Airborne Particles Are a Risk Factor for Hospital Admissions for Heart and Lung Disease.

We examined the association between particulate matter [is less than or equal to] 10 [micro]m; ([PM.sub.10]) and hospital admission for heart and lung disease in ten U.S. cities. Our three goals were to determine whether there was an association, to estimate how the association was distributed across various lags between exposure and response, and to examine socioeconomic factors and copollutants as effect modifiers and confounders. We fit a Poisson regression model in each city to allow for city-specific differences and then combined the city-specific results. We examined potential confounding by a rectaregression of the city-specific results. Using a model that considered simultaneously the effects of [PM.sub.10] up to lags of 5 days, we found a 2.5% [95% confidence interval (CI), 1.8-3.3] increase in chronic obstructive pulmonary disease, a 1.95% (CI, 1.5-2.4) increase in pneumonia, and a 1.27% increase (CI, 1-1.5) in CVD for a 10 [micro]g/[m.sub.3] increase in [PM.sub.10]. We found similar effect estimates using the mean of [PM.sub.10] on the same and previous day, but lower estimates using only [PM.sub.10] for a single day. When using only days with [PM.sub.10] [is less than] 50 mg/[m.sub.3], the effect size increased by [is greater than or equal to] 20% for all three outcomes. These effects are not modified by poverty rates or minority status. The results were stable when controlling for confounding by sulfur dioxide, ozone, and carbon monoxide. These results are consistent with previous epidemiology and recent mechanistic studies in animals and humans. Key words: air pollution, distributed lag, hierarchical model, hospital admissions, meta-analysis, meta-regression. Environ Health Perspect 108:1071-1077 (2000). [Online 23 October 2000]

http://ehpnet1.niehs.nih.gov/docs/2000/108p1071-1077zanobetti /abstract.html

In the last decade many studies have assessed the association between daily deaths or hospital admissions and air pollution, both in Europe and in the United States (1-12). Almost all of these studies reported associations between airborne particles (and sometimes other pollutants) and deaths or hospital admissions within a few days of exposure, but they have differed in the exact lag between exposure and outcome used. They have also differed in whether they examined only associations with a 24-hr averaged exposure or considered effects spread out over several days.

When studies have considered the possibility of lags or multiday effects, they usually have used ad hoc approaches based on the best fit in individual cities, which can be subject to substantial variability due to stochastic error. A systematic approach, which used a multicity analysis to overcome stochastic variability, would help clarify this situation. This has recently been applied successfully to the association between particulate matter [is less than or equal to] 10 [micro]m ([PM.sub.10]) and mortality (13). Past studies have traditionally relied on simple moving averages of pollution to assess the potential for the effect of air pollution on health to persist for more than 1 day after exposure. However, it is quite possible that the effect of air pollution decreases gradually over several days, perhaps after first rising to a peak. In that case, a weighted moving average, with weights that decline to zero after several days, would be more appropriate than a simple moving average or single day's exposure (13).

It is possible to include air pollution values on multiple days to directly estimate the effect of different lags, but this approach is limited in single-city analyses because multi-collinearity makes the estimated effects of different lags very noisy. Although these estimates have large variance, they are unbiased, and hence a multiple-city analysis, which can average out the noise, makes this approach feasible (13). We have applied such a multicity approach to estimate the association between [PM.sub.10] and hospital admissions for heart and lung disease, including the distribution of effects over time.

A multicity approach estimating the association between air pollution and hospital admissions has several other advantages. The National Academy of Sciences has stated that identifying individuals most sensitive to the adverse effects of particulate air pollution is a research priority (14). Multicity analyses allow us to investigate whether demographic or economic factors are modifiers of the pollution effect. In addition, multicity approaches provide opportunities to separate the effect of different air pollutants, analyses which are of limited utility in single-city analyses (15). The present analysis examined distributed lag effects on hospital admissions, confounding by copollutants, and effect modification by socioeconomic factors in 10 locations from across the United States with daily measurements of [PM.sub.10] but widely varying relationships between [PM.sub.10] and other pollutants.

