Evidence map›Paper›PMID 41370817›Full record

ArticleJMIR formative research2025

Changes in the Neighborhood Built Environment and Chronic Health Conditions in Washington, DC, in 2014-2019: Longitudinal Analysis.

Quynh C Nguyen, Riki Doumbia, Thu T Nguyen, Xiaohe Yue, Heran Mane, Junaid Merchant, Tolga Tasdizen, Mitra Alirezaei, Pankaj Dipankar, Dapeng Li and 4 more

Abstract read
In one paragraph

Article in JMIR formative research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Quynh C NguyenNational Institute of Nursing Research, National Institutes of Health, Bethesda, MD, United States.ORCID 0000-0003-4745-6681
Riki Doumbia *Urban Studies Program, Brown University, Providence, RI, United States.ORCID 0009-0002-2589-0014
Thu T Nguyen *Department of Epidemiology and Biostatistics, University of Maryland School of Public Health, College Park, MD, United States.ORCID 0000-0003-1185-045X
Xiaohe Yue *Department of Epidemiology and Biostatistics, University of Maryland School of Public Health, College Park, MD, United States.ORCID 0000-0001-5922-1949
Heran Mane *Department of Epidemiology and Biostatistics, University of Maryland School of Public Health, College Park, MD, United States.ORCID 0000-0001-8859-3282
Junaid Merchant *Department of Epidemiology and Biostatistics, University of Maryland School of Public Health, College Park, MD, United States.ORCID 0000-0002-4315-6211
Tolga Tasdizen *Department of Electrical and Computer Engineering, Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, United States.ORCID 0000-0001-6574-0366
Mitra Alirezaei *Department of Electrical and Computer Engineering, Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, United States.ORCID 0000-0003-2417-1659
Pankaj Dipankar *National Institute of Nursing Research, National Institutes of Health, Bethesda, MD, United States.ORCID 0000-0001-9002-4679
Dapeng Li *Department of Geography and the Environment, University of Alabama, Tuscaloosa, AL, United States.ORCID 0000-0002-3255-6084
Penchala Sai Priya Mullaputi *Department of Epidemiology and Biostatistics, University of Maryland School of Public Health, College Park, MD, United States.ORCID 0009-0002-6005-2227
Amrutha Alibilli *Department of Epidemiology and Biostatistics, University of Maryland School of Public Health, College Park, MD, United States.ORCID 0009-0006-3932-264X
Yulin Hswen *Department of Epidemiology and Biostatistics, University of Maryland School of Public Health, College Park, MD, United States.ORCID 0000-0003-3203-1322
Xin He *Department of Epidemiology and Biostatistics, University of Maryland School of Public Health, College Park, MD, United States.ORCID 0000-0002-9814-1666

Funding

Sci-Tech Core-Maryland Population Research CenterP2CHD041041 · NICHD · UNIV OF MARYLAND, COLLEGE PARK · PI Susan W Parker · 2018 to 2026
$3.9M
Risk and strength: determining the impact of area-level sentiment and protective factors on birth outcomesR01MD015716 · NIMHD · UNIV OF MARYLAND, COLLEGE PARK · PI NGUYEN, THU · 2021 to 2025
$3.6M
Rosie the Chatbot: Leveraging Automated and Personalized Health Information Communication to Reduce Disparities in Maternal and Child HealthR01MD016037 · NIMHD · UNIV OF MARYLAND, COLLEGE PARK · PI NGUYEN, THU, NORELL, ELIZABETH MARIE · 2021 to 2025
$3.3M
Neighborhood Looking Glass: 360 Degree Automated Characterization of the Built Environment for Neighborhood Effects ResearchR01LM012849 · NLM · UNIV OF MARYLAND, COLLEGE PARK · PI NGUYEN, QUYNH · 2018 to 2021
$1.3M
HashtagHealthZIANR000043 · NINR · NATIONAL INSTITUTE OF NURSING RESEARCH · PI NGUYEN, QUYNH · 2025 to 2025
$773k
Intramural NIH HHS ZIA NR000043NICHD NIH HHS P2C HD041041NIMHD NIH HHS R01 MD015716NIMHD NIH HHS R01 MD016037NLM NIH HHS R01 LM012849
6 · The paper itself

Abstract

backgroundGoogle Street View (GSV) images offer a unique and scalable alternative to in-person audits for examining neighborhood built environment characteristics. Additionally, most prior neighborhood studies have relied on cross-sectional designs.

objectiveThis study aimed to use GSV images and computer vision to examine longitudinal changes in the built environment, demographic shifts, and health outcomes in Washington, DC, from 2014 to 2019.

methodsIn total, 434,115 GSV images were systematically sampled at 100 m intervals along primary and secondary road segments. Convolutional neural networks, a type of deep learning algorithm, were used to extract built environment features from images. Census tract summaries of the neighborhood built environment were created. Multilevel mixed-effects linear models with random intercepts for years and census tracts were used to assess associations between built environment changes and health outcomes, adjusting for covariates, including median age, percentage male, percentage Hispanic, percentage African American, percentage college educated, percentage owner-occupied housing, and median household income.

resultsWashington, DC, experienced a shift toward higher-density housing, with non-single-family homes rising from 66% to 72% of the housing stock. Single-lane roads increased from 37% to 42%, suggesting a shift toward more sustainable and compact urban forms. Gentrification trends were reflected in a rise in college-educated residents (16%-41%), a US $17,490 increase in the median household income, and a US $159,600 increase in property values. Longitudinal analyses revealed that increased construction activity was associated with lower rates of obesity, diabetes, high cholesterol, and cancer, while growth in non-single-family housing was correlated with reductions in the prevalence of obesity and diabetes. However, neighborhoods with higher proportions of African American residents experienced reduced construction activity.

conclusionsWashington, DC, has experienced significant urban transformation, marked by substantial changes in neighborhood built environments and demographic shifts. Urban development is associated with reduced prevalence of chronic conditions. These findings highlight the complex interplay between urban development, demographic changes, and health, underscoring the need for future research to explore the broader impacts of neighborhood built environment changes on community composition and health outcomes. GSV imagery, along with advances in computer vision, can aid in the acceleration of neighborhood studies.

Indexed as

Built EnvironmentNeighborhood CharacteristicsResidence CharacteristicsAdultChronic DiseaseDistrict of ColumbiaFemaleHousingHumansLongitudinal StudiesMaleMiddle Agedartificial intelligencecomputer visionGoogle Street Viewhealth outcomestemporal analysisurban development

Identifiers

PMID41370817
PMCPMC12739454

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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.