Page
1
of
7
FIFRA
SCIENTIFIC
ADVISORY
PANEL
(
SAP)
OPEN
MEETING
APRIL
29­
APRIL
30,
2004
FIFRA
SAP
WEB
SITE
http://
www.
epa.
gov/
scipoly/
sap/
OPP
Docket
Telephone:
(
703)
305­
5805
Docket
Number:
OPP­
2004­
0071
THURSDAY,
APRIL
29,
2004
Holiday
Inn
Hotel
1900
North
Fort
Myer
Drive
Arlington,
VA
22209
(
703)
807­
2000
A
MODEL
COMPARISON:
DIETARY
AND
AGGREGATE
EXPOSURE
IN
CALENDEX,
CARES,
AND
LIFELINE

8:
30
AM
Introduction
and
Identification
of
Panel
Members
­
Stephen
M.
Roberts,
Ph.
D.
(
FIFRA
SAP
Chair)


8:
40
AM
Administrative
Procedures
by
Designated
Federal
Official
­
Ms.
Myrta
R.
Christian

8:
45
AM
Welcome
­
Mr.
Joseph
J.
Merenda,
Jr.
(
Director,
Office
of
Science
Coordination
and
Policy,
EPA)


8:
50
AM
Opening
Remarks
­
Mr.
Jim
Jones
(
Director,
Office
of
Pesticide
Programs,
EPA)


8:
55
AM
Opening
Remarks
­
Randolph
Perfetti,
Ph.
D.
(
Health
Effects
Division,
Office
of
Pesticide
Programs,
EPA)


9:
00
AM
Background,
Goals,
Objectives
and
Focus
of
Current
Model
Comparisons
in
OPP;
Future
Model
Comparisons
in
OPP
­

Mr.
Francis
Suhre
(
Health
Effects
Division,
Office
of
Pesticide
Programs,
EPA)


9:
20
AM
Introduction
to
Probabilistic
Models
(
DEEM,
Calendex,
LifeLine
and
CARES)
and
Reviews
of
Past
SAPs
­
Mr.
David
Miller
(
Health
Effects
Division,
Office
of
Pesticide
Programs,
EPA)


9:
40
AM
Side­
by­
Side
Comparisons
of
Dietary
Exposure
Estimates
Using
DEEM
and
LifeLine
­
Mr.
David
Miller
(
Health
Effects
Division,
Office
of
Pesticide
Programs,
EPA)


10:
00
AM
BREAK

10:
15
AM
Approximating
Consumption
Distributions
for
Probabilistic
Models
(
DEEM/
Calendex,
LifeLine
and
CARES)
Using
SAS
Simulations
­
Steve
Nako,
Ph.
D.
(
Health
Effects
Division,
Office
Page
2
of
7
of
Pesticide
Programs,
EPA)


11:
15
AM
Case
Study
­
Dietary
(
Food
and
Water)
Estimates
Using
the
DEEM/
Calendex,
CARES,
and
LifeLine
Models
and
a
Common
Data
Set
­
Mr.
David
Miller
(
Health
Effects
Division,
Office
of
Pesticide
Programs,
EPA)


11:
40
AM
Summary
­
Mr.
Francis
Suhre
(
Health
Effects
Division,
Office
of
Pesticide
Programs,
EPA)


11:
45
AM
LUNCH

12:
45
PM
Public
Comments

1:
45
PM
Questions
to
the
Panel
1.
General
Approach
of
Approximating
Models:

A.
While
the
three
probabilistic
risk
assessment
models
described
in
this
SAP
presentation
each
project
pesticide
exposure
for
the
`
US
population',
they
differ
in
their
basic
design
in
a
number
of
ways.
EPA
has
identified
and
investigated
four
model
design
features
associated
with
these
models,
as
follows:

!
`
Reference
Population':

"
DEEM­
Calendex
is
based
on
the
CSFII
survey
design
"
CARES
is
based
on
the
US
Census
PUMs
"
Lifeline
is
based
on
the
NCHS
Natality
database
!
`
Binning
food
diaries'
to
generate
longitudinal
consumption
profiles:

"
DEEM­
Calendex
draws
from
the
individuals'
two
day
diaries
"
CARES
uses
the
Gower
dissimilarity
index
"
Lifeline
`
bins'
food
diaries
based
on
age
and
season
!
`
Model
weights'
to
project
simulated
exposure
days
up
to
the
modeled
(
US)
population
"
DEEM­
Calendex
uses
the
CSFII
survey
weights.

"
CARES
uses
weights
developed
from
its
stratified
sampling
design,
and
"
Lifeline
weights
each
person
equally.

!
`
Body
weight'

"
DEEM­
Calendex
assigns
food
consumption
values
to
each
individual
on
the
basis
of
the
grams
food/
kg
body
weight
as
reported
by
the
CSFII
respondents.

"
CARES
also
assigns
food
consumption
values
to
each
individual
on
the
basis
of
the
grams
food/
kg
body
weight
as
Page
3
of
7
reported
by
the
CSFII
respondents.
However,
since
the
CARES
body
weights
are
different
from
the
CSFII
Body
weights,
CARES
adjusts
the
amount
of
food
consumed
to
reflect
the
CARES
body
weight.

