ORIGINAL CONTRIBUTION

Lung Cancer, Cardiopulmonary Mortality,

and Long-term Exposure

to Fine Particulate Air Pollution

C. Arden Pope III, PhD

Richard T. Burnett, PhD

Michael J. Thun, MD

Eugenia E. Calle, PhD

Daniel Krewski, PhD

Kazuhiko Ito, PhD

George D. Thurston, ScD

B

ASED ON SEVERAL SEVERE AIR

pollution events,1-3 a temporal

correlation between extremely

high concentrations of particulate and sulfur oxide air pollution and

acute increases in mortality was well

established by the 1970s. Subsequently, epidemiological studies published between 1989 and 1996 reported health effects at unexpectedly low

concentrations of particulate air pollution.4 The convergence of data from

these studies, while controversial,5

prompted serious reconsideration of

standards and health guidelines6-10 and

led to a long-term research program designed to analyze health-related effects

due to particulate pollution.11-13 In 1997,

the Environmental Protection Agency

adopted new ambient air quality standards that would impose regulatory limits on fine particles measuring less than

2.5 μm in diameter (PM2.5). These new

standards were challenged by industry

groups, blocked by a federal appeals

court, but ultimately upheld by the US

Supreme Court.14

Although most of the recent epidemiological research has focused on effects of short-term exposures, several

studies suggest that long-term exposure may be more important in terms

of overall public health.4 The new standards for long-term exposure to PM2.5

were originally based primarily on 2

prospective cohort studies,15,16 which

evaluated the effects of long-term pollution exposure on mortality. Both of

these studies have been subjected to

much scrutiny,5 including an extensive independent audit and reanalysis

of the original data.17 The larger of these

2 studies linked individual risk factor

and vital status data with national ambient air pollution data.16 Our analysis

uses data from the larger study and

Author Affiliations: Brigham Young University, Provo,

Utah (Dr Pope); Health Canada, Ottawa, Ontario (Dr

Burnett); University of Ottawa, Ottawa, Ontario (Drs

Burnett and Krewski); American Cancer Society, Atlanta, Ga (Drs Thun and Calle); and New York University School of Medicine, Tuxedo, NY (Drs Ito and

Thurston).

Corresponding Author and Reprints: C. Arden Pope

III, PhD, Department of Economics, Brigham Young

University, 142 FOB, Provo, UT 84602 (e-mail: cap3

@email.byu.edu).

Context Associations have been found between day-to-day particulate air pollution

and increased risk of various adverse health outcomes, including cardiopulmonary mortality. However, studies of health effects of long-term particulate air pollution have

been less conclusive.

Objective To assess the relationship between long-term exposure to fine particulate air pollution and all-cause, lung cancer, and cardiopulmonary mortality.

Design, Setting, and Participants Vital status and cause of death data were collected by the American Cancer Society as part of the Cancer Prevention II study, an ongoing prospective mortality study, which enrolled approximately 1.2 million adults in 1982.

Participants completed a questionnaire detailing individual risk factor data (age, sex, race,

weight, height, smoking history, education, marital status, diet, alcohol consumption, and

occupational exposures). The risk factor data for approximately 500000 adults were linked

with air pollution data for metropolitan areas throughout the United States and combined with vital status and cause of death data through December 31, 1998.

Main Outcome Measure All-cause, lung cancer, and cardiopulmonary mortality.

Results Fine particulate and sulfur oxide–related pollution were associated with allcause, lung cancer, and cardiopulmonary mortality. Each 10-μg/m3 elevation in fine

particulate air pollution was associated with approximately a 4%, 6%, and 8% increased risk of all-cause, cardiopulmonary, and lung cancer mortality, respectively. Measures of coarse particle fraction and total suspended particles were not consistently

associated with mortality.

Conclusion Long-term exposure to combustion-related fine particulate air pollution is an important environmental risk factor for cardiopulmonary and lung cancer

mortality.

JAMA. 2002;287:1132-1141 www.jama.com

1132 JAMA, March 6, 2002—Vol 287, No. 9 (Reprinted) ©2002 American Medical Association. All rights reserved.
(1) doubles the follow-up time to more

than 16 years and triples the number

of deaths; (2) substantially expands exposure data, including gaseous copollutant data and new PM2.5 data, which

have been collected since the promulgation of the new air quality standards; (3) improves control of occupational exposures; (4) incorporates

dietary variables that account for total

fat consumption, and consumption of

vegetables, citrus, and high-fiber grains;

and (5) uses recent advances in statistical modeling, including the incorporation of random effects and nonparametric spatial smoothing components

in the Cox proportional hazards model.