Data and Methods

Data

To examine the effect of [PM.sub.10] at multiple lags, we needed cities with daily [PM.sub.10] monitoring, rather than the more usual 1 day in 6 monitoring schemes. We selected 10 cities from across the United States that met this criterion: Canton, Ohio; Birmingham, Alabama; Chicago, Illinois; Colorado Springs, Colorado; Detroit, Michigan; Minneapolis/ St. Paul, Minnesota; New Haven, Connecticut; Pittsburgh, Pennsylvania; Seattle, Washington; and Spokane, Washington. We chose the metropolitan county containing each city, except for Minneapolis and St. Paul, which were combined and analyzed as one city. We analyzed daily counts of hospital admissions for cardiovascular disease [CVD; International Classification of Disease, 9th revision (ICD-9) 390-429], chronic obstructive pulmonary disease (COPD; ICD-9 490-496, except 493), and pneumonia (ICD-9 480-487), in persons [is greater than or equal to] 65 years of age. The data were extracted from the Health Care Financing Administration (Medicare; Baltimore, MD) billing records, which we obtained for the years 1986-1994. The Medicare system provides hospital coverage for all U.S. citizens aged 65 and over.

Daily meteorologic measurements such as mean temperature, relative humidity, and barometric pressure, were obtained from the nearest National Weather Service surface station for each county (EarthInfo CD NCDC Surface Airways, EarthInfo Inc., Boulder, CO).

Air pollution data for [PM.sub.10] were obtained from the U.S. Environmental Protection Agency's Aerometric Information Retrieval System (AIRS) (16). Many of the cities have more than one monitoring location. To ensure that our exposure measure best represented general population exposure and not local conditions, monitors within the lowest 10th percentile of the correlation among monitors across all counties were excluded. Some monitors only measure [PM.sub.10] 1 day in 6, and different monitors have different means and standard deviations. Therefore, we needed a scheme for computing the daily pollution value that did not change our exposure estimates day to day because of which monitors reported, as opposed to differences in actual ambient levels. Thus, the annual mean was computed for each monitor for each year and subtracted from the daily values of that monitor. We then standardized these daily deviances from each monitor's annual average by dividing by the standard deviation for that monitor. The daily standardized deviations for each monitor on each day were averaged, producing a daily averaged standardized deviation. We multiplied this by the standard deviation of all of the monitor readings for the entire year, and added back in the annual average of all of the monitors. This approach has been described previously (13).

We excluded days when [PM.sub.10] exceeded the ambient air quality standard of 150 [micro]g/[m.sub.3] for the 24-hr mean in order to study the association at common concentrations. We also excluded days with hospital admissions outliers, defined as those days with daily counts more than four times the interquartile range above the median for pneumonia and CVD. For COPD, the outliers were defined as values that were three times the interquartile range above the median, or when the observations were at least 100% higher than the mean of the nearby data. These can occur for clerical reasons; for example, records without the date of admission are coded to the first of the month or year. Alternatively, these outliers may represent epidemics. This exclusion eliminated a total of 2 days of data for CVD, 44 days for pneumonia, and 13 days of data for COPD in all the 10 cities. The exclusion of these outliers did not have a marked effect on the regression coefficients for the [PM.sub.10] effect.

Methods

In each city the associations between hospital admissions and [PM.sub.10] were investigated with a generalized additive robust Poisson regression model (17). In the generalized additive model, the outcome is assumed to depend on a sum of nonparametric smooth functions for each variable. This allows us to better model the nonlinear dependence of daily admission on weather and season. The model is of the form:

log[E([Y.sub.t])] = [[Alpha].sub.0]+ [S.sub.1] ([X.sub.] + ... + [S.sub.p]([X.sub.p]),

where E([Y.sub.t]) is the expected value of the daily count of admissions ([Y.sub.t]) and [S.sub.i] are the locally weighted, running-line, smooth functions (Loess) of the covariates [X.sub.i] (18).

All nonparametric smoothing functions are characterized by a smoothing parameter, which determines the smoothness of the fit. To control for weather variables (24-hr means of temperature, relative humidity, and barometric pressure) and day of the week, we chose the smoothing parameters in each city that minimized the Akaike's Information Criteria (19).

