Studies have shown short-term effects of

particulate pollution (PM) on hospital admissions and deaths from cardiovascular causes

(Anderson et al. 2003; Braga et al. 2001;

Dockery 2001; Katsouyanni et al. 1996; Pope

et al. 2004a; Samet et al. 2000; Schwartz

1999; Zanobetti et al. 2000). Myocardial

infarctions (MIs) have been shown to be susceptible to being triggered by PM (Braga

et al. 2001; D’Ippoliti et al. 2003; Lanki et al.

2006; Peters et al. 2001a; von Klot et al.

2005; Zanobetti and Schwartz 2005). These

studies have not addressed whether persons

who survive an MI are at risk of death in

response to subsequent particle exposure.

A few studies have addressed this question

with respect to acute exposure. For example,

Bateson and Schwartz (2004) reported subjects in Chicago who were discharged alive

for an MI had twice the risk of death due to

acute air pollution exposure as other subjects. 

In a recent study of five European cities,

von Klot et al. (2005) found that ambient air

pollution was associated with increased risk of

hospital cardiac readmissions of MI survivors.

Peters et al. (2006) examined the probability

of recurrent hospitalization in a cohort of MI

survivors; compared the use of time-series,

case–crossover, and survival analysis for analyzing short-term health effects; and found

that the three methods gave similar results.

None of these studies examined the effect of

longer-term exposure on survival. 

Several studies have examined the effect

of longer-term PM exposure on survival in

general cohorts. The Harvard Six Cities Study

(Dockery et al. 1993) demonstrated an association between mortality and chronic exposure

to PM. A recent reanalysis of the Harvard Six

Cities Study, which extended the mortality

follow-up period, used a similar approach to

our study and included PM2.5 (PM with aerodynamic diameter < 2.5 μm) as a time-varying

exposure; Laden et al. (2006) found significant association of PM2.5 with mortality.

Two articles (Pope et al. 2002, 2004a)

have shown an association between PM and

mortality in the American Cancer Society

(ACS) Cancer Prevention Study population,

including an association between PM and

deaths from all cardiovascular disease (Pope

et al. 2004a). 

These studies have treated air pollution as

a city-level variable, whereas two European

cohort studies have assessed exposure at an

individual level. Hoek et al. (2002) found an

association between estimated long-term

exposure to traffic-related particles at each

participant’s home and cause-specific mortality in the Netherlands Cohort Study on Diet

and Cancer. Nafstad et al. (2004) found an

association between estimated nitrogen dioxide concentrations at the subject’s home and

the risk of dying for total, respiratory, lung

cancer, and ischemic heart disease mortality

in a cohort of men in Oslo, Norway. Particle

data was not available in that study, and NO2

was used as a marker of traffic pollution. 

Although these studies have reported an

association of PM with survival, they have

not evaluated the role of preexisting cardiac

disease or whether risk changed with annual

changes in exposure, nor have they treated

pollution as a time-varying covariate. 

Reduced heart rate variability has been

associated with decreased survival of MI

patients (Ewing 1991; Malik et al. 1989), and

PM has been associated with reduced heart

rate variability (Creason et al. 2001; Gold et al.

2000; Liao et al. 1999; Park et al. 2005; Pope

et al. 1999), which in turn is associated with

decreased post-MI survival (Stein et al. 2005).

Other studies have suggested that PM may be

associated with increased C-reactive protein

(Brook et al. 2003; Peters et al. 2001b), which

is associated with mortality risk following an

MI (Kinjo et al. 2005). PM and its components have also been shown to increase oxidative stress in the heart (Bouthillier et al. 1998;

Brook et al. 2003; Dhalla et al. 2000; Sorensen

et al. 2003), to decrease plaque stability (Suwa

et al. 2002), and to increase atherosclerosis

(Künzli et al. 2005). Based on this, we investigated whether annual PM exposure was associated with progression of disease or reduced

survival in a study of 196,000 persons discharged alive following an acute MI.

Materials and Methods

Study population. Using Medicare data for

persons ≥ 65 years of age, we constructed a

cohort of survivors of acute MI, defining cases

as emergency admissions for a primary discharge diagnosis of MI [International

Classification of Diseases, Ninth Revision

(ICD-9; World Health Organization 1975)

code 410] discharged alive between 1985 and

1999 in any of 21 cities chosen to represent a

broad range of the country. We obtained from

Medicare the date of death for each subject, or

whether they were still alive as of the end of

1999. We also retrieved information on age,

sex, race, and the number of coronary intensive care days and Medical intensive care days. 

