Racial differences in the built environment--body mass index relationship? A geospatial analysis of adolescents in urban neighborhoods.
Racial differences in the built environment--body mass index relationship? A geospatial analysis of adolescents in urban neighborhoods.
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DOI：
10.1186/1476-072x-11-11
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发表时间：
2012-04-26
影响因子：
4.9
通讯作者：
Bennett GG
中科院分区：
文献类型：
--
作者：
Duncan DT;Castro MC;Gortmaker SL;Aldstadt J;Melly SJ;Bennett GG
社区的建成环境特征可能与青少年肥胖有关，并可能与肥胖相关的健康差异有关。本研究的目的是探讨各种建筑环境特征与青少年身体质量指数（BMI） z-score之间的空间关系，并探讨种族/民族是否会改变这些关系。第二个目标是评估结果对分析的空间尺度（即400米和800米的街道网络缓冲）的敏感性。数据来自2008年波士顿青年调查，这是一个以马萨诸塞州波士顿公立高中学生为基础的学校样本。分析包括从具有地理参考居住信息和完整有效数据的学生收集的数据，以计算BMI z-score （n = 1,034）。我们使用地理信息系统建立了一个空间数据库，其中包含与步行目的地和社区设计相关的各种特征。利用Global Moran’s I统计量计算关键研究变量的空间自相关性。我们拟合了传统的普通最小二乘（OLS）回归和空间同步自回归误差模型，这些模型可以适当地控制数据中的空间自相关。模型是使用青少年的总样本进行的，包括种族/民族的相互作用术语，调整了几个潜在的个人和社区水平的混杂因素以及学校内学生的群集。我们发现，在建筑环境特征中存在显著的正空间自相关性（Global Moran 's I大多数≥0.60，所有p = 0.001），但在BMI z-score中没有（Global Moran 's I = 0.07, p = 0.28）。因为我们在OLS回归残差中发现了显著的空间自相关，所以我们拟合了空间自回归模型。大多数建筑环境特征与BMI z-score无关。公交车站密度与白人较高的BMI z-score相关（系数：0.029,p < 0.05）。亚洲人在零售目的地与BMI z-score之间的相互作用项具有统计学意义，呈负相关。人行道完整性与总样本较高的BMI z得分显著相关（系数：0.010,p < 0.05）。在800米缓冲区中发现了这些显著的关联。建筑环境与青少年BMI z-score之间的一些关系出乎意料。我们的研究结果总体上表明，建成环境并不能解释青少年BMI z分数的很大一部分差异或青少年肥胖的种族差异。然而，不同种族/民族的青少年之间存在一些差异，需要进一步研究。
Built environment features of neighborhoods may be related to obesity among adolescents and potentially related to obesity-related health disparities. The purpose of this study was to investigate spatial relationships between various built environment features and body mass index (BMI) z-score among adolescents, and to investigate if race/ethnicity modifies these relationships. A secondary objective was to evaluate the sensitivity of findings to the spatial scale of analysis (i.e. 400- and 800-meter street network buffers). Data come from the 2008 Boston Youth Survey, a school-based sample of public high school students in Boston, MA. Analyses include data collected from students who had georeferenced residential information and complete and valid data to compute BMI z-score (n = 1,034). We built a spatial database using GIS with various features related to access to walking destinations and to community design. Spatial autocorrelation in key study variables was calculated with the Global Moran’s I statistic. We fit conventional ordinary least squares (OLS) regression and spatial simultaneous autoregressive error models that control for the spatial autocorrelation in the data as appropriate. Models were conducted using the total sample of adolescents as well as including an interaction term for race/ethnicity, adjusting for several potential individual- and neighborhood-level confounders and clustering of students within schools. We found significant positive spatial autocorrelation in the built environment features examined (Global Moran’s I most ≥ 0.60; all p = 0.001) but not in BMI z-score (Global Moran’s I = 0.07, p = 0.28). Because we found significant spatial autocorrelation in our OLS regression residuals, we fit spatial autoregressive models. Most built environment features were not associated with BMI z-score. Density of bus stops was associated with a higher BMI z-score among Whites (Coefficient: 0.029, p < 0.05). The interaction term for Asians in the association between retail destinations and BMI z-score was statistically significant and indicated an inverse association. Sidewalk completeness was significantly associated with a higher BMI z-score for the total sample (Coefficient: 0.010, p < 0.05). These significant associations were found for the 800-meter buffer. Some relationships between the built environment and adolescent BMI z-score were in the unexpected direction. Our findings overall suggest that the built environment does not explain a large proportion of the variation in adolescent BMI z-score or racial disparities in adolescent obesity. However, there are some differences by race/ethnicity that require further research among adolescents.