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Effect of Outdoor Airborne Particulate Matter on Daily Death Counts

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Abstract and Figures

To investigate the possible relationship between airborne particulate matter and mortality, we developed regression models of daily mortality counts using meteorological covariates and measures of outdoor PM10. Our analyses included data from Cook County, Illinois, and Salt Lake County, Utah. We found no evidence that particulate matter < or = 10 microns (PM10) contributes to excess mortality in Salt Lake County, Utah. In Cook County, Illinois, we found evidence of a positive PM10 effect in spring and autumn, but not in winter and summer. We conclude that the reported effects of particulates on mortality are unconfirmed.
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Effect
of
Outdoor
Airborne
Particulate
Matter
on
Daily
Death
Counts
Patricia
Styer,
Nancy
McMillan,1
Feng
Gao,
Jerry
Davis,'3
and
Jerome
Sacks1
1National
Institute
of
Statistical
Sciences,
Research
Triangle
Park,
NC
27709-4162
USA;
2Battelle
Pacific
Northwest
Laboratories,
Richland,
WA
99352
USA;
3Department
of
Marine,
Earth
and
Atmospheric
Sciences,
North
Carolina
State
University,
Raleigh,
NC
27695-8208
USA
To
determine
if
airborne
particulates
con-
tribute
to
excess
mortality,
researchers
have
adopted
multiple
regression
techniques
to
measure
the
effects
of
particulates
on
daily
death
counts
(1,2).
Other
factors,
such
as
extreme
temperatures,
can
affect
mortality,
and
regression
techniques
are
used
to
adjust
for
these
other
known
influences.
Though
many
factors
could
be
involved,
research
has
generally
limited
attention
to
meteorological
sources
such
as
temperature
and
humidity.
In
some
cases,
other
air
pol-
lution
measures
such
as
sulfur
dioxide
and
ozone
are
included.
The
regression
coeffi-
cient
corresponding
to
a
measure
of
partic-
ulate
level
is
then
interpreted
as
the
effect
of
particulate
pollution
on
mortality,
accounting
for
stress
from
the
other
influ-
ences.
If
this
coefficient
is
a
statistically
sig-
nificant
positive
number,
the
conclusion
is
that
mortality
increases
with
increasing
lev-
els
of
particulates.
This
association
is
then
elevated
to
a
causal
interpretation:
particu-
lates
cause
death,
and
researchers
estimate
that
soot
at
levels
well
below
the
maximum
set
by
federal
law
"kills
up
to
60,000
in
U.S.
each
year"
(3,4),
and
similar
calcula-
tions
"put
the
annual
toll
in
England
and
Wales
at
10,000"
(5).
Studies
vary
as
to
the
particulate
mea-
sures
used
and
the
locations
analyzed.
In
the
analyses
presented
here,
we
used
PM1O,
which
specifies
particulate
matter
with
an
aerodynamic
diameter
<10
pm
(6).
The
current
U.S.
EPA
standard
is
based
on
this
measure.
The
locations
we
analyzed,
Cook
County,
Illinois,
and
Salt
Lake
County,
Utah,
both
have
relatively
long
records
of
PM1O
monitoring.
The
monitoring
data
are
discussed
in
more
detail
in
Methods.
The
data
used
in
the
analyses
(meteo-
rological
conditions,
particulate
levels,
death
counts)
are
observational;
that
is,
data
that
are
measured
and
recorded
with-
out
control
or
intervention
by
researchers.
Deducing
causal
relationships
from
obser-
vational
data
is
perilous.
A
practical
approach
described
by
Mosteller
and
Tukey
(P)
involves
considerations
beyond
regression
analysis.
In
particular,
consider-
ation
should
be
given
to
whether
the
asso-
ciation
between
particulate
levels
and
mor-
tality
is
consistent
across
"settings,"
whether
there
are
plausible
common
causes
for
elevated
particulate
levels
and
mortali-
ty,
and
whether
the
derived
models
reflect
reasonable
physical
relationships.
There
is
a
high
degree
of
association
of
PM1O
with
meteorology,
and
a
high
degree
of
association
of
mortality
with
weather.
For
example,
in
the
summer
in
Cook
County
the
correlation
coefficient
between
the
daily
average
of
PM1O
and
daily
mean
tempera-
ture
is
0.52
and
the
correlation
between
daily
elderly
(age
65
or
older)
mortality
and
mean
temperature
is
0.25.
