Environmental Health Perspectives • volume 120 | number 7 | July 2012 965

Research

All-cause, cardiopulmonary, cardiovascular, 

and lung-cancer mortality have been associated with chronic air pollution exposure in 

prospective studies that controlled for individual covariates (Abbey et al. 1999; Beelen 

et al. 2008b; Beeson et al. 1998; Cao et al. 

2011; Dockery et al. 1993; Eftim et al. 2008; 

Filleul et al. 2005; Gehring et al. 2006; 

Katanoda et al. 2011; Laden et al. 2006; 

Miller et al. 2007; Nafstad et al. 2004; Ostro 

et al. 2010; Pope et al. 2002; Puett et al. 

2009; Yorifuji et al. 2011). The studies that 

specifically considered lung-cancer mortality 

associations with fine particles (aerodynamic 

diameter < 2.5 μm; PM2.5), all found positive 

associations (Beelen et al. 2008b; Dockery 

et al. 1993; Laden et al. 2006; McDonnell 

et al. 2000), although this association was 

only statistically significant (p < 0.05) in the 

American Cancer Society study (ACS) (Pope 

et al. 2002; Turner et al. 2011).

Although compelling evidence supports the harmful effects of PM2.5 on longevity, concerns have been raised regarding 

the sensitivity of the results to model specifications. In particular, Moolgavkar (2005, 

2007) suggested that covariates may not be 

proportional and hence were not controlled 

for properly in proportional hazards models; 

that the concentration–response relation 

may not be linear; and that there are few 

observations at levels as low as or below the 

current World Health Organization and U.S. 

Environmental Protection Agency (EPA) air 

quality standards. In addition, the relative toxicity of particle elements is still controversial, 

and most of the recent reduction in PM2.5

concentrations in the United States has come 

from sulfate control. Hence it is of interest 

whether the concentration–response curve has 

changed over time as particle composition has 

changed. Health impact assessments in the 

United States assume that health benefits of 

reducing particles are only fully realized after 

20 years (U.S. EPA 2010), so examination 

of the lag between exposure and mortality is 

also relevant for consideration of changes in 

the standard.

Our goal was to test the robustness of 

the association between chronic exposure to 

PM2.5 and mortality observed in the original study (Dockery et al. 1993), and the first 

extended follow-up of the Harvard Six Cities 

study (Laden et al. 2006) by replicating the 

analyses using 11 additional years of follow-up 

with exposures well below the U.S. annual 

standard (15 μg/m3) (U.S. EPA 1997). We 

examined different lags of exposure, tested the 

shape of the PM2.5 concentration–mortality 

relationship, tested for changes in this slope 

over time, and relaxed the proportion assumption by allowing the effects of covariates to 

vary each year. We reexamined the association 

of PM2.5 with specific causes of death such as 

lung cancer and examined the effects of PM2.5

depending on participants’ chronic conditions 

and smoking status.

Methods

Study population. The Harvard Six Cities 

study population has been previously described 

(Dockery et al. 1993). Briefly, adults were randomly sampled from six cities in the eastern 

and midwestern United States between 1974 

and 1977: in 1974, Watertown, Massachusetts; 

in 1975, Kingston and Harriman, Tennessee, 

and specific census tracts of St. Louis, 

Missouri; in 1976, Steubenville, Ohio, and 

Portage, Wyocena, and Pardeeville, Wisconsin; 

and in 1977, Topeka, Kansas. Information 

on age, sex, weight, height, educational level, 

smoking history, hypertension, and diabetes 

was collected by questionnaire at enrollment. 

All participants underwent spirometry tests at 

enrollment (Dockery et al. 1985) and chronic 

obstructive pulmonary disease (COPD) was 

defined as having

(FEV1 ÷ FVC) < 70%, 

where FEV1 is forced expiratory volume in 

1 sec, and FVC is forced vital capacity. This 

analysis, as in the previous analyses, was 

restricted to 8,096 white participants with 

acceptable pulmonary function measurements. 

The study was approved by the Harvard 

School of Public Health Human Subjects 

Committee and all participants signed an 

informed consent before participation.

Mortality follow-up. Vital status and 

cause of death were determined by searching 

the National Death Index (NDI) for calendar 

years 1979–2009. Deaths before the NDI 

started in 1979 were identified by next of kin 

and Social Security records, and the cause of 

death was determined by a certified nosologist 

Address correspondence to J. Lepeule, Landmark 

Center West, Room 404C, 401 Park Dr., Boston, 

MA 02215, USA. Telephone: (617) 384-8807. Fax: 

(617) 384-8728. E-mail: jlepeule@hsph.harvard.edu

Supplemental Material is available online (http://

dx.doi.org/10.1289/ehp.1104660).