Environmental Health Perspectives • VOLUME 115 | NUMBER 5 | May 2007 769

Research | Environmental Medicine

Address correspondence to A. Zanobetti, Department

of Environmental Health, Exposure Epidemiology

and Risk Program, Harvard School of Public Health,

401 Park Dr., Landmark Center, Suite 415, P.O. Box

15698, Boston, MA 02215 USA. Telephone: (617)

384-8751. Fax: (617) 384-8745. E-mail: azanobet@

hsph.harvard.edu

This study was supported by the U.S. Environmental Protection Agency (EPA)/Harvard Center on

Ambient Particle Health Effects EPA PM Center

(U.S. EPA grant R827353).

The authors declare they have no competing

financial interests.

Received 25 March 2006; accepted 20 February

2007.

Particulate Air Pollution, Progression, and Survival after Myocardial Infarction

Antonella Zanobetti and Joel Schwartz

Department of Environmental Health, Harvard School of Public Health, Boston, Massachusetts, USA

OBJECTIVE: Several studies have examined the effect of particulate pollution (PM) on survival in

general populations, but less is known about susceptible groups. Moreover, previous cohort studies

have been cross-sectional and subject to confounding by uncontrolled differences between cities. 

DESIGN: We investigated whether PM was associated with progression of disease or reduced survival

in a study of 196,000 persons from 21 U.S. cities discharged alive following an acute myocardial

infarction (MI), using within-city between-year exposure to PM. We constructed city-specific

cohorts of survivors of acute MI using Medicare data between 1985 and 1999, and defined three

outcomes on follow-up: death, subsequent MI, and a first admission for congestive heart failure

(CHF). Yearly averages of PM10 (particulate matter with aerodynamic diameter < 10 μm) were

merged to the individual annual follow-up in each city. We applied Cox’s proportional hazard

regression model in each city, with adjustment for individual risk factors. In the second stage of the

analysis, the city-specific results were combined using a meta-regression.

RESULTS: We found significant associations with a hazard ratio for the sum of the distributed lags of

1.3 [95% confidence interval (CI), 1.2–1.5] for mortality, a hazard ratio of 1.4 (95% CI, 1.2–1.7)

for a hospitalization for CHF, and a hazard ratio of 1.4 (95% CI, 1.1–1.8) for a new hospitalization

for MI per 10 μg/m3 PM10.

CONCLUSIONS: This is the first long-term study showing a significant association between particle

exposure and adverse post-MI outcomes in persons who survived an MI.

KEY WORDS: air pollution, epidemiology, heart diseases, myocardial infarction, survival. Environ

Health Perspect 115:769–775 (2007). doi:10.1289/ehp.9201 available via http://dx.doi.org/ [Online

20 February 2007]
To study progression of disease, we traced

each subject through subsequent Medicare

records and identified admissions for a subsequent MI or for congestive heart failure

(CHF; ICD-9 code 428).

Subjects alive the first of January of the

year following the admission were entered into

the cohort, and follow-up periods were calendar years. We excluded subjects whose death

or subsequent admission occurred within the

first 3 months of their index admission. 

We also used a unique identifier for each

subject to assess medical factors that might

modify the risk of survival or progression, such

as whether they had any primary or secondary

diagnosis of chronic obstructive pulmonary

disease (COPD; ICD-9 codes 490–496,

except 493), diabetes (ICD-9 code 250), or

essential hypertension (ICD-9 code 401); or if

they had previous admissions for atrial fibrillation (ICD-9 code 427.3). 

During 1985–1999 changes in treatment

occurred, such as introduction of thrombolytics and increased angioplasty. To control for

changes in postdischarge survival, we used

strata to allow a different underling hazard for

each 5-year interval in the study. 

We defined a categorical variable for type

of MI as given by the fourth digit of the

ICD-9 code. There are 10 different types of

MI: ICD-9 code 410.0, MI of anterolateral

wall; 410.1, other anterior wall; 410.2, inferolateral wall; 410.3, inferoposterior wall; 410.4,

other inferior wall; 410.5, other lateral wall;

410.6, true posterior wall infarction; 410.7,

subendocardial infarction; 410.8, other specified sites; and 410.9, unspecified sites. 

City characteristics, such as population

density and percentage of population

≥ 65 years of age in poverty status, were

obtained from the 1990 U.S. census (U.S.

Census Bureau 2000). The average annual

mortality rate for emphysema among people

≥ 65 years of age during 1989–2000 were

obtained from the National Center for

Health Statistics (Hyattsville, MD) and used

as an indirect measure of smoking history in

each city. 

The accuracy of the Medicare claims-based

diagnosis of MI has been recently validated

(Kiyota et al. 2004). 

