Evidence map›Paper›PMID 33512524›Full record

ArticleClinical infectious diseases : an official publication of the Infectious Diseases Society of America2021

Transmission Dynamics of Clostridioides difficile in 2 High-Acuity Hospital Units.

Karim Khader, L Silvia Munoz-Price, Ryan Hanson, Vanessa Stevens, Lindsay T Keegan, Alun Thomas, Liliana E Pezzin, Ann Nattinger, Siddhartha Singh, Matthew H Samore

Abstract read
In one paragraph

Article in Clinical infectious diseases : an official publication of the Infectious Diseases Society of America, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Retrospective Study of the Epidemiology ofInfection and drug resistance · 2023
    Article
  2. Review
  3. Modeling transmission of pathogens in healthcare settings.Current opinion in infectious diseases · 2021
    Review
  4. 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

10 authors.

Karim KhaderInformatics, Decision-Enhancement, and Analytic Sciences (IDEAS) Center of Innovation, Veterans Affairs Salt Lake City Health Care System, Salt Lake City, Utah, USA.
L Silvia Munoz-PriceDepartment of Medicine, Medical College of Wisconsin, Milwaukee, Wisconsin, USA.
Ryan HansonCollaborative for Healthcare Delivery Science, Medical College of Wisconsin, Milwaukee, Wisconsin, USA.
Vanessa StevensInformatics, Decision-Enhancement, and Analytic Sciences (IDEAS) Center of Innovation, Veterans Affairs Salt Lake City Health Care System, Salt Lake City, Utah, USA.
Lindsay T KeeganInformatics, Decision-Enhancement, and Analytic Sciences (IDEAS) Center of Innovation, Veterans Affairs Salt Lake City Health Care System, Salt Lake City, Utah, USA.
Alun ThomasInformatics, Decision-Enhancement, and Analytic Sciences (IDEAS) Center of Innovation, Veterans Affairs Salt Lake City Health Care System, Salt Lake City, Utah, USA.
Liliana E PezzinCollaborative for Healthcare Delivery Science, Medical College of Wisconsin, Milwaukee, Wisconsin, USA.
Ann NattingerDepartment of Medicine, Medical College of Wisconsin, Milwaukee, Wisconsin, USA.
Siddhartha SinghDepartment of Medicine, Medical College of Wisconsin, Milwaukee, Wisconsin, USA.
Matthew H SamoreInformatics, Decision-Enhancement, and Analytic Sciences (IDEAS) Center of Innovation, Veterans Affairs Salt Lake City Health Care System, Salt Lake City, Utah, USA.

Funding

Utah Center for Clinical and Translational ScienceUL1TR002538 · NCATS · UNIVERSITY OF UTAH · PI HESS, RACHEL, MAJERSIK, JENNIFER JUHL · 2018 to 2022
$26.0M
University of Utah Center for Clinical and Translational Science-UL1UL1TR000105 · NCATS · UNIVERSITY OF UTAH · PI MCCLAIN, DONALD A. · 2012 to 2013
$5.1M
NCATS NIH HHS UL1 TR000105NCATS NIH HHS UL1 TR002538NCEZID CDC HHS U01 CK000585NCEZID CDC HHS U54 CK000456
6 · The paper itself

Abstract

backgroundThe key epidemiological drivers of Clostridioides difficile transmission are not well understood. We estimated epidemiological parameters to characterize variation in C. difficile transmission, while accounting for the imperfect nature of surveillance tests.

methodsWe conducted a retrospective analysis of C. difficile surveillance tests for patients admitted to a bone marrow transplant (BMT) unit or a solid tumor unit (STU) in a 565-bed tertiary hospital. We constructed a transmission model for estimating key parameters, including admission prevalence, transmission rate, and duration of colonization to understand the potential variation in C. difficile dynamics between these 2 units.

resultsA combined 2425 patients had 5491 admissions into 1 of the 2 units. A total of 3559 surveillance tests were collected from 1394 patients, with 11% of the surveillance tests being positive for C. difficile. We estimate that the transmission rate in the BMT unit was nearly 3-fold higher at 0.29 acquisitions per percentage colonized per 1000 days, compared to our estimate in the STU (0.10). Our model suggests that 20% of individuals admitted into either the STU or BMT unit were colonized with C. difficile at the time of admission. In contrast, the percentage of surveillance tests that were positive within 1 day of admission to either unit for C. difficile was 13.4%, with 15.4% in the STU and 11.6% in the BMT unit.

conclusionsAlthough prevalence was similar between the units, there were important differences in the rates of transmission and clearance. Influential factors may include antimicrobial exposure or other patient-care factors.

Indexed as

Clostridioides difficileClostridium InfectionsClostridioidesHospital UnitsHumansRetrospective StudiesClostridioides difficiledynamic transmission modelMarkov chain Monte Carlo

Identifiers

PMID33512524
PMCPMC7844587

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.