We chose city-specific smoothing parameters for season, which assure seasonal patterns have been removed, and to minimize autocorrelation of residuals. In some cases it was necessary to use autoregressive terms to eliminate serial correlation (12,20).

[PM.sub.10] was treated as a linear term in our analysis to allow examination of how its effects were distributed over different lags and to allow the use of meta-analytic techniques to combine results across cities.

It has been argued that there are thresholds for the effects of air pollution and that no adverse responses occur on most days. To test this we repeated our analysis, restricting it to days when [PM.sub.10] was [is less than] 50 [micro]g/[m.sub.3], which is one-third of the current U.S. 24-hr mean national ambient air quality standard (21).

Distributed lag models. Distributed lag models were introduced by Almon (22) and have been mainly applied in econometrics and social sciences. These models allow us to examine the possibility that air pollution can influence hospital admissions on the same day, but also on subsequent days.

The unconstrained distributed lag model of order q is

[1] log[E([Y.sub.t])] = [Alpha] + covariates + [[Beta].sub.0][Z.sub.t] +[[Beta].sub.1][Z.sub.t-1] + ... + [[Beta].sub.q][Z.sub.t-q]

Hence, the outcome [Y.sub.t] at time t may depend on the exposure ([Z.sub.t]) measured not only on the current day but also on previous days. The overall impact of a unit change in exposure on one day is the sum of its impact on that day and its impacts on subsequent days (i.e., [[Beta].sub.0] + [[Beta].sub.1] + ... + [[Beta].sub.q]). The problem is that [Z.sub.t] is correlated with [Z.sub.t-1], ..., [Z.sub.t-q] and the high degree of collinearity will result in unstable estimates of the [[Beta].sub.j]. However, both the [[Beta].sub.j] and the sum of all [[Beta].sub.j] will be unbiased estimators of the effects at each lag and of the overall effects. Because they are unbiased, combining results across cities will produce more stable unbiased estimates.

A 1-day exposure model can be seen as a constrained model, where [[Beta].sub.j] = 0 for j = 1 ... q. If we have no strong biological reason for that constraint, it is preferable to let the data tell us what the actual pattern looks like. While the 1-day model may be an unreasonably strong constraint, which risks introducing bias, a more flexible constraint may reduce the variance of the individual [Beta] with less risk of bias. One common approach is to constrain the [Beta] values to follow a flexible polynomial (13,23-25).

We have used the unconstrained model as our primary approach, relying on the combined results across cities to cancel out noise and provide stable estimates. We used quadratic distributed lag models as a sensitivity analysis. In both cases we estimated lags of up to 5 days between exposure and hospitalization. For comparison to previous results, we estimated the effect of [PM.sub.10] on the same day, and on the mean of the same and previous day as exposure variables.

Hierarchical modeling. Hierarchical modeling is a multistage approach in which a set of models are fit in (in our case) individual cities, and the results of those regressions are analyzed in a second-stage regression to examine issues of effect modification and confounding (26). In the second stage of the analysis we first used inverse-variance-weighted averages to combine results across cities. These were computed for both the estimated overall effect (the sum of the [[Beta].sub.i]) and for the effect of each lag. More formally, we assumed the effect of [PM.sub.10] in city i (i = 1-10) was [[Beta].sub.i] ~ N([micro], V), and we estimated [micro] from the 10 city-specific [[Beta].sub.i] values and their variances by computing an inverse-variance-weighted average. We then extended this approach to a full second-stage regression. To examine effect modification by socioeconomic status, for example, we fit a weighted, least-squares regression:

[2] [[Beta].sub.i]= [Beta]* + [Delta][P.sub.1] + [[Epsilon].sub.i],

where [[Beta].sub.i] is the estimated [PM.sub.10] effect in city i, [P.sub.i] is the socioeconomic index in that city, and, again, inverse variance weighting is done. The variable [Delta] then tells us how much the effect of [PM.sub.10] changes for a unit increase in the social index. We examined the percentage of the population living below the federal poverty level and the percentage of the population that was nonwhite as potential modifiers of the effect of [PM.sub.10] on hospital admissions of the elderly.

