Health Effects of Particulate
Matter Air Pollution
C. Arden Pope III
Mary Lou Fulton Professor of Economics
Presented at
EPA Wood Smoke Health Effects Webinar
July 28, 2011
What we breath impacts our health
Pure Air--nitrogen (78%),Oxygen (21%), Argon, CO2. . .
+ Various gaseous pollutants including:
– SO2, NO2, CO, O3 . . .
+ Particulate matter:
– Course particles (> 2.5 mm in diameter)
– Fine particles (< 2.5 mm in diameter)
+
Other air toxics
Wood
Smoke
Introduction to Particulate Matter
(PM)
EPA PM Criteria Document, 2004 EPA PM Criteria Document, 2004
How small are fine particles?
Human Hair
(60 mm diameter)
PM10
(10 mm)
PM2.5
(2.5 mm)
Magnified ambient particles (www.nasa.gov/vision/earth/environment)
This presentation not organized
chronologically, but methodologically Studies of short-term exposure (hours-days)
Episode
Population-based daily time-series
Panel-based acute exposure
Case-crossover
Studies of long-term exposure (years-decades)
Population-based cross-sectional
Cohort-based mortality
Cohort- and panel-based morbidity
Case-control studies
Intervention/natural experiment (months-years)
Controlled experimental human and animal
This presentation not organized
chronologically, but methodologically Studies of short-term exposure (hours-days)
Episode
Population-based daily time-series
Panel-based acute exposure
Case-crossover
Studies of long-term exposure (years-decades)
Population-based cross-sectional
Cohort-based mortality
Cohort- and panel-based morbidity
Case-control studies
Intervention/natural experiment (months-years)
Controlled experimental human and animal
Q
&
A
This presentation not organized
chronologically, but methodologically Studies of short-term exposure (hours-days)
Episode
Population-based daily time-series
Panel-based acute exposure
Case-crossover
Studies of long-term exposure (years-decades)
Population-based cross-sectional
Cohort-based mortality
Cohort- and panel-based morbidity
Case-control studies
Intervention/natural experiment (months-years)
Controlled experimental human and animal
Early “Killer smog” episodes demonstrated that air pollution at extreme
levels can contribute to respiratory and cardiovascular disease and death
Dec. 5-9, 1952: London--1000’s of excess deaths
Dec. 1-5, 1930: Meuse Valley, Belgium
60 deaths (10x expected)
Oct. 27-31, 1948: Donora, PA
20 deaths, ½ the town’s population fell ill
Respiratory and cardiovascular
disease and death
London Fog Episode, Dec. 1952
From: Brimblecombe P. The Big Smoke, Methuen 1987
Utah Valley, 1980s
• Winter inversions trap local pollution
• Natural test chamber
• Local Steel mill contributed ~50% PM2.5
• Shut down July 1986-August 1987
• Natural Experiment
Large difference in air quality
when inversions trap air pollution in valley
Utah Valley: Clean day
Utah Valley: Dirty day
(PM10 = 220 mg/m3)
mg
/m3/N
um
be
rs o
f A
dm
issio
ns
0
50
100
150
200
250
300
PM10 concentrations Children's respiratory hospital admissions
Mean PM10
levels forMonthsIncluded
Mean HighPM
10
levels forMonthsIncluded
PneumoniaandPleurisy
Bronchitisand Asthma
Total
Children's Respiratory Hospital AdmissionsFall and Winter Months, Utah Valley
Sources: Pope. Am J Pub Health.1989; Pope. Arch Environ Health. 1991
When the steel mill was open, total children’s hospital
admissions for respiratory conditions approx. doubled. m
g/m
3/N
um
be
rs o
f A
dm
issio
ns
0
50
100
150
200
250
300
PM10 concentrations Children's respiratory hospital admissions
Mean PM10
levels forMonthsIncluded
Mean HighPM
10
levels forMonthsIncluded
PneumoniaandPleurisy
Bronchitisand Asthma
Total
Children's Respiratory Hospital AdmissionsFall and Winter Months, Utah Valley
Sources: Pope. Am J Pub Health.1989; Pope. Arch Environ Health. 1991
Mill
Open
Mill
Closed
This presentation not organized
chronologically, but methodologically Studies of short-term exposure (hours-days)
Episode
Population-based daily time-series
Panel-based acute exposure
Case-crossover
Studies of long-term exposure (years-decades)
Population-based cross-sectional
Cohort-based mortality
Cohort- and panel-based morbidity
Case-control studies
Intervention/natural experiment (months-years)
Controlled experimental human and animal
Health studies take advantage of highly variable air
pollution levels that result from inversions.
