Biostatistics Seminar Series

Methods for Assessing Airborne Chemical Exposure - Caroline Groth, West Virginia University

Thursday 11/7 3:30PM - 4:30PM
Public Health Lecture Hall (A115)

Methods for Assessing Airborne Chemical Exposure for Workers Involved in the Response and Clean-up of the Deepwater Horizon Oil Spill

In April 2010 the Deepwater Horizon oil rig caught fire and sank, sending approximately 5 million barrels of oil into the Gulf of Mexico over the ensuing three months. Thousands of workers were involved in the response and clean-up efforts. Many harmful chemicals were released into the air from crude oil, including total hydrocarbons (THC), benzene, toluene, ethylbenzene, xylene, hexane (BTEXH), and volatile organic compounds (VOCs). NIEHS’s GuLF STUDY investigators are estimating the exposures the workers experienced related to their response and clean-up work and evaluating associations between the exposures and detrimental health outcomes.

As a collaborator on this study, my research focused on developing statistical methods to quantify airborne chemical exposures under analytical method and data collection limitations. All analytical methods used to measure chemical concentrations have a limit of detection (LOD), or a threshold below which exposure cannot be detected with the analytical method (measurements below the LOD are censored). However, even these low exposures must be assessed to provide accurate estimates of exposure. Similarly, due to the scope of this event, it was not possible to take measurements in all scenarios.

In this talk, I describe several methods we developed to allow us to better quantify exposures under these limitations. First, I describe a Bayesian linear model for quantifying exposure while accounting for censoring in both a chemical predictor and a response. Next, I explain how we used a database of over 26 million VOC measurements to supplement information in THC and BTEXH. Finally, I conclude with some discussion of future research directions.

Last Updated On Friday, November 1, 2019 by Tang, Lu
Created On Monday, August 12, 2019