In a recent study, leveraging 16 years of data—68.5 million Medicare enrollees—we have provided strong evidence of the causal link between long-term air pollution exposure and mortality under a set of causal inference assumptions. Assessing whether long-term exposure to air pollution increases the severity of COVID-19 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. We describe these challenges in the context of one of the first preliminary investigations of this question in the US, 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 these two studies, we lay the groundwork for future research on this important topic, describe the complex challenges, and outline promising directions and opportunities in developing statistics and causal inference methods in environmental health research.
About the Speaker:
Xiao Wu is a Ph.D. candidate in the Department of Biostatistics at Harvard University, where he is advised by Dr. Francesca Dominici and Dr. Danielle Braun. His research interests lie in developing causal inference methods to address methodological needs in environmental health and public policy evaluation using health claims databases. His dissertation work focuses on developing robust and interpretable causal inference methods to handle error-prone, continuous, and time-series exposures. He is also working on collaborative projects to design Bayesian clinical trials, meta-analysis, and real-world evidence studies.
Place: Tecent Meeting
ID: 924 441 531
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