98 99 00 01 02 03 04 05 06 07 08 09 10
0
10
20
30
40
50
60
70
80
90
100
98 99 00 01 02 03 04 05 06 07 08 09 10
0
10
20
30
40
50
60
70
80
90
100
PM2.5 concentrations January 1 1998-December 12 2009. Black dots, 24-hr PM2.5; Red line, 30-day moving average PM2.5;
Green line, 1-yr moving average PM2.5.
Utah Valley (Lindon Monitor)
g/m
3
Salt Lake Valley (Hawthorn Monitor)
g/m
3
Salt Lake Valley (Hawthorn Monitor)
Daily changes in air pollution daily death counts
Time (days)
# o
f D
eath
s
Utah Valley
Poisson Regression
Count data (non-negative integer values). Counts of independent and
random occurrences classically modeled as being generated by a Poisson
process with a Poisson distribution:
Prob (Y = r) = e(-λ)
Note: λ = mean and variance. If λ is constant across time, we have a
stationary Poisson process. If λ changes over time due to changes in
pollution (P), time trends, temperature, etc., this non-stationary Poisson
process can model as:
ln λt = α + β(w0Pt + w1Pt-1 + w2Pt-2 + . . .) + s1(t) + s2(tempt) + . . .
λr
r!
How to construct
the lag structure?
(MA, PDL, etc.)
How aggressive do you
fit time? (harmonics vs
GAMs, df, span, loess,
cubic spline, etc.)
How to control for
weather? (smooths of
temp & RH, synoptic
weather, etc.)
Modeling
controversies
Also: How to combine or integrate information from multiple cities
% incre
ase in m
ort
alit
y
0
1
2
3
Estimates frommeta analysis
Estimates from Multicity studies
29 c
itie
s(L
evy e
t a
l. 2
00
0)
GA
M-b
ase
d s
tud
ies
(Stie
b e
t a
l. 2
00
2,
200
3)
Un
ad
juste
d(A
nd
ers
on
et
al. 2
00
5)
6 U
.S.
citie
s(K
lem
m a
nd
Ma
so
n 2
00
3)
8 C
ana
dia
n c
itie
s(B
urn
ett
an
d G
old
be
rg 2
00
3)
9 C
alif
orn
ian c
itie
s(O
str
o e
t a
l. 2
00
6)
10 U
.S c
itie
s(S
ch
wa
rtz 2
00
0,
200
3)
14 U
.S c
itie
s,
ca
se
-cro
sso
ve
r(S
ch
wa
rtz 2
00
4)
NM
MA
PS
, 2
0-1
00
U.S
. citie
s(D
om
inic
i e
t a
l. 2
00
3)
AP
HE
A-2
, 1
5-2
9 E
uro
pe
an
citie
s(K
ats
ouya
nn
i e
t a
l. 2
00
3)
9 F
rench
citie
s(L
e T
ert
re e
t a
l. 2
00
2)
13 J
apa
ne
se
citie
s(O
mo
ri e
t a
l. 2
00
3)
No
n G
AM
-base
d s
tud
ies
(Stie
b e
t a
l. 2
00
2,
200
3)
Pub
lica
tio
n b
ias a
dju
ste
d
(And
ers
on
et
al. 2
00
5)
7 K
ore
an
citie
s(L
ee
et
al. 2
00
0)
20
g/m
3 P
M1
0
20
g/m
3 P
M1
0
20
g/m
3 P
M1
0
20
g/m
3 P
M1
0
20
g/m
3 P
M1
0
10
g/m
3 P
M2.5
20
g/m
3 P
M1
0
10
g/m
3 P
M2.5
10
g/m
3 P
M2.5
20
g/m
3 P
M1
0
20
g/m
3 P
M1
0
20
g/m
3 P
M1
0
20
g/m
3 B
S
40
g/m
3 T
SP
20
g/m
3 S
PM
Re
vie
w o
f A
sia
n L
it.-
-8 s
tudie
s(H
EI
Re
po
rt,
Ta
ble
TS
2)
20
g/m
3 P
M1
0
20
g/m
3 P
M1
0
20
g/m
3 P
M1
0
Estimates frommeta analysisfrom Asian Lit
PA
PA
Stu
die
s--
4 s
tudie
s(H
EI
Re
po
rt,
Ta
ble
TS
2)
Asia
n L
it.
