Associations between ambient air pollutants exposure and case fatality rate of COVID-19: a multi-city ecological study in China

  1. Tiantian Zhang
  2. Guoli Zhao
  3. Li Luo
  4. Yongzhen Li
  5. Wenming Shi

Reviewed by

Read the full article See related articles

Listed in

Log in to save this article

Abstract

Background

Environmental factors, including air pollution, can strongly impact on spatio-temporal patterns of infectious diseases outbreak. In this study, we aimed to investigate the association and correlation between ambient air pollutants and case fatality rate (CFR) of the novel coronavirus disease (COVID-19) in China.

Methods

Publicly accessible data on COVID-19 average CFR were utilized in the data analysis. The ambient daily air pollutants including fine particulate matter (PM 2.5 ), inhalable particles (PM 10 ) and nitrogen dioxide (NO 2 ) during the period from December 25, 2019 to March 5, 2020 were obtained from National Air Quality Real-time Publishing System of China. Ecological analysis was performed to explore the association and correlation between the cumulative average exposure of ambient air pollutants at different lag days (14 and 28 days) and average CFR in China outside Hubei and cities in Hubei province via model fitting.

Results

The average case fatality rate was highest in Wuhan city (4.53%) and the cumulative average exposure of ambient PM 2.5 , PM 10 and NO 2 at lag 28 days was 55.8±12.1μg/m 3 , 66.8±9.2μg/m 3 , 20.7±4.4μg/m 3 , respectively in Hubei province during the study period. Ecological analysis showed that ambient PM 2.5 , PM 10 and NO 2 exposure at both lag 14 and 28 days was positively correlated with average CFR in China outside Hubei (province-level). For city-level analysis in Hubei, significant associations were only found between cumulative ambient NO 2 exposure and average CFR(r=0.693 for Lag0-14, r=0.697 for Lag0-28, respectively) during the same period.

Conclusion

Our findings suggested ambient PM 2.5 , PM 10 and NO 2 exposure, especially at 28 lag days, positively associated with the case fatality rate of COVID-19 in China. These results could help provide guidance for identifying potential exposure window and preventing and controlling the epidemic.

Article activity feed

  1. ScreenIT

    SciScore for 10.1101/2020.05.06.20088682: (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

    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:
    However, our study has some limitations. First, ecological study designs were adopted in the study, other city-level factors including implementation ability of control policy of COVID-19, the availability of medical resources might confound our findings. Second, the study period may not represent a whole air pollutants pattern associated with the average case fatality rate of COVID-19. Furthermore, the exposure misclassification might bias the study result due to the different activity patterns of people. For all these limitations, the findings should be interpreted with caution before extension to other population and research.

    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 10.1101/2020.05.06.20088682v1 on medRxiv