The
confound-
ing
effects
of
weather
as
a
partial
cause
of
both
particulate
levels
and
mortality
may
not
be
controllable
by
standard
regression
methods;
the
appearance
of
an
effect
for
particulates,
i.e.,
a
positive
coefficient
for
the
PM1O
term,
may,
as
a
result,
be
spurious
(see
Appendix
B).
We
have
not
addressed
the
issue
of
errors
in
variables,
which
can
also
be
a
cause
for
spurious
relationships.
The
concern
about
errors
in
variables
arises
from
the
differences
between
measured
PM1O
and
the
actual
PM1O
exposure
experi-
enced
by
the
population.
PM1O
measure-
ments
are
taken
outdoors,
but
people
tend
to
spend
most
of
their
time
indoors,
espe-
cially
the
sick
and
elderly
who
are
believed
to
be
the
most
vulnerable.
Similarly,
the
meteorological
covariates
we
include
repre-
sent
outdoor
conditions.
And
again,
when
explanatory
variables
are
measured
with
error,
the
result
is
not
necessarily
attenua-
tion
of
the
regression
surface.
In
multiple
regression,
the
result
can
be
an
artificial
increase
in
the
magnitude
of
the
estimated
coefficients.
The
results
for
Cook
County
and
Salt
Lake
County
show
that
the
appearance
and
size
of
a
PMIO
effect
is
quite
sensitive
to
model
specification.
In
particular,
the
treatment
of
season
affects
the
estimates
of
the
PMIO
effect.
In
Cook
County,
we
found
a
significant
interaction
between
the
time
of
year
and
PM10.
Using
a
standard
Poisson
regression
model,
we
found
that
PMIO
appears
to
be
significantly
associated
with
mortality
in
the
spring
and
fall,
but
not
in
the
winter
and
summer.
Using
a
semi-parametric
model
(Appendix
A),
we
found
that
only
the
months
of
May
and
September
exhibit
a
particulate
effect.
In
Salt
Lake
County,
the
semi-parametric
model
suggests
a
similarly
isolated
PMIO
effect
limited
to
the
month
of
June,
but
we
found
no
evidence
of
a
PMIO
effect
in
any
model
using
Poisson
regression.
Hence,
we
conclude
there
is
no
evidence
of
a
consistent
association
between
partic-
ulates
and
mortality.
Several
studies
carried
on
at
various
locations
in
the
United
States
have
report-
ed
small
yearly
increases
in
mortality
resulting
from
increases
in
particulates.
In
our
Cook
County
analyses,
the
effect
of
PM1O
in
the
spring
and
fall
induces
a
simi-
lar
positive
yearly
increase
in
mortality
from
increases
in
particulates,
but
the
increase
is
from
one-half
to
one-third
the
size
usually
reported
in
other
studies
depending
on
the
analyses
performed.
In
Salt
Lake
County,
the
size
of
the
yearly
effect
is
far
smaller
and
statistically
insignificant.
What
remains
unexplained
is
why,
in
Cook
County,
effects
should
appear
in
the
spring
but
not
in
the
sum-
mer,
and
in
the
fall
but
not
in
the
winter.
Neither
is
it
clear
why
the
effect
of
particu-
lates
on
mortality
should
not
appear
in
any
season
in
Salt
Lake
County.
The
appearance
of
a
PM10
effect
in
the
spring
and
fall
in
Cook
County
led
to
the
speculation
that
pollen
may
be
implicated,
but
no
such
evidence
was
found
using
pollen
data
monitored
in
the
city
of
Chicago,
the
major
population
component
of
Cook
County.
Other
analyses
carried
out
for
the
fall
season
in
Cook
County
on
differ-
ent
subgroups
of
the
population
produced
no
definitive
differences
among
subgroups.
The
inconsistency
of
the
regression
analyses,
the
unresolved
status
of
plausible
common
causes
of
particulate
levels
and
mortality,
the
confounding
effects
of
weather,
and
the
unavailability
of
plausible
biophysical
mechanisms
to
explain
the
Address
correpondence
to
J.
Sacks,
National
Institute
of
Statistical
Sciences,
PO
Box
14162,
Research
Triangle
Park,
NC
27709-4162
USA.
This
research
was
supported
in
part
by
the
U.S.
Environmental
Protection
Agency
under
coopera-
tive
agreement
CR819638-01-0
and
by
a
National
Science
Foundation
Grant
(DMS-9208758).
Received
27
October
1994;
accepted
15
February
1995.