inco
rpora
tin
g P
AP
A s
tud
ies
(HE
I R
epo
rt,
Ta
ble
TS
2)
18 L
atin
Am
. stu
die
s
(PA
HO
20
05)
20
g/m
3 P
M1
0
10 mg/m3 PM2. or 20 mg/m3 PM10 → 0.4% to 1.5%
increase in relative risk of mortality—Small but
remarkably consistent across meta-analyses and multi-city studies.
Daily time-series studies ***of over 200 cities***
This presentation not organized
chronologically, but methodologically Studies of short-term exposure (hours-days)
Episode
Population-based daily time-series
Panel-based acute exposure
Case-crossover
Studies of long-term exposure (years-decades)
Population-based cross-sectional
Cohort-based mortality
Cohort- and panel-based morbidity
Case-control studies
Intervention/natural experiment (months-years)
Controlled experimental human and animal
Panel studies of asthmatics and non-asthmatics
Summary of early Utah Valley epidemiological studies
Health effects
• Increased hospital admissions
• Increased respiratory
symptoms
• Reduced lung function
• Increased school absences
• Increased respiratory and
cardiovascular deaths
Study References
Pope (1989) Am. J. Public Health
Pope (1991) Arch. Environ. Health
Pope, Dockery, Spengler, Raizenne
(1991) Am. Rev. Resp. Dis.
Pope, Dockery (1992)
Am. Rev. Resp. Dis.
Pope, Kanner (1993)
Am. Rev. Resp. Dis.
Ransom, Pope (1992)
Environ. Res
Pope, Schwartz, Ransom (1992)
Arch. Environ. Health
Pope, Kalkstein (1996)
Environ. Health Perspect.
Pope, Hill, Villegas (1999)
Environ. Health Perspect.
This presentation not organized
chronologically, but methodologically Studies of short-term exposure (hours-days)
Episode
Population-based daily time-series
Panel-based acute exposure
Case-crossover
Studies of long-term exposure (years-decades)
Population-based cross-sectional
Cohort-based mortality
Cohort- and panel-based morbidity
Case-control studies
Intervention/natural experiment (months-years)
Controlled experimental human and animal
Methods:
Case-crossover study of acute
ischemic coronary events (heart
attacks and unstable angina) in
12,865 well-defined and followed up
cardiac patients who lived on Utah’s
Wasatch Front
…and who underwent coronary
angiography
Jeffrey Anderson
2006;114:2443-48
yt
0
1
t
Binary Data, classic time-series
yt
0
1
t
Binary Data, case-crossover
Conditional Logistic Reg.
Each subject serves as his/her own control.
Control for subject-specific effects, day of week, season,
time-trends, etc.—by matching
Prob (Yt = 1)
1 - Prob (Yt = 1)
α1 + α2 + α3 + . . . + α12,865 + β(w0Pt + w1Pt-1 + w2Pt-2 + . . .)
Control by matching for:
All cross-subject differences
(in this case, 12,865 subject-level fixed effects),
Season and/or month of year,
Time trends,
Day of week
( ) = ln
Conditional logistic regression:
Modeling controversies: How to select control or referent periods. Time
stratified referent selection approach (avoids bias that can occur due to time
trends in exposure) (Holly Janes, Lianne Sheppard, Thomas Lumley
Statistics in Medicine and Epidemiology 2005)
%
0.00
5.00
10.00
15.00
Figure 1. Percent increase in risk (and 95% CI) of acute coronary
events associated with 10 g/m3 of PM
2.5, or PM
10 for
different lag structures.
Co
ncu
rre
nt d
ay
Co
ncu
rre
nt d
ay
1-d
ay
lag
1-d
ay
lag
2-d
ay
lag
2-d
ay
lag
3-d
ay
lag
3-d
ay
lag
2-d
ay
mo
vin
g a
v.
2-d
ay
mo
vin
g a
v.
4-d
ay
mo
vin
g a
v.
3-d
ay
mo
vin
g a
v.
3-d
ay
mo
vin
g a
v.