Environmental data. We obtained data

for PM10 (particulate air matter with aerodynamic diameter < 10 μm) for 1985–1999

from the U.S. Environmental Protection

Agency’s Aerometric Information Retrieval

System (Nehls and Akland 1973). 

We selected the following cities with daily

PM10 monitoring that represent a geographic

distribution across the country: Birmingham,

Alabama; Boulder, Colorado; Canton, Ohio;

Chicago, Illinois; Cincinnati, Ohio; Cleveland,

Ohio; Colorado Springs, Colorado; Columbus,

Ohio; Denver, Colorado; Detroit, Michigan;

Honolulu, Hawaii; Houston, Texas;

Minneapolis-St.Paul, Minnesota; Nashville,

Tennessee; New Haven, Connecticut;

Pittsburgh, Pennsylvania; Provo-Orem, Utah;

Salt Lake City, Utah; Seattle, Washington;

Steubenville, Ohio; and Youngstown, Ohio. 

For most cities, the metropolitan county

encompassed the city and much of its suburbs,

but we used multiple counties for MinneapolisSt. Paul (Ramsey and Hennepin), Birmingham

(Blount, Jefferson, St. Clair, Shelby, and

Walker), Steubenville (Jefferson, OH; Brooke

and Hancock, WV), and Youngstown

(Columbiana and Mahoning). 

For each subject and follow-up period we

created yearly averages (January–December)

of pollution for that year and lags up to the

3 previous years.

Analytical strategy. We defined the cohort

as follows: We assumed that a subject admitted for MI enters the cohort if he survived at

least 3 months and/or is alive the first January

of the year following the admission. For each

subject, the follow-up periods were 1 year

periods (January–December) until the year in

which they die (or suffer a subsequent MI or

CHF admission for those analyses) or until

December 1999 (censoring). 

City-specific cohorts were created for the

three survival analyses, one where failure was

defined as death, one where failure was

defined as a new MI, and one where failure

was defined as a first admission for CHF.

We analyzed the data with an extended

Cox’s proportional hazard regression model,

which allows for time-varying covariates in

survival analysis (Kleinbaum and Klein 2005).

The model for the hazard h at time t is

h[t,X(t)] = 

h0(t) exp[Σi = 1 to p1 βi Xi + Σj=1 to p2 δj Zj

(t)], [1]

where t is time since a subject entered the

cohort (January) after the admission for MI

and is represented by 1 year; X1, ... Xp1 are

time-invariant variables such as sex; and Z1(t),

..., Zp2(t) are time-varying variables such as

air pollution. 

We adjusted for individual risk factors

including age, sex, race, type of MI, number

of days of coronary care and intensive care,

previous diagnoses for atrial fibrillation, and

secondary or previous diagnoses for COPD,

diabetes, and hypertension, and for season of

initial event as cold (December–February),

hot (June–August), and transitional. To allow

for possible nonproportionality of the survival rates, time period (three categories:

1985–1989, 1990–1994, and 1995–1999),

age (5-year categories), sex, race (white, black,

others), and type of MI (10 categories) were

treated as stratification variables. 

Zanobetti and Schwartz

770 VOLUME 115 | NUMBER 5 | May 2007 • Environmental Health Perspectives

Table 1. Characteristics of the study population among residents of 21 U.S. cities.

Mean 

Characteristic No. of events Percent (5th–95th percentiles)

MI 196,131

Failure

Deaths 89,249 45.5

CHF 33,764 17.2

Subsequent MI 22,552 11.5

Baseline characteristics

Age 76.1 (66.5–89.1)

Sex 

Male 98,822 50.4

Female 97,309 49.6

Race

White 165,549 84.4

Black 19,759 10.1

Other 10,823 5.5

No. of days in coronary care 1.3 (0–6.5)

No. of days in intensive care 1.5 (0–6.5)

Type of MI

Anterolateral wall 10,088 5.1

Other anterior wall 37,993 19.4

Inferolateral wall 6,434 3.3

Inferoposterior wall 4,291 2.2

Other inferior wall 44,923 22.9

Other lateral wall 5,326 2.7

True posterior wall infarction 2,009 1.0

Subendocardial infarction 66,628 34.0

Other specified sites 3,951 2.0

Unspecified sites 14,488 7.4

Secondary or previous diagnoses

COPD 28,509 14.5

Diabetes 44,686 22.8

Hypertension 72,154 36.8

Previous admissions

Atrial fibrillation 11,374 5.8
Ties were treated using the approach of

Kalbfleisch and Prentice (1980). 

As a sensitivity analysis, we considered an

alternative definition of our follow-up period.

