The Use of Heuristics and Exposure Models in Improving Exposure Judgment Accuracy

Abstract

Exposure assessments provide the foundation for determining whether occupational and environmental exposure risks are efficiently and effectively managed. The American Industrial Hygiene Association’s (AIHA) strategy is well-known and provides a simple yet elegant framework for exposure assessments. Judgments are made by identifying the exposure control category in which the 95th percentile of the exposure distribution is most likely located for a given job or task Acceptability is commonly evaluated by comparing the true group 95th percentile to the occupational exposure limit (OEL), and based on this comparison the exposure is classified into one of four categories: “highly-controlled”, “well controlled”, “controlled”, or “poorly controlled”. Qualitative assessments, made without personal exposure data and quantitative exposure assessments are performed after a thorough review of available information and data related to the workforce, jobs, materials, worker interviews, exposure agents, exposure limits, work practices, engineering controls and protective equipment. Recent studies suggest that when exposure assessments are conducted in an ad hoc manner using subjective inputs, the accuracy of these assessments is low; in many cases no more accurate than random chance. Moreover, they tend to underestimate the true exposure. Thus, there is an urgent need to improve the accuracy of these assessments. This research was focused on investigating exposure assessment inputs that improve exposure judgment accuracy when assessments are conducted without personal exposure data. Specifically, the use of heuristics and exposure models were investigated. A Study was conducted to assess the impact of objective inputs used in a checklist format on improving exposure judgment accuracy by practicing and novice industrial hygienists. The results indicated exposure judgment accuracy is significantly higher, relative to random chance and conventional approaches when guided by the checklist tool developed for this research. Two studies were conducted to systematically evaluate two widely used exposure models under highly controlled and real world conditions. This was an important first step in evaluating the potential utility of these models in improving exposure judgment accuracy. The results for the majority of the tests conducted met the performance criteria, suggesting the models will be useful for generating reasonably accurate exposure estimates.