All R analytical code for the following paper (preprint link):
Evaluating social and spatial inequalities of large scale rapid lateral flow SARS-CoV-2 antigen testing in COVID-19 management: An observational study of Liverpool, UK (November 2020 to January 2021)
- Mark A. Green^ PhD, Senior Lecturer in Health Geography, Department of Geography & Planning, University of Liverpool, Liverpool, UK. Email: [email protected]. Tel: (+44) 0151 794 2854
- Marta García-Fiñana PhD, Professor of Health Data Science, Department of Health Data Science, University of Liverpool, Liverpool, UK
- Ben Barr PhD, Professor in Applied Public Health Research, Department of Public Health and Policy, University of Liverpool, Liverpool, UK
- Girvan Burnside PhD, Senior Lecturer in Biostatistics, Department of Health Data Science, University of Liverpool, Liverpool, UK
- Christopher P. Cheyne PhD, Research Associate, Department of Health Data Science, University of Liverpool, Liverpool, UK
- David Hughes PhD, Lecturer in Health Data Science, Department of Health Data Science, University of Liverpool, Liverpool, UK
- Matthew Ashton FFPH, Director of Public Health, Liverpool City Council, Liverpool, UK
- Sally Sheard PhD, Andrew Geddes and John Rankin Professor of Modern History, Department of Public Health and Policy, University of Liverpool, Liverpool, UK
- Iain E. Buchan MD, Chair in Public Health and Clinical Informatics, Department of Public Health and Policy, University of Liverpool, Liverpool, UK
^ Corresponding author
Abstract
Background: Large-scale asymptomatic testing of communities in Liverpool (UK) for SARS-CoV-2 was used as a public health tool for containing COVID-19. The aim of the study is to explore social and spatial inequalities in uptake and case-detection of rapid lateral flow SARS-CoV-2 antigen tests (LFTs) offered to people without symptoms of COVID-19.
Methods: Linked pseudonymised records for asymptomatic residents in Liverpool who received a LFT for COVID-19 between 6th November 2020 to 31st January 2021 were accessed using the Combined Intelligence for Population Health Action resource. Bayesian Hierarchical Poisson Besag, York, and Mollié models were used to estimate ecological associations for uptake and positivity of testing.
Findings: 214 525 residents (43%) received a LFT identifying 5192 individuals as positive cases of COVID-19 (1.3%). Uptake was highest in November when there was military assistance. High uptake was observed again in the week preceding Christmas and was sustained into a national lockdown. Overall uptake and repeat testing were lower among males (e.g. 40% uptake over the whole period), Black Asian and other Minority Ethnic groups (e.g. 27% uptake for ‘Mixed’ ethnicity) and in the most deprived areas (e.g. 32% uptake in most deprived areas). These population groups were also more likely to have received positive tests for COVID-19. Models demonstrated that uptake and repeat testing were lower in areas of higher deprivation, areas located further from test sites and areas containing populations less confident in the using Internet technologies. Positive tests were spatially clustered in deprived areas.
Interpretation: Large-scale voluntary asymptomatic community testing saw social, ethnic, digital and spatial inequalities in uptake. COVID-19 testing and support to isolate need to be more accessible to the vulnerable communities most impacted by the pandemic, including non-digital means of access.
Funding: Department of Health and Social Care (UK) and Economic and Social Research Council).