Current levels of ambient air pollution in the

Americas, Europe, and Australia have been

associated with increased respiratory and cardiac morbidity and mortality in the general

population (Bascom et al. 1996; Dockery and

Pope 2002; Morgan et al. 1998). The primary

focus has usually been either adults or children

of all ages combined, but research is needed to

know whether the results are generalizable to

more narrowly defined age groups which have

special characteristics. Associations between

ambient air pollution and pregnancy outcomes,

including preterm birth, low birth weight, and

intrauterine growth restriction (IUGR), have

been studied in several countries (Ritz et al.

2002; Wang et al. 1997). Liu et al. (2003)

found that maternal exposure to increased concentration of gaseous air pollutants, sulfur dioxide, nitrogen dioxide, and carbon monoxide

during pregnancy were positively associated

with preterm birth and IUGR. The number of

SIDS (sudden infant death syndrome) deaths

increased on days of high gaseous air pollution

in Canada (Dales et al. 2004). Neonates, 0–28

days of age, have a unique spectrum of morbidity including clinical presentations such as

“acute life-threatening events” and “respiratory

distress syndrome.” To understand better the

contribution of pollution to neonatal morbidity,

we used time-series studies to test the hypothesis that gaseous air pollutants are associated

with hospital admissions for respiratory diseases

among neonates in 11 large Canadian cities.

We compared hospitalization rates on higher

and lower air pollution days for the same population. As stated by Peng et al. (2006) the

results from multicity time-series studies are

robust against alternative methodologies for

confounding adjustment. To bias the results, a

variable would need to be associated with daily

changes in air pollution and also be a risk factor

for neonatal hospitalization for asthma, making

confounding unlikely.

Methods

The study population. The study population

was all emergency admissions to hospital for

respiratory disease in patients from birth to the

age of 27 days between 1 January 1986 and 31

December 2000 in 11 of the largest Canadian

cities from the Atlantic to Pacific coasts:

Calgary, Edmonton, Halifax, London,

Hamilton, Ottawa, Saint John, Toronto,

Vancouver, Windsor, and Winnipeg. The hospitalization data were provided by the

Canadian Institute for Health Information

(http://secure.cihi.ca/cihiweb/splash.html),

which collects information on all urgent and

emergency admissions in Canada; the availability of this information is made possible by the

fact that acute care general medical hospitals in

Canada are publicly funded. There were a total

of 9,542 hospital discharge records for cases in

which the principal reason for hospitalization

was a respiratory disorder described by the

International Classification of Disease, 9th

Revision (ICD-9; World Health Organization

1975) code: asphyxia (799.0), respiratory failure (799.1), dyspnea and respiratory abnormalities (786.0), respiratory distress syndrome

(769), unspecified birth asphyxia in live-born

infant (768.9), other respiratory problems after

birth (770.8), and pneumonia (486).

Meteorologic and air pollution data. For

each city, the following daily data were provided by Environment Canada and the

National Air Pollution Monitoring System

(http://www.mb.ec.gc.ca/index.en.html):

mean, maximum, and minimum temperature; mean relative humidity; maximum 24-

hr change in barometric pressure; and daily

measurements of SO2, NO2, CO, and ozone

throughout the 15-year study period. Most

cities had several monitoring stations.

Averaging data over all monitors within each

city created daily summary pollution exposure measures. Daily average values for NO2,

SO2, CO, and O3 were used in our analysis.

Twenty-four–hour averaged particulate matter < 10 μm in median aerodynamic diameter

(PM10) and sulfates (SO4) were measured

every sixth day.

Statistical Methods

The air pollution, neonatal respiratory hospital admission, and weather variables, measured

over the course of the study, were each

expressed as a time series. We correlated daily

variations in the number of neonatal hospitalization with daily variations in ambient

concentrations of air pollutants by using a random-effects regression model for count data

(Burnett et al. 1997). Because the response of

interest is counts, the residual variation was

assumed to be proportional to the expected

response, accommodating overdispersion or

underdispersion relative to Poisson variation.

