Expert Decision-making in Exposure Assessment

描述(由申请人提供)：通常，没有足够的测量方法来正确描述相似暴露组(SEG)工人的暴露情况。在这种情况下，职业卫生员使用他们的专业判断和其他定性和半定量的信息来补充实际的暴露测量，以做出决定。显然有必要验证这种对暴露的主观评估，并制定一个逻辑上连贯的框架，将这些主观判断与实际的暴露测量结合起来，从而利用这两个信息来源。拟议研究的总体目标是开发和验证一种有效的、基于绩效的暴露评估战略，该战略将主观判断和实际测量相结合，并使用最少的测量次数得出正确的决定。研究的具体目标是：(1)评估专业卫生员对暴露的主观评估的准确性；(2)确定准确的主观评估的决定因素 评估职业卫生员的暴露情况(如资历、暴露评估经验、教育背景)，并确定他们的主观暴露评估对这些决定因素的敏感性；(3)开发一个贝叶斯概率框架，以便有效地作出关于暴露的决策，该框架将实际监测数据和对暴露的主观估计结合在一起，以最少的测量得出正确的决定。该方法将使用3M公司的大型职业暴露数据集，以及从3M公司雇用的职业卫生员那里获得的概率专业判断来制定。

DESCRIPTION (provided by applicant): Frequently, insufficient measurements are available for properly characterizing the exposure profiles of similarly exposed groups (SEGs) of workers. In such circumstances, occupational hygienists use their professional judgment and other qualitative and semi-quantitative information to supplement the actual exposure measurements in arriving at decisions. There is clearly a need to validate such subjective assessments of exposure and develop a logically coherent framework that integrates these subjective judgments with actual exposure measurements, and thus leverage both sources of information. The overall goal of the proposed research is to develop and validate an efficient, performance-based exposure assessment strategy that combines subjective judgments and actual measurements, and arrives at the correct decision using the least number of measurements. The specific goals of the research are: (1) to assess the accuracy of subjective assessments of exposure by professional hygienists; (2) to identify the determinants of accurate subjective assessments of exposure by occupational hygienists (such as seniority, exposure assessment experience, educational backgrounds), and to determine the sensitivity of their subjective exposure assessments to these determinants; (3) to develop a Bayesian probabilistic framework for efficient decision making regarding exposures that integrates actual monitoring data and subjective estimates of exposures to arrive at correct decisions using the least number of measurements. The methodology will be developed using a large occupational exposure dataset from 3M Company, and probabilistic professional judgments obtained from occupational hygienists employed at 3M Company.