SHORT COMMUNICATION

Giuseppe Migliaretti Æ Ennio Cadum Æ Enrica Migliore

Franco Cavallo

Traffic air pollution and hospital admission for asthma:

a case–control approach in a Turin (Italy) population

Received: 13 April 2004 / Accepted: 4 August 2004 / Published online: 22 February 2005

 Springer-Verlag 2005

Abstract Objectives: This study investigated the relationship between traffic air pollution and asthma, using

a case–control design applied to routinely collected data.

Methods: Subjects resident in Turin during the period

1997–1999 and admitted for asthma were defined as

cases; patients admitted for causes other than respiratory diseases or heart diseases were defined as controls.

Nitrogen dioxide and total suspended particulate were

considered as indicators of traffic air pollution. Statistical analysis were performed, separately for young (0–

14 years), adult (15–64 years) and elderly (>64 years)

patients, with a logistic regression model; results are

expressed as percentage of risk modification for a 10 g/

m3 increase in exposure to pollutants. Results: The risk

of emergency admissions for asthma rose significantly

with increased exposure to nitrogen dioxide [2.4%, 95%

Confidence Interval: 0.5%; 4.3%], and total suspended

particulate [2.3%, 95% Confidence Interval: 1.1%–

3.6%]. The significant association was evident, in particular, among young and elderly patients for both

pollutants. Conclusion: Using a case–control design both

easy to use and manage, the study confirms the significant association between hospital emergency admissions

for asthma and exposure to nitrogen dioxide and total

suspended particulate pollutants.

Keywords Air pollution Æ Nitrogen dioxide Æ Total

suspended particulate Æ Asthma Æ Respiratory health Æ

Emergency admission Æ Case–controls study

Introduction

International literature has documented the short-term

and long-term effects that air pollution has on public

health and mortality (Dockery et al. 1993; Pope et al.

1995; Barbone et al. 1994; Biggeri et al. 1996; Pope et al.

2002). The defining of the potential effects of environmental pollution on health remains a issue in both the

attribution of exposure and the identification of many

confounding factors, which are often difficult to control.

However, one usual outcome of the short-term effects of

air pollution is the hospital admission for respiratory

causes. Close correlation between an increase in air

pollution and the number of hospital admissions for

respiratory causes has been shown in European (Vigotti

et al. 1996; Spix et al. 1998; Atkinson et al. 2001; Fusco

et al. 2001; Biggeri et al. 2001; Sunyer et al. 2002, 2003)

and non-European (Bates and Szito 1987; Thurston et al

1992; Burnet et al. 1994; Schwartz 1994) studies; in

particular close association has been shown between

exposure to urban air pollution and emergency admissions for asthma.

In most of those studies, the statistical methods were

based on time–series data to control the association for

the effects of long-term seasonal variations and/or

cyclical components in changes in exposure and frequency of health events. Few studies have used a case–

control design to determine the relationship between

hospital admission and urban air pollution. A case–

control study carried out on the population of Ravenna

(Ciccone et al. 1995), to analyse the relationship between

the number of hospital admissions and sulphur dioxide

levels, showed an association between the risk of

admission for chronic bronchitis and increased exposure

to sulphur dioxide. A similar study on the Novara

population (Panella et al. 2000) showed a positive

association between nitrogen dioxide exposure and

emergency admissions for respiratory causes.

The literature also reports no association between

urban air pollution and hospital admission for respiraG. Migliaretti (&) Æ E. Migliore Æ F. Cavallo

Department of Public Health and Microbiology,

University of Turin, Via Santena 5 bis, Turin, Italy

E-mail: giuseppe.migliaretti@unito.it

Tel.: +39-011-6706559

Fax: +39-011-6706551

E. Cadum

Unit of Environmental Epidemiology,

Regional Agency of Environmental Protection,

Turin, Italy

Int Arch Occup Environ Health (2005) 78: 164–169

DOI 10.1007/s00420-004-0569-3
tory causes (Wilkinson et al. 1999). For these reasons

further investigation are required on the effects of traffic

air pollution on public health.

