Incidence of COVID-19 and Connections with Air Pollution Exposure: Evidence from the Netherlands

  1. Bo Pieter Johannes Andrée

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Abstract

The fast spread of severe acute respiratory syndrome coronavirus 2 has resulted in the emergence of several hot-spots around the world. Several of these are located in areas associated with high levels of air pollution. This study investigates the relationship between exposure to particulate matter and COVID-19 incidence in 355 municipalities in the Netherlands. The results show that atmospheric particulate matter with diameter less than 2.5 is a highly significant predictor of the number of confirmed COVID-19 cases and related hospital admissions. The estimates suggest that expected COVID-19 cases increase by nearly 100 percent when pollution concentrations increase by 20 percent. The association between air pollution and case incidence is robust in the presence of data on health-related preconditions, proxies for symptom severity, and demographic control variables. The results are obtained with ground-measurements and satellite-derived measures of atmospheric particulate matter as well as COVID-19 data from alternative dates. The findings call for further investigation into the association between air pollution and SARS-CoV-2 infection risk. If particulate matter plays a significant role in COVID-19 incidence, it has strong implications for the mitigation strategies required to prevent spreading.

Highlights

Background

Research on viral respiratory infections has found that infection risks increase following exposure to high concentrations of particulate matter. Several hot-spots of Severe Acute Respiratory Syndrome Coronavirus 2 infections are in areas associated with high levels of air pollution.

Approach

This study investigates the relationship between exposure to particulate matter and COVID-19 incidence in 355 municipalities in the Netherlands using data on confirmed cases and hospital admissions coded by residence, along with local PM 2.5 , PM 10 , population density, demographics and health-related pre-conditions. The analysis utilizes different regression specifications that allow for spatial dependence, nonlinearity, alternative error distributions and outlier treatment.

Results

PM 2.5 is a highly significant predictor of the number of confirmed COVID-19 cases and related hospital admissions. Taking the WHO guideline of 10mcg/m3 as a baseline, the estimates suggest that expected COVID-19 cases increase by nearly 100% when pollution concentrations increase by 20%.

Conclusion

The findings call for further investigation into the association between air pollution on SARS-CoV-2 infection risk. If particulate matter plays a significant role in the incidence of COVID-19 disease, it has strong implications for the mitigation strategies required to prevent spreading, particularly in areas that have high levels of pollution.

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  1. ScreenIT

    SciScore for 10.1101/2020.04.27.20081562: (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: Thank you for sharing your code and data.

    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

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  2. Version published to 10.1101/2020.04.27.20081562v1 on medRxiv