When: Feb 25 2021 @ 1:30 PM

Title: Statistical Approaches to Studying Air Pollution Mixtures and Health
Abstract: The control of ambient air quality in the United States has been a major public health success since the passing of the Clean Air Act, with particulate matter (PM) reductions resulting in an estimated 160,000 premature deaths prevented in 2010 alone. Currently, public policy is oriented around lowering the levels of individual pollutants and this focus has driven the nature of much epidemiological research. Recently, attention has been given to viewing air pollution as a complex mixture and to developing a multi-pollutant approach to controlling ambient concentrations. We discuss current approaches to studying air pollution mixtures and detail their strengths and weaknesses. We also present a new statistical method for estimating the health effects of environmental mixtures using a mixture-altering contrast, which is any comparison, intervention, policy, or natural experiment that changes a mixture’s composition. As a demonstration, we apply this approach to assess the health effects of wildfire particulate matter air pollution in the Western United States.
Bio: Dr. Roger D. Peng is a Professor of Biostatistics at the Johns Hopkins Bloomberg School of Public Health where his research focuses on the development of statistical methods for addressing environmental health problems. He has led some of the largest national studies on the health effects of ambient air pollution in the United States. Dr. Peng is the author of the popular book R Programming for Data Science and 10 other books on data science and statistics. He is also the co-creator of the Johns Hopkins Data Science Specialization, the Simply Statistics blog where he writes about statistics for the public, the Not So Standard Deviations podcast with Hilary Parker, and The Effort Report podcast with Elizabeth Matsui. Dr. Peng is a Fellow of the American Statistical Association and is the recipient of the Mortimer Spiegelman Award from the American Public Health Association, which honors a statistician who has made outstanding contributions to public health.

Here is the new link and meeting ID+passcode:
Meeting ID: 914 6737 5713
Passcode: 272254