We defined yearly follow-up (and exposure

averages) using a 12-month period starting

the month of their index admission. We continued to construct 12-month average PM

exposure for each subject for each subsequent

year of follow-up (using month of initial

event as the anniversary) until censoring

or failure. In the last follow-up period for

each subject, the person time at risk was

< 12 months, and this was incorporated in

the model. However, PM exposure was kept

as a 12-month average to maintain comparability with other periods. When the last follow-up period was ≤ 3 months, subjects were

censored at their last complete 12-month

period of follow-up because the exposure

interval was judged too short to be comparable to the 12-month exposure used for the

follow-up periods. 

We also restricted the mortality and CHF

cohorts to subjects with a second MI, with

follow-up beginning after the occurrence of

their second MI, or including only those subjects who were admitted for their primary MI

between 1985 and 1996, allowing at least

3 years of follow-up to all subjects in the

analysis. 

For each subject in each follow-up period,

we considered the following possible exposure

indexes: a) the average PM10 in their city in

that follow-up period; and b) a model containing simultaneously the exposure during

the follow-up period and each of the three

previous years (distributed lag), to see if we

could determine how the PM dropped off

over time. 

We first performed city-specific analyses;

in the second stage of the analysis, the results

were combined using the meta-regression

technique of Berkey et al. (1998). To be conservative, we report the results incorporating a

random effect, whether or not there was a significant heterogeneity. 

Effect modification by individual risk factors was examined by fitting separate proportionate hazard models for each group (e.g.,

sex) in each city, controlling for covariates and

combining across cities as in the main analysis.

In addition, we examined effect modification

by city characteristics by entering them as predictor variables in the meta-regression. These

included measures of socioeconomic condition

(percent in poverty), exposure-related measures (mean and interquartile range of PM10 in

the city), general social factors (population

density), and the emphysema death rate in

persons ≥ 65 years of age as a surrogate for the

smoking history of the population. The

results are expressed as hazard ratio (HR) for

10 μg/m3 PM10. 

Results 

There were 196,131 eligible MIs in the

21 cities during the study period. Table 1

shows characteristics of the study population

for all the cities. Of the population, 45.5%

died by the end of follow-up, 17% had a

CHF admission after the index MI, and

11.5% had a subsequent MI. In the cohort,

63% of the subjects were ≥ 75 years of age.

The most common types of MI were subendocardial infarction (34%), MI of other

inferior wall (23%), and MI of other anterior

wall (19%). 

The average duration of the follow-up was

5.1 years for mortality, 3.7 years for CHF,

and 3.6 years for subsequent MI. The range

of survival times in all the cohorts varied from

1 to 14 years. 

Table 2 presents city-specific characteristics, including the total population, PM concentrations, counts of hospital admissions

for MI, and the numbers of deaths, subsequent acute MI, and first hospitalizations for

CHF. The average PM10 across all cities was

28.8 μg/m3. 

Table 3 presents the city-specific incidence

rates for the three outcomes. In total, the incidence rates were 0.091 for death, 0.054 for

CHF admission, and 0.027 for subsequent

MI. The incidence rates among all cities were

examined by year (Table 4); these were higher

in the first 5 years but changed little during

Air pollution and MI survival

Environmental Health Perspectives • VOLUME 115 | NUMBER 5 | May 2007 771

Table 3. Accrued person-time and incidence rate for the three survival analyses.

Person-years Incidence rate

City Deaths CHF 2nd MI Deaths CHF 2nd MI

Birmingham, AL 44,672 28,995 39,495 0.120 0.063 0.026

Boulder, CO 6,292 4,014 5,305 0.084 0.044 0.018

Canton, OH 23,826 15,333 20,336 0.108 0.052 0.029

Chicago, IL 207,151 125,216 180,216 0.123 0.061 0.028

Cincinnati, OH 39,967 25,530 35,255 0.118 0.054 0.026

Cleveland, OH 83,790 52,392 72,310 0.116 0.060 0.026

Colorado Springs, CO 10,475 7,813 9,430 0.080 0.036 0.018

Columbus, OH 37,725 24,977 33,535 0.120 0.055 0.031

Denver, CO 22,424 15,187 20,178 0.084 0.036 0.018

Detroit, MI 87,612 55,024 74,111 0.123 0.063 0.028

Honolulu, HI 21,831 14,842 19,632 0.113 0.043 0.025

Houston, TX 52,910 36,734 46,250 0.113 0.049 0.021

Minneapolis, MN 51,318 34,124 46,421 0.096 0.040 0.022

Nashville, TN 20,710 14,039 18,168 0.113 0.050 0.025

New Haven, CT 46,935 28,516 40,926 0.113 0.051 0.027

Pittsburg, PA 101,145 63,399 82,726 0.124 0.058 0.034

Provo/Orem, UT 8,138 5,401 6,798 0.081 0.048 0.020

Salt Lake City, UT 19,021 13,236 17,136 0.080 0.032 0.015

Seattle, WA 52,168 34,519 46,465 0.094 0.038 0.020

Steubenville, OH 11,674 6,986 9,311 0.123 0.071 0.032

Youngstown, OH 27,146 16,902 23,182 0.120 0.061 0.033

Total 976,930 623,179 847,186 0.091 0.054 0.027

Table 2. City-specific counts of hospital admissions for MI, second MI, first CHF, and deaths and for distribution of PM10.

