A Decision Analytic Framework for Considering the Economic Value of

Improved Risk Assessment Data

Eric Ruder Henry Roman

Industrial Economics

October 12, 2011

IEcOverview of Presentation

• Objective: Explore the potential value of improved risk assessment data in the context of environmental regulation and research priority setting (both public and private sector)

• Nature of the problem

• Analytical framework.

• Examples• Results of Case Studies: Lead regulation and mobile

source related air toxics requirements

• Hypothetical scenario of research to support product development

• Implications2

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Nature of the Problem – Evaluating Improvements in Risk Assessment

• There are numerous sources of uncertainty in risk assessment that may be addressed (at least partially) by improved risk assessment methods and/or data:• Improving methods for evaluating the potential risks of

chemicals. • Identifying susceptible populations and characterizing factors

that may place them at higher risk. • Understanding how chemicals move and change along

pathways from sources to potentially exposed populations.

• Advances in these areas seems of obvious value in the field of risk assessment and the protection of human health and the environment, but are there quantifiable net benefits for society or economic benefits for the private sector?

IEcValue of Information Analysis

• Value of information (VOI) represents the improvement in the expected value of a decision outcome that would result from collecting additional information about one or more factors affecting a decision.

• Decision analysis approaches can quantify the value of collecting additional information before making a specific decision.

• Involves comparison of the expected outcomes of a suite of alternative regulatory choices made with and without information believed critical for the issue.

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IEcWhy is VOI Important?

• Structuring decision helps decision-makers make better decisions• identifies key decision inputs• helps minimize expected loss / maximize expected

gain• increases chances of good outcome• makes explicit the "costs" of uncertainty

• Results provide a measure of social willingness-to-pay for new research

• Helps prioritize competing research opportunities to allocate resources efficiently

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IEcEvaluations of VOI as a Tool

• NRC’s Science and Decisions: Advancing Risk Assessment (2009) • Discusses VOI as a tool for understanding the

tradeoffs between timely decision-making and the desire to refine the underlying science.

• Barriers to applying formal VOI broadly• Underlying concepts and structure still valuable:

linkages between information, decision-maker behavior, and decision making objectives

• Recommends informal VOI or value of methods analyses to inform risk assessment design.

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IEcEvaluations of VOI as a Tool (cont’d)

• 2010 Report of EPA’s Board of Scientific Counselors (BOSC) decision analysis workshop with ORD/NRMRL• VOI can be valuable, but is challenging to implement

• Need extensive data, including probabilities for decision options & how p’s change conditional on new information

• Nonetheless, group mentions VOI as possible method to inform several case studies – e.g., regulations of chemicals with biomarker data, prioritizing IRIS evaluations

• Key to consider benefits as the net improvements in decision outcomes minus the costs of obtaining improved information.

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VOI Analytic Framework• Decision analysis approaches can quantify the value of

collecting additional information before making a specific decision.

• Involves comparison of the expected outcomes of a suite of alternative regulatory choices made with and without information believed critical for the issue.

• Framework in a regulatory context can be illustrated using two diagrams:• Influence diagram - showing the role of exposure data and

other inputs in estimating NSB.• Decision tree - depicting a choice among:

• Taking no further regulatory action.• Instituting additional control measures based on current information.• Collecting additional exposure information prior to deciding whether to

institute new controls.

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VOI Framework

Implement Controls?

Effectiveness of

Controls

Monetized Benefits (Avoided

Health Effects)

Change in Health Effects

Baseline Exposur

e

Change in

Exposure

Net Benefits

Value ofHealth Effects

Dose-Response

Information

Control Costs

Influence Diagram

IEcVOI Framework (continued)

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Hypothetical Decision Tree: VOI = $500 - $437.5 = $62.5

Collect ExposureInformation

High.25

Medium.5

Low.25

No Controls

Controls

No Controls

Control Decision Net Social BenefitsExposure Distribution

Controls

High.25

Medium.5

Low.25

Controls

No Controls

Controls

No Controls

$0

$1,000

$500

$-250

$0

$1,000

$0

$500

$0

$-250

Policy Decision

[$437.5]

[$500]

All values in millions of $

IEcVOI Framework (continued)

• Variations in complexity of analysis:• Value of perfect information -- assumes that

collection of information will eliminate all uncertainty in exposure.

