1616 volume 119 | number 11 | November 2011 • Environmental Health Perspectives

Research

Active cigarette smoking is the major cause 

of lung cancer and an important established cause of cardiovascular disease (CVD)

mortality (U.S. Department of Health and 

Human Services 2004, 2010). Risks have 

been shown to increase with even light or 

intermittent active smoking (Bjartveit and 

Tverdal 2005; Schane et al. 2010; U.S. 

Department of Health and Human Services 

2010). Secondhand smoke (SHS) is also an 

established cause of both lung cancer and 

CVD (U.S. Department of Health and 

Human Services 2004, 2006, 2010). Since 

the early 1990s, growing evidence has linked 

long-term exposure to fine particulate matter (particulate matter with an aerodynamic 

diameter ≤ 2.5 μm; PM2.5) air pollution with 

increases in the risk of cardiovascular mortality (Brook et al. 2010; Dockery et al. 1993; 

Krewski et al. 2009; Laden et al. 2006; Miller 

et al. 2007; Pope et al. 1995, 2004; Pope and 

Dockery 2006) and, to a lesser extent, lung 

cancer (Chen et al. 2008; Pope et al. 2002).

In a recent analysis, we evaluated the 

exposure–response relationship for cardiovascular mortality in relation to PM2.5 from 

active cigarette smoking, SHS, and ambient 

air pollution (Pope et al. 2009). The results 

suggested a relatively steep exposure–response 

function at very low levels of exposure and a 

flattening out of CVD risk at high exposure 

levels. Previous efforts to estimate the disease 

burden attributable to PM2.5 exposure have 

assumed that the adverse effects of PM2.5 on 

both cardiovascular and lung cancer mortality flatten out above 50 μg/m3 (Cohen et al. 

2004). If the lung cancer effects of PM2.5, 

however, do not flatten out at high levels of 

exposure similar to CVD, the estimates of the 

lung cancer excess disease burden from air pollution would be substantially underestimated 

in areas with high levels of PM2.5 pollution.

In the present analysis, therefore, we 

explicitly conducted an original evaluation of 

the shape of the PM2.5-mortality exposure–

response relationship for lung cancer. For 

direct comparison, we also reevaluated the 

shape of the PM2.5-mortality exposure–

response relationship for CVD mortality using 

the same cohort, follow-up period, exclusion 

criteria, covariates, and related statistical modeling approaches. In an integrative way, our 

approach evaluates exposure–response relationships using three basic sources of exposure 

(active smoking, passive smoking, and ambient air pollution) in relation to two major 

health end points (cardiovascular and lung 

cancer mortality), using PM2.5 as the common index of exposure. There are limitations 

and uncertainties related to using any single 

measure as a common index of exposure. 

Nevertheless, this analysis allows for direct 

comparisons of the exposure–response relations for lung cancer versus CVD mortality 

and provides the opportunity to evaluate the 

implications of the observed differences when 

estimating the disease burden from PM2.5.

Methods

Cohort data. Estimates of adjusted relative 

risks (RRs) over different increments of active 

cigarette smoking are based on data collected 

by the American Cancer Society (ACS) as part 

of the Cancer Prevention Study II (CPS-II), 

an ongoing prospective cohort mortality study 

involving nearly 1.2 million adults. Ethics 

approval for the CPS-II was obtained from 

the Emory University School of Medicine 

Human Investigations Committee. More 

detailed descriptions of this cohort are provided elsewhere (Calle and Terrel 1993; Chao 

et al. 2000; Hoover et al. 2003; Krewski et al. 

2009; Pope et al. 2002). Participants from 

throughout the United States were enrolled by 

Address correspondence to C.A. Pope III, 142 FOB, 

Brigham Young University, Provo, UT 84602 USA. 

Telephone: (801) 422-2157. Fax: (801) 422-0194. 

E-mail: cap3@byu.edu

This study was supported partially by the 

California Air Resources Board. C.A.P. was supported partially by Mary Lou Fulton Professorship, 

Brigham Young University. D.K. was supported partially by Natural Sciences and Engineering Research 

Council Chair in Risk Science at the University 

of Ottawa. M.C.T. was supported with a Canada 

Graduate Scholarship from the Canadian Institutes 

of Health Research (CIHR). S.M.G. and M.J.T. 

are both employed by the American Cancer Society, 

Atlanta, Georgia, USA. A.C. is employed by the 

Health Effects Institute, Boston, Massachusetts, 

USA, which is sponsored by the U.S. Environmental 

Protection Agency (U.S. EPA), the automobile and 

engine manufacturing industry, and other organizations and industries. 

The views expressed in this paper are those of the 

authors and do not necessarily reflect the views of 

the Health Effects Institute or its sponsors.

The authors declare they have no actual or 

potential competing financial interests.

Received 4 March 2011; accepted 18 July 2011.

