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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

Overview of attention for article published in BMC Infectious Diseases, September 2017
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (88th percentile)
  • High Attention Score compared to outputs of the same age and source (97th percentile)

Mentioned by

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1 news outlet
policy
2 policy sources
twitter
2 X users

Citations

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35 Dimensions

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82 Mendeley
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Title
Possible explanations for why some countries were harder hit by the pandemic influenza virus in 2009 – a global mortality impact modeling study
Published in
BMC Infectious Diseases, September 2017
DOI 10.1186/s12879-017-2730-0
Pubmed ID
Authors

Kathleen F. Morales, John Paget, Peter Spreeuwenberg

Abstract

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. 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. 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. 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.

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X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 82 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 82 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 17 21%
Student > Master 15 18%
Student > Bachelor 7 9%
Student > Ph. D. Student 6 7%
Student > Doctoral Student 4 5%
Other 12 15%
Unknown 21 26%
Readers by discipline Count As %
Medicine and Dentistry 25 30%
Nursing and Health Professions 7 9%
Economics, Econometrics and Finance 4 5%
Business, Management and Accounting 2 2%
Agricultural and Biological Sciences 2 2%
Other 17 21%
Unknown 25 30%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 17. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 02 February 2022.
All research outputs
#2,069,136
of 25,271,884 outputs
Outputs from BMC Infectious Diseases
#575
of 8,521 outputs
Outputs of similar age
#38,853
of 326,519 outputs
Outputs of similar age from BMC Infectious Diseases
#5
of 140 outputs
Altmetric has tracked 25,271,884 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 8,521 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.7. This one has done particularly well, scoring higher than 93% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 326,519 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 88% of its contemporaries.
We're also able to compare this research output to 140 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 97% of its contemporaries.