Short Communication

Disparities in diabetes and sedentary behavior across Florida counties

Introduction

Diabetes and a sedentary lifestyle are health issues that may occur independently or together, resulting in decreased life expectancy.¹ Research links sedentary lifestyle to abdominal obesity and impaired insulin, which increases the risk of diabetes and poor outcomes.² With over 38.4 million people with diabetes—representing 11.6% of the population³—the prevalence of diabetes is a public health concern and growing epidemic in the United States.

Florida is considered part of the “diabetes belt,⁴” a geographic region in United States with clustered patterns of higher-than-average diabetes prevalence that spans across 15 Southern states. For years, Florida has consistently ranked higher than the national average for diabetes prevalence.⁵ the adult diagnosis rate was approximately 13.1%, with another 9.9% of adult Floridians diagnosed with prediabetes.^6,7 Among Black Americans over the age of 18, 12.2% were diagnosed with diabetes in 2018-2019, and the mortality rate for Black men in 2019 was 47.1%.⁸Unfortunately, almost 550 000 Floridians are unaware they have diabetes,⁷ and the consequences including increased medical expenses and indirect cost from the inability to work, further compound the negative health outcomes.

Diabetes is more prevalent among rural populations compared to their urban counterparts, and managing the condition is particularly challenging due to the need for lifelong care.^9,10 Evidence indicates significant disparities in diabetes control and outcomes among rural dwellers across various countries.^11-13 These healthcare disparities between rural and urban dwellers have been well-documented. Studies reveal that access to care is often limited in rural areas, and rural residents may experience higher diabetes mortality rates than those in urban settings. Various social determinants, such as long distances to health facilities and lack of access to specialty care, exercise facilities, internet, and efficient telecommunication, are known to impact diabetes management in rural settings. Consequently, living in rural areas not only hampers healthcare access but also promotes sedentary lifestyles due to the lack of exercise facilities. A sedentary lifestyle is a known risk factor for the onset of type 2 diabetes and adversely affects blood glucose control.¹⁴

Defining the specific geographic distributions of health issues for interventional programs and research is important for investigators, practitioners, and policymakers to inform appropriate resource allocation and priority areas for targeted interventions.¹⁵ While other studies have investigated the geographic distribution of diabetes at the state level,¹⁶ few studies have explored more granular spatial patterns between diabetes prevalence and race/ethnicity to identify diabetes hotspots, provide evidence for appropriate interventions, and combat disparities in diabetes prevalence.

Methods

This study aimed to define the geographical distribution of all Floridians who reported having diabetes between 2017-2019 and understand how this relates to sedentary behavior in Florida.

Study design

Sample survey data from the Florida Behavioral Risk Factor Surveillance System (BRFSS) was retrieved from civilian, noninstitutionalized US adults aged 18 years or older. The annual survey is distributed via random-digit-dialed landlines and cell phones. We analyzed data on diabetes and sedentary behavior from the 2017-2019 county-level BRFSS.¹⁷

Data collection

Diabetes status was assessed by the answer to the question, “Have you ever been told by a doctor, nurse, or other health professional that you have diabetes?” Individuals who answered “No” to the question “During the past month, other than your regular job, did you participate in any physical activities such as running, calisthenics, golf, gardening, or walking for exercise?” were classified as sedentary. We excluded those who declined to answer these questions or reported “not sure” or “don’t know.”

Data analysis

All analyses were conducted using appropriate BRFSS design variables and weights, ensuring estimates were representative of the Florida population. SAS version 9.4 (Cary, NC: SAS Institute Inc) was used to calculate the overall prevalence of diabetes cases across Florida and determine the weighted mean of diabetes across the 67 counties.

Results

While the statewide prevalence of diabetes diagnosis was 11.7% (95% CI: 10.8–12.6), the geographic distribution of diabetes prevalence varied substantially across counties in Florida, ranging from a high of 20.8% (95% CI: 14–27.6) in Glades County to a low of 6.4% (95% CI: 4.5–8.3) in Leon County. Many counties in the central region of the state tended to have higher diabetes prevalence compared to southern coastal counties. Additionally, higher diabetes prevalence was observed in some northern panhandle counties near the Florida-Georgia border.

