Greenspace and depression incidence in the US-based nationwide Nurses' Health Study II: A deep learning analysis of street-view imagery

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.