Environmental
Health
Perspectives
490
A
I
-S
-
-
M
d
empirical
analyses
prevent
us
from
conclud-
ing
that
there
is
an
effect
between
"today's"
mortality
and
"yesterday's"
particulates.
The
question
appears
to
be
unresolved.
Methods
Data
The
data
used
for
the
statistical
studies
have
three
main
components:
mortality
counts,
particulate
levels,
and
meteorology.
The
sources
of
the
data
are
described
in
this
sec-
tion
along
with
some
summary
statistics.
Mortality
data.
Daily
death
counts
for
the
period
1985
through
1990
came
from
death
certificate
records
for
Cook
and
Salt
Lake
County
residents,
collected
by
the
National
Center
for
Health
Statistics,
and
made
available
to
us
by
John
Creason,
EPA.
Although
mortality
data
are
avail-
able
for
longer
periods,
PM1O
data
are
unavailable
before
1985.
Each
death
record
contains
a
cause
of
death
code
and
some
basic
demographic
information.
In
compiling
daily
death
counts,
we
exclud-
ed
all
deaths
from
accidental
causes,
as
well
as
deaths
of
county
residents
occur-
ring
in
other
locations.
We
refer
to
the
remaining
number
of
deaths
as
total
deaths.
The
main
analyses
were
per-
formed
with
total
deaths
among
the
pop-
ulation
aged
65
or
older
(elderly
deaths).
We
carried
out
additional
analyses
for
total
deaths,
unrestricted
by
age,
for
deaths
classified
by
specific
causes,
and
for
selected
population
subgroups
such
as
elderly
blacks
and
elderly
males.
We
clas-
sified
the
disease-specific
causes
of
death
by
the
International
Classification
of
Diseases
(ICD)
codes
that
appear
on
the
mortality
records.
We
adopted
the
classi-
fication
scheme
detailed
in
Fairley
(8),
extracting
cancer
deaths
(ICD
categories
140-209),
circulatory
deaths
(ICD
cate-
gories
390-459),
and
respiratory
deaths
(ICD
categories
11,
35,
472-519,710.0,
710.2,710.4).
In
Cook
County,
there
was
an
average
of
117
nonaccidental
deaths
per
day
for
all
ages.
Among
residents
aged
65
and
over,
there
was
an
average
of
83
deaths
per
day.
Death
counts
vary
by
time
of
year,
with
higher
numbers
in
winter
and
fewer
deaths
in
summer.
In
Salt
Lake
County,
there
was
an
average
of
9
nonaccidental
deaths
for
all
ages
and
7
nonaccidental
deaths
for
resi-
dents
65
and
over.
As
in
Cook
County,
there
are
slightly
more
deaths
in
the
win-
ter.
Table
1
displays
some
summary
statis-
tics
for
both
Cook
County
and
Salt
Lake
County
mortality.
Particulate
data.
In
current
monitoring
efforts,
particulates
are
measured
through-
out
the
United
States.
There
are
both
24-hr
and
annual
ambient
air
quality
standards
for
particulate
matter
(6).
In
the
first
case,
the
standard
is
attained
when
the
expected
number
of
days
per
calendar
year
with
a
24-hr
average
concentration
above
150
pg/m3
is
equal
to
or
less
than
one.
In
the
second
case,
the
standard
is
attained
when
the
expected
annual
arithmetic
mean
con-
centration
is
less
than
or
equal
to
50
pg/m3.
To
comply
with
these
standards,
it is
suffi-
cient to
collect
samples
from
each
monitor-
ing
site
only
once
every
6
days,
though
there
are
a
few
locations
with
monitors
that
operate
on
a
daily
basis.
For
Cook
County,
the
particulate
data
come
from
a
network
of
PMIO
monitors
reported
in
the
EPA
Aerometric
Information
Retrieval
System
(AIRS)
for
the
period
1985
through
1990.
During
this
time,
there
were
20
separate
monitors
in
operation,
though
several
mon-
itors
were
operated
for
only
a
brief
period
of
time.
The
Cook
County
network
includes
one
daily
station
where
PMlo
sam-
ples
are
collected
on
a
daily
basis.
The
remaining
stations
collected
samples
every
sixth
day.
The
daily
station
observations
are
frequently
missing,
with
69%
of
the
values
recorded
once
the
monitoring
station
began
operation
in
April
1985.
To
fill
in
some
of
the
missing
values,
we
used
the
daily
means
of
all
available
monitoring
data
as
the
basis
for
constructing
our
measures
of
PMIO.
