Bureau of Chronic Disease Prevention, Division of Community Health Promotion, Florida Department of Health, Tallahassee, Florida, United States of America.
Biomedical and Diagnostic Sciences, College of Veterinary Medicine, The University of Tennessee, Knoxville, Tennessee, United States of America.
PLoS One. 2019 Aug 30;14(8):e0218708. doi: 10.1371/journal.pone.0218708. eCollection 2019.
Stroke 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.
County-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.
Overall, 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.
Geographic 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.
由于与中风相关的发病率和死亡率，中风是一个主要的公共卫生关注点。确定中风高发的地理区域对于告知公共卫生干预措施非常重要。因此，本研究的目的是调查佛罗里达州中风发病率的地理差异，并确定其地理热点。
从佛罗里达州卫生部行为风险因素监测系统（BRFSS）获得 2013 年县级中风发病率数据。使用 Kulldorff 的圆形空间扫描统计（CSSS）和 Tango 的灵活空间扫描统计（FSSS）调查中风发病率的地理聚类，假设泊松模型。使用精确 McNemar 检验比较两种方法识别的聚类县的比例。计算 Cohen's Kappa 和偏置调整 Kappa 以评估 CSSS 和 FSSS 聚类检测方法之间的一致性水平。使用聚类信息准则比较模型的拟合优度。对识别出的聚类和选定的中风危险因素进行映射。
总体而言，佛罗里达州有 3.7%的成年人报告称，他们曾被医疗保健专业人员告知患有中风。CSSS 和 FSSS 方法均识别出具有显著高发病率的中风空间聚类。然而，CSSS 识别出的聚类往往比 FSSS 识别出的聚类大。FSSS 的拟合优于 CSSS。大多数识别出的聚类可以用评估的已知风险因素的患病率分布来解释。
佛罗里达州存在中风风险的地理差异，一些县存在显著的高中风发病率热点。这些信息对于指导未来的研究和控制工作以解决这一问题非常重要。Kulldorff 的 CSSS 和 Tango 的 FSSS 相互补充，应一起使用，以更全面地了解健康结果的空间聚类分布。
