Michael D. Sohn, Randy L. Maddalena, Thomas E. McKone Indoor Environment Department Lawrence Berkeley National Laborator

1. Value of Information: Concepts and Potential Applications Michael D. Sohn, Randy L. Maddalena, Thomas E. McKone Indoor Environment Department Lawrence Berkeley National Laboratory Exposure-Based Chemical Prioritization Workshop: Exploring Opportunities for Collaboration April 6-7, Research Triangle Park, North Carolina The research discussed in this presentation was funded in parts by the S&T Directorate of the US Dept. of Homeland Security, the Defense Threat Reduction Agency, the US Environmental Protection Agency, and LBNL’s Laboratory Directed Research and Development Fund, and was performed by LBNL under US DOE Contract No. DE-AC03-76SF00098.

2. Goal of this Talk • Introduce the overarching concepts of value of information analysis • Emphasize that it can be a natural extension of existing EPA analysis procedures • Suggest possible places where it may be applied

3. Not a Goal of this Talk • Show you how to perform a value of information analysis: • literature is becoming rich (past 10 years) • gaining acceptance as an effective decision analysis tool • experts in engineering and finance fields exist, though fewer in environmental quality, public health, and chemical hazard fields

4. Value of Information Introductory Concepts “Risk assessors attempting to use probabilistic approaches to describe uncertainty often find themselves in a data-sparse situation ... In determining whether or not to collect additional data ... it is useful to consider the expected value of the information.” (Raiffa 1968, NRC 1996, Hammitt and Shlyakhter 1999). • Key points in the above statement: • framed as a decision analysis question • uncertainty is the prime-driver • analysis is quantitative and prescriptive: conduct analysis before acting • additional data may not be needed • implies an approach that merges models and data in a hypothesis-based study

5. air plants soil layers sediment Air (moving phase) Air Non-moving phases (floor, carpet, walls, dust, surface films) Cleaning Soil tracking Reactions water liver Energy use Industry Agriculture Buildings Transportation Consumer products Sources & emissions Personal air Exposure events nasal Risk Characterization & Communication saliva dermal lung oral skin fat muscle Intake and uptake Biokinetics & toxicology

6. air plants soil layers sediment Air (moving phase) Air Non-moving phases (floor, carpet, walls, dust, surface films) Soil tracking Cleaning Reactions water liver µ µ µ µ µ µ µ E E E E E M M M M M Energy use Industry Agriculture Buildings Transportation Consumer products = monitoring = experiments = modeling Sources & emissions Personal air Exposure events nasal Risk Characterization & Communication saliva dermal lung oral skin fat muscle Intake and uptake Biokinetics & toxicology

7. The Need to Broaden the Utility of Risk Assessment (NRC, 2009) In “Science and Decisions” the National Research Council focused on the need for • Treatment of uncertainty, variability, vulnerability • Consistent approach to carcinogens and non-carcinogens • A focus on disease burden and community health • Cumulative exposure and aggregate risk • Focus on solutions Science and Decisions (NRC, 2009)

8. Variability, Susceptibility, Vulnerability Variation in vulnerability Variability in susceptibility (endogenous factors) Variability in exposure Age, gender Genetics Pre-existing disease Variability in susceptibility (exogenous factors) Exposures to other agents Overall variability in risk relative to a median or baseline risk for a population Modernizing Risk Assessment, February 2010

9. What is VOI? • Approach or framework for comparing decisions or consequences where uncertainty affects end results • Often employs Bayesian inference and event tree methods • easy to propagate uncertainties • tools exist for data-poor analyses • communicating results is straight-forward • Can be used to combined different types of limited observations and data to confirm or refute hypotheses • biomarker data • monitoring data • modeling results

10. How does VOI Fit into EPA Programs • Prioritize research efforts • What data or modeling study is expected to refute beliefs? • What data or modeling study is expected to reduce uncertainties? • Is a reduction in uncertainty expected to alter decisions/rankings? • How much “resources” should we expend/demand, and why? • What are the key unknowns? Morgan, and Henrion, 1990

11. VOI applied in a Chemical Screening decision based on a priori data (excerpted from OPPTS, 2009)

12. VOI applied in a Chemical Screening (cont.) • What are the consequences of the decision? P[safe] , P[not safe] E[loss in benefit due to false decision] • What data can refute the decision? Information has value if it can lead to a change in the decision option preferred a priori and that the preferred decision changes depending on the outcome of the experiment or study [Small MJ, 2010]

13. apply models and beliefs a priori estimate of hazard and exposure broad distribution of source and exposure conditions apply Bayes’ rule estimate benefits propose data (µ, s) Update Input and Output Uncertainties

18. Metrics • Expected Value of Perfect Information: • “If I had perfect information, would it change my decision?” • “What are the limits to reducing uncertainty/unknowns?” • Expected Value of Sample Information: • “Given the expected noise/error/variability in data/modeling/etc, will more studies change my decision?” • “What is the likely reduction in uncertainty/unknowns?” • Expected Value of Consequences

19. Defining Some Jargon Likelihood Function Prior P(O|Y)P(Y) P(Y|O) = P(O) Bayes’ Rule P(O|Y) = f(distribution, error assessment) Normalizing Function

20. Other Possible Applications for VOI Analysis • Grade relative rankings in exposure-based prioritization by comparing the relative certainty in exposure pathways/assessment µChemA < µChemB sChemA > sChemB • Extrapolate from known chemicals to unknown chemicals using a precautionary principle (esp. for data-poor chemicals)

21. Concluding Remarks • The point of this talk is not to promote another “tool.” VOI is not a magic bullet for Exposure-Based Chemical Prioritization • If EPA is to include Exposure in its chemical managing then uncertainty and the consequences of uncertainty must be included in the analysis. • VOI or some other forms decision analysis methods are suitable tools, with a long list of successful applications • Move from ad hoc methods of decision analysis to transparent and quantitative method to prioritize data-gathering and further modeling studies

22. Important References • Taylor et al., The Value of Animal Test Information in Environmental Control Decisions, Risk Analysis 13(4): 403-412, 1993. VOI used to evaluate the benefit of performing animal bioasses to assess cancer potency. • Dakins, The Value of the Value of Information, Human and Ecolog. Risk Assess., 5(2):281-289, 1999. Easy description of key concepts. • Yokota & Thompson, Value of Information Analysis in Environmental Health Risk Management Decisions: Past, Present, and Future, Risk Analysis (24(4):635-650, 2004. Evaluation and recommendation for health risk management.