Evidence map›Paper›PMID 29092049›Full record

ArticleJournal of the American Medical Informatics Association : JAMIA2018

FACETS: using open data to measure community social determinants of health.

Michael N Cantor, Rajan Chandras, Claudia Pulgarin

Abstract read
In one paragraph

Article in Journal of the American Medical Informatics Association : JAMIA, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.

0numbers the graph read from it
0cells of the map it votes in
17citing 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

17 citing papers in PubMed.

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  7. Migrating a research data warehouse to a public cloud: challenges and opportunities.Journal of the American Medical Informatics Association : JAMIA · 2022
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  9. Observational
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  14. Teaching students to identify and document social determinants of health.Advances in medical education and practice · 2019
    Article
  15. Article
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  17. 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.

Michael N CantorDepartment of Medicine, New York University School of Medicine, New York, NY, USA.
Rajan ChandrasMedical Center Information Technology, NYU Langone Health, New York, NY, USA.
Claudia PulgarinDepartment of Population Health, New York University School of Medicine, New York, NY, USA.

Funding

Project-005UL1TR001445 · NCATS · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · PI BREDELLA, MIRIAM ANTOINETTE, HOCHMAN, JUDITH S · 2015 to 2025
$103.5M
NCATS NIH HHS UL1 TR001445
6 · The paper itself

Abstract

Objective: To develop a dataset based on open data sources reflective of community-level social determinants of health (SDH). Materials and Methods: We created FACETS (Factors Affecting Communities and Enabling Targeted Services), an architecture that incorporates open data related to SDH into a single dataset mapped at the census-tract level for New York City. Results: FACETS (https://github.com/mcantor2/FACETS) can be easily used to map individual addresses to their census-tract-level SDH. This dataset facilitates analysis across different determinants that are often not easily accessible. Discussion: Wider access to open data from government agencies at the local, state, and national level would facilitate the aggregation and analysis of community-level determinants. Timeliness of updates to federal non-census data sources may limit their usefulness. Conclusion: FACETS is an important first step in standardizing and compiling SDH-related data in an open architecture that can give context to a patient's condition and enable better decision-making when developing a plan of care.

Indexed as

Datasets as TopicSocial Determinants of HealthVulnerable PopulationsGovernment AgenciesHumansNew York City

Identifiers

PMID29092049
PMCPMC5885799

What OpenQuestion holds

Textmetadata
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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.