Performance of Existing and Novel Symptom- and Antigen Testing–Based COVID-19 Case Definitions in a Community Setting

  1. Scott Lee
  2. Olivia Almendares
  3. Jessica Prince-Guerra
  4. Mark Anderson
  5. Charles M Heilig
  6. Jacqueline E Tate
  7. Hannah L Kirking

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Abstract

Point-of-care antigen tests are an important tool for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) detection, yet are less clinically sensitive than real-time reverse-transcription polymerase chain reaction (RT-PCR), affecting their efficacy as screening procedures. Our goal in this analysis was to see whether we could improve this sensitivity by considering antigen test results in combination with other relevant information, namely exposure status and reported symptoms. In November 2020, we collected 3,419 paired upper respiratory specimens tested by RT-PCR and the Abbott BinaxNOW (Abbott Laboratories, Abbott Park, Illinois) antigen test at 2 community testing sites in Pima County, Arizona. We used symptom, exposure, and antigen-testing data to evaluate the sensitivity and specificity of various symptom definitions in predicting RT-PCR positivity. Our analysis yielded 6 novel multisymptom case definitions with and without antigen test results, the best of which overall achieved a Youden’s J index of 0.66, as compared with 0.53 for antigen testing alone. Using a random forest as a guide, we show that this definition, along with our others, does not lose the ability to generalize well to new data despite achieving optimal performance in our sample. Our methodology is broadly applicable, and our code is publicly available to aid public health practitioners in developing or fine-tuning their own case definitions.

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  1. Version published to 10.1093/aje/kwac196
  2. ScreenIT

    SciScore for 10.1101/2022.05.10.22274914: (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
    Software and hardware: The random forest, combinatorial search, and statistical analysis were implemented in Python 3.8 with the scikit-learn (23) and statsmodels (24) packages.
    Python
    suggested: (IPython, RRID:SCR_001658)

    Results from OddPub: Thank you for sharing your code.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Our analysis has several limitations. First, the CSTE COVID-19 case definition (27) has been updated since the time of this investigation to add additional symptoms for consideration which were not included in our investigation and cannot be evaluated, although many symptoms added to the most recent definition are more severe in nature (confusion or change in mental status; persistent chest pain/pressure; blue-colored skin, lips, or nail beds, depending on skin tone; and inability to wake or stay awake), and it is unlikely they would have been captured in an outpatient community setting. Second, although SARS-CoV-2 samples were not sequenced as part of this investigation, data collection occurred in November 2020, well before the two variants of concern, Delta (B.1.617.2) and Omicron (B.1.1.529), were documented in the United States. The candidate definitions we propose above, which rely heavily on loss of taste or smell to boost the sensitivity of antigen testing, may not generalize well to populations heavily affected by these variants if the symptoms they cause differ substantially from those cause by the original strain. Additionally, it is unclear whether these results may apply to re- infections or vaccine-breakthrough infections as this study was conducted prior to vaccine availability and widespread concern for re-infection. Because our analysis could easily be rerun with the availability of new data, we hope these limitations will be addressed by future research. Thi...

    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.

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

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  3. Version published to 10.1101/2022.05.10.22274914v1 on medRxiv