Journal of Environmental Protection, 2013, 4, 18-32
Published Online December 2013 (http://www.scirp.org/journal/jep)
http://dx.doi.org/10.4236/jep.2013.412A1003
Open Access JEP
Short-Term Effects of Ozone and PM2.5 on Mortality in 12
Canadian Cities*
Nawal Farhat1#, Tim Ramsay1, Michael Jerrett2, Daniel Krewski1,3
1Department of Epidemiology and Community Medicine, University of Ottawa, Ottawa, Canada; 2School of Public Health, Univer-
sity of California at Berkeley, Berkeley, USA; 3McLaughlin Center for Population Health Risk Assessment, University of Ottawa,
Ottawa, Canada.
Received September 19th, 2013; revised October 18th, 2013; accepted November 15th, 2013
Copyright © 2013 Nawal Farhat et al. This is an open access article distributed under the Creative Commons Attribution License,
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. In accor-
dance of the Creative Commons Attribution License all Copyrights © 2013 are reserved for SCIRP and the owner of the intellectual
property Nawal Farhat et al. All Copyright © 2013 are guarded by law and by SCIRP as a guardian.
ABSTRACT
Numerous recent epidemiological studies have linked health effects with short-term exposure to air pollution levels
commonly found in North America. The association between two key pollutants—ozone and fine particulate matter—
and mortality in 12 Canadian cities was explored in a time-series study. City-specific estimates were obtained using
Poisson regression models, adjusting for the effects of seasonality and temperature. Estimates were then pooled across
cities using the inverse variance method. For a 10 ppb increase in 1-hr daily maximum ozone levels, significant associa-
tions were in the range of 0.56% - 2.47% increase in mortality. For a 10 μg/m3 increase in the 24-hr average PM2.5 con-
centration of, significant associations varied between 0.91% and 3.17% increase in mortality. Generally, stronger asso-
ciations were found among the elderly. Effects estimates were robust to adjustment for seasonality, but were sensitive to
lag structures. There was no evidence for effect modification of the mortality-exposure association by city-level ecolo-
gic covariates.
Keywords: Air Pollution; Ozone; Particulate Matter; Mortality; Canada
1. Introduction
Health effects of air pollution have become a major pub-
lic health concern in North America, Europe and other
developed regions in the past several years. The World
Health Organization estimated 1.34 million premature
deaths (2.4% of total deaths) were attributable to outdoor
air pollution in 2008 [1]. Further, using satellite imaging
data to predict tropospheric PM2.5 concentrations glob-
ally, Evans et al. [2] recently estimated that 7% of global
mortality may be attributable to particulate air pollution.
In Canada, the Canadian Medical Association’s (CMA)
report No Breathing Room—National Illness Costs of
Air Pollution published in 2008, stated that air pollution
results in considerable health and economic damages that
will only increase over time. It was estimated that 21,000
deaths and 92,000 emergency department visits in Can-
ada could be attributed to short- and long-term exposure
to air pollution in year 2008. Associated economic costs
for the year, including worker absenteeism, higher health
care costs, loss of life, and other factors were expected to
exceed $10 billion [3].
Numerous studies have shown positive and significant
associations between adverse health effects and short-
term exposure to ozone (O3) and particulate matter (PM),
both of which are major components of smog in Canada
[4-16]. Both pollutants have been linked to various health
effects including premature mortality, deaths, and hospi-
tal admissions due to respiratory or cardiovascular dis-
eases. Other effects reported include decreases in lung
*Declaration of interest: Funding was provided through the McLaughlin
Center for Population Health Risk Assessment. D. Krewski holds a
N
atural Sciences and Engineering Research Council Industrial Re-
search Chair in Risk Science at the University of Ottawa, a peer-re-
viewed Canadian university-industry partnership program. He also
serves as Chief Risk Scientist for Risk Sciences International, a Cana-
dian company established in partnership with the University of Ottawa
(www.risksciences.com) which has conducted air pollution risk as-
sessments for public and private sector clients.
#Corresponding author.

Short-Term Effects of Ozone and PM2.5 on Mortality in 12 Canadian Cities 19
function and the exacerbation of existing chronic respi-
ratory and cardiovascular diseases. The elderly [6,17-20]
and children [21-25] have been reported to be at greater
risk than the general population. Exposure to high levels
of air pollution during pregnancy has also been linked to
low birth weight in Canada and other countries [26].
Health effects of short term exposure to air pollution
are typically assessed using time-series studies, where
associations between daily variations in air pollution lev-
els and daily counts of deaths in a given area are esti-
mated by Poisson regression models. One of the central
issues in statistical modeling of time-series studies is
adequate control for potential confounding. Confounders
typically controlled for are those that change over rela-
tively short periods such as seasonality, day of the week,
and weather variables that are associated with both pollu-
tion levels and health outcomes. Control for confounding
is usually achieved using smooth functions for time and
temperature variables in the Poisson regression models
[27]. For calendar time, penalized splines (PS) or natural
cubic splines (NS) are commonly used as the smoothing
functions. The degree of smoothing is determined by the
degrees of freedom (df) allowed in the smoothing func-
tions [28]. This must be selected carefully to ensure that
there is neither over-fitting (too many df), which would
fit the “noise”, nor under-fitting (too few df), which
would not remove the confounding effects of potential
confounders in order to avoid bias in the effect estimate.
Typically, 3 - 12 df per year have been used. Another
important consideration in time-series models is the lag
structure, which refers to the period between exposure to
the pollutant and the event (health outcome). Lag periods
used can be described using single-day lag models,
which allow for a lag period of a number of days, or
combined lag models. In combined models, the pollution
exposure levels are averages of multiple single-day lags.
Distributed lag models look at the effect of cumulative
exposure to pollution over the course of several days,
thus allowing each day to have an effect on health out-
comes.
There have been many advances in time-series models
since they were first used to study short-term effects of
exposure to air pollution in the 1980s [28]. Early statisti-
cal approaches included standard regression models,
which have now been replaced by semi-parametric mod-
els. The two main statistical models currently used are
based on Generalized Linear Models (GLM) with para-
metric splines or Generalized Additive Models (GAM)
with non-parametric splines. GAMs offer increased flexi-
bility in estimating the smooth component of the model
relative to GLM, and had been preferred over GLMs
until 2002. It was then discovered that these methods
underestimated the standard errors of linear terms in the
model (the air pollution regression coefficients) and over-
estimated the effect of air pollution on health outcomes
[28-30]. The discovery of these methodological and com-
putational issues came at the time when the United States
Environmental Protection Agency (EPA) was finalizing
its most recent review of the epidemiologic evidence on
particulate matter air pollution. As a result, all of the
findings from time-series studies that had been based on
GAMs were re-evaluated using alternative methods. Ap-
proximately 40 original studies from the US, Canada and
Europe were reanalyzed and then peer reviewed by a
panel appointed by the Health Effects Institute (HEI)
[31]. The HEI re-analysis report stated that no optimal
analytic method could be recommended to estimate the
air pollution health effects. Studies that have compared
different approaches have found that, although there may
be some sensitivities in the results, the effects remain sta-
tistically significant with the common approaches used
[11].
The purpose of the present paper is to quantitatively
assess the impact of fine particulate matter (PM2.5) and
ozone on mortality (total, cardiovascular and respiratory)
in Canada, and explore the sensitivity of the air pollution
effects estimates to different model specifications. A
secondary objective is to explore potential effect modifi-
cation of socioeconomic and demographic variables on
the effect of air pollution and health.
2. Materials and Methods
2.1. Data
2.1.1. Location, Exposure and Outcome
Air pollution and mortality data were analyzed for the
following 12 Canadian cities: Calgary, Edmonton, Hali-
fax, Hamilton, Montreal, Ottawa, Toronto, Quebec City,
St John, Vancouver, Windsor and Winnipeg. Data sets
were previously assembled and provided by Health Can-
ada. The air pollution data were obtained through the
National Air Pollution Surveillance (NAPS) program
administered by Environment Canada, which is subject
to an extensive quality assurance program. A single daily
measurement for each pollutant was available and repre-
sented the average of the measurements of all monitors in
that city. On days when one or more monitors were not
functioning, daily measurements were derived from the
remaining monitors. Daily ozone concentrations collected
include the 1-hour (1-hr) and the 8-hour (8-hr) maximum
concentrations. The 1-hr maximum, available on a daily
basis from 1980-2001, was used in the analyses to facili-
tate comparison of results with previous findings. Par-
ticulate matter measurements represent the 24-hour av-
erage cumulative mass measurements from all the moni-
tors in one city. PM2.5 was measured once every six days.
However, the data had occasional random periods with
missing data in many of the cities. In general, the time
Open Access JEP

