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 and Klea Katsouyanni 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 ϭ 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 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 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 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- creased hospitalizations, 14 decreased heart rate variabil- ity, 15 etc, thus suggesting that increased recruitment is possible. Recently, Zelikoff et al 16 have 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, 17 Boston, 18 and Chicago 19 after 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. 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- 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 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 (par- ticulate air matter less than 10 ␮ M in aerodynamic diameter) in four cities, as PM 13 (particulate air matter with aerodynamic diameter less than 13 ␮ M) in Paris, and PM 15 (particulate air matter with aerodynamic di- ameter less than 15 ␮ M) in Rome. The Paris data were assumed to be equivalent to PM 10 in this study. Rome data were converted to PM 10 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 PM 10 and 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, 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 PM 10 . Further details have been previously reported. 23 Covariate Control Generalized additive regression models 25 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. 23 Because 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. 27 In 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 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, 31 which 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. 32 This 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)onPM 10 on 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 EPIDEMIOLOGY January 2002, Vol. 13 No. 1 AIR POLLUTION AND MORTALITY DISPLACEMENT 89 For Poisson regression, the unconstrained distributed lag model may be written as: 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 lag constrains the ␤ j to follow a polynomial pattern in the lag number, that is: ␤ j ϭ ͸ kϭ0 d ␩ k j k , for j ϭ 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 the ␩ k in a Poisson model, have been pub- lished previously. 34 Too 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 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 coefficients ␩ ik , using the multivariate maximum likeli- hood method. 35 We assume that: ␩ ˆ i ϳ MVN͑ ␩ k ,S ˆ i ϩ D) where ˆ ␩ i is the vector of ␩ k in city i, ˆ S i is 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 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 ␤ 2 ϭ ␤ 3 ϭ ϭ ␤ 40 ϭ 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 PM 10 (per 10 ␮ g/m 3 and 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 (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 ␮ g/m 3 . 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 PM 10 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 PM 10 does 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 PM 10 effect 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 (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 Paris 0.42 0.23 1.82 2.48 0.46 5.40 2.54 0.46 5.53 2.45 0.46 5.30 Prague 0.11 0.18 0.60 0.66 0.33 1.99 0.72 0.34 2.13 0.53 0.35 1.49 Rome 1.51 0.27 5.56 Ϫ0.90 0.48 Ϫ1.90 Ϫ0.74 0.48 Ϫ1.55 Ϫ0.72 0.48 Ϫ1.50 Stockholm 0.36 0.88 0.41 1.88 2.02 0.93 1.93 2.02 0.95 1.40 2.04 0.68 Tel Aviv 0.67 0.26 2.62 0.53 0.38 1.42 0.65 0.38 1.71 0.89 0.44 2.05 Meta-analysis (with random effect) 0.70 0.14 5.13 1.57 0.67 2.33 1.61 0.30 5.32 1.61 0.39 4.13 * 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 Ͻ10 ␮ 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 Ͻ10 ␮ M in aerodynamic diameter with daily deaths, with a cubic-degree distributed lag model with random effect in ten cities. EPIDEMIOLOGY January 2002, Vol. 13 No. 1 AIR POLLUTION AND MORTALITY DISPLACEMENT 91 Discussion 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 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 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 al 21 produced 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 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. 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 paper 23 (which did not address harvesting), and the mean concentration of NO 2 and 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). References 1. Schwartz J, Dockery DW. Increased mortality in Philadelphia associated with daily air pollution concentrations. Am Rev Respir Dis 1992;145:600– 604. 92 Zanobetti et al EPIDEMIOLOGY January 2002, Vol. 13 No. 1 2. Pope CA, Dockery DW, Schwartz J. Review of epidemiologic evidence of health effects of particulate air pollution. Inhal Toxicol 1995;7:1–18. 3. Schwartz J. Air pollution and daily mortality: a review and meta- analysis. Environ Res 1994;64:36 –52. 4. 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 1994;23:957–967. 5. Saldiva PH, Pope CA, Schwartz J, et al. Air pollution and mor- tality in elderly people: a time series study in Sao Paulo, Brazil. Arch Environ Health 1995;50:159 –163. 6. Rossi G, Vigotti MA, Zanobetti A, Repetto F, Giannelle V, Schwartz J. Air pollution and cause specific mortality in Milan, Italy, 1980–1989. Arch Environ Health 1999;54:158 –164. 7. Hoek G, Schwartz J, Groot B, Eilers P. Effects of ambient partic- ulate matter and ozone on daily mortality in Rotterdam, The Netherlands. Arch Environ Health 1997;52:455– 463. 8. Ostro B, Sanchez JM, Aranda C, Eskeland GS. Air pollution and mortality: results from a study of Santiago, Chile. J Expo Anal Environ Epidemiol 1996;6:97–114. 