MI CHF 2nd MI No. ≥ 65 PM10a (%)

City admissions Deaths events events years of ageb 10 50 90

Birmingham, AL 8,927 4,281 1,814 1,028 120 22.8 27.0 38.2

Boulder, CO 1,117 434 176 97 17 18.0 20.6 28.9

Canton, OH 4,788 2,061 797 594 53 22.1 25.2 28.4

Chicago, IL 42,091 20,333 7,673 5,130 632 29.5 33.4 38.5

Cincinnati, OH 7,961 3,778 1,381 906 115 25.5 30.7 38.2

Cleveland, OH 16,648 7,767 3,140 1,899 221 35.2 37.6 42.5

Colorado Springs, CO 2,054 672 282 173 32 18.4 21.0 24.9

Columbus, OH 7,859 3,574 1,370 1,044 92 25.6 28.5 31.6

Denver, CO 3,919 1,551 546 357 64 26.6 28.9 36.5

Detroit, MI 18,437 8,490 3,439 2,098 264 28.0 31.3 37.9

Honolulu, HI 4,528 1,952 633 484 91 14.8 16.3 18.7

Houston, TX 10,885 4,764 1,812 975 196 26.0 29.7 32.3

Minneapolis, MN 10,123 3,962 1,360 1,019 176 22.3 24.7 31.8

Nashville, TN 4,081 1,877 697 449 59 27.1 30.2 38.5

New Haven, CT 9,232 4,245 1,447 1,119 118 22.2 24.0 29.3

Pittsburg, PA 20,663 10,007 3,653 2,800 233 25.2 29.5 34.3

Provo/Orem, UT 1,504 535 258 136 18 26.3 32.4 38.5

Salt Lake City, UT 3,535 1,233 425 256 61 28.2 34.1 43.3

Seattle, WA 9,674 4,011 1,325 921 167 16.0 22.6 31.7

Steubenville, OH 2,502 1,130 499 302 24 26.4 33.9 37.7

Youngstown, OH 5,603 2,592 1,037 765 61 27.2 29.2 33.3

aDistribution of the individually assigned 1-year PM10 mean in each city. bPopulation ≥ 65 years of age (× 1,000).
the following years, justifying the use of three

categories to describe the time period. 

We found significant associations in the

three survival analyses adjusting for confounders (Table 5), with a hazard ratio for the

sum of the distributed lag for mortality of 1.3

[95% confidence interval (CI), 1.2–1.5] per

10 μg/m3 PM10, a hazard ratio of 1.4 (95%

CI, 1.2–1.7) for CHF, and a hazard ratio of

1.4 (95% CI, 1.1–1.8) per 10 μg/m3 PM10

for a new hospitalization for MI. The distributed lag model shows greater effects at lags 1

and 2 exposure, with an overall effect considerably larger than for a single year. 

Table 6 presents the results of the sensitivity analyses. When we restricted the mortality

and CHF analysis to subjects with a second

MI with follow-up beginning after the occurrence of their second MI, the effect of PM10

for the distributed lag model showed an

HR of 1.3 (95% CI, 1.15–1.55) in the mortality cohort and an HR of 1.4 (95% CI,

1.22–1.65) in the CHF cohort. Including

only those subjects who were admitted for

their primary MI between 1985 and 1996,

that is, with at least 3 years of follow-up, we

found higher estimates than the main results

reported in Table 5.

In Table 6 we also present the result of

the sensitivity analysis in which we modified

the definition of the cohort. For the distributed lag, we found an HR for mortality of 1.3

(95% CI, 1.15–1.4) per 10 μg/m3 PM10; this

HR is similar to the main result in Table 5.

Figure 1 shows the results of the analysis

of effect modification by sex and age groups

(65–75 years of age and ≥ 76 years). We did

not find effect modification by sex, but we

did find a higher effect in the older age

group. Following the method of Payton et al.

(2003) to determine whether the difference

between the age groups was significant, we

found a p-value for mortality of 0.064,

whereas the p-value for subsequent MI and

CHF was 0.082. 

We used meta-regression (Berkey et al.

