The Concentration-Response Relation between PM2.5 and Daily Deaths

Abstract

Particulate air pollution at commonly occurring concentrations is associated with daily deaths. Recent attention has focused on the shape of the concentration-response curve, particularly at low doses. Several recent articles have reported that particulate matter with aerodynamic diameter < or = 10 microm (PM(10)) was associated with daily deaths with no evidence of a threshold. These reports have used smoothing or spline methods in individual cities and pooled the results across multiple cities to obtain estimates that are more robust. To date, fine particulate matter (aerodynamic diameter Less than or equal to 2.5 microm; PM(2.5)), a component of PM(10), has not been examined in this regard. We examined this association in a hierarchical model in six U.S. cities. In the first stage, we fit log-linear models including smooth functions of PM(2.5) in each city, controlling for season, weather, and day of the week. These smooth functions allowed for nonlinearities in the city-specific associations. We combined the estimated curves across cities using a hierarchical model that allows for heterogeneity. We found an essentially linear relationship down to 2 microg/m(3). The same approach was applied to examine the concentration response to traffic particles, controlling for particles from other sources. Once again, the association showed no sign of a threshold. The magnitude of the association suggests that controlling fine particle pollution would result in thousands of fewer early deaths per year.

Full text

Environmental Health Perspectives
•
VOLUME 110 |NUMBER 10 |October 2002
1025
The Concentration–Response Relation between PM2.5 and Daily Deaths
Joel Schwartz,1,2,3 Francine Laden,1,3 and Antonella Zanobetti1
1Environmental Epidemiology Program, Department of Environmental Health, Harvard School of Public Health, Boston, Massachusetts,
USA; 2Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts, USA; 3Channing Laboratory, Brigham and
Women’s Hospital, and Department of Medicine, Harvard Medical School, Boston, Massachusetts, USA
In the last decade, a series of studies reported
associations between daily concentrations of
airborne particles and daily deaths (1–3). The
magnitude of the regression coefficients in
those studies indicated that particulate air pol-
lution was associated with between 50 and
100,000 early deaths per year in the United
States, and similar numbers were found in
Europe. More recently, a number of large,
multicity studies (4–7) have reported associa-
tions between airborne particles, measured in
various ways, and daily deaths. The largest
study demonstrated that gaseous air pollutants
did not confound the association, and that
none of the gaseous air pollutants showed an
independent effect on daily deaths (7). These
studies assumed a linear concentration–
response relation between airborne particles
and daily deaths and did not address the ques-
tion of what the association looked like for
particle constituents, characterized by size,
physiochemical composition, or source.
In a recent study of six U.S. cities (5), we
demonstrated that daily mortality was associ-
ated with fine particulate matter (aerody-
namic diameter ≤2.5 µm; PM
2.5
) and not
with coarse particulate matter (aerodynamic
diameter between 2.5 and 10 µm; PM
2.5–10
).
Each 10 µg/m
3
increase in the 2-day mean
concentration of PM
2.5
was associated with a
1.5% (95% confidence interval, 1.1–1.9%)
increase in daily mortality.
Ambient PM
2.5
consists mainly of com-
bustion particles from motor vehicles and the
burning of coal, fuel oil, and wood, but also
contains some crustal particles from finely pul-
verized road dust and soils. These sources pro-
duce particles with different characteristics,
and the relative toxicity of those sources and
characteristics is an area of relative recent but
intense interest. In a follow-up study (8), we
used the elemental composition of size-frac-
tionated particles to identify several distinct
source-related fractions of fine particles. We
then examined the association of these frac-
tions with daily mortality in each of the six
cities and combined the city-specific results in
a meta-analysis to derive overall relative risks
for each fraction. We found positive associa-
tions with particles from traffic, particles from
coal, and particles from residual oil combus-
tion when included jointly in the model pre-
dicting daily deaths (8). The largest effect size
was for residual oil particles, followed by traffic
particles and then coal particles. Only the latter
two associations were statistically significant,
however. Again, as traditional, these analyses
assumed a linear association between the vari-
ous particle constituents and daily deaths.
The shape of the concentration–response
relationship is critical for public health assess-
ment, and in particular, some have speculated
that thresholds might exist.
