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RESEARCH Open Access
Urban air pollution and emergency room
admissions for respiratory symptoms: a case-
crossover study in Palermo, Italy
Fabio Tramuto
1*
, Rosanna Cusimano
2,3
, Giuseppe Cerame
1
, Marcello Vultaggio
4
, Giuseppe Calamusa
1
,
Carmelo M Maida
1
and Francesco Vitale
1
Abstract
Background: Air pollution from vehicular traffic has been associated with respiratory diseases. In Palermo, the
largest metropolitan area in Sicily, urban air pollution is mainly addressed to traffic-related pollution because of lack
of industrial settlements, and the presence of a temperate climate that contribute to the limited use of domestic
heating plants. This study aimed to investigate the association between traffic-related air pollution and emergency
room admissions for acute respiratory symptoms.
Methods: From January 2004 through December 2007, air pollutant concentrations and emergency room visits
were collected for a case-crossover study conducted in Palermo, Sicily. Risk estimates of short-term exposures to
particulate matter and gaseous ambient pollutants including carbon monoxide, nitrogen dioxide, and sulfur dioxide
were calculated by using a conditional logistic regression analysis.
Results: Emergency departments provided data on 48,519 visits for respiratory symptoms. Adjusted case-crossover
analyses revealed stronger effects in the warm season for the most part of the pollutants considered, with a
positive association for PM
10
(odds ratio = 1.039, 95% confidence interval: 1.020 - 1.059), SO
2
(OR = 1.068, 95% CI:
1.014 - 1.126), nitrogen dioxide (NO
2
: OR = 1.043, 95% CI: 1.021 - 1.065), and CO (OR = 1.128, 95% CI: 1.074 - 1.184),
especially among females (according to an increase of 10 μg/m
3
in PM
10
,NO
2
,SO
2
, and 1 mg/m
3
in CO exposure).
A positive association was observed either in warm or in cold season only for PM
10
.
Conclusions: Our findings suggest that, in our setting, exposure to ambient levels of air pollution is an important
determinant of emergency room (ER) visits for acute respiratory symptoms, particularly during the warm season. ER
admittance may be considered a good proxy to evaluate the adverse effects of air pollution on respiratory health.
Background
The p revalence of respiratory diseases has dramatically
increased during the last decades in industrialized coun-
tries [1,2] and there is some evidence to correlate both
high levels of motor-vehicle emissions and urban life-
styles with the rising trend in respiratory diseases [3,4].
Several studies, in Europe [5-7] and elsewhere [8-10],
have reported the adverse effects of traffic-related air-
pollution on human health focusing on particulate
matter as the most common investigated traffic-related
air pollutant [11].
The burden of air pollution on health system is gener-
ally underestimated for the difficulties to clearly evaluate
the possible linkage between air pollution level and
adverse health outcomes part ially due to the variabi lity
of personal exposure, to the influence of individual
effect modifiers [12] but also because respiratory symp-
toms are often neither consulted nor registered in medi-
cal records as related to air pollution [13].
Several epidemiological studies were reported on
emergencyroom(ER)visitsandurbanairpollution
worldwide, but mainly focused on asthma in young age
[14-18]. In Italy, the relationship between air pollution
* Correspondence: fabio.tramuto@unipa.it
1
Department for Health Promotion Sciences “G. D’Alessandro” - Hygiene
section, University of Palermo, Via del Vespro 133, 90127 Palermo, Italy
Full list of author information is available at the end of the article
Tramuto et al. Environmental Health 2011, 10:31
http://www.ehjournal.net/content/10/1/31
© 2011 Tramuto et al; licensee BioMed Central Ltd. This is an Open Acces s article distributed under the terms of the Creative
Commons Attribution License (http://creativecommons.org/lic enses/by/2.0), which permits unrestricted use, distribution, and
reproductio n in any medium, provided the original work is properly cited.
and health effects has been previou sly investigated both
in terms of mortality and hospital admission [19-22].