Lung Cancer and Cardiovascular Disease Mortality Associated with Ambient Air 

Pollution and Cigarette Smoke: Shape of the Exposure–Response Relationships

C. Arden Pope III,1 Richard T. Burnett,2 Michelle C. Turner,3 Aaron Cohen,4 Daniel Krewski,3 Michael Jerrett,5

Susan M. Gapstur,6 and Michael J. Thun6

1Economics Department, Brigham Young University, Provo, Utah, USA; 2Population Studies Division, Health Canada, Ottawa, Ontario, 

Canada; 3McLaughlin Center for Population Health Risk Assessment, University of Ottawa, Ottawa, Ontario, Canada; 4Health Effects 

Institute, Boston, Massachusetts, USA; 5Division of Environmental Health Science, School of Public Health, University of California, 

Berkeley, California, USA; 6Epidemiology Research Program, American Cancer Society, Atlanta, Georgia, USA 

Background: Lung cancer and cardiovascular disease (CVD) mortality risks increase with 

smoking, secondhand smoke (SHS), and exposure to fine particulate matter < 2.5 μm in diameter

(PM2.5) from ambient air pollution. Recent research indicates that the exposure–response relationship for CVD is nonlinear, with a steep increase in risk at low exposures and flattening out at higher 

exposures. Comparable estimates of the exposure–response relationship for lung cancer are required 

for disease burden estimates and related public health policy assessments.

Objectives: We compared exposure–response relationships of PM2.5 with lung cancer and cardiovascular mortality and considered the implications of the observed differences for efforts to estimate 

the disease burden of PM2.5.

Methods: Prospective cohort data for 1.2 million adults were collected by the American Cancer 

Society as part of the Cancer Prevention Study II. We estimated relative risks (RRs) for increments 

of cigarette smoking, adjusting for various individual risk factors. RRs were plotted against estimated daily dose of PM2.5 from smoking along with comparison estimates for ambient air pollution 

and SHS.

Results: For lung cancer mortality, excess risk rose nearly linearly, reaching maximum RRs > 40 

among long-term heavy smokers. Excess risks for CVD mortality increased steeply at low exposure 

levels and leveled off at higher exposures, reaching RRs of approximately 2–3 for cigarette smoking.

Conclusions: The exposure–response relationship associated with PM2.5 is qualitatively different 

for lung cancer versus cardiovascular mortality. At low exposure levels, cardiovascular deaths are 

projected to account for most of the burden of disease, whereas at high levels of PM2.5, lung cancer 

becomes proportionately more important.

Key words: air pollution, cardiovascular disease, lung cancer, mortality, particulate matter, smoking. Environ Health Perspect 119:1616–1621 (2011). http://dx.doi.org/10.1289/ehp.1103639

[Online 19 July 2011]
Air pollution, cigarette smoke, CVD, and lung cancer

Environmental Health Perspectives • volume 119 | number 11 | November 2011 1617

> 77,000 volunteers between September 1982 

and February 1983. Enrollment was restricted 

to persons who were ≥ 30 years of age and who 

were members of households with one or more 

members ≥ 45 years of age. At enrollment, 

participants completed a self-administered confidential questionnaire that captured data on a 

range of demographic, lifestyle, medical, and 

other individual characteristics (ACS 2011). 

Through 1988, vital status of study participants was ascertained by both personal contact 

and subsequently through linkage with the 

National Death Index (Calle and Terrel 1993). 

Death certificates were obtained and coded for 

cause of death.

Cause of death was coded according to 

two-digit ACS codes that were consolidations 

of International Classification of Diseases, 9th 

Revision (ICD-9) codes (Pope et al. 2004; 

World Health Organization 1977). Lung 

cancer deaths included deaths due to malignant neoplasms of the trachea, bronchus, and 

lung (ICD-9 162). As discussed elsewhere 

(Pope et al. 2009), cardiovascular diseases and 

cardiopulmonary diseases (CPDs) have substantial common comorbidity. Cross-coding 

and misclassification of primary causes of 

death are inherent in the use of death certificate data, making it unclear which ICD-9 

groupings most generally or specifically indicate death due to CVD. For example, inflammation associated with pulmonary disease 

contributes to cardiovascular risk (van Eeden 

et al. 2005), and individuals with chronic 

obstructive pulmonary disease are likely to be 

coded as dying of CVD based on death certificate data (Sin et al. 2005; Speizer et al. 1989). 

Because of this cross-classification problem, 

we examined the exposure–response function 

for CVD deaths using three different outcome 

groupings, namely, a) ischemic heart disease 

(IHD) (ICD-9 410–414), b) CVD (ICD-9 

401–459), and c) CPD (ICD-9 401–459 and 

460–519).

Because additional information regarding cigarette smoking was not collected after 

enrollment, the present analysis was based on 

a restricted follow-up period of approximately 

6 years through 31 December 1988. The 

unrestricted CPS-II cohort included a total of 

1,184,881 participants. For this analysis, subjects were excluded if they had prevalent cancer 

(except nonmelanoma skin cancer) at baseline 

(82,329), or if they had missing or erroneous 

data for vital status (419), race (5,318), education (16,946), marital status (5,239), body 

mass index (30,291), SHS exposure (9,799), or 

cigarette smoking habits or history (208,835). 