Recently, three reports have explored this
question for particulate air pollution, using
multicity studies in the United States. In one
study, Daniels et al. (9) used data from 20 U.S.
cities, five of which had daily measurements of
PM
10
, with the rest having measurements
only one day in six. They used regression
splines to model the concentration–response
curve in each city and combined the results
across cities. They found no evidence for a
threshold. In fact, the concentration–response
relation was quite linear across the entire
range of exposure. In another report,
Schwartz and Zanobetti (10) used data from
10 cities, all of which had daily measurements
of PM
10
, resulting in slightly more days of
study than in the first report. They used non-
parametric smoothing to model the concen-
tration–response curve between air pollution
and daily deaths in each city and combined
the results across cities. Again, a linear, no-
threshold relationship was seen. Schwartz and
Zanobetti also performed simulations to con-
firm the ability of this approach to detect
thresholds and other types of nonlinearity
(10). Schwartz et al. (11), using data from
eight Spanish cities, similarly reported a linear
association between daily deaths and black
smoke, an optical measure of black particles.
These results held after adjusting for SO
2
. To
date, no similar examination of the concen-
tration–response curve has been done for
PM
2.5
, or for any source components.
Because PM
2.5
is now the regulated form of
particulate air pollution in the United States,
we here report results of such an analysis.
Materials and Methods
Air pollution data. As part of the Harvard Six
Cities studies (12), dichotomous virtual
impactor samplers were placed at a central
residential monitoring site in six U.S. metro-
politan areas: Boston, Massachusetts;
Knoxville, Tennessee; St. Louis, Missouri;
Stuebenville, Ohio; Madison, Wisconsin; and
Topeka, Kansas. Separate filter samples were
collected of fine particles (PM
2.5
) and of the
coarse mass (PM
2.5–10
) fraction. Integrated
24-hr samples were collected at least every
other day from 1979 until the late 1980s,
with daily sampling during health survey
periods. For fine and coarse particle samples,
mass concentration was determined separately
by beta-attenuation (13). Except for a period
Address correspondence to J. Schwartz, Environmental
Epidemiology Program, Harvard School of Public
Health, 665 Huntington Ave., Boston, MA 02115
USA. Telephone: (617) 384-8752. Fax: (617) 384-
8745. E-mail: jschwrtz@hsph.Harvard.edu
This work was supported by U.S. EPA Grant
R827353 and NIEHS Grant ES 00002.
Received 21 December 2001; accepted 20 March
2002.
Articles
Particulate air pollution at commonly occurring concentrations is associated with daily deaths.
Recent attention has focused on the shape of the concentration–response curve, particularly at low
doses. Several recent articles have reported that particulate matter with aerodynamic diameter
≤10 µm (PM10) was associated with daily deaths with no evidence of a threshold. These reports
have used smoothing or spline methods in individual cities and pooled the results across multiple
cities to obtain estimates that are more robust. To date, fine particulate matter (aerodynamic
diameter ≤2.5 µm; PM2.5), a component of PM10, has not been examined in this regard. We
examined this association in a hierarchical model in six U.S. cities. In the first stage, we fit log-lin-
ear models including smooth functions of PM2.5 in each city, controlling for season, weather, and
day of the week. These smooth functions allowed for nonlinearities in the city-specific associa-
tions. We combined the estimated curves across cities using a hierarchical model that allows for
heterogeneity. We found an essentially linear relationship down to 2 µg/m3. The same approach
was applied to examine the concentration response to traffic particles, controlling for particles
from other sources. Once again, the association showed no sign of a threshold. The magnitude of
the association suggests that controlling fine particle pollution would result in thousands fewer
early deaths per year. Key words: meta-analysis, mortality, particulate air pollution, smoothing,
time series, traffic. Environ Health Perspect 110:1025–1029 (2002).
[Online 27 August 2002]
http://ehpnet1.niehs.nih.gov/docs/2002/110p1025-1029schwartz/abstract.html

between October 1981 and January 1984 in
all cities, elemental composition of fine and
coarse mass was determined by X-ray fluores-
cence (14). Elemental composition was avail-
able on 97% of these samples. In the fine
fraction, 15 elements were routinely found
above the limit of detection: silicon, sulfur,
chlorine, potassium, calcium, vanadium,
manganese, aluminum, nickel, zinc, sele-
nium, bromine, lead, copper, and iron.