However, fewer studies have analysed more generic end-
points, such as respiratory symptoms in general popula-
tion, in association with ER admissions [23,24]. The
latter ones, that are certainly more frequent events than
hospitalisation, could be considered a n indicator of
urban air pollution associated with a significant worsen-
ing in the quality of life, especially in large metropolitan
areas [25,26].
In Sicily, the main island of the Mediterranean Sea,
Palermo represents the largest metropolitan area. It is
characterized by a temperate climate and a very active
commercial and touristic port. Due to limited use of
domestic heating plants and to the lack of industrial set-
tlements in residential areas, motor vehicles, including
boats, contribute to the most part of urban air pollutant
emissions, conferring to this geographical setting dis-
tinctive key features suitable for modelling studies on
traffic-related pollution on health effects.
In the current study, a case-crossover approach was
carried out on a three years routinely collected data in
order to analyse the association between hospital ER
attendance for respiratory causes and traffic-related air
pollutants among adult individuals residents of Palermo,
the largest city in Sicily (Italy).
Methods
Geographic setting
In this study, we considered the municipality of
Palermo, a seaside town capital of Sicily, with a resident
population of about 700,000 inhabitants (82.5% > 14
years of age, 47.8% males) [27], and a mediterrane an cli-
mate with hot summers and temperate winters. Palermo
has a very active commercial and tourist port, regular
stop of many Mediterranean cruises, and a historic cen-
tre characterized by narrow streets and hea vy traffic
congestion, particularly in rush hours. Due to limited
use of domestic heating plant s and to the lack of indus-
trial plants in residential areas, motor vehicles, including
boats, contributes to at least 70-75% of total a ir pollu-
tant emissions [28].
Air pollution and climatic data
Ten automated fixed-site monitoring stations (seven
“urban traffic” ,two“background”, and one meteo-cli-
matic monitoring stat ions, respectively), located either
in densely populated or peripheral urban areas, collected
the daily air pollution levels geographica lly dispersed on
a metropolitan area of about 56 km
2
(Figure 1) [29].
Data were obtained for particulate matter (Ø ≤ 10
microns - PM
10
;inμg/m
3
), nitrogen dioxide (NO
2
;in
μg/m
3
), sulfur dioxide (SO
2
;inμg/m
3
), and carbon
monoxide (CO; in mg/m
3
). Pollutants were hourly
collected by d irect gravimetric determination method
for PM
10
, by chemiluminescence for NO
2
, by ultraviolet
fluorescence spectroscopy for SO
2
, a nd by infrared-ray
absorption for CO.
PM
10
,SO
2
,NO
2
daily mean exposure estimates were
used. Exposures to CO were based on the 8-hours mov-
ing average maximum value.
The meteo-climatic monitoring station specifically col-
lected air temperature, relative humidity percent, wind
speed, atmospheric pressure, and precipitation.
The completeness criteria for the data recorded at the
nine stations were based on estimating the missing
value using the available measurements in the other
monitoring stations on the same day, weighted by a fac-
tor equal to the ratio of the annual mean for the missing
station over the corresponding mean from all the other
stations available on that particular day [30].
Daily pollution levels were considered missing if any
of the other measurements were not available.
Overall, there were l ess t han 10 % of missing va lues i n
the air pollutant and meteo-climatic hourly measurements.
Health data
The i nclusion criteria for the selection of partecipating
hospitals were: a) location within the city limits of
Palermo, b) 24-hour service ER department and emer-
gency physicians, and c) electronic registration of patient
admissions.
Overall, six public general hospitals are present in the
urban area of Palermo. Of them, five were included in
the study, while only one hospital (about 37,000 ER vis-
its/per year) did not meet the third criterio n (Figure 1).
On the whole, study population accounted for 89.1% of
the ER visits totally collected in Palermo during the per-
iod 2005-2007.
Figure 1 Map of Palermo (Sicily). Air quality monitoring stations
and hospitals.
