### 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 extremelyhigh concentrations of particulate and sulfur oxide air pollution and acute increases in mortality was wellestablished by the 1970s. Subsequently, epidemiological studies published between 1989 and 1996 reported health effects at unexpectedly lowconcentrations of particulate air pollution. 4 

### The convergence of data from these studies, while controversial, 5 

### prompted serious reconsideration of standards and health guidelines 6-10 

### andled to a long-term research program designed to analyze health-related effects due to particulate pollution. 11-13 

### In 1997, the Environmental Protection Agencyadopted new ambient air quality standards that would impose regulatory limits on fine particles measuring less than 2.5 μm in diameter (PM 2.5 

### ). 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, severalstudies suggest that long-term exposure may be more important in terms of overall public health. 4 

### The new standards for long-term exposure to PM 2.5 

### were originally based primarily on 2 prospective cohort studies, 15,16 

### whichevaluated 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 factorand 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 (DrsBurnett 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 pollutionand 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 linkedwith 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/m 3 elevation in fineparticulate 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 numberof deaths; (2) substantially expands exposure data, including gaseous copollutant data and new PM 2.5 

### data, whichhave 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 weregenerally 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 confidentialquestionnaire, 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 wasascertained by ACS volunteers in September of the following years: 1984, 1986, and 1988. Reported deaths wereverified with death certificates. Subsequently, through December 31, 1998,vital status was ascertained through automated linkage of the CPS-II studypopulation with the National Death Index. 19 

### Ascertainment of deaths wasmore 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 participantswith 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 sizeof 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). Manyof the particulate pollution indices, including PM 2.5 

### , were available from datafrom the Inhalable Particle Monitoring Network for 1979-1983 and datafrom 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 AgencyAerometric 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 databaseas 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 meanvalues 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 inthe late 1980s from total suspended particles to particles measuring less than 10 μm in diameter (PM 10 

### ), resulting inthe majority of total suspended particle data being available in the early to mid-1980s and PM 10 

### data being mostly available in the early to mid-1990s.

As a consequence of the new PM 2.5

### standard, a large number of sites began collecting PM 2.5 

### data in 1999. Daily
PM 2.5 

### data were extracted from the AIRSdatabase 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 45total sampling days available. Measurements were averaged first by site and then by metropolitan area. Although no network of PM 2.5 

### monitoring existed in the United States between the early1980s and the late 1990s, the integrated average of PM 2.5 

### concentrations during the period was estimated by averaging the PM 2.5 

### concentration for early and later periods.

Mean sulfate concentrations for 1980- 1981 were available for many citiesbased on data from the Inhalable Particle Monitoring Network and theNational Aerometric Database. Recognizing that sulfate was artifactually overestimated due to glass fiber filtersused at that time, season and regionspecific adjustments were made. 17 

### Since few states analyzed particulate samplesfor sulfates after the early 1980s, individual states were directly contacted fordata regarding filter use. Ion chromatography was used to analyze PM 10 

### filters and this data could be obtained from metropolitan areas across the MORTALITY AND LONG-TERM EXPOSURE TO AIR POLLUTION

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### United States. Filters were collected for a single reference year (1990) in themiddle of the 1982-1998 study period. The use of quartz filters virtuallyeliminated 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 riskestimates of pollution-related mortality in previous longitudinal cohort studies. 15,16 

### The standard Cox model implicitly assumes that observations arestatistically independent after controlling for available risk factors, resultingin 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 thanparticipants living in communities farther apart, which results in spatial autocorrelation. If this spatial autocorrelation is due to missing or systematicallymismeasured risk factors that are spatially correlated with air pollution, then the risk estimates of pollution-relatedmortality 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-effectscomponent, 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)
PM 2.5 μg/m 3 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)
PM 10 μg/m 3 1982-1998 AIRS NYU 102 415 28.8 (5.9)
PM 15 μg/m 3 1979-1983 IPMN HEI 63 359 40.3 (7.7)
PM 15-2.5 μg/m 3 1979-1983 IPMN HEI 63 359 19.2 (6.1) Total suspended particles μg/m 3 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/m 3 1980-1981 IPMN and NAD, artifact adjusted HEI 149 572 6.5 (2.8)

