barbara d beck ph d dabt fats gradient corporation january 23 2008 n.
Download
Skip this Video
Loading SlideShow in 5 Seconds..
Barbara D. Beck, Ph.D., DABT, FATS Gradient Corporation January 23, 2008 PowerPoint Presentation
Download Presentation
Barbara D. Beck, Ph.D., DABT, FATS Gradient Corporation January 23, 2008

Loading in 2 Seconds...

play fullscreen
1 / 20
hedwig

Barbara D. Beck, Ph.D., DABT, FATS Gradient Corporation January 23, 2008 - PowerPoint PPT Presentation

143 Views
Download Presentation
Barbara D. Beck, Ph.D., DABT, FATS Gradient Corporation January 23, 2008
An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript

  1. Determining Risks to Background Arsenic Using a Margin – of – Exposure ApproachPresentation atSociety of Risk Analysis, New England Chapter Barbara D. Beck, Ph.D., DABT, FATS Gradient Corporation January 23, 2008

  2. How Can Epidemiology be Used to Inform the Understanding of Background Risks from Inorganic Arsenic? • Multiple opportunities, e.g. • Identification of plausible “No Observed Effect Level” for carcinogenicity • Intake distributions, e.g. from food • Host factors that modify carcinogenicity • Evaluating plausibility of modeled dose estimates through use of urine arsenic population studies • Understanding the relationship between arsenic metabolism and disease

  3. Background • Ingestion of inorganic arsenic (Asi) • Associated with skin, bladder, and lung cancer • Studies demonstrating carcinogenicity – Taiwan, Bangladesh, Inner Mongolia, etc. • Relatively high exposures, frequently in poorly nourished populations • No confirmed association in US populations • Challenges in developing animal model of Asi carcinogenesis

  4. Background (cont’d) • Prior risk assessments • Cancer Slope Factors (CSFs) range from 1.5 to 23 (mg/kg-d)-1 • All based on Taiwan data • Different cancer types (skin, bladder, lung), absolute vs. relative risk models, etc. • All assume low dose linearity

  5. Identification of NOEL Cancer Data from Taiwan Figure from: Lamm, SH; Engel, A; Penn, CA; Chen, R; Feinleib, M. January 13, 2006. "Arsenic cancer risk factor in SW Taiwan dataset." Environ. Health Perspect. 39p.

  6. Analysis of Taiwan Data by Township ◊ = Townships 2, 4, 6 □ = Townships 0, 3, 5 Figure from: Lamm, SH; Engel, A; Penn, CA; Chen, R; Feinleib, M. 2006. “Arsenic cancer risk confounder in southwest Taiwan data set.” Environ. Health Perspect. 114: 1077-1082.

  7. Analysis of Taiwan Data by Township • Suggests high background of bladder and lung cancer in townships 0, 3, 5 • Clear dose-response only in in townships 2, 4, 6 • SMR > 100 at median H2O concentrations > 150 µg/L (CI = 42 - 229 µg/L)

  8. Implications • Offers alternate approach to LNT for evaluating cancer risks from ingestion of inorganic arsenic • Determine the “No Effect” Drinking Water Level based on epidemiological data and convert to a dose • Equivalent to 0.013 mg/kg-d ( “NOEL”) • Use Margin of Exposure (MOE) to compare population dose to NOEL • Approach compatible with US EPA cancer guidelines and current understanding of arsenic mode of action

  9. Monte Carlo Exposure Analysis for US Populations • 3 main sources for background exposure • Diet • Water • Soil • Intake estimates based on population surveys

  10. Drinking Water

  11. Soil

  12. Food

  13. Summary of Results of Probabilistic Exposure Analysis

  14. Intake at 50th Percentile

  15. Use of Epidemiology to Evaluate Plausibility of Intakes • 50th percentile = 7.1 x 10-5 mg/kg-d • Can convert to potential urine concentration • 70kg • 0.8 – 2 L urine/day • 100% excreted in urine (our estimate) • = ~ 2.5 to 6.2 µg arsenic/L urine • Comparable to 7.5 µg/L median from Kalman

  16. Risk Calculation Results Notes: a – MOE calculation based on Point of Departure Value of 0.013 mg/kg-day b – Calculation based on CSF value presented in EPA’s IRIS database: 1.5 (mg/kg-day)-1 c – Calculation based on alternative CSF value used in recent EPA risk assessments: 3.67 (mg/kg-day) -1

  17. Implication of Analysis • Choice of dose-response model critical • 95th percentile risks exceed permissible criteria using recent CSF, based on LNT • 95th percentile risks do not exceed criteria using epidemiologically-based NOEL

  18. Sensitivity Analysis • Use of alternate assumptions • Increased or decreased intake from each medium by 50% • Greatest impact was change in adult dietary intake • Changed Average Lifetime Daily Dose by +/- 23%

  19. Sensitivity Analysis (cont’d) • Uncertainty in NOEL • Use of lower confidence unit on dose of 42 µg/L (instead of 150 µg/L) • 95th percentile MOE – 19 (versus 58) • Estimates of NOEL based on different diet and water intakes in Taiwan – more “restrictive” NOELs, MOEs all > 13

  20. Implications • Use of epidemiological data to assess risks of ingestion of inorganic arsenic -- informative on multiple levels • Toxicity quantification • Exposure assumptions • Plausibility of results