METHODS

Study Population

The analysis is based on data collected

by the American Cancer Society (ACS)

as part of the Cancer Prevention Study

II (CPS-II), an ongoing prospective

mortality study of approximately 1.2

million adults.18,19 Individual participants were enrolled by ACS volunteers in the fall of 1982. Participants resided in all 50 states, the District of

Columbia, and Puerto Rico, and were

generally friends, neighbors, or acquaintances of ACS volunteers. Enrollment was restricted to persons who

were aged 30 years or older and who

were members of households with at

least 1 individual aged 45 years or older.

Participants completed a confidential

questionnaire, which included questions about age, sex, weight, height,

smoking history, alcohol use, occupational exposures, diet, education, marital status, and other characteristics.

Vital status of study participants was

ascertained by ACS volunteers in September of the following years: 1984,

1986, and 1988. Reported deaths were

verified with death certificates. Subsequently, through December 31, 1998,

vital status was ascertained through automated linkage of the CPS-II study

population with the National Death Index.19 Ascertainment of deaths was

more than 98% complete for the period of 1982-1988 and 93% complete

after 1988.19 Death certificates or codes

for cause of death were obtained for

more than 98% of all known deaths.

Cause of death was coded according to

the International Classification of Diseases, Ninth Revision (ICD-9). Although the CPS-II cohort included approximately 1.2 million participants

with adequate questionnaire and causeof-death data, our analysis was restricted to those participants who resided in US metropolitan areas with

available pollution data. The actual size

of the analytic cohort varied depending on the number of metropolitan areas for which pollution data were available. TABLE 1 provides the number of

metropolitan areas and participants

available for each source of pollution

data.

Air Pollution Exposure Estimates

Each participant was assigned a metropolitan area of residence based on address at time of enrollment and 3-digit

ZIP code area.20 Mean (SD) concentrations of air pollution for the metropolitan areas were compiled from various

primary data sources (Table 1). Many

of the particulate pollution indices, including PM2.5, were available from data

from the Inhalable Particle Monitoring Network for 1979-1983 and data

from the National Aerometric Database for 1980-1981, periods just prior

to or at the beginning of the follow-up

period. An additional data source was

the Environmental Protection Agency

Aerometric Information Retrieval System (AIRS). The mean concentration

of each pollutant from all available

monitoring sites was calculated for each

metropolitan area during the 1 to 2

years prior to enrollment.17

Additional information on ambient

pollution during the follow-up period

was extracted from the AIRS database

as quarterly mean values for each routinely monitored pollutant for 1982

through 1998. All quarterly averages

met summary criteria imposed by the

Environmental Protection Agency and

were based on observations made on at

least 50% of the scheduled sampling

days at each site. The quarterly mean

values for all stations in each metropolitan area were calculated across the

study years using daily average values

for each pollutant except ozone. For

ozone, daily 1-hour maximums were

used and were calculated for the full

year and for the third quarter only (ie,

July, August, September). While gaseous pollutants generally had recorded data throughout the entire follow-up period of interest, the particulate

matter monitoring protocol changed in

the late 1980s from total suspended particles to particles measuring less than

10 μm in diameter (PM10), resulting in

the majority of total suspended particle data being available in the early to

mid-1980s and PM10 data being mostly

available in the early to mid-1990s.

As a consequence of the new PM2.5

standard, a large number of sites began collecting PM2.5 data in 1999. Daily

PM2.5 data were extracted from the AIRS

database for 1999 and the first 3 quarters of 2000. For each site, quarterly averages for each of the 2 years were computed. The 4 quarters were averaged

when at least 1 of the 2 corresponding

quarters for each year had at least 50%

of the sixth-day samples and at least 45

total sampling days available. Measurements were averaged first by site and

then by metropolitan area. Although no

network of PM2.5 monitoring existed in

the United States between the early

1980s and the late 1990s, the integrated average of PM2.5 concentrations during the period was estimated

by averaging the PM2.5 concentration for

early and later periods.

Mean sulfate concentrations for 1980-

1981 were available for many cities

based on data from the Inhalable Particle Monitoring Network and the

National Aerometric Database. Recognizing that sulfate was artifactually

overestimated due to glass fiber filters

used at that time, season and regionspecific adjustments were made.17 Since

few states analyzed particulate samples

for sulfates after the early 1980s, individual states were directly contacted for

data regarding filter use. Ion chromatography was used to analyze PM10 filters and this data could be obtained

from metropolitan areas across the

MORTALITY AND LONG-TERM EXPOSURE TO AIR POLLUTION

©2002 American Medical Association. All rights reserved. (Reprinted) JAMA, March 6, 2002—Vol 287, No. 9 1133
United States. Filters were collected for

a single reference year (1990) in the

middle of the 1982-1998 study period. The use of quartz filters virtually

eliminated the historical overestimation of sulfate. Mean sulfate concentrations for 1990 were estimated using sulfate from AIRS, data reported

directly from individual states, and

analysis of archived filters.

