Document ID: EPA-HQ-OW-2002-0033-0144
Agency: epa
Document Type: Supporting & Related Material
Title: 
Posted Date: 2003-04-14T04:00Z

United
States
EPA
Science
Advisory
EPA­
SAB­
EC­
01­
008
Environmental
Board
(
1400A)
August
2001
Protection
Agency
Washington,
DC
www.
epa.
gov/
sab
ARSENIC
RULE
BENEFITS
ANALYSIS:
AN
SAB
REVIEW
A
REVIEW
BY
THE
ARSENIC
RULE
BENEFITS
REVIEW
PANEL
(
ARBRP)
OF
THE
US
EPA
SCIENCE
ADVISORY
BOARD
(
SAB)
1
UNITED
STATES
ENVIRONMENTAL
PROTECTION
AGENCY
WASHINGTON,
D.
C.
20460
August
30,
2001
OFFICE
OF
THE
ADMINISTRATOR
SCIENCE
ADVISORY
BOARD
EPA­
SAB­
EC­
01­
008
Honorable
Christine
Todd
Whitman
Administrator
U.
S.
Environmental
Protection
Agency
1200
Pennsylvania
Avenue,
NW
Washington,
DC
20460
Subject:
Arsenic
Rule
Benefits
Analysis;
An
EPA
Science
Advisory
Board
Review
Dear
Governor
Whitman:

On
July
19
and
20,
2001
the
Arsenic
Rule
Benefits
Review
Panel
(
ARBRP)
of
the
US
EPA
Science
Advisory
Board
(
SAB)
met
to
review
the
EPA
report
Arsenic
in
Drinking
Water
Rule
Economic
Analysis
(
EPA
815­
R­
00­
026).
As
part
of
the
review
process,
the
Panel
responded
to
five
charge
questions:

Charge
Question
1:
How
should
latency
be
addressed
in
the
benefits
estimates
when
existing
literature
does
not
provide
specific
quantitative
estimates
of
latency
periods
associated
with
exposure
to
arsenic
in
drinking
water?

Charge
Question
2:
How
should
health
endpoints
(
other
than
bladder
and
lung
cancer)
be
addressed
in
the
analysis,
when
[
existing]
literature
does
not
provide
specific
quantification,
to
ensure
appropriate
consideration
by
decision
makers
and
the
public?

Charge
Question
3:
Should
reduction/
elimination
of
exposure
be
evaluated
as
a
separate
benefits
category,
in
addition
to
or
in
conjunction
with
mortality
and
morbidity
reduction?

Charge
Question
4:
How
should
total
benefits
and
costs
and
incremental
benefits
and
costs
be
addressed
in
analyzing
regulatory
alternatives
to
ensure
appropriate
consideration
by
decision
makers
and
the
public?

Charge
Question
5:
How
should
uncertainties
be
addressed
in
the
analysis
to
ensure
appropriate
consideration
by
decision
makers
and
the
public?

Detailed
answers
to
these
questions
are
found
in
the
body
of
the
report.
The
major
findings
and
recommendations
are:
1
The
ED01
is
that
dose
which
produces
a
response
in
1%
of
the
population.
It
is
equivalent
to
a
1
in
100
risk.

2
1.
Charge
Question
1
In
evaluating
the
health
benefits
of
a
reduction
in
exposure
to
a
carcinogen,
what
matters
is
the
cessation­
lag
between
a
reduction
in
exposure
and
a
reduction
in
risk.
While
`
latency'
is
the
term
used
in
the
charge,
in
fact,
`
cessation­
lag'
is
the
more
appropriate
term,
and
the
two
are
not
necessarily
equivalent.
In
other
words,
time
between
initiation
of
exposure
and
the
increase
in
risk
(
latency)
does
not
necessarily
equal
time
between
cessation
of
exposure
and
the
reduction
in
risk.

The
length
of
the
cessation­
lag
determines
the
number
of
cancer
cases
avoided
each
year
after
a
policy
is
implemented.
If,
for
example,
people
previously
exposed
to
50
µ
g/
L
of
arsenic
in
drinking
water
are
exposed,
beginning
in
2006,
to
only
10
µ
g/
L,
cancer
risks
in
the
population
will
eventually
decline
to
a
steady­
state
level
associated
with
a
lifetime
of
exposure
to
10
µ
g/
L.
How
fast
this
reduction
in
risk
occurs
depends
on
the
cessation­
lag
following
reduction
in
exposure.
If
the
cessation­
lag
is
zero,
this
steady­
state
level
will
be
reached
immediately.

We
believe
that
the
current
arsenic
benefits
analysis
is
flawed
for
two
reasons:
(
a)
the
primary
analysis
considers
only
the
case
of
a
zero
cessation­
lag;
(
b)
when
the
analysis
considers
alternate
`
latency
periods'
it
incorrectly
assumes
no
reduction
in
cancer
cases
until
the
end
of
the
latency
period.
The
correct
approach
is
to
clearly
identify
the
assumption
of
a
zero
cessation­
lag
as
an
upper
bound
to
benefits
and
to
consider
alternate,
plausible
cessation­
lags
in
the
primary
benefits
analysis.
In
the
report,
we
suggest
ways
in
which
the
length
of
the
cessation­
lag
could
be
estimated.
To
each
assumption
there
corresponds
a
time
path
of
cancer
cases
avoided
that
gradually
approaches
the
steady­
state
number
of
cancer
cases
avoided.

2.
Charge
Question
2
The
scientific
literature
on
health
effects
due
to
arsenic
exposure
includes
studies
of
a
number
of
endpoints
other
than
cancer,
as
well
as
studies
of
several
cancer
sites
for
which
the
risks/
benefits
have
not
been
quantified
(
USEPA
2000).
The
quality
of
these
studies
varies,
as
does
the
strength
of
evidence
they
provide.
Specifically,
it
appears
to
us
that
it
should
be
possible
to
quantify
mortality
from
ischemic
heart
disease,
diabetes
mellitus,
hypertension
and
skin
cancer,
and
that
the
evidence
is
reasonably
strong
relating
arsenic
to
these
endpoints.
Although
the
strength
of
evidence
is
lower,
the
Panel
recommends
serious
consideration
be
given
to
quantification
of
benefits
from
reductions
in
prostate
cancer,
nephritis
and
nephrosis,
hypertensive
heart
disease
and
non­
malignant
respiratory
disease.
The
literature
that
would
permit
quantification
of
cases
avoided
for
these
endpoints
is
discussed
in
Section
2.2.2
of
the
report.

Ideally,
quantification
would
take
the
form
of
a
dose
response
function
that
would
permit
the
Agency
to
estimate
the
number
of
cases
of
mortality
and
morbidity
avoided
by
the
regulation.
If,
however,
the
shape
of
the
dose­
response
function
cannot
reliably
be
estimated
at
doses
relevant
to
the
regulation
considered,
it
would
be
useful
to
compare
benchmark
doses
for
the
non­
quantified
endpoints
(
e.
g.,
the
ED01)
with
benchmark
doses
for
the
quantified
endpoints.
1
This
will
indicate
whether
non­
quantified
effects
are,
in
fact,
seen
at
similar
exposures
in
the
study
populations
as
the
bladder
and
lung
cancer
outcomes.
3
In
addition
to
these
comparisons,
the
type
of
information
that
should
be
provided
in
a
benefit­
cost
analysis
about
endpoints
that
have
not
been
quantified
is
described
in
tables,
such
as
those
presented
in
Appendix
2.2
of
this
report.
Studies
must
first
be
selected
according
to
welldefined
criteria.
The
information
that
should
be
provided
for
each
study
(
grouped
by
health
endpoint
of
interest)
includes:

a)
Nature
of
the
study
design
b)
How
exposure
was
measured
c)
Range
of
exposures
observed
d)
What
type
of
statistical
analysis
was
conducted
and
what
confounding
factors
were
controlled
for
in
the
analysis
e)
Measure
of
association
(
e.
g.,
odds
ratio)
and
level
of
statistical
significance
of
the
association
In
some
cases
the
literature
may
be
so
extensive
that
a
summary
of
results
is
required
in
the
text
of
the
report.
As
much
as
possible,
this
summary
should
focus
on
clinical
measures
that
are
clear
indications
of
morbidity
and
that
affect
individuals'
well­
being
and
activities
so
as
to
make
it
possible
to
link
these
endpoints
to
the
available
data
on
individuals'
valuations
of
improvements
in
health.
It
should
also
provide
some
discussion
of
the
mechanism
by
which
the
toxin
would
be
expected
to
exert
an
effect.
The
summary
should
also
indicate
the
level
at
which
effects
were
observed
in
the
studies
reported
(
including
benchmark
doses
where
possible)
and
should
comment
on
the
likelihood
of
observing
these
effects
at
the
levels
relevant
to
the
regulatory
decision.

3.
Charge
Question
3
Regarding
Charge
Question
3,
we
believe
that
reductions
in
exposure
in
this
case
should
not
be
considered
a
separate
category
of
benefits
in
a
benefit
cost
analysis.
The
damage
function
approach
to
valuing
benefits
currently
used
by
the
Agency
separates
the
measurement
of
the
relationship
between
exposure
and
response
(
e.
g.,
risk
of
fatal
or
non­
fatal
cancer)
from
the
valuation
of
reductions
in
risk
of
death
or
illness.
Epidemiologists
estimate
dose­
response
functions
and
economists
measure
the
value
people
place
on
reductions
in
risk
of
death
or
illness
associated
with
them.
To
add
a
separate
value
for
reductions
in
exposure
to
arsenic
per
se
would
double
count
the
health
benefits
estimated
using
the
damage
function
approach.

We
do
recognize
that
some
people
may
value
the
existence
of
lower
levels
of
arsenic
in
drinking
water,
possibly
for
psychological
reasons
(
e.
g.,
dread
of
being
exposed),
and
we
believe
that
existence
values
are
a
legitimate
category
of
benefits.
Existence
values
are
not
accommodated
within
a
damage
function
approach
to
benefit
quantification.
Reliable
estimates
of
these
values
would
need
to
identify
the
marginal
benefit
to
individuals
associated
with
a
change
in
concentration,
separate
from
the
change
in
health
risks
associated
with
the
change
in
exposure.
We
found
no
empirical
evidence
to
support
or
contradict
such
a
relationship
in
the
case
of
arsenic.
In
the
absence
of
any
empirical
data,
there
is
no
basis
for
estimating
an
existence
value
in
this
case.

4.
Charge
Question
4
We
applaud
the
Agency
for
presenting
the
costs
and
benefits
associated
with
various
possible
maximum
contaminant
levels
rather
than
presenting
only
the
costs
and
benefits
associated
with
a
single
standard
that
the
Agency
proposes
to
implement.
We
believe,
however,
that
in
the
primary
analysis
(
and
in
the
Executive
Summary)
benefits
and
costs
should
be
calculated
on
a
water
supply
system
basis,
with
the
results
summarized
in
a
format
that
breaks
4
them
down
by
system
size.
Because
of
the
large
economies
of
scale
associated
with
drinking
water
treatment,
the
net
benefits
(
benefits
minus
costs)
are
likely
to
vary
substantially
by
system
size,
and
this
information
should
be
made
clear
to
policy
makers
and
the
public.

Such
an
analysis
would
allow
decision
makers
to
evaluate
a
range
of
alternative
strategies
rather
than
a
one­
size­
fits­
all
approach.
The
high
cost
of
arsenic
control
is
driven
by
the
tail
of
a
distribution
involving
a
number
of
small
systems.
The
analysis
needs
to
make
this
clear
so
that
decision
makers
can
consider
this
fact
in
formulating
an
appropriate
response.
For
example,
other
policy
measures
that
could
be
considered
include
efforts
to
promote
the
consolidation
of
very
small
systems,
or
the
provision
of
bottled
water
by
very
small
systems
to
meet
their
customers'
needs
for
potable
water.

We
also
believe
that
benefits
(
and
incremental
benefits
associated
with
different
maximum
contaminant
levels)
should
be
presented
in
terms
of
cases
of
morbidity
and
mortality
avoided
as
well
as
in
monetary
terms,
and
that
the
age
distribution
of
cases
avoided
should
be
presented
whenever
possible.
The
description
of
cases
avoided
allows
readers
to
consider
alternatives
to
monetization
of
benefits.
Information
about
the
age
distribution
of
health
benefits
is
important
in
evaluating
the
incidence
of
regulations,
and
benefit­
cost
analyses
should
make
this
task
as
easy
as
possible.

5.
Charge
Question
5
Benefit­
cost
analyses
of
drinking
water
regulations
are
likely
to
entail
uncertainties
in
the
(
a)
measurement
of
exposure,
(
b)
measurement
of
dose­
response,
(
c)
valuation
of
health
outcomes
and
(
d)
measurement
of
costs.
The
sources
of
these
uncertainties
include
measurement
error
(
uncertainty
about
the
average
level
of
arsenic
in
tap
water
or
of
the
amount
of
tap
water
consumed)
as
well
as
uncertainty
about
which
model
to
use
in
describing
the
relationship
between
exposure
and
response
at
low
doses.
In
general,
there
are
two
approaches
to
handling
these
sources
of
uncertainty
 
sensitivity
analysis
and
Monte
Carlo
simulation.
In
a
sensitivity
analysis
various
assumptions
are
made
about
the
correct
model
(
e.
g.,
dose
response
function)
or
parameter
(
e.
g.,
discount
rate)
to
use
in
the
analysis
and
results
are
presented
for
each
set
of
assumptions.
In
a
Monte
Carlo
analysis
a
distribution
is
assumed
for
a
key
parameter
or
set
of
parameters
(
e.
g.,
the
slope
of
the
dose­
response
function)
and
several
thousand
draws
are
made
from
this
distribution.
Benefits
are
calculated
for
each
value
of
the
parameter
drawn.
This
yields
a
probability
distribution
of
benefits,
whose
parameters
(
e.
g.,
the
10th
and
90th
percentiles)
can
be
reported.

We
believe
that,
in
the
case
of
model
uncertainty,
it
is
appropriate
to
rely
on
sensitivity
analysis;
however,
the
assumptions
underlying
each
sensitivity
analysis
should
be
clearly
spelled
out
when
presenting
results.
It
is
particularly
inappropriate
to
present
only
the
highest
and
lowest
numbers
associated
with
a
set
of
sensitivity
analyses,
which
may
give
the
reader
the
false
impression
that
these
constitute
the
upper
and
lower
bounds
of
a
uniform
distribution.
For
parameters
for
which
it
is
possible
to
specify
a
distribution,
Monte
Carlo
analysis
is
desirable
(
for
example,
in
the
case
of
the
slope
of
the
dose­
response
function).

6.
General
Comments
on
the
Benefit­
Cost
Analysis
for
Arsenic
The
document
Arsenic
in
Drinking
Water
Rule:
Economic
Analysis
makes
a
serious
attempt
at
analyzing
the
benefits
and
costs
of
alternate
MCLs
for
arsenic
in
drinking
water.
Many
aspects
of
the
analysis
deserve
commendation.
These
include
calculating
benefits
and
costs
for
different
possible
MCLs,
presenting
some
breakdown
of
benefits
and
costs
by
system
size,
and
presenting
cost­
effectiveness
information
(
cost
per
cancer
case
avoided)
that
would
5
enable
the
drinking
water
standard
for
arsenic
to
be
compared
to
other
public
health
programs.

We
do,
however,
have
certain
criticisms
of
the
computation
of
the
benefits,
the
computation
of
the
costs,
and
with
the
presentation
of
the
results,
especially
as
they
appear
in
the
Executive
Summary.

a)
Computation
of
Benefits
(
1)
In
calculating
cancer
cases
avoided,
the
primary
(
central
case)
analysis
assumes
no
cessation­
lag
between
reduction
in
exposure
to
arsenic
and
reduction
in
cancer
risk.
This
assumption
yields
an
upper
bound
to
the
number
of
cancer
cases
avoided
by
any
MCL.
It
should
be
noted
that
this
assumption
produces
an
upper
bound
to
benefits.
Furthermore,
alternate
assumptions
regarding
the
length
of
the
cessation­
lag
should
be
included
in
the
primary
analysis
and
reported
in
the
Executive
Summary.

(
2)
Estimates
of
cancer
cases
avoided
should
be
broken
down
by
age.
The
underlying
dose­
response
function
(
Morales
et
al.
2000)
predicts
reductions
in
risk
by
age
group;
hence
cancer
cases
avoided
can
be
broken
down
by
age
group.
It
is
important
for
policy
makers
and
the
public
to
know
how
many
beneficiaries
of
a
regulation
are
seven
years
old
and
how
many
are
70.

(
3)
We
believe
that
it
is
possible
to
quantify
more
health
endpoints
than
lung
and
bladder
cancers.
Specifically,
it
appears
to
us
that
the
data
permit
quantification
of
mortality
from
ischemic
heart
disease,
diabetes
mellitus,
hypertension
and
skin
cancer,
for
which
substantial
evidence
supports
an
association,
as
well
as
for
prostate
cancer,
nephritis
and
nephrosis,
hypertensive
heart
disease
and
non­
malignant
respiratory
disease,
for
which
some
evidence
points
to
an
association
with
arsenic
exposure.
However,
this
recommendation
should
be
considered
in
light
of
the
more
definitive
analysis
by
the
NAS
Arsenic
Subcommittee.

(
4)
The
benefit
analysis
should
present
detailed
information
on
non­
quantified
health
effects
in
the
manner
suggested
in
this
report
(
see
Section
2.2
and
Appendix
2.2),
rather
than
simply
listing
possible
health
effects.

(
5)
Estimates
of
avoided
non­
fatal
cancers
and
other
non­
fatal
diseases
should
be
computed
in
the
same
fashion
as
estimates
of
avoided
fatal
cancers.
The
length
of
the
cessation­
lag
should
also
be
treated
in
a
parallel
fashion.

(
6)
To
value
non­
fatal
bladder
cancers,
the
Agency
used
a
value
for
chronic
bronchitis
provided
by
Viscusi,
et
al.
(
1991).
This
study
is
based
on
a
small
sample
and
values
a
different
kind
of
health
endpoint.
There
is
one
study
(
Magat
et
al.
1996)
that
values
a
different
form
of
non­
fatal
cancer
(
non­
fatal
lymphoma),
but
it
is
also
based
on
a
relatively
small
and
probably
not
representative
sample.
We
recommend
that
the
value
used
in
the
report
and
the
alternative
we
have
identified
be
used
as
bounds
in
an
uncertainty
analysis.

(
7)
We
believe
that
the
central
estimate
of
$
6.1
million
for
the
value
of
a
statistical
life
(
VSL)
is
appropriate.
On
the
question
of
whether
to
add
a
6
value
for
cancer
morbidity
before
death,
we
do
not
believe
that
there
is
an
adequate
basis
in
the
literature
for
doing
this.
But
we
can
endorse
adding
estimates
of
the
medical
costs
of
treatment
and
amelioration
for
fatal
cancers
to
the
VSL
as
a
lower
bound
on
the
true
value
of
avoiding
fatal
cancers.

b)
Computation
of
Costs
(
1)
Costs
should
be
computed
using
data
for
the
systems
affected
by
the
proposed
arsenic
standard(
s)
rather
than
national
cost
data.

(
2)
The
costs
of
complying
with
the
proposed
MCLs
may
be
overstated
to
the
extent
that
(
a)
removal
of
arsenic
may
also
remove
other
toxic
substances;
(
b)
possibilities
for
combining
ground
and
surface
water
to
meet
the
MCL
have
been
overlooked.

(
3)
The
capital
costs
of
drinking
water
treatment
should
be
amortized
using
the
interest
rate
that
each
water
system
must
pay
to
borrow
money,
not
at
the
rate
of
7%
(
or
3%)
used
in
the
current
analysis.

c)
Presentation
of
Results
(
1)
The
Executive
Summary
should
clearly
state
the
size
of
the
population
affected
by
each
MCL
considered
in
the
analysis,
as
well
as
the
number
of
systems
affected.

(
2)
The
Executive
Summary
should
present
benefits
in
terms
of
cases
of
mortality
and
morbidity
avoided,
as
well
as
in
monetary
terms,
including
the
age
distribution
of
avoided
cancers
(
and
other
health
endpoints,
if
possible).

(
3)
The
primary
case
analysis
should
include
sensitivity
to
the
length
of
the
cessation­
lag,
and
this
should
be
reported
in
the
Executive
Summary.

(
4)
Benefits
and
costs
should
be
broken
down
and
compared
by
system
size.

We
recommend
that
the
Agency
modify
its
analysis
to
take
account
of
the
issues
we
have
raised
regarding
the
computation
of
benefits
and
costs
associated
with
the
arsenic
standard.

This
report
was
reviewed
and
approved
by
the
SAB
Executive
Committee
in
a
public
meeting
held
on
August
27,
2001.
We
appreciate
the
opportunity
to
review
and
provide
advice
on
this
important
report.
The
EPA
Science
Advisory
Board
would
be
pleased
to
expand
on
any
of
the
findings
described
in
our
report,
and
we
look
forward
to
your
response.

Sincerely,

/
S
/
/
S
/
Dr.
William
H.
Glaze,
Chair
Dr.
Maureen
Cropper,
Chair
EPA
Science
Advisory
Board
Arsenic
Rule
Benefits
Review
Panel
EPA
Science
Advisory
Board
i
NOTICE
This
report
has
been
written
as
part
of
the
activities
of
the
EPA
Science
Advisory
Board,
a
public
advisory
group
providing
extramural
scientific
information
and
advice
to
the
Administrator
and
other
officials
of
the
Environmental
Protection
Agency.
The
Board
is
structured
to
provide
balanced,
expert
assessment
of
scientific
matters
related
to
problems
facing
the
Agency.
This
report
has
not
been
reviewed
for
approval
by
the
Agency
and,
hence,
the
contents
of
this
report
do
not
necessarily
represent
the
views
and
policies
of
the
Environmental
Protection
Agency,
nor
of
other
agencies
in
the
Executive
Branch
of
the
Federal
government,
nor
does
mention
of
trade
names
or
commercial
products
constitute
a
recommendation
for
use.