Confounding is usually examined by including potential confounders in what is here the first stage of a hierarchical regression model. However, because weather tends to increase or decrease all pollutants in parallel, that approach risks substantial collinearity problems. Although most pollutants increase and decrease together, the incremental increase in one pollutant (in micrograms per cubic meter) that is associated with each microgram per cubic meter increase in another pollutant varies substantially across locations. We have used this variation to examine confounding in the second stage of our analysis.

To illustrate this approach, suppose the true association is between our outcome and pollutant [X.sub.1]:

[3] Y= [[Beta].sub.0] + [[Beta].sub.1] [X.sub.1] + [[Epsilon].sub.t].

Assume [X.sub.1] is correlated with another pollutant, [X.sub.2], that is not causal for Y. It is possible to quantify the association between them by

[4] [X.sub.1] = [[Gamma].sub.0] + [[Gamma].sub.1][X.sub.2] + [[Epsilon].sub.t],

Substituting Equation 4 in Equation 3 it follows that:

Y = [[Beta].sub.0] + [[Beta].sub.1][[Gamma].sub.0] + [[Beta].sub.1][[Gamma].sub.1][X.sub.2] + [[Epsilon].sub.t],

and we see that the induced coefficient for the noncausal variable [X.sub.2] depends on [[Gamma].sub.1], the slope of the relationship between [X.sub.1] and [X.sub.2]. From this, we can see that it is natural to extend our meta-regression approach to use the slope between pollutants as an explanatory factor in the second-stage model. That is,

[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII]

where [[Gamma].sub.i] is the slope between [SO.sub.2] and [PM.sub.10], for example, [Beta]*, the intercept term in this regression, is the estimated effect of [PM.sub.10] in a city where it had no correlation with [SO.sub.2]. This is the unconfounded effect of [PM.sub.10]. This approach has recently been applied to mortality data (27).

Simulation study. To test the power of our two-stage approach to detect confounding, we did a simulation study. We simulated the case where one pollutant was really standing for another, and looked to see whether the association with the noncausal pollutant disappeared in our two-stage approach. Specifically, we examined a scenario where analyses were done in 10 cities, with 2,000 days of data in each location. This is somewhat fewer data than we have. In each location, we generated two exposure variables that were multivariate normal, with a correlation coefficient of 0.70. However, the regression coefficient between the two pollutants was chosen from a uniform distribution with a 3-fold variation in slopes. This is less variation than is present in the actual data we were analyzing.

We then generated a random Poisson count with a log relative risk for one pollutant of 0.05, and no true association with the other pollutant. We fit a Poisson regression in each of the 10 locations and estimated the regression coefficient of the noncausal pollutant in each location. Then we regressed those 10 coefficients against the 10 slopes relating the two pollutants and took the intercept term in that regression as the estimate of the nonconfounded effect of the noncausal pollutant. We repeated this 500 times and looked at the median and 95% confidence interval for the noncausal pollutant to see if they were centered on zero and with magnitude that would clearly distinguish them from 0.05.

Results

Table 1 shows the 25th, 50th, and 75th percentiles of each of the variables used in the analysis in each city. Colorado Springs had the lowest median [PM.sub.10] concentration, and Spokane had the highest. Table 1 also shows the dates during which daily [PM.sub.10] measurements were available in each city. Table 2 presents the population [is greater than or equal to] 65 years of age and the percentile values for the hospital admissions data. Table 3 shows the correlation between [PM.sub.10] and the weather variables. The correlations were always modest and, for temperature and barometric pressure, include both positive and negative correlations. In one city (Spokane) [PM.sub.10] was essentially uncorrelated with temperature.

Table 1. 25th, 50th, and 75th percentile values for the environmental
variables in the 10 cities.

