A Bayesian approach to retrospective exposure assessment.

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.