Evidence map›Paper›PMID 39888883›Full record

ArticlePloS one2025

A data pipeline for secure extraction and sharing of social determinants of health.

Tyler Schappe, Lisa M McElroy, Moronke Ogundolie, Roland Matsouaka, Ursula Rogers, Nrupen A Bhavsar

Abstract read
In one paragraph

Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

6 authors.

Tyler SchappeDepartment of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, North Carolina, United States of America.ORCID 0000-0003-3523-3333
Lisa M McElroyDepartment of Population Health Sciences, Duke University School of Medicine, Durham, North Carolina, United States of America.ORCID 0000-0003-2366-2579
Moronke OgundolieDepartment of Surgery, Duke University School of Medicine, Durham, North Carolina, United States of America.
Roland MatsouakaDepartment of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, North Carolina, United States of America.
Ursula RogersDepartment of Surgery, Duke University School of Medicine, Durham, North Carolina, United States of America.ORCID 0000-0002-2838-3918
Nrupen A BhavsarDepartment of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, North Carolina, United States of America.

Funding

Support for QA/QC for Prior Approval ProcessUL1TR002553 · NCATS · DUKE UNIVERSITY · PI LI, JENNIFER S, MCNAMARA, JAMES O. · 2018 to 2023
$58.5M
Research Project 3U54MD012530 · NIMHD · DUKE UNIVERSITY · PI JOHNSON, KIMBERLY S. · 2017 to 2022
$8.8M
Increasing Access to Transplant through Improved Clinical Decision MakingK08MD017632 · NIMHD · DUKE UNIVERSITY · PI Lisa M McElroy · 2022 to 2026
$551k
NCATS NIH HHS UL1 TR002553NIMHD NIH HHS K08 MD017632NIMHD NIH HHS U54 MD012530
6 · The paper itself

Abstract

objectivesLinking neighborhood- and patient-level data provides valuable information about the influence of upstream social determinants of health (SDOH). However, sharing of these data across health systems presents challenges. We set out to develop a pipeline to acquire, deidentify, and share neighborhood-level SDOH data across multiple health systems.

methodsWe created a pipeline centered around Decentralized Geomarker Assessment for Multi-Site Studies (DeGAUSS) that utilizes containerization to geocode patient addresses and obtain neighborhood-level SDOH variables. We compared DeGAUSS to a third-party vendor geocoding tool available at Duke Health using a cohort of adult patients referred for abdominal transplant from January 1, 2016, to December 31, 2022. We calculated Cohen's Kappa and percent disagreement at census block group and tract levels, and by Area Deprivation Index, urbanicity, and year.

resultsThe pipeline successfully generated SDOH data for 97.8% of addresses. There was high concordance between DeGAUSS and the vendor tool at the census block group (0.93) and tract levels (0.95). At the block group level, disagreement proportion differed by year and urbanicity, with larger disagreement in the rural category than in micropolitan and metropolitan categories (13%, 7%, 6.2%, respectively). DISCUSSION AND

conclusionWe describe a novel pipeline that can facilitate the secure acquisition and sharing of neighborhood-level SDOH without sharing PHI. The pipeline can be scaled to include additional social, climate, and environmental variables, and can be extended to an unlimited number of health systems.

Indexed as

Information DisseminationSocial Determinants of HealthAdultFemaleHumansMaleMiddle AgedNeighborhood CharacteristicsResidence Characteristics

Identifiers

PMID39888883
PMCPMC11785280

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LicenceCC BY
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Registered trials

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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.