9. Katsouyanni K, Touloumi G, Spix C, et al. Short-term effects of ambient sulfur dioxide and particulate mater on mortality in 12 European cities: results from time series data from the APHEA project. BMJ 1997;314:1658 –1663. 10. Dominici F, Samet J, Zeger SL. Combining evidence on air pol- lution and daily mortality from the largest 20 U.S. cities: a hier- archical modeling strategy. R Stat Soc Ser A 2000;163:263–302. 11. Zanobetti A, Schwartz J, Dockery DW. Airborne particles are a risk factor for hospital admissions for heart and lung disease. Environ Health Perspect 2000;108:1071–1077. 12. Schwartz J. Assessing confounding, effect modification, and thresholds in the association between ambient particles and daily deaths. Environ Health Perspect 2000;108:563–568. 13. Anderson HR. Health effects of air pollution episodes. In: Holgate ST, Sament J M, Koren HS, Maynard RL, eds. Air Pollution and Health. London: Academic Press, 1999. 14. Peters A, Liu E, Verrier RL, et al. Air pollution and incidence of cardiac arrhythmia. Epidemiology 2000;11:11–17. 15. Gold DR, Litonjua A, Schwartz J, Litonjua A, Schwartz J. Am- bient pollution and heart rate variability. Circulation 2000;101: 1267–1273. 16. Zelikoff JT, Nadziejko C, Fang T, Gordon C, Premdass C, Cohen MD. Short term, low-dose inhalation of ambient particulate mat- ter exacerbates ongoing pneumococcal infections in Streptococcus pneumoniae-infected rats. In: Phalen RF, Bell YM, eds. Proceedings of the Third Colloquium on Particulate Air Pollution and Human Health, Air Pollution Health Effects Laboratory. vol. 8. Irvine: University of California, 1999;94 –101. 17. Zeger SL, Dominici F, Samet J. Harvesting-resistant estimates of air pollution effects on mortality. Epidemiology 1999;10:171–175. 18. Schwartz J. Harvesting and long-term exposure effects in the relationship between air pollution and mortality. Am J Epidemiol- ogy 2000;151:440– 448. 19. Schwartz J. Is there harvesting in the association of airborne particles with daily deaths and hospital admissions? Epidemiology 2001;12:55– 61. 20. Murray CJ, Nelson CR. State-space modeling of the relationship between air quality and mortality. J Air Waste Manag Assoc 2000; 50:1075–1080. 21. Zanobetti A, Wand MP, Schwartz J, Ryan L. Generalized additive distributed lag models: quantifying mortality displacement. Biosta- tistics 2000;1:3:279–292. 22. Katsouyanni K, Schwartz J, Spix C, et al. Short term effects of air pollution on health: a European approach using epidemiologic time-series data: the APHEA protocol. J Epidemiol Community Health 1996;50(suppl 1):S12–S18. 23. Katsouyanni K, Touloumi G, Samoli E, et al. Confounding and effect modification in the short-term effects of ambient particles on total mortality: results from 29 European cities within the APHEA2 project. Epidemiology 2001;12:521–531. 24. Michelozzi P, Forastiere F, Fusco D, et al. Air pollution and daily mortality in Rome, Italy. Occup Environ J 1998;55:605– 610. 25. Hastie T, Tibshirani R. Generalized Additive Models. London: Chapman and Hall, 1990. 26. Deleted in proof. 27. Cleveland WS, Devlin SJ. Robust locally weighted regression and smoothing scatterplots. J Am Stat Assoc 1988;74:829– 836. 28. Akaike H. Information theory and an extension of the maximum likelihood principal. In: Petrov BN, Csaki F, eds. Second Inter- national Symposium on Information Theory. Budapest: Aka- demiai Kiado, 1973. 29. Schwartz J, Spix C, Touloumi G, et al. Methodological issues in studies of air pollution and daily counts of deaths or hospital admissions. J Epidemiol Community Health 1996;50(suppl 1):S3– S11. 30. Schwartz J. Air pollution and hospital admissions for heart disease in eight U.S. counties. Epidemiology 1999;10:17–22. 31. Diggle PJ. Time Series. New York: Oxford University Press, 1990. 32. Brumback BA, Ryan LM, Schwartz J, Neas LM, Stark PC, Burge HA. Transitional regression models with application to environ- mental time series. J Am Stat Assoc 2000;95:16 –28. 33. Pope CA III, Schwartz J. Time series for the analysis of pulmonary health data. Am J Respir Crit Care Med 1996;154:S229 –S233. 34. Schwartz J. The distributed lag between air pollution and daily deaths. Epidemiology 2000;11:320 –26. 35. Berkey CS, Hoaglin DC, Antczak-Bouckoms A, Mosteller F, Colditz GA. Meta-analysis of multiple outcomes by regression with random effects. Stat Med 1998;17:2537–2550. 36. Kelsall JE, Samet JM, Zeger SL, Xu J. Air pollution and mor- tality in Philadelphia: 1974 –1988. Am J Epidemiol 1997;146: 750 –762. 37. Samet JM, Zegar SL, Dominici F, et al. The National Morbidity, Mortality, and Air Pollution Study. Part II. Morbidity and mor- tality from air pollution in the United States. Res Rep Health Effects Institute 2000;94(pt 2):5–79. 38. Dockery DW, Pope CA III, Xu X, et al. An association between air pollution and mortality in six U.S. cities. N Engl J Med 1993;329: 1753–1759. 39. Pope CA III, Thun MJ, Namboodiri M, et al. Particulate air pollution as a predictor of mortality in a prospective study of U.S. adults. Am J Respir Crit Care Med 1995;151:669 – 674. 40. Sunyer J, Schwartz J, Tobias A, Macfarlane D, Garcia J, Anto JM. Patients with chronic obstructive pulmonary disease are at in- creased risk of death associated with urban particle air pollution: a case-crossover analysis. Am J Epidemiol 2000;151:50–56. EPIDEMIOLOGY January 2002, Vol. 13 No. 1 AIR POLLUTION AND MORTALITY DISPLACEMENT 93 . The Temporal Pattern of Mortality Responses to Air Pollution: A Multicity Assessment of Mortality Displacement Antonella Zanobetti, 1 Joel Schwartz, 1 Evi. 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