Quantitative Risk Analysis of Air Pollution Health Effects:
Gespeichert in:
| 1. Verfasser: | |
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| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Cham
Springer International Publishing AG
2020
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| Schriftenreihe: | International Series in Operations Research and Management Science Ser.
v.299 |
| Schlagworte: | |
| Online-Zugang: | HWR01 |
| Beschreibung: | Description based on publisher supplied metadata and other sources |
| Beschreibung: | 1 Online-Ressource (543 Seiten) |
| ISBN: | 9783030573584 |
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| 245 | 1 | 0 | |a Quantitative Risk Analysis of Air Pollution Health Effects |
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| 490 | 0 | |a International Series in Operations Research and Management Science Ser. |v v.299 | |
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| 505 | 8 | |a Intro -- Preface -- Acknowledgments -- Contents -- Part I: Estimating and Simulating Dynamic Health Risks -- Chapter 1: Scientific Method for Health Risk Analysis: The Example of Fine Particulate Matter Air Pollution and COVID-19 Mortality Risk -- Introduction: Scientific Method for Quantitative Risk Assessment -- Scientific Method vs. Weight-of-Evidence Consensus Judgments as Paradigms for Regulatory Risk Analysis -- A Recent Example: PM2.5 and COVID-19 Mortality -- Do Positive Regression Coefficients Provide Evidence of Causation? -- Positive Regression Coefficients Created by Model Specification Error and Other Causes -- Conclusion: Regression Models and Judgment Should Complement Science, not Substitute for it -- Appendix 1: Data -- References -- Chapter 2: Modeling Nonlinear Dose-Response Functions: Regression, Simulation, and Causal Networks -- Introduction -- Why Does Nonlinearity Matter? -- Hazard Identification -- Challenges for Regression-Based Hazard Identification -- Significant Regression Coefficients Arising from Trends and from Omitted Confounders -- Significant Regression Coefficients Arising from Measurement Errors in Confounders -- Significant Regression Coefficients Arising from Model Specification Errors -- Significant Regression Coefficients Arising from Residual Confounding -- Surrogate Variables -- Variable Selection -- Significant Regression Coefficients Arising from Competing Explanations -- Significant Regression Coefficients Arising from Attribution of Joint Effects -- Some Alternatives to Regression for Hazard Identification -- Dose-Response Modeling -- Challenges for Regression-Based Dose-Response Modeling -- Bayesian Networks for Dose-Response Modeling -- Dynamic Simulation for Dose-Response Modeling -- Exposure Assessment -- Risk Characterization, Uncertainty Characterization, and Risk Communication | |
| 505 | 8 | |a LNT Rationales: Additivity to Background, Population Heterogeneity, and Upper-Bound Estimates -- Use of Sensitivity Analysis and Scientific Judgment -- Discussion and Conclusions: Risk Management and Risk Assessment Implications of Nonlinearity -- References -- Chapter 3: Simulating Exposure-Related Health Effects: Basic Ideas -- Introduction -- Chronic Inflammation and Biological Roles of the Inflammasome in Exposure-Associated Diseases -- Modeling the Time for Internal Doses to Reach Activation Thresholds -- Internal Doses After Exposures to Constant Concentrations with Elimination -- Internal Doses After Exposures to Constant Concentrations Without Elimination -- Activation of Chronic Inflammation -- Internal Doses After Time-Varying Exposure Concentrations -- Discussion -- Conclusions -- References -- Chapter 4: Case Study: Occupational Health Risks from Crystalline Silica -- Introduction -- Setting: Linear No-Threshold (LNT) vs. Threshold Dose-Response Models -- Biological Thresholds for NLRP3 Inflammasome Priming, Assembly, and Activation -- Modeling the Transition from Transient to Chronic Inflammation -- A PBPK Model for Internal Doses of Respirable Crystalline Silica (RCS) -- Sensitivity to Interindividual Variability and Physical and Chemical Properties of RCS -- Inflammation-Mediated Promotion of Lung Cancer in a Two-Stage Clonal Expansion Model -- Discussion and Conclusions: Implications for RCS Dose-Response Modeling -- References -- Chapter 5: Case Study: Health Risks from Asbestos Exposures -- Introduction -- Is There a Threshold for Asbestos Carcinogenicity? -- Recent Progress in Elucidating the Roles of the NLRP3 Inflammasome and Chronic Inflammation -- A Risk Assessment Paradigm for NLRP3 Inflammasome-Mediated Carcinogenesis -- Qualitative Aspects of NLRP3 Inflammasome-Mediated Dose-Response | |
| 505 | 8 | |a Quantitative Dose-Response Modeling for Asbestos: Methods and Models -- PBPK Model for Asbestos Fibers -- Chronic Inflammation Model -- Carcinogenesis Models for Lung Cancer (LC) and Malignant Mesothelioma (MM) -- Results: Dose-Response and Age-Specific Risk Implications of the I-TSCE Model -- Discussion -- Limitations and Robustness of the I-TSCE Model -- Contributions and Relationships to Previous Research Literature -- Conclusions -- Appendix 1: A Physiologically Based Pharmacokinetics (PBPK) Model for Asbestos Fibers in the Human Body -- Appendix 2: A Model of the Transition from Acute to Chronic NLRP3-Inflammasome-Mediated Inflammation -- Appendix 3: Two-Stage Clonal Expansion (TSCE) Models of Lung Cancer (LC) and Malignant Mesothelioma (MM) Risk -- References -- Chapter 6: Nonlinear Dose-Time-Response Risk Models for Protecting Worker Health -- Introduction -- Science-Policy Background: Do Linear No Threshold (LNT) Assumptions Protect Worker Health? -- Biological Background: NLRP3 Inflammasomes and Response Thresholds, Revisited -- Data and Methods: Physiologically-Based Pharmacokinetic (PBPK) Modeling -- Results -- Constant Exposure Concentrations -- Time-Varying Exposures -- Discussion -- Conclusions -- References -- Part II: Statistics, Causality, and Machine Learning for Health Risk Assessment -- Chapter 7: Why Not Replace Quantitative Risk Assessment Models with Regression Models? -- Introduction: Use and Abuse of Regression Models in Public Health Risk Assessments -- A Methodological Thought Experiment: Reduced-Form Model Regression Coefficients Are Not Causal Coefficients -- Discussion: Why the Difference? -- Conclusions -- Appendix: Data Sources -- References -- Chapter 8: Causal vs. Spurious Spatial Exposure-Response Associations in Health Risk Analysis -- Introduction -- Spatial Exposure-Response Associations in Risk Assessment | |
