Evidence map›Paper›PMID 28592870›Full record

ArticleScientific reports2017

Influence of a patient transfer network of US inpatient facilities on the incidence of nosocomial infections.

Juan Fernández-Gracia, Jukka-Pekka Onnela, Michael L Barnett, Víctor M Eguíluz, Nicholas A Christakis

Abstract read
In one paragraph

Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed, 1 pooled it
–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

17 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Applications of network analysis to routinely collected health care data: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2018
    Pooled it
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  7. Real-time updating of dynamic social networks for COVID-19 vaccination strategies.Journal of ambient intelligence and humanized computing · 2023
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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

5 authors.

Juan Fernández-GraciaHarvard T.H. Chan School of Public Health, 677 Huntington Ave, Boston, MA, 02115, USA. jfernand@hsph.harvard.edu.
Jukka-Pekka OnnelaHarvard T.H. Chan School of Public Health, 677 Huntington Ave, Boston, MA, 02115, USA.
Michael L BarnettHarvard T.H. Chan School of Public Health, 677 Huntington Ave, Boston, MA, 02115, USA.
Víctor M EguíluzInstitute for Cross-Disciplinary Physics and Complex Systems, Campus Universitat de les Illes Balears, Carretera de Valldemossa, km 7,5 Edificio Científico-Técnico, 07122, Palma de Mallorca, Islas Baleares, Spain.ORCID 0000-0003-1133-1289
Nicholas A ChristakisDepartment of Medicine, Department of Sociology, and Yale Institute for Network Science, Yale University, P.O. Box 208263, New Haven, CT, 06520-8263, USA.ORCID 0000-0001-5547-1086

Funding

Understanding and controlling antibiotic resistanceU54GM088558 · NIGMS · HARVARD SCHOOL OF PUBLIC HEALTH · PI LIPSITCH, MARC · 2009 to 2018
$26.4M
The Spread of Health Behaviors in the Framingham Heart Study Social NetworkP01AG031093 · NIA · YALE UNIVERSITY · PI LANDON, BRUCE E. · 2008 to 2012
$10.7M
NIA NIH HHS P01 AG031093NIGMS NIH HHS U54 GM088558
6 · The paper itself

Abstract

Antibiotic-resistant bacterial infections are a substantial source of morbidity and mortality and have a common reservoir in inpatient settings. Transferring patients between facilities could be a mechanism for the spread of these infections. We wanted to assess whether a network of hospitals, linked by inpatient transfers, contributes to the spread of nosocomial infections and investigate how network structure may be leveraged to design efficient surveillance systems. We construct a network defined by the transfer of Medicare patients across US inpatient facilities using a 100% sample of inpatient discharge claims from 2006-2007. We show the association between network structure and C. difficile incidence, with a 1% increase in a facility's C. difficile incidence being associated with a 0.53% increase in C. difficile incidence of neighboring facilities. Finally, we used network science methods to determine the facilities to monitor to maximize surveillance efficiency. An optimal surveillance strategy for selecting "sensor" hospitals, based on their network position, detects 80% of the C. difficile infections using only 2% of hospitals as sensors. Selecting a small fraction of facilities as "sensors" could be a cost-effective mechanism to monitor emerging nosocomial infections.

Indexed as

Health FacilitiesInpatientsPatient TransferAgedAged, 80 and overClostridioides difficileClostridium InfectionsCross InfectionFemaleHospitalsHumansIncidenceMaleMiddle AgedSpatial AnalysisUnited States

Identifiers

PMID28592870
PMCPMC5462812

What OpenQuestion holds

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LicenceCC BY
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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.