The single-lag model reflects the percentage increase in mortality for
a 10-ppb increase in ozone on a single day. The distributed-lag model reflects
the percentage change in mortality for a 10-ppb increase in ozone during the
previous week. Error bars indicate 95% posterior intervals.
Results are obtained with a 2-stage constrained distributed-lag model
applied to the same data set (days with data for ozone and particulate matter
<10 μm [PM10]). The distributed-lag model reflects the percentage
increase in mortality for a 10-ppb increase in ozone during the previous week.
The large blue circle indicates the national average effect.
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Bell ML, McDermott A, Zeger SL, Samet JM, Dominici F. Ozone and Short-term Mortality in 95 US Urban Communities, 1987-2000. JAMA. 2004;292(19):2372–2378. doi:10.1001/jama.292.19.2372
Author Affiliations: School of Forestry and
Environmental Studies, Yale University, New Haven, Conn (Dr Bell); Departments
of Biostatistics (Drs McDermott, Zeger, and Dominici) and Epidemiology (Dr
Samet), Johns Hopkins Bloomberg School of Public Health, Baltimore, Md.
Context Ozone has been associated with various adverse health effects, including
increased rates of hospital admissions and exacerbation of respiratory illnesses.
Although numerous time-series studies have estimated associations between
day-to-day variation in ozone levels and mortality counts, results have been
Objective To investigate whether short-term (daily and weekly) exposure to ambient
ozone is associated with mortality in the United States.
Design and Setting Using analytical methods and databases developed for the National Morbidity,
Mortality, and Air Pollution Study, we estimated a national average relative
rate of mortality associated with short-term exposure to ambient ozone for
95 large US urban communities from 1987-2000. We used distributed-lag models
for estimating community-specific relative rates of mortality adjusted for
time-varying confounders (particulate matter, weather, seasonality, and long-term
trends) and hierarchical models for combining relative rates across communities
to estimate a national average relative rate, taking into account spatial
Main Outcome Measure Daily counts of total non–injury-related mortality and cardiovascular
and respiratory mortality in 95 large US communities during a 14-year period.
Results A 10-ppb increase in the previous week’s ozone was associated
with a 0.52% increase in daily mortality (95% posterior interval [PI], 0.27%-0.77%)
and a 0.64% increase in cardiovascular and respiratory mortality (95% PI,
0.31%-0.98%). Effect estimates for aggregate ozone during the previous week
were larger than for models considering only a single day’s exposure.
Results were robust to adjustment for particulate matter, weather, seasonality,
and long-term trends.
Conclusions These results indicate a statistically significant association between
short-term changes in ozone and mortality on average for 95 large US urban
communities, which include about 40% of the total US population. The findings
indicate that this widespread pollutant adversely affects public health.
Exposure to tropospheric ozone is widespread in the United States,1,2 occurring also outside southern California,
where ozone formation was first recognized.3 Short-term
exposure to ozone has been linked to adverse health effects, including increased
rates of hospital admissions and emergency department visits, exacerbation
of chronic respiratory conditions (eg, asthma), and decreased lung function.4-8 Numerous
time-series studies have addressed the relationship between ozone levels and
mortality counts on short-term intervals of 1 or a few days, including some
studies involving multiple locations; however, their findings have been inconsistent.9-17 Interpretation
of this evidence is constrained by the limited range of locations included
in these reports, the variability of methods used, and the imprecision of
estimates from some of the studies. The study of ozone and health is complicated
by the complex, nonlinear chemical formation of tropospheric ozone, which
is temperature driven, with higher ozone levels at higher temperatures.18
In 1997, the US Environmental Protection Agency (EPA) proposed revisions
to the National Ambient Air Quality Standard (NAAQS) for ozone, adding a daily
maximum 8-hour standard of 80 ppb (parts per billion by volume) while phasing
out the daily hourly maximum standard of 120 ppb. These changes were prompted
by evidence from epidemiologic, controlled human exposure, and toxicologic
studies that identified adverse health effects at ozone concentrations below
the existing 1-hour NAAQS.19 Because of the
relevance of epidemiologic evidence to the NAAQS for ozone and other pollutants,
updated and expanded time-series studies of ozone are informative to the regulatory
With the National Morbidity, Mortality, and Air Pollution Study (NMMAPS),
we have developed national approaches for multisite time-series analyses of
particulate matter with an aerodynamic diameter less than 10 μm (PM10) and mortality and hospitalization data that provided evidence for
decision making.20-26 As
part of the NMMAPS, we developed 2-stage statistical models20,21,27 for
estimating the percentage increase in mortality associated with exposure to
PM10 or other pollutants. In our 2-stage approach, a time-series
analysis is first performed within each community, and in the second stage
of analysis, the results are combined across communities to produce a national
average estimate that accounts for the within-community statistical uncertainty
and the heterogeneity of the effects across the country.20,21
We use an updated NMMAPS database, including 95 large US urban areas
for 1987-2000, to perform a multisite time-series study of ozone and mortality.
