Tuberculosis and COVID-19: Lessons from the Past Viral Outbreaks and Possible Future Outcomes

  1. Radu Crisan-Dabija
  2. Cristina Grigorescu
  3. Cristina-Alice Pavel
  4. Bogdan Artene
  5. Iolanda Valentina Popa
  6. Andrei Cernomaz
  7. Alexandru Burlacu

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Abstract

Background . The threat of contagious infectious diseases is constantly evolving as demographic explosion, travel globalization, and changes in human lifestyle increase the risk of spreading pathogens, leading to accelerated changes in disease landscape. Of particular interest is the aftermath of superimposing viral epidemics (especially SARS-CoV-2) over long-standing diseases, such as tuberculosis (TB), which remains a significant disease for public health worldwide and especially in emerging economies. Methods and Results . The PubMed electronic database was systematically searched for relevant articles linking TB, influenza, and SARS-CoV viruses and subsequently assessed eligibility according to inclusion criteria. Using a data mining approach, we also queried the COVID-19 Open Research Dataset (CORD-19). We aimed to answer the following questions: What can be learned from other coronavirus outbreaks (focusing on TB patients)? Is coinfection (TB and SARS-CoV-2) more severe? Is there a vaccine for SARS-CoV-2? How does the TB vaccine affect COVID-19? How does one diagnosis affect the other? Discussions . Few essential elements about TB and SARS-CoV coinfections were discussed. First, lessons from past outbreaks (other coronaviruses) and influenza pandemic/seasonal outbreaks have taught the importance of infection control to avoid the severe impact on TB patients. Second, although challenging due to data scarcity, investigating the pathological pathways linking TB and SARS-CoV-2 leads to the idea that their coexistence might yield a more severe clinical evolution. Finally, we addressed the issues of vaccination and diagnostic reliability in the context of coinfection. Conclusions . Because viral respiratory infections and TB impede the host’s immune responses, it can be assumed that their lethal synergism may contribute to more severe clinical evolution. Despite the rapidly growing number of cases, the data needed to predict the impact of the COVID-19 pandemic on patients with latent TB and TB sequelae still lies ahead. The trial is registered with NCT04327206 , NCT01829490 , and NCT04121494 .

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  1. Version published to 10.1155/2020/1401053
  2. ScreenIT

    SciScore for 10.1101/2020.04.28.20082917: (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
    The electronic database of PubMed was systematically searched for relevant articles from the inception until March 2020.
    PubMed
    suggested: (PubMed, RRID:SCR_004846)
    In order to identify emerging coinfection particularities of novel coronavirus SARS-CoV-1 we queried the COVID-19 Open Research Dataset (CORD-19), the current largest open dataset available with over 47000 scholarly articles, including over 36000 with full text about COVID-19, SARS-CoV-2 and other coronaviruses from the following sources: PubMed’s PMC open access corpus, a corpus maintained by the WHO, bioRxiv and medRxiv preprints.
    bioRxiv
    suggested: (bioRxiv, RRID:SCR_003933)

    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: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.

    Results from TrialIdentifier: We found the following clinical trial numbers in your paper:

    IdentifierStatusTitle
    NCT04327206RecruitingBCG Vaccination to Protect Healthcare Workers Against COVID-…

    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.

  3. Version published to 10.1101/2020.04.28.20082917v1 on medRxiv