Neonatal respiratory admissions, air pollution,

and weather can vary seasonally and may have

other unexplained time trends that can cause

confounding (Peng et al. 2006). We adjusted

for temporal trends in neonatal respiratory

admissions, including day of the week, and

effects of weather on neonatal respiratory

admissions using piecewise constant functions

that varied by 30, 90, 180, 270, and 365 days

throughout the study period. This approach,

used to address the low counts of neonatal

respiratory admissions in some cities, has

been previously used by Dales et al. (2004) to

study SIDS.

We then selected a model with time

period that either minimized the Akaike

Information Criterion (AIC; Akaike 1973),

Environmental Health Perspectives • VOLUME 114 | NUMBER 11 | November 2006 1751

Research | Children’s Health

Address correspondence to R. Dales, The Ottawa

Hospital (General Campus), 501 Smyth Rd., Box

211, Ottawa, Ontario K1H 8L6 Canada.

Telephone: (613) 737-8198. Fax: (613) 739-6266.

E-mail: rdales@ohri.ca

The authors declare they have no competing

financial interests.

Received 26 January 2006; accepted 3 August

2006.

Gaseous Air Pollutants and Hospitalization for Respiratory Disease in the

Neonatal Period

Robert E. Dales,1,2,3 Sabit Cakmak,3 and Marc Smith Doiron3 

1University of Ottawa, Ottawa, Ontario, Canada; 2Ottawa Hospital Research Institute, Ottawa, Ontario, Canada; 3Health Canada, Ottawa,

Ontario, Canada

OBJECTIVE: Current levels of ambient air pollution are associated with morbidity and mortality in

the general population. To determine the influence of gaseous air pollutants on neonatal respiratory

morbidity, we tested the association between daily respiratory hospitalizations and daily concentrations of ambient air pollution gases: ozone, carbon monoxide, sulfur dioxide, and nitrogen dioxide,

in 11 large Canadian cities.

STUDY DESIGN: Daily time-series analyses were employed and results were adjusted for day of the

week, temperature, barometric pressure, and relative humidity. 

RESULTS: The percent increases in hospitalization associated with an increase in air pollution equivalent to its interquartile range were 3.35 [95% confidence interval (CI), 1.73–4.77] for O3, 2.85

(95% CI, 1.68–4.02) for NO2, 1.66 (95% CI, 0.63–2.69) for SO2, and 1.75 (95% CI, 0.48–3.02)

for CO. The independent effect of all pollutants combined was 9.61% (95% CI, 4.52–14.7%). 

CONCLUSION: Our results suggest that neonates are experiencing adverse effects of air pollution at

current levels in Canada, and that accounts for a significant proportion of hospitalizations in this

subgroup. 

KEY WORDS: carbon monoxide, hospital admission, infants, ozone, sulfur dioxide. Environ Health

Perspect 114:1751–1754 (2006). doi:10.1289/ehp.9044 available via http://dx.doi.org/ [Online

3 August 2006]
or maximized the evidence that the model

residuals did not display any type of structure,

including serial correlation, using Bartlett’s

test. We examined serial correlation structure

in the residuals after fitting models with functions that were constant over time intervals.

The above steps were implemented separately

for each city. 

Using S-PLUS (version 6.2; Insightful,

Seattle, WA, USA), we tested the association

between weather and respiratory admissions

with a nonlinear model and natural spline functions of weather variables to remove unwanted

temporal trends. These variables included daily

minimum and maximum temperature, mean

daily temperature, 24-hr change in barometric

pressure, and mean relative humidity. Three

lags (0, 1, and 2 days) were examined for each

of these weather variables. The model containing the latter three weather variables minimized the AIC and was selected as the

predictive weather model for daily neonatal

respiratory hospitalization. Finally, air pollutants, with lags, were added to the model containing weather variables. We tested time lags

between daily pollution concentration and

daily neonatal respiratory admissions of 0–5

days. Results for the lag associated with the

largest T ratio—the ratio of percentage increase

in admissions to the standard error. 

Once the final model was selected, the percent change in daily hospitalization and the

accompanying standard errors in relative risks

were generated for each city. The results for

each city were pooled. We calculated the estimates of the pooled effect size mean ± SE by

using a fixed- or random-effects model. This

weights the individual city effects by the

inverse of the between and within-city variance. We used a random-effects model whenever the between-city variance was > 0, even if

the heterogeneity among effect-size estimates

for individual cities was not statistically significant. We assessed the presence of statistical heterogeneity in effect size between cities using the

chi-square statistic. Pooled estimates were

expressed as the percentage increase in neonatal

hospitalization associated with an increase in

the concentration of air pollutants equivalent

to the interquartile range among all cities

(Burnett et al. 1995). As stated by Peng et al.

