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Combining aggregate and individual-level data to estimate individual-level associations between air pollution and COVID-19 mortality in the United States

Author

Listed:
  • Sophie M Woodward
  • Daniel Mork
  • Xiao Wu
  • Zhewen Hou
  • Danielle Braun
  • Francesca Dominici

Abstract

Imposing stricter regulations for PM2.5 has the potential to mitigate damaging health and climate change effects. Recent evidence establishing a link between exposure to air pollution and COVID-19 outcomes is one of many arguments for the need to reduce the National Ambient Air Quality Standards (NAAQS) for PM2.5. However, many studies reporting a relationship between COVID-19 outcomes and PM2.5 have been criticized because they are based on ecological regression analyses, where area-level counts of COVID-19 outcomes are regressed on area-level exposure to air pollution and other covariates. It is well known that regression models solely based on area-level data are subject to ecological bias, i.e., they may provide a biased estimate of the association at the individual-level, due to within-area variability of the data. In this paper, we augment county-level COVID-19 mortality data with a nationally representative sample of individual-level covariate information from the American Community Survey along with high-resolution estimates of PM2.5 concentrations obtained from a validated model and aggregated to the census tract for the contiguous United States. We apply a Bayesian hierarchical modeling approach to combine county-, census tract-, and individual-level data to ultimately draw inference about individual-level associations between long-term exposure to PM2.5 and mortality for COVID-19. By analyzing data prior to the Emergency Use Authorization for the COVID-19 vaccines we found that an increase of 1 μg/m3 in long-term PM2.5 exposure, averaged over the 17-year period 2000-2016, is associated with a 3.3% (95% credible interval, 2.8 to 3.8%) increase in an individual’s odds of COVID-19 mortality. Code to reproduce our study is publicly available at https://github.com/NSAPH/PM_COVID_ecoinference. The results confirm previous evidence of an association between long-term exposure to PM2.5 and COVID-19 mortality and strengthen the case for tighter regulations on harmful air pollution and greenhouse gas emissions.

Suggested Citation

  • Sophie M Woodward & Daniel Mork & Xiao Wu & Zhewen Hou & Danielle Braun & Francesca Dominici, 2023. "Combining aggregate and individual-level data to estimate individual-level associations between air pollution and COVID-19 mortality in the United States," PLOS Global Public Health, Public Library of Science, vol. 3(8), pages 1-20, August.
    • Handle: RePEc:plo:pgph00:0002178
    DOI: 10.1371/journal.pgph.0002178
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    References listed on IDEAS

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    1. Janet Currie & John Voorheis & Reed Walker, 2023. "What Caused Racial Disparities in Particulate Exposure to Fall? New Evidence from the Clean Air Act and Satellite-Based Measures of Air Quality," American Economic Review, American Economic Association, vol. 113(1), pages 71-97, January.
    2. Janet Currie & John Voorheis & Reed Walker, 2020. "What Caused Racial Disparities in Particulate Exposure to Fall? New Evidence from the Clean Air Act and Satellite-Based Measures of Air Quality," NBER Working Papers 26659, National Bureau of Economic Research, Inc.
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    Cited by:

    1. Torres David J. & Rouson Damain, 2024. "Investigating the ecological fallacy through sampling distributions constructed from finite populations," Monte Carlo Methods and Applications, De Gruyter, vol. 30(4), pages 331-363.

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