Robert L. Sielken Jr., Ph.D. Sielken & Associates Consulting Inc

1. Experiences Helping Develop More Effective Regulations via Interactions Between the Public, Universities, Regulated Entities and Regulators Robert L. Sielken Jr., Ph.D. Sielken & Associates Consulting Inc 3833 Texas Avenue, Suite, 230, Bryan, TX 77802 Tel: 979-846-5175; Fax: 979-846-2671; Email: SielkenAssoc@aol.com Air Toxics Workshop II: Air Toxics Research Implications of Research on Policies to Protect Public Health Session II: Interactive Processes in Toxicity Assessments Houston, Texas Tuesday 10:20 am - 12:00 Noon, June 12, 2007

2. Scientists Regulators and Risk Managers Academic researchers Consultants, specialists Industrial hygienists and scientists Interactive Processes Concerned citizens

3. As a statistician, researcher, consultant, and university professor in the field of quantitative human and environmental risk assessment, I have had the opportunity to interact with risk assessors and managers in numerous contexts: States: Texas Florida California Minnesota Wisconsin Michigan Illinois Indiana Ohio Pennsylvania New York etc. Federal Government: Congress EPA FDA OSHA NIH NIEHS NCTR NAS/NRC etc. Litigation: Missouri Texas Louisiana Virginia California Colorado Arizona Mississippi Hawaii Delaware Canada etc. Task Forces: Cancer Risk Assessment Benchmark Dose Food Protection Great Lakes ILSI ACC CMA AIHC SOT SRA SETAC ISRTP ASA etc. Universities: Texas A&M U. of Texas Harvard Center for Risk Analysis etc. Industry: > 100 Clients

4. My Job: Bridger between Risk Assessors and Managers and Other Scientists -- Ask Questions No One Else Dares Ask Bridger between Regulators and those being Regulated -- Be Someone All Sides Respect and Trust

5. My Job: Help all those involved to avoid the feeling that they are being “hurt” or “fooled” by someone’s incorrect, inappropriate, incomplete, or inadequate treatment of the available data.

6. My Job: Help Risk Assessors and Risk Managers -- recognize the limitations of default methodology -- understand the opportunities available due to recent advances in risk assessment methodology and risk management techniques -- avoid the pitfalls associated with poorly understood mathematical and statistical procedures

7. Partial List of Findings that Impacted the Most Recent US EPA Draft Risk Assessment for Ethylene Oxide: Common Failures: Failure to use the newest data. Failure to use all of the data. Failure to use a valid dose-response modeling approach. Errors in calculations. Results fail simple reality checks.

8. Partial List of Findings that Impacted the Most Recent US EPA Draft Risk Assessment for Ethylene Oxide: It is critically important to do dose-response assessment using the exposure and outcome data for the individuals in the cohort rather than on summaries of groups of individuals (e.g., odds ratios). Conclusions about lower-dose risks based on high-to-low-dose extrapolation using fitted dose-response models dominated by the high-dose portion of the data may be contradicted by the observed lower-dose data. The BEIR IV life-table methodology for calculating excess risk is mathematically correct when the response of concern is mortality but is incorrect when the response of concern is incidence. Excess risk calculations for incidence using estimated dose-response models for mortality are inappropriate. The most common implementation of the age-dependent adjustment factor (ADAF) is mathematically incorrect.

9. Partial List of Findings that Impacted the Most Recent US EPA Draft Risk Assessment for Ethylene Oxide: Assumptions frequently dominate excess risk calculations. Assuming an 85-year exposure lifetime instead of a 70-year exposure lifetime substantially impacts calculations of excess risk for many toxic endpoints (e.g., most cancers). In order to fairly compare the risks of two substances, the risks must both be calculated using the same assumptions.

10. Partial List of Findings that Impacted the Most Recent US EPA Draft Risk Assessment for Ethylene Oxide: The existence of repair and other background defense mechanisms can imply that the extrapolation below a point of departure (POD) should not be done linearly.

11. Partial List of Findings that Impacted the Most Recent US EPA Draft Risk Assessment for Ethylene Oxide: The existence of repair and other background defense mechanisms can imply that the extrapolation below a point of departure (POD) should not be done linearly.

