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Possible explanations for why some countries were harder hit by the pandemic influenza virus in 2009 – a global mortality impact modeling study
BACKGROUND: A global pandemic mortality study found prominent regional mortality variations in 2009 for Influenza A(H1N1)pdm09. Our study attempts to identify factors that explain why the pandemic mortality burden was high in some countries and low in others. METHODS: As a starting point, we identif...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5613504/ https://www.ncbi.nlm.nih.gov/pubmed/28946870 http://dx.doi.org/10.1186/s12879-017-2730-0 |
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author | Morales, Kathleen F. Paget, John Spreeuwenberg, Peter |
author_facet | Morales, Kathleen F. Paget, John Spreeuwenberg, Peter |
author_sort | Morales, Kathleen F. |
collection | PubMed |
description | BACKGROUND: A global pandemic mortality study found prominent regional mortality variations in 2009 for Influenza A(H1N1)pdm09. Our study attempts to identify factors that explain why the pandemic mortality burden was high in some countries and low in others. METHODS: As a starting point, we identified possible risk factors worth investigating for Influenza A(H1N1)pdm09 mortality through a targeted literature search. We then used a modeling procedure (data simulations and regression models) to identify factors that could explain differences in respiratory mortality due to Influenza A(H1N1)pdm09. We ran sixteen models to produce robust results and draw conclusions. In order to assess the role of each factor in explaining differences in excess pandemic mortality, we calculated the reduction in between country variance, which can be viewed as an effect-size for each factor. RESULTS: The literature search identified 124 publications and 48 possible risk factors, of which we were able to identify 27 factors with appropriate global datasets. The modelling procedure indicated that age structure (explaining 40% of the mean between country variance), latitude (8%), influenza A and B viruses circulating during the pandemic (3–8%), influenza A and B viruses circulating during the preceding influenza season (2–6%), air pollution (pm10; 4%) and the prevalence of other infections (HIV and TB) (4–6%) were factors that explained differences in mortality around the world. Healthcare expenditure, levels of obesity, the distribution of antivirals, and air travel did not explain global pandemic mortality differences. CONCLUSIONS: Our study found that countries with a large proportion of young persons had higher pandemic mortality rates in 2009. The co-circulation of influenza viruses during the pandemic and the circulation of influenza viruses during the preceding season were also associated with pandemic mortality rates. We found that real time assessments of 2009 pandemic mortality risk factors (e.g. obesity) probably led to a number of false positive findings. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12879-017-2730-0) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-5613504 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-56135042017-10-11 Possible explanations for why some countries were harder hit by the pandemic influenza virus in 2009 – a global mortality impact modeling study Morales, Kathleen F. Paget, John Spreeuwenberg, Peter BMC Infect Dis Research Article BACKGROUND: A global pandemic mortality study found prominent regional mortality variations in 2009 for Influenza A(H1N1)pdm09. Our study attempts to identify factors that explain why the pandemic mortality burden was high in some countries and low in others. METHODS: As a starting point, we identified possible risk factors worth investigating for Influenza A(H1N1)pdm09 mortality through a targeted literature search. We then used a modeling procedure (data simulations and regression models) to identify factors that could explain differences in respiratory mortality due to Influenza A(H1N1)pdm09. We ran sixteen models to produce robust results and draw conclusions. In order to assess the role of each factor in explaining differences in excess pandemic mortality, we calculated the reduction in between country variance, which can be viewed as an effect-size for each factor. RESULTS: The literature search identified 124 publications and 48 possible risk factors, of which we were able to identify 27 factors with appropriate global datasets. The modelling procedure indicated that age structure (explaining 40% of the mean between country variance), latitude (8%), influenza A and B viruses circulating during the pandemic (3–8%), influenza A and B viruses circulating during the preceding influenza season (2–6%), air pollution (pm10; 4%) and the prevalence of other infections (HIV and TB) (4–6%) were factors that explained differences in mortality around the world. Healthcare expenditure, levels of obesity, the distribution of antivirals, and air travel did not explain global pandemic mortality differences. CONCLUSIONS: Our study found that countries with a large proportion of young persons had higher pandemic mortality rates in 2009. The co-circulation of influenza viruses during the pandemic and the circulation of influenza viruses during the preceding season were also associated with pandemic mortality rates. We found that real time assessments of 2009 pandemic mortality risk factors (e.g. obesity) probably led to a number of false positive findings. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12879-017-2730-0) contains supplementary material, which is available to authorized users. BioMed Central 2017-09-25 /pmc/articles/PMC5613504/ /pubmed/28946870 http://dx.doi.org/10.1186/s12879-017-2730-0 Text en © The Author(s). 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided 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 Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Article Morales, Kathleen F. Paget, John Spreeuwenberg, Peter Possible explanations for why some countries were harder hit by the pandemic influenza virus in 2009 – a global mortality impact modeling study |
title | Possible explanations for why some countries were harder hit by the pandemic influenza virus in 2009 – a global mortality impact modeling study |
title_full | Possible explanations for why some countries were harder hit by the pandemic influenza virus in 2009 – a global mortality impact modeling study |
title_fullStr | Possible explanations for why some countries were harder hit by the pandemic influenza virus in 2009 – a global mortality impact modeling study |
title_full_unstemmed | Possible explanations for why some countries were harder hit by the pandemic influenza virus in 2009 – a global mortality impact modeling study |
title_short | Possible explanations for why some countries were harder hit by the pandemic influenza virus in 2009 – a global mortality impact modeling study |
title_sort | possible explanations for why some countries were harder hit by the pandemic influenza virus in 2009 – a global mortality impact modeling study |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5613504/ https://www.ncbi.nlm.nih.gov/pubmed/28946870 http://dx.doi.org/10.1186/s12879-017-2730-0 |
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