A Bayesian Approach to Exposure Risk Characterization and Medical Surveillance Decision-Making in the U.S. Department of Energy.

OBJECTIVE: Apply Bayesian posterior probabilities to identify workers at high risk for elemental mercury exposure and optimize targeted industrial hygiene (IH) sampling and medical surveillance strategies. METHODS: Bayesian modeling estimated probabilities of exceeding ACGIH, OSHA, and NIOSH occupational exposure limits. Posterior probability distributions for the 90th and 95th exposure percentiles were generated by job title and used to assign exposure-based risk bands that informed IH sampling frequencies and medical surveillance recommendations. RESULTS: Bayesian posterior probabilities identified worker groups with an elevated probability of exceeding occupational exposure limits. Several job titles consistently demonstrated high probabilities, supporting enhanced medical surveillance. Risk classifications differed across exposure limits. CONCLUSIONS: Bayesian analysis provides a risk-based framework for prioritizing exposure monitoring and medical surveillance while improving resource allocation and protecting worker health.