Short-Term Effects of Ozone and PM2.5 on Mortality in 12 Canadian Cities 20
period with data available for each city varied between 6
- 16 years since 1984. Records of the daily mean tem-
perature for the time-series were also available.
Health outcome data for this study were obtained from
the Canadian Institute for Health Information (CIHI). To
ensure the quality of data collected, CIHI regularly per-
forms quality checks of its databases. Deaths were classi-
fied using the ICD-9 (International Statistical Classifica-
tion of Diseases) codes. Records that had been classified
using the ICD-10-CA scheme were converted to the ICD-9
classification scheme by CIHI and were then subject to a
quality assurance program. Mortality data for the 12 Ca-
nadian cities were available for the 20-year period 1981-
2001. The databases included information on residence
(city), age, date of death, and single underlying cause of
death.
2.1.2. Potential Effect Modifiers
Data on 29 ecologic covariates representing city-level
demographic, socioeconomic, health care, and lifestyle
determinants were used to explore effect modification.
The data were initially compiled for use in the interna-
tional study, Air Pollution and Health: A combined
European and North American Approach (APHENA)
[32], but was of limited use due to the lack of uniform
data of variables in the US and Europe. To explore effect
modification, city-specific risk coefficients, βs, were re-
gressed on the city-level covariates, by weighted linear
regression with weights inversely proportional to the
variance of each city’s β. Twenty-nine variables that
might modify the exposure-mortality association were
considered by including them in the time series models
individually.
2.1.3. Analyses
1) First stage (city-specific estimates)
Poisson regression models allowing for over disper-
sion were used to estimate the associations of ozone and
PM2.5 with mortality. The city-specific model is pre-
sented in Equation (1):
  

12
log, ,3
t
t
Estime dfstempdf
P DOWholiday

t



  (1)
where E(μt) is the expected value of the Poisson distrib-
uted variable μt, which represents the daily counts of
events (deaths) on day t. The term s1(time, df) controls
for seasonality, where s1 is a smooth function with natu-
ral cubic splines as basis functions for the time variable
and df is the degrees of freedom that allows s1 to take
various functional forms. The function s1 models the
non-linear association between time-varying covariates,
calendar time, and daily mortality. To control for weather,
the term s2(temp, 3) was included, where s2 is a smooth-
ing function of temperature on day t with 3 df. P is the
pollutant concentration (ozone in ppb or PM2.5 in μg/m3)
on day t; DOW and holiday are dummy variables in-
cluded in the model representing the day of week and
holidays. The regression coefficient β represents the log
relative increase (if β is positive) in the number of events
in the target population per unit increase in pollutant
concentration. Time-series studies generally report re-
sults as percent change in mortality per 10 units change
in pollutant concentration (This value is simply obtained
by multiplying the regression coefficient β by a factor of
1000).
Three mortality outcomes for each age group were
considered based on the ICD-9 codes: <800 for total
mortality corresponding to all non-accidental causes of
death, 390 - 459 for cardiovascular causes of death, and
460 - 519 for respiratory causes of death.
Sensitivity analyses exploring the effect of degrees of
freedom allowed for seasonality control in the smooth
function of calendar time and the effect of varying the
lag period were conducted. These analyses were limited
to ozone, which had daily data. Effects estimates for
ozone on all outcomes across all ages were determined
with models allowing for 1 - 20 df for the time variable
per year of data available. For temperature, three df were
allowed in all analyses, based on previous findings that
indicate that results are robust to varying degrees df used
for temperature. Models included the same lagged term
for temperature variable as was used for the pollutant
under consideration.
To compare the effect of natural splines and penalized
splines, a sensitivity analyses on the risk estimates was
carried out on the Toronto data set. After the results of
the df and lag period analyses were examined, three val-
ues of the df (4, 8 and 12 per year) and three lag periods
(for ozone: lag1, lag02 and dist02) were selected for
analyses of the data for the remaining cities. Combined
lag structures could not be applied to PM2.5 since data
were available for every sixth day; thus, three single-day
lag periods were selected for this pollutant (lag0, lag1,
lag2).
2) Second stage (pooled estimates)
City-specific estimates were combined to arrive at
pooled estimates by applying fixed effects (FEM) and
random effects (REM) regression models. In the fixed
effects approach, effects estimates (β’s) were assumed to
be normally distributed around an overall estimate and
were pooled using inverse variance weighting, with
weights proportional to the inverse variance of each
city’s β. In the random effects regression approach, the
city-specific βs were assumed to form a sample of inde-
pendent observations from a normal distribution with the
same mean and with variance equal to the sum of the
between-city variance and the variance of β. The be-
tween-city variance is added to the city-specific variance
Open Access JEP

Short-Term Effects of Ozone and PM2.5 on Mortality in 12 Canadian Cities
Open Access JEP
21
and is estimated using the maximum likelihood estima-
tion (MLE) method [33].
2.2. Effect Modification
Heterogeneity between city specific estimates was as-
sessed by the I2 index, which is a measure of the total
variability among effect sizes that can be attributed to
true heterogeneity (between-city variability) [34]. In gen-
eral, an I2 less than 25% suggests low heterogeneity be-
tween cities. To explore potential effect modification,
weighted linear regression of the city-specific estimates
was performed onto each ecologic covariate. Weights
were inversely proportional to the variance of each city
specific risk estimate (β). Models where the data showed
a significant linear association at the 95% confidence
level between the potential effect modifier and the risk
estimates were assumed to potentially modify the pollut-
ant-health outcome relationship.
All analyses were completed using R statistical soft-
ware for Windows version 2.6.1 [35].
3. Results
Descriptive statistics for the mortality data are provided
Tables 1 and 2. There were a total of approximately 1.6
million deaths among all age groups between 1981 and
2000 across the 12 Canadian cities considered. The total
exposed population was approximately 9.1 million. Indi-
vidual city population ranged from 100,000 in St John to
2.3 million in Toronto, based on the 1991 Census. Mean
daily death counts varied between 0 (respiratory mortal-
ity) and 48 deaths (total mortality), depending on the size
of the city.
Descriptive statistics of the exposure database are pre-
sented in Table 3. Mean annual temperatures for the 12
cities ranged from 2.4˚C (Edmonton) to 10.6˚C (Van-
couver). Ozone measurements were available on a daily
basis during the period 1981-2000, with few missing data
(except for Halifax). The mean measurements of the 1-hr
maximum ozone levels were in the range 27 - 37 ppb.
For PM2.5, the mean 24-hour levels varied between 9
μg/m3 and 16 μg/m3. The time periods during which
PM2.5 data was available were not uniform across cites.
PM2.5 was generally measured every sixth day for most
cities, with occasional intermittent missing data across
longer periods. Ozone and PM2.5 levels were not strongly
correlated, with the highest correlation coefficients being
0.46 and 0.41 for Windsor and St John, respectively.
The city level socio-demographic, health services, and
lifestyle ecological variables were assessed for potential
effect modification of the association between air pollu-
tion and mortality. Several of the variables listed were
highly correlated.
Table 1. Total number of death counts in the 12 Canadian
cities in the 1981-2000 period.
Outcome/Age group Total counts
All-cause mortality
all ages 1,564,583
75 and over 748,498
under 75 815,978
Cardiovascular mortality
all ages 641,072
75 and over 369,177
under 75 271,855
Respiratory mortality
all ages 134,663
75 and over 85,971
under 75 48,683
Table 2. Summary of the population size and mean number of daily mortality counts by cause and age group in the 12 Cana-
dian cities.
All-cause mortality Cardiovascular mortality Respiratory mortality
City Population (×1000)All ages 75 and overUnder 75 All ages75 and overUnder 75 All ages 75 and overUnder 75
Calgary 711 10 5 5 4 2 2 1 1 0
Edmonton 617 11 5 6 5 3 2 1 1 0
Halifax 231 6 3 3 2 1 1 1 0 0
Hamilton 319 10 4 5 4 2 2 1 0 0
Montreal 1776 48 22 26 19 10 9 4 2 2
Ottawa 880 15 7 8 6 4 3 1 1 0
Quebec City 540 17 8 9 7 4 3 1 1 1
St John 103 3 1 1 1 1 1 0 0 0
Toronto 2276 47 22 24 18 11 8 4 3 1
Vancouver 1832 29 15 14 12 8 4 3 2 1
Windsor 191 6 3 3 3 2 1 0 0 0
Winnipeg 615 14 7 7 6 4 2 1 1 0

