The spatial clustering of obesity: does the built environment matter?
The spatial clustering of obesity: does the built environment matter?
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DOI：
10.1111/jhn.12279
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发表时间：
2015-12-01
影响因子：
3.3
通讯作者：
Drewnowski, A.
中科院分区：
文献类型：
--
作者：
Huang, R.;Moudon, A. V.;Drewnowski, A.
背景美国的肥胖率呈现出明显的地理分布模式。本研究采用空间聚类检测方法和个人层面的数据，定位肥胖集群，并分析他们在附近的建成environment.MethodsThe 2008-2009年西雅图肥胖研究提供的数据，自我报告的身高，体重和社会人口特征的1602金郡成年人。家庭住址都有地理编码。使用Anselin的Local Moran's I和回归模型的空间扫描统计来确定高或低体重指数的集群，该回归模型从残差中搜索未测量的邻域水平因素，并对测量的个人水平协变量进行调整。空间连续值的客观测量功能的本地邻里建成环境（SmartMaps）构建了7个变量，从税收卷和商业databases.ResultsBoth本地莫兰的我和空间扫描统计确定类似的空间浓度的肥胖。高和低肥胖集群衰减调整后的年龄，性别，种族，教育和收入，他们消失了，一旦邻里住宅物业价值和住宅密度包括在model.ConclusionsUsing个人水平的数据来检测肥胖集群与两个集群检测方法，本研究表明，肥胖的空间集中是完全由邻里组成和社会经济特征解释。这些特征可能有助于更准确地定位肥胖预防和干预计划。
BackgroundObesity rates in the USA show distinct geographical patterns. The present study used spatial cluster detection methods and individual-level data to locate obesity clusters and to analyse them in relation to the neighbourhood built environment.MethodsThe 2008-2009 Seattle Obesity Study provided data on the self-reported height, weight, and sociodemographic characteristics of 1602 King County adults. Home addresses were geocoded. Clusters of high or low body mass index were identified using Anselin's Local Moran's I and a spatial scan statistic with regression models that searched for unmeasured neighbourhood-level factors from residuals, adjusting for measured individual-level covariates. Spatially continuous values of objectively measured features of the local neighbourhood built environment (SmartMaps) were constructed for seven variables obtained from tax rolls and commercial databases.ResultsBoth the Local Moran's I and a spatial scan statistic identified similar spatial concentrations of obesity. High and low obesity clusters were attenuated after adjusting for age, gender, race, education and income, and they disappeared once neighbourhood residential property values and residential density were included in the model.ConclusionsUsing individual-level data to detect obesity clusters with two cluster detection methods, the present study showed that the spatial concentration of obesity was wholly explained by neighbourhood composition and socioeconomic characteristics. These characteristics may serve to more precisely locate obesity prevention and intervention programmes.