Bayesian Models for Analyzing Worker Exposure to Airborne Chemicals During the Deepwater Horizon Oil Spill Cleanup and Response
2017-07
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Bayesian Models for Analyzing Worker Exposure to Airborne Chemicals During the Deepwater Horizon Oil Spill Cleanup and Response
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2017-07
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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 3 months. Thousands of workers were involved in the response and cleanup 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 cleanup work and evaluating associations between the exposures and detrimental health outcomes. My research focuses on developing statistical methods to quantify airborne chemical exposures in response to this event and to other settings in environmental health. Factors complicating the exposure estimation include 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 called censored measurements). 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 where workers were involved in the response. Therefore, we must develop methods that allow us to estimate exposures under these limitations. I introduce a strategy that uses chemical linear relationships to inform exposure estimates. We describe a Bayesian linear model for quantifying exposure while accounting for censoring in both a chemical predictor and a response. We further expand this model to quantify exposure in multiple EGs. Then, I describe a multivariate Bayesian linear model used to quantify exposures under various amounts of LOD censoring in the chemical response and multiple chemical predictors. We assess our model's performance against simpler models at a variety of censoring levels using WAIC. We apply our model to assess vapor exposures from measurements of volatile substances in crude oil on the Ocean Intervention III taken during the Deepwater Horizon oil spill response and cleanup. Next, I explain how we used a database of over 26 million VOC measurements to supplement information in THC and BTEXH. I discuss the methods we used to convert this large VOC database into a exposure metric that could be compared with THC exposure. Then, I describe how we used the VOC exposure metrics to estimate THC and BTEXH exposure when VOC information was available but THC/BTEXH measurements were unavailable. Finally, I expand the Bayesian linear framework to a spatial setting that allows us to estimate exposure for particular areas in the Gulf of Mexico while accounting for values below LOD in both the response and predictor of interest. We also investigate imputation strategies designed to allow us to estimate exposure to our chemical predictor (providing input to our model) so we can better estimate our chemical response. I conclude with a brief description of our current investigation of environmental exposures during the Deepwater Horizon response and cleanup efforts.
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University of Minnesota Ph.D. dissertation.July 2017. Major: Biostatistics. Advisors: Sudipto Banerjee, Gurumurthy Ramachandran. 1 computer file (PDF); xiii, 157 pages.
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Groth, Caroline. (2017). Bayesian Models for Analyzing Worker Exposure to Airborne Chemicals During the Deepwater Horizon Oil Spill Cleanup and Response. Retrieved from the University Digital Conservancy, https://hdl.handle.net/11299/206663.
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