Evidence map›Paper›PMID 40502238›Full record

ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2025

Investigating the Impact of Social Determinants of Health on Diagnostic Delays and Access to Antifibrotic Treatment in Idiopathic Pulmonary Fibrosis.

Rui Li, Qiuhao Lu, Andrew Wen, Jinlian Wang, Sunyang Fu, Xiaoyang Ruan, Liwei Wang, Hongfang Liu

Abstract read
In one paragraph

Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed, 1 pooled it
–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

1 citing paper in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors.

Rui LiMcWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA.
Qiuhao LuMcWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA.
Andrew WenMcWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA.
Jinlian WangMcWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA.
Sunyang FuMcWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA.
Xiaoyang RuanMcWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA.
Liwei WangMcWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA.
Hongfang LiuMcWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA.

Funding

Predictive modeling of Alzheimer's Disease Related Dementias (ADRD) in the elderly population empowered by knowledge-driven data miningR01HG012748 · NHGRI · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI HONGFANG LIU · 2023 to 2026
$3.1M
NHGRI NIH HHS R01 HG012748
6 · The paper itself

Abstract

Idiopathic pulmonary fibrosis (IPF) is a rare disease that is challenging to diagnose. Patients with IPF often spend years awaiting a diagnosis after the onset of initial respiratory symptoms, and only a small percentage receive antifibrotic treatment. In this study, we examine the associations between social determinants of health (SDoH) and two critical factors: time to IPF diagnosis following the onset of initial respiratory symptoms, and whether the patient receives antifibrotic treatment. To approximate individual SDoH characteristics, we extract demographic-specific averages from zip code-level data using the American Community Survey (via the U.S. Census Bureau API). Two classification models are constructed, including logistic regression and XGBoost classification. The results indicate that for time-to-diagnosis, the top three SDoH factors are education, gender, and insurance coverage. Patients with higher education levels and better insurance are more likely to receive a quicker diagnosis, with males having an advantage over females. For antifibrotic treatment, the top three SDoH factors are insurance, gender, and race. Patients with better insurance coverage are more likely to receive antifibrotic treatment, with males and White patients having an advantage over females and patients of other ethnicities. This research may help address disparities in the diagnosis and treatment of IPF related to socioeconomic status.

Identifiers

PMID40502238
PMCPMC12150710

What OpenQuestion holds

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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.