Document ID: EPA-HQ-OPP-2005-0172-0063
Agency: epa
Document Type: Supporting & Related Material
Title: 
Posted Date: 2005-08-29T04:00Z

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1
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27
Preliminary
N­
Methyl
Carbamate
Cumulative
Risk
Assessment
Preliminary
N­
Methyl
Carbamate
Cumulative
Risk
Assessment
FIFRA
Scientific
Advisory
Panel
August
23­
26,
2005
FIFRA
Scientific
Advisory
Panel
August
23­
26,
2005

Session
1
°
Public
Comments
°
Hazard
Assessment

Session
2
°
Drinking
Water
Exposure
Assessment

Session
3
°
Food
&
Residential
Exposure
Assessment

Session
4
°
Model
Results
Comparison,
Cumulative
(

Multipathway
Analysis,
&
Risk
Characterization
Sessions
Roadmap
Slide
2
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27
Slide
3
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27
Session
3
Preliminary
N­
Methyl
Carbamate
Cumulative
Risk
Assessment:

Dietary
Assessment
Session
3
Preliminary
N­
Methyl
Carbamate
Cumulative
Risk
Assessment:

Dietary
Assessment
David
Hrdy
Health
Effects
Division
Office
of
Pesticide
Programs
David
Hrdy
Health
Effects
Division
Office
of
Pesticide
Programs
Slide
4
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27
Outline
of
Presentation

Sources
of
Input
data

Residue
Manipulations
°
Index
Equivalent
Residues
(
RPF
method)

°
Risk
estimated
using
PoD
(
BMDL)

and
exposure

Dietary
Risk
Assessment
Results

Input
Assumptions

Question
to
the
Panel
Slide
5
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27
Cumulative
Dietary
Exposure
Exposure
=
Residue
X
Consumption
Cumulative
Residues
Slide
6
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27
Exposure
=
Residue
x
Consumption

Residues
°
USDA
Pesticide
Data
Program
(
PDP)

65
food
forms
in
the
PDP
data
with
data
between
1994
and
2003

Available
at:

http://
www.
ams.
usda.
gov/
science/
pdp
°
FDA
Data

Consumption
°
CSFII
1994­
96/
1998

20,607
individual
participants
interviewed
over
two
discontinuous
days
(~
3­
10
days
apart)
Slide
7
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27
Foods
Based
on
Translated
PDP
Data
Eggplant
Pepper(
other
than
green)

Citrus
(
other
than
orange)

Apricots
Plums/
Prunes
Rye
Beets­
garden
Horseradish
Parsnips
Radishes
Rutabagas
Turnips
Various
leafy
greens
Brussels
sprouts
Cabbage
Cauliflower
Melons
(
other
than
cantaloupe)

Pumpkins
Squash­
summer
Slide
8
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27
Relative
Potency
Factor
(
RPF)

Method
of
Estimating
Cumulative
Residues
in
Foods
Converted
chemical
specific
residues
on
food
samples
to
a
common
residue
Index
Equivalent
Residue
(
Residue
IE)
Calculation
of
Cumulative
Residues

Residue:

°
PDP
residue
data
by
sample

PF:

°
Processing
factor
(
if
applicable)


RPF:

°
Relative
Potency
Measure
ResidueIE
=
Residue
X
PF
X
RPF
Cumulative
ResidueIE=
 
ResidueIE
(
per
PDP
sample)


×
=
foods
all
IE
n
Consumptio
Residue
Cumulative
PoD
MOE
Slide
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27
Slide
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27
NMC
CRA
Food
Residue
Database

Residue
Data

Processing
Factors

Relative
Potency
Factors

Data
Translation
schemes

Algorithms
for
estimating
cumulative
residue
distributions
Slide
11
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Exposure
Assessment
Models

DEEM­
FCID
 

CARES
 

LifeLine
 
were
used
to
estimate
cumulative
food
exposure
for
this
part
of
the
case
study
°
Cumulative
results
for
DEEM/
Calendex
 
,
Lifeline
 
,
and
CARES
 
will
be
discussed
in
a
later
session
Slide
12
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27
Exposure
Assessment
Models

Probabilistic
(
Monte­
Carlo)
procedures

Inputs
°
Distributions
for
consumption
°
Distributions
or
point
estimates
for
residue
concentrations

Outputs
°
Distribution
of
one­
day
dietary
exposures
°
Distribution
of
associated
risks

i.
e.,
MOEs
Slide
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Populations
Groups
Assessed

Dietary
Exposure
Assessments
were
based
on
survey
information
on
the
following
age
groups:

°
Infants
less
than
one
year
old
°
Children
1­
2
years
old
°
Children
3­
5
years
old
°
Children
6­
12
°
Youth
13­
19
°
Adults
20­
49
years
old
°
Adults
50
years
of
age
and
older
°
Females
13­
49
°
US
General
Population

Revised
Aggregate
assessments
will
include
detailed
discussion
of
all
of
EPA's
age
groups
104
0.001346
596
0.000235
3889
0.000036
Females
13­
49
yrs
110
0.001273
551
0.000254
3182
0.000044
Adults
50+
yrs
109
0.001279
633
0.000221
4118
0.000034
Adults
20­
49
yrs
98
0.001428
606
0.000231
4375
0.000032
Youth
13­
19
yrs
61
0.002278
329
0.000426
2000
0.000070
Children
6­
12
yrs
42
0.003368
201
0.000696
1094
0.000128
Children
3­
5
yrs
37
0.003773
188
0.000745
979
0.000143
Children
1­
2
yrs
109
0.001288
741
0.000189
3415
0.000041
All
infants
<
1
yrs
86
0.001619
483
0.000290
2979
0.000047
U.
S.
Population
MOE
Exp
MOE
Exp
MOE
Exp
Population
99.9th
Percentile
99th
Percentile
95th
Percentile
Slide
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N­
Methyl
Carbamate
MOEs
and
Exposures
For
Various
Subpopulations
(
DEEM)

