290 VOLUME 113 | NUMBER 3 | March 2005 • Environmental Health Perspectives

Research | Article

Poor environmental quality has been an

important public health issue for some time

now. Research using large-scale data sets has

shown a fairly consistent relationship between

air pollutant levels and respiratory diseases in

a variety of communities in the industrialized

world (e.g., Atkinson et al. 1999; Dockery

et al. 1993; Lin et al. 2002, 2004; Pope et al.

1995; Schwartz 1994).

In Canada, several reports have been published linking air pollution to adverse population health in cities based on data that were

collected in the 1980s and early 1990s (e.g.,

Burnett et al. 1994, 1999; Goldberg et al.

2001). Windsor, Ontario, with a population of

208,402, is one of the cities that has been identified as heavily polluted (Burnett et al. 1998).

The city is one of the most industrialized cities

in Canada, with major industries including

three automobile assembly plants, an engine

plant, a foundry, and a scrap metal recycling

plant. In addition, there is the outstanding

problem of transboundary air and water pollution from the U.S. states of Ohio, Illinois, and

Michigan. The city is immediately downwind

of major steel mills with associated coking operations in Detroit, Michigan, the wastewater

treatment plant of Detroit and associated sludge

incineration facilities, and a major power plant

that until recently was coal fired. Consequently,

Windsor and surrounding communities have

been identified as an “area of concern” and in

need of further health investigation (Health

Canada 2000).

Furthermore, in line with Windsor’s ranking as a city with a high level of pollution

compared with other Canadian cities (Burnett

et al. 1998), a recent community-health profile by Gilbertson and Brophy (2001) indicated mortality and morbidity rates from

various cancers, circulatory, and respiratory

disorders were higher in Windsor than in the

rest of the province of Ontario. This work

aroused a lot of public sentiments, and several

calls were made for further investigation into

the “alarming trends” of morbidity and mortality. To respond to the call for an in-depth

analysis of the health of Windsorites, we

assessed the association between daily ambient air quality and cardiovascular disease hospitalization (Fung et al. 2005). We reported,

among other things, that short-term effects of

sulfur dioxide were associated significantly

with daily cardiac hospital admissions for people ≥ 65 years of age. The main focus of this

article is on respiratory diseases. We used the

most recent hospitalization data available from

1995 through 2000 to quantify the association between ambient air pollution and respiratory hospitalization, with temperature,

humidity, and change in barometric pressure

as covariates. We are especially interested in

investigating whether there is an age or sex

difference in respiratory admissions. This

research will provide policy makers as well as

the public with estimates of current risks of

respiratory hospitalization as a result of poor

ambient air quality.

Materials and Methods

Data acquisition. The study population consisted of all people who were admitted into one

of the four hospitals in Windsor with primary

diagnoses of respiratory disease [International

Classification of Diseases, 9th Revision (ICD-9)

codes 460–519 (World Health Organization

[WHO] 1975)] from 1 April 1995 through

31 December 2000 and were registered with

the Ontario Health Insurance Plan (OHIP).

Daily hospital admission records for OHIP

patients were obtained from the Canadian

Institute for Health Information (CIHI)

Discharge Abstract Database (CIHI 2002).

The data included date of respiratory admission, age, and sex. Our analysis focused on

finding the association between air pollution

and daily respiratory hospitalizations. It was

not able to address events that happened after

admission.

The hourly air pollution data from the

four fixed monitoring stations in Windsor

were obtained from the Ontario Ministry of

the Environment (MOE 2000). To capture

the effects of exposure, the highest reading for

each day was used for the analysis (see Chock

et al. 2000). The pollutants were nitrogen

dioxide, SO2, carbon monoxide, ozone, inhalable particles [particulate matter ≤ 10 μm in

diameter (PM10)], coefficient of haze (COH),

and total reduced sulfur compounds (TRS).

We included COH in our analysis following

the recommendation by Goldberg et al. (2001).

Address correspondence to I.N. Luginaah, Department of Geography, Room 1409 Social Science

Centre, University of Western Ontario, London,

Ontario, N6A 5C2 Canada. Telephone: (519) 661-

2111, Ext. 86944. Fax: (519) 661-3750. E-mail:

iluginaa@uwo.ca

This research was supported in part by a Natural

Sciences and Engineering Research Council of

Canada operating grant to K.Y.F., a Canadian

Institutes of Health Research (CIHR) investigator

award to K.M.G., and an associated CIHR partnership appointment to I.N.L.

The authors declare they have no competing

financial interests.

Received 28 May 2004; accepted 14 December

2004.

Association of Ambient Air Pollution with Respiratory Hospitalization in a

Government-Designated “Area of Concern”: The Case of Windsor, Ontario

Isaac N. Luginaah,1 Karen Y. Fung,2 Kevin M. Gorey,3 Greg Webster,4 and Chris Wills2

1Department of Geography, University of Western Ontario, London, Ontario, Canada; 2Department of Mathematics and Statistics, 

and 3School of Social Work, University of Windsor, Windsor, Ontario, Canada; 4Canadian Institute for Health Information, Toronto,

Ontario, Canada

This study is part of a larger research program to examine the relationship between ambient air

quality and health in Windsor, Ontario, Canada. We assessed the association between air pollution and daily respiratory hospitalization for different age and sex groups from 1995 to 2000. The

pollutants included were nitrogen dioxide, sulfur dioxide, carbon monoxide, ozone, particulate

matter ≤ 10 μm in diameter (PM10), coefficient of haze (COH), and total reduced sulfur (TRS).