                                             Temperature    Relative
City                   Date of study        ([degrees] F)   humidity

Akron              1 Jan 1989-24 Dec 1994        36            66
                                                 51            74
                                                 66            82
Birmingham         1 Apr 1987-31 Dec 1993        51            62
                                                 65            71
                                                 76            80
Chicago            1 Mar 1988-24 Dec 1994        35            62
                                                 51            70
                                                 67            79
Colorado Springs   1 Jul 1987-24 Dec 1994        36            39
                                                 51            51
                                                 64            66
Detroit            1 May 1986-24 Dec 1994        36            64
                                                 52            71
                                                 67            79
Minneapolis        1 Apr 1987-24 Dec 1994        31            60
                                                 49            69
                                                 67            78
New Haven          1 May 1987-31 Dec 1991        38            57
                                                 53            67
                                                 68            77
Pittsburgh         1 Jan 1987-24 Dec 1994        37            61
                                                 53            70
                                                 68            79
Seattle            1 Jan 1986-24 Dec 1994        45            67
                                                 52            77
                                                 60            85
Spokane            1 Oct 1985-24 Dec 1994        35            49
                                                 47            68
                                                 61            84

                         Barometric          [PM.sub.10]
City                      pressure          ([micro]g/[m.sup.3])

Akron                       28.6                 19
                            28.8                 26
                            28.9                 34
Birmingham                  29.3                 20
                            29.4                 31
                            29.5                 46
Chicago                     29.2                 23
                            29.3                 33
                            29.4                 46
Colorado Springs            23.9                 18
                            24.0                 23
                            24.1                 31
Detroit                     29.2                 21
                            29.3                 32
                            29.4                 49
Minneapolis                 29.0                 17
                            29.1                 24
                            29.2                 35
New Haven                   29.7                 17
                            29.8                 26
                            30.0                 38
Pittsburgh                  28.6                 19
                            28.8                 30
                            28.9                 47
Seattle                     29.5                 18
                            29.6                 27
                            29.7                 39
Spokane                     27.4                 23
                            27.5                 36
                            27.7                 57
Table 2. Population and 25th, 50th, and 75th percentile values
for the daily counts of hospital admissions for
CVD, COPD, and pneumonia in the 10 cities.

                       Population ([is greater
City                      than or equal to]      CVD   COPD   Pneumonia
                          65 years of age)

Canton                         52,900              7     0         1
                                                   9     1         2
                                                  12     2         3
Birmingham                     119,000            14     1         3
                                                  17     1         5
                                                  21     2         7
Chicago                        633,000            86     4        20
                                                 103     7        25
                                                 117    11        31
Colorado Springs               31,700              2     0         0
                                                   3     0         1
                                                   4     1         2
Detroit                        263,900            41     2         7
                                                  50     4        10
                                                  59     6        13
Minneapolis/St. Paul           176,000            13     1         3
                                                  16     1         5
                                                  20     3         7
New Haven                      118,200            12     0         2
                                                  16     1         4
                                                  20     1         5
Pittsburgh                     232,500            38     3         7
                                                  48     5        10
                                                  56     8        13
Seattle                        167,300            13     1         3
                                                  17     1         4
                                                  20     2         6
Spokane                        48,000              4     0         1
                                                   6     1         1
                                                   7     1         3
Table 3. Correlation between [PM.sub.10] and other
environmental variables in the 10 cities.

                           Temp               Barometric
City                   ([degrees F)    RH      pressure

Canton                     0.42       -0.16      0.15
Birmingham                 0.26       -0.3       0.12
Chicago                    0.36       -0.3      -0.02
Colorado Springs          -0.34       -0.11     -0.01
Detroit                    0.37       -0.14     -0.05
Minneapolis/St. Paul       0.29       -0.35     -0.03
New Haven                  0.05       -0.15      0.11
Pittsburgh                 0.45       -0.23      0.14
Seattle                   -0.22       -0.11      0.24
Spokane                   -0.01       -0.19      0.16

Abbreviations: RH, relative humidity; Temp, temperature.

Overall effects of [PM.sub.10]. Table 4 shows the combined overall estimate for the constrained (1-day mean, 2-day mean, quadratic distributed lag) and the unconstrained distributed lag model, for a 10 [micro]g/[m.sub.3] increase in [PM.sub.10]. The effect size estimate for the 2-day mean and the quadratic distributed lag are similar to the effect estimate using the unconstrained model, and all three are always higher than the 1-day lag. When the analysis using the 2-day mean of [PM.sub.10] was repeated using only days with [PM.sub.10] [is less than] 50 [micro]g/[m.sub.3], the effect size increased by [is greater than or equal to] 20% for all three outcomes.