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Each participating emergency department provided all
their patient data collected between January 2005 and
December 2007. Basic data for each patient, only resi-
dent of Palermo, included sex, age, and a unique identi-
fication (ID) number.
Each ER admission record collected during hospital
triage evaluation, which included terms as respiratory
deficiency, emphysema, dyspnea/shortness of breath,
cough, asthma, pneumonia, bronchopathy, or other
obstructive pulmonary diseases, was defined as “event of
interest” only if followed by a medical diag nosis of
respiratory distress.
Moreover, the number of ER visits by the same person
in a day was preliminary checked, and evidence of
repeated access was found. Therefore, in order to pre-
vent any possible overestimation of independent visits,
although small, only one ER visit per person/day (within
each month) was included in the analyses.
Statistical analysis
Descriptive statistics were calculated for the demo-
graphics of patients with ER hospital admission for
respiratory disorders and for meteorological factors and
air pollutant levels, and a matrix of Pearson’s correlation
coefficients (r) was generated to better define the asso-
ciations between air pollutants and meteorological
parameters.
A case -crossover design [31] was adopted f ollowing a
time-stratified approach, where for an “event of interest”
occurring on a given day of the week, “control days”
were considered all the same days of the other weeks
throughout the rest of the month. For example, if the
subject went to hospital ER on Saturday, all other Satur-
days of the same month would be used as controls
(thus, three or four days) [32,33].
Stratified analyses were similarly conducted by sex,
age-groups (1 6-44, 45-54, 55-64, 65-74, 75-84, ≥85), and
seasons (winter: October - March, summer: April -
September).
Moreover, to highlight sufficient variation around a
non-zero mean value as suggested in case-crossover stu-
dies [34], we calculated the “ relevant exposure term”
which is the absolute difference between each pollutant’s
levels corresponding to the “event of interest” ("event
days”) and its average concentrations over the “control
days”.
To control for potential impact of meteo-climatic
parameters, a sam e-day mean temperature was used to
control for immedia te effects and the average of the lags
1-3 of mean temperature to represent the delayed
effects.
In the w arm season, temper ature was c onsidered as
daily mean “apparent temperature” (AT), following the
methodology described by other authors [35,36].
Because risk may vary non-linearly with temperature,
a natural cubic spline (with three degrees of freedom)
was used for both the same day and the moving average
of the previous three days; both terms were included
simultaneously in the models.
The relevant daily data of other meteorogical para-
meters (relative humidity percent, wind speed, atmo-
spheric pressure, and precipitation) as well as the
influenza epidemic peaks, defined between the 3
rd
and
the 7
th
week of each year (National Surveillance System
by the Italian National Institute of Health), were consid-
ered as confounding factors.
Pollutant measurements were entered into the ana-
lyses as linear variables.
The association between daily levels of traffic-related
air pollutants and ER attendance for respiratory causes
was analysed by a conditio nal logistic regression model,
and odds ratios ( OR) of exposures were calculated to
quantify the increase i n risk according to an increase of
10 μg/m
3
in PM
10
,NO
2
,SO
2
,and1mg/m
3
in CO
exposure; 95% confidence intervals (CI) were calculated.
To examine the hazard period of air pollution for
respiratory s ymptoms, a distributed lag model was also
used to evaluate the effect of air pollutants; the hazard
period was defined as the same day (lag 0), o r the pre-
vious day up to the 5
th
day prior to the hospital visit.
Finally, risk estimates were calculated by using a single
pollutant model, given the general collinearity between
the pollutants.
All statistical analyses were conducted using STATA
v10.1 MP for Macintosh ( Apple) by using the CLOGIT
command [37].
Results
“ Events of interest” were recorded in 48,519 o ut of
1,014,272 (5%) ER visits accounting for a mean numb er
of daily admissions of 44.9 (range: 17-96), with a higher
proportion of visits during the winter (53.1%). Moreover,
about 53% of visits occurred in indivi duals ≤ 64 years of
age, with a fairly predominance of males (55.5%). 608
(1.2%) ER visits were excluded as duplicates within the
same day by individual patients (Table 1).
