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 |
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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. |
doi_str_mv | 10.1007/s00477-011-0470-z |
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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.</description><identifier>ISSN: 1436-3240</identifier><identifier>EISSN: 1436-3259</identifier><identifier>DOI: 10.1007/s00477-011-0470-z</identifier><language>eng</language><publisher>Berlin/Heidelberg: Springer-Verlag</publisher><subject>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</subject><ispartof>Stochastic environmental research and risk assessment, 2012-03, Vol.26 (3), p.451-465</ispartof><rights>Springer-Verlag 2011</rights><rights>Springer-Verlag 2012</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c371t-63607e23d0b78e6c967c14d108d2bbfd908f1c5b56d10d2c89241e9fd3bc58493</citedby><cites>FETCH-LOGICAL-c371t-63607e23d0b78e6c967c14d108d2bbfd908f1c5b56d10d2c89241e9fd3bc58493</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s00477-011-0470-z$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s00477-011-0470-z$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,41488,42557,51319</link.rule.ids></links><search><creatorcontrib>Liu, Kevin Fong-Rey</creatorcontrib><creatorcontrib>Lu, Che-Fan</creatorcontrib><creatorcontrib>Chen, Cheng-Wu</creatorcontrib><creatorcontrib>Shen, Yung-Shuen</creatorcontrib><title>Applying Bayesian belief networks to health risk assessment</title><title>Stochastic environmental research and risk assessment</title><addtitle>Stoch Environ Res Risk Assess</addtitle><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.</description><subject>air</subject><subject>Air pollution</subject><subject>Aquatic Pollution</subject><subject>Bayesian analysis</subject><subject>Belief networks</subject><subject>Chemistry and Earth Sciences</subject><subject>Computational Intelligence</subject><subject>Computer Science</subject><subject>Earth and Environmental Science</subject><subject>Earth Sciences</subject><subject>Environment</subject><subject>epidemiological studies</subject><subject>Hazardous materials</subject><subject>Health risk assessment</subject><subject>Health risks</subject><subject>Math. Appl. in Environmental Science</subject><subject>morbidity</subject><subject>mortality</subject><subject>Original Paper</subject><subject>Physics</subject><subject>pollutants</subject><subject>Probability Theory and Stochastic Processes</subject><subject>risk</subject><subject>Risk assessment</subject><subject>risk characterization</subject><subject>Statistics for Engineering</subject><subject>toxic substances</subject><subject>uncertainty</subject><subject>Waste Water Technology</subject><subject>Water Management</subject><subject>Water Pollution Control</subject><issn>1436-3240</issn><issn>1436-3259</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2012</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp9kE1Lw0AQQIMoWGp_gCeDF0_Rmexms4unWvwCwYP2vGySTRubJnEnRdpf79aIggdPOyzvDcMLglOESwRIrwiAp2kEiJEfINodBCPkTEQsTtThz8zhOJgQVZl3EqYUwii4nnZdva2aRXhjtpYq04SZrStbho3tP1q3orBvw6U1db8MXUWr0BBZorVt-pPgqDQ12cn3Ow7md7evs4fo6fn-cTZ9inKWYh8JJiC1MSsgS6UVuRJpjrxAkEWcZWWhQJaYJ1ki_F8R51LFHK0qC5blieSKjYOLYW_n2veNpV6vK8ptXZvGthvSKuFCSPwiz_-Qb-3GNf44rVDxGFgiPYQDlLuWyNlSd65aG7fVCHrfUw89te-p9z31zjvx4JBnm4V1v4v_k84GqTStNgtfT89fYsAEACWXPGWfBQOAnA</recordid><startdate>20120301</startdate><enddate>20120301</enddate><creator>Liu, Kevin Fong-Rey</creator><creator>Lu, Che-Fan</creator><creator>Chen, Cheng-Wu</creator><creator>Shen, Yung-Shuen</creator><general>Springer-Verlag</general><general>Springer Nature B.V</general><scope>FBQ</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7ST</scope><scope>7XB</scope><scope>88I</scope><scope>8AO</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>KR7</scope><scope>L6V</scope><scope>M2P</scope><scope>M7S</scope><scope>PATMY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>Q9U</scope><scope>S0W</scope><scope>SOI</scope><scope>7T2</scope><scope>7TV</scope><scope>7U1</scope><scope>7U2</scope></search><sort><creationdate>20120301</creationdate><title>Applying Bayesian belief networks to health risk assessment</title><author>Liu, Kevin Fong-Rey ; Lu, Che-Fan ; Chen, Cheng-Wu ; Shen, Yung-Shuen</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c371t-63607e23d0b78e6c967c14d108d2bbfd908f1c5b56d10d2c89241e9fd3bc58493</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2012</creationdate><topic>air</topic><topic>Air pollution</topic><topic>Aquatic Pollution</topic><topic>Bayesian analysis</topic><topic>Belief networks</topic><topic>Chemistry and Earth Sciences</topic><topic>Computational Intelligence</topic><topic>Computer Science</topic><topic>Earth and Environmental Science</topic><topic>Earth Sciences</topic><topic>Environment</topic><topic>epidemiological studies</topic><topic>Hazardous materials</topic><topic>Health risk assessment</topic><topic>Health risks</topic><topic>Math. Appl. in Environmental Science</topic><topic>morbidity</topic><topic>mortality</topic><topic>Original Paper</topic><topic>Physics</topic><topic>pollutants</topic><topic>Probability Theory and Stochastic Processes</topic><topic>risk</topic><topic>Risk assessment</topic><topic>risk characterization</topic><topic>Statistics for Engineering</topic><topic>toxic substances</topic><topic>uncertainty</topic><topic>Waste Water Technology</topic><topic>Water Management</topic><topic>Water Pollution Control</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Liu, Kevin Fong-Rey</creatorcontrib><creatorcontrib>Lu, Che-Fan</creatorcontrib><creatorcontrib>Chen, Cheng-Wu</creatorcontrib><creatorcontrib>Shen, Yung-Shuen</creatorcontrib><collection>AGRIS</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Environment Abstracts</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Science Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>Civil Engineering Abstracts</collection><collection>ProQuest Engineering Collection</collection><collection>Science Database</collection><collection>Engineering Database</collection><collection>Environmental Science Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Engineering Collection</collection><collection>Environmental Science Collection</collection><collection>ProQuest Central Basic</collection><collection>DELNET Engineering & Technology Collection</collection><collection>Environment Abstracts</collection><collection>Health and Safety Science Abstracts (Full archive)</collection><collection>Pollution Abstracts</collection><collection>Risk Abstracts</collection><collection>Safety Science and Risk</collection><jtitle>Stochastic environmental research and risk assessment</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Liu, Kevin Fong-Rey</au><au>Lu, Che-Fan</au><au>Chen, Cheng-Wu</au><au>Shen, Yung-Shuen</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Applying Bayesian belief networks to health risk assessment</atitle><jtitle>Stochastic environmental research and risk assessment</jtitle><stitle>Stoch Environ Res Risk Assess</stitle><date>2012-03-01</date><risdate>2012</risdate><volume>26</volume><issue>3</issue><spage>451</spage><epage>465</epage><pages>451-465</pages><issn>1436-3240</issn><eissn>1436-3259</eissn><abstract>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.</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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