Air pollution, SARS-CoV-2 transmission, and COVID-19 outcomes: A state-of-the-science review of a rapidly evolving research area

  1. A Bhaskar
  2. J Chandra
  3. D Braun
  4. J Cellini
  5. F Dominici

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Abstract

Background

As the coronavirus pandemic rages on, 692,000 (August 7, 2020) human lives and counting have been lost worldwide to COVID-19. Understanding the relationship between short- and long-term exposure to air pollution and adverse COVID-19 health outcomes is crucial for developing solutions to this global crisis.

Objectives

To conduct a scoping review of epidemiologic research on the link between short- and long-term exposure to air pollution and COVID-19 health outcomes.

Method

We searched PubMed, Web of Science, Embase, Cochrane, MedRxiv, and BioRxiv for preliminary epidemiological studies of the association between air pollution and COVID-19 health outcomes. 28 papers were finally selected after applying our inclusion/exclusion criteria; we categorized these studies as long-term studies, short-term time-series studies, or short-term cross-sectional studies. One study included both short-term time-series and a cross-sectional study design.

Results

27 studies of the 28 reported evidence of statistically significant positive associations between air pollutant exposure and adverse COVID-19 health outcomes; 11 of 12 long-term studies and all 16 short-term studies reported statistically significant positive associations. The 28 identified studies included various confounders, spatial and temporal resolutions of pollution concentrations, and COVID-19 health outcomes.

Discussion

We discuss methodological challenges and highlight additional research areas based on our findings. Challenges include data quality issues, ecological study design limitations, improved adjustment for confounders, exposure errors related to spatial resolution, geographic variability in testing, mitigation measures and pandemic stage, clustering of health outcomes, and a lack of publicly available data and code.

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  1. ScreenIT

    SciScore for 10.1101/2020.08.16.20175901: (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

    Experimental Models: Organisms/Strains
    SentencesResources
    Two screeners (AB and JC) reviewed each identified paper from the database searches in the Covidence online platform.
    AB
    suggested: RRID:BDSC_203)
    Software and Algorithms
    SentencesResources
    Search strategy: We conducted a literature search of the National Library of Medicine’s PubMed database (National Institutes of Health et al.)
    PubMed
    suggested: (PubMed, RRID:SCR_004846)
    , Elsevier’s Embase Database,
    Embase
    suggested: (EMBASE, RRID:SCR_001650)
    r, Cold Spring Harbor Laboratory’s medRxiv, and Cold Spring Harbor Laboratory’s bioRxiv.
    Cold Spring Harbor Laboratory’s
    suggested: None
    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: 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 10.1101/2020.08.16.20175901v1 on medRxiv