(2006), the results from multicity time-series

studies are robust against alternative methodologies for confounding adjustment. To bias

the results, a variable would need to be associated with daily changes in air pollution and

also be a risk factor for neonatal hospitalization

for asthma, making confounding unlikely.

Results

The frequency of occurrence of the ICD codes

used in this study, presented in Table 1, was

relatively low over the 15-year period of observation, with respiratory distress syndrome being

the most common of the respiratory diagnoses

studied and asphyxia the least common.

Details on population size and the mean

of air pollution and meteorologic data are

presented in Table 2. O3 concentrations

ranged from 13.3 ppb in Vancouver to 23.1

ppb in Saint John, with a populationweighted average of 17.0. NO2 concentrations ranged from 9.2 ppb in Saint John to

25.6 ppb in Calgary, with a pooled average of

21.8. SO2 concentrations ranged from 1.2

ppb in Winnipeg to 10.1 in Halifax, with a

pooled average of 4.3 ppb, and CO concentrations ranged from 0.4 in London and to

1.2 ppm in Toronto, with a pooled average of

1.0 ppm. 

Pairwise Pearson correlations between

pollutants were quite variable between cities.

Correlations between NO2, SO2, and CO

were all positive although they varied from

0.13 to 0.76 (Table 3). Correlations between

PM10 and O3 and other pollutants varied

from negative to positive, –0.55 to 0.71. 

We tested the association between air pollutants and hospitalization using lags of 0–5

days. Averaged over cities, the mean lags providing the greatest association were 2 days for

O3, 2 days for SO2, 1 day for NO2, and 1.8

days for CO. Based on minimization of the

AIC, temperature was included in the models

for Edmonton and Halifax. Relative humidity

on the same day was included in all models

Dales et al.

1752 VOLUME 114 | NUMBER 11 | November 2006 • Environmental Health Perspectives

Table 1. Frequency of occurrence of respiratory diagnoses in 11 Canadian cities, 1 January 1986 to 31 December 2000. 

1996 Census Dyspnea and Respiratory Unspecified Other respiratory

population Respiratory respiratory distress birth asphyxia problems All respiratory

City (× 105) Asphyxia failure abnormalities syndrome in live born after birth Pneumonia diagnoses

Calgary 7.68 2 13 209 272 19 270 26 811

Edmonton 6.16 0 9 215 143 53 268 54 742

Halifax 1.80 0 1 46 1 0 14 8 70

Hamilton 2.45 1 6 181 107 4 76 27 402

London 1.25 1 11 82 21 4 110 24 253

Ottawa 9.39 1 14 173 99 26 104 32 449

Saint John 1.02 0 1 57 1 0 12 3 74

Toronto 23.85 9 22 821 789 189 543 212 2,585

Vancouver 18.32 4 30 555 1,510 82 367 111 2,659

Windsor 1.98 2 4 105 22 3 34 21 191

Winnipeg 6.88 0 15 97 21 11 110 96 350

Total 20 126 2,541 2,986 391 1,908 614 8,586

Census data from Statistics Canada 1996.

Table 2. Population size, 24-hr mean air pollution levels (5th, 95th percentiles), and weather variables for 11 Canadian cities, 1 January 1986 to 31 December 2000. 

O3 NO2 SO2 CO Mean temperature 24-hr change in % Relative

City (ppb) (ppb) (ppb) (ppb) (°C) barometric pressure humidity

Calgary 17.8 (4.7, 32.3) 25.6 (13.3, 41.0) 3.6 (1.0, 8.0) 0.9 (0.4, 2.0) 4.5 (–15.5, 18.4) 0.0 (–1.1, 1.13) 61.2 (37, 86)

Edmonton 17.0 (4.0, 33.1) 24.6 (11.5, 43) 2.7 (0, 6.0) 1.1 (0.4, 2.4) 3.0 (–19.5, 18.1) 0.0 (–1.2, 1.2) 68.6 (47, 88)

