1614 VOLUME 116 | NUMBER 12 | December 2008 • Environmental Health Perspectives

Research

Particulate matter (PM) air pollution is a

global public health problem (Cohen et al.

2004). In developing countries, levels of airborne PM still reach concentrations at which

serious health consequences are well documented (Chhabra et al. 2001; Ostro et al.

1999a, 1999b; Vichit-Vadakan et al. 2001).

In developed countries, recent epidemiologic

studies show evidence of continued adverse

effects, even though PM levels have declined

in the last two decades (Dominici et al. 2006;

Jerrett et al. 2005; Laden et al. 2006; Pope

et al. 2002). Increased mortality associated

with higher levels of PM air pollution has

been of particular concern, giving an imperative for stronger protective regulations

(Bachmann 2007; Samet et al. 2006).

The evidence on PM and health shows

acute and chronic effects (Pope and Dockery

2006). The London Fog of 1952 provides dramatic evidence of the risk of extremely high

levels of PM air pollution over a period of

about a week (Bell and Davis 2001; Bell et al.

2004; Logan 1953). Multisite time-series studies estimate associations between the risk of

death and the level of air pollution shortly

before death (shorter-term effects). These studies have provided evidence that far lower levels

of PM than those that occur during events like

the London Fog are still associated with

increased risk over several days (Dominici et al.

2006, 2007; Katsouyanni et al. 1997; Lee et al.

2000; Samoli et al. 2001). Cohort studies estimate associations between time to death and

exposure to air pollution over multiple years

(longer-term effects). The design of these studies involves follow-up of cohorts for mortality

over periods of years to decades and an assessment of mortality risk in association with estimated longer-term exposure to air pollution

(Dockery et al. 1993; Hoek et al. 2002; Jerrett

et al. 2005; Krewski et al. 2004; Laden et al.

2006; Pope et al. 1995, 2002). The exposure

indicator in these studies was long-term average air pollution concentration, and time-varying exposures were not used, except in the

most recent updates of several cohorts (Laden

et al. 2006; Pope et al. 2002). Hence, inferences about the relative risks of chronic exposure derive from comparisons across study

cohorts in geographic units with differing

long-term PM levels.

Künzli et al. (2001) have reviewed and

compared time-series studies and cohort studies. They point out that air pollution might

increase a) the risk of underlying diseases leading to frailty and the shorter-term risk of death

among frail persons, b) the risk of chronic diseases leading to frailty but without relation to

timing of death, and c) the shorter-term risk of

death among frail persons but unrelated to risk

of chronic diseases. They note that time-series

studies capture items a and c but do not provide any information on item b; can be

affected by confounding bias due to lack of

control of time-varying covariates; and are useful to establish causation and to assess relative

magnitude of effects across subgroups. On the

other hand, Künzli et al. (2001) note that

cohort studies capture items a and b but, in the

absence of time-varying exposure, provide very

little information on item c; can be affected by

ecologic bias due to comparison of mortality

risks across heterogeneous groups; and can be

used to estimate years of life lost.

Because of their complexity and costs, only

a small number of cohort studies have been

conducted. The most rigorously executed,

including the Harvard Six Cities Study and the

American Cancer Society’s Cancer Prevention

Study II (CPS-II), have provided generally

consistent evidence for an association between

average exposure to PM air pollution over a

decade and increased all-cause and cardiorespiratory mortality (Dockery et al. 1993; Laden

et al. 2006; Pope et al. 1995, 2002). Both

studies compared mortality rates across counties or larger geographic units with different

long-term PM levels to estimate relative risks.

Address correspondence to J.M. Samet, Department

of Epidemiology, Johns Hopkins Bloomberg School

of Public Health, 615 North Wolfe St., Suite

W6041, Baltimore, MD 21205 USA. Telephone:

(410) 955-3286. Fax: (410) 614-0467. E-mail:

jsamet@jhsph.edu

Supplemental Material is available online at

http://www.ehponline.org/members/2008/11449/

suppl.pdf

A.D. had full access to all of the data in the study

and takes responsibility for the integrity of the data

and the accuracy of the data analysis. We thank C.

Gerczak for editorial input.

Funding was provided by the U.S. Environmental

Protection Agency (U.S. EPA) (RD-8324170-3;

S.L.Z., F.D., A.D., J.M.S.), the National Institute of

Environmental Health Sciences (NIEHS) Center in

Urban Environmental Health (P30 ES 03819; S.L.Z.,

F.D., J.M.S.), the NIEHS (ES012054-05; S.L.Z.,

F.D.), and U.S. Environmental Protection Agency

Science to Achieve Results (STAR)/Health Outcome

Indicators grant RD-83362201. Although the research

described in this article has been funded wholly or in

part by the U.S. EPA through grant agreement

RD-83241701 to Johns Hopkins University, it has

not been subjected to the agency’s required peer and

policy review and therefore does not necessarily reflect

the views of the agency, and no official endorsement

should be inferred. 

The authors declare they have no competing

financial interests.

Received 6 March 2008; accepted 12 August 2008.

Mortality in the Medicare Population and Chronic Exposure to

Fine Particulate Air Pollution in Urban Centers (2000–2005)

Scott L. Zeger,1 Francesca Dominici,1 Aidan McDermott,1 and Jonathan M. Samet2

1Department of Biostatistics, and 2Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore,

Maryland, USA

BACKGROUND: Prospective cohort studies constitute the major source of evidence about the mortality

effects of chronic exposure to particulate air pollution. Additional studies are needed to provide

evidence on the health effects of chronic exposure to particulate matter ≤ 2.5 μm in aerodynamic

diameter (PM2.5) because few studies have been carried out and the cohorts have not been

representative.

