Predictors of County-level Diabetes-related Mortality Risks in Florida, USA: a Retrospective Ecological Study

Overview

Journal PeerJ

Date 2025 Jan 20

PMID 39830967

Authors

Nirmalendu Deb Nath

Agricola Odoi

Affiliations

Soon will be listed here.

Abstract

Background: Diabetes is an increasingly important public health problem due to its socioeconomic impact, high morbidity, and mortality. Although there is evidence of increasing diabetes-related deaths over the last ten years, little is known about the population level predictors of diabetes-related mortality risks (DRMR) in Florida. Identifying these predictors is important for guiding control programs geared at reducing the diabetes burden and improving population health. Therefore, the objective of this study was to identify geographic disparities and predictors of county-level DRMR in Florida.

Methods: The 2019 mortality data for the state of Florida were obtained from the Florida Department of Health. The 10th International Classification of Disease codes E10-E14 were used to identify diabetes-related deaths which were then aggregated to the county-level. County-level DRMR were computed and presented as number of deaths per 100,000 persons. Geographic distribution of DRMR were displayed in choropleth maps and ordinary least squares (OLS) regression model was used to identify county-level predictors of DRMR.

Results: There was a total 6,078 diabetes-related deaths in Florida during the study time period. County-level DRMR ranged from 9.6 to 75.6 per 100,000 persons. High mortality risks were observed in the northern, central, and southcentral parts of the state. Relatively higher mortality risks were identified in rural counties compared to their urban counterparts. Significantly high county-level DRMR were observed in counties with high percentages of the population that were: 65 year and older ( < 0.001), current smokers ( = 0.032), and insufficiently physically active ( = 0.036). Additionally, percentage of households without vehicles ( = 0.022) and percentage of population with diabetes ( < 0.001) were significant predictors of DRMR.

Conclusion: Geographic disparities of DRMR exist in Florida, with high risks being observed in northern, central, and southcentral counties of the state. The study identified county-level predictors of these identified DRMR disparities in Florida. The findings are useful in guiding health professionals to better target intervention efforts.

References

Xu J, Murphy S, Kochanek K, Arias E . Mortality in the United States, 2021. NCHS Data Brief. 2023; (456):1-8. View

Maynard C . The association of state per capita income and military service deaths in the Vietnam and Iraq wars. Popul Health Metr. 2009; 7:1. PMC: 2621124. DOI: 10.1186/1478-7954-7-1. View

Tian Y, Li C, Shilko T, Sosunovsky V, Zhang Y . The relationship between physical activity and diabetes in middle-aged and elderly people. Medicine (Baltimore). 2023; 102(6):e32796. PMC: 9907994. DOI: 10.1097/MD.0000000000032796. View

Turi K, Grigsby-Toussaint D . Spatial spillover and the socio-ecological determinants of diabetes-related mortality across US counties. Appl Geogr. 2022; 85:62-72. PMC: 9555791. DOI: 10.1016/j.apgeog.2017.05.005. View

Boyle J, Honeycutt A, Narayan K, Hoerger T, Geiss L, Chen H . Projection of diabetes burden through 2050: impact of changing demography and disease prevalence in the U.S. Diabetes Care. 2001; 24(11):1936-40. DOI: 10.2337/diacare.24.11.1936. View

Lord J, Roberson S, Odoi A . Investigation of geographic disparities of pre-diabetes and diabetes in Florida. BMC Public Health. 2020; 20(1):1226. PMC: 7425001. DOI: 10.1186/s12889-020-09311-2. View

Taylor W, Floyd M, Whitt-Glover M, Brooks J . Environmental justice: a framework for collaboration between the public health and parks and recreation fields to study disparities in physical activity. J Phys Act Health. 2007; 4 Suppl 1:S50-63. DOI: 10.1123/jpah.4.s1.s50. View