4-d
ay
mo
vin
g a
v.
PM2.5
PM10
%
-10.00
-5.00
0.00
5.00
10.00
15.00
20.00
Figure 2. Percent increase in risk (and 95% CI) of acute coronary events associated with
10 mg/m3 of PM
2.5, stratified by various characteristics.
All
acute
coro
nary
Subsequent M
I
Unsta
ble
Angin
a
Age<
65
Age>
=65
Male
Fem
ale
Sm
okin
g
Non S
mokin
g
BM
I<30
BM
I>=
30 C
HF
,yes
CH
F,n
o
Hyp
ert
ensio
n,y
es
Hyp
ert
ensio
n,n
o
Hyp
erlip
idem
ia,y
es
Hyp
erlip
idem
ia,n
o
Dia
bete
s,y
es
Dia
bete
s,n
o
Fam
ily h
isto
ry,y
es
Fam
ily h
isto
ry,n
o
# ofDiseasedVessels
# of Risk Factors
01
2
3
0
1
2
3
4+
Index M
I
Short-term PM exposures contributed to acute coronary events,
especially among patients with underlying coronary artery disease.
This presentation not organized
chronologically, but methodologically Studies of short-term exposure (hours-days)
Episode
Population-based daily time-series
Panel-based acute exposure
Case-crossover
Studies of long-term exposure (years-decades)
Population-based cross-sectional
Cohort-based mortality
Cohort- and panel-based morbidity
Case-control studies
Intervention/natural experiment (months-years)
Controlled experimental human and animal
Any
Questions?
This presentation not organized
chronologically, but methodologically Studies of short-term exposure (hours-days)
Episode
Population-based daily time-series
Panel-based acute exposure
Case-crossover
Studies of long-term exposure (years-decades)
Population-based cross-sectional
Cohort-based mortality
Cohort- and panel-based morbidity
Case-control studies
Intervention/natural experiment (months-years)
Controlled experimental human and animal
Median PM2.5
for aprox. 1980
8 10 12 14 16 18 20 22 24 26 28 30 32 34
Ad
juste
d M
ort
alit
y f
or
19
80
(D
ea
ths/Y
r/1
00
,00
0)
600
650
700
750
800
850
900
950
1000
Mean sulfate for aprox. 1980
4 6 8 10 12 14 16 18 20 22 24
Ad
juste
d M
ort
alit
y
for
19
80
(D
ea
ths/Y
r/1
00
,00
0)
600
650
700
750
800
850
900
950
1000
Mean PM2.5
for aprox. 1980
8 10 12 14 16 18 20 22 24 26 28 30 32 34
600
650
700
750
800
850
900
950
1000
Mean TSP for aprox. 1980
15 30 45 60 75 90 105 120 135 150 165
600
650
700
750
800
850
900
950
1000
Figure 1. Age-, sex-, and race-adjusted population-based mortality rates in U.S. cities for 1980 plotted over various indices of particulate air pollution.
Age-, sex-, and race- adjusted population-based mortality rates in U.S.
cities for 1980 plotted over various indices of particulate air pollution
(From Pope 2000).
This presentation not organized
chronologically, but methodologically Studies of short-term exposure (hours-days)
Episode
Population-based daily time-series
Panel-based acute exposure
Case-crossover
Studies of long-term exposure (years-decades)
Population-based cross-sectional
Cohort-based mortality
Cohort- and panel-based morbidity
Case-control studies
Intervention/natural experiment (months-years)
Controlled experimental human and animal
An Association Between Air Pollution and
Mortality in Six U.S. Cities
1993
Dockery DW, Pope CA III, Xu X, Spengler JD,
Ware JH, Fay ME, Ferris BG Jr, Speizer FE.
Methods:
14-16 yr prospective follow-up of 8,111 adults living in
six U.S. cities.
Monitoring of TSP PM10, PM2.5, SO4, H+, SO2, NO2, O3 .
Data analyzed using survival analysis, including
Cox Proportional Hazards Models.
Controlled for individual differences in: age, sex, smoking,
BMI, education, occupational exposure.
Average
Polluted
cities
Highly
Polluted
cities
Clean
cities
Cox Proportional Hazards Survival Model
Cohort studies of outdoor air pollution have commonly used the CPH Model
to relate survival experience to exposure while simultaneously controlling for
other well known mortality risk factors. The model has the form
)()()( )(0
)()( txexptt l
i
Tll
i
Hazard function
or instantaneous
probability of
death for the ith
subject in the lth
strata.