We calculated relative risk (RR) estimates using both time-series and case-crossover methods after

controlling for appropriate confounders (temperature, humidity, and change in barometric pressure). The results of both analyses were consistent. We found associations between NO2, SO2, CO,

COH, or PM10 and daily hospital admission of respiratory diseases especially among females. For

females 0–14 years of age, there was 1-day delayed effect of NO2 (RR = 1.19, case-crossover

method), a current-day SO2 (RR = 1.11, time series), and current-day and 1- and 2-day delayed

effects for CO by case crossover (RR = 1.15, 1.19, 1.22, respectively). Time-series analysis showed

that 1-day delayed effect of PM10 on respiratory admissions of adult males (15–64 years of age),

with an RR of 1.18. COH had significant effects on female respiratory hospitalization, especially

for 2-day delayed effects on adult females, with RRs of 1.15 and 1.29 using time-series and casecrossover analysis, respectively. There were no significant associations between O3 and TRS with

respiratory admissions. These findings provide policy makers with current risks estimates of respiratory hospitalization as a result of poor ambient air quality in a government designated “area of concern.” Key words: air pollution, area of concern, Ontario, respiratory disease, Windsor. Environ

Health Perspect 113:290–296 (2005). doi:10.1289/ehp.7300 available via http://dx.doi.org/ [Online

14 December 2004]
According to Goldberg et al. (2001), despite

the infrequent use of the COH in time-series

analyses, it is a reliable measure of the concentration of ambient carbon particles (generally

from internal combustion), with only limited

contributions from other pollutants, such as

sulfates, nitrates, or particle mass. Respirable

particles (PM ≤ 2.5 μm in diameter) data were

available only from 1998 through 2001 and

were not included in our analysis. Daily

weather data including maximum and minimum temperature, humidity, and change in

maximum or minimum barometric pressure

from the previous day were obtained from the

Environment Canada (2002).

Statistical analysis. First, we linked

together > 2,000 days of records from several

databases comprising pollutants, temperature,

humidity and pressure, and number of respiratory admissions. Data from CIHI were

given to us in a ready-to-use format. Because

we used the maximum of daily hourly pollutant values from four stations, there were not

many missing values (< 1%). If missing values

were sporadic, we replaced the missing values

by the mean of nearby points (3 days before

and 3 days after). If missing values occurred

for a series of days, we substituted the linear

trend value for those points using other pollutants and covariates as predictors. In very few

cases, if the highest hourly maximum was

deemed extreme, it was replaced by the next

highest value.

To relate short-term effects of air pollution on the number of respiratory hospitalizations, we used two different statistical

techniques: time-series and case crossover

methods. Both procedures have been used

extensively to analyze this type of data

(Burnett et al. 1994; Goldberg et al. 2001;

Lee and Schwartz 1999; Lin et al. 2002,

2004; Neas et al. 1999). Detailed formulas

are available in the literature.

Since 2002, significant developments in

these methodologies have taken place. For time

series, the usual smoothing method that has

been used for producing residuals with no

seasonality was locally weighted regression

smoothers (LOESS) within the generalized

additive models (GAMs) (Hastie and Tibshirani

1990). It was later discovered (Dominici et al.

2002; Ramsay et al. 2003; Samet et al. 2003)

that the default settings of the GAM function

in the software package S-Plus (Insightful

Corp. 2001) do not assure convergence of its

iterative estimation procedure and can provide

biased estimates of regression coefficients and

standard errors, especially when the concurvity

is high. Dominici et al. (2002) reanalyzed the

National Morbidity, Mortality, and Air

Pollution Study data with the default implementation and found that the estimates were

biased upward (i.e., higher than they should

be). Since then, either the default option was

set to a smaller number, such as 10–8 (S-Plus

has already done that in their new release), or

another smoother called natural splines has

been used in the general linear model function.

For case-crossover analysis, Navidi modified his bidirectional design (Navidi 1998)

and proposed the semisymmetric bidirectional

design (Navidi and Weinhandl 2002). Fung

et al. (2003) compared all these methods using

simulations, and we used what was recommended in that report—natural splines (ns) in

time series and bidirectional case crossover.

For the time-series analysis in this article,

daily concentrations of each pollutant and

covariates were related to the natural logarithm

of hospital admissions, y, by the model

log E(y) = pollutant + ns(time, df) 

+ DOW + ns(temperature, 4) 

+ ns(humidity, 4) + ns(pressure, 4),

where E(y) is the mean of y and DOW is the

day-of-the-week effect, which takes on values

1–7. For each age and sex group, we first

found the degrees of freedom (df) for ns(time)

such that after fitting the smoothed time effect

and DOW, we had a time series of residuals

that is as close to white noise as possible, as

determined by Bartlett’s test (Priestly 1981).

We then extended the model by incorporating

the smoothed weather variables. Different

combinations of smoothed weather variables

(minimum or maximum temperature, humidity, and change in barometric pressure) were

examined, and the combination that yielded

the lowest Akaike Information Criterion

(Akaike 1973) was chosen. Last, we added the

air pollutant into the model. Regression models with current-day pollution value (lag 1),

average of current day and yesterday (lag 2),

and average of current and 2 previous days

(lag 3) were examined. Relative risk (RR) was

calculated as exp(β

ˆ × IQR), where βˆ is the estimated regression coefficient for pollutant in

the above log-linear model and IQR is the

interquartile range (75th percentile to 25th

percentile) of the pollutant. This implies that

the percentage change in the mean number of

daily hospitalizations is (RR – 1) × 100% for

an increase of IQR unit of pollutant. Ninetyfive percent confidence intervals (CIs) of the

RRs were obtained under the assumption that

the estimated regression coefficients were

normally distributed.

