Introduction and epidemiology of meningococcal disease

Trotter, Caroline; Ramsay, Mary; Harrison, Lee H.

doi:10.1007/978-3-319-28119-3_1

Cited by 4 publications

(3 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Meningococcal disease in the UK is primarily endemic, with episodic hyperendemic waves [ 45 , 56 , 57 ]. We calibrate the age-dependent risks of IMD given infection to be …”

Section: Methodsmentioning

confidence: 99%

Fast and accurate dynamic estimation of field effectiveness of meningococcal vaccines

Argante

Tizzoni

Medini

2016

BMC Med

View full text Add to dashboard Cite

BackgroundEstimating 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.MethodsWe 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.ResultsMCML 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.ConclusionsResults 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.Electronic supplementary materialThe online version of this article (doi:10.1186/s12916-016-0642-2) contains supplementary material, which is available to authorized users.

show abstract

“…Meningococcal disease in the UK is primarily endemic, with episodic hyperendemic waves [ 45 , 56 , 57 ]. We calibrate the age-dependent risks of IMD given infection to be …”

Section: Methodsmentioning

confidence: 99%

Fast and accurate dynamic estimation of field effectiveness of meningococcal vaccines

Argante

Tizzoni

Medini

2016

BMC Med

View full text Add to dashboard Cite

show abstract

“…The only known reservoir for the meningococcus is humans and it is spread by droplet transmission or direct contact with respiratory secretions from an infected host (5). The meningococcus serotypes responsible for causing the majority of disease are the A, B, C, W, X and Y strains (8).…”

Section: Background Rationalementioning

confidence: 99%

Care of the Patient with Invasive Meningococcal Disease by Emergency Medical Service Clinicians: A Scoping Review Protocol

Pearce

Peters

May

et al. 2019

Australasian Journal of Paramedicine

View full text Add to dashboard Cite

BackgroundInvasive meningococcal disease (IMD) is a rare but potentially fatal disease. It can rapidly progress within minutes to hours and carries a significant burden of disease worldwide. The role of emergency medical service (EMS) clinicians in providing early care for the patient with IMD is important, but the literature regarding the pre-hospital phase of care is sparse.AimThe aim of this scoping review is to systematically map the literature to identify the scope, depth and key concepts in the evidence, and to identify gaps in knowledge regarding the care of the patient with IMD by EMS clinicians.MethodsA comprehensive electronic search strategy will be used to locate sources of evidence that fit the inclusion criteria of the study. Data will be extracted, charted and reported in accordance with current best practice for scoping reviews.DiscussionThis protocol provides the framework for the first scoping review to identify the scope, depth and key concepts in the evidence, and to identify gaps in knowledge regarding the care of the patient with IMD by EMS clinicians. The review report will present the current state of knowledge, inform clinical practice guideline development and identify gaps in the evidence requiring further research.Scoping review registration:The protocol for this scoping review was registered prospectively with the Open Science Framework (https://osf.io/z639u/).

show abstract

“…Meningococcal disease outbreaks in the United States are public health emergencies due to their high case fatality rate [1][2][3]. CDC guidelines provide flexible thresholds for outbreak declarations, based on detecting multiple primary cases of the same meningococcal serogroup during a 3-month period; outbreaks in organizations may be declared after 2-3 cases, while outbreaks in geographically defined communities require an increased disease incidence [4].…”

Section: Introductionmentioning

confidence: 99%

Using Neisseria meningitidis genomic diversity to inform outbreak strain identification

et al. 2021

View full text Add to dashboard Cite

Meningococcal disease is a life-threatening illness caused by the human-restricted bacterium Neisseria meningitidis. Outbreaks in the USA involve at least two cases in an organization or community caused by the same serogroup within three months. Genome comparisons, including phylogenetic analysis and quantification of genome distances can provide confirmatory evidence of pathogen transmission during an outbreak. Interpreting genome distances depends on understanding their distribution both among isolates from outbreaks and among those not from outbreaks. Here, we identify outbreak strains based on phylogenetic relationships among 141 N. meningitidis isolates collected from 28 outbreaks in the USA during 2010–2017 and 1516 non-outbreak isolates collected through contemporaneous meningococcal surveillance. We show that genome distance thresholds based on the maximum SNPs and allele distances among isolates in the phylogenetically defined outbreak strains are sufficient to separate most pairs of non-outbreak isolates into separate strains. Non-outbreak isolate pairs that could not be distinguished from each other based on genetic distances were concentrated in the clonal complexes CC11, CC103, and CC32. Within each of these clonal complexes, phylodynamic analysis identified a group of isolates with extremely low diversity, collected over several years and multiple states. Clusters of isolates with low genetic diversity could indicate increased pathogen transmission, potentially resulting in local outbreaks or nationwide clonal expansions.

show abstract

Introduction and epidemiology of meningococcal disease

Cited by 4 publications

References 28 publications

Fast and accurate dynamic estimation of field effectiveness of meningococcal vaccines

Fast and accurate dynamic estimation of field effectiveness of meningococcal vaccines

Care of the Patient with Invasive Meningococcal Disease by Emergency Medical Service Clinicians: A Scoping Review Protocol

Using Neisseria meningitidis genomic diversity to inform outbreak strain identification

Contact Info

Product

Resources

About