SARS-CoV-2 Testing in Florida, Illinois, and Maryland: Access and Barriers

  1. Steven J. Clipman
  2. Amy Wesolowski
  3. Shruti H. Mehta
  4. Smisha Agarwal
  5. Sarah E. Cobey
  6. Derek A.T. Cummings
  7. Dustin G. Gibson
  8. Alain B. Labrique
  9. Gregory D. Kirk
  10. Sunil S. Solomon

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Abstract

Objective

To characterize the SARS-CoV-2 testing cascade and associated barriers in three US states.

Methods

We recruited participants from Florida, Illinois, and Maryland (∼1000/state) for an online survey September 16 – October 15, 2020. The survey covered demographics, COVID-19 symptoms, and experiences around SARS-CoV-2 PCR testing in the prior 2 weeks. Logistic regression was used to analyze associations with outcomes of interest.

Results

Overall, 316 (10%) of 3,058 respondents wanted/needed a test in the two weeks prior to the survey. Of these, 166 (53%) were able to get tested and 156 (94%) received results; 53% waited ≥ 8 days to get results from when they wanted/needed a test. There were no significant differences by state. Among those wanting/needing a test, getting tested was significantly less common among men (aOR: 0.46) and those reporting black race (aOR: 0.53) and more common in those reporting recent travel (aOR: 3.35).

Conclusions

There is an urgent need for a national communication strategy on who should get tested and where one can get tested. Additionally, measures need to be taken to improve access and reduce turn-around-time.

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  1. ScreenIT

    SciScore for 10.1101/2020.12.23.20248789: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementConsent: Study Sample: All participants were ≥ 18 years, provided consent, and resided in the state.
    RandomizationDynata maintains a database of potential participants who are randomized to specific surveys if they meet the demographic targets of the survey; additionally, participants can select a survey from a list of potential options (survey topic not provided).
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    Software and Algorithms
    SentencesResources
    Statistical Methods: Statistical analyses were carried out using Python (v3.7.3) and R (v3.5.1).
    Python
    suggested: (IPython, RRID:SCR_001658)

    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:
    There are limitations of online surveys; individuals need internet access to participate and so these surveys may underrepresent lower income/less educated individuals. However, in a constantly evolving pandemic where face-to-face data collection is nearly impossible, this approach allows for the collection of individual-level data across diverse geographies and demographics in a rapid and safe manner. If anything, we are likely overestimating access to testing. Moreover, care was taken to balance targets on state demographic characteristics and estimates of flu vaccine coverage are comparable to samples based on random digit dialing.11 Additionally, there is a possibility respondent misclassified the type of test. Regardless, these data reflecting common testing barriers across three US states clearly underscore the importance of a unified national strategy with clear messaging on who, where, when, and how to get a test. Concurrently, there should be a focus on improved turn-around-time by incorporating newer approaches such as rapid lateral flow assays.

    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.
    • Thank you for including a protocol registration statement.

    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 10.1101/2020.12.23.20248789v1 on medRxiv