Statistical Analysis

The basic statistical approach used in this

analysis is an extension of the standard

Cox proportional hazards survival

model,21 which has been used for risk

estimates of pollution-related mortality in previous longitudinal cohort studies.15,16 The standard Cox model implicitly assumes that observations are

statistically independent after controlling for available risk factors, resulting

in 2 concerns with regard to risk estimates of pollution-related mortality.22

First, if the assumption of statistical independence is not valid, the uncertainty in the risk estimates of pollutionrelated mortality may be misstated.

Second, even after controlling for available risk factors, survival times of participants living in communities closer together may be more similar than

participants living in communities farther apart, which results in spatial autocorrelation. If this spatial autocorrelation is due to missing or systematically

mismeasured risk factors that are spatially correlated with air pollution, then

the risk estimates of pollution-related

mortality may be biased due to inadequate control of these factors. Therefore, in this analysis, the Cox proportional hazards model was extended by

incorporating a spatial random-effects

component, which provided accurate esTable 1. Summary of Alternative Pollution Indices*

Pollutant

(Years of Data

Collection) Units Source of Data

Data

Compilation

Team†

No. of

Metropolitan

Areas

No. of

Participants,

in Thousands Mean (SD)

PM2.5 μg/m3

1979-1983 IPMN HEI 61 359 21.1 (4.6)

1999-2000 AIRS NYU 116 500 14.0 (3.0)

Average 51 319 17.7 (3.7)

PM10 μg/m3

1982-1998 AIRS NYU 102 415 28.8 (5.9)

PM15 μg/m3

1979-1983 IPMN HEI 63 359 40.3 (7.7)

PM15-2.5 μg/m3

1979-1983 IPMN HEI 63 359 19.2 (6.1)

Total suspended particles μg/m3

1980-1981 NAD HEI 156 590 68.0 (16.7)

1979-1983 IPMN HEI 58 351 73.7 (14.3)

1982-1998 AIRS NYU 150 573 56.7 (13.1)

Sulfate μg/m3

1980-1981 IPMN and NAD,

artifact adjusted

HEI 149 572 6.5 (2.8)

1990 Compilation and analysis

of PM10 filters

NYU 53 269 6.2 (2.0)

Sulfur dioxide ppb AIRS

1980 HEI 118 520 9.7 (4.9)

1982-1998 NYU 126 539 6.7 (3.0)

Nitrogen dioxide ppb AIRS

1980 HEI 78 409 27.9 (9.2)

1982-1998 NYU 101 493 21.4 (7.1)

Carbon monoxide ppm AIRS

1980 HEI 113 519 1.7 (0.7)

1982-1998 NYU 122 536 1.1 (0.4)

Ozone ppb AIRS

1980 HEI 134 569 47.9 (11.0)

1982-1998 NYU 119 525 45.5 (7.3)

1982-1998‡ NYU 134 557 59.7 (12.8)

*PM2.5 indicates particles measuring less than 2.5 μm in diameter; PM10, particles measuring less than 10 μm in diameter; PM15, particles measuring less than 15 μm in diameter;

PM15-2.5, particles measuring between 2.5 and 15 μm in diameter; μg/m3

, micrograms per cubic meter; ppb, parts per billion; ppm, parts per million; IPMN, Inhalable Particle

Monitoring Network; AIRS, Aerometric Information Retrieval System [Environmental Protection Agency]; and NAD, National Aerometric Database.

†HEI indicates data were compiled by the Health Effects Institute reanalysis team, which was previously published.17 NYU indicates data were compiled at the New York University

School of Medicine, Nelson Institute of Environmental Medicine (K.I. and G.D.T.).

‡Daily 1-hour maximums were used. Values were calculated only for the third quarter (ie, July, August, September).

MORTALITY AND LONG-TERM EXPOSURE TO AIR POLLUTION

1134 JAMA, March 6, 2002—Vol 287, No. 9 (Reprinted) ©2002 American Medical Association. All rights reserved.
timates of the uncertainty of effect estimates. The model also evaluated spatial autocorrelation and incorporated a

nonparametric spatial smooth component (to account for unexplained spatial structure). A more detailed description of this modeling approach is

provided elsewhere.22

The baseline analysis in this study estimated adjusted relative risk (RR) ratios for mortality by using a Cox proportional hazards model with inclusion

of a metropolitan-based randomeffects component. Model fitting involved a 2-stage process. In the first

stage, survival data were modeled using the standard Cox proportional hazards model, including individual level

covariates and indicator variables for

each metropolitan area (without pollution variables). Output from stage 1

provided estimates of the metropolitanspecific logarithm of the RRs of mortality (relative to an arbitrary reference community), which were adjusted

for individual risk factors. The correlation between these values, which was

induced by using the same reference

community, was then removed.23 In the

second stage, the estimates of adjusted metropolitan-specific health responses were related to fine particulate air pollution using a linear randomeffects regression model.24 The time

variable used in the models was survival time from the date of enrollment. Survival times of participants who

did not die were censored at the end of

the study period. To control for age, sex,

and race, all of the models were stratified by 1-year age categories, sex, and

race (white vs other), which allowed

each category to have its own baseline

hazard. Models were estimated for allcause mortality and for 3 separate mortality categories: cardiopulmonary