1998) to examine predictors of heterogeneity

across city (Table 7), and we found that most

of the predictors were not significant as modifiers of the PM10 effect. 

Discussion

We found a significant effect of long-term

exposure to airborne particles on the risk of

death, progression to heart failure, and a subsequent MI in a large multicity study of subjects

discharged alive following an acute MI. This

association was not due to differences between

cities in exposure, but resulted from the association of year-to-year changes in mortality risk

with year-to-year changes in exposure. We

found that association persisted for several

years of lag, but was falling off by lag 3. 

Although several previous studies have

reported an association of PM with mortality

in survival analysis, this is the first long-term

study that investigated persons discharged

alive following an acute MI and showed that

persons who survive an MI are at risk of death

in response to subsequent particle exposure.

The present study is the first large cohort

study focused on the elderly.

One key difference between the present

study and previous cohorts comes from the

source of variation in exposure. In the other

cohort studies, the source of exposure variation is across geographic area. For example,

the ACS study (Pope et al. 2004a) contrasted

covariate-adjusted survival in each city with

long-term average pollution in that city.

Using such an approach, unmeasured factors

that vary across city are potential confounders. For example, substantial geographic

variability in the use of cardiovascular medication has been reported in a number of studies, and this was not controlled in previous

cohort studies. In the present study the basic

analysis was conducted within each city, and

exposure variation comes from temporal

changes in pollution concentration. This

eliminates those potential confounders as a

concern. By focusing on 12-month average

exposures, it also eliminates the potential confounding by short-term weather factors that

are an issue in time-series studies. Obviously,

factors that fluctuate from year to year within

each city are potential confounders in this

study design. The advantage of this approach

is that it allows us to use an analytical

methodology with different vulnerabilities to

confounding than in previous studies; to

determine whether an association between

PM and mortality risk persists; and to examine an intermediate time period of exposure—in contrast to the use of daily exposure

in time-series studies—and exposure over

many years in other cohorts. 

The use of longitudinal rather than crosssectional exposure gradients in this study may

also explain some of the differences in effect

size estimates, because the variation in central

station monitoring and personal exposure over

time may be more correlated than similar variations over space. Moreover, the ACS study

Zanobetti and Schwartz

772 VOLUME 115 | NUMBER 5 | May 2007 • Environmental Health Perspectives

Table 4. Accrued person-years, number of deaths,

and incidence rate (IR) across all cities by year for

the mortality cohort.

Year Person-years No. of deaths IR

1986 10,015 1,423 0.142

1987 22,094 2,808 0.127

1988 32,640 3,675 0.113

1989 41,852 4,173 0.100

1990 50,173 4,786 0.095

1991 59,419 5,581 0.094

1992 67,713 6,223 0.092

1993 76,769 6,711 0.087

1994 85,574 7,709 0.090

1995 93,209 8,427 0.090

1996 100,260 8,803 0.088

1997 107,150 9,152 0.085

1998 113,019 9,617 0.085

1999 117,043 10,161 0.087

Table 5. HR and 95% CI for 10-μg/m3 increase in

PM10 for the year of failure and for the distributed

lag from the year of failure up to 3 previous years.

Failure HR 95% CI p-Values

Death

PM10 annual 1.11 1.05–1.19 0.001

Distributed lag model

Lag 0 1.04 0.96–1.14 0.336

Lag 1 1.07 0.99–1.14 0.070

Lag 2 1.14 1.10–1.18 0.000

Lag 3 1.06 0.99–1.12 0.077

Sum lags 0–3 1.34 1.17–1.52 0.000

CHF

PM10 annual 1.11 1.03–1.21 0.009

Distributed lag model

Lag 0 1.09 1.01–1.18 0.030

Lag 1 1.09 1.01–1.19 0.038

Lag 2 1.13 1.02–1.25 0.014

Lag 3 1.04 0.97–1.12 0.260

Sum lags 0–3 1.41 1.19–1.66 0.000

Second MI

PM10 annual 1.17 1.05–1.31 0.003

Distributed lag model

Lag 0 1.09 0.92–1.30 0.325

Lag 1 1.12 0.97–1.30 0.108

Lag 2 1.15 1.08–1.23 0.000

Lag 3 1.01 0.94–1.09 0.783

Sum lags 0–3 1.43 1.12–1.82 0.005

Models controlled for season, days of coronary care and

intensive care, previous diagnosis for atrial fibrillation, and

secondary or previous diagnoses for COPD, diabetes, and

hypertension; we adjusted for time period, age, sex, race,

and type of MI as stratification variables. 

Table 6. HR and 95% CI for 10 μg/m3 increase in PM10 (sum of previous 3 years distributed lag) for the sensitivity analyses.

