Information given by websites selling home self-sampling COVID-19 tests: an analysis of accuracy and completeness

  1. Sian Taylor-Phillips
  2. Sarah Berhane
  3. Alice J Sitch
  4. Karoline Freeman
  5. Malcolm James Price
  6. Clare Davenport
  7. Julia Geppert
  8. Isobel M Harris
  9. Osemeke Osokogu
  10. Magdalena Skrybant
  11. Jonathan J Deeks

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Abstract

To assess the accuracy and completeness of information provided by websites selling home self-sampling and testing kits for COVID-19.

Design

Cross-sectional observational study.

Setting

All websites (n=27) selling direct to user home self-sampling and testing kits for COVID-19 (41 tests) in the UK (39 tests) and USA (two tests) identified by a website search on 23 May 2020.

Main outcome measures

Thirteen predefined basic information items to communicate to a user, including who should be tested, when and how testing should be done, test accuracy, and interpretation of results.

Results

Many websites did not provide the name or manufacturer of the test (32/41; 78%), when to use the test (10/41; 24%), test accuracy (12/41; 29%), and how to interpret results (21/41; 51%). Sensitivity and specificity were the most commonly reported test accuracy measures (either reported for 27/41 [66%] tests): we could only link these figures to manufacturers’ documents or publications for four (10%) tests. Predictive values, most relevant to users, were rarely reported (five [12%] tests reported positive predictive values). For molecular virus tests, 9/23 (39%) websites explained that test positives should self-isolate, and 8/23 (35%) explained that test negatives may still have the disease. For antibody tests, 12/18 (67%) websites explained that testing positive does not necessarily infer immunity from future infection. Seven (39%) websites selling antibody tests claimed the test had a CE mark, when they were for a different intended use (venous blood rather than finger-prick samples).

Conclusions

At the point of online purchase of home self-sampling COVID-19 tests, users in the UK are provided with incomplete, and, in some cases, misleading information on test accuracy, intended use, and test interpretation. Best practice guidance for communication about tests to the public should be developed and enforced for online sales of COVID-19 tests.

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  1. ScreenIT

    SciScore for 10.1101/2020.08.18.20177360: (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
    Two researchers performed the searches independently on the same day (23rd May 2020) using the Google search engine in incognito mode in Google Chrome, with geo-locations for the UK and for the USA.
    Google
    suggested: (Google, RRID:SCR_017097)

    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: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    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.

  2. Version published to 10.1136/bmjopen-2020-042453
  3. Version published to 10.1101/2020.08.18.20177360 on medRxiv