Because this study is focused on effects of longterm exposure, we also excluded participants 

who started smoking after 25 years of age 

(30,921). The final analytic cohort included 

794,784 subjects, among whom 3,194 lung 

cancer deaths, 11,607 IHD deaths, 19,290 

cardiovascular deaths, and 22,021 cardiopulmonary deaths occurred during the approximately 6-year follow-up period.

Statistical analysis. Cox proportional hazards survival models (Fleming and Harrington 

1991) were estimated separately for lung cancer, ischemic heart, cardiovascular, and cardiopulmonary deaths. Data from participants who 

died of other causes were censored at time of 

death. Survival time from the date of enrollment was used as the time axis. Baseline hazard functions were stratified by 1-year age 

categories, sex, and race (white, black, other). 

The models included indicator variables for 

smoking increments of ≤ 3, 4–7, 8–12, 13–17, 

18–22, 23–27, 28–32, 33–37, 38–42, and 

≥ 43 cigarettes per day for current smokers 

relative to never smokers. To control for smoking in previous smokers relative to never smokers, former-smoking indicator variables using 

the same cigarettes-per-day increments were 

included in the models. Additionally, the models included variables to control for education 

(two variables that indicate high school education or more than high school education 

versus less than high school education), marital 

status (two variables that indicate separated/

divorced/widowed or single versus married), 

body mass (two variables representing linear 

and squared terms for body mass index), alcohol consumption (six variables that indicate 

consumption of or missed reporting of beer, 

wine, or other alcohol versus nondrinkers), 

occupational exposures [one variable indicating 

self-reported exposure to dust and fumes in the 

workplace and seven additional variables that 

indicate different rankings of an occupational 

dirtiness index versus a referent category, as 

has been developed and described elsewhere 

(Siemiatycki et al. 2003)], and diet [eight 

indicator variables that contrasted quintiles of 

dietary fat consumption and quintiles of combined consumption of vegetables, citrus, and 

high-fiber grains (Chao et al. 2000)].

To evaluate potential effect modification 

by sex, we also modeled the Cox proportional 

hazards estimates described above separately 

for males and females. To account for the 

impact of smoking duration on effect estimates and the shape of the exposure–response 

relationship, we conducted further analyses 

that stratified all of the cigarettes-per-day 

smoking increments by three levels of smoking duration. Specifically, the cigarettes-perday smoking increment indicator variables 

(for current and previous smokers) were each 

replaced by three variables that indicated the 

specific cigarettes-per-day increments for three 

smoking durations: < 30 years, 30–39 years, 

and ≥ 40 years.

Plotting exposure–response relationships. 

The adjusted RRs (as estimated by the hazard ratios from the Cox proportional hazard 

model) associated with different increments 

of PM2.5 exposure from active cigarette smoking, SHS, and ambient air pollution were 

plotted against estimated average daily inhaled 

dose of PM2.5. For active smoking, the average inhaled dose was assumed to be 12 mg 

PM2.5 per cigarette. The actual amount of 

PM2.5 inhaled per cigarette is influenced 

more by individual inhalation and smoking 

patterns than by the tar level measured by 

machine smoking (National Cancer Institute 

2001). Nevertheless, the estimated salesweighted average of PM from cigarettes sold 

in the United States in the 1980s and 1990s 

was approximately 12–14 mg per cigarette 

(National Cancer Institute 2001). Because 

of uncertainty regarding estimates of PM2.5

exposure per cigarette smoked, reference exposure scaling expressed as actual increments of 

cigarettes smoked per day is also provided in 

the exposure–response plots.

Comparative estimates of excess risk of 

mortality from long-term exposure to ambient air pollution come from several key prospective cohort studies, including estimates 

from analyses using ACS CPS-II cohort 

participants who lived in metropolitan areas 

with available air pollution data (Pope et al. 

1995, 2002, 2004), analyses of the Harvard 

Six Cities (HSC) study (Dockery et al. 

1993; Laden et al. 2006), and the Women’s 

Health Initiative (WHI) study (Miller et al. 

2007). The RRs of mortality are taken from 

the published estimates from these studies. 

The average daily dose of inhaled PM2.5 is 

estimated by multiplying the relevant average ambient PM2.5 concentrations by average 

daily inhalation rates (cubic meters per day). 

Actual ventilation rates depend on age, sex, 

body size, activity levels, and other factors. 

The estimated average volume of air inhaled 

daily by adults ranges from 13 to 23 m3/day 

(Allan et al. 2008; Brochu et al. 2006; Layton 

1993; Stifelman 2007; U.S. Environmental 

Protection Agency 1997). Our analyses use 

18 m3/day (Allan et al. 2008; Brochu et al. 

2006; Stifelman 2007) in calculating PM2.5

dosage at various levels of air pollution. Based 

on the key prospective cohort studies cited 

above, the range of average ambient PM2.5

concentrations is approximately 5–30 μg/m3, 

resulting in estimated daily dose of PM2.5

from ambient air pollution potentially ranging from 0.09 to 0.54 mg. In our analyses, 

however, the plotted RRs represent changes in 

risk across contrasts in exposure that are internal to and reported by the specific studies.