| 505 | 8 | |a Data and Methods -- Results -- Fatal Car Accidents and Proximity to Putative Asbestos Sources (Ultramafic Rock Deposits) -- Kaposi's Sarcoma and Proximity to Ultramafic Rock Deposits -- Mesothelioma and Proximity to Ultramafic Rock Deposits -- Results for Point Sources -- Theoretical Interpretation: What Does a Spatial Regression Coefficient Show? -- Discussion and Conclusions -- References -- Chapter 9: Methods of Causal Analysis for Health Risk Assessment with Observational Data -- Introduction -- Science-Policy Setting: Bradford-Hill Considerations, False Causal Conclusions, and Reproducibility Challenges -- Background on Modern Causal Discovery Concepts and Terminology -- Causal Graph Concepts and Terminology -- Causal Concepts -- Why these Distinctions Matter in Practice: The CARET Trial as an Example -- Strength of Association and Mutual Information -- Some Limitations of Strength of Association -- The Mutual Information Criterion -- Criteria for Orienting Arrows in Causal Graphs: Temporality, Directed Information, Homoscedasticity, Exogeneity, Knowledge-Based Constraints, and Quasi-Experiments -- Temporality and Directed Information -- Consistency Checks: Internal and External Consistency and Generalization and Synthesis across Studies -- Learning from Apparent Inconsistencies -- Causal Coherence, Biological Plausibility, and Valid Analogy: Explanations of Exposure-Response Dependencies via Directed Paths of Causal Mechanisms -- Specificity and Biological Gradient: Special Cases of Testable Implications -- Summary: Making Limited Progress in the Spirit of Hill -- Discussion -- A Dose of Humility: How Useful are Our Proposed Causal Criteria for Observational Data? -- The CARET Trial Reconsidered -- Elevating the Goals for Causality Criteria: Returning to Manipulative Causation -- Conclusions | |
| 505 | 8 | |a Appendix 1: Directed Acyclic Graph (DAG) Concepts and Methods -- Adjustment Sets, Partial Dependence Plots, and Estimation of Predictive Causal Effects -- Appendix 2: Information Theory and Causal Graph Learning -- Some Limitations of Graph-Learning Algorithms: Mutual Information Does not Necessarily Imply Causality -- Appendix 3: Concepts of Causation -- Appendix 4: Software for Dynamic Bayesian Networks and Directed Information -- Appendix 5: Non-temporal Methods for Inferring Direction of Information Flow -- Homoscedastic Errors and LiNGAM -- Knowledge-Based and Exogeneity Constraints -- Quasi-Experiments (QEs) and Assumption-Based Constraints on Arrow Directions -- References -- Chapter 10: Clarifying Exposure-Response Regression Coefficients with Bayesian Networks: Blood Lead-Mortality Associations an Example -- Introduction -- Science-Policy Challenge: Determining Whether Data Sets Provide Evidence that Risk Depends on Exposure -- Example Data Set -- Statistical Analysis Methods -- Results -- Results from Logistic Regression Modeling -- Results for Bayesian Network (BN) Modeling -- Discussion -- Limitations of Bayesian Network Modeling -- Contributions of Bayesian Network Modeling to Improving Risk Assessment -- Practical Implications -- Conclusions -- Appendix: Variables and Data -- References -- Chapter 11: Case Study: Does Molybdenum Decrease Testosterone? -- Introduction -- Data -- Statistical Analysis Methods -- Results -- Linear Correlation and Descriptive Statistics Results -- Scatterplot and Simple Linear Regression Results -- Multiple Linear Regression Results -- Nonparametric Modeling Results: Splines, CART Trees and Random Forest Partial Dependence Plots -- Bayesian Network Analysis Results -- Insights from Comparing Linear and Nonparametric Nonlinear Regression Models -- Discussion and Conclusions -- References | |
| 505 | 8 | |a Chapter 12: Case Study: Are Low Concentrations of Benzene Disproportionately Dangerous? | |
| 650 | 4 | |a Air-Pollution-Risk assessment | |
| 650 | 4 | |a Health risk assessment-Statistical methods | |
| 776 | 0 | 8 | |i Erscheint auch als |n Druck-Ausgabe |a Cox Jr., Louis Anthony |t Quantitative Risk Analysis of Air Pollution Health Effects |d Cham : Springer International Publishing AG,c2020 |z 9783030573577 |
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| contents | Intro -- Preface -- Acknowledgments -- Contents -- Part I: Estimating and Simulating Dynamic Health Risks -- Chapter 1: Scientific Method for Health Risk Analysis: The Example of Fine Particulate Matter Air Pollution and COVID-19 Mortality Risk -- Introduction: Scientific Method for Quantitative Risk Assessment -- Scientific Method vs. Weight-of-Evidence Consensus Judgments as Paradigms for Regulatory Risk Analysis -- A Recent Example: PM2.5 and COVID-19 Mortality -- Do Positive Regression Coefficients Provide Evidence of Causation? -- Positive Regression Coefficients Created by Model Specification Error and Other Causes -- Conclusion: Regression Models and Judgment Should Complement Science, not Substitute for it -- Appendix 1: Data -- References -- Chapter 2: Modeling Nonlinear Dose-Response Functions: Regression, Simulation, and Causal Networks -- Introduction -- Why Does Nonlinearity Matter? -- Hazard Identification -- Challenges for Regression-Based Hazard Identification -- Significant Regression Coefficients Arising from Trends and from Omitted Confounders -- Significant Regression Coefficients Arising from Measurement Errors in Confounders -- Significant Regression Coefficients Arising from Model Specification Errors -- Significant Regression Coefficients Arising from Residual Confounding -- Surrogate Variables -- Variable Selection -- Significant Regression Coefficients Arising from Competing Explanations -- Significant Regression Coefficients Arising