"
Lifeline
uses
a
reported
consumption
value
for
each
individual
on
the
basis
of
the
grams
food
as
reported
in
CSFII)
and
estimates
the
individual's
body
weight
based
on
physiometric
growth
models
developed
for
various
demographic
groups
(
based
on
gender,
race
and
ethnicity)
using
NHANES
data.

Question
1.1
The
SAP
is
asked
to
please
comment
on
whether
the
above
cited
model
design
features
reflect
those
most
likely
to
result
in
differences
in
dietary
[
food
and
water]
exposure
estimates
based
on
identical
data
sets.
If
not,
what
other
model
design
features
are
likely
to
cause
different
dietary
exposure
estimates?

B.
In
an
attempt
to
further
elucidate
differences
between
predicted
exposures
among
the
three
models
(
DEEM­
Calendex,
CARES
and
LifeLine)
,
OPP
developed
SAS
approximation
models.
These
SAS
approximation
models
permit
the
isolation
of
factors
related
to
the
Reference
Population,
Binning
Procedures,
Sampling
Weights,
and
individual
Body
Weights
which
cannot
be
isolated
by
running
the
individual
models.
Section
IV
of
the
background
document,
provided
to
the
SAP,
describes
the
development
of
these
SAS
approximation
models
and
some
analyses
performed
by
the
Agency
using
these
SAS
approximation
models
to
compare
and
contrast
model
design
features
of
DEEM­
Calendex,
CARES,
and
LifeLine.
Based
on
these
analyses
the
Agency
concluded
that
the
SAS
approximation
models
track
actual
model
results
very
closely
for
single
Raw
Agricultural
Commodity
(
RAC)
analyses,
and
reasonably
well
for
the
multi­
RAC
analyses.

Question
1.2
The
SAP
is
asked
to
please
comment
on
the
approach
taken
by
the
Agency
to
develop
and
use
SAS
approximation
models
(
see
Section
IV
of
the
background
document)
to
attribute
differences
in
model
predictions
from
differences
in
model
designs.
Please
suggest
possible
improvements
or
refinements
to
these
SAS
approximation
models
and
to
alternative
methods
for
comparing
model
predictions.

2.
Reference
Population
&
Model
Weights
Page
4
of
7
The
DEEM­
Calendex
program
uses
the
CSFII
survey
respondents
as
its
reference
population;
as
such,
the
DEEM­
Calendex
model
estimates
use
the
CSFII­
specific
sample
(
or
model)
weights
to
estimate
exposures.
Each
simulated
day
is
weighted
to
project
that
exposure
day
to
represent
a
group
of
similar
individuals
from
the
U.
S.
population.
CARES
and
Lifeline
use
alternative
data
sources
(
i.
e.,
U.
S.
Census
PUMS,
and
NCHS
Natality)
to
generate
their
respective
Reference
populations.
The
CARES
model
developed
its
Reference
Population
by
taking
a
stratified
random
sample
of
100,000
persons
from
the
US
Census
PUMS.
The
stratified
sampling
design
enabled
CARES
to
over­
represent
sub­
populations
of
interest
(
e.
g.,
20,003
Infants)
in
its
reference
population
which
are
subsequently
downweighted
to
permit
projection
to
the
U.
S.
population.
The
Lifeline
model
uses
the
Natality
data
to
generate
its
Reference
population.
Lifeline
provides
the
option
of
using
CSFII
survey
weights
to
affect
the
probability
of
selecting
diaries
from
each
of
the
dietary
bins.
If
this
option
is
not
selected,
Lifeline
will
weight
each
modeled
individual
equally
since
these
modeled
lives
are
drawn
randomly
from
the
Natality
statistics.

Question
2.1
The
SAP
is
asked
to
please
comment
on
the
different
approaches
used
by
the
three
models
in
developing
their
Reference
Populations
and
model
weights.


3:
30
PM
BREAK

3:
45
PM
Questions
to
the
Panel
Continued
3.
Binning
Design
&
Frequencies
of
using
CSFII
diaries
These
models
differ
in
the
expected
(
or
actual)
frequencies
that
each
CSFII
diary
is
used
in
the
probabilistic
risk
assessment.
DEEM­
Calendex
uses
only
the
individuals
that
provided
two
days
of
food
diaries
in
its
reference
population,
and
sets
aside
approximately
1,000
one
day
food
diaries
in
estimating
dietary
exposure.
CARES
employs
a
Gower
dissimilarity
index
in
its
algorithm
to
generate
longitudinal
consumption
profiles
for
its
Reference
Population.
The
result
is
use
of
some
CSFII
diaries
much
more
often
than
other
diaries
in
simulating
exposure
(
as
much
as
4,000
times
for
certain
diaries
versus
once
for
others).
Approximately
1,000
CSFII
diaries
are
not
included
in
the
CARES
Food
Match
table.
The
Lifeline
model
uses
a
very
general
bin
based
on
age
and
season,
such
that
all
food
diaries
within
a
particular
bin
have
the
same
expected
frequency
of
being
used
in
its
exposure
assessment.
In
order
to
evaluate
the
effect
of
these
differing
frequencies
and
modeling
weights,
EPA
approximated
all
three
models
using
the
Lifeline
recipes
(
i.
e.,
keeping
recipes
constant).