(ICD-9 401-440 and 460-519), lung

cancer (ICD-9 162), and all others.

Models were estimated separately for

each of the 3 fine particle variables,

PM2.5 (1979-1983), PM2.5 (1999-

2000), and PM2.5 (average). Individual level covariates were included in

the models to adjust for various important individual risk factors. All of these

variables were classified as either indicator (ie, yes/no, binary, dummy) variables or continuous variables. Variables used to control for tobacco smoke,

for example, included both indicator

and continuous variables. The smoking indicator variables included: current cigarette smoker, former cigarette smoker, and a pipe or cigar smoker

only (all vs never smoking) along with

indicator variables for starting smoking before or after age 18 years. The

continuous smoking variables included: current smoker’s years of smoking, current smoker’s years of smoking squared, current smoker’s cigarettes

per day, current smoker’s cigarettes per

day squared, former smoker’s years of

smoking, former smoker’s years of

smoking squared, former smoker’s cigarettes per day, former smoker’s cigarettes per day squared, and the number of hours per day exposed to passive

cigarette smoke.

To control for education, 2 indicator variables, which indicated completion of high school or education beyond high school, were included.

Marital status variables included indicator variables for single and other vs

married. Both body mass index (BMI)

values and BMI values squared were included as continuous variables. Indicator variables for beer, liquor, and wine

drinkers and nonresponders vs nondrinkers were included to adjust for alcohol consumption. Occupational exposure was controlled for using various

indicator variables: regular occupational exposure to asbestos, chemicals/

acids/solvents, coal or stone dusts, coal

tar/pitch/asphalt, diesel engine exhaust, or formaldehyde, and additional indicator variables that indicated 9 different rankings of an

occupational dirtiness index that has

been developed and described elsewhere.17,25 Two diet indices that accounted for fat consumption and consumption of vegetables, citrus, and

high-fiber grains were derived based on

information given in the enrollment

questionnaire.18 Quintile indicator variables for each of these diet indices were

also included in the models.18

In addition to the baseline analysis,

several additional sets of analysis were

conducted. First, to more fully evaluate the shape of the concentrationresponse function, a robust locally

weighted regression smoother26 (within

the generalized additive model framework27) was used to estimate the relationship between particulate air pollution and mortality in the second stage

of model fitting. Second, the sensitivity of the fine particle mortality risk estimates compared with alternative modeling approaches and assumptions was

evaluated. Standard Cox proportional

hazards models were fit to the data including particulate air pollution as a

predictor of mortality and sequentially adding (in a controlled forward

stepwise process) groups of variables

to control for smoking, education, marital status, BMI, alcohol consumption,

occupational exposures, and diet.

In addition, to evaluate the sensitivity of the estimated pollution effect

while more aggressively controlling for

spatial differences in mortality, a 2-dimensional term to account for spatial

trends was added to the models and was

estimated using a locally weighted regression smoother. The “span” parameter, which controls the complexity of

the surface smooth, was set at 3 different settings to allow for increasingly aggressive fitting of the spatial structure.

These included a default span of 50%,

the span that resulted in the lowest unexplained variance in mortality rate between metropolitan areas, and the span

that resulted in the strongest evidence

(highest P value) to suggest no residual spatial structure. The risk estimates and SEs (and thus the confidence intervals) were estimated using

generalized additive modeling27 with

S-Plus statistical software,28 which provides unbiased effect estimates, but may

underestimate SEs if there is significant spatial autocorrelation and significant correlations between air pollution and the smoothed surface of

mortality. Therefore, evidence of spatial autocorrelation was carefully evaluated and tested using the Bartlett test.29

The correlations of residual mortality

MORTALITY AND LONG-TERM EXPOSURE TO AIR POLLUTION

©2002 American Medical Association. All rights reserved. (Reprinted) JAMA, March 6, 2002—Vol 287, No. 9 1135
with distance between metropolitan areas were graphically examined.

Analyses were also conducted of

effect modification by age, sex, smoking status, occupational exposure, and

education. Finally, models were fit using a variety of alternative pollution indices, including gaseous pollutants.