1990 Compilation and analysis of PM 10 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)

* PM 2.5 indicates particles measuring less than 2.5 μm in diameter; PM 10 , particles measuring less than 10 μm in diameter; PM 15 , particles measuring less than 15 μm in diameter;

PM 15-2.5 , particles measuring between 2.5 and 15 μm in diameter; μg/m 3 , 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

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### timates of the uncertainty of effect estimates. The model also evaluated spatial autocorrelation and incorporated anonparametric 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 inclusionof a metropolitan-based randomeffects component. Model fitting involved a 2-stage process. In the firststage, survival data were modeled using the standard Cox proportional hazards model, including individual level covariates and indicator variables foreach metropolitan area (without pollution variables). Output from stage 1provided estimates of the metropolitanspecific logarithm of the RRs of mortality (relative to an arbitrary reference community), which were adjustedfor individual risk factors. The correlation between these values, which was induced by using the same reference community, was then removed. 23 

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

### The timevariable 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 baselinehazard. 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,
PM 2.5 

### (1979-1983), PM 2.5 

### (1999- 2000), and PM 2.5 

### (average). Individual level covariates were included inthe 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 indicatorand 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 withindicator variables for starting smoking before or after age 18 years. Thecontinuous 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 ofsmoking 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 winedrinkers and nonresponders vs nondrinkers were included to adjust for alcohol consumption. Occupational exposure was controlled for using variousindicator variables: regular occupational exposure to asbestos, chemicals/ acids/solvents, coal or stone dusts, coaltar/pitch/asphalt, diesel engine exhaust, or formaldehyde, and additional indicator variables that indicated 9 different rankings of an occupational dirtiness index that hasbeen 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 wereconducted. First, to more fully evaluate the shape of the concentrationresponse function, a robust locally weighted regression smoother26 

### (withinthe generalized additive model framework27

### ) was used to estimate the relationship between particulate air pollution and mortality in the second stageof model fitting. Second, the sensitivity of the fine particle mortality risk estimates compared with alternative modeling approaches and assumptions was evaluated. Standard Cox proportionalhazards models were fit to the data including particulate air pollution as apredictor of mortality and sequentially adding (in a controlled forward stepwise process) groups of variablesto 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 forspatial differences in mortality, a 2-dimensional term to account for spatial trends was added to the models and wasestimated using a locally weighted regression smoother. The “span” parameter, which controls the complexity ofthe 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 modeling 27 

### with S-Plus statistical software, 28 

### which provides unbiased effect estimates, but mayunderestimate SEs if there is significant spatial autocorrelation and significant correlations between air pollution and the smoothed surface ofmortality. 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

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### with distance between metropolitan areas were graphically examined.

Analyses were also conducted ofeffect modification by age, sex, smoking status, occupational exposure, andeducation. Finally, models were fit using a variety of alternative pollution indices, including gaseous pollutants. Specifically, models were estimatedseparately for each of the pollution variables listed in Table 1, while also including all of the other risk factor variables.

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

### 1 plots mean PM 2.5 

### concentrations for 1999-2000 over mean PM 2.5 

### concentrations for 1979-1983 for the 51 cities in which paired data were available. The concentrations of PM 2.5 

### were lower in 1999-2000 than in 1979-1983 for most cities, with the largest reduction observed in the cities with thehighest concentrations of pollution during 1979-1983. Mean PM 2.5 

### levels in the 2 periods were highly correlated ( r =0.78). The rank ordering of cities by relative pollution levels remainednearly 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 indicesof fine particulate air pollution were associated with all-cause, cardiopulmonary, and lung cancer mortality, but notmortality from all other causes combined. F IGURE 

### 2 presents the nonparametric smoothed exposure response relationships between cause-specific mortality and PM 2.5 

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

The fine particle mortality RR ratiosfrom various alternative modeling approaches and assumptions are presented in F IGURE 

### 3 . After controlling forsmoking, education, and marital status, the controlled forward stepwise inclusion of additional covariates had little influence on the estimated associationswith fine particulate air pollution on cardiopulmonary and lung cancer mortality. As expected, cigarette smoking washighly 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 lungcancer 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 werealso 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 theintroduction 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 thevarious individual risk factors. Furthermore, graphical examination of the correlations of the residual mortality with distance between metropolitan areas didnot reveal significant spatial autocorrelation (results not shown). Nevertheless, the incorporation of spatialsmoothing was included to further investigate the robustness of the estimated particulate pollution effect. Effect estimates were not highly sensitive tothe incorporation of spatial smoothing to account for regional clustering or other spatial patterns in the data.