We thank A. Zanobetti for providing some data.

This work was supported by grants from the 

U.S. Environmental Protection Agency (R832416 

and R84479801) and the National Institute of 

Environmental Health Sciences (ES000002). 

The authors declare they have no actual or potential 

competing financial interests.

Received 25 October 2011; accepted 28 March 

2012.

Chronic Exposure to Fine Particles and Mortality: An Extended Follow-up 

of the Harvard Six Cities Study from 1974 to 2009

Johanna Lepeule,1 Francine Laden,1,2,3 Douglas Dockery,1,2,3 and Joel Schwartz1,2,3

1Department of Environmental Health, and 2Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts, 

USA; 3Channing Laboratory, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA

Background: Epidemiologic studies have reported associations between fine particles (aerodynamic 

diameter ≤ 2.5 μm; PM2.5) and mortality. However, concerns have been raised regarding the sensitivity of the results to model specifications, lower exposures, and averaging time.

Objective: We addressed these issues using 11 additional years of follow-up of the Harvard Six 

Cities study, incorporating recent lower exposures.

Methods: We replicated the previously applied Cox regression, and examined different time lags, 

the shape of the concentration–response relationship using penalized splines, and changes in the 

slope of the relation over time. We then conducted Poisson survival analysis with time-varying 

effects for smoking, sex, and education.

Results: Since 2001, average PM2.5 levels, for all six cities, were < 18 μg/m3. Each increase in 

PM2.5 (10 μg/m3) was associated with an adjusted increased risk of all-cause mortality (PM2.5

average on previous year) of 14% [95% confidence interval (CI): 7, 22], and with 26% (95% CI: 

14, 40) and 37% (95% CI: 7, 75) increases in cardiovascular and lung-cancer mortality (PM2.5

average of three previous years), respectively. The concentration–response relationship was linear 

down to PM2.5 concentrations of 8 μg/m3. Mortality rate ratios for PM2.5 fluctuated over time, 

but without clear trends despite a substantial drop in the sulfate fraction. Poisson models produced 

similar results.

Conclusions: These results suggest that further public policy efforts that reduce fine particulate 

matter air pollution are likely to have continuing public health benefits.

Key words: air pollution, cohort studies, concentration–response, follow-up studies, lag, lung 

cancer, mortality, particles, PM2.5, threshold. Environ Health Perspect 120:965–970 (2012). http://

dx.doi.org/10.1289/ehp.1104660 [Online 28 March 2012]
Lepeule et al.

966 volume 120 | number 7 | July 2012 • Environmental Health Perspectives

who reviewed death certificates (Dockery 

et al. 1993).

Survival time. Survival times were calculated from enrollment until death or the 

end of follow-up (31 December 2009). For 

the 6 participants who were lost to follow-up 

before 1979, the censored survival times were 

calculated from enrollment to date of the last 

follow-up contact plus 6 months or the first 

day of the NDI (1 January 1979), whichever 

came first. For each cause of death category, 

participants who died from another cause 

were censored at time of death.

Air pollution estimates. Annual PM2.5 concentration was assigned for each participant 

until death or censoring. PM2.5 concentration 

was measured in the participant’s city by a centrally located monitor from 1979 to 1986–

1988, depending on the city (Dockery et al. 

1993). Therefore, the study has no spatial contrast on the within-city scale. PM2.5 concentrations for the years before monitoring started 

were assumed to be equal to the earliest monitored year. From the end of monitoring until 

1998, PM2.5 concentration was estimated from 

PM10 (aerodynamic diameter < 10 μm) data 

from U.S. EPA monitors and visibility (extinction) data from the National Weather Service 

(Laden et al. 2006). From 1999 through 2009, 

direct measurements of PM2.5 were available 

from U.S. EPA monitors. For sensitivity analyses, we also predicted PM2.5 for 1999–2009 

(correlation between predicted and measured 

was 0.97) using the formula applied to derive 

exposure estimates during the earlier period 

when PM2.5 was not measured.