Distribution
and
Availability:
This
EPA
Science
Advisory
Board
report
is
provided
to
the
EPA
Administrator,
senior
Agency
management,
appropriate
program
staff,
interested
members
of
the
public,
and
is
posted
on
the
SAB
website
(
www.
epa.
gov/
sab).
Information
on
its
availability
is
also
provided
in
the
SAB's
monthly
newsletter
(
Happenings
at
the
Science
Advisory
Board).
Additional
copies
and
further
information
are
available
from
the
SAB
Staff
[
US
EPA
Science
Advisory
Board
(
1400A),
1200
Pennsylvania
Avenue,
NW,
Washington,
DC
20460­
0001;
202­
564­
4546].
ii
U.
S.
Environmental
Protection
Agency
EPA
Science
Advisory
Board
Arsenic
Rule
Benefits
Review
Panel*

CHAIR
Dr.
Maureen
L.
Cropper,
Lead
Economist,
The
World
Bank,
Washington,
DC
and
Professor
of
Economics,
University
of
Maryland;
Member:
Advisory
Council
on
Clean
Air
Compliance
Analysis
OTHER
SAB
MEMBERS
Dr.
Richard
Bull,
Consulting
Toxicologist,
MoBull
Consulting,
Kennewick,
WA
Member:
Research
Strategies
Advisory
Committee
and
Drinking
Water
Committee
Dr.
W.
Michael
Hanemann,
Professor,
University
of
California,
Berkeley,
CA
Member:
Environmental
Economics
Advisory
Committee
Dr.
V.
Kerry
Smith,
University
Distinguished
Professor,
Department
of
Agricultural
and
Resource
Economics,
North
Carolina
State
University,
Raleigh,
NC
Member:
Advisory
Council
on
Clean
Air
Compliance
Analysis
CONSULTANTS
Dr.
A.
Myrick
Freeman,
Professor,
Department
of
Economics,
Bowdoin
College,
Brunswick,
ME
Dr.
Dale
Hattis,
Research
Associate
Professor,
Center
for
Technology,
Environment,
and
Development,
Clark
University,
Worcester,
MA
Dr.
Irva
Hertz­
Picciotto,
Professor,
Department
of
Epidemiology,
University
of
North
Carolina,
Chapel
Hill,
NC.

SCIENCE
ADVISORY
BOARD
STAFF
Mr.
Thomas
Miller,
Designated
Federal
Officer,
1200
Pennsylvania
Avenue,
NW,
Washington,
DC
Ms.
Rhonda
Fortson,
Management
Assistant,
1200
Pennsylvania
Avenue,
NW,
Washington,
DC
Ms.
Wanda
Fields,
Management
Assistant,
1200
Pennsylvania
Avenue,
NW,
Washington,
DC
iii
TABLE
OF
CONTENTS
1.
INTRODUCTION
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1
1.1
Background
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1
1.2
Charge
to
the
Panel
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1
2.
RESPONSES
TO
CHARGE
QUESTIONS
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3
2.1
The
impact
of
timing
of
exposure
on
avoided
cancers
(
Charge
Question
1)
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3
2.1.1.
Introduction
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3
2.1.2.
Calculation
of
reduced
cancer
fatalities
associated
with
reduced
exposure
to
a
carcinogen
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4
2.1.2.1
The
timing
of
the
exposure­
response
relationship
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4
2.1.2.2
Calculating
the
time
path
of
cancer
cases
avoided
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5
2.1.3
Quantifying
the
relationship
between
exposure
and
mortality
risk
.
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5
2.2
Characterization
of
non­
quantified
health
endpoints
(
Charge
Question
2)
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7
2.2.1
Overview
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7
2.2.2.
Quantifiability
of
particular
health
endpoints
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9
2.2.2.1
Cardiovascular
disease
endpoints
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9
2.2.2.2
Diseases
of
the
endocrine
system
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10
2.2.2.3
Other
cancer
sites
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11
2.2.2.4
Non­
malignant
respiratory
diseases
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11
2.2.2.5
Reproductive
effects
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11
2.2.2.6
Neurologic
and
neurodevelopmental
endpoints
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12
2.2.3
Valuation
of
non­
quantified
health
endpoints
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12
2.3
Exposure
reduction
as
a
benefit
category
(
Charge
Question
3)
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12
2.4
Comparison
of
benefits
and
costs
(
Charge
Question
4)
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13
2.4.1
Comparison
of
benefits
and
costs
by
system
size
.
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13
2.5
Incorporation
of
uncertainty
into
benefits
measures
(
Charge
Question
5)
.
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.
14
3.
GENERAL
COMMENTS
ON
THE
ECONOMIC
ANALYSIS
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16
3.1
Comments
on
exposure
assessment
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16
3.1.1
Characterization
of
U.
S.
population
exposure
in
the
analysis
.
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16
3.2
Comments
on
the
computation
of
benefits
.
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16
3.2.1
Treatment
of
latency
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16
3.2.2
Treatment
of
age
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17
3.2.3
Valuing
avoided
cancer
morbidity
.
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17
3.2.4
Valuing
avoided
cancer
morality
.
.
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17
3.3
Comments
on
the
computation
of
costs
.
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18
3.3.1
Factors
that
may
cause
costs
to
be
overstated
and/
or
benefits
to
be
understated18
3.3.2
Amortization
of
costs
.
.
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18
3.3.3
Unanticipated
costs
.
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19
3.3.4
Policy
implications
of
regulatory
costs
.
.
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20
iv
REFERENCES
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R­
1
APPENDICES
Appendix
1
­
Background;
NDWAC
Benefits
Working
Group
Comments
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Appendix
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Supplementary
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Appendix
2.1
Appendix
to
charge
question
1
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Appendix
2.2
Appendix
to
charge
question
2
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A­
13
1
1.
INTRODUCTION
1.1
Background
According
to
information
provided
by
EPA
(
letter
from
Diane
Regas,
June
9,
2001),
studies
have
linked
long­
term
exposure
to
arsenic
in
drinking
water
to
cancer
of
the
bladder,
lungs,
skin,
kidney,
nasal
passages,
liver,
and
prostate.
Non­
cancer
effects
of
ingesting
arsenic
include
cardiovascular,
pulmonary,
immunological,
neurological,
and
endocrine
(
e.
g.,
diabetes).
The
current
standard
of
50
µ
g/
L
was
set
by
EPA
in
1975,
based
on
a
Public
Health
Service
standard
originally
established
in
1942.
A
March
1999
report
by
the
National
Academy
of
Sciences
concluded
that
the
current
standard
does
not
achieve
EPA's
goal
of
protecting
public
health
and
should
be
lowered
as
soon
as
possible.

The
Safe
Drinking
Water
Act
(
SDWA)
requires
EPA
to
revise
the
existing
50
microgram
per
liter
(
µ
g/
L)
arsenic
standard.
In
response
to
this
mandate,
the
Agency
published
a
standard
of
10
µ
g/
L
to
protect
consumers
against
the
effects
of
long­
term,
chronic
exposure
to
arsenic
in
drinking
water
on
January
22,
2001.
The
rule
is
significant
in
that
it
is
the
second
drinking
water
regulation
for
which
EPA
has
used
the
discretionary
authority
under
§
1412(
b)(
6)
of
the
SDWA
to
set
the
Maximum
Contaminant
Level
(
MCL)
higher
than
the
technically
feasible
level,
which
is
3
µ
g/
L
for
arsenic
­­
based
on
a
determination
that
the
costs
would
not
justify
the
benefits
at
this
level.
The
January
22,
2001
arsenic
rule
is
based
on
the
conclusion
that
a
10
µ
g/
L
MCL
maximizes
health
risk
reduction
at
a
cost
justified
by
the
benefits.

Key
stakeholder
concerns
about
the
benefits
component
of
the
economic
analysis
include
the
following
issues:
(
a)
the
timing
of
health
benefits
accrual;
(
b)
the
use
of
the
Value
of
Statistical
Life
as
a
measure
of
health
benefits;
(
c)
the
use
of
alternative
methodologies
for
benefits
estimation;
(
d)
how
the
Agency
considered
non­
quantifiable
benefits
in
its
regulatory
decision­
making
process;
(
e)
the
analysis
of
incremental
costs
and
benefits;
and
(
f)
the
Agency's
assumption
that
health
risk
reduction
benefits
will
begin
to
accrue
at
the
same
time
costs
begin
to
accrue.

The
January
22,
2001
rule
will
apply
to
all
54,000
community
water
systems
and
requires
compliance
by
2006.
A
community
water
system
is
a
system
that
serves
15
locations
or
25
residents
year­
round,
and
includes
most
cities
and
towns,
apartments,
and
mobile
home
parks
with
their
own
water
supplies.
EPA
estimates
that
roughly
five
percent,
or
3000,
of
community
water
systems,
serving
11
million
people,
will
have
to
take
corrective
action
to
lower
the
current
levels
of
arsenic
in
their
drinking
water.
The
new
standard
will
also
apply
to
20,000
"
noncommunity
water
systems
that
serve
at
least
25
of
the
same
people
more
than
six
months
of
the
year,
such
as
schools,
churches,
nursing
homes,
and
factories.
EPA
estimates
that
five
percent,
or
1,100,
of
these
water
systems,
serving
approximately
2
million
people,
will
need
to
take
measures
to
comply
with
the
January
22,
2001
rule.
Of
all
of
the
affected
systems,
97
percent
are
small
systems
that
serve
fewer
than
10,000
people
each.

1.2
Charge
to
the
Panel
The
Science
Advisory
Board
(
SAB)
was
asked
to
conduct
a
review
of
the
benefits
analysis
prepared
by
EPA
in
support
of
the
arsenic
drinking
water
standard
which
is
contained
in
its
regulatory
support
document
Arsenic
in
Drinking
Water
Rule
Economic
Analysis
(
USEPA
2000).
The
Agency
asked
that
the
Panel
evaluate
whether
the
components,
methodology,
criteria
and
estimates
reflected
in
EPA's
analysis
are
reasonable
and
appropriate
in
light
of
(
1)
the
Science
Advisory
Board's
(
SAB)
benefits
transfer
report
(
SAB
2000;
Report
on
EPA's
White
Paper,
Valuing
the
Benefits
of
Fatal
Cancer
Risk
Reduction),
(
2)
EPA
Guidelines
for
Preparing
2
Economic
Analyses
(
USEPA
2000a),
(
3)
relevant
requirements
of
SDWA,
(
4)
the
Report
of
the
Benefits
Working
Group
of
the
National
Drinking
Water
Advisory
Council
(
NDWAC
unpublished,
October
1998),
and
(
5)
recent
literature.
Specifically,
the
Agency
asked
that
the
Panel
consider
the
following
issues:

Charge
Question
1:
How
should
latency
be
addressed
in
the
benefits
estimates
when
existing
literature
does
not
provide
specific
quantitative
estimates
of
latency
periods
associated
with
exposure
to
arsenic
in
drinking
water?

Charge
Question
2:
How
should
health
endpoints
(
other
than
bladder
and
lung
cancer)
be
addressed
in
the
analysis,
when
[
existing]
literature
does
not
provide
specific
quantification,
to
ensure
appropriate
consideration
by
decision
makers
and
the
public?

Charge
Question
3:
Should
reduction/
elimination
of
exposure
be
evaluated
as
a
separate
benefits
category,
in
addition
to
or
in
conjunction
with
mortality
and
morbidity
reduction?

Charge
Question
4:
How
should
total
benefits
and
costs
and
incremental
benefits
and
costs
be
addressed
in
analyzing
regulatory
alternatives
to
ensure
appropriate
consideration
by
decision
makers
and
the
public?

Charge
Question
5:
How
should
uncertainties
be
addressed
in
the
analysis
to
ensure
appropriate
consideration
by
decision
makers
and
the
public?

Responses
to
these
questions,
and
to
other
issues
the
Committee
wishes
to
address,
are
provided
to
the
Agency
below.
3
2.
RESPONSE
TO
THE
CHARGE
QUESTIONS
2.1
The
Impact
of
the
Timing
of
Exposure
on
Avoided
Cancers
Charge
Question
1:
How
should
latency
be
addressed
in
the
benefits
estimates
when
existing
literature
does
not
provide
specific
quantitative
estimates
of
latency
periods
associated
with
exposure
to
arsenic
in
drinking
water?

2.1.1.
Introduction
A
central
component
in
analyzing
the
benefits
of
reduced
exposure
to
a
carcinogen
is
the
prediction
of
the
annual
reduction
in
cancer
cases
following
reduction
in
exposure.
If
a
population
previously
exposed
to
50
µ
g/
L
of
arsenic
in
drinking
water
is
exposed,
beginning
in
2006,
to
only
10
µ
g/
L,
cancer
risks
in
the
population
will
eventually
decline
to
a
steady­
state
level
associated
with
a
lifetime
of
exposure
to
10
µ
g/
L.
How
fast
this
reduction
in
risk
occurs
depends
on
the
cessation­
lag
following
reduction
in
exposure.
We
believe
that
this
is
more
appropriately
termed
a
"
cessation­
lag,"
rather
than
"
latency."
This
distinction
is
clarified
below.

In
order
to
explain
what
should
be
done
when
the
length
of
this
cessation­
lag
is
unknown,
we
must
describe
how
the
timing
of
the
relationship
between
exposure
and
response
(
death
due
to
cancer)
should
be
treated
in
a
benefits
analysis.
We
emphasize
that
we
believe
that
this
is
how
such
an
analysis
is
conducted;
it
does
not
refer
to
the
approach
taken
in
the
arsenic
benefits
analysis.
As
in
the
case
of
arsenic,
we
analyze
a
policy
that
would
reduce
exposure
from
a
current
level
of
d0
(
e.
g.,
50
µ
g/
L)
to
d
N
(
e.
g.,
10
µ
g/
L).
We
assume
that
this
policy
would
continue
into
the
indefinite
future.

For
a
benefits
analysis
we
would
like
to:

a)
Calculate
the
expected
number
of
cancer
fatalities
avoided
each
year,
as
a
result
of
the
policy,
beginning
with
the
year
in
which
the
policy
is
implemented
and
continuing
into
the
future.

If
benefits
are
to
be
monetized
in
accordance
with
conventional
economic
practice:

b)
The
expected
number
of
cancer
fatalities
avoided
each
year
should
be
multiplied
by
the
value
of
a
statistical
life
in
that
year.
This
will
give
the
dollar
value
of
benefits
each
year,
beginning
with
the
year
in
which
the
policy
in
implemented.
The
dollar
value
of
benefits
in
each
year
should
be
discounted
to
the
year
in
which
the
policy
is
implemented
and
summed.
The
present
discounted
value
of
benefits,
so
calculated,
should
be
compared
with
the
present
discounted
value
of
costs,
calculated
over
the
same
period.

The
timing
of
the
relationship
between
exposure
and
cancer
mortality
is
implicit
in
the
calculations
in
(
a).
As
described
more
fully
below,
if
the
lag
between
reduction
in
exposure
and
reduction
in
risk
of
death
is
long,
there
will
be
fewer
cancer
fatalities
avoided
in
years
immediately
following
the
policy
than
if
the
lag
were
shorter.
Uncertainties
in
the
timing
of
the
exposure­
response
relationship
will
be
reflected
in
uncertainties
in
the
number
of
cancer
fatalities
reduced
each
year
after
the
policy
is
implemented.
These
uncertainties
should
be
treated
as
described
in
the
answer
to
Charge
Question
5.
2
A
proportional
hazard
model
(
Pope
et
al.
1995)
is
also
used
to
measure
the
association
between
particulate
matter
and
all­
cause
mortality
in
The
Benefits
and
Costs
of
the
Clean
Air
Act
1970­
1990
(
USEPA
1997)
and
The
Benefits
and
Costs
of
the
Clean
Air
Act
1990­
2010
(
USEPA
1999).
The
issue
of
the
length
of
the
cessation­
lag
after
a
reduction
in
exposure
also
arises
in
these
studies.
3The
function
ft
(
)
could
also
be
conditioned
on
other
factors
such
as
smoking.

4
2.1.2
Calculation
of
Reduced
Cancer
Fatalities
Associated
with
Reduced
Exposure
to
a
Carcinogen
The
approach
taken
here
is
to
relate
the
age­
adjusted
risk
of
death
due
to
cancer
to
the
history
of
exposure
to
the
carcinogen.
This
relationship,
together
with
information
on
the
age
distribution
of
the
population
affected
by
the
policy,
can
be
used
to
calculate
the
expected
number
of
cancer
fatalities
avoided
by
the
policy.

The
epidemiology
underlying
the
arsenic
benefits
analysis
(
Morales
et
al.
2000)
assumes
that
the
conditional
probability
of
dying
from
cancer
at
age
t,
h(
t)
is
related
to
cumulative
exposure
to
a
carcinogen
as
of
age
t,
xt,
by
a
proportional
hazard
model:

(
1)
h(
t,
x)
=
h0(
t)
g(
xt)

where
h0(
t)
=
baseline
risk
of
dying
from
cancer
at
age
t
(
assuming
survival
to
age
t)
and
g(
xt)
represents
the
impact
of
exposure
incurred
up
to
age
t
on
risk
of
death.
2
2.1.2.1
The
Timing
of
the
Exposure­
Response
Relationship
The
key
question
is
how
cumulative
exposure
(
xt)
depends
on
the
dose
of
arsenic
received
at
ages
0
through
t.
Let
di
=
dose
received
at
age
i.
A
general
form
that
this
relationship
could
take
is3:

(
2)
xt
=
ft(
d0,
d1,...,
dt)

The
exact
form
of
this
function
reflects
the
answers
to
the
following
four
questions
(
Tollerud
et
al.
1999):

(
a)
How
long
does
it
take
after
an
exposure
until
an
increase
in
risk
is
observed?
(
b)
How
long
does
the
effect
of
an
exposure
last
after
exposure
has
terminated?
(
c)
How
does
the
effect
of
exposure
vary
by
the
age
at
which
it
was
received?
(
d)
Does
the
exposure
act
at
an
early
or
late
stage
in
the
carcinogenic
process?

The
relevant
questions
for
the
implementation
of
changes
in
the
drinking
water
standard
for
arsenic
are
questions
(
b)­(
d).
In
contrast,
most
of
the
epidemiologic
literature
addressing
the
issue
of
latency
has
focused
on
question
(
a),
which
is
the
usual
definition
of
latency.
The
committee
wishes
to
underscore
that
data
addressing
question
(
a)
do
not
necessarily
provide
information
answering
questions
(
b)­(
d).
Unfortunately,
much
less
work
has
been
done
to
evaluate
questions
(
b)­(
d)
in
the
epidemiologic
literature
in
general,
and
in
the
research
on
arsenic
carcinogenicity
in
particular.

The
NAS
report
Veterans
and
Agent
Orange:
Update
1998
(
Tollerud
et
al.
1999)
addresses
the
second
question,
regarding
how
long
effects
last
after
cessation
of
exposure.
With
5
respect
to
arsenic
in
drinking
water,
the
charge
of
our
committee
is
an
expansion
of
this
question:
when
does
the
excess
risk
(
compared
to
a
lifetime
of
exposure
to
d
N
(
e.
g.,
10
µ
g/
L))
begin
to
attenuate
and
how
long
does
it
take
until
all
of
the
excess
is
eliminated?
Since
the
term
latency
has
a
traditional
usage
that
is
not
the
charge
given
to
this
committee,
we
have
coined
the
phrase
"
cessation­
lag"
to
clarify
and
emphasize
the
difference.

An
important
point
is
that
the
time
to
benefits
from
reducing
arsenic
in
drinking
water
may
not
equal
the
estimated
time
since
first
exposure
to
an
adverse
effect.
A
good
example
is
cigarette
smoking:
the
latency
between
initiation
of
exposure
and
an
increase
in
lung
cancer
risk
is
approximately
20
years.
However,
after
cessation
of
exposure,
risk
for
lung
cancer
begins
to
decline
rather
quickly.
A
benefits
analysis
of
smoking
cessation
programs
based
on
the
observed
latency
would
greatly
underestimate
the
actual
benefits.
We
return
to
the
issue
of
how
to
estimate
the
length
of
the
cessation­
lag
below.

2.1.2.2
Calculating
the
Time
Path
of
Cancer
Cases
Avoided
If
the
relationships
in
(
1)
and
(
2)
are
known,
it
is,
in
principle,
a
simple
matter
to
compute
the
expected
number
of
cancer
fatalities
avoided
at
age
t
(
and,
by
analogy,
for
all
other
ages)
in
each
year
following
the
policy.
In
the
first
year
of
the
policy
it
is
only
the
most
recent
dose
of
the
carcinogen
(
dt
for
persons
who
are
age
t
in
the
year
the
policy
is
implemented)
that
is
affected
by
the
policy.
The
expected
reduction
in
risk
of
death
due
to
cancer
at
age
t
in
the
first
year
of
the
policy
is:

(
3)
h0(
t)[
g(
ft(
d0
0,
d1
0,...,
dt
0))
­
g(
ft(
d0
0,
d1
0,...,
dt
N
)
)]

where
the
superscripts
0
and
N
refer
to
doses
with
and
without
the
policy,
respectively.
In
the
second
year
of
the
policy,
for
persons
of
age
t,
both
dt­
1
and
dt
are
affected
by
the
policy,
and
the
formula
in
(
3)
would
be
adjusted
accordingly.
Eventually,
a
steady­
state
will
be
reached
in
which
persons
of
age
t
face
the
same
mortality
risk
from
cancer
as
people
who
have
been
exposed
to
the
lower
level
of
the
carcinogen
(
d
N
)
throughout
their
lifetime.

In
each
year,
the
number
of
fatalities
avoided
by
the
policy
among
persons
of
age
t
would
be
the
expression
similar
to
(
3)
multiplied
by
the
number
of
persons
of
age
t.
Similar
computations
would
be
performed
for
persons
of
all
ages.
In
this
manner,
it
should
be
possible
to
compute
the
expected
number
of
fatalities
avoided,
by
age
(
or
age­
group),
in
each
year
following
the
implementation
of
the
policy.
Because
the
age
distribution
of
avoided
cancer
fatalities
is
calculated,
it
should
be
reported
in
a
benefits
analysis
even
if
information
on
the
age
distribution
of
avoided
fatalities
is
not
used
in
valuing
avoided
mortality.

2.1.3
Quantifying
the
Relationship
Between
Exposure
and
Mortality
Risk
Most
epidemiologic
studies
ignore
the
time
pattern
of
exposure
in
estimating
the
proportional
hazard
model
in
equation
(
1).
For
example,
Morales
et
al.
(
2000)
effectively
assume
that
t
(
4)
xt
=
E
di
.
i=
0
4Latencies
and
cessation­
lags
would
be
expected
to
vary
by
cancer
site,
would
probably
be
shorter
for
cardiovascular
disease
than
for
cancer,
and
may
be
shortest
for
reproductive
effects.
5
We
emphasize
that
the
same
model
should
be
used
to
estimate
the
time
pattern
of
exposure
and
response
as
is
used
to
estimate
the
potency
of
the
carcinogen.

6
Given
sufficient
data,
the
time
pattern
of
exposure
and
effect
can
be
estimated
in
the
context
of
equations
(
1)
and
(
2).
4
In
order
to
properly
study
effects
of
protracted
exposures,
detailed
exposure
histories
for
each
study
subject,
including
the
dates
and
ages
when
the
individual
was
exposed
and
the
level
of
exposure
at
all
points
in
time,
are
needed.
Appropriate
statistical
methods
have
been
developed
for
an
investigation
of
the
effect
of
exposure
accrued
as
a
function
of
time
since
that
exposure
(
Thomas
1983;
Breslow
and
Day
1987;
Thomas
1988).
In
general,
the
ability
to
investigate
the
issues
of
timing
of
exposure
in
a
given
data
set
will
depend
on
the
quality
of
the
exposure
measure,
the
quality
of
the
timing
of
exposure
information,
the
number
of
people
developing
the
disease
of
interest,
and
variation
of
exposure
over
time
within
the
study
group.
These
aspects
of
study
quality
are,
of
course,
important
in
evaluating
any
epidemiologic
investigation.
But
there
are
special
problems
that
arise
in
the
evaluation
of
timerelated
factors
(
Enterline
and
Henderson
1973;
Thomas
1987).

