Abstract:
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On April 20, 2010 the Deepwater Horizon oil rig caught fire, exploded, 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 clean-up efforts. Many harmful chemicals were released into the air from crude oil including benzene, toluene ethylbenzene, xylene, and hexane. NIEHS's GuLF STUDY investigators are working to quantify the exposure the workers experienced related to the event and evaluate associations between the exposure and detrimental health outcomes. Approximately 150,000 personal exposure measurements were collected but a high percentage of the measurements were below the analytical methods' limit of detection and denoted as censored. In this presentation, we propose a model where one chemical is estimated by the other chemicals in a Bayesian linear regression setting, with each chemical having its own limits of detection. This multivariate extension of a simple linear Bayesian framework accounting for censoring in both X and Y, should allow for even stronger predictions and unbiased estimates of exposure for different groups of workers.
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