ARTICLE IN PRESS
Health & Place 16 (2010) 399–408
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Health & Place
journal homepage: www.elsevier.com/locate/healthplace
Who is affected more by air pollution—Sick or healthy? Some evidence from
a health survey of schoolchildren living in the vicinity of a coal-ﬁred power
plant in Northern Israel
Tamar Yogev-Baggio a, Haim Bibi b, Jonathan Dubnov c,d, Keren Or-Hen e,
Rafael Carel d, Boris A. Portnov a,n
Department of Natural Resources & Environmental Management, University of Haifa, Israel
Carmel Medical Center, Haifa, Israel
Haifa District Health Ofﬁce, Ministry of Health, Israel
School of Public Health, University of Haifa, Israel
Graduate School of Education, University of Haifa, Israel
a r t i c l e in fo abstract
Article history: Objective: To evaluate the effects of exposure to air pollution by NOx and SO2 on the development of
Received 17 July 2009 pulmonary function of children, characterized by different health status.
Received in revised form Methods: A cohort of 1181 schoolchildren from the 2nd to 5th grades, residing near a major coal-ﬁred
18 November 2009
power plant in the Hadera district of Israel, were subdivided into three health status groups, according
Accepted 22 November 2009
to the diagnosis given by a physician at the beginning of the study period in 1996: (a) healthy children;
(b) children experiencing chest symptoms, and (c) children with asthma or spastic bronchitis.
Keywords: Pulmonary Function Tests (PFTs) were performed twice (in 1996 and 1999) and analyzed in conjunction
Children with air pollution estimates at the children’s places of residence and several potential confounders
—height, age, gender, parental education, passive smoking, housing density, length of residence in the
Pulmonary function test
study area and proximity to the main road.
Results: A signiﬁcant negative association was found between changes in PFT results and individual
exposure estimates to air pollution, controlled for socio-demographic characteristics of children and
their living conditions. A sensitivity analysis revealed a decrease in the Forced Expiratory Volume
during the First Second (FEV1) of about 19.6% for children with chest symptoms, 11.8% for healthy
children, and approximately 7.9% for children diagnosed with asthma. Results of a sensitivity test for
the Forced Vital Capacity (FVC) were found to be similar.
Conclusion: Exposure to air pollution appeared to have had the greatest effect on children with chest
symptoms. This phenomenon may be explained by the fact that this untreated symptomatic group
might experience the most severe insult on their respiratory system as a result of exposure to ambient
air pollution, which is reﬂected by a considerable reduction in their FEV1 and FVC. Since asthmatic
children have lower baseline and slower growth rates, their PFT change may be affected less by
exposure to air pollution, reﬂecting a well known relationship between pulmonary function change and
height growth, according to which age-speciﬁc height is very similar for preadolescent children, but
shifts upward with age during the growth spurt.
& 2009 Elsevier Ltd. All rights reserved.
1. Introduction 2007; Schwartz, 2004). Several studies indicated that gaseous
pollutants and particulate matters tended to increase the risk of
According to several epidemiological studies, children are chronic chest symptoms and might have lifelong effects on
more susceptible to air pollution than adults due to their children’s health (Bateson and Schwartz, 2008; Gauderman et al.,
increased respiratory rate and immature host defense mechan- 2004; Kajekar, 2007; Schwartz, 2004). Genetic factors, which
isms, which lead to the increased absorption of air pollutants regulate antioxidant defenses, may act together with environ-
(Bateson and Schwartz, 2008; Gauderman et al., 2000, 2002, 2004, mental factors further elevating a potential risk of respiratory
diseases in children (Gilliland et al., 1999, 2002; Kajekar, 2007; Sly
and Flack, 2008; Romieu et al., 2002; Schwartz, 2009).
Corresponding author. Fax: + 972 4 8249971. However, the question whether sensitivity to air pollution
E-mail address: Portnov@nrem.haifa.ac.il (B.A. Portnov). differs among children characterized by different health status
1353-8292/$ - see front matter & 2009 Elsevier Ltd. All rights reserved.
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400 T. Yogev-Baggio et al. / Health & Place 16 (2010) 399–408
(e.g., healthy children vs. asthmatic children) remains largely
unanswered. Thus, for instance, Gauderman et al. (2004) did not
ﬁnd any signiﬁcant effect of air pollution on asthmatic children,
concluding that it may be inﬂuenced by a small sample size. Only
a handful of studies looked into these differences, indicating that
the long-term effects of air pollutants on pulmonary function (PF)
growth may depend on children’s initial health status (see inter
alia, Avol et al., 2001; Goren and Hellmann, 1997; Jedrychowski
et al., 2002).
