Risk Assessment

1. Risk Assessment • a systematic process for describing and quantifying the risks associated with hazardous substances, processes, action or events • release assessment • exposure assessment • consequence assessment • risk estimation

2. Risk Assessment Policy Guidelines for value judgement and policy choices which may need to be applied at specific decision points in the risk assessment process.

3. Risk Assessment Method • any self-contained systematic procedure conducted as part of a risk assessment • any procedure that can be used to help generate a probability distribution for health or environmental consequences

4. NRC-NAS model of risk assessment (1983) • hazard identification • determining whether a specified chemical causes a particular health effects • dose-response assessment • determining the relationship between the magnitude of exposure and the probability of occurrence of the health effects in question • exposure assessment • determining the extent of human exposure before and after application of regulatory controls • risk characterization • determining the nature and magnitude of human risk, including attendant uncertainty

5. Covello-Merkhofer model of risk assessment (1994) • release assessment • quantifying the potential of a risk source to introduce risk agents into the environment • exposure assessment • quantifying the exposures to risk agents resulting under specified release conditions • consequence assessment • quantifying the relationship between exposures to risk agents and health and environmental consequences • risk estimation • estimating the likelihood, timing, nature and magnitude of adverse consequences

6. Monitoring release monitoring monitoring source status administrative records laboratory analysis Performance testing component and system failure tests accelerated-life tests accident simulations stress analysis mental movies Release Assessment

7. Accident investigation field investigation laboratory investigation accident reconstruction Statistical methods actuarial risk assessment named probability distributions Baye's theorem statistical sampling regression analysis extreme value theory hypothesis testing Release Assessment (cont’d)

8. Release assessment (cont’d) • modeling methods • engineering failure analysis • logic trees, event trees, fault trees, Markov models • analytic process models • biological models for pests • containment models • discharge models • BLEVE models

9. Exposure Assessment • monitoring • personal exposure monitors (PEMs) • media contamination(site monitoring) of air, surface water, sediment, soil, groundwater • remote geological monitoring: aerial photography, multispectral overhead imagery • biological monitoring: chemical residues, bioaccumulation/degradation, physiology, indicator species • testing • scale models • laboratory tests • field experimentation

10. Exposure Assessment • the process of measuring or estimating the intensity, frequency and duration of human or other population exposures to risk agents • often the most difficult task of a risk assessment; individual personal habits have a strong influence on human exposure; also synergistic effects • monitoring through direct (such as personal exposure monitors -- PEM) or indirect (pollutants in air) methods

11. Exposure assessment (cont’d) • calculation of dose • based on exposure time • co-existing or decay substances • material deposition in tissue • pollution transport-and-fate modeling • air: analytic models, trajectory models, transformation models • surface water: dissolved oxygen models • groundwater: travel-time models, absorption models • overland • food-chain models • multimedia models

12. Exposure assessment (cont’d) • exposure-route models • population-at-risk models • census, sensitive groups, trip-generation models

13. health surveillance hazard screening molecular structure analysis short-term tests animal tests acute toxicity studies sub-chronic toxicity studies chronic toxicity studies tests on humans laboratory setting field setting Consequence assessment

14. epidemiology case-control study cohort study retrospective study prospective study molecular epidemiology animal-to-animal extrapolation models dose-response models threshold tolerance mechanistic time-to-response Consequence assessment (cont’d)

15. pharmacokinetic models ecosystem monitoring tests on the natural environment field tests laboratory tests microcosms, macrocosms, mesocosms ecological effects models dynamic matrix stochastic Mark harvest pollution response Consequence assessment (cont’d)

16. Dose-response models -- good • a means of estimating adverse effects in the absence of direct data • the relationships on which the model is based are described explicitly through mathematical equations or computer codes, and the logic is therefore open to review and criticism • pharmacokinetic models, especially physiologically-based ones, possess a high degree of predictive power for estimating the adverse effects of exposures to toxic chemicals

17. Dose-response models -- bad • limited by the availability of data, knowledge and understanding • extrapolation outside the range of observation in laboratory experiments • appropriate conversion factors for translating data from laboratory animals to humans due to differences in body size, life span, and metabolic processes, among others • dose-response models are generally gross oversimplifications of complex biological processes

18. relative risk models model coupling risk indexes individual risk societal risk nominal risk outcomes worst-case analysis sensitivity analysis point parametric rank correlations stochastic closed loop Risk estimation

19. statistical methods probability encoding debiasing interval method probability wheel behavioural aggregation mechanical aggregation uncertainty propagation method of moments Monte Carlo analysis response surfaces probability trees Risk estimation (cont’d)

20. Risk estimation (cont’d) • quantitative uncertainty analysis • confidence bounds • credibility analysis • uncertainty partitioning • qualitative uncertainty analysis

21. Groups  Individuals The principal limitation on the use of statistical methods for release assessment, even with copious amounts of data, is that estimating the probability that a particular driver will be in an accident or that a particular homeowner will experience a fire requires that the entity be catalogued as belonging to some representative group. This group defines the universe for the statistical model. The specification of this group represents a judgment that may be highly subjective.

