Evidence map›Paper›PMID 39411222›Full record

ArticleOpen forum infectious diseases2024

Sociodemographic Disparities in Hepatitis B Treatment: A Real-World Analysis of 3 Safety-Net Health Systems in the United States.

Robert J Wong, Mamta K Jain, Bolin Niu, George Therapondos, Onkar Kshirsagar, Mae Thamer

Abstract read
In one paragraph

Article in Open forum infectious diseases, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Article
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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.

Robert J WongDivision of Gastroenterology and Hepatology, Stanford University School of Medicine, Palo Alto, California, USA.ORCID https://orcid.org/0000-0002-8923-2806
Mamta K JainDivision of Infectious Diseases, Department of Internal Medicine, University of Texas Southwestern and Parkland Health and Hospital System, Dallas, Texas, USA.
Bolin NiuDivision of Gastroenterology and Hepatology, MetroHealth System, Cleveland, Ohio, USA.
George TherapondosMulti-Organ Transplant Institute, Ochsner Health System, New Orleans, Louisiana, USA.
Onkar KshirsagarMedical Technology and Practice Patterns Institute, Bethesda, Maryland, USA.
Mae ThamerMedical Technology and Practice Patterns Institute, Bethesda, Maryland, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Timely treatment of chronic hepatitis B (CHB) reduces risks of cirrhosis and hepatocellular carcinoma. Gaps in timely treatment persist, especially among underserved safety-net populations. We aim to evaluate gaps and disparities in CHB treatment in the United States. Methods: Adults with treatment-naive CHB without human immunodeficiency virus were identified from 2010 to 2018 across 3 safety-net health systems. CHB treatment eligibility was assessed using American Association for the Study of Liver Diseases (AASLD) criteria and alternative criteria, including the Simplified Approach for Hepatitis B Algorithm. Differences in CHB treatment between groups were evaluated using χ Results: Among 3749 patients with treatment-naive CHB (51.5% women, 38.7% White, 33.7% African American, 19.6% Asian, 24.6% cirrhosis), 30.0% were AASLD treatment eligible, among whom 31.0% were treated. Men were more likely than women to be treated (33.5% vs 26.6%, Conclusions: Among an ethnically diverse multicenter safety-net cohort of CHB patients, less than one-third of treatment-eligible patients received antiviral treatment. Significant disparities in CHB treatment were observed by sociodemographic characteristics.

Indexed as

antiviraldisparitiesgenderhepatitis Bsafety net

Identifiers

PMID39411222
PMCPMC11475814

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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.