Several earlier studies demonstrated that the effects of air
pollution on children’s PFTs differed between healthy children
and asthmatics. In particular, such differences were detected for
NOx, SO2, O3 and PM exposures (see inter alia, Gauderman et al.,
2004; Gilliland, 2009). Healthy preadolescents residing in neigh-
borhoods with elevated levels of SO2 and suspended particulate
matters (SPM) were found to exhibit a delay in their pulmonary
function growth (Jedrychowski et al., 2002), while pollutant
initiated inﬂammation was found to worsen the pulmonary
function (PF) performance among asthmatic children (Lee et al.,
2002; Pikhart et al., 2000; Raizenne et al., 1996). Exposure to air
pollution was also found to increase the number of asthmatic
children residing in urban areas (Delﬁno, 2002; Islam et al., 2007;
Jerrett et al., 2008), thus supporting a causality relationship
between air pollution and asthma.
A major uncertainty of epidemiological studies is often
associated with the estimation of individual exposure levels. A
commonly used approach assumes that each person in a
concerned region has the same exposure level, since the pollution
often originates from the same pollution source (Elliott and
Watenberg, 2000, 2004; Nuckols et al., 2004; Thurston et al.,
2009). Exposure level assigned to each individual are often Fig. 1. Map of the study area.
obtained from a few air quality monitors and reﬂect the mean
concentrations in the entire urban area or community (Nuckols
et al., 2004; Portnov et al., 2007). This approach may under-
estimate the individual exposure to air pollutants in the most
2.1. Study area
polluted areas and overestimate it in the least polluted neighbor-
hoods, thus leading to a measurement error known as exposure
misclassiﬁcation (Elliott and Savitz, 2008; Nuckols et al., 2004; The Hadera district of Israel, which forms our study area, is
Portnov et al., 2007). Furthermore, the use of mean air pollution situated on the Mediterranean shore, some 50 km north-east of
levels ‘‘smoothes’’ air pollution ‘‘spikes’’ (or so-called ‘‘air Tel Aviv. Its total population amounts to approx. 350,000
pollution events’’), and may thus result in unreliable estimates residents (ICBS (Central Bureau of Statistics, Jerusalem, Israel),
of health effects, which may be especially harmful for children. 2008). The study area hosts two townships (Hadera and Pardes-
Several epidemiological surveys carried out in Israel compared Hana-Karkur), two small suburban neighborhoods (Beit Eliezer
the prevalence of respiratory symptoms, related to asthma and and Givat Olga), and a major coal-ﬁred power plant with
bronchitis, among subgroups of children residing in highly production capacity of about 2590 MW (see Fig. 1). Sources of
polluted areas with the prevalence of these symptoms among ambient air pollution in the area originate mainly from the power
children residing in less polluted areas. Most of these studies plant’s smokestacks, as well as from several small industrial
detected the attenuation of PF development and an increase in the plants scattered throughout the study area, and from motor
prevalence of respiratory morbidity in polluted areas (Goren et al., vehicles.
1990, 1991; Goren and Hellmann, 1997; Peled et al., 2005).