• Value of imperfect information -- examines the potential that addition of information will resolve some, but not all of the uncertainty in exposure.

• Value of partial information -- considers the impact of uncertainty in other inputs to the decision (e.g., toxicity).

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Summary of Case Study Results

• Lead Case Study• There are significant benefits of improving data ($13.2 billion to $24.3

billion).• This value is robust when we consider potential delay in obtaining

exposure data and the magnitude of uncertainties in other inputs.• Conclusion depends on the ability to resolve the uncertainty regarding

lead uptake, especially among the highly exposed (i.e., children).

• Mobile Source Air Toxics Case Study• There are potential benefits to improving exposure data, up to $45

million annually.• This value decreases substantially when we consider the larger

uncertainty in toxicity for these compounds.

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Background

• Decision we analyzed: regulating lead in residences under TSCA Section 403.

• Rule sets three standards: floor dust loading (µg/ft2); window sill dust loading (µg/ft2); and soil concentration (ppm). Violation triggers abatement.

• Economic and risk analysis supporting the rule looked at 1000 combinations of these three standards.

• Risk and economic estimates based on two alternative models for relationship between environmental lead and children’s blood lead:• IEUBK: more sensitive, says regulate stringently• The “Empirical” model: much less sensitive, says do not

regulate• November 1998 proposed standards reflect EPA’s balancing of

these two exposure outcomes.

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VOI Case Study - Lead ExposureCOMPARISON OF NET BENEFITS, DUST STANDARDS, AND

SOIL STANDARDS FOR TWO LEAD UPTAKE MODELS

Standards that Maximize Net Benefits IEUBK Model

Proposed Standards

Floor Dust Standard Window Sill Dust Standard Soil Standard Total Cost Total Benefit Net Benefit

40 g/ft2

100 g/ft2

250 ppm $100.4 billion $273.6 billion $173.2 billion

50 g/ft2

250 g/ft2

2,000 ppm $52.8 billion

$160.1 billion $107.2 billion

Standards that Maximize Net Benefits

Empirical Model

Floor Dust Standard Window Sill Dust Standard Soil Standard Total Cost Total Benefit Net Benefit

80 g/ft2

310 g/ft2

4,350 ppm $44.0 billion $35.1 billion -$8.9 billion

IEcLead Rule RIA Influence Diagram

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LEAD RULE REGULATORY IMPACT ANALYSISINFLUENCE DIAGRAM

ImplementAbatementActions?

Lead AbatementEffectiveness

(Adjustment Factors)

MonetizedBenefits (Avoided

IQ Changes)

Change inPersons with

IQ<70

Change in IQ(points/person)

BaselineExposure (HUD

Survey Data)

Change inExposure (BloodLead Distribution)

Net Benefits

Value of IQ Changes

Lead UptakeEfficiency

Lead Dose-Response Function

Abatement Costs

IEcDecision Tree for Lead Regulatory Decision

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No_Controls 0

[0]

IEUBK $173.2

.250 Empir

$-34.9 .750

HighControls [$17.1]

IEUBK $107.2

.250 Empir

$-10.4 .750

Medium_Controls [$19]

IEUBK $77.9 .250 Empir

$ -8.9 .750

Low_Controls [$12.8]

High_Controls $173.2

Medium_Controls $107.2

Low_Controls $77.9

No_Controls $0

IEUBK

.250

[$173.2]

High_Controls $-34.9

Medium_Controls $-10.4

Low_Controls $-8.9

No_Controls $0

Empir

.750

[$0]

Exposure_Data [$43.3]

VOI = $43.3b - $19b = $24.3b

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Effect of Delay on VOI

• Delay scenarios assume: • Benefits are delayed if “collect exposure

data” option is chosen.• Discounted over delay period at 3 percent

annually.

• Results: • Perfect VOI, no delay: $24.3 billion• Perfect VOI, five-year delay: $18.4 billion• Perfect VOI, ten-year delay: $13.2 billion.

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Value of Imperfect Information

• Value of imperfect information analysis asks: What if improved data cannot completely resolve uptake uncertainty?

• Looked at best and worst case scenarios for power of NHEXAS-like data to resolve IEUBK/Empirical uncertainty (from 50 percent predictive accuracy to 90 percent predictive accuracy).