OBJECTIVES: This study was designed to estimate the relative risk of death associated with longterm exposure to PM2.5 by region and age groups in a U.S. population of elderly, for the period

2000–2005.

METHODS: By linking PM2.5 monitoring data to the Medicare billing claims by ZIP code of residence of the enrollees, we have developed a new retrospective cohort study, the Medicare Cohort

Air Pollution Study. The study population comprises 13.2 million participants living in 4,568 ZIP

codes having centroids within 6 miles of a PM2.5 monitor. We estimated relative risks adjusted by

socioeconomic status and smoking by fitting log-linear regression models.

RESULTS: In the eastern and central regions, a 10-μg/m3 increase in 6-year average of PM2.5 is associated with 6.8% [95% confidence interval (CI), 4.9–8.7%] and 13.2% (95% CI, 9.5–16.9)

increases in mortality, respectively. We found no evidence of an association in the western region or

for persons ≥ 85 years of age.

CONCLUSIONS: We established a cohort of Medicare participants for investigating air pollution and

mortality on longer-term time frames. Chronic exposure to PM2.5 was associated with mortality in

the eastern and central regions, but not in the western United States.

KEY WORDS: ecologic bias, fine particulate matter (PM2.5), heterogeneity, log-linear models,

Medicare, mortality, prospective studies. Environ Health Perspect 116:1614–1619 (2008).

doi:10.1289/ehp.11449 available via http://dx.doi.org/ [Online 12 August 2008]
The results of these studies, rather than of the

time-series studies, have been used to quantify

the risks of PM exposure for consideration of

alternative values for the U.S. National

Ambient Air Quality Standard for PM [U.S.

Environmental Protection Agency (EPA)

2003]. These results have also been used to

estimate the global burden of disease attributable to air pollution (Cohen et al. 2005).

Additional cohort studies are needed to

confirm associations between multiyear average exposure to PM and mortality, to broaden

the populations studied, to reduce the degree

of geographic averaging of the exposure measure—a source of ecologic bias—and to refine

the estimates by regions, age, and socioeconomic status (SES) categories across which

PM exposures may vary. Toward this end, we

have used data from the U.S. Medicare system, which covers nearly all persons ≥ 65

years of age in the United States. We linked

Medicare mortality data to the PM2.5 (PM ≤

2.5 μm in aerodynamic diameter) air pollution monitoring data to create a new retrospective cohort study, the Medicare Cohort

Air Pollution Study (MCAPS), a study population of 13.2 million persons residing in

4,568 ZIP codes in urban areas having geographic centroids within 6 miles of a PM2.5

monitor. We have previously described this

general approach and the comparability of

risk estimates based on MCAPS with estimates from the Harvard Six Cities Study and

CPS-II (Eftim et al. 2008). In this article, we

report on the relationship between 6-year

average exposure to PM2.5 and mortality risk

in the MCAPS over the period 2000–2005.

Our objective is to provide new evidence

about the relative risk of death associated with

chronic exposure to urban PM2.5 by region

and age-defined subgroups.

Materials and Methods

MCAPS is a retrospective study of a cohort of

13.2 million persons ≥ 65 years of age

enrolled in the U.S. Medicare system during

the 6-year period 2000–2005. To create the

cohort, we used the Medicare enrollment file

for the study period, which provides a listing

of all Medicare enrollees, along with demographic information (age, race, and sex) and

ZIP code of residence. New participants enter

each year as they enroll in Medicare, making

this a “dynamic cohort.”

More specifically, the cohort consists of all

those ≥ 65 years of age who enrolled in

Medicare between 2000 and 2005 with ZIP

code centroids within 6 miles of a U.S. EPA

PM2.5 monitoring station. Although the

Social Security Administration maintains the

addresses of those enrolled in Medicare, the

Center for Medicaid and Medicare (CMS)

provides an annual report of Medicare

enrollees by ZIP code (often referred to as the

enrollee file). Medicare enrollees enter the

cohort on reaching their 65th birthday or on

1 January 1999 should they be ≥ 65 on that

date. A small number of individuals enroll in

Medicare the year after their 65th birthday,

and those individuals enter the cohort on

January 1 of the year of their enrollment.

Individuals contribute time to the cohort

until they die or are otherwise censored.

Censorship occurs when individuals move to

a ZIP code > 6 miles from a U.S. EPA PM2.5

monitoring station or are no longer reported

in the enrollee file. We calculated age-specific

mortality rates as the total number of deaths

occurring within an age group and ZIP code

divided by the total person-years contributed

by that age group and ZIP code.

We obtained the date of death from the

CMS. The date of death is provided to CMS

by the Social Security Administration, rather

than by the National Center for Health

Statistics (NCHS), which maintains the

national death certificate system. To validate

the mortality data from the CMS, we compared annual age- and sex-adjusted mortality

rates from the CMS with the corresponding

rates calculated from NCHS data for the 250

largest counties for the year 2000. The correlation coefficient was 0.998, indicating a high

level of agreement between the two sources of

mortality data aggregated to the county

level—the finest partition available from the

NCHS—for the 1-year period.