Source identification. In separate analyses
for each city, we used specific rotation factor
analysis to identify up to five common factors
from the 15 specified elements. We specified a
single element as the tracer for each factor and
maximized the projection of these elements
using the Procrustes rotation, a variant of the
oblique rotation method (15). The Procrustes
method allows us to use known tracers for dif-
ferent sources as targets for the different fac-
tors and to maximize their loadings on those
factors instead of having factors defined in an
entirely data-driven manner. To rescale the
factor scores from the normalized scale to the
mass scale (in micrograms per cubic meter),
we regressed the total daily fine particle con-
centrations on the daily factor scores for all of
the factors in separate regression models for
each city and took the product of each factor
score with its regression coefficient (16). Only
sources that were significant predictors of total
fine particle mass (p< 0.10) were considered
in the mortality analyses. Further details have
been published previously (8).
Meteorologic data. We obtained meteo-
rologic data from the National Center for
Atmospheric Research, including hourly
measures of temperature, dew point tem-
perature, and precipitation from the
National Oceanographic and Atmospheric
Administration weather station nearest to
each city (17). We calculated 24-hr mean
values for temperature and dew point
temperature.
Mortality data. We defined the six metro-
politan areas in this study as the county con-
taining the air pollution monitor and
contiguous counties (5). We extracted daily
deaths from annual detail mortality tapes
(National Center for Health Statistics) (18)
for people who lived and died in the selected
counties for the time periods with fine partic-
ulate measurements. After excluding all
deaths caused by accidents and other external
causes [International Classification of Diseases,
9th Revision (ICD-9)(19), clinical modifica-
tion codes 800–999], we analyzed the
remaining total daily deaths.
Poisson regression of mortality. We inves-
tigated the association of daily deaths with
sources of fine particles separately for each
city using Poisson regression in a generalized
additive model (GAM) (20,21). That is, in
each city we assumed
[1]
where Y
t
is the number of deaths in the city
on day tand X
it
is the value of covariate ion
day t. GAMs are distinguished by allowing us
to use smooth functions S
i
instead of linear
terms to control for covariates, such as tem-
perature, that may affect daily deaths in a
nonlinear way. Linear functions may be used
where appropriate. This approach was intro-
duced for time series of counts in 1994 (22)
and is now standard (23,24).
To control for trend and season, we used a
locally weighted linear regression (LOESS)
smooth function of date with a span of 0.05
(25). For the smooth functions of temperature
and dew point temperature, we used LOESS
functions with spans of 0.80. Indicator vari-
ables for day of the week also were included in
the models. This is the identical model used
by Schwartz et al. (5) and Laden et al. (8), and
more details are provided there. To these mod-
els we added a smooth function of the mean
PM
2.5
concentration on the day of death and
the previous day, instead of the linear term
previously used by Schwartz et al. (5). The
smoothing window included 50% of the data,
which corresponds to between four and five
degrees of freedom for the air pollution rela-
tion in each city. Alternatively, we added the
estimated mass for each of the source factor
scores (in micrograms per cubic meter ) simul-
taneously in the model. That is, the estimate of
the mobile source factor is in a model control-
ling for coal-derived particles, crustal particles,
and the other source factors, and vice versa.
Because only the particles from traffic showed
a strong linear association, and because the
exposure ranges for the exposures to coal parti-
cles did not overlap sufficiently, we only used a
smooth function for the traffic particles and
followed Laden et al. (8) in treating the particle
mass from the other sources as linear terms.
Hierarchical model. To combine the
smooth curves across cities, we applied the
approach of Schwartz and Zanobetti (10), as
modified by Schwartz et al. (11). In each city,
the predicted log relative risk and its point-
wise standard error was computed for each 2
Log EY S X
tiit
()
[]
=+∑
()
βο,
Articles •Schwartz et al.
1026
VOLUME 110 |NUMBER 10 |October 2002
•
Environmental Health Perspectives
Table 1. Mean daily deaths in six U.S. cities and mean concentrations of PM2.5 overall, and from the three
source categories showing evidence of an association with daily deaths in Laden et al. (8).