Objectives

Using a case–control design based on routinely collected

data, our study analysed the association between exposure to nitrogen dioxide and total suspended particulate

and emergency hospital admissions for asthma in subjects resident in Turin in the period from January 1997

to December 1999.

Materials and methods

Air-pollution data

Air-pollution data were provided by the Regional

Agency for Environmental Protection in Piedmont

(ARPA), Air Quality Control Division. During the

period from January 1997 to December 1999, reliable

data were available from ten monitoring stations located

in different quadrants of Turin. In agreement with the

literature (World Health Organization 1992), in this

study, the levels of nitrogen dioxide [NO2, measured

in microgrammes per cubic metre (lg/m3

)] and total

suspended particulate [TSP, measured in microgrammes

per cubic metre (lg/m3

)] were considered as indicators of

traffic air pollution (World Health Organization 1999;

MicromedeX 2002). NO2 is a surrogate of components

of motor vehicle emission, and TSP, usually considered

a proxy indicator for overall air pollution, is also considered a good indicator of exposure to vehicular pollution; for those reasons both pollutants have been

considered in our study. All stations monitored hourly

NO2, using a chemiluminescence method, and TSP,

using a b-attenuation method. Other details of the

monitoring sites, measurement methods and indicators

can be found in the literature (Citta` di Torino 1999).

The various components of airborne pollutants dissolve at different rates; some remain suspended for a

long time, while others dissolve more rapidly. As suggested by other studies on the short-term effects of air

pollution (Katsouyanni et al. 1996; Vigotti et al. 1996;

Sunyer et al. 1997, 2002, 2003; Spix et al. 1998; Atkinson

et al. 2001; Fusco et al. 2001; Biggeri et al. 2001), the

best indicator for NO2 exposure is the daily mean level,

computed as means of the measurements at the monitoring stations, and for TSP exposure, the means of the

daily total levels measured by each monitoring station.

Those methods were also used in this study.

Pollution data: how to complete missing data

The missing values at each monitoring station, ranging

from 3.3% (36 of the total 1,095 days) to 13.5% (148 of

the total 1,095 days) [values comparable with other

studies (Sunyer et al. 1997)], were replaced with available

measurements from the other monitoring stations, following a common procedure used in other studies

(Katsouyanni et al. 1996; Sunyer et al. 1997, 2002,

Vigotti et al. 1996; Spix et al. 1998; Atkinson et al. 2001):

(a) Stations that lacked over 25% of data for the entire

study period were excluded

(b) The daily data were defined as lacking when either

fewer than 75% of the daily hourly data, or over

25% of the 8-h moving average, were not available,

with the specification that, for each 8-h interval, at

least 75% of the hourly data were required. When

data were not available at one or more stations on

certain days, they were substituted with the weighted

mean of values from other monitoring stations when

available.

Briefly: Xestimate 1⁄4 Xd 

Xy

Xk

 ;

where Xd = daily mean of the other monitoring sites, Xy

= k yearly mean of the j monitoring site, Xk = mean for

the k year of all monitoring sites

(c) When information was not available for all monitoring stations, the days with missing values were

replaced with the average of the values of the pollutant recorded the previous and the next day, if

these were not missing as well.

In all other cases, the data were reported as missing.

Meteorological data

Meteorological information was drawn from the daily

recordings of the Environmental Protection Agency for

Piedmont (ARPA), Air Quality Division. Daily mean

temperature and humidity were the indicators selected

on the basis of previous epidemiological evidence (Katsouyanni et al. 1996); other meteorological data used in

the study were wind velocity and solar radiation.

Admission data

Admission data were extracted from the regional archive

of hospital discharge records routinely collected by the

Epidemiology Service of the Piedmont Region. The

study was based on emergency admissions of patients

resident in Turin 1 in the period from January 1997 to

December 1999, including those admitted during the

final months of 1999 and discharged in 2000. For each

patient demographic data, place of residence, period and

1

The population resident in Turin in the period 1997–1999 was

approximately 870,000 and there were 12 hospitals in Turin (Banca

Dati Demografica Evolutiva Piemonte 2003; Citta` di Torino 1999;

Costa et al. 1998).