Comparative estimates of excess risk of 

mortality from long-term exposure to SHS 

come from pooled estimates from the 2006 

Surgeon General’s Report (SGR) (U.S. 

Department of Health and Human Services 

2006). These pooled estimates include exposure assessments based simply on SHS exposure 

at home or work (for example, nonsmokers 
Pope et al.

1618 volume 119 | number 11 | November 2011 • Environmental Health Perspectives

living with a spouse who smokes or nonsmokers employed in an SHS environment) and 

on more quantified assessment of SHS exposure including low to moderate SHS exposure 

(passive exposure to either 1–14 or 1–19 cigarettes per day) and moderate to high exposure 

(≥ 15 or ≥ 20 cigarettes per day). Additionally, 

risk estimates for acute myocardial infarction 

from the INTERHEART study (Teo et al. 

2006) of 52 countries associated with 1–7 hr 

per week of SHS exposure or exposure from 

living with a spouse who smoked are included. 

Estimated average PM2.5 exposure is approximately 20 μg/m3 for low to moderate SHS 

exposure and exposure incurred over an interval of 1–7 hr per week, 50 μg/m3 for moderate 

to high exposure, 30 μg/m3 for living with a 

spouse who smokes, and 40 μg/m3 for working 

in a workplace with reported SHS exposure. 

These estimates are based on limited data from 

studies that sampled the PM2.5 concentrations from SHS in various settings over time 

(Dockery and Spengler 1985; Jenkins et al. 

1996; Leaderer and Hammond 1991; Spengler 

1991; Spengler et al. 1985; U.S. Department 

of Health and Human Services 2006). The 

average daily dose of inhaled PM2.5 from SHS 

is estimated by multiplying the estimated average concentrations by the inhalation rate of 

18 m3/day.

To help illustrate the integrated exposure–response relationship, the risk and dose 

estimates for different increments of active 

smoking, SHS, and ambient PM2.5 were used 

to fit a simple power function of the form 

[RR = 1 + α(dose)β] for both lung cancer and 

cardiovascular mortality. This functional form 

was selected because it represents a simple, 

well-behaved, nonlinear, monotonic function that goes through the origin (RR = 1 at 

a dose of zero). The functions were fit using 

iterative nonlinear regression (PROC NLIN 

in SAS, release 9.2; SAS Institute Inc., Cary, 

NC, USA) and were plotted along with the 

specific point estimates.

Results

Table 1 presents selected summary statistics 

for the primary ACS CPS-II analytic cohort 

used to estimate the adjusted RRs for different 

increments of cigarette smoking. Table 2 presents adjusted RRs and estimated daily dose 

of PM2.5 for various increments of exposure 

from cigarette smoking, SHS, and ambient 

air pollution from the present analysis and the 

selected comparison studies. Figure 1A presents the adjusted RR estimates for lung cancer 

mortality plotted against estimated daily dose 

of PM2.5 from different increments of current 

cigarette smoking (relative to never smokers) 

and from different exposures from ambient 

air pollution and SHS. Figure 1B presents 

the adjusted RRs for IHD, CVD, and CPD

plotted against estimated daily dose of PM2.5

from different increments of current cigarette 

smoking (relative to never smokers) and from 

different exposures from ambient air pollution and SHS. Because the estimated doses 

from different increments of active smoking 

are dramatically larger than estimated doses 

from ambient air pollution or SHS, associations at lower exposure levels (due to ambient 

air pollution and SHS) are shown as insets 

with a magnified scale. The comparative 

mortality risk estimates for PM2.5 and SHS 

represent changes in risk across contrasts in 

exposure that are internal to the specific or 

pooled studies.

Table 1. Selected summary statistics for the ACS 

analytic cohort.

Characteristic

Summary 

statistic

Total subjects in analytic cohort (n) 794,784

Age at enrollment [years (mean ± SD)] 56.0 ± 10.5

Body mass index (mean ± SD) 25.1 ± 4.0

Percentage of cohort

Female 61.0

White 94.3

High school education 33.1

> High school education 53.7

Current smoker 19.9

Former smoker 26.8

Percentage of current smokers who smoked

≤ 3 cigarettes/day 3.7

4–7 cigarettes/day 5.8

8–12 cigarettes/day 12.2

13–17 cigarettes/day 7.2

18–22 cigarettes/day 33.4

23–27 cigarettes/day 5.0

28–32 cigarettes/day 13.9

33–37 cigarettes/day 1.6

38–42 cigarettes/day 13.3

≥ 43 cigarettes/day 3.9

Percentage of current smokers with smoking duration

< 30 years 31.2

30–39 years 38.8

≥ 40 years 30.0

Table 2. Adjusted RR estimatesa for various increments of exposure from cigarette smoking (versus never smokers), SHS, and ambient air pollution from the 

present analysis and selected comparison studies.