If
possible,
it
would
be
desirable
to
use
information
about
the
mechanism
by
which
cancer
occurs
in
estimating
the
length
of
the
cessation­
lag.
5
For
example,
if
arsenic
primarily
exerts
a
late­
stage
effect
in
the
cancer
formation
process,
the
cessation­
lag
will
be
shorter
than
if
arsenic
primarily
exerts
an
early­
stage
effect.
Appendix
2.1
to
this
report
discusses
how
the
time
pattern
of
exposure
and
response
could
be
estimated
in
the
context
of
the
multi­
stage
model
of
cancer
formation.

In
addition,
two
published
studies
have
attempted
to
address
either
latency
or
cessationlag
or
the
stage
at
which
arsenic
acts
in
the
carcinogenic
pathway.
Brown
and
Chu
(
1983,
1987)
attempted
an
analysis
based
on
one
of
the
arsenic­
exposed
occupational
cohorts
and
demonstrated
that
two
models
provided
good
fit
to
the
data:
one
with
only
a
late­
stage
effect
and
the
other
with
both
an
early­
and
late­
stage
effect.
There
was
a
slightly
better
fit
for
the
model
with
only
a
late­
stage
effect
but
the
difference
in
fit
was
not
sufficient
to
exclude
an
early­
stage
effect.
A
more
recent
analysis
(
Hazelton
et
al.
2000)
examined
an
occupational
cohort
with
exposures
to
arsenic,
radon
and
tobacco
using
biologically
based
models.
They
evaluated
the
time
between
generation
of
the
first
malignant
cell
and
death
from
lung
cancer.
This
would
appear
to
assume
an
early­
stage
effect
only;
nevertheless,
it
is
notable
that
the
best
fit
was
given
for
a
gamma
distribution
of
lags
that
had
a
mean
of
4.1
years
and
a
variance
of
2.9
years.
Under
this
distribution,
which
is
consistent
with
a
minimal
first
stage
effect
of
arsenic,
the
bulk
of
the
benefit
following
cessation
would
be
expected
to
occur
within
the
first
five
years
after
exposure
is
reduced.

It
thus
appears
that
some
information
about
the
length
of
the
cessation­
lag
is
available
in
the
case
of
arsenic.
Additional
information
on
the
length
of
the
cessation­
lag
could
be
evaluated
from
data
on
arsenic­
exposed
populations
in
Taiwan
and
Chile,
and
we
urge
that
such
research
be
undertaken.
In
Taiwan,
the
water
supply
was
changed
in
the
early
1970'
s,
thereby
eliminating
the
arsenic
exposure.
In
Antofagasta,
Chile,
water
treatment
beginning
in
1970
reduced
the
arsenic
concentration
from
800
to
110
µ
g/
L
within
a
short
time,
and
over
a
few
more
years
to
40­
50
µ
g/
L.

If,
however,
such
information
were
not
available
(
as
the
charge
question
assumes),
what
could
be
done?
One
extreme
assumption
that
would
yield
an
upper
bound
to
the
benefits
of
the
program
is
to
assume
that
the
program
immediately
attains
the
steady­
state
result,
i.
e.,
that
the
7
reduction
in
the
age­
t
mortality
rate
is
given
by:

(
5)
h0(
t)[
g(
ft(
d0
0,
d1
0,...,
dt
0))
­
g(
ft(
d0
N
,
d1
N
,
...,
dt
N
)
)].

This
is
the
assumption
made
in
the
Agency's
primary
analysis.

If
it
should
prove
infeasible
to
estimate
the
cessation­
lag
and
account
for
it
as
described
above,
it
would
still
be
desirable
to
examine
the
influence
of
this
lag
by
performing
sensitivity
analyses
similar
to
those
carried
out
for
the
PM­
mortality
relationship
in
the
Agency's
analysis
of
The
Benefits
and
Costs
of
the
Clean
Air
Act:
1990­
2010
(
USEPA
1999).
In
the
context
of
the
multi­
stage
model
described
in
Appendix
2.1,
we
would
suggest
that
the
testing
of
extreme
cases
of
potential
mechanisms
(
i.
e.,
arsenic's
effects
being
exerted
entirely
at
an
early
stage
v.
all
at
a
late
stage)
be
done
as
part
of
the
uncertainty
analysis.

2.2.
Characterization
of
Non­
Quantified
Health
Endpoints
Charge
Question
2:
How
should
health
endpoints
(
other
than
bladder
and
lung
cancer)
be
addressed
in
the
analysis,
when
[
existing]
literature
does
not
provide
specific
quantification,
to
ensure
appropriate
consideration
by
decision
makers
and
the
public?

2.2.1
Overview
The
scientific
literature
on
health
effects
due
to
arsenic
exposure
includes
studies
of
a
number
of
endpoints
other
than
cancer,
as
well
as
studies
of
several
cancer
sites
for
which
the
risks/
benefits
have
not
been
quantified
(
USEPA
2000).
The
quality
of
these
studies
varies,
as
does
the
strength
of
evidence
they
provide.
Nevertheless,
this
body
of
evidence
is
relevant
for
the
determination
of
an
MCL
and
needs
to
be
addressed
more
fully.
In
some
cases,
the
nonquantified
effects
can
and
should
be
quantified.
In
other
words,
the
lack
of
quantification
by
EPA,
to
date,
of
these
effects
should
not
be
construed
to
mean
that
they
are
not
quantifiable.

Of
the
49
non­
quantified
non­
carcinogenic
health
effects
listed
in
the
Benefits
Analysis
(
USEPA
2000),
some
would
not
be
relevant
at
low
exposure
levels,
e.
g.,
at
or
below
the
current
standard.
These
would
include
gangrene
in
adults
or
children,
hepatic
enlargement,
Raynaud's
syndrome
and
others.
The
main
categories
for
which
there
may
be
concern
at
lower
exposure
levels
are:
several
cardiovascular
and
cerebrovascular
diseases,
endocrine
effects
(
diabetes
mellitus),
reproductive
health
outcomes,
and
non­
malignant
respiratory
diseases.
Some
data
have
emerged
for
neurologic
or
neurodevelopmental
outcomes,
but
this
evidence
is
currently
somewhat
sparse.

Studies
addressing
the
major
categories
of
both
non­
cancer
outcomes
and
other
cancer
sites
of
concern
(
besides
lung
and
bladder)
at
lower
exposure
levels
are
listed
in
the
tables
in
Appendix
2.2
(
which
are
not
comprehensive,
but
rather,
representative).
These
studies
demonstrate
a
broad
array
of
related
endpoints
and
indicate
the
range
and
weight
of
evidence,
qualitatively,
as
well
as
the
consistency
with
which
these
effects
are
related
to
arsenic
exposure.
Such
consistency,
particularly
when
at
least
some
of
the
studies
are
of
high
quality
and
have
adjusted
for
individual­
level
confounders,
strengthens
the
evidence
for
causality.

Given
(
a)
the
consistency
of
results,
including
supportive
in
vivo
animal
experiments;
(
b)
epidemiologic
studies
with
individual
level
data
on
exposure,
outcomes,
and
confounders;
and
(
c)
evidence
suggesting
plausibility
of
effects
at
low
exposures:
the
Panel
finds
that
for
several
of
these
health
endpoints,
the
benefits
can
and
should
be
quantified.
These
include,
at
a
minimum,
mortality
from
ischemic
heart
disease,
diabetes
mellitus,
hypertension
and
skin
6Notably,
these
outcomes
are
not
all
independent.
For
instance,
arsenic
is
associated
with
increased
prevalence
of
hypertension,
and
with
increased
incidence
of
ischemic
heart
disease.
Within
the
studies
assessing
the
latter,
hypertension
was
a
strong
risk
factor.
Thus,
hypertension
may
be
one
step
along
one
or
more
pathways
by
which
arsenic
increases
risk
for
ischemic
heart
disease.
Nonetheless,
hypertension
can
itself
be
a
cause
of
death,
though
this
occurs
much
more
rarely
than
death
due
to
ischemic
heart
disease.
7
For
an
example
of
such
criteria
see
Table
5­
2
in
The
Benefits
and
Costs
of
the
Clean
Air
Act
1990­
2010
(
USEPA
1999)
which
lists
the
criteria
used
to
select
studies
that
examine
the
health
effects
of
the
criteria
air
pollutants.

8
cancer.
Serious
consideration
should
also
be
given
to
prostate
cancer,
nephritis
and
nephrosis,
hypertensive
heart
disease,
and
non­
malignant
respiratory
disease,
for
which
there
is
some
evidence
of
an
association
and
data
that
would
permit
quantification
of
effects.
The
discussion
below
briefly
assesses
the
broad
groupings
of
outcomes,
highlighting
those
for
which
quantification
appears
to
be
eminently
feasible.
6
By
`
quantification'
we
mean
estimation
of
a
dose­
response
function
that
would
permit
the
Agency
to
predict
the
number
of
cases
of
cancer
and
non­
cancer
effects
avoided
by
the
regulation.
When
the
shape
of
the
dose­
response
function
cannot
reliably
be
estimated
at
doses
relevant
to
the
regulation,
it
may
be
possible
to
suggest
the
importance
of
non­
quantified
health
effects
in
other
ways.
For
example,
Appendix
2.2
compares
the
total
non­
cancer
mortality
and
mortality
from
cancers
other
than
bladder
and
lung
associated
with
arsenic
exposure
in
Taiwan
with
excess
deaths
due
to
lung
and
bladder
cancer.
These
data
indicate
the
total
excess
cancer
mortality
to
be
about
double
that
of
lung
and
bladder
alone;
the
numbers
are
similar
for
males
and
for
females.
The
excess
from
non­
cancer
endpoints
is
between
75%
and
95%
of
that
from
lung
and
bladder
cancers
combined.
This
calculation
gives
a
very
approximate
example
of
how
important
the
other
mortality
endpoints
could
be,
and
indicates
that
the
total
excess
mortality
might
be
as
high
as
three
times
that
from
lung
and
bladder
cancer
alone.

Another
approach
is
to
compare
the
benchmark
doses
at
which
effects
of
arsenic
have
been
found
in
other
studies
(
for
example,
in
producing
mortality
from
ischemic
heart
disease
and
diabetes)
with
the
benchmark
doses
in
the
studies
for
lung
and
bladder
cancer.
This
allows
one
to
determine
whether
non­
quantified
effects
have
occurred
at
similar
doses
as
cancer
endpoints.
Other
approaches
are
possible
(
Hattis
et
al.
1999,
2001).

In
addition
to
these
comparisons,
the
type
of
information
that
should
be
provided
in
a
benefit­
cost
analysis
about
endpoints
that
have
not
been
quantified
is
listed
in
the
tables
in
Appendix
2.2.
For
each
health
endpoint
(
e.
g.,
cardiovascular
morbidity),
studies
that
pass
certain
scientific
criteria
should
be
listed.
7
The
information
that
should
be
provided
for
each
study
includes:

(
a)
Nature
of
the
study
design
(
b)
How
exposure
was
measured
(
c)
Range
of
exposures
observed
(
d)
What
type
of
statistical
analysis
was
conducted
and
what
confounding
factors
were
controlled
for
in
the
analysis
(
e)
Measure
of
association
(
e.
g.,
odds
ratio)
and
level
of
statistical
significance
of
the
association
In
some
cases
the
literature
may
be
so
extensive
that
a
summary
of
results
is
required
in
the
text
of
the
report.
This
summary
should
focus
on
health
endpoints
that
have
meaning
to
9
humans,
and
should
provide
some
discussion
of
the
mechanism
by
which
the
toxin
would
be
expected
to
exert
an
effect.
The
summary
should
also
indicate
the
level
at
which
effects
were
observed
in
the
studies
reported
and
should
comment
on
the
likelihood
of
observing
these
effects
at
the
levels
relevant
to
the
regulatory
decision.

2.2.2
Quantifiability
of
Particular
Health
Endpoints
2.2.2.1
Cardiovascular
Disease
Endpoints
(
see
Tables
I,
II,
and
III
in
Appendix
2.2)

Both
human
and
animal
studies
provide
evidence
that
arsenic
affects
the
cardiovascular
system,
possibly
via
several
mechanisms.
The
human
studies
have
included
both
occupational
cohorts
for
which
exposure
is
primarily
by
inhalation,
and
communities
for
which
exposure
is
primarily
via
drinking
water.
Both
morbidity
(
Lagerkvist
et
al.
1986;
Chen
et
al.
1988;
Chen
et
al.
1995,
Tseng
et
al.
1996,
Chiou
et
al.
1997,
Rahman
et
al.
1999,
Hsueh
et
al.
1998,
Tsai
et
al.
1999),
and
mortality
(
Axelson
et
al.
1978;
Wu
et
al.
1989;
Engel
et
al.
1994;
Chen
et
al.
1996;
Tsai
et
al.
1999;
Lewis
et
al.
1999;
Hertz­
Picciotto
et
al.
2000)
have
been
addressed
in
these
investigations.
Several
tables
in
Appendix
2.2
illustrate
the
range
of
types
of
studies
and
exposure
levels
at
which
these
effects
have
been
observed.

The
Taiwanese
study
by
Chen
et
al.
(
1996)
on
mortality
from
ischemic
heart
disease
is
particularly
interesting,
in
that
a
wide
range
of
individual­
level
confounding
factors
were
adjusted
in
the
analysis,
including
age,
sex,
smoking,
body
mass
index,
serum
cholesterol
level,
serum
triglyceride
level,
blackfoot
disease,
hypertension
and
diabetes.
Their
adjustment
for
the
latter
two
chronic
diseases
that
may
themselves
contribute
to
ischemic
heart
disease
risk
could
have
attenuated
the
effects,
although
the
relative
risks
are
reduced
only
modestly
by
the
inclusion
of
the
confounders
other
than
blackfoot
disease.
Nevertheless,
there
is
a
strong
doseresponse
relationship,
rising
from
2­
fold
to
5­
fold
increased
risks
according
to
the
cumulative
exposure
level.

Another
study
from
Taiwan,
by
Tsai
et
al.
(
1999),
relied
on
vital
statistics,
and
hence
did
not
collect
the
individual­
level
confounding
data
included
by
Chen
and
colleagues.
However,
these
authors
present
analyses
for
a
broader
list
of
causes
of
mortality,
including
diabetes,
hypertension,
pulmonary
heart
disease,
cerebrovascular
disease,
liver
cirrhosis,
and
a
host
of
other
non­
cancer
and
cancer
endpoints.
The
findings
on
lung
and
bladder
cancer
confirm
those
of
numerous
other
investigators;
results
for
ischemic
heart
disease
are
similarly
consistent
with
those
of
Chen
et
al.
(
1996)
and
others.
Additionally,
the
study
presents
information
on
some
health
outcomes
not
previously
observed
in
arsenic­
exposed
populations.

Whereas
most
of
the
studies
on
cardiovascular
endpoints
have
been
conducted
in
communities
with
long
and
heavy
exposures,
a
few
were
conducted
in
a
population
with
more
relevant
levels.
For
instance,
Lewis
et
al.
(
1999)
examined
records
from
the
Mormon
Church
from
towns
in
Utah
with
concentrations
in
drinking
water
of
18­
164
µ
g/
L.
These
authors
found
mortality
due
to
hypertensive
heart
disease
to
be
elevated
in
both
males
and
females.
Although
individual­
level
confounder
data
were
not
available,
the
church's
prohibitions
on
consumption
of
alcohol
and
caffeine
would
tend
to
minimize
this
problem;
the
extremely
low
rates
of
respiratory
cancer
and
non­
malignant
respiratory
disease
attest
to
the
low
level
of
smoking
in
this
population,
which
may
also
explain
the
low
incidence
of
ischemic
heart
disease.

In
another
study
relevant
for
evaluating
the
plausibility
of
effects
at
low
level
exposures,
Gomez­
Caminero
(
2001)
examined
several
biomarkers
of
subclinical
cardiovascular
damage
comparing
a
population
exposed
at
45
µ
g/
L
in
drinking
water
to
one
with
negligible
exposures
8
The
von
Willebrand
factor
is
a
protein
that
promotes
normal
clotting
of
the
blood.

10
(<
2
µ
g/
L).
Among
pregnant
women
residing
in
the
exposed
community,
the
levels
of
von
Willebrand
factor
were
significantly
reduced
as
compared
with
those
in
unexposed
pregnant
women.
8
The
important
point
is
that
these
data
suggest
damage
to
the
endothelium
of
the
arterial
walls
at
levels
just
under
the
current
standard
of
50
µ
g/
L.
The
vascular
endothelium
serves
as
a
barrier
between
blood
plasma
and
the
arterial
smooth
muscle
and
regulates
the
flow
of
lipoproteins
between
these
compartments.
Arsenic
may
damage
the
endothelium
directly
or
restrict
its
repair
or
regenerative
capacity,
by
inhibiting
endothelial
cell
hyperplasia.
Reduced
von
Willebrand
factor
could
play
a
role
in
this
process.

It
is
also
notable
that,
in
the
past,
clinical
cardiovascular
effects
normally
only
seen
in
adults
were
observed
in
children
at
very
high
exposure
levels.
The
possibility
that
subclinical
damage
to
the
cardiovascular
system
occurs
in
early
life,
setting
the
stage
for
severe
and
potentially
fatal
events
at
older
ages,
should
be
considered.

The
Panel
concludes
that
cardiovascular
effects
of
arsenic
could
be
occurring
at
current
levels
in
drinking
water.
Despite
uncertainty
in
the
shape
of
the
dose­
response
curve,
a
benchmark
dose
approach
would
be
a
reasonable
starting
point
for
incorporating
these
benefits
into
the
risk/
benefit
analysis
associated
with
reduction
of
the
MCL.
To
place
the
epidemiologic
findings
with
regard
to
ischemic
heart
disease
in
context,
over
500,000
deaths
occurred
in
the
U.
S.
in
1999
due
to
this
cause,
or
22%
of
all
deaths.
Undoubtedly
the
overwhelming
majority
of
these
are
not
due
to
arsenic.
However,
the
same
can
be
said
for
lung
and
bladder
cancer
in
the
general
population.
Given
the
large
background
incidence
of
ischemic
heart
disease,
the
committee
believes
these
effects/
benefits
should
be
quantified.
A
similar
argument
would
apply
to
the
morbidity
and
mortality
from
hypertension.

Peripheral
vascular
disease
is
a
well­
established
effect
of
high
exposures
to
arsenic,
to
the
extent
that
the
presence
of
one
severe
form
of
this
condition,
blackfoot
disease,
has
been
used
as
an
indicator
of
exposure.
There
is
probably
little
direct
relevance
of
the
extreme
manifestations
of
this
condition
for
lower
exposures.
The
likelihood
of
less
severe
conditions
at
low
exposures
is
uncertain.

2.2.2.2
Diseases
of
the
Endocrine
System
(
see
Table
IV,
Appendix
2.2).

Most
of
the
epidemiologic
literature
demonstrating
increased
risk
of
diabetes
in
association
with
arsenic
exposure
has
been
published
in
the
last
five
years
(
Tsai
et
al.
1999;
Lai
et
al.
1994;
Tseng
et
al.
2000;
Rahman
et
al.
1998).
Studies
include
occupational
and
drinking
water
sources
for
exposure,
and
both
mortality
and
morbidity
studies
have
found
significant
excesses.
Generally
speaking,
because
diabetes
is
not
a
common
cause
of
death,
mortality
studies
would
be
expected
to
observe
only
the
tip
of
the
iceberg
in
terms
of
increased
incidence.
However,
even
when
not
fatal,
diabetes
engenders
large
medical
costs
and
has
a
serious,
lifelong
impact
on
the
quality
of
life.

Besides
overt
clinical
disease,
subclinical
indicators
potentially
relevant
to
the
development
of
diabetes
have
been
examined
in
studies
of
arsenic­
exposed
populations.
Specifically,
glucosuria
and
elevated
glycosylated
hemoglobin
have
both
been
found
in
association
with
arsenic
exposure
(
Jensen
and
Hansen
1998;
Rahman
et
al.
1999;
Gomez­
Caminero
2001).
These
are
biologically
significant
markers
of
impaired
glucose
metabolism.
Glycosylated
hemoglobin
represents
an
indicator
of
long­
term
glycemic
control.
The
Chilean
population
examined
by
Gomez­
Caminero
(
2001),
for
which
exposures
were
~
45
µ
g/
L,
was
11
found
to
have
significantly
elevated
glycosylated
hemoglobin,
both
when
this
biomarker
was
treated
as
a
continuous
measure
(%
of
hemoglobin
glycosylated),
and
when
it
was
dichotomized
(>
6.5%
vs.
<
6.5%).
Since
these
women
were
pregnant,
the
age
range
was
fairly
young
and
therefore
the
majority
were
born
after
levels
were
reduced
to
about
110
µ
g/
L,
which
occurred
around
1970
(
Hopenhayn­
Rich
et
al.
2000).
As
the
risk
of
diabetes
increases
with
age,
the
findings
may
indicate
that
the
effects
of
arsenic
on
glycemic
status
could
begin
early,
laying
the
basis
for
clinical
disease
that
manifests
primarily
beyond
the
reproductive
years
(
i.
e.,
Type
II
diabetes).

Evidence
for
the
diabetogenicity
of
arsenic
is
mounting,
plausible
mechanisms
have
been
shown,
subclinical
markers
of
altered
glycemic
control
have
been
observed,
and
there
appears
to
be
relevance
at
low
exposures.
Diabetes
was
directly
responsible
for
68,000
deaths
in
the
U.
S.
in
1999,
representing
2.9%
of
deaths,
more
than
five
times
as
many
as
occurred
due
to
bladder
cancer.
Quantification
of
the
benefits
of
reducing
the
arsenic
MCL
in
terms
of
diabetes
mortality,
as
well
as
the
multidimensional
costs
associated
with
chronic
illness,
is
appropriate.
Any
effect
that
arsenic
has
in
increasing
the
incidence
or
advancing
the
onset
of
Type
II
diabetes
will
contribute
to
the
risks
of
many
other
diseases
associated
with
arsenic
exposure
(
e.
g.
hypertension,
cardiovascular
disease,
liver
cancer,
peripheral
vascular
disease).

2.2.2.3
Other
Cancer
Sites
(
see
Table
V,
Appendix
2.2).

Increased
risks
for
kidney,
liver,
skin,
bone,
prostate,
laryngeal,
nasal
and
other
sites
are
observed
to
occur
in
populations
exposed
to
arsenic
through
ingestion
(
Lewis
et
al.
1999;
Smith
et
al.
1992;
Tsai
et
al.
1999).
A
comprehensive
accounting
of
benefits
from
the
reduction
in
the
arsenic
MCL
should
quantitate
at
least
the
strongest
of
these
effects,
accounting
for
uncertainty.
Recent
studies
on
the
mechanisms
for
arsenic
carcinogenicity
do
not
suggest
that
lung
and
bladder
would
be
the
only
sites
affected.
An
excess
of
prostate
cancer
was
associated
with
cumulative
arsenic
exposures
above
1
mg/
L
year
in
Utah.