Short-Term Effects of Ozone and PM2.5 on Mortality in 12 Canadian Cities 22
Table 3. Descriptive statistics for the study period, air pollutants and temperature data used in the analyses of mortality out-
comes.
City Calgary Edmonton Halifax Hamilton. Montreal OttawaQuebec CitySt JohnToronto Vancouver Windsor Winnipeg
Temp (˚C)
Mean 4.5 3.0 6.5 8.0 6.6 6.4 4.4 5.2 8.1 10.5 9.8 3.1
25th centile −1.9 −5 -0.8 0.1 −2.1 −2.7 −4.6 −1.70.2 6.3 1.5 −7.4
Median 5.6 4.7 6.9 8.2 7.6 7.455.4 6.1 8.3 10.3 10.2 4.7
75th centile 13 13 14.6 17 16.7 17 14.8 13.717.2 15.3 19.1 15.4
Ozone (ppb)
Time period
(month/year)
01/81-
12/00
01/81-
12/00
01/81-
12/00
01/81-
12/00
01/81-
12/00
01/81-
12/00
01/81-
12/00
01/81-
12/00
01/81-
12/00
01/81-
12/00
01/81-
12/00
01/81-
12/00
No. obs.1 7305 7302 5945 7290 7304 73037208 714473057292 7225 7159
No. missing2 0 3 1360 15 1 2 97 161 2 13 80 146
Mean (1 hr max) 33.1 31.2 29.0 34.8 28.7 28.828.8 34.534.2 27.0 36.9 30.0
Maximum
(1 hr max) 94 110 100 129 115.7 105135 160 144.1104.6 159 99
25th centile 25 22 21 22.3 18.8 20 20 26 22.3 19.1 21 21.5
Median 33 30.5 28 31 26.2 27 28.5 32.330.5 26.6 31.5 29
75th centile 41 40 35 44 35.9 35.735.0 40.042.3 34 49 37.5
PM2.5 (μg/m3)
Time period
(month/year)
12/84-
12/00
12/84-
12/00
12/84-
12/96
01/95-
12/00
12/84-
12/00
12/84-
12/00
12/85-
12/00
09/92-
09/99
12/84-
12/00
12/84-
12/00
12/87-
12/00
09/84-
09/00
No. obs. 891 791 657 418 1180 807524 112515371082 1031 816
No. missing 6414 6514 6648 6887 6125 64986781 618057706223 6274 6489
Mean 10.2 10.1 11.0 15.3 14.7 10.711.3 7.7 14.7 11.8 16.3 9.0
Maximum 66.1 64.0 45.5 74.1 72.0 53.850.4 53.271.0 67.0 85.6 71.3
25th centile 5.7 5.3 6.1 7.7 7.8 5.1 6.0 3.8 7.3 6.7 8.7 5.2
Median 8.3 8 9.15 12.5 12 8.329 6.3 12.349.8 13.7 7.3
75th centile 12.1 12 13.5 20.3 18.8 13.814.0 9.9 19.5 14.1 20.7 11.0
1Total number of observations; 2Number of missing observations.
Figure 1 presents the pooled percent increase (random
effects) in mortality outcomes across all ages associated
with an increase of 10 ppb in the previous day’s ozone
concentrations (lag1). The number degrees of freedom
allowed per year of data available was varied between 1
and 20 in the smooth function of time (natural splines) in
the city-specific models. Estimates stabilized after al-
lowing 4 - 5 df per year, displaying slight decreases as
the degrees of freedom increased. In the absence of sub-
stantial heterogeneity among city specific estimates,
fixed effects and random effects models gave comparable
results across cities. Estimates were positive and statistic-
cally significant across all outcomes.
Results showing the effect of varying the lag period
between exposure to ozone and day of death are pre-
sented in Figure 2. The figure represents the pooled re-
sults (random effects) for all ages at 4, 8 and 12 df per
year of data. Higher estimates were observed for com-
bined lag models relative to single day lags. Wider con-
fidence intervals for respiratory mortality compared to
other mortality outcomes were observed, a result of the
low daily counts for this outcome. For total and cardio-
vascular mortality, effects estimates were significant at
all lag structures and df examined.
The mortality effects estimates for ozone and PM2.5
across all age groups with eight degrees of freedom for
seasonality control and three lag structures are summa-
rized in Tables 4 and 5, respectively. For ozone, results
were statistically significant across all age groups, with
higher estimates observed with the combined lags rela-
Open Access JEP

Short-Term Effects of Ozone and PM2.5 on Mortality in 12 Canadian Cities 23
Total Mortality
Degrees of freedom
5 1015 20
0.4 0.6 0.8 1.0 1.2 1.4 1.6
Percent increase
Cardiovascular Mortality
Degrees of freedom
5 10 15 20
0.5
Percent increase
1.0 1.5
Respirat ory Mortali ty
Degrees of freedom
5 10 15 20
0 Percent increase
1 2 3 4
Figure 1. Pooled percent increase in mortality and 95% CI
associated with a 10 ppb increase in the 1-hr maximum
ozone levels in 12 Canadian cities with a 1-day lag with
varying degrees of freedom per year for seasonality control.
All mortality
-1
0
1
2
3
lag 0lag 1lag 2l ag 01lag 02dist02
Respira tory mortality
-1
0
1
2
3
lag 0l ag 1l ag 2l ag 01lag 02dist02
Cardiovascular mortality
-1
0
1
2
3
l ag 0lag 1l ag 2lag 01lag 02dist02
Figure 2. Pooled percent change in mortality (all ages) asso-
ciated with an increase of 10 ppb in the 1-hr maximum
ozone concentrations in 12 Canadian cities. Results are
from random effects models with various df allowed for
seasonality control. (∆ 4 df; ● 8 df; □ 12 df).
tive to the single day lag period. Effects estimates were
generally stronger among the elderly. However, respira-
tory mortality estimates were stronger for the <75 age
group compared to the ≥75 group, contrary to what might
be expected.
For PM2.5, three single-day lag structures were evalu-
ated with exposure on the same day (lag0), the previous
day (lag1) and two days prior (lag2). Effects represent
the percent increase in the outcome associated with a 10
μg/m3 increase in the 24-hour average PM2.5 levels. Gen-
erally, fewer outcomes were statistically significant in
relation to PM2.5, compared to ozone. Estimates at 1- or
2-day lag periods were higher relative to effects at same
ay exposure, indicating that PM2.5 may have a delayed d
Open Access JEP

Short-Term Effects of Ozone and PM2.5 on Mortality in 12 Canadian Cities
Open Access JEP
24
Table 4. Pooled percent increase in mortality outcomes associated with a 10 ppb increases in the 1-hour maximum ozone lev-
els for three lag structures. Results are from fixed effects models with 8 df allowed for seasonality control.
Percent increase (95% CI)
Outcome/Age group
lag1 lag02 dist02
All mortality
All ages 0.64 (0.45 - 0.82) 1.12 (0.87 - 1.38) 1.03 (0.77 - 1.30)
Under 75 0.59 (0.33 - 0.84) 1.01 (0.66 - 1.36) 0.95 (0.59 - 1.31)
75 and over 0.69 (0.42 - 0.96) 1.25 (0.87 - 1.62) 1.05 (0.66 - 1.45)
Cardiovascular mortality
All ages 0.56 (0.27 - 0.84) 1.25 (0.85 - 1.65) 1.20 (0.79 - 1.61)
Under 75 0.07 (−0.36 - 0.51) 0.76 (0.16 - 1.37) 1.63 (0.01 - 1.25)
75 and over 0.93 (0.55 - 1.31) 1.62 (1.09 - 2.15) 1.40 (0.84 - 1.96)
Respiratory mortality
All ages 0.86 (0.21 - 1.51) 1.34 (0.44 - 2.25) 1.49 (0.55 - 2.43)
Under 75 0.98 (−0.08 - 2.04) 2.14 (0.67 - 3.62) 2.47 (0.95 - 3.98)
75 and over 0.79 (−0.04 - 1.62) 0.86 (−0.29 - 2.01) 1.58 (0.33 - 2.84)
Table 5. Pooled percent increase in mortality outcomes associated with a 10 μg/m3 increase in the 24-hr average PM2.5 con-
centrations. Results are from fixed effects models with 8 df allowed for seasonality control.
Percent increase (95% CI)
Outcome/Age group
lag0 lag1 lag2
All mortality
All ages 0.35 (−0.23 - 5.94) 1.43 (0.84 - 2.31) 0.98 (0.39 - 1.57)
Under 75 0.57 (−0.26 - 1.39) 0.91 (0.08 - 1.73) 0.14 (−0.68 - 4.97)
75 and over 0.12 (−0.72 - 5.96) 1.98 (1.14 - 2.81) 1.85 (1.01 - 2.68)
Cardiovascular mortality
All ages −0.23 (−1.18 - 0.72) 1.03 (0.09 - 1.97) 1.77 (0.83 - 2.71)
Under 75 0.11 (−1.37 - 1.68) −1.44 (−4.84 - 1.96) 0.88 (−0.61 - 2.36)
75 and over −0.45 (−1.68 - 0.78) 2.14 (0.92 - 3.35) 2.39 (1.17 - 3.61)
Respiratory mortality
All ages −1.12 (−3.19 - 1.95) 0.30 (−1.72 - 2.33) 1.31 (−0.75 - 3.36)
Under 75 0.04 (−3.80 - 3.99) −0.27 (−3.87 - 3.33) −1.77 (−5.53 - 2.88)
75 and over −0.95 (−3.54 - 1.64) 0.73 (−1.74 - 3.21) 3.17 (0.61 - 5.72)
effect on health outcomes. Effects for mortality on the
same day of exposure (lag0) were not statistically sig-
nificant. Positive and significant effects were seen for
total and cardiovascular mortality in 1- and 2-day lag
models. Respiratory mortality effects were only signifi-
cant at 2-day lag and among the elderly. Effects were
consistently higher for older age groups.
There was no substantial heterogeneity between city-
specific estimates in the majority of models applied. Two
outcomes that displayed the highest heterogeneity based
on the I2 index were selected for effect modification
analyses: cardiovascular mortality with ozone (assessed
at lag02, 8 df for time, for <75 years, I2 index 22%) and
cardiovascular mortality with PM2.5 (assessed at lag0, 8
df for time, for <75 years, I2 index 16%). Table 6 pre-
sents statistically significant results (at the 95% confi-
dence level) of the effect modification analysis of PM2.5
and ozone. Results represent the percent increases in
daily number of deaths associated with an increase of 10
units in PM2.5 or ozone at two different values for the
effect modifier, corresponding to the 25th and the 75th
ercentiles of the city-specific distribution that variable. p