Specifically, models were estimated

separately for each of the pollution variables listed in Table 1, while also including all of the other risk factor variables.

RESULTS

Fine particulate air pollution generally declined in the United States during the follow-up period of this study.

FIGURE 1 plots mean PM2.5 concentrations for 1999-2000 over mean PM2.5

concentrations for 1979-1983 for the

51 cities in which paired data were

available. The concentrations of PM2.5

were lower in 1999-2000 than in 1979-

1983 for most cities, with the largest reduction observed in the cities with the

highest concentrations of pollution during 1979-1983. Mean PM2.5 levels in the

2 periods were highly correlated

(r=0.78). The rank ordering of cities

by relative pollution levels remained

nearly the same. Therefore, the relative levels of fine particle concentrations were similar whether based on

measurements at the beginning of the

study period, shortly following the

study period, or an average of the 2.

As reported in TABLE 2, all 3 indices

of fine particulate air pollution were associated with all-cause, cardiopulmonary, and lung cancer mortality, but not

mortality from all other causes combined. FIGURE 2 presents the nonparametric smoothed exposure response relationships between cause-specific

mortality and PM2.5 (average). The log

RRs for all-cause, cardiopulmonary, and

lung cancer mortality increased across

the gradient of fine particulate matter.

Goodness-of-fit tests indicated that the

associations were not significantly different from linear associations (P.20).

The fine particle mortality RR ratios

from various alternative modeling approaches and assumptions are presented in FIGURE 3. After controlling for

smoking, education, and marital status, the controlled forward stepwise inclusion of additional covariates had little

influence on the estimated associations

with fine particulate air pollution on cardiopulmonary and lung cancer mortality. As expected, cigarette smoking was

highly significantly associated with elevated risk of all-cause, cardiopulmonary, and lung cancer mortality

(P.001). Estimated RRs for an average current smoker (men and women

combined, 22 cigarettes/day for 33.5

years, with initiation before age 18 years)

were equal to 2.58, 2.89, and 14.80 for

all-cause, cardiopulmonary, and lung

cancer mortality, respectively. Statistically significant, but substantially smaller

and less robust associations, were also

observed for education, marital status,

BMI, alcohol consumption, occupational exposure, and diet variables. Although many of these covariates were

also statistically associated with mortality, the risk estimates of pollutionrelated mortality were not highly sensitive to the inclusion of these additional

covariates.

Figure 3 also demonstrates that the

introduction of the random-effects component to the model resulted in larger

SEs of the estimates and, therefore,

somewhat wider 95% confidence intervals. There was no evidence of statistically significant spatial autocorrelation in the survival data based on the

Bartlett test (P.20) after controlling

for fine particulate air pollution and the

various individual risk factors. Furthermore, graphical examination of the correlations of the residual mortality with

distance between metropolitan areas did

not reveal significant spatial autocorrelation (results not shown). Nevertheless, the incorporation of spatial

smoothing was included to further investigate the robustness of the estimated particulate pollution effect. Effect

estimates were not highly sensitive to

the incorporation of spatial smoothing to account for regional clustering

or other spatial patterns in the data.

FIGURE 4 presents fine particle air

pollution–related mortality RR ratios after stratifying by age, sex, education,

and smoking status, and adjusting for

all other risk factors. The differences

across age and sex strata were not generally consistent or statistically significant. However, a consistent pattern

emerged from this stratified analysis: the

association with particulate pollution

was stronger for both cardiopulmoFigure 1. Mean Fine Particles Measuring

Less Than 2.5 μm in Diameter (PM2.5)

30

15

25

20

10

5

5 10 15 20 25 30

PM2.5 (1979-1983)

PM2.5 (1999-2000)

r =0.78

Mean PM2.5 concentrations in micrograms per meters cubed for 1999-2000 are plotted along with concentrations for 1979-1983 for the 51 metropolitan areas with paired pollution data. The dotted line is a

reference 45°-equality line.

Table 2. Adjusted Mortality Relative Risk (RR) Associated With a 10-μg/m3 Change in Fine

Particles Measuring Less Than 2.5 μm in Diameter

Cause of Mortality

Adjusted RR (95% CI)*

1979-1983 1999-2000 Average

All-cause 1.04 (1.01-1.08) 1.06 (1.02-1.10) 1.06 (1.02-1.11)

Cardiopulmonary 1.06 (1.02-1.10) 1.08 (1.02-1.14) 1.09 (1.03-1.16)

Lung cancer 1.08 (1.01-1.16) 1.13 (1.04-1.22) 1.14 (1.04-1.23)

All other cause 1.01 (0.97-1.05) 1.01 (0.97-1.06) 1.01 (0.95-1.06)

*Estimated and adjusted based on the baseline random-effects Cox proportional hazards model, controlling for age,

sex, race, smoking, education, marital status, body mass, alcohol consumption, occupational exposure, and diet.

