We analyzed this issue with a distributed lag model in a multicity hierarchic modeling ap-proach, within the Air Pollution and Health: A European Ap-proach APHEA-2 study.. Our study co
Trang 1to Air Pollution:
A Multicity Assessment of Mortality Displacement
Antonella Zanobetti,1 Joel Schwartz,1 Evi Samoli,2 Alexandros Gryparis,2
Abstract: Although the association between particulate matter
and mortality or morbidity is generally accepted, controversy
remains about the importance of the association If it is due solely
to the deaths of frail individuals, which are brought forward by
only a brief period of time, the public health implications of the
association are fewer than if there is an increase in the number of
deaths Recently, other research has addressed the mortality
dis-placement issue in single-city analysis We analyzed this issue with
a distributed lag model in a multicity hierarchic modeling
ap-proach, within the Air Pollution and Health: A European
Ap-proach (APHEA-2) study We fit a Poisson regression model and
a polynomial distributed lag model with up to 40 days of delay in
each city In the second stage we combined the city-specific
results We found that the overall effect of particulate matter less than 10 M in aerodynamic diameter (PM 10 ) per 10 g/m 3 for the fourth-degree distributed lag model is a 1.61% increase in daily deaths (95% CI ⫽ 1.02–2.20), whereas the mean of PM 10 on the same day and the previous day is associated with only a 0.70% increase in deaths (95% CI ⫽ 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.
Air 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 and daily deaths at some longer lag An example of such
a hypothetical pattern, called mortality displacement or harvesting effect, is seen in Figure 1 Were such a phe-nomenon to exist, it should be detected readily in studies
of acute episodes, but those patterns have not been observed in air pollution episodes.13
It is useful to examine the reason for such a phenom-enon Assume there is a pool of people at high risk of dying at any given time An air pollution episode, by
From the 1 Environmental Epidemiology Program, Harvard School of Public
Health, Boston, MA; 2 University of Athens Medical School, Athens, Greece;
3 Department of Public Health Sciences, St George’s Hospital Medical School,
London, United Kingdom; 4 Environmental Health Unit, National Institute of
Public Health Surveillance, Saint-Maurice, France; 5 Municipal Institute of
Pub-lic Health, Budapest, Hungary; 6 Charles University Medical Faculty, Prague,
Czech Republic; 7 Department of Epidemiology, Tel Aviv University, Tel Aviv,
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
Trang 2increasing 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-creased hospitalizations,14 decreased heart rate
variabil-ity,15 etc, thus suggesting that increased recruitment is
possible Recently, Zelikoff et al16have shown that
par-ticle exposure exacerbates pneumonia in animals
Hence, air pollution may intensify some illnesses,
in-creasing the size of the risk pool Further, this may occur
with a different lag than that between exposure and
death out of the risk pool Hence, the direction of the
effect of an air pollution episode on the size of the risk
pool, and the effect of the risk pool on the death rate
over time, may be positive or negative
Recently, three papers have examined this issue
in-directly, by estimating the association between air
pol-lution and daily deaths in Philadelphia,17Boston,18and
Chicago19after filtering out such rebounds None of the
studies found any evidence that the effect size for air
pollution was reduced as a result of the mortality
dis-placement, and indeed all three studies reported that the
effect size approximately doubled Schwartz18interpreted
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 paper21applied a different approach
that explicitly tests this hypothesis Zanobetti et al21
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-ric smoothed distributed lag, looking out to 40 days after exposure, to estimate the effect of air pollution on daily deaths in Milan between 1980 and 1989 This con-strained the estimated effects of air pollution to vary smoothly with the number of days of lag between expo-sure and death This required special software that is not generally available However, in a sensitivity analysis, they showed that essentially identical results could be obtained using a cubic polynomial distributed lag model, which can be implemented in any Poisson regression package In both cases, the coefficients of air pollution at each lag are constrained to fit a smooth shape, in which the latter case is a polynomial If the polynomial is flexible enough to fit the true pattern of the data rea-sonably well, little bias will be introduced
We have adopted that approach for a systematic examination of the lag between air pollution and daily deaths in the Air Pollution and Health: A European Approach (APHEA-2) study.22,23 This analysis focuses
on particulate air pollution in a multicity hierarchic model
Subjects and Methods
Health Data
The APHEA-2 study is a comprehensive, multicenter study that examines the association between air pollu-tion and daily deaths in 30 cities across Europe and
associated regions (eg, Tel Aviv) Data collection
in-cluded daily counts of all-cause mortality, excluding
deaths from external causes (International Classification of
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.