Adjusted RR (95% confidence interval) Estimated daily 

dose PM2.5 (mg)b Source of risk estimate Increments of exposure Lung cancer IHD CVD CPD

ACS-present analysis ≤ 3 (1.5) cigarettes/day 10.44 (7.30, 14.94) 1.61 (1.27, 2.03) 1.58 (1.32, 1.89) 1.72 (1.46, 2.03) 18

ACS-present analysis 4–7 (5.5) cigarettes/day 8.03 (5.89, 10.96) 1.64 (1.37, 1.96) 1.73 (1.51, 1.97) 1.84 (1.63, 2.08) 66

ACS-present analysis 8–12 (10) cigarettes/day 11.63 (9.51, 14.24) 2.07 (1.84, 2.31) 2.01 (1.84, 2.19) 2.10 (1.94, 2.28) 120

ACS-present analysis 13–17 (15) cigarettes/day 13.93 (11.04, 17.58) 2.18 (1.89, 2.52) 1.99 (1.77, 2.23) 2.08 (1.87, 2.32) 180

ACS-present analysis 18–22 (20) cigarettes/day 19.88 (17.14, 23.06) 2.36 (2.19, 2.55) 2.42 (2.28, 2.56) 2.52 (2.39, 2.66) 240

ACS-present analysis 23–27 (25) cigarettes/day 23.82 (18.80, 30.18) 2.29 (1.91, 2.75) 2.33 (2.02, 2.69) 2.33 (2.03, 2.67) 300

ACS-present analysis 28–32 (30) cigarettes/day 26.82 (22.54, 31.91) 2.22 (1.97, 2.49) 2.17 (1.98, 2.38) 2.39 (2.19, 2.60) 360

ACS-present analysis 33–37 (35) cigarettes/day 26.72 (18.58, 38.44) 2.58 (1.91, 3.47) 2.52 (1.98, 3.19) 2.83 (2.28, 3.52) 420

ACS-present analysis 38–42 (40) cigarettes/day 30.63 (25.79, 36.38) 2.30 (2.05, 2.59) 2.37 (2.16, 2.59) 2.61 (2.40, 2.84) 480

ACS-present analysis ≥ 43 (45) cigarettes/day 39.16 (31.13, 49.26) 2.00 (1.62, 2.48) 2.17 (1.84, 2.56) 2.37 (2.04, 2.76) 540

ACS-air pol original 24.5 μg/m3

 ambient PM2.5 — — — 1.31 (1.17, 1.46) 0.44

ACS-air pol extend 10 μg/m3

 ambient PM2.5 1.14 (1.04, 1.23) 1.18 (1.14, 1.23) 1.12 (1.08, 1.15) 1.09 (1.03, 1.16) 0.18

HSC-air pol original 18.6 μg/m3

 ambient PM2.5 — — — 1.37 (1.11, 1.68) 0.33

HSC-air pol extend 10 μg/m3

 ambient PM2.5 1.21 (0.92, 1.69) — 1.28 (1.13, 1.44) — 0.18

WHI-air pol 10 μg/m3

 ambient PM2.5 — — 1.24 (1.09, 1.41)c — 0.18

SGR-SHS Low–moderate SHS exposure — — 1.16 (1.03, 1.32) — 0.36

SGR-SHS Moderate–high SHS exposure — — 1.26 (1.12, 1.42) — 0.90

SGR-SHS Live with smoking spouse 1.21 (1.13, 1.30) — — — 0.54

SGR-SHS Work with SHS exposure 1.22 (1.13, 1.33) — — — 0.72

INTERHEART 1–7 hr/week SHS exposure — 1.24 (1.17, 1.32)d — — 0.36

INTERHEART Live with smoking spouse — 1.28 (1.12, 1.47)d — — 0.54

Study name abbreviations: ACS-present analysis, the present analysis of the ACS CPS-II cohort; ACS-air pol original, the original analysis of air pollution and mortality using the ACS 

CPS-II cohort (Pope et al. 1995); ACS-extend, extended analyses of air pollution and mortality using the ACS CPS-II cohort (Pope et al. 2002 and 2004); HSC-air pol original, the original 

analysis of air pollution and mortality using the HSC cohort (Dockery et al. 1993); HSC-air pol extend, an extended analysis of air pollution and mortality using the HSC cohort (Laden 

et al. 2006); WHI-air pol, an analysis of air pollution and fatal and non-fatal cardiovascular events using the WHI cohort (Miller et al. 2007); SGR-SHS, SGR that provides pooled estimates for SHS (U.S. Department of Health and Human Services 2006); INTERHEART, a 52-country case–control study of tobacco use and risk of myocardial infarction (Teo et al. 2006). aAdjusted RR estimates are for mortality unless otherwise noted. bThe estimated daily dose assumes an inhalation rate of 18 m3

/day and a dose of 12 mg/cigarette. cFirst CVD event. dMyocardial infarction.
Air pollution, cigarette smoke, CVD, and lung cancer

Environmental Health Perspectives • volume 119 | number 11 | November 2011 1619

The dotted lines in Figure 1A and B represent the fitted nonlinear power function. 