Short-Term Effects of Ozone and PM2.5 on Mortality in 12 Canadian Cities 25
Table 6. Percent change in mortality associated with a 10 μg/m3 increase in PM2.5 concentrations at the 25th and 75th percen-
tile of the city-specific distributions of covariates that displayed significant effect modification.
Percent change (95% CI)
Outcome/ Effect modifier
25th centile 75th centile
p-value
Cardiovascular mortality and PM2.5
Area −0.72 (−3.45, 2.02) −0.17 (−2.79, 2.46) 0.05
Unemployment, males 1.72 (−1.32, 4.77) 0.16 (−2.44, 2.76) 0.05
Manufacturing 2.75 (−0.88, 6.38) −0.24 (−2.85, 2.37) 0.04
Stress 1.6 (−0.89, 4.09) 0.47 (−1.8, 2.75) 0.01
Results can be seen as showing pollutant effects in a city
characterized by a level of the effect modifier corre-
sponding to the 25th or 75th percentile.
4. Discussion
This study presents results of the short-term effects of
ozone and PM2.5 exposure on mortality across 12 Cana-
dian cities. Statistically significant associations were ob-
served across the three mortality outcomes, with esti-
mates being generally higher among the elderly. Risk
estimates were robust to seasonality control when more
than five degrees of freedom per year of data available
were allowed. Sensitivity of risk estimates was observed
to varying lag structures with higher estimates when us-
ing combined lag structures for ozone, and with 1 or 2
day lags for PM2.5. Analyses of socio-demographic, health
services and lifestyle covariates did not identify any
potential effect modifiers that warrant further investiga-
tion.
4.1. Degrees of Freedom for Seasonality
Across the three mortality outcomes, effects estimates
were found to stabilize beyond five degrees of freedom
per year in the smoothing function of calendar time. Re-
sults obtained are in agreement with other studies that
have explored the sensitivity of degrees of freedom for
seasonality control. Peng et al. [20] conducted a simula-
tion study that compared various methods commonly
used to adjust for seasonal and long-term trends. By ex-
amining the variability of the regression coefficient, β,
using 1 - 20 df per year, results indicated that the bias in
β was only serious for df between 1 and 4 with natural
splines (and between 1 and 6 df with penalized splines)
and was stable afterwards. Another study in California
found that effects estimates decreased with increasing df
when evaluated at 4, 8 and 12 df per year with a greater
reduction observed going from 4 to 8 df [13].
Although there is no preferred method to choose the
optimal degrees of freedom, Touloumi et al. [36] suggest
that the approach followed in NMMAPS (7 df per year)
yields conservative air pollution effects estimates, since
this value of df is large enough to ensure adequate con-
trol for seasonal and long-term trends. Many previous
studies have selected a fixed value for df (generally be-
tween 4 - 12 df per year) to be used in analyses, based on
sensitivity analyses or previous results [13,14,37,38].
The analyses in this study evaluated all the outcomes for
different age groups at 4, 8 and 12 df per year to compare
the effects across this range.
Natural cubic splines were used in all analyses pre-
sented. Penalized splines have also been used in time-
series studies and both methods are believed to yield
comparable results. Mortality risk estimates associated
with ozone obtained from both approaches were com-
pared in this study using the data set for one of the 12
cities (Toronto). Risk estimates varied between −4% and
6% when comparing both approaches at values of 6 - 14
df per year for time, confirming the comparability of risk
estimates based on natural splines and penalized splines.
The effect of varying the df for the temperature vari-
able was not explored in this study. It is generally ac-
cepted that the effects estimates are not as sensitive to the
method used to control for temperature as they are to
controlling for calendar time [39]. The approach fol-
lowed in APHENA for controlling for temperature was
adopted in this study, where three degrees of freedom
were allowed in the smooth function of temperature in all
models.
4.2. Effects Estimates
The use of the 1-hr maximum daily average for ozone
facilitated the comparison of results with previous find-
ings, as many of previous studies used this measure. The
World Health Organization suggests that the 8-hour av-
erage may be a better indicator for respiratory function
and lung inflammation [40]. However, correlation coef-
ficients between the 1-hr and 8-hr maximum ozone levels
were in the 0.94 - 0.97 range across the 12 Canadian cit-
ies. Thus, similar results are to be expected using either
measurement. Results of both measures were compared
Open Access JEP

Short-Term Effects of Ozone and PM2.5 on Mortality in 12 Canadian Cities 26
in the project Air Pollution and Health—A European Ap-
proach 2 (APHEA 2), and have been reported to give
comparable results [11].
Pooled effects estimates across cities for ozone were
positive and significant for total and cause specific mor-
tality. Significant results observed were in the range of
0.56% to 2.47% increase in mortality for a 10 ppb in-
crease in the 1-hr maximum ozone levels. In general,
higher effects were obtained for the ≥75 age group.
These results are comparable with previous studies
[9,10,15,16,32]. A slightly stronger effect was detected
for respiratory mortality, which is also consistent with
other studies. In APHEA 2, a stronger association be-
tween ozone and respiratory mortality was found (2.26%)
relative to other mortality outcomes (0.9% cardiovascular
mortality and 0.66% for total mortality) per 10 ppb in-
crease in the 1-hr maximum ozone levels [11]. In a study
within NMMAPS that looked at 95 US urban communi-
ties, a positive and significant association (0.64% in-
crease) per 10 ppb increase in the previous week’s ozone
levels was estimated for respiratory and cardiovascular
mortality, slightly higher than the estimate for total mor-
tality (0.52%). Further, in an Australian study of four
cities, a significant association was obtained for respire-
tory mortality (2.2% increases per 10 ppb increase), but
not for other outcomes (Simpson et al. 2005). The great-
est effects estimates were observed with combined lag
models in this study. The higher effects estimated with
the 3-day average (lag02) and the distributed lag models
(dist02) suggest that the effect of ozone may not only
depend on same day exposure, but also on the exposure
over the previous 2 days. This indicates that single day
lag models may underestimate the cumulative effect of
ozone on mortality due to repeated exposure to high lev-
els of ozone. Hence, the combined and distributed lag
models may be more appropriate for estimating ozone
health effects. This is in agreement with previous find-
ings that suggest multi-day exposure lags are higher than
single day lags [9,10,32,41]. Studies that investigated lag
models taking into account the previous week’s ozone
levels in 95 US cities and found that effects were consis-
tently higher than those of single day lag models [9,10].
Meta-analyses that have looked at the health effects of
ozone have found positive effects for both total and car-
diovascular mortality [8] or only total mortality [16].
Estimates from this study are lower than the Canadian
estimates from APHENA for all-cause mortality and car-
diovascular mortality outcomes for ozone as the exposure
pollutant. The data sets used in APHENA represent a
subset of the data used in this study, covering a shorter
time period (1987-1996). It is hypothesized that the use
of a shorter time-series and the inclusion of additional
covariates in the models (two terms for temperature con-
trol) in APHENA, may have led to different results. A
sensitivity analysis exploring the effect of varying the
number of temperature terms included in the time series
was carried out. Results show that the use of one term
(same day temperature (temp0)) or two terms (same day
and previous day temperature (temp01)) for the tem-
perature variable produced comparable risk estimates.
Further investigation is needed to more fully explain the
difference in results between this study and the Canadian
APHENA results.
Fine particulate matter showed statistically significant
effects estimates combined across cities. For total and
cardiovascular mortality, significant estimates were in
the range of 0.91% - 2.39% increase per 10 µg/m3 in-
crease in PM2.5. Results of this study are in agreement
with previous study findings where estimates reported
have generally been in the range of 0.8% - 2.4% increase
[42]. A number of previous studies have reported com-
parable results [12-14], although others have found no
significant effect on mortality [8,15]. The only signifi-
cant estimate detected for respiratory mortality was a
3.17% increase for ≥65 at a 2-day lag. It is unusual to
detect a relatively strong association for this age group
when other groups considered did not show any signifi-
cant effects. Compared to respiratory diseases, cardio-
vascular diseases are more prevalent which leads to in-
creased power to detect weak associations [43]. It is pos-
sible that due to low number of respiratory related deaths,
the models applied were not able to detect the weak as-
sociation and that the 3.17% increase observed was ob-
tained by chance.
Effects were consistently higher for older age groups,
supporting the hypothesis that the elderly may be more
susceptible to the effects of PM2.5; this may be a result of
exacerbation of pre-existing conditions that are more
prevalent among individuals in this age group or due to
reduced antioxidant defenses [44].
Effects of mortality on the same day of exposure (lag0)
were not significant. Rather, across all mortality out-
comes and two age groups (all ages and ≥75), the effect
of PM2.5 was strongest at 1-day lag (and sometimes at
2-day lag) compared to the effect of same day exposure,
suggesting a delayed PM2.5 effect. As with the case of
ozone, findings for PM2.5 reported in the literature have
been inconsistent. For example, a study in Montreal
found that cardiovascular mortality was more affected by
exposure to PM2.5 in previous days [18], whereas a study
in 10 US cities found a stronger same-day exposure ef-
fect [41]. Results have also been inconsistent for respire-
tory deaths. Previous studies have reported stronger ef-
fects on day exposure levels [38] or exposure in the prior
1 or 2 days [45]. This inconsistency may be a result of
the different chemical components of the PM2.5 mixture
with different chemicals responsible for immediate or
delayed responses in individuals across the various study
Open Access JEP

