The impact of community asymptomatic rapid antigen testing on COVID-19 hospital admissions: a synthetic control study

  1. Xingna Zhang
  2. Ben Barr
  3. Mark Green
  4. David Hughes
  5. Matthew Ashton
  6. Dimitrios Charalampopoulos
  7. Marta García-Fiñana
  8. Iain Buchan

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Abstract

Objective

To analyse the impact on hospital admissions for COVID-19 of large-scale, voluntary, public open access rapid testing for SARS-CoV-2 antigen in Liverpool (UK) between 6 th November 2020 and 2 nd January 2021.

Design

Synthetic control analysis comparing hospital admissions for small areas in the intervention population to a group of control areas weighted to be similar in terms of prior COVID-19 hospital admission rates and socio-demographic factors.

Intervention

COVID-SMART (Systematic Meaningful Asymptomatic Repeated Testing), a national pilot of large-scale, voluntary rapid antigen testing for people without symptoms of COVID-19 living or working in the City of Liverpool, deployed with the assistance of the British Army from the 6 th November 2020 in an unvaccinated population. This pilot informed the UK roll-out of SARS-CoV-2 antigen rapid testing, and similar policies internationally.

Main outcome measure

Weekly COVID-19 hospital admissions for neighbourhoods in England.

Results

The intensive introduction of COVID-SMART community testing was associated with a 43% (95% confidence interval: 29% to 57%) reduction in COVID-19 hospital admissions in Liverpool compared to control areas for the initial period of intensive testing with military assistance in national lockdown from 6 th November to 3 rd December 2020. A 25% (11% to 35%) reduction was estimated across the overall intervention period (6 th November 2020 to 2 nd January 2021), involving fewer testing centres, before England’s national roll-out of community testing, after adjusting for regional differences in Tiers of COVID-19 restrictions from 3 rd December 2020 to 2 nd January 2021.

Conclusions

The world’s first voluntary, city-wide SARS-CoV-2 rapid antigen testing pilot in Liverpool substantially reduced COVID-19 hospital admissions. Large scale asymptomatic rapid testing for SARS-CoV-2 can help reduce transmission and prevent hospital admissions.

Summary box

What is already known on this topic

  • –

    Previous studies on managing the spread of SARS-CoV-2 have identified asymptomatic transmission as significant challenges for controlling the pandemic.

  • –

    Along with non-pharmaceutical measures, many countries rolled out population-based asymptomatic testing programmes to further limit transmission.

  • –

    Evidence is required on whether large scale voluntary testing of communities for COVID-19 reduces severe disease, by breaking chains of transmission.

    • What this study adds

  • –

    The findings of this study suggest that large scale rapid antigen testing of communities for SARS-CoV-2, within an agile local public health campaign, can reduce transmission and prevent hospital admissions.

  • –

    The results indicate that policy makers should integrate such testing into comprehensive, local public health programmes targeting high risk groups, supporting those required to isolate and adapting promptly to prevailing biological, behavioural and environmental circumstances.

    • Article activity feed

    1. ScreenIT

      SciScore for 10.1101/2022.04.19.22274050: (What is this?)

      Please note, not all rigor criteria are appropriate for all manuscripts.

      Table 1: Rigor

      NIH rigor criteria are not applicable to paper type.

      Table 2: Resources

      Software and Algorithms
      SentencesResources
      21,22] The synthetic control method is a generalisation of difference-in-difference methods, whereby an untreated version of the intervention areas (i.e. a synthetic control) is created using a weighted combination of areas that were not exposed to the intervention, and the intervention effect is estimated by comparing the trend in outcomes in the intervention areas to that in the synthetic control areas following the intervention.[23] As there would be an expected time lag between the introduction of SMART and reduced hospital admissions, we assume the minimum plausible period from the start of the testing programme to the time when we might expect an impact on hospitalisation to be two weeks.
      SMART
      suggested: None

      Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).

      Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
      This is important as the parallel trends assumptions of simple difference-in-difference methods are not sufficient for analysis of infectious diseases, where the rate of change is intrinsically linked to the levels of infection at baseline.[24] There are several limitations. Firstly, although we were able to match areas to ensure a good balance of potential confounding factors prior to the intervention, it is possible that concurrent changes in the intervention and/or control populations could bias the results. The major policy change that affected transmission at this time was the introduction of tiered restrictions and we have sought to adjust for these in our analysis and present sensitivity analysis assuming different effects of this policy on transmission. The adjustments we make for these differences in restrictions assume the effect of Tier 2 restrictions on transmission in Liverpool was the same as the average effect across Tier 2 areas in England. The effect could have, however, been greater in Liverpool, because unlike other Tier 2 areas, most of the areas surrounding Liverpool were in Tier 3. Being an island of lower restrictions may have seen surrounding populations using the restaurants and other facilities open in Liverpool that were closed in their own areas at the time. In addition, some other areas saw Tier 4 restriction in late December 2020. So, our estimates for the effect of SMART may be overly conservative. Secondly, there are potential spill-over effect...

      Results from TrialIdentifier: No clinical trial numbers were referenced.

      Results from Barzooka: We did not find any issues relating to the usage of bar graphs.

      Results from JetFighter: We did not find any issues relating to colormaps.

      Results from rtransparent:
      • Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
      • No funding statement was detected.
      • No protocol registration statement was detected.

      Results from scite Reference Check: We found no unreliable references.

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    2. Version published to 10.1101/2022.04.19.22274050 on medRxiv