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Identifying the effect of patient sharing on between-hospital genetic differentiation of methicillin-resistant Staphylococcus aureus

Overview of attention for article published in Genome Medicine, February 2016
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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 (81st percentile)
  • Average Attention Score compared to outputs of the same age and source

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15 X users

Citations

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22 Dimensions

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46 Mendeley
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2 CiteULike
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Title
Identifying the effect of patient sharing on between-hospital genetic differentiation of methicillin-resistant Staphylococcus aureus
Published in
Genome Medicine, February 2016
DOI 10.1186/s13073-016-0274-3
Pubmed ID
Authors

Hsiao-Han Chang, Janina Dordel, Tjibbe Donker, Colin J. Worby, Edward J. Feil, William P. Hanage, Stephen D. Bentley, Susan S. Huang, Marc Lipsitch

Abstract

Methicillin-resistant Staphylococcus aureus (MRSA) is one of the most common healthcare-associated pathogens. To examine the role of inter-hospital patient sharing on MRSA transmission, a previous study collected 2,214 samples from 30 hospitals in Orange County, California and showed by spa typing that genetic differentiation decreased significantly with increased patient sharing. In the current study, we focused on the 986 samples with spa type t008 from the same population. We used genome sequencing to determine the effect of patient sharing on genetic differentiation between hospitals. Genetic differentiation was measured by between-hospital genetic diversity, F ST , and the proportion of nearly identical isolates between hospitals. Surprisingly, we found very similar genetic diversity within and between hospitals, and no significant association between patient sharing and genetic differentiation measured by F ST . However, in contrast to F ST , there was a significant association between patient sharing and the proportion of nearly identical isolates between hospitals. We propose that the proportion of nearly identical isolates is more powerful at determining transmission dynamics than traditional estimators of genetic differentiation (F ST ) when gene flow between populations is high, since it is more responsive to recent transmission events. Our hypothesis was supported by the results from coalescent simulations. Our results suggested that there was a high level of gene flow between hospitals facilitated by patient sharing, and that the proportion of nearly identical isolates is more sensitive to population structure than F ST when gene flow is high.

X Demographics

X Demographics

The data shown below were collected from the profiles of 15 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 46 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 2%
Netherlands 1 2%
Unknown 44 96%

Demographic breakdown

Readers by professional status Count As %
Researcher 15 33%
Student > Ph. D. Student 6 13%
Student > Master 5 11%
Student > Doctoral Student 4 9%
Student > Bachelor 2 4%
Other 6 13%
Unknown 8 17%
Readers by discipline Count As %
Agricultural and Biological Sciences 12 26%
Medicine and Dentistry 6 13%
Biochemistry, Genetics and Molecular Biology 6 13%
Immunology and Microbiology 5 11%
Mathematics 2 4%
Other 8 17%
Unknown 7 15%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 8. 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 13 October 2016.
All research outputs
#4,538,981
of 24,832,302 outputs
Outputs from Genome Medicine
#884
of 1,529 outputs
Outputs of similar age
#75,539
of 411,881 outputs
Outputs of similar age from Genome Medicine
#24
of 42 outputs
Altmetric has tracked 24,832,302 research outputs across all sources so far. Compared to these this one has done well and is in the 81st percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,529 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 27.2. This one is in the 42nd percentile – i.e., 42% of its peers scored the same or lower than it.
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 411,881 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 81% of its contemporaries.
We're also able to compare this research output to 42 others from the same source and published within six weeks on either side of this one. This one is in the 45th percentile – i.e., 45% of its contemporaries scored the same or lower than it.