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Standardizing Plasmodium falciparum infection prevalence measured via microscopy versus rapid diagnostic test

Overview of attention for article published in Malaria Journal, November 2015
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (77th percentile)
  • High Attention Score compared to outputs of the same age and source (82nd percentile)

Mentioned by

twitter
9 tweeters

Citations

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22 Dimensions

Readers on

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50 Mendeley
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Title
Standardizing Plasmodium falciparum infection prevalence measured via microscopy versus rapid diagnostic test
Published in
Malaria Journal, November 2015
DOI 10.1186/s12936-015-0984-9
Pubmed ID
Authors

Bonnie Mappin, Ewan Cameron, Ursula Dalrymple, Daniel J. Weiss, Donal Bisanzio, Samir Bhatt, Peter W. Gething

Abstract

Large-scale mapping of Plasmodium falciparum infection prevalence relies on opportunistic assemblies of infection prevalence data arising from thousands of P. falciparum parasite rate (PfPR) surveys conducted worldwide. Variance in these data is driven by both signal, the true underlying pattern of infection prevalence, and a range of factors contributing to 'noise', including sampling error, differing age ranges of subjects and differing parasite detection methods. Whilst the former two noise components have been addressed in previous studies, the effect of different diagnostic methods used to determine PfPR in different studies has not. In particular, the majority of PfPR data are based on positivity rates determined by either microscopy or rapid diagnostic test (RDT), yet these approaches are not equivalent; therefore a method is needed for standardizing RDT and microscopy-based prevalence estimates prior to use in mapping. Twenty-five recent Demographic and Health surveys (DHS) datasets from sub-Saharan Africa provide child diagnostic test results derived using both RDT and microscopy for each individual. These prevalence estimates were aggregated across level one administrative zones and a Bayesian probit regression model fit to the microscopy- versus RDT-derived prevalence relationship. An errors-in-variables approach was employed to account for sampling error in both the dependent and independent variables. In addition to the diagnostic outcome, RDT type, fever status and recent anti-malarial treatment were extracted from the datasets in order to analyse their effect on observed malaria prevalence. A strong non-linear relationship between the microscopy and RDT-derived prevalence was found. The results of regressions stratified by the additional diagnostic variables (RDT type, fever status and recent anti-malarial treatment) indicate that there is a distinct and consistent difference in the relationship when the data are stratified by febrile status and RDT brand. The relationships defined in this research can be applied to RDT-derived PfPR data to effectively convert them to an estimate of the parasite prevalence expected using microscopy (or vice versa), thereby standardizing the dataset and improving the signal-to-noise ratio. Additionally, the results provide insight on the importance of RDT brands, febrile status and recent anti-malarial treatment for explaining inconsistencies between observed prevalence derived from different diagnostics.

Twitter Demographics

The data shown below were collected from the profiles of 9 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 50 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Kenya 1 2%
Unknown 49 98%

Demographic breakdown

Readers by professional status Count As %
Student > Master 10 20%
Researcher 10 20%
Student > Ph. D. Student 7 14%
Student > Bachelor 4 8%
Student > Postgraduate 3 6%
Other 7 14%
Unknown 9 18%
Readers by discipline Count As %
Medicine and Dentistry 16 32%
Agricultural and Biological Sciences 5 10%
Nursing and Health Professions 4 8%
Social Sciences 2 4%
Biochemistry, Genetics and Molecular Biology 2 4%
Other 8 16%
Unknown 13 26%

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 26 November 2015.
All research outputs
#2,527,787
of 11,530,863 outputs
Outputs from Malaria Journal
#736
of 3,401 outputs
Outputs of similar age
#68,380
of 307,487 outputs
Outputs of similar age from Malaria Journal
#29
of 164 outputs
Altmetric has tracked 11,530,863 research outputs across all sources so far. Compared to these this one has done well and is in the 78th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,401 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.2. This one has done well, scoring higher than 77% 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 307,487 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 77% of its contemporaries.
We're also able to compare this research output to 164 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 82% of its contemporaries.