Excess of COVID-19 cases and deaths due to fine particulate matter exposure during the 2020 wildfires in the United States

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Abstract

High levels of PM _2.5 during the 2020 wildfires in the western United States led to an excess of COVID-19 cases and deaths.

Abstract

The year 2020 brought unimaginable challenges in public health, with the confluence of the COVID-19 pandemic and wildfires across the western United States. Wildfires produce high levels of fine particulate matter (PM _2.5). Recent studies reported that short-term exposure to PM _2.5 is associated with increased risk of COVID-19 cases and deaths. We acquired and linked publicly available daily data on PM _2.5, the number of COVID-19 cases and deaths, and other confounders for 92 western U.S. counties that were affected by the 2020 wildfires. We estimated the association between short-term exposure to PM _2.5 during the wildfires and the epidemiological dynamics of COVID-19 cases and deaths. We adjusted for several time-varying confounding factors (e.g., weather, seasonality, long-term trends, mobility, and population size). We found strong evidence that wildfires amplified the effect of short-term exposure to PM _2.5 on COVID-19 cases and deaths, although with substantial heterogeneity across counties.

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The 2020 report of The Lancet Countdown on health and climate change: responding to converging crises

Nick Watts, Markus Amann, Nigel W Arnell … (2021)

For the Chinese, French, German, and Spanish translations of the abstract see Supplementary Materials section.

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Real estimates of mortality following COVID-19 infection

David Baud, Xiaolong Qi, Karin Nielsen-Saines … (2020)

As of March 1, 2020, 79 968 patients in China and 7169 outside of China had tested positive for coronavirus disease 2019 (COVID-19). 1 Among Chinese patients, 2873 deaths had occurred, equivalent to a mortality rate of 3·6% (95% CI 3·5–3·7), while 104 deaths from COVID-19 had been reported outside of China (1·5% [1·2–1·7]). However, these mortality rate estimates are based on the number of deaths relative to the number of confirmed cases of infection, which is not representative of the actual death rate; patients who die on any given day were infected much earlier, and thus the denominator of the mortality rate should be the total number of patients infected at the same time as those who died. Notably, the full denominator remains unknown because asymptomatic cases or patients with very mild symptoms might not be tested and will not be identified. Such cases therefore cannot be included in the estimation of actual mortality rates, since actual estimates pertain to clinically apparent COVID-19 cases. The maximum incubation period is assumed to be up to 14 days, 2 whereas the median time from onset of symptoms to intensive care unit (ICU) admission is around 10 days.3, 4 Recently, WHO reported that the time between symptom onset and death ranged from about 2 weeks to 8 weeks. 5 We re-estimated mortality rates by dividing the number of deaths on a given day by the number of patients with confirmed COVID-19 infection 14 days before. On this basis, using WHO data on the cumulative number of deaths to March 1, 2020, mortality rates would be 5·6% (95% CI 5·4–5·8) for China and 15·2% (12·5–17·9) outside of China. Global mortality rates over time using a 14-day delay estimate are shown in the figure , with a curve that levels off to a rate of 5·7% (5·5–5·9), converging with the current WHO estimates. Estimates will increase if a longer delay between onset of illness and death is considered. A recent time-delay adjusted estimation indicates that mortality rate of COVID-19 could be as high as 20% in Wuhan, the epicentre of the outbreak. 6 These findings show that the current figures might underestimate the potential threat of COVID-19 in symptomatic patients. Figure Global COVID-19 mortality rates (Feb 11 to March 1, 2020) Current WHO mortality estimates (total deaths divided by total confirmed cases), and mortality rates calculated by dividing the total number of deaths by the total number of confirmed cases 14 days previously.

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Is Open Access

Distributed lag non-linear models

A Gasparrini, Timothy Armstrong, M. Kenward (2010)

Environmental stressors often show effects that are delayed in time, requiring the use of statistical models that are flexible enough to describe the additional time dimension of the exposure–response relationship. Here we develop the family of distributed lag non-linear models (DLNM), a modelling framework that can simultaneously represent non-linear exposure–response dependencies and delayed effects. This methodology is based on the definition of a ‘cross-basis’, a bi-dimensional space of functions that describes simultaneously the shape of the relationship along both the space of the predictor and the lag dimension of its occurrence. In this way the approach provides a unified framework for a range of models that have previously been used in this setting, and new more flexible variants. This family of models is implemented in the package dlnm within the statistical environment R. To illustrate the methodology we use examples of DLNMs to represent the relationship between temperature and mortality, using data from the National Morbidity, Mortality, and Air Pollution Study (NMMAPS) for New York during the period 1987–2000. Copyright © 2010 John Wiley & Sons, Ltd.

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Author and article information

Journal

Journal ID (nlm-ta): Sci Adv

Journal ID (iso-abbrev): Sci Adv

Journal ID (publisher-id): SciAdv

Journal ID (hwp): advances

Title: Science Advances

Publisher: American Association for the Advancement of Science

ISSN (Electronic): 2375-2548

Publication date Collection: August 2021

Publication date (Electronic): 13 August 2021

Volume: 7

Issue: 33

Electronic Location Identifier: eabi8789

Affiliations

[1 ]Environmental Systems Research Institute, Redlands, CA, USA.

[2 ]Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.

[3 ]Harvard John A. Paulson School of Engineering and Applied Sciences, Cambridge, MA, USA.

[4 ]Department of Earth and Planetary Sciences, Harvard University, Cambridge, MA, USA.

[5 ]Department of Global Health and Population, Harvard University, Boston, MA, USA.

[6 ]Harvard Data Science Initiative, Cambridge, MA, USA.

Author notes

[* ]Corresponding author. Email: fdominic@ 123456hsph.harvard.edu

[†]

These authors contributed equally to this work.

Author information

Xiaodan Zhou http://orcid.org/0000-0002-9250-768X

Kevin Josey http://orcid.org/0000-0003-2490-6272

Miah C. Caine http://orcid.org/0000-0002-9989-7166

Tianjia Liu http://orcid.org/0000-0003-3129-0154

Loretta J. Mickley http://orcid.org/0000-0002-7859-3470

Matthew Cooper http://orcid.org/0000-0002-7133-3801

Francesca Dominici http://orcid.org/0000-0002-9382-0141

Article

Publisher ID: abi8789

DOI: 10.1126/sciadv.abi8789

PMC ID: 8363139

PubMed ID: 34389545

SO-VID: 99d987a6-78fa-4f3c-8999-6c47c7b24763

Copyright © Copyright © 2021 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).

License:

This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial license, which permits use, distribution, and reproduction in any medium, so long as the resultant use is not for commercial advantage and provided the original work is properly cited.