The prevalence of sedentary behavior also varied widely across the state, compared with the statewide rate of 26.5% (95% CI: 25–28) Prevalence of sedentary behavior was ranked by county and ranged from a high of 42.7% (95% CI: 35.5–49.9) in Hardee County to a low of 19.3% (95% CI: 14.9–23.8) in Leon County (Table 1). More than half of Florida counties had a prevalence rate of sedentary behavior greater than 30%. Many of the counties with high diabetes prevalence also had elevated rates of sedentary behavior. Some of the counties with both a high prevalence of diabetes and sedentary behavior were located in rural northern panhandle counties. For example, in Gadsden County, a county where 38.9% (95% CI: 29.6–48.3) of the population was sedentary, nearly 20% (19.9%) reported a diabetes diagnosis. Similarly, in neighboring Holmes County, 40.0% (95% CI: 34.3–45.6) of respondents were sedentary and 18.4% had diabetes.

Discussion

These findings suggest that many Florida counties have both an elevated prevalence of diabetes and sedentary behavior, particularly in more rural areas of the state. This trend will negatively impact health outcomes in these populations by increasing their risk of other chronic diseases such as cancers. For men who have diabetes, androgen levels tend to be moderately reduced, and the interaction between metabolic and androgen factors may increase their risk of prostate cancer.² This might contribute to the disparities in prostate cancer prevalence and increased mortality risk observed in Black men.¹⁸

The higher prevalence of diabetes and sedentary lifestyle observed in the northern panhandle counties could impact productivity and economic status, further enforcing health disparities within the underserved and rural communities. Compounding these effects, research shows, an inverse correlation was found between sedentary behavior and socioeconomic factors among older adults, and status of health improves with increased social advantage.¹⁹

Identifying these regions in Florida at increased risk for diabetes and sedentary behaviors is critical for deploying targeted interventions and improving access to care for vulnerable populations. This vivid snapshot unveils a growing menace of health disparities gripping rural communities predominantly inhabited by individuals who experience lack of healthcare access, resulting in poorer health outcomes.

Several limitations should be noted related to survey methodology and design. First, BRFSS data are cross-sectional, which limits our ability to determine causal pathways for diabetes risk. Second, BRFSS data are self-reported. Although research has demonstrated alignment between self-reported diabetes diagnosis and externally verified diagnosis of diabetes,²⁰ our results could underestimate the true prevalence of diabetes in the population due to the inability to capture undiagnosed diabetes. Sedentary behavior may also be subject to recall and social desirability bias, which may result in an underestimate of sedendary behavior. Lastly, BRFSS design excludes the institutionalized population and findings may not be generalizable to this population. Despite these limitations, our study has substantial strengths in its ability to leverage county-level data to pinpoint geographic areas with elevated risk for diabetes and sedentary behavior across the state.

Conclusion

This study reveals that higher diabetes and sedentary lifestyles may be more concentrated in the northern part of Florida counties. Our mapping provides initial evidence that may bolster support for county-specific level interventions for high-prevalence counties, leading to more precise identification of high-risk populations. In spite of the disconcerting surge of diabetes and sedentary behavior incidence in tandem, there lies an immense opportunity to mobilize community and outreach services to more effectively target diabetes management and prevention efforts, as well as physical activity interventions, ensuring their timely deployment to these vulnerable landscapes.

Competing Interests

The authors have no funding or conflicts of interest to disclose.

Ethical Approval

This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Mayo Clinic Florida Institutional Review Board (IRB#23-000375).

