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Evaluating the Use of Multilocus Variable Number Tandem Repeat Analysis of Shiga Toxin-Producing Escherichia coli O157 as a Routine Public Health Tool in England

Overview of attention for article published in PLOS ONE, January 2014
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  • Good Attention Score compared to outputs of the same age (73rd percentile)
  • Above-average Attention Score compared to outputs of the same age and source (64th percentile)

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1 policy source
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2 X users

Citations

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22 Dimensions

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30 Mendeley
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Title
Evaluating the Use of Multilocus Variable Number Tandem Repeat Analysis of Shiga Toxin-Producing Escherichia coli O157 as a Routine Public Health Tool in England
Published in
PLOS ONE, January 2014
DOI 10.1371/journal.pone.0085901
Pubmed ID
Authors

Lisa Byrne, Richard Elson, Timothy J. Dallman, Neil Perry, Philip Ashton, John Wain, Goutam K. Adak, Kathie A. Grant, Claire Jenkins

Abstract

Multilocus variable number tandem repeat analysis (MLVA) provides microbiological support for investigations of clusters of cases of infection with Shiga toxin-producing E. coli (STEC) O157. All confirmed STEC O157 isolated in England and submitted to the Gastrointestinal Bacteria Reference Unit (GBRU) during a six month period were typed using MLVA, with the aim of assessing the impact of this approach on epidemiological investigations. Of 539 cases investigated, 341 (76%) had unique (>2 single locus variants) MLVA profiles, 12% of profiles occurred more than once due to known household transmission and 12% of profiles occurred as part of 41 clusters, 21 of which were previously identified through routine public health investigation of cases. The remaining 20 clusters were not previously detected and STEC enhanced surveillance data for associated cases were retrospectively reviewed for epidemiological links including shared exposures, geography and/or time. Additional evidence of a link between cases was found in twelve clusters. Compared to phage typing, the number of sporadic cases was reduced from 69% to 41% and the diversity index for MLVA was 0.996 versus 0.782 for phage typing. Using MLVA generates more data on the spatial and temporal dispersion of cases, better defining the epidemiology of STEC infection than phage typing. The increased detection of clusters through MLVA typing highlights the challenges to health protection practices, providing a forerunner to the advent of whole genome sequencing as a diagnostic tool.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 30 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 3%
Germany 1 3%
Unknown 28 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 9 30%
Researcher 5 17%
Student > Master 3 10%
Student > Bachelor 3 10%
Other 2 7%
Other 4 13%
Unknown 4 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 12 40%
Medicine and Dentistry 3 10%
Biochemistry, Genetics and Molecular Biology 2 7%
Computer Science 2 7%
Immunology and Microbiology 2 7%
Other 4 13%
Unknown 5 17%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 19 June 2017.
All research outputs
#6,401,232
of 22,741,406 outputs
Outputs from PLOS ONE
#76,823
of 194,090 outputs
Outputs of similar age
#75,885
of 304,589 outputs
Outputs of similar age from PLOS ONE
#1,873
of 5,548 outputs
Altmetric has tracked 22,741,406 research outputs across all sources so far. This one has received more attention than most of these and is in the 70th percentile.
So far Altmetric has tracked 194,090 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 15.1. This one has gotten more attention than average, scoring higher than 59% 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 304,589 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 73% of its contemporaries.
We're also able to compare this research output to 5,548 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 64% of its contemporaries.