Note:
No
target
MOE
or
regulatory
percentile
has
been
established
101
0.001389
628
0.000223
3812
0.000037
Lifeline
 
110
0.001278
605
0.000231
3913
0.000036
CARES
 
109
0.001279
633
0.000221
4118
0.000034
DEEM/
Calendex
 
20
to
49
yr
olds
40
0.003474
208
0.000672
1151
0.000122
Lifeline
 
41
0.003449
204
0.000688
1199
0.000117
CARES
 
42
0.003368
201
0.000696
1094
0.000128
DEEM/
Calendex
 
3
to
5
yr
olds
33
0.004207
210
0.000666
1076
0.000130
Lifeline
 
37
0.003771
194
0.000720
1053
0.000133
CARES
 
37
0.003773
188
0.000745
979
0.000143
DEEM/
Calendex
 
1
to
2
yr
olds
118
0.001189
779
0.000180
3128
0.000045
Lifeline
 
111
0.001257
823
0.000170
3892
0.000036
CARES
 
109
0.001288
741
0.000189
3415
0.000041
DEEM/
Calendex
 
Infants
<
1
years
old
MOE
Exposure
MOE
Exposure
MOE
Exposure
Model
99.9th
Percentile
99th
Percentile
95th
Percentile
Slide
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Note:
No
target
MOE
or
regulatory
percentile
has
been
established
Comparison
of
MOEs
and
Exposures
For
Various
Subpopulations
from
Three
Models
Slide
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27
Input
Assumptions

These
assumptions
are
the
same
as
those
that
have
been
extensively
characterized
in
the
Organophosphorus
Cumulative
Risk
Assessment
Slide
17
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Input
Assumptions:
1

Residues
in
composite
samples
from
PDP
adequately
reflect
any
residues
that
may
occur
on
single
serving
of
food
contained
in
a
homogenate
Slide
18
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27
Input
Assumptions:
2

In
cases,
where
PDP
multiresidue
methods
did
not
analyze
for
all
N­
Methyl
Carbamates
on
a
given
sample,
we
assumed
that
no
residues
of
that
missing
analyte
were
present
Slide
19
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27
Input
Assumptions:
3

In
those
cases
where
PDP
did
not
detect
and
report
<
LOD
residues,

we
did
not
assume
any
residues
were
present
Slide
20
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27
Input
Assumptions:
4

PDP
food
commodity
collection
procedures
adequately
capture
the
temporal
and
geographical
variations
in
uses
of
pesticides
Slide
21
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27
Input
Assumptions:
5

Residue
data
from
over
a
nine
year
period
were
included
in
this
analysis
°
It
is
assumed
that
combining
data
over
this
time
span
adequately
captures
the
current
exposure
situation
Slide
22
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27
Input
Assumptions:
6

Translation
of
PDP
data
from
one
food
commodity
to
related
ones
(
for
example
cantaloupe
to
watermelon)
would
adequately
surrogate
residues
if
the
N­
methyl
carbamate
use
patterns
are
similar
on
these
crops
Slide
23
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27
Input
Assumptions

Past
experience
with
the
OP
CRA
suggests
that
these
assumptions
have
negligible
effects
on
estimated
exposure

We
will
verify
that
this
is
also
true
for
the
N­
Methyl
carbamate
Cumulative
Risk
Assessment
Slide
24
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27
Conclusions

EPA
plans
to
perform
additional
analyses
before
reaching
specific
conclusions
about
risks
associated
with
exposure

Data
inputs
and
assumptions
will
be
verified

Results
for
the
higher
percentiles
of
exposure
for
children's
age
groups
will
be
evaluated

EPA
is
in
the
process
of
conducting
sensitivity
analyses
on
contributors
or
sources
of
potential
risks
associated
with
the
food
pathway
Slide
25
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27
Session
3
Preliminary
N­
Methyl
Carbamate
Cumulative
Risk
Assessment:

Food
Exposure
Assessment
Questions
to
the
Panel
Session
3
Preliminary
N­
Methyl
Carbamate
Cumulative
Risk
Assessment:

Food
Exposure
Assessment
Questions
to
the
Panel
Health
Effects
Division
Office
of
Pesticide
Programs
Health
Effects
Division
Office
of
Pesticide
Programs
Slide
26
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27
FOOD
QUESTION

The
food
portion
of
the
N­
methyl
carbamate
cumulative
risk
assessment
used
similar
data
sources
and
techniques
to
those
used
for
the
organophosphate
pesticide
for
estimating
cumulative
risk
from
food.

This
included
use
of
both
the
USDA's
Continuing
Survey
of
Food
Intakes
by
Individuals
(
CSFII)
as
a
data
source
for
food
consumption
and
Pesticide
Data
Program
Data
(
PDP)
as
a
data
source
for
food
residues.
Slide
27
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27
FOOD
QUESTION
F1.
Please
comment
on
the
planned
intermediate­
and
longer­
term
activities
associated
with
sensitivity
analyses
identified
in
Section
I
of
the
document.
Does
the
Panel
have
any
suggestions
for
other
or
additional
activities
which
the
Agency
should
consider?