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Risk Zoom: Dynamic wide-angle depth-of-field - Realisms in high-risk focus for integrated risk assessment (IRA) Timo Assmuth, Finn Environ Inst (SYKE)

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Presentation on theme: "Risk Zoom: Dynamic wide-angle depth-of-field - Realisms in high-risk focus for integrated risk assessment (IRA) Timo Assmuth, Finn Environ Inst (SYKE)"— Presentation transcript:

1 Risk Zoom: Dynamic wide-angle depth-of-field - Realisms in high-risk focus for integrated risk assessment (IRA) Timo Assmuth, Finn Environ Inst (SYKE) 1st Open NoMiracle Workshop, Intra 8-9 June 2006

2 Definitions Wide angle Narrow angle Focal plane Small depth of field Narrow focus (one/few risks) – large depth; see trees Wide angle = broad focus (many risks) – small depth due to lacking detail and study capability but see wood Photography Real-life observation and reflection Large depth of field - IRA: cf. Cumul RA, Compar RA - Focus, depth, angle:

3 Integrative relations of RA and other activities (cf. unidirectional model from science to policy) Science -nat, appl nat, other Risk assessment - id., charact, eval. Risk management -pol, tech etc Communication Other influences Communication Other influences (structural/econ. etc.) Other influences Social and cultural (historical) context

4 Some key balancing acts in IRA How to balance broad focus and depth How to balance detail and generality How to balance precaution and evidence Different notions of realism and rationality (and reasonableness)

5 1. Width of angle/breadth of focus: How integrated risk assessment ? Wide angle needed in integration, prevents tunnel vision (also artificial limitations on IPs) but … … wide angle small depth (real life) Focusing on some chemicals and risks: may blur/conceal others high-risk scenarios Appropriate integration varies by case & dimension Dynamic wide-angle: focusing/refocusing (zooming)

6 Multiple foci on multi-D risks: DLCs in BS fish Modified from Assmuth, Jalonen, TemaNord 2005:568 Zoom

7 2. High-risk or realistic worst case ? Relevant situations and concerns of also small groups, even individuals, and other (eco)systems = Particularistic and pluralistic approach May be justified also by common good But, high-risk defaults and lacking breadth cause biases

8 For realism: The golden medium E.g., Maimonides ( ), Guide for the Perplexed: … the Law … was not given with a view to things that are rare … but it has … the most prevailing conditions in mind = Averaging and unifying approach Necessary to avoid particularism ad absurdum When integrating sectors high risk often unclear Comparative risk analysis

9 3. Precautionary and evidence-based RA: Meanings of precaution Proactive precaution may mean panic action = actually too little/limited precaution to avoid harm Too much precaution may also mean inaction (paralysis by analysis, U trap, see e.g. Pierke 2005) Combine Hi & Average R scenarios; contextualize Interim decisions pending on new evidence

10 Paths to action by high-risk (re)focusing A risk is indicated Assessed on PP – 1st high-risk focus Contextualized / related to other risks - 2nd high-risk focus; CRA New information is acquired, also on RM (R/BA, MODA) Evaluation revised (up/down) - 3rd high-risk focus Action is taken or not, implying over / underreaction To fast response To considered response

11 (I)RA under REACH: NoM Challenges RA extended & diversified and streamlined Quick assess: Indicator (Hi-Risk) substances; R mapping; links and combinations with in-depth assessment Integrate knowledge: Value; data models; other areas Integrate policy areas: Alternatives & trade-offs Intelligent Testing Strategies & RA guidance = ? Balancing detail with generality (realism) Communication: On sound methods etc

12 Mapping & communicating complex risks Expo vulnerability effect risk manage. Scale & detail: GIS but beware of data/tool fixation Multifactorial causality: Clearing event jungles Policy-relevant R features: High-reward areas Uncertainty representations: Guide framing/focusing Revisability Interaction, communication, memory Internalized complexity: simplified as far as possible but giving needed detail, context, relativism - attention to other(s) concepts of reality