For this article, the outcome measure is

the 6-year (2000–2005) mortality rate for

persons residing within each of 4,568 ZIP

codes for each of three age strata: 65–74,

75–84, and ≥ 85 years of age.

We obtained the PM2.5 data from the U.S.

EPA’s AirData database (http://www.epa.gov/

oar/data/), which included 1,006 monitors for

the period 2000–2005. We calculated mean

annual PM2.5 values for the study period for all

4,568 ZIP codes with centroids within 6 miles

of a monitor with > 10 months of data per

year. If three or more observations were available for a month, we considered this amount

of data sufficient because PM concentration

was measured every sixth day at many locations. Because the focus of this study was to

estimate the effect of long-term exposure to

PM2.5, we used a ZIP code 6-year average of

PM2.5 as a measure of the long-term exposure

to PM 2.5 for an individual living within a ZIP

code both during the 6 years of follow-up and

for some time before cohort enrollment. We

omitted the 1999 PM2.5 data because this was

the initial year of the U.S. EPA monitoring

program and coverage was limited.

An advantage of MCAPS is that it comprises persons ≥ 65 years of age from nearly all

of the major urban ZIP codes in the United

States, and large numbers of deaths are

reported within each age stratum and region.

We have therefore estimated the age- and

region-specific relative risks of chronic PM2.5

exposure for a) the eastern region of the

United States, with 2,938 ZIP codes in 421

counties; b) the central region, with 990 ZIP

codes within 185 counties located between the

Mississippi River and the Sierra Nevada range;

and c) the western United States, with 640

ZIP codes within 62 counties extending from

Washington State to Southern California.

Figure 1 shows the location of the 4,568 ZIP

code centroids, the three geographic regions,

and the spatially smoothed levels of the 6-year

average PM2.5. These spatially smoothed

PM2.5 levels should be interpreted with caution because of the sparseness of monitors in

some areas.

We conducted the analyses separately

within each of these three geographic regions

Mortality risks from chronic exposure to fine particulate matter

Environmental Health Perspectives • VOLUME 116 | NUMBER 12 | December 2008 1615

Figure 1. Map of spatially smoothed averages of PM2.5 during the study period 2000–2005. The map also

indicates 4,568 ZIP code centroid locations (black circles) and western, central, and eastern U.S. regions. 

PM2.5 μg/m3

< 5

6

7

8

9

10

11

12

13

14

15

16

≥ 17
and for three distinct age strata: 65–74,

75–84, and ≥ 85 years of age. We also stratified initial analyses by sex and by the ZIP

codes that were above and below the national

median for education and income variables.

Because the estimated effects for men and

women and for high- and low-SES subgroups

were very similar, we did not stratify the

analyses reported here by sex or SES. The

results of these stratified analyses are available

in the Supplemental Material, Table 1 (online

at http://www.ehponline.org/members/2008/

11449/suppl.pdf).

In estimating the effect on mortality of

PM or other air pollutants, previous cohort

studies and this new study rely entirely on

cross-sectional comparisons of covariateadjusted mortality rates across geographic

locations with different PM levels, because

PM is not time varying in the analyses.

Previous studies have accounted for potential

confounding by a) individual-level lifestyle

factors, including age and smoking, and

b) area-level characteristics such as countylevel SES. The MCAPS provides individuallevel age, sex, and race data but not data on

lifestyle factors. To account for SES at the ZIP

code level, we used age-specific SES variables

from the 2000 U.S. Census. After preliminary

analysis, we selected five SES variables at the

ZIP code level from the U.S. Census Bureau’s

Summary File 3. We restricted the analysis to

those enrollees who report ZIP codes to CMS

that correspond to ZIP code tabulation areas

recognized by the U.S. Census Bureau. We

selected two education variables, percentage of

the population with a high school diploma

and the percentage with a higher education

degree, along with two household income

measures, percentage of households living

below the poverty level and median household

income, as well as percentage unemployed. To

create a univariate measure of SES by which to

stratify the analysis, we averaged the ranks of

the five SES variables for each county.

Previous cohort studies have found little

effect of adjusting for self-reported smoking

status (Krewski et al. 2000). Area-level differences in cigarette smoking, however, could

potentially confound the association between

PM2.5 and mortality. Because the MCAPS

data have neither individual- nor area-level

smoking information, we used data from the

NCHS to calculate the standardized mortality

ratio (SMR) for chronic obstructive pulmonary disease (COPD) for the period

1993–2002, adjusted for age, race, and sex for

each county. Because the vast majority of

deaths from COPD in the United States are

attributable to smoking (U.S. Department of

Health and Human Services 2004), we used

the SMR for COPD as a surrogate indicator

of the long-term smoking pattern of its residents. We included the county-level COPD

SMR in the regression model, assigning the

county value to all ZIP codes within a county.

For exposure, reliance on ZIP code–level

rather than county-level PM concentration is

a strength, but person-level covariate information is unavailable. To assess the potential

consequences of imperfect control for confounding variables, we estimated the main

models with three levels of adjustment: no

control for ZIP code–level confounders, control for ZIP code–level SES variables, and

control for ZIP code–level SES and countylevel COPD SMRs.

Within each age stratum, we estimated

the following log-linear regression models

(McCullagh and Nelder 1989):

logE(Yi) = logNi + β0 + βPMZi + βXXi , [1]

where Yi, Ni, Zi, and Xi are the number of

deaths, number of person-years at risk, PM2.5,

and SES and COPD SMR for ZIP code i.