However, these studies were rather inconclusive in regard to the 2.2. Air pollution estimates
differential effect of air pollution on children characterized by
different health status. Air quality data for our study were obtained from the network
Following our previous studies (Dubnov et al., 2007; Portnov of 12 air quality monitoring stations (AQMSs), maintained by the
et al., 2007), in the present analysis, we attempt to determine Hadera Association of Towns for the Environmental Protection
whether the previously detected effect of air pollution on children’s (ATEP) and providing continuous measurements of air pollution
PF development vary across subgroups of children characterized by by SO2 and NOx. In the present study, we adopted the ‘‘event’’
different health status, i.e., healthy children, asthmatics, and children approach, used in several previous studies and found to be an
with chest symptoms. effective and sensitive measure for local air pollution levels
The study is based on a cohort of 1181 schoolchildren residing (Dubnov et al., 2007; Portnov et al., 2007). According to this
in the Hadera district of Israel (Fig. 1), and subdivided into three approach, half-an-hour concentrations of NOx and SO2 that
health status groups—(a) healthy children (children without simultaneously exceeded some predeﬁned air pollution levels
known respiratory diseases and symptoms); (b) children who (i.e., 0.125 ppm for NOx and 0.070 ppm for SO2) were multiplied
experienced chest symptoms during the year preceding the by the average concentrations during the ‘‘air pollution events’’
pulmonary function test; and (c) children diagnosed by a and then summed up over the entire study period, 1996–1999
physician as suffering from either asthma or spastic bronchitis. (for more detail on this approach, see Dubnov et al., 2007). The
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T. Yogev-Baggio et al. / Health & Place 16 (2010) 399–408 401
aforementioned concentration levels are quarters of the local air 2007) being similar to those used elsewhere (Leonardi et al.,
pollution standards for NOx and SO2, respectively, and are 2002). In particular, the following three ‘‘health status’’ categories
recommended by ATEP for air pollution event analysis (ATEP (subgroups) were distinguished:
(Association of Towns for Environmental Protection), 2005; Goren
et al., 1995). 1. Healthy subgroup: The child was never diagnosed by a
In order to insure a time lag between the air pollution data and physician with either spastic bronchitis or asthma and had
PFT results, air pollution data were taken for calendar years (i.e., not had any chest symptoms during the last 12 months prior to
January 1st through December 31st), while PF tests were carried PFT.
out a few months later after the start of a calendar year, i.e., 2. Chest symptoms subgroup: The child had at least one of the
between April and June (see the subsection on Study Population). following symptoms during the 12 months preceding the PFT
Over the entire study period (1996–1999), air pollution events test: wheezing with or without upper respiratory tract
occurred approximately 0.3% of the time in the least polluted part infection (URTI); coughing with or without URTI; coughing
of the study area and about 1.1% of the time in its most polluted with phlegm or wheezing with dyspnea.
part. Whereas the overall annual numbers of ‘‘air pollution 3. Asthma/spastic bronchitis subgroup: The child was diagnosed by
events’’ appeared to be stable in 1996–1999, the integrated a physician with either asthma or spastic bronchitis and
concentration values of the air pollutants exhibited a general exhibited at least one of the above respiratory symptoms, as
tendency to rise (see Appendix 1). idem in (2).
The NOx Â SO2 ‘‘event’’ concentrations, observed at the location
of AQMSs were then interpolated by the kriging interpolation In accordance with the ‘‘intent-to-treat principle’’ for a ﬁxed
method (Beers and Kleijnen, 2003; Dubnov et al., 2007; Kim et al., cohort analysis (Rothman et al., 2008), the classiﬁcation was
2009), which enabled us to obtain a continuous surface of based on the results of the 1996 survey, that is, on the health
NOx Â SO2 air pollution estimates covering the entire study area status of the children at the beginning of the study period. Only
(see Fig. 1). the children with a known health status in 1996, who did not
To illustrate differences in the children’s PF performances change their home address during the entire length of the study
across communities with different levels of air pollution, we also period (that is, 1181 children; see Section 2.3), were included in
estimated the average NOx Â SO2 ‘‘event’’ concentrations for 20 the analysis.
Small Census Areas (SCAs), into which the study area is divided,
and then grouped these SCAs into three air pollution zones,
2.5. Individual exposure estimates
with distinctively different air pollution levels: low pollution
(NOx Â SO2 o312 ppm); medium pollution (312oNOx Â SO2
o2640 ppm), and high pollution (NOx Â SO2 42640 ppm) (see In the present study, we used an indirect approach to
Fig. 2). The classiﬁcation was performed using the Jenks’ ‘‘natural estimating the individual air pollution exposure (Dubnov et al.,
breaks’’ method in the ArcGIS9.xTM software. This method 2007; Jerrett et al., 2008; Kim et al., 2009). According to this
determines the best arrangement of values into classes by approach, the homes of the children participating in the survey
comparing the sum of squared differences of values from the were positioned on a map and superimposed upon NOx Â SO2 air
means of their classes and thus identiﬁes ‘‘natural break points’’ in pollution levels at corresponding locations (see Section 2.2 and
the data distribution by picking the class breaks that best group Fig. 1). Next, the levels of NOx and SO2 air pollution, to which each
similar values and maximize differences between the classes child was exposed, were calculated. The task was performed in the
(Minami, 2000). ArcGIS 9TM software, using its ‘‘spatial join’’ tool, which makes it
possible to match between different geographic layers (maps)
based the spatial relationship between them (Minami, 2000).
