Robert L. Sielken Jr., Ph.D. Sielken & Associates Consulting Inc

Sielken & Associates Consulting, Inc.1

Robert L. Sielken Jr., Ph.D.Sielken & Associates Consulting Inc

3833 Texas Avenue, Suite, 230, Bryan, TX 77802Tel: 979-846-5175; Fax: 979-846-2671; Email:

[email protected]

Air Toxics Workshop II: Air Toxics ResearchImplications of Research on Policies to Protect Public Health

Session II: Interactive Processes in Toxicity AssessmentsHouston, Texas

Tuesday 10:20 am - 12:00 Noon, June 12, 2007

Experiences Helping Develop More Effective Regulations via Interactions

Between the Public, Universities, Regulated Entities and Regulators


Interactive Processes

Scientists

Industrial hygienists and scientists

Academic researchers

Concerned citizens

Regulators and Risk Managers

Consultants, specialists


As a statistician, researcher, consultant, and university professorin the field of quantitative human and environmental risk assessment,I have had the opportunity to interact with risk assessors and managers in numerous contexts:

States:

TexasFloridaCaliforniaMinnesotaWisconsinMichiganIllinoisIndianaOhioPennsylvaniaNew Yorketc.

Federal Government:

CongressEPAFDAOSHANIHNIEHSNCTRNAS/NRCetc.

Universities:

Texas A&MU. of TexasHarvard Center for Risk Analysis

etc.

Task Forces:

Cancer Risk AssessmentBenchmark DoseFood ProtectionGreat LakesILSIACCCMAAIHCSOTSRASETACISRTPASAetc.

Litigation:

MissouriTexasLouisianaVirginiaCaliforniaColoradoArizonaMississippiHawaiiDelawareCanadaetc.

Industry:> 100 Clients


My Job:

Bridger between Risk Assessors and Managersand Other Scientists

-- Ask Questions No One Else Dares Ask

Bridger between Regulators and those being Regulated

-- Be Someone All Sides Respect and Trust


My Job:

Help all those involved

to avoid the feeling

that they are being “hurt” or “fooled”

by someone’s

incorrect, inappropriate, incomplete, or inadequate

treatment of the available data.


My Job:

Help Risk Assessors and Risk Managers

-- recognize the limitations of default methodology

-- understand the opportunities available due torecent advances in risk assessment methodologyand risk management techniques

-- avoid the pitfalls associated with poorly understoodmathematical and statistical procedures


Partial List of Findings that Impacted the Most RecentUS EPA Draft Risk Assessment for Ethylene Oxide:

Common Failures:

Failure to use the newest data.

Failure to use all of the data.

Failure to use a valid dose-response modeling approach.

Errors in calculations.

Results fail simple reality checks.


The most common implementation of the age-dependentadjustment factor (ADAF) is mathematically incorrect.

Excess risk calculations for incidence using estimated dose-response models for mortality are inappropriate.

The BEIR IV life-table methodology for calculating excess risk ismathematically correct when the response of concern is mortality but is incorrect when the response of concern is incidence.

Conclusions about lower-dose risks based onhigh-to-low-dose extrapolation using fitted dose-response modelsdominated by the high-dose portion of the datamay be contradicted by the observed lower-dose data.

It is critically important to do dose-response assessment using theexposure and outcome data for the individuals in the cohortrather than on summaries of groups of individuals (e.g., odds ratios).



Assuming an 85-year exposure lifetime instead of a 70-year exposure lifetime substantially impacts calculations ofexcess risk for many toxic endpoints (e.g., most cancers).

Assumptions frequently dominate excess risk calculations.

In order to fairly compare the risks of two substances,the risks must both be calculated using the same assumptions.




The existence of repair and other background defense mechanismscan imply that the extrapolation below a point of departure (POD)should not be done linearly.



The existence of repair and other background defense mechanismscan imply that the extrapolation below a point of departure (POD)should not be done linearly.



You cannot conclude what the SHAPE of the dose-response relationship is

if you only fit models of a specified shape to the data.

For example,if you only fit linear models, then the fitted shape is linear; however, that does not mean that the true shape of the dose-response relationship is linear.

Similarly, if you only fit supra-linear models, then the fitted shape is supra-linear; however, that does not mean that the true shape of the dose-response relationship is supra-linear.