The case-crossover design of Maclure

(1991) has recently been suggested as an alternative to time-series analysis. This design is

essentially a case–control design in which cases

serve as their own controls. Risk estimates are

based on within subject comparisons of exposures at failure times with exposure at times

both before and after failure, using matched

case–control methods. This procedure is used

to investigate whether a recent exposure has

triggered the occurrence of a particular adverse

health outcome and is particularly useful for

estimating effects that are transient or acute.

Because each subject serves as its own control,

the case-crossover approach controls for effects

of stable subject specific covariates such as sex

and race, and for potential time varying confounders such as seasonal effects or personal

habits such as smoking. In this study, we used

the bidirectional design (Navidi 1998), which

Article | Air pollution and respiratory admissions

Environmental Health Perspectives • VOLUME 113 | NUMBER 3 | March 2005 291

Table 1. Summary statistics of the daily high concentrations of air pollutants and respiratory admissions,

1 April 1995 through 31 December 2000.

Variable (unit) Mean ± SD Minimum Maximum (AAQCa)

0–14 years

Female (n = 626) 0.33 ± 0.60 0 4

Male (n = 976) 0.52 ± 0.79 0 6

15–64 years

Female (n = 573) 0.30 ± 0.56 0 4

Male (n = 310) 0.16 ± 0.41 0 3

≥ 65 years

Female (n = 938) 0.50 ± 0.75 0 5

Male (n = 791) 0.42 ± 0.66 0 5

Total (n = 4,214) 2.23 ± 1.76 0 14

SO2 (ppb) 27.5 ± 16.5 0 129 (100/24 hr)

NO2 (ppb) 38.9 ± 12.3 0 117 (100/24 hr)

O3 (ppb) 39.3 ± 21.4 1 129 (80/hr)

CO (ppm) 1.3 ± 1.0 0 11.82 (3/hr)

TRS (ppb) 8.1 ± 10.6 0 132 (27/hr)

PM10 (μg/m3) 50.6 ± 35.5 9 349 (30/24 hr)

COH 0.6 ± 0.4 0 3.6 (1.0/24 hr)

Maximum temperature (°C) 14.2 ± 11.2 –15.8 35.7

Minimum temperature (°C) 5.3 ± 9.8 –21.4 25.6

Maximum humidity 86.1 ± 9.2 50.0 100.0

Minimum humidity 53.4 ± 15.0 17.0 98.0

Maxp 0.00 ± 0.54 –2.36 2.06

Minp 0.00 ± 0.70 –3.42 3.12

Abbreviations: Maxp, change in maximum barometric pressure from the previous day; Minp, change in minimum barometric

pressure from the previous day.

aAmbient air quality criteria (MOE 2000).
can control for different patterns of time trends

in exposures and outcomes and gives the least

biased estimate compared with the pre- or postunidirectional design (Fung et al. 2003). We

selected an interval of 2 weeks between case

and control periods to minimize autocorrelation between case and control exposures and to

control for seasonal effects. Conditional logistic

regression analysis using the same covariates as

time series were performed via the Cox proportional hazards model. Maximum likelihood

estimates of the parameters were obtained by

choosing the “exact” option in S-Plus. Details

of this model can be found in Navidi (1998) or

Fung et al. (2003).

Results

A total of 4,214 overall admissions due to

respiratory diseases occurred in the study

period. Table 1 gives the summary statistics of

daily respiratory admissions for the three age

groups (0–14, 15–64, ≥ 65 years). Overall,

there seem to be more male hospitalizations

than female in the early years, but the opposite is true for later years. Summary statistics

of weather variables and daily high concentrations of all the pollutants are also provided in

Table 1. An analysis of the Windsor yearly

air pollution data for the period 1990–2000

showed an overall decreasing trend in ambient air pollutants (NO2, SO2, CO, COH),

likely due to regulatory measures implemented by the government in the preceding

10 years (MOE 2000). There was an increasing trend in O3 and TRS, whereas PM10 did

not change much. Based on the air quality

index, there were 165 days of poor air quality,

583 days of moderate air quality, and 1,352

days of good air quality during the entire

study period.

Table 2 gives the correlation coefficients

for the air pollutants and weather variables.

Most of the pollutants are positively correlated with each other, except SO2 and O3

(r = –0.02), and TRS and O3 (r = –0.01).

Maximum temperature and minimum

humidity were highly correlated with O3.

Tables 3 and 4 give the time-series and

case-crossover RR estimates by age and sex

groups. 95% CIs were also given for the current day (lag 1), lag 2, and lag 3 of the pollutants that were used in the analyses.

The time-series analysis showed elevated

effects of NO2 on the respiratory admissions

of females overall and the 0–14 and 15–64 age

groups (Table 3). The results of the casecrossover analysis somewhat concurred with

those of the time series. We found NO2 lag 2

to be significantly associated with respiratory

hospitalization of females 0–14 years age,

with an RR of 1.19 (95% CI, 1.002–1.411)

(Table 4). Although the effects of NO2 on

women in the 15–64 and ≥ 65 age groups

were all elevated, none of these were significant. There were no significant associations

between NO2 and male hospitalization in any

of the age groups (Tables 3 and 4).

Time-series results showed a significant

current-day effect of SO2 on the admission of

females 0–14 years of age, with an RR of

1.11(95% CI, 1.011–1.221). The casecrossover method also showed an RR of 1.12,

and it is almost significant. Other than this,

there were no significant association between

SO2 and hospitalization for respiratory diseases

in females and males using both methods of

analysis. However, the effects of SO2 on female

respiratory admissions were consistently elevated in all age groups.

Although the time-series analysis showed

elevated effects of CO on respiratory hospitalization of females, only CO lag 2 was significantly associated with the hospitalization of

females 0–14 years of age (RR = 1.07; 95% CI,

1.001–1.139). The case-crossover results

showed that CO had both immediate and

delayed effects on respiratory admissions for

females 0–14 years of age, with RRs of

1.15 (95% CI, 1.006–1.307), 1.19 (95%

CI, 1.020–1.379), and 1.22 (95% CI,

1.022–1.459) for lags 1, 2, and 3, respectively.