2.2.2.4
Non­
malignant
Respiratory
Diseases
(
see
Table
VI,
Appendix
2.2).

The
increased
incidence
of
bronchitis,
emphysema,
respiratory
symptoms,
and
chronic
airway
obstruction
are
surprising
for
exposures
that
do
not
occur
via
inhalation.
At
high
exposures,
strong
dose­
response
relationships
were
found
for
respiratory
symptoms
(
Mazumder
et
al.
2000).
Plausibility
for
these
effects
at
low
exposures
is
uncertain.
Shortness
of
breath
was
elevated
at
50­
199
µ
g/
L
in
West
Bengal
(
Mazumder
et
al.
2000),
and
an
ecologic
study
in
the
U.
S.
found
mortality
was
increased
from
chronic
airways
obstruction
and
emphysema
at
levels
as
low
as
5­
10
µ
g/
L,
with
the
highest
risk
at
>
20
µ
g/
L
(
Engel
and
Smith
1994).
This
latter
finding
suggests
the
possibility
that
communities
with
somewhat
higher
arsenic
concentrations
in
drinking
water
(
e.
g.,
>
20
µ
g/
L)
may
also
include
a
higher
proportion
of
smokers.
Two
concerns
are:
first,
that
smoking
could
be
a
confounder,
and
second,
that
smoking
and
arsenic
could
have
synergistic
effects.
Since
smoking
acts
synergistically
with
arsenic
in
producing
lung
cancer
(
Hertz­
Picciotto
et
al.
1992),
a
similar
interaction
for
non­
malignant
respiratory
diseases
is
possible.
Although
smoking
is
a
voluntary
risk,
smokers
do
constitute
a
susceptible
subgroup.

2.2.2.5
Reproductive
Effects
(
see
Table
VII,
Appendix
2.2).

Few
reproductive
endpoints
have
been
examined
in
more
than
one
study.
Most
of
the
spontaneous
abortion
studies
were
conducted
in
populations
with
high
exposures;
those
that
were
not
did
not
have
individual
data
on
confounders,
and
hence
little
confidence
can
be
placed
in
the
results.
The
time
trend
analyses
by
Hopenhayn­
Rich
et
al.
(
2000)
suggest
that
stillbirths
and
postneonatal
mortality
are
increased
at
high
exposures
but
not
at
levels
between
40
and
70
µ
g/
L;
12
the
decline
in
rates
in
the
exposed
region
after
arsenic
levels
are
reduced
may
be
partially
attributable
to
other
improvements
in
water
quality
and
standard
of
living.
In
contrast,
an
effect
on
birth
weight
may
be
seen
at
lower
levels,
based
on
the
studies
to
date.
Transfer
of
arsenic
to
the
fetus
has
been
shown;
interestingly,
blood
plasma
arsenic
was
essentially
all
in
the
form
of
DMA,
and
pregnant
women
had
a
higher
proportion
of
their
urinary
arsenic
as
DMA
than
nonpregnant
women
(
Concha
et
al.
1998),
suggesting
more
efficient
methylation
during
pregnancy.

2.2.2.6
Neurologic
and
Neurodevelopmental
Endpoints
(
see
Table
VIII,
Appendix
2.2).

There
have
been
studies
indicating
associations
between
environmental
exposures
and
pathologies,
symptoms,
and
developmental
deficit.

2.2.3
Valuation
of
Non­
Quantified
Health
Endpoints
The
preceding
discussion
suggests
that
some
health
endpoints
affected
by
arsenic
exposure,
including
skin
cancer
and
ischemic
heart
disease
could
be
quantified.
That
is,
the
expected
reduction
in
cases
could
be
calculated
for
each
endpoint
(
possibly
by
age
group)
for
each
year
following
the
reduction
in
exposure.
If
the
magnitudes
of
these
effects
can
be
characterized,
valuation
should
be
done
in
the
same
way
as
for
bladder
and
lung
cancers.
(
See
Charge
Question
1.)

Two
issues,
however,
arise:
(
a)
Do
unit
values
exit
for
all
of
the
health
endpoints
that
can
be
quantified?
(
b)
Should
valuation
be
done
if
effects
cannot
be
quantified?

To
answer
the
first
question,
unit
values
that
measure
what
individuals
would
pay
to
avoid
adverse
health
effects
(
Willingness­
to­
Pay
estimates)
do
not
exist
for
all
health
endpoints
mentioned
in
our
answer
to
Charge
Question
2.
The
Benefits
and
Costs
of
the
Clean
Air
Act
1990­
2010
(
USEPA
1999)
contains
a
recent
review
of
the
available
data
for
at
least
some
of
the
relevant
endpoints.
Where
only
cost
of
illness
estimates
are
available,
they
can
be
used
but
should
be
clearly
described
as
providing
lower
bounds
on
true
willingness
to
pay
(
Freeman
1993).
The
EPA
Cost
of
Illness
Handbook
is
a
recent
source
of
cost
of
illness
data
for
some
relevant
endpoints
(
USEPA
2001a)

To
make
economic
valuation
possible,
it
is
important
to
express
and
characterize
these
other
endpoints
in
terms
of
effects
on
people's
activity
levels
and
sense
of
well­
being,
as
much
as
possible.
There
is
a
fairly
extensive
body
of
data
on
the
economic
values
of
reducing
days
experiencing
various
symptoms,
restricted
activity
days,
hospitalizations,
required
treatments,
etc.
It
would
be
difficult
to
use
this
body
of
data
to
value
many
of
the
health
effects
listed
in
Exhibit
5­
1
(
p.
5­
4
of
the
arsenic
economic
analysis)
such
as
hepatic
enlargement,
anemia,
leukopenia,
peripheral
neuropathy,
since
the
clinical
significance
and
impact
on
individuals'
activities
of
these
effects
may
vary
significantly.

To
answer
the
second
question
raised
above,
it
is
not
possible
to
value
health
effects
that
have
not
been
quantified.

2.3
Exposure
Reduction
as
a
Benefit
Category
Charge
Question
3:
Should
reduction/
elimination
of
exposure
be
evaluated
as
a
separate
benefits
category,
in
addition
to
or
in
conjunction
with
mortality
and
morbidity
reduction?
13
Regarding
Charge
Question
3,
the
Panel
believes
in
this
case
that
reductions
in
exposure
should
not
be
considered
a
separate
category
of
benefits
in
a
benefit
cost
analysis.
The
Agency
has
adopted
a
damage
function
approach
to
quantifying
the
benefits
associated
with
reducing
people's
exposure
to
arsenic.
The
damage
function
framework
to
estimating
benefits
separates
the
measurement
of
the
relationship
between
exposure
and
response
(
e.
g.,
the
risk
of
fatal
or
non­
fatal
cancer)
from
the
valuation
of
reductions
in
the
risk
of
each
of
these
health
endpoints.

Under
the
damage
function
approach,
epidemiologists
estimate
dose­
response
functions
and
economists
measure
the
value
people
place
on
reductions
in
risk
of
death
or
illness.
Reductions
in
exposure
are
therefore
already
valued
when
one
values
the
reductions
in
the
risk
of
death
or
illness
associated
with
those
exposures
under
the
damage
function
approach.
Adding
a
separate
value
for
reductions
in
exposure
to
arsenic
per
se
would
require
that
the
be
associated
with
some
additional
source
of
benefits.

We
do
recognize
that
some
people
may
value
the
existence
of
lower
levels
of
arsenic
in
drinking
water,
possibly
for
psychological
reasons
(
e.
g.,
dread
of
being
exposed),
and
we
believe
that
existence
values
are
a
legitimate
category
of
benefits.
Existence
values
are
not
accommodated
within
a
damage
function
approach
to
benefit
quantification.
Reliable
estimates
of
these
values
would
need
to
identify
the
marginal
benefit
to
individuals
associated
with
a
change
in
concentration,
separate
from
the
change
in
health
risks
associated
with
the
change
in
exposure.
We
found
no
empirical
evidence
to
support
or
contradict
such
a
relationship
in
the
case
of
arsenic.
In
the
absence
of
any
empirical
data,
there
is
no
basis
for
estimating
an
existence
value
in
this
case.

2.4
Comparison
of
Benefits
and
Costs
Charge
Question
4:
How
should
total
benefits
and
costs
and
incremental
benefits
and
costs
be
addressed
in
analyzing
regulatory
alternatives
to
ensure
appropriate
consideration
by
decision
makers
and
the
public?

2.4.1
Comparison
of
Benefits
and
Costs
by
System
Size
One
noteworthy
feature
of
the
arsenic
in
drinking
water
problem
is
that
for
the
most
part,
those
who
would
receive
the
health
benefits
from
reductions
in
the
concentrations
of
arsenic
in
drinking
water
will
also
bear
the
costs
of
achieving
them.
These
costs
will
take
the
form
of
higher
rates
and
prices
for
water
supply
and/
or
higher
taxes
to
cover
these
costs.
Thus
it
is
important
to
try
to
determine
whether
those
who
receive
these
benefits
would
be
willing
to
bear
the
costs
of
reducing
arsenic
concentrations
in
their
drinking
water.
This
is
the
question
that
benefit­
cost
analysis
attempts
to
answer,
because
in
principle
the
benefits
of
a
program
are
defined
as
the
sum
of
the
affected
individuals'
willingness
to
pay
for
these
improvements.
If
all
benefits
and
costs
of
a
regulation
are
measured
accurately,
and
if
the
benefits
received
by
the
members
of
a
group
are
less
than
the
costs
paid
by
the
members
of
the
group,
this
is
a
signal
that
the
members
of
the
group
would
consider
themselves
to
be
made
worse
off
by
the
regulation.
Conversely,
if
benefits
exceed
costs,
the
policy
would
make
the
members
of
the
group
better
off.

For
this
reason,
we
recommend
that
benefits
and
costs
should
be
calculated
on
a
water
supply
system
basis.
Specifically,
we
recommend
that
total
benefits
and
costs
and
marginal
benefits
and
costs
be
calculated
for
all
the
systems
that
are
affected
by
the
standard,
and
the
system­
level
results
then
be
aggregated
to
the
national
level.
Because
of
the
large
economies
of
scale
associated
with
drinking
water
treatment,
the
net
benefits
(
benefits
minus
costs)
are
likely
to
vary
substantially
by
system
size,
and
this
information
should
be
made
clear
to
policy
makers
and
the
public.
Such
an
analysis
would
allow
decision
makers
to
evaluate
a
range
of
alternative
14
strategies
rather
than
a
one­
size­
fits­
all
approach.
The
high
cost
of
arsenic
control
is
driven
by
the
tail
of
a
distribution
involving
a
number
of
small
systems.
The
analysis
needs
to
make
this
clear
so
that
decision
makers
can
consider
this
fact
in
formulating
an
appropriate
policy
response.

When
there
are
too
many
affected
systems
to
perform
a
separate
cost
analysis
tailored
to
the
specific
circumstances
of
every
system,
some
data
aggregation
may
be
necessary.
Nevertheless,
the
existing
cost
analysis
appears
to
be
too
generic
and
too
little
tailored
to
the
specific
circumstances
of
the
particular
utilities
affected
by
arsenic
regulation
(
e.
g.,
water
supply
systems
in
the
west
and
southwest
that
use
groundwater).
Rather
than
using
national
cost
functions,
an
attempt
should
be
made
to
employ
cost
functions
tailored
to
these
affected
utilities.
Grouping
utilities
into
size
classes
and
conducting
an
analysis
by
size
class
is
acceptable
if
this
is
done
with
specific
reference
to
size
classes
that
are
meaningful
for
the
systems
affected
by
the
arsenic
regulation
and
using
data
specific
to
these
systems.
In
the
existing
analysis,
individual
cost
analyses
were
performed
only
for
water
utilities
that
serve
more
than
a
million
people
("
very
large
systems");
we
recommend
lowering
the
threshold
population
size
for
performing
individual
cost
analyses,
for
example
to
a
service
population
of
250,000
or
more.

2.5
Incorporation
of
Uncertainty
into
Benefits
Measures
Charge
Question
5:
How
should
uncertainties
be
addressed
in
the
analysis
to
ensure
appropriate
consideration
by
decision
makers
and
the
public?

Doing
one's
best
to
understand
and
communicate
uncertainty
is
a
basic
obligation
of
technical
analysts
to
their
audience.
Ideally,
the
goal
should
be
to
enable
the
audience
to
make
as
informed
a
choice
among
risk
acceptance/
risk
control
options
as
if
the
audience
members
themselves
had
been
able
to
go
through
the
process
of
analysis.
Good
uncertainty
assessments
help
decision­
makers
take
appropriate
precautions,
where
indicated,
against
the
possibility
that
future
improved
data
will
alter
the
balance
of
benefits
and
costs
projected
from
current
information.
If
applied
consistently
and
comparably
across
different
types
of
information
(
i.
e.
costs
and
benefits
of
various
types)
uncertainty
analyses
also
can
help
planners
make
judgments
about
the
relative
productivity
of
investments
in
different
kinds
of
information­
gathering
activities
for
future
regulatory
choices
(
including,
for
example,
the
timing
of
implementation
measures).

Benefit­
cost
analyses
of
drinking
water
regulations
are
likely
to
entail
uncertainties
in
the
(
a)
measurement
of
exposure,
(
b)
measurement
of
dose­
response,
(
c)
valuation
of
health
outcomes
and
(
d)
measurement
of
costs.
The
sources
of
these
uncertainties
include
measurement
error
(
uncertainty
about
the
average
level
of
arsenic
in
tap
water
or
of
the
amount
of
tap
water
consumed)
as
well
as
uncertainty
about
which
model
to
use
in
describing
the
relationship
between
exposure
and
response
at
low
doses.
In
general,
there
are
two
approaches
to
handling
these
sources
of
uncertainty
 
sensitivity
analysis
and
Monte
Carlo
simulation.
In
a
sensitivity
analysis
various
assumptions
are
made
about
the
correct
model
(
e.
g.,
dose
response
function)
or
parameter
(
e.
g.,
discount
rate)
to
use
in
the
analysis
and
results
are
presented
for
each
set
of
assumptions.
In
a
Monte
Carlo
analysis
a
distribution
is
assumed
for
a
key
parameter
or
set
of
parameters
(
e.
g.,
the
Value
of
a
Statistical
Life)
and
several
hundred
draws
are
made
from
this
distribution.
Benefits
are
calculated
for
each
value
of
the
parameters
drawn.
This
yields
a
probability
distribution
of
benefits,
whose
parameters
(
e.
g.,
the
10th
and
90th
percentiles)
can
be
reported.

We
believe
that,
in
the
case
of
model
uncertainty,
it
is
appropriate
to
rely
on
sensitivity
analysis;
however,
the
assumptions
underlying
each
sensitivity
analysis
should
be
clearly
spelled
15
out
when
presenting
results.
It
is
particularly
inappropriate
to
present
only
the
highest
and
lowest
numbers
associated
with
a
set
of
sensitivity
analyses,
which
may
give
the
reader
the
false
impression
that
these
constitute
the
upper
and
lower
bounds
of
a
uniform
distribution.
For
parameters
for
which
it
is
possible
to
specify
a
distribution,
Monte
Carlo
analysis
is
desirable
(
for
example,
in
the
case
of
the
slope
of
the
dose­
response
function).

The
EPA
analysis
of
the
Arsenic
in
Drinking
Water
Rule
discusses
some
of
the
sources
of
uncertainty
in
benefit
estimates
and
handles
them
by
performing
sensitivity
analyses.
Specifically,
it
focuses
on
the
impact
of
alternate
assumptions
about
the
parameters
of
the
doseresponse
function,
which
will
vary
depending
on
what
fraction
of
arsenic
in
the
Taiwanese
population
(
the
population
used
to
estimate
the
dose
response
function)
is
assumed
to
come
from
drinking
water.
A
"
high"
and
"
low"
estimate
of
benefits
are
generated
based
on
alternate
assumptions
about
the
sources
of
arsenic
exposure
in
Taiwan.

The
other
set
of
sensitivity
analyses
that
are
performed
pertain
to
the
Value
of
a
Statistical
Life
(
VSL).
This
is
varied
to
allow
for
(
a)
changes
in
the
VSL
as
incomes
grow,
(
b)
the
involuntary
nature
of
drinking
water
risks
and
(
c)
the
length
of
the
latency
period.
As
we
explain
in
more
detail
in
the
next
section,
latency
(
or,
more
correctly,
the
cessation­
lag
between
reduction
in
exposure
and
reduction
in
risk)
is
not
handled
correctly
in
the
arsenic
benefits
analysis.
We
also
have
a
criticism
of
the
treatment
of
the
adjustment
for
the
involuntary
nature
of
drinking
water
risks.
In
principle,
however,
there
is
nothing
wrong
with
handling
these
sources
of
uncertainty
through
a
sensitivity
analysis.
The
choice
of
discount
rate
is
also
correctly
handled
via
sensitivity
analysis.

The
report
could,
however,
improve
in
its
reporting
of
the
results
of
these
sensitivity
analyses
in
two
ways.
First,
the
presentation
of
the
details
of
the
analysis
in
the
Executive
Summary
and
in
the
body
of
the
report
does
not
provide
a
sufficiently
clear
description
of
the
specific
details
of
all
aspects
of
the
uncertainty
analysis.
With
considerable
effort
it
is
possible
to
develop
a
more
complete
understanding
of
how
the
analysis
was
undertaken
by
studying
the
appendices
to
the
report.
Second,
when
the
results
of
two
alternate
assumptions
are
presented,
for
example,
the
"
high"
and
"
low"
benefit
estimates
in
the
Executive
Summary,
it
is
important
to
state
that
these
are
not
the
endpoints
of
a
uniform
distribution.
16
3.
GENERAL
COMMENTS
ON
THE
ECONOMIC
ANALYSIS
3.1
Comments
on
Exposure
Assessment
3.1.1
Characterization
of
U.
S.
Population
Exposure
in
the
Analysis
There
are
a
few
opportunities
to
improve
the
presentation
of
arsenic
exposures
in
the
benefits
analysis.
First,
although
the
report
gives
national
estimates
of
the
proportion
of
water
systems
of
various
types
that
exceed
various
average
arsenic
levels,
and
Tables
III.
C­
5
and
C­
6
give
helpful
breakdowns
by
geographic
region
and
the
system
size
(
population
served
per
system),
there
does
not
appear
to
be
an
accessible
presentation
of
the
national
or
regional
numbers
of
people
or
population
aggregate
exposures
broken
down
in
the
same
ways.
A
breakdown
of
the
numbers
of
people
in
these
categories
is
important
for
understanding
the
distributional
burdens
of
both
current
arsenic
exposures/
health
harm
and
the
prospective
compliance
costs.
A
breakdown
of
the
amounts
of
population
aggregate
exposure
in
these
categories
is
very
important
for
understanding
the
extent
to
which
the
national
aggregate
arsenicin
drinking
water
problem
would
be
reduced
by
different
MCLs.

3.2
Comments
on
the
Computation
of
Benefits
3.2.1
Treatment
of
`
Latency'

As
the
answer
to
Charge
Question
1
implies,
we
do
not
believe
that
the
lag
between
reduction
in
exposure
and
reduction
in
fatal
cancers
has
been
treated
correctly
in
the
benefits
analysis.
The
correct
approach
is
to
predict
the
number
of
fatal
cancers
avoided
each
year
based
on
an
assumption
about
the
percent
of
the
steady­
state
reduction
in
cancer
cases
that
will
be
achieved
each
year
following
the
policy.
For
example,
in
The
Benefits
and
Costs
of
the
Clean
Air
Act
1990­
2010
(
USEPA
1999),
it
was
assumed
that
25%
of
the
steady­
state
benefits
from
reducing
air
pollution
would
be
achieved
in
the
first
year
of
the
policy,
50%
by
the
second
year,
and
(
increasing
gradually),
100%
of
the
benefits
by
the
end
of
the
5th
year
of
the
policy.

Once
this
time
path
is
established,
the
number
of
fatal
cancers
avoided
in
year
t
should
be
multiplied
by
the
Value
of
a
Statistical
Life
in
year
t
and
the
result
discounted
to
the
first
year
of
the
policy.
The
sum
of
these
present
discounted
values
over
the
horizon
of
the
analysis
yields
the
present
discounted
value
of
benefits
of
the
policy.
It
is,
of
course,
possible
to
annualize
this
number
by
calculating
the
constant
annual
value
of
benefits
that
produces
the
same
present
discounted
value
of
benefits.

In
its
primary
analysis
the
Agency
makes
no
adjustment
for
the
cessation­
lag
in
its
calculation
of
cancer
mortalities
avoided.
It
simply
assumes
that
the
cancer
mortality
risk
will
drop
immediately
to
the
new
steady­
state
level
upon
implementation
of
the
new
standard.
Then
in
a
sensitivity
analysis
(
Section
5.5),
it
accounts
for
the
cessation­
lag
not
with
alternative
calculations
of
cancer
mortalities
avoided,
but
by
discounting
the
Value
of
a
Statistical
Life
applied
to
these
avoided
deaths
for
three
alternative
lag
periods,
5,
10,
and
20
years.
In
terms
of
the
calculated
monetary
benefits,
this
is
equivalent
to
assuming
there
is
no
reduction
in
cancer
mortalities
avoided
for
the
first
5,
10,
20
years
after
the
regulation
is
implemented,
after
which
the
cancer
mortality
risk
drops
immediately
to
the
new
steady­
state
level.

In
valuing
avoided
nonfatal
cancers,
the
cessation­
lag
should
be
taken
into
account
in
estimating
the
numbers
of
cases
avoided
in
the
same
way
that
we
described
for
fatal
cancers
in
Section
2.1.
17
3.2.2
Treatment
of
Age
There
is
sufficient
information
in
the
dose­
response
function
in
Morales
et
al.
(
2000)
to
calculate
cancer
cases
avoided
by
age
group.
We
believe
that
this
should
be
done.
The
doseresponse
function
used
to
compute
the
number
of
cancer
cases
avoided
in
the
benefits
analysis
(
Model
1
of
Morales
et
al.
2000)
is
a
special
case
of
equation
(
1)
in
which
"
the
relative
risk
of
mortality
at
any
time
is
assumed
to
increase
exponentially
with
a
linear
function
of
dose
and
a
quadratic
function
of
age
(
p.
B­
7)."
Instead
of
using
this
equation
to
predict
risks
by
age
group,
the
information
contained
in
the
equation
is
aggregated
to
compute
a
lifetime
cancer
risk.

3.2.3
Valuing
Avoided
Cancer
Morbidity
To
value
nonfatal
bladder
cancers,
the
Agency
used
a
value
for
avoiding
a
statistical
case
of
chronic
bronchitis
obtained
by
Viscusi,
Magat,
and
Huber
(
1991).
We
have
two
reservations
about
this.
First,
this
study
used
a
small
sample
obtained
in
a
shopping
mall
in
North
Carolina
and
thus
may
not
be
representative
of
either
the
U.
S.
population
as
a
whole
or
the
population
of
individuals
at
risk
of
bladder
cancer.
Second,
we
have
no
basis
for
determining
that
avoiding
a
case
of
chronic
bronchitis
has
the
same
value
as
avoiding
a
nonfatal
case
of
bladder
cancer.

