U.S. EP A Of fice of Pesticide Programs Public W orkshop ~ April 2014 ~ Arlington, V A 

## Assessing Pesticide Risks Quantitative and W eight - of - Evidence Approaches **Bruce K. Hope, Ph.D.**2 

###  **Committee on Ecological Risk**  **Assessment Under**  **FIFRA and ESA** 

– **Report: April 2013**  

###  **CropLife Science Forum** 

– **May 2013**  

###  **Agency efforts** 

– “Interim Approaches for National - Level Endangered Species...Assessments” 

# National Research Council (NRC)

# Interim Memo: It’ s a Start, but...  Point of departure for exposure is not defined

– What will the EEC represent? Median, mean, 95%? 

###  Point to point comparisons (EEC to LD0.000001, etc.) are not “risk” estimates

– They are hazard or threshold assessments 

###  Step 1 & 2 hazard assessments produce “risk quotients” that are not easily transferable to Step 3 stochastic population models

– A common basis in probability (risk) is missing 3

# Informing Decisions with Risk  If I plan to make decision X (to register a pesticide)...  What is the probability (p(Y)) that detrimental outcome Y will occur in the future? [where p(Y) is the risk estimate]  What is my confidence in that estimate of p(Y)?

– Where confidence is af fected by variability and incertitude 

###  Acceptability of p(Y)’ s value is strictly a policy choice

– But knowledge of it’ s size (large or very small) is an important component of informed decision making 45 

# Risk Characterization
**Risk Estimate** Probability of eliciting a specified response in an organism. 

### CONCENTRA

**TION P** **R O** **B A** **B I** **L I** **T Y**  **EXPOSURE -**  **RE SPONSE**  **EXPOSURE (EE C)** 

# Risk as Common Ground

**Probability of Effect on**  **Po pulation**  **Exposure Exposure**  **** **Response Exposure**  **Scenario Population**  **Model(s)** 

### EP A: Steps 1 & 2

Services: Step 3 **Risk estimate**  **(%)**

6 **Risk Characterization**  **CONCENTRA**

**TION P** **R O** **B A** **B I** **L I** **T Y** 

# Beyond the Risk Estimate  But what if a “risk estimate” isn’t enough?

– For decision makers, constituents, or stakeholders? 

###  Sadly , both hazard assessments (thresholds) and risk assessments (probabilities) are viewed with suspicion

– “Risking it away” (with numbers), cancer lottery , etc. 

###  Fortunately , there are methods for bringing more evidence – other than just a risk estimate - to bear on a pesticide registration decision
7

# We ight - of

- Evidence (WOE)  Multiple lines - of

- evidence (LOEs) are defined by the risk hypothesis (or question) – from Problem Formulation  We ighting of each LOE is determined by how well it addresses that hypothesis (question)

– We ighting criteria, assigning weights, scaling weights 

###  Multiple LOEs (for the same hypothesis) converge to provide a WOE informed estimate of risk and of our confidence in that estimate 89 

# WOE Within EP

A’ s ERA Framework

Analysis Plan Measure of Exposure Measure of Effect Measure of Characteristics **Line of Evidence** Exposure **Line of Evidence** Characteristics **Line of Evidence** Exposure/Response **Tools** Field (surveys) Field (manipulation) Laboratory Testing Models Legacy Data Literature Expert Elicitation Graphic stressor>exposure>response **Conceptual Model** Narrative Risk hypothesis Exposure (x) to stressor (y) could, with probability (p), yield an adverse response (z) of magnitude (m). Attribute Entity (receptor) **Assessment Endpoint**  Pr oblem F ormulation
**To Analysis & Risk**  **Char acterization**  **Phases**10 

# Integrating Lines - of

- Evidence **Line of Evidence** Exposure **Line of Evidence** Characteristics **Line of Evidence** Exposure/Response RE,  W

**Evidence Group 1**

**Line of Evidence** Exposure **Line of Evidence** Characteristics **Line of Evidence** Exposure/Response RE,  W

**Evidence Group n**    

## WOE MA TRIX11 

# WOE Interpretation: An Example

Assessment Endpoint (entity, attribute) : Salmon population, recruitment Effect Endpoints : Premature egg hatching, juvenile mortal ity

R isk Hypotheses RH 1, RH 2: Likelihood that effect endpoint will be realized due to DO concentrations in su rface wa ter. RH 3 , RH 4 : Likelihood that effect e ndpoint will be realized due to Cu concentrations in sur fa ce wat er. **Evidence Group Weight**  **(**  **W) 1**  **- 2**  **2 -**  **3 3**  **- 4**  **4 -**  **5 R** **I S** **K E** **S T** **I M** **A T** **E > 75%**  **51 -**  **75% 25**  **- 50%**  

##  **5 -**  **24%** 

##   **< 5%** 

##  Moderate to high confidence of a low to moderate risk

Evidence Group #

# We ight - of

- Evidence Pros & Cons  Pros

– Organize and utilize myriad types of ecological data

– Condense complex analyses for use by decision makers

– Can easily combine dif ferent tests / metrics

– Much more robust than a single LOE (e.g., a “risk” quotient) 

###  Cons

– Regulatory restrictions on use (CW A standards)

– Lack of established federal or state guidance

– Limited prior use in a variety of regulatory environments

– The enduring comfort of threshold - based decision making

– Budget and time constraints / additional expertise 12