| Title: |
Assessing greenspace and cardiovascular disease risk through deep learning analysis of street-view imagery in the US-based nationwide Nurses' Health Study |
| Authors: |
James, Peter; Suel, Esra; Lin, Pi-I Debby; Hart, Jaime E; Rimm, Eric B; Laden, Francine; Hystad, Perry; Hankey, Steve; Larkin, Andrew; Zhang, Wenwen; Klompmaker, Jochem; Coull, Brent; Yi, Li; Pescador Jimenez, Marcia |
| Source: |
ENVIRONMENTAL EPIDEMIOLOGY , 10 (1) , Article ARTN e442. (2026) |
| Publisher Information: |
LIPPINCOTT WILLIAMS & WILKINS |
| Publication Year: |
2026 |
| Collection: |
University College London: UCL Discovery |
| Subject Terms: |
Science & Technology; Life Sciences & Biomedicine; Environmental Sciences; Public; Environmental & Occupational Health; Environmental Sciences & Ecology; Street-view imagery; Greenspace; Cardiovascular disease; Nurses' Health Study; ASSOCIATIONS |
| Description: |
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. |
| Document Type: |
article in journal/newspaper |
| File Description: |
text |
| Language: |
English |
| Relation: |
https://discovery.ucl.ac.uk/id/eprint/10221089/ |
| Availability: |
https://discovery.ucl.ac.uk/id/eprint/10221089/1/Assessing%20greenspace%20and%20cardiovascular%20disease%20risk%20through%20deep%20learning%20analysis%20of%20street-view%20imagery%20in%20the%20US-based%20n.pdf; https://discovery.ucl.ac.uk/id/eprint/10221089/ |
| Rights: |
open |
| Accession Number: |
edsbas.606C1009 |
| Database: |
BASE |