from Attribution of Joint Effects -- Some Alternatives to Regression for Hazard Identification -- Dose-Response Modeling -- Challenges for Regression-Based Dose-Response Modeling -- Bayesian Networks for Dose-Response Modeling -- Dynamic Simulation for Dose-Response Modeling -- Exposure Assessment -- Risk Characterization, Uncertainty Characterization, and Risk Communication LNT Rationales: Additivity to Background, Population Heterogeneity, and Upper-Bound Estimates -- Use of Sensitivity Analysis and Scientific Judgment -- Discussion and Conclusions: Risk Management and Risk Assessment Implications of Nonlinearity -- References -- Chapter 3: Simulating Exposure-Related Health Effects: Basic Ideas -- Introduction -- Chronic Inflammation and Biological Roles of the Inflammasome in Exposure-Associated Diseases -- Modeling the Time for Internal Doses to Reach Activation Thresholds -- Internal Doses After Exposures to Constant Concentrations with Elimination -- Internal Doses After Exposures to Constant Concentrations Without Elimination -- Activation of Chronic Inflammation -- Internal Doses After Time-Varying Exposure Concentrations -- Discussion -- Conclusions -- References -- Chapter 4: Case Study: Occupational Health Risks from Crystalline Silica -- Introduction -- Setting: Linear No-Threshold (LNT) vs. Threshold Dose-Response Models -- Biological Thresholds for NLRP3 Inflammasome Priming, Assembly, and Activation -- Modeling the Transition from Transient to Chronic Inflammation -- A PBPK Model for Internal Doses of Respirable Crystalline Silica (RCS) -- Sensitivity to Interindividual Variability and Physical and Chemical Properties of RCS -- Inflammation-Mediated Promotion of Lung Cancer in a Two-Stage Clonal Expansion Model -- Discussion and Conclusions: Implications for RCS Dose-Response Modeling -- References -- Chapter 5: Case Study: Health Risks from Asbestos Exposures -- Introduction -- Is There a Threshold for Asbestos Carcinogenicity? -- Recent Progress in Elucidating the Roles of the NLRP3 Inflammasome and Chronic Inflammation -- A Risk Assessment Paradigm for NLRP3 Inflammasome-Mediated Carcinogenesis -- Qualitative Aspects of NLRP3 Inflammasome-Mediated Dose-Response Quantitative Dose-Response Modeling for Asbestos: Methods and Models -- PBPK Model for Asbestos Fibers -- Chronic Inflammation Model -- Carcinogenesis Models for Lung Cancer (LC) and Malignant Mesothelioma (MM) -- Results: Dose-Response and Age-Specific Risk Implications of the I-TSCE Model -- Discussion -- Limitations and Robustness of the I-TSCE Model -- Contributions and Relationships to Previous Research Literature -- Conclusions -- Appendix 1: A Physiologically Based Pharmacokinetics (PBPK) Model for Asbestos Fibers in the Human Body -- Appendix 2: A Model of the Transition from Acute to Chronic NLRP3-Inflammasome-Mediated Inflammation -- Appendix 3: Two-Stage Clonal Expansion (TSCE) Models of Lung Cancer (LC) and Malignant Mesothelioma (MM) Risk -- References -- Chapter 6: Nonlinear Dose-Time-Response Risk Models for Protecting Worker Health -- Introduction -- Science-Policy Background: Do Linear No Threshold (LNT) Assumptions Protect Worker Health? -- Biological Background: NLRP3 Inflammasomes and Response Thresholds, Revisited -- Data and Methods: Physiologically-Based Pharmacokinetic (PBPK) Modeling -- Results -- Constant Exposure Concentrations -- Time-Varying Exposures -- Discussion -- Conclusions -- References -- Part II: Statistics, Causality, and Machine Learning for Health Risk Assessment -- Chapter 7: Why Not Replace Quantitative Risk Assessment Models with Regression Models? -- Introduction: Use and Abuse of Regression Models in Public Health Risk Assessments -- A Methodological Thought Experiment: Reduced-Form Model Regression Coefficients Are Not Causal Coefficients -- Discussion: Why the Difference? -- Conclusions -- Appendix: Data Sources -- References -- Chapter 8: Causal vs. Spurious Spatial Exposure-Response Associations in Health Risk Analysis -- Introduction -- Spatial Exposure-Response Associations in Risk Assessment Data and Methods -- Results -- Fatal Car Accidents and Proximity to Putative Asbestos Sources (Ultramafic Rock Deposits) -- Kaposi's Sarcoma and Proximity to Ultramafic Rock Deposits -- Mesothelioma and Proximity to Ultramafic Rock Deposits -- Results for Point Sources -- Theoretical Interpretation: What Does a Spatial Regression Coefficient Show? -- Discussion and Conclusions -- References -- Chapter 9: Methods of Causal Analysis for Health Risk Assessment with Observational Data -- Introduction -- Science-Policy Setting: Bradford-Hill Considerations, False Causal Conclusions, and Reproducibility Challenges -- Background on Modern Causal Discovery Concepts and Terminology -- Causal Graph Concepts and Terminology -- Causal Concepts -- Why these Distinctions Matter in Practice: The CARET Trial as an Example -- Strength of Association and Mutual Information -- Some Limitations of Strength of Association -- The Mutual Information Criterion -- Criteria for Orienting Arrows in Causal Graphs: Temporality, Directed Information, Homoscedasticity, Exogeneity, Knowledge-Based Constraints, and Quasi-Experiments -- Temporality and Directed Information -- Consistency Checks: Internal and External Consistency and Generalization and Synthesis across Studies -- Learning from Apparent Inconsistencies -- Causal Coherence, Biological Plausibility, and Valid Analogy: Explanations of Exposure-Response Dependencies via Directed Paths of Causal Mechanisms -- Specificity and Biological Gradient: Special Cases of Testable Implications -- Summary: Making Limited Progress in the Spirit of Hill -- Discussion -- A Dose of Humility: How Useful are Our Proposed Causal Criteria for Observational Data? -- The CARET Trial Reconsidered -- Elevating the Goals for Causality Criteria: Returning to Manipulative Causation -- Conclusions Appendix 1: Directed Acyclic Graph (DAG) Concepts and Methods -- Adjustment Sets, Partial Dependence Plots, and Estimation of Predictive Causal Effects -- Appendix 2: Information Theory and Causal Graph Learning -- Some