Baseline
hazard
function,
common to all
subjects within
a strata.
Regression equation that
modulates the baseline
hazard. The vector Xi(l)
contains the risk factor
information related to the
hazard function by the
regression vector β which
can vary in time.
Adjusted risk ratios (and 95% CIs) for
cigarette smoking and PM2.5
Cause of
Death
Current Smoker,
25 Pack years
Most vs. Least
Polluted City
All 2.00 (1.51-2.65)
1.26 (1.08-1.47)
Lung
Cancer
8.00 (2.97-21.6)
1.37 (0.81-2.31)
Cardio-
pulmonary
2.30 (1.56-3.41)
1.37 (1.11-1.68)
All
other
1.46 (0.89-2.39)
1.01 (0.79-1.30)
Particulate Air Pollution as a Predictor of Mortality in a
Prospective Study of U.S. Adults
Pope CA III, Thun MJ, Namboodiri MM,
Dockery DW, Evans JS, Speizer FE, Heath CW Jr.
1995
Methods: Linked and
analyzed ambient air
pollution data from 51-
151 U.S. metro areas
with risk factor data for
over 500,000 adults
enrolled in the ACS-
CPSII cohort.
Clark Heath Michael Thun
Adjusted mortality risk ratios (and 95% CIs) for cigarette
smoking the range of sulfates and fine particles
Cause of
Death
Current
Smoker
Sulfates Fine
Particles
All 2.07 (1.75-2.43)
1.15 (1.09-1.22)
1.17 (1.09-1.26)
Lung
Cancer
9.73 (5.96-15.9)
1.36 (1.11-1.66)
1.03 (0.80-1.33)
Cardio-
Pulmonary
2.28 (1.79-2.91)
1.26 (1.16-1.37)
1.31 (1.17-1.46)
All other 1.54 (1.19-1.99)
1.01 (0.92-1.11)
1.07 (0.92-1.24)
Dan Krewski
Rick Burnett
Mark Goldberg
and 28 others
2004;109:71-
77. R
R (
95
% C
I)
0.65
0.70
0.75
0.80
0.85
0.90
0.95
1.00
1.05
1.10
1.15
1.20
1.25
1.30
1.35
1.40
All
Card
iovascula
rplu
s D
iabe
tes
Ische
mic
hea
rtdis
ea
se
Dysrh
yth
mia
s,H
ea
rtfa
ilure
, C
ard
iac a
rrest
Hype
rten
siv
edis
ea
se
Oth
er
Ath
ero
scle
rosis
,ao
rtic
ane
ury
sm
s
Cere
bro
-vascula
r
Oth
er
Card
io-
vascula
r
Dia
bete
s
Respirato
ryD
iseases
CO
PD
an
dalli
ed c
ond
itio
ns
Pne
um
on
ia,
Influe
nza
All
oth
er
respirato
ry
Figure 1. Adjusted relative risk ratios for cardiovascular and respiratory mortality associated with
a 10 mg/m3 change in PM
2.5 for 1979-1983, 1999-2000, and the average of the two periods.
(Relative size of the dots correspond to the relative number of deaths for each cause.)
John Godleski
Perc
en
t in
cre
as
e i
n m
ort
ali
ly r
isk (
95%
CI)
-10
0
10
20
30
40
50
60
70
80
90
100
110
120180
190
All Cause CPD CVD IHD
Six
Citie
s (
Dockery
et al 1993;
Kre
wski et al. 2
000; Laden e
t al. 2
008)
Six
Citie
s, M
edic
are
(E
fim
et al. 2
008)
ACSACS
Oslo
, m
en a
nd w
om
en, 51-7
0 (
Naess e
t al. 2
007)
ACS
AH
SM
OG
, M
ale
s (
McD
onnell
et al. 2
000)
VA
, H
ypert
ensiv
e, M
ale
s (
Lip
fert
et al. 2
006)
11 C
A c
ounty
, eld
erly
(Enstr
om
2005)
Fre
nch P
AA
RC
(F
illeul et al. 2
005)
Dutc
h C
ohort
(B
eele
n e
t al. 2
008)
Six
Citie
s Six
Citie
s
AC
S
LA
LA
LA
Wom
en's
Health Initia
tive (
Mill
er
et al. 2
007)
AH
SM
OG
, F
em
ale
(C
hen e
t al. 2
005)
Oslo
, m
en a
nd w
om
en, 71-9
0 (
Naess e
t al. 2
007)
Gem
an w
om
en (
Gehring e
t al. 2
006)
U.S
. M
edic
are
; E
ast/C
entr
al/W
est (Z
egar
et al. 2
008)
AC
S M
edic
are
(E
fim
et al. 2
008)
AH
SM
OG
, M
ale
s (
McD
onnell
et al. 2
000)
Fre
nch P
AA
RC
(F
illeul et al. 2
005)
Gem
an w
om
en (
Gehring e
t al. 2
006)
Dutc
h C
ohort
(B
eele
n e
t al. 2
008)
Nurs
es' H
ealth S
tudy
(Puett e
t al. 2
008)
Nurs
es' H
ealth S
tudy
(Puett e
t al. 2
008)
Pope e
t al. 1
995; K
rew
ski et al. 2
000; P
ope e
t al.