• Results show VOI remains substantial when improved data achieves a high level of predictive accuracy and the prior likelihood that IEUBK is correct is less than 50 percent.

IEcExpected Value of Imperfect Lead Exposure Information

19

0

2

4

6

8

10

12

14

16

18

0.5 0.6 0.7 0.8 0.9

Predictive Power of Improved Exposure Data

EV

I (B

illio

ns

of

$)

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

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Conclusions of Lead Case Study

• Value of exposure information could be very high; under best case conditions, a national exposure survey could have value in the tens of billions of dollars.

• Effect of delay in provision of information is to reduce VOI, but value of perfect information remains substantial even with 10-year delay.

• Improved exposure information must achieve a predictive accuracy of 80 to 90 percent certainty in forecasting the "correct" lead uptake model for VOI to remain substantial (e.g., no more than a 20 percent chance of predicting IEUBK if the empirical model is correct); lower levels of certainty quickly erode VOI.

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VOI Case Study: Mobile Air Toxics

• Retrospective analysis of VOI of exposure data for benzene and 1,3-butadiene, two air toxics regulated under the 1990 CAAA.

• Developed estimates of exposure and risk in 2000 both with and without control programs -- based on information available as rules were promulgated.

• Other inputs: control costs (over $2 billion annually); other benefits of air toxics reductions (about $850 million annually).

• Estimate value of improved information for mobile source air toxics relative to improvements in other key uncertain inputs (e.g., toxicity).

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Mobile Source Air Toxics - Exposure Uncertainty

Alternative Exposure/Distributions for 1,3-Butadiene

0%

5%

10%

15%

20%

25%

30%

0.0 0.5 1.0 1.5 2.0 2.5 3.0

Concentration (ug/m3)

Pe

rce

nt

of

Po

pu

lati

on

5th

25th

50th

75th

95th

IEcCalculation of Net Social Benefits

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Exhibit 2

CALCULATION OF NET SOCIAL BENEFITS

1 This includes characterizing the uncertainty in the exposure description.

Baseline(No-Control)

ExposureDistribution1

(g/m3)

Post-ControlExposure

Distribution1

(g/m3)

BaselineRisk

BaselineRisk

BaselineHealth

Damages

Post-ControlHealth

Damages

Other Healthand WelfareBenefits of

ControlMeasures

Cost ofControl

Measures

Direct HealthBenefits

Net SocialBenefits ofEmissionControls

Potency

Potency

HealthEffects

Valuation

HealthEffects

Valuation

ExposureCharacterization

Estimation of Direct Health BenefitsOther Benefits and

Control Costs

IEcDecision Tree for Motor Vehicle Air Toxics

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No_Control [0]*

Control [$103]

But_exp

N5th

.15

[$141]

But_exp

N25th

.225

[$160]

But_exp

N50th

.25

[$187]

But_exp

N75th

.225

[$220]

No_Control

Control

$435

$0

Decision2

N5th

.15

[0]

No_Control

Control

-$240

$0

Decision2

N25th

.225

[0]

No_Control

Control

$901

$0

Decision2

N50th

.25

[$190]

No_Control

Control

-$13

$0

Decision2

N75th

.225

[$435]

No_Control

Control

$190

$0

Decision2

N95th

.15

[$901]

But_exp

N95th

.15

[$281]

Collect_Exposure_Data Benz_exp

[$196]

Decision_1

VOI = $93 million (in 1995 $)

* Numbers in brackets are expected values of net social benefits at decision or chance nodes.

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Conclusions of Mobile Sources Case Study

• Expected value of perfect information = $93 million.

• Explored value of partial information by considering impact of uncertainty in toxicity of 1,3-butadiene and benzene.

• Value of exposure information decreases to $9 million.

• Value of toxicity information is greater, $118 million.

• Explored impact of varying dollar value of a statistical life (VSL).

• Exposure information has value across a wide range of VSL (approximately $3 million to $7 million).

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• Studies are routinely performed to better understand exposure and toxicity prior to making a product development decision in the private sector, but these are usually still uncertain elements, and there may be concerns about study assumptions or study quality that can compound this uncertainty.

• What is the value of research that improves both our fundamental understanding of exposure and toxicity and the methods used to assess these important decision elements? Improved methods can lead to increased confidence and reduced uncertainty in results that will help inform firm decision-making and regulators.