Limitations of Graph-Learning Algorithms: Mutual Information Does not Necessarily Imply Causality -- Appendix 3: Concepts of Causation -- Appendix 4: Software for Dynamic Bayesian Networks and Directed Information -- Appendix 5: Non-temporal Methods for Inferring Direction of Information Flow -- Homoscedastic Errors and LiNGAM -- Knowledge-Based and Exogeneity Constraints -- Quasi-Experiments (QEs) and Assumption-Based Constraints on Arrow Directions -- References -- Chapter 10: Clarifying Exposure-Response Regression Coefficients with Bayesian Networks: Blood Lead-Mortality Associations an Example -- Introduction -- Science-Policy Challenge: Determining Whether Data Sets Provide Evidence that Risk Depends on Exposure -- Example Data Set -- Statistical Analysis Methods -- Results -- Results from Logistic Regression Modeling -- Results for Bayesian Network (BN) Modeling -- Discussion -- Limitations of Bayesian Network Modeling -- Contributions of Bayesian Network Modeling to Improving Risk Assessment -- Practical Implications -- Conclusions -- Appendix: Variables and Data -- References -- Chapter 11: Case Study: Does Molybdenum Decrease Testosterone? -- Introduction -- Data -- Statistical Analysis Methods -- Results -- Linear Correlation and Descriptive Statistics Results -- Scatterplot and Simple Linear Regression Results -- Multiple Linear Regression Results -- Nonparametric Modeling Results: Splines, CART Trees and Random Forest Partial Dependence Plots -- Bayesian Network Analysis Results -- Insights from Comparing Linear and Nonparametric Nonlinear Regression Models -- Discussion and Conclusions -- References Chapter 12: Case Study: Are Low Concentrations of Benzene Disproportionately Dangerous? |
| ctrlnum | (ZDB-30-PQE)EBC6386164 (ZDB-30-PAD)EBC6386164 (ZDB-89-EBL)EBL6386164 (OCoLC)1204208128 (DE-599)BVBBV048224468 |
| dewey-full | 363.7392 |
| dewey-hundreds | 300 - Social sciences |
| dewey-ones | 363 - Other social problems and services |
| dewey-raw | 363.7392 |
| dewey-search | 363.7392 |
| dewey-sort | 3363.7392 |
| dewey-tens | 360 - Social problems and services; associations |
| discipline | Soziologie |
| discipline_str_mv | Soziologie |
| format | Electronic eBook |
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-- Recent Progress in Elucidating the Roles of the NLRP3 Inflammasome and Chronic Inflammation -- A Risk Assessment Paradigm for NLRP3 Inflammasome-Mediated Carcinogenesis -- Qualitative Aspects of NLRP3 Inflammasome-Mediated Dose-Response</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">Quantitative Dose-Response Modeling for Asbestos: Methods and Models -- PBPK Model for Asbestos Fibers -- Chronic Inflammation Model -- Carcinogenesis Models for Lung Cancer (LC) and Malignant Mesothelioma (MM) -- Results: Dose-Response and Age-Specific Risk Implications of the I-TSCE Model -- Discussion -- Limitations and Robustness of the I-TSCE Model -- Contributions and Relationships to Previous Research Literature -- Conclusions -- Appendix 1: A Physiologically Based Pharmacokinetics (PBPK) Model for Asbestos Fibers in the Human Body -- Appendix 2: A Model of the Transition from Acute to Chronic NLRP3-Inflammasome-Mediated Inflammation -- Appendix 3: Two-Stage Clonal Expansion (TSCE) Models of Lung Cancer (LC) and Malignant Mesothelioma (MM) Risk -- References -- Chapter 6: Nonlinear Dose-Time-Response Risk Models for Protecting Worker Health -- Introduction -- Science-Policy Background: Do Linear No Threshold (LNT) Assumptions Protect Worker Health? -- Biological Background: NLRP3 Inflammasomes and Response Thresholds, Revisited -- Data and Methods: Physiologically-Based Pharmacokinetic (PBPK) Modeling -- Results -- Constant Exposure Concentrations -- Time-Varying Exposures -- Discussion -- Conclusions -- References -- Part II: Statistics, Causality, and Machine Learning for Health Risk Assessment -- Chapter 7: Why Not Replace Quantitative Risk Assessment Models with Regression Models? -- Introduction: Use and Abuse of Regression Models in Public Health Risk Assessments -- A Methodological Thought Experiment: Reduced-Form Model Regression Coefficients Are Not Causal Coefficients -- Discussion: Why the Difference? -- Conclusions -- Appendix: Data Sources -- References -- Chapter 8: Causal vs. Spurious Spatial Exposure-Response Associations in Health Risk Analysis -- Introduction -- Spatial Exposure-Response Associations in Risk Assessment</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">Data and Methods -- Results -- Fatal Car Accidents and Proximity to Putative Asbestos Sources (Ultramafic Rock Deposits) -- Kaposi's Sarcoma and Proximity to Ultramafic Rock Deposits -- Mesothelioma and Proximity to Ultramafic Rock Deposits -- Results for Point Sources -- Theoretical Interpretation: What Does a Spatial Regression Coefficient Show? -- Discussion and Conclusions -- References -- Chapter 9: Methods of Causal Analysis for Health Risk Assessment with Observational Data -- Introduction -- Science-Policy Setting: Bradford-Hill Considerations, False Causal Conclusions, and Reproducibility Challenges -- Background on Modern Causal Discovery Concepts and Terminology -- Causal Graph Concepts and Terminology -- Causal Concepts -- Why these Distinctions Matter in Practice: The CARET Trial as an Example -- Strength of Association and Mutual Information -- Some Limitations of Strength of Association -- The Mutual Information Criterion -- Criteria for Orienting Arrows in Causal Graphs: Temporality, Directed Information, Homoscedasticity, Exogeneity, Knowledge-Based Constraints, and Quasi-Experiments -- Temporality and Directed Information -- Consistency Checks: Internal and External Consistency and Generalization and Synthesis across Studies -- Learning from Apparent Inconsistencies -- Causal Coherence, Biological Plausibility, and Valid Analogy: Explanations of Exposure-Response Dependencies via Directed Paths of Causal Mechanisms -- Specificity and Biological Gradient: Special Cases of Testable Implications -- Summary: Making Limited Progress in the Spirit of Hill -- Discussion -- A Dose of Humility: How Useful are