Statistical Analysis of Occupational Exposure Data

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PROJECT SUMMARY/ABSTRACT I am an Assistant Professor in the Environmental and Occupational Health Sciences (EOHS) Division, at the School of Public Health (SPH) at the University of Illinois at Chicago (UIC). My career goal is to be an independent investigator, recognized for contributions to occupational health through exposure assessment. My research has had two themes: (1) the development, evaluation and application of mathematical models of exposure, and (2) exposure and risk assessment for infectious agents. While my research has been highly quantitative, I have focused on the use of models and simulation. Since coming to UIC I have grown increasingly interested in the statistical analysis and interpretation of occupational exposure data, within the frameworks of risk assessment and occupational epidemiology. Few exposure scientists have appropriate statistical training to conduct research in this area; and it is rare to find statisticians with the time and interest to study occupational exposure assessment. I recognize these skills as a gap in my training and in the profession. While I have taught myself new statistical techniques, the formal training of this award is required for independence. These skills will complement my ongoing research themes, and enable me to have a comprehensive, independent research career in exposure assessment. The proposed research career development plan includes three aims to be attained through coursework and independent study. Aim 1 is to develop competency in advanced statistical concepts for occupational exposure assessment. Aim 2 will be to increase knowledge of methods in occupational epidemiology. Aim 3 is to gain conceptual and practical computer science skills. Mentors have been identified for their expertise in biostatistics, occupational epidemiology, and exposure assessment. The primary mentor for this award is Dr. Donald Hedeker, Professor of Biostatistics at UIC. Co-mentors at UIC include Drs. Hakan Demirtas and Leslie Stayner who have expertise in biostatistics and occupational epidemiology, respectively. Dr. Gurumurthy Ramachandran of the University of Minnesota will serve as co-mentor, contributing expertise in exposure assessment. The proposed research project includes two studies. Study 1 will characterize the magnitude, variability and determinants of lead exposures among ironworkers during the preparation of bridges for painting. I will explore the magnitude, variability and determinants of exposure and exposure variability using mixed-effects models; and determine whether time-varying area monitoring data is a better predictor of personal breathing zone exposures than data about the metal content of paint, which is not time-varying. Some data in this study is missing - either lost or not collected. Traditionally, only observed data are analyzed for exposure assessment, but this approach can lead to a loss of information and inaccurate standard errors. Multiple imputation, is an increasingly poplar technique outside of occupational exposure assessment that fills in missing values. Using these data, I will determine the impact of multiple imputation relative to complete cases analysis on the exposure characterization, and evaluate the equivalence of model selection methods for multiple imputation. Study 2 will develop and test an epidemiologic exposure-response model in which the mean exposure of individual workers is described by a probability distribution. I will test the method using the Hanford Cohort Mortality Study, 1989, which includes annual radiation doses and cause-specific mortality for more than 30,000 workers at the Hanford nuclear site in Washington State. This study is motivated by the observation that using individual mean exposure assessments yields attenuated exposure-response relationships relative to those obtained with group mean exposure assessments in the context of linear regression. This attenuation arises because of imprecision in exposure estimates, suggesting that attenuation may be minimized or eliminated if imprecision in exposure is explicitly addressed in the exposure-response model. This study represents one approach to management of exposure misclassification, a ubiquitous problem in occupational epidemiology.