Statistical analysis. We first replicated 

the original analysis separately for all-cause 

mortality, cardiovascular mortality as coded by 

the International Classification of Diseases, 9th 

Revision [ICD-9; World Health Organization 

(WHO) 1977] or the 10th Revision (ICD-10; 

WHO 1992), 400.0–440.9, I10.0–I70.9, 

respectively, lung-cancer mortality (ICD-9 

162, ICD-10 C33.0–C34.9), and COPD 

mortality (ICD-9 490.0–496.0, ICD-10 

J40.0–J47.0) for the 36-year follow-up from 

1974 to 2009 using a Cox proportional hazards model with follow-up time as the time 

scale (Dockery et al. 1993; Laden et al. 2006). 

PM2.5 was included in each model as an 

annual time-dependent variable. The model 

was stratified by sex, age (1-year intervals) 

and time in the study (1-year intervals), so 

that each age/sex group had its own baseline hazard for each year of follow-up. The 

analysis was adjusted for potential confounders collected at baseline: smoking status 

(never, former, current), cumulative smoking (pack-years included separately for current and former smokers), educational level 

(< high-school, ≥ high school), and a linear 

and quadratic term for body mass index 

(BMI; kilograms per meter squared), using 

the Cox proportional hazards model formulated as follows:

his(t) = h0 s(t) exp[β1Xi + β2Zi(t)], [1]

where hi is the instantaneous hazard probability of death for subject i in stratum s (defined 

by sex, age, and time in the study), h0 s

(t) is 

the baseline hazard function, Xi

 is the vector of 

time-independent variables, and Zi

(t) is the vector of time-dependent variables. We evaluated 

models with 1-year (i.e., exposure during the 

year before death or censure) to 5-year lagged 

moving averages and chose the best fit model 

using Akaike’s information criterion (AIC) 

(Akaike 1973). The best fit moving average 

was determined from participants who survived 

at least 5 years from enrollment, so that AIC 

criteria were evaluated among populations with 

comparable sizes. We then estimated mortality 

rate ratios (RR) associated with PM2.5 exposure 

during the best fit moving average on the whole 

sample size. Once the best exposure window 

was determined, we fit a penalized spline model 

using a cubic regression spline with 12 knots 

to estimate the shape of the concentration–

response relation, and chose the optimal degree 

of freedom by minimizing AIC and evaluated 

nonlinearity with a Wald test. We investigated 

whether PM2.5 advanced date of death for participants with chronic conditions at enrollment. 

We also investigated the potential for effect 

modification of PM2.5 on mortality by smoking status at enrollment using interaction terms 

between such variables and PM2.5. Finally, we 

tested the hypothesis that the effect of PM2.5

changed over time by dividing the follow-up 

into four equally spaced time periods and 

testing interactions between period and PM2.5.

Sensitivity analyses. We performed sensitivity analyses using a second-degree polynomial 

distributed lag model to allow the effects of 

PM2.5 exposure to be distributed from 1 to 

5 years before death or censor (Lepeule et al. 

2006; Schwartz 2000); using predicted PM2.5

concentrations after 1999 instead of the measured PM2.5; considering only deaths from 

natural causes, with external causes of deaths 

(ICD-9 E800–E999, ICD-10 S00–T88 and 

V00–Y99) being censored at time of death; 

and considering only deaths that occurred in 

the state where the participants lived at enrollment. We next investigated the robustness of 

the results to alternative modeling assumptions by using a Poisson model with dummy 

variables for each year of follow-up, which is 

equivalent to a piecewise exponential proportionate hazard model with the baseline hazard 

changing each year (Laird and Oliver 1981):

log μit = log Eit + γtTt + β1Xi + β2Zi(t), [2]

where μit is the expected value of the death 

indicator for subject i at time t, Eit is the 

exposure duration of subject i at time t 

(log Eit being the offset), Tt is the vector of 

dummy variables for time by 1 year (piecewise baseline hazard), Xi is the vector of the 

time-independent covariates, and Zi(t) is the 

vector of time-dependent variables. Using this 

Poisson survival analysis, we first compared 

the results to the Cox model and then relaxed 

the proportionate hazard assumption for 

sex, education, and cumulative smoking by 

including interaction terms of these variables 

with each year of follow-up. As an alternative 

to the previous analyses (Dockery et al. 1993; 

Laden et al. 2006), we used age in 5-year 

groups as the time scale, and adjusted the 

model for time trends (linear term). For specific 

causes of death, convergence issues led us to 

group age by 10 years. We then fit penalized 

spline models. Because RRs may vary over time 

and period-specific RRs may be biased, we used 

the Poisson model to calculate adjusted survival 

curves (Hernan 2010). We included product 

terms between PM2.5 and time in model 2 

[Equation 2], thereby allowing the effect of 

PM2.5 to flexibly vary from year to year. We 

then predicted the survival probability for each 

year of follow-up for each participant under 

three scenarios using concentrations of PM2.5

throughout the entire follow-up period equal 

to 10, 15, or 20 μg/m3.

p-Values < 0.05 were considered statistically 

significant. All analyses were repeated separately 

for all- and specific-causes of deaths. Analyses 

were conducted with SAS software, version 9.2 

(SAS Institute Inc., Cary, NC) and R statistical software, version 2.12.2 (R Foundation for 

Statistical Computing, Vienna, Austria).

