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Occurrence of invasive pneumococcal disease and number of excess cases due to influenza

BACKGROUND: Influenza is characterized by seasonal outbreaks, often with a high rate of morbidity and mortality. It is also known to be a cause of significant amount secondary bacterial infections. Streptococcus pneumoniae is the main pathogen causing secondary bacterial pneumonia after influenza an...

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Autores principales: Grabowska, Katarzyna, Högberg, Liselotte, Penttinen, Pasi, Svensson, Åke, Ekdahl, Karl
Formato: Texto
Lenguaje:English
Publicado: BioMed Central 2006
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1534049/
https://www.ncbi.nlm.nih.gov/pubmed/16549029
http://dx.doi.org/10.1186/1471-2334-6-58
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author Grabowska, Katarzyna
Högberg, Liselotte
Penttinen, Pasi
Svensson, Åke
Ekdahl, Karl
author_facet Grabowska, Katarzyna
Högberg, Liselotte
Penttinen, Pasi
Svensson, Åke
Ekdahl, Karl
author_sort Grabowska, Katarzyna
collection PubMed
description BACKGROUND: Influenza is characterized by seasonal outbreaks, often with a high rate of morbidity and mortality. It is also known to be a cause of significant amount secondary bacterial infections. Streptococcus pneumoniae is the main pathogen causing secondary bacterial pneumonia after influenza and subsequently, influenza could participate in acquiring Invasive Pneumococcal Disease (IPD). METHODS: In this study, we aim to investigate the relation between influenza and IPD by estimating the yearly excess of IPD cases due to influenza. For this purpose, we use influenza periods as an indicator for influenza activity as a risk factor in subsequent analysis. The statistical modeling has been made in two modes. First, we constructed two negative binomial regression models. For each model, we estimated the contribution of influenza in the models, and calculated number of excess number of IPD cases. Also, for each model, we investigated several lag time periods between influenza and IPD. Secondly, we constructed an "influenza free" baseline, and calculated differences in IPD data (observed cases) and baseline (expected cases), in order to estimate a yearly additional number of IPD cases due to influenza. Both modes were calculated using zero to four weeks lag time. RESULTS: The analysis shows a yearly increase of 72–118 IPD cases due to influenza, which corresponds to 6–10% per year or 12–20% per influenza season. Also, a lag time of one to three weeks appears to be of significant importance in the relation between IPD and influenza. CONCLUSION: This epidemiological study confirms the association between influenza and IPD. Furthermore, negative binomial regression models can be used to calculate number of excess cases of IPD, related to influenza.
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spelling pubmed-15340492006-08-10 Occurrence of invasive pneumococcal disease and number of excess cases due to influenza Grabowska, Katarzyna Högberg, Liselotte Penttinen, Pasi Svensson, Åke Ekdahl, Karl BMC Infect Dis Research Article BACKGROUND: Influenza is characterized by seasonal outbreaks, often with a high rate of morbidity and mortality. It is also known to be a cause of significant amount secondary bacterial infections. Streptococcus pneumoniae is the main pathogen causing secondary bacterial pneumonia after influenza and subsequently, influenza could participate in acquiring Invasive Pneumococcal Disease (IPD). METHODS: In this study, we aim to investigate the relation between influenza and IPD by estimating the yearly excess of IPD cases due to influenza. For this purpose, we use influenza periods as an indicator for influenza activity as a risk factor in subsequent analysis. The statistical modeling has been made in two modes. First, we constructed two negative binomial regression models. For each model, we estimated the contribution of influenza in the models, and calculated number of excess number of IPD cases. Also, for each model, we investigated several lag time periods between influenza and IPD. Secondly, we constructed an "influenza free" baseline, and calculated differences in IPD data (observed cases) and baseline (expected cases), in order to estimate a yearly additional number of IPD cases due to influenza. Both modes were calculated using zero to four weeks lag time. RESULTS: The analysis shows a yearly increase of 72–118 IPD cases due to influenza, which corresponds to 6–10% per year or 12–20% per influenza season. Also, a lag time of one to three weeks appears to be of significant importance in the relation between IPD and influenza. CONCLUSION: This epidemiological study confirms the association between influenza and IPD. Furthermore, negative binomial regression models can be used to calculate number of excess cases of IPD, related to influenza. BioMed Central 2006-03-20 /pmc/articles/PMC1534049/ /pubmed/16549029 http://dx.doi.org/10.1186/1471-2334-6-58 Text en Copyright © 2006 Grabowska et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( (http://creativecommons.org/licenses/by/2.0) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Grabowska, Katarzyna
Högberg, Liselotte
Penttinen, Pasi
Svensson, Åke
Ekdahl, Karl
Occurrence of invasive pneumococcal disease and number of excess cases due to influenza
title Occurrence of invasive pneumococcal disease and number of excess cases due to influenza
title_full Occurrence of invasive pneumococcal disease and number of excess cases due to influenza
title_fullStr Occurrence of invasive pneumococcal disease and number of excess cases due to influenza
title_full_unstemmed Occurrence of invasive pneumococcal disease and number of excess cases due to influenza
title_short Occurrence of invasive pneumococcal disease and number of excess cases due to influenza
title_sort occurrence of invasive pneumococcal disease and number of excess cases due to influenza
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1534049/
https://www.ncbi.nlm.nih.gov/pubmed/16549029
http://dx.doi.org/10.1186/1471-2334-6-58
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