RiteeshGrandhi/sciclaimeval-shared-task huggingface dataset:
Strengths and limitations
This is the first study investigating geographic disparities of non-gestational diabetes-related ED visits in Florida using rigorous statistical approaches. Identifying areas with high diabetes-related ED visit rates is crucial for guiding resource allocation and improving access to primary diabetes care. This study also investigated sociodemographic predictors of non-gestational diabetes-related ED visits in Florida, the findings of which are important for guiding programs aimed at reducing disparities in the availability of diabetes care and improving the health of populations with diabetes in Florida. Although this study was conducted in Florida, the findings of this study showed that spatial statistics are useful for identifying geographic disparities of ED visit rates across states in the US. However, this study is not without limitation. Coding errors might have occurred in the use of ICD-10 codes to report diabetes and diabetes-related conditions in ED data due to the complex nature of the ICD-10 coding system that might result in coding errors potentially leading to misclassification of the outcome and hence misclassification bias ( American Academy of Family Physicians, 2013 ; Burles et al., 2017 ). Similarly, errors in identifying geographic location might have resulted in location misclassification bias. However, this is expected to be minimal, if at all present, and is expected to have little to no impact on study findings since only aggregated county-level data were used. Additionally, since this was a retrospective study that used previously collected administrative data, the investigation was limited to only variables that were available in the datasets used for the study. It is important to note that since this study was conducted at the county-level, it is potentially subject to ecological bias so our inferences to the individual-level should be considered with caution. Finally, BRFSS data used in this study may be prone to reporting bias. However, previous research has shown that findings generated from BRFSS data are representative of the population ( Stein et al., 1995 ; Bowlin et al., 1996 ). These limitations notwithstanding, the findings of this study provide useful information for guiding health planners in allocating resources and reducing diabetes burden in Florida.
Conclusions
This study identified geographic disparities of non-gestational diabetes-related ED visit rates in Florida with high-rate areas being observed in the rural northern and southcentral parts of the state. Lack of healthcare access, high diabetes prevalence, low levels of insurance coverage, and certain demographic factors were identified as significant predictors of high non-gestational diabetes-related ED visit rates. These findings are useful for guiding public health efforts geared at reducing disparities and improving diabetes outcomes in Florida.
Figure 1: Florida map showing geographic distribution of rural and urban counties.
Base maps source: the United States Census Bureau, https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-line-file.2019.html.

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DOI: 10.7717/peerj.18897/fig-1
Figure 2: Temporal patterns of diabetes-related emergency department visit rates in Florida, January 2016-December 2019.

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DOI: 10.7717/peerj.18897/fig-2
Figure 3: Geographic distribution of diabetes-related emergency department visit rates in Florida, 2016–2019.
Base maps source: the United States Census Bureau, https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-line-file.2019.html.

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DOI: 10.7717/peerj.18897/fig-3
Figure 4: Statistically significant non-overlapping spatial clusters of high diabetes-related emergency department visit rates identified in Florida using Tango’s flexible spatial scan statistics, 2016–2019.
Base maps source: the United States Census Bureau, https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-line-file.2019.html.

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DOI: 10.7717/peerj.18897/fig-4
Figure 5: Statistically significant non-overlapping spatial clusters of high diabetes-related emergency department visit rates identified in Florida using Kulldorff’s circular spatial scan statistics, 2016–2019.
Base maps source: the United States Census Bureau, https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-line-file.2019.html.

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DOI: 10.7717/peerj.18897/fig-5
Figure 6: Distribution of statistically significant predictors of diabetes-related emergency department visit rates in Florida, 2019.
Base maps source: the United States Census Bureau, https://www.census.gov/geographies/mapping-files/time-series/geo/tiger-line-file.2019.html.

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DOI: 10.7717/peerj.18897/fig-6
Summary statistics of variables considered as potential predictors of county-level non-gestational diabetes-related emergency department visit rates in Florida, 2019.
Statistically significant non-overlapping spatial clusters of high diabetes-related emergency department visit rates identified in Florida using Tango’s flexible spatial scan statistics, 2016–2019.
Statistically significant non-overlapping spatial clusters of high diabetes-related emergency department visit rates identified in Florida using Kulldorff’s circular spatial scan statistics, 2016–2019.
Results of univariable negative binomial regression models examining the associations between county characteristics and the rate of diabetes-related emergency department visits in Florida, 2019.
Results of a multivariable negative binomial regression model examining the associations between county characteristics and the rate of diabetes-related emergency department visits in Florida, 2019.