Short-Term Effects of Ozone and PM2.5 on Mortality in 12 Canadian Cities 27
locations. This may also be explained by the different
population structures where certain subpopulations are
more vulnerable to air pollution [46].
4.3. Effect Modification
City-specific results in general did not display significant
heterogeneity across outcomes based on the I2 index,
which was generally in the range of 0% - 25%. The lack
of heterogeneity between estimates of Canadian cities is
supported by findings of APHENA [32]. Two mortality
outcomes that showed some level of heterogeneity among
cites were examined for effect modification by 29 eco-
logical variables. None of the variables were found to
modify the ozone-mortality relationship (p > 0.05). With
PM2.5, the association was statistically significant for four
variables: area of city, percent of unemployed males,
percent manufacturing and percent of population stressed.
The PM-mortality association is not likely to be affected
by the geographic area of city per se, but by other factors
associated with it. The remaining city-level variables
identified were found to modify the effect in the opposite
sense of what would be expected. For example, mortality
was seen to decrease with higher percentage of stress
levels.
Previous findings on effect modification have been
inconsistent, with several studies concluding that the
effect of air pollution is not modified by city-level vari-
ables or reporting only geographical variations [11,12,16,
38]. However, Ostro et al. [13], reported that effect of
PM2.5 was higher among females, whites, diabetics, or
persons with less than high school education. In addition,
Bell and Dominici [10] looked at effect modification
patterns in 98 US communities and reported that higher
estimates were associated with higher unemployment,
fraction of African American population, public trans-
portation use, lower temperature and lower prevalence of
central air conditioning. In APHEA 2, life expectancy
was identified as an effect modifier.
The use of only 12 cities in this study may have lim-
ited the effect modification analyses. Although the PM2.5
effect was found to be modified by several city-specific
characteristics, results cannot be considered as providing
strong evidence of effect modification. As several previ-
ous studies have reported, it is possible that effect modi-
fication with the covariates considered does not occur in
the case of short-term exposure to air pollution. Repeat-
ing this analysis with a greater number of cities would
give greater power to detect heterogeneity—if present—
and allow stronger conclusions to be made regarding
effect modification.
4.4. Biological Mechanisms
The exact biological mechanisms by which air pollution
leads to morbidity and premature deaths remain under
active investigation. However, much of the current evi-
dence suggests that exposure to ozone and PM induces
oxidative stress and inflammation in the lung tissue that
lead to local and systemic events. The inflammatory re-
sponse in the lungs has been demonstrated in animal and
controlled human studies [47-49]. Inflammation in the
lungs triggers the release of cytokines and chemokines
that lead to sub-clinical systemic inflammation that may
alter the vascular system [48,50,51].
Observed cardiovascular effects can be partially ex-
plained by activation of pulmonary neural reflexes that
result from interactions between pollutants and lung re-
ceptors. Increases in fibrinogen levels and reductions in
heart rate, two risk factors for cardiac diseases that lead
to hospital admissions, have been associated with expo-
sure to air pollution. Reductions in heart rate can lead to
decreased parasympathetic input, which may in turn lead
to arrhythmia and cardiovascular mortality [52,53]. Lung
inflammation is also believed to exacerbate underlying
lung diseases by weakening lung defense mechanisms.
Animals with chronic obstructive pulmonary diseases
(COPD) or chronic lung inflammation have been found
to be more vulnerable to combustion particles compared
to normal animals [52,53]. Influenza infections have also
been shown to be exacerbated by air pollution in experi-
ments [54,55]. Further, studies on mice and humans in-
dicate that PM2.5 may accelerate the development of
atherosclerosis [48,56]. Other studies have detected PM
in the heart muscle and brain cells indicating its ability to
diffuse into the bloodstream which may lead to direct
toxic effects [48,50].
4.5. Strengths and Limitations
This study examined the associations of two ambient air
pollutants and health outcomes in 12 Canadian cities,
with a total exposed population of 9 - 10 million Cana-
dians. Statistical methods applied were uniform across all
cities enabling the direct pooling of city-specific results.
The literature on the health effects of short-term ex-
posure to PM2.5 is somewhat limited [38], as its use in
time series studies is relatively recent. Many previous
studies have focused on larger particles rather than fine
PM due to the availability of data, or have used conver-
sion factors to convert between the two particle fractions.
This study adds to the literature quantitative evidence of
the significant effects of fine PM. This study was based
on measurements of PM2.5 as recorded by fixed monitors
in each city; hence, errors inherent in conversion factors
were not introduced into the measurements. Further,
analyses in previous studies have sometimes been hin-
dered by the different measurements methods that were
used for each city [57]. However, air pollution and mor-
Open Access JEP

Short-Term Effects of Ozone and PM2.5 on Mortality in 12 Canadian Cities 28
tality data were collected under a common framework
and subject to the same quality assurance programs
across the 12 Canadian cities included in this study.
Concentrations of ambient air pollution obtained from
fixed outdoor monitors throughout each city were used as
a surrogate measure for the population average personal
exposure. This method assumes that exposure among all
individuals in a given area is identical and does not take
into account the differences in activity patterns (such as
time spent outdoors) [58]. The feasibility of obtaining
such data, usually collected for regulatory purposes, at
low cost and no burden to study subjects, has made it
convenient to use in time-series studies. As a result, time-
series studies are believed to be subject to exposure mis-
classification, especially among subpopulations that are
at a higher risk since their activity patterns may differ
from that of the general population [58,59]. However, the
use of fixed monitor measurements is supported by pre-
vious studies that have shown a strong correlation be-
tween outdoor, indoor and personal exposure to particu-
late matter [60,61]. It has also been reported that the
presence of error in measurement of the exposure would
lead to non-differential misclassification of exposure and
hence underestimation of health effects [62-64]. Jerret et
al. [63], showed that the health effects were three times
higher when analyses were based on individual’s prox-
imity to high traffic regions compared to using commu-
nity average concentrations. Models that correct for
measurement error have also been developed [64].
Another limitation in this study was the systematically
missing exposure data for PM2.5. The incomplete expo-
sure data may have resulted in an underestimation of the
true effect estimates, based on the findings of a recent
study by Samoli et al. [65] that showed systematically
missing daily PM10 and ozone data gave considerably
lower effect estimates. Having PM2.5 measurements for
every sixth day may have also led to effect estimates
with greater uncertainty than those calculated for ozone
(which had daily data). In Canada, PM2.5 data have been
collected on a daily basis since the late 1990s. The un-
availability of daily PM data may have also contributed
to greater heterogeneity between city estimates, thereby
increasing the possibility of observing spurious associa-
tions as effect modifiers.
Confounding of co-pollutants in the PM2.5 effect has
been looked at in other studies. PM2.5 is highly correlated
with other co-pollutants, and it is often difficult to disen-
tangle which component of the air pollution mixture is
the one responsible for the observed health effects [66,67].
This study did not look at potential confounding by
co-pollutants beyond ozone. Some previous studies have
looked at the effect of seasonal variation in the levels of
ozone, where higher effects were detected in the summer
when ozone levels are typically higher [11,66,68]. This
was not explored in this study.
Further, the power to detect heterogeneity between
city estimates and consequently potential effect modifiers
was limited by the low number of cities. It is recom-
mended to repeat effect modification analyses with a
larger number of cities to arrive at more conclusive re-
sults regarding potential effect modifiers.
Finally, the potential biases associated with the use of
mortality data obtained from death certificates needs to
be considered. A Canadian study by Stieb et al. [69],
looked at the classification of cardio-respiratory diseases
in emergency department visits. Findings found a fair
degree of agreement in the diagnosis of seven independ-
ent assessments, with no evidence of diagnostic bias in
relation to daily air pollution. The databases in this study
have been subject to quality control by CIHI. Neverthe-
less, if errors were present in the management of data,
this would result in non-differential misclassification bias,
as such errors would not likely be related to variation in
air pollution levels.
4.6. Public Health Implications
Results of this study indicate a substantial public health
burden from ozone and PM2.5 pollution. Further reduc-
tions in the levels of these two pollutants would bring
considerable health and economic benefits to Canadians.
For example, based on the calculated effects in this study,
a 10 ppb increase in 1-hr maximum ozone levels would
correspond to an additional 1,368 (95% CI, 985-759)
premature deaths each year in the 12 cities considered in
this study (based on lag02 model with 8 df and average
annual mortality between 1980 and 2000). Similarly, a
10 µg/m3 increase in daily average of PM2.5 would cor-
respond to 1148 (95% CI, 521-2319) premature deaths
annually in these cities (based on lag2 model with 8 df
and the average annual mortality between 1980 and
2000). These figures will be higher when considering the
total Canadian population and the inclusion deaths asso-
ciated with long-term exposure to these pollutants. Long-
term effects related to ozone and PM2.5 exposure have
been reported to be much greater than short-term effects
[3].
Previous studies have looked at the exposure-response
relationship between air pollutants and mortality in an
attempt to identify a threshold concentration, below
which air pollution would not lead to increases in deaths
[32,70,71]. However, recent epidemiologic findings have
consistently detected associations at low ambient pollu-
tion levels, without clear evidence supporting the exis-
tence of a threshold concentration [32,66,71].
5. Conclusion
This study supports previous findings that have linked
Open Access JEP

