Cumulative COVID ‐19 incidence, mortality and prognosis in cancer survivors: A population‐based study in Reggio Emilia, Northern Italy

  1. Lucia Mangone
  2. Francesco Gioia
  3. Pamela Mancuso
  4. Isabella Bisceglia
  5. Marta Ottone
  6. Massimo Vicentini
  7. Carmine Pinto
  8. Paolo Giorgi Rossi

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Abstract

The aim of this population‐based study was to evaluate the impact of being a cancer survivor (CS) on COVID‐19 risk and prognosis during the first wave of the pandemic (27 February 2020 to 13 May 2020) in Reggio Emilia Province. Prevalent cancer cases diagnosed between 1996 and 2019 were linked with the provincial COVID‐19 surveillance system. We compared CS' cumulative incidence of being tested, testing positive for severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2), being hospitalized and dying of COVID‐19 with that of the general population; we compared COVID‐19 prognosis in CS and in patients without cancer. During the study period, 15 391 people (1527 CS) underwent real‐time polymerase chain reaction for SARS‐CoV‐2, of whom 4541 (447 CS) tested positive; 541 (113 CS) died of COVID‐19. CS had higher age‐ and sex‐adjusted incidence rate ratios (IRR) of testing (1.28 [95% confidence interval, CI = 1.21‐1.35]), of positive test (IRR 1.06 [95% CI = 0.96‐1.18]) and of hospitalization and death (IRR 1.27 [95% CI = 1.09‐1.48] and 1.39 [95%CI = 1.12‐1.71], respectively). CS had worse prognosis when diagnosed with COVID‐19, particularly those below age 70 (adjusted odds ratio [OR] of death 5.03; [95% CI = 2.59‐9.75]), while the OR decreased after age 70. The OR of death was higher for CS with a recent diagnosis, that is, <2 years (OR = 2.92; 95% CI = 1.64‐5.21), or metastases (OR = 2.09; 95% CI = 0.88‐4.93). CS showed the same probability of being infected, despite a slightly higher probability of being tested than the general population. Nevertheless, CS were at higher risk of death once infected.

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  1. Version published to 10.1002/ijc.33601
  2. Version published to 10.1101/2020.11.18.20233833v2 on medRxiv
  3. ScreenIT

    SciScore for 10.1101/2020.11.18.20233833: (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
    STATA v.
    STATA
    suggested: (Stata, RRID:SCR_012763)
    (StataCorp LP 4905 Lakeway Drive, Texas 77845 USA) was used for all analyses.
    StataCorp
    suggested: (Stata, RRID:SCR_012763)

    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:
    The main limitation of our study is that we do not have any information on treatment or on comorbidities, which could have influenced outcomes. Furthermore, because we could not include cancer patients with a diagnosis occurring in 2020, we could not observe the phase of diagnosis and disease assessment, which for many cancer sites is very intensive in terms of access to healthcare facilities.

    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.

  4. Version published to 10.1101/2020.11.18.20233833v1 on medRxiv