Table 2 summarize the descriptive statistics of the
urban air pollutant levels and meteo-c limatic variables.
Daily average concentrations of SO
2
,NO
2
,andCO
were costantly lower than the law’ s threshold in Italy
[38]; the daily mean level of PM
10
was 36.0 μg/m
3
(annual law limit = 40 μg/ m
3
) although, on a cumula-
tive basis, about 45% of the daily observations exceeded
threshold.
Moreover, a consistent difference was observed
between the mean daily levels of each pollutant regis-
tered in the “ event days” an d “ control days” ,
respectively.
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Table 1 Descriptive statistics of ER hospital admissions for respiratory symptoms in total and by year, age-group, sex,
and season
Characteristic Number of visits (%)
ER admissions for all causes 1,014,272
ER admissions for respiratory symptoms 49,127
2005 16,960 (34.5)
2006 15,932 (32.4)
2007 16,235 (33,1)
Daily ER admissions [mean (range)] 44.9 (17-96)
Duplicates within the same day for each study subject 608 (1.2)
Total ER visits w/o same day duplicates 48,519
Season
Warm (April to September) 22,759 (46.9)
Cold (October to March) 25,760 (53.1)
Age group (years)
16-44 14,988 (30,9)
45-54 4,698 (9.7)
55-64 5,936 (12.2)
65-74 9,236 (19.0)
75-84 10,139 (20.9)
≥85 3,522 (7.3)
Age subjects [years, mean (SD)] 56.4 (37)
Sex
Female 21,516 (44.4)
Male 26,934 (55.5)
(missing) 69 (0.1)
Table 2 Statistics for urban air pollutant, weather variables, and distribution of the absolute differences between the
daily levels of each pollutant ("event days”) and the average concentrations over the “control days”.
Parameter Unit Mean Percentiles
10 25 50 75 90
Pollutants
PM
10
μg/m
3
36.0* 21.6 26.3 33.2 41.5 52.6
NO
2
μg/m
3
41.5 24.8 32.7 40.8 49.7 58.6
SO
2
μg/m
3
3.4 0.6 1.2 2.6 4.5 6.9
CO mg/m
3
1.1 0.4 0.6 0.9 1.5 2.1
Differences “event-control” days
PM
10
μg/m
3
11.8 1.4 4.2 8.9 15.6 24.1
NO
2
μg/m
3
10.8 1.7 4.1 9.0 15.2 22.1
SO
2
μg/m
3
2.2 0.3 0.6 1.4 2.8 5.0
CO mg/m
3
0.4 0.0 0.1 0.3 0.6 0.9
Weather variables
Air temperature °C 18.6 10.7 13.3 18.7 23.8 26.7
Relative humidity % % 58.8 44.2 51.3 59.8 66.9 72.1
Atmospheric pressure mbars 994.2 987.6 990.7 993.9 997.8 1001.3
Precipitation mm 0.1 0.0 0.0 0.0 0.0 0.3
Wind speed m/s 3.2 1.6 2.0 2.6 4.1 6.1
January 2005 - December 2007.
*on a cumulative basis, about 45% of the daily observations exceeded threshold.
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During the study period the climate was temperate,
with a mean air temperature of 18.6°C and a relative
humidity of 58.8%, with little rain or wind.
There was moderately high collinearity among pollu-
tants, including SO
2
and NO
2
(r = 0.571), PM
10
and
NO
2
(r = 0.451), and especially CO and NO
2
(r = 0.592).
Rain correlated negatively with all pollutants, whereas
relative humidity percent did not. PM
10
,SO
2
,andNO
2
did not f ollow a s easonal pattern and w ere not corre-
lated with temperature (see Additional file 1: Matrix of
linear corre lation coefficients, Table S1 for an overview
of all variables). Moreover, the monthly levels of the
pollutants measured during the study period are
reported in Additional file 2: Monthly distribution of
the pollutants, Figure S1.