With
all
available
data,
there
are
observa-
tions
for
75%
of
the
days
after
1
April
1985.
Since
many
of
the
20
monitoring
stations
were
in
operation
for
a
short
peri-
od,
there
is
a
maximum
of
12
observations
on
any
single
day.
Furthermore,
the
6-day
monitoring
stations
tend
to
operate
on
the
same
schedule,
so
many
of
the
days
have
only
the
single
daily
monitor
contributing
to
the
daily
mean.
In
Cook
County,
PM10
levels
are
generally
highest
in
the
summer.
Figure
1
shows
the
distribution
of
daily
PM1O
val-
ues
by
month.
It
is
clear
that
mean
levels
are
generally
well
below
the
EPA
stan-
dard
of
150
pg/m3.
In
Table
2,
the
daily
means
from
all
available
stations
are
com-
pared
with
the
values
from
the
single
daily
monitoring
station.
These
show
close
agreement,
with
three
observations
over
the
EPA
standard
for
the
daily
sta-
tion
and
two
observations
over
150
for
the
daily
means.
In
Salt
Lake
County,
there
were
six
PMIO
monitors
operating
between
June
1985
and
December
1990.
The
monitoring
network
includes
two
daily
stations.
We
use
the
observations
from
just
one
of
the
daily
stations,
station
12,
in
this
analysis.
Station
12
is
centrally
located
in
Salt
Lake
County.
The
second
daily
monitor
is
located
in
a
more
remote
section
of
the
county
and
was
considered
to
be
unreliable
to
use
in
mea-
suring
general
exposure
levels.
Figure
1
shows
the
distribution
of
daily
PM10
values
by
month
for
the
centrally
located
daily
sta-
tion
(station
12).
The
distribution
of
PMIO
in
Salt
Lake
County
differs
slightly
from
the
distribution
in
Cook
County.
The
overall
levels
are
similar,
though
there
are
more
days
in
Salt
Lake
County
with
PM1O
levels
over
150
pg/m3.
Unlike
Cook
County,
there
is
an
increase
in
overall
levels
in
winter
(December-February),
though
isolated
occurrences
of
high
particulate
lev-
els
occur
throughout
the
spring
and
sum-
mer.
In
Table
2,
we
present
some
summary
statistics
from
the
single
daily
station
used
in
this
analysis.
Meteorological
data.
The
meteorologi-
cal
data
used
in
this
study
are
based
on
hourly
surface
observations
taken
at
O'Hare
International
Airport
(Cook
County)
and
Salt
Lake
City
International
Airport
(Salt
Lake
County).
We
extracted
the
data
from
the
National
Climatic
Data
Center's
National
Solar
and
Meteoro-
logical
Surface
Observation
Network
(1961-1990)
database,
which
contains
hourly
surface
observations
in
addition
to
solar
radiation
data.
Our
primary
analyses
concentrated
on
three
meteorological
vari-
ables:
temperature,
specific
humidity,
and
barometric
pressure.
We
excluded
other
variables
such
as
solar
radiation,
cloud
cover,
wind
speed,
and
wind
direction.
These
variables
were
omitted
to
make
our
primary
analyses
more
directly
comparable
with
other
research
and
because
factors
like
wind
may
have
more
direct
connection
with
PM1O
than
those
included.
For
each
variable
we
did
include,
we
calculated
the
daily
mean,
based
on
hourly
values.
And,
because
weather
may
have
a
lagged
effect
on
mortality,
we
also
included
the
values
of
temperature,
humidity,
and
pressure
Table
1.
Mean
daily
mortality
for
nonaccidental
causes
of
death
Cook
County
Salt
Lake
County
Elderlya
Totalb
Circulatoryc
Cancer
Respiratory
Elderly
Total
Winter
90.4
126.7
62.5
28.9
11.8
7.4
10.2
Spring
82.3
116.7
56.3 28.3
10.2
6.8
9.2
Summer
77.0
110.6
52.6
28.4
8.8
6.3
8.5
Fall
81.5
115.6
54.9
29.2
9.6
6.6
8.9
aElderly
mortality
indicates
the
subset
of
these
deaths
among
county
residents
aged
65
and
older.
bTotal
mortality
indicates
the
mean
number
of
daily
deaths
of
county
residents
of
all
ages,
excluding
accidental
deaths,
homicides,
and
suicides.
cCirculatory,
cancer,
and
respiratory
deaths
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