2002, 2004; Jerr
ett e
t al. 2
005; K
rew
ski et al. 2
009)
Cal. T
eachers
Stu
dy
(Ostr
o e
t al. 2
009)
Cal. T
eachers
Stu
dy
(Ostr
o e
t al. 2
009)
Cal. T
eachers
Stu
dy
(Ostr
o e
t al. 2
009)
Other cohort studies have shown associations between exposure
to fine PM and increased risk of cardiovascular death.
Six-Cities
studies
ACS studies
Other studies
Pe
rce
nt
inc
rea
se
in
mo
rta
lily
ris
k (
95
% C
I)
-10
0
10
20
30
40
50
60
70
80
90
100
110
120180
190
All Cause CPD CVD IHD
Joel Kaufman Lianne Sheppard
Women’s Health Initiative Study
Pe
rce
nt
inc
rea
se
in
mo
rta
lily
ris
k (
95
% C
I)
-10
0
10
20
30
40
50
60
70
80
90
100
110
120180
190
All Cause CPD CVD IHD Lung Cancer
Nurses’ Health Study:
•Puett et al. Am. J. Epidemiology 2008
Stronger association with CVD than
with all cause.
Frank Speizer
Pe
rce
nt
inc
rea
se
in
mo
rta
lily
ris
k (
95
% C
I)
-10
0
10
20
30
40
50
60
70
80
90
100
110
120180
190
All Cause CPD CVD IHD Lung Cancer
Netherlands, Germany, Norway studies:
Beelen et al. EHP 2008
Gehring et al. Epidemiology 2006
Naess et al. Am. J. Epidemiology 2007
Again, positive associations, generally
Stronger for cardiovascular disease.
Brunekreef (summary paper)
JESEE 2007
Bert Brunekreef
Pe
rce
nt
inc
rea
se
in
mo
rta
lily
ris
k (
95
% C
I)
-10
0
10
20
30
40
50
60
70
80
90
100
110
120180
190
All Cause CPD CVD IHD
U.S. Medicare Cohort studies:
•Eftim et al. Epidemiology 2008
•Zegar et al. EHP 2008
Cohorts of Medicare participants cities of the 6-cities and ACS study, plus all U.S.
U.S. Medicare Cohort Studies
This presentation not organized
chronologically, but methodologically Studies of short-term exposure (hours-days)
Episode
Population-based daily time-series
Panel-based acute exposure
Case-crossover
Studies of long-term exposure (years-decades)
Population-based cross-sectional
Cohort-based mortality
Cohort- and panel-based morbidity
Case-control studies
Intervention/natural experiment (months-years)
Controlled experimental human and animal
Southern California Children’s Health Study
Effects of air pollution on
children’s health, especially
lung function growth.
David Bates, Advisor
W. James Gauderman John Peters
Southern California Children’s Health Study, has shown that
air pollution impacts lung development in children.
Children living in cities with higher air pollution and
living near major traffic sources showed greater
deficits in lung function growth.
Gauderman et al. 2007
This presentation not organized
chronologically, but methodologically Studies of short-term exposure (hours-days)
Episode
Population-based daily time-series
Panel-based acute exposure
Case-crossover
Studies of long-term exposure (years-decades)
Population-based cross-sectional
Cohort-based mortality
Cohort- and panel-based morbidity
Case-control studies
Intervention/natural experiment (months-years)
Controlled experimental human and animal
Any
Questions?