13 Conclusions and recommendations Realism entails value judgments & subjectivity KTailor realism (frame, detail, safety) to case KConsider the relation of high to average risks KBalance precaution with full use of (sci) info KExpress multiple scenarios (for communication) KCommunicate about the meanings of Rs, Us Attention to management processes and links

14 Conclusions - II Risk zooming may put initial Hi-Rs in new light – diminishing them but also revealing new aspects and contingencies in them Dynamism and flexibility in risk integration; more integration with less complication (But, key problems of zoom include distortive optics and low light power …)

15 Integrating risk co-factors and dimensions: A risk-based upstream process of deriving quantitative human health risk management criteria for DLCs in fish (Assmuth & Jalonen 2005)

16 Example of balancing precaution and science-based RA Conclusions from Hrudey & Leiss, EHP 111;13(2003):1577- … best practices for the management of risks from well-characterized low-frequency hazards have an inevitable dominance of false positives over true positives and false negatives; this implies inherent substantial precaution … the critical question is: how precautionary should we be in a particular case? … dealing with well-characterized hazards, we sometimes unwittingly want to be more precautionary than it is possible to be, ensuring a self-defeating outcome the same applies to poorly characterized (uncertain) hazards of dread type, causing panic and self-defeating, while other hazards go unnoticed and escalate … manager needs to maintain a healthy tension by considering the likelihood and concequences of both false positives and false negatives, seeking an appropriate balance …, rather than absolute elimination of false-negative errors in a futile search for zero risk In addition, options for and consequences of risk management to be considered

17 Integration of RA and related activities Env H Sciences Env H R Assess Env H R Manage - General publ health sci. - Other env res (ecol etc) -- Res. in env R manage. - Epidemiol RA (e.g. multifactor) - Tech saf assess - General. ERA (multistressor) -- Resource use RA - Overall health care - Environ management -- Safety management - General nat resource management -- Enterprise manage (chem etc)- Other policy areas Monitoring expo/effects Mapping risks & Us (environ, health) Testing of chemicals Methods development Advanced original R&D also in applied processes !

18 IRA of mixtures under REACH: specific issues Agents: poor integration esp. of pharmaca, precursors & metabolites Environs: incorporating regional features Receptors: more human+non-human integration; (eco)epidemiol exp info Effects: multiple, indirect; M-O-A inclusion (aggregate/specific)

19 Intelligent testing strategies for REACH Only relevant non-redundant in vivo - depends on relevance definition SAR applicability varies by endpoint - and by purpose/desired realism In vitro developments: esp. screening; but reality-checks needed Toxicokin. models (to focus testing) – also depends on realism desired Read-across chemical groups: depends on similarity criteria (MOA) + Read-across taxa: hum + non-hum RA Waiving based on expo: manage links Relate to overall pros/cons of REACH ! Synopsis of IHCP 2005 discussion paper + comments with a view to integrated RA

20 Dixon B, Appl Geogr. 35;2005):327- Map of risk index spatial distribution for the benthic community (estimated through the quotient method and the TEL benchmark) Critto & al., Env Int 31(2005):1094- Andreo C et al. Sci Total Environ. 357(23006):54- Aquifer vulnerability (fuzzy) Groundw R = f (vulnerabil, hazard/load) Ecotox R = f (expo, sensitivity proxies) - Some integration (prioritiz.) of agents

21 Probability fields used to create incrementally different maps of mortality risk by sequential Gaussian simulation Simulated risk maps for breast cancer, and results of the local cluster analysis Estimation of NW US breast cancer mortality risk from empirical frequencies by Poisson kriging Endpoint-based health R mapping - Inherent integration of agents Goovaerts P, Int J Health Geographics 4(2005):31- Model verification/eval & uncertainty analysis Goovaerts P, Int J Health Geographics 5;7(2006):1-

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