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

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journal contribution
posted on 2024-07-01, 09:23 authored by Koen B Pouwels, David W Eyre, Thomas House, Ben Aspey, Philippa C Matthews, Nicole Stoesser, John N Newton, Ian Diamond, Ruth Studley, Nick GH Taylor, John I Bell, Jeremy Farrar, Jaison Kolenchery, Brian D Marsden, Sarah Hoosdally, E Yvonne Jones, David I Stuart, Derrick W Crook, Tim EA Peto, A Sarah Walker, COVID−19 Infection Survey Team
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.

Funding

Crick (Grant ID: CC2223, Grant title: Matthews CC2223)

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