Assessing greenspace and cardiovascular disease risk through deep learning analysis of street-view imagery in the US-based nationwide Nurses' Health Study
James, Peter Suel, Esra Lin, Pi-I Debby Hart, Jaime E Rimm, Eric B Laden, Francine Hystad, Perry Hankey, Steve Larkin, Andrew Zhang, Wenwen
James, Peter
Suel, Esra
Lin, Pi-I Debby
Hart, Jaime E
Rimm, Eric B
Laden, Francine
Hystad, Perry
Hankey, Steve
Larkin, Andrew
Zhang, Wenwen
Series / Report no.
Open Access
Type
Journal Article
Article
Article
Language
en
Date of publication
2026-01-20
Year of publication
Research Projects
Organizational Units
Journal Issue
Title
Assessing greenspace and cardiovascular disease risk through deep learning analysis of street-view imagery in the US-based nationwide Nurses' Health Study
Translated Title
Published in
Environ Epidemiol 2026; 10(1):e442
Abstract
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.