Short-Term Effects of Ozone and PM2.5 on Mortality in 12 Canadian Cities 29
short-term exposure to ozone and PM2.5 with mortality.
Effects estimates were robust to confounding adjustment
of seasonality but sensitive to lag structures. Statistically
significant central estimates of the increase in mortality
associated with a 10 ppb increase the 1-hr maximum
ozone ranged from 0.56% (95% CI 0.27-0.84) to 2.14%
(95% CI 0.95-3.98). For PM2.5, significant central esti-
mates of the increases in mortality ranged from 0.91%
(95% CI 0.08-1.73) to 3.17% (95% CI 0.61-5.72). Al-
though estimated effects are relatively weak, they repre-
sent a substantial health burden given the size of the ex-
posed population. Based on these results, it is reasonable
to assume that reductions in air pollution would likely
lead to health benefits by reducing premature mortality.
REFERENCES
[1] World Health Organization, “Tackling the Global Clean
Air Challenge,” 2011.
http://www.who.int/mediacentre/news/releases/2011/air_
pollution_20110926/
[2] J. Evans, A. van Donkelaar, R. Martin, R. T. Burnett, D.
Rainham, N. Birkett and D. Krewski, “Estimates of Glo-
bal Mortality Due to Particulate Air Pollution Using Sat-
ellite Imagery,” Environmental Research, Vol. 120, 2013,
pp. 33-42. http://dx.doi.org/10.1016/j.envres.2012.08.005
[3] Canadian Medical Association, “No Breathing Room—
National Cost of Illness Study,” Summary Report, 2008.
http://www.cma.ca/multimedia/CMA/Content_Images/In
side_cma/Office_Public_Health/ICAP/CMA_ICAP_sum
_e.pdf
[4] S. Shang, Z. Sun, J. Cao, X. Wang, L. Zhong, X. Bi, H.
Li, W. Liu, T. Zhu and W. Huang, “Systematic Review of
Chinese Studies of Short-Term Exposure to Air Pollution
and Daily Mortality,” Environment International, Vol. 54,
2013, pp. 100-111.
http://dx.doi.org/10.1016/j.envint.2013.01.010
[5] A. Zanobetti, M. Franklin, P. Koutrakis and J. Schwartz,
“Fine Particulate Air Pollution and Its Components in
Association with Cause-Specific Emergency Admissions,”
Environmental Health, Vol. 8, 2009.
[6] E. Samoli, M. Stafoggia, S. Rodopoulou, B. Ostro, C.
Declercq, E. Alessandrini, J. Díaz, A. Karanasiou, A. G.
Kelessis, A. Le Tertre, G. Randi, C. Scarinzi, S. F. Zau-
li-Sajani and F. Forastiere, “Associations between Fine
and Coarse Particles and Mortality in Mediterranean Cit-
ies: Results from the MED-PARTICLES Project,” Envi-
ronmental Health Perspectives, Vol. 121, 2013, pp. 932-
938.
[7] R. D. Peng, E. Samoli, L. Pham, F. Dominici, G. Tou-
loumi, T. Ramsay, R. T. Burnett, D. Krewski, A. Le Ter-
tre, A. Cohen, R. W. Atkinson, H. R. Anderson, K. Kat-
souyani and J. M. Samet, “Acute Effects of Ambient
Ozone on Mortality in Europe and North America: Re-
sults from the APHENA Study,” Air Quality, Atmosphere
& Health, Vol. 6, No. 2, 2013, pp. 445-453.
http://dx.doi.org/10.1007/s11869-012-0180-9
[8] H. R. Anderson, R. Atkinson, J. L. Peacock, L. Martson
and K. Konstantinou, “Meta-Analysis of Times Series
Studies and Panel Studies of Particulate Matter and Ozone,”
Report of a WHO Task Group, World Health Organiza-
tion, Copenhagen, 2004.
[9] M. L. Bell, A. McDermott, S. L. Zeger, J. M. Samet and
F. Dominici, “Ozone and Short-Term Mortality in 95 US
Urban Communities, 1987-2000,” Jama-Journal of the
American Medical Association, Vol. 292, No. 19, 2004,
pp. 2372-2378.
http://dx.doi.org/10.1001/jama.292.19.2372
[10] M. L. Bell and F. Dominici, “Effect Modification by
Community Characteristics on the Short-Term Effects of
Ozone Exposure and Mortality in 98 US Communities,”
American Journal of Epidemiology, Vol. 167, 2008, pp.
986-997. http://dx.doi.org/10.1093/aje/kwm396
[11] A. Gryparis, B. Forsberg, K. Katsouyanni, A. Analitis, G.
Touloumi, J. Schwartz, E. Samoli, S. Medina, H. R. An-
derson, E. M. Niciu, H. E. Wichmann, B. Kriz, M. Kos-
nik, J. Skorkovsky, J. M. Vonk and Z. Dortbudak, “Acute
Effects of Ozone on Mortality from the ‘Air Pollution and
Health: A European Approach’ Project,” American Jour-
nal of Respiratory and Critical Care Medicine, Vol. 170,
2004, pp. 1080-1087.
http://dx.doi.org/10.1164/rccm.200403-333OC
[12] R. Klemm and R. Mason, “Replication of Reanalysis of
Harvard Six-City Mortality Study,” In: Revised Analyses
of Time Series Studies of Air Pollution and Health, Spe-
cial Report, Health Effects Institute, Boston, 2003, pp.
165-172.
[13] B. Ostro, R. Broadwin, S. Green, W. Y. Feng and M.
Lipsett, “Fine Particulate Air Pollution and Mortality in
Nine California Counties: Results from CALFINE,” En-
vironmental Health Perspectives, Vol. 114, 2006, pp. 29-
33. http://dx.doi.org/10.1289/ehp.8335
[14] S. Schwartz, “Daily Deaths Associated with Air Pollution
in Six US Cities and Short-Term Mortality Displacement
in Boston,” Revised Analyses of Time Series Studies of
Air Pollution and Health, Special Report, Health Effects
Institute, Boston, 2003, pp. 219-226.
[15] R. Simpson, G. Williams, A. Petroeschevsky, T. Best, G.
Morgan, L. Denison, A. Hinwood, G. Neville and A.
Neller, “The Short-Term Effects of Air Pollution on Daily
Mortality in Four Australian Cities”, Australian and New
Zealand Journal of Public Health, Vol. 29, 2005, pp.
205-212.
http://dx.doi.org/10.1111/j.1467-842X.2005.tb00758.x
[16] D. M. Stieb, S. Judek and R. T. Burnett, “Meta-Analysis
of Time-Series Studies of Air Pollution and Mortality:
Effects of Gases and Particles and the Influence of Cause
of Death, Age, and Season,” Journal of the Air & Waste
Management Association, Vol. 52, No. 4, 2002, pp. 470-
484. http://dx.doi.org/10.1080/10473289.2002.10470794
[17] Y. Chen, Q. Y. Yang, D. Krewski, Y. Shi, R. T. Burnett
and K. McGrail, “Influence of Relatively Low Level of
Particulate Air Pollution on Hospitalization for COPD in
Elderly People,” Inhalation Toxicology, Vol. 16, 2004, pp.
21-25. http://dx.doi.org/10.1080/08958370490258129
[18] M. S. Goldberg, R. T. Burnett, J. C. Bailar, J. Brook, Y.
Bonvalot, R. Tamblyn, R. Singh, M. F. Valois and R. Vin-
Open Access JEP

