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Statistical Analysis of Occupational Exposure Data

职业暴露数据统计分析

基本信息

批准号：
9084273
负责人：
Rachael Mary Jones
金额：
$ 10.78万
依托单位：
UNIVERSITY OF ILLINOIS AT CHICAGO
依托单位国家：
美国
项目类别：
财政年份：
2014
资助国家：
美国
起止时间：
2014-04-01 至 2017-03-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9084273
关键词：
Statistical Analysis Occupational Exposure Data

项目摘要

DESCRIPTION (provided by applicant): 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 dat are analyzed for exposure assessment, but this approach can lead to a loss of information and inaccurate standard errors. Multiple imputation is an increasingly popular 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.

描述（由申请人提供）：我是伊利诺伊大学芝加哥分校 (UIC) 公共卫生学院 (SPH) 环境与职业健康科学 (EOHS) 部门的助理教授。我的职业目标是成为一名独立调查员，通过暴露评估对职业健康做出的贡献而受到认可。我的研究有两个主题：（1）暴露数学模型的开发、评估和应用，以及（2）传染源的暴露和风险评估。虽然我的研究是高度定量的，但我专注于模型和模拟的使用。自从来到伊利诺伊大学芝加哥分校以来，我对风险评估和职业流行病学框架内职业暴露数据的统计分析和解释越来越感兴趣。很少有暴露科学家接受过适当的统计培训来开展该领域的研究；很少有统计学家有时间和兴趣来研究职业暴露评估。我认识到这些技能是我的培训和职业中的差距。虽然我自学了新的统计技术，但独立性需要接受该奖项的正式培训。这些技能将补充我正在进行的研究主题，并使我能够在暴露评估方面拥有全面、独立的研究生涯。拟议的研究职业发展计划包括通过课程作业和独立学习实现的三个目标。目标 1 是培养职业暴露评估先进统计概念的能力。目标 2 是增加对职业流行病学方法的了解。目标 3 是获得概念性和实用的计算机科学技能。导师因其在生物统计学、职业流行病学和暴露评估方面的专业知识而被确定。该奖项的主要导师是UIC生物统计学教授Donald Hedeker博士。 UIC 的共同导师包括博士。 Hakan Demirtas 和 Leslie Stayner 分别拥有生物统计学和职业流行病学方面的专业知识。明尼苏达大学的 Gurumurthy Ramachandran 博士将担任联合导师，贡献暴露评估方面的专业知识。拟议的研究项目包括两项研究。研究 1 将描述钢铁工人在桥梁涂漆准备过程中铅暴露的程度、变异性和决定因素。我将使用混合效应模型探讨暴露和暴露变异性的幅度、变异性和决定因素；并确定随时间变化的区域监测数据是否比不随时间变化的油漆金属含量数据更能预测个人呼吸区暴露。这项研究中的一些数据缺失——要么丢失，要么没有收集。传统上，仅分析观察到的数据以进行暴露评估，但这种方法可能会导致信息丢失和标准误差不准确。多重插补是职业暴露评估之外越来越流行的一种技术，可以填补缺失值。使用这些数据，我将确定多重插补相对于完整案例分析对暴露特征的影响，并评估多重插补模型选择方法的等效性。研究 2 将开发和测试流行病学暴露-反应模型，其中个体工人的平均暴露通过概率分布来描述。我将测试方法使用 1989 年汉福德队列死亡率研究，其中包括华盛顿州汉福德核电站 30,000 多名工人的年度辐射剂量和特定原因死亡率。这项研究的动机是观察到，相对于在线性回归背景下通过群体平均暴露评估获得的暴露-反应关系，使用个体平均暴露评估会产生减弱的暴露-反应关系。这种衰减是由于暴露估计的不精确性而产生的，这表明如果在暴露-响应模型中明确解决了暴露的不精确性，则可以最小化或消除衰减。这项研究代表了一种管理暴露错误分类的方法，这是职业流行病学中普遍存在的问题。