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How Do Exposure Estimation Errors Affect Estimated Exposure-Response Relations?

In: Quantitative Risk Analysis of Air Pollution Health Effects

Author

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  • Louis Anthony Cox Jr.

    (Cox Associates and University of Colorado)

Abstract

Associations between fine particulate matter (PM2.5) exposure concentrations and a wide variety of undesirable outcomes are routinely reported. Adverse outcomes associated with PM2.5 range from autism, auto theft, and COVID-19 mortality to elderly mortality, suicide, and violent crime. Many influential articles argue that reducing National Ambient Air Quality Standards for PM2.5 is desirable to reduce these outcomes; of late, it has become fashionable to explicitly interpret estimated statistical associations causally, implying that reducing PM2.5 would reduce associated adverse outcomes, typically by making untested modeling assumptions to justify interpreting association as causation. Yet, other studies have found that reducing particulate pollution dramatically, by as much as 70% and dozens of micrograms per cubic meter, has not detectably affected all-cause mortality rates even after decades, despite strong, statistically significant positive exposure concentration-response (C-R) associations between them (Zigler and Dominici 2014; see also Chap. 17 ).

Suggested Citation

  • Louis Anthony Cox Jr., 2021. "How Do Exposure Estimation Errors Affect Estimated Exposure-Response Relations?," International Series in Operations Research & Management Science, in: Quantitative Risk Analysis of Air Pollution Health Effects, edition 1, chapter 0, pages 449-474, Springer.
    • Handle: RePEc:spr:isochp:978-3-030-57358-4_16
    DOI: 10.1007/978-3-030-57358-4_16
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