165
details of admission, and primary and secondary diagnosis were available.

In the study we considered separately young patients

(0–14 years), adult patients (15–64 years) and elderly

patients (>64 years), because some respiratory illnesses,

such as the diagnostic category of asthma, may include

syndromes of a different nature in children from those in

adults and elderly people (Martinez et al. 1995). Case

and control groups were defined in accordance with the

literature (Ciccone et al. 1995; Panella et al. 2000).

Based on the patient’s primary diagnosis, coded

according to the International Classification of Diseases,

9th Revision (ICD-9), cases were defined as those patients resident in Turin and admitted for asthma (ICD-9

code 493) (ncases=1,401). As suggested in the literature,

patients admitted for causes other than respiratory

diseases (ICD-9 codes 460–487, 490–492, 494–496 and

500–519) or heart diseases (ICD-9 codes 390–405 and

410–429), because they are associated with air pollution

exposures were defined as controls (ncontrols=201,071).

Definition of exposure and control factors

For each pollutant, the average of the level measured on

the admission day and on the previous 2 days (lag 2)

and the average of the level measured on the admission

day and on the previous 3 days (lag 3) were considered

as the indicators of exposure. As the literature confirms

(World Health Organization 1992; Biggeri et al. 2001),

lag 3 was thought to be a good indicator of a patient’s

total exposure.

As confirmed by the literature (MicromedeX 2002),

nitrogen dioxide and the total suspended particulate, as

other pollutants, were influenced by variations in the

weather (seasonality) (Fig. 1). In the analysis, the month

of admission, coded as summer (April–September) and

winter (October–March) in accordance with other

studies (Sunyer et al. 2003) was considered as the seasonality indicator; meteorological parameters considered in the analysis were temperature, humidity, solar

radiation and wind velocity. In accordance with other

studies (Fusco et al. 2001; Sunyer et al. 2002) temperature and humidity were considered the principal meteorological factors influencing the pollutants in the study.

The geographical characteristics of Turin suggested a

general homogeneity in the distribution of meteorological and air pollution levels for day (information by the

Public Health Office, Public Health Laboratory, USSL

1, Turin, Italy); for this reason a uniform distribution of

the meteorological parameters and the air pollution

levels of the city were considered. The other effects of the

season on health were considered to be randomly distributed among cases and controls.

The different probability of admission, according to

the day of admission, was controlled by the day of the

week on which the patient was admitted, classified as

day of week (coded 1: Monday, 7: Sunday) and workday

or holiday (coded 1: workday, 2: holiday). Other

adjustment factors were gender (coded 1: male, 2: female) and age of patient (considered as continuous

variable).

Since information about the subjects’ tobacco use

and social status were unavailable; education levels

(coded 1: elementary, 1–4: graduate) were used to

compensate partially for this gap.

Statistical analysis

The pollutants and meteorological factors were considered in the analysis as continuous variables to quantify

the increase in risk by unit increase of exposure.

The correlation between pollutants and temperature

and humidity was analysed by Pearson coefficient

(overall period and separately for season).

We used logistic regression models (Clayton and Hills

1998) to analyse the association between emergency

admission for asthma and nitrogen dioxide and total

suspended particulate concentrations. The models,

which included gender, age, daily mean temperature,

daily mean humidity, season (winter/summer), day of

the week and holidays, were used separately for each

pollutant. To analyse the independent effects of two

pollutants, in subsequent analysis both pollutants were

included in the same model. On the basis of odds ratio

Fig. 1 Nitrogen dioxide and total suspended particulate levels by

day of recording (period: 1 January 1997–31 December 1999)

166
values estimated by the models, the results have been

shown as percentage increase (increase%) in risk and

relative 95% confidence interval (95% CI), by 10 lg/m3

increase in nitrogen dioxide and total suspended particulate exposure.