PM2.5 Traffic Coal Residual oil Dates
City Deaths (µg/m3)(µg/m3)(µg/m3)(µg/m3)(month/year)
Boston 59 16.5 4.8 8.3 0.5 5/79–1/86
Knoxville 12 21.1 4.4 6.8 — 1/80–12/87
St. Louis 55 19.2 2.9 5.6 — 9/79–1/87
Steubenville 3 30.5 1.5 19.2 0.9 4/79–9/87
Madison 11 11.3 3.1 4.9 — 3/79–12/97
Topeka 3 12.2 2.1 7.0 — 9/79–10/88
Figure 1. Overall estimated dose–response relation between total PM2.5 and daily deaths in six U.S. cities.
The estimate is obtained by combining the estimated smoothed curves in each of the cities, after control-
ling for weather, season, and day of the week. The shaded area indicates the pointwise 95% confidence
intervals at each point. The line shown is a least-squares regression line through the estimated points.
6
4
2
0
–2
0102030
Percent increase in deaths
PM2.5 (µg/m3)

µg/m
3
increment in exposure. These esti-
mates are provided by the GAM function in
S-plus (MathSoft, Inc., Seattle, WA). To suc-
cessfully combine data across cities, we need
to use a range of exposures that is common to
all cities. Because high concentrations of
PM
2.5
were rare, the curves were combined
only in the range of 0–35 µg/m
3
. The first
phase of the analysis produced estimated
effect sizes (log relative risks) Y
ˆ
ij
in each city i
for each exposure category j. A pointwise
standard error of the estimate is also esti-
mated by GAM. To produce the combined
curve, we regressed these estimates against
indicator variables for each level, using inverse
variance weighting and allowing for a random
variance component to capture heterogeneity
in the association across cities. That is, we
assumed
[2]
where d
j
are dummy variables for the jexpo-
sure levels, V
ij
is the estimated variance in city
iat level j, and δis the estimated random
variance component.
We used the iterative meta-regression
approach of Berkey et al. (26) to obtain a
maximum likelihood estimate of the random
variance component.
The nonparametric smooth functions we
use to estimate the shape of the concentration
response relation use four to five degrees of
freedom, and it is not clear that the source-
specific relations can support so many degrees
of freedom, which would entail a total of 20
degrees of freedom for all the PM
2.5
sources.
In our previous report (8), the relation
between PM
2.5
from traffic and daily deaths
was estimated with considerably greater preci-
sion than for particles from other sources,
most of which were not significant. Further,
the range of overlap in exposures across cities
was lower for coal, crustal, and residual oil
factors. Therefore, in our source-specific
models, we only modeled the traffic source
particles using a nonparametric smooth,
while controlling for PM
2.5
from the other
sources using linear terms, as in Laden et al.
(8). We then combined the estimated con-
centration–response relations for traffic parti-
cles similarly to what we did for PM
2.5
from
the other sources.
Results
Table 1 shows the daily deaths, PM
2.5
levels,
and estimated concentrations of PM
2.5
from
each source. Figure 1 shows the meta-smooth
dose–response relation between PM
2.5
and
daily deaths in the six cities. There is no evi-
dence of a threshold, and the relation occurs
well below the U.S. Environmental Protection
Agency standard of 65 µg/m
3
(27). The line
shows the least-squares fit of a linear relation
through the estimated points.
The next results come from the source
component models. These models had a
smooth function of PM
2.5
from traffic and
linear functions of PM
2.5
from the other
sources in each city. Figure 2 shows the
results when we combined the estimated
dose–response curves for traffic particles
across the six cities. Again, there is no evi-
dence of a threshold, and the association is
essentially linear. If anything, the slope is
steeper at lower concentrations. To test the
robustness of the association with traffic par-
ticles to our method of controlling for parti-
cles from other sources, we re-estimated the
relationship controlling for smooth functions
of the estimated particle mass from other
sources, rather than the linear terms. This
association is shown in Figure 3 and differs
little from that shown in Figure 2. We also fit
linear regressions through the points shown
on Figures 1 and 2. We obtained a slope of
1.5% increase in deaths per 10 µg/m
3
increase in PM
2.5
and 3% increase in deaths
per 10 µg/m
3
increase in particles from traf-
fic, which is the same as the results reported
by Laden et al. (8). These lines are shown on
the figures. This supports the assumption of a
linear relationship.