Our Proposed Causal Criteria for Observational Data? -- The CARET Trial Reconsidered -- Elevating the Goals for Causality Criteria: Returning to Manipulative Causation -- Conclusions</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">Appendix 1: Directed Acyclic Graph (DAG) Concepts and Methods -- Adjustment Sets, Partial Dependence Plots, and Estimation of Predictive Causal Effects -- Appendix 2: Information Theory and Causal Graph Learning -- Some Limitations of Graph-Learning Algorithms: Mutual Information Does not Necessarily Imply Causality -- Appendix 3: Concepts of Causation -- Appendix 4: Software for Dynamic Bayesian Networks and Directed Information -- Appendix 5: Non-temporal Methods for Inferring Direction of Information Flow -- Homoscedastic Errors and LiNGAM -- Knowledge-Based and Exogeneity Constraints -- Quasi-Experiments (QEs) and Assumption-Based Constraints on Arrow Directions -- References -- Chapter 10: Clarifying Exposure-Response Regression Coefficients with Bayesian Networks: Blood Lead-Mortality Associations an Example -- Introduction -- Science-Policy Challenge: Determining Whether Data Sets Provide Evidence that Risk Depends on Exposure -- Example Data Set -- Statistical Analysis Methods -- Results -- Results from Logistic Regression Modeling -- Results for Bayesian Network (BN) Modeling -- Discussion -- Limitations of Bayesian Network Modeling -- Contributions of Bayesian Network Modeling to Improving Risk Assessment -- Practical Implications -- Conclusions -- Appendix: Variables and Data -- References -- Chapter 11: Case Study: Does Molybdenum Decrease Testosterone? -- Introduction -- Data -- Statistical Analysis Methods -- Results -- Linear Correlation and Descriptive Statistics Results -- Scatterplot and Simple Linear Regression Results -- Multiple Linear Regression Results -- Nonparametric Modeling Results: Splines, CART Trees and Random Forest Partial Dependence Plots -- Bayesian Network Analysis Results -- Insights from Comparing Linear and Nonparametric Nonlinear Regression Models -- Discussion and Conclusions -- References</subfield></datafield><datafield tag="505" ind1="8" ind2=" "><subfield code="a">Chapter 12: Case Study: Are Low Concentrations of Benzene Disproportionately Dangerous?</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Air-Pollution-Risk assessment</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Health risk assessment-Statistical methods</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Erscheint auch als</subfield><subfield code="n">Druck-Ausgabe</subfield><subfield code="a">Cox Jr., Louis Anthony</subfield><subfield code="t">Quantitative Risk Analysis of Air Pollution Health Effects</subfield><subfield code="d">Cham : Springer International Publishing AG,c2020</subfield><subfield code="z">9783030573577</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">ZDB-30-PQE</subfield></datafield><datafield tag="999" ind1=" " ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-033605201</subfield></datafield><datafield tag="966" ind1="e" ind2=" "><subfield code="u">https://ebookcentral.proquest.com/lib/hwr/detail.action?docID=6386164</subfield><subfield code="l">HWR01</subfield><subfield code="p">ZDB-30-PQE</subfield><subfield code="q">HWR_PDA_PQE</subfield><subfield code="x">Aggregator</subfield><subfield code="3">Volltext</subfield></datafield></record></collection> |
| id | DE-604.BV048224468 |
| illustrated | Not Illustrated |
| index_date | 2024-07-03T19:50:39Z |
| indexdate | 2024-07-10T09:32:29Z |
| institution | BVB |
| isbn | 9783030573584 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-033605201 |
| oclc_num | 1204208128 |
| open_access_boolean | |
| owner | DE-2070s |
| owner_facet | DE-2070s |
| physical | 1 Online-Ressource (543 Seiten) |
| psigel | ZDB-30-PQE ZDB-30-PQE HWR_PDA_PQE |
| publishDate | 2020 |
| publishDateSearch | 2020 |
| publishDateSort | 2020 |
| publisher | Springer International Publishing AG |
| record_format | marc |
| series2 | International Series in Operations Research and Management Science Ser. |
| spelling | Cox Jr., Louis Anthony Verfasser aut Quantitative Risk Analysis of Air Pollution Health Effects Cham Springer International Publishing AG 2020 ©2021 1 Online-Ressource (543 Seiten) txt rdacontent c rdamedia cr rdacarrier International Series in Operations Research and Management Science Ser. v.299 Description based on publisher supplied metadata and other sources Intro -- Preface -- Acknowledgments -- Contents -- Part I: Estimating and Simulating Dynamic Health Risks -- Chapter 1: Scientific Method for Health Risk Analysis: The Example of Fine Particulate Matter Air Pollution and COVID-19 Mortality Risk -- Introduction: Scientific Method for Quantitative Risk Assessment -- Scientific Method vs. Weight-of-Evidence Consensus Judgments as Paradigms for Regulatory Risk Analysis -- A Recent Example: PM2.5 and COVID-19 Mortality -- Do Positive Regression Coefficients Provide Evidence of Causation? -- Positive Regression Coefficients Created by Model Specification Error and Other Causes -- Conclusion: Regression Models and Judgment Should Complement Science, not Substitute for it -- Appendix 1: Data -- References -- Chapter 2: Modeling Nonlinear Dose-Response Functions: Regression, Simulation, and Causal Networks -- Introduction -- Why Does Nonlinearity Matter? -- Hazard Identification -- Challenges for Regression-Based Hazard Identification -- Significant Regression Coefficients Arising from Trends and from Omitted Confounders -- Significant Regression Coefficients Arising from Measurement Errors in Confounders -- Significant Regression Coefficients Arising from Model Specification Errors -- Significant Regression Coefficients Arising from Residual Confounding -- Surrogate Variables -- Variable Selection -- Significant Regression Coefficients Arising from Competing Explanations -- Significant Regression Coefficients Arising from Attribution of Joint Effects -- Some Alternatives to Regression for Hazard Identification -- Dose-Response Modeling -- Challenges for Regression-Based Dose-Response Modeling -- Bayesian Networks