Department of Epidemiology and Biostatistics, School of Public Health, University of Maryland, College Park, MD 20742, USA.
Department of Family and Community Medicine, University of California San Francisco, San Francisco, CA 94110, USA.
Int J Environ Res Public Health. 2021 Oct 3;18(19):10428. doi: 10.3390/ijerph181910428.
Characteristics of the neighborhood built environment influence health and health behavior. Google Street View (GSV) images may facilitate measures of the neighborhood environment that are meaningful, practical, and adaptable to any geographic boundary. We used GSV images and computer vision to characterize neighborhood environments (green streets, visible utility wires, and dilapidated buildings) and examined cross-sectional associations with chronic health outcomes among patients from the University of California, San Francisco Health system with outpatient visits from 2015 to 2017. Logistic regression models were adjusted for patient age, sex, marital status, race/ethnicity, insurance status, English as preferred language, assignment of a primary care provider, and neighborhood socioeconomic status of the census tract in which the patient resided. Among 214,163 patients residing in California, those living in communities in the highest tertile of green streets had 16-29% lower prevalence of coronary artery disease, hypertension, and diabetes compared to those living in communities in the lowest tertile. Conversely, a higher presence of visible utility wires overhead was associated with 10-26% more coronary artery disease and hypertension, and a higher presence of dilapidated buildings was associated with 12-20% greater prevalence of coronary artery disease, hypertension, and diabetes. GSV images and computer vision models can be used to understand contextual factors influencing patient health outcomes and inform structural and place-based interventions to promote population health.
社区建成环境的特点会影响健康和健康行为。谷歌街景（GSV）图像可以促进对有意义、实用且可适应任何地理边界的社区环境进行测量。我们使用 GSV 图像和计算机视觉来描述社区环境（绿色街道、可见的公用事业线和破旧的建筑物），并研究了这些环境特征与 2015 年至 2017 年间在加利福尼亚大学旧金山卫生系统就诊的患者的慢性健康结果之间的横断面关联。逻辑回归模型调整了患者的年龄、性别、婚姻状况、种族/民族、保险状况、首选语言、初级保健提供者的分配以及患者居住的普查区的社区社会经济地位。在加利福尼亚州的 214163 名患者中，与居住在绿色街道比例最低的社区的患者相比，居住在绿色街道比例最高的社区的患者患冠状动脉疾病、高血压和糖尿病的比例分别低 16-29%。相反，架空可见的公用事业线较多与冠状动脉疾病和高血压的发生率增加 10-26%有关，而破旧建筑物较多与冠状动脉疾病、高血压和糖尿病的发病率增加 12-20%有关。GSV 图像和计算机视觉模型可用于了解影响患者健康结果的环境因素，并为促进人口健康的结构性和基于场所的干预措施提供信息。
