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Sample size calculation for estimating key epidemiological parameters using serological data and mathematical modelling
BACKGROUND: Our work was motivated by the need to, given serum availability and/or financial resources, decide on which samples to test in a serum bank for different pathogens. Simulation-based sample size calculations were performed to determine the age-based sampling structures and optimal allocat...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6407263/ https://www.ncbi.nlm.nih.gov/pubmed/30845904 http://dx.doi.org/10.1186/s12874-019-0692-1 |
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author | Blaizot, Stéphanie Herzog, Sereina A. Abrams, Steven Theeten, Heidi Litzroth, Amber Hens, Niel |
author_facet | Blaizot, Stéphanie Herzog, Sereina A. Abrams, Steven Theeten, Heidi Litzroth, Amber Hens, Niel |
author_sort | Blaizot, Stéphanie |
collection | PubMed |
description | BACKGROUND: Our work was motivated by the need to, given serum availability and/or financial resources, decide on which samples to test in a serum bank for different pathogens. Simulation-based sample size calculations were performed to determine the age-based sampling structures and optimal allocation of a given number of samples for testing across various age groups best suited to estimate key epidemiological parameters (e.g., seroprevalence or force of infection) with acceptable precision levels in a cross-sectional seroprevalence survey. METHODS: Statistical and mathematical models and three age-based sampling structures (survey-based structure, population-based structure, uniform structure) were used. Our calculations are based on Belgian serological survey data collected in 2001–2003 where testing was done, amongst others, for the presence of Immunoglobulin G antibodies against measles, mumps, and rubella, for which a national mass immunisation programme was introduced in 1985 in Belgium, and against varicella-zoster virus and parvovirus B19 for which the endemic equilibrium assumption is tenable in Belgium. RESULTS: The optimal age-based sampling structure to use in the sampling of a serological survey as well as the optimal allocation distribution varied depending on the epidemiological parameter of interest for a given infection and between infections. CONCLUSIONS: When estimating epidemiological parameters with acceptable levels of precision within the context of a single cross-sectional serological survey, attention should be given to the age-based sampling structure. Simulation-based sample size calculations in combination with mathematical modelling can be utilised for choosing the optimal allocation of a given number of samples over various age groups. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12874-019-0692-1) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-6407263 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-64072632019-03-21 Sample size calculation for estimating key epidemiological parameters using serological data and mathematical modelling Blaizot, Stéphanie Herzog, Sereina A. Abrams, Steven Theeten, Heidi Litzroth, Amber Hens, Niel BMC Med Res Methodol Research Article BACKGROUND: Our work was motivated by the need to, given serum availability and/or financial resources, decide on which samples to test in a serum bank for different pathogens. Simulation-based sample size calculations were performed to determine the age-based sampling structures and optimal allocation of a given number of samples for testing across various age groups best suited to estimate key epidemiological parameters (e.g., seroprevalence or force of infection) with acceptable precision levels in a cross-sectional seroprevalence survey. METHODS: Statistical and mathematical models and three age-based sampling structures (survey-based structure, population-based structure, uniform structure) were used. Our calculations are based on Belgian serological survey data collected in 2001–2003 where testing was done, amongst others, for the presence of Immunoglobulin G antibodies against measles, mumps, and rubella, for which a national mass immunisation programme was introduced in 1985 in Belgium, and against varicella-zoster virus and parvovirus B19 for which the endemic equilibrium assumption is tenable in Belgium. RESULTS: The optimal age-based sampling structure to use in the sampling of a serological survey as well as the optimal allocation distribution varied depending on the epidemiological parameter of interest for a given infection and between infections. CONCLUSIONS: When estimating epidemiological parameters with acceptable levels of precision within the context of a single cross-sectional serological survey, attention should be given to the age-based sampling structure. Simulation-based sample size calculations in combination with mathematical modelling can be utilised for choosing the optimal allocation of a given number of samples over various age groups. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12874-019-0692-1) contains supplementary material, which is available to authorized users. BioMed Central 2019-03-07 /pmc/articles/PMC6407263/ /pubmed/30845904 http://dx.doi.org/10.1186/s12874-019-0692-1 Text en © The Author(s). 2019 Open AccessThis 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 Blaizot, Stéphanie Herzog, Sereina A. Abrams, Steven Theeten, Heidi Litzroth, Amber Hens, Niel Sample size calculation for estimating key epidemiological parameters using serological data and mathematical modelling |
title | Sample size calculation for estimating key epidemiological parameters using serological data and mathematical modelling |
title_full | Sample size calculation for estimating key epidemiological parameters using serological data and mathematical modelling |
title_fullStr | Sample size calculation for estimating key epidemiological parameters using serological data and mathematical modelling |
title_full_unstemmed | Sample size calculation for estimating key epidemiological parameters using serological data and mathematical modelling |
title_short | Sample size calculation for estimating key epidemiological parameters using serological data and mathematical modelling |
title_sort | sample size calculation for estimating key epidemiological parameters using serological data and mathematical modelling |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6407263/ https://www.ncbi.nlm.nih.gov/pubmed/30845904 http://dx.doi.org/10.1186/s12874-019-0692-1 |
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