Evidence map›Paper›PMID 41807767›Full record

ArticleCommunications medicine2026

Analysis of a PCORnet

Lauren Alpert, Xiaotao Zhang, Rachel Smith, Marcia Lange, Tatyana Kushner

Abstract read
In one paragraph

Article in Communications medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

5 authors.

Lauren AlpertDepartment of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA. lauren.alpert2@mountsinai.org.ORCID http://orcid.org/0009-0006-9163-0252
Xiaotao ZhangDivision of Liver Diseases, Icahn School of Medicine at Mount Sinai, New York, NY, USA.ORCID http://orcid.org/0000-0002-3968-5030
Rachel SmithDivision of Gastroenterology, Hepatology, and Nutrition, UPMC Children's Hospital of Pittsburgh, Pittsburgh, PA, USA.ORCID http://orcid.org/0000-0003-2382-2317
Marcia LangeDepartment of Internal Medicine, Brigham and Women's Hospital, Boston, MA, USA.ORCID http://orcid.org/0000-0003-2700-2279
Tatyana KushnerDepartment of Gastroenterology and Hepatology, Weill Cornell Medicine, New York, NY, USA.ORCID http://orcid.org/0000-0001-6663-6253

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundAlthough New York City (NYC) is a hotspot for hepatitis delta virus (HDV) in the United States (US), the epidemiology of HDV remains poorly understood. We aimed to determine HDV testing and positivity rates, calculate prevalence of liver disease progression by HDV status, and assess the association between HDV and community resources.

methodsWe utilized the INSIGHT database, which contains data from the five major NYC health systems, to identify adults with laboratory and/or diagnosis code evidence of hepatitis B virus (HBV) from 2010-2023. HDV positivity included HDV RNA ≥ 20 IU/mL and/or positive results for HDV Ab, RNA, and Ag. Prevalence of liver-related complications was calculated. Community deprivation index was used to evaluate socioeconomic disparities by HDV testing and positivity status.

resultsHere we show that among 106,210 patients with HBV, 5,388 (5.1%) have received HDV testing; 294 (5.5%) are HDV + . HDV-tested individuals are more likely to be older and Asian, receive comprehensive HBV care, and have more community resources. HDV+ individuals are more likely to be female, White, and have higher prevalence of liver disease progression.

conclusionsHDV is undertested and under-detected, even in a high-prevalence US region. Early linkage to treatment is essential given high rates of liver disease progression.

Identifiers

PMID41807767
PMCPMC13106837

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LicenceCC BY-NC-ND
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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.