Discussion
We have explored the concentration–response
relation between PM
2.5
and daily deaths in six
U.S. cities and combined the results to obtain
greater stability, while accounting for hetero-
geneity in response. The population mean
curve shows no evidence of a threshold down
to the lowest levels of PM
2.5
. In fact, the curve
is quite linear over the exposure range from 0
to 35 µg/m
3
. This is consistent with previous
results using a similar methodology but with
PM
10
(10) and black smoke (11) as the expo-
sure metric. In addition, a different methodol-
ogy, using regression splines, was applied by
Daniels et al. (9) to PM
10
data in different
cities. They combined these spline models
across 20 cities. Again, the association
appeared to be quite linear without any evi-
dence of a threshold. A spline model had pre-
viously been applied by Schwartz (22) to the
PM
2.5
data from Boston, with a similar find-
ing. Indeed, the original study of these data by
Schwartz, Dockery, and Neas (5) found a sig-
nificant association when limited to days
below 30 µg/m
3
, with a slightly larger slope.
The consistency of the results on two conti-
nents, and using different techniques, suggests
that this finding is robust. The concentra-
tion–response curve seen here for PM
2.5
is
steeper than that previously reported (per
µg/m
3
) for PM
10
(10). This is consistent with
the previous report from this study (5) that
coarse mass (the difference between PM
10
and
PM
2.5
) is not associated with daily deaths. We
note that Schwartz and Zanobetti (10)
demonstrated in simulation studies that mea-
surement error was not likely to distort the
shape of the association. Similarly, recent
studies of “harvesting” have shown that effect
sizes increase rather than decrease when longer
lags are taken into account; for example, high
ˆ~,,YNd d dV
ij k k ij
ββ β δ
11 2 2
++⋅⋅+ +
()
Articles •PM2.5 and daily deaths
Environmental Health Perspectives
•
VOLUME 110 |NUMBER 10 |October 2002
1027
Figure 2. Overall estimated dose–response relation between PM2.5 from traffic and daily deaths in six U.S.
cities. The estimate is obtained by combining the estimated smoothed curve in each of the cities, after
controlling for weather, season, and day of the week and for PM2.5 from crustal sources, coal combustion,
residual oil, salt, and metal processes as linear terms. The line shown is a least-squares regression line
through the estimated points.
10
5
0
01020
Percent increase in deaths
Traffic particles (µg/m3)
155

days producing harvesting that mutes the
effect on the next high day is unlikely to have
distorted the shape of the association.
These results are also biologically plausi-
ble. Schwartz (28) pointed out that if thresh-
olds exist in individuals, but there is a
distribution of those thresholds among indi-
viduals, and if multiple genetic and predis-
posing illnesses each contributed to the
distribution of those thresholds, then by the
central limit theorem, the distribution of
thresholds should approach a normal distrib-
ution. Hence, the population concentration–
response curve should approach a cumulative
normal curve. But the low-dose end of the
cumulative normal curve is linear. To see this,
consider that typical death rates in U.S. cities
are 8/1,000 per year, or 2 ×10
–6
per day. The
normal range of variation in daily deaths in
U.S. cities is a factor of two or less. Hence,
the normal range of daily death probabilities
in response to all risk factors is from 1 to 3 ×
10
–6
. Figure 4 shows the cumulative normal
curve in that range of probabilities, which is
quite linear. Because we are clearly in the low-
dose regime, in the sense that the exposures
to particles are well below the threshold for
mortality for most people, this linearity is
exactly what would be expected.
Figure 1 also indicates that the association
reported here has public health significance.
The difference between mean PM
2.5
concen-
trations of 10 µg/m
3
and 20 µg/m
3
, which is
a difference found between U.S. cities, is
associated with about a 1.5% increase in
deaths. In a metropolitan area of a million
inhabitants, this would amount to about 130
additional early deaths per year, and in the
country as a whole, these results indicate that
a reduction of 10 µg/m
3
would be expected to
result in about 36,000 fewer early deaths per
year. Although this study does not indicate
the extent to which these deaths are brought
forward, other studies of the harvesting issue
(29–32) suggest that they are considerable.
