Evidence map›Paper›PMID 38195669›Full record

ArticleScientific reports2024

Regional patient transfer patterns matter for the spread of hospital-acquired pathogens.

Hanjue Xia, Johannes Horn, Monika J Piotrowska, Konrad Sakowski, André Karch, Mirjam Kretzschmar, Rafael Mikolajczyk

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2024. 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
3.4field-weighted citation impact, top 7% 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

4 citing papers in PubMed, 3 citations in OpenAlex.

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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

7 authors at 4 institutions in 3 countries.

Hanjue XiaInstitute for Medical Epidemiology, Biometrics and Informatics (IMEBI), Interdisciplinary Centre for Health Sciences, Medical School of the Martin Luther University Halle-Wittenberg, 06108, Halle, Saale, Germany. hanjue.xia@uk-halle.de.
Johannes HornInstitute for Medical Epidemiology, Biometrics and Informatics (IMEBI), Interdisciplinary Centre for Health Sciences, Medical School of the Martin Luther University Halle-Wittenberg, 06108, Halle, Saale, Germany.
Monika J PiotrowskaInstitute of Applied Mathematics and Mechanics, University of Warsaw, 02-097, Warsaw, Poland.
Konrad SakowskiInstitute of Applied Mathematics and Mechanics, University of Warsaw, 02-097, Warsaw, Poland.
André KarchInstitute for Epidemiology and Social Medicine, University of Münster, 48149, Münster, Germany.
Mirjam KretzschmarJulius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, 3584 CG, Utrecht, The Netherlands.
Rafael MikolajczykInstitute for Medical Epidemiology, Biometrics and Informatics (IMEBI), Interdisciplinary Centre for Health Sciences, Medical School of the Martin Luther University Halle-Wittenberg, 06108, Halle, Saale, Germany.
Martin Luther University Halle-Wittenberg · DEUniversity of Warsaw · PLUniversity of Münster · DEUtrecht University · NL

Funding

Bundesministerium für Bildung und Forschung (BMBF), Germany 01KI1704CNational Science Center, Poland 2016/22/Z/ST1/00690The Netherlands Organization for Health Research and Development 547001005
6 · The paper itself

Abstract

Pathogens typically responsible for hospital-acquired infections (HAIs) constitute a major threat to healthcare systems worldwide. They spread via hospital (or hospital-community) networks by readmissions or patient transfers. Therefore, knowledge of these networks is essential to develop and test strategies to mitigate and control the HAI spread. Until now, no methods for comparing healthcare networks across different systems were proposed. Based on healthcare insurance data from four German federal states (Bavaria, Lower Saxony, Saxony and Thuringia), we constructed hospital networks and compared them in a systematic approach regarding population, hospital characteristics, and patient transfer patterns. Direct patient transfers between hospitals had only a limited impact on HAI spread. Whereas, with low colonization clearance rates, readmissions to the same hospitals posed the biggest transmission risk of all inter-hospital transfers. We then generated hospital-community networks, in which patients either stay in communities or in hospitals. We found that network characteristics affect the final prevalence and the time to reach it. However, depending on the characteristics of the pathogen (colonization clearance rate and transmission rate or even the relationship between transmission rate in hospitals and in the community), the studied networks performed differently. The differences were not large, but justify further studies.

Indexed as

Cross InfectionPatient TransferCommunity NetworksHealth FacilitiesHospitals, CommunityHumans

Identifiers

PMID38195669
PMCPMC10776674
OpenAlexW4390735152

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.