The parameter βPM denotes the log relative

risk of mortality associated with a 1-μg/m3

difference in average PM2.5 comparing ZIP

codes that are otherwise similar with respect

to SES and COPD SMR.

We report results for each region by age

stratum and aggregated over the three age

groups. To obtain the aggregated value, we fit

a single log-linear regression with a common

PM effect across the strata. We use generalized estimating equations (Diggle et al. 2002)

to account for the correlation among age

groups from the same ZIP code.

We carried out all analyses with the statistical programs R (R Development Core

Team, Vienna, Austria) and SAS (version 9.1;

SAS Institute Inc., Cary, NC). Programs are

available from the authors.

Results

Table 1 presents the total number of ZIP

codes, PM2.5 monitors, study population,

person-years of follow-up, number of deaths,

and crude death rates for the eastern, central,

and western regions. The study population

comprises 19.1 million persons followed for a

total of 92.6 million person-years or an average of 4.8 years per person. An individual can

contribute person-time to two age categories,

so the age-specific numbers of people do not

add to the total size of the population. There

were 4.88 million deaths, for a crude mortality rate of 52.6 deaths per 1,000 person-years.

The crude mortality in the western region was

lower by roughly 4 deaths per 1,000 personyears compared with the other two regions,

reflecting its younger population.

Table 2 presents the median and interquartile range of the ZIP code values of average PM2.5 for 2000–2005, five SES variables,

Zeger et al.

1616 VOLUME 116 | NUMBER 12 | December 2008 • Environmental Health Perspectives

Table 1. Numbers of ZIP codes, counties, monitoring sites, Medicare enrollees, person-years of follow-up,

deaths, and crude death rates stratified by region and age group for MCAPS.

U.S. region

Characteristic Eastern Central Western All

ZIP codes 2,938 990 640 4,568

Counties 421 185 62 668

Monitoring sites 613 280 119 1,006

Persons (millions) 12.5 3.7 3.1 19.1

65–74 years 7.7 2.3 1.9 11.9

75–84 years 5.9 1.7 1.4 8.9

≥ 85 years 2.4 0.7 0.6 3.6

Person-years (millions) 61.1 17.2 14.4 92.6

65–74 years 30.2 8.8 7.3 46.3

75–84 years 22.6 6.2 5.3 34.1

≥ 85 years 8.2 2.2 1.9 12.3

Deaths (millions) 3.26 0.91 0.70 4.88

65–74 years 0.77 0.22 0.16 1.66

75–84 years 1.31 0.36 0.28 1.95

≥ 85 years 1.18 0.33 0.26 1.77

Crude death rate (deaths/1,000 person-years) 53.4 53.1 48.8 52.6

65–74 years 25.6 25.3 22.4 25.0

75–84 years 57.8 58.7 53.3 57.2

≥ 85 years 143.6 148.9 139.1 143.9

Table 2. Median (interquartile range) ZIP code–level SES values, median county-level COPD SMR, and

median ZIP code–level PM2.5, by region in MCAPS.

U.S. region

Characteristic Eastern Central Western All 

Percent with high school degree 50.0 (41.8–56.0) 49.6 (42.1–56.1) 44.4 (37.0–51.3) 49.3 (40.8–55.6)

Percent with higher degree 28.0 (19.7–41.9) 29.2 (18.2–43.9) 31.4 (21.0–44.7) 28.7 (19.5–43.0)

Percent in poverty 10.1 (5.5–18.5) 12.4 (6.9–20.6) 12.0 (7.0–19.3) 10.9 (6.0–19.2)

Percent unemployment 5.1 (3.5–8.3) 5.3 (3.6–8.2) 6.4 (4.5–8.9) 5.3 (3.6–8.4)

Median income (thousands US$) 40.6 (31.6–52.6) 37.2 (29.8–48.2) 43.9 (35.0–56.4) 40.4 (31.5–52.3)

COPD SMR 94.0 (84.0–108.7) 109.2 (95.2–101.9) 101.9 (100.5–115.0) 99.6 (88.6–113.3)

PM2.5 (μg/m3) 14.0 (12.3–15.3) 10.7 (9.8–12.2) 13.1 (10.4–18.5) 13.2 (11.1–14.9)
and COPD SMRs by region. A scatterplot

matrix [see Supplemental Material, Figure 1

(online at http://www.ehponline.org/members/

2008/11449/suppl.pdf)] provides an X–Y

graph for each variable against each other variable. The proportions have been transformed

to the log odds (logit) scale {log[p/(1 – p)]} to

allow them to range over the whole real line

rather than in (0,1); we show SES variables on

a log scale to linearize their associations with

mortality and to reduce the impact of a few

ZIP codes with larger average incomes. The

bottom row of Table 2 shows the pattern of

pairwise associations between the logit of mortality and each of the covariates or PM2.5. As

expected, mortality has a strong negative association with each of the SES variables and a

positive association with COPD SMRs.

