Evidence map›Paper›PMID 40814956›Full record

ArticleJournal of the American Medical Informatics Association : JAMIA2025

Examining housing insecurity and transportation barriers in pediatric hospital readmissions: insights from structured and unstructured data.

Shivani Mehta, William Brown, Urmimala Sarkar, Nathan Tran, Yulin Hswen, Matthew S Pantell

Abstract read
In one paragraph

Article in Journal of the American Medical Informatics Association : JAMIA, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

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3 · Its place in the literature

Who cites it

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No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Shivani MehtaDepartment of Epidemiology and Biostatistics, University of California, San Francisco (UCSF), San Francisco, CA 94158, United States.ORCID 0000-0002-1417-6694
William BrownDepartment of Epidemiology and Biostatistics, University of California, San Francisco (UCSF), San Francisco, CA 94158, United States.
Urmimala SarkarCenter for Vulnerable Populations, University of California, San Francisco (UCSF), San Francisco, CA 94110, United States.ORCID 0000-0003-4213-4405
Nathan TranDepartment of Pediatrics, University of California, San Francisco (UCSF), San Francisco, CA 94158, United States.
Yulin HswenDepartment of Epidemiology and Biostatistics, University of California, San Francisco (UCSF), San Francisco, CA 94158, United States.
Matthew S PantellDepartment of Pediatrics, University of California, San Francisco (UCSF), San Francisco, CA 94158, United States.

Funding

Developing a Clinical Decision Support Tool that Assesses Risk of Opioid Use Disorder Using Natural Language Processing, Machine Learning, and Social Determinants of Health from Clinical NotesK01DA055081 · NIDA · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI BROWN III, WILLIAM · 2022 to 2025
$760k
A natural language processing approach to identify social risk factors from neonatal intensive care unit patients’ (NICU) electronic health recordsF31MD019214 · NIMHD · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI MEHTA, SHIVANI · 2024 to 2024
$30k
AHRQ HHS K08 HS028473NIDA NIH HHS K01 DA055081NIH HHS F31MD019214NIH HHS K01DA055081NIMHD NIH HHS F31 MD019214U.S. Department of Health and Human Services K08HS028473
6 · The paper itself

Abstract

backgroundPediatric hospital readmissions increase healthcare costs and highlight gaps in care. Social determinants of health (SDOH), such as housing and transportation insecurity, significantly impact outcomes but are underexplored in pediatric populations.

objectivesThis study evaluates the impact of housing and transportation-related SDOH on pediatric readmissions, comparing structured ICD-10-CM Z-codes alone to a combination of structured and unstructured data extracted via natural language processing (NLP). MATERIALS AND

methodsWe conducted a retrospective cohort study of pediatric patients (ages 2-17) discharged from UCSF Benioff Children's Hospital between January 2018 and January 2023. SDOH exposure was identified using structured Z-codes and NLP-extracted data. The primary outcome was hospital readmission within 365 days. Cox proportional hazards models assessed associations between SDOH and readmission risk.

resultsAmong 8928 patients, only 0.8% were identified as exposed using structured data, compared to 31.7% using combined data. Patients identified through combined data had a higher readmission risk (HR: 2.64, 95% CI: 2.34-2.98) compared to those identified with structured data alone (HR: 1.99, 95% CI: 1.27-3.13). ED utilization was also higher among exposed patients. In the structured-only analysis, exposed patients had a significantly higher hazard of ED readmission (HR: 2.26, 95% CI: 1.65-3.10), whereas the association was slightly attenuated in the combined analysis (HR: 1.49, 95% CI: 1.37-1.62).

conclusionLeveraging unstructured data enhances SDOH identification and reveals stronger associations with hospital and ED readmissions. A hybrid approach enables improved risk stratification and targeted interventions to address pediatric health disparities.

Indexed as

Hospitals, PediatricHousing InstabilityPatient ReadmissionSocial Determinants of HealthTransportationAdolescentChildChild, PreschoolFemaleHealth Care CostsHealth InequitiesHumansMaleNatural Language ProcessingProportional Hazards ModelsRetrospective Studiesclinical noteselectronic health recordsnatural language processingpediatricssocial determinants of health

Identifiers

PMID40814956
PMCPMC12626208

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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.