Results

Table 1 presents a description of nitrogen dioxide and

total suspended particulate levels separately for cases

and controls; a description of temperature, humidity and

number of admission by day (day of the week and

workday/holiday) is also included.

Figure 1 shows daily pollutant levels in the period

January 1997–December 1999, presenting different

concentrations for cold and warm seasons. A low correlation between daily pollutant, temperature and

humidity levels was found (Table 2).

Subsequent analyses presented a significant association between a general increase in the risk of emergency

admissions for asthma and an increase in exposure to

both pollutants (Table 3, model including one pollutant). The results showed a mean increase of 2.4%, (95%

CI: 0.5%; 4.3%) and 2.3% (95% CI: 1.1%; 3.6%) for a

10 lg/m3 increase in exposure to NO2 and TSP,

respectively.

The association was evident, principally, among

young and elderly patients for both pollutants: 2.3%

(95% CI: 0.3%; 4.4%) and 7.7%, (95% CI: 0.2%;

15.2%) for NO2; 1.9% (95% CI: 0.4%; 3.4%) and 8.3%

(95% CI: 2.9%; 13.7%) for TSP, respectively, for young

and elderly patients. A positive, but not statistically

significant, association was found for the adult patients

for both pollutants.

The association between pollutants and the outcome

was generally more unstable when both pollutants were

included in the same model (Table 3), suggesting that

the effects observed may be due to particulates derived

from source correlated with NO2 levels, probably trafficrelated sources.

Conclusions and discussion

Turin, for its geographical characteristics, presents a

more homogeneous distribution of meteorological and

air pollution levels, by day, throughout the whole city

area, in comparison with other cities (Vigotti et al. 1996;

Fusco et al. 2001; Biggeri et al. 2001). We found no

significant differences (Kruskal–Wallis test p=0.18 and

P=0.32 for NO2 and TSP, respectively) and a positive

correlation (Pearson’s correlation coefficient: r=0.71,

p=0.002 and r=0.79, p=0.001 for NO2 and TSP,

respectively) among the ten monitoring stations.

For those reasons, the daily mean level for NO2 and the

daily total level for TSP, computed from measurements

provided by each station, were considered to be good

Table 1 Summary of environmental variables and numbers

admitted, by admission day (work/holiday), for cases and controls

Parameter Temperature

(C)

Humidity

(%)

NO2

(lg/m3)

TSP

(lg/m3)

Controls

Mean 14.3 58.7 112.7 105.3

SD 7.6 17.5 30.2 44.2

Median 14.5 60.7 107.7 95.9

Cases

Mean 13.1 58.7 117.1 113.9

SD 7.1 17.6 30.0 43.8

Median 13.2 60.2 113.0 107.2

Total

Mean 14.3 58.7 112.7 105.3

SD 7.6 17.5 30.2 44.2

Median 14.5 60.7 107.7 96.0

Workday Holiday Total

Controls

n 197,653 3,418 201,071

Row (%) 98.3 1.7

Cases

n 1,365 3,643 1,401

Row (%) 97.4 2.6

Table 2 Pearson correlation coefficient for NO2, TSP and meteorological parameters, total and separately for season

Parameter NO2 TSP Temperature

Total

NO2 1

TSP 0.80 1

Temperature 0.33 0.45 1

Humidity 0.20 0.04 0.05

Winter

NO2 1

TSP 0.77 1

Temperature 0.09 0.10 1

Humidity 0.24 0.08 0.14

Summer

NO2 1

TSP 0.69 1

Temperature 0.19 0.07 1

Humidity 0.38 0.22 0.23

Table 3 Percentage increase in hospital admissions for asthma by

10 lg/m3 increase in NO2 and TSP exposure (and relative 95% CI)

