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. Cumulative Risk Assessment for Pesticide Regulation.

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Cumulative risk assessment for pesticide regulation a risk characterization challenge l.jpg

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 Characterization Challenge

  • 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 Defined Characterization ChallengeNAS 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 Characterization Challenge

  • 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 Characterization Challenge

  • 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 Characterization Challenge

  • 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 Characterization ChallengeGuiding 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 Characterization Challenge

  • Transparency

    • Explicitness

  • Clarity

    • Easy to understand

  • Consistency

    • Consistent with other EPA actions

  • Reasonableness

    • Based on sound judgment


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Transparency Criteria Characterization Challenge

  • 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 Characterization Challenge

  • 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 Characterization Challenge

  • 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 Characterization Challenge

  • 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 Characterization Challenge


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Experiment to examine importance of recipes Characterization Challenge

  • 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 Characterization ChallengeForty 1000-Iteration Replicates, Different Random Number Seeds


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Within Model Exposure Variability Characterization ChallengeForty 1000-Iteration Replicates, Different Random Number Seeds

On par with US EPA findings for 1000-iteration runs


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Exposure variability findings in context Characterization ChallengePreliminary data files, Children 1-2, Single 1000 iteration runs

Average DEEM vs. FCID difference is 15%


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Risk Metric Comparison – 15% Difference Characterization Challenge

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 Characterization Challenge

  • Risk characterization is incomplete

  • Good guidance on risk characterization for complex models

  • Continue to work and share findings


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