Because ozone concentrations are typically available daily, at the first stage
of our analysis we extend previous approaches to develop constrained and unconstrained
distributed-lag models.26,28-34 Distributed-lag
models are appropriate for estimating relative rates of mortality associated
with exposure to pollution levels during several previous days, thus allowing
more flexibility for exploring the lag between exposure and death than single-lag
models. At the second stage, we use hierarchical models35-38 to
combine the relative rate estimates obtained from the community-specific distributed-lag
models to produce a national average estimate. With this 2-stage model, variation
across communities in the short-term effects of ozone can be explored and
an effect estimated for the nation.
This analysis is based on daily cause-specific mortality counts for
1987-2000 obtained from the National Center for Health Statistics on 95 large
urbanized areas. The mortality counts for each urban community are at the
county level (either a single county or multiple adjacent counties representing
the metropolitan area). The outcome measure was the daily number of deaths
in each community, excluding those of nonresidents and those caused by injuries
and other external causes (International Classification
of Diseases, Ninth Revision [ICD-9] codes
800 and above, International Statistical Classification
of Diseases, 10th Revision [ICD-10] codes
S and above). International Classification of Diseases,
Ninth Revision codes were used for 1987-1998 and the ICD-10 for 1999 and 2000. Mortality was further categorized by cardiovascular
causes (ICD-9 codes 390-448, ICD-10 chapter I with codes <800) and respiratory causes, including chronic
obstructive pulmonary disease and related disorders (ICD-9 codes 480-486, 490-497, or 507, ICD-10 chapter
J with codes 100-118, 120-189, 209-499, or 690-700). The average daily death
rate ranged from 2.2 deaths per day (Arlington, Va) to 190 deaths per day
(New York, NY) and averaged 20 deaths per day across all communities. Deaths
for people aged 75 years and older comprised approximately half of total deaths
in these 95 communities.
Air-pollution data for ozone and PM10 were supplied by the
US EPA’s Aerometric Information Retrieval Service (now called the Air
Quality System database). To protect against outliers, a 10% trimmed mean
was used to average across monitors after correction for yearly averages for
each monitor.23 For ozone, the 24-hour average,
maximum 8-hour average, and maximum hourly concentrations were calculated
for each day. In several locations, ozone values were measured only during
the peak ozone season, often April to October. (Descriptive statistics on
each community are provided at http://www.ihapss.jhsph.edu/data/NMMAPS/descriptives/frame.htm.)
Daily average values for dew point and temperature were calculated from
hourly values obtained from the National Climatic Data Center on the Earth-Info
CD database.39 Daily averages were chosen according
to extensive analyses conducted for related work.23,24 Measurements
from multiple weather stations were averaged to provide weather variables
representing each community.40
A 2-stage statistical model35-38 was
used to estimate a national average association between short-term ambient
ozone levels and mortality risk, accounting for other factors such as weather,
seasonality, long-term trends, and PM10. In the first stage, distributed-lag
overdispersed Poisson regression models26,28,41,42 were
used for estimating community-specific relative rates of mortality associated
with exposure to ozone in the last week. First-stage community-specific models
included indicator variables for the day of the week to allow for different
baseline mortality rates for each day of the week. Smooth functions of calendar
time (natural cubic splines) were used to adjust for seasonality and long-term
trends, such as influenza epidemics. We also added interaction terms between
smooth functions of time and age-specific indicators (<65, 65-74, ≥75
years) to further adjust for seasonal mortality patterns that could vary by
age group. We controlled for the potential confounding effect of weather by
including smooth functions of temperature, the average of the 3 previous days’
temperature, dew point, and the average of the 3 previous days’ dew
At the second stage, we combined the community-specific relative rates
to generate a national average estimate of the association between ozone and
mortality that accounts for within-community and across-community variability
(also called heterogeneity).27,43 The
second-stage model also provides community-specific Bayesian estimates that
are approximately equal to a weighted average of the maximum likelihood estimate
(from the first stage) for that community and the national average with shrinkage
weights equal to w and 1−w, respectively. The degree of shrinkage w of
each Bayesian estimate to the national average is inversely proportional to
the heterogeneity of the community-specific relative rates. The higher the
heterogeneity, the less the Bayesian estimates shrink toward the overall mean.