2.3. Study population
2.6. Statistical analysis
The health, demographic and PFT data used in the present
analysis are described in detail in Dubnov et al. (2007). In brief,
The analysis was carried out in four phases. During the ﬁrst
the same cohort of 1492 schoolchildren performed pulmonary
phase, changes in the proportional shares of different health status
function tests (FVC and FEV1) in 1996 and, again, in 1999. In total
groups in the total population between 1996 and 1999 was
1181 out of 1492 children resided at the same address and had
examined separately for the sub-regions with low, medium and
information about their health status at the beginning of the
high air pollution (see Section 3.1 and Fig. 2). During the second
study period. This group of 1181 children formed the study cohort
phase of the analysis, the interaction between the ‘‘health status’’
covered by the present analysis. Changes in the results of
groups (see Section 3.1) and air pollution levels (Section 3.2) was
pulmonary functions tests (DFVC and DFEV1) between the years
investigated by the analysis of variance (ANOVA), which was used
were calculated as the percent difference between the observed
to determine whether the mean DPFT values, observed in
and expected lung function volumes (Hankinson et al., 1999). For
different health status groups of children, differ by air pollution
each child in the sample, health and socioeconomic data were
zones. During the third phase of the analysis, the multiple
obtained from individual questionnaires (Dubnov et al., 2007;
regression analysis was run to indentify and measure the effect
Feris, 1978), ﬁlled out by the children’s parents. The question-
of air pollution on 1996–1999 changes in the children’s PF test
naires included questions on housing density; exposure to
(both DFEV1 and DFVC), adjusted for several potential confoun-
tobacco smoking; parental education and duration of living in
ders, such as the duration of residence in the study area, passive
the study area. The same questionnaire was used in both 1996
smoking in the family, housing density, father’s education, and
proximity to the main road. The analysis was performed
separately for each health status subgroup covered by the study
2.4. Health status classiﬁcation (see Section 3.3). During the analysis, the normality and the
heterogeneity of variance assumptions were veriﬁed and the
The classiﬁcation of children into health status subgroups was results were found to be satisfactory. Lastly, during the fourth
based on a health questionnaire described above (Dubnov et al., phase of the analysis, an estimation test was run to determine the
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402 T. Yogev-Baggio et al. / Health & Place 16 (2010) 399–408
Fig. 2. Subdivision of the study area into air pollution regions.
magnitude of changes in the children’s DFEV1 and DFVC, with known health status in both 1996 and 1999.] As the table
attributed to plausible changes in ambient air pollution levels. shows, the percent of children, diagnosed as healthy, decreased
The general characteristics of the entire cohort of school- between 1996 and 1999 in all air pollution regions, but most
children surveyed are reported in Table 1, while Table 2 reports substantially in the ‘‘high air pollution’’ zone. For example, the
the characteristics of children in different health status groups. number of healthy children in the least polluted (‘‘low air
pollution’’) zone dropped from 46.7% children diagnosed as
healthy in 1996 to 40.4% such children in 1999. Concurrently,
3. Results the corresponding decrease in the number of healthy children in
the ‘‘high air pollution’’ zone was much higher, from 45.9% of
3.1. Changes in the health status groups’ sizes children diagnosed as healthy in 1996 to 34.5% of such children in
Table 3 illustrates changes in the numbers of children in each The opposite trend is found in the ‘‘chest symptoms’’ group.
health status group in different air pollution regions, which The number of children in this health status group increased in all
occurred between 1996 and 1999. [The table reports only children air pollution zones, but, again, most substantially, in the ‘‘high air
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pollution’’ region. In particular, the number of children exhibiting Characteristically, the direction of changes appear to be less
chest symptoms in this air pollution zone increased between consistent in respect to the ‘‘pulmonary diseases’’ subgroup,
1996 and 1999 by nearly twofold, from 20 children diagnosed which may be explained by a relatively small numbers of children
with chest symptoms in 1996 to 37 such children in 1999, in this group overall, which is generally insufﬁcient for general-
or by 85%. ization.
General characteristics of the schoolchildren surveyed in 1996 and 1999.
Variable 1996 1999
Mean S.D. Mean S.D.