References

1. Jang DK, Nam HS, Park M, Kim YH. Differences in associated factors of sedentary behavior by diabetes mellitus status: a nationwide cross-sectional study. J Clin Med 2023; 12(17):5453. doi: 10.3390/jcm12175453 [Crossref] [ Google Scholar]
2. Grossmann M, Wittert G. Androgens, diabetes and prostate cancer. Endocr Relat Cancer 2012; 19(5):F47-62. doi: 10.1530/erc-12-0067 [Crossref] [ Google Scholar]
3. Centers for Disease Control and Prevention (CDC). National Diabetes Statistics Report, 2021. Diabetes. June 6, 2024. Available from: https://www.cdc.gov/diabetes/php/data-research/index.html. Accessed June 11, 2024.
4. Barker LE, Kirtland KA, Gregg EW, Geiss LS, Thompson TJ. Geographic distribution of diagnosed diabetes in the US: a diabetes belt. Am J Prev Med 2011; 40(4):434-9. doi: 10.1016/j.amepre.2010.12.019 [Crossref] [ Google Scholar]
5. Centers for Disease Control and Prevention (CDC). United States Diabetes Surveillance System. 2024. Available from: https://gis.cdc.gov/grasp/diabetes/DiabetesAtlas.html. Accessed April 15, 2024.
6. Florida Department of Health. 2023 Florida Diabetes Report. 2023. Available from: https://www.floridahealth.gov/provider-and-partner-resources/dac/_documents/2023-dac-report.pdf. Accessed April 15, 2024.
7. American Diabetes Association. Economic costs of diabetes in the US in 2017. Diabetes Care 2018; 41(5):917-28. doi: 10.2337/dci18-0007 [Crossref] [ Google Scholar]
8. Office of Minority Health. Diabetes and African Americans. United States Department of Health and Human Services; 2018. Available from: https://minorityhealth.hhs.gov/diabetes-and-african-americans. Accessed February 27, 2024.
9. National Center for Health Statistics. Percentage of Diagnosed Diabetes for Adults Aged 18 and Over, United States, 2019-2021. National Health Interview Survey; 2023.
10. Foss R, Fischer K, Lampman MA, Laabs S, Halasy M, Allen SV. Disparities in diabetes care: differences between rural and urban patients within a large health system. Ann Fam Med 2023; 21(3):234-9. doi: 10.1370/afm.2962 [Crossref] [ Google Scholar]
11. MacCarthy S, Elliott MN, Martino SC, Klein DJ, Haviland AM, Weech-Maldonado R. Rural disparities in the quality of clinical care are notable and larger for males. J Rural Health 2023; 39(3):636-42. doi: 10.1111/jrh.12710 [Crossref] [ Google Scholar]
12. Flood D, Geldsetzer P, Agoudavi K, Aryal KK, Brant LC, Brian G. Rural-urban differences in diabetes care and control in 42 low- and middle-income countries: a cross-sectional study of nationally representative individual-level data. Diabetes Care 2022; 45(9):1961-70. doi: 10.2337/dc21-2342 [Crossref] [ Google Scholar]
13. Chen HF, Lee HE, Chen IT, Huang YT, Ho PS, Karim SA. Rural-urban disparities in the incidence and treatment intensity of periodontal disease among patients with diabetes. Front Public Health 2023; 11:1241150. doi: 10.3389/fpubh.2023.1241150 [Crossref] [ Google Scholar]
14. Molina RT, Ríos García AL, Vergara TA, Florez-Garcia VA, Gutierrez VR, Lozano KF. Predictors of diabetes risk in urban and rural areas in Colombia. Heliyon 2022; 8(1):e08653. doi: 10.1016/j.heliyon.2021.e08653 [Crossref] [ Google Scholar]
15. Wang F. Why public health needs GIS: a methodological overview. Ann GIS 2020; 26(1):1-12. doi: 10.1080/19475683.2019.1702099 [Crossref] [ Google Scholar]
16. Danaei G, Friedman AB, Oza S, Murray CJ, Ezzati M. Diabetes prevalence and diagnosis in US states: analysis of health surveys. Popul Health Metr 2009; 7:16. doi: 10.1186/1478-7954-7-16 [Crossref] [ Google Scholar]
17. Florida Department of Health. 2017-2019 Behavorial Risk Factor Surveillance System (BRFSS). Available from: https://www.floridahealth.gov/statistics-and-data/survey-data/behavioral-risk-factor-surveillance-system/2019county/BradfordCombinedReport.pdf. Accessed June 16, 2023.
18. Siegel RL, Giaquinto AN, Jemal A. Cancer statistics, 2024. CA Cancer J Clin 2024; 74(1):12-49. doi: 10.3322/caac.21820 [Crossref] [ Google Scholar]
19. Rhee TG, Lee K, Schensul JJ. Black-White disparities in social and behavioral determinants of health index and their associations with self-rated health and functional limitations in older adults. J Gerontol A Biol Sci Med Sci 2021; 76(4):735-40. doi: 10.1093/gerona/glaa264 [Crossref] [ Google Scholar]
20. Okura Y, Urban LH, Mahoney DW, Jacobsen SJ, Rodeheffer RJ. Agreement between self-report questionnaires and medical record data was substantial for diabetes, hypertension, myocardial infarction and stroke but not for heart failure. J Clin Epidemiol 2004; 57(10):1096-103. doi: 10.1016/j.jclinepi.2004.04.005 [Crossref] [ Google Scholar]