HR 95% CI p-Values

Death

Subjects with subsequent MIa 1.33 1.15–1.55 0.000

Subjects admitted between 1985 and 1996b 1.45 1.26–1.68 0.000

Second definition of cohortc 1.29 1.15–1.44 0.000

CHF

Subjects with subsequent MIa 1.42 1.22–1.65 0.000

Subjects admitted between 1985 and 1996b 1.51 1.26–1.81 0.000

Subsequent MI

Subjects admitted between 1985 and 1996b 1.62 1.23–2.13 0.001

aFollow-up started after subsequent MI. bIncludes only primary admission for MI during 1985 and 1996. cYearly follow-up

and 12-month average PM10 exposure for each subject for each subsequent year of follow-up starting from the month of

the index admission until censoring or failure.
Air pollution and MI survival

Environmental Health Perspectives • VOLUME 115 | NUMBER 5 | May 2007 773

(Pope et al. 2004a) used monitors within the

multicounty metropolitan areas to assign exposure, whereas our subjects are matched to monitors in the same city or county. A recent

reanalysis of ACS data restricting to persons

living in the same county of the monitor

reported a larger risk (Pope et al. 2004a; Willis

et al. 2003). 

The sensitivity analysis in the present

study showed that subjects with a second MI

have a higher risk of a PM-associated subsequent death. Hence, this appears to represent

a particularly susceptible group. 

In a recent case–crossover study examining

this association, Bateson and Schwartz (2004)

reported much smaller relative risks (1.02; 95%

CI, 0.99–1.04). In another case–crossover

study (Zanobetti and Schwartz 2005) on 21

U.S. cities, we analyzed the short-term effect of

PM10 on the MI hospitalization in these cities.

In that analysis, we also found a smaller relative

risk (1.007; 95% CI, 1.003–1.01). Although

part of the association reported here may be

acute, the evidence indicates cumulative exposure over a year or more elevates risk above and

beyond the effects of acute exposure. 

We observed a high degree of heterogeneity among the cities. In multicities time-series

studies of short-term effect of air pollution on

health, heterogeneity has been found among

cities (Katsouyanni et al. 2001; Le Tertre et al.

2002; Samet et al. 2000). Heterogeneity has

been attributed to, for example, differences in

particle characteristics, ventilation rates of

buildings, average PM10 concentrations, and

social conditions. Previously published cohort

studies could not address the issue of heterogeneity because the studies were essentially

cross-sectional; the source of exposure variation

was across geographic area. This is the first

long-term study to show significant heterogeneity among cities in response to long-term

exposure, and the cause of this heterogeneity

needs to be determined. 

This analysis advances the field a) by

reporting an association between particle exposure and survival in a cohort study of MI survivors that eliminates geographic variation in

risk factors as a confounder, but where exposure variation comes from temporal changes in

pollution concentration; and b) by focusing on

associations on an intermediate time scale. If

PM increases progression of atherosclerosis

(Künzli et al. 2005; Suwa et al. 2002) or

impairs endothelial function (Brook et al.

2004) or autonomic function (Liao et al. 1999;

O’Neill et al. 2005), these changes may have

greater impact in populations with greater

underlying impairment, such as the elderly. 

A primary candidate for explaining these

risks must be the acute MI itself (Berger et al.

1992; Guidry et al. 1999; Kannel et al. 1979).

Subjects surviving MIs have enhanced risk of

dying and decreased heart rate variability

(Malik et al. 1989), and are likely to have

greater susceptibility to subsequent insults. 

This possibility of multiple pathways to

mortality makes it plausible that the all-cause

mortality risk might exceed the risk of specific

cardiac events, as we observe for subsequent

MI. For example, PM has been associated

with arrhythmias (Peters et al. 2000), pneumonia (Zanobetti et al. 2000), and COPD

(Schwartz 1993), creating potential additional

pathways by which exposure could increase

mortality risk independent of MI risk. 

One limitation of the present study is that

Medicare does not provide the underlying

cause of death. If the cause of death were

available, we could understand better the

possible pathways. 

Future studies examining cohorts with

more detailed clinical data on the MI survivors

should be fruitful. The increased risk of heart

failure following the MI also suggests that further evaluation of this outcome is warranted. 

The present study presents additional limitations, the main one being the absence of

information on subject characteristics such as

smoking, body mass index, or medicine use,

and information on whether the patients

migrated out of the study areas after their last

Medicare contact. However, we were able to

adjust for other important characteristics such

as age, race, sex, the type of MI, and previous

and secondary diagnoses. Missing those other

characteristics would only confound the

association with air pollution if they were

correlated with pollution. However, we conducted a city-specific analysis to remove location-specific differences in the analyses. Hence,

differences across cities in smoking rates, for

example, cannot confound the association,

because only the temporal variability in

Table 7. Modification of the PM10 association in the three survival analyses by city characteristics across

21 U.S. cities expressed as HR and 95% CI for 10-μg/m3 increase in PM10 (distributed lag) estimated at the

25th percentile and the 75th percentile of the effect modifier.