Results

Study population. The 8,096 participants were 

25–74 years of age at enrollment (mean ± SD, 

49.6 ± 13.4) and 54.7% were female. More 

than half of the participants had a high school 

degree or higher, 35.8% were current smokers, and 23.9% were former smokers and the 

average BMI was 25.8 ± 4.5. As for chronic 

conditions, 17.8% reported hypertension, 

11.6% COPD, and 6.9% diabetes.

Mortality rates and PM2.5 levels. At the 

end of 2009, there were 212,067 person-years 

of follow-up and 55.5% of the participants had 

died, among whom 40.8% died from cardiovascular diseases, 7.8% from lung cancer, and 

5.5% from COPD (Table 1). Overall, PM2.5

concentration decreased during the study 

period (Figure 1). After 1998, annual average levels declined by 1.8 μg/m3 in St. Louis 

and by 10.5 μg/m3 in Steubenville, whereas 

levels increased by 1.5 μg/m3 in the Portage–

Wyocena–Pardeeville area. Since 2000, all 

the cities experienced average PM2.5 levels 

< 15 μg/m3 except Kingston–Harriman and 

Steubenville, which had average concentrations 

of ≤ 18 μg/m3.
PM2.5 and mortality: the Harvard Six Cities study

Environmental Health Perspectives • volume 120 | number 7 | July 2012 967

Association between PM2.5 and mortality. 

Using the Cox proportional hazards model, 

statistically significant associations between 

PM2.5 exposure and all-cause, cardiovascular, 

and lung-cancer mortality were observed 

(Table 2). The AIC indicated lag 1 (i.e., exposure during the previous year) to be the best 

fit exposure window for all-cause mortality 

[see Supplemental Material, Table 1 (http://

dx.doi.org/10.1289/ehp.1104660)]. For 

cause-specific mortality, the best fit moving 

average differed between the Cox and the 

Poisson regressions. Because the differences 

in AIC were very small between the 1- and 

5-year moving averages for both the Cox and 

Poisson regressions, we chose the longer of 

the two moving averages to produce more 

stable results, specifically, a 1- to 3-year moving average for cardiovascular and lung-cancer 

mortality, and a 1- to 5-year moving average 

for COPD mortality. Each 10-μg/m3 increase 

in PM2.5 was associated with a 14% increased 

risk of all-cause death [95% confidence interval (CI): 7%, 22%], a 26% increase in cardiovascular death (95% CI: 14%, 40%), and a 

37% increase in lung-cancer death (95% CI: 

7%, 75%). For both all-cause mortality and 

specific causes of death, the model fit was better without the spline (p-values between 0.24 

and 0.43), indicating a linear relationship 

with PM2.5. Results restricted to participants 

with chronic conditions at enrollment (i.e., 

hypertension, COPD, or diabetes) were consistent with those estimated for all participants 

(Table 2). Although, the interaction between 

smoking status and PM2.5 was not statistically 

significant, there was a trend for a stronger 

estimated effect of PM2.5 on mortality in current and former smokers. However, positive 

associations between PM2.5 and all-cause and 

cardiovascular mortality were still evident in 

never smokers. RR for PM2.5 fluctuated over 

time for all-cause mortality and specific causes 

of death, without clear trends (Table 2).

Sensitivity analysis. For both all causes and 

specific causes of death, the cumulative effects 

estimated from the polynomial distributed 

lag model were similar to the effect estimates 

obtained with the selected moving averages 

(Table 2). However, the five lags were too 

correlated (between 0.90 and 0.96) to disentangle the relative importance of each one. 

Using predicted PM2.5 instead of measured 

PM2.5 for exposures after 1999, excluding the 

138 deaths from external causes and excluding 

the 702 participants who died in a state other 

than the state where they lived at enrollment, 

did not change the results (data not shown) 

except for the lung-cancer mortality association with PM2.5, which was slightly attenuated 

(increased risk of 28%; 95% CI: –2%, 67% 

compared with 37%; 95% CI: 7%, 75%) 

when the 702 participants were excluded.

