Indicators of recent COVID-19 infection status: findings from a large occupational cohort of staff and postgraduate research students from a UK university

  1. Katrina A. S. Davis
  2. Ewan Carr
  3. Daniel Leightley
  4. Valentina Vitiello
  5. Gabriella Bergin-Cartwright
  6. Grace Lavelle
  7. Alice Wickersham
  8. Michael H. Malim
  9. Carolin Oetzmann
  10. Catherine Polling
  11. Sharon A. M. Stevelink
  12. Reza Razavi
  13. Matthew Hotopf

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Abstract

Background

Researchers conducting cohort studies may wish to investigate the effect of episodes of COVID-19 illness on participants. A definitive diagnosis of COVID-19 is not always available, so studies have to rely on proxy indicators. This paper seeks to contribute evidence that may assist the use and interpretation of these COVID-indicators.

Methods

We described five potential COVID-indicators: self-reported core symptoms, a symptom algorithm; self-reported suspicion of COVID-19; self-reported external results; and home antibody testing based on a 'lateral flow' antibody (IgG/IgM) test cassette. Included were staff and postgraduate research students at a large London university who volunteered for the study and were living in the UK in June 2020. Excluded were those who did not return a valid antibody test result. We provide descriptive statistics of prevalence and overlap of the five indicators.

Results

Core symptoms were the most common COVID-indicator (770/1882 participants positive, 41%), followed by suspicion of COVID-19 ( n  = 509/1882, 27%), a positive symptom algorithm ( n  = 298/1882, 16%), study antibody lateral flow positive ( n  = 124/1882, 7%) and a positive external test result ( n  = 39/1882, 2%), thus a 20-fold difference between least and most common. Meeting any one indicator increased the likelihood of all others, with concordance between 65 and 94%. Report of a low suspicion of having had COVID-19 predicted a negative antibody test in 98%, but positive suspicion predicted a positive antibody test in only 20%. Those who reported previous external antibody tests were more likely to have received a positive result from the external test (24%) than the study test (15%).

Conclusions

Our results support the use of proxy indicators of past COVID-19, with the caveat that none is perfect. Differences from previous antibody studies, most significantly in lower proportions of participants positive for antibodies, may be partly due to a decline in antibody detection over time. Subsequent to our study, vaccination may have further complicated the interpretation of COVID-indicators, only strengthening the need to critically evaluate what criteria should be used to define COVID-19 cases when designing studies and interpreting study results.

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  1. Version published to 10.1186/s12889-022-13889-0
  2. Version published to 10.1101/2020.12.07.20245183v2 on medRxiv
  3. ScreenIT

    SciScore for 10.1101/2020.12.07.20245183: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: Ethical approval has been gained from King’s Psychiatry, Nursing and Midwifery Research Ethics Committee (HR-19/20-18247).
    Consent: Participants provided informed consent and most opted into follow-up: 90% agreed to two-monthly surveys, 89% also agreed to shorter fortnightly surveys.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Antibodies
    SentencesResources
    We report potential indicators from surveys at P0 (baseline) to P5 which took place between April and June 2020 and antibody testing in June 2020.
    P5
    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:
    Strengths and weaknesses: The strengths of this study include the survey repeating every fortnight to minimise recall bias. We incorporated a symptom checklist that has been previously evaluated. The antibody test kit was highly specific for SARS-CoV-2, suited to minimise false positives in population screening. While our conclusions could have been strengthened by the presence of a hospital standard diagnosis against which to compare other outcomes, the paper aimed to show what results can be gathered in the community. Home testing maximised uptake of the test at a time when people may have been hesitant about attending a clinic. The lateral flow cassette is designed for use by a trained person but, from our pilot and the high proportion of people returning valid results, we believe that with illustrated instructions and a responsive email enquiry address most participants were able to perform the test.[12] Nevertheless, the potential for errors and inconsistencies when carrying out tests out of the laboratory.[16, 30] The analysis utilised results from all with valid antibody testing, regardless of survey completion. Our sensitivity analysis showed that restricting to a more complete sample made little difference, possibly because COVID-19 infections were much less common in May/June 2020 than they had been in March,[37, 38] so we would expect relatively few positives to occur after the April baseline. Finally, our cohort comprised staff and PGRs from a single university, w...

    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.

  4. Version published to 10.1101/2020.12.07.20245183v1 on medRxiv