Assessing greenspace and cardiovascular disease risk through deep learning analysis of street-view imagery in the US-based nationwide Nurses’ Health Study
Year: 2026
Type: article
Study design: Observational Study
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.
Source: Environmental Epidemiology
Institutions: Harvard University, University of California, Davis, University of Zurich, University College London, Harvard Pilgrim Health Care, Brigham and Women's Hospital, Oregon State University, Virginia Tech, Rutgers, The State University of New Jersey, Utrecht University, National Institute for Public Health and the Environment, Boston University
Language: English
Topic: Urban Green Space and Health
Subfield: Health, Toxicology and Mutagenesis
Field: Environmental Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,200
Awards: Built Environment Assessment through Computer visiON (BEACON): Applying Deep Learning to Street-Level and Satellite Images to Estimate Built Environment Effects on Cardiovascular Health, Harvard Chan School NIEHS Center for Environmental Health, Race/ethnicity and Environmental Stressors: POtential drivers of Dementia and stroke inequities RESPOnD, Environmental Resources for Individual Cognitive Health/Resilience (EnRICH)
OpenAlex ID: W7124999707