Evidence map›Paper›PMID 35789918›Full record

ReviewCurrent epidemiology reports2022

Machine Learning Approaches for Measuring Neighborhood Environments in Epidemiologic Studies.

Andrew G Rundle, Michael D M Bader, Stephen J Mooney

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Current epidemiology reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. 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

3 authors.

Andrew G RundleDepartment of Epidemiology, Mailman School of Public Health, Columbia University, New York City, NY USA.
Michael D M BaderDepartment of Sociology, Johns Hopkins University, Baltimore, MD USA.
Stephen J MooneyDepartment of Epidemiology, School of Public Health, University of Washington, Seattle, WA USA.

Funding

Elucidating Genetic and Environmental Second Hits in Racial and Ethnic Minorities with APOL1 High-Risk GenotypesR01DK127139 · NIDDK · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · PI NADKARNI, GIRISH NITIN · 2021 to 2025
$4.0M
Temperature, shade, and adolescent psychopathology: understanding how place shapes healthR01MH128734 · NIMH · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI KEYES, KATHERINE M., RUNDLE, ANDREW G · 2021 to 2025
$3.4M
Identifying alcohol-related and built environment factors that can be modified to prevent pedestrian road traffic deathR01AA028552 · NIAAA · COLUMBIA UNIVERSITY HEALTH SCIENCES · PI RUNDLE, ANDREW G · 2021 to 2024
$2.6M
The Automatic Context Measurement Tool: bringing environmental data to non-specialistsR00LM012868 · NLM · UNIVERSITY OF WASHINGTON · PI MOONEY, STEPHEN JOHN · 2019 to 2021
$687k
NIAAA NIH HHS R01 AA028552NIDDK NIH HHS R01 DK127139NIMH NIH HHS R01 MH128734NLM NIH HHS R00 LM012868
6 · The paper itself

Abstract

Purpose of review: Innovations in information technology, initiatives by local governments to share administrative data, and growing inventories of data available from commercial data aggregators have immensely expanded the information available to describe neighborhood environments, supporting an approach to research we call Urban Health Informatics. This review evaluates the application of machine learning to this new wealth of data for studies of the effects of neighborhood environments on health. Recent findings: Prominent machine learning applications in this field include automated image analysis of archived imagery such as Google Street View images, variable selection methods to identify neighborhood environment factors that predict health outcomes from large pools of exposure variables, and spatial interpolation methods to estimate neighborhood conditions across large geographic areas. Summary: In each domain, we highlight successes and cautions in the application of machine learning, particularly highlighting legal issues in applying machine learning approaches to Google's geo-spatial data.

Indexed as

Google Street ViewMachine learningNeighborhood environmentsNeighborhood-wide association studiesSpatial interpolationUrban health informatics

Identifiers

PMID35789918
PMCPMC9244309

What OpenQuestion holds

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LicenceCC BY
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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.