Halifax 20.8 (9, 35) 15.1 (3, 28) 10.1 (2, 23) 0.8 (0.3, 1.7) 6.4 (–10.4, 10.3) 0.0 (–1.7, 1.6) 77.5 (54, 96)

Hamilton 19.0 (3.3, 41.8) 20.8 (11, 34) 8.2 (1.7, 17.5) 0.9 (0.2, 1.6) 7.9 (–9.4, 22.8) 0.0 (–1.3, 1.3) 73.5 (50, 95)

London 22.3 (6, 46) 20.0 (8, 35) 3.7 (0, 11) 0.4 (0, 1.2) 7.9 (–9.7, 22.9) 0.0 (–1.25, 1.27) 75.7 (55, 93)

Ottawa 16.4 (4.5, 31.0) 21.2 (7, 38) 3.9 (0, 10) 0.9 (0.2, 1.9) 6.3 (–15, 23) 0.0 (–1.5, 1.5) 69.4 (46, 91)

Saint John 23.1 (10.7, 38.5) 9.2 (2, 21) 8.3 (0.5, 23.5) 0.7 (0.1, 1.7) 5.1 (–12.6, 18.6) 0.0 (–1.6, 1.5) 75.4 (52, 95)

Toronto 18.3 (5, 36.7) 25.1 (14, 39) 4.5 (0.2, 11.3) 1.2 (0.6, 1.9) 8.1 (–9.6, 23.4) 0.0 (–1.4, 1.3) 71.9 (52, 90)

Vancouver 13.3 (3.2, 24.9) 19.0 (11.4, 30.2) 4.6 (1.2, 9.8) 0.9 (6.4, 1.9) 10.5 (1.5, 19.1) 0.0 (–1.1, 1.2) 79.3 (64, 94)

Windsor 18.7 (3, 42) 24.9 (11, 41) 7.6 (1.7, 15.7) 0.8 (0, 1.5) 9.8 (–7.3, 25.1) 0.0 (–1.2, 1.3) 70.8 (51, 91)

Winnipeg 18.5 (6, 34) 15.2 (6, 28) 1.2 (0, 3.5) 0.6 (0.3, 1.0) 3.1 (–22.6, 22.1) 0.0 (–1.4, 1.4) 71.9 (49, 91)

Population weighted average 17.0 21.8 4.3 1.0 7.2 48.1 72.3
except the two aforementioned cities. In addition, relative humidity lagged 1 day was

included for Calgary, Vancouver, Windsor,

and Winnipeg. Change in barometric pressure over the day did not enter the models.

All individual pollutants were associated

with increased respiratory hospitalizations,

with NO2 having the strongest effect whether

or not adjusted for the other gases (Table 4).

The independent effects of each gas remained

stable whether or not adjusted for the effects

of the other gases. The percent increases in

hospitalization associated with an increase in

air pollution equivalent to its interquartile

range were 3.35 [95% confidence interval

(CI), 1.73–4.77] for O3, 2.85 (95% CI,

1.68–4.02) for NO2, 1.66 (95% CI,

0.63–2.69) for SO2, and 1.75 (95% CI,

0.48–3.02) for CO. The total effect of all

gaseous pollutants was a 9.61% (4.52–14.7%)

increase in respiratory admissions, with an

increase in pollutants equivalent to their

interquartile range.

To determine the degree to which PM10

may have confounded the findings, we

restriced the analyses to every sixth day, when

these pollutants were measured during the

years 1986–2000. The gases remained statistically significant, with the largest difference in

effect-size estimate found for the association

between hospitalizations and SO2: 2.06%

(95% CI, 1.04–3.08%) for the daily model

versus 1.41% (95% CI, 0.35–2.47%) for the

model restricted to every sixth day.

Discussion 

Although admissions for respiratory disease are

uncommon in neonates compared with adults,

we were able to detect a significant association

with gaseous air pollutants. If the association

was causal, air pollution at ambient levels seen

in Canada may account for 16% of admissions. Our results are consistent with previously observed associations in older children

and adults between air pollution and respiratory symptoms, lung function, hospitalization,

and mortality. The two strongest effects

observed were with NO2 and O3. 