We assessed the relationship between ambient ozone concentrations and
the risk of mortality on subsequent days at various single-day lags. For instance,
a lag of 0 days corresponds to the association between ozone concentrations
on a given day and the risk of mortality on that same day. A lag of 2 days
refers to the association between ozone levels on a given day and the risk
of mortality 2 days later. In the single-lag models, we considered pollution
for the same day and up to 3 days before.
We also investigated cumulative exposure during several days. Our distributed-lag
model estimates the association between the risk of mortality and the cumulative
exposure to ambient ozone levels during the previous week, allowing each day
to have an effect. We used an unconstrained distributed-lag model that simultaneously
included variables for the same day and up to 6 days before to estimate the
effect of the previous week’s ozone levels on current-day mortality.
We also used a constrained distributed-lag model to estimate how the previous
week’s pollution levels affected mortality. This model constrains lag-specific
regression coefficients to be a step function by including variables for the
average of the same day’s concentration and that of up to 6 days before,
the average of the same day’s and the previous 3 days’ concentrations
minus the average of the same day’s and previous 6 days’ ozone
levels, and the current day’s concentration minus the average of the
same day’s and previous 3 days’ concentrations.
We examined the sensitivity of key findings with respect to (1) the
specification of the statistical model: constrained and unconstrained distributed-lag
models26,28 and single-day lag
for ozone exposure; (2) inclusion of PM10 in the statistical model
as a potential confounder; (3) exclusion of days with temperatures above specified
thresholds to control for the potential confounding effect of temperature
and heat waves; (4) specification of the degrees of freedom (df )
in the smooth functions of time to control for seasonality and long-term trends;
and (5) use of different ozone exposure metrics: daily average, 8-hour maximum,
and 1-hour maximum.
Calculations were implemented using the statistical software S-Plus44 and with strict convergence parameters.45 The
data and statistical models used have been made available on the Internet-based
Health and Air Pollution Surveillance System (http://www.ihapss.jhsph.edu/index.htm), maintained by the Johns Hopkins Bloomberg School of Public Health
and sponsored by the Health Effects Institute.
The daily ozone concentrations varied by community, averaging approximately
26 ppb across the 95 urbanized communities. Daily PM10 concentrations
were generally not highly correlated with ozone concentrations (correlations
within communities ranged from –0.38 to 0.63, averaging 0.30) or with
temperature (correlations within communities ranged from 0.34 to 0.61, averaging
0.33). The correlation between daily ozone and temperature ranged from –0.41
for Honolulu, Hawaii, which also had the lowest ozone concentrations, to 0.87
for Louisville, Ky, and averaged 0.52.
Figure 1 provides the national
average estimates for total mortality obtained under several model specifications.
Results from the constrained distributed-lag model indicated that a 10-ppb
increase in daily ozone levels of the previous week corresponded to a 0.52%
(95% posterior interval [PI], 0.27%-0.77%) increase in daily mortality. The
95% PI is the Bayesian formulation of the 95% confidence interval. The unconstrained
distributed-lag model provided similar estimates. This result was statistically
significant, with a posterior probability of 1 that this overall effect is
larger than zero. Results from single-lag models indicated that ozone concentrations
from the same day and a few preceding days affected mortality. The relative
rate estimates decreased as the lag increased, indicating that exposure to
ozone on more recent days, such as the same or previous day, was associated
with a larger risk of mortality than exposure on less recent days, such as
2 or 3 days ago. However, single-day lag models underestimate the cumulative
effect of ozone on mortality because they take into account only 1 day’s
ozone exposure. Distributed-lag models estimate cumulative relative rates
of mortality associated with ozone concentrations in the few preceding days.
Effects of ozone on mortality for 3 age groups (<65, 65-74, and ≥75
years) were estimated separately by using the constrained distributed-lag
model to investigate whether the elderly are particularly susceptible to the
effects of ozone on mortality. Effect estimates for the 65- to 74-year age
category were 0.70% (95% PI, 0.28%-1.12%) for a 10-ppb increase in daily ozone,
whereas estimates for the youngest and eldest groups were similar to the overall
estimate for the total population, at 0.50% (95% PI, 0.10%-0.92%) and 0.52%
(95% PI, 0.18%-0.87%), respectively, for a 10-ppb increase.
