@article{Pouwels_K-2024_179384,
title = {Improving the representativeness of UK’s national COVID-19 Infection Survey through spatio-temporal regression and post-stratification},
author = {Pouwels, K. and Eyre, D. and House, T. and Aspey, B. and Matthews, P. and Stoesser, N. and Newton, J. and Diamond, I. and Studley, R. and Taylor, N. and Bell, J. and Farrar, J. and Kolenchery, J. and Marsden, B. and Hoosdally, S. and Jones, E. and Stuart, D. and Crook, D. and Peto, T. and Walker, A.},
month = {jun},
year = {2024},
abstract = {Population-representative estimates of SARS-CoV-2 infection prevalence and antibody levels in specific geographic areas at different time points are needed to optimise policy responses. However, even population-wide surveys are potentially impacted by biases arising from differences in participation rates across key groups. Here, we used spatio-temporal regression and post-stratification models to UK’s national COVID-19 Infection Survey (CIS) to obtain representative estimates of PCR positivity (6,496,052 tests) and antibody prevalence (1,941,333 tests) for different regions, ages and ethnicities (7-December-2020 to 4-May-2022). Not accounting for vaccination status through post-stratification led to small underestimation of PCR positivity, but more substantial overestimations of antibody levels in the population (up to 21 percentage points), particularly in groups with low vaccine uptake in the general population. There was marked variation in the relative contribution of different areas and age-groups to each wave. Future analyses of infectious disease surveys should take into account major drivers of outcomes of interest that may also influence participation, with vaccination being an important factor to consider.},
volume = {15},
journal = {Nature Communications},
publisher = {Springer Nature},
url = {https://doi.org/10.1038/s41467-024-49201-4},
}
