Risk Assessment to Support Decision Making

Katherine von Stackelberg, ScD

Center for Climate, Health and the Global Environment

Harvard Center for Risk Analysis

NEK Associates LTD

kvon@hsph.harvard.edu

1.617.998.1037

RISK ASSESSMENT TO SUPPORT DECISION MAKING

Risk = p(Adverse Effect|Exposure, Effects)

Exposure Effects (hazard)

Probability of adverse ecological effect Risk

Fate, transport, transformationmovement in the environment

Laboratory studies

Environmental source of stressors

Fieldstudies

Probabilistic Approaches in Risk Assessment

Range of Values

Freq

ue

ncy

Most are at this value: highest frequency

Source: Using Probabilistic

Methods to Enhance the Role of

Risk Analysis in Decision Making -

Managers' Summary, 2009

Prepared by the EPA Risk

Assessment Forum Working

Group

Concentration-Response Function

Probabilistic Ecological Risk Assessment

% reduction in fecundity

0 50 1000

% of population

1.0

0.5

Probability of

exceedance of a

threshold

Probability of

exceeding an

increasing magnitude

of effect

Conceptual Model of Bt Exposure

Source: Carstens et al. 2012 Transgenic Research 21:813-842

Example: Invasive Species

Source: Kolar et al. 2002 Science 298(8):1233

Example of an Invasive Species Model

Source: http://www.indiana.edu/~preserve/InvasiveSpread/model.html

Output: visualization of probability of invasion

Need to Understand Conditions Under Which Impacts Occur

Can be downloaded from www.biorxiv.org

Examples of outcomes

Offtarget effectsResistance over timeEfficacy – target effects

• The Biosafety Clearing-House (BCH) is a mechanism set up by the Cartagena Protocol on Biosafety to facilitate the exchange of information on Living Modified Organisms (LMOs) and assist the Parties to better comply with their obligations under the Protocol. Global access to a variety of scientific, technical, environmental, legal and capacity building information is provided in the six official languages of the UN.

• https://bch.cbd.int/onlineconferences/ahteg_ra.shtml

• An Ad Hoc Technical Expert Group (AHTEG) on Risk Assessment and Risk Management has a mandate to develop risk assessment guidelines

• Review Article: What Risk Assessments of Genetically Modified Organisms Can Learn from Institutional Analyses of Public Health Risks

• S. Ravi Rajan and Deborah K. Letourneau, Journal of Biomedicine and Biotechnology, Vol 2012, Article ID 203093, http://dx.doi.org/10.1155/2012/203093

Some Examples of Risk from the Literature

Ecosystem Services: Context for Ecological Risk

Source: Munns et al. 2015 Integrated Environmental Assessment and Management, DOI: 10.1002/ieam.1707

Decision Analytic Approaches for Evaluating Alternatives

Risk is not just an academic exercise. There must be a decision context.

Possible alternatives

Criteria against which to compare

alternatives

Example Metrics for Forest Ecosystem Health

Metric Measurable Outcome

Primary productivity Wood growth + litterfall or eddy covariance CO2 flux

Nutrient acquisition Foliar chemistry

Mortality Mortality rate or standing dead trees

Nutrient retention Leaching rate

Water use/evapotranspiration (Precip. – streamflow) or eddy covariance H2O flux

Resilience to moderate stress (pest, disease) Mortality after stress event

Physical structure, age structure, and plant species composition

Forest inventory / canopy assessment

Food web structure Monitoring animal populations

Soil quality Soil chemistry

Biodiversity Abundance

Demand (products, recreation) Market or nonmarket value

Adapted from Gary Lovett’s presentation http://nas-sites.org/dels/files/2018/01/Gary-Lovett-Presentation-Updated.pdf

Take Away 1

• All non-trivial decisions involve the potential for both good and bad outcomes

– Risk applies to more than just “bad” outcomes

• Decisions are informed by risk as a probability and as a perception

• Risk as a probability

– If we make a decision today, what are the probabilities of detrimental and beneficialoutcomes in the future, and how confident are we in our estimates of these probabilities?

Take Away 2

• Risk as perception

– Heuristics; Individual; Voluntary; Familiar (Known)

– “Safe”

• Your audience is regulatory decision and policy makers, their constituencies, and associated stakeholders• Their acceptance of “risk” will likely be driven more by

perception (“safe”) than by any scientific determination (i.e., calculated probabilities)

• Risk of bad outcomes to humans will almost always out-weigh those to ecological resources

Take Away 3

• New ecological assessment tools are available:– Bayesian belief networks

– Weight-of-evidence analysis

– Ecosystem services

– Decision analytics

• But: what constitutes a gene drive hazard/adverse effect?

• Things could go easier if you show that:– Your technology is self-limiting (can’t “escape”)

– Indirect consequences won’t happen• e.g., loss of the target population won’t affect non-target populations

or communities, etc.