Should We Risk It?,9780691004266

Should We Risk It?

by Kammen, Daniel M.; Hassenzahl, David M.
ISBN13: 9780691004266
ISBN10: 0691004269
Format: Hardcover
Pub. Date: 1999-05-01
Publisher(s): Princeton Univ Pr
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Summary

This book will be very useful as a text in a risk-analysis class. It will also be a valuable reference for practitioners of risk assessment in industry, government, and consulting. . . . The organization of the book is logical and effective.

Author Biography

Daniel Kammen is Associate Professor of Energy and Society in the Energy and Resources Group at the University of California, Berkeley David Hassenzahl is a graduate student in the Science, Technology, and Environmental Policy Program at the Woodrow Wilson School of Public and International Affairs, Princeton University

Table of Contents

Preface xv
Acknowledgments xix
Introduction
3(28)
Defining Risk
3(3)
Structure of the Book
6(2)
Risk Analysis and Public Policy
8(22)
Getting Started
16(5)
Data Needs
21(2)
Using Data
23(1)
Additional Cases
24(1)
Additional Curves
24(1)
Does the Dose Make the Poison?
25(1)
One in a Million Risks
25(1)
Surfing and Smoking
26(1)
Risks of Nuclear Power
26(4)
References
30(1)
Basic Models and Risk Problems
31(52)
Introduction
31(1)
Basic Modeling
32(33)
Volatile Organic Emissions from Household Materials: Wallpaper Glue
35(2)
Indoor Radon Exposure
37(13)
Revisited
50(1)
Equilibrium Concentration
50(1)
Simple PBPK Model--Continuous Dose
51(5)
Alternative Depictions
56(1)
PBPK--Finite Dose of Barium
56(7)
How Much Resolution Is Too Much?
63(1)
How Much Information Is Needed?
64(1)
Sensitivity Analysis
64(1)
Cause and Effect Relationships
65(4)
Radon and Cancer
65(4)
Mechanistic Models and Curve Fitting
69(12)
Conceiving ``Mechanistic'' Models
69(4)
Using the Wrong Model, Getting the Model Wrong
73(2)
Empirically Derived Dose Response
75(2)
Earthquakes versus Traffic Risks
77(4)
Conclusion
81(1)
References
82(1)
Review of Statistics for Risk Analysis
83(39)
Introduction: Statistics and the Philosophy of Risk Assessment
83(21)
Average Radon Exposure
85(2)
Radon Exposures in Different Regions
87(1)
Working with Data
87(3)
Mean and Median: Why Worry?
90(3)
Sample Data Revisited
93(3)
Hypothesis Testing and Confidence Intervals
96(6)
Making Decisions
102(2)
Distributions
104(17)
Moving Away from Ignorance
104(7)
Fitting a Model
111(6)
R2 Versus X2
117(1)
How Many Bins?
117(1)
Distributional Models
117(3)
Fitting the Lognormal Distribution
120(1)
Dealing with Grouped Data
120(1)
References
121(1)
Uncertainty, Monte Carlo Methods, and Bayesian Analysis
122(31)
Introduction
122(6)
Measuring the Speed of Light
124(2)
Energy Forecasts
126(2)
Forecasting the Impacts of Climate Change
128(1)
Bayesian Statistics
128(14)
Interpreting Test Results
132(2)
Bayesian Experts
134(1)
Bayesian Analysis of Radon Concentrations
134(8)
Monte Carlo Analysis
142(9)
Exposure to Tap Water in the Home
143(7)
Uncertainty or Incommensurability?
150(1)
References
151(2)
Toxicology
153(46)
Introduction
153(1)
Critical Assumptions for Modeling Disease
154(5)
Assumptions Specific to Toxicology
159(4)
Assumptions Specific to Epidemiology
163(34)
Test Data and Carcinogenesis: The Kil-EZ Example
166(8)
Calculating LEDIO
174(1)
Maximum Tolerated Dose
174(1)
1,3-Butadiene
174(1)
Fitting Data to Mechanistic Models: One-Hit, Two-Hit, Two-Stage Problem
175(14)
The Cost of Better Data
189(1)
Model-Free Extrapolation
190(1)
Variation in Cancer Susceptibility
190(1)
Variation in Sensitivity and Exposure
191(1)
Additional Data Set
191(1)
Exact Two-Stage Formulation
191(3)
Noncarcinogenic Effects: The EPA Approach
194(2)
Additional Noncancer End Points
196(1)
Formaldehyde
196(1)
References
197(2)
Epidemiology
199(32)
Introduction
199(31)
Cigarette Smoking and Cancer
199(7)
The Heavy Smoker
206(1)
All Deaths in the United States
206(1)
Risk in a Time of Cholera
207(3)
Pooled Data
210(1)
What Might Be Missing?
211(1)
Measurement Error
211(1)
Benzene Revisited: The Pliofilm Cohort Study
211(3)
Additional Data
214(1)
