↓ Skip to main content

Fast and accurate dynamic estimation of field effectiveness of meningococcal vaccines

Overview of attention for article published in BMC Medicine, June 2016
Altmetric Badge

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 (82nd percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

twitter
17 tweeters

Citations

dimensions_citation
12 Dimensions

Readers on

mendeley
31 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Fast and accurate dynamic estimation of field effectiveness of meningococcal vaccines
Published in
BMC Medicine, June 2016
DOI 10.1186/s12916-016-0642-2
Pubmed ID
Authors

Lorenzo Argante, Michele Tizzoni, Duccio Medini

Abstract

Estimating the effectiveness of meningococcal vaccines with high accuracy and precision can be challenging due to the low incidence of the invasive disease, which ranges between 0.5 and 1 cases per 100,000 in Europe and North America. Vaccine effectiveness (VE) is usually estimated with a screening method that combines in one formula the proportion of meningococcal disease cases that have been vaccinated and the proportion of vaccinated in the overall population. Due to the small number of cases, initial point estimates are affected by large uncertainties and several years may be required to estimate VE with a small confidence interval. We used a Monte Carlo maximum likelihood (MCML) approach to estimate the effectiveness of meningococcal vaccines, based on stochastic simulations of a dynamic model for meningococcal transmission and vaccination. We calibrated the model to describe two immunization campaigns: the campaign against MenC in England and the Bexsero campaign that started in the UK in September 2015. First, the MCML method provided estimates for both the direct and indirect effects of the MenC vaccine that were validated against results published in the literature. Then, we assessed the performance of the MCML method in terms of time gain with respect to the screening method under different assumptions of VE for Bexsero. MCML estimates of VE for the MenC immunization campaign are in good agreement with results based on the screening method and carriage studies, yet characterized by smaller confidence intervals and obtained using only incidence data collected within 2 years of scheduled vaccination. Also, we show that the MCML method could provide a fast and accurate estimate of the effectiveness of Bexsero, with a time gain, with respect to the screening method, that could range from 2 to 15 years, depending on the value of VE measured from field data. Results indicate that inference methods based on dynamic computational models can be successfully used to quantify in near real time the effectiveness of immunization campaigns against Neisseria meningitidis. Such an approach could represent an important tool to complement and support traditional observational studies, in the initial phase of a campaign.

Twitter Demographics

The data shown below were collected from the profiles of 17 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 31 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Italy 1 3%
Unknown 30 97%

Demographic breakdown

Readers by professional status Count As %
Researcher 5 16%
Student > Ph. D. Student 5 16%
Student > Bachelor 4 13%
Student > Master 4 13%
Student > Postgraduate 3 10%
Other 4 13%
Unknown 6 19%
Readers by discipline Count As %
Medicine and Dentistry 6 19%
Nursing and Health Professions 3 10%
Agricultural and Biological Sciences 3 10%
Immunology and Microbiology 2 6%
Biochemistry, Genetics and Molecular Biology 2 6%
Other 8 26%
Unknown 7 23%

Attention Score in Context

This research output has an Altmetric Attention Score of 9. 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 July 2016.
All research outputs
#3,540,390
of 22,880,230 outputs
Outputs from BMC Medicine
#1,883
of 3,438 outputs
Outputs of similar age
#63,155
of 351,542 outputs
Outputs of similar age from BMC Medicine
#29
of 42 outputs
Altmetric has tracked 22,880,230 research outputs across all sources so far. Compared to these this one has done well and is in the 84th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,438 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 43.6. This one is in the 45th percentile – i.e., 45% 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 351,542 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 82% 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 30th percentile – i.e., 30% of its contemporaries scored the same or lower than it.