Table 3 presents the estimated relative

risks stratified by region. The MCAPS data

provide evidence of an association between

long-term exposure to PM2.5 and mortality in

the eastern and central regions. For the eastern ZIP codes, we found that a ZIP code with

10 μg/m3 higher long-term average of PM2.5

compared with another ZIP code with comparable age distribution, SES, and COPD

SMR has a 6.8% higher mortality [95% confidence interval (CI), 4.9–8.7]. For the central

ZIP codes, a 10-μg/m3 increase in the longterm average of PM2.5 is associated with a

13.2% increase in mortality (95% CI,

9.5–16.9). For the ZIP codes in the western

region, the association between PM2.5 and

mortality does not achieve statistical significance. In the eastern region, adjustment for

SES and COPD SMR substantially attenuates

the association from 15.5% down to 6.8%

per 10 μg/m3increase. 

Table 4 presents the estimated regionspecific log relative risks of death for each of

the three age groups. In the western region,

there is no evidence of an association for any

of the three age groups. In the eastern and

central regions, the largest effect is for the

youngest group, 65- to 74-year-olds (11.4%

and 20.4% per 10-μg/m3 increase, respectively). The effects are smaller for the 75- to

84-year-olds and close to 0 for the oldest

group, those ≥ 85 years of age. Hence, there is

no evidence of a PM effect for persons ≥ 85

years of age in any of the three regions.

We verified the sensitivity of the inferences to the specific choice of model used to

control for SES and COPD mortality rate,

and the results are qualitatively robust, as

shown in Table 3. We also conducted analyses stratified by sex and by ZIP codes above

and below the national median for education

and income variables. We found that the estimated effects for men and women and for

high- and low-SES subgroups were very similar. We report the results of these analyses in

the Supplemental Material, Table 1 (online at

http://www.ehponline.org/members/

2008/11449/suppl.pdf).

Discussion

In this article we present results from MCAPS,

the largest study of potential health effects of

chronic exposure to air pollution on morbidity

and mortality to date, with 4.88 million deaths

during more than 92 million person-years of

follow-up. In comparison, a total of 20,765

deaths in the subcohort of the American

Cancer Society (Krewski et al. 2000) were

included in the analyses of air pollution and

mortality, less than one-tenth the number in

our study. However, CPS-II had an extensive

set of individual-level risk factors. Given the

availability of 1,006 air pollution monitors and

mortality data from 4,568 ZIP codes within

668 urban counties, we have stratified the

analyses geographically, choosing strata that

broadly reflected differing source mixes and

background disease patterns. This stratification

also controls for potential confounders that

vary on broad geographic scales.

Our estimated associations between longterm exposure to PM2.5 and mortality for the

eastern and central ZIP codes give results

qualitatively similar to those previously published from the Six Cities Study (Dockery

et al. 1993) and CPS-II (Pope et al. 2002).

We previously reported the comparability of

MCAPS estimates to estimates from these

studies, with the MCAPS cohort restricted to

the 110 and the 6 counties corresponding to

the 50 metropolitan areas and the 6 counties

included in CPS-II and the Six Cities Study,

respectively (Eftim et al. 2008). The MCAPS

relative risk estimates, based on the 4,568 ZIP

codes, are 11.4% and 20.4% per 10-μg/m3

increase in the eastern and central regions

(95% CI, 8.8–14.1% and 15.0–25.8%,

respectively) for the youngest age group, compared with the Six Cities Study and CPS-II

values of 15.3% and 12.4%, respectively.

Although the MCAPS data lack individuallevel risk factor information, the MCAPS

results were not qualitatively changed with

inclusion of ZIP code–level or county-level

SES indicators and the COPD SMR in the

log-linear regression model (Tables 3 and 4).

The size of the positive estimates does change

with control for SES and COPD SMRs in the

eastern region.

In MCAPS, we found compelling evidence of differing PM relative risks by age

and geographic location. MCAPS estimates of

the PM relative risk decline with increasing

age category (Table 4), with no evidence of an

association among persons ≥ 85 years of age.

This decline may reflect the many competing

causes of death for which the hazard of death

increases with age. If only a subset of the

competing causes is influenced by exposure to

PM, then the PM-associated relative risk will

reduce with age.

The MCAPS results indicate that the estimated positive association between PM2.5

concentration and mortality derives entirely

from the eastern and central United States. A

provocative finding is that the MCAPS data

show no evidence of a positive association

between ZIP code–level PM2.5 and mortality

rates for the 640 urban ZIP codes in the western region. This lack of association is largely

because the Los Angeles basin counties

(California) have higher PM levels than other

Mortality risks from chronic exposure to fine particulate matter

Environmental Health Perspectives • VOLUME 116 | NUMBER 12 | December 2008 1617

Table 3. Percentage increase (95% CI) in mortality rate per 10-μg/m3 increase in PM2.5 from the log-linear

regression model and stratified by three regions, and relative risks for three levels of adjustment for demographic and socioeconomic variables.

U.S. region

Adjustment Eastern (n = 2,938 ZIP codes) Central (n = 990 ZIP codes) Western (n = 640 ZIP codes)

Age 15.5 (13.0 to 18.0) 17.8 (13.3 to 22.2) 0.3 (–1.9 to 2.5)

Age + SES 10.5 (8.4 to 12.5) 8.9 (5.2 to 12.5) –0.3 (–2.2 to 1.6)

Age + SES + COPD 6.8 (4.9 to 8.7) 13.2 (9.5 to 16.9) –1.1 (–3.0 to 0.8)

Table 4. Percentage increase (95% CI) in mortality rate per 10-μg/m3 increase in PM2.5 from log-linear regression using MCAPS regional data adjusting for three levels of demographic and socioeconomic variables.