For lung cancer, the fitted function [RR = 1 

+ 0.3195(dose)0.7433] represents a monotonic, 

nearly linear exposure–response relationship 

with fairly constant marginal increases in 

RR with increasing exposure. As reported in 

Table 2 and as can be seen in Figure 1, the 

adjusted RR of lung cancer rises with increased 

exposure throughout the full observed range 

of exposure. For CVD (and related cause-ofdeath groupings) the fitted function [RR = 1 

+ 0.2685(dose)0.2730] indicates an exposure–

response relationship that is substantially nonlinear, that is, much steeper at the very low 

levels of exposure compared with higher levels 

of exposure. At very low levels of exposure—

within the range of exposures associated with 

ambient air pollution and SHS—excess mortality risks are similar for lung cancer and CVD

mortality (compare the rescaled inserts in 

Figure 1). For lung cancer mortality, the RRs

steadily increase to nearly 40 at the highest 

increment of cigarette smoking (> 42 cigarettes 

per day), whereas for CVD mortality, the RRs

level off at approximately 2.0–2.5.

A similar, near-linear exposure–response 

function for lung cancer mortality was observed 

for both men and women when adjusted risk 

ratios were stratified by sex (results not shown). 

For CVD mortality, the adjusted RRs associated with smoking ≤ 3 cigarettes per day were 

notably lower for women than for men, but at 

higher levels of smoking (≥ 18–22 cigarettes/

day), the adjusted RRs were somewhat higher 

for women than for men. However, for both 

men and women, the excess risk for CVD mortality tended to level off at high exposures.

Models that also accounted for different strata of smoking duration (< 30 years, 

30–39 years, or ≥ 40 years) did not indicate 

consistent differences in the exposure–response 

relationship for CVD mortality, and nonlinear exposure–response relationships for all 

three smoking-duration strata were consistent 

with the exposure–response relationship for 

the population as a whole (data not shown). 

Exposure–response curves for lung cancer 

mortality were nearly linear for all strata of 

smoking duration, consistent with results for 

the population as a whole, but the slopes varied by smoking duration, with the exposure–

response function much less steep for those 

with a smoking duration of < 30 years. Our 

ability to evaluate effects of smoking duration independent of age was limited by design, 

because this analytic cohort was constrained 

to smokers who started smoking as teenagers 

or young adults, and smoking duration was 

therefore highly correlated with age (correlation coefficient 0.83).

Discussion

Results for CVD mortality were consistent 

with those from a previous analysis (Pope 

et al. 2009). Specifically, the exposure–

response function is relatively steep at very 

low levels of exposure (in the range associated 

with exposure to SHS and ambient air pollution) and flattens out at high exposure levels 

(consistent with exposure from active cigarette 

smoking). Results from recent studies that 

have found surprisingly sizable reductions in 

acute cardiovascular events after public bans 

on smoking (Institute of Medicine 2010) are 

qualitatively consistent with our findings.

In the present analysis, we further evaluated the PM2.5 exposure–response relationship for lung cancer mortality and contrasted it 

with comparably estimated exposure–response 

relationships for cardiovascular mortality using 

the same cohort, follow-up period, statistical 

models, and adjustment for other risk factors. 

The exposure–response function for PM2.5 and 

lung cancer mortality is different than that for 

cardiovascular mortality in two important ways. 

First, the basic shapes of the exposure–response 

functions differ. For lung cancer mortality, 

excess risks rise nearly linearly throughout the 

full range of exposure from SHS, air pollution, 

and active smoking, reaching maximum RRs

> 40 for heavy smokers.

Second, smoking duration appeared to 

have a much larger impact on lung cancer 

mortality than on CVD mortality. A more 

complete discussion of how CVD mortality 

risk is affected by the duration and intensity of exposure, provided elsewhere (Pope 

et al. 2011), suggests that most of the cardiovascular effects are associated with exposure most proximal in time and over exposure 

duration windows of a few years to approximately a decade. Consistent with these findings, stratifying by exposure durations of 

< 30 years, 30–39 years, or ≥ 40 years did 

not result in substantially different exposure–

response relationships for CVD mortality 

risk, in contrast with our findings for lung 

cancer mortality. The importance of duration of smoking for lung cancer was established in the British Doctor’s Study (Doll and 

Peto 1978) and has been confirmed by more 

complete analyses of age, duration of smoking, and daily cigarette consumption based 

Figure 1. Adjusted RRs [with 95% confidence intervals (CIs)] of lung cancer mortality (A) and IHD, cardiovascular, and cardiopulmonary mortality (B) plotted over 

estimated daily exposure of PM2.5 (milligrams) and increments of cigarette smoking relative to never smokers (cigarettes/day). Diamonds represent comparative 

mortality risk estimates (with 95% CIs) for PM2.5 from air pollution from the comparative studies (Dockery et al. 1993; Laden et al. 2006; Miller et al. 2007; Pope et al 

1995, 2002, 2004). Stars represent comparable pooled RR estimates (with 95% CIs) associated with SHS exposure from comparative studies (Teo et al. 2006; U.S. 