A large meta-analysis of studies from

around the world found consistent associations

between mortality and NO2 even when

adjusted for other pollutants (Stieb et al.

2002). Associations have also been found linking all air pollutant gases with lung function

impairment in both children and adults

(Gauderman et al. 2000; Peters et al. 1999a,

1999b). In other observational studies, acute

changes in NO2 have been associated with sudden infant death syndrome in Canada, infant

mortality in Mexico City, and asthma symptoms in children (Dales et al. 2004; Delfino

et al. 2002; Loomis et al. 1999). Exposures

during pregnancy have been associated with

low birth weight in Seoul, Korea, and IUGR

in Vancouver, Canada (Ha et al. 2001; Liu

et al. 2003). Controlled human exposures to

NO2 have demonstrated small increases in

bronchial reactivity (Folinsbee 1992) and an

enhanced effect of allergen-induced bronchoconstriction (Strand et al. 1998). In mice,

NO2 exposure increases morbidity from

Mycoplasma pulmonis (Parker et al. 1989) and

mortality from Klebsiella Pneumoniae (Ehrlich

1966; Gauderman et al. 2000; Peters et al.

1999a, 1999b; Stieb et al. 2002). The effects of

NO2 in large observational studies have been

found at lower concentrations than those

investigated in laboratory studies. Several possible reasons exist. The much larger sample size

in population studies allows the ability to

detect smaller effects. The laboratory studies

control for other confounding variables better

than can be done in an observational study.

Perhaps NO2 is simply a marker of other toxicants in a complex mixture of pollutants that

share a common source. Nevertheless, our

findings of increased neonatal morbidity from

air pollution gases are consistent with other

studies of early life.

Particulates such as NO2 are also markers

of exposure to vehicular traffic. Although our

study focused on air pollution gases, we had an

opportunity to assess the effects of PM10 measured every sixth day. A 10-μg/m3 increase in

PM10 has been associated with a 0.51–1%

increase in all-cause mortality (Burnett et al.

1995) and a 1–4% increase in hospital visits

(Pope et al. 1995). Our results were of similar

magnitude, although in a different age group.

We found a 1.5–2.1% increase in hospitalization among neonates associated with a

15-μg/m3 increase in PM10, of similar magnitude to studies of other age groups. A more

recent cohort study of cystic fibrosis patients

found that living in an area with a 10-μg/m3

higher concentration of PM10 was associated

with an 8% (95% CI, 2–15%) increase in the

odds of at least two disease exacerbations

requiring hospitalization or home intravenous

antibiotics (Goss et al. 2004). 

For a 100-ppb increase in the daily onehour maximum of O3, Thurston and Ito

(1999) reported that the relative risk of respiratory hospitalization for all ages was 1.18

(95% CI, 1.1–1.3), and the 1-sec forced expiratory volume in children attending summer

camps decreased by 50.0 (SE = 7.0) mL. 

Most of the literature on air pollution and

health includes both adults and children

together. The many reports of air pollution

and children’s health either excluded neonates

(Farrow et al. 1997; Gouveia and Fletcher

2000; Ha et al. 2003; Woodruff et al. 1997) or

did not study them separately from all children

(Burnett et al. 2001, 1994; Loomis et al.

1999). Yet neonates have a unique set of respiratory diagnoses such as acute life-threatening

events and respiratory distress syndrome,

whereas older children commonly suffer from

asthma, croup, and bronchiolitis, and adults

are admitted with asthma, pneumonia and

chronic obstructive pulmonary disease. We

could not find previous studies of air pollution

and hospitalization for neonatal respiratory disease, but the findings are consistent with the

few published studies focusing on neonatal

mortality. Lin et al. (2004) reported that a

23.3-μg/m3 increase in PM10 was associated

with a 4% (95% CI, 2–6%) increase in total

daily mortality in neonates in São Paulo,

Brazil, whereas a 9.2-μg/m3 increase in SO2

was associated with a 6% (95% CI, 4–8%)

increase in mortality. The combined effects of

the two pollutants was 6.3% (95% CI,

6.1–6.5%), indicating that the SO2 alone

could explain most of the effect. No significant

Air pollution, social status, and respiratory illness

Environmental Health Perspectives • VOLUME 114 | NUMBER 11 | November 2006 1753

Table 3. Range of Pearson pairwise correlations between pollutants by city for 11 Canadian cities,