Age group (years)

U.S. region/adjustment All 65–74 75–84 ≥ 85

Eastern

Age 15.5 (13.0 to 18.0) 31.1 (26.8 to 35.5) 17.6 (14.9 to 20.4) –1.4 (–3.5 to 0.8)

Age + SES 10.5 (8.4 to 12.5) 17.3 (14.6 to 20.0) 12.4 (10.1 to 14.6) 1.4 (–0.7 to 3.5)

Age + SES + COPD 6.8 (4.9 to 8.7) 11.4 (8.8 to 14.1) 8.9 (6.8 to 11.0) 1.7 (–0.3 to 3.7)

Central

Age 17.8 (13.3 to 22.2) 39.0 (29.7 to 48.2) 17.5 (12.7 to 22.2) –2.1 (–5.9 to 1.6)

Age + SES 8.9 (5.2 to 12.5) 16.5 (10.9 to 22.1) 8.8 (4.6 to 13.0) –0.7 (–4.2 to 2.8)

Age + SES + COPD 13.2 (9.5 to 16.9) 20.4 (15.0 to 25.8) 12.0 (7.6 to 16.4) –0.3 (–4.0 to 3.3)

Western

Age 0.3 (–1.9 to 2.5) 6.0 (2.3 to 9.6) 0.4 (–2.0 to 2.7) –5.2 (–7.2 to 3.2)

Age + SES –0.3 (–2.2 to 1.6) –2.1 (–5.0 to 0.8) 0.3 (–1.8 to 2.5) 0.9 (–0.8 to 2.7)

Age + SES + COPD –1.1 (–3.0 to 0.8) –1.5 (–4.2 to 1.1) –0.2 (–2.2 to 1.9) –0.5 (–2.5 to 1.5)
West Coast urban centers, but not higher

adjusted mortality rates.

Recent multisite time-series studies of the

same Medicare data also suggest that the

effects of airborne PM vary by region and season. In a study of cause-specific cardiovascular

and respiratory hospital admissions and daily

PM2.5 levels in Medicare enrollees, Dominici

et al. (2006) found strong regional patterns of

effect across the 204 U.S. counties included

during the period 1999–2002. Effect estimates for most of the cardiovascular causes

were statistically significant in the eastern

United States, but not in the western United

States. These results were confirmed by a

recent study (Bell ML et al., in press) that

covered the period 2000–2005.

Previous studies of the mortality effects of

chronic PM exposure or surrogates for populations in the western region have reported a

range of relative risks. Most recently, Jerrett

et al. (2005) investigated the PM–mortality

association in a subset of the CPS-II cohort

living in Los Angeles. They estimated an 11%

increase in mortality per 10-μg/m3 increase

PM2.5 (95% CI, –1% to 25%), using a

chronic PM exposure interpolated with a statistical model of measured PM, traffic patterns, and proximity to freeways. Abbey et al.

(1999) reported a follow-up analysis of data

from the Adventist Health Study (Hodgkin

et al. 1984) of > 6,000 nonsmoking residents

of three air basins in California—San

Francisco, Los Angeles, and San Diego—

enrolled in 1977. They found a nonsignificant increase in all-cause deaths of roughly

5% per 10-μg/m3 increase PM10 (PM with

aerodynamic diameter < 10 μm) in males and

no effect in females. They reported a statistically significant association in respiratory

deaths with the fraction of days > 100 μg/m3

PM10 for both sexes. Enstrom (2005) tracked

mortality from 1973 through 2002 in about

50,000 California participants in the first

national cohort study carried out by the

American Cancer Society. Using PM2.5 data

for 11 counties in 1979–1983, he found no

association across the full follow-up period

and evidence of a small effect during the first

decade of follow-up. Misclassification arising

from the limited exposure data available may

have biased this study toward the null.

Regional differences in effect estimates

may be related to heterogeneity in the PM

mixture. For example, higher PM2.5 sulfate

levels are observed in the eastern United

States and higher PM2.5 nitrate in the western

United States. A recent analysis of the chemical composition of PM2.5 from 2000 to 2005

characterized seasonal and regional variation

for > 50 chemical components (Bell et al.

2007); several other studies have investigated

the chemical composition of PM in specific

regions of the United States (Ostro et al., in

press; Shen et al. 2007; Subramanian et al.

2007; Vega et al. 2007; Viana et al. 2008). 

The relative risks estimated in this study

might be affected by ecologic bias due to

using aggregate rather than individual-level

air pollution exposure and confounding factors. We estimate long-term exposure by taking averages of PM2.5 concentrations during

the study period 2000–2005 for each of the

1,006 monitors. We then assign this monitorspecific long-term average to the ZIP code of

residence of each enrollees with a centroid

located within 6 miles from the monitor. Bias

in a cross-sectional study such as this one can

occur if the difference between average personal PM exposure in a ZIP code and the

average ambient value used in this study

covaries with PM levels across the region after

adjusting for SES and COPD SMRs. By

including only ZIP codes whose centroids

were within 6 miles of a monitor as the units

of analysis, we used exposure values that are,

on average, geographically closer to residences

and thus reduced the potential for this type of

ecologic bias.

In a few cohort studies, exposures have

been estimated at the individual level using

models and residence location (Hoek et al.

2002; Jerrett et al. 2005). This approach can

assign person-specific estimates of exposure,

potentially reducing the effects of exposure

measurement error, depending upon the

accuracy and precision of the exposure model.