Table 4. Results of the combined analysis: percentage increase in
admissions for a 10 [micro]g/[m.sup.3] increase in
[PM.sub.10] in 10 U.S. cities.

                                          COPD         Pneumonia

Model                              Percent    SE     Percent   SE

Constrained lag
  1-Day mean(a)                     1.48     0.23      1.57   0.15
  2-Day mean(b)                     2.04     0.25      2.03   0.17
  [PM.sub.10] < 50 [micro]          2.41     0.47      2.96   0.33
    g/[m.sup.3] (2-day mean)(b)
  Quadratic distributed lag         2.56     0.36      1.73   0.22
Unconstrained distributed lag       2.54     0.36      1.95   0.23

                                         CVD

Model                              Percent    SE

Constrained lag
  1-Day mean(a)                     1.09     0.08
  2-Day mean(b)                     1.21     0.08
  [PM.sub.10] < 50 [micro]          1.51     0.15
    g/[m.sup.3] (2-day mean)(b)
  Quadratic distributed lag         1.22     0.11
Unconstrained distributed lag       1.27     0.11

(a) Lag O.

(b) Mean of lag 0 and lag 1.

Distributed lag over time. Figures 1-3 show the combined city estimate of the unconstrained distributed lag association between [PM.sub.10] and the three analyzed causes of admissions. For COPD admissions (Figure 1) the effect is similar for [PM.sub.10] on the concurrent day and the previous day and goes to near zero at lag 2 and subsequent days. For pneumonia admissions (Figure 2) the effect decreases continuously for lags 0-2 and then oscillates about zero for further lags. Cardiovascular admissions (Figure 3) show a higher effect at lag 0, dropping to a more modest effect at lags 1 and 2, and then oscillate about zero.

[GRAPHS OMITTED]

Second-Stage Models

Social factors. Neither the percentage of the population living in poverty nor the percentage of the population that was nonwhite was a significant modifier of the [PM.sub.10] slopes in our cities. Table 5 shows the change from the baseline [PM.sub.10] effect size (as percent increase in admission per 10 [micro]g/[m.sub.3] increase in concentration) associated with a 5-point increase in the percentage of the population living below the federal poverty level or the percentage of the population that is not white.

Table 5. Effect modification by percentage of the
population living in poverty or nonwhite.

                      Poverty             Nonwhite

Disease             % (95% Cl)           % (95% Cl)

CVD              0.15 (-0.19-0.50)    0.06 (-0.03-0.15)
COPD            -0.17 (-1.95-0.55)   -0.21 (-0.53-0.11)
Pneumonia       -0.53 (-1.34-0.29    -0.05 (-0.28-0.18)

Results are shown for a 10 [micro]g[m.sup.3] increase in
[PM.sub.10] and a 5 percentage point increase in the effect modifiers.

Copollutants. Figures 4 and 5 show the data for of the meta-regression. Figure 4 shows, for COPD and pneumonia, the effect of [PM.sub.10] in each city plotted against the regression coefficients relating [SO.sub.2] and ozone to [PM.sub.10] in each city. Figure 5 presents the CVD results, where we considered also the regression coefficients of CO versus [PM.sub.10].

[GRAPHS OMITTED]

These plots give an idea of the range of the results by city. These vary from a negative effect to effects higher than a 6% increase for 10 [micro]g/[m.sub.3] [PM.sub.10] for COPD or pneumonia, while for CVD the higher effects are around 2%. They also show the range of regression coefficients relating [PM.sub.10] to the other pollutants. For [O.sub.3] they include both positive and negative slopes and vary considerably within each sign, with a wider range among the positive slopes. For [SO.sub.2] and CO the slopes are always positive, but vary by almost an order of magnitude.

