’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

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:

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 CommunicationOther influences(structural/econ. etc.)

Other influences

Social and cultural (historical) context

Social and cultural (historical) context

Some key balancing acts in IRA

How to balance broad focus and depthHow to balance detail and generalityHow to balance precaution and evidence

Different notions of realism and rationality (and reasonableness)

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 onsome chemicals and risks: may blur/conceal othershigh-risk scenarios

Appropriate integration varies by case & dimension Dynamic wide-angle: focusing/refocusing (zooming)

Multiple foci on multi-D risks: DLCs in BS fish

Modified from Assmuth, Jalonen, TemaNord 2005:568

Zoom

Zoom

Zoom

Zoom

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

For realism: ’The golden medium’ E.g., Maimonides (1135-1206), 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

3. Precautionary and evidence-based RA: Meanings of precaution

’Proactive’ precaution may mean panic action = actually too little/limited precaution to avoid harmToo 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

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

(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 assessmentIntegrate knowledge: Value; data models; other areasIntegrate policy areas: Alternatives & trade-offs’Intelligent’ Testing Strategies & RA guidance = ?

Balancing detail with generality (’realism’)Communication: On ’sound’ methods etc

Mapping & communicating complex risks

Expo vulnerability effect risk manage.Scale & detail: GIS but beware of data/tool fixationMultifactorial causality: Clearing ’event jungles’ Policy-relevant R features: ’High-reward’ areasUncertainty representations: Guide framing/focusingRevisability Interaction, communication, memory

’Internalized’ complexity: simplified as far as possible but giving needed detail, context, relativism - attention to other(’s) concepts of reality

Conclusions and recommendations

’Realism entails value judgments & subjectivity

Tailor realism (frame, detail, safety) to case Consider the relation of high to average risks Balance precaution with full use of (sci) info Express multiple scenarios (for communication) Communicate about the meanings of Rs, Us

Attention to management processes and links

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 …)

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)

Example of balancing precaution and science-based RAConclusions 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

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 !

IRA of mixtures under REACH: specific issues

Agents: poor integration esp. of pharmaca, precursors & metabolitesEnvirons: incorporating regional features Receptors: more human+non-human integration; (eco)epidemiol exp infoEffects: multiple, indirect; M-O-A inclusion (aggregate/specific)

’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

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

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 agentsGoovaerts P, Int J Health Geographics 4(2005):31-

Model verification/eval & uncertainty analysisGoovaerts P, Int J Health Geographics 5;7(2006):1-