Abstract
BackgroundOur 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.MethodsStatistical 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.ResultsThe 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.ConclusionsWhen 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.
Highlights
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
The objectives of this paper are i) to give insights into the age structure best suited to estimate the parameters with acceptable levels of precision; ii) to provide an order of magnitude of the sample size required to attain a specified precision for a particular parameter; and iii) to give insights into the optimal allocation of a fixed sample size among age groups
Models Here, we briefly present an overview of the methods used to derive key epidemiological parameters from serological survey data and we refer to Hens et al [2] for a more in-depth explanation of the methodology
Summary
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. In a crosssectional serological survey, samples taken from individuals at a certain time point provide information about whether or not these individuals have been immunised before that time point (depicting current status data). The antibody levels are typically compared to a predetermined cut-off level to determine the individuals’ humoral immunological status. The usefulness of these surveys in epidemiology has recently been highlighted [1].
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