Total number of children surveyed 1181 1181
Observed FVC (l) 2.0 0.5 2.9 0.7
Predicted FVC (%) 94.3 12.4 93.9 11.0
Observed FEV1 (l) 1.8 0.4 2.7 0.6
Predicted FEV1 (%) 100.7 13.7 97.6 11.6
Female gender (%) 54 56
Age (years) 9.2 1.5 12.2 1.5
Height (cm) 136.3 10.6 153.2 10.6
Father’s education (years of schooling) 12.7 2.7 12.9 2.8
Passive smoking in the family %a 53 48
Duration of residence in the study area (years) 8.0 2.7 11.0 2.7
Proximity to the main road (%)b 36.0
Housing density (persons per room) 1.3 0.7 1.2 0.5
At least one person smokes in the child’s home.
Living within 50 m from a main road (the ﬁrst row of buildings).
Characteristics of children in different health status groups (according to the 1996 survey).
Variable Healthy (N = 537) Chest symptoms (N = 357) Pulmonary diseases (N= 287) Prob.
1996 1996 1996
Mean S.D. Mean S.D. Mean S.D.
Age (years) 9.3 1.6 9.1 1.5 9.2 1.5 0.310
Father’s education (years of schooling) 12.8 2.6 12.5 2.9 12.8 2.7 0.285
Female gender (%) 56.4 55.7 47.7 0.044
Height (cm) 136.6 10.7 135.9 10.8 136.2 10.3 0.598
Housing density (person/room) 1.3 0.7 1.4 0.6 1.3 0.7 0.400
Length of residence in the study area (years) 8.4 2.5 7.7 2.8 7.8 2.8 o0.001
Observed FEV1 (l) 1.9 0.4 1.8 0.4 1.8 0.4 0.078
Observed FVC (l) 2.0 0.5 2.0 0.5 2.0 0.5 0.094
Passive smoking in the family %a 47.1 56.7 58.5 0.002
Predicted FEV1 (%) 101.6 13.8 100.1 13.6 99.6 13.6 0.092
Predicted FVC (%) 94.9 12.0 93.5 12.9 94.5 12.4 0.249
Proximity to the main road (%)b 39.1 32.5 33.1 0.076
At least one person smokes in the child’s home.
Percent of children living within 50-m from a main road.
Changes in the number of children in different health status groups by air pollution regions in 1996–1999.a
Air pollution region Health group Number of children in 1996 Number of children in 1999 % Change Prob.
Low Healthy 268 232 13.4 o0.001
Chest symptoms 170 208 + 22.4
Pulmonary diseases 136 134 –1.5
Total 574 574
Medium Healthy 97 74 –23.7 o0.001
Chest symptoms 77 84 + 9.1
Pulmonary diseases 40 56 + 40.0
Total 214 214
High Healthy 40 30 –25.0 o0.01
Chest symptoms 20 37 + 85.0
Pulmonary diseases 27 20 –25.9
Total 87 87
Includes only children with reported health status in both 1996 and 1999 (875 children out of 1181 children in the sample).
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Interaction of health status groups with air pollution levels (method: analysis of variance (ANOVA); dependent variables—DFEV1)1.
Health status group Air pollution zone Fa(df.)
Low Medium High
Mean (S.D.) N Mean (S.D.) N Mean (S.D.) N
Healthy À 2.12 (12.77) 360 À 4.44 (13.22) 136 À 11.47 (11.98) 40 10.16 (2,535) P o0.001
Chest symptoms À 2.75 (12.95) 230 À 1.40 (11.18) 104 À 16.07 (17.59) 20 11.33 (2,356) P o0.001
Pulmonary diseases À 1.82 (11.96) 194 À 3.23 (12.87) 66 À 5.92 (11.84) 27 1.48 (2,286) P =0.229
Note: Total number of children—1181.
F-statistic and its degree of freedom (df), post-hoc differences.
Factors affecting the pulmonary function change by children subgroups (dependent variable—DFEV1; method—multiple regression analysis).
