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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. Cumulative Risk Assessment for Pesticide Regulation.

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cumulative risk assessment for pesticide regulation a risk characterization challenge

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

cumulative risk assessment for pesticide regulation
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?
risk characterization defined nas 1996
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 …
outline
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
resources
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
presidential commission 1997
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)
excerpts from guiding principles of monte carlo analysis us epa 1997
Excerpts fromGuiding 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.
risk characterization handbook 2000
Risk Characterization Handbook, 2000
  • Transparency
    • Explicitness
  • Clarity
    • Easy to understand
  • Consistency
    • Consistent with other EPA actions
  • Reasonableness
    • Based on sound judgment
transparency criteria
Transparency Criteria
  • Describe assessment approach, assumptions
  • Describe plausible alternative assumptions
  • Identify data gaps
  • Distinguish science from policy
  • Describe uncertainty
  • Describe relative strengths of assessment
key elements of risk characterization
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
evaluation of the revised op cumulative assessment
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
recipes essential to dietary model
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
experiment to examine importance of recipes
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
between model exposure variability forty 1000 iteration replicates different random number seeds
Between Model Exposure Variability Forty 1000-Iteration Replicates, Different Random Number Seeds
within model exposure variability forty 1000 iteration replicates different random number seeds
Within Model Exposure VariabilityForty 1000-Iteration Replicates, Different Random Number Seeds

On par with US EPA findings for 1000-iteration runs

slide17
Exposure variability findings in contextPreliminary data files, Children 1-2, Single 1000 iteration runs

Average DEEM vs. FCID difference is 15%

risk metric comparison 15 difference
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

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