Estimating the Economic Value of Research in the Private Sector – Product Decision

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Influence Diagram for Estimating the Economic Value of Research

Produce Chemical?

Price

Demand/Volume

Sold

Profit

ExposureCancerPotency

Revenue

RegulatoryAction

Cost

Research

ExposureStudy

ToxStudy

ExposureStudy

ToxStudy

RegulatoryAction

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MOA__1__High -100 .50

MOA__2__Low 200 .50

Toxicity Yes [50]

No 0 [0]

Yes -100

No 0

Produce_Chemical2 MOA__1__High .500

[0]

Yes 200

No 0

Produce_Chemical2 MOA__2__Low

.50 [200]

Toxicity Research [100]

Produce_Chemical_ [100]

Example: Bioassays for compound X in two different animal species suggest two possible modes of action (MOAs) with very different implications for cancer potency, and hence for regulatory action and/or consumer demand and, ultimately, potential profit. Each MOA appears equally plausible at present. The firm can proceed with production or abandon the chemical. However, additioanl research may help to resolve which MOA is relevant for humans at expected ambient levels and lead to a better informed decision. (Note: This example assumes exposure is well known.)

In the ideal case, research fully resolves the uncertainty. The value of this “perfect” information is the difference between the expected value of the Research branch ($100 million) and the next best alternative, the Yes branch ($50 million) = $50 million. This represents an upper bound estimate on the value of research on toxicity.

Values in millions of dollars. Bracketed numbers represent expected values for the outcome measure.

Value of Perfect Toxicity Information

.50

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MOA__1__High

-100 .50 MOA__2__Low

200 .50

Toxicity Yes [50]

No

0

[0]

MOA__1__High

-100 .80 MOA__2__Low

200 .20

Toxicity_Given_High_Result Yes [-40]

No

0

[0]

Produce_Chemical2 MOA__1__High

.50

[0]

MOA__1__High

-100 .20 MOA__2__Low

200 .80

Toxicity_Given_Low_Result Yes [140]

No

0

[0]

Produce_Chemical2 MOA__2__Low

.50

[140]

Study_Prediction LRI_Study [70]

Produce_Chemical_ [70]

Value of Imperfect Toxicity Information

Research is unlikely to fully resolve uncertainty, yet It may still be valuable if it reduces the level of uncertainty. In the bottom branch following the study prediction, there is still some residual uncertainty about the MOA. However, if the study (e.g., PBPK modeling for MeCl2) is viewed as highly credible, the uncertainty in the MOA, given the study prediction, could be significantly reduced (80/20 in favor of the study prediction in this hypothetical example).

In this case of “imperfect” information, the research would still be worth pursuing, though the value would be less ($70 million - $50 million = $20 million). When using this type of VOI tool it is possible to explore ranges of values associated with different levels of confidence in the predictive power of LRI studies (or their likelihood of influencing regulatory decisions and customer demand).

Values in millions of dollars.Bracketed numbers represent expectedvalues for the outcome measure.

IEcApplications / Requirements

• VOI framework can be applied to variety of issues (pollutant regulation, global warming) and focus on different inputs (exposure, toxicity, economic valuation).

• Key constraint is data availability.• Need to be able to estimate outcomes (ideally, net social

benefits) in dollars

• Need to quantitatively characterize decision inputs (e.g., costs, population exposure levels, toxicity, non-health effects, benefit values)

• Need to quantify uncertain elements either discretely or using distributions (e.g., lognormal) pre- and post-information -- may require expert elicitation

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Identifying Decisions Where VOI May be High

• Stakes are high; divergent outcomes• Decision expected to be sensitive to health

outcomes and not dictated by non health-related factors

• Uncertainty can be represented using small number of scenarios (e.g., alternative MOAs) and associated probabilities

• Additional basic research can change beliefs about these states (e.g., improved PBPK modeling, interpretation of biomarker data, MOA research)

• Uncertainty in other decision inputs not significantly greater than uncertainty in input of interest.

IEcINDUSTRIAL ECONOMICS, INCORPORATED

Eric Ruder, Principal eruder@indecon.com

Henry Roman, Principal hroman@indecon.com\

617.354.0074