Yi Li, Rifas-Shiman Sheryl, Pescador Jimenez Marcia, Lin Pi-I Debby, Suel Esra, Hystad Perry, Larkin Andrew, Hankey Steve, Zhang Wenwen, Klompmaker Jochem, Oken Emily, Hivert Marie-France, Aris Izzuddin, James Peter
Division of Chronic Disease Research Across the Lifecourse, Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, USA.
Division of Chronic Disease Research Across the Lifecourse, Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA, USA.
Environ Res. 2025 Jan 15;265:120459. doi: 10.1016/j.envres.2024.120459. Epub 2024 Nov 26.
Accurately capturing individuals' experiences with greenspace at ground-level can provide valuable insights into their impact on children's health. However, most previous research has relied on coarse satellite-based measurements.
We utilized CVH and residential address data from Project Viva, a US-based pre-birth cohort, tracking participants from mid-childhood to late adolescence (2007-21). A deep learning segmentation algorithm was applied to street-view images across the US to estimate % street-view trees, grass, and other greenspace (flowers, field, and plants). Exposure estimates were derived by linking street-view greenspace metrics to 500m of participants' residences during mid-childhood, early and late adolescence. CVH scores (range 0-100; higher indicate better CVH) were calculated using the American Heart Association's Life's Essential 8 algorithm at these three time points, incorporating four biomedical components (body weight, blood lipids, blood glucose, blood pressure) and four behavioral components (diet, physical activity, nicotine exposure, sleep). Linear regression models were used to examine cross-sectional and cumulative associations between street-view greenspace metrics and CVH scores. Generalized estimating equations models were used to examine associations between street-view greenspace metrics and changes in CVH scores across three timepoints. All models were adjusted for individual and neighborhood-level confounders.
Adjusting for confounders, a one-SD increase in street-view trees within 500m of residence was cross-sectionally associated with a 1.92-point (95%CI: 0.50, 3.35) higher CVH score in late adolescence, but not mid-childhood or early adolescence. Longitudinally, street-view greenspace metrics at baseline (either mid-childhood or early adolescence) were not associated with changes in CVH scores at the same and all subsequent time points. Cumulative street-view greenspace metrics across the three time points were also not associated with CVH scores in late adolescence.
In this US cohort of children, we observed few evidence of associations between street-level greenspace children's CVH, though the impact may vary with children's growth stage.
准确获取个人在地面层面与绿地空间的体验，可为了解其对儿童健康的影响提供有价值的见解。然而，以往的大多数研究都依赖于基于卫星的粗略测量。
我们利用了来自美国一项产前队列研究“活力计划”（Project Viva）的儿童健康纵向研究（CVH）和居住地址数据，对参与者从中童年期到青少年后期（2007 - 2021年）进行跟踪。一种深度学习分割算法被应用于美国各地的街景图像，以估计街景中树木、草地和其他绿地空间（花卉、田野和植物）的占比。通过将街景绿地空间指标与参与者在童年中期、青少年早期和晚期居住地址周围500米范围内的情况相关联，得出暴露估计值。在这三个时间点，使用美国心脏协会的“生命八大要素”算法计算CVH分数（范围为0 - 100；分数越高表明CVH越好），该算法纳入了四个生物医学成分（体重、血脂、血糖、血压）和四个行为成分（饮食、体育活动、尼古丁暴露、睡眠）。线性回归模型用于检验街景绿地空间指标与CVH分数之间的横断面和累积关联。广义估计方程模型用于检验街景绿地空间指标与三个时间点之间CVH分数变化的关联。所有模型都对个体和邻里层面的混杂因素进行了调整。
在对混杂因素进行调整后，居住地址周围500米内街景树木每增加一个标准差，与青少年后期的CVH分数高出1.92分（95%置信区间：0.50，3.35）存在横断面关联，但在童年中期或青少年早期则不然。从纵向来看，基线（童年中期或青少年早期）的街景绿地空间指标与同一时间点及所有后续时间点的CVH分数变化均无关联。三个时间点的累积街景绿地空间指标与青少年后期的CVH分数也无关联。
在这个美国儿童队列中，我们观察到几乎没有证据表明街道层面的绿地空间与儿童的CVH之间存在关联，尽管这种影响可能因儿童的生长阶段而异。