Figure 2 shows the community-specific
Bayesian estimates and their 95% PIs. The heterogeneity, which denotes the
between-location SD of the community-specific relative rates in relation to
their mean, was 0.64. The mean effect of 0.52% indicated that 95% of the true
community-specific relative rates were within the interval −0.73% to
We evaluated whether the estimated national average effect was due entirely
to days with ozone levels higher than the current regulatory standard of 80
ppb for the daily 8-hour maximum, which is roughly equivalent to a 60-ppb
daily average. With this restriction, we repeated the statistical analyses
using only days with ozone levels less than 60 ppb and using a single day
of exposure at a lag of 1 day. We found that the national average effect was
equal to a 0.15% increase in daily mortality (95% PI, 0.04%-0.27%) for a 10-ppb
increase in ozone of the previous day. The effect estimate using all days
was 0.18% (95% PI, 0.06%-0.30%).
Relative rate estimates for cardiovascular and respiratory mortality
are shown in Figure 1. The national
estimate for cardiovascular and respiratory mortality was slightly larger
than the one for total mortality: 0.64% (95% PI, 0.31%-0.98%) increase in
mortality for a 10-ppb increase in the preceding week’s ozone levels.
We found the key results for ozone to be robust to adjustment for PM10. We adjusted for 1-day lag PM10 because this lag yielded
the largest effect estimate in previous time-series analysis of PM and mortality
for 90 US urban communities.22-24 Because
monitoring for PM10 is required for only 1 of every 6 days, we
limited this sensitivity analysis to those days for which PM10 and
ozone were available. In single-lag models, the relative rates estimated for
mortality and ozone at various lags (0, 1, and 2 days) were affected little
by inclusion of PM10 at lags 0, 1, or 2 days. Figure 3 compares the maximum likelihood estimates for the constrained
distributed-lag model for ozone, with and without adjustment for PM10, and shows that the community-specific estimates for ozone were also
robust to the adjustment for PM10.
We explored whether the association between ozone and mortality was
modified by the long-term average of PM2.5 (PM with an aerodynamic
diameter less than 2.5 μm) by performing a weighted second-stage linear
regression with the community-specific estimate of ozone’s effect on
mortality as the dependent variable and the long-term PM2.5 average
as the independent variable. No association was observed.
The national average effect of ozone on mortality was similar for the
following data sets: all 95 urban communities using all data, all communities
for days from April to October, and 55 communities that have yearly data for
ozone. Estimates for these 3 sets of analyses using the constrained distributed-lag
model were 0.52% (95% PI, 0.27%-0.77%), 0.39% (95% PI, 0.13%-0.65%), and 0.48%
(95% PI, 0.16%-0.78%) increase in mortality for a 10-ppb increase in the previous
week’s ozone level. Further, the national average estimate of short-term
effects of ozone (using all days) on mortality was robust to the exclusion
of days with high temperatures with a cutoff as low as 29°C (85°F);
the range of effects for these analyses was 0.50% (95% PI, 0.25%-0.75%) to
0.55% (95% PI, 0.30%-0.80%) for a 10-ppb increase in the previous week’s
daily ozone concentration. Effect estimates were slightly higher at lower
The national average estimate of the constrained distributed-lag model
was robust to the degrees of freedom for smoothing of calendar time (ie, long-term
trends). The central estimate of the mortality increase associated with a
10-ppb increase in the previous week’s ozone concentrations ranged from
0.41% to 0.54%, with smoothing of calendar time varying from 7 to 21 df per year.
Statistically significant relationships were found for multiple ozone
concentration metrics by using the constrained distributed-lag model. The
concentrations of the different metrics were highly correlated, although relationships
among them varied because of differences in weather and the nature of sources.
The increase in mortality was 0.52% (95% PI, 0.27%-0.77%) for a 10-ppb increase
in the daily average, 0.64% (95% PI, 0.41%-0.86%) for a 15-ppb increase in
the daily 8-hour maximum, and 0.67% (95% PI, 0.42%-0.92%) for a 20-ppb increase
in the daily hourly maximum.