On
this
second
point,
there
is
one
study
of
willingness
to
pay
to
avoid
a
nonfatal
case
on
one
type
of
cancer.
Magat,
Viscusi,
and
Huber
estimated
the
willingness
to
pay
to
avoid
a
case
of
nonfatal
lymphoma
to
be
$
3.6
million
(
Magat,
et
al.
1996).
This
value
was
obtained
from
a
similar
shopping
mall
intercept
survey
with
a
substantially
larger
sample
size.
So,
although
the
endpoint
being
valued
more
nearly
corresponds
to
nonfatal
bladder
cancer,
there
is
still
the
question
of
the
representativeness
of
the
sample.
We
also
note
that
the
value
obtained
is
at
least
20
times
larger
than
the
cost
of
illness
for
nonfatal
bladder
cancer
cited
in
Exhibit
5­
10.
Thus
we
do
not
have
a
lot
of
confidence
in
this
number.
Therefore,
we
recommend
that
the
value
used
in
the
report
and
the
alternative
discussed
here
be
used
as
bounds
in
an
uncertainty
analysis.
However,
this
range
should
be
clearly
identified
as
displaying
the
two
extreme
estimates
available
in
the
literature
so
it
is
not
misconstrued
as
a
confidence
interval.

3.2.4
Valuing
Avoided
Cancer
Mortality
The
Agency
should
recognize
the
uncertainty
in
the
estimated
VSL
used
to
value
fatal
cancers
either
by
sensitivity
analysis
or
incorporating
the
uncertainty
in
Monte
Carlo
analyses.

The
committee
believes
that
the
adjustments
to
the
VSL
for
the
voluntariness/
controllability
of
risk
does
not
conform
to
standard
economic
practice.
The
SAB
Review
of
the
EPA's
White
Paper,
Valuing
the
Benefits
of
Fatal
Cancer
Risk
Reductions
recommended
that
no
such
adjustments
be
made.

We
believe
that
the
central
estimate
of
about
$
6.1
million
for
the
VSL
is
appropriate.
In
an
earlier
report,
the
SAB
said:
"
To
the
extent
that
cancer
victims
suffer
greater
morbidity,
fear,
or
dread
than
the
victims
of
the
causes
of
death
involved
in
VSL
studies,
it
would
be
appropriate
to
attach
a
"
cancer
premium"
to
the
value
of
an
avoided
death
from
cancer."
It
went
on
to
say
that
there
was
little
reliable
information
on
what
this
premium
should
be.
We
agree
with
this
conclusion.

One
possibility
would
be
to
add
to
the
VSL
a
number
representing
the
value
of
avoiding
a
nonfatal
case
of
the
same
type
of
cancer.
We
can
not
endorse
that
approach
here
for
there
is
no
18
reason
to
believe
that
either
the
medical
costs
(
cost
of
illness),
the
duration
of
the
morbidity,
or
its
severity
would
be
the
same
for
a
nonfatal
case
and
a
fatal
case
of
cancer.
In
fact,
we
can
think
of
reasons
why
they
could
be
quite
different.
We
can
endorse
adding
estimates
of
the
medical
costs
of
treatment
and/
or
amelioration
for
fatal
cancers
to
the
VSL
as
a
lower
bound
on
the
true
value
of
avoiding
fatal
cancers.

3.3
Comments
on
the
Computation
of
Costs
3.3.1
Factors
that
May
Cause
Costs
to
Be
Overstated
and/
or
Benefits
to
Be
Understated
Two
features
of
the
existing
cost
analysis
may
lead
it
to
overstate
the
costs
of
arsenic
regulation,
at
least
to
some
degree:
We
recommend
that
the
Agency
attempt
to
take
account
of
these
factors.
(
1)
To
the
extent
that
arsenic
removal
is
a
joint
product
of
water
treatment
together
with
the
removal
of
other
contaminants,
the
existing
cost
analysis
may
overstate
the
costs
(
or
understate
the
benefits)
of
arsenic
regulation.
Utilities
may
already
have
pre­
existing
installed
treatment
processes
for
other
contaminants
that
lower
the
cost
of
arsenic
removal
in
a
manner
not
reflected
in
the
current
analysis,
or
utilities
may
adopt
new
treatment
processes
in
response
to
arsenic
regulation
that
yield
other
improvements
in
drinking
water
quality
as
a
by­
product.
(
2)
In
two
of
three
cases,
the
existing
cost
analyses
for
the
very
large
systems
affected
by
the
arsenic
regulations
note
that
the
costs
may
be
overstated
because
they
do
not
account
for
options
that
may
be
available
to
lower
costs
associated
with
ground
water
entry
points.
In
those
two
cases
it
is
stated
that:
"
Depending
on
the
spatial
distribution
of
the
wells,
it
may
be
possible
to
implement
centralized
treatment,
with
reduced
compliance
costs.
It
may
also
be
possible
to
achieve
compliance
without
treatment
by
blending
ground
water
with
surface
water.
Finally,
depending
on
the
additional
capacity
available
from
surface
water
and
unaffected
well,
the
city
could
shut
down
affected
wells."
Presumably,
the
same
considerations
apply
to
some
of
the
other
systems
affected
by
arsenic
regulation
and
we
recommend
that
the
Agency
attempt
to
take
them
into
account.

3.3.2
Amortization
of
Costs
In
the
arsenic
benefits
analysis
capital
costs
are
amortized
(
expressed
as
annual
equivalent
flows)
by
using
a
discount
rate
of
7%.
An
alternative
calculation
based
on
a
3%
rate
is
also
presented.
However,
what
matters
for
the
impact
on
utility
finances
and
utility
customers
is
the
actual
interest
rate
at
which
the
affected
utilities
will
finance
these
investments.
We
recommend
that
the
Agency
estimate
this
when
calculating
the
regulatory
costs
(
Freeman
1993,
pp.
213­
216;
Kolb
and
Scheraga
1990).

Exhibit
6­
7
of
the
arsenic
economic
analysis
presents
data
showing
recommended
cost
of
capital
estimates
for
various
types
of
water
utility
ranging
from
4.17%
to
5.94%.
Having
reviewed
the
report
from
which
they
derive,
we
do
not
believe
these
estimates
are
adequate.
First,
while
the
analysis
allows
for
the
use
of
different
sources
of
capital
by
non­
small
utilities
of
different
sizes
(
those
serving
10,001
­
50,000
and
those
serving
over
50,000)
it
assumes
that
the
costs
of
various
types
of
capital
 
long­
term
debt,
short­
term
debt,
equity
capital,
municipal
bonds
 
are
the
same
regardless
of
size
for
all
systems
serving
over
10,000.
We
do
not
believe
this
assumption
is
likely
to
be
accurate.
Second,
with
investor
owned
utilities
the
report
states
that
an
after­
tax
figure
is
appropriate
for
the
required
analysis.
We
disagree
and
instead
recommend
(
1)
using
a
before­
tax
figure
for
the
cost
of
capital
for
investor
owned
utilities,
and
(
2)
using
a
separate
account
to
track
the
revenue
gains
to
the
government
sector
from
taxes
from
the
water
system
debt.
19
By
way
of
illustration,
suppose
an
investor
owned
water
utility
and
a
public
owned
water
utility
both
need
to
borrow
$
1
million.
Suppose
the
investor
owned
utility
issues
bonds
with
an
interest
rate
of
8.5%.
The
publicly
owned
utility
can
borrow
at
a
lower
interest
rate
since
the
interest
paid
on
its
bonds
is
tax
exempt;
it
can
borrow
at
5.19%,
to
use
the
figure
from
page
29
of
the
report
on
Public
Water
System
Cost
of
Capital.
The
difference
of
3.31%
(=
8.5
­
5.1)
is
the
savings
due
to
the
tax
exemption
on
publicly
owned
system
debt.
The
report
recommends
using
5.19%
as
the
cost
of
capital
for
investor
owned
utility
debt
as
well
as
publicly
owned
utility
debt,
because
it
views
the
3.31%
interest
increment
as
merely
a
transfer
payment.
While
this
is
not
incorrect,
it
is
misleading
with
respect
to
the
policy
implications.
Because
the
investor
owned
utility
pays
a
higher
interest
rate
for
its
debt
than
the
publicly
owned
utility,
its
customers
will
face
a
larger
cost
increase
than
those
of
the
publicly
owned
utility.
We
believe
this
should
be
made
explicit
in
the
analysis.

Third,
for
similar
reasons
we
disagree
with
the
way
in
which
the
report
treats
the
financing
of
capital
costs
on
a
pay­
as­
you­
go
basis
out
of
current
revenues
or
accumulated
capital
reserves.
This
type
of
financing
accounts
for
about
20­
30%
of
cost
of
capital
expenditures
for
non­
small
systems,
and
20­
60%
for
small
systems.
The
report
imputes
an
opportunity
cost
of
capital
to
funds
from
this
source
as
though
they
were
amortized
over
15
or
30
years.
For
example,
if
a
small
system
needs
to
fund
$
1
million
of
water
supply
improvement
from
cash
flow,
the
report
recommends
amortizing
this
as
though
the
funds
were
being
borrowed
with
unrated
or
low
rated
general
obligation
bonds
at
an
interest
rate
of
5.47%
amortized
over
15
years.
Suppose
the
investment
were
being
made
over
a
5­
year
period.
If
the
utility
had
made
no
provision
for
a
sinking
fund,
it
would
need
to
raise
the
$
1
million
from
higher
water
rates
over
the
5­
year
period.
To
the
extent
there
is
a
sinking
fund,
the
impact
on
water
rates
will
be
less
severe.
It
is
clear,
however,
that
using
an
imputed
cost
of
capital
may
not
give
an
accurate
assessment
of
the
short­
term
impact
on
water
rates
when
financing
water
system
investments
from
cash
flow.

3.3.3
Unanticipated
Costs
Some
comments
received
by
the
Committee
from
the
City
of
Albuquerque
question
whether
the
costs
of
arsenic
regulation
may
have
understated
the
costs
of
proper
disposal
of
residuals
from
treatment
and
omitted
certain
external
costs
such
as
the
cost
of
road
accidents
caused
by
the
increased
transportation
of
materials
used
in
water
treatment.
To
the
extent
that
significant
external
costs
or
benefits
may
be
incurred
as
the
result
of
arsenic
regulation,
these
should
be
accounted
for
in
the
analysis.

In
this
specific
case,
from
the
information
currently
available
to
us
we
do
not
know
whether
there
would
be
a
significant
external
cost
of
accidents
as
a
result
of
arsenic
regulation.
The
analysis
of
increased
truck
and
car
accidents
presented
by
the
City
of
Albuquerque
used
estimates
of
the
crash,
injury
and
death
rates
per
hundred
million
vehicle
miles
based
on
data
for
1998
statewide
interstate
commercial
truck
traffic,
Albuquerque
truck
traffic,
and
Albuquerque
car
traffic.
We
are
not
able
to
assess
whether
these
are
reliable
estimates
of
the
increase
in
road
accidents
that
could
be
expected
to
occur
as
the
result
of
arsenic
regulation
for
at
least
two
reasons:
(
1)
What
is
needed
is
not
the
average
number
of
accidents
per
vehicle
mile
but
rather
the
marginal
increment
in
accidents
per
increment
in
vehicle
miles;
if
the
ratio
of
accidents
to
vehicle
miles
were
a
constant
it
would
measure
what
is
needed,
but
we
do
not
know
this.
(
2)
We
do
not
know
whether
the
marginal
accident,
injury
and
death
rates
for
an
average
Albuquerque
driver
are
the
same
as
the
marginal
accident
rate
for
drivers
employed
by
the
City
of
Albuquerque
Public
Works
Department.
The
data
presented
by
the
City
of
Albuquerque
do
not
control
for
this
and,
as
with
all
observational
data,
one
needs
to
be
wary
of
potential
confounding
factors
and
omitted
variables.
If
would
be
useful
to
know,
for
example,
whether
the
City
of
20
Albuquerque
has
any
corroborating
data
on
its
existing
experience
with
road
accidents
in
connection
with
the
transportation
of
water
treatment
materials.

3.3.4
Policy
Implications
of
Regulatory
Costs
The
Agency
should
give
some
attention
to
policy
measures
that
could
be
undertaken
to
mitigate
the
financial
impacts
on
smaller
systems
that
lack
economies
of
scale
and
therefore
face
very
high
compliance
costs
per
account.
Implicit
in
the
cost
of
capital
estimates
used
in
the
arsenic
benefits
analysis
are
some
assumptions
about
the
role
of
existing
government
loan
and
grant
programs
in
financing
costs
of
compliance.
The
cost
of
capital
report
assumes
that
these
loan
and
grant
programs
account
for
5%
of
capital
cost
financing
for
publicly
owned
systems
serving
over
50,000,
8%
for
publicly
owned
systems
serving
10,001­
50,000,
26%
for
publicly
owned
systems
serving
501­
10,000,
55%
for
publicly
owned
systems
serving
1­
500,
4%
for
investor
owned
systems
serving
over
10,000,
and
55%
for
private
systems
serving
under
10,000
(
pp
28,
41,
47).
It
would
be
useful
for
the
Agency
to
assess
whether
these
existing
loan
and
grant
programs
will
be
adequate
to
support
the
volume
of
demand
generated
by
the
arsenic
regulations
and
whether
they
need
to
be
supplemented
with
additional
programs
of
financial
assistance.

Other
policy
measures
that
could
be
considered
include
efforts
to
promote
the
consolidation
of
very
small
systems,
or
the
provision
of
bottled
water
by
very
small
systems
to
meet
their
customers'
needs
for
potable
water.
If
the
latter
option
is
considered,
it
would,
of
course,
be
necessary
to
calculate
the
reduction
in
all
drinking
water
contaminants
that
the
provision
of
bottled
water
would
achieve.
R­
1
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A­
1
APPENDICES
APPENDIX
1
­
BACKGROUND
NDWAC
Benefits
Workgroup
Recommendations,
October
1998
The
National
Drinking
Water
Advisory
Council
(
NDWAC)
was
charged
with
providing
EPA
with
recommendations
on
which
benefits
should
be
routinely
considered
in
developing
its
regulations.
They
were
to
address
what
categories
of
benefits
should
be
considered,
how
to
consider
qualitative
benefits,
and
how
to
compare
the
results
of
benefits
assessments
with
cost
analyses.
NDWAC
adopted
the
following
recommendations
from
the
Working
Group:

Recommendation
1:
EPA
should
focus
its
benefits
analysis
efforts
primarily
on
assessing
effects
on
human
health,
defining
these
effects
as
clearly
as
possible
and
using
the
best
available
data
to
value
them.
It
is
also
recommended
that
EPA
consider
1)
health
risk
reductions,
2)
taste
and
odor
improvements,
3)
reduction
in
water
system
materials
damage,
4)
commercial
water
treatment
cost
reductions,
5)
benefits
due
to
source
water
protection,
and
6)
benefits
derived
from
the
provision
of
information
on
drinking
water
quality.

Recommendation
2:
EPA
should
devote
substantial
efforts
to
better
understanding
the
health
effects
of
drinking
water
contaminants,
including
the
types
of
effects,
their
severity
and
affected
sensitive
subpopulations.
Better
information
is
also
needed
on
exposures
and
the
effects
of
different
exposure
levels,
particularly
for
contaminants
with
threshold
effects.
These
efforts
should
pay
particular
attention
to
obtaining
improved
information
concerning
impacts
on
children
and
other
sensitive
populations.

Recommendation
3:
EPA
should
clearly
identify
and
describe
the
uncertainties
in
the
benefits
and
costs
analysis,
including
descriptions
of
factors
that
may
lead
the
analysis
to
significantly
understate
or
overstate
total
benefits
and
costs.
Factors
that
may
have
significant
but
indeterminate
effects
on
the
benefits
and
costs
estimates
should
also
be
described.

Recommendation
4:
EPA
should
consider
both
quantified
and
non­
quantified
benefits
in
regulatory
decision
making.
The
information
about
quantified
and
non­
quantified
(
qualitative)
benefits
should
be
presented
together
in
a
format,
such
as
a
table,
to
ensure
that
decision­
makers
consider
both
kinds
of
information.

Recommendation
5:
EPA
should
consider
incremental
benefits
and
costs,
total
benefits
and
costs,
the
distribution
of
benefits
and
costs,
and
cost­
effectiveness
in
regulatory
decisionmaking
This
information
should
be
presented
together
in
a
format,
such
as
a
table,
to
ensure
its
consideration
by
decision­
makers.

Recommendation
6:
Whenever
EPA
considers
regulation
of
a
drinking
water
contaminant,
it
should
evaluate
and
consider,
along
with
water
treatment
requirements
to
remove
a
contaminant,
source
water
protection
options
to
prevent
such
[
a]
contaminant
from
occurring.
The
full
range
of
benefits
of
those
options
should
be
considered.
A­
2
APPENDIX
2
Appendix
2.1
Supplemental
Information
to
Charge
Question
1
Estimates
of
latency
can
be
approached
by
developing
classical
Armitage­
Doll
multi­
stage
models
of
the
morbidity
and
mortality
from
various
cancers
in
the
U.
S.
population
and
then
exploring
mathematically
the
expected
distributions
of
times
to
diagnosis
and
death
from
various
cancers,
making
various
plausible
assumptions
about
where
arsenic
might
act
in
the
sequence
of
genetic
changes
leading
to
the
different
cancers.
Recent
(
1994­
98)
U.
S.
morbidity
and
mortality
data
for
different
cancers
are
available
from
the
"
SEER"
program
[
Ries,
L.
A.
G.,
Eisner,
M.
P.,
Kosary,
C.
L.
Hankey,
B.
F.,
Miller,
B.
A.,
Clegg,
L.,
and
Edwards,
B.
K.
(
2001)
SEER
Cancer
Statistics
Review
1973­
1998,
National
Cancer
Institute,
Bethesda,
Md.].

The
most
straightforward
approach
to
specifying
the
models
is
to
do
a
simple
set
of
weighted
regression
analyses
to
these
data
of
the
form:

Log(
Incidence
or
Mortality
Rate
in
cases/
100,000
population
per
year)
=
k*
Log(
Age
 
L)
+
b
In
this
equation,
L
is
a
lag
period
that
represents
the
typical
time
between
the
unobserved
birth
of
the
first
cancer
cell
and
either
cancer
diagnosis
or
cancer
death
(
for
morbidity
v.
mortality
data,
respectively),
and
k
+
1
is
the
number
of
"
stages"
(
sequential
genetic
changes)
in
the
cancer
model.
Some
fits
derived
from
the
data
from
Taiwan
are
contained
in
Attachment
1.
The
"
U.
S.
incidence
data"
worksheet
contains
SEER
incidence
and
mortality
data
for
lung
and
bladder
cancer
for
each
sex,
but
the
model
fitting
has
not
yet
been
done.
The
"
5­
stage
male
smoker"
worksheet
(
see
Attachment
2)
shows
an
example
of
a
5­
stage
lung
cancer
model
created
several
years
ago
to
represent
the
expected
time
pattern
of
development
of
lung
cancer
in
smokers
who
began
smoking
at
age
13.
[
See
Hattis,
D.,
and
Silver,
K.
"
Use
of
Mechanistic
Data
in
Occupational
Health
Risk
Assessment­­
The
Example
of
Diesel
Particulates,"
in
Chemical
Risk
Assessment
and
Occupational
Health­­
Current
Applications,
Limitations,
and
Future
Prospects,
C.
Mark
Smith,
David
C.
Christiani,
and
Karl
T.
Kelsey,
eds.,
Greenwood
Publishing
Group,
Inc.,
Westport
CT
1994,
pp.
167­
177
for
an
example
of
prior
use
of
this
approach]

Such
a
model
makes
it
straightforward
to
explore
the
implications
of
different
assumptions
about
which
stages
are
affected
by
arsenic
exposures.
Additional
data
available
in
the
literature
may
help
judge
the
relative
likelihood
of
different
stage­
of­
action
assumptions.
In
addition
to
the
Chen
et
al.
(
1991)
paper
cited
above,
the
following
by
Tsai
et
al.
(
1998)
might
be
useful
in
estimating
the
rates
at
which
risks
for
various
health
effects
might
decrease
when
exposure
is
decreased
[
Tsai,
SM,
Wang,
TN,
and
Ko,
YC.
Cancer
mortality
trends
in
a
blackfoot
disease
endemic
community
of
Taiwan
following
water
source
replacement.
J.
Toxicol
Environ.
Health
55(
6):
389­
404
1998].
It
is
important
that
the
latent
benefits
from
lowering
exposure
to
individuals
that
have
had
prior
arsenic
exposure
be
estimated
utilizing
the
same
model
utilized
to
estimate
potency.
Mode
of
action
has
implications
for
how
rapidly
and
completely
the
effects
in
the
exposed
population
are
reversed
as
it
does
when
exposure
increases
to
increase
the
risk
of
cancer.
Thus,
it
is
important
to
be
consistent
in
the
utilization
of
mode
of
action
information
in
the
final
treatment
of
risks.

As
indicated
above,
in
the
ideal
circumstance
there
needs
to
be
some
consideration
or
at
least
acknowledgment
of
the
different
ages
at
the
time
the
rule
is
put
into
effect.
Benefits
will
accrue
over
a
lifetime
for
children
conceived
after
treatment
is
instituted.
However,
at
that
moment
there
will
be
people
of
different
ages
who
will
gain
some
benefit.
Benefits
to
these
individuals
A­
3
could
be
significantly
larger
if
arsenic
were
largely
a
late­
stage
carcinogen.
This
appears
to
be
the
basis
of
the
reduction
in
lifetime
risks
associated
with
discontinuation
of
smoking
even
after
several
years.
Arsenic
produces
a
variety
of
effects
at
the
molecular
and
cellular
level
that
can
contribute
to
cancer
risk.
It
is
probable
that
there
will
be
insufficient
data
to
come
to
hard
conclusions
about
how
different
modes
of
action
are
contributing
to
the
cancer
incidence
at
different
doses
or
dose
rates.
Because
the
experimental
data
(
i.
e.
mechanistic
data)
that
is
available
today
indicate
the
possibility
of
several
distinctly
different
modes
of
action
with
different
metabolic
forms
of
arsenic
at
different
doses
such
an
exercise
will
be
viewed
as
being
highly
speculative
by
scientists.
Thus,
unless
more
certainty
can
be
brought
to
the
analysis
than
was
apparent
in
the
Panel's
brief
review
of
the
literature,
it
is
suggested
that
such
analyses
be
confined
to
the
uncertainty
analysis
as
it
has
the
distinct
possibility
of
confusing
the
more
straightforward
derivation
of
latency
information
from
existing
data.
It
is
strongly
suggested
that
the
sophistication
of
the
methodology
applied
be
limited
by
and
consistent
with
recommendations
of
the
National
Research
Council
(
NRC)
panel,
which
has
been
charged
with
making
recommendations
on
the
risk
assessment
methodology
that
should
be
used.
A­
4
Attachment
1
to
Appendix
2.1
US
Incidence
and
Mortality
data
for
various
cancers
All
incidence
and
mortality
rates
are
for
1994­
98,
obtained
from
SEER
website
(
Ries,
L.
A.
G.,
Eisner,
M.
P.,
Kosary,
C.
L.
Hankey,
B.
F.,
Miller,
B.
A.,
Clegg,
L.,
and
Edwards,
B.
K.
(
2001)
SEER
Cancer
Statistics
Review
1973­
1998,
National
Cancer
Institute,
Bethesda,
Md.)

