Incorporating Exposure Measurement Data from Similar Exposure Scenarios to Inform Exposure Modeling Estimates: A Demonstration Using Cluster Analysis and Bayesian Modeling.

Addressing occupational health and safety concerns early in the design stage anticipates hazards and enables health professionals to recommend control measures that can best protect workers' health. This method is a well-established tool in public health. Importantly, its success depends on a comprehensive exposure assessment that incorporates previous exposure data and outcomes. Traditional methods for characterizing similar occupational exposure scenarios rely on expert judgment or qualitative descriptions of relevant exposure data, which often include undisclosed underlying assumptions about specific exposure conditions. Thus, improved methods for predicting exposure modeling estimates based on available data are needed. This study proposes that cluster analysis can be used to quantify the relevance of existing exposure scenarios that are similar to a new scenario. We demonstrate how this method improves exposure predictions. Exposure data and contextual information of the scenarios were collected from past exposure assessment reports. Prior distributions for the exposure distribution parameters were specified using Stoffenmanager® 8 predictions. Gower distance and k-Medoids clustering algorithm analyses grouped existing scenarios into clusters based on similarity. The information was used in a Bayesian model to specify the degree of correlation between similar scenarios and the scenarios to be assessed. Using the distance metric to characterize the degree of similarity, the performance of the Bayesian model was improved in terms of the average bias of model estimates and measured data, reducing from 0.77 (SD: 2.0) to 0.49 (SD: 1.8). Nevertheless, underestimation of exposures still occurred for some rare scenarios, which tended to be those with highly variable exposure data. In conclusion, the cluster analysis approach may enable transparent selection of similar exposure scenarios for factoring into design-phase assessments and thereby improve exposure modeling estimates.