Table 3 reports the associations between air pollution
exposure and respiratory effects calculated for the single
pollutant model, by controlling the influence of different
climatic parameters and influenza epidemic peaks.
In the full year analysis, positive effect estimates were
found with all the pollutants, sho wing an increased risk
of 2.2% (95% CI: 1.3-3.1), 4.4% (95% CI: 0.3-8.6), 2.3%
(95% CI: 0.1-4.7) and 1.5% (95% CI: 0.4-2.6) for PM
10
,
SO
2
,COandNO
2
, respectively. Stronger associations
were observ ed during the summer with i ncrements ran-
ging from 3.9% to 12.8%; only PM
10
demonstrated a
clear association in the cold season too.
Moreover, risk estimates decreased over time for each
pollutant at differ ent lags (0-5 days prior to ER visit),
and mostly the same day exposure was significant;
therefore, lag 0 exposure will be considered as the
hazard time (Figure 2).
For each pollutant, analyses were replicated for different
age groups and sex (Figure 3 and 4). Overal l, the most
marked associations between ER visits and PM
10
air pollu-
tion levels occurred among the age groups 16-44 years
and ≥85 years d uring the summer (OR = 1.059, 9 5% CI:
1.023-1.096 and OR = 1.087, 95% CI: 1.015-1.165, respec-
tively), preferentially among women (OR = 1.064, 95% CI:
1.012-1.119 and OR = 1.121, 95% CI: 1.023-1.229).
A similar result was also observed in females 75-84
years old for the SO
2
(OR = 1.222, 95% CI: 1.026-1.457),
while the highest OR values were observed with CO
exposure (OR = 1.292; 95% CI: 1.1 27-1.481) among
females and during the warm season.
Discussion
In this study, a positive association between ER atten-
dance for respiratory symptoms and ambient exposure
to motor-vehicle pollutants such as PM
10
, nitrogen diox-
ide, sulfure oxide, and carbon monoxide was found, and
a clear difference by season was observed. PM
10
was the
sole pollutant that showed positive OR values in both
the warm and cold seasons.
Villeneuve et al. [14] desc ribed a positive association
for asthma visits with outdoor air pollution levels but
Table 3 Adjusted odds ratio (OR)
a
for emergency department visits for respiratory causes among all patients,
by season
All seasons Season
Cold (October to March) Warm (April to September)
Pollutants OR 95% CI OR 95% CI OR 95% CI
PM
10
1.022 1.013-1.031 1.018 1.008-1.029 1.039 1.020-1.058
SO
2
1.044 1.003-1.086 0.983 0.908-1.064 1.068 1.014-1.126
CO
b
1.023 1.001-1.047 0.991 0.965-1.017 1.128 1.074-1.184
NO
2
1.015 1.004-1.026 1.000 0.984-1.015 1.043 1.021-1.065
a
Odds ratios were calculated in relation to an increase of 10 μg/m
3
of selected air pollutants and were adjusted for meteo-climatic parameters, and influenza
epidemic peaks (see Methods - Statistical analysis).
b
For an increase of 1 mg/m
3
.
Figure 2 Odds ratio (OR) for emergency respiratory symptoms
calls according to various lag times, Palermo, Sicily, 2005-2007.
Lag 0 is for pollutant concentrations averaged on the day of the
call, lag 1 is for pollutant concentrations averaged for the previous
day of the call, and so on. Associations are expressed as adjusted
OR [95% confidence interval (CI)] in relation to an increase of 10
μg/m
3
of selected air pollutants (CO: an increase of 1 mg/m
3
). ORs
adjusted for meteo-climatic parameters, and influenza epidemic
peaks (see Methods - Statistical analysis).