U.
S.
Population
data
for
1995
by
age:

Age
group
Male
Female
all
15+
98,760,045
106,269,617
15­
24
18,352,667
17,594,592
25­
34
20,431,905
20,441,238
35­
44
21,061,700
21,406,031
45­
54
15,181,658
15,897,104
55­
64
10,044,054
11,087,025
65­
74
8,342,097
10,417,067
75+
4,346,564
9,426,584
Interpolated
5­
year
age
groups
beginning
at
various
ages:

Age
midpoint
of
range
Age
group
Male
pop
Female
pop
15
9,176,334
8,797,296
22.4970298
20
9,176,334
8,797,296
27.4969777
25
10,215,953
10,220,619
32.4961483
30
10,215,953
10,220,619
37.495086
35
10,530,850
10,703,016
42.4935055
40
10,530,850
10,703,016
47.491253
45
7,590,829
7,948,552
52.4873845
50
7,590,829
7,948,552
57.4814267
55
5,022,027
5,543,513
62.4719425
60
5,022,027
5,543,513
67.462545
65
4,171,049
5,208,534
72.4506775
70
4,171,049
5,208,534
77.4426083
75
1,243,504
2,717,212
82.437223
80
1,071,621
2,363,956
91.7883669
85
2,031,439
4,345,416
A­
5
Age
group
Male
blad
inc
per
100K
Male
blad
cases
Log(
Male
blad
inc/
100K)
Female
blad
inc
Male
blad
mort
Female
blad
mort
15
.
.
.
.
.
.
20
.
.
.
.
.
.
25
0.8
82
­
0.096910
30
1.3
133
0.113943
0.1
35
3.1
326
0.491362
0.9
0.2
0.1
40
6.2
653
0.792392
2
0.5
0.2
45
13.5
1025
1.130334
4
1.1
0.5
50
26.8
2034
1.428135
9.1
2.7
0.9
55
50.2
2521
1.700704
14.4
5.5
1.8
60
83.9
4213
1.923762
23.8
10.5
3.4
65
138.7
5785
2.142076
32.8
19.7
5.9
70
191.8
8000
2.282849
50.3
33.3
10.1
75
237.8
2957
2.376212
57.8
52.1
15.6
80
286.8
3073
2.457579
67.7
82.7
25.7
85
296.6
6025
2.472171
75
135.1
41.7
Age
group
Male
kidney
inc
Female
kidney
inc
Male
kidney
mort
Female
kidney
mort
15
.
.
0.1
0.1
20
.
.
0.1
0.1
25
0.6
0.1
0.1
30
1.2
1.1
0.2
0.2
35
3
1.9
0.6
0.3
40
6.8
3.4
1.6
0.7
45
13.2
6.1
3.6
1.5
50
22.2
10.7
7
2.8
55
35.1
16.2
11.4
4.8
60
45.1
21.6
16.7
7.3
65
55.8
29.3
22.3
10
70
71.5
32.9
28
13.4
75
72.7
37.3
34.8
16.6
80
70.8
38.7
41.1
21.3
85
71.7
32.9
48.5
24.3
A­
6
Age
group
Male
liver
inc
Female
liver
inc
Male
liver
mort
Female
liver
mort
15
0.1
0.1
20
0.1
0.1
25
0.2
0.1
30
0.3
0.2
35
1.3
0.5
0.8
0.3
40
3.1
0.8
2.1
0.7
45
6.8
1.6
4.4
1.3
50
8.8
2.9
6.6
2.4
55
15.2
4.2
10.3
3.9
60
21.6
7.1
17
6.5
65
29.1
9.5
23.1
10.1
70
35.3
13.7
30.9
14
75
39.4
18
36.6
18.4
80
36.5
20.4
43.6
22.8
85
39.9
19.7
45.4
26.7
Age
group
Male
lung
inc
Female
lung
inc
Male
lung
mort
Female
lung
mort
15
20
0.1
25
0.6
0.3
0.2
30
1.8
1.5
1.1
0.9
35
5.1
4.8
3.6
3
40
13.3
10.2
11
7.4
45
31.9
26.1
27.4
17.6
50
76
57.3
67.1
40.7
55
151.7
104.6
133.6
76.5
60
256
166.4
237
126.8
65
389
235
357
180.9
70
508
287.3
471.1
230.6
75
556.3
294.5
525.7
247.1
80
553.6
268.4
577
243.3
85
448.3
171.9
521.3
185.5
A­
7
Attachment
2
Appendix
2.1
Example
of
a
5­
Stage
Multistage
Model,
Tuned
to
Represent
The
Influence
of
Smoking
at
Stages
1
and
4
(
Observed
data
quoted
by
Whittemore
for
U.
S.
Veterans
study)
Part
I
All
smokers
Age
Lung
Cancer
Cases
Observed
Lung
cancer
(
Hundreds
of
Personyears
at
risk)
Model
predicted
Incidence
per
100,000
Model
Predicted
Cases
Expected
Chi^
2
Average
Cigarettes
Per
Day
Smoking/
Average
Background
mutation
rate
0.000181965
Smoking
increment
to
stage
1
mut
rate
0.000432
35
6
1127
19.09
21.52
1.12E+
01
20.93
1.05
(
stage
1
and
4,
begin
age
13)
45
14
342
63.46
21.70
2.73E+
00
21.65
1.08
(
Stage
4
effect
is
2X
stage1
effect)
55
522
3195
150.09
479.55
3.76E+
00
20.57
1.03
65
527
1977
280.90
555.33
1.45E+
00
18.49
0.92
75
30
72
440.52
31.72
9.30E­
02
16.03
0.80
Total:
19.22050
20.02
A­
8
Attachment
2
Appendix
2.1
(
Continued)

Example
of
a
5­
Stage
Multistage
Model,
Tuned
to
Represent
The
Influence
of
Smoking
at
Stages
1
and
4
(
Observed
data
quoted
by
Whittemore
for
U.
S.
Veterans
study)
(
Table
Continued)
Part
II
Numbers
of
Susceptible
Lung
Cells
In
Various
Stages:

Age
(
Year)
Stage
0
Stage
1
Stage
2
Stage
3
Stage
4
Stage
5
(
tumor
hits)
For
age+
5
Fraction
of
People
with
Tumors
Incidence
Per
Year
Per
100,000
0
2.00E+
09
0.00E+
00
0.00E+
00
0.00E+
00
0.00E+
00
0
0
0.5
2.00E+
09
1.82E+
05
0.00E+
00
0.00E+
00
0.00E+
00
0
0
1
2.00E+
09
3.64E+
05
1.66E+
01
0.00E+
00
0.00E+
00
0
0
1.5
2.00E+
09
5.46E+
05
4.97E+
01
1.51E­
03
0.00E+
00
0
0
2
2.00E+
09
7.28E+
05
9.93E+
01
6.02E­
03
1.37E­
07
0
0
2.5
2.00E+
09
9.09E+
05
1.66E+
02
1.51E­
02
6.85E­
07
1.25e­
11
1.2469E­
11
3
2.00E+
09
1.09E+
06
2.48E+
02
3.01E­
02
2.06E­
06
7.48e­
11
7.4805E­
11
3.5
2.00E+
09
1.27E+
06
3.48E+
02
5.27E­
02
4.80E­
06
2.62e­
10
2.6179E­
10
4
2.00E+
09
1.45E+
06
4.63E+
02
8.43E­
02
9.59E­
06
6.98e­
10
6.9805E­
10
4.5
2.00E+
09
1.64E+
06
5.96E+
02
1.26E­
01
1.73E­
05
1.57e­
09
1.5705E­
09
5
2.00E+
09
1.82E+
06
7.44E+
02
1.81E­
01
2.88E­
05
3.14e­
09
3.1407E­
09
5.5
2.00E+
09
2.00E+
06
9.10E+
02
2.48E­
01
4.52E­
05
5.76e­
09
5.7574E­
09
6
2.00E+
09
2.18E+
06
1.09E+
03
3.31E­
01
6.78E­
05
9.87e­
09
9.8689E­
09
6.5
2.00E+
09
2.36E+
06
1.29E+
03
4.30E­
01
9.79E­
05
0
1.6035E­
08
7
2.00E+
09
2.54E+
06
1.50E+
03
5.48E­
01
1.37E­
04
0
2.4942E­
08
7.5
2.00E+
09
2.73E+
06
1.74E+
03
6.85E­
01
1.87E­
04
0
3.7409E­
08
8
2.00E+
09
2.91E+
06
1.98E+
03
8.43E­
01
2.49E­
04
0
5.4409E­
08
8.5
2.00E+
09
3.09E+
06
2.25E+
03
1.02E+
00
3.26E­
04
0
7.7072E­
08
9
2.00E+
09
3.27E+
06
2.53E+
03
1.23E+
00
4.19E­
04
0
1.0671E­
07
9.5
2.00E+
09
3.45E+
06
2.83E+
03
1.46E+
00
5.30E­
04
0
1.448E­
07
10
2.00E+
09
3.63E+
06
3.14E+
03
1.71E+
00
6.63E­
04
0
1.9305E­
07
10.5
2.00E+
09
3.81E+
06
3.47E+
03
2.00E+
00
8.19E­
04
0
2.5336E­
07
11
2.00E+
09
4.00E+
06
3.82E+
03
2.32E+
00
1.00E­
03
0
3.2784E­
07
11.5
2.00E+
09
4.18E+
06
4.18E+
03
2.66E+
00
1.21E­
03
0
4.1887E­
07
12
2.00E+
09
4.36E+
06
4.56E+
03
3.04E+
00
1.45E­
03
0
5.2905E­
07
12.5
2.00E+
09
4.54E+
06
4.96E+
03
3.46E+
00
1.73E­
03
0
6.6125E­
07
13
1.99E+
09
5.15E+
06
5.37E+
03
3.91E+
00
3.54E­
03
0
8.1862E­
07
A­
9
13.5
1.99E+
09
5.76E+
06
5.84E+
03
4.39E+
00
5.58E­
03
0
1.1405E­
06
14
1.99E+
09
6.37E+
06
6.36E+
03
4.92E+
00
7.88E­
03
0
1.6482E­
06
14.5
1.99E+
09
6.99E+
06
6.94E+
03
5.50E+
00
1.05E­
02
0
2.3648E­
06
15
1.99E+
09
7.60E+
06
7.58E+
03
6.13E+
00
1.33E­
02
0
3.3155E­
06
15.5
1.99E+
09
8.21E+
06
8.27E+
03
6.81E+
00
1.65E­
02
0
4.5276E­
06
16
1.99E+
09
8.82E+
06
9.01E+
03
7.56E+
00
2.01E­
02
0
6.031E­
06
16.5
1.99E+
09
9.43E+
06
9.81E+
03
8.38E+
00
2.40E­
02
0
7.8582E­
06
17
1.99E+
09
1.00E+
07
1.07E+
04
9.27E+
00
2.84E­
02
0
1.0045E­
05
17.5
1.99E+
09
1.06E+
07
1.16E+
04
1.02E+
01
3.33E­
02
0
1.263E­
05
18
1.99E+
09
1.13E+
07
1.26E+
04
1.13E+
01
3.86E­
02
0
1.5655E­
05
18.5
1.99E+
09
1.19E+
07
1.36E+
04
1.24E+
01
4.45E­
02
0
1.9167E­
05
19
1.99E+
09
1.25E+
07
1.47E+
04
1.36E+
01
5.10E­
02
0
2.3214E­
05
19.5
1.99E+
09
1.31E+
07
1.58E+
04
1.50E+
01
5.81E­
02
0
2.7852E­
05
20
1.99E+
09
1.37E+
07
1.70E+
04
1.64E+
01
6.60E­
02
0
3.3138E­
05
20.5
1.99E+
09
1.43E+
07
1.82E+
04
1.79E+
01
7.45E­
02
0
3.9135E­
05
21
1.99E+
09
1.49E+
07
1.95E+
04
1.96E+
01
8.39E­
02
0
4.5912E­
05
21.5
1.98E+
09
1.55E+
07
2.09E+
04
2.13E+
01
9.41E­
02
0
5.354E­
05
22
1.98E+
09
1.61E+
07
2.23E+
04
2.32E+
01
1.05E­
01
0
6.2099E­
05
22.5
1.98E+
09
1.67E+
07
2.38E+
04
2.53E+
01
1.17E­
01
0
7.1672E­
05
23
1.98E+
09
1.73E+
07
2.53E+
04
2.74E+
01
1.31E­
01
0
8.2349E­
05
23.5
1.98E+
09
1.80E+
07
2.68E+
04
2.97E+
01
1.45E­
01
0
9.4225E­
05
24
1.98E+
09
1.86E+
07
2.85E+
04
3.21E+
01
1.60E­
01
0
0.0001074
24.5
1.98E+
09
1.92E+
07
3.02E+
04
3.47E+
01
1.77E­
01
0
0.00012199
25
1.98E+
09
1.98E+
07
3.19E+
04
3.74E+
01
1.95E­
01
0
0.0001381
25.5
1.98E+
09
2.04E+
07
3.37E+
04
4.03E+
01
2.15E­
01
0
0.00015586
26
1.98E+
09
2.10E+
07
3.56E+
04
4.33E+
01
2.36E­
01
0
0.0001754
26.5
1.98E+
09
2.16E+
07
3.75E+
04
4.66E+
01
2.59E­
01
0
0.00019684
27
1.98E+
09
2.22E+
07
3.94E+
04
4.99E+
01
2.83E­
01
0
0.00022035
27.5
1.98E+
09
2.28E+
07
4.14E+
04
5.35E+
01
3.09E­
01
0
0.00024607
28
1.98E+
09
2.34E+
07
4.35E+
04
5.72E+
01
3.37E­
01
0
0.00027415
28.5
1.98E+
09
2.40E+
07
4.56E+
04
6.12E+
01
3.67E­
01
0
0.00030478
29
1.98E+
09
2.46E+
07
4.78E+
04
6.53E+
01
3.99E­
01
0
0.00033812
29.5
1.97E+
09
2.52E+
07
5.00E+
04
6.96E+
01
4.33E­
01
0
0.00037436
30
1.97E+
09
2.58E+
07
5.23E+
04
7.41E+
01
4.69E­
01
0
0.0004137
30.5
1.97E+
09
2.64E+
07
5.47E+
04
7.88E+
01
5.08E­
01
0
0.00045634
31
1.97E+
09
2.70E+
07
5.71E+
04
8.38E+
01
5.49E­
01
0.001
0.0005025
31.5
1.97E+
09
2.76E+
07
5.95E+
04
8.89E+
01
5.93E­
01
0.001
0.0005524
A­
10
32
1.97E+
09
2.82E+
07
6.20E+
04
9.43E+
01
6.39E­
01
0.001
0.00060626
32.5
1.97E+
09
2.88E+
07
6.46E+
04
9.99E+
01
6.89E­
01
0.001
0.00066435
33
1.97E+
09
2.94E+
07
6.72E+
04
1.06E+
02
7.41E­
01
0.001
0.0007269
33.5
1.97E+
09
3.00E+
07
6.99E+
04
1.12E+
02
7.96E­
01
0.001
0.00079419
34
1.97E+
09
3.06E+
07
7.26E+
04
1.18E+
02
8.54E­
01
0.001
0.00086649
34.5
1.97E+
09
3.12E+
07
7.54E+
04
1.25E+
02
9.16E­
01
0.001
0.00094408
35
1.97E+
09
3.18E+
07
7.82E+
04
1.31E+
02
9.81E­
01
0.001
0.00102726
35.5
1.97E+
09
3.24E+
07
8.11E+
04
1.38E+
02
1.05E+
00
0.0011
0.00111635
36
1.97E+
09
3.30E+
07
8.41E+
04
1.46E+
02
1.12E+
00
0.0012
0.00121165
36.5
1.97E+
09
3.36E+
07
8.71E+
04
1.53E+
02
1.20E+
00
0.0013
0.00131351
37
1.97E+
09
3.42E+
07
9.01E+
04
1.61E+
02
1.28E+
00
0.0014
0.00142227
37.5
1.97E+
09
3.49E+
07
9.32E+
04
1.69E+
02
1.36E+
00
0.0015
0.00153828
38
1.96E+
09
3.55E+
07
9.64E+
04
1.78E+
02
1.45E+
00
0.0017
0.00166191
38.5
1.96E+
09
3.60E+
07
9.96E+
04
1.86E+
02
1.54E+
00
0.0018
0.00179355
39
1.96E+
09
3.66E+
07
1.03E+
05
1.95E+
02
1.64E+
00
0.0019
0.00193359
39.5
1.96E+
09
3.72E+
07
1.06E+
05
2.05E+
02
1.74E+
00
0.0021
0.00208243
40
1.96E+
09
3.78E+
07
1.10E+
05
2.14E+
02
1.85E+
00
0.0022
0.00224049
18.2678851
40.5
1.96E+
09
3.84E+
07
1.13E+
05
2.24E+
02
1.96E+
00
0.0024
0.00240821
41
1.96E+
09
3.90E+
07
1.17E+
05
2.34E+
02
2.08E+
00
0.0026
0.00258604
41.5
1.96E+
09
3.96E+
07
1.20E+
05
2.45E+
02
2.20E+
00
0.0028
0.00277443
42
1.96E+
09
4.02E+
07
1.24E+
05
2.55E+
02
2.33E+
00
0.003
0.00297386
42.5
1.96E+
09
4.08E+
07
1.27E+
05
2.67E+
02
2.46E+
00
0.0032
0.00318482
43
1.96E+
09
4.14E+
07
1.31E+
05
2.78E+
02
2.60E+
00
0.0034
0.00340782
43.5
1.96E+
09
4.20E+
07
1.35E+
05
2.90E+
02
2.75E+
00
0.0037
0.00364335
44
1.96E+
09
4.26E+
07
1.39E+
05
3.02E+
02
2.90E+
00
0.0039
0.00389197
44.5
1.96E+
09
4.32E+
07
1.42E+
05
3.14E+
02
3.06E+
00
0.0042
0.00415421
45
1.96E+
09
4.38E+
07
1.46E+
05
3.27E+
02
3.22E+
00
0.0044
0.00443064
45.5
1.96E+
09
4.44E+
07
1.50E+
05
3.40E+
02
3.39E+
00
0.0047
0.00472182
46
1.95E+
09
4.50E+
07
1.54E+
05
3.54E+
02
3.57E+
00
0.005
0.00502836
46.5
1.95E+
09
4.56E+
07
1.58E+
05
3.68E+
02
3.75E+
00
0.0054
0.00535085
47
1.95E+
09
4.62E+
07
1.63E+
05
3.82E+
02
3.94E+
00
0.0057
0.00568991
47.5
1.95E+
09
4.68E+
07
1.67E+
05
3.96E+
02
4.14E+
00
0.0061
0.00604619
48
1.95E+
09
4.74E+
07
1.71E+
05
4.11E+
02
4.35E+
00
0.0064
0.00642032
48.5
1.95E+
09
4.80E+
07
1.75E+
05
4.27E+
02
4.57E+
00
0.0068
0.00681298
49
1.95E+
09
4.86E+
07
1.80E+
05
4.42E+
02
4.79E+
00
0.0073
0.00722485
49.5
1.95E+
09
4.92E+
07
1.84E+
05
4.59E+
02
5.02E+
00
0.0077
0.00765663
50
1.95E+
09
4.98E+
07
1.89E+
05
4.75E+
02
5.26E+
00
0.0081
0.00810902
58.6853364
A­
11
50.5
1.95E+
09
5.04E+
07
1.93E+
05
4.92E+
02
5.51E+
00
0.0086
0.00858276
51
1.95E+
09
5.10E+
07
1.98E+
05
5.09E+
02
5.76E+
00
0.0091
0.00907859
51.5
1.95E+
09
5.16E+
07
2.02E+
05
5.27E+
02
6.03E+
00
0.0096
0.00959727
52
1.95E+
09
5.22E+
07
2.07E+
05
5.45E+
02
6.30E+
00
0.0102
0.01013957
52.5
1.95E+
09
5.27E+
07
2.12E+
05
5.64E+
02
6.59E+
00
0.0108
0.01070629
53
1.95E+
09
5.33E+
07
2.16E+
05
5.83E+
02
6.88E+
00
0.0114
0.01129822
53.5
1.95E+
09
5.39E+
07
2.21E+
05
6.02E+
02
7.19E+
00
0.012
0.01191619
54
1.95E+
09
5.45E+
07
2.26E+
05
6.22E+
02
7.50E+
00
0.0126
0.01256105
54.5
1.94E+
09
5.51E+
07
2.31E+
05
6.42E+
02
7.83E+
00
0.0133
0.01323363
55
1.94E+
09
5.57E+
07
2.36E+
05
6.63E+
02
8.16E+
00
0.014
0.0139348
55.5
1.94E+
09
5.63E+
07
2.41E+
05
6.84E+
02
8.51E+
00
0.0148
0.01466546
56
1.94E+
09
5.69E+
07
2.46E+
05
7.05E+
02
8.86E+
00
0.0155
0.01542649
56.5
1.94E+
09
5.75E+
07
2.51E+
05
7.28E+
02
9.23E+
00
0.0164
0.01621882
57
1.94E+
09
5.81E+
07
2.57E+
05
7.50E+
02
9.61E+
00
0.0172
0.01704335
57.5
1.94E+
09
5.87E+
07
2.62E+
05
7.73E+
02
1.00E+
01
0.0181
0.01790105
58
1.94E+
09
5.93E+
07
2.67E+
05
7.96E+
02
1.04E+
01
0.019
0.01879285
58.5
1.94E+
09
5.98E+
07
2.73E+
05
8.20E+
02
1.08E+
01
0.0199
0.01971974
59
1.94E+
09
6.04E+
07
2.78E+
05
8.45E+
02
1.13E+
01
0.0209
0.0206827
59.5
1.94E+
09
6.10E+
07
2.83E+
05
8.69E+
02
1.17E+
01
0.0219
0.02168272
60
1.94E+
09
6.16E+
07
2.89E+
05
8.95E+
02
1.21E+
01
0.023
0.02272081
146.117854
60.5
1.94E+
09
6.22E+
07
2.95E+
05
9.21E+
02
1.26E+
01
0.0241
0.023798
61
1.94E+
09
6.28E+
07
3.00E+
05
9.47E+
02
1.31E+
01
0.0252
0.02491532
61.5
1.94E+
09
6.34E+
07
3.06E+
05
9.74E+
02
1.36E+
01
0.0264
0.02607383
62
1.94E+
09
6.40E+
07
3.12E+
05
1.00E+
03
1.41E+
01
0.0277
0.02727457
62.5
1.94E+
09
6.46E+
07
3.17E+
05
1.03E+
03
1.46E+
01
0.0289
0.02851864
63
1.93E+
09
6.51E+
07
3.23E+
05
1.06E+
03
1.52E+
01
0.0303
0.02980709
63.5
1.93E+
09
6.57E+
07
3.29E+
05
1.09E+
03
1.57E+
01
0.0316
0.03114104
64
1.93E+
09
6.63E+
07
3.35E+
05
1.12E+
03
1.63E+
01
0.0331
0.03252158
64.5
1.93E+
09
6.69E+
07
3.41E+
05
1.15E+
03
1.69E+
01
0.0345
0.03394983
65
1.93E+
09
6.75E+
07
3.47E+
05
1.18E+
03
1.75E+
01
0.0361
0.03542691
65.5
1.93E+
09
6.81E+
07
3.53E+
05
1.21E+
03
1.81E+
01
0.0377
0.03695394
66
1.93E+
09
6.87E+
07
3.59E+
05
1.24E+
03
1.87E+
01
0.0393
0.03853207
66.5
1.93E+
09
6.93E+
07
3.66E+
05
1.27E+
03
1.93E+
01
0.041
0.04016245
67
1.93E+
09
6.98E+
07
3.72E+
05
1.30E+
03
2.00E+
01
0.0427
0.04184622
67.5
1.93E+
09
7.04E+
07
3.78E+
05
1.34E+
03
2.07E+
01
0.0446
0.04358453
68
1.93E+
09
7.10E+
07
3.85E+
05
1.37E+
03
2.14E+
01
0.0464
0.04537857
68.5
1.93E+
09
7.16E+
07
3.91E+
05
1.40E+
03
2.21E+
01
0.0484
0.04722948
A­
12
69
1.93E+
09
7.22E+
07
3.97E+
05
1.44E+
03
2.28E+
01
0.0504
0.04913844
69.5
1.93E+
09
7.28E+
07
4.04E+
05
1.47E+
03
2.36E+
01
0.0525
0.05110663
70
1.93E+
09
7.34E+
07
4.11E+
05
1.51E+
03
2.43E+
01
0.0546
0.05313521
304.144013
70.5
1.93E+
09
7.39E+
07
4.17E+
05
1.55E+
03
2.51E+
01
0.0568
0.05522537
71
1.93E+
09
7.45E+
07
4.24E+
05
1.58E+
03
2.59E+
01
0.0591
0.05737827
71.5
1.92E+
09
7.51E+
07
4.31E+
05
1.62E+
03
2.68E+
01
0.0614
0.05959509
72
1.92E+
09
7.57E+
07
4.37E+
05
1.66E+
03
2.76E+
01
0.0639
0.06187701
72.5
1.92E+
09
7.63E+
07
4.44E+
05
1.70E+
03
2.85E+
01
0.0664
0.06422518
73
1.92E+
09
7.69E+
07
4.51E+
05
1.74E+
03
2.94E+
01
0.069
0.06664078
73.5
1.92E+
09
7.74E+
07
4.58E+
05
1.78E+
03
3.03E+
01
0.0716
0.06912496
74
1.92E+
09
7.80E+
07
4.65E+
05
1.82E+
03
3.12E+
01
0.0744
0.07167888
74.5
1.92E+
09
7.86E+
07
4.72E+
05
1.86E+
03
3.21E+
01
0.0772
0.07430367
75
1.92E+
09
7.92E+
07
4.79E+
05
1.90E+
03
3.31E+
01
0.0801
0.07700048
75.5
1.92E+
09
7.98E+
07
4.86E+
05
1.95E+
03
3.41E+
01
0.0831
0.07977041
76
1.92E+
09
8.04E+
07
4.94E+
05
1.99E+
03
3.51E+
01
0.0862
0.0826146
76.5
1.92E+
09
8.09E+
07
5.01E+
05
2.03E+
03
3.62E+
01
0.0894
0.08553413
77
1.92E+
09
8.15E+
07
5.08E+
05
2.08E+
03
3.72E+
01
0.0927
0.08853009
77.5
1.92E+
09
8.21E+
07
5.16E+
05
2.12E+
03
3.83E+
01
0.0961
0.09160355
78
1.92E+
09
8.27E+
07
5.23E+
05
2.17E+
03
3.94E+
01
0.0996
0.09475555
78.5
1.92E+
09
8.33E+
07
5.31E+
05
2.21E+
03
4.05E+
01
0.103
0.09798714
79
1.92E+
09
8.38E+
07
5.38E+
05
2.26E+
03
4.17E+
01
0.107
0.10129931
79.5
1.92E+
09
8.44E+
07
5.46E+
05
2.31E+
03
4.29E+
01
0.111
0.10469307
80
1.91E+
09
8.50E+
07
5.53E+
05
2.36E+
03
4.41E+
01
0.114
0.10816936
550.341531
80.5
1.91E+
09
8.56E+
07
5.61E+
05
2.41E+
03
4.53E+
01
0.118
0.11172914
81
1.91E+
09
8.62E+
07
5.69E+
05
2.46E+
03
4.66E+
01
0.123
0.11537332
81.5
1.91E+
09
8.67E+
07
5.76E+
05
2.51E+
03
4.78E+
01
0.127
0.11910277
82
1.91E+
09
8.73E+
07
5.84E+
05
2.56E+
03
4.91E+
01
0.131
0.12291836
82.5
1.91E+
09
8.79E+
07
5.92E+
05
2.61E+
03
5.05E+
01
0.136
0.1268209
A­
13
APPENDIX
2.2
Supplement
to
Charge
Question
2
Studies
addressing
the
major
categories
of
concern
at
lower
exposure
levels
are
listed
in
the
tables
(
which
are
not
comprehensive,
but
rather,
representative).
These
studies
demonstrate
a
broad
array
of
related
endpoints
and
indicate
the
range
and
weight
of
evidence,
qualitatively,
as
well
as
the
consistency
with
which
these
effects
are
related
to
arsenic
exposure.
Such
consistency,
particularly
when
at
least
some
of
the
studies
are
of
high
quality
and
have
adjusted
for
individual­
level
confounders,
strengthens
the
evidence
for
causality.

