Biomedical and Diagnostic Sciences, University of Tennessee, Knoxville, United States of America.
Florida Department of Health, Tallahassee, United States of America.
PeerJ. 2023 Apr 20;11:e15107. doi: 10.7717/peerj.15107. eCollection 2023.
Diabetes and its complications represent a significant public health burden in the United States. Some communities have disproportionately high risks of the disease. Identification of these disparities is critical for guiding policy and control efforts to reduce/eliminate the inequities and improve population health. Thus, the objectives of this study were to investigate geographic high-prevalence clusters, temporal changes, and predictors of diabetes prevalence in Florida.
Behavioral Risk Factor Surveillance System data for 2013 and 2016 were provided by the Florida Department of Health. Tests for equality of proportions were used to identify counties with significant changes in the prevalence of diabetes between 2013 and 2016. The Simes method was used to adjust for multiple comparisons. Significant spatial clusters of counties with high diabetes prevalence were identified using Tango's flexible spatial scan statistic. A global multivariable regression model was fit to identify predictors of diabetes prevalence. A geographically weighted regression model was fit to assess for spatial non-stationarity of the regression coefficients and fit a local model.
There was a small but significant increase in the prevalence of diabetes in Florida (10.1% in 2013 to 10.4% in 2016), and statistically significant increases in prevalence occurred in 61% (41/67) of counties in the state. Significant, high-prevalence clusters of diabetes were identified. Counties with a high burden of the condition tended to have high proportions of the population that were non-Hispanic Black, had limited access to healthy foods, were unemployed, physically inactive, and had arthritis. Significant non-stationarity of regression coefficients was observed for the following variables: proportion of the population physically inactive, proportion with limited access to healthy foods, proportion unemployed, and proportion with arthritis. However, density of fitness and recreational facilities had a confounding effect on the association between diabetes prevalence and levels of unemployment, physical inactivity, and arthritis. Inclusion of this variable decreased the strength of these relationships in the global model, and reduced the number of counties with statistically significant associations in the local model.
The persistent geographic disparities of diabetes prevalence and temporal increases identified in this study are concerning. There is evidence that the impacts of the determinants on diabetes risk vary by geographical location. This implies that a one-size-fits-all approach to disease control/prevention would be inadequate to curb the problem. Therefore, health programs will need to use evidence-based approaches to guide health programs and resource allocation to reduce disparities and improve population health.
在美国，糖尿病及其并发症是一个重大的公共卫生负担。一些社区患这种疾病的风险过高。发现这些差异对于指导政策和控制工作以减少/消除不平等现象和改善人口健康状况至关重要。因此，本研究的目的是调查佛罗里达州糖尿病流行的地理高发集群、时间变化和预测因素。
佛罗里达州卫生署提供了 2013 年和 2016 年行为风险因素监测系统的数据。使用比例相等性检验来确定 2013 年至 2016 年期间糖尿病患病率发生显著变化的县。使用 Simes 方法对多个比较进行调整。使用 Tango 的灵活空间扫描统计数据识别具有高糖尿病流行率的县的显著空间聚类。拟合全局多变量回归模型以确定糖尿病患病率的预测因素。拟合地理加权回归模型以评估回归系数的空间非平稳性并拟合局部模型。
佛罗里达州的糖尿病患病率略有但呈上升趋势（2013 年为 10.1%，2016 年为 10.4%），并且该州 61%（41/67）的县的患病率呈统计学显著上升。确定了显著的高流行率糖尿病集群。该疾病负担沉重的县往往具有高比例的非西班牙裔黑人人口、获得健康食品的机会有限、失业、身体不活跃和患有关节炎。观察到以下变量的回归系数存在显著的非平稳性：身体不活跃的人口比例、获得健康食品机会有限的比例、失业比例和患关节炎比例。然而，健身和娱乐设施的密度对糖尿病患病率与失业率、身体不活动和关节炎水平之间的关联有混杂作用。在全球模型中包含此变量会降低这些关系的强度，并减少局部模型中具有统计学意义关联的县的数量。
本研究中确定的糖尿病流行的持续地理差异和时间增加令人担忧。有证据表明，这些决定因素对糖尿病风险的影响因地理位置而异。这意味着一刀切的疾病控制/预防方法不足以遏制这个问题。因此，健康计划将需要使用基于证据的方法来指导健康计划和资源分配，以减少不平等现象并改善人口健康状况。