Variable Model 1 (all children) Model 2 (healthy)
Ba Sig.b 95% CIc Ba Sig.b 95% CIc
Lower bound Upper bound Lower bound Upper bound
Age 2.564 o0.001 1.889 3.240 2.728 o0.001 1.601 3.855
Father’s education 0.139 0.221 À 0.084 0.361 0.393 0.030 0.038 0.749
Gender À 0.084 0.889 À 1.265 1.097 0.325 0.725 À 1.489 2.140
Height 0.396 o0.001 0.302 0.490 0.341 o0.001 0.187 0.495
Housing density 0.264 0.594 À 0.705 1.233 0.199 0.800 À 1.344 1.741
NOx Â SO2 À 3.81E À 03 o0.001 À 0.005 À 0.003 À 3.02E À 03 o0.001 À 0.005 À 0.001
Passive smoking 1.098 0.072 À 0.097 2.293 1.868 0.044 0.053 3.683
Road proximity À 1.062 0.092 À 2.298 0.175 À 0.147 0.877 À 2.007 1.713
Duration of residence 0.124 0.349 À 0.135 0.383 0.268 0.252 À 0.191 0.728
(Constant) À 82.449 o0.001 À 92.102 À 72.796 À 82.952 o0.001 À 98.307 À 67.596
R2 0.41 0.40
No. of children 1181 537
Variable Model 3 (chest symptoms) Model 4 (pulmonary diseases)
Ba Sig.b 95% CIc Ba Sig.b 95% CIc
Lower bound Upper bound Lower bound Upper bound
Age 1.996 0.001 0.813 3.179 2.808 o 0.001 1.597 4.019
Father’s education À 0.168 0.394 À 0.557 0.220 0.146 0.500 À 0.279 0.570
Gender 0.053 0.963 À 2.163 2.268 À 0.228 0.843 À 2.491 2.034
Height 0.515 o 0.001 0.352 0.679 0.366 o 0.001 0.189 0.542
Housing density À 0.608 0.540 À 2.557 1.341 0.966 0.242 À 0.656 2.588
NOx Â SO2 À 7.58E–03 o 0.001 À 0.010 À 0.005 À 2.04E À 03 0.069 À 0.004 0.000
Passive smoking À 0.864 0.448 À 3.102 1.374 1.776 0.135 À 0.558 4.109
Road proximity À 1.908 0.115 À 4.282 0.466 À 1.111 0.364 À 3.516 1.294
Duration of residence À 0.198 0.399 À 0.659 0.263 0.281 0.205 À 0.154 0.716
(Constant) À 83.299 o 0.001 À 100.841 À 65.758 À 83.032 o 0.001 À 101.156 À 64.908
R2 0.45 0.44
No. of children 357 287
Unstandardized regression coefﬁcient.
Two-tailed signiﬁcance level.
95% conﬁdence interval.
3.2. Air pollution—health status groups’ interaction 3.3. Multivariate regression analysis
Table 4 reports the results of the ANOVA analysis and Table 5 reports the results of the multivariate regression
illustrates the signiﬁcance of differences in the average DFEV1 analysis of the factors affecting the FEV1 change across different
values observed in different health status groups and in different health status groups of children between 1996 and 1999 (The
air pollution zones. (The results of the analysis for DFVC are results for the FVC change were found to be similar and can be
essentially similar and are not reported here for brevity’s sake.) obtained from the authors upon request.) In the analysis,
As Table 4 shows, the effect of ‘‘air pollution levels—the individual air pollution estimates (see Section 2.5) were
children’s health status’’ interaction on DPFT appears to be controlled for several potential confounders: age, height, gender,
statistically signiﬁcant (Roy’s=2.37, Po0.05), thus indicating that duration of residence in the study area, passive smoking in the
the effect of air pollution on a child’s PFT change does appear to family, housing density, father’s education, and proximity to the
depend on the child’s health status at the beginning of the study closest main road (see Appendix 1). The models appear to provide
period (that is, in 1996). a reasonably good ﬁt (R2 =0.40–0.45).
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Changes in the DFEV1 values as a response to plausible changes in NOx Â SO2, air pollution levels.
Air pollution levels (ppm) Healthy Chest symptoms Pulmonary diseases
Low pollution (NOx Â SO2 = 106) À 4.005 0.050 À 2.578
Medium pollution NOx Â SO2 =1507) À 8.235 À 10.566 À 5.437
High pollution (NOx Â SO2 = 2700) À 11.837 À 19.606 À 7.872
Note: Based on the models in Table 5; input constants are set to the average values as follows: age= 9.2 (average for the sample); proximity to the main road = 0 ( 450 m);
passive smoke= 0 (no); gender= male (1); duration of living in the study area= 8 years (average for the sample); housing density= 1.3 persons per room (average for the
sample); father’s education =12.71 years (average for the sample); height = 136.28 cm (average for the sample).
