Statistical Analysis of Occupational Exposure Data

职业暴露数据统计分析

• 批准号: 8843283
• 负责人: Rachael Mary Jones
• 金额: $ 10.79万
• 依托单位: UNIVERSITY OF ILLINOIS AT CHICAGO
• 依托单位国家: 美国
• 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/8843283
• 关键词: Statistical; Analysis; Occupational; Exposure; Data

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.

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部(EOHS)助理教授。我的职业目标是成为一名独立的调查员，通过暴露评估对职业健康的贡献得到认可。我的研究有两个主题：(1)暴露的数学模型的发展、评估和应用，以及(2)感染性病原体的暴露和风险评估。虽然我的研究是高度量化的，但我把重点放在模型和模拟的使用上。自从第一次来UIC以来，在风险评估和职业流行病学的框架内，对职业暴露数据的统计分析和解释越来越感兴趣。很少有接触科学家接受过适当的统计培训来进行这一领域的研究；而且很少能找到有时间和兴趣研究职业的统计学家 暴露评估。我认识到这些技能在我的培训和职业中都是一个空白。虽然我自学了新的统计技术，但这个奖项的正式培训是独立所必需的。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学习和自主学习实现的三个目标。目标1是培养职业暴露评估的高级统计概念的能力。目标2将是增加对职业流行病学方法的了解。目标3是获得概念性和实践性的计算机科学技能。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的联合导师包括Hakan Demirtas博士和Leslie Stayner博士，他们分别在生物统计学和职业流行病学方面拥有专业知识。明尼苏达大学的Gurumurthy Ramachandran博士将担任共同导师，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将描述钢铁工人在准备油漆桥梁过程中铅暴露的大小、变异性和决定因素。我将使用混合效应模型探索暴露和暴露可变性的大小、可变性和决定因素；并确定时变的区域监测数据是否比油漆中金属含量的数据更能预测个人呼吸区的暴露，后者不是时变的。这项研究中的一些数据丢失了--要么丢失了，要么没有收集到。传统上，只有观察到的数据被分析用于暴露评估，但这种方法可能导致信息损失和不准确的标准误差。在填补缺失值的职业暴露评估之外，多重归罪是一种越来越受欢迎的技术。利用这些数据，我将确定相对于完整案例分析的多重归因对暴露表征的影响，并评估多重归因模型选择方法的等价性。研究2将开发和测试一个流行病学暴露-反应模型，在该模型中，每个工人的平均暴露是通过概率分布描述的。我会测试一下 方法使用1989年的汉福德队列死亡率研究，该研究包括华盛顿州汉福德核电站3万多名工人的年度辐射剂量和特定原因的死亡率。这项研究的动机是观察到，在线性回归的背景下，相对于使用群体平均暴露评估所获得的关系，使用个人平均暴露评估会产生减弱的暴露-反应关系。这种衰减是由于曝光估计的不精确造成的，这表明如果曝光-反应模型中明确解决了曝光不精确的问题，则可以最大限度地减少或消除衰减。这项研究代表了一种处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。