Partial List of Additional Findings that Impacted Risk Assessments:

Other Risk Assessments


Risk extrapolations from occupational to environmental scenarios

need to account for the differences in these scenarios

especially with respect to

exposure magnitude, duration, and temporal spacing

as well as confounding factors like

exposures to other substances

and the number of high intensity tasks.



The default procedure used by some risk assessors(e.g., in EPA and California) to bound cancer potencies

is dominated by default assumptions and does not reflect the observed experimental data:

The linearized multistage model upper bound on the cancer slope (q1*)fails to adequately reflect the shape of the observeddose-response relationship and especially the outcomesin the low-dose region, which is the region ofprimary interest in risk assessment.


20%

0% Dose0 0.25 0.50 1.0

Re

spo

nse

Fre

qu

en

cyLinearizedMultistage

Model Slope = 0.035

20%

0% Dose0 0.25 0.50 1.0

Re

spo

nse

Fre

qu

en

cy

LinearizedMultistage

Model Slope = 0.332

20%

0% Dose0 0.25 0.50 1.0

Re

spo

nse

Fre

qu

en

cy


Model Slope = 0.311

20%

0% Dose0 0.25 0.50 1.0

Re

spo

nse

Fre

qu

en

cy


Model Slope = 0.065

20%

0% Dose0 0.25 0.50 1.0

Re

spo

nse

Fre

qu

en

cy


Model Slope = 0.084

20%

0% Dose0 0.25 0.50 1.0

Re

spo

nse

Fre

qu

en

cy


Model Slope = 0.120

100% at High Dose

100% at High Dose

100% at High Dose

= observedresponse frequency

Although the outcomes for each of the 6 experiments are very different,the slopes q1* differ by less than 10 fold (one order of magnitude).


This example and similar other exampleshave promoted several regulatory agenciesto emphasize

Best Estimates instead of Bounds

especially when the dose-response data are human data.

For example, emphasizing

the fitted dose-response modelinstead of an upper bound on that model

ECs instead of LECs

BMDs instead of BMDLs



Errors:

Almost any data collection involves some error

-- Measurement Error-- Reporting Error

etc.


Errors usually cause

upper sample percentiles

to have an

OVERESTIMATION BIAS.


Impact of Errors: Simple Example

True Concentration = 10

Concentration

Concentration with Error

Upper PercentileGreater ThanTrue Concentration

10

10


The Impact of Errors on the Sample Percentiles is Least for Central Tendency and Greatest for Extreme Percentiles

1,000 Monte Carlo Samples of Size 1000F

req

ue

nc

y

20%

40%

0%

60%

80%

100%

99.9th Percentile

Fre

qu

en

cy

20%

40%

0%

60%

80%

100%

95th Percentile

Fre

qu

en

cy

20%

40%

0%

60%

80%

100%

90th Percentile

Fre

qu

en

cy

20%

40%

0%

60%

80%

100%

50th Percentile

Ratios: (Sample Percentile / True Percentile)

0 0.0010.01

0.10.9 1 1.1

10 1001000


If the data collection involves errors, then the inflationary errors at the extremes of the data distributions make

the extreme percentiles of the assessment the shakiest foundation for good decision making

and the least reliable basis for differentiation between different chemicals or situations.



Lessons learned in the development of the

Integrated Endangerment Assessment /Risk Characterization

for the Rocky Mountain Arsenal (RMA)near Denver, Colorado


Bounds on exposure should NOT be determined by simply evaluating an exposure equation or model with each exposure

parameter's distribution

replaced by

a bounding constant.


For example,if visitation hours per lifetime were evaluated as

(Hours per Day)x (Days per Year)

x (Years per Lifetime)

then the 95th percentile of the corresponding probability distribution for Recreational Visitors to RMA would be approximately

200 hours per lifetime


However, if each component variable was simply replaced by its 95th percentile, then

(Hours per Day)0.95

x (Days per Year)0.95

x (Years per Lifetime) 0.95

= 1200 hours per lifetime

or approximately 6 times greater than the true 95th percentile

200 hours per lifetime


Furthermore, if each component variable was replaced by a default "reasonable maximum exposure" (RME) value, then

(Hours per Day)RME

x (Days per Year)RME

x (Years per Lifetime)RME

= 10,400 hours per lifetime

or more than 50 times greater than the true 95th percentile

200 hours per lifetime.



