Greenspace and depression incidence in the US-based nationwide Nurses' Health Study II: A deep learning analysis of street-view imagery
Year: 2025
Type: article
Study design: Observational Study
Abstract: Background Greenspace exposure is associated with lower depression risk. However, most studies have measured greenspace exposure using satellite-based vegetation indices, leading to potential exposure misclassification and limited policy relevance. We examined the association of street-view greenspace measures with incident depression in a prospective cohort of US women. Methods We applied deep learning segmentation models to 350 million US street-view images nationwide (2007–2020) to derive ground-level greenspace metrics, including percentage of trees, grass, and other greenspace (plants/flowers/fields), and linked metrics to Nurses’ Health Study II participants’ residences (N = 33,490) within 500 m each year. Cox proportional hazards models estimated the relationship between street-view greenspace metrics and incident depression, assessed through self-report of clinician-diagnosed depression or regular antidepressant use and adjusted for individual- and area-level factors. Findings In adjusted models, higher percentages of street-view trees were inversely associated with incident depression (HR per IQR, 0.98; 95%CI: 0.94–1.01) and specifically clinician-diagnosed depression (HR per IQR, 0.94; 95%CI: 0.90–0.99). Higher percentages of street-view grass were also inversely associated with incident depression, but only in areas with low particulate matter (PM 2.5 ) levels (HR per IQR, 0.79; 95%CI: 0.71–0.86). Results were consistent after adjusting for additional spatial and behavioral factors, and persisted after adjusting for traditional satellite-based vegetation indices. Conclusion and relevance We observed participants who lived in areas with more trees visible in street-view images had a lower risk of depression. Our findings suggest tree-planting interventions may reduce depression risk.
Source: Environment International
Institutions: Harvard University, Harvard Pilgrim Health Care, Brigham and Women's Hospital, Portland State University, Oregon Health & Science University, Dana-Farber Cancer Institute, Boston University, University College London, Oregon State University, Virginia Tech, Rutgers, The State University of New Jersey, Massachusetts General Hospital, University of California, Davis
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: $4,750
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, Neurodegenerative diseases and the role of green space: A deep learning assessment, Harvard Chan School NIEHS Center for Environmental Health, Neurodegenerative diseases and the role of green space: A deep learning assessment, G9000014778, G9000058679, Integrating lifecourse approaches, biologic and digital phenotypes in support of heart and lung disease epidemiologic research, Life Course Cancer Epidemiology Cohort in Women, Environmental Resources for Individual Cognitive Health/Resilience (EnRICH)
OpenAlex ID: W4409201782