health effects of particulate matter air pollution · plotted over various indices of particulate...

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



Episode



Case-crossover








Q

&

A



Episode



Case-crossover








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


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


Mean HighPM

10


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



Episode



Case-crossover








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***



Episode



Case-crossover








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.



Episode



Case-crossover








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.



Episode



Case-crossover








Any

Questions?



Episode



Case-crossover








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).



Episode



Case-crossover








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.

http://content.nejm.org/

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

http://jama.ama-assn.org/

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


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


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



Episode



Case-crossover








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



Episode



Case-crossover








Any

Questions?



Episode



Case-crossover








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

http://content.nejm.org/

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

http://content.nejm.org/content/vol360/issue4/images/large/10t2.jpeg

Reduction in PM2.5

, 1980-2000

0 2 4 6 8 10 12 14

Chan

ge in L

E,

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199

0s

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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

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0.5

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A

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Episode



Case-crossover








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.)


Linear regression

Survival Analyses,

Cox Proportional Hazards

models (random effect,

spatial autocorr., etc.)

Various regression

modeling strategies (fixed

effects, mixed models. . .)


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.



Episode



Case-crossover








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


, 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


, 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.

health effects of particulate matter air pollution · plotted over various indices of particulate...

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