Evidence map›Paper›PMID 33115427›Full record

ArticleBMC infectious diseases2020

Comparing intervention strategies for reducing Clostridioides difficile transmission in acute healthcare settings: an agent-based modeling study.

Brittany Stephenson, Cristina Lanzas, Suzanne Lenhart, Eduardo Ponce, Jason Bintz, Erik R Dubberke, Judy Day

Open access · goldAbstract read
In one paragraph

Article in BMC infectious diseases, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
0.4field-weighted citation impact, top 26% of its field
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

13 citing papers in PubMed, 24 citations in OpenAlex.

  1. Molecular epidemiology and clinical differentiation betweenAntimicrobial stewardship & healthcare epidemiology : ASHE · 2026
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  5. Systematic Design of Adenosine Analogs as Inhibitors of aJournal of medicinal chemistry · 2023
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  9. Inactivation of Spores and Vegetative Forms ofInternational journal of environmental research and public health · 2022
    Review
  10. Article
  11. Article
  12. Modeling transmission of pathogens in healthcare settings.Current opinion in infectious diseases · 2021
    Review
  13. Review
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

7 authors at 5 institutions in 1 country.

Brittany StephensonDepartment of Engineering, Computing, and Mathematical Sciences, Lewis University, 1 University Parkway, Romeoville, 60446, IL, USA. bstephenson@lewisu.edu.ORCID http://orcid.org/0000-0001-5478-4072
Cristina LanzasDepartment of Population Health and Pathobiology, North Carolina State University, 1052 William Moore Drive, Raleigh, 27606, NC, USA.
Suzanne LenhartDepartment of Mathematics, University of Tennessee, 1403 Circle Drive, Knoxville, 37996, TN, USA.
Eduardo PonceDepartment of Electrical Engineering and Computer Science, University of Tennessee, 1520 Middle Drive, Knoxville, 37996, TN, USA.
Jason BintzSchool of Arts and Sciences, Johnson University, Knoxville, 37998, TN, USA.
Erik R DubberkeDivision of Infectious Disease, Washington University School of Medicine, 660 S Euclid Ave, St. Louis, 63110, MO, USA.
Judy DayDepartment of Mathematics, University of Tennessee, 1403 Circle Drive, Knoxville, 37996, TN, USA.
University of Tennessee at Knoxville · USJohnson University · USLewis University · USNorth Carolina State University · USWashington University in St. Louis · US

Funding

Modeling and control of environmentally transmitted pathogensR01GM113239 · NIGMS · UNIVERSITY OF TENNESSEE KNOXVILLE · PI LANZAS, CRISTINA · 2014 to 2017
$624k
NIGMS NIH HHS R01 GM113239NIH HHS R01GM113239
6 · The paper itself

Abstract

backgroundClostridioides difficile infection (CDI) is one of the most common healthcare infections. Common strategies aiming at controlling CDI include antibiotic stewardship, environmental decontamination, and improved hand hygiene and contact precautions. Mathematical models provide a framework to evaluate control strategies. Our objective is to evaluate the effectiveness of control strategies in decreasing C. difficile colonization and infection using an agent-based model in an acute healthcare setting.

methodsWe developed an agent-based model that simulates the transmission of C. difficile in medical wards. This model explicitly incorporates healthcare workers (HCWs) as vectors of transmission, tracks individual patient antibiotic histories, incorporates varying risk levels of antibiotics with respect to CDI susceptibility, and tracks contamination levels of ward rooms by C. difficile. Interventions include two forms of antimicrobial stewardship, increased environmental decontamination through room cleaning, improved HCW compliance, and a preliminary assessment of vaccination.

resultsIncreased HCW compliance with CDI patients was ranked as the most effective intervention in decreasing colonizations, with reductions up to 56%. Antibiotic stewardship practices were highly ranked after contact precaution compliance. Vaccination and reduction of high-risk antibiotics were the most effective intervention in decreasing CDI. Vaccination reduced CDI cases to up to 90%, and the reduction of high-risk antibiotics decreased CDI cases up to 23%.

conclusionsOverall, interventions that decrease patient susceptibility to colonization by C. difficile, such as antibiotic stewardship, were the most effective interventions in reducing both colonizations and CDI cases.

Indexed as

Systems AnalysisAnti-Bacterial AgentsAntimicrobial StewardshipClostridioides difficileClostridium InfectionsCross InfectionHand HygieneHealth PersonnelHumansInfection ControlModels, TheoreticalVaccinationAnti-Bacterial AgentsAgent-based modelClostridioides difficileEnvironmental transmissionHealthcareStochastic model

Identifiers

PMID33115427
PMCPMC7594474
OpenAlexW3097259048

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.