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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...

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Detalles Bibliográficos
Autores principales: Blaizot, Stéphanie, Herzog, Sereina A., Abrams, Steven, Theeten, Heidi, Litzroth, Amber, Hens, Niel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
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
Descripción
Sumario: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.