The association of daily deaths with traf-
fic particles also has no threshold and is some-
what steeper than the association with all
PM
2.5
. This is consistent with the results of
Laden et al. (8), except that they used linear
terms instead of smooth functions. This study
confirms that this association extends to low
levels. This result has considerable public
policy relevance. Recently, automotive
companies have proposed using diesel engines
to achieve higher fuel economy in the future.
However, diesel engines produce substantially
greater emissions of particles and particle pre-
cursors such as NO
x
. The present results indi-
cate that such an expansion of diesel engine
use in the United States before diesel engines
can meet the same particle emission levels as
gasoline engines may result in important pub-
lic health problems. A 1 µg/m
3
increase in the
concentration of traffic particles in the
United States, for example, could be associ-
ated with about 7,000 additional early deaths
per year in the United States.
REFERENCES AND NOTES
1. Schwartz J, Dockery DW. Increased mortality in
Philadelphia associated with daily air pollution concentra-
tions. Am Rev Respir Dis 145:600–604 (1992).
2. Pope CA, Dockery DW, Schwartz J. Review of epidemio-
logic evidence of health effects of particulate air pollution.
Inhal Toxicol 7:1–18 (1995).
3. Touloumi G, Pocock SJ, Katsouyanni K, Trichopoulos D.
Short-term effects of air pollution on daily mortality in Athens:
a time-series analysis. Int J Epidemiol 23:957–967 (1994).
4. Katsouyanni K, Touloumi G, Spix C, Schwartz J, Balducci
F, Medina S, Rossi G, Wojtyniak D, Sunyer J, Bacharova L,
et al. Short term effects of ambient sulphur dioxide and
particulatematter on mortality in 12 European cities:
results from time series data from the APHEA project. Br
Med J 314:1658–1663 (1997).
5. Schwartz J, Dockery DW, Neas LM. Is daily mortality
associated specifically with fine particles? J Air Waste
Manag Assoc 46:2–14 (1996).
6. Schwartz J. Assessing confounding, effect modification,
and thresholds in the association between ambient parti-
cles and daily deaths. Environ Health Perspect
108:563–568 (2000).
7. Samet JM, Zegar SL, Dominici F, Curriero F, Coursac I,
Dockery DW, Schwartz J, Zanobetti A. The National
Morbidity, Mortality, and Air Pollution Study Part II:
Morbidity, Mortality, and Air Pollution in the United States.
Report 94. Boston, MA:Health Effects Institute, 2000.
8. Laden F, Neas LM, Dockery DW, Schwartz J. Association
of fine particulate matter from different sources with daily
mortality in six U.S. cities. Environ Health Perspect
108(10):941–947 (2000).
9. Daniels MJ, Dominici F, Samet JM, Zeger SL. Estimating
particulate matter mortality dose response curves and
threshold levels: an analysis of daily time series for the 20
largest U.S. cities. Am J Epidemiol 152:397–406 (2000).
10. Schwartz J, Zanobetti A. Using meta-smoothing to esti-
mate dose-response trends across multiple studies,
with application to air pollution and daily death.
Epidemiology 11(6):666–672 (2000).
11. Schwartz J, Ballester F, Saez M, Pérez-Hoyos S, Bellido J,
Cambra K, Arribas F, Cañada A, Pérez-Boillos MJ, Jordi
Sunyer J. The concentration–response between air pollu-
tion and daily deaths. Environ Health Perspect
109:1001–1006 (2001).
12. Ferris BG Jr, Speizer FE, Spengler JD, Dockery DW,
Bishop YMM, Wolfson M, Humble C. Effects of sulfur
oxides and respirable particulates on human health:
methodology and demography of populations in study. Am
Rev Respir Dis 120:767–779 (1979).
13. Courtney WJ, Shaw RW, Dzubay TD. Precision and accu-
racy of a beta-gauge for aerosol mass determinations.
Environ Sci Technol 16:236–239 (1982).
14. Spengler JD, Thurston G. Mass and elemental composi-
tion of fine and coarse particles in six U.S. cities. J Air
Pollut Control Assoc 33:1162–1171 (1983).
15. Browne MW. On oblique Procrustes rotation. Psychometrika
32:125–132 (1967).
16. Thurston GD, Spengler JD. A quantitative assessment of
source contributions to inhalable particulate matter pollu-
tion in metropolitan Boston. Atmos Environ 19:9–25 (1985).
