Spatial analysis and correlates of county-level diabetes prevalence, 2009-2010.
Spatial analysis and correlates of county-level diabetes prevalence, 2009-2010.
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DOI：
10.5888/pcd12.140404
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发表时间：
2015-01-22
影响因子：
5.5
通讯作者：
Chalise N
中科院分区：
文献类型：
--
作者：
Hipp JA;Chalise N
有关糖尿病患病率与建筑环境属性之间关系的信息可以使公共卫生计划更好地针对糖尿病风险人群。本研究旨在确定美国糖尿病的空间患病率以及这种分布与常见糖尿病的地理分布之间的关系。整合来自疾病控制与预防中心和美国人口普查局的数据，对以下变量在县一级进行地理加权回归：非白人人口百分比、西班牙裔人口百分比、教育水平、失业百分比、生活在联邦贫困线以下的百分比、人口密度、肥胖百分比、缺乏身体活动的百分比、骑自行车或步行上班的人口百分比以及社区食物沙漠百分比。我们发现美国县级糖尿病患病率存在显着的空间聚集性；然而，糖尿病患病率与重要预测因素的相关性不一致。在某些地区，生活在联邦贫困线以下的百分比和非白人人口的百分比与糖尿病有关。骑自行车或步行上班的人口百分比是与糖尿病相关的唯一重要的建筑环境相关变量，而且这种关联在全国范围内的程度各不相同。在美国一些地区，社会人口统计学和建筑环境相关变量与糖尿病患病率相关。这些关系的大小和方向的变化凸显了在预防和维持糖尿病方面了解当地情况的必要性。地理加权回归显示了公共卫生研究在检测跨地理空间的健康行为、结果和预测因素之间关联的变化方面的前景。
Information on the relationship between diabetes prevalence and built environment attributes could allow public health programs to better target populations at risk for diabetes. This study sought to determine the spatial prevalence of diabetes in the United States and how this distribution is associated with the geography of common diabetes correlates. Data from the Centers for Disease Control and Prevention and the US Census Bureau were integrated to perform geographically weighted regression at the county level on the following variables: percentage nonwhite population, percentage Hispanic population, education level, percentage unemployed, percentage living below the federal poverty level, population density, percentage obese, percentage physically inactive, percentage population that cycles or walks to work, and percentage neighborhood food deserts. We found significant spatial clustering of county-level diabetes prevalence in the United States; however, diabetes prevalence was inconsistently correlated with significant predictors. Percentage living below the federal poverty level and percentage nonwhite population were associated with diabetes in some regions. The percentage of population cycling or walking to work was the only significant built environment–related variable correlated with diabetes, and this association varied in magnitude across the nation. Sociodemographic and built environment–related variables correlated with diabetes prevalence in some regions of the United States. The variation in magnitude and direction of these relationships highlights the need to understand local context in the prevention and maintenance of diabetes. Geographically weighted regression shows promise for public health research in detecting variations in associations between health behaviors, outcomes, and predictors across geographic space.
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