The effects of CO on the respiratory admissions of females in the 15–64 and ≥ 65 age

groups were elevated, but none were significant.

There were no significant associations between

CO and respiratory admissions in any of the

male age groups.

We also found no significant association

between O3 and respiratory admissions on

either females or males, although the effects

were elevated mostly among the young and

elderly age groups in the case-crossover analysis.

The time-series results showed that PM10

lag 2 is significantly associated with respiratory

hospitalization for males 15–64 years of age,

with an RR of 1.18 (95% CI, 1.036–1.332).

In the case-crossover analysis, the effects of

PM10 on respiratory admissions were mostly

elevated, but not significant, in all the groups

except for males 0–14 years of age.

COH (lag 3) was significantly associated

with the admission of all females (RR = 1.07;

95% CI, 1.004–1.135) and for females

15–64 years of age (RR = 1.15; 95% CI,

1.020–1.296) in the time-series analysis.

When all the age groups were combined, the

case-crossover analysis also showed that COH

had an immediate effect on the admission of

women for respiratory disease, with an RR of

1.09 (95% CI, 1.037–1.176). COH lags 2

and 3 were also significantly associated with respiratory admissions for females 15–64 years of

age, with RRs of 1.20 (95% CI, 1.003–1.426)

and 1.29 (95% CI, 1.051–1.582) respectively.

None of the effects of COH on the hospitalization of females 0–14 and ≥ 65 years of age for

respiratory disease was significant. Furthermore,

none of the male groups showed a significant

association between COH and respiratory

admissions.

Using both methods, we found no significant associations between TRS and respiratory

admissions for any group, but the case-crossover

results suggested there might be a delayed effect

of TRS on the younger age groups.

Taken together, both the time-series and

case-crossover analyses show that young

(0–14 years) and adult (15–64 years) females

were more likely to be admitted for airpollution–induced respiratory diseases than

were males.

Article | Luginaah et al.

292 VOLUME 113 | NUMBER 3 | March 2005 • Environmental Health Perspectives

Table 2. Correlation coefficients between air pollutants and weather variables.

NO2 SO2 CO O3 COH PM10 TRS Mint Minh Maxt Maxh Maxp Minp

PM10

NO2 1.00

SO2 0.22 1.00

CO 0.38 0.16 1.00

O3 0.26 –0.02 0.10 1.00

COH 0.49 0.14 0.31 0.23 1.00

PM10 0.33 0.22 0.21 0.33 0.39

TRS 0.06 0.13 0.11 –0.01 0.15 0.05 1.00

Mint –0.22 –0.12 –0.06 –0.45 –0.16 –0.26 –0.10 1.00

Minh 0.06 –0.06 0.02 0.67 0.21 0.25 0.08 –0.19 1.00

Maxt 0.15 –0.01 0.08 0.74 0.28 0.34 0.06 0.95 –0.34 1.00

Maxh –0.09 –0.08 0.03 –0.20 0.03 –0.09 0.09 –0.02 0.63 –0.07 1.00

Maxp –0.06 –0.03 –0.08 –0.04 –0.05 –0.14 –0.02 –0.13 –0.18 –0.14 –0.23 1.00

Minp –0.03 –0.01 –0.04 –0.04 –0.05 –0.13 0.04 –0.13 –0.18 –0.15 –0.27 0.67 1.00

Abbreviations: Maxh, maximum humidity; Maxp, change in maximum barometric pressure from the previous day; Maxt, maximum temperature; Minh, minimum humidity; Minp, change in

minimum barometric pressure from the previous day; Mint, minimum temperature.
Discussion

Although ambient pollution levels (NO2, SO2,

CO, COH) in Windsor “area of concern”

decreased during the study period, we still see

existing levels of some pollutants that had significant effects on respiratory hospitalization.

Consistent with Lin et al. (2002), we saw some

differences in results between time-series and

case-crossover analyses. CIs on RR estimates

from the bidirectional case-crossover analysis

were slightly wider than those from time series,

implying lower statistical power for the bidirectional case-crossover design, as documented

previously (Bateson and Schwartz 1999; Fung

et al. 2003). Because of the sex dimension we

introduced into our analysis, together with differences in analytical approaches, control variables, populations studied, exposure variable

averaging times, and cut points, comparison of

our findings with other studies is not entirely

straightforward (e.g., Burnett et al. 1997).

Despite the fact that no comparable RRs can

be given, our findings are consistent with those

of existing studies qualitatively.

Although NO2 has been known to increase

susceptibility to respiratory infections (Speizer

et al. 1980), results of different studies that

examined the link between NO2 and respiratory outcomes continued to vary. For instance,

Atkinson et al. (1999), working in London,

Article | Air pollution and respiratory admissions

Environmental Health Perspectives • VOLUME 113 | NUMBER 3 | March 2005 293

Table 3. RRs (95% CIs) for single-pollutant models using time-series method for an increase in IQR.a

All age groups 0–14 years 15–64 years ≥ 65 years

Pollutants (IQR) Female Male Female Male Female Male Female Male

NO2 (16 ppb)

Lag 1 1.035 (0.971–1.104) 0.944 (0.886–1.006) 1.114 (0.994–1.248) 0.955 (0.866–1.054) 1.013 (0.893–1.150) 0.942 (0.790–1.122) 1.020 (0.930–1.1198) 0.9196 (0.832–1.016)