With the Poisson framework, using basic 

assumptions, relaxed proportionate hazard 

assumption for covariates, or age as the time 

scale, the effect estimates and p-values fluctuated slightly but without any notable change 

in the results compared with estimates from 

the Cox models for all-cause mortality and for 

specific-causes of death (data not shown). The 

penalized spline models also indicated linear 

concentration–response relationships without 

a threshold for PM2.5 and mortality from allcauses and specific-causes [see Supplemental 

Material, Figure 1 (http://dx.doi.org/10.1289/

ehp.1104660)]. With the Poisson survival 

analysis, we predicted survival assuming every 

participant was exposed to a constant concentration of PM2.5 (10, 15, or 20 μg/m3) 

during the entire follow-up period. Adjusted 

Table 1. Number of participants, mortality, and average PM2.5 levels in the Harvard Six Cities study, 1974–2009.

Characteristic

Six cities 

(combined) Steubenville Kingston–Harriman St. Louis Watertown Topeka

Portage–Wyocena–

Pardeeville

Participants (n) 8,096 1,346 1,258 1,292 1,332 1,238 1,630

Person-years (n) 212,067 33,276 33,067 32,225 36,818 32,877 43,804

Cause of death

All causes [n (%)] 4,495 (55.5) 822 (61.1) 733 (58.3) 827 (64.0) 700 (52.6) 617 (49.8) 796 (48.8)

Cardiovascular (%) 40.8 45.3 41.1 42.2 39.3 37.4 38.6

Lung cancer (%) 7.8 9.0 8.0 8.7 6.6 7.3 6.8

COPD (%) 5.5 4.9 7.0 5.1 4.9 7.3 4.6

1974–2009 average of individual 

PM2.5 concentrations

15.9 23.6 19.1 16.7 14.0 12.2 11.4

Figure 1. Annual mean PM2.5 levels during 1974–2009 in the Harvard Six Cities study.

35

40

25

30

Steubenville

Kingston–Harriman

St. Louis

Watertown

Topeka

Portage–Wyocena–Pardeeville 

20

10

15

5

PM2.5 (μg/m3

)

1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 2000 2001 2002 2003 2004 2005 200 1999 6 2007 2008 2009
Lepeule et al.

968 volume 120 | number 7 | July 2012 • Environmental Health Perspectives

for individual covariates, the lowest PM2.5

concentration was associated with the highest 

survival (Figure 2). The three adjusted survival 

curves showed that the proportionate hazard 

was a reasonable assumption for PM2.5 and 

that PM2.5 effects were quite stable over time.

Discussion

Including more recent observations with 

PM2.5 exposures down to 8 μg/m3, we continued to find a statistically significant association between chronic exposure to PM2.5

and all-cause and cardiovascular mortality. 

Furthermore, in the present extended followup, PM2.5 exposure was also statistically 

significantly associated with lung-cancer mortality. Our study indicated no sensitivity of 

the results for all-cause mortality and specific 

causes of death when we allowed the effects 

of smoking, education, and sex to vary over 

time, or when we used age as the time scale 

instead of follow-up time. Using very flexible 

modeling assumptions, our results did not 

show any rationale for change of PM2.5 effect 

size over the whole study period, as indicated 

by the adjusted survival curves and the lack 

of a clear interaction of PM2.5 with the four 

study periods. The concentration–response 

relationship was linear without any threshold, 

even at exposure levels below the U.S. annual 

15-μg/m3 standard (U.S. EPA 1997). Taken 

together with the results of a previous reanalysis of the Harvard Six Cities study (Krewski 

et al. 2005b), there is evidence for a robust 

association between chronic PM2.5 exposure 

and early mortality.

Consistency of the results. Our results indicated a statistically significant 14% increase 

in all-cause mortality for a 10-μg/m3 annual 

increase in PM2.5, which is similar to the results 

of the previous follow-ups (Dockery et al. 

1993; Laden et al. 2006). The Netherlands 

Cohort Study on Diet (NLCS–Air) in Europe 

(Beelen et al. 2008b), the Adventist Study 

(McDonnell et al. 2000), and the male Health 

Professionals Follow-up Study in the United 

States (Puett et al. 2011) did not show statistically significant associations between PM2.5

and all-cause mortality. However, our current 

results are consistent with those from the ACS 

cohort (Pope et al. 2002), the Nurses’ Health 

Study (Puett et al. 2009), and the Medicare 

cohort (Eftim et al. 2008), which indicated 

mortality increases ranging from 3–26% per 

10-μg/m3 increase in PM2.5.