As Table 5 shows, DFEV1 is signiﬁcantly and positively using the regression models reported in Table 5 and holding all
correlated with the age and height of a child (Po0.01). The effect other variables (accept for air pollution) constant.
of the NOx Â SO2 interaction term on the children’s PFT perfor- As Table 6 shows, the largest DFEV1 deﬁcit associated with
mance appears to be negative and highly signiﬁcant in most of the living in the high air pollution area is observed in the ‘‘chest
models (Po0.01), implying that increasing air pollution levels symptoms’’ subgroup ( À19.6%), followed by healthy children
appear to have, ceteris paribus, a signiﬁcant and negative effect on ( À 11.8%) and children with chronic respiratory diseases ( À 7.9%),
the children’s PF growth. Characteristically, the strongest thus suggesting that healthy children and children with chest
‘‘NOx Â SO2—DFEV1’’ association is detected in the ‘‘chest symp- symptoms appear to be most susceptible to chronic respiratory
toms’’ subgroup and among the healthy (Po0.001; see Table 5; effects associated with their exposure to air pollution.
Models 2–3). However, this association is much weaker for the Possible explanations of this trend may be attributed to differences
‘‘PF diseases’’ subgroup (P 40.05; see Table 5; Model 4), implying in PFT change among children in different health status groups. The
that PF growth in children with chronic pulmonary diseases relation between pulmonary function and height is well known: the
appears to be least affected by air pollution, as compared with age-speciﬁc height is very similar for the preadolescent children, but
other groups under study. A possible explanation for this shifts upward with age during the growth spurt (Wang et al., 1993).
phenomenon will be suggested in the following section. Some studies have shown that children with asthma exhibited
impaired growth (Balfour-Lynn, 1986; Littlewood et al., 1988; Murray
et al., 1976; Wales et al., 1991; Wolthers and Pedersen, 1991). This
4. Discussion slow growth may lessen the effect of air pollution on PF changes and
differences in measurements between ‘‘initial performance values’’
Since individuals differ in their metabolic rate and immune (FEV1_1996) and ‘‘ﬁnal performance values’’ (FEV1_1999). As Wang
system functioning, some are likely to be more susceptible to et al. (1993) reported, FEV1 of asthmatic children was, on the average,
ambient air pollution than others (Arshad et al., 2005; D’Amato 1.5–3.0% lower than that of children who had no asthma or wheezing.
et al., 2005; Kim, 2004; Paden, 2005). In the present study, a In particular, asthmatic children tend to exhibit airway hyper-
cohort of 1181 schoolchildren divided into three subgroups based responsiveness and obstruction, which is reﬂected by low values
on their health status was tested twice for their PFT performance of polluted air they inhale (GINA (Global Strategy for Asthma
in 1996 and again in 1999. Management and Prevention), 2008; Sears et al., 2003).
As the analysis revealed, the number of healthy children in the Thus, Xuan et al. (2000) carried an 8-year long survey in which
study cohort decreased over the study period overall, but most ﬁve follow-up studies were conducted at 2-year intervals. Height
substantially in the most air polluted areas. In particular, the has been established as the best predictor of lung function
number of healthy children in the ‘‘low pollution’’ zone dropped growth, in both cross-sectional and longitudinal comparisons. The
during the study period by some 13.4%, from 268 children relationship between height and age in both sexes was found to
diagnosed as healthy in 1996 to 232 such children in 1999. be linear for 8–12 year old, but had a curved relation for older
Concurrently, the corresponding decrease in the number of healthy children. In our study, the PFT values were measured twice during
children in the ‘‘high air pollution’’ zone was 25%. Since the analysis a 3-year period and the results were found to be close to those of
covered only children who resided at the same address during the Xuan et al. (2000) with data collected during a longer survey. Even
entire study period, these changes in the size of the health status if the children were equally exposed to air pollution before the
groups appear to reﬂect a clinical situation in which air pollution in study period, the changes we observed during the 3 years, covered
the study area insults the children’s respiratory system. the study, are likely to reﬂect the impact of recent air pollution
The analysis of variance also conﬁrmed that the interaction exposure. This assumption is based mainly on the fact that the
between air pollution levels and the children’s health status was growth spurt in our children (the 8–12 age group) occurred
associated signiﬁcantly with the children’s PFT change during the study period and the relationship between their height
(P40.001), thus indicating that the PF development of children growth and DPFT was linear.
