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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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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (87th percentile)
  • High Attention Score compared to outputs of the same age and source (81st percentile)

Mentioned by

news
1 news outlet
policy
1 policy source
twitter
4 tweeters

Citations

dimensions_citation
25 Dimensions

Readers on

mendeley
72 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.

Twitter Demographics

The data shown below were collected from the profiles of 4 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 72 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 72 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 16 22%
Student > Master 13 18%
Student > Ph. D. Student 6 8%
Student > Bachelor 6 8%
Student > Doctoral Student 4 6%
Other 10 14%
Unknown 17 24%
Readers by discipline Count As %
Medicine and Dentistry 18 25%
Nursing and Health Professions 9 13%
Economics, Econometrics and Finance 4 6%
Pharmacology, Toxicology and Pharmaceutical Science 3 4%
Agricultural and Biological Sciences 2 3%
Other 15 21%
Unknown 21 29%

Attention Score in Context

This research output has an Altmetric Attention Score of 15. 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 06 October 2020.
All research outputs
#1,625,782
of 19,019,810 outputs
Outputs from BMC Infectious Diseases
#418
of 6,666 outputs
Outputs of similar age
#37,011
of 288,903 outputs
Outputs of similar age from BMC Infectious Diseases
#4
of 16 outputs
Altmetric has tracked 19,019,810 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 6,666 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.6. 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 288,903 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 87% of its contemporaries.
We're also able to compare this research output to 16 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 81% of its contemporaries.