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Influence of a patient transfer network of US inpatient facilities on the incidence of nosocomial infections
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, linke...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5462812/ https://www.ncbi.nlm.nih.gov/pubmed/28592870 http://dx.doi.org/10.1038/s41598-017-02245-7 |
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author | Fernández-Gracia, Juan Onnela, Jukka-Pekka Barnett, Michael L. Eguíluz, Víctor M. Christakis, Nicholas A. |
author_facet | Fernández-Gracia, Juan Onnela, Jukka-Pekka Barnett, Michael L. Eguíluz, Víctor M. Christakis, Nicholas A. |
author_sort | Fernández-Gracia, Juan |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-5462812 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-54628122017-06-08 Influence of a patient transfer network of US inpatient facilities on the incidence of nosocomial infections Fernández-Gracia, Juan Onnela, Jukka-Pekka Barnett, Michael L. Eguíluz, Víctor M. Christakis, Nicholas A. Sci Rep Article 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. Nature Publishing Group UK 2017-06-07 /pmc/articles/PMC5462812/ /pubmed/28592870 http://dx.doi.org/10.1038/s41598-017-02245-7 Text en © The Author(s) 2017 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Fernández-Gracia, Juan Onnela, Jukka-Pekka Barnett, Michael L. Eguíluz, Víctor M. Christakis, Nicholas A. Influence of a patient transfer network of US inpatient facilities on the incidence of nosocomial infections |
title | Influence of a patient transfer network of US inpatient facilities on the incidence of nosocomial infections |
title_full | Influence of a patient transfer network of US inpatient facilities on the incidence of nosocomial infections |
title_fullStr | Influence of a patient transfer network of US inpatient facilities on the incidence of nosocomial infections |
title_full_unstemmed | Influence of a patient transfer network of US inpatient facilities on the incidence of nosocomial infections |
title_short | Influence of a patient transfer network of US inpatient facilities on the incidence of nosocomial infections |
title_sort | influence of a patient transfer network of us inpatient facilities on the incidence of nosocomial infections |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5462812/ https://www.ncbi.nlm.nih.gov/pubmed/28592870 http://dx.doi.org/10.1038/s41598-017-02245-7 |
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