The 26% increase in cardiovascular mortality for each 10-μg/m3 increase in PM2.5

exposure during the previous 3 years estimated in this extended follow-up is similar 

to the previous estimate (Laden et al. 2006). 

Although the NLCS–Air study (Beelen et al. 

2008b) found no statistically significant association, the magnitude of the estimated effect 

reported here is between the 12% increase 

estimated for the ACS cohort (Pope et al. 

2004) and the 76% increase estimated for the 

Women’s Health Initiative study (Miller et al. 

2007). Puett et al. (2009) also estimated a 

100% increase in fatal coronary heart diseases 

for a 10-μg/m3 increase in PM2.5 during the 

prior year. Underlying mechanisms for the 

effects of PM2.5 on cardiovascular mortality 

are still poorly understood, but changes in 

vasoconstriction might explain the associations (Anderson et al. 2011).

The previous extended follow-up of the 

Harvard Six Cities study showed an elevated, 

but not statistically significant, risk of lungcancer mortality (Laden et al. 2006), whereas 

the present extended follow-up estimated a 

statistically significant 37% increase in lungcancer mortality (for each 10-μg/m3 increase 

in PM2.5), which is greater than that estimated 

for both the ACS cohort (14%) (Pope et al. 

2002) and a Japanese cohort (27%) (Katanoda 

et al. 2011). Lungs are one of the organs that 

are most directly affected by particulate air 

pollution. Fine particles, which may carry 

toxic chemicals of carcinogenic potential 

(Laden et al. 2000), can reach lung alveoli 

where the clearance is slow (Pinkerton et al. 

1995) and induce durable pulmonary and systemic inflammation (Riva et al. 2011). Recent 

findings in the ACS cohort indicated that a 

10-μg/m3 increase in PM2.5 concentration 

was associated with a statistically significant 

15% to 27% increase in lung-cancer mortality in never smokers (Turner et al. 2011). 

Table 2. Adjusteda association between PM2.5b and mortality, for the 8,096 participants and certain subpopulations of the Harvard Six Cities study, 1974–2009.

Cause of death/stratum-specific estimates 

according to characteristics at enrollment

n participants 

(n person-years)

RR (95% CI) for 10-μg/m3

increase in PM2.5

All-cause 8,096 (212,067) 1.14 (1.07, 1.22)

Chronic conditionsc

Hypertension 1,439 (30,540) 1.17 (1.03, 1.32)

COPD 942 (17,723) 1.09 (0.95, 1.26)

Diabetes 563 (11,473) 1.04 (0.85, 1.27)

Smoking status (p-interaction = 0.58)

Never smoker 3,265 (90,372) 1.09 (0.98, 1.21)

Former smoker 1,934 (48,049) 1.17 (1.04, 1.30)

Current smoker 2,897 (73,646) 1.17 (1.06, 1.28)

Follow-up period (p-interaction = 0.06)

1974–1982 8,096 (58,798) 1.06 (0.96, 1.17)

1983–1991 7,478 (63,129) 1.32 (1.16, 1.50)

1992–2000 6,391 (51,800) 1.11 (0.98, 1.27)

2001–2009 4,910 (38,340) 1.19 (0.91, 1.55)

Cardiovascular 7,961 (195,941) 1.26 (1.14, 1.40)

Smoking status (p-interaction = 0.45)

Never smoker 3,232 (83,861) 1.21 (1.04, 1.41)

Former smoker 1,891 (44,205) 1.21 (1.02, 1.44)

Current smoker 2,838 (67,875) 1.36 (1.17, 1.58)

Follow-up period (p-interaction = 0.07)

1974–1982 7,961 (42,672) 1.08 (0.92, 1.27)

1983–1991 7,478 (63,129) 1.46 (1.21, 1.76)

1992–2000 6,391 (51,800) 1.30 (1.06, 1.59)

2001–2009 4,910 (38,340) 1.57 (1.01, 2.43)

Lung cancer 7,961 (195,941) 1.37 (1.07, 1.75)

Smoking status (p-interaction = 0.15)

Never smoker 3,232 (83,861) 1.25 (0.54, 2.89)

Former smoker 1,891 (44,205) 1.96 (1.29, 2.99)