This multisite time-series study of 95 large US urban communities throughout
a 14-year period provides strong evidence of an association between mortality
and short-term exposure to ozone. On average across the 95 communities, we
estimated a 0.52% (95% PI, 0.27%-0.77%) increase in daily mortality for a
10-ppb increase in the previous week’s ozone concentration. We found
that the community-specific estimates were heterogeneous. Air pollution effect
estimates may be heterogeneous because of many factors, including city-specific
differences in pollution characteristics, the use of air conditioning, time
spent indoors vs outdoors, and socioeconomic factors.
The estimated effect was relatively robust to estimation with several
statistical models and to the degree of confounding adjustment for seasonality,
long-term trends, and temperature. The results indicate a substantial health
burden from ozone pollution. For example, according to our national average
estimate from the constrained distributed-lag model, a 10-ppb increase in
daily ozone would correspond to an additional 319 (95% PI, 168-472) annual
premature deaths for New York City and 3767 (95% PI, 1976-5562) premature
deaths annually for the 95 urban communities, based on mortality data from
2000. This value is probably an underestimate of the total mortality burden
from such an increase in ozone because it accounts for only the short-term
effects. Further, we found a relationship between mortality and ozone at pollution
levels below the current regulatory standard. Our analysis focused on 95 large
urbanized areas, although rural communities may also experience elevated ozone
levels, especially because of large biogenic emissions of volatile organic
compounds and the movement of ozone and ozone precursors from other regions.
Our study resolves inconsistencies in the findings of previous time-series
studies of ozone and mortality. The national average estimate was comparable
to those from other pooled analyses, including meta-analyses and other, smaller
multicity studies. To compare results across these studies, which have used
diverse metrics for ozone exposure, we converted the estimates from all of
the studies to a common metric, the daily average. Although the relationship
between different ozone concentration metrics can vary by location, we used
ratios of 2.5 and 1.33 to convert estimates according to the daily 1-hour
maximum and 8-hour maximum, respectively, to the daily average, as has been
done by others.46,47
Earlier multicity time-series studies of ozone and mortality have estimated
a broad range of effects. A 10-ppb increase in daily ozone was associated
with estimated increases in daily mortality of 2.84% (95% PI, 0.95%-4.77%)
for 4 European cities,48 0.61% (95% PI, −0.38%
to 1.60%) for 7 Spanish cities,49 1.40% (95%
PI, 0.68%-2.12%) for 6 French cities,50 and
0.43% (95% PI, 0.23%-0.63%) for 80 US urban centers from 1987 to 199422; however, a negative, nonstatistically significant
association was reported for 7 major Korean cities for 1991-1997.51
Recent meta-analyses were reported by Thurston and Ito,47 who
combined results of 16 studies and considered differences in their approaches
to the modeling of weather; Levy et al,46 who
used 4 US studies based in Cook County, Illinois, and Philadelphia; Stieb
et al,52,53 who extracted results
from 109 single- and multicity studies for random effects pooling; and Anderson
et al,54 who conducted a meta-analysis of ozone and PM as part
of a World Health Organization project. The overall estimates from the meta-analysis
studies, expressed as the percentage increase in daily mortality for a 10-ppb
increase in daily ozone, are 0.89% (95% CI, 0.56%-1.22%)47;
1.37% (95% CI, 0.78%-1.96%),47 considering
only studies that allow nonlinear associations between temperature and mortality;
0.98% (95% CI, 0.59%-1.38%)46; 1.12% (95% CI,
0.32%-1.92%)52,53; and 0.78% (95%
CI, 0.39%-1.18%).54 Our distributed-lag model’s
estimate was 0.52% (95% PI, 0.27%-0.77%) for a 10-ppb increase in the previous
week’s ozone levels, whereas our estimates for a single day’s
lag was 0.25% (95% PI, 0.12%-0.39%) and 0.18% (95% PI, 0.06%-0.30%) for a
10-ppb increase in the same day’s and previous day’s ozone concentrations,
respectively. The lower value estimated by our model could be due to publication
bias in the single-city studies that are incorporated into the meta-analyses.
Because the same statistical approach was applied to time-series data from
the 95 large US urban communities, our results are not subject to publication
A key advance in our study is the use of distributed-lag models, rather
than models that estimate the effect of a single day or several days at a
particular lag. Using single-day lagged models and the distributed-lag approach,
we found statistically significant associations between ozone levels on the
preceding days (primarily the current day and 2 previous days) and daily mortality.
