Ramachandran G, Vincent J H
Division of Environmental and Occupational Health, University of Minnesota, Minneapolis, USA.
Appl Occup Environ Hyg. 1999 Aug;14(8):547-57. doi: 10.1080/104732299302549.
A variety of health effects are caused by chronic, cumulative exposure over time to pollutants. In these cases, to establish dose-response relationships for epidemiological and risk assessment purposes, it is vital to determine the exposures of individuals or cohorts as functions of time. Most existing occupational exposure databases, however, do not contain continuous records of historical exposures to airborne contaminants. These gaps in the historical record may be filled by using the knowledge base that experts and professionals in the field possess. In this article we present a new framework, based on Bayesian probabilistic reasoning, for obtaining estimates of exposure histories for airborne particulates from limited historical measurements, using subjective expert judgment. The framework has great potential applications in instances where there is sparse information or missing data on past exposures. Expert judgment, in the form of inputs to physical models, provides additional knowledge to retrospectively estimate exposure as a function of time from discrete and incomplete measurements. The expert judgments are informed by knowledge of historical plant conditions and work practices, and models describing process-dependent aerosol generation, ventilation, and worker activity patterns. The result will be probability distributions of the exposure of task-groups of workers as a function of time, in the form of a matrix.
长期慢性累积接触污染物会引发多种健康影响。在这些情况下，为了进行流行病学和风险评估以建立剂量反应关系，确定个体或队列随时间变化的接触情况至关重要。然而，大多数现有的职业接触数据库并不包含空气中污染物历史接触的连续记录。历史记录中的这些空白可以通过利用该领域专家和专业人员所拥有的知识库来填补。在本文中，我们提出了一个基于贝叶斯概率推理的新框架，用于利用主观专家判断，从有限的历史测量数据中获取空气中颗粒物接触历史的估计值。该框架在过去接触信息稀少或数据缺失的情况下具有巨大的潜在应用价值。以物理模型输入形式的专家判断，提供了额外的知识，以便根据离散和不完整的测量数据回顾性地估计随时间变化的接触情况。专家判断依据历史工厂条件和工作实践的知识，以及描述与工艺相关的气溶胶生成、通风和工人活动模式的模型。结果将是以矩阵形式呈现的工人任务组随时间变化的接触概率分布。