Trang 3Tel 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 paper23 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 PM10
(par-ticulate air matter less than 10 M in aerodynamic
diameter) in four cities, as PM13 (particulate air matter
with aerodynamic diameter less than 13M) in Paris,
and PM15 (particulate air matter with aerodynamic
di-ameter less than 15M) in Rome The Paris data were
assumed to be equivalent to PM10 in this study Rome
data were converted to PM10 using a site-specific
con-version factor based on colocated measurements.24 In
Athens, data were routinely collected only on black
smoke Because traffic is the dominant source of particles
in Athens, there were some days of colocated PM10and
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
PM10as a function of both black smoke (total suspended
particulate for Budapest) and season, again on the basis
of regression modeling with limited PM10data
We conducted a weighted metaregression with a
dummy variable equal to 1 for cities where the other
particle measures were converted to PM10on the basis of
site-specific calibration We found a somewhat higher
coefficient in the converted cities (1.98% per 10g/m3
increase in PM10compared with 1.48% in the cities that
measured PM10), but the confidence interval for the
incremental 0.5% effect was⫾1.93% These results
in-dicate that the coefficients could in fact be 0 Further,
three of the five cities where the conversion occurred
were in southern Europe, where a previous hierarchic
model of all 29 cities in APHEA-2 showed larger
coef-ficients We conclude that there is little reason to
be-lieve the effect estimates differ between the cities where
the air pollutant measurement has been converted and
the other cities Hence, results were reported as the
effect of PM10 Further details have been previously
reported.23
Covariate Control
Generalized additive regression models25 were fitted
in each of the ten cities, controlling for seasonal
pat-terns, long-term time trends for weather, influenza
epi-demics, holidays, and day of the week The models were
built following the APHEA-2 methodology.23Because of
the substantial variability in seasonal patterns and weather between, for example, Stockholm and Tel Aviv, separate models were chosen in each city All models controlled for temperature and humidity on the same day using nonparametric smooth function.27In addition,
we examined whether nonparametric functions of 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 criterion28
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,31which can distort the association with air pollution Therefore, we chose a smoothing parameter for time to reduce the residuals to white noise Sometimes it was necessary to introduce autoregressive terms to accomplish this.32This approach has been used in a number of recent studies.6,12,30
Distributed Lag Model
The goal of our analysis was to estimate the
depen-dence of daily deaths (on day t) on PM10on that day and
up to the previous 40 days If the pollution-related deaths are only being advanced by a few days to a few weeks, we will see this effect as a negative association between air pollution and deaths several days to several weeks subsequently The net effect of air pollution, net
of any such short-term rebound up to 40 days, is the sum
of the effect estimates for all 41 days In addition,
plot-ting individual effect size estimates vs lag number gives
us a direct estimate of what Figure 1 really looks like This is an example of a distributed lag model, which has been described previously.33,34
Trang 4For Poisson regression, the unconstrained distributed
lag model may be written as:
Log(E[Yt])⫽␣ ⫹ covariates ⫹ 0Zt⫹1Zt⫺1⫹
⫹qZt⫺q (1)
where Zt⫽ pollution variable delayed over time, for
j ⫽ 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
lag constrains the jto follow a polynomial pattern in
the lag number, that is:
j⫽k⫽0冘d
kjk, for j⫽ 0 q (2)
where j is the number of lag of delay and k is the
degree of the polynomial Further details, including how
to estimate thek in a Poisson model, have been
pub-lished previously.34Too much constraint risks bias,
pro-ducing a distorted shape, whereas too little constraint
produces estimates that are too noisy to be informative
Although a cubic polynomial was sufficient to match the
results of the smoothed distributed lag in Milan,21 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 coefficients0 .4for
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 ˆik values are estimated in each city i, as
described in Eqs 1 and 2
In the second stage, we combined the city-specific coefficientsik, using the multivariate maximum likeli-hood method.35
We assume that:
ˆi⬃ MVN共 k ,Sˆ i⫹ D) where ˆiis the vector ofk in city i, ˆS iis the estimated
variance-covariance matrix in city i, and D is the
ran-dom variance-covariance matrix component, reflecting heterogeneity in response among the cities
After combining the coefficients ˆikby city, the com-bined coefficients by lag ( ˆj) 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 PM10concentration 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 forcing1⫽ 0, and2
⫽3⫽ ⫽40⫽ 0 All analyses were done using the S-plus software (Mathsoft Inc, Seattle, WA)
Results Table 1 shows the ten cities, their populations, the study period in each location, and the mean and stan-dard deviation of the number of daily deaths and envi-ronmental variables Further details of the baseline mod-els for each city have been published previously.23
Table 2 shows, for each city, the estimated regression coefficients of PM10 (per 10 g/m3and its 95% confi-dence interval) for the traditional model (mean of the current and previous day), and the overall effect from the fourth-degree polynomial, the cubic, and
unre-TABLE 1 Study Period, Population, Mean, and Standard Deviation of the Number of Daily Deaths and the Environmental Variables in the Ten Cities
Years of Study
Population ( ⫻1,000)
Total Mortality PM10( g/m 3 )
5th–95th Percentile
Trang 5stricted distributed lag models The overall effect is the
sum of thejper 10g/m3 It also shows the combined
effect estimates across all of the ten cities, based on a
random-effect model to combine results across cities
Apart from Rome, the estimated effect of PM10
in-creased, and in many cities was more than doubled,
when the lagged effects were considered, rather than
reduced These results are seen in all of the distributed
lag models that we applied, including the unconstrained
model
The reason for this increase is clear from Figure 2,
which shows the estimated effect at each lag, and its
confidence interval from the fourth-degree polynomial
It shows that the effect of PM10does decrease to close to
0 with a lag of 10 days, but remains positive, and rises
again to a second smaller peak, before dying out to 0 by
lag 40
Figure 3 shows the combined effect for the cubic polynomial The PM10effect decreases with a minimum
at 14 days of lag and then rises again Although they differ in some detail, both figures show the same general pattern The initial effect declines to 0 with a lag of 1–2 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 (⫻1000) was 0.922 ⫾ 0.184 for the first
10 days of exposure, and 0.688⫾ 0.261 for the deaths associated with PM10 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 ( ⴛ1,000) for the Mean of PM 10 Lags 0 –1, and the Cubic, Fourth-Degree, and Unrestricted Distributed Lag Models for
40 Lags
* Mean of PM 10 on day of death and day before death.
