Materials and Methods
This retrospective ecological study was conducted between 2022 and 2023. Temporal changes in monthly non-gestational diabetes-related ED visit rates (per 100,000 person-months) were investigated. Annual non-gestational diabetes-related ED visit rates (per 100,000 person-years) were computed at the county level and clusters of counties with high non-gestational diabetes-related ED visit rates were identified and displayed on maps. Socioeconomic and demographic characteristics, health-related behavior, and environmental factors were investigated as potential predictors of non-gestational diabetes-related ED visit rates.

The study area encompassed the entire state of Florida and covered the time period from January 1, 2016 to December 31, 2019 ( Figure 1 ). As of 2020, Florida was the most populous state in the southeastern US with approximately 21.6 million people ( Florida Department of Health, 2020a ). Twenty-two percent of Florida’s population was 0–19 years old, 31% was 20–44 years old, 26.1% was 45–64 years old, and the rest (20.9%) were 65 years old or older. Approximately half of the population was female. By race, White represented the majority (77.2%) of the population, Black were 17.0%, and all other races comprised the rest (5.8%). By ethnicity, 26.7% of the population was Hispanic-Latino while the rest were non-Hispanic (of any race) ( Florida Department of Health, 2020a ). The most urban and populous county was Miami-Dade (population: 2.9 million) located in the southern part of the state, while the most rural and least populous county was Lafayette (population: 8,721) located in the northern part of the state ( Florida Department of Health, 2020a ). In total, Florida has 67 counties many of which are considered part of the diabetes belt which is an area located in the southeastern part of the US where diabetes prevalence is higher (11.7%) compared to the rest of the US (8.5%) ( Barker et al., 2011 ).

ED data for the time period from January 1, 2016 to December 31, 2019 were obtained from the Agency for Healthcare Administration, Florida. Records of ED visits related to non-gestational diabetes and non-gestational diabetes-related complications, for people of all ages, were extracted from the ED data using the following International Classification of Diseases (ICD)-10 codes listed in primary or secondary diagnoses: E08 (diabetes mellitus due to underlying condition); E09 (drug or chemical induced diabetes mellitus); E10 (type 1 diabetes mellitus); E11 (type 2 diabetes mellitus); and E13 (other specified diabetes mellitus) ( Kostick, 2012 ). However, pregnancy related diabetes and neonatal diabetes were excluded.

The 2017–2019 Behavioral Risk Factor Surveillance System (BRFSS) data, which contains questionnaire survey data for individuals aged 18 years or older, were obtained from the Florida Department of Health (FDH) ( Florida Department of Health, 2020b ). The following variables were extracted from the BRFSS: respondent’s county of residence, race, gender, education, income, marital status, overall health status, Body Mass Index (BMI), level of daily physical activity, daily fruit and vegetable consumption, smoking and drinking habits, usage of tobacco, snuff, or e-cigarettes, healthcare accessibility, presence of comorbidities such as diabetes, heart disease, stroke, arthritis, kidney diseases, hypertension, hypercholesterolemia, and depression.

County-level percentages of population that are unemployed, lack access to healthy food and exercise opportunities, do not have food security, and live in rural areas were obtained from the County Health Rankings and Roadmap (CHRR) website ( Robert Wood Johnson Foundation and The University of Wisconsin Population Health Institute, 2020 ). Additionally, county-level number of primary care physicians per 100,000 population and air pollution were extracted from the CHRR website. Data on total number of people per county were obtained from the population dashboard of the FDH ( Florida Department of Health, 2020a ). Rural and urban counties were classified based on the population density in counties. Counties with a population density of <100 persons per square mile were classified as rural, while those with a density of ≥100 were classified as urban. This classification table was obtained from the FDH ( Florida Department of Health, 2023 ). County-level age distribution of the population and household vehicle availability data were extracted from the 2015–2019 American Community Survey (ACS) 5-year average estimate ( US Census Bureau, 2020 ). A cartographic boundary file for performing county-level geographic analyses was downloaded from the United States Census Bureau TIGER Geodatabase ( United States Census Bureau, 2021 ).

All data preparation and descriptive analyses were performed in SAS 9.4 ( SAS Institute Inc, 2017 ). Since the BRFSS data were collected using a complex survey design, all county-level estimates were calculated using SURVEYFREQ procedure of SAS specifying strata variable (_STSTR), cluster variable (_PSU), and sampling weight variable (_CNTYWT). Weighted county-level percentages of categorical variables were computed and presented in a table ( Table 1 ). Annual county-level non-gestational diabetes-related ED visit rates were computed by dividing the number of non-gestational diabetes-related ED visits in a year by the total number of populations of the county and multiplying the result by 100,000.