This presentation not organized
chronologically, but methodologically Studies of short-term exposure (hours-days)
Episode
Population-based daily time-series
Panel-based acute exposure
Case-crossover
Studies of long-term exposure (years-decades)
Population-based cross-sectional
Cohort-based mortality
Cohort- and panel-based morbidity
Case-control studies
Intervention/natural experiment (months-years)
Controlled experimental human and animal
Fine-Particulate Air Pollution and Life Expectancy in the United States
C. Arden Pope, III, Ph.D., Majid Ezzati, Ph.D., and Douglas W. Dockery, Sc.D.
January 22, 2009
Majid Ezzati
Matching PM2.5 data for
1979-1983 and 1999-2000 in
51 Metro Areas
Life Expectancy data for
1978-1982 and 1997-2001 in
211 counties in 51 Metro areas
Evaluate changes in Life
Expectancy with changes in
PM2.5 for the 2-decade period
of approximately 1980-2000.
Doug Dockery
Covariates included in the regression models
Changes in socio-economic and demographic variables (from U.S.
Census Data):
Per capita income
Population
5-yr in-migration
High-school graduates
Urban population
Black proportion of population
Hispanic proportion of population
Proxy cigarette smoking variables—available for all 211 counties
COPD mortality rates
Lung Cancer mortality rates
Survey-based metro-area estimates of smoking prevalence
National Health Interview Survey (1978-1980)
Behavioral Risk Factor Surveillance System (1998-2000)
Matching data available for only 24 of 51 metro areas
Clustered standard errors (clustered by the 51 metro areas) were estimated for all models
except for analysis that included only the 51 largest counties in each metro area.
A 10 µg/m3 decrease in PM2.5 was associated with
a 7.3 (± 2.4) month increase in life expectancy.
This increase in life expectancy persisted even after controlling for
socio-economic, demographic, or smoking variables
Reduction in PM2.5
, 1980-2000
0 2 4 6 8 10 12 14
Chan
ge in L
E,
19
80
s -
199
0s
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
5.0
5.5
22
47
49
45
4
10
17
19
46
48
6
43
50
24 21
36
8
34
20
7
25
1
11
12
14 44
51
27
3
28
30
32
13
18 26
9
29
23
37
38
40
15
33
5
2
35 31
16 39
41
42
Reduction in PM2.5
, 1980-2000
0 2 4 6 8 10 12 14
Resid
ual changes in
LE
contr
olli
ng f
or
covariate
s
-2.5
-2.0
-1.5
-1.0
-0.5
0.0
0.5
1.0
1.5
2.0
2.5
22
47 49
45
4
10 17
19
46
48
6 43 50
24
21
36
8
34
20 7
25
1
11
12
14
44
51
27 3 28
30
32 13
18
26
9
29
23
37
38
40
15
33
5
2
35
31
16
39
41 42
A
B
This presentation not organized
chronologically, but methodologically Studies of short-term exposure (hours-days)
Episode
Population-based daily time-series
Panel-based acute exposure
Case-crossover
Studies of long-term exposure (years-decades)
Population-based cross-sectional
Cohort-based mortality
Cohort- and panel-based morbidity
Case-control studies
Intervention/natural experiment (months-years)
Controlled experimental human and animal
Cardiovascular disease as part of chronic and acute inflammatory
processes.
By the early 2000s, there was increasingly compelling evidence that
inflammation is a major accomplice with LDL cholesterol in the initiation and
progression of atherosclerosis.
Furthermore, inflammation contributes to acute thrombotic complications of
atherosclerosis, increasing the risk of making atherosclerotic plaques more
vulnerable to rupture, clotting, and precipitating acute cardiovascular or
cerebrovascular events (MI or ischemic stroke).
Interactive effects of hs-CRP (marker of inflammation) and blood lipids.
Ridker PM. 2001;103:1813-1818.
Paul Ridker
Fine Particulate
exposure
↓
Pulmonary and
systemic
inflammation
and oxidative stress
(along with blood lipids)
↓
Progression and
destabilization of
atherosclerotic
plaques
Experimental evidence of biological effects of PM extracted from filters
(Ghio, Costa, Devlin, Kennedy, Frampton, Dye, et al. 1998-2004)
• Acute airway injury and inflammation in rats and humans
• In vitro oxidative stress and release of proinflammatory mediators by
cultured respiratory epithelial cells
• Differential toxicities of PM when the mill was operating versus when it was
not (metals content and mixtures?)