CI indicates confidence interval.

MORTALITY AND LONG-TERM EXPOSURE TO AIR POLLUTION

1136 JAMA, March 6, 2002—Vol 287, No. 9 (Reprinted) ©2002 American Medical Association. All rights reserved.
nary and lung cancer mortality for participants with less education. Also, for

both cardiopulmonary and lung cancer mortality, the RR estimates were

higher for nonsmokers.

FIGURE 5 summarizes the associations between mortality risk and air pollutant concentrations listed in Table 1.

Statistically significant and relatively

consistent mortality associations existed for all measures of fine particulate exposure, including PM2.5 and sulfate particles. Weaker less consistent

mortality associations were observed

with PM10 and PM15. Measures of the

coarse particle fraction (PM15-2.5) and

total suspended particles were not consistently associated with mortality. Of

the gaseous pollutants, only sulfur dioxide was associated with elevated mortality risk. Interestingly, measures of

PM2.5 were associated with all-cause cardiopulmonary, and lung cancer mortality, but not with all other mortality.

However, sulfur oxide pollution (as

measured by sulfate particles and/or sulfur dioxide) was significantly associated with mortality from all other causes

in addition to all-cause, cardiopulmonary, and lung cancer mortality.

COMMENT

This study demonstrated associations between ambient fine particulate air pollution and elevated risks of both cardiopulmonary and lung cancer mortality.

Each 10-μg/m3 elevation in long-term average PM2.5 ambient concentrations was

associated with approximately a 4%, 6%,

and 8% increased risk of all-cause, cardiopulmonary, and lung cancer mortality, respectively, although the magnitude of the effect somewhat depended

on the time frame of pollution monitoring. In addition, this analysis addresses

many of the important questions concerning the earlier, more limited analysis of the large CPS-II cohort, including the following issues.

First, does the apparent association

between pollution and mortality persist with longer follow-up and as the cohort ages and dies? The present analysis more than doubled the follow-up

time to more than 16 years, resulting

in approximately triple the number of

deaths, yet the associations between

pollution and mortality persisted.

Second, can the association between

fine particulate air pollution and increased cardiopulmonary and lung cancer mortality be due to inadequate control of important individual risk factors?

After aggressively controlling for smoking, the estimated fine particulate pollution effect on mortality was remarkably robust. When the analysis was

stratified by smoking status, the estimated pollution effect on both cardiopulmonary and lung cancer mortality

was strongest for never smokers vs

former or current smokers. This analysis also controlled for education, marital status, BMI, and alcohol consumption. This analysis used improved

variables to control for occupational exposures and incorporated diet variables

that accounted for total fat consumption, as well as for consumption of vegetables, citrus, and high-fiber grains. The

mortality associations with fine particulate air pollution were largely unaffected by the inclusion of these individualriskfactorsinthemodels.Thedata

on smoking and other individual risk factors, however, were obtained directly by

questionnaire at time of enrollment and

do not reflect changes that may have occurred following enrollment. The lack of

risk factor follow-up data results in some

misclassification of exposure, reduces the

precision of control for risk factors, and

constrains our ability to differentiate time

dependency.

Third, are the associations between

fine particulate air pollution and mortality due to regional or other spatial differences that are not adequately controlled for in the analysis? If there are

unmeasured or inadequately modeled

risk factors that are different across locations, then spatial clustering will occur. If this clustering is independent or

random across metropolitan areas, then

the spatial clustering can be modeled

by adding a random-effects component to the Cox proportional hazards

model as was done in our analysis. The

clustering may not be independent or

random across metropolitan areas due

to inadequately measured or modeled

Figure 2. Nonparametric Smoothed Exposure Response Relationship Log RR (95% CI)

0.2

0

0.1

–0.1

–0.2

–0.4

–0.3

A All-Cause Mortality B Cardiopulmonary Mortality

Log RR (95% CI)

0.2

0

0.1

–0.1

–0.2

–0.4

–0.3

10 15 20

PM2.5, μg/m3

C Lung Cancer Mortality

10 15 20

PM2.5, μg/m3

D All Other Cause Mortality

Vertical lines along x-axes indicate rug or frequency plot of mean fine particulate pollution; PM2.5, mean fine

particles measuring less than 2.5 μm in diameter; RR, relative risk; and CI, confidence interval.