Short-Term Effects of Ozone and PM2.5 on Mortality in 12 Canadian Cities 30
cent, “The Association between Daily Mortality and Am-
bient Air Particle Pollution in Montreal, Quebec 2.
Cause-Specific Mortality,” Environmental Research, Vol.
86, No. 1, 2001, pp. 26-36.
http://dx.doi.org/10.1006/enrs.2001.4243
[19] S. Parodi, M. Vercelli, E. Garrone, V. Fontana and A.
Izzotti, “Ozone Air Pollution and Daily Mortality in
Genoa, Italy between 1993 and 1996,” Public Health, Vol.
119, No. 9, 2005, pp. 844-850.
http://dx.doi.org/10.1016/j.puhe.2004.10.007
[20] R. D. Peng, F. Dominici and T. A. Louis, “Model Choice
in Time Series Studies of Air Pollution and Mortality,”
Journal of the Royal Statistical Society Series A—Statis-
tics in Society, Vol. 169, No. 2, 2006, pp. 179-198.
http://dx.doi.org/10.1111/j.1467-985X.2006.00410.x
[21] R. T. Burnett, M. Smith-Doiron, D. Stieb, M. E. Raizenne,
J. R. Brook, R. E. Dales, J. A. Leech, S. Cakmak and D.
Krewski, “Association between Ozone and Hospitaliza-
tion for Acute Respiratory Diseases in Children Less than
2 Years of Age,” American Journal of Epidemiology, Vol.
153, No. 5, 2001, pp. 444-452.
http://dx.doi.org/10.1093/aje/153.5.444
[22] B. B. Jalaludin, T. Chey, B. I. O’Toole, W. T. Smith, A.
G. Capon and S. R. Leeder, “Acute Effects of Low Levels
of Ambient Ozone on Peak Expiratory Flow Rate in a
Cohort of Australian Children,” International Journal of
Epidemiology, Vol. 29, No. 3, 2000, pp. 549-557.
http://dx.doi.org/10.1093/ije/29.3.549
[23] C. A. Lin, M. A. Martins, S. C. L. Farhat, C. A. Pope, G.
M. S. Conceicao, V. M. Anastacio, M. Hatanaka, W. C.
Andrade, W. R. Hamaue, G. M. Bohm and P. H. N. Sal-
diva, “Air Pollution and Respiratory Illness of Children in
Sao Paulo, Brazil,” Paediatric and Perinatal Epidemiol-
ogy, Vol. 13, No. 4, 1999, pp. 475-488.
http://dx.doi.org/10.1046/j.1365-3016.1999.00210.x
[24] A. Peters, D. W. Dockery, J. Heinrich and H. E. Wich-
mann, “Short-Term Effects of Particulate Air Pollution on
Respiratory Morbidity in Asthmatic Children,” European
Respiratory Journal, Vol. 10, 1997, pp. 872-879.
[25] Q. Y. Yang, Y. Chen, Y. Shi, R. T. Burnett, K. McGrail
and D. Krewski, “Association between Ozone and Respi-
ratory Admissions among Children and the Elderly in
Vancouver, Canada,” Inhalation Toxicology, Vol. 15, No.
13, 2003, pp. 1297-1308.
http://dx.doi.org/10.1080/08958370390241768
[26] Health Canada, “Canada-United States Border Air Qual-
ity Strategy: Great Lakes Basin Airshed Management
Framework,” 2006.
http://www.hc-sc.gc.ca/ewh-semt/air/out-ext/great_lakes-
grands_lacs-eng.php#exposure
[27] T. F. Bateson, B. A. Coull, B. Hubbell, K. Ito, M. Jerrett,
T. Lumley, D. Thomas, S. Vedal and M. Ross, “Panel
Discussion Review: Session Three—Issues Involved in
Interpretation of Epidemiologic Analyses—Statistical Mo-
deling,” Journal of Exposure Science and Environmental
Epidemiology, Vol. 17, 2007, pp. S90-S96.
http://dx.doi.org/10.1038/sj.jes.7500631
[28] F. Dominici, A. McDermott and T. J. Hastie, “Improved
Semiparametric Time Series Models of Air Pollution and
Mortality,” Journal of the American Statistical Associa-
tion, Vol. 99, 2004, pp. 938-948.
http://dx.doi.org/10.1198/016214504000000656
[29] T. O. Ramsay, R. T. Burnett and D. Krewski, “The Effect
of Concurvity in Generalized Additive Models Linking
Mortality to Ambient Particulate Matter,” Epidemiology,
Vol. 14, 2003, Vol. 18-23.
[30] J. M. Samet, F. Dominici, A. McDermott and S. L. Zeger,
“New Problems for an Old Design: Time Series Analyses
of Air Pollution and Health,” Epidemiology, Vol. 14,
2003, pp. 11-12.
http://dx.doi.org/10.1097/00001648-200301000-00006
[31] Health Effects Institute, “Revised Analyses of Time-
Series Studies of Air Pollution and Health—Special Re-
port,” Health Effects Institute, Boston, 2003.
[32] K. Katsouyani, J. M. Samet, H. R. Anderson, R. W. At-
kinson, A. Le Tertre, S. Medina, E. Samoli, G. Touloumi,
R. T. Burnett, D. Krewski, T. Ramsay, F. Dominici, R. D.
Peng, J. Schwartz and A. Zanobetti, “Air Pollution and
Health: A European and North American Approach
(APHENA),” Health Effects Institute, 2009.
[33] C. S. Berkey, D. C. Hoaglin, A. Antczak-Bouckoms, F.
Mosteller and G. A. Colditz, “Meta-Analysis of Multiple
Outcomes by Regression with Random Effects,” Statistics
in Medicine, Vol. 17, 1998, pp. 2537-2550.
http://dx.doi.org/10.1002/(SICI)1097-0258(19981130)17:
22<2537::AID-SIM953>3.0.CO;2-C
[34] T. B. Huedo-Medina, J. Sanchez-Meca, F. Marin-Marti-
nez and J. Botella, “Assessing Heterogeneity in Meta-
Analysis: Q Statistic or I-2 Index?” Psychological Meth-
ods, Vol. 11, No. 2, 2006, pp. 193-206.
http://dx.doi.org/10.1037/1082-989X.11.2.193
[35] R Development Core Team, “R: A Language and Envi-
ronment for Statistical Computing,” R Foundation for
Statistical Computing, Vienna, 2007.
[36] G. Touloumi, R. Atkinson, A. Le Tertre, E. Samoli, J.
Schwartz, C. Schindler, J. M. Vonk, G. Rossi, M. Saez, D.
Rabszenko and K. Katsouyanni, “Analysis of Health
Outcome Time Series Data in Epidemiological Studies,”
Environmetrics, Vol. 15, No. 2, 2004, pp. 101-117.
http://dx.doi.org/10.1002/env.623
[37] F. Ballester, P. Rodriguez, C. Iniguez, M. Saez, A.
Daponte, I. Galan, M. Taracido, F. Arribas, J. Bellido, F.
B. Cirarda, A. Canada, J. J. Guillen, F. Guillen-Grima, E.
Lopez, S. Perez-Hoyos, A. Lertxundi and S. Toro, “Air
Pollution and Cardiovascular Admissions Association in
Spain: Results within the EMECAS Project,” Journal of
Epidemiology and Community Health, Vol. 60, No. 4, 2006,
pp. 328-336. http://dx.doi.org/10.1136/jech.2005.037978
[38] F. Dominici, R. D. Peng, M. L. Bell, L. Pham, A. Mc-
Dermott, S. L. Zeger and J. M. Samet, “Fine Particulate
Air Pollution and Hospital Admission for Cardiovascular
and Respiratory Diseases,” Journal of the American Medi-
cal Association, Vol. 295, No. 10, 2006, pp. 1127-1134.
http://dx.doi.org/10.1001/jama.295.10.1127
[39] L. J. Welty and S. L. Zeger, “Are the Acute Effects of
Particulate Matter on Mortality in the National Morbidity,
Mortality, and Air Pollution Study the Result of Inadequate
Control for Weather and Season? A Sensitivity Analysis
Open Access JEP