Notes:

The Shapiro-Wilk test and Quantile-Quantile (Q-Q) plot were used to assess the normality of continuous county-level variables. Means, standard deviations, median (50th percentile), and lower-upper quartiles were reported for all variables. Temporal trends in monthly diabetes-related ED visit rates were investigated using the Cochran-Armitage trend test.

Circular- and irregularly-shaped spatial clusters of high non-gestational diabetes-related ED visit rates were investigated using Tango’s flexible spatial scan statistics implemented in FlexScan 3.1.2 ( Tango & Takahashi, 2005 ). Poisson probability model with restricted log likelihood (LLR) ratio (specifying α = 0.2) and a maximum cluster size of 15 counties was used to ensure that each cluster includes areas with high ED visit rates, based on the geographic distribution of ED visit rates in Florida and prior studies identifying regions with higher disease burdens ( Lord, Roberson & Odoi, 2020 ; Khan et al., 2021 ; Khan, Odoi & Odoi, 2023 ). For statistical inference, 999 Monte Carlo replications and a critical p -value of 0.05 were used to assess statistical significance. Significant clusters were ranked based on their restricted LLR values. The cluster with the highest LLR value was considered the primary cluster, while the rest of the statistically significant clusters were considered secondary clusters. Only statistically significant non-overlapping clusters that had what we considered an epidemiologically notable higher rate of ED visits compared to the overall rate ( i.e ., observed/expected ≥1.2) were reported.

Kulldorff’s circular spatial scan statistics (CSSS), implemented in SaTScan 9.6, was used to identify circular non-overlapping purely spatial high-rate clusters of non-gestational diabetes-related ED visit rates. A discrete Poisson probability model specifying a maximum circular window size of 13.5% of population at risk was used in the analysis. The window size was set based on the geographic distribution of high ED visit rates in Florida, and prior knowledge of areas with high disease rates in Florida ( Lord, Roberson & Odoi, 2020 ; Khan et al., 2021 ; Khan, Odoi & Odoi, 2023 ). This window size also ensures that all counties have a chance of being in a cluster regardless of their population size. Like our analyses using Tango’s method, the cluster with the highest LLR value was considered the primary cluster, and 999 Monte Carlo replications and a critical p -value of 0.05 were used to identify statistically significant clusters, and clusters with rate ratios ≥1.2 were reported.

For investigation of county-level predictors of non-gestational diabetes-related ED visit rates, only data for 2019 were used to ensure that the identified associations were based on the most current available data. To do this, a global negative binomial model was built in SAS using county-level data obtained from the BRFSS, CHRR, FDH, and ACS ( SAS Institute Inc, 2017 ). A causal diagram was constructed based on literature review and biological knowledge to identify potential predictors. The model building process involved first assessing univariable associations between each of the potential predictors and county-level diabetes-related ED visit rates using a relaxed p -value of ≤ 0.15. The log of total number of populations (per 100,000) was used as an offset. Correlations among the potential predictors were assessed using Spearman’s rank correlation coefficients. To avoid multicollinearity, only one of a pair of highly correlated variables (r ≥ |0.7|) was retained for assessment in the multivariable model. The decision of which variable of the pair to keep was based on its proximal relationship with the outcome as well as its p -value (variable with the lowest p -value was retained). Variables that were significant potential predictors and not highly correlated were used to build the multivariable negative binomial model. The final main effects model was built using manual backward elimination approach specifying a critical p -value of ≤ 0.05. Potential confounders and intervening variables were identified using the causal diagram and assessed during the model building process. Thus, confounding was assessed by running the model with and without a suspected confounder and assessing the changes in regression coefficients of variables in the model. A variable was kept in the final main effects model as a confounder if its removal from the model resulted in a change of 20% or more of the coefficients of any other variables in the model. Biologically meaningful two-way interaction terms (such as percent non-Hispanic Black*percent of having diabetes; percent non-Hispanic Black*percent without insurance coverage), were assessed and only the statistically significant ones were retained in the final model. Goodness-of-fit of the final model was assessed using deviance χ 2 goodness-of-fit test. Cook’s Distance was used to identify highly influential observations.

All cartographic displays were performed using QGIS ( QGIS Development Team, 2021 ). The geographic distribution of non-gestational diabetes-related ED visit rates and significant ( p ≤ 0.05) spatial clusters were displayed on maps. Critical intervals for choropleth maps were determined using Jenk’s optimization classification scheme.

This study was reviewed and approved by the University of Tennessee Institutional Review Board (IRB) (IRB Number: UTK IRB-20-05707-XM). The IRB determined that the study was eligible for exempt review under 45 CFR 46.101 “Category 4: Secondary research for which consent is not required: Secondary research uses of identifiable private information or identifiable biospecimens, if the information, which may include information about biospecimens, is recorded by the investigator in such a manner that the identity of the human subjects cannot readily be ascertained directly or through identifiers linked to the subjects, the investigator does not contact the subjects, and the investigator will not re-identify subjects.”