Assessing greenspace and cardiovascular health through deep-learning analysis of street-view imagery in a cohort of US children

Abstract

Background: 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.

Methods: 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.

Results: 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.

Conclusion and relevance: 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.

Keywords: Children and adolescents; Deep learning algorithms; Environmental epidemiology; Green space; Life's essential 8; Street-view imagery.