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Activity Number: 222 - Cross-Disciplinary Research on Health Data Science
Type: Invited
Date/Time: Tuesday, August 9, 2022 : 8:30 AM to 10:20 AM
Sponsor: National Institute of Statistical Sciences
Abstract #320557
Title: Causal Inference Methods in Air Pollution Research
Author(s): Francesca Dominici and Xiao Wu*
Companies: Harvard and Stanford
Keywords: generalized propensity score; air pollution; observational data; matching
Abstract:

Many studies link long-term fine particle (PM2.5) exposure to mortality, even at levels below current U.S. air quality standards (12 micrograms per cubic meter). These findings have been disputed with claims that the use of traditional statistical approaches does not guarantee causality. Leveraging 16 years of data (68.5 million Medicare enrollees), we provide strong evidence of the causal link between long-term PM2.5 exposure and mortality under a set of causal inference assumptions. Using five distinct approaches, we found that a decrease in PM2.5 (by 10 micrograms per cubic meter) leads to a statistically significant 6 to 7% decrease in mortality risk. Based on these models, lowering the air quality standard to 10 micrograms per cubic meter would save 143,257 lives (95% confidence interval, 115,581 to 170,645) in one decade. Our study provides the most comprehensive evidence to date of the link between long-term PM2.5 exposure and mortality, even at levels below current standards.


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