Symptoms and SARS-CoV-2 positivity in the general population in the UK

  1. Karina-Doris Vihta
  2. Koen B. Pouwels
  3. Tim Peto
  4. Emma Pritchard
  5. David W. Eyre
  6. Thomas House
  7. Owen Gethings
  8. Ruth Studley
  9. Emma Rourke
  10. Duncan Cook
  11. Ian Diamond
  12. Derrick Crook
  13. Philippa C. Matthews
  14. Nicole Stoesser
  15. Ann Sarah Walker
  16. the COVID-19 Infection Survey team

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Abstract

Background

Several community-based studies have assessed the ability of different symptoms to identify COVID-19 infections, but few have compared symptoms over time (reflecting SARS-CoV-2 variants) and by vaccination status.

Methods

Using data and samples collected by the COVID-19 Infection Survey at regular visits to representative households across the UK, we compared symptoms in new PCR-positives and comparator test-negative controls.

Results

From 26/4/2020-7/8/2021, 27,869 SARS-CoV-2 PCR-positive episodes occurred in 27,692 participants (median 42 years (IQR 22-58)); 13,427 (48%) self-reported symptoms (“symptomatic positive episodes”). The comparator group comprised 3,806,692 test-negative visits (457,215 participants); 130,612 (3%) self-reported symptoms (“symptomatic negative visit”). Reporting of any symptoms in positive episodes varied over calendar time, reflecting changes in prevalence of variants, incidental changes (e.g. seasonal pathogens, schools re-opening) and vaccination roll-out. There was a small increase in sore throat reporting in symptomatic positive episodes and negative visits from April-2021. After May-2021 when Delta emerged there were substantial increases in headache and fever in positives, but not in negatives. Although specific symptom reporting in symptomatic positive episodes vs. negative visits varied by age, sex, and ethnicity, only small improvements in symptom-based infection detection were obtained; e.g. adding fatigue/weakness or all eight symptoms to the classic four symptoms (cough, fever, loss of taste/smell) increased sensitivity from 74% to 81% to 90% but tests per positive from 4.6 to 5.3 to 8.7.

Conclusions

Whilst SARS-CoV-2-associated symptoms vary by variant, vaccination status and demographics, differences are modest and do not warrant large-scale changes to targeted testing approaches given resource implications.

Summary

Within the COVID-19 Infection Survey, recruiting representative households across the UK general population, SARS-CoV-2-associated symptoms varied by viral variant, vaccination status and demographics. However, differences are modest and do not currently warrant large-scale changes to targeted testing approaches.

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2021.08.19.21262231: (What is this?)

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

    Table 1: Rigor

    EthicsConsent: Having obtained verbal agreement, each household is visited by a study worker, and written informed consent obtained for individuals aged ≥2y (from parents/carers for those 2-15y, with those 10-15y also providing written assent).
    Sex as a biological variablenot detected.
    RandomizationThe Office for National Statistics (ONS) COVID-19 Infection Survey[12] (ISRCTN21086382, https://www.ndm.ox.ac.uk/covid-19/covid-19-infection-survey/protocol-and-information-sheets) continuously randomly selects private households from address lists and previous surveys.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    Recombinant DNA
    SentencesResources
    We assessed the impact on performance of adding each of the other eight symptoms to the four classic symptoms currently prompting testing in the UK, and of every combination of 1-8 symptoms, and any of the 12 elicited symptoms, using standard metrics (details in Supplementary Methods, epiR (v.1.0-15), pROC (v.1.16.2) packages) plus tests per case (TPC)=1/positive predictive value (PPV) and the inflation factor=episodes/visits with any included symptom/episodes/visits with classic symptoms.
    pROC
    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:
    The main limitation is that the survey collected information on only 12 specific COVID-19 symptoms, plus one generic question, to minimise participant burden. We therefore could not evaluate some symptoms more recently proposed for inclusion in expanded case definitions, such as coryza[13,14]. Parents/carers reported symptoms for children; symptom reporting may be affected by other cultural differences we could not adjust for as well as public awareness (e.g. increased reporting of loss of taste/smell once this became a “recognised” symptom). Power was limited within some subgroups, e.g. children and specific non-white ethnic groups. Our survey does not include those in care homes and with severe disease admitted to hospital who may have different symptom profiles. Testing was predominantly monthly; although individuals were followed longitudinally, we had limited resolution to assess the short-term evolution of symptoms during an infection. The main study strengths are its size and population representativeness, particularly capturing episodes of mild infection in the community. We took a stringent approach to defining our ‘test-negative’ comparator to limit possible contamination from undetected positives/ongoing COVID-19. We report over periods that include different dominant viral variants. We took a pragmatic approach to comparing test performance, taking into account trade-offs between overall accuracy, sensitivity and TPC over different background prevalences, reflecti...

    Results from TrialIdentifier: We found the following clinical trial numbers in your paper:

    IdentifierStatusTitle
    ISRCTN21086382NANA

    Results from Barzooka: We found bar graphs of continuous data. We recommend replacing bar graphs with more informative graphics, as many different datasets can lead to the same bar graph. The actual data may suggest different conclusions from the summary statistics. For more information, please see Weissgerber et al (2015).

    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.
    • Thank you for including a protocol registration statement.

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

    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.

  2. Version published to 10.1101/2021.08.19.21262231 on medRxiv