Exposure and risk characterizationscan be very differentdepending on whether or not variability is incorporated.

Since variability is a part of reality,the most realistic exposure and risk characterizationsincorporate variability.


Distribution of Lifetime Average Daily DoseModeling With and Without Year-to-Year Variability

mg / kg / day

Fre

qu

enc

y

10%

20%

30%

40%

0.000001

0.000005

0.00001

0.00005

0.0001

0.0005

0.001

WithYear-to-Year Variability

WithoutYear-to-Year Variability



Potentially exposed populations are comprised of people who do NOTconduct their lives in an identical fashion, but people who vary widely in theiractivities, diets, hobbies, desires, preferences,interests, obligations, and motivations. Such variation is more fully reflected in a distribution than a constant.


Examples of Quantitative Impact of Incorporating the Exposure Variability

Within the Population


The quantitative impact on the distribution of the lifetime average daily dose from drinking water ingestionof assuming that the exposure duration is either 70 years or less than a full lifetime (e.g., one residence duration)

0

20

40

60

80

Per

cen

tag

e

Duration ofExposure

70 years

One Residence Duration

0 to 1E-10

1E-91E-8

1E-70.000001

0.00001

0.0001

0.0010.01

Lifetime Average Daily Dose (mg / kg / day)


Pesticide: Water Concentration (ppb)

The distributional characterization of the concentration in the drinking water in 9 of the 18 major use states with sample data in the data base:Variability from State to State and Person to Person within a State

MD

ILIN

HI

CADE

FL

IA

KS

1E-10

1E-91E-8

1E-70.000001

0.00001

0.0001

0.001

0.010.1

1 10 100

Per

cen

tag

e

0

20

40

60

80

100


NY

NE

MNMI

MONC

OH

PAWI

Pro

po

rtio

n

1.00

0.80

0.60

0.40

0.20

0.00

Margin of Exposure

10,000,000,000

1,000,000,000

100,000,000

10,000,000

1,000,000

100,000

10,000

1,000

100

101

The distribution of the concentration in the drinking water and theVariability from State to State and Person to Person within a Statecarries forward to the distribution of the margin of exposure associated with drinking water ingestion in 9 major use states



Hence, it can be important to incorporatetemporal, spatial and demographic variabilityinto exposure and risk characterizations.

Temporal, spatial and/or demographic variabilitycan have a major impact on exposure and risk distributions.


Temporal Integration: Hypothetical Example: Target = Three Month Exposure

Drinking Water

Spring Summer Fall Winter Without TemporalIntegration

Food

+ + + + Combined

Non-Dietary

SpringAggregate

SummerAggregate

FallAggregate

CombinedAggregate

+ + + + Combined


WithoutTemporalIntegration

WithTemporalIntegration

Temporal Integration: Hypothetical Example: Target = Three Month ExposureImportance of Pooling Exposures

in the Same Season versus Mis-Matched Seasons

Fre

qu

en

cy

10%

20%

30%

40%

0%

mg / kg / day



Populations are often comprised of subpopulations(e.g., males and females, andprivate well and community water supply users).

Population characterizations can incorporatethe size and other characteristics of the component subpopulations.

The distribution of exposures in the population is NOT THE SAME AS the distribution of exposures in the most exposed subpopulation..


Suppose that a population

is comprised of

two subpopulations,

A and B


0

10

20

30

40

50

0 5 10 15 20 25 30

Distributional Characterizations of a Population Comprised of Two Subpopulations:50% Subpopulation A, 50% Subpopulation B

Correct: A and B

Incorrect: A + BP

erce

nta

ge

AB

The distribution in the population comprised of subpopulations A and Bis not the distribution of the values for the sum of A and B.


The relative sizes of

the subpopulations comprising

the population impact

the distributional characterization

of the population.