for Dose-Response Modeling -- Dynamic Simulation for Dose-Response Modeling -- Exposure Assessment -- Risk Characterization, Uncertainty Characterization, and Risk Communication LNT Rationales: Additivity to Background, Population Heterogeneity, and Upper-Bound Estimates -- Use of Sensitivity Analysis and Scientific Judgment -- Discussion and Conclusions: Risk Management and Risk Assessment Implications of Nonlinearity -- References -- Chapter 3: Simulating Exposure-Related Health Effects: Basic Ideas -- Introduction -- Chronic Inflammation and Biological Roles of the Inflammasome in Exposure-Associated Diseases -- Modeling the Time for Internal Doses to Reach Activation Thresholds -- Internal Doses After Exposures to Constant Concentrations with Elimination -- Internal Doses After Exposures to Constant Concentrations Without Elimination -- Activation of Chronic Inflammation -- Internal Doses After Time-Varying Exposure Concentrations -- Discussion -- Conclusions -- References -- Chapter 4: Case Study: Occupational Health Risks from Crystalline Silica -- Introduction -- Setting: Linear No-Threshold (LNT) vs. Threshold Dose-Response Models -- Biological Thresholds for NLRP3 Inflammasome Priming, Assembly, and Activation -- Modeling the Transition from Transient to Chronic Inflammation -- A PBPK Model for Internal Doses of Respirable Crystalline Silica (RCS) -- Sensitivity to Interindividual Variability and Physical and Chemical Properties of RCS -- Inflammation-Mediated Promotion of Lung Cancer in a Two-Stage Clonal Expansion Model -- Discussion and Conclusions: Implications for RCS Dose-Response Modeling -- References -- Chapter 5: Case Study: Health Risks from Asbestos Exposures -- Introduction -- Is There a Threshold for Asbestos Carcinogenicity? -- Recent Progress in Elucidating the Roles of the NLRP3 Inflammasome and Chronic Inflammation -- A Risk Assessment Paradigm for NLRP3 Inflammasome-Mediated Carcinogenesis -- Qualitative Aspects of NLRP3 Inflammasome-Mediated Dose-Response Quantitative Dose-Response Modeling for Asbestos: Methods and Models -- PBPK Model for Asbestos Fibers -- Chronic Inflammation Model -- Carcinogenesis Models for Lung Cancer (LC) and Malignant Mesothelioma (MM) -- Results: Dose-Response and Age-Specific Risk Implications of the I-TSCE Model -- Discussion -- Limitations and Robustness of the I-TSCE Model -- Contributions and Relationships to Previous Research Literature -- Conclusions -- Appendix 1: A Physiologically Based Pharmacokinetics (PBPK) Model for Asbestos Fibers in the Human Body -- Appendix 2: A Model of the Transition from Acute to Chronic NLRP3-Inflammasome-Mediated Inflammation -- Appendix 3: Two-Stage Clonal Expansion (TSCE) Models of Lung Cancer (LC) and Malignant Mesothelioma (MM) Risk -- References -- Chapter 6: Nonlinear Dose-Time-Response Risk Models for Protecting Worker Health -- Introduction -- Science-Policy Background: Do Linear No Threshold (LNT) Assumptions Protect Worker Health? -- Biological Background: NLRP3 Inflammasomes and Response Thresholds, Revisited -- Data and Methods: Physiologically-Based Pharmacokinetic (PBPK) Modeling -- Results -- Constant Exposure Concentrations -- Time-Varying Exposures -- Discussion -- Conclusions -- References -- Part II: Statistics, Causality, and Machine Learning for Health Risk Assessment -- Chapter 7: Why Not Replace Quantitative Risk Assessment Models with Regression Models? -- Introduction: Use and Abuse of Regression Models in Public Health Risk Assessments -- A Methodological Thought Experiment: Reduced-Form Model Regression Coefficients Are Not Causal Coefficients -- Discussion: Why the Difference? -- Conclusions -- Appendix: Data Sources -- References -- Chapter 8: Causal vs. Spurious Spatial Exposure-Response Associations in Health Risk Analysis -- Introduction -- Spatial Exposure-Response Associations in Risk Assessment Data and Methods -- Results -- Fatal Car Accidents and Proximity to Putative Asbestos Sources (Ultramafic Rock Deposits) -- Kaposi's Sarcoma and Proximity to Ultramafic Rock Deposits -- Mesothelioma and Proximity to Ultramafic Rock Deposits -- Results for Point Sources -- Theoretical Interpretation: What Does a Spatial Regression Coefficient Show? -- Discussion and Conclusions -- References -- Chapter 9: Methods of Causal Analysis for Health Risk Assessment with Observational Data -- Introduction -- Science-Policy Setting: Bradford-Hill Considerations, False Causal Conclusions, and Reproducibility Challenges -- Background on Modern Causal Discovery Concepts and Terminology -- Causal Graph Concepts and Terminology -- Causal Concepts -- Why these Distinctions Matter in Practice: The CARET Trial as an Example -- Strength of Association and Mutual Information -- Some Limitations of Strength of Association -- The Mutual Information Criterion -- Criteria for Orienting Arrows in Causal Graphs: Temporality, Directed Information, Homoscedasticity, Exogeneity, Knowledge-Based Constraints, and Quasi-Experiments -- Temporality and Directed Information -- Consistency Checks: Internal and External Consistency and Generalization and Synthesis across Studies -- Learning from Apparent Inconsistencies -- Causal Coherence, Biological Plausibility, and Valid Analogy: Explanations of Exposure-Response Dependencies via Directed Paths of Causal Mechanisms -- Specificity and Biological Gradient: Special Cases of Testable Implications -- Summary: Making Limited Progress in the Spirit of Hill -- Discussion -- A Dose of Humility: How Useful are Our Proposed Causal Criteria for Observational Data? -- The CARET Trial Reconsidered -- Elevating the Goals for Causality Criteria: Returning to Manipulative Causation -- Conclusions Appendix 1: Directed Acyclic Graph (DAG) Concepts and Methods -- Adjustment Sets, Partial Dependence Plots, and Estimation of Predictive Causal Effects -- Appendix 2: Information Theory and Causal Graph Learning -- Some Limitations of Graph-Learning Algorithms: Mutual Information Does not Necessarily Imply Causality -- Appendix 3: Concepts of Causation -- Appendix 4: Software for Dynamic Bayesian Networks and Directed