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only during the warm season, documenting similar
results with higher OR values among elderly individuals
(OR = 1.09 vs 1.10, respectively). In contrast, Fusco et
al.[39]didnotreportanyoverall effect with same-day
levels of suspended particles for total respiratory
admissions.
Zanobetti et al. [35], using a case-crossover approach,
found a significant association betwe en black carbon
and p neumonia hospitalization (11.7% increase of risk).
However, they found no associations with pneumonia
ER admissions in the warm season.
In Italy, Bedeschi et al. [23] reported a 2.7% increase
of risk between PM
10
exposure and ER visits for all
respiratory diso rders, even if amo ng children and at lag
3; however, the delayed time observed might raise speci-
fic considerations in a such particular setting of
individuals.
Different considerations have to point out on sulfur
dioxide. Air concentration of this gaseous pollutant
has been drastically decreased worldwide [40,41] due
to the adoption of low-sulphur fuels for urban vehicle
engines. Consequently, it could be considered of minor
importance in the evaluation of the possible linkage
between traffic related air pollution and health effects.
However, since new regulations in maritime transpor-
tation haven’ t been fully implemented yet, sea
Figure 3 Single pollutant model results for all respiratory causes according to the same-day exposures, Palermo, Sicily, 2005-2007 (Air
pollutants: PM
10
and SO
2
). Associations are expressed as adjusted odds ratio (OR) [95% confidence interval (CI)] in relation to an increase of 10
μg/m
3
of selected air pollutants, according to age groups, sex, and seasons (cold season: October to March, warm season: April to September).
ORs adjusted for meteo-climatic parameters, and influenza epidemic peaks (see Methods - Statistical analysis).
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transports may be actually considered the most impor-
tant source of SO
2
pollution in deep-rooted maritime
vocation cities [42,43]. In our context, where the port
is located not far from the city centre and a heavy
maritime traffic is present from spring through early
autumn, the potential effects of ambient SO
2
levels on
respiratory health cannot be excluded. Therefore, SO
2
was considered in the analyses reported in the p resent
study.
The effects of SO
2
on respiratory hospitalization varies
considerably, especially at low levels of exposure, and
conflicting results were documented by several authors
[14,44,45].
Wong et al. [46] observed significant short-term
effects between SO
2
and admissions for respiratory
causes in elderly subjects but not among younger age
groups. Consistent with these findings, our study
showed a positive association between SO
2
and respira-
tory events among elderly individuals, especially in
warm season, confirming the possible r ole of maritime
traffic pollution in coastal cities as also observed in
North Europe [42].
Overall, a significant association was observed between
CO exposure and respiratory disorders especially in the
warm season (OR = 1.128, 95% CI: 1.074 - 1.184), as
similarly reported in large metropolitan centres either in
Figure 4 Single pollutant model results for all respiratory causes according to the same-day exposures, Palermo, Sicily, 2005-2007 (Air
pollutants: CO and NO
2
). Associations are expressed as adjusted odds ratio (OR) [95% confidence interval (CI)] in relation to an increase of 10
μg/m
3
of selected air pollutants (CO: an increase of 1 mg/m
3
), according to age groups, sex, and seasons (cold season: October to March, warm
season: April to September). ORs adjusted for meteo-climatic parameters, and influenza epidemic peaks (see Methods - Statistical analysis).
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Italy or elsewhere [14,39,46], while Bedeschi et al . [23]
found no association between CO and respiratory ER
visits among children.
NO
2
has been known to increase susceptibility to
respiratory infections [47].
Positive associations were observed both in France
[48] and in Rome [39] particularly during the summer,
as well as in England although at lag2 and in infants
[49]. On the contrary, no significant associations were
reported, also in different groups of age, either in Lon-
don [50] or in northern Alberta (Canada) [14].
In our setting, NO
2
correla ted with increasing respira-
tory symptoms mostly in summer but without a clear
age dependence.