Subpopulation A = 50% of PopulationSubpopulation B = 50% of Population

vsSubpopulation A = 90% of PopulationSubpopulation B = 10% of Population

vs Subpopulation A = 10% of PopulationSubpopulation B = 90% of Population

0 1 5 10 15 20 25 30

Per

cen

tag

e

50% A , 50% B

10% A, 90% B

90% A, 10% B

0

10

20

30

40

50


The distribution of exposures in the population is not the same as the distribution of exposures in the most exposed subpopulation

PopulationDistribution

Most ExposedSubpopulationDistribution

0

10

20

30

40

50

Per

cen

tag

e

0 1 5 10 15 20 25 30


CORN

Grower Commercial

M/LMixer / Loader

AApplicator

Aerial

M/L A M/L Pilot

Tree for Assessment of Pesticide Handler Exposure

Ground

OpenPour

ClosedSystem

OpenCab

ClosedCab

OpenPour

ClosedSystem

Both (M/L & A)

Open ClosedPour System

Open ClosedPour System

Open ClosedCab Cab

OpenCab

ClosedCab

OpenCab

ClosedCab

7/10 3/10 3/10 7/10 7/10 3/10

3/10 7/10 3/10 7/10 3/10 7/10 2/10 8/10 3/10 7/10

1/3 1/3 1/3

1/2 1/2 1/2 1/2

97% 3%

86% 14%


PESTICIDE HANDLING Corn Production, Flowable Formulation

Population and Subpopulations

Margin of Exposure

ApplicatorMixer/Loader

Commercial Aerial Applicator

Mixer/Loader

Mixer/Loader & ApplicatorCommercial Ground

Mixer/LoaderApplicator

GrowersAll Pesticide Handlers

10,000,000,000

1,000,000,000

100,000,000

10,000,000

1,000,000

100,000

10,000

1,000

100

1010.00

0.20

0.40

0.60

0.80

1.00



Multiple Choice: Is 100

1) NEAR A + B2) GREATER THAN A + B3) LESS THAN A + B

4) NEAR the 95th Percentile of A + B5) GREATER THAN the 95th Percentile of A + B6) LESS THAN 95th Percentile of A + B

Answer: All of these outcomes are possible!

For example, suppose95th Percentile of A + 95th Percentile of B

= 50 + 50 = 100

When A and B are characteristics that have distributions, thenyou usually must use a technique like Monte Carlo simulationto determine the distribution of a combination of A and B(e.g., A + B). For example, you can’t determine the 95-th percentile of A+B simply by knowing the 95-th percentile of A and the 95-th percentile of B.



There is often more than one way to calculate a bound,and different ways can produce very different bounds.

-- not all bounds have the form: best estimate ± a few standard deviations

See your local statisticianfor the best bounding methodology!


There can be a huge difference between anEstimated Risk

and a Bound on Risk.

Therefore, it can be very important to state whethera stated risk is an estimate or a bound.


Statistical confidence limits are intended toreflect experimental variability

notmisspecified model families,

unsatisfied assumptions,alternative choices,

etc.

A statistical 95% upper confidence limit is not gospel.

-- On the one hand, it may capture only a small part of what is unknown.

-- On the other hand, it may be a gross exaggeration because of the method chosen for its calculation and the assumptions incorporated.



Some bounding procedures exaggerate risks far more for some underlying dose-response relationships than others.

For example, the linearized multistage model that is often usedto generate upper bounds (q1*) on the cancer potencygenerally exaggerates the risk

-- 1 to 2 fold for linear dose-response relationships

-- 5 to 10 fold for linear-quadratic dose-response relationships

-- 100 to 1,000 fold for quadratic dose-response relationships

Thus, bounds exaggerate the risks associated with “safer” substances more than they exaggerate the risks for “less safe” substances.

Thus, risk comparisons between substances should be based on best estimates rather than bounds.


1,0000.5 1 2 5 100 20Ratio

50 100

100%Frequency

1,0000.5 1 2 5 100 20Ratio

50 100

100%Frequency

1,0000.5 1 2 5 100 20Ratio

50 100

100%Frequency

1%

94%

72%

6%

20%

14%

8%

85%

True Dose-Response Model:Multistage Model:

Linear: P(d) = 1 – exp [ -0.92d]


Linear-Quadratic: P(d) = 1 – exp [ -0.08d + 0.84d2]


Quadratic: P(d) = 1 – exp [ -0.92d2]

True Risk Specific Dose [ RSD (1/100,000) ] Ratio = ----------------------------------------------------------------- Linearized Multistage Model Lower Bound on RSD



Experiences Helping Develop More Effective Regulations via Interactions

Between the Public, Universities, Regulated Entities and Regulators

There is a vast collection of examples of


Thank You

Documents

Robert L. Sielken Jr., Ph.D. Sielken & Associates Consulting Inc