Results
There was a total of 34,285,646 ED visits reported in Florida from January 1, 2016 to December 31, 2019. This study included only 2,871,326 ED visits, which had a diagnosis of non-gestational diabetes or non-gestational diabetes-related complications.

Overall, non-gestational diabetes-related ED visit rates in Florida increased significantly ( p < 0.001) from 266 visits per 100,000 person-months in January 2016 to 332 visits per 100,000 person-months in December 2019 ( Figure 2 ). The highest non-gestational diabetes-related ED visit rate (332 ED visits per 100,000 person-months) was observed in December 2019, while the lowest (259 ED visits per 100,000 person-months) was in June 2016.

Geographic distribution of non-gestational diabetes-related ED visit rates varied across counties in Florida ranging from 1,448 to 10,211 visits per 100,000 person-years ( Figure 3 ). Overall, more than half of the counties had high non-gestational diabetes-related ED visit rates (>3,385 ED visits per 100,000 person-years) during the study period. Almost all counties in rural northern Florida, including the entire panhandle area up to the westernmost part of the state, had higher non-gestational diabetes-related ED visits than counties in the southern part of the state ( Figs. 1 and 3 ). However, a few counties in the north-central portion had low ED visit rates in 2016 and 2017. Most of the counties in the central part of the state tended to have high non-gestational diabetes-related ED visit rates, while low rates were consistently observed in the southernmost part and urban coastal areas during the study period ( Figs. 1 and 3 ).

Consistent with high non-gestational diabetes-related ED visit rates observed in the northern and central parts of Florida, significant ( p < 0.05) high non-gestational diabetes-related ED visit rate clusters were identified in these areas ( Figs. 3 , 4 , and 5 ). Overall, clusters were similar in size and location across years ( Tables 2 and 3 , Figs. 4 and 5 ). Using Tango’s FSSS, the primary high-rate clusters were consistently identified in the south-central portion of the state and mainly included rural counties (Hardee, Highlands, Okeechobee, and Hendry) ( Figs. 1 and 4 ). Similarly, several small high-rate clusters located across the panhandle area included only rural counties. However, a high-rate cluster was identified in the rural-urban interface of north-central Florida and included both rural and urban counties. Although high-rate clusters were not identified in the southern and coastal urban areas, a single county high-rate cluster (Escambia) was consistently identified in the westernmost urban part of the state ( Figs. 1 and 4 ). Similar to the findings of Tango’s FSSS, Kulldorff’s CSSS identified significant high non-gestational diabetes-related ED visit rate clusters in the panhandle area, north-central, and central portions of the state and included mostly rural counties. However, unlike Tango’s FSSS, larger but fewer clusters were identified by Kulldorff’s CSSS ( Tables 2 and 3 , Figs. 4 and 5 ).

Notes:

Spatial scan parameters: Maximum cluster size = 15 counties; Test = one-tailed (high rates); RR for reporting clusters ≥ 1.2.

Geographic distribution of the clusters is shown in Figure 4 .

Notes:

Spatial scan parameters: Maximum window size = 13.5% of total population; Test = one-tailed (high rates); RR for reporting clusters ≥ 1.2.

Geographic distribution of the clusters is presented in Figure 5 .

Tables 4 and 5 show the results of the univariable and final multivariable negative binomial models used to investigate associations between county-level sociodemographic variables and county-level non-gestational diabetes-related ED visit rates, respectively. Several of the variables assessed had univariable statistically significant associations with the outcome based on a relaxed critical p -value of 0.15. Almost half of these variables were dropped during the variable selection process due to the presence of high collinearity and the fact that none of the dropped variables were important confounders. Additionally, no statistically significant two-way interactions were identified. Thus, only variables that had statistically significant ( p < 0.05) associations with non-gestational diabetes related ED visit rates were kept in the final model.

Notes:

Notes:

Based on the final multivariable negative binomial model, the following independent variables had significant high county-level non-gestational diabetes-related ED visit rates: percentages of population who were non-Hispanic Black, current smokers, had diabetes, and had no insurance coverage ( Table 5 ). However, percentage of population that were married had statistically significant low diabetes-related ED visit rate ( Table 5 ). The p -value of the deviance χ 2 goodness-of-fit test was not significant ( p = 0.259) indicating a good model fit. Based on the Cook’s Distance, Glades and Union counties were identified as highly influential observations. Glades county had the highest diabetes prevalence, while Union County had the highest percentage of smokers.

Geographic distributions of the significant predictors of non-gestational diabetes-related ED visit rates are shown in Figure 6 . Counties in the central and eastern panhandle rural area tended to have high percentages of population that were non-Hispanic Black, current smokers, had diabetes, and had no insurance coverage. These counties overlapped with many counties with high non-gestational diabetes-related ED visit rates ( Figs. 1 , 3 , and 6 ). On the other hand, counties in the southernmost urban area of the state had relatively low non-gestational diabetes-related ED visit rates but tended to have high percentages of non-Hispanic Black and low percentages of married, current smokers, and those with diabetes. Although counties with high percentages of population with diabetes were concentrated in the mid to south-central parts of the state, these counties had high percentages of uninsured people ( Figs. 1 , 3 , and 6 ).