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 Defined NAS 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 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.

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, 99.9 th %ile, exposures in mg/kg-day

15. Between Model Exposure Variability Forty 1000-Iteration Replicates, Different Random Number Seeds DEEM ™ Estimate DEEM-FCID ™ Estimate Difference% Difference Minimum7.43 x 10 -4 8.54 x 10 -4 1.02 x 10 -4 13.56 % Maximum7.63 x 10 -4 8.80 x 10 -4 1.32 x 10 -4 17.74 % Mean7.53 x 10 -4 8.69 x 10 -4 1.17 x 10 -4 15.48 %

16. 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

17. Exposure variability findings in context Preliminary data files, Children 1-2, Single 1000 iteration runs CommoditiesDEEM-FCID ™ Estimate Difference% Difference from complete model Exclude grapes 0.001570.0002415.29 % Exclude apples 0.001630.0001811.00 % All included0.00181 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