Cumulative Risk Assessment for Pesticide Regulation: A Risk Characterization Challenge

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Cumulative Risk Assessment for Pesticide
Regulation A Risk Characterization Challenge

• Mary A. Fox, PhD, MPH
• Linda C. Abbott, PhD
• USDA Office of Risk Assessment and Cost-Benefit
  Analysis

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Cumulative Risk Assessment for Pesticide
Regulation

• Debut of multi-chemical assessment of pesticide
  exposure through food, water, and residential
  uses
• Highly refined dose-response and exposure
  assessment
• Nationally representative dietary assessment
• What do we know about risk characterization for
  such complex assessments?

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Risk Characterization DefinedNAS 1996

• From Understanding Risk
• A synthesis and summary of information about a
  potentially hazardous situation that addresses
  the needs and interests of decision makers and
  interested and affected parties
• Analytic-deliberative process
• The process of organizing, evaluating, and
  communicating

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Outline

• Identify key elements of risk characterization
  for probabilistic assessments
• Evaluate the risk characterization chapter of the
  revised organophosphate (OP) assessment
• Review example highlighting importance of
  uncertainty and sensitivity analyses

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Resources

• Presidential/Congressional Commission on Risk
  Assessment/Management, 1997
• US EPA Guidance
• Principles for Monte-Carlo Analysis, 1997
• Risk Characterization Handbook, 2000
• US EPA Revised OP Cumulative Risk Assessment,
  2002
• DEEM and DEEM-FCID
• Data files for methamidophos

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Presidential Commission, 1997

• Quantitative and qualitative descriptions of risk
• Summarize weight of evidence
• Include information on the assessment itself
• Describe uncertainty and variability
• Use probability distributions as appropriate
• Use sensitivity analyses to identify key
  uncertainties
• Discuss costs and value of acquiring additional
  information
• Did not recommend
• Use of formal quantitative analysis of
  uncertainties for routine decision-making (i.e.
  local, low-stakes)

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Excerpts from Guiding Principles of Monte Carlo
Analysis, US EPA 1997

• Selecting Input Data and Distributions
• Conduct preliminary sensitivity analyses
• Evaluating Variability and Uncertainty
• Separate variability and uncertainty to provide
  greater accountability and transparency.
• Presenting the Results
• Provide a complete and thorough description of
  the model. The objectives are transparency and
  reproducibility.

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Risk Characterization Handbook, 2000

• Transparency
• Explicitness
• Clarity
• Easy to understand
• Consistency
• Consistent with other EPA actions
• Reasonableness
• Based on sound judgment

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Transparency Criteria

• Describe assessment approach, assumptions
• Describe plausible alternative assumptions
• Identify data gaps
• Distinguish science from policy
• Describe uncertainty
• Describe relative strengths of assessment

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Key Elements of Risk Characterization

• Separately track and describe uncertainty and
  variability
• Conduct sensitivity analyses
• Conduct formal uncertainty analyses
• Transparency and reproducibility
• Model components
• Basic operational details

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Evaluation of the Revised OP Cumulative Assessment

• Track and describe uncertainty and variability
• Sensitivity analyses
• Uncertainty analyses
• Yes, but spotty, qualitative, not comprehensive
• Transparency/reproducibility No
• Significance of many inputs unknown
• No mention of random seed, iterations used

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Recipes essential to dietary model

• Break down foods reported in dietary recall
  records to commodities that can be matched with
  pesticide residue data
• Recipes are representative with nutritional
  basis
• May not accurately reflect commodities eaten
• E.g. beef stew with vegetables recipe includes
  carrots but could be broccoli or leafy greens
• DEEM proprietary recipes
• DEEM-FCID EPA USDA collaboration
• Policy relevant

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Tomato Soup Recipe
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Experiment to examine importance of recipes

• Focus on one chemical- methamidophos
• Look at dietary exposure using DEEM and
  DEEM-FCID
• Forty 1000 iteration replicates with different
  random number seeds
• 1-6 year olds, 99.9th ile, exposures in
  mg/kg-day

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Between Model Exposure Variability Forty
1000-Iteration Replicates, Different Random
Number Seeds
DEEM Estimate DEEM-FCID Estimate Difference Difference
Minimum 7.43 x 10-4 8.54 x 10-4 1.02 x 10-4 13.56
Maximum 7.63 x 10-4 8.80 x 10-4 1.32 x 10-4 17.74
Mean 7.53 x 10-4 8.69 x 10-4 1.17 x 10-4 15.48
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Within Model Exposure Variability Forty
1000-Iteration Replicates, Different Random
Number Seeds
DEEM DEEM-FCID
Within model exposure variability 2.69 3.04
On par with US EPA findings for 1000-iteration
runs
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Exposure variability findings in
contextPreliminary data files, Children 1-2,
Single 1000 iteration runs
Commodities DEEM-FCID Estimate Difference Difference from complete model
Exclude grapes 0.00157 0.00024 15.29
Exclude apples 0.00163 0.00018 11.00
All included 0.00181
Average DEEM vs. FCID difference is 15
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Risk Metric Comparison 15 Difference
Margin of Exposure (MOE) Toxicological
Benchmark
Exposure Estimate Revised OPCRA Tox.
Benchmark for dietary 0.08 mg/kg-d MOE average
exposure DEEM 0.08 / 0.000753 106 MOE
average exposure FCID 0.08 / 0.000869 92
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Conclusions

• Risk characterization is incomplete
• Good guidance on risk characterization for
  complex models
• Continue to work and share findings