MORTALITY AND LONG-TERM EXPOSURE TO AIR POLLUTION

©2002 American Medical Association. All rights reserved. (Reprinted) JAMA, March 6, 2002—Vol 287, No. 9 1137
Figure 3. Mortality Relative Risk (RR) Ratio Associated With 10-μg/m3 Differences of PM2.5 Concentrations RR (95% CI)

1.15

1.20

1.05

1.10

1.00

0.95

Stratification

by Age, Sex,

and Race

+Smoking +Education

and Marital

Status

+Body Mass

Index

+Alcohol +Occupation

and Dust

Exposure

+Diet All

Covariates

Span-50% Lowest

Variance

Highest

P Value

D All Other Cause Mortality

Standard Cox Models Random-Effects Models

All Covariates Plus Spatial Smoothing

RR (95% CI)

1.15

1.20

1.05

1.10

1.00

0.95

C Lung Cancer Mortality

RR (95% CI)

1.15

1.20

1.05

1.10

1.00

0.95

B Cardiopulmonary Mortality

RR (95% CI)

1.15

1.20

1.05

1.10

1.00

0.95

A All-Cause Mortality

Data presented are for 1979-1983 for the different causes of death, with various levels of controlling for individual risk factors, and using alternative modeling approaches. The 3 models with spatial smoothing allow for increasingly aggressive fitting of the spatial structure. Plus sign indicates model included previous variables (ie,

smoking included stratification by age, sex, and race); PM2.5, mean fine particles measuring less than 2.5 μm in diameter; and CI, confidence interval.

MORTALITY AND LONG-TERM EXPOSURE TO AIR POLLUTION

1138 JAMA, March 6, 2002—Vol 287, No. 9 (Reprinted) ©2002 American Medical Association. All rights reserved.
risk factors (either individual or ecological). If these inadequately measured or modeled risk factors are also

spatially correlated with air pollution,

then biased pollution effects estimates

may occur due to confounding. However, in this analysis, significant spatial autocorrelation was not observed

after controlling for fine particulate air

pollution and the various individual risk

factors. Furthermore, to minimize any

potential confounding bias, sensitivity analyses, which directly modeled

spatial trends using nonparametric

smoothing techniques, were conducted. A contribution of this analysis

is that it included the incorporation of

both random effects and nonparametric spatial smoothing components to the

Cox proportional hazards model. Even

after accounting for random effects

across metropolitan areas and aggressively modeling a spatial structure that

accounts for regional differences, the association between fine particulate air

pollution and cardiopulmonary and

lung cancer mortality persists.

Fourth, is mortality associated primarily with fine particulate air pollution or is mortality also associated with

other measures of particulate air pollution, such as PM10, total suspended

particles, or with various gaseous pollutants? Elevated mortality risks were

associated primarily with measures of

fine particulate and sulfur oxide pollution. Coarse particles and gaseous pollutants, except for sulfur dioxide, were

generally not significantly associated

with elevated mortality risk.

Fifth, what is the shape of the concentration-responsefunction?Withinthe

range of pollution observed in this analysis, the concentration-response function appears to be monotonic and nearly

linear. However, this does not preclude

a leveling off (or even steepening) at

much higher levels of air pollution.

Sixth, how large is the estimated mortality effect of exposure to fine particulate air pollution relative to other risk factors? A detailed description and

interpretation of the many individual risk

factors that are controlled for in the

analysis goes well beyond the scope of

Figure 4. Adjusted Mortality Relative Risk (RR) Ratio Associated With 10-μg/m3 Differences

of PM2.5 Concentrations

A All-Cause Mortality

RR (95% CI)1.20

1.30

1.40

1.10

1.00

0.90

0.80

Age, y Sex Education Smoking Status

RR (95% CI)

All

Combined

<60 60-69 ≥70 Men Women <High

School

High

School

>High

School

Current Former Never

D All Other Cause Mortality

1.20

1.30

1.40

1.10

1.00

0.90

0.80

C Lung Cancer Mortality

RR (95% CI)1.20

1.30

1.40

1.10

1.00

0.90

0.80

B Cardiopulmonary Mortality

RR (95% CI)1.20

1.30

1.40

1.10

1.00

0.90

0.80

Data presented are for 1979-1983 for the different causes of death stratified by age, sex, education, and smoking status. PM2.5 indicates mean fine particles measuring less than 2.5 μm in diameter; CI, confidence interval.

