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 500 000 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 all-cause, 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
Based 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
(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
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 cause-of-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
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 region-specific 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 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.
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 pollution-related 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 estimates 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 random-effects 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 metropolitan-specific 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
random-effects 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 all-cause 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
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 concentration-response
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 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.
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 pollution-related
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 cardiopulmonary 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.
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 individual risk factors in the models. The data 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 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-response function? Within
the 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
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 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.
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