Question
3.1
The
SAP
is
asked
to
please
comment
on
the
frequency
Page
5
of
7
that
CSFII
diaries
are
used
by
the
various
models.
Are
there
any
potential
biases
that
may
arise
in
the
respective
dietary
exposure
estimates
for
these
models
as
a
result
of
how
they
used
CSFII
records?
Considering
Lifeline's
current
dietary
bin
design
(
age,
season),
please
comment
with
respect
to
the
use
of
the
CSFII
survey
weight
option.
Is
either
Lifeline
option
(
CSFIIweighted
or
not)
generally
more
appropriate
than
the
other
or
are
there
circumstances
in
which
one
might
be
preferable
to
the
other?


5:
00
PM
ADJOURNMENT
Page
6
of
7
FIFRA
SCIENTIFIC
ADVISORY
PANEL
(
SAP)
OPEN
MEETING
APRIL
29­
APRIL
30,
2004
FIFRA
SAP
WEB
SITE
http://
www.
epa.
gov/
scipoly/
sap/
OPP
Docket
Telephone:
(
703)
305­
5805
Docket
Number:
OPP­
2004­
0071
FRIDAY,
APRIL
30,
2004
Holiday
Inn
Hotel
1900
North
Fort
Myer
Drive
Arlington,
VA
22209
(
703)
807­
2000
A
MODEL
COMPARISON:
DIETARY
AND
AGGREGATE
EXPOSURE
IN
CALENDEX,
CARES,
AND
LIFELINE

8:
30
AM
Introduction
and
Identification
of
Panel
Members
­
Stephen
M.
Roberts,
Ph.
D.
(
FIFRA
SAP
Chair)


8:
40
AM
Administrative
Procedures
by
Designated
Federal
Official
­
Ms.
Myrta
R.
Christian

8:
45
AM
Follow­
up
from
Previous
Day's
Discussion
­
Mr.
Francis
Suhre
(
Health
Effects
Division,
Office
of
Pesticide
Programs,
EPA)


9:
00
AM
Questions
to
the
Panel
Continued
4.
Commodity
Exposure
Contribution
Analyses
An
important
aspect
of
any
dietary
risk
assessment
is
the
ability
to
identify
significant
contributors
at
the
upper
percentiles
of
exposure.
The
CARES
and
DEEM
models
both
include
an
output
report
option
known
as
the
Critical
Exposure
Contribution
(
CEC)
analysis.
A
comparable
report
option
is
expected
to
be
developed
for
the
LifeLine
model
in
the
near
future.
These
CEC
reports
quantify
the
contribution
of
specific
food
commodities
(
RAC­
FF)
to
the
total
exposure
at
the
upper
percentiles
(
e.
g.,
top
0.2%)
of
the
exposure
distribution.
An
alternate
or
complementary
approach
(
frequency­
exceeded),
also
used
by
various
model
developers,
tabulates
the
frequency
that
a
particular
commodity
(
RAC­
FF)
causes
exposure
to
exceed
some
level
of
concern
.
As
was
the
case
with
predictive
exposure
estimates,
model
design
can
affect
the
outcome
of
commodity
exposure
contribution
analyses.
Section
IV.
G
of
the
background
document
describes
the
CEC
and
`
frequency­
exceeded'
approaches
for
identifying
significant
contributors
at
the
upper
end
of
the
exposure
distribution.
Tables
13
and
14
show
CEC
reports
and
`
frequency
of
occurrence'
data
for
DEEM­
FCID
and
CARES
analyses
for
Page
7
of
7
3
­
5
year
olds
and
20
­
49
year
olds,
respectively.
Tables
15
and
16
show
SAS
approximations
for
the
model
CEC
reports
and
`
number
of
occurrences
>
aPAD'
for
these
same
age
groups.
Although
there
is
certainly
a
degree
of
similarity
between
model
results
and
between
the
model
results
and
the
SAS
approximation
results,
differences
do
occur.

Question
4.1
The
SAP
is
asked
to
please
comment
on
the
relative
merits
of
the
two
approaches
described
above
(
CEC
and
frequency­
exceeded)
for
identifying
significant
contributors
(
RAC­
FF)
to
exposure
at
the
upper
percentiles
of
exposure.
Are
there
other
methods
or
techniques
which
the
Panel
might
recommend
for
accomplishing
this
important
part
of
the
dietary
exposure
assessment?


10:
30
AM
ADJOURNMENT
Please
be
advised
that
agenda
times
are
approximate.
For
further
information,
please
contact
the
Designated
Federal
Official
for
this
meeting,
Ms.
Myrta
Christian,
via
telephone:
(
202)
564­
8450;
fax:
(
202)
564­
8382;
or
email:
christian.
myrta@
epa.
gov
