Background: Living near greenspace is associated with decreased cardiovascular disease (CVD). Greenspace estimates, however, typically represent all types of vegetation using top-down satellite images, which incorporate exposure misclassification and limit policy relevance. Objective: We studied the association between street-view greenspace measures with incident CVD using a large, long-term prospective US cohort of female nurses. Methods: We estimated the percentage of streetscapes composed of visible trees, grass, and other green (plants/flowers/fields) from 350 million street-view images using deep learning models. Estimates were applied to Nurses’ Health Study participants (N = 88,788) within 500 m of their residential addresses. We used Cox models to estimate associations from 2000 to 2018 between street-view greenspace measures and risk of incident CVD, assessed through self-report, medical record review, or death certificates, and adjusted for individual- and area-level factors. Results: In adjusted models, higher percentages of visible trees were associated with lower CVD incidence (hazard ratio HR per interquartile range IQR 0.96 (95% confidence interval 0.93, 1.00]), while higher percentages of visible grass (HR 1.06 1.02, 1.11) and other green space types (HR 1.03 1.01, 1.04) were associated with higher CVD incidence. We did not observe evidence of effect modification by population density, Census region, air pollution, satellite-based vegetation, or neighborhood socioeconomic status. Findings were robust to adjustment for other spatial and behavioral factors and persisted even after adjustment for traditional satellite-based vegetation indices. Discussion: Specific greenspace types may be protective or harmful for CVD. Aggregating greenspace into a single exposure category limits epidemiological research and potential interventions to increase health-promoting greenspace.
James et al. (2026) studied this question.