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System dynamic modelling of healthcare associated influenza -a tool for infection control

BACKGROUND: The transmission dynamics of influenza virus within healthcare settings are not fully understood. Capturing the interplay between host, viral and environmental factors is difficult using conventional research methods. Instead, system dynamic modelling may be used to illustrate the comple...

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Autores principales: Sansone, Martina, Holmstrom, Paul, Hallberg, Stefan, Nordén, Rickard, Andersson, Lars-Magnus, Westin, Johan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9136787/
https://www.ncbi.nlm.nih.gov/pubmed/35624510
http://dx.doi.org/10.1186/s12913-022-07959-7
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author Sansone, Martina
Holmstrom, Paul
Hallberg, Stefan
Nordén, Rickard
Andersson, Lars-Magnus
Westin, Johan
author_facet Sansone, Martina
Holmstrom, Paul
Hallberg, Stefan
Nordén, Rickard
Andersson, Lars-Magnus
Westin, Johan
author_sort Sansone, Martina
collection PubMed
description BACKGROUND: The transmission dynamics of influenza virus within healthcare settings are not fully understood. Capturing the interplay between host, viral and environmental factors is difficult using conventional research methods. Instead, system dynamic modelling may be used to illustrate the complex scenarios including non-linear relationships and multiple interactions which occur within hospitals during a seasonal influenza epidemic. We developed such a model intended as a support for health-care providers in identifying potentially effective control strategies to prevent influenza transmission. METHODS: By using computer simulation software, we constructed a system dynamic model to illustrate transmission dynamics within a large acute-care hospital. We used local real-world clinical and epidemiological data collected during the season 2016/17, as well as data from the national surveillance programs and relevant publications to form the basic structure of the model. Multiple stepwise simulations were performed to identify the relative effectiveness of various control strategies and to produce estimates of the accumulated number of healthcare-associated influenza cases per season. RESULTS: Scenarios regarding the number of patients exposed for influenza virus by shared room and the extent of antiviral prophylaxis and treatment were investigated in relation to estimations of influenza vaccine coverage, vaccine effectiveness and inflow of patients with influenza. In total, 680 simulations were performed, of which each one resulted in an estimated number per season. The most effective preventive measure identified by our model was administration of antiviral prophylaxis to exposed patients followed by reducing the number of patients receiving care in shared rooms. CONCLUSIONS: This study presents an system dynamic model that can be used to capture the complex dynamics of in-hospital transmission of viral infections and identify potentially effective interventions to prevent healthcare-associated influenza infections. Our simulations identified antiviral prophylaxis as the most effective way to control in-hospital influenza transmission. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12913-022-07959-7.
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spelling pubmed-91367872022-05-28 System dynamic modelling of healthcare associated influenza -a tool for infection control Sansone, Martina Holmstrom, Paul Hallberg, Stefan Nordén, Rickard Andersson, Lars-Magnus Westin, Johan BMC Health Serv Res Research BACKGROUND: The transmission dynamics of influenza virus within healthcare settings are not fully understood. Capturing the interplay between host, viral and environmental factors is difficult using conventional research methods. Instead, system dynamic modelling may be used to illustrate the complex scenarios including non-linear relationships and multiple interactions which occur within hospitals during a seasonal influenza epidemic. We developed such a model intended as a support for health-care providers in identifying potentially effective control strategies to prevent influenza transmission. METHODS: By using computer simulation software, we constructed a system dynamic model to illustrate transmission dynamics within a large acute-care hospital. We used local real-world clinical and epidemiological data collected during the season 2016/17, as well as data from the national surveillance programs and relevant publications to form the basic structure of the model. Multiple stepwise simulations were performed to identify the relative effectiveness of various control strategies and to produce estimates of the accumulated number of healthcare-associated influenza cases per season. RESULTS: Scenarios regarding the number of patients exposed for influenza virus by shared room and the extent of antiviral prophylaxis and treatment were investigated in relation to estimations of influenza vaccine coverage, vaccine effectiveness and inflow of patients with influenza. In total, 680 simulations were performed, of which each one resulted in an estimated number per season. The most effective preventive measure identified by our model was administration of antiviral prophylaxis to exposed patients followed by reducing the number of patients receiving care in shared rooms. CONCLUSIONS: This study presents an system dynamic model that can be used to capture the complex dynamics of in-hospital transmission of viral infections and identify potentially effective interventions to prevent healthcare-associated influenza infections. Our simulations identified antiviral prophylaxis as the most effective way to control in-hospital influenza transmission. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12913-022-07959-7. BioMed Central 2022-05-27 /pmc/articles/PMC9136787/ /pubmed/35624510 http://dx.doi.org/10.1186/s12913-022-07959-7 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Sansone, Martina
Holmstrom, Paul
Hallberg, Stefan
Nordén, Rickard
Andersson, Lars-Magnus
Westin, Johan
System dynamic modelling of healthcare associated influenza -a tool for infection control
title System dynamic modelling of healthcare associated influenza -a tool for infection control
title_full System dynamic modelling of healthcare associated influenza -a tool for infection control
title_fullStr System dynamic modelling of healthcare associated influenza -a tool for infection control
title_full_unstemmed System dynamic modelling of healthcare associated influenza -a tool for infection control
title_short System dynamic modelling of healthcare associated influenza -a tool for infection control
title_sort system dynamic modelling of healthcare associated influenza -a tool for infection control
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9136787/
https://www.ncbi.nlm.nih.gov/pubmed/35624510
http://dx.doi.org/10.1186/s12913-022-07959-7
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