Modeling Pediatric Body Mass Index and Neighborhood Environment at Different Spatial Scales.
Modeling Pediatric Body Mass Index and Neighborhood Environment at Different Spatial Scales.
复制标题
DOI：
10.3390/ijerph15030473
复制
发表时间：
2018-03-08
影响因子：
--
通讯作者：
Wheeler DC
中科院分区：
文献类型：
--
作者：
Grant LP;Gennings C;Wickham EP;Chapman D;Sun S;Wheeler DC
在公共卫生研究中，地理位置在影响健康结果方面发挥着重要作用已经得到充分证实。近年来，人们越来越重视社区或环境因素作为儿童肥胖的潜在风险因素的影响。与儿童肥胖相关的一些社区因素包括食物来源的获取、娱乐设施的获取、社区安全和社会经济地位（SES）变量。在多个空间尺度（SS）或地理单元（如人口普查区组和人口普查区）上存在邻域或区域级变量是很常见的，区域级变量的空间尺度选择可以看作是一个模型选择问题。本文以美国弗吉尼亚联邦大学（VCU）医学中心的儿科患者为研究对象，对其体重指数（BMI）的变化进行了建模，并采用最近提出的4种空间尺度选择算法：SS前向逐步回归、SS增量前向阶段回归、SS最小角回归和SS lasso，对多个空间尺度上的邻域水平变量进行了空间尺度的选择。对于儿童BMI，我们发现在个体水平上，访问年龄和黑人种族、在人口普查区水平上的西班牙裔白人百分比、在人口普查区水平上的西班牙裔黑人百分比以及在人口普查区水平上的空置住房百分比之间存在显著的正相关。我们还发现，人口密度与人口普查区水平的人口密度、人口普查区水平的家庭收入中位数、人口普查区水平的租房率和人口普查区水平的运动器材支出之间存在显著的负相关。SS算法在不同的空间尺度上选择协变量，与传统模型相比，产生了更好的拟合优度，传统模型中所有面积水平的协变量都是在相同的尺度上建模的。这些发现强调了在进行模型选择时考虑空间尺度的重要性。
In public health research, it has been well established that geographic location plays an important role in influencing health outcomes. In recent years, there has been an increased emphasis on the impact of neighborhood or contextual factors as potential risk factors for childhood obesity. Some neighborhood factors relevant to childhood obesity include access to food sources, access to recreational facilities, neighborhood safety, and socioeconomic status (SES) variables. It is common for neighborhood or area-level variables to be available at multiple spatial scales (SS) or geographic units, such as the census block group and census tract, and selection of the spatial scale for area-level variables can be considered as a model selection problem. In this paper, we model the variation in body mass index (BMI) in a study of pediatric patients of the Virginia Commonwealth University (VCU) Medical Center, while considering the selection of spatial scale for a set of neighborhood-level variables available at multiple spatial scales using four recently proposed spatial scale selection algorithms: SS forward stepwise regression, SS incremental forward stagewise regression, SS least angle regression (LARS), and SS lasso. For pediatric BMI, we found evidence of significant positive associations with visit age and black race at the individual level, percent Hispanic white at the census block group level, percent Hispanic black at the census tract level, and percent vacant housing at the census tract level. We also found significant negative associations with population density at the census tract level, median household income at the census tract level, percent renter at the census tract level, and exercise equipment expenditures at the census block group level. The SS algorithms selected covariates at different spatial scales, producing better goodness-of-fit in comparison to traditional models, where all area-level covariates were modeled at the same scale. These findings underscore the importance of considering spatial scale when performing model selection.
登录
查看更多内容
影响因子：
5.4
作者：
Flowerdew, Robin;Manley, David J.;Sabel, Clive E.
通讯作者：
Sabel, Clive E.
影响因子：
4.9
作者：
Gubbels, J. S.;Kremers, S. P. J.;Thijs, C.
通讯作者：
Thijs, C.
影响因子：
2
作者：
Gelman, Andrew
通讯作者：
Gelman, Andrew
影响因子：
2
作者：
Grant, Lauren P.;Gennings, Chris;Wheeler, David C.
通讯作者：
Wheeler, David C.
影响因子：
2.5
作者：
Crowder K;South SJ
通讯作者：
South SJ