One-Hit Model and Epidemiological Data
214(2)
Additional Data
216(1)
Catching Cold: Exponential Spread of Disease
216(2)
Graphical Presentation
218(1)
Small Groups
218(1)
The Spread of AIDS: An Empirical Analysis, or, Does the Model Fit the Data?
218(7)
Double-Blind Study
225(3)
Side Effects: Test for Safety
228(1)
Low-Probability Effects
229(1)
Death by Cheese?
229(1)
Cancer Clusters--Real or Not?
229(1)
References
230(1)
Exposure Assessment
231(35)
Introduction
231(33)
Assessment of Exposures and Risks: The ChemLawn Claim
231(6)
Can Adults and Children Be Treated the Same?
237(1)
Contaminated Milk
237(2)
Biomass Fuels and Childhood Disease
239(2)
Bioaccumulation of Heptachlor in Beef
241(6)
Sensitive Receptors: Exposure to Children
247(1)
Exposure via Breast Milk
247(1)
Tricholoroethylene Exposure at Woburn, Massachusetts
247(9)
TCE at Woburn: The Big Picture
256(1)
Probability Distribution for Radon Exposures (or Risks)
257(1)
PBPK Models and Gender Differences in the Uptake of Benzene
258(2)
Acme Landfill
260(4)
References
264(2)
Technological Risk
266(38)
Introduction
266(5)
Lethality of Plutonium
267(3)
Cassini Spacecraft Reentry Risk
270(1)
Event Trees and Fault Trees
271(31)
Simple Pressure Relief System
272(6)
Additional Fault Tree
278(1)
Calculations from Fault Trees
278(1)
Missing Components, Common-Mode Failures, and the Human Element
278(2)
Additional Fault Trees
280(1)
Identifying Problems
281(1)
Anticipating the Unknown
281(1)
Oleum and the ``Clever-Proofing'' Problem
281(1)
Coal-Burning Power Plant Emissions
282(3)
Commercial Nuclear Power Safety: An Empirical Analysis
285(2)
The Risk from Nuclear Accidents
287(3)
Long-Term Risks from High-Level Nuclear Waste: A Case of Extreme Uncertainty
290(5)
Military Fighter Aircraft
295(4)
Who Decides What's Important?
299(1)
Risk of Domestic Airplane Flight
299(2)
Verifast Airlines
301(1)
Risk of International Airplane Flight
301(1)
References
302(2)
Decision Making
304(49)
Introduction
304(5)
Comparing Risk Reduction Measures By Dollar Values
304(4)
Cost Per Life-Year versus One in a Million
308(1)
Too Much or Too Little Spending?
308(1)
Mandatory Helmet Laws
309(1)
Discussion
309(1)
Is It Worth It?
309(1)
High-Level Nuclear Waste
309(1)
Organizing Processes
309(43)
Event Trees and Decision Analysis
310(7)
Who Lives There (and Does It Matter)?
317(1)
Additional Calculations
318(1)
Additional Uncertainty
318(1)
Analysis or Abuse? Transmission of Hoof Blister
318(8)
Dollars and Decisions
326(1)
Health and Environmental Technology Policy: Superfund Remediation
326(9)
Discounting or Dodging?
335(1)
Taking a Viewpoint
336(1)
Event-Decision Tree
336(1)
Regulatory Impact Analysis: Where the Rubber Meets the Road
336(16)
References
352(1)
Risk Perception and Communication
353(40)
Introduction
353(35)
Same Numbers, Different Stories
354(3)
Opening Dialogue
357(1)
Explaining Numbers
357(1)
Framing a Question: Loss or Gain?
358(2)
A Project to ``Restore'' or ``Improve'' a Wetland
360(1)
A Little Bit or a Lot? Violent Agreement on the Numbers
360(1)
Risk Level, Risk Perceptions, and Psychometric Models
361(2)
Cognitive Maps
363(1)
Radiation By Any Other Name
364(1)
Surfers and the Sun Revisited
365(1)
Ranking the Risks: Are the Experts Right?
366(3)
Implications of Differing Perspectives
369(1)
Risk, Trust, and Rationality
369(1)
Who Is More Concerned?
370(1)
The Availability Bias
370(2)
Aggregation
372(1)
How Intuitive Are Statistics? The Case of Electromagnetic Fields and Cancer
373(4)
Alternative Interpretations
377(1)
Use of Point Estimates versus Distributions
377(4)
Alternative Options
381(1)
When Is the New Leaf Turned?
381(1)
What Will They Think It Means?
382(1)
What To Tell Them?
383(1)
Alternative Labeling
384(1)
Situational Differences
384(1)
Saccharin and Alar: Why the Difference?
384(3)
When To Spin?
387(1)
Can or Should ``Zero Risk'' Be a Goal?
387(1)
Wrapping Up: Putting the Pieces Together
388(2)
References
390(3)
Appendix A. Z-Scores 393(2)
Appendix B. Student's T-test 395(3)
Appendix C. Chi-Squared Distribution 398(3)
Index 401

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