Air pollution and COVID-19 mortality in the United States: strengths and limitations of an ecological regression analysis (Updated September 18, 2020)

By using the contents on this website and the Github repo, you agree to cite:

Wu, X., Nethery, R. C., Sabath, M. B., Braun, D. and Dominici, F., 2020. Air pollution and COVID-19 mortality in the United States: Strengths and limitations of an ecological regression analysis. Science advances6, p.eabd4049. 

Abstract: Assessing whether long-term exposure to air pollution increases the severity of health outcomes, including death, is an important public health objective. Limitations in COVID-19 data availability and quality remain obstacles to conducting conclusive studies on this topic. At present, publicly available COVID-19 outcomes data for representative populations are available only as area- level counts. Therefore, studies of long-term exposure to air pollution and COVID-19 outcomes using these data must employ use an ecological regression analysis, which precludes controlling for individual-level COVID-19 risk factors. We describe these challenges in the context of one of the first preliminary investigations of this question in the United StatesUS, where we found that higher historical PM2.5 exposures are positively associated with higher county-level COVID-19 mortality rates after accounting for many area-level confounders. Motivated by this study, we lay the groundwork for future research on this important topic, describe the complex challenges, and outline promising directions and opportunities.

Background: United States government scientists estimate that COVID-19 may kill tens of thousands of Americans. Many of the pre-existing conditions that increase the risk of death in those with COVID-19 are the same diseases that are affected by long-term exposure to air pollution. We investigated whether long-term average exposure to fine particulate matter (PM2.5) is associated with an increased risk of COVID-19 death in the United States.

Design: A nationwide, cross-sectional ecological study using county-level data.

Data sources: COVID-19 death counts were collected for more than 3,000 counties in the United States (representing 98% of the population) from Johns Hopkins University, Center for Systems Science and Engineering Coronavirus Resource Center.

Methods: We fit negative binomial mixed models using county-level COVID-19 deaths as the outcome and county-level long-term average of PM2.5 as the exposure. In the main analysis, we adjusted by 20 potential confounding factors including population size, age distribution, population density, time since the beginning of the outbreak, time since state’s issuance of stay-at-home order, hospital beds, number of individuals tested, weather, and socioeconomic and behavioral variables such as obesity and smoking. We included a random intercept by state to account for potential correlation in counties within the same state. We conducted more than 68 additional sensitivity analyses.

Results: We found that higher historical PM2.5 exposures are positively associated with higher county-level COVID-19 mortality rates after accounting for many area-level confounders. The results were statistically significant and robust to secondary and sensitivity analyses.

Conclusions: A small increase in long-term exposure to PM2.5 leads to a large increase in the COVID-19 death rate. Despite inherent limitations of the ecological study design, our results underscore the importance of continuing to enforce existing air pollution regulations to protect human health both during and after the COVID-19 crisis. The data and code are publicly available so our analyses can be updated routinely.

rr_time04180731rr_updated_time08011023Figure: COVID-19 mortality rate ratios (MRR) per 1 μg/m3 increase in PM2.5 and 95% CI using daily cumulative COVID-19 death counts from April 18, 2020 to Octorber 23, 2020. Since August 1, 2020, we have started to use reprocessed PM2.5 exposures and census variables which rely on more accurate aggregation. Note the current statistical model may not be able to capture newcome confounding factors, e.g., the lifting or changes of the policy interventions and the avaliability of newly-identifed experimental and approval drugs (e.g., Remdesivir and dexamethasone).

Data and Code:

Our data and code is available on github here. (Updated Oct 23, 2020)

Manuscript and Supplemental Material

- Manuscript

- MedRxiv: https://www.medrxiv.org/content/10.1101/2020.04.05.20054502v2

- By using the contents on this website and the Github repo, you agree to cite:

  1. Wu, X., Nethery, R. C., Sabath, M. B., Braun, D. and Dominici, F., 2020. Air pollution and COVID-19 mortality in the United States: Strengths and limitations of an ecological regression analysis. Science advances6, p.eabd4049.

  2. A pre-print version can be found at: Exposure to air pollution and COVID-19 mortality in the United States. Xiao Wu, Rachel C. Nethery, Benjamin M. Sabath, Danielle Braun, Francesca Dominici. medRxiv 2020.04.05.20054502; doi: https://doi.org/10.1101/2020.04.05.20054502

Acknowledgments

We appreciate the work of Aaron Van Donkelaar, Randall Martin, and his team for providing us with access to their estimates of PM2.5 exposure. Their data (V4.NA.02.MAPLE) can be found on Randall Martin's website here: https://sites.wustl.edu/acag/datasets/surface-pm2-5/

The data was produced as part of the following paper:
van Donkelaar, A., R. V. Martin, C. Li, R. T. Burnett, Regional Estimates of Chemical Composition of Fine Particulate Matter using a Combined Geoscience-Statistical Method with Information from Satellites, Models, and Monitors, Environ. Sci. Technol., doi: 10.1021/acs.est.8b06392, 2019. 

We would like to thank Lena Goodwin and Stacey Tobin for editorial assistance in the preparation of this manuscript.