Department of Biomedical Informatics and Medical Education, University of Washington, Seattle.
Institute for Health Metrics and Evaluation, University of Washington, Seattle.
JAMA Netw Open. 2018 Aug 3;1(4):e181535. doi: 10.1001/jamanetworkopen.2018.1535.
More than one-third of the adult population in the United States is obese. Obesity has been linked to factors such as genetics, diet, physical activity, and the environment. However, evidence indicating associations between the built environment and obesity has varied across studies and geographical contexts.
To propose an approach for consistent measurement of the features of the built environment (ie, both natural and modified elements of the physical environment) and its association with obesity prevalence to allow for comparison across studies.
The cross-sectional study was conducted from February 14 through October 31, 2017. A convolutional neural network, a deep learning approach, was applied to approximately 150 000 high-resolution satellite images from Google Static Maps API (application programing interface) to extract features of the built environment in Los Angeles, California; Memphis, Tennessee; San Antonio, Texas; and Seattle (representing Seattle, Tacoma, and Bellevue), Washington. Data on adult obesity prevalence were obtained from the Centers for Disease Control and Prevention's 500 Cities project. Regression models were used to quantify the association between the features and obesity prevalence across census tracts.
Model-estimated obesity prevalence (obesity defined as body mass index ≥30, calculated as weight in kilograms divided by height in meters squared) based on built environment information.
The study included 1695 census tracts in 6 cities. The age-adjusted obesity prevalence was 18.8% (95% CI, 18.6%-18.9%) for Bellevue, 22.4% (95% CI, 22.3%-22.5%) for Seattle, 30.8% (95% CI, 30.6%-31.0%) for Tacoma, 26.7% (95% CI, 26.7%-26.8%) for Los Angeles, 36.3% (95% CI, 36.2%-36.5%) for Memphis, and 32.9% (95% CI, 32.8%-32.9%) for San Antonio. Features of the built environment explained 64.8% (root mean square error [RMSE], 4.3) of the variation in obesity prevalence across all census tracts. Individually, the variation explained was 55.8% (RMSE, 3.2) for Seattle (213 census tracts), 56.1% (RMSE, 4.2) for Los Angeles (993 census tracts), 73.3% (RMSE, 4.5) for Memphis (178 census tracts), and 61.5% (RMSE, 3.5) for San Antonio (311 census tracts).
This study illustrates that convolutional neural networks can be used to automate the extraction of features of the built environment from satellite images for studying health indicators. Understanding the association between specific features of the built environment and obesity prevalence can lead to structural changes that could encourage physical activity and decreases in obesity prevalence.
美国超过三分之一的成年人口肥胖。肥胖与遗传、饮食、体育活动和环境等因素有关。然而，不同研究和地理背景下的证据表明，建筑环境与肥胖之间存在关联。
提出一种一致测量建筑环境特征（即物理环境的自然和人为元素）及其与肥胖流行率之间关联的方法，以便在研究之间进行比较。
这项横断面研究于 2017 年 2 月 14 日至 10 月 31 日进行。应用卷积神经网络（一种深度学习方法）从 Google Static Maps API（应用程序编程接口）中约 150000 张高分辨率卫星图像中提取加利福尼亚州洛杉矶、田纳西州孟菲斯、得克萨斯州圣安东尼奥和华盛顿州西雅图（代表西雅图、塔科马和贝尔维尤）的建筑环境特征。成人肥胖流行率的数据来自疾病控制与预防中心的 500 个城市项目。回归模型用于量化整个普查区建筑环境特征与肥胖流行率之间的关联。
基于建筑环境信息的模型估计肥胖流行率（肥胖定义为体重指数≥30，计算为体重（千克）除以身高（米）的平方）。
该研究包括 6 个城市的 1695 个普查区。调整年龄后的肥胖流行率为：贝尔维尤 18.8%（95%CI，18.6%-18.9%），西雅图 22.4%（95%CI，22.3%-22.5%），塔科马 30.8%（95%CI，30.6%-31.0%），洛杉矶 26.7%（95%CI，26.7%-26.8%），孟菲斯 36.3%（95%CI，36.2%-36.5%），圣安东尼奥 32.9%（95%CI，32.8%-32.9%）。建筑环境特征解释了所有普查区肥胖流行率变化的 64.8%（均方根误差[RMSE]，4.3）。单独来看，西雅图（213 个普查区）的解释变异为 55.8%（RMSE，3.2），洛杉矶（993 个普查区）为 56.1%（RMSE，4.2），孟菲斯（178 个普查区）为 73.3%（RMSE，4.5），圣安东尼奥（311 个普查区）为 61.5%（RMSE，3.5）。
本研究表明，卷积神经网络可用于从卫星图像中自动提取建筑环境特征，以研究健康指标。了解建筑环境特定特征与肥胖流行率之间的关联，可以促使进行结构变化，从而鼓励体育活动并降低肥胖流行率。
