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. Se...

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Veröffentlicht in:Stochastic environmental research and risk assessment 2012-03, Vol.26 (3), p.451-465
Hauptverfasser: Liu, Kevin Fong-Rey, Lu, Che-Fan, Chen, Cheng-Wu, Shen, Yung-Shuen
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container_issue 3
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container_title Stochastic environmental research and risk assessment
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creator Liu, Kevin Fong-Rey
Lu, Che-Fan
Chen, Cheng-Wu
Shen, Yung-Shuen
description 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.
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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.</abstract><cop>Berlin/Heidelberg</cop><pub>Springer-Verlag</pub><doi>10.1007/s00477-011-0470-z</doi><tpages>15</tpages></addata></record>
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subjects air
Air pollution
Aquatic Pollution
Bayesian analysis
Belief networks
Chemistry and Earth Sciences
Computational Intelligence
Computer Science
Earth and Environmental Science
Earth Sciences
Environment
epidemiological studies
Hazardous materials
Health risk assessment
Health risks
Math. Appl. in Environmental Science
morbidity
mortality
Original Paper
Physics
pollutants
Probability Theory and Stochastic Processes
risk
Risk assessment
risk characterization
Statistics for Engineering
toxic substances
uncertainty
Waste Water Technology
Water Management
Water Pollution Control
title Applying Bayesian belief networks to health risk assessment
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