No cohort studies have measured personal

exposure directly because this is not feasible

with current technologies.

For MCAPS, the covariate information

about SES was available only at the ZIP code

level. Smoking data were represented by the

COPD SMR for the county of residence,

because the direct data on prevalence were

unavailable. With the data reported here, we

cannot directly evaluate the potential for ecologic bias from these terms. However, Krewski

et al. (2000) have done so for CPS-II and the

Six Cities Study by comparing relative risks

with and without controlling for individuallevel characteristics, including smoking, exercise, education, and occupational exposures.

They found little change in the PM relative

risk with adjustment, suggesting that ecologic

bias is negligible for those personal characteristics measured in these two cohort studies.

Despite these methodologic complexities,

we have shown that a cohort can be established using Medicare participants and routine monitoring data for investigating air

pollution and mortality on longer-term time

frames. In our initial analyses of the MCAPS

data, we confirmed the association between

PM2.5 and mortality found in other studies

but we found substantial and unexplained

geographic heterogeneity in the effect of

PM2.5 across the United States.

REFERENCES

Abbey DE, Nishino N, McDonnell WF, Burchette RJ, Knutsen

SF, Lawrence BW, et al. 1999. Long-term inhalable particles and other air pollutants related to mortality in nonsmokers. Am J Respir Crit Care Med 159:373–382.

Bachmann J. 2007. Will the circle be unbroken: a history of the

U.S. National Ambient Air Quality Standards. J Air Waste

Manag Assoc 57:652–697.

Bell ML, Davis DL. 2001. Reassessment of the lethal London fog

of 1952: novel indicators of acute and chronic consequences of acute exposure to air pollution. Environ Health

Perspect 109(suppl 3):389–394.

Bell ML, Davis DL, Fletcher T. 2004. A retrospective assessment of mortality from the London smog episode of 1952:

the role of influenza and pollution. Environ Health Perspect

112:6–8.

Bell ML, Dominici F, Ebisu K, Zeger SL, Samet JM. 2007. Spatial

and temporal variation in PM2.5 chemical composition in

the United States for health effects studies. Environ Health

Perspect 115:989–995.

Bell ML, Ebisu K, Peng RD, Walker J, Samet JM, Zeger SL,

et al. In press. Seasonal and regional short-term effects of

fine particles on hospital admissions in 202 U.S. counties,

1999–2005. Am J Epidemiol.

Chhabra SK, Chhabra P, Rajpal S, Gupta RK. 2001. Ambient air

pollution and chronic respiratory morbidity in Delhi. Arch

Environ Health 56:58–64.

Cohen AJ, Anderson HR, Ostro B, Pandey KD, Krzyzanowski M,

Künzli N, et al. 2004. Urban air pollution. In: Comparative

Quantification of Health Risks. Global and Regional Burden

of Disease Attributable to Selected Major Risk Factors

(Ezzati M, Lopez AD, Rodgers A, Murray CJ, eds).

Geneva:World Health Organization, 1353–1434.

Cohen AJ, Ross AH, Ostro B, Pandey KD, Krzyzanowski M, Künzli

N, et al. 2005. The global burden of disease due to outdoor

air pollution. J Toxicol Environ Health A 68:1301–1307.

Diggle P, Heagerty P, Liang KY, Zeger SL. 2002. Analysis of

Longitudinal Data. 2nd ed. Oxford, UK:Oxford University

Press.

Dockery DW, Pope CA III, Xu X, Spengler JD, Ware JH, Fay

ME, et al. 1993. An association between air pollution and

mortality in six U.S. cities. N Engl J Med 329:1753–1759.

Dominici F, Peng RD, Bell ML, Pham L, McDermott A, Zeger SL,

et al. 2006. Fine particulate air pollution and hospital

admission for cardiovascular and respiratory diseases.

JAMA 295:1127–1134.

Dominici F, Peng RD, Zeger SL, White RH, Samet JM. 2007.

Particulate air pollution and mortality in the United States:

did the risks change from 1987 to 2000? Am J Epidemiol

166:880–888.

Eftim SE, Samet JM, Janes H, McDermott A, Dominici F. 2008.

Fine particulate matter and mortality: a comparison of the

Six Cities and American Cancer Society cohorts with a

Medicare cohort. Epidemiology 19:209–216.

Enstrom JE. 2005. Fine particulate air pollution and total mortality among elderly Californians, 1973–2002. Inhal Toxicol

17:803–816.

Hodgkin JE, Abbey DE, Euler GL, Magie AR. 1984. COPD prevalence in nonsmokers in high and low photochemical air

pollution areas. Chest 86:830–838.

Hoek G, Brunekreef B, Goldbohm S, Fischer P, van den Brandt

PA. 2002. Association between mortality and indicators of

traffic-related air pollution in the Netherlands: a cohort

study. Lancet 360:1203–1209.

Jerrett M, Burnett RT, Ma R, Pope CA III, Krewski D, Newbold

KB, et al. 2005. Spatial analysis of air pollution and mortality in Los Angeles. Epidemiology 16:727–736.

Katsouyanni K, Touloumi G, Spix C, Schwartz J, Balducci F,

Medina S, et al. 1997. Short-term effects of ambient sulphur dioxide and particulate matter on mortality in 12

European cities: results from the APHEA project. Br Med J

314:1658–1663.