Lag 2 1.027 (0.967–1.094) 0.958 (0.900–1.021) 1.107 (0.990–1.238) 0.918 (0.833–1.012) 1.044 (0.918–1.187) 0.992 (0.833–1.182) 0.987 (0.881–1.106) 0.9620 (0.854–1.084)

Lag 3 1.036 (0.970–1.107) 0.970 (0.909–1.036) 1.108 (0.987–1.245) 0.927 (0.838–1.025) 1.121 (0.978–1.285) 1.012 (0.841–1.216) 0.962 (0.847–1.093) 0.9773 (0.854–1.118)

SO2 (19.25 ppb)

Lag 1 1.041 (0.987–1.098) 0.953 (0.900–1.009) 1.111 (1.011–1.221)* 0.952 (0.874–1.037) 1.031 (0.930–1.144) 0.971 (0.845–1.115) 1.030 (0.951–1.115) 0.9409 (0.860–1.029)

Lag 2 1.041 (0.979–1.107) 0.984 (0.925–1.048) 1.090 (0.977–1.216) 0.981 (0.892–1.078) 1.068 (0.950–1.202) 1.046 (0.898–1.218) 1.030 (0.927–1.145) 0.9490 (0.845–1.066)

Lag 3 1.046 (0.982–1.114) 0.987 (0.925–1.053) 1.066 (0.952–1.194) 0.995 (0.904–1.096) 1.054 (0.931–1.192) 0.985 (0.837–1.159) 1.074 (0.949–1.215) 0.9561 (0.834–1.096)

CO (1.17 ppm)

Lag 1 1.049 (0.993–1.108) 0.989 (0.932–1.049) 1.077 (0.979–1.184) 1.034 (0.949–1.126) 1.072 (0.962–1.195) 0.994 (0.854–1.157) 1.029 (0.947–1.118) 0.9010 (0.817–0.994)

Lag 2 1.032 (0.993–1.188) 0.986 (0.946–1.029) 1.068 (1.001–1.139)* 0.996 (0.933–1.062) 1.025 (0.944–1.112) 0.988 (0.884–1.104) 1.030 (0.928–1.144) 0.9041 (0.803–1.019)

Lag 3 1.051 (0.993–1.112) 0.987 (0.929–1.048) 1.100 (0.997–1.213) 0.968 (0.881–1.064) 1.081 (0.963–1.213) 0.951 (0.806–1.121) 1.013 (0.899–1.142) 0.9632 (0.845–1.098)

O3 (29 ppb)

Lag 1 0.947 (0.819–1.096) 1.039 (0.923–1.170) 1.048 (0.830–1.322) 0.944 (0.745–1.196) 0.817 (0.621–1.075) 0.959 (0.661–1.393) 0.945 (0.777–1.150) 1.0961 (0.920–1.306)

Lag 2 1.006 (0.852–1.188) 1.063 (0.917–1.232) 1.084 (0.829–1.433) 0.955 (0.731–1.246) 0.759 (0.549–1.048) 1.268 (0.832–1.932) 1.008 (0.807–1.259) 1.0624 (0.852–1.325)

Lag 3 1.043 (0.873–1.246) 1.057 (0.891–1.254) 1.092 (0.796–1.497) 1.001 (0.755–1.328) 0.893 (0.633–1.261) 1.346 (0.851–2.128) 0.963 (0.763–1.215) 0.9767 (0.757–1.261)

PM10 (31 μg/m3

)

Lag 1 0.996 (0.950–1.044) 1.008 (0.965–1.054) 1.023 (0.948–1.104) 0.980 (0.912–1.053) 1.047 (0.962–1.140) 1.096 (0.982–1.222) 0.967 (0.900–1.040) 1.0033 (0.934–1.078)

Lag 2 1.015 (0.963–1.069) 1.036 (0.986–1.089) 1.035 (0.948–1.130) 1.001 (0.925–1.083) 1.049 (0.946–1.163) 1.175 (1.036–1.332)* 0.993 (0.913–1.079) 1.0298 (0.941–1.127)

Lag 3 1.022 (0.968–1.078) 1.027 (0.974–1.083) 1.047 (0.956–1.147) 0.980 (0.901–1.065) 1.030 (0.922–1.150) 1.080 (0.938–1.243) 0.998 (0.910–1.094) 1.0768 (0.972–1.193)

COH (0.5)

Lag 1 1.051 (0.994–1.113) 0.977 (0.922–1.036) 1.085 (0.986–1.195) 1.004 (0.923–1.093) 1.103 (0.994–1.223) 0.955 (0.820–1.113) 0.996 (0.912–1.088) 0.9381 (0.852–1.033)

Lag 2 1.032 (0.982–1.086) 0.991 (0.942–1.043) 1.066 (0.979–1.161) 0.980 (0.907–1.058) 1.056 (0.958–1.164) 0.996 (0.871–1.141) 0.989 (0.884–1.107) 0.9841 (0.876–1.106)

Lag 3 1.067 (1.004–1.135)* 1.001 (0.940–1.066) 1.094 (0.985–1.216) 0.972 (0.884–1.070) 1.150 (1.020–1.296)* 0.948 (0.799–1.126) 0.998 (0.875–1.137) 1.0609 (0.928–1.213)

TRS (8 ppb)

Lag 1 0.990 (0.939–1.038) 0.997 (0.961–1.035) 0.957 (0.887–1.031) 0.993 (0.938–1.052) 1.013 (0.942–1.090) 0.981 (0.896–1.074) 0.997 (0.945–1.051) 1.0126 (0.958–1.070)

Lag 2 0.987 (0.939–1.038) 0.999 (0.950–1.051) 1.002 (0.913–1.100) 0.982 (0.908–1.063) 1.023 (0.926–1.130) 1.015 (0.904–1.140) 0.961 (0.892–1.034) 1.0089 (0.9341–1.090)

Lag 3 0.976 (0.924–1.032) 1.003 (0.949–1.060) 1.063 (0.965–1.171) 0.990 (0.909–1.079) 0.980 (0.874–1.100) 0.988 (0.866–1.128) 0.925 (0.845–1.011) 1.0227 (0.934–1.120)

aAdjusted for temperature, humidity, and change in barometric pressure. *Statistically significant at p < 0.05.