Current smoker 2,838 (67,875) 1.25 (0.95, 1.64)

Follow-up period (p-interaction = 0.19)

1974–1982 7,961 (42,672) 1.45 (0.98, 2.15)

1983–1991 7,478 (63,129) 0.94 (0.58, 1.52)

1992–2000 6,391 (51,800) 1.54 (0.98, 2.41)

2001–2009 4,910 (38,340) 2.84 (1.06, 7.59)

COPD 7,805 (180,106) 1.17 (0.85, 1.62)

Smoking status (p-interaction = 0.35)

Never smoker 3,191 (77,422) 0.85 (0.36, 2.02)

Former smoker 1,847 (40,453) 1.64 (0.92, 2.93)

Current smoker 2,767 (62,231) 1.10 (0.74, 1.62)

Follow-up period (p-interaction = 0.35)

1974–1982 7,805 (26,837) 0.79 (0.36, 1.72)

1983–1991 7,478 (63,129) 1.52 (0.90, 2.56)

1992–2000 6,391 (51,800) 1.31 (0.74, 2.31)

2001–2009 4,910 (38,340) 0.68 (0.25, 1.83)

aCox proportional hazards model stratified by sex, age, and time in the study and adjusted for BMI, education, and smoking history. bPM2.5 moving average was 1 year before death or censure for all-cause deaths, 1–3 years for cardiovascular 

and lung-cancer deaths, and 1–5 years for COPD deaths. cEstimates restricted to participants with the specified chronic 

condition.
PM2.5 and mortality: the Harvard Six Cities study

Environmental Health Perspectives • volume 120 | number 7 | July 2012 969

We did not find such an association in our 

study, which might be due to a lack of statistical power (350 lung-cancer deaths, 26 among 

never smokers). However, estimated effects of 

PM2.5 on all-cause and cardiovascular mortality were also statistically significant (or borderline significant) in never smokers, and higher 

in current smokers compared to never or former smokers (Table 2).

Regarding COPD mortality, we found 

a positive but not statistically significant risk 

of COPD death associated with PM2.5 exposure. In the ACS cohort, Pope et al. (2004) 

estimated an unexpected inverse association 

between PM2.5 exposure and COPD mortality, whereas Katanoda et al. (2011) estimated 

an inverse but not statistically significant 

association between PM2.5 and COPD in a 

Japanese cohort.

Chronic conditions at enrollment and 

mortality. The central deposition of particles in lungs has been shown to be enhanced 

in COPD patients (Bennett et al. 1997). 

Although PM2.5 has been associated with early 

mortality in COPD patients (Zanobetti et al. 

2008), and ozone has been associated with 

early mortality in susceptible subjects (i.e., 

with COPD, diabetes, heart failure, or myocardial infarction) (Zanobetti and Schwartz 

2011), our results did not indicate stronger 

associations in participants with such chronic 

conditions at enrollment compared with the 

population as a whole,. This might have been 

due to a lack of statistical power as few participants had COPD (n = 942) or diabetes 

(n = 563) at enrollment.

Exposure assessment. Use of outdoor 

measurements from central monitoring stations as a proxy measure of mean personal 

exposure to PM2.5 is prone to measurement 

error because the measures do not capture 

fine spatial contrasts that may occur within 

a city, which may bias the results. Recent 

reanalyses of the ACS cohort using land use 

regression models showed that the impact on 

the PM2.5–mortality association was heterogeneous depending on the city (Krewski et al. 

2009). However, other recent studies have 

suggested that considering a more precise 

exposure model focused on the home address 

might not improve health effects estimates in 

terms of bias and variance (Kim et al. 2009; 

Lepeule et al. 2010; Szpiro et al. 2011). In 

the Harvard Six Cities study, there were not 

enough monitors in the cities to implement a 

land use regression model.

Strengths and limitations. Our results were 

adjusted for baseline factors, but there is potential for residual confounding for risk factors 

after enrollment and for unmeasured factors 

such as occupational exposures or medication 

use if those factors co-vary with PM2.5. Some 

other limitations are that we did not measure 

PM2.5 in the same locations throughout the 

study period, that death certificates might have 

listed misclassified specific causes of death, and 

that hypertension and diabetes were assessed 

by questionnaire only. An extensive body of 

methodological work has been performed 

regarding the sensitivity of estimated associations between long-term exposure to air pollution and mortality, especially for the ACS and 

Harvard Six Cities study cohorts. More specifically, it has been shown that results were robust 

to alternative model specifications, alternative 

metrics of PM2.5, and adjustment for individual and ecological risk factors such as occupational exposures and socioeconomic variables 