F IGURE 

### 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 differencesacross age and sex strata were not generally consistent or statistically significant. However, a consistent pattern emerged from this stratified analysis: the association with particulate pollutionwas stronger for both cardiopulmoFigure 1. Mean Fine Particles Measuring Less Than 2.5 μm in Diameter (PM 2.5)

30

15 25

20

10

5

5 10 30 15 20 25 PM 2.5(1979-1983)PM2.5(1999-2000)

r = 0.78

Mean PM 2.5 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/m 3 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

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### nary and lung cancer mortality for participants with less education. Also, forboth cardiopulmonary and lung cancer mortality, the RR estimates were higher for nonsmokers.
F IGURE 

### 5 summarizes the associations between mortality risk and air pollutant concentrations listed in Table 1. Statistically significant and relativelyconsistent mortality associations existed for all measures of fine particulate exposure, including PM 2.5 

### and sulfate particles. Weaker less consistent mortality associations were observed with PM 10 

### and PM 15 

### . Measures of the coarse particle fraction (PM 15-2.5 

### ) andtotal suspended particles were not consistently associated with mortality. Ofthe gaseous pollutants, only sulfur dioxide was associated with elevated mortality risk. Interestingly, measures of
PM 2.5 

### were associated with all-cause cardiopulmonary, and lung cancer mortality, but not with all other mortality. However, sulfur oxide pollution (asmeasured by sulfate particles and/or sulfur dioxide) was significantly associated with mortality from all other causesin addition to all-cause, cardiopulmonary, and lung cancer mortality.

COMMENTThis 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 PM 2.5 

### 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 dependedon the time frame of pollution monitoring. In addition, this analysis addressesmany of the important questions concerning the earlier, more limited analysis of the large CPS-II cohort, including the following issues. First, does the apparent associationbetween 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 betweenfine 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 wasstratified by smoking status, the estimated pollution effect on both cardiopulmonary and lung cancer mortality was strongest for never smokers vsformer or current smokers. This analysis also controlled for education, marital status, BMI, and alcohol consumption. This analysis used improvedvariables to control for occupational exposures and incorporated diet variablesthat accounted for total fat consumption, as well as for consumption of vegetables, citrus, and high-fiber grains. Themortality associations with fine particulate air pollution were largely unaffected by the inclusion of these individualriskfactorsinthemodels.Thedataon smoking and other individual risk factors, however, were obtained directly by questionnaire at time of enrollment anddo 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 betweenfine 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 modeledrisk 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 modeledby 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 RelationshipLog RR (95% CI) 0.2

0 0.1

–0.1

–0.2

–0.4 –0.3 All-Cause Mortality A Cardiopulmonary Mortality BLog RR (95% CI) 0.2

0 0.1

–0.1

–0.2

–0.4 –0.3

10 15 20 PM 2.5 , μg/m 3 Lung Cancer Mortality C

10 15 20 PM 2.5 , μg/m 3 All Other Cause Mortality D

Vertical lines along x-axes indicate rug or frequency plot of mean fine particulate pollution; PM 2.5 , 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 1137Figure 3. Mortality Relative Risk (RR) Ratio Associated With 10-μg/m 3 Differences of PM 2.5 ConcentrationsRR (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 All Other Cause Mortality D

Standard Cox Models Random-Effects Models All Covariates Plus Spatial SmoothingRR (95% CI) 1.15 1.20

