The US COVID-19 Trends and Impact Survey: Continuous real-time measurement of COVID-19 symptoms, risks, protective behaviors, testing, and vaccination

  1. Joshua A. Salomon
  2. Alex Reinhart
  3. Alyssa Bilinski
  4. Eu Jing Chua
  5. Wichada La Motte-Kerr
  6. Minttu M. Rönn
  7. Marissa B. Reitsma
  8. Katherine A. Morris
  9. Sarah LaRocca
  10. Tamer H. Farag
  11. Frauke Kreuter
  12. Roni Rosenfeld
  13. Ryan J. Tibshirani

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Abstract

The US COVID-19 Trends and Impact Survey (CTIS) is a large, cross-sectional, internet-based survey that has operated continuously since April 6, 2020. By inviting a random sample of Facebook active users each day, CTIS collects information about COVID-19 symptoms, risks, mitigating behaviors, mental health, testing, vaccination, and other key priorities. The large scale of the survey—over 20 million responses in its first year of operation—allows tracking of trends over short timescales and allows comparisons at fine demographic and geographic detail. The survey has been repeatedly revised to respond to emerging public health priorities. In this paper, we describe the survey methods and content and give examples of CTIS results that illuminate key patterns and trends and help answer high-priority policy questions relevant to the COVID-19 epidemic and response. These results demonstrate how large online surveys can provide continuous, real-time indicators of important outcomes that are not subject to public health reporting delays and backlogs. The CTIS offers high value as a supplement to official reporting data by supplying essential information about behaviors, attitudes toward policy and preventive measures, economic impacts, and other topics not reported in public health surveillance systems.

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  1. Version published to 10.1073/pnas.2111454118
  2. ScreenIT

    SciScore for 10.1101/2021.07.24.21261076: (What is this?)

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

    Table 1: Rigor

    EthicsIRB: The study was approved by the Carnegie Mellon University Institutional Review Board, under protocol STUDY2020_00000162.
    Sex as a biological variablenot detected.
    RandomizationFacebook uses stratified random sampling within US states to randomly select a sample of its users to see the survey invitation at the top of their News Feed.
    Blindingnot detected.
    Power Analysisnot detected.

    Table 2: Resources

    No key resources detected.

    Results from OddPub: Thank you for sharing your code and data.

    Results from LimitationRecognizer: We detected the following sentences addressing limitations in the study:
    Several limitations are important to note. First, because the survey uses Facebook active users as its sampling frame and because participation in the survey is strictly voluntary, respondents may not be fully representative of the U.S. population despite incorporation of survey weights, which adjust for non-response and coverage biases based on a limited number of covariates. Comparison to the American Community Survey indicates that our sample over-represents respondents who are college-educated. Research users of the survey microdata can use additional demographic or other survey variables to construct improved post-stratification adjustments to correct this for their purposes. However, any non-response biases not accounted for by Facebook’s non-response weights would be much more difficult to correct. Additionally, many of the outcome measures related to COVID-19 are based on self-reports, which may diverge from more objective measures due to recall bias, social desirability bias, and other sources of survey bias and measurement error. On the other hand, broad comparisons of indicators such as cumulative COVID-19 diagnoses suggest that measurement of key COVID-19 outcomes are relatively robust to response biases that may be present in the sample. Ultimately, the value of such a large-scale survey is not in accuracy afforded by its sample size, since survey biases persist no matter the size of the survey; smaller surveys more carefully constructed to reduce sampling biases...

    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.

    About SciScore

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  3. Version published to 10.1101/2021.07.24.21261076 on medRxiv