(Krewski et al. 2005a, 2005b). It was also 

shown that using a spatial covariance structure 

did not change the results (Pope et al. 2002), 

but with only six locations, that methodology 

is not applicable in our study. Whereas the 

primary analysis from the Harvard Six Cities 

study (Dockery et al. 1993) estimated associations were based on between–city contrasts 

in exposure, in the current study, with age 

used as time scale, the exposure relied on both 

between– and within–city contrasts, limiting 

the potential for residual cross-sectional confounding. The strengths of the present study 

are the randomly sampled participants and 

its extended follow-up through 2009, which 

included more observations of participants 

with lower exposures during recent years and 

provided more statistical power.

Critical periods of PM2.5 exposure. Our 

results indicated that the best fit moving average for PM2.5 was 1 year for all-cause mortality. For cardiovascular and lung-cancer 

mortality, no clear pattern was identified 

because of the high correlation between PM2.5

concentrations in the 5 lagged years tested. 

These results suggest that PM2.5 exposure 

can act to promote cardiovascular diseases 

and lung-cancer growth, although the design 

of this study precludes us from determining 

whether PM2.5 initiates these diseases as suggested by other studies (Beelen et al. 2008a; 

Beeson et al. 1998). These results agree with 

the literature (Gehring et al. 2006; Krewski 

et al. 2009; Puett et al. 2009; Schwartz et al. 

2008) and suggest that health improvements 

can be expected almost immediately after a 

reduction in air pollution. This conclusion 

should be taken into account for cost–benefit 

analyses related to air pollution standards.

Role of sulfates and public health implications. Although RRs for PM2.5 fluctuated over 

time, our extended follow-up did not indicate 

any clear pattern over time during the study 

period. Between 1979–1988 (Laden et al. 

2000) and 2009 (Nehls and Akland 1973), the 

sulfates/PM2.5 ratio for exposures measured for 

the Harvard Six Cities study dropped between 

13% and 54%, depending on the city. If sulfates are unrelated to mortality, as some have 

argued (Grahame and Schlesinger 2005), the 

elimination of a substantial fraction of nontoxic material from PM2.5 mass should result 

in a substantial increase in the PM2.5 coefficient, which would otherwise have been suppressed by the large fraction of mass that was 

nontoxic. This was not the case, and hence our 

results indicate that sulfate particles are about 

as toxic as the average fine particle. This is consistent with the results of Pope et al. (2007), 

who found that the 2.5-μg/m3 decrease in sulfate particle concentrations observed during an 

8-month smelters strike were associated with a 

2.5% decrease in the number of deaths in the 

region. In comparison, a 2.5-μg/m3 decrease 

in PM2.5 in our follow-up of the Harvard 

Six Cities study was associated with a 3.5% 

reduction in all-cause deaths, but that was for 

reductions in PM2.5 lasting at least a year, not 

Figure 2. Survival probabilities under three hypothetical scenarios: participants of the Harvard Six Cities 

study are exposed to 10, 15, or 20 μg/m3 PM2.5 during the entire follow-up period of 1974–2009.

0.65

0.75

0.85

0.95

Survival function (probability)

0.35

0.45

0.55

Years of follow-up

0 5 10 15 20 25 30 35

10 μg/m3

15 μg/m3

20 μg/m3
Lepeule et al.

970 volume 120 | number 7 | July 2012 • Environmental Health Perspectives

8 months. Given that there were 2,423,712 

deaths in the United States in 2007 (Xu et al. 

2010) and that the average PM2.5 level was 

11.9 μg/m3 (U.S. EPA 2011), our estimated 

association between PM2.5 and all-cause mortality implies that a decrease of 1 μg/m3 in 

population-average PM2.5 would result in 

approximately 34,000 fewer deaths per year.

Conclusion

Including recent observations with PM2.5 exposures well below the U.S. annual standard of 

15 μg/m3 and down to 8 μg/m3, the relationship between chronic exposure to PM2.5 and 

all-cause, cardiovascular, and lung-cancer mortality was found to be linear without a threshold. Our results were not sensitive to various 

model specifications. Furthermore, estimated 

effects of PM2.5 did not change over time, 

suggesting a stable toxicity of PM2.5, even at 

lower exposure levels and with a lower sulfates 

proportion. These results suggest that further 

public policy efforts that reduce fine particulate 

matter air pollution are likely to have continuing public health benefits.

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