Information -- Appendix 5: Non-temporal Methods for Inferring Direction of Information Flow -- Homoscedastic Errors and LiNGAM -- Knowledge-Based and Exogeneity Constraints -- Quasi-Experiments (QEs) and Assumption-Based Constraints on Arrow Directions -- References -- Chapter 10: Clarifying Exposure-Response Regression Coefficients with Bayesian Networks: Blood Lead-Mortality Associations an Example -- Introduction -- Science-Policy Challenge: Determining Whether Data Sets Provide Evidence that Risk Depends on Exposure -- Example Data Set -- Statistical Analysis Methods -- Results -- Results from Logistic Regression Modeling -- Results for Bayesian Network (BN) Modeling -- Discussion -- Limitations of Bayesian Network Modeling -- Contributions of Bayesian Network Modeling to Improving Risk Assessment -- Practical Implications -- Conclusions -- Appendix: Variables and Data -- References -- Chapter 11: Case Study: Does Molybdenum Decrease Testosterone? -- Introduction -- Data -- Statistical Analysis Methods -- Results -- Linear Correlation and Descriptive Statistics Results -- Scatterplot and Simple Linear Regression Results -- Multiple Linear Regression Results -- Nonparametric Modeling Results: Splines, CART Trees and Random Forest Partial Dependence Plots -- Bayesian Network Analysis Results -- Insights from Comparing Linear and Nonparametric Nonlinear Regression Models -- Discussion and Conclusions -- References Chapter 12: Case Study: Are Low Concentrations of Benzene Disproportionately Dangerous? Air-Pollution-Risk assessment Health risk assessment-Statistical methods Erscheint auch als Druck-Ausgabe Cox Jr., Louis Anthony Quantitative Risk Analysis of Air Pollution Health Effects Cham : Springer International Publishing AG,c2020 9783030573577 |
| spellingShingle | Cox Jr., Louis Anthony Quantitative Risk Analysis of Air Pollution Health Effects Intro -- Preface -- Acknowledgments -- Contents -- Part I: Estimating and Simulating Dynamic Health Risks -- Chapter 1: Scientific Method for Health Risk Analysis: The Example of Fine Particulate Matter Air Pollution and COVID-19 Mortality Risk -- Introduction: Scientific Method for Quantitative Risk Assessment -- Scientific Method vs. Weight-of-Evidence Consensus Judgments as Paradigms for Regulatory Risk Analysis -- A Recent Example: PM2.5 and COVID-19 Mortality -- Do Positive Regression Coefficients Provide Evidence of Causation? -- Positive Regression Coefficients Created by Model Specification Error and Other Causes -- Conclusion: Regression Models and Judgment Should Complement Science, not Substitute for it -- Appendix 1: Data -- References -- Chapter 2: Modeling Nonlinear Dose-Response Functions: Regression, Simulation, and Causal Networks -- Introduction -- Why Does Nonlinearity Matter? -- Hazard Identification -- Challenges for Regression-Based Hazard Identification -- Significant Regression Coefficients Arising from Trends and from Omitted Confounders -- Significant Regression Coefficients Arising from Measurement Errors in Confounders -- Significant Regression Coefficients Arising from Model Specification Errors -- Significant Regression Coefficients Arising from Residual Confounding -- Surrogate Variables -- Variable Selection -- Significant Regression Coefficients Arising from Competing Explanations -- Significant Regression Coefficients Arising from Attribution of Joint Effects -- Some Alternatives to Regression for Hazard Identification -- Dose-Response Modeling -- Challenges for Regression-Based Dose-Response Modeling -- Bayesian Networks for Dose-Response Modeling -- Dynamic Simulation for Dose-Response Modeling -- Exposure Assessment -- Risk Characterization, Uncertainty Characterization, and Risk Communication LNT Rationales: Additivity to Background, Population Heterogeneity, and Upper-Bound Estimates -- Use of Sensitivity Analysis and Scientific Judgment -- Discussion and Conclusions: Risk Management and Risk Assessment Implications of Nonlinearity -- References -- Chapter 3: Simulating Exposure-Related Health Effects: Basic Ideas -- Introduction -- Chronic Inflammation and Biological Roles of the Inflammasome in Exposure-Associated Diseases -- Modeling the Time for Internal Doses to Reach Activation Thresholds -- Internal Doses After Exposures to Constant Concentrations with Elimination -- Internal Doses After Exposures to Constant Concentrations Without Elimination -- Activation of Chronic Inflammation -- Internal Doses After Time-Varying Exposure Concentrations -- Discussion -- Conclusions -- References -- Chapter 4: Case Study: Occupational Health Risks from Crystalline Silica -- Introduction -- Setting: Linear No-Threshold (LNT) vs. Threshold Dose-Response Models -- Biological Thresholds for NLRP3 Inflammasome Priming, Assembly, and Activation -- Modeling the Transition from Transient to Chronic Inflammation -- A PBPK Model for Internal Doses of Respirable Crystalline Silica (RCS) -- Sensitivity to Interindividual Variability and Physical and Chemical Properties of RCS -- Inflammation-Mediated Promotion of Lung Cancer in a Two-Stage Clonal Expansion Model -- Discussion and Conclusions: Implications for RCS Dose-Response Modeling -- References -- Chapter 5: Case Study: Health Risks from Asbestos Exposures -- Introduction -- Is There a Threshold for Asbestos Carcinogenicity? -- Recent Progress in Elucidating the Roles of the NLRP3 Inflammasome and Chronic Inflammation -- A Risk Assessment Paradigm for NLRP3 Inflammasome-Mediated Carcinogenesis -- Qualitative Aspects of NLRP3 Inflammasome-Mediated Dose-Response Quantitative Dose-Response Modeling for Asbestos: Methods and Models -- PBPK Model for Asbestos Fibers -- Chronic Inflammation Model -- Carcinogenesis Models for Lung Cancer (LC) and Malignant Mesothelioma (MM) -- Results: Dose-Response and Age-Specific Risk Implications of the I-TSCE Model -- Discussion -- Limitations and Robustness of the I-TSCE Model -- Contributions and Relationships to Previous Research Literature -- Conclusions -- Appendix 1: A Physiologically Based Pharmacokinetics (PBPK) Model for Asbestos Fibers in the Human Body -- Appendix 2: A Model