Statistical Analysis of Occupational Exposure Data

职业暴露数据统计分析

• 批准号: 8615011
• 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
• 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/8615011
• 关键词: Statistical; Analysis; Occupational; Exposure; Data

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.

项目摘要/摘要 我是环境与职业健康科学(EOHS)部门的助理教授， 在伊利诺伊大学芝加哥分校公共卫生学院(SPH)。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的贡献得到认可。 我的研究有两个主题：(1)数学模型的发展、评价和应用 暴露，以及(2)感染源的暴露和风险评估。虽然我的研究高度重视 定量方面，我重点介绍了模型的使用和模拟。自从来到UIC以来，我成长了 对职业暴露数据的统计分析和解释越来越感兴趣，在 风险评估和职业流行病学框架。很少有科学家有合适的接触 在这一领域进行研究的统计培训；很少能找到有时间和兴趣的统计学家 学习职业暴露评估。我认识到这些技能是我在培训中和在 职业。虽然我已经自学了新的统计技术，但这个奖项的正式培训是必需的 为了独立。这些技能将补充我正在进行的研究主题，并使我能够 在暴露评估方面的全面、独立的研究生涯。 拟议的研究职业发展计划包括三个目标，通过以下途径实现 课业和自学。目标1是培养高级统计概念方面的能力 职业暴露评估。目标2将是增加对职业方法的了解 流行病学。目标3是获得概念性和实践性的计算机科学技能。已经确定了导师 他们在生物统计学、职业流行病学和暴露评估方面的专业知识。主要导师 该奖项的获得者是UIC生物统计学教授唐纳德·海德克博士。UIC的联合导师包括Hakan博士 德米尔塔斯和莱斯利·斯泰纳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，在以下方面贡献专业知识 暴露评估。 拟议的研究项目包括两项研究。研究1将描述其大小、可变性 以及钢铁工人在油漆桥梁准备过程中铅暴露的决定因素。这就做 利用混合效应探讨暴露和暴露可变性的大小、可变性和决定因素 模型；并确定时变区域监测数据是否能更好地预测个人呼吸 区域曝光量比涂料中金属含量的数据要少，这不是时变的。这项研究中的一些数据 丢失了--要么丢失了，要么没有找回。传统上，只对观测数据进行曝光分析 但这种方法可能会导致信息丢失和不准确的标准误差。多重 在职业暴露评估之外，归罪是一种日益流行的技术，它填补了 缺少值。使用这些数据，我将确定多重归罪相对于完整案例的影响 分析暴露特征，并评估模型选择方法的等价性 多重归罪。研究2将开发和测试流行病学暴露-反应模型，在该模型中 个体工人的平均暴露剂量用概率分布来描述。我将使用 汉福德队列死亡率研究，1989年，其中包括每年的辐射剂量和原因特定死亡率 华盛顿州汉福德核电站的3万多名工人。这项研究的动机是 观察到使用个人平均暴露评估会产生减弱的暴露反应 与线性背景下的群体平均暴露评估所获得的关系 回归。这种衰减是由于暴露估计的不精确造成的，这表明衰减 如果曝光反应中明确解决了曝光不精确的问题，则可以将其最小化或消除 模特。这项研究代表了一种管理暴露错误分类的方法，这是一种普遍存在的问题 职业流行病学中的问题。