Evidence map›Paper›PMID 41567830›Full record

ArticleEnvironmental epidemiology (Philadelphia, Pa.)2026

Assessing greenspace and cardiovascular disease risk through deep learning analysis of street-view imagery in the US-based nationwide Nurses' Health Study.

Peter James, Esra Suel, Pi-I Debby Lin, Jaime E Hart, Eric B Rimm, Francine Laden, Perry Hystad, Steve Hankey, Andrew Larkin, Wenwen Zhang and 4 more

Abstract read
In one paragraph

Article in Environmental epidemiology (Philadelphia, Pa.), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors.

Peter JamesDivision of Environmental and Occupational Health, Department of Public Health Sciences, University of California, Davis School of Medicine, Davis, California.ORCID https://orcid.org/0000-0002-2858-1973
Esra SuelCentre for Advanced Spatial Analysis, The Bartlett Faculty of the Built Environment, University College London, London, United Kingdom.ORCID https://orcid.org/0000-0001-9246-3966
Pi-I Debby LinDivision of Chronic Disease Research Across the Lifecourse (CoRAL), Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts.ORCID https://orcid.org/0000-0003-3564-4255
Jaime E HartDepartment of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, Massachusetts.ORCID https://orcid.org/0000-0002-0826-1163
Eric B RimmChanning Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts.ORCID https://orcid.org/0000-0002-1402-7250
Francine LadenDepartment of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, Massachusetts.ORCID https://orcid.org/0000-0002-2813-2174
Perry HystadCollege of Health, Oregon State University, Corvallis, Oregon.ORCID https://orcid.org/0000-0002-9454-4986
Steve HankeyUrban Affairs and Planning (UAP), School of Public and International Affairs, Virginia Polytechnic Institute and State University, Blacksburg, Virginia.ORCID https://orcid.org/0000-0002-7530-6077
Andrew LarkinCollege of Health, Oregon State University, Corvallis, Oregon.
Wenwen ZhangEdward J Bloustein School of Planning and Public Policy, Rutgers, The State University of New Jersey, New Brunswick, New Jersey.
Jochem KlompmakerNational Institute for Public Health and the Environment (RIVM) and IRAS, Utrecht University, Bilthoven, The Netherlands.ORCID https://orcid.org/0000-0002-3260-9692
Brent CoullDepartment of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts.ORCID https://orcid.org/0000-0002-1808-4156
Li YiDivision of Chronic Disease Research Across the Lifecourse (CoRAL), Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts.
Marcia Pescador JimenezDepartment of Epidemiology, Boston University School of Public Health, Boston, Massachusetts.

Funding

Translational Research Support CoreP30ES000002 · NIEHS · HARVARD UNIVERSITY (SCH OF PUBLIC HLTH) · PI JAIME ELIZABETH HART · 1985 to 2026
$44.6M
Built Environment Assessment through Computer visiON (BEACON): Applying Deep Learning to Street-Level and Satellite Images to Estimate Built Environment Effects on Cardiovascular HealthR01HL150119 · NHLBI · UNIVERSITY OF CALIFORNIA AT DAVIS · PI JAMES, PETER · 2020 to 2024
$3.9M
Role of Environmental Stressors as POtential drivers of Dementia and stroke RESPOnDR01NS139186 · NINDS · BOSTON UNIVERSITY MEDICAL CAMPUS · PI Marcia Ixchel Pescador Jimenez · 2024 to 2026
$2.4M
Environmental Resources for Individual Cognitive Health/Resilience (EnRICH)R01AG087199 · NIA · BOSTON UNIVERSITY MEDICAL CAMPUS · PI Marcia Ixchel Pescador Jimenez · 2024 to 2026
$2.3M
NHLBI NIH HHS R01 HL150119NIA NIH HHS R01 AG087199NIEHS NIH HHS P30 ES000002NINDS NIH HHS R01 NS139186
6 · The paper itself

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.

Indexed as

Cardiovascular diseaseGreenspaceNurses’ Health StudyStreet-view imagery

Identifiers

PMID41567830
PMCPMC12818865

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.