The Temporal Pattern of Mortality Responses
to Air Pollution:
A Multicity Assessment of Mortality Displacement
Antonella Zanobetti,
1
Joel Schwartz,
1
Evi Samoli,
2
Alexandros Gryparis,
2
Giota Touloumi,
2
Richard Atkinson,
3
Alain Le Tertre,
4
Janos Bobros,
5
Martin Celko,
6
Ayana Goren,
7
Bertil Forsberg,
8
Paola Michelozzi,
9
Daniel Rabczenko,
10
Emiliano Aranguez Ruiz,
11

increase in deaths (95% CI ϭ 0.43– 0.97). This result is un-
changed using an unconstrained distributed lag model. Our study
confirms that the effects observed in daily time-series studies are
not due primarily to short-term mortality displacement. The effect
size estimate for airborne particles more than doubles when we
consider longer-term effects, which has important implications for
risk assessment. (E
PIDEMIOLOGY 2002;13:87–93)
Key words: air pollution, mortality, mortality displacement.
A
ir pollution, especially airborne particles, has
been consistently reported to be associated with
daily deaths in reports from all over the
world.
1– 8
More recently, systematic multicity analyses
have confirmed these findings.
9 –12
Nevertheless, some
have questioned the public health significance of these
associations, arguing that if these deaths are occurring
only in those who would have died in a few days anyway,
the public health significance of exposure is small. Were
that the case, the increase in deaths during and imme-
diately after exposure would be counterbalanced by a
deficit in daily deaths a few days later, when those deaths
would have otherwise occurred. If such a pattern were
true, the positive correlation seen between daily deaths
and exposure shortly before the death would be coun-
terbalanced by a negative correlation between exposure

Israel;
8
Department of Public Health and Clinical Medicine, Umeå University,
Umeå, Sweden;
9
Agency for Public Health, Lazio Region, Rome, Italy;
10
Na-
tional Institute of Hygiene, Department of Medical, Statistics, Warsaw, Poland;
and
11
Municipal Department of Public Health, Madrid, Spain.
Address correspondence to: Antonella Zanobetti, Department of Environmental
Health, Environmental Epidemiology Program, Harvard School of Public Health,
665 Huntington Avenue, Boston, MA 02115; azanob@sparc6a. harvard.edu
This research was part of the APHEA-2 project, which was funded by the
European Union contract number ENV4-CT97-0534. Joel Schwartz was also
supported by U.S. Environmental Protection Agency Grant R827353.
Submitted October 16, 2000; final version accepted August 21, 2001.
Copyright © 2001 by Lippincott Williams & Wilkins, Inc.
87
increasing the risk in that pool, would increase the death
rate out of the pool and result in a smaller pool size. The
finite size of the risk pool creates the possibility of a
negative association with pollution at some lags. This
rebound (ie, drop in the number of deaths, after an
initial increase) presupposes that air pollution does not
affect recruitment into the pool. Yet numerous epidemi-
ologic studies have shown particulate air pollution to be
associated with exacerbation of illness, including in-

effect size approximately doubled. Schwartz
18
interpreted
this as suggesting that, far from depleting the pool of
critically ill people, air pollution increased the size of the
pool over longer time scales by increasing the intensity
of illness in general. None of these studies provided any
direct estimate of what the time course of the rise and
fall of mortality after exposure might be (eg, Figure 1).
One additional analysis has recently been pub-
lished.
20
These authors assumed a model in which air
pollution could only deplete the pool of susceptible
individuals at high risk of dying and could not increase
recruitment into that pool. This is equivalent to assum-
ing that the correlation between air pollution and daily
deaths must become negative after a lag of several days.
That assumption is a testable hypothesis.
Another recent paper
21
applied a different approach
that explicitly tests this hypothesis. Zanobetti et al
21
estimated the association of air pollution at multiple lags
simultaneously, providing a direct estimate of Figure 1.
Because air pollution is generally correlated, putting a
large number of lags of a pollutant into a model produces
high levels of multicolinearity and unstable results. To
counter this problem, these authors used a nonparamet-

deaths from external causes (International Classification of
Diseases, 9th revision, code Ͼ800). The years of study
were 1990 through 1997, although mortality data in
most cities were available only through 1995 or 1996. In
some cases, air pollution data were available only for part
of the period.
Because of resource and time constraints, it was de-
cided a priori to limit the analysis of mortality displace-
ment to ten cities. To maximize the power of the study,
we chose the largest cities in the study, with the stipu-
lation that only one city could be chosen in each coun-
try. The ten cities selected were Athens, Budapest, Lodz,
London, Madrid, Paris, Prague, Rome, Stockholm, and
FIGURE 1. Hypothetical lag structure corresponding to the
mortality displacement effect.
88 Zanobetti et al EPIDEMIOLOGY January 2002, Vol. 13 No. 1
Tel Aviv. Together, they comprise a population of about
28 million people, which is two-thirds of the population
in the full study, and they represent northern Europe,
central Europe, and the Mediterranean region. An ear-
lier paper
23
examined the association of particulate air
pollution in all available cities and addressed the issue of
heterogeneity in response. That analysis did not exam-
ine the “harvesting” issue addressed in this paper.
Daily measurements of particulate air pollution were
provided by each city participating in the APHEA-2
project. Particulate matter was measured as PM
10

