Improving the representativeness of UK’s national COVID-19 Infection Survey through spatio-temporal regression and post-stratification

Koen B. Pouwels (), David W. Eyre, Thomas House, Ben Aspey, Philippa C. Matthews, Nicole Stoesser, John N. Newton, Ian Diamond, Ruth Studley, Nick G. H. Taylor, John I. Bell, Jeremy Farrar, Jaison Kolenchery, Brian D. Marsden, Sarah Hoosdally, E. Yvonne Jones, David I. Stuart, Derrick W. Crook, Tim E. A. Peto and A. Sarah Walker
Additional contact information
Koen B. Pouwels: University of Oxford
David W. Eyre: The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford
Thomas House: University of Manchester
Ben Aspey: Office for National Statistics
Philippa C. Matthews: The Francis Crick Institute
Nicole Stoesser: The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford
John N. Newton: University of Exeter
Ian Diamond: Office for National Statistics
Ruth Studley: Office for National Statistics
Nick G. H. Taylor: Office for National Statistics
John I. Bell: University of Oxford
Jeremy Farrar: Wellcome Trust
Jaison Kolenchery: Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital
Brian D. Marsden: University of Oxford
Sarah Hoosdally: University of Oxford
E. Yvonne Jones: University of Oxford
David I. Stuart: University of Oxford
Derrick W. Crook: The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford
Tim E. A. Peto: The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford
A. Sarah Walker: The National Institute for Health Research Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at the University of Oxford

Nature Communications, 2024, vol. 15, issue 1, 1-12

Abstract: 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.

Date: 2024
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DOI: 10.1038/s41467-024-49201-4

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