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Fast and accurate dynamic estimation of field effectiveness of meningococcal vaccines

BACKGROUND: 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...

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Autores principales: Argante, Lorenzo, Tizzoni, Michele, Medini, Duccio
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4929770/
https://www.ncbi.nlm.nih.gov/pubmed/27363534
http://dx.doi.org/10.1186/s12916-016-0642-2
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author Argante, Lorenzo
Tizzoni, Michele
Medini, Duccio
author_facet Argante, Lorenzo
Tizzoni, Michele
Medini, Duccio
author_sort Argante, Lorenzo
collection PubMed
description BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12916-016-0642-2) contains supplementary material, which is available to authorized users.
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spelling pubmed-49297702016-07-02 Fast and accurate dynamic estimation of field effectiveness of meningococcal vaccines Argante, Lorenzo Tizzoni, Michele Medini, Duccio BMC Med Research Article BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12916-016-0642-2) contains supplementary material, which is available to authorized users. BioMed Central 2016-06-30 /pmc/articles/PMC4929770/ /pubmed/27363534 http://dx.doi.org/10.1186/s12916-016-0642-2 Text en © The Author(s) 2016 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Argante, Lorenzo
Tizzoni, Michele
Medini, Duccio
Fast and accurate dynamic estimation of field effectiveness of meningococcal vaccines
title Fast and accurate dynamic estimation of field effectiveness of meningococcal vaccines
title_full Fast and accurate dynamic estimation of field effectiveness of meningococcal vaccines
title_fullStr Fast and accurate dynamic estimation of field effectiveness of meningococcal vaccines
title_full_unstemmed Fast and accurate dynamic estimation of field effectiveness of meningococcal vaccines
title_short Fast and accurate dynamic estimation of field effectiveness of meningococcal vaccines
title_sort fast and accurate dynamic estimation of field effectiveness of meningococcal vaccines
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4929770/
https://www.ncbi.nlm.nih.gov/pubmed/27363534
http://dx.doi.org/10.1186/s12916-016-0642-2
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