Krewski D, Burnett RT, Goldberg MS, Hoover K, Siemiatycki J,

Abrahamowicz M, et al. 2000. Reanalysis of the Harvard

Six Cities Study and the American Cancer Society Study of

Particulate Air Pollution and Mortality. Investigators’

Reports Parts I and II. Cambridge, MA:Health Effects

Institute.

Krewski D, Burnett RT, Goldberg MS, Hoover K, Siemiatycki J,

Abrahamowicz M, et al. 2004. Validation of the Harvard Six

Cities Study of particulate air pollution and mortality.

N Engl J Med 350:198–199.

Zeger et al.

1618 VOLUME 116 | NUMBER 12 | December 2008 • Environmental Health Perspectives
Mortality risks from chronic exposure to fine particulate matter

Environmental Health Perspectives • VOLUME 116 | NUMBER 12 | December 2008 1619

Künzli N, Medina S, Kaiser R, Quenel P, Horak F Jr, Studnicka

M. 2001. Assessment of deaths attributable to air pollution:

should we use risk estimates based on time series or on

cohort studies? Am J Epidemiol 153:1050–1055.

Laden F, Schwartz J, Speizer FE, Dockery DW. 2006. Reduction

in fine particulate air pollution and mortality: extended follow-up of the Harvard Six Cities study. Am J Respir Crit

Care Med 173:667–672.

Lee JT, Kim H, Hong YC, Kwon HJ, Schwartz J, Christiani DC.

2000. Air pollution and daily mortality in seven major cities

of Korea, 1991–1997. Environ Res 84:247–254.

Logan WPD. 1953. Mortality in the London fog incident, 1952.

Lancet 1:336–338.

McCullagh P, Nelder JA. 1989. Generalized Linear Models.

New York:Chapman and Hall.

Ostro B, Chestnut L, Vichit-Vadakan N, Laixuthai A. 1999a. The

impact of particulate matter on daily mortality in Bangkok,

Thailand. J Air Waste Manag Assoc 49:100–107.

Ostro BD, Eskeland GS, Sanchez JM, Feyzioglu T. 1999b. Air

pollution and health effects: a study of medical visits

among children in Santiago, Chile. Environ Health

Perspect 107:69–73.

Ostro BD, Feng WY, Broadwin R, Malig BJ, Green RS, Lipsett

MJ. In press. The impact of components of fine particulate

air pollution on cardiovascular mortality in susceptible

subpopulations. Occup Environ Med. 

Pope CA III, Burnett RT, Thun MJ, Calle EE, Krewski D, Ito K,

et al. 2002. Lung cancer, cardiopulmonary mortality, and

long-term exposure to fine particulate air pollution. JAMA

287:1132–1141.

Pope CA III, Dockery DW. 2006. Health effects of fine particulate air pollution: lines that connect. J Air Waste Manag

Assoc 56:709–742.

Pope CA III, Thun MJ, Namboodiri MM, Dockery DW, Evans

JS, Speizer FE, et al. 1995. Particulate air pollution as a

predictor of mortality in a prospective study of U.S. adults.

Am J Resp Crit Care Med 151:669–674.

Samet JM, Brauer M, Schlesinger RB. 2006. Particulate matter.

In: Air Quality Guidelines: Global Update 2005—Particulate

Matter, Ozone, Nitrogen Dioxide and Sulfur Dioxide.

Copenhagen:World Health Organization, 217–305.

Samoli E, Schwartz J, Wojtyniak B, Touloumi G, Spix C,

Balducci F, et al. 2001. Investigating regional differences

in short-term effects of air pollution on daily mortality in

the APHEA project: a sensitivity analysis for controlling

long-term trends and seasonality. Environ Health Perspect

109:349–353.

Shen ZX, Cao JJ, Arimoto R, Zhang RJ, Jie DM, Liu SX, et al.

2007. Chemical composition and source characterization

of spring aerosol over Horqin sand land in northeastern

China. J Geophys Res Atmos 112:D14315; doi:10.1029/

2006JD007991 [Online 12 November 2007].

Subramanian R, Donahue NM, Bernardo-Bricker A, Rogge WF,

Robinson AL. 2007. Insights into the primary-secondary

and regional-local contributions to organic aerosol and

PM2.5 mass in Pittsburgh, Pennsylvania. Atmos Environ

41:7414–7433.

U.S. Department of Health and Human Services. 2004. The

Health Effects of Active Smoking: A Report of the Surgeon

General. Washington, DC:U.S. Government Printing Office.

U.S. EPA. 2003. Particulate Matter Health Risk Assessment for

Selected Urban Areas: Draft Report. Washington, DC:U.S.

Environmental Protection Agency.

Vega E, Ruiz H, Martinez-Villa G, Sosa G, Gonzalez-Avalos E,

Reyes E, et al. 2007. Fine and coarse particulate matter

chemical characterization in a heavily industrialized city in

central Mexico during winter 2003. J Air Waste Manag

Assoc 57:620–633.

Viana M, Querol X, Ballester F, Llop S, Esplugues A, FernandezPatier R, et al. 2008. Characterising exposure to PM

aerosols for an epidemiological study. Atmos Environ

42:1552–1586.

Vichit-Vadakan N, Ostro BD, Chestnut LG, Mills DM,

Aekplakorn W, Wangwongwatana S, et al. 2001. Air pollution and respiratory symptoms: results from three panel

studies in Bangkok, Thailand. Environ Health Perspect

109(suppl 3):381–387.