Lord J, Odoi A . Determinants of disparities of diabetes-related hospitalization rates in Florida: a retrospective ecological study using a multiscale geographically weighted regression approach. Int J Health Geogr. 2024; 23(1):1. PMC: 10771651. DOI: 10.1186/s12942-023-00360-5. View

Lim R, Wee W, For W, Ananthanarayanan J, Soh Y, Goh L . Correlates, facilitators and barriers of physical activity among primary care patients with prediabetes in Singapore - a mixed methods approach. BMC Public Health. 2020; 20(1):1. PMC: 6941241. DOI: 10.1186/s12889-019-7969-5. View

Wolfe M, McDonald N, Holmes G . Transportation Barriers to Health Care in the United States: Findings From the National Health Interview Survey, 1997-2017. Am J Public Health. 2020; 110(6):815-822. PMC: 7204444. DOI: 10.2105/AJPH.2020.305579. View

Danaei G, Friedman A, Oza S, Murray C, Ezzati M . Diabetes prevalence and diagnosis in US states: analysis of health surveys. Popul Health Metr. 2009; 7:16. PMC: 2764564. DOI: 10.1186/1478-7954-7-16. View

10.

Stokes A, Preston S . Deaths Attributable to Diabetes in the United States: Comparison of Data Sources and Estimation Approaches. PLoS One. 2017; 12(1):e0170219. PMC: 5266275. DOI: 10.1371/journal.pone.0170219. View

11.

Hamilton M, Hamilton D, Zderic T . Sedentary behavior as a mediator of type 2 diabetes. Med Sport Sci. 2014; 60:11-26. PMC: 4364419. DOI: 10.1159/000357332. View

12.

Khan M, Roberson S, Reid K, Jordan M, Odoi A . Geographic disparities and temporal changes of diabetes prevalence and diabetes self-management education program participation in Florida. PLoS One. 2021; 16(7):e0254579. PMC: 8284795. DOI: 10.1371/journal.pone.0254579. View

13.

Zheng Z, Peng F, Xu B, Zhao J, Liu H, Peng J . Risk factors of critical & mortal COVID-19 cases: A systematic literature review and meta-analysis. J Infect. 2020; 81(2):e16-e25. PMC: 7177098. DOI: 10.1016/j.jinf.2020.04.021. View

14.

Okwechime I, Roberson S, Odoi A . Prevalence and Predictors of Pre-Diabetes and Diabetes among Adults 18 Years or Older in Florida: A Multinomial Logistic Modeling Approach. PLoS One. 2015; 10(12):e0145781. PMC: 4699892. DOI: 10.1371/journal.pone.0145781. View

14.

Elias C, Antunes D, Coelho M, de Lima C, Rassi N, de Melo A . Evaluation of the hypothalamic-pituitary-adrenal axis in a case series of familial partial lipodystrophy. Diabetol Metab Syndr. 2019; 11:1. PMC: 6317180. DOI: 10.1186/s13098-018-0396-4. View

15.

Cederholm J, Eeg-Olofsson K, Eliasson B, Zethelius B, Nilsson P, Gudbjornsdottir S . Risk prediction of cardiovascular disease in type 2 diabetes: a risk equation from the Swedish National Diabetes Register. Diabetes Care. 2008; 31(10):2038-43. PMC: 2551651. DOI: 10.2337/dc08-0662. View

16.

Hu G, Eriksson J, Barengo N, Lakka T, Valle T, Nissinen A . Occupational, commuting, and leisure-time physical activity in relation to total and cardiovascular mortality among Finnish subjects with type 2 diabetes. Circulation. 2004; 110(6):666-73. DOI: 10.1161/01.CIR.0000138102.23783.94. View

17.

Lord J, Roberson S, Odoi A . A retrospective investigation of spatial clusters and determinants of diabetes prevalence: scan statistics and geographically weighted regression modeling approaches. PeerJ. 2023; 11:e15107. PMC: 10122841. DOI: 10.7717/peerj.15107. View