Cumulative Risk Assessment for Pesticide Regulation: A Risk Characterization Challenge

1. 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

2. 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?

3. 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 …

4. 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

5. 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

6. 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)

7. 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.

8. Risk Characterization Handbook, 2000 • Transparency • Explicitness • Clarity • Easy to understand • Consistency • Consistent with other EPA actions • Reasonableness • Based on sound judgment

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

10. 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

11. 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

12. 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

13. Tomato Soup Recipe

14. 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

15. Between Model Exposure Variability Forty 1000-Iteration Replicates, Different Random Number Seeds

16. Within Model Exposure VariabilityForty 1000-Iteration Replicates, Different Random Number Seeds On par with US EPA findings for 1000-iteration runs

17. Exposure variability findings in contextPreliminary data files, Children 1-2, Single 1000 iteration runs Average DEEM vs. FCID difference is 15%

18. 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

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