Hispanic black population, low median household income, and low proportion of the population without health insurance coverage. Conclusions Geographic disparities of pre-diabetes continues to exist in Florida with most counties reporting significant increases in prevalence between 2013 and 2016. These findings are critical for guiding health planning, resource allocation and intervention programs.

Determinants of disparities of diabetes-related hospitalization rates in Florida: a retrospective ecological study using a multiscale geographically weighted regression approach

“…Population density, which was used as a measure of rurality in this study, was significantly associated with DRH rates, with more densely populated areas tending to have lower rates of hospitalizations. In the present study, the association between population density and DRH rates was strongest in northern Florida, where spatial clusters of pre-diabetes and diabetes prevalence [ 20 , 22 , 88 ], as well as stroke prevalence [ 89 ] and myocardial infarction mortality [ 90 ] have been identified. Previous research suggests that rural residents are more likely to report delaying health care due to cost than urban residents [ 91 ].…”

A retrospective investigation of spatial clusters and determinants of diabetes prevalence: scan statistics and geographically weighted regression modeling approaches

“…Our recent study demonstrated the application of FSSS for the identification of significant spatial clusters, and investigated temporal changes and determinants of pre-diabetes prevalence within Florida during the same time period. In that study, persistent geographic disparities were identified, as were temporal increases in the prevalence of pre-diabetes, indicating that a similar investigation was thus warranted for diabetes ( Lord, Roberson & Odoi, 2021 ). The results of the current study indicate that geographic disparities in diabetes prevalence also continue to exist in Florida, with high-prevalence clusters being identified in 2013 and again in 2016.…”

“…These findings suggest that thorough, county-level investigations are valuable to inform targeted, evidence-based health planning. Furthermore, a follow-up analysis identified an increase in pre-diabetes prevalence from 8.0% to 10.5% as well as changes in high-risk cluster locations between 2013 and 2016, in addition to identifying significant predictors of the observed spatial patterns ( Lord, Roberson & Odoi, 2021 ). Since the locations of high-risk clusters of pre-diabetes and diabetes may not be identical ( Lord, Roberson & Odoi, 2020 ), ongoing surveillance is warranted for diabetes to enable periodic reassessment of spatial patterns and to identify any changes in these patterns over time.…”

Investigation of geographic disparities of diabetes-related hospitalizations in Florida using flexible spatial scan statistics: An ecological study

“…Previous research in Florida identified county-level geographic disparities in pre-diabetes and diabetes prevalence [ 17 – 20 ]. The locations of those identified high-prevalence diabetes clusters and the high-rate DRH clusters identified in this study exhibited some overlap, particularly in south-central Florida and near Tallahassee [ 18 – 20 ].…”