As explained in "Methods," if the [PM.sub.10] effect were due to confounding with other pollutants, the plots would show a significantly increasing trend with increasing slope between the pollutants. Figures 4 and 5 show little evidence of such a pattern. These results are confirmed by the meta-regression estimates, shown in Figure 6. Here the baseline estimate is the result of the distributed lag meta-analysis. Plotted above each pollutant is the estimated intercept in the meta-regression of the [PM.sub.10] coefficients against the slopes between that copollutant and [PM.sub.10]. For all three outcomes the results appear quite stable to control for confounding by gaseous pollutants. Moreover, there are no consistent patterns indicative of confounding. For example, the effect of [PM.sub.10] on pneumonia admissions increases somewhat after control for [SO.sub.2] and decreases after control for [O.sub.3]; for COPD the pattern is the opposite. None of the copollutants was a significant predictor of the [PM.sub.10] slope.

Weather variables. The wide range of weather patterns, shown in Table 3, give considerable support to the conclusion that these results are not confounded by inadequate control for weather. Figure 7, plotting the effect size estimates for the distributed lag [PM.sub.10] versus the correlation of [PM.sub.10] with temperature and relative humidity, shows similar effects sizes across a broad range of correlations. Hence, these results are unlikely to be confounded by weather. In the formal meta-regression we found that the coefficient for temperature was not significant for all the three outcomes, but for relative humidity we found some negative confounding with COPD. The effect size of [PM.sub.10] is not modified by temperature; the percentage increase of 10 [micro]g/[m.sup.3] of [PM.sub.10] is 1.2% for CVD (SE = 0.2); 3.3% for COPD (SE = 0.7), and 2.1% for pneumonia (SE = 0.5). There is no effect modification due to relative humidity for CVD (1.8%; SE = 0.4) and for pneumonia (1.7%; SE = 1.1), while the [PM.sub.10] effect increased for COPD with a 5.5% increase (SE = 1.2).

[GRAPH OMITTED]

Simulation. The 95% confidence interval for the slopes between the two simulated pollutants ranged from 0.48 to 1.27, reflecting the 3-fold range that was our target. In the meta-regression, the intercept term was taken as the non-confounded effect of the non-causal pollutant, as in our analysis of real data. The median estimate for this was -0.00008, and the 95% confidence interval was -0.0098-0.0102. This demonstrates that our approach has the power to detect significant confounding in a 10-cities study, with a smaller range of variation in pollutant-pollutant slopes than was seen in the study.

Discussion

There are four main findings of this study. First, [PM.sub.10] is associated with increased hospital admissions for CVD, COPD, and pneumonia. Second, the effect of a 24-hr increase in [PM.sub.10] is spread over that day and several subsequent days, and single-day analyses underestimate the impact of [PM.sub.10]. Third, these effects are not modified by poverty rates or minority status and are relatively stable to control for potential confounding by [SO.sub.2], O3, and CO. And fourth, these effects persist at common concentrations well below the current air quality standards. We discuss each of these findings in turn.

The finding that airborne particles are associated with hospital admissions for heart and lung disease has been reported in many other studies. In general, the effect-size estimate reported here is consistent with those previous studies. The advantage of this study is that it involves more years of follow-up than most previous studies and l0 cities spread across the continent, with a wide range of coincident weather and copollutants.

For all three outcomes, the effect of PM10 appears to be spread over more than 1 day, and Table 4 shows that the use of a single exposure day will underestimate the effect of PM10, sometimes by a substantial factor. This suggests that integrative summaries of the health data need to address this issue. Most studies of air pollution have used multiday averages but some have not, and this will need to be taken into account in any future meta-analysis. A recent analysis of daily deaths in these same cities found the use of a single day's exposure underestimated the effect of PM10 on daily deaths by more than a factor of 2, for instance (13).

Confounding by gaseous pollutants has been raised as a major issue regarding previous studies (28). We found that the effect-size estimate for [PM.sub.10] and hospital admissions for CVD, COPD, and pneumonia changed little after control for potential confounding by gaseous air pollutants in our second-stage regression. The standard errors increased because our second-stage analysis had a limited sample size (10 points in a regression estimating an intercept and a slope), but overall the evidence for confounding was small. Temperature did not appear to confound the [PM.sub.10] association either, whereas for relative humidity there seemed to be some negative confounding for COPD admissions.