1 January 1986 to 31 December 2000.

PM10 O3 NO2 SO2

PM10

O3 –0.29 to 0.41

NO2 –0.26 to 0.69 –0.55 to 0.05

SO2 –0.09 to 0.61 –0.41 to 0.13 0.20 to 0.67

CO –0.13 to 0.71 –0.54 to –0.01 0.13 to 0.76 0.19 to 0.66

Table 4. The pooled estimate of percent increase (95% CI) in neonatal respiratory hospital admissions

associated with an increase in air pollution concentration increase equal in magnitude to its interquartile

range. Data from 11 Canadian cities, 1 January 1986 to 31 December 2000.

Multipollutant modelb

Interquartile Single- Multi- restricted to days 

Air pollutants range pollutant modela pollutant modelb with PM10 measures

O3 (ppb) 12.0 3.21 (1.72 to 4.70) 3.35 (1.73 to 4.77) 2.67 (0.98 to 4.39) 

NO2 (ppb) 10.0 2.94 (1.93 to 3.95) 2.85 (1.68 to 4.02) 2.48 (1.18 to 3.80) 

SO2 (ppb) 3.8 2.06 (1.04 to 3.08) 1.66 (0.63 to 2.69) 1.41 (0.35 to 2.47) 

CO (ppm) 0.5 1.75 (0.63 to 2.87) 1.75 (0.48 to 3.02) 1.30 (0.13 to 2.49) 

PM10 (μg/m3) 15.2 2.13 (–0.5 to 4.76) 1.45 (–1.90 to 4.80)

aEffect size is adjusted for long-term temporal trends, day of week effects, and weather variables. bEffect size is adjusted

for other gases and size is adjusted for long-term temporal trends, day of week effects, and weather variables. 
effect of NO2, CO, or O3 could be detected,

although an average of 6.1 deaths occurred

daily over the 1,096 days of study.

Bobak and Leon (1999) performed a

case–control study of live births in the Czech

Republic between 1989 and 1991. Each infant

death was matched with 20 living infants who

were of the same sex and born on the same

day. No significant effect of total suspended

particulates (TSP), SO2, or NO2 was found in

the 1,684 cases of death in the neonatal period,

adjusted for unspecified socioeconomic factors,

gestational age, and birth weight. Exposure to

environmental tobacco smoke was not known.

TSP and SO2 were significantly associated with

respiratory (n = 111 cases) but not all-cause

deaths (n = 810) in the postneonatal period,

with rate ratios of 1.95 (95% CI, 1.1–3.5) and

1.74 (95% CI, 1.0–3.0) respectively for an

increase of 50 μg/m3. 

Lipfert et al. (2000) matched the U.S.

Environmental Protective Agency’s air pollution database with the National Linked

Birth/Infant Death Data Set of the National

Center for Health Statistics for 1990. Annual

averages of air pollution were estimated for

each infant based on the county of birth. SO4

and PM10 were positively associated with total

neonatal deaths, and PM10 was associated

with neonatal respiratory deaths, adjusted for

mother’s smoking, marital status, race,

infant’s month of birth, and average heating

degree days. There were no associations with

CO, and associations with SO2 and NO2

were not reported.

Dales et al. (2004) reported that between

1984 and 1999, daily increases in SO2 and

NO2 equivalent to their interquartile ranges

were associated with an 18% increase in daily

SIDS. The study was based on infant deaths

occurring in the same Canadian cities on

which the present study is based.

Even earlier in life, there is emerging evidence suggesting adverse effects of air pollution. In British Columbia, lower birth weight

has been found in areas of higher air pollution

adjusting for maternal age, parity, infant sex,

gestational age or birth weight, and month of

birth (Liu et al. 2003). However, socioeconomic status, a potential confounder, was

not accounted for in the study. In conclusion,

current Canadian levels of ambient air pollution are associated with a significant proportion of hospitalizations for respiratory

disorders in neonates. This is consistent with

findings of adverse effects in both older and

younger age groups. 

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1754 VOLUME 114 | NUMBER 11 | November 2006 • Environmental Health Perspectives