12. Partial List of Findings that Impacted the Most Recent US EPA Draft Risk Assessment for Ethylene Oxide: You cannot conclude what the SHAPE of the dose-response relationship is if you only fit models of a specified shape to the data. For example, if you only fit linear models, then the fitted shape is linear; however, that does not mean that the true shape of the dose-response relationship is linear. Similarly, if you only fit supra-linear models, then the fitted shape is supra-linear; however, that does not mean that the true shape of the dose-response relationship is supra-linear.

13. Partial List of Additional Findings that Impacted Risk Assessments: Other Risk Assessments

14. Risk extrapolations from occupational to environmental scenarios need to account for the differences in these scenarios especially with respect to exposure magnitude, duration, and temporal spacing as well as confounding factors like exposures to other substances and the number of high intensity tasks.

16. The default procedure used by some risk assessors (e.g., in EPA and California) to bound cancer potencies is dominated by default assumptions and does not reflect the observed experimental data: The linearized multistage model upper bound on the cancer slope (q1*) fails to adequately reflect the shape of the observed dose-response relationship and especially the outcomes in the low-dose region, which is the region of primary interest in risk assessment.

17. 100% at High Dose Linearized Multistage Model Slope = 0.120 Linearized Multistage Model Slope = 0.332 Linearized Multistage Model Slope = 0.311 Linearized Multistage Model Slope = 0.084 Linearized Multistage Model Slope = 0.065 Linearized Multistage Model Slope = 0.035 20% 20% 20% 20% 20% 20% Response Frequency Response Frequency Response Frequency Response Frequency Response Frequency Response Frequency = observed response frequency Although the outcomes for each of the 6 experiments are very different, the slopes q1* differ by less than 10 fold (one order of magnitude). 0% 0% 0% 0% 0% 0% Dose Dose Dose Dose Dose Dose 100% at High Dose 0 0 0 0 0 0 0.25 0.25 0.25 0.25 0.25 0.25 0.50 0.50 0.50 0.50 0.50 0.50 1.0 1.0 1.0 1.0 1.0 1.0 100% at High Dose

18. This example and similar other examples have promoted several regulatory agencies to emphasize Best Estimates instead of Bounds especially when the dose-response data are human data. For example, emphasizing the fitted dose-response model instead of an upper bound on that model ECs instead of LECs BMDs instead of BMDLs

20. Errors: Almost any data collection involves some error -- Measurement Error -- Reporting Error etc.

21. Errors usually cause upper sample percentiles to have an OVERESTIMATION BIAS.

22. Impact of Errors: Simple Example True Concentration = 10 Concentration 10 Concentration with Error 10 Upper Percentile Greater Than True Concentration

23. TheImpact of Errorson the Sample Percentiles is Least for Central Tendency and Greatest for Extreme Percentiles 1,000 Monte Carlo Samples of Size 1000 100% 80% Frequency 99.9th Percentile 60% 40% 20% 0% 100% 80% Frequency 95th Percentile 60% 40% 20% 0% 100% 80% Frequency 90th Percentile 60% 40% 20% 0% 100% 80% Frequency 50th Percentile 60% 40% 20% 0% 10 0.01 0.9 100 0.001 1.1 1000 0.1 0 1 Ratios: (Sample Percentile / True Percentile)

24. If the data collection involves errors, then the inflationary errors at the extremes of the data distributions make the extreme percentiles of the assessment the shakiest foundation for good decision making and the least reliable basis for differentiation between different chemicals or situations.

26. Lessons learned in the development of the Integrated Endangerment Assessment / Risk Characterization for theRocky Mountain Arsenal (RMA) near Denver, Colorado

27. Bounds on exposure shouldNOTbe determined by simply evaluating an exposure equation or model with each exposure parameter's distribution replaced by a bounding constant.

28. For example, if visitation hours per lifetime were evaluated as (Hours per Day) x (Days per Year) x (Years per Lifetime) then the95th percentileof the corresponding probability distribution for Recreational Visitors to RMA would be approximately 200 hours per lifetime

29. However,if each component variable was simply replaced by its 95th percentile, then (Hours per Day)0.95 x (Days per Year)0.95 x (Years per Lifetime) 0.95 = 1200 hours per lifetime or approximately6 times greaterthan the true 95th percentile 200 hours per lifetime

30. Furthermore,if each component variable was replaced by a default "reasonable maximum exposure" (RME) value, then (Hours per Day)RME x (Days per Year)RME x (Years per Lifetime)RME = 10,400 hours per lifetime or more than50 times greaterthan the true 95th percentile 200 hours per lifetime.