Department of Health and Human Services 2006). The dotted lines represent the nonlinear power function fit through the origin and the estimates (including active 

smoking, SHS, ambient PM2.5). Estimated doses from different increments of active smoking are dramatically larger than estimated doses from ambient air pollution or SHS; therefore, associations at lower exposure levels (due to ambient air pollution and SHS) are shown as insets with a magnified scale. 

40

35

30

25

20

15

10

5

3.0

2.5

2.0

1.5

1.0

1.50

1.25

1.00

1.50

1.25

1.00

Adjusted RRAdjusted RR

0.0 0.5 1.0 0.0 0.5 1.0

Lung cancer Ischemic heart

Cardiovascular

Cardiopulmonary

0

(< 3)

60

(4–7)

120

(8–12)

180

(13–17)

240

(18–22)

300

(23–27)

360

(28–32)

420

(33–37)

480

(38–42)

540

(> 42)

0

(< 3)

60

(4–7)

120

(8–12)

180

(13–17)

240

(18–22)

300

(23–27)

360

(28–32)

420

(33–37)

480

(38–42)

540

(> 42)

Estimated daily exposure, mg of PM2.5, and increments of cigarettes/day Estimated daily exposure, mg of PM2.5, and increments of cigarettes/day
Pope et al.

1620 volume 119 | number 11 | November 2011 • Environmental Health Perspectives

on the ACS CPS-II cohort (Flanders et al. 

2003; Thun et al. 1997) and case–control 

studies of residential radon and lung cancer 

(Lubin et al. 2007). In the present analysis, 

we integrated exposure and risk information 

for active smoking, passive cigarette smoking, 

and air pollution to evaluate the shape of the 

exposure–response relationship. The range of 

duration of active smoking was constrained 

by design to facilitate comparisons with studies of long-term exposure to air pollution and 

SHS. Strong correlations between age and 

exposure duration, the fact that the Cox proportional hazards models strictly controlled 

for age (by allowing separate baseline hazards 

for each age, sex, race strata), and changes in 

baseline mortality risk with aging limit our 

ability to evaluate effects of exposure duration 

separately from effects of age. Nevertheless, 

these results clearly demonstrate fundamental differences in the shapes of the exposure–

response functions for lung cancer mortality 

versus cardiovascular mortality.

The exposure–response relationship for 

PM2.5 and lung cancer mortality appears to 

be nearly linear. These results are consistent 

with previous analyses that demonstrated that 

lung cancer mortality risk estimates for SHS 

are similar to risk estimates calculated indirectly 

from linear extrapolations of excess risk from 

active smokers (Hackshaw et al. 1997). The differences in the exposure–response relationships 

for lung cancer versus CVD may have mechanistic implications, because the pathogenicity 

of PM2.5 for cardiovascular end points may 

be mediated more by the particles themselves, 

whereas the lung cancer hazard is thought to be 

mediated largely by carcinogenic compounds 

carried on the particles. Carcinogens found 

in tobacco smoke and in combustion-source 

air pollution are the likely agents responsible 

for the excess lung cancer risk. Fine particles 

transport many of the toxic and carcinogenic 

substances in smoke and may contribute to pulmonary and systemic inflammation. Chronic 

inflammation may promote genetic and possibly epigenetic changes that transform a normal 

cell through a multistep process toward malignancy. More complete reviews and discussions 

regarding the mechanistic pathways that particulate exposure can contribute to lung cancer 

and CVD are provided elsewhere (Brook et al. 

2010; U.S. Department of Health and Human 

Services 2010).

There are at least four possible explanations 

for the very steep exposure–response for CVD

mortality at low levels of exposure (within the 

ranges associated with air pollution and SHS) 

and the leveling off at high exposures (for active 

cigarette smoking): a) systematic scaling distortions, b) differential toxicities, c) self-selected 

smokers who are less susceptible to cardiovascular effects, and d) the occurrence of a saturation phenomenon with relatively low levels 

of exposure capable of activating relevant biological pathways. Use of the estimated average 

daily inhaled dose of PM2.5 as the common 

exposure metric to plot the exposure–response 

relationship as well as the potential for systematic measurement error in PM2.5 exposure estimates could result in scaling distortions. For 

example, information on cigarette smoking and 

residence history was not collected after enrollment in the ACS cohort. Changes in smoking 

habits, including quitting smoking, may result 

in underestimation of effects, although this bias 

would likely be minimal over the short 6-year 

follow-up. Furthermore, changes in cigarette 

design and compensatory smoking behavior 

may have changed the inhaled dose of PM2.5

per cigarette. Changes in assumptions used to 

calculate doses (such as ventilation rates, yields 

of PM2.5 per cigarette, and estimates of average concentrations from SHS) would alter the 

exposure estimates and dose scaling presented in 

Figure 1. Although it is possible that systematic 

scaling issues would fundamentally influence 

the shape of the exposure–response relationship, we used the same exposure scaling for 

lung cancer and cardiovascular mortality, and 

the fact that the lung cancer exposure–response 

was nearly linear suggests that the exposure scaling may be reasonable. For lung cancer, the primary anomaly was the relatively high adjusted 

RR (approximately 10) estimated for very light 

smokers (≤ 3 cigarettes/day).