† Exposure up to 40 days before death, subject to constraints to keep the estimated effect from changing too much from one lag to the next The constraint was a cubic polynomial See method section for more details.
‡ As above but with a 4th-degree polynomial constraint.
§ All 41 PM 10 lags included in the model without constraints.
FIGURE 2. The estimated shape of the association of
par-ticulate matter ⬍10 M in aerodynamic diameter with daily
deaths, with a fourth-degree distributed lag model with random
effect in ten cities.
FIGURE 3. The estimated shape of the association of par-ticulate matter ⬍10 M in aerodynamic diameter with daily deaths, with a cubic-degree distributed lag model with random effect in ten cities.
Trang 6Discussion Previous studies have addressed the mortality
dis-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 results23suggests 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
results This gives considerable assurance that the results
are not due to a chance selection of the study locations
or selection bias Second, our study provides insight into
the shape of the longer-term response to particulate air
pollution In particular, it suggests that the adverse
re-sponse to pollution persists up to a month or longer
Moreover, the smoothed distributed lag model of
Zano-betti et al21produced a very similar curve of effect over
time in Milan There was a prolonged response out to a
month in that study as well, with the same dip after 1–2
weeks
The curves shown in Figures 2 and 3 reflect two
processes One is the pattern of risk over time that
occurs in an individual after exposure This is
presum-ably positive definite, as pollution cannot be expected to
improve health The second is the effect of pollution on
the sensitive pool, which can be to expand or shrink that
pool One possible explanation for the observed results is
that the effects of air pollution persist for over a month
(ie, longer-term average exposures have cumulative
ef-fects), but that this is partially countered by a drop in the
size of the frail pool in the week or two after exposure A
second possibility is that the direct effects of air
pollu-tion trail off by a week or so, but that enhanced
recruit-ment into the frail pool results in a long tail of excess
deaths triggered by other factors This is an important
issue that remains to be investigated If there is a pro-longed increase in individual risks, it should be possible
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 studies38,39 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.11,37Exploration of the cause of such heterogene-ity is now a major priorheterogene-ity Demographic factors do not appear to be major predictors.11,37 Chronic obstructive pulmonary disease has been noted as an effect modifier
in one study.40 The factors responsible for this hetero-geneity in the APHEA-2 cities was the focus of an earlier paper23 (which did not address harvesting), and the mean concentration of NO2and the mean temper-ature appeared to explain most of the variability Be-cause this analysis is more limited, we have not at-tempted to repeat those analyses
Acknowledgments
The APHEA-2 collaborative group consists of: K Katsouyanni, G Touloumi, E Samoli, A Gryparis, Y Monopolis, E Aga, and D Panagiotakos (Greece, coordinating center); C Spix, A Zanobetti, and H E Wichmann (Germany);
H R Anderson, R Atkinson, and J Ayres (U.K.); S Medina, A Le Tertre, P Quenel, L Pascale, and A Boumghar (Paris); J Sunyer, M Saez, F Ballester, S Perez-Hoyos, J M Tenias, E Alonso, K Kambra, E Aranguez, A Gandarillas,
I Galan, J M Ordonez (Spain); M A Vigotti, G Rossi, E Cadum, G Costa,
L Albano, D Mirabelli, P Natale, L Bisanti, A Bellini, M Baccini, A Biggeri,
P Michelozzi, V Fano, A Barca, and F Forastiere (Italy); D Zmirou and F Balducci (Grenoble, France); J Schouten and J Vonk (The Netherlands); J Pekkanen and P Tittanen (Finland); L Clancy and P Goodman (Ireland); A Goren and R Braunstein (Israel); C Schindler (Switzerland); B Wojtyniak, D Rabczenko, and K Szafraniek (Poland); B Kriz, M Celko, and J Danova (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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