1 probabilistic risk assessment in environmental toxicology risk: perception, policy & practice...
TRANSCRIPT
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Probabilistic Risk Assessmentin Environmental Toxicology
RISK: Perception, Policy & Practice Workshop October 3-4, 2007
SAMSI, Research Triangle Park, NC
John W. Green, Ph.D., Ph.D.
Senior Consultant: Biostatistics
DuPont Applied Statistics Group
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Topics Addressed in Environmental Risk Assessment
• Present & proposed regulatory methods– Concerns– Micro- vs macro-assessments
• Variability vs Uncertainty• Exposure and Toxicity
– Exposure models (Monte Carlo, PBA)• extensive literature on exposure
– Toxicity• Species Sensitivity Distributions (Monte Carlo)
– Combining the two for risk assessment
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Deterministic Probabilistic
Toxicity
Exposure
TERTER
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Assessment of Toxicity
• Species level assessments– Laboratory toxicity experiments– Greenhouse studies– Field studies
• Ecosystem level assessment– Most sensitive species– Mesocosm studies– Species Sensitivity Distribution
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Species Level Assessment:NOEC (aka NOAEL) and ECx
• LOEC = lowest tested conc at which a statistically significant adverse effect is observed
• NOEC = highest tested conc < LOEC – LOEC, NOEC depend on experimental
design & statistical test
• ECx = conc producing x% effect– ECx depends on experimental design and
model and choice of x
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Ecosystem level assessment
Current Method Determine the NOEC (or EC50) for each
species representing an ecosystem Find the smallest NOEC (or EC50) Divide it by 10, 100, or 1000
(uncertainty factor) Regulate from this value
or argue against it
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• Collect a consistent measure of toxicity from a representative set of species– EC50s or NOECs (not both)
• Fit a distribution (SSD) to these numerical measures
• Estimate concentration, HC5, that protects 95% of species in ecosystem
• Advantages and problems with SSDs
Ecosystem level assessmentProbabilistic Approach
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SSD by Habitat
Visual groupings are not taxonomic classes but defined by habitat , possibly related to mode of action
Selection of Toxicity Data
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How Many Species?
• Newman’s method: 40 to 60 species – Snowball’s chance…– Might reduce this by good choice of
groups to model
• Aldenberg-Jaworski: 1 species will do– If you make enough assumptions,…
• 8 is usual target
• 5 is common
• 20-25 in some non-target plant studies
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Which Distribution to Fit?
• Normal, log-normal, log-logistic, Burr III…?– With 5-8 data points, selecting the “right”
distribution is a challenge• Next slide gives simulation results
• Does it matter?– Recent simulation study suggests yes
• 2nd slide following: uniform [0,1] generated• Various distributions fit
– Actual laboratory data suggests yes
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Power to Detect non-LognormalityExponential Distribution Generated
SW KS AD CM Sample Size
10 11 10 8 4
16 13 16 15 5
24 19 24 23 6
35 26 32 31 8
46 31 43 40 10
68 43 62 58 15
84 60 77 72 20
97 78 93 91 30
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Does it Matter?Q05 Simulations: True value =0.05
Uniform [0,1] Generated
Distribution 3rd Qrtl Q5median Ist Qrtl Size
Exponential 0.2341 0.08295 -0.02438 4
Normal 0.19371 0.02227 -0.09323 4
Exponential 0.19859 0.06788 -0.01593 5
Lognormal 0.26667 0.1385 0.064521 5
Normal 0.16495 0.02547 -0.08768 5
Exponential 0.16714 0.05756 -0.01171 6
Lognormal 0.23317 0.13017 0.065593 6
Normal 0.13695 0.02157 -0.07665 6
Exponential 0.139 0.05249 -0.00116 8
Lognormal 0.1993 0.11927 0.063502 8
Normal 0.12884 0.02709 -0.05738 8
Exponential 0.11034 0.04692 0.004643 10
Lognormal 0.17223 0.10481 0.060777 10
Normal 0.10975 0.02209 -0.04842 10
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Which Laboratory Species?
One EUFRAM case study fits an SSD to the following
Aquatic toxicologists can comment (and have)
on whether these values belong to a meaningful population
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Variability and Uncertainty
Uncertainty reflects lack of knowledge of thesystem under study
Ex1: what distribution to fit for SSDEx2: what mathematical model to use to
estimate ECx
Increased knowledge will reduce uncertainty
Variability reflects lack of controlinherent variation or noise among individuals.
Increased knowledge of the animal or plant species will not reduce variability
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Variability & Uncertainty• The fitted distribution is assumed log-normal
– Defined by the population mean and variance
• Motivated in part by standard relationship shown below – Randomly sample from the χ2
(n-1) distribution.
– Then randomly sample from a normal with the above variance, and mean equal to sample mean
– Note: If formulas below are used, only variability is captured
1)1(
n
stx n
2)1(
2
n
ns
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Spaghetti plot Probabilities (vertical variable values) associated with a given value of log(EC50) are themselves distributed
For a given log(EC50) value, the middle 95% of these secondary probabilities defines 95% confidence interval for proportion of species affected at that conc
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For a given proportion (value of y), the values of Log(EC50) (horizontal variable) that might have produced the given y-value are distributed.
For a given y value, the middle 95% of these x-values defines 95% confidence bounds on the distribution of log(ECy) values.
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Summary Plot for SSD
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Putting it All Together
Joint Probability Curves
Plot exposure and toxicity distributions together to understand the likelihood of the exposure concentration exceeding the toxic threshold of a given percent of the population
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Calculating Risk
The risk is given by
Pr[Xe>Xs]
where Xe = exposure, Xs =sensitivity or toxicity
This is an “average” probability that exposure
will exceed the sensitivity of species exposed
Not clear that this captures the right risk
Work needed here
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Conclusions
• PRA can bring increased reality to risk management by– communicating uncertainty more realistically– separating uncertainty from variability– clarifying risk of environmental effects
• PRA is only as good as the assumptions and theories on which it rests
• The bad news is that implementation is running ahead of understanding
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Conclusions
• SSDs based on tiny datasets unreliable• Need to identify what populations are
appropriate subjects for SSD is vital • 2-D Monte Carlo methods often assume
independent inputs or specific correlations– Not realistic in many cases
• PBA can accommodate dependent inputs– But can lead to wide bounds– Have other limitations restricting use
• MCMC can accommodate correlated inputs– But are mathematically demanding