of the Transition from Acute to Chronic NLRP3-Inflammasome-Mediated Inflammation -- Appendix 3: Two-Stage Clonal Expansion (TSCE) Models of Lung Cancer (LC) and Malignant Mesothelioma (MM) Risk -- References -- Chapter 6: Nonlinear Dose-Time-Response Risk Models for Protecting Worker Health -- Introduction -- Science-Policy Background: Do Linear No Threshold (LNT) Assumptions Protect Worker Health? -- Biological Background: NLRP3 Inflammasomes and Response Thresholds, Revisited -- Data and Methods: Physiologically-Based Pharmacokinetic (PBPK) Modeling -- Results -- Constant Exposure Concentrations -- Time-Varying Exposures -- Discussion -- Conclusions -- References -- Part II: Statistics, Causality, and Machine Learning for Health Risk Assessment -- Chapter 7: Why Not Replace Quantitative Risk Assessment Models with Regression Models? -- Introduction: Use and Abuse of Regression Models in Public Health Risk Assessments -- A Methodological Thought Experiment: Reduced-Form Model Regression Coefficients Are Not Causal Coefficients -- Discussion: Why the Difference? -- Conclusions -- Appendix: Data Sources -- References -- Chapter 8: Causal vs. Spurious Spatial Exposure-Response Associations in Health Risk Analysis -- Introduction -- Spatial Exposure-Response Associations in Risk Assessment Data and Methods -- Results -- Fatal Car Accidents and Proximity to Putative Asbestos Sources (Ultramafic Rock Deposits) -- Kaposi's Sarcoma and Proximity to Ultramafic Rock Deposits -- Mesothelioma and Proximity to Ultramafic Rock Deposits -- Results for Point Sources -- Theoretical Interpretation: What Does a Spatial Regression Coefficient Show? -- Discussion and Conclusions -- References -- Chapter 9: Methods of Causal Analysis for Health Risk Assessment with Observational Data -- Introduction -- Science-Policy Setting: Bradford-Hill Considerations, False Causal Conclusions, and Reproducibility Challenges -- Background on Modern Causal Discovery Concepts and Terminology -- Causal Graph Concepts and Terminology -- Causal Concepts -- Why these Distinctions Matter in Practice: The CARET Trial as an Example -- Strength of Association and Mutual Information -- Some Limitations of Strength of Association -- The Mutual Information Criterion -- Criteria for Orienting Arrows in Causal Graphs: Temporality, Directed Information, Homoscedasticity, Exogeneity, Knowledge-Based Constraints, and Quasi-Experiments -- Temporality and Directed Information -- Consistency Checks: Internal and External Consistency and Generalization and Synthesis across Studies -- Learning from Apparent Inconsistencies -- Causal Coherence, Biological Plausibility, and Valid Analogy: Explanations of Exposure-Response Dependencies via Directed Paths of Causal Mechanisms -- Specificity and Biological Gradient: Special Cases of Testable Implications -- Summary: Making Limited Progress in the Spirit of Hill -- Discussion -- A Dose of Humility: How Useful are Our Proposed Causal Criteria for Observational Data? -- The CARET Trial Reconsidered -- Elevating the Goals for Causality Criteria: Returning to Manipulative Causation -- Conclusions Appendix 1: Directed Acyclic Graph (DAG) Concepts and Methods -- Adjustment Sets, Partial Dependence Plots, and Estimation of Predictive Causal Effects -- Appendix 2: Information Theory and Causal Graph Learning -- Some Limitations of Graph-Learning Algorithms: Mutual Information Does not Necessarily Imply Causality -- Appendix 3: Concepts of Causation -- Appendix 4: Software for Dynamic Bayesian Networks and Directed Information -- Appendix 5: Non-temporal Methods for Inferring Direction of Information Flow -- Homoscedastic Errors and LiNGAM -- Knowledge-Based and Exogeneity Constraints -- Quasi-Experiments (QEs) and Assumption-Based Constraints on Arrow Directions -- References -- Chapter 10: Clarifying Exposure-Response Regression Coefficients with Bayesian Networks: Blood Lead-Mortality Associations an Example -- Introduction -- Science-Policy Challenge: Determining Whether Data Sets Provide Evidence that Risk Depends on Exposure -- Example Data Set -- Statistical Analysis Methods -- Results -- Results from Logistic Regression Modeling -- Results for Bayesian Network (BN) Modeling -- Discussion -- Limitations of Bayesian Network Modeling -- Contributions of Bayesian Network Modeling to Improving Risk Assessment -- Practical Implications -- Conclusions -- Appendix: Variables and Data -- References -- Chapter 11: Case Study: Does Molybdenum Decrease Testosterone? -- Introduction -- Data -- Statistical Analysis Methods -- Results -- Linear Correlation and Descriptive Statistics Results -- Scatterplot and Simple Linear Regression Results -- Multiple Linear Regression Results -- Nonparametric Modeling Results: Splines, CART Trees and Random Forest Partial Dependence Plots -- Bayesian Network Analysis Results -- Insights from Comparing Linear and Nonparametric Nonlinear Regression Models -- Discussion and Conclusions -- References Chapter 12: Case Study: Are Low Concentrations of Benzene Disproportionately Dangerous? Air-Pollution-Risk assessment Health risk assessment-Statistical methods |
| title | Quantitative Risk Analysis of Air Pollution Health Effects |
| title_auth | Quantitative Risk Analysis of Air Pollution Health Effects |
| title_exact_search | Quantitative Risk Analysis of Air Pollution Health Effects |
| title_exact_search_txtP | Quantitative Risk Analysis of Air Pollution Health Effects |
| title_full | Quantitative Risk Analysis of Air Pollution Health Effects |
| title_fullStr | Quantitative Risk Analysis of Air Pollution Health Effects |
| title_full_unstemmed | Quantitative Risk Analysis of Air Pollution Health Effects |
| title_short | Quantitative Risk Analysis of Air Pollution Health Effects |
| title_sort | quantitative risk analysis of air pollution health effects |
| topic | Air-Pollution-Risk assessment Health risk assessment-Statistical methods |
| topic_facet | Air-Pollution-Risk assessment Health risk assessment-Statistical methods |
| work_keys_str_mv | AT coxjrlouisanthony quantitativeriskanalysisofairpollutionhealtheffects |