black smoke monitoring that allowed the establishment
of a site-specific selective conversion. Also in Lodz only
data for black smoke were available, whereas in Budapest
the original data were measured as total suspended par-
ticulate. In these three cities, data were converted to
PM
10
as a function of both black smoke (total suspended
particulate for Budapest) and season, again on the basis
of regression modeling with limited PM
10
data.
We conducted a weighted metaregression with a
dummy variable equal to 1 for cities where the other
particle measures were converted to PM
10
on the basis of
site-specific calibration. We found a somewhat higher
coefficient in the converted cities (1.98% per 10
␮
g/m
3
increase in PM
10
compared with 1.48% in the cities that
measured PM
10
), but the confidence interval for the
incremental 0.5% effect was Ϯ1.93%. These results in-
dicate that the coefficients could in fact be 0. Further,

weather variables on the previous day or up to 3 previous
days or the average of a few days improved model fit
(defined as lowering the Akaike information criterion
28
for the model). We similarly chose the number of de-
grees of freedom for each weather variable to minimize
the Akaike information criterion. This approach has
been used and discussed previously.
29,30
Seasonal patterns are controlled because there are
unmeasured predictors of death, such as diet, which vary
seasonally and have long-term trends over time. Because
air pollution also shows seasonal variations and long-
term trends, this creates a potential for confounding.
Shorter-term fluctuations in diet are unlikely to be cor-
related with air pollution. Hence, the goal of our smooth
function of time is to remove seasonal and long-term
fluctuations.
Various smoothing parameters exist for producing
residuals with no seasonality. To choose among them,
we examined the partial autocorrelation function of the
residuals. This is because, although each death is an
independent event, seasonal patterns in the mortality
data produce correlations between the number of deaths
on one day and on the previous day. Eliminating short-
term serial correlation is therefore a measure of how
successful our seasonal control has been. On the other
hand, the use of excessive degrees of freedom for sea-
sonal control induces negative serial correlation in the
residuals of the mortality series,

Log(E[Y
t
]) ϭ
␣
ϩ covariates ϩ
␤
0
Z
t
ϩ
␤
1
Z
tϪ1
ϩ
ϩ
␤
q
Z
tϪq
(1)
where Z
t
ϭ pollution variable delayed over time, for
j ϭ 0 q days.
Because this model produces unstable estimates for
large q, it is common to constrain the coefficients to vary
smoothly with lag number.
33
A polynomial distributed

we
have chosen a fourth-degree polynomial in this study, to
ensure enough degrees of freedom to fit the pattern of
response over time. Such a polynomial has enough de-
grees of freedom to model a curve such as that shown in
Figure 1, or any other plausible shape. Therefore, we
estimated in each city the five coefficients
␩
0

␩
4
for
the fourth-degree polynomial that defines the shape of
the distributed lag. As a sensitivity analysis, we used a
cubic polynomial and an unconstrained distributed lag
model. The unconstrained distributed lag model is too
noisy to provide any information about the shape of the
effect size vs lag, but it does give an unbiased estimate of
the overall effect. A separate distributed lag model was
fit for each of the ten cities.
Second-Stage Modeling
The hierarchic model has two stages. In the first
stage, the
ˆ
␩
ik
values are estimated in each city i,as
described in Eqs 1 and 2.
In the second stage, we combined the city-specific

dom variance-covariance matrix component, reflecting
heterogeneity in response among the cities.
After combining the coefficients
ˆ
␩
ik
by city, the com-
bined coefficients by lag (
ˆ
␤
j
) for the distributed lag
model were obtained from Eq 2.
To see how the results compare with more traditional
models, we fit the same model in each city using as our
exposure index the mean PM
10
concentration on the day
of death and the previous day.
11,34,36,37
Note that this
model is a highly constrained variant of our distributed
lag model, with the constraints forcing
␤
1
ϭ
␤
0
, and
␤

Study
Population
(ϫ1,000)
Total Mortality PM
10
(
␮
g/m
3
)
5th–95th
Percentile
Temperature Humidity
Mean SD Mean SD Mean SD Mean SD
Athens 1992–1996 3073 72.9 13.2 42.7 12.9 33.4 –48.7 17.8 7.4 61.7 13.6
Budapest 1992–1995 1931 80.0 11.6 41.0 9.1 34.2 –45.6 12.8 8.8 70.1 12.6
Lodz 1990–1996 828 29.5 6.3 53.5 15.5 40.7 –61.9 8.4 8.4 79.0 12.4
London 1992–1996 6905 168.5 25.2 28.8 13.7 19.3 –34.0 11.8 5.4 69.3 11.3
Madrid 1992–1995 3012 60.8 11.1 37.8 17.7 26.9 –41.7 14.5 7.4 61.8 16.7
Paris 1992–1996 6700 123.3 15.7 22.5 11.5 14.5 –27.9 12.1 6.5 75.6 12.5
Prague 1992–1995 1212 38.2 7.2 76.2 45.7 46.9 –91.4 11.0 8.0 69.4 14.1
Rome 1992–1996 2775 56.2 10.4 58.7 17.4 61.8 –92.2 16.8 6.7 61.6 11.9
Stockholm 1994–1996 1126 28.9 6.1 15.5 7.9 9.9 –19.5 7.7 8.1 71.4 15.8
Tel Aviv 1993–1996 1141 27.4 6.3 50.3 57.5 32.0 –55.0 20.6 5.4 65.6 11.0
90 Zanobetti et al EPIDEMIOLOGY January 2002, Vol. 13 No. 1
stricted distributed lag models. The overall effect is the
sum of the
␤
j
per 10