Table 4. RRs (95% CIs) for single-pollutant models using case-crossover method for an increase in IQR.a

All age groups 0–14 years 15–64 years ≥ 65 years

Pollutants (IQR) Female Male Female Male Female Male Female Male

NO2 (16 ppb)

Lag 1 1.078 (0.995–1.168) 0.957 (0.883–1.036) 1.145 (0.996–1.317) 0.981 (0.873–1.103) 1.004 (0.870–1.159) 0.988 (0.806–1.210) 1.081 (0.964–1.212) 0.915 (0.810–1.034)

Lag 2 1.100 (0.998–1.213) 0.960 (0.873–1.055) 1.189 (1.002–1.411)* 0.933 (0.810–1.074) 1.055 (0.883–1.260) 1.004 (0.789–1.277) 1.063 (0.925–1.222) 0.959 (0.832–1.105)

Lag 3 1.085 (0.972–1.210) 0.951 (0.854–1.057) 1.178 (0.973–1.427) 0.910 (0.777–1.066) 1.114 (0.915–1.356) 0.972 (0.744–1.268) 1.001 (0.856–1.172) 0.973 (0.829–1.142)

SO2 (19.25 ppb)

Lag 1 1.047 (0.978–1.122) 0.939 (0.874–1.009) 1.119 (0.995–1.259) 0.923 (0.831–1.025) 1.002 (0.879–1.141) 0.944 (0.798–1.116) 1.020 (0.924–1.126) 0.968 (0.867–1.082)

Lag 2 1.062 (0.969–1.164) 1.003 (0.914–1.101) 1.126 (0.957–1.325) 0.984 (0.859–1.128) 1.057 (0.893–1.252) 1.071 (0.859–1.334) 1.011 (0.888–1.152) 0.994 (0.861–1.147)

Lag 3 1.073 (0.963–1.195) 0.989 (0.886–1.103) 1.100 (0.907–1.335) 0.961 (0.819–1.126) 1.055 (0.864–1.289) 1.022 (0.785–1.330) 1.044 (0.896–1.216) 1.008 (0.852–1.192)

CO (1.17 ppm)

Lag 1 1.037 (0.968–1.111) 0.950 (0.884–1.020) 1.147 (1.006–1.307)* 1.003 (0.904–1.113) 1.005 (0.884–1.141) 1.036 (0.870–1.233) 1.014 (0.922–1.116) 0.867 (0.775–0.970)

Lag 2 1.063 (0.976–1.158) 0.945 (0.862–1.036) 1.186 (1.020–1.379)* 0.997 (0.871–1.141) 1.007 (0.859–1.181) 1.033 (0.821–1.299) 1.024 (0.907–1.156) 0.865 (0.752–0.994)

Lag 3 1.087 (0.982–1.203) 0.965 (0.866–1.075) 1.221 (1.022–1.459)* 0.970 (0.824–1.141) 1.032 (0.858–1.240) 0.991 (0.760–1.293) 1.035 (0.893–1.200) 0.946 (0.807–1.109)

O3 (29 ppb)

Lag 1 1.013 (0.766–1.339) 1.064 (0.930–1.217) 1.046 (0.800–1.367) 1.070 (0.854–1.340) 0.937 (0.723–1.214) 0.899 (0.630–1.282) 1.122 (0.919–1.369) 1.095 (0.896–1.339)

Lag 2 1.066 (0.778–1.462) 1.037 (0.889–1.211) 1.084 (0.797–1.474) 1.024 (0.797–1.316) 0.838 (0.625–1.123) 0.974 (0.651–1.457) 1.147 (0.912–1.444) 1.039 (0.826–1.308)

Lag 3 1.007 (0.712–1.424) 1.015 (0.855–1.207) 1.013 (0.721–1.425) 1.032 (0.786–1.355) 0.877 (0.639–1.203) 0.972 (0.625–1.513) 1.161 (0.901–1.496) 0.987 (0.765–1.273)

PM10 (31 μg/m3

)

Lag 1 1.034 (0.974–1.098) 0.997 (0.942–1.056) 1.040 (0.944–1.146) 0.965 (0.887–1.050) 1.038 (0.937–1.151) 1.055 (0.926–1.203) 1.027 (0.936–1.125) 0.999 (0.912–1.094)

Lag 2 1.045 (0.972–1.124) 1.022 (0.953–1.097) 1.032 (0.916–1.162) 0.948 (0.857–1.048) 1.051 (0.920–1.200) 1.136 (0.964–1.339) 1.051 (0.943–1.171) 1.059 (0.942–1.191)

Lag 3 1.054 (0.970–1.145) 1.008 (0.930–1.092) 1.052 (0.919–1.204) 0.914 (0.815–1.025) 1.020 (0.872–1.194) 1.026 (0.852–1.236) 1.073 (0.949–1.214) 1.125 (0.985–1.284)

COH (0.5)

Lag 1 1.092 (1.037–1.176)* 0.974 (0.906–1.048) 1.101 (0.971–1.245) 1.025 (0.925–1.134) 1.135 (0.997–1.292) 1.013 (0.845–1.214) 1.058 (0.946–1.184) 0.898 (0.799–1.008)