Age category NO2 TSP

Increase

(%)a 95% CI Increase

(%)a 95% CI

Inf Sup Inf Sup

Model including one pollutant

<15 years 2.30 0.30 4.40 1.90 0.40 3.40

15–64 years 3.10 0.01 7.70 2.30 0.01 5.20

>64 years 7.70 0.20 15.20 8.30 2.91 13.68

Total 2.40 0.50 4.30 2.30 1.10 3.60

Model including both pollutants

<15 years 1.71 0.02 5.00 0.12 0.03 2.50

15–64 years 1.20 0.06 6.50 0.90 0.04 5.61

>64 years 0.91 0.08 5.91 1.2 0.01 4.32

Total 1.10 0.02 3.82 0.91 0.02 3.11

a

Computed as (adjusted OR · 10 lg/m3 · 100)

167
indicators of the NO2 and TSP daily concentration in

Turin.

As the literature shows (Holme ́n et al. 1997; Sunyer

et al. 1997), asthma morbidity has been increasing in the

past few years, and the reasons have not yet been

explained (Department of Health Advisor Group on the

Medical Effects of Air Pollution Episodes 1995); in particular, traffic air pollution could exacerbate the existing

asthma symptoms (Fusco et al. 2001). Our paper focused

the effects of traffic air pollution on asthma admissions,

showing a positive, significant, association between

asthma admission during the period 1997–1999 and

exposure to nitrogen dioxide and total suspended particulate. The association was particularly evident among

the young and elderly patients. The positive but nonsignificant association among the adults patients, in

accord with other studies (Sunyer et al. 1997; Fusco et al.

2001; Biggeri et al. 2001), could also be attributable to the

difficulty in defining the exposure for the adults (children

and elderly people are more sedentary than adults).

Moreover, we tried to analyse the independent effects

of two pollutants with a model, including both pollutants in the same model. These models were limited by

the collinearity among NO2 and TSP, generated by the

same source (as the correlation coefficient confirms); for

this reason the model that included both pollutants was

more unstable, and suggests that the effects observed

may be due to particulates derived from traffic-related

sources correlated with NO2.

In the interpretation of the results of this study the

possible effect of passive smoking (the regional archive

of hospital discharge forms did not permit exact control

for the subjects’ smoking status), and the risk of disease

misclassification, frequent in asthma diagnosis, especially in the pre-school population, should be taken into

consideration.

A confounding factor not considered in the study was

indoor exposure; in Turin, residences present a really

homogeneous distribution for heating (mostly central

heating) and cooking (mostly gas cooking) (Costa et al.

1998); for this reason indoor exposure should not affect

the results.

Studies on the effects of atmospheric pollution on

health contend with thorny difficulties, foremost of

which are the heterogeneity of pollutants and the difficulty in measuring them accurately. Several groups of

common air pollutants in urban air have been the object

of studies by the International Agency for Cancer

Research (IARC). However, none of the monitors in the

air quality control network manages to measure all

components directly; thus, no air quality parameter

currently measured can be considered exhaustive in the

complex and variable mixture that is summarized under

the term ‘urban air pollution’. In this respect the nitrogen dioxide and total suspended particulate pollutants

could be considered to be good indicators of exposure to

vehicular traffic pollution (World Health Organization

1999; MicromedeX 2002). The case–control approach

used in this study takes into the model the time of

occurrence of the events, even for the series of controls.

It has a natural baseline, not influenced by exposure, so

that the occurrence among cases and controls can be

compared; it needs no adjustments for complex cyclical

trends or for introducing smoothing parameters.

Despite some limitations, our study shows results

comparable to the risks reported in other studies on

Italian and European populations and based on design

and statistical methods that are difficult to carry out

(Burnett et al. 1994; Schwartz et al. 1994; Spix et al.

1998; Atkinson et al. 2001; Fusco et al. 2001; Biggeri

et al. 2001; Sunyer et al. 2002, 2003). In conclusion,

using a study design that was both easy to use and to be

managed, we have shown that our results are comparable to those from studies with a more complex design

that are published in the literature; in particular, the

results show the importance of nitrogen dioxide and

total suspended particles in the analysis of the effects of

traffic air pollution on public health and underline the

need for further research in this area.

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