I.
Human
morbidity
studies
of
cardiovascular
endpoints
Outcome
Authors/
year
&
location
Design
Exposure
assessment
Dose­
response
analysis:
Measure
of
association
Range
of
exposures
Cerebrovascular
disease/
cerebral
infarction
Chiou
et
al.
1997
Taiwan
Retrospective
cohort
Cumulative
exposure
Avg
concentr'n
in
H
2
O
Significant;
adjusted
for
age,
sex,
cigarettes,
alcohol
Odds
ratio
<
0.1,
0.1­
4.9,
>
5.0
mg/
L­
year;
<
0.1,
0.1­
50,
50.1­
2999.9,
>
300
ug/
L
Ischemic
heart
disease
Hsueh
et
al.
1998
Taiwan
Retrospective
cohort
Duration
of
exposure
via
H
2
O
Significant,
adjusted
for
total
cholesterol,
BMI,
hypertension,
serum
"
­
and
$
­
carotene
Odds
ratio
<
13,
13­
29,
>
30
years
drinking
artesian
well
water
Electrocardio­
graphic
abnormalities
Ohnishi
et
al.
2000
Japan
Prospective,
patients
with
promyelocytic
leukemia
As
Tx
for
promyelocytic
leukemia
Prolonged
QT
intervals
in
all
8
patients,
serious
arrhythmias
in
4
­­
15
mg/
kg
for
20­
79
days
Hypertension
Chen
et
al.
1995
Taiwan
Retrospective
cohort
Cumulative
exposure
[
Avg
conc
in
H
2
O]*
Significant;
adjusted
for
age,
sex,
diabetes,
proteinuria,
BMI
Odds
ratio
0,
0.1­
6.3,
6.4­
10.8,
10.9­
14.7
mg/
L­
years;
0,
.01­.
70,
>.
70
mg/
L
"
Rahman
et
al.
1999
Bangladesh
Retrospective
cohort
Cumulative
exposure
Avg
concentr'n
in
H
2
O
Significant;
adjusted
for
age,
sex,
BMI
Prevalence
ratio
0,
<
1.0,
1.0­
5.0,
>
5.0­
10.0
mg/
Lyears
<
0.5,
0.5
to
1.0,
>
1.0
mg/
L
Systolic
blood
pressure
Jensen
&
Hansen
1998
Denmark
Retrospective
cohort
Job
with
arsenic
exposure,
urinary
As
Difference
in
means
Mean
of
22.3
nmol/
mmol
As
in
creatinine
vs.
12.0
nmol/
mmol
for
referents
Vasospastic
tendency
(
finger
systolic
pressure,
upon
cooling)
Lagerkvist
et
al.
1986
Sweden
X­
sectional
Urinary
As
available
but
not
used­
Estimated
exposure
at
300
ug/
day,
or
4
g
over
23
years
No
dose­
response
analysis
conducted
Difference
in
prevalence
10­
340
ug/
L
(
mean=
70)
in
urine
among
exposed;
5­
20
ug/
L
among
referents,
highest
quartile
had
mean
of
180
ug/
L
A­
14
I.
Human
morbidity
studies
of
cardiovascular
endpoints
(
con't)

Outcome
Authors/
year
&
location
Design
Exposure
assessment
Dose­
response
analysis:
Measure
of
association
Range
of
exposures
Blackfoot
disease**
Chen
et
al.
1988
Taiwan
Retrospective
cohort
Duration
of
exposure
via
H2O
0
(
referent)
1­
29,
>
30
years
drinking
artesian
well
water
Peripheral
vascular
disease***
Tseng
et
al.
1996
Taiwan
Retrospective
cohort
Cumulative
exposure
Duration
well
water
use
Duration
living
in
Bf
area
Significant
in
highest
exposure
group,
adjusted
for
age,
sex,
BMI,
cigarette
smoking,
diabetes
hypertension,
serum
total
cholesterol,
&
triglycerides
Odds
ratio
0
(
referent),
0.1­
19.9,
>
20
mg/
L­
years
0,
1­
19,
20­
29,
>
30
years
drinking
artesian
well
water
Raynaud
phenomenon,
numbness
&
other
symptoms
Lagerkvist
et
al.
1988
Sweden
Time
trend
 
start
to
end
of
vacation
No
dose­
response
analysis
conducted.
Significant
difference
in
numbness
&
other
signs,
Difference
in
prevalence
Exposed:
mean
of
61
ug/
L
urine
von
Willebrand
factor
Gomez­
Caminero
2001
Chile
Prospective
cohort
of
pregnant
women
Exposed
vs.
unexposed
town
Significant
vs.
referents
Difference
in
means,
odds
ratio
for
lowest
tertile
<
2
ug/
L
(
referent),
~
45
ug/
L
(
exposed)

*
The
analysis
for
this
exposure
metric
did
not
adjust
for
all
factors
in
the
next
column
**
Blackfoot
disease
has
been
used
as
an
indicator
of
exposure
to
arsenic
&/
or
susceptibility
to
the
effects
of
arsenic,
due
to
its
close
association
with
elevated
arsenic
exposures.
***
Diagnosed
by
Doppler
ultrasound,
ABI<
0.9
on
either
side
of
extremity
A­
15
II.
Human
mortality
studies
of
cardiovascular
&
renal
endpoints
Outcome
Authors/
year
&
location
Design
Exposure
assessment
Dose­
response
analysis:
Measure
of
association
Range
of
exposures
Circulatory
disease
Tsai
et
al.
1999
Taiwan
Retrospective
cohort
1971­
1994
Townships
with
arsenic
contaminated
water
from
1900'
s
to
mid­
1970'
s
Significant
in
both
sexes,
adjusted
for
age,
calendar
year
Standardized
mortality
ratio
0.78
mg/
L,
referents:
local
county,
and
national
rates
"
Hertz­
Picciotto
et
al,
2000
US
smelter
workers
Retrospective
cohort
Cumulative
occupational
exposure
over
the
worklife
Significant
dose
response
adjusted
for
age,
year
of
hire,
and
the
healthy
worker
survivor
effect
Rate
ratio
<
750
(
referent),
750­
1999,
2000­
3999,
4000­
7999,
8000­
19,999,
>
20,000
ug/
m3
 
years
Cardiovascular
disease
Wu
et
al.
1989
Taiwan
Retrospective
cohort
1973­
1986
Villages
with
arsenic
contaminated
water
Significant,
adjusted
for
age,
sex
Mortality
ratio
<
0.3,
0.3­
0.59,
>.
60
mg/
L
"
Axelson
et
al.
1978
Sweden,
area
around
smelter
Case­
control
Employment
in
exposed
jobs
Significant
dose
response
Mantel­
Haenszel
rate
ratio
Not
employed
at
smelter
(
referent),
employed
at
smelter:
`
close
to'
0.5
mg/
m3
"
Hertz­
Picciotto
et
al,
2000
US
smelter
workers
Retrospective
cohort
Cumulative
occupational
exposure
over
the
worklife
Significant
dose
response
adjusted
for
age,
year
of
hire,
and
the
healthy
worker
survivor
effect
Rate
ratio
<
750
(
referent),
750­
1999,
2000­
3999,
4000­
7999,
8000­
19,999,
>
20,000
ug/
m3
 
years
Ischemic
heart
Disease
Chen
et
al.
1996
Taiwan
Two
prospective
cohorts
1985­
1993,
and
1988­
1995
Avg
concentr'n
in
H2O
Cumulative
exposure
Monotonic
dose
response,
models
adjusted
for
age,
sex,
baseline
BMI,
cigarette
smoking,
serum
cholesterol,
triglycerides,
diabetes,
hypertension,
blackfoot
disease*
Hazard
ratio
from
Cox
proportional
hazards
model
0
(
referent),
0.1­
9.9,
10.0­
19.9,
20.0+
mg/
L
years
"
Tsai
et
al.
1999
Taiwan
Retrospective
cohort
1971­
1994
Townships
with
arsenic
contaminated
water
from
1900'
s
to
mid­
1970'
s
Significant
in
both
sexes,
adjusted
for
age,
calendar
year
Standardized
mortality
ratio
0.78
mg/
L,
referents:
local
county,
and
national
rates
Hypertensive
heart
disease
Lewis
et
al.
1999
Utah,
USA
Retrospective
cohort
Cumulative
exposure.
Means
in
towns
ranged
from
18.1­
164.4
µ
g/
L
Significant
excess
in
men
and
women
Standardized
mortality
ratio
<
1,
1­
4.999,
>
5.0
mg/
L­
years,
range
A­
16
II.
Human
mortality
studies
of
cardiovascular
&
renal
endpoints
(
con't)

Outcome
Authors/
year
&
location
Design
Exposure
assessment
Dose­
response
analysis:
Measure
of
association
Range
of
exposures
Cerebrovascular
disease
Wu
et
al.
1989
Taiwan
Retrospective
cohort
1973­
1986
Villages
with
arsenic
contaminated
water
Significant,
adjusted
for
age,
sex
Mortality
ratio
<
0.3,
0.3­
0.59,
>.
60
mg/
L
"
Tsai
et
al.
1999
Taiwan
Retrospective
cohort
1971­
1994
Townships
with
arsenic
contaminated
water
from
1900'
s
to
mid­
1970'
s
Significant
in
both
sexes,
adjusted
for
age,
calendar
year
Standardized
mortality
ratio
0.78
mg/
L,
referents:
local
county,
and
national
rates
Peripheral
vascular
disease
Wu
et
al.
1989
Taiwan
Retrospective
cohort
1973­
1986
Concentr'n
in
H
in
villages
with
arsenic
contaminated
water
Significant,
adjusted
for
age,
sex
Mortality
ratio
<
0.3,
0.3­
0.59,
>.
60
mg/
L
"
Tsai
et
al.
1999
Taiwan
Retrospective
cohort
1971­
1994
Townships
with
arsenic
contaminated
water
from
1900'
s
to
mid­
1970'
s
No
dose
measure
used,
adjusted
for
age,
sex,
calendar
year
Standardized
mortality
ratio
0.78
mg/
L,
referents:
local
county,
and
national
rates
"
Engel
&
Smith
1994
USA
Ecologic
study
at
the
county
level
Avg
concentr'n
in
H2O
No
clear
monotonic
dose
response,
but
elevated
risk
at
each
level
>
5
µ
g/
L
Standardized
mortality
ratio
5­
10,
10­
20,
>
20
µ
g/
L
Pulmonary
heart
disease
Tsai
et
al.
1999
Taiwan
Retrospective
cohort
1971­
1994
Townships
with
arsenic
contaminated
water
from
1900'
s
to
mid­
1970'
s
No
dose
measure
used,
adjusted
for
age,
sex,
calendar
year
Standardized
mortality
ratio
0.78
mg/
L,
referents:
local
county,
and
national
rates
**
Engel
et
al.
1994
Nephritis,
nephrosis
Tsai
et
al.
1999
Taiwan
Retrospective
cohort
1971­
1994
Townships
with
arsenic
contaminated
water
from
1900'
s
to
mid­
1970'
s
No
dose
measure
used,
adjusted
for
age,
sex,
calendar
year
Standardized
mortality
ratio
0.78
mg/
L,
referents:
local
county,
and
national
rates
"
Lewis
et
al.
1999
Utah,
USA
Retrospective
cohort
Cumulative
exposure.
Means
in
towns
ranged
from
18.1­
164.4
µ
g/
L
Significant
excess
in
men
and
women
Standardized
mortality
ratio
<
1,
1­
4.999,
>
5.0
mg/
L­
years,
range
*
Adjustment
for
Blackfoot
disease
attenuated
but
did
not
eliminate
the
association
of
arsenic
exposure
with
ISHD
**
For
further
mortality
and
morbidity
studies
of
cardiovascular
endpoints,
see
Table
6,
Engel
et
al.
1994.
A­
17
III.
Animal
morbidity
studies
of
cardiovascular
endpoints
Outcome
Authors/
year
Design
Exposure
assessment
Dose­
response
analysis
adjusted
for:
Measure
of
association
Exposure
level
Animal
Studies
Vasoreactivity
Bekemeir
&
Hirschelmann
1989
Experiment
Not
applicable
 
controlled
dosing
Only
one
dose
group
15
mg/
kg,
orally
Vasoreactivity
Carmignano
et
al.
1983
Experiment
"
Only
one
dose
group
50
µ
g/
mL
drinking
water
Potentiation
of
$
­
adrenoreceptor
stimulation
"
"
Only
one
dose
group
Stroke
volume,
cardiac
output
Carmignano
et
al.
1985
Experiment
"
Only
one
dose
group
50
µ
g/
mL
drinking
water
Vasoreactivity*
"
Only
one
dose
group
*
after
administration
of
isoprenaline,
clonidine,
tyramine,
etc.
A­
18
IV.
Human
mortality
and
morbidity
studies
of
endocrinologic/
metabolic
conditions
and
biomarkers
Outcome
Authors/
year
&
location
Design
Exposure
assessment
Dose­
response
analysis:
Measure
of
association
Range
of
exposures
Diabetes
mellitus
mortality
Tsai
et
al.
1999
Taiwan
Retrospective
cohort
1971­
1994
Townships
with
arsenic
contaminated
water
from
1900'
s
to
mid­
1970'
s
No
dose
measure
used,
adjusted
for
age,
sex,
calendar
year
Standardized
mortality
ratio
0.78
mg/
L,
referents:
local
county,
and
national
rates
Diabetes
mellitus
incidence
Lai
et
al.
1994
Taiwan
Retrospective
cohort
Cumulative
exposure
Duration
well
water
use*
Significant,
adjusted
for
age,
sex,
BMI,
physical
activity
Odds
ratio
0
(
referent),
0.1­
15.0,
>
15.1
mg/
L­
yrs;
0
(
referent,
1­
10,
11­
20,
>
21
years
drinking
artesian
well
water
"
Rahman
et
al.
1996
Sweden
Retrospective
cohort
Job
in
glassworks
with
likely
exposure
Significant
in
those
with
highest
exposure,
adjusted
for
age
Odds
ratio
No
quantitation
available
"
Tseng
et
al
2000
Taiwan
Prospective
cohort,
~
2.5
years
follow­
up
Cumulative
exposure
from
H2O
Significant,
adjusted
for
age,
sex,
BMI
Hazard
ratio
from
Cox
model
<
17
mg/
L
years
(
referent),
>
17
mg/
L
years
Glycosylated
hemoglobin
Jensen
&
Hansen
1998
Denmark
Retrospective
cohort
Jobs
with
arsenic
exposure
(
taxidermists,
construction
workers,
wood
&
electric
pylon
impregnators
Significant
vs.
referents
Difference
in
medians
6­
44
nmol/
mmol
urinary
As
in
creatinine
(
referents);
12­
295
nmol/
mmol
(
exposed)

"
Gomez­
Caminero
2001
Chile
Prospective
cohort
of
pregnant
women
Exposed
vs.
unexposed
town
Significant
vs.
referents
Difference
in
means,
odds
ratio
for
>
6.5%
<
2
µ
g/
L
(
referent),
~
45
µ
g/
L
(
exposed)

Glucosuria
Rahman
et
al.
1999
Bangladesh
Retrospective
cohort
Avg
concentr'n
in
H2O
Cumulative
exposure
Significant,
adjusted
for
age
and
sex,
using
cumulative
exposure
Prevalence
ratio
<
0.5,
0.5­
1.0,
>
1.0
mg/
L;
<
1.0,
1.0­
5.0,
>
5.0­
10.0,
>
10.0
mg/
L­
years
Hepatic
function:
bilirubin
excretion,
ALP
activity
Hernandez­
Zavala
et
al.
1998
Mexico
Retrospective
cohort
Mean
water
concentration
in
each
of
three
towns
Significant
differences,
adjusted
for
age,
alcohol,
tobacco,
pesticides
Difference
in
means
Means:
14.0
µ
g/
L
(
referent),
116
µ
g/
L
and
239
µ
g/
L
in
two
exposed
towns
*
The
analysis
for
this
exposure
metric
did
not
adjust
for
all
factors
in
the
next
column
A­
19
V.
Human
studies
of
cancers
other
than
lung
and
bladder
Outcome
Authors/
year
&
location
Design
Exposure
assessment
Dose­
response
analysis:
Measure
of
association
Range
of
exposures
Kidney
cancer
Smith
et
al.
1992
Taiwan
Retrospective
cohort
Cumulative
exposure
in
H2O
Significant,
adjusted
for
age,
sex
Rate
ratio
Liver
cancer
"
"
"
"
"
"
Prostate
cancer
Tsai
et
al.
1999
Taiwan
Retrospective
cohort
1971­
1994
Townships
with
arsenic
contaminated
water
from
1900'
s
to
mid­
1970'
s
Adjusted
for
age,
sex,
calendar
year
Standardized
mortality
ratio
0.78
mg/
L,
referents:
local
county,
and
national
rates
"
Lewis
et
al.
1999
Utah,
USA
Retrospective
cohort
Cumulative
exposure.
Means
in
towns
ranged
from
18.1­
164.4
µ
g/
L
Significant
excess
Standardized
mortality
ratio
<
1,
1­
4.999,
>
5.0
mg/
L­
years,
range
Stomach
cancer*
Tsai
et
al.
1999
Taiwan
Retrospective
cohort
1971­
1994
Townships
with
arsenic
contaminated
water
from
1900'
s
to
mid­
1970'
s
Adjusted
for
age,
sex,
calendar
year
Standardized
mortality
ratio
0.78
mg/
L,
referents:
local
county,
and
national
rates
Colon
cancer*
"
"
"
"
"
"
Rectum
cancer*
"
"
"
"
"
"
Liver
cancer*
"
"
"
"
"
"
Nasal
cancer*
"
"
"
"
"
"
Laryngeal
ca*
"
"
"
"
"
"
Skin
cancer*
"
"
"
"
"
"
Bone
cancer*
"
"
"
"
"
"
Lymphoma*
"
"
"
"
"
"