This temporal pattern of effect would be anticipated for ozone, which produces
acute inflammatory responses in the lung; adaptation of this inflammatory
response with several days of repeated exposure has been demonstrated.55,56 Although the temporal dynamics of
the underlying processes linking ozone exposure to increased mortality may
differ from those of the inflammatory response, inflammation has been postulated
as having a central role in the increased mortality and morbidity associated
Several groups within the population have been considered at increased
risk from ozone exposure, including older persons and those with underlying
chronic heart and lung diseases. We did not find evidence of significantly
greater risk for these 2 groups; the estimated increments in risk were similar
across age groups and for total mortality and cardiorespiratory mortality.
However, this lack of evidence for increased susceptibility should be interpreted
in the context of effect modification on the relative risk scale in the statistical
models that were used. In these models, higher underlying mortality rates
are increased multiplicatively by the effect of ozone, implying a substantially
greater absolute effect of ozone in older persons or those with cardiac or
pulmonary diseases. Because the older population has a larger baseline mortality
rate than the general population, the same relative rate estimate for the
older and the general populations leads to a larger number of extra deaths
for the elderly.
One critical concern is the extent to which effect estimates may be
confounded by either temperature or other pollutants. In the communities included
in the present analysis, the concentration of ozone was not correlated with
concentrations of PM10. This lack of correlation and the stability
of the ozone estimate with inclusion of PM10, and vice versa, in
the models provide strong evidence against confounding of the effects of these
2 pollutants. The ozone and mortality results do not appear to be confounded
by temperature, as evidenced by analyses using subsets of the data at various
temperature levels and periods.
However, the estimated effect of ozone, although robust to the adjustment
for PM10, may still reflect the risk from the photochemical pollution
mixture more generally. Atmospheric photochemistry produces several hazardous
pollutants, in addition to ozone, such as peroxyacyl nitrates.18 Ozone
may act as a surrogate indicator for this highly complex and geographically
variable mixture and is likely to be an imperfect measure of potential toxicity.
The degree to which ozone functions as a surrogate for other pollutants or
the pollutant mixture in general, and thereby misclassifies toxicity, may
vary across locations and depend on the mix of sources and meteorologic factors.
Although statistically significant relationships were identified for all ozone
concentration metrics considered, the analysis did not identify a particular
metric as the optimum predictor of mortality.
Ozone pollution is now widespread in urban areas in the United States
and many other countries. Its rise reflects primarily increased numbers of
motor vehicles and miles traveled; vehicle emissions are a major source of
precursor hydrocarbons and nitrogen oxides. In the United States, more than
a hundred areas are not in compliance with the 8-hour NAAQS for ozone, with
the most extreme violations in California.2 Our
findings, interpreted in the context of the already extensive epidemiologic
and toxicologic evidence on ozone toxicity, indicate that this widespread
pollutant adversely affects mortality, in addition to other health effects
that have been associated with ozone.4-6 The
consequences of control strategies for public health can be tracked with the
methods and databases described in this report.
Corresponding Author: Michelle L. Bell,
PhD, Yale University, School of Forestry and Environmental Studies, 205 Prospect
St, New Haven, CT 06511 (firstname.lastname@example.org).
Author Contributions: Dr Bell had full access
to all of the data in the study and takes responsibility for the integrity
of the data and the accuracy of the data analysis.
Study concept and design: Bell, Zeger, Samet,
Acquisition of data: McDermott, Zeger.
Analysis and interpretation of data: Bell,
McDermott, Zeger, Dominici.
Drafting of the manuscript: Bell, Dominici.
Critical revision of the manuscript for important
intellectual content: Bell, McDermott, Zeger, Samet, Dominici.
Statistical analysis: Bell, McDermott, Zeger,
Obtained funding: Zeger, Dominici.
Study supervision: Zeger, Samet, Dominici.
Funding/Support: Funding for Drs Bell, Dominici,
and McDermott was provided by the US Environmental Protection Agency (EPA
3D-6867-NAEX). Funding for Drs Dominici, Samet, and Zeger was also provided
by the National Institute for Environmental Health Sciences (NIEHS; ES012054-01)
and by the NIEHS Center in Urban Environmental Health (P30 ES 03819).
Role of the Sponsors: None of the funding agencies
played any role in the design and conduct of the study; collection, management,
analysis, and interpretation of the data; and preparation, review, or approval
of the manuscript.
Acknowledgment: We thank Roger Peng, PhD, for