Lag 2 1.097 (0.998–1.206) 1.001 (0.913–1.098) 1.119 (0.953–1.314) 1.004 (0.880–1.144) 1.196 (1.003–1.426)* 1.040 (0.823–1.315) 1.029 (0.897–1.181) 0.966 (0.837–1.115)

Lag 3 1.104 (0.989–1.232) 1.020 (0.915–1.136) 1.086 (0.903–1.307) 0.995 (0.853–1.160) 1.289 (1.051–1.582)* 0.968 (0.740–1.267) 1.016 (0.865–1.193) 1.048 (0.886–1.241)

TRS (8 ppb)

Lag 1 1.007 (0.961–1.054) 0.990 (0.945–1.037) 0.982 (0.899–1.072) 0.991 (0.923–1.063) 0.985 (0.903–1.076) 0.994 (0.895–1.103) 1.030 (0.965–1.098) 0.990 (0.925–1.061)

Lag 2 1.000 (0.940–1.064) 1.009 (0.948–1.075) 1.056 (0.941–1.184) 1.015 (0.921–1.118) 0.960 (0.858–1.074) 1.035 (0.907–1.181) 0.987 (0.903–1.078) 0.992 (0.902–1.092)

Lag 3 1.005 (0.935–1.081) 1.018 (0.944–1.098) 1.144 (0.999–1.310) 1.015 (0.933–1.185) 0.932 (0.813–1.069) 1.016 (0.867–1.192) 0.967 (0.872–1.073) 0.991 (0.886–1.110)

aAdjusted for temperature, humidity, and change in barometric pressure. *Statistically significant at p < 0.05.
reported no significant associations between

NO2 and respiratory admissions overall or

within any of three age groups (0–14, 15–64,

and ≥ 65 years). As part of the Air Pollution

and Health: A European Approach (APHEA)

project, Spix et al. (1998) reported no significant association between NO2 and respiratory

admissions for the 15–64 and ≥ 65 year age

groups. In Paris, France (Dab et al. 1996),

and in Birmingham, England (Wordley et al.

1997), a lack of associations between NO2

and hospital admissions for respiratory diseases was observed. On the other hand, Wong

et al. (1999) reported significant associations

between NO2 and respiratory admissions for

0–4, 5–64 and ≥ 65 year age groups in Hong

Kong. Similarly, in London, England, Ponce

de Leon et al. (1996) found a significant association between summer exposure to NO2

lag 2 and respiratory admissions for children

0–14 years of age. In the present analysis, we

found a significant association between NO2

lag 2 and respiratory admissions for females

0–14 years of age, but not for any of the other

female or male groups.

The effect of SO2 on respiratory hospitalization varies considerably, especially at low

levels of exposure. For example, Spix et al.

(1998), Sunyer et al. (2003), and Wordley

et al. (1997) reported no consistent association between SO2 and respiratory admissions.

However, studies in Milan, Italy (Vigotti

et al. 1996), in Paris, France (Dab et al.

1996), and in London, England (Walters

et al. 1994), found SO2 levels influenced hospital admissions for all respiratory diseases.

Atkinson et al. (1999) reported a strong association between SO2 and respiratory admissions among 0- to 14-year-olds. Wong et al.

(1999) observed significant short-term effects

between SO2 and respiratory admissions in

the ≥ 65 age group but not among younger

age groups. Furthermore, Ponce de Leon et al.

(1996) found a positive association between

SO2 lag 1 (in cool season) with respiratory

admissions for adults 15–64 years of age; there

was no significant association in either the

0–14 or ≥ 65 age groups. Bates and Sizto

(1987) found an association between SO2

(2-day lag) and respiratory admissions in

southern Ontario. Consistent with these findings, the time-series analysis in this study

showed a significant association between SO2

(lag 1) and respiratory admissions for females

0–14 years of age. However, the significance of

SO2 in all other age groups may be minimal

because ambient concentrations of SO2 in

Ontario have decreased by more than 86% over

recent decades (MOE 2000). Nonetheless,

there is a need for continuous attention because

of the number of people exposed and the existence of high-risk groups.

According to Burnett et al. (1999), because

there is a strong correlation between CO and

other pollutants regularly used in air pollution

studies, it is usually difficult to examine the

effects of CO independent of all other pollutants. It is therefore not surprising that the

literature on the effects of CO on respiratory

illness has also been mixed at best. For

instance, Atkinson et al. (1999) found no association between CO and respiratory admissions either overall or by age group. However,

in Korea, Cho et al. (2000) after controlling

for seasonal and temperature effects, found an

association between CO and hospital admissions for respiratory disease with RRs ranging

from 1.21 to 3.55, depending on whether the

area is rural or urban. In this study, we found

that females 0–14 years of age were more

likely to be admitted as a result of their exposure to CO in both the time-series and casecrossover models, although only CO lag 2 was

significant in the former case. Although the

effects of CO on respiratory admissions of

women ≥ 65 years of age were elevated for all

lags, they were not statistically significant. It is

important to note that significant reduction in

CO had been achieved in the preceding 10 years

in Windsor (mean = 1.0 ppm in 1991 to 0.3 in

2000) because of more stringent regulatory

effort in air quality (MOE 2000).

There are contrasting reports on the effect

of O3 on respiratory admissions. For instance,

studies in The Netherlands (Schouten et al.

1996), in London (Atkinson et al. 1999) and

in Paris (Dab et al. 1996) found no significant

associations between O3 and respiratory hospitalization. However, Burnett et al. (1997)

reported an association between O3 and respiratory admissions in several Canadian cities,

using data from 1981 through 1991. In Hong

Kong, Wong et al. (1999) reported that O3

had a significant effect on respiratory admissions with an RR of 1.022. Ponce de Leon

et al. (1996) found an association between O3

and daily respiratory admissions for the 15–64

and ≥ 65 age groups but not for the 0–14 age

group. Spix et al. (1998) observed a consistent

association between O3 and respiratory admissions in five European cities, and the effects

were stronger in the ≥ 65 age group. In our

analysis, we found elevated risk in the 0–14

and ≥ 65 age groups; however, none of these

RRs was statistically significant.