22. Objective vs. Subjectivehow to deal with uncertainty • methods for quantifying and propagating uncertainty through models differ significantly according to whether an objective or a subjective perspective is adopted for the analysis • the choice of perspective is critical because it determines the meaning assigned to probability and also because it affects both the interpretation and quantitative values of the computed risk measures

23. Objective • sees risk as a measurable property of the physical world • uses methods based on the classical theory of probability and statistics, where probabilities are numbers associated with events • events are interpreted as possible outcomes of repeatable experiments.

24. Subjective • risk is a product of perceptions • meet the 18th century mathematician Reverend Thomas Bayes • Baysian or judgmental view holds that probability is a number expressing a state of knowledge or degree of belief that depends on the information, experience and theories of the individual who assigns it

25. Subjective (cont’d) • probability is therefore a function not only of the event, but of the state of information • different people may assign different probabilities and the probability assigned by any one person may change over time as new information is acquired

26. Sources of error • inaccurate data processing • inappropriate assumptions for extrapolation • fitting models to sparse data • data aggregation • the use of surrogate data

27. Sources of error (cont’d) • relying on underqualified experts or experts who do not represent a full range of scientific opinion • discretizing continuous decision variables • utilizing models based on poor data or inadequate theory • incomplete models

28. Solutions? • always use an iterative approach • comparing model predictions with the intuition of experts and decision makers is useful; if models and experts disagree, then either the model is wrong or the analysis • fully disclose sources of uncertainty to avoid a false sense of accuracy

29. Sources of Uncertainty • statistical uncertainty • parameter uncertainty • judgmental uncertainty • model uncertainty • completeness uncertainty • a crucial flaw in many risk assessments is the failure to describe and characterize uncertainties in the estimates of risk outcomes

30. But ...?? • A study by a NAS committee estimated that the number of bladder cancers resulting from the consumption of saccharin over a lifetime of exposure ranged between 0.22 and 1,144,000 cases • A study by the Department of Energy estimated that fatalities associated with emissions from coal-fired power plants ranged between 1 and 305 per year • A study by the Nuclear Regulatory Commission estimated that the risk of a core-melt at a nuclear power plant ranged from between 1 chance in 10,000 and 1 chance in 1,000,000

31. Practicality • animal bioassays can cost more than $2 million and take 2-5 years to complete • under the U.S. Toxic Substances Control Act, EPA is charged with the task of screening the roughly 70,000 chemical substances now in use and the more than 1,000 chemicals that enter the market each year

32. Practicality (cont’d) • a large fault tree/event tree model for an industrial facility can cost more than $500,000 to develop and take more than 2 years to complete • the U.S. Consumer Product Safety Commission deals with more than 2.5 million firms, more than 10,000 products, and some 30,000 consumer deaths and 20 million consumer injuries each year

33. Nevertheless, still useful to ... • show how different estimates of risk are derived • provide the logic by which different regulatory actions might reduce risk • present a range of plausible risk consequences reflecting uncertainty about underlying theory and data • reduce the range of uncertainty in decision making and identify which estimates are most likely to be accurate • help set priorities and develop standards

34. And ... • describe and quantify levels of risk that remain after application of risk-reduction technologies • provide an empirical foundation for balancing risks against benefits • identify subpopulations that are especially sensitive or vulnerable • identify crucial areas where the resolution of uncertainty can be most effective in reducing risk

35. Hazard Identification Exposure Assessment Dose-Response Assessment Risk Characterization Risk Estimate with Attendant Uncertainty Structure of example DFT model for microbial risk assessment for E. coli O157:H7 in hamburger[Marks et al., 1998] occurrence Consumption density Pathogen in food serving Growth/decline Thermal heat transfer Predictive microbiology Ingested number of pathogens Threshold model Non-threshold model

36. Value judgments in risk assessment • institutional affiliations • trust in information provider • prior experience with similar risk situations • power to influence the source of the risk

37. Preliminary Pathway Analysis (Possibilistic Hazard Analysis) Source: Institute for Risk Research, March 22, 1996 Unreported Scrapied Sheep Pre-detection Scrapied Sheep Undetected Scrapied Sheep Imported Animal Protein Detected Scrapied Sheep Other As Yet Unknown Contaminated Feed Livestock from “Uninfected” Countries Exposure to CWD in Elk or other SEs Mad Cow Disease In Canada Indigenous Genetic Predisposed Cow Biologics Undiagnosed Mad Cow Disease In Canada Unreported BSE in Canada Importation via U.S. from U.K. Imported Bovine Embryo