Environmental exposures are complex. Traffic-related
air pollution includes gaseous species and PM from
combustion, tire and brake wear, and resuspended road-
way dusts. Moreover, because there is a strong correla-
tion between different pollutants regularly investigated
in environmental studies [44], it is usually difficult to
glean the contribution of each pollutant on health
effects.
Furthermore, quality and distribution of air pollutants
could be probably affected by the geo-orographical char-
acteristics, human activities, and cl imatic condit ions that
may vary between cities. Thus, concomitant causes
could explain the partial inconsistency in the results of
the various investigations.
Although studies on air pollution and health were his-
torically carried out by using a time series design, the
case-crossover approach has been increasingly applied
more recently [51]. In our study, values relative to the
“relevant exposure term” were also calculated for each
pollutant to evaluate the presence of sufficient variation
around a non-zero mean value between ambient con-
centrations of event and control days, since a scarse
variability between event and control days could lead to
a wrong interpret ation of the results, limiting the power
to detect health effects [34].
Moreover, because some controversies regarding the
use of multipollut ant modelling in air pollutant research
were rais ed [39], in this study we applied a monopollu-
tant regression model controlling for different meteo-cli-
matic variables and flu epidemic peaks as possible
confounders. Furthermore, we have preliminarly
checked the effect of air pollutants without meteo-cli-
matic f actors in the logistic regression model. Not sur-
prisingly, we found stronge r effects with temperature,
considering the climate of our geographic area charac-
terized by hot and humid summers.
Overall, the present study documented a strong sea-
sonality of air pollution effects on human respiratory
health. According to other authors [52,53], this could be
partially explained as the warm season represents the
period when individuals spend a greater portion of their
time outdoor dedicated to physical activity practice,
resulting in higher respiratory volumes and exposure to
ambient pollution.
More elevated risk estimates were observed among
females, although the reasons for these differences are
yet unclear and the literature is far from consistent.
However, there is growing epidemiologic evidence of
differing associations between air pol lution and respira-
tory health for females and males and suggestive inter-
pretations have been proposed for existing differences in
relation to sex [54].
It is unclear whet her observed modification is attribu-
table primarily to sex-linked biological distinctions, to
work-related exposure differences between men and
women (e.g. cooking exhaust and cleaning products), to
socially derived activities and roles, or to some i nterplay
thereof.
Hormonal status or differences in the rates of lung
growth and decline may influence vascular functions [55]
or inflammation of the respiratory tract [56,57]. More-
over, the deposition of air pollution particles in the lung
has been shown to be greater in f emales compared with
males, leading to a more female susceptibility to respira-
tory diseases [58,59]. Furthermore, in Sicily, because
some domestic jobs continue to be usually performed by
women such as cooking, dusting, cleaning, and child
care, these and other reasons might lea d women to show
greater health effects to air-related risk factors.
Finally, at least three limitations of this study could be
considered. Firstly, we were not able to separately inves-
tigate the effects of individual behaviours, as possible
confounders, such as tobacco use, because informations
usually were not available in ER admission archives.
Secondly, t he lack of ICD codes in admission records
might have affected the ability to critically choose the
“events of interest”.
Thirdly, for each air pollutant, a single value was aver-
aged by a fixed number of monitoring stations instead
of individual passive samplers for personal exposure
measurements, leading to a spatial misalignment
between pollutants levels and health data.
However, the distribution of pollutants throughout the
study area was preliminarly checked by calculating a set
of both correlation and concordance coefficients
between pair of monitoring stations, showing a stro ng
homogeneity in the pollutant distribution (mean r =
0.801; range: 0.687 - 0.900).
Nevertheless, this study implicates motor-vehicle
emissions as a relevant indicator of urban air pollution
and as a determinant of deterioration of respiratory
health status with evidence of exacerbati on in the warm
season. These findings persisted after adjustment for
meteo-climatic variables and seasonal flu epidemics.