Several studies have reported positive and

significant effects of PM10 on respiratory

admissions. In Toronto, Canada (Burnett et al.

1999), and in Hong Kong (Wong et al. 1999),

PM10 has been found to be associated with respiratory admissions. A study by Schwartz

(1996) in Spokane, Washington (USA), found

PM10 to be significantly associated with respiratory hospitalization of women ≥ 65 years of

age. The association between PM10 and respiratory admission was demonstrated further by

Atkinson et al. (1999), who found significant

effects in all age groups (0–14, 15–64, and

≥ 65), although the effect was strongest in the

0–14 age group. In the present study, PM10

(lag 2) was significantly associated with respiratory admission of males 15–64 years of age.

The elevated effects of PM10 found in this

study for all female age groups and for adult

and elderly males are in line with the notion

that PM10 influences inflammatory mechanisms in respiratory organs (Hitzfeld et al.

1997).

Compared with other pollutants, the effect

of COH on respiratory admissions has not

been frequently examined (Goldberg et al.

2001). However, one study found that COH

was the strongest predictor of hospitalizations

for respiratory diseases among particle-related

pollutants examined in both single- and multiple-pollutant regression models (Burnett et al.

1997). Consistent with this later report, we

found COH to be significantly related to

female respiratory hospitalization, and more so

among adult females.

Overall, our results show that there were

more elevated effects with female respiratory

hospitalization in relation to ambient air pollution compared with males. The reasons for

these differences are unclear. However, several

authors have suggested possible explanations

for existing sex differences observed in respiratory health. According to Redline and Gold

(1994), sex differences in respiratory diseases

relate to differences in hormonal status,

potentially influencing airway inflammation

and smooth muscle and vascular functions.

Differences may also be related to differences

in the rates of lung growth and decline, and

the relative changes in airway and parenchymal

size, in females and males. For instance, the

deposition of pollution particles in the lung

has been shown to vary by sex, with greater

lung deposition fractions of 1-μm particles in

females compared with males (Kim and Hu

1998; Kohlhaufl et al. 1999), leading to a more

female susceptibility to respiratory diseases.

Additionally, despite significant social

progress, industrial and domestic jobs continue to be different for men and women. In

particular, women as a group are poorer than

men and may experience different psychosocial stresses. Also, women usually perform the

bulk of child care, cooking, dusting, and vacuum cleaning. It is therefore possible that

women experience greater exposures to viral

infections, nitrogen oxides, household irritants, and aeroallergens (Redline and Gold,

1994). Moreover, some studies have shown

that women are more sensitive than men to

the effect of smoking, with the effects of

smoking on lung function greater in women

than in men (e.g., Chen et al. 1999; Prescott

et al. 1997; Xu et al. 1994). The increased

probability of female hospitalization for respiratory disease probably reflects the increase in

smoking among women, relative to men, in

Article | Luginaah et al.

294 VOLUME 113 | NUMBER 3 | March 2005 • Environmental Health Perspectives
Article | Air pollution and respiratory admissions

Environmental Health Perspectives • VOLUME 113 | NUMBER 3 | March 2005 295

the last half-century (Canadian Council for

Tobacco Control 2002).

Sex differences in respiratory morbidity,

may also reflect differences in the management

of morbidity. For instance, Goodman et al.

(1994) suggested that increased asthma morbidity in women may relate to their less adequate medical management. The complex

social and biologic differences in women and

men, underscore the need for more work to

aid in our understanding of the bases for a

female susceptibility to respiratory diseases.

Limitations of this study are the same as

in studies of this kind. They include the adequacy of covariate control and the impact of

measurement error in the exposure and outcome variables. However, for most of the risk

factors such as the presence of chronic conditions and cigarette smoking, there is no reason to believe that the individual risk factors

are correlated with the daily changes in air

pollution; hence, they are not likely to be

confounders in this study. Furthermore, the

limitations of using fixed monitors to represent the entire population in environmental

exposure studies have been frequently discussed (Goldberg et al. 2001). Hence, these

results must be interpreted with caution.

Nevertheless, the findings still have implications for public health policy.

Conclusion

This study has found associations between

ambient air pollution (NO2, SO2, CO, COH,

and PM10) and daily hospital admission of respiratory diseases especially among females in

the Windsor “area of concern.” The findings

are generally consistent with other studies.

Even though the risks of respiratory disease due

to ambient air pollution in the general population may seem low, it is reasonable to assume

that the risks are much higher among susceptible groups, and our findings here support this

hypothesis especially for females in the 0–14

age group. Hence, we recommend that in

addressing the intense public concern about

the health impacts of environmental quality in

this “area of concern” must not only involve

stricter guidelines (which will be beneficial) but

also include environmental risk communication, aimed at improving public perception of

risk due to poor air quality. In addition, the

events of 11 September 2001 brought renewed

concerns about the effects of air pollution in

the Windsor area. There have been increasing

delays resulting in long lines of trucks at the

border crossing points. The idling trucks are

spewing toxic pollutants from their archaic

exhaust systems into the air. With Windsor

located on the downwind side of Detroit,

which is a major source of industrial pollutants,

the combined effect of these factors is that

the improvements that have been suggested

here may no longer be possible to attain.

We recommend that more frequent studies

examining the link between ambient air quality

and health effects be conducted to monitor any

changes that may be taking place.

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