1.05 1.10

1.00

0.95 Lung Cancer Mortality CRR (95% CI) 1.15 1.20

1.05 1.10

1.00

0.95 Cardiopulmonary Mortality BRR (95% CI) 1.15 1.20

1.05 1.10

1.00

0.95 All-Cause Mortality A

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 include d previous variables (ie, smoking included stratification by age, sex, and race); PM 2.5 , mean fine particles measuring less than 2.5 μm in diameter; and CI, confidence interval. MORTALITY AND LONG-TERM EXPOSURE TO AIR POLLUTION

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### 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 estimatesmay 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 anypotential confounding bias, sensitivity analyses, which directly modeled spatial trends using nonparametricsmoothing techniques, were conducted. A contribution of this analysis is that it included the incorporation ofboth random effects and nonparametric spatial smoothing components to the Cox proportional hazards model. Even after accounting for random effectsacross metropolitan areas and aggressively modeling a spatial structure thataccounts 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 withother measures of particulate air pollution, such as PM 10 

### , total suspendedparticles, or with various gaseous pollutants? Elevated mortality risks were associated primarily with measures offine 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?Withintherange 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/m 3 Differences of PM 2.5 Concentrations

All-Cause Mortality ARR (95% CI) 1.20 1.30 1.40

1.10

1.00

0.90

0.80

Smoking Status Education Age, y SexRR (95% CI)

All Combined < 60 60-69 ≥ 70 Men Women < High School High School > High School Current Former Never All Other Cause Mortality D

1.20 1.30 1.40

1.10

1.00

0.90

0.80 Lung Cancer Mortality CRR (95% CI) 1.20 1.30 1.40

1.10

1.00

0.90

0.80 Cardiopulmonary Mortality BRR (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. PM 2.5 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 1139Figure 5. Adjusted Mortality Relative Risk (RR) Ratio Evaluated at Subject-Weighted Mean Concentrations

All-Cause Mortality ARR (95% CI) 1.30 1.40 1.50

1.00 1.10 1.20

0.90

0.80

Gaseous Pollutants Inhalable, Coarse Particles Ozone Total Suspended Particles Fine Particles

(PM 2.5) Carbon Monoxide Nitrogen Dioxide Sulfur Dioxide Sulfate ParticlesRR (95% CI)

1979-1983 1999-2000 Average 1987-1996 (PM 10 ) 1979-1983 (PM 15 ) 1979-1983 (PM 15-2.5

) 1980-1981 1979-1983 1982-1986 1980-1981 1990 1980 1982-1998 1980 1980 1982-1998 1980-1981 1982-1998 1982-1998 Third Quarter 1982-1998 All Other Cause Mortality D

1.30 1.40 1.50

1.00 1.10 1.20

0.90

0.80RR (95% CI) Lung Cancer Mortality C

1.30 1.40 1.50

1.00 1.10 1.20

0.90

0.80RR (95% CI) Cardiopulmonary Mortality B

1.30 1.40 1.50

1.00 1.10 1.20

0.90

0.80

PM 2.5 indicates particles measuring less than 2.5 μm in diameter; PM 10 , particles measuring less than 10 μm in diameter; PM 15 , particles measuring less than 15 μm in diameter; PM 15-2.5 , particles measuring between 2.5 and 15 μm in diameter; and CI, confidence interval. MORTALITY AND LONG-TERM EXPOSURE TO AIR POLLUTION

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### this report. However, the mortality risk associated with cigarette smoking has been well documented using the CPS-II cohort. 16 

### The risk imposed by exposureto fine particulate air pollution is obviously much smaller than the risk of cigarette smoking. Another risk factor that has been well documented using theCPS-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/m 2 

### , 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 theproportional hazards models. Consistent with previous ACS analysis, 30 

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

### , andthe 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 

### (vsoptimal) would be up to approximately 1.33 for never smokers. Based onthese calculations, mortality risks associated with fine particulate air pollution at levels found in more polluted USmetropolitan 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 todate 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 extendedfollow-up have provided an unprecedented opportunity to evaluate associations between air pollution and lungcancer mortality. Elevated fine particulate air pollution exposures were associated with significant increases in lungcancer 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. SamuelMcLaughlin 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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