Tramuto et al. Environmental Health 2011, 10:31
http://www.ehjournal.net/content/10/1/31
Page 8 of 11
Our results specifically incremented the evidence of
association between air pollution exposure and short-
term respiratory health effects in a residential area char-
acterized by the lack of industrial settlements and by a
limited use of domestic heating plants.
Although these results must be interpreted with cau-
tion, they can provide helpful information to the field of
public healt h and may have implications for local envir-
onmental and social policies.
Conclusions
This study suggests that , in our setting, urban air pollu-
tion exposure is an important determinant of ER visits
for acute respiratory symptoms. Air pollution effects are
not homogenous and differences in the magnitude
might be associated with different seasons and age-
groups. Moreover, the study shows that warm season
increases the risk of respiratory health effects due to
motor vehicle-related air pollution, especially in females.
ER admittance may be considered a good proxy to
evaluate the adverse effects of air pollution on respira-
tory health and the identification of sex-related suscepti-
ble groups reinforces the need for public policy
measures to better control air pollution.
Additional material
Additional file 1: Table S1 Matrix of linear correlation coefficients.
Text document that provides a matrix of linear correlation coefficients
between urban air pollutants and weather variables. January 2005 -
December 2007.
Additional file 2: Figure S1 Monthly distribution of the pollutants.
EPS File that shows the monthly distribution of the pollutants over the
three-year period.
List of abbreviations
AT: apparent temperature; CI: confidence interval; CO: carbon monoxide; ER:
emergency room; ID: identification number; OR: odds ratio; PM: particulate
matter; Press: Atmospheric pressure; NO
2
: nitrogen dioxide; Prec:
Precipitation; r: Pearson’s correlation coefficient; RH%: relative humidity %;
SO
2
: sulfur dioxide; Temp: Air temperature; Wind: wind speed;
Acknowledgements
Fabio Tramuto was partially supported by the Master in Epidemiology,
University of Turin and San Paolo Foundation.
The authors thank Prof. Rossella Miglio and Prof. Franco Merletti for their
scientific and technical support.
The authors like to thank all members of the APRES (Air Pollution and
Respiratory Syndromes) Study Group:
Luigi Aprea, Salvatore Paterna, Vittorio Giuliano (A.O.U.P. “ P. Giaccone” -
Palermo); Giovanna Volo, Michelangelo Pecorella (A.R.N.A.S. Civico - Palermo);
Gabriella Filippazzo, Manlio De Simone (Az. Osp. “V. Cervello” - Palermo);
Salvatore Requirez, Baldassare Seidita (Az. Osp. “Villa Sofia”); Giampiero
Seroni, Michele Zagra (Az. Osp. “Buccheri La Ferla).
Author details
1
Department for Health Promotion Sciences “G. D’Alessandro” - Hygiene
section, University of Palermo, Via del Vespro 133, 90127 Palermo, Italy.
2
Department of Public Health, Epidemiology and Preventive Medicine - ASP6
Palermo, Via Siracusa 45, 90141 Palermo, Italy.
3
Palermo Province Cancer
Registry, Department for Health Promotion Sciences “G. D’Alessandro” -
Hygiene section, University of Palermo, Via del Vespro 133, 90127 Palermo,
Italy.
4
AMIA SpA, Via Pietro Nenni 28, 90146 Palermo, Italy.
Authors’ contributions
FT participated in the design of the study, contributed in the acquisition of
air pollution/health data, performed the statistical analysis, and helped to
draft the manuscript. RC participated in the design of the study and helped
to draft the manuscript. GCE participated in the design of the study and in
the acquisition of air pollution/health data. MV carried out the modeling of
traffic, congestion, and emissions. GCA contributed in the acquisition of air
pollution/health data. CMM helped to draft the manuscript. FV conceived of
the study, participated in its design and coordination, and helped to draft
the manuscript. All authors read and approved the final manuscript.
Competing interests
The authors declare that they have no competing interests.
Received: 16 November 2010 Accepted: 13 April 2011
Published: 13 April 2011
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