City p-Value HR at the 25% percentile HR at the 75% percentile

characteristic for modifier 1st quartile HR 95% CI 3rd quartile HR 95% CI

Population ≥ 65 years of age in poverty status (%)

Death 0.60 8.0 1.34 1.06–1.70 11.4 1.27 1.05–1.53

CHF 0.83 1.36 1.05–1.75 1.32 1.08–1.62

MI 0.61 1.29 0.91–1.82 1.19 0.91–1.56

Annual mortality rate for emphysema ≥ 65 years of age

Death 0.57 32.9 1.33 1.07–1.67 47.6 1.27 1.04–1.54

CHF 0.37 1.42 1.12–1.79 1.30 1.06–1.60

MI 0.97 1.26 0.91–1.73 1.25 0.94–1.66

Mean PM10

Death 0.60 25.5 1.25 1.01–1.55 32.1 1.33 1.07–1.65

CHF 0.74 1.36 1.08–1.72 1.30 1.03–1.64

MI 0.04 1.02 0.76–1.37 1.43 1.08–1.91

IQR PM10

Death 0.70 3.3 1.31 1.07–1.62 5.5 1.26 1.02–1.56

CHF 0.38 1.40 1.12–1.74 1.26 1.01–1.58

MI 0.28 1.12 0.83–1.50 1.33 0.98–1.79

Population density

Death 0.67 594.8 1.33 1.06–1.67 2076.9 1.27 1.05–1.54

CHF 0.75 1.30 1.02–1.66 1.35 1.10–1.65

MI 0.74 1.17 0.84–1.64 1.23 0.93–1.62

IQR, interquartile range. 

Figure 1. Effect modification by sex and age group in the three survival analyses, in which failure

is defined as death, a subsequent MI, or CHF. The effects were computed by stratified analysis for

each sex or age group (65–75 years or > 75 years) and PM10 averaged over 3 years. Effects are computed

for 10 μg/m3 PM10.

Male Female Age

65–75

Age

> 75

Death

Age

65–75

Age

> 75

CHF

HR per 10-μg/m3 PM10 increase

Age

65–75

Age

> 75

Second MI

1.50

1.40

1.30

1.20

1.10

1.00

0.90 Male Female Male Female
pollution within city contributes to the association. Smoking could only confound this association if year-to-year variations in smoking

rates within city covaried with year-to-year

variations in PM10 concentrations. Moreover,

we examined the emphysema death rate in

persons > 65 years of age as an effect modifier

because it is associated with smoking history

in the population, and found that it did not

modify the PM-associated risk. Thus, smoking

is unlikely to be a confounder in our study. 

Our findings that both subsequent MIs

and CHF admissions, as well as mortality

risk, are elevated suggests that multiple pathways are involved in the particle effects. 

Other human and animal studies (Godleski

et al. 2000) have shown associations between

particulate pollution and changes in heart rate

variability (Gold et al. 2000; Liao et al. 1999;

Pope et al. 1999); increases in plasma viscosity

(Peters et al. 1997), C-reactive protein (Brook

et al. 2003; Peters et al. 2001b; Pope et al.

2004b), plasma fibrinogen (Ghio et al. 2000),

white blood cell counts (Salvi et al. 1999;

Schwartz 2001), blood pressure (Ibald-Mulli

et al. 2001; Linn et al. 1999; Zanobetti et al.

2004), and oxidative stress (Bouthillier et al.

1998; Brook et al. 2003; Sorensen et al. 2003);

decreases in plaque stability (Suwa et al. 2002);

or occurrence of thrombotic complications after

exposure to pollutants (Nemmar et al. 2003).

In a recent study O’Neill et al. (2005) reported

that PM was associated with flow-mediated

dilation of the brachial arteries. 

Many of these associations are with acute

exposure, not long-term exposures such as

those used in the present study. Nevertheless,

the associations suggest that the hypotheses to

explain the potential mechanisms for the particle effects might involve systemic inflammation, changes in autonomic function, or

oxidative stress capable of influencing both

cardiovascular and pulmonary physiology. 

Our findings suggest that persons surviving

an MI are at risk from exposure to particulate

pollution. This is a large group, and hence this

finding has substantial public health implications. Our results also suggest that it would be

beneficial to examine this population in

mechanistic studies. 

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