32. Exposure and risk characterizations can be very different depending on whether or not variability is incorporated. Since variability is a part of reality, the most realistic exposure and risk characterizations incorporate variability.

33. Distribution of Lifetime Average Daily Dose Modeling With and Without Year-to-Year Variability 40% 30% Frequency 20% Without Year-to-Year Variability 10% With Year-to-Year Variability 0.001 0.0001 0.0005 0.00001 0.00005 0.000001 0.000005 mg / kg / day

35. Potentially exposed populations are comprised of people who do NOT conduct their lives in an identical fashion, but people whovarywidely in their activities, diets, hobbies, desires, preferences, interests, obligations, and motivations. Suchvariationis more fully reflected in a distribution than a constant.

36. Examples of Quantitative Impact of Incorporating the Exposure Variability Within the Population

37. The quantitative impact on the distribution of the lifetime average daily dose from drinking water ingestion of assuming that the exposure duration is either 70 years or less than a full lifetime (e.g., one residence duration) 80 Duration of Exposure 60 Percentage 40 70 years 20 One Residence Duration 0 0.01 1E-9 1E-8 1E-7 0.001 0.0001 0.00001 0.000001 0 to 1E-10 Lifetime Average Daily Dose (mg / kg / day)

38. The distributional characterization of the concentration in the drinking water in 9 of the 18 major use states with sample data in the data base: Variability from State to State and Person to Person within a State 100 80 60 Percentage 40 MD KS IN 20 IL IA HI FL DE 0 CA 1 10 0.1 100 0.01 1E-9 1E-8 1E-7 0.001 1E-10 0.0001 0.00001 0.000001 Pesticide: Water Concentration (ppb)

39. The distribution of the concentration in the drinking water and the Variability from State to State and Person to Person within a State carries forward to the distribution of the margin of exposure associated with drinking water ingestion in 9 major use states 1.00 0.80 0.60 Proportion WI PA 0.40 OH NY NE 0.20 NC MO MN MI 0.00 10 100 1 10,000 1,000 1,000,000 100,000 100,000,000 10,000,000 10,000,000,000 1,000,000,000 Margin of Exposure

41. Temporal, spatial and/or demographic variability can have a major impact on exposure and risk distributions. Hence, it can be important to incorporate temporal, spatial and demographic variability into exposure and risk characterizations.

42. Temporal Integration: Hypothetical Example: Target = Three Month Exposure SpringSummerFall Winter Without Temporal Integration Drinking Water + + + + Combined Food + + + + Combined Non-Dietary Fall Aggregate Spring Aggregate Summer Aggregate Combined Aggregate

43. Temporal Integration: Hypothetical Example: Target = Three Month Exposure Importance of Pooling Exposures in the Same Season versus Mis-Matched Seasons 40% 30% Frequency 20% Without Temporal Integration 10% With Temporal Integration 0% mg / kg / day

45. Populations are often comprised of subpopulations (e.g., males and females, and private well and community water supply users). Population characterizations can incorporate the size and other characteristics of the component subpopulations. The distribution of exposures in the population is NOT THE SAME AS the distribution of exposures in the most exposed subpopulation..

46. Suppose that apopulation is comprised of two subpopulations, AandB

47. Distributional Characterizations of a Population Comprised of Two Subpopulations: 50% Subpopulation A, 50% Subpopulation B Percentage Incorrect: A + B Correct: A and B B A The distribution in the population comprised of subpopulations A and B is not the distribution of the values for the sum of A and B.

48. The relative sizes of the subpopulations comprising the population impact the distributional characterization of the population.

49. 50 40 30 20 10 0 0 1 5 10 15 20 25 30 Subpopulation A = 50% of Population Subpopulation B = 50% of Population vs Subpopulation A = 90% of Population Subpopulation B = 10% of Population vs Subpopulation A = 10% of Population Subpopulation B = 90% of Population Percentage 10% A, 90% B 90% A, 10% B 50% A , 50% B

50. The distribution of exposures in the population is not the same as the distribution of exposures in the most exposed subpopulation 50 40 30 Percentage 20 Most Exposed Subpopulation Distribution 10 Population Distribution 0 15 5 20 25 30 0 1 10