17. U.S. National Climate Data Center. EarthInfo CD. NCDC
Surface Airways. Boulder, CO:EarthInfo Inc.
18. U.S. Department of Health and Human Services, Public
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VOLUME 110 |NUMBER 10 |October 2002
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Environmental Health Perspectives
Figure 4. Cumulative normal curve versus a stan-
dardized predictor (the sum of the effects of all risk
factors) over the range of exposures that corre-
spond to daily death rates of between 1 and 3 per
million, which is the observed range of variation in
U.S. cities. It is quite linear in the predictor.
3.0
2.5
2.0
1.5
0
10 20
Probability of death (×
10–6
)
515
Scaled linear predictor
Figure 3. Overall estimated dose–response relation between PM2.5 from traffic and daily deaths in six U.S.
cities. The estimate is obtained by combining the estimated smoothed curve in each of the cities, after
controlling for weather, season, and day of the week. Instead of linear terms for particles from other
sources, in this analysis we controlled for smoothed terms for PM2.5 from crustal sources, coal combus-
tion, residual oil, and salt. The line shown is a least-squares regression line through the estimated points.
10
5
0
–5
01020
Percent increase in deaths
Traffic particles (µg/m3)
155

Health Service, Centers for Disease Control and
Prevention, National Center for Health Statistics. Public
Use Data Tape Documentation. Hyattsville, MD:U.S.
Department of Health and Human Services, 1993.
19. International Classification of Diseases, 9th revision, Vol 1.
Commission on Professional and Hospital Activities. Ann
Arbor, MI:Edward Brother Inc, 1979.
20. Hastie T, Tibshirani R. Generalized Additive Models.
London:Chapman and Hall, 1990.
21. Schwartz J. Generalized additive models in epidemiol-
ogy. Invited Papers: Proceedings of the 17th
International Biometric Conference. Hamilton, Ontario,
Canada:International Biometric Society, 1994:55–80.
22. Schwartz J. Air pollution and daily mortality: a review and
meta analysis. Environ Res 64:36–52 (1994).
23. Hoek G, Schwartz J, Groot B, Eilers P. Effects of ambient
particulate matter and ozone on daily mortality in
Rotterdam, The Netherlands. J Arch Environ Health
52:455–463 (1997).
24. Michelozzi P, Forastiere F, Fusco D, Perucci CA, Ostro B,
Ancona C, Pallotti G. Air pollution and daily mortality in
Rome, Italy. Occup Environ Med 55(9):605–610 (1998).
25. Cleveland WS, Devlin SJ. Robust locally-weighted regres-
sion and smoothing scatterplots. J Am Stat Assoc
74:829–836 (1988).
26. Berkey CS, Hoaglin DC, Mosteller F, Colditz GA. A ran-
dom–effects regression model for meta-analysis. Stat
Med 14:395–411 (1995).
27. U.S. EPA. National Ambient Air Quality Standards for
Ozone and Particulate Matter, Proposed Decision.
Fed Reg 62:7743–7744 (1997). Available: http://www.epa.gov/
fedrgstr/EPA-AIR/1997/February/Day-20/a4330.htm [cited
31 July 2002].
28. Schwartz J. Health effects of particulate air pollution: is
there a threshold? In: Relationship between Respiratory
Disease and Exposure to Air Pollution (Mohr U, ed).
Washington, DC:ILSI Press, 1998;195–206.
29. Zeger SL, Dominici F, Samet JM. Harvesting resistent esti-
mates of air pollution effects on mortality. Epidemiology
10:171–175 (1999).
30. Zanobetti A, Wand MP, Schwartz J, Ryan L. Generalized
additive distributed lag models: quantifying mortality dis-
placement. Biostatistics 1:279–292 (2000).
31. Schwartz J. Is there harvesting in the association of air-
borne particles with daily deaths and hospital admis-
sions? Epidemiology 12:55–61 (2001).
32. Zanobetti A, Schwartz J, Samoli E, Gryparis A, Touloumi G,
Atkinson R, Le Tertre A, Bobros J, Celko M, Goren A, et al.
The temporal pattern of mortality responses to air pollu-
tion. Epidemiology 13(1):87–93 (2002).
Articles •PM2.5 and daily deaths
Environmental Health Perspectives
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