PM exposure
↓
Pulmonary inflammation
↓
Systemic inflammatory responses
(including release of inflammatory
mediators, bone marrow stimulation
and release of leukocytes and platelets)
↓
Progression and destabilization of
atherosclerotic plaques
In rabbits naturally prone to develop atherosclerosis they found that:
PM exposure
↓↓
Accelerated progression of atherosclerotic plaques
with greater vulnerability to plaque rupture
A series of studies by van Eeden, Hogg, Suwa et al. (1997-2002) suggest:
Stephan van Eeden
James Hogg
Sun et al. (JAMA 2005)
Representative Photomicrographs
of Aortic Arch Sections Normal Chow High-Fat Chow
Clean
Filtered Air Clean
Filtered Air PM Polluted Air PM Polluted Air
apoE-/- mouse Sun QH Lippmann M
Common Statistical
Modeling Approaches
Simple Comparative Stats,
Graphs
Poisson reg., (GAMs,
smooths for time , weather
etc.)
Linear and Logistic Reg.,
(fixed effects, temporal
autocorr., etc.)
Conditional Logistic Reg.
Linear regression
Survival Analyses,
Cox Proportional Hazards
models (random effect,
spatial autocorr., etc.)
Various regression
modeling strategies (fixed
effects, mixed models. . .)
Conditional Logistic Reg.
Various comparative stats
and regression models
Various comparative stats
and regression models
Many studies using various
study designs and approaches
with companion statistical
modeling approaches and
techniques have provided
remarkably coherent evidence.
This presentation not organized
chronologically, but methodologically Studies of short-term exposure (hours-days)
Episode
Population-based daily time-series
Panel-based acute exposure
Case-crossover
Studies of long-term exposure (years-decades)
Population-based cross-sectional
Cohort-based mortality
Cohort- and panel-based morbidity
Case-control studies
Intervention/natural experiment (months-years)
Controlled experimental human and animal
Any
Questions?
0 60 120 180 240
Ad
juste
d R
ela
tive R
isk
1.0
1.5
2.0
estimated daily dose of PM2.5
, mg
18-22cigs/day
Pack-a-day smoker:
RR ~ 2
Daily inhaled dose ~ 240 mg
Live in polluted city or
With smoking spouse
RR ~ 1.15 – 1.35
Daily inhaled dose ~ 0.2–1.0 mg
0 60 120 180 240 300
Ad
jus
ted
Re
lati
ve
Ris
k
1.0
1.5
2.0
2.5
<3cigs/day
estimated daily dose of PM2.5
, mg
23+cigs/day
8-12cigs/day
13-17cigs/day
18-22cigs/day
4-7cigs/day
Pope, Burnett, Krewski, et al. 2009.
Figure 1. Adjusted relative
risks (and 95% CIs) of IHD
(light gray), CVD (dark
gray), and CPD (black)
mortality plotted over
estimated daily dose of
PM2.5 from different
increments of current
cigarette smoking.
Diamonds represent
comparable mortality risk
estimates for PM2.5 from air
pollution. Stars represent
comparable pooled relative
risk estimates associated
with SHS exposure from
the 2006 Surgeon
General’s report and from
the INTERHEART study.
0.1 1.0 10.0 100.0
Ad
jus
ted
Re
lati
ve
Ris
k
1.0
1.5
2.0
2.5
estimated daily dose of PM2.5
, mg
Exposure from
Second hand cigarette smoke: Stars, from 2006 Surgeon General Report and INTERHEART studyAnd air pollution: Hex, from Womens Health Initiative cohort Diamonds, from ACS cohort Triangles, Harvard Six Cities cohort
Exposure from smoking<3, 4-7, 8-12, 13-17, 18-22, and 23+
cigarettes/day
Figure 2. Adjusted relative
risks (and 95% CIs) of
ischemic heart disease
(light gray), cardiovascular
(dark gray), and
cardiopulmonary (black)
mortality plotted over
baseline estimated daily
dose (using a log scale) of
PM2.5 from current
cigarette smoking (relative
to never smokers), SHS,
and air pollution.