weeks and then shows a second peak.
To test whether the effect at longer lags made an
important contribution to the overall effect, we com-
puted the overall effect (and its standard error) for the
first 10 days and for days 11– 40 before the death. The
effect estimate (ϫ1000) was 0.922 Ϯ 0.184 for the first
10 days of exposure, and 0.688 Ϯ 0.261 for the deaths
associated with PM
10
11– 40 days before. Hence, al-
though the exposure in the first week (and indeed the
first 2 days) before the event had a stronger impact, the
exposure in the preceding month substantially increased
the estimate of the overall effect.
TABLE 2. Results for the Ten Cities and Combined for the Estimated Particulate Matter <10
␮
M in Diameter (PM
10
)
Effect (؋1,000) for the Mean of PM
10
Lags 0–1, and the Cubic, Fourth-Degree, and Unrestricted Distributed Lag Models for
40 Lags
Mean 0–1* Cubic† 4th degree‡ Unrestricted§
bSEt bSEt bSEt bSEt
Athens 1.64 0.29 5.60 3.26 0.57 5.67 3.54 0.57 6.16 3.49 0.57 6.10
Budapest 0.28 0.46 0.61 1.20 0.85 1.41 1.41 0.86 1.65 1.01 0.87 1.16
Lodz 0.59 0.42 1.41 3.99 0.61 6.57 3.88 0.62 6.30 3.44 0.62 5.51
London 0.70 0.18 3.94 1.05 0.44 2.38 1.17 0.44 2.63 1.15 0.44 2.59
Madrid 0.52 0.24 2.22 2.35 0.52 4.53 2.34 0.52 4.52 2.57 0.52 4.92

placement issue in single-city analysis. Although these
studies were both methodologically innovative and pro-
duced valuable information on the issue, the heteroge-
neity of response to air pollution that has been reported
in single-city results
23
suggests that a multicity approach,
in various locations and using a predefined sampling
framework, would be quite valuable in furthering discus-
sion of this issue. Such a study would be necessary to
obtain reliable estimates of effect size by lag. Our study is
the first report to obtain such stable estimates of effect
size by lag in multiple locations.
Qualitatively, our study confirms the basic finding of
the previous four studies that did not force harvesting to
occur: we do not find that most of the effect of air
pollution is short-term harvesting. These results have
now been shown in five studies using three different
methodologies and in 13 of 14 cities, suggesting that the
finding is robust. These findings are also consistent with
the results of the episode studies.
13
Quantitatively, our
study also confirms the previous results by showing that
the effect size estimate for airborne particles more than
doubles when longer-term effects are taken into
consideration.
Our study adds several things to the previous litera-
ture. One is the weight of ten cities, which were not
selected haphazardly or according to having positive

to identify intermediary biomarkers that remain elevated
for some time.
The two-fold increase in risk associated with longer
time scales is consistent with the report of higher risk
estimates in cohort studies
38,39
than in previous time-
series studies, given that the cohort studies incorporate
effects of longer-term exposure. Together with those
studies, it suggests that risk assessment based on the
short-term associations likely underestimate the number
of early deaths that are advanced by a significant
amount, and that estimates based on the cohort studies,
or studies such as this one, would more accurately assess
the public health impact. Nevertheless, it is important
to note that the exposure on the day of death and the
immediately preceding day have the greatest impact.
This finding suggests that there are important short-term
influences at work, which is consistent with recent re-
ports of changes in electrocardiogram patterns within
hours of exposure to airborne particles.
15
We note that there appears to be heterogeneity in the
response to particles evident in Table 2. This heteroge-
neity in response has been noted in several studies re-
cently.
11,37
Exploration of the cause of such heterogene-
ity is now a major priority. Demographic factors do not
appear to be major predictors.

(Prague); A. Paldy, J. Bobvos, A. Vamos, G. Nador, I. Vincze, P. Rudnai, and A.
Pinter (Hungary); E. Niciu, V. Frunza, and V. Bunda, (Romania); M. Macarol-
Hitti and P. Otorepec (Slovenia); Z. Dörtbudak and F. Erkan (Turkey); B.
Forsberg and B. Segerstedt, (Sweden); F. Kotesovec and J. Skorkovski (Teplice,
Czech Republic).
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EPIDEMIOLOGY January 2002, Vol. 13 No. 1 AIR POLLUTION AND MORTALITY DISPLACEMENT 93