Short-Term Effects of Ozone and PM2.5 on Mortality in 12 Canadian Cities 31
Using Flexible Distributed Lag Models,” American Journal
of Epidemiology, Vol. 162, No. 1, 2005, pp. 80-88.
http://dx.doi.org/10.1093/aje/kwi157
[40] World Health Organization, “Health Aspects of Air Pollu-
tion with Particulate Matter, Ozone and Nitrogen Dioxide,”
Report on a WHO Working Group. WHO Regional Office
for Europe, Copenhagen, 13-15 January 2003, pp. 1-92.
http://www.euro.who.int/__data/assets/pdf_file/0005/112
199/E79097.pdf
[41] A. L. F. Braga, A. Zanobetti and J. Schwartz, “The Lag
Structure between Particulate Air Pollution and Respira-
tory and Cardiovascular Deaths in 10 US Cities,” Journal
of Occupational and Environmental Medicine, Vol. 43,
No. 11, 2001, pp. 927-933.
http://dx.doi.org/10.1097/00043764-200111000-00001
[42] EPA, “Fourth External Review for Air Quality Criteria
for Particulate Matter (Draft),” 2003.
[43] Y. H. Zhang, W. Huang, S. J. London, G. X. Song, G. H.
Chen, L. L. Jiang, N. Q. Zhao, B. H. Chen and H. D. Kan,
“Ozone and Daily Mortality in Shanghai, China,” Envi-
ronmental Health Perspectives, Vol. 114, No. 8, 2006, pp.
1227-1232. http://dx.doi.org/10.1289/ehp.9014
[44] F. J. Kelly, “Oxidative Stress: Its Role in Air Pollution
and Adverse Health Effects,” Occupational and Environ-
mental Medicine, Vol. 60, No. 8, 2003, pp. 612-616.
http://dx.doi.org/10.1136/oem.60.8.612
[45] J. Schwartz, D. W. Dockery and L. M. Neas, “Is Daily Mor-
tality Associated Specifically with Fine Particles?” Journal
of the Air & Waste Management Association, Vol. 46, No.
10, 1996, pp. 927-939.
http://dx.doi.org/10.1080/10473289.1996.10467528
[46] H. Kim, Y. Kim and Y. C. Hong, “The Lag-Effect Pattern
in the Relationship of Particulate Air Pollution to Daily
Mortality in Seoul, Korea,” International Journal of Bio-
meteorology, Vol. 48, No. 1, 2003, pp. 25-30.
http://dx.doi.org/10.1007/s00484-003-0176-0
[47] S. Ahmad, A. Ahmad, G. McConville, B. K. Schneider, C.
B. Allen, R. Manzer, R. J. Mason and C. W. White,
“Lung Epithelial Cells Release ATP during Ozone Expo-
sure: Signaling for Cell Survival,” Free Radical Biology
and Medicine, Vol. 39, No. 2, 2005, pp. 213-226.
http://dx.doi.org/10.1016/j.freeradbiomed.2005.03.009
[48] N. Kunzli, M. Jerrett, W. J. Mack, B. Beckerman, L.
LaBree, F. Gilliland, D. Thomas, J. Peters and H. N.
Hodis, “Ambient Air Pollution and Atherosclerosis in Los
Angeles,” Environmental Health Perspectives, Vol. 113,
No. 2, 2005, pp. 201-206.
http://dx.doi.org/10.1289/ehp.7523
[49] I. S. Mudway, N. Stenfors, A. Blomberg, R. Helleday, C.
Dunster, S. L. Marklund, A.J. Frew, T. Sandstrom and F.
J. Kelly, “Differences in Basal Airway Antioxidant Con-
cen-Trations Are Not Predictive of Individual Respon-
siveness to Ozone: A Comparison of Healthy and Mild
Asthmatic Subjects,” Free Radical Biology and Medicine,
Vol. 31, No. 8, 2001, pp. 962-974.
http://dx.doi.org/10.1016/S0891-5849(01)00671-2
[50] R. D. Brook, B. Franklin, W. Cascio, Y. L. Hong, G. Ho-
ward, M. Lipsett, R. Luepker, M. Mittleman, J. Samet, S.
C. Smith and I. Tager, “Air Pollution and Cardiovascu-
lar Disease—A Statement for Healthcare Professionals
from the Expert Panel on Population and Prevention Science
of the American Heart Association,” Circulation, Vol. 109,
No. 21, 2004, pp. 2655-2671.
http://dx.doi.org/10.1161/01.CIR.0000128587.30041.C8
[51] W. P. Watkinson, M. J. Campen, J. P. Nolan and D. L. Costa,
“Cardiovascular and Systemic Responses to Inhaled Pollut-
ants in Rodents: Effects of Ozone and Particulate Matter,”
Environmental Health Perspectives, Vol. 109, No. S4,
2001, pp. 539-546.
[52] P. S. Gilmour, D. M. Brown, T. G. Lindsay, P. H. Beswick,
W. MacNee and K. Donaldson, “Adverse Health Effects of
PM(10) Particles: Involvement of Iron in Generation of
Hydroxyl Radical,” Occupational and Environmental Me-
dicine, Vol. 53, No. 12, 1996, pp. 817-822.
http://dx.doi.org/10.1136/oem.53.12.817
[53] R. J. Pritchard, A. J. Ghio, J. R. Lehmann, D. W. Winsett,
J. S. Tepper, P. Park, M. I. Gilmour, K. L. Dreher and D.
L. Costa, “Oxidant Generation and Lung Injury after Par-
ticulate Air Pollutant Exposure Increase with the Concen-
trations of Associated Metals,” Inhalation Toxicology,
Vol. 8, No. 5, 1996, pp. 457-477.
http://dx.doi.org/10.3109/08958379609005440
[54] E. W. Spannhake, S. P. M. Reddy, D. B. Jacoby, X. Y. Yu,
B. Saatian and J. Y. Tian, “Synergism between Rhinovirus
Infection and Oxidant Pollutant Exposure Enhances Air-
way Epithelial Cell Cytokine Production,” Environmental
Health Perspectives, Vol. 110, No. 7, 2002, pp. 665-670.
[55] J. T. Zelikoff, M. W. Frampton, M. D. Cohen, P. E. Mor-
row, M. Sisco, Y. Tsai, M. J. Utell and R. B. Schlesinger,
“Effects of Inhaled Sulfuric Acid Aerosols on Pulmonary
Immunocompetence: A Comparative Study in Humans
and Animals,” Inhalation Toxicology, Vol. 9, No. 8, 1997,
pp. 731-752. http://dx.doi.org/10.1080/089583797197971
[56] Q. H. Sun, A. X. Wang, X. M. Jin, A. Natanzon, D. Du-
quaine, R. D. Brook, J. G. S. Aguinaldo, Z. A. Fayad, V.
Fuster, M. Lippmann, L. C. Chen and S. Rajagopalan,
“Long-Term Air Pollution Exposure and Acceleration of
Atherosclerosis and Vascular Inflammation in an Animal
Model,” Journal of the American Medical Association,
Vol. 294, No. 23, 2005, pp. 3003-3010.
http://dx.doi.org/10.1001/jama.294.23.3003
[57] A. Le Tertre, S. Medina, E. Samoli, B. Forsberg, P. Mi-
chelozzi, A. Boumghar, J. M. Vonk, A. Bellini, R. Atkinson,
J. G. Ayres, J. Sunyer, J. Schwartz and K. Katsouyanni,
“Short-Term Effects of Particulate Air Pollution on Car-
diovascular Diseases in Eight European Cities,” Journal
of Epidemiology and Community Health, Vol. 56, No. 10,
2002, pp. 773-779.
http://dx.doi.org/10.1136/jech.56.10.773
[58] M. L. Bell, J. M. Samet and F. Dominici, “Time-Series
Studies of Particulate Matter,” Annual Review of Public
Health, Vol. 25, 2004, pp. 247-280.
http://dx.doi.org/10.1146/annurev.publhealth.25.102802.1
24329
[59] M. S. Goldberg, R. T. Burnett and J. R. Brook, “Counter-
point: Time-Series Studies of Acute Health Events and
Environmental Conditions Are Not Confounded by Per-
sonal Risk Factors,” Regulatory Toxicology and Phar-
macology, Vol. 51, No. 2, 2008, pp. 141-147.
Open Access JEP

Short-Term Effects of Ozone and PM2.5 on Mortality in 12 Canadian Cities
Open Access JEP
32
http://dx.doi.org/10.1016/j.yrtph.2008.03.006
[60] N. A. Janssen, J. J. de Hartog, G. Hoek, B. Brunekreef, T.
Lanki, K. L. Timonen and J. Pekkanen, “Personal Exposure
to Fine Particulate Matter in Elderly Subjects: Relation
between Personal, Indoor, and Outdoor Concentrations,”
Journal of the Air & Waste Management Association, Vol.
50, No. 7, 2000, pp. 1133-1143.
http://dx.doi.org/10.1080/10473289.2000.10464159
[61] D. Kim, A. Sass-Kortsak, J. T. Purdham, R. E. Dales and
J. R. Brook, “Associations between Personal Exposures
and Fixed-Site Ambient Measurements of Fine Particulate
Matter, Nitrogen Dioxide, and Carbon Monoxide in To-
ronto, Canada,” Journal of Exposure Science and Environ-
mental Epidemiology, Vol. 16, No. 2, 2006, pp. 172-183.
http://dx.doi.org/10.1038/sj.jea.7500446
[62] G. Hoek, B. Brunekreef, S. Goldbohm, P. Fischer and P.
A. van den Brandt, “Association between Mortality and
Indicators of Traffic-Related Air Pollution in the Nether-
lands: A Cohort Study,” Lancet, Vol. 360, No. 9341, 2002,
pp. 1203-1209.
http://dx.doi.org/10.1016/S0140-6736(02)11280-3
[63] M. Jerrett, R. T. Burnett, R. J. Ma, C. A. Pope, D. Krewski,
K. B. Newbold, G. Thurston, Y. L. Shi, N. Finkelstein, E. E.
Calle and M. J. Thun, “Spatial Analysis of Air Pollution
and Mortality in Los Angeles,” Epidemiology, Vol. 16,
No. 6, 2005, pp. 727-736.
http://dx.doi.org/10.1097/01.ede.0000181630.15826.7d
[64] S. L. Zeger, D. Thomas, F. Dominici, J. M. Samet, J.
Schwartz, D. Dockery and A. Cohen, “Exposure Measure-
ment Error in Time-Series Studies of Air Pollution: Con-
cepts and Consequences,” Environmental Health Perspec-
tives, Vol. 108, No. 5, 2000, pp. 419-426.
[65] E. Samoli, R. D. Peng, T. Ramsay, G. Touloumi, F. Do-
minici, R. W Atkinson, A. Zanobetti, A. Le Tertre, H. R.
Anderson, J. Schwartz, A. Cohen, D. Krewski, J. M. Sa-
met and K. Katsouyani, “What Is the Impact of System-
atically Missing Exposure Data on Air Pollution Health
Effect Estimates?”
[66] P. Bellini, M. Baccini, A. Biggeri and B. Terracini, “The
Meta-Analysis of the Italian Studies on Short-Term Ef-
fects of Air Pollution (MISA): Old and New Issues on the
Interpretation of the Statistical Evidences,” Environmet-
rics, Vol. 18, No. 3, 2007, pp. 219-229.
http://dx.doi.org/10.1002/env.836
[67] M. Goldberg, “On the Interpretation of Epidemiological
Studies of Ambient Air Pollution,” Journal of Exposure
Science and Environmental Epidemiology, Vol. 17, No.
S2, 2007, pp. S66-S70.
http://dx.doi.org/10.1038/sj.jes.7500629
[68] R. T. Burnett, J. R. Brook, W. T. Yung, R. E. Dales and
D. Krewski, “Association between Ozone and Hospitali-
zation for Respiratory Diseases in 16 Canadian Cities,”
Environmental Research, Vol. 72, No. 1, 1997, pp. 24-31.
http://dx.doi.org/10.1006/enrs.1996.3685
[69] D. M. Stieb, R. C. Beveridge, B. H. Rowe, S. D. Walter
and S. Judek, “Assessing Diagnostic Classification in an
Emergency Department: Implications for Daily Time Se-
ries Studies of Air Pollution,” American Journal of Epi-
demiology, Vol. 148, No. 7, 1998, pp. 666-670.
http://dx.doi.org/10.1093/aje/148.7.666
[70] M. J. Daniels, F. Dominici, J. M. Samet and S. L. Zeger,
“Estimating Particulate Matter-Mortality Dose-Response
Curves and Threshold Levels: An Analysis of Daily Time-
Series for the 20 Largest US Cities,” American Journal of
Epidemiology, Vol. 152, No. 5, 2000, pp. 397-406.
http://dx.doi.org/10.1093/aje/152.5.397
[71] J. Schwartz, “Assessing Confounding, Effect Modifica-
tion, and Thresholds in the Association between Ambient
Particles and Daily Deaths,” Environmental Health Per-
spectives, Vol. 108, No. 6, 2000, pp. 563-568.