We have not found evidence that obvious socioeconomic factors such as poverty and race are modifiers of these effects. There may be several reasons for this. First, it is important to realize that Poisson models are relative risk models. They have multiplicativity built in. That is, a given change in [PM.sub.10] is associated with a given percent increase in admissions. If a town with more poverty or larger percentage of nonwhites has a higher baseline rate of admission, then a 3% increase in the admissions rate from baseline will be a greater increase (per 10,000 persons [is greater than or equal to] 65 years of age) in that town than in a town with a lower baseline rate.

It may be that the medical conditions that predispose to higher risk are not well captured by these socioeconomic factors and that more specific medical conditions, rather than social factors, are needed to explore effect modification. Finally, we used county-level data for these social factors because our admissions data are on that level. But the variation in socioeconomic status within the typical urban county is usually considerably larger than the variation across counties. Our social factors may be too ecologic to be meaningful. In this case, future studies using finer geographical data may be able to find some modification.

If these associations are causal, as we have argued, then it is crucial for public health impact assessment to know whether the associations are dominated by only a few high pollution days or whether they persist at the concentrations seen on most days. When we restricted our analysis to days with concentrations of one-third of current air quality standard or less ([is less than] 50 [micro]g/[m.sup.3]), we still found a significant association between [PM.sub.10] and admissions for all three illnesses. Moreover, the effect size increased by 20% or more. This increase in effect size at lower concentrations has been noted previously in a mortality study (6). For a significant association to persist, and grow in size, on days with levels [is less than] 50 [micro]g/[m.sup.3], any threshold would have to be far below that level, and likely down to background levels. The more likely scenario is that the true concentration-response curve is curvilinear, with higher slopes at lower concentrations and no threshold.

In addition to this statistical evidence, there has been a substantial increase in evidence for the biological plausibility of these effects. Recent studies have reported that particulate air pollution is associated with reduced heart rate variability and increased fibrinogen levels in animals (29-31). These are known risk factors for arrhythmia and ischemic events, which are the major sources of hospital admissions for heart disease. Human studies have reported airborne particles associated with increased plasma viscosity (32) and decreased heart rate variability (33-35), paralleling animal studies. Airborne particles have also been associated with increased fibrinogen and platelet levels in humans (36); and they are associated with increased heart rate (37, 38). These changes in risk factors for arrhythmia are supported by a recent study of patients with implanted cardiac defibrillators. Defibrillator discharges to halt arrhythmic events were associated with particulate air pollution and [NO.sub.2] (39). Further, the increase in mortality associated with airborne particles was particularly strong for sudden death (40), which is again consistent with these recent animal and human results.

Animals with COPD or chronic lung inflammation have been shown to have increased vulnerability to combustion particles (41-44). And exposure to concentrated air particles of animals previously infected with strep pneumonia resulted in a doubling of lung area involved with pneumonia, and of bacterial burdens (45). Influenza infections have similarly been shown to be exacerbated by air pollution (46).

Given the consistent epidemiologic evidence, the indications of little, if any, confounding by gaseous copollutants and weather, the mechanistic animal studies showing airborne particles can exacerbate these illnesses, and the more recent mechanistic human studies, we believe that there is a strong case for causal associations between [PM.sub.10] and heart and lung diseases.

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Antonella Zanobetti, Joel Schwartz, Douglas W. Dockery

Environmental Epidemiology Program, Department of Environmental Health, Harvard School of Public Health, Boston, Massachusetts, USA

Address correspondence to A. Zanobetti, Environmental Epidemiology Program, Department of Environmental Health, Harvard School of Public Health, 665 Huntington Avenue, Boston, MA 02115 USA. Telephone: [phone]. Fax: [phone]. E-mail: [e-mail]

This work was supported in part by Health Effects Institute contract 70972 and National Institute of Environmental Health Sciences grant ES-07937.

Received 9 February 2000; accepted 3 July 2000.