title: Bivariate Left-Censored Bayesian Model for Predicting Exposure: Preliminary Analysis of Worker Exposure during the Deepwater Horizon Oil Spill.
abstract: In April 2010, the Deepwater Horizon oil rig caught fire and exploded, releasing almost 5 million barrels of oil into the Gulf of Mexico over the ensuing 3 months. Thousands of oil spill workers participated in the spill response and clean-up efforts. The GuLF STUDY being conducted by the National Institute of Environmental Health Sciences is an epidemiological study to investigate potential adverse health effects among these oil spill clean-up workers. Many volatile chemicals were released from the oil into the air, including total hydrocarbons (THC), which is a composite of the volatile components of oil including benzene, toluene, ethylbenzene, xylene, and hexane (BTEXH). Our goal is to estimate exposure levels to these toxic chemicals for groups of oil spill workers in the study (hereafter called exposure groups, EGs) with likely comparable exposure distributions. A large number of air measurements were collected, but many EGs are characterized by datasets with a large percentage of censored measurements (below the analytic methods' limits of detection) and/or a limited number of measurements. We use THC for which there was less censoring to develop predictive linear models for specific BTEXH air exposures with higher degrees of censoring. We present a novel Bayesian hierarchical linear model that allows us to predict, for different EGs simultaneously, exposure levels of a second chemical while accounting for censoring in both THC and the chemical of interest. We illustrate the methodology by estimating exposure levels for several EGs on the Development Driller III, a rig vessel charged with drilling one of the relief wells. The model provided credible estimates in this example for geometric means, arithmetic means, variances, correlations, and regression coefficients for each group. This approach should be considered when estimating exposures in situations when multiple chemicals are correlated and have varying degrees of censoring.
authors.affiliation: Division of Biostatistics, University of Minnesota, Minneapolis, MN 55455, USA.
authors.forename: Caroline
authors.initials: C
authors.lastname: Groth
authors.affiliation: Department of Biostatistics, University of California-Los Angeles, Los Angeles, CA 90095, USA.
authors.forename: Sudipto
authors.initials: S
authors.lastname: Banerjee
authors.affiliation: Department of Environmental Health and Engineering, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD 21205, USA.
authors.forename: Gurumurthy
authors.initials: G
authors.lastname: Ramachandran
authors.affiliation: Exposure Assessments Applications, LLC, Arlington, VA 22207, USA.
authors.forename: Mark R
authors.initials: MR
authors.lastname: Stenzel
authors.affiliation: National Institute of Environmental Health Sciences, Research Triangle Park, NC 27709, USA.
authors.forename: Dale P
authors.initials: DP
authors.lastname: Sandler
authors.affiliation: National Cancer Institute, Bethesda, MD 20892, USA.
authors.forename: Aaron
authors.initials: A
authors.lastname: Blair
authors.affiliation: Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
authors.forename: Lawrence S
authors.initials: LS
authors.lastname: Engel
authors.affiliation: National Institute of Environmental Health Sciences, Research Triangle Park, NC 27709, USA.
authors.forename: Richard K
authors.initials: RK
authors.lastname: Kwok
authors.affiliation: Stewart Exposure Assessments, LLC, Arlington, VA 22207, USA.
authors.forename: Patricia A
authors.initials: PA
authors.lastname: Stewart
mesh_terms: D001554:Benzene / Q000032:analysis; D003627:Data Interpretation, Statistical; D004190:Disasters; D004784:Environmental Monitoring / Q000379:methods; D060851:Gulf of America; D006801:Humans; D019570:Inhalation Exposure / Q000032:analysis*; D057230:Limit of Detection; D015233:Models, Statistical; D016273:Occupational Exposure / Q000032:analysis; D009790:Occupations / Q000706:statistics & numerical data; D059392:Petroleum Pollution / Q000009:adverse effects*; D011084:Polycyclic Aromatic Hydrocarbons / Q000032:analysis*; D018570:Risk Assessment; D014874:Water Pollutants, Chemical / Q000032:analysis
chemical_list: D011084:Polycyclic Aromatic Hydrocarbons; D014874:Water Pollutants, Chemical; D001554:Benzene
keywords: Bayesian statistics; Deepwater Horizon oil spill; bivariate left censoring; correlation; exposure assessment; total hydrocarbons (THC)