ArticlePloS one2019
An exploratory investigation of geographic disparities of stroke prevalence in Florida using circular and flexible spatial scan statistics.
Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- A Geographic Weighted Regression Analysis of the Health Opportunity Index and Stroke Prevalence in Health and Human Services Region 3.International journal of environmental research and public health · 2025Article
- Article
- Determinants of disparities of diabetes-related hospitalization rates in Florida: a retrospective ecological study using a multiscale geographically weighted regression approach.International journal of health geographics · 2024Article
- A simulation study for geographic cluster detection analysis on population-based health survey data using spatial scan statistics.International journal of health geographics · 2022Article
- Geographic disparities, determinants, and temporal changes in the prevalence of pre-diabetes in Florida.PeerJ · 2021Article
- Article
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Authors and funding
4 authors.
Funding
Abstract
backgroundStroke is a major public health concern due to the morbidity and mortality associated with it. Identifying geographic areas with high stroke prevalence is important for informing public health interventions. Therefore, the objective of this study was to investigate geographic disparities and identify geographic hotspots of stroke prevalence in Florida. MATERIALS AND
methodsCounty-level stroke prevalence data for 2013 were obtained from the Florida Department of Health's Behavioral Risk Factor Surveillance System (BRFSS). Geographic clusters of stroke prevalence were investigated using the Kulldorff's circular spatial scan statistics (CSSS) and Tango's flexible spatial scan statistics (FSSS) under Poisson model assumption. Exact McNemar's test was used to compare the proportion of cluster counties identified by each of the two methods. Both Cohen's Kappa and bias adjusted Kappa were computed to assess the level of agreement between CSSS and FSSS methods of cluster detection. Goodness-of-fit of the models were compared using Cluster Information Criterion. Identified clusters and selected stroke risk factors were mapped.
resultsOverall, 3.7% of adults in Florida reported that they had been told by a healthcare professional that they had suffered a stroke. Both CSSS and FSSS methods identified significant high prevalence stroke spatial clusters. However, clusters identified using CSSS tended to be larger than those identified using FSSS. The FSSS had a better fit than the CSSS. Most of the identified clusters are explainable by the prevalence distributions of the known risk factors assessed.
conclusionsGeographic disparities of stroke risk exists in Florida with some counties having significant hotspots of high stroke prevalence. This information is important in guiding future research and control efforts to address the problem. Kulldorff's CSSS and Tango's FSSS are complementary to each other and should be used together to provide a more complete picture of the distributions of spatial clusters of health outcomes.
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