*
Excess
observed
in
both
genders.
Cancers
found
in
excess
in
only
one
gender
not
included.
A­
20
VI.
Human
morbidity
&
mortality
studies
of
non­
malignant
respiratory
endpoints
Outcome
Authors/
year
&
location
Design
Exposure
assessment
Dose­
response
analysis:
Measure
of
association
Range
of
exposures
Respiratory
effects:
cough,
shortness
of
breath
Mazumder
et
al.
2000
West
Bengal,
India
X­
sectional
Current
concentration
measured
in
well
water
Significant,
adjusted
for
age
&
sex,
smokers
excluded
Prevalence
odds
ratio
<
50,
50­
199,
200­
499,
500­
799,
>
800
µ
g/
L
Bronchitis
Tsai
et
al.
1999
Taiwan
Retrospective
cohort
1971­
1994
Townships
with
arsenic
contaminated
water
from
1900'
s
to
mid­
1970'
s
Adjusted
for
age,
sex,
calendar
year
Standardized
mortality
ratio
0.78
mg/
L,
referents:
local
county,
and
national
rates
Chronic
airways
obstruction
Engel
&
Smith
1994
USA
Ecologic
study
at
county
level
Avg
concentr'n
in
H2O
Adjusted
for
age,
sex,
and
calendar
year
Standardized
mortality
ratio
5­
10,
10­
20,
>
20
µ
g/
L
Emphysema
"
"
"
"
"
"
A­
21
VII.
Human
reproductive
studies
Outcome
Authors/
year
&
location
Design
Exposure
assessment
Dose­
response
analysis:
Measure
of
association
Range
of
exposures
Spontaneous
abortion
Nordstrom
et
al.
1978
Sweden
Retrospective
cohort
of
pregnancies
Residential
proximity
to
a
smelter
Trend
in
frequency
by
distance
of
region
to
smelter
Prevalence
ratio
No
quantitation
"
Nordstrom
et
al.
1979
Sweden
Retrospective
cohort
of
pregnancies
Employment
in
smelter
prior
to
or
during
pregnancy
Highest
prevalence
among
those
living
near
the
smelter
during
or
after
their
employment
Prevalence
ratio
"

"
Borzsonyi
et
al
1992
Hungary
Retrospective
cohort
Concentration
in
H2O
Significant
difference
comparing
high
vs.
low
arsenic
region
Prevalence
rate
difference
Low
(
not
quantitated
referent),
170­
330
µ
g/
L
"
Ahmad
et
al.
2001
Bangladesh
Retrospective
cohort
of
pregnancies
Concentration
in
H2O
Duration
of
residence
in
high
arsenic
area
Significant
difference
comparing
high
vs.
low
arsenic
region,
and
for
those
with
longer
duration
Prevalence
rate
difference
<
20
(
referent),
>
100
µ
g/
L
"
Aschengrau
et
al.
1989
Massachussetts
Case­
control
Concentration
in
H2O
Trend
in
risk
Odds
ratio
<
0.8,
0.8­
1.3,
1.4­
1.9
µ
g/
L
Stillbirth
"
_
"
_
"
_
"
_
"
_
"
"
Borzsonyi
et
al
1992
Hungary
Retrospective
cohort
Concentration
in
H2O
Significant
difference
comparing
high
vs.
low
arsenic
region
Prevalence
rate
difference
Low
(
not
quantitated
referent),
170­
330
µ
g/
L
"
Hopenhayn­
Rich
et
al.
2000
Chile
Retrospective
vital
statistics
Concentration
in
H2O
Comparison
of
two
communities
Significant
difference
during
period
when
exposures
were
very
high
Mortality
rate
difference
and
ratio
<
5
(
referent),
various
levels
to
>
800
µ
g/
L
Preterm
birth
Ahmad
et
al.
2001
Bangladesh
Retrospective
cohort
of
pregnancies
Concentration
in
H2O
Duration
of
residence
in
high
arsenic
area
Significant
difference
comparing
high
vs.
low
arsenic
region,
and
for
those
with
longer
duration
Prevalence
rate
difference
<
20
(
referent),
>
100
µ
g/
L
A­
22
VII.
Human
reproductive
studies
(
con't)

Outcome
Authors/
year
&
location
Design
Exposure
assessment
Dose­
response
analysis:
Measure
of
association
Range
of
exposures
Birthweight
Nordstrom
et
al.
1978
Sweden
Retrospective
cohort
of
pregnancies
Residential
proximity
to
smelter
or
employment
Lowest
birthweight
among
those
living
nearest
the
smelter
Difference
in
birthweight
No
quantitation
Low
birthweight
Hopenhayn
et
al.
2001
Chile
Prospective
cohort
&
review
of
vital
statistics
Concentration
in
H2O
Comparison
of
two
communities
Significantly
increased
risk
of
low
birth
weight
Odds
ratio
for
low
birthweight
<
2
(
referent),
40­
50
µ
g/
L
Congenital
malformations
Nordstrom
et
al.
1979
Sweden
Retrospective
cohort
of
pregnancies
Employment
in
the
smelter
Higher
prevalence
of
congenital
malformations
among
employed
mothers
Prevalence
ratio
"

Coarctation
of
the
aorta
Zierler
et
al
1988
Massachussetts
Case­
control
Routine
monitoring
of
water
Above
vs.
below
the
limit
of
detection,
three­
fold
increased
risk,
adjusted
for
seven
other
contaminants,
source
of
water,
maternal
education
Odds
ratio
<
limit
of
detection
(
0.8
µ
g/
L),
>
limit
of
detection
Neonatal
mortality
Hopenhayn­
Rich
et
al.
2000
Chile
Retrospective
vital
statistics
Concentration
in
H2O
Comparison
of
two
communities
Significant
difference
during
period
when
exposures
were
very
high
Mortality
rate
difference
and
ratio
<
5
(
referent),
various
levels
to
>
800
µ
g/
L
Postneonatal
mortality
"
"
"
"
"
"
A­
23
VIII.
Human
studies
of
neurologic
and
neurodevelopmental
endpoints
Outcome
Authors/
year
&
location
Design
Exposure
assessment
Dose­
response
analysis:
Measure
of
association
Range
of
exposures
Peripheral
neuropathy
Gerr
et
al.
2000
Georgia,
USA
Cross­
sectional
Dust
&
soil
arsenic
measurements
Significant
trend,
adjusted
for
age,
education,
sex,
verbal
intellectual
score,
alcohol
Odds
ratio
House
dust:
1­
1200
µ
g/
g
Window
sill
dust:
0.5­
192
Attic
dust
1.2­
2635
µ
g/
g
Soil
2.0­
1845
µ
g/
g
Various
neurobehavioral
parameters*
"
"
"
"
Linear
regression
"

Verbal
IQ
Calderon
et
al.
2001
Mexico
Cross­
sectional
Urinary
arsenic
Significant
inverse
correlation
Partial
correlation
coefficient
<
50,
50­
100,
>
100
µ
g
As/
g
creatinine;
Range:
27.5­
186.2
µ
g/
g
creatinine
*
Vibrotactile
threshold,
standing
steadiness,
tremor
intensity
A­
24
A
Public
Health
Based
Approach
to
Calculating
the
Magnitude
of
Unquantified
Health
Effects
Several
of
the
analyses
of
the
health
effects
of
arsenic
in
Taiwan
use
Standardized
Mortality
Ratios
(
SMRs)
to
compare
death
rates
in
villages
with
high
levels
of
arsenic
in
drinking
water
to
death
rates
in
unexposed
areas.
The
analysis
below
compares
the
number
of
excess
deaths
due
to
lung
and
bladder
cancers
(
based
on
SMRs)
with
excess
deaths
due
to
other
cancers
and
due
to
vascular
disease.
The
goal
is
to
compare
the
magnitude
of
excess
deaths
for
endpoints
for
which
dose­
response
has
not
been
quantified
to
excess
deaths
for
endpoints
for
which
dose­
response
functions
exist.
This
suggests
the
possible
magnitude
of
effects
that
might
be
established
if
dose­
response
functions
were
estimated.

The
spreadsheet
in
Attachment
1
to
Appendix
2.2,
performs
this
analysis
using
data
reported
in
Wu
et
al.
(
1989)
and
Tsai
et
al.
(
1999).
For
the
Wu
et
al.
data
the
basic
findings
are
that
(
1)
cancers
other
than
lung
and
bladder
have
similar
aggregate
excess
deaths
as
the
sum
of
lung
plus
bladder
cancer
excess
deaths,
and
(
2)
vascular
deaths
are
comparable
in
number
to
the
sum
of
lung
plus
bladder
cancer
excess
deaths.
This
suggests
that
the
total
mortality
effect
at
the
high
exposure
levels
in
the
Wu
et
al.
study
is
about
three
times
the
effect
of
the
previously
quantified
lung
and
bladder
cancers.
For
the
Tsai
et
al.
data,
the
basic
findings
are
similar
for
total
excess
cancer
deaths
 
about
double
those
from
lung
plus
bladder
cancer
by
themselves.
However,
the
vascular
excess
deaths
for
these
data
are
just
over
half
the
excess
deaths
from
lung
plus
bladder
cancers.
This
apparent
difference
from
the
Wu
et
al.
results
may
be
related
to
the
fact
that
more
of
the
Tsai
et
al.
data
are
from
a
somewhat
later
period
relative
to
the
end
of
exposure
than
the
earlier
Wu
et
al.
data.
One
possible
interpretation
of
this
is
that
the
vascular
deaths
may
tend
to
have
a
shorter
average
lag
time
relative
to
exposures
than
the
cancer
deaths.
A­
25
Attachment
1
to
Appendix
2.2
Analysis
of
Data
of
Wu
et
al.
for
the
Population
Aggregate
Excess
Deaths
from
Various
Causes
(
Mortality
from
1973­
1986)
A.
Data
from
Tables
3
and
4
(
all
data
are
age­
adjusted
mortality
per
100,000
per
year)

­­­­­­­­­­­­­­
Males­­­­­­­­­­­­­­
­­­­­­­­­­­­­­
Females­­­­­­­­­­­­­­

<
.3
mg/
L
.3­.
59
mg/
L
?
.6
mg/
L
<
.3
mg/
L
.3­.
59
mg/
L
?
.6
mg/
L
Cancers
All
sites
224.56
405.12
534.61
162.22
277.2
487.2
Bladder
22.64
61.02
92.71
25.6
57.02
111.3
Kidney
8.42
18.9
25.26
3.42
19.42
57.98
Skin
2.03
14.01
32.41
1.73
14.75
18.66
Lung
49.16
100.67
104.08
36.71
60.82
122.16
Liver
47.78
67.62
86.73
21.4
24.18
31.75
Prostate
0.95
9
9.18
Leukemia
4.87
6.52
2.69
3.03
4.55
0.00
Nasopharynx
3.58
8.16
8.58
1.59
5.81
4.89
Esophagus
7.62
9.37
6.55
1.83
3.64
0.00
Stomach
25.66
17.82
56.42
6.71
18.72
5.98
Colon
7.94
8.3
12.51
9.05
8.16
17.21
Uterine
Cervix
0.91
5.46
3.92
Unidentified
sites
43.91
83.73
97.49
50.24
54.67
113.35
Vascular
Diseases
All
vascular
diseases
364.1
421.47
572.68
277.5
370.79
386.41
Peripheral
vascular
diseases
22.54
57.8
60.4
18.2
48.00
35.82
Cardiovascular
diseases
125.87
153.98
259.51
91.14
153.07
144.74
Cerebrovascular
accidents
137.8
145.36
175.72
92.42
98.11
120.68
Unidentified
vascular
disease
77.89
64.33
77.05
75.74
71.61
85.17
A­
26
B.
Excess
Death
Rates/
100,000
Over
<
.3
mg/
L
Group
Males
Females
Mean,
Both
Sexes
Ratio
to
Lung+
Bladder
Ca
.3­.
59
mg/
L
?
.6
mg/
L
.3­.
59
mg/
L
?
.6
mg/
L
.3­.
59
mg/
L
?
.6
mg/
L
.3­.
59
mg/
L
?
.6
mg/
L
Cancers
All
sites
180.56
310.05
114.98
324.98
147.77
317.52
2.03
2.14
Bladder
38.38
70.07
31.42
85.7
34.9
77.89
0.48
0.53
Kidney
10.48
16.84
16
54.56
13.24
35.70
0.18
0.24
Skin
11.98
30.38
13.02
16.93
12.5
23.66
0.17
0.16
Lung
51.51
54.92
24.11
85.45
37.81
70.19
0.52
0.47
Liver
19.84
38.95
2.78
10.35
11.31
24.65
0.16
0.17
Prostate
8.05
8.23
0
0
4.025
4.12
0.06
0.03
Leukemia
1.65
­
2.18
1.52
­
3.03
1.585
­
2.61
0.02
­
0.02
Nasopharynx
4.58
5
4.22
3.3
4.4
4.15
0.06
0.03
Esophagus
1.75
­
1.07
1.81
­
1.83
1.78
­
1.45
0.02
­
0.01
Stomach
­
7.84
30.76
12.01
­
0.73
2.085
15.02
0.03
0.10
Colon
0.36
4.57
­
0.89
8.16
­
0.265
6.37
0.00
0.04
Uterine
Cervix
0
0
4.55
3.01
2.275
1.51
0.03
0.01
Unidentified
sites
39.82
53.58
4.43
63.11
22.13
58.35
0.30
0.39
Vascular
Diseases
All
vascular
diseases
57.37
208.58
93.29
108.91
75.33
158.75
1.04
1.07
Peripheral
vascular
diseases
35.26
37.86
29.8
17.62
32.53
27.74
0.45
0.19
Cardiovascular
diseases
28.11
133.64
61.93
53.6
45.02
93.62
0.62
0.63
Cerebrovascular
accidents
7.56
37.92
5.69
28.26
6.625
33.09
0.09
0.22
Unidentified
vascular
disease
­
13.56
­
0.84
­
4.13
9.43
­
8.845
4.295
­
0.12
0.03
Wu,
M.
M.,
Kuo,
T.
L.,
Hwang,
Y.
H.,
and
Chen,
C.
J.
Dose­
response
relation
between
arsenic
concentration
in
well
water
and
mortality
from
cancers
and
vascular
diseases.
Am
J.
Epidemiology
130:
1123­
1132
A­
27
Analysis
of
Population
Aggregate
Excess
Deaths
from
Various
Causes
from
the
Data
of
Tsai
et
al.
(
All
mortality
data
are
for
1971­
1994­­
after
nearly
all
phaseout
of
the
arsenic
in
drinking
water
exposure
in
the
mid­
1970'
s.
Expected
deaths
are
based
on
the
local
comparison
group.)

A.
Numbers
of
Deaths
for
Men
Numbers
of
Deaths
for
Men
Observed
Expected
SMR
95%
LCL
SMR
95%
UCL
SMR
Excess
Deaths
Ratio
to
Lung
+
Bladder
Ca
All
Causes
11193
8265.76
1.32
1.29
1.35
2927
3.90
Cancers
All
sites
2774
1263.95
2.19
2.11
2.28
1510
2.01
Oral
23
20
3
0.00
Pharyngeal,
except
NPC
24
17.75
6
0.01
Nasopharyngeal
60
50.59
9
0.01
Esophagus
69
41.2
1.67
1.3
2.12
28
0.04
Stomach
195
143.84
1.36
1.17
1.46
51
0.07
Intestine
15
7.15
8
0.01
Colon
91
61.05
30
0.04
Rectum
46
31.96
14
0.02
Liver
631
345.27
1.83
1.69
1.98
286
0.38
Gallbladder
13
11.68
1
0.00
Pancreas
30
24.57
5
0.01
Nasal
40
13.3
3
2.14
4.09
27
0.04
Laryngeal
30
16.81
1.78
1.2
2.55
13
0.02
Lung
699
225.39
3.1
2.88
3.34
474
0.63
Bone
41
16.64
2.46
1.77
3.34
24
0.03
Skin
66
13.65
4.83
3.74
6.15
52
0.07
Breast
Cervical
Ovary
Prostate
48
19.07
2.52
1.86
3.34
29
0.04
Bladder
312
34.99
8.92
7.96
9.96
277
0.37
Kidney
94
13.91
6.76
5.46
8.27
80
0.11
Brain
19
15.03
1.26
0.76
1.97
4
0.01
Lymphoma
56
34.4
1.63
1.23
2.11
22
0.03
Leukemia
67
50.07
1.34
1.04
1.7
17
0.02
Diabetes
mellitus
188
139.69
1.35
1.16
1.55
48
0.06
All
listed
vascular
diseases
2563
2193.62
1.17
369
0.49
A­
28
Hypertension
158
216.83
0.73
0.62
0.85
­
59
­
0.08
Ischemic
heart
disease
445
254.68
1.75
1.59
1.92
190
0.25
Pulmonary
heart
disease
33
65.39
0.5
0.35
0.71
­
32
­
0.04
Heart
disease
534
503.37
31
0.04
Cerebrovascular
disease
1286
1123.26
1.14
1.08
1.21
163
0.22
Vascular
disease
107
30.09
3.56
2.91
4.3
77
0.10
Bronchitis
157
106.38
1.48
1.25
1.73
51
0.07
Emphysema
31
38.09
­
7
­
0.01
Asthma
147
166.13
­
19
­
0.03
Liver
cirrhosis
428
360.05
1.18
1.08
1.31
68
0.09
Nephritis,
nephrotic
syndrome,
nephrosis
206
176.01
1.17
1.02
1.34
30
0.04
Congenital
anomalies
86
75.68
10
0.01
A­
29
B.
Numbers
of
Deaths
for
Women
Observed
Expected
SMR
95%
LCL
SMR
95%
UCL
SMR
Excess
Deaths
Ratio
to
Lung
+
Bladder
Ca
All
Causes
8875
6329.72
1.4
1.37
1.43
2545
4.03
Cancers
All
sites
2029
843.9
2.4
2.3
2.51
1185
1.88
Oral
12
7.46
5
0.01
Pharyngeal,
except
NPC
10
4.24
2.36
1.13
4.34
6
0.01
Nasopharyngeal
29
31.13
­
2
0.00
Esophagus
12
7.59
4
0.01
Stomach
111
79.46
1.4
1.15
1.68
32
0.05
Intestine
8
5.81
2
0.00
Colon
83
58.47
1.42
1.13
1.76
25
0.04
Rectum
33
21.98
1.5
1.03
2.11
11
0.02
Liver
224
119.28
1.88
1.64
2.14
105
0.17
Gallbladder
11
12.18
­
1
0.00
Pancreas
19
19.75
­
1
0.00
Nasal
29
5.82
4.98
3.33
7.15
23
0.04
Laryngeal
13
2.73
4.76
2.53
8.15
10
0.02
Lung
471
114.02
4.13
3.77
4.52
357
0.57
Bone
34
15.11
2.25
1.56
3.14
19
0.03
Skin
68
11.96
5.68
4.41
7.21
56
0.09
Breast
47
46.48
1
0.00
Cervical
122
96.09
1.27
1.05
1.52
26
0.04
Ovary
15
13.78
1
0.00
Prostate
Bladder
295
20.96
14.07
12.51
15.78
274
0.43
Kidney
128
14.4
8.89
7.42
10.57
114
0.18
Brain
21
11.99
1.75
1.08
2.68
9
0.01
Lymphoma
35
20.57
1.7
1.18
2.37
14
0.02
Leukemia
40
37.36
3
0.00
Diabetes
mellitus
343
221.72
1.55
1.39
1.72
121
0.19
All
listed
vascular
diseases
2462
2077.06
1.19
385
0.61
Hypertension
239
198.69
1.2
1.06
1.37
40
0.06
Ischemic
heart
disease
283
197.02
1.44
1.27
1.61
86
0.14
Pulmonary
heart
disease
27
51.18
0.53
0.35
0.77
­
24
­
0.04
A­
30
Heart
disease
493
511.25
­
18
­
0.03
Cerebrovascular
disease
1352
1089.41
1.24
1.18
1.31
263
0.42
Vascular
disease
68
29.51
2.3
1.78
2.93
38
0.06
Bronchitis
148
96.55
1.53
1.3
1.8
51
0.08
Emphysema
16
13.96
2
0.00
Asthma
103
123.14
­
20
­
0.03
Liver
cirrhosis
164
157.71
6
0.01
Nephritis,
nephrotic
syndrome,
nephrosis
196
168.39
1.16
1.01
1.39
28
0.04
Congenital
anomalies
70
59.96
10
0.02
A­
31
C.
Men
and
Women
Combined
Excess
Deaths
Ratio
to
Lung
+
Bladder
Ca
All
Causes
5473
3.96
Cancers
All
sites
2695
1.95
Oral
8
0.01
Pharyngeal,
except
NPC
12
0.01
Nasopharyngeal
7
0.01
Esophagus
32
0.02
Stomach
83
0.06
Intestine
10
0.01
Colon
54
0.04
Rectum
25
0.02
Liver
390
0.28
Gallbladder
0
0.00
Pancreas
5
0.00
Nasal
50
0.04
Laryngeal
23
0.02
Lung
831
0.60
Bone
43
0.03
Skin
108
0.08
Breast
1
0.00
Cervical
26
0.02
Ovary
1
0.00
Prostate
29
0.02
Bladder
551
0.40
Kidney
194
0.14
Brain
13
0.01
Lymphoma
36
0.03
Leukemia
20
0.01
Diabetes
mellitus
170
0.12
All
listed
vascular
diseases
754
0.55
Hypertension
­
19
­
0.01
A­
32
Ischemic
heart
disease
276
0.20
Pulmonary
heart
disease
­
57
­
0.04
Heart
disease
12
0.01
Cerebrovascular
disease
425
0.31
Vascular
disease
115
0.08
Bronchitis
102
0.07
Emphysema
­
5
0.00
Asthma
­
39
­
0.03
Liver
cirrhosis
74
0.05
Nephritis,
nephrotic
syndrome,
nephrosis
58
0.04
Congenital
anomalies
20
0.01