The shape of the CVD mortality exposure–

response relationship might also reflect variation in biological mechanisms, characteristics of 

exposure, and the toxicity of PM2.5 depending 

on the composition of fine particles from different sources—from active smoking, SHS, or 

air pollution. Components or combinations 

of components of fine particulates from cigarette smoke and other combustion sources, 

as well as the mechanisms responsible for the 

observed adverse cardiovascular health effects, 

have yet to be fully elucidated. However, the 

differential toxicity explanation is not fully 

consistent with the fact that in the range of 

exposures associated with active smoking alone 

the exposure–response curve is remarkably 

flat and, if extrapolated back to zero, would 

not go through the origin. Assuming there is 

no excess risk at zero exposure (i.e., that the 

dose–response curve should go through the 

origin in the absence of exposure), a monotonic 

exposure–response function based only on the 

evidence of excess risk due to active smoking would require a nonlinear function that is 

steeper at low exposures and flattens out at high 

exposures. This explanation could be tested further in future analyses that include estimates of 

cardiovascular mortality associated with exposures to high concentrations of fine particles 

from diverse combustion and noncombustion 

sources. Studies of occupational groups, such as 

underground miners exposed to diesel exhaust 

and women exposed to high levels of PM from 

household fuel combustion, both coal and biomass, could potentially provide such estimates.

The third potential explanation for the leveling off of the exposure–response relationship 

for CVD mortality in the range of exposures 

from active smoking is that individuals who 

are less susceptible to adverse cardiopulmonary 

effects of smoking may be more likely to 

smoke and continue smoking than those who 

are more susceptible. Exposure to ambient 

air pollution (and SHS, to a lesser degree) is 

largely involuntary and therefore is not likely 

to be biased by self-selection. This explanation 

implies that in the absence of self-selection, the 

relative effects of high exposures from active 

smoking would be larger than estimated in 

this analysis. However, this explanation also 

assumes that self-selection would not be relevant to lung cancer susceptibility. In addition, 

similar to the discussion of differential toxicity 

above, this explanation is not fully consistent 

with the fact that in the range of exposure 

due to active smoking alone, the exposure–

response function is remarkably flat.

The fourth potential explanation regarding 

the steep exposure–response for CVD mortality at low levels of exposure and the leveling 

off at high exposures is a saturation phenomenon whereby relatively low levels of exposure 

are capable of activating relevant biological 

pathways. There is substantial and growing 

evidence that long-term exposures to PM2.5

from cigarette smoke, ambient air pollution, 

or both affect multiple physiologic pathways. 

Even low levels of exposure from SHS and 

ambient air pollution have been associated 

with pulmonary and systemic oxidative stress, 

inflammatory vascular dysfunction, increased 

platelet activation and blood viscosity, atherosclerosis, IHD, and altered cardiac autonomic 

function (Ambrose and Barua 2004; Brook 

et al. 2010; Pope et al. 2004; U.S. Department 

of Health and Human Services 2006, 2010). 

An evaluation of the pathophysiology of cigarette smoke and CVD suggests that underlying 

biochemical and cellular processes may become 

saturated with small doses of toxic components 

(Ambrose and Barua 2004). A recent study, 

for example, reported small airway epithelium 

responses (transcriptome modifications) to even 

the lowest levels of cigarette smoke exposures 

(Strulovici-Barel et al. 2010). There is evidence 

that relevant biological pathways for CVD may 

be activated at low levels of exposure and that 

increasing exposure further increases risk, but 

at a decreasing marginal rate.

The empirical findings of this analysis have 

important public health implications (Smith 

and Peel 2010). Better quantification of the 

exposure–response gradients across environmentally relevant ranges of exposure helps 

inform public health policy decisions. Estimates 

of the burden of disease attributable to air 
Air pollution, cigarette smoke, CVD, and lung cancer

Environmental Health Perspectives • volume 119 | number 11 | November 2011 1621

pollution are sensitive to assumptions regarding 

the shape of the exposure–response relationship, but most of the studies of the effects on 

cardiovascular mortality of long-term exposure 

to PM2.5 have been conducted in areas where 

annual average concentrations range between 

approximately 5 and 35 μg/m3. Average concentrations of particulate air pollution in major 

population centers of China, India, and other 

developing countries are often much higher, 

exceeding 100 μg/m3 according to recent estimates (Sivertsen 2006; van Donkelaar et al. 

2010). For CVD, inappropriate extrapolations 

of linear exposure–response functions may 

result in substantial overestimates in areas with 

very high exposures and, in some cases, potential underestimates in areas with relatively low 

exposures. These results confirm our previous 

finding (Pope et al. 2009) that, for CVD, the 

exposure–response relationship is nonlinear, 

with a steep increase in risk at low exposures 

and flattening out at higher exposures. These 

results, however, indicate a substantively different exposure–response relationship for lung 

cancer mortality and suggest that it may be 

reasonable to assume a near linear exposure–

response function when estimating burdens of 

disease across a wide range of exposures.

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