Persistence of symptoms up to 10 months following acute COVID-19 illness

  1. Roy H. Perlis
  2. Jon Green
  3. Mauricio Santillana
  4. David Lazer
  5. Katherine Ognyanova
  6. Matthew Simonson
  7. Matthew A. Baum
  8. Alexi Quintana
  9. Hanyu Chwe
  10. James Druckman
  11. John Della Volpe
  12. Jennifer Lin

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Abstract

Importance

COVID-19 symptoms are increasingly recognized to persist among a subset of individual following acute infection, but features associated with this persistence are not well-understood.

Objective

We aimed to identify individual features that predicted persistence of symptoms over at least 2 months at the time of survey completion.

Design: Non-probability internet survey. Participants were asked to identify features of acute illness as well as persistence of symptoms at time of study completion. We used logistic regression models to examine association between sociodemographic and clinical features and persistence of symptoms at or beyond 2 months.

Setting

Ten waves of a fifty-state survey between June 13, 2020 and January 13, 2021.

Participants

6,211 individuals who reported symptomatic COVID-19 illness confirmed by positive test or clinician diagnosis.

Exposure

symptomatic COVID-19 illness

Results

Among 6,211 survey respondents reporting COVID-19 illness, with a mean age of 37.8 (SD 12.2) years and 45.1% female, 73.9% white, 10.0% Black, 9.9% Hispanic, and 3.1% Asian, a total of 4946 (79.6%) had recovered within less than 2 months, while 491 (7.9%) experienced symptoms for 2 months or more. Of the full cohort, 3.4% were symptomatic for 4 months or more and 2.2% for 6 months or more. In univariate analyses, individuals with persistent symptoms on average reported greater initial severity. In logistic regression models, older age was associated with greater risk of persistence (OR 1.10, 95% CI 1.01-1.19 for each decade beyond 40); otherwise, no significant associations with persistence were identified for gender, race/ethnicity, or income. Presence of headache was significantly associated with greater likelihood of persistence (OR 1.44, 95% CI 1.11-1.86), while fever was associated with diminished likelihood of persistence (OR 0.66, 95% CI 0.53-0.83).

Conclusion and Relevance

A subset of individuals experience persistent symptoms from 2 to more than 10 months after acute COVID-19 illness, particularly those who recall headache and absence of fever. In light of this prevalence, strategies for predicting and managing such sequelae are needed.

Trial Registration

NA

Key Points

Question

Which individuals are at greatest risk for post-acute sequelae of COVID-19?

Findings

In this non-probability internet survey, among 6,211 individuals with symptomatic COVID-19 illness, 7.9% experienced persistence of symptoms lasting 2 months or longer. Older age, but not other sociodemographic features, was associated with risk for persistence, as was headache.

Meaning

Identifying individuals at greater risk for symptomatic persistence may facilitate development of targeted interventions.

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2021.03.07.21253072: (What is this?)

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

    Table 1: Rigor

    Institutional Review Board StatementIRB: The study was determined to be exempt by the Institutional Review Board of Harvard University; all participants signed consent online prior to survey access.
    Consent: The study was determined to be exempt by the Institutional Review Board of Harvard University; all participants signed consent online prior to survey access.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Sex as a biological variablenot detected.

    Table 2: Resources

    No key resources detected.

    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:
    Multiple caveats must be considered in interpreting our findings. First, as the study used pre-empaneled respondents in a non-probability design, we cannot reliably calculate response rate; as such, non-response bias cannot be estimated. However, we note that in other domains these non-probability surveys have closely mirrored results with more traditional designs10. Furthermore, our cross-sectional design does not allow more precise estimate of symptom persistence, and relies on participant recall in some cases nearly a year after initial illness. Still, this misclassification should bias our results toward smaller estimates of effect, such that any associations we identify may actually represent conservative estimates. For a subset of participants, we also cannot correctly classify their status, as follow-up is insufficient to allow determination of persistence; we report the features of this group, and include survival curves for time to remit, rather than simply excluding them from all analyses. Finally, and most notably, we rely on self-report of symptoms. Here too, this limitation contributes to misclassification, particularly of individuals who may be predisposed to less reliable reporting of symptoms for any reason. As a result, prospective studies will be necessary to confirm our results. Despite these limitations, we also emphasize the strengths of this systematic assessment, namely that by design it should be more representative than other single-cohort studies bec...

    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 10.1101/2021.03.07.21253072 on medRxiv