Descriptive comparison of admission characteristics between pandemic waves and multivariable analysis of the association of the Alpha variant (B.1.1.7 lineage) of SARS-CoV-2 with disease severity in inner London

  1. Luke B Snell
  2. Wenjuan Wang
  3. Adela Alcolea-Medina
  4. Themoula Charalampous
  5. Rahul Batra
  6. Leonardo de Jongh
  7. Finola Higgins
  8. Gaia Nebbia
  9. COG-UK HOCI Investigators
  10. Yanzhong Wang
  11. Jonathan Edgeworth
  12. Vasa Curcin

Reviewed by

Read the full article See related articles

Listed in

Log in to save this article

Abstract

The Alpha variant (B.1.1.7 lineage) of SARS-CoV-2 emerged and became the dominant circulating variant in the UK in late 2020. Current literature is unclear on whether the Alpha variant is associated with increased severity. We linked clinical data with viral genome sequence data to compare admitted cases between SARS-CoV-2 waves in London and to investigate the association between the Alpha variant and the severity of disease.

Methods

Clinical, demographic, laboratory and viral sequence data from electronic health record systems were collected for all cases with a positive SARS-CoV-2 RNA test between 13 March 2020 and 17 February 2021 in a multisite London healthcare institution. Multivariate analysis using logistic regression assessed risk factors for severity as defined by hypoxia at admission.

Results

There were 5810 SARS-CoV-2 RNA-positive cases of which 2341 were admitted (838 in wave 1 and 1503 in wave 2). Both waves had a temporally aligned rise in nosocomial cases (96 in wave 1 and 137 in wave 2). The Alpha variant was first identified on 15 November 2020 and increased rapidly to comprise 400/472 (85%) of sequenced isolates from admitted cases in wave 2. A multivariate analysis identified risk factors for severity on admission, such as age (OR 1.02, 95% CI 1.01 to 1.03, for every year older; p<0.001), obesity (OR 1.70, 95% CI 1.28 to 2.26; p<0.001) and infection with the Alpha variant (OR 1.68, 95% CI 1.26 to 2.24; p<0.001).

Conclusions

Our analysis is the first in hospitalised cohorts to show increased severity of disease associated with the Alpha variant. The number of nosocomial cases was similar in both waves despite the introduction of many infection control interventions before wave 2.

Article activity feed

  1. Version published to 10.1136/bmjopen-2021-055474
  2. ScreenIT

    SciScore for 10.1101/2021.03.16.21253377: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board Statementnot detected.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    ] running Jupyter Notebook 6.0.3, R 3.6.3 and Python 3.7.6.
    Python
    suggested: (IPython, RRID:SCR_001658)

    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:
    Limitations: Our study population comes from a single inner-city healthcare institution and therefore needs to be compared with findings from other centres. Our dataset included cases identified by SARS-CoV-2 RNA testing in our laboratory, so some cases diagnosed by external laboratories prior to admission may not be represented unless subsequently testing positive in our laboratory. The impact of differences in testing strategy and capacity during both waves needs further investigation, particularly the impact of the increase in asymptomatic cases in wave two. Finally, the analysis of clinical characteristics in this study was restricted to those recorded on admission to hospital. A follow-up study will include outcome data, when the wave two cohort has completed hospital stay, alongside other unstructured data points such as radiology or genome sequence results in clinical or external reports, to facilitate linkage with national and international research studies.

    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.
    • Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
    • No protocol registration statement was detected.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  3. Version published to 10.1101/2021.03.16.21253377v1 on medRxiv