Applying Bayesian belief networks to health risk assessment

The health risk of noncarcinogenic substances is usually represented by the hazard quotient (HQ) or target organ-specific hazard index (TOSHI). However, three problems arise from these indicators. Firstly, the HQ overestimates the health risk of noncarcinogenic substances for non-critical organs. Secondly, the TOSHI makes inappropriately the additive assumption for multiple hazardous substances affecting the same organ. Thirdly, uncertainty of the TOSHI undermines the accuracy of risk characterization. To address these issues, this article proposes the use of Bayesian belief networks (BBN) for health risk assessment (HRA) and the procedure involved is developed using the example of road constructions. According to epidemiological studies and using actual hospital attendance records, the BBN-HRA can specifically identify the probabilistic relationship between an air pollutant and each of its induced disease, which can overcome the overestimation of the HQ for non-critical organs. A fusion technique of conditional probabilities in the BBN-HRA is devised to avoid the unrealistic additive assumption. The use of the BBN-HRA is easy even for those without HRA knowledge. The input of pollution concentrations into the model will bring more concrete information on the morbidity and mortality rates of all the related diseases rather than a single score, which can reduce the uncertainty of the TOSHI.