MORTALITY AND LONG-TERM EXPOSURE TO AIR POLLUTION

©2002 American Medical Association. All rights reserved. (Reprinted) JAMA, March 6, 2002—Vol 287, No. 9 1139
Figure 5. Adjusted Mortality Relative Risk (RR) Ratio Evaluated at Subject-Weighted Mean Concentrations

A All-Cause Mortality

RR (95% CI)

1.30

1.40

1.50

1.00

1.10

1.20

0.90

0.80

Inhalable, Coarse Particles Gaseous Pollutants

Total Suspended Ozone

Particles

Fine Particles

(PM2.5)

Carbon

Monoxide

Nitrogen

Dioxide

Sulfur

Dioxide

Sulfate

Particles

RR (95% CI)

1979-19831999-2000

Average

1987-1996 (PM10)1979-1983 (PM15)

1979-1983 (PM15-2.5)

1980-19811979-19831982-19861980-1981

19901980

1982-1998

19801980

1982-19981980-19811982-1998

1982-1998

Third Quarter

1982-1998

D All Other Cause Mortality

1.30

1.40

1.50

1.00

1.10

1.20

0.90

0.80

RR (95% CI)

C Lung Cancer Mortality

1.30

1.40

1.50

1.00

1.10

1.20

0.90

0.80

RR (95% CI)

B Cardiopulmonary Mortality

1.30

1.40

1.50

1.00

1.10

1.20

0.90

0.80

PM2.5 indicates particles measuring less than 2.5 μm in diameter; PM10, particles measuring less than 10 μm in diameter; PM15, particles measuring less than 15 μm in

diameter; PM15-2.5, particles measuring between 2.5 and 15 μm in diameter; and CI, confidence interval.

MORTALITY AND LONG-TERM EXPOSURE TO AIR POLLUTION

1140 JAMA, March 6, 2002—Vol 287, No. 9 (Reprinted) ©2002 American Medical Association. All rights reserved.
this report. However, the mortality risk

associated with cigarette smoking has

been well documented using the CPS-II

cohort.16 The risk imposed by exposure

to fine particulate air pollution is obviously much smaller than the risk of cigarette smoking. Another risk factor that

has been well documented using the

CPS-II cohort data is body mass as measured by BMI.30 The Word Health Organization has categorized BMI values

between 18.5-24.9 kg/m2 as normal; 25-

29.9 kg/m2

, grade 1 overweight; 30-

39.9 kg/m2

, grade 2 overweight; and 40

kg/m2 or higher, grade 3 overweight.31

In the present analysis, BMI values and

BMI values squared were included in the

proportional hazards models. Consistent with previous ACS analysis,30 BMI

was significantly associated with mortality, optimal BMI was between approximately 23.5 and 24.9 kg/m2

, and

the RR of mortality for different BMI values relative to the optimal were dependent on sex and smoking status. For example, the RRs associated with BMI

values between 30.0 and 31.9 kg/m2 (vs

optimal) would be up to approximately 1.33 for never smokers. Based on

these calculations, mortality risks associated with fine particulate air pollution at levels found in more polluted US

metropolitan areas are less than those associated with substantial obesity (grade

3 overweight), but comparable with the

estimated effect of being moderately

overweight (grade 1 to 2).

In conclusion, the findings of this

study provide the strongest evidence to

date that long-term exposure to fine particulate air pollution common to many

metropolitan areas is an important risk

factor for cardiopulmonary mortality. In

addition, the large cohort and extended

follow-up have provided an unprecedented opportunity to evaluate associations between air pollution and lung

cancer mortality. Elevated fine particulate air pollution exposures were associated with significant increases in lung

cancer mortality. Although potential effects of other unaccounted for factors

cannot be excluded with certainty, the

associations between fine particulate air

pollution and lung cancer mortality, as

well as cardiopulmonary mortality, are

observed even after controlling for cigarette smoking, BMI, diet, occupational

exposure, other individual risk factors,

and after controlling for regional and

other spatial differences.

Author Contributions: Study concept and design:

Pope, Burnett, Krewski, Thurston.

Acquisition of data: Thun, Calle, Krewski, Ito, Thurston.

Analysis and interpretation of data: Pope, Burnett,

Krewski, Thurston.

Drafting of the manuscript: Pope, Burnett, Ito, Thurston.

Critical revision of the manuscript for important intellectual content: Pope, Thun, Calle, Krewski, Thurston.

Statistical expertise: Pope, Burnett, Krewski.

Obtained funding: Pope, Thun, Thurston.

Administrative, technical, or material support: Pope,

Calle, Krewski, Ito, Thurston.

Study supervision: Pope, Krewski.

Funding/Support: The research for this article was supported largely by grant ES09560-01A1 from the National Institutes of Health/National Institute of Environmental Health Sciences (NIEHS). It was also

supported in part by grant ES00260 from the New York

University Center/NIEHS, grant R-827351 from the

Environmental Protection Agency PM Health Effects

Research Center, and funding from the R. Samuel

McLaughlin Centre for Population Health Risk Assessment at the University of Ottawa.

Acknowledgment: We thank Morton Lippmann, PhD,

for his help in developing the research grant application and various comments and suggestions and Yuanli

Shi, MD, for computer programming and statistical

analysis support.

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©2002 American Medical Association. All rights reserved. (Reprinted) JAMA, March 6, 2002—Vol 287, No. 9 1141