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Department of Epidemiology and Biostatistics Seminar Series

Wednesday, March 10, 2021

2:30 PM-3:30 PM

Join the Dornsife School of Public Health's Department of Epidemiology and Biostatistics for our next seminar series event.

Our speaker will be Joshua Warren, PhD, Associate Professor of Biostatistics at Yale School of Public Health, who will discuss "Critical Window Variable Selection for Mixtures: Estimating the Impact of Multiple Air Pollutants on Stillbirth."

More about Dr. Warren's research:

Understanding the role of time-varying pollution mixtures on human health is critical as people are simultaneously exposed to multiple pollutants during their lives. For vulnerable sub-populations who have well-defined exposure periods (e.g., pregnant women), questions regarding critical windows of exposure to these mixtures are important for mitigating harm. We extend Critical Window Variable Selection (CWVS) to the multipollutant setting by introducing CWVS for Mixtures (CWVSmix), a hierarchical Bayesian method that combines smoothed variable selection and correlated Dirichlet-distributed parameters to (i) identify critical windows of exposure to mixtures of time-varying pollutants, (ii) estimate the time-varying relative importance of each individual pollutant within the mixture, and (iii) quantify the impact of the mixtures on health. Through simulation study, we show that CWVSmix outperforms competing methods in each of these categories. We use these approaches to investigate the impact of exposure to 12 gas and particulate ambient air pollutants on the risk of stillbirth in New Jersey, 2005-2014. We find consistent elevated risk in gestational weeks 16-17 for non-Hispanic Black mothers, with pollution mixtures dominated by nitrogen oxides, carbon monoxide, nitrogen dioxide, and nitrate (week 16), and ammonium, nitrate (week 17). The method is available in the R package CWVSmix.

To RSVP contact Nancy Colon-Anderson.

Contact Information

Nancy Colon-Anderson
nanderson@drexel.edu

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Location

Via Zoom

Audience

  • Everyone