in different health status groups (that is, healthy children, As Xuan et al. (2000) also found, subjects who had wheeze
children with chest symptoms and children with pulmonary and/or asthma during the course of the study were likely to have
diseases) appeared to respond differently to air pollution, bronchoconstriction, and hence lower levels of FEV1. This would
estimated for the places of the children’s residence. cause such children to have an overall lower level of lung function
Regression models, adjusted for the children’s health and (FEV1) but would not explain the observed reduced rate of growth
socio-demographic characteristics, also indicated that air pollu- in PFT since there is no reason to suspect that the degree of
tion estimates (measured in this study by the NOx Â SO2 bronchoconstriction would increase over time. Hence, we believe
interaction term) appeared to have a stronger negative effect on that these background data provide an explanation why asthma
the PFT change in healthy children and children with chest may be associated with a reduced rate of growth in FEV1.
symptoms, than in children with already diagnosed chronic In our study the most affected population were children not
respiratory diseases (i.e., spastic bronchitis and asthma). diagnosed as asthmatics but experienced chest symptoms. In fact,
A test of DFEV1 response to plausible changes in NOx Â SO2 air- those children may belong to an ‘‘undiagnosed’’ and, therefore,
pollution levels is reported in Table 6. The test was performed by ‘‘untreated-for-asthma’’ group. This group of children may
ARTICLE IN PRESS
406 T. Yogev-Baggio et al. / Health & Place 16 (2010) 399–408
experience the most severe insult on their respiratory system as An additional sort of bias, attributed to exposure misclassiﬁca-
reﬂected by a greater reduction of their PFT from the baseline.1 tion, can also arise from an assumption that average exposure
Our results are generally consistent with the results of levels estimated for townships and SCAs are used to approximate
previous studies carried out in the same region (Dubnov et al., the air pollution exposure of individuals (Jerrett et al., 2008;
2007; Portnov et al., 2007), which reported a decline of children’s Portnov et al., 2007; Kim et al., 2009). Since, in the present study,
PFT performance in line with increasing air pollution levels. As in we used individual exposure estimates, this source of bias was
these previous studies, we used the individual exposure estimate also minimized.
approach, based on geographic information systems (GIS) tech- While children spend most of their time at home, a signiﬁcant
nologies, which are more sensitive than zonal approaches, proportion of their ‘‘active’’ time is spent at school which
traditionally used in the epidemiological research (Jerrett et al., contributes to the total exposure. Since according to the Israeli
2008; Sahsuvaroglu et al., 2009; Kim et al., 2009). Earlier municipal law, schoolchildren of the elementary and secondary
evidence, accumulated during the 1990s, also indicated a rise in schools (1st through 8th grade) attend schools near their homes,
the prevalence of acute pulmonary symptoms (upper respiratory most of them stay in the same air pollution zone, which
tract infection (URTI), and lower respiratory tract infection (LRTI) minimizes the chance of exposure misclassiﬁcation.
among asthmatics and non-asthmatics children (Brauer et al., Lastly, the effect of air pollution on asthmatic children, who
2002; Goren and Hellmann, 1997). Although preadolescent (6–10 receive medical treatment, and the effect of air pollution on
year old) children exhibited a more acute prevalence of these children suffering from chest symptoms, but not taking medica-
symptoms, no parallel decline in their PFTs was reported. These tions, may be different (Delﬁno et al., 2008; Liu et al., 2009).
adverse effects air pollution on lung development responses were However, we had no robust information on such ‘‘undiagnosed’’
also detected by studies carried out elsewhere (Gauderman et al., asthmatic children and had to rely on information obtained from
2002, 2004; Horak et al., 2002). questionnaires ﬁlled out by the children’s parents.
Although there is no shortage of studies investigating the
association between air pollution and children pulmonary func-
tion performance (Gauderman et al., 2000, 2002, 2004, 2007; 6. Conclusion
Horak et al., 2002; Jedrychowski et al., 1999, 2002; Lee et al.,
2002; Peters et al., 1999a, 1999b), we are unaware of other As the present study indicates, the long-term exposure to
studies that evaluated the effect of air pollution on children ambient air pollution appears to have more detrimental effects on
subgroups characterized by different initial health status and used pulmonary function growth among children with chest symptoms
individual exposure estimates approach. and healthy than among asthmatics, with the estimated PFT
deﬁcit ranging from À 7.9% for asthmatics to À19.6% for children
exhibiting chest symptoms. A likely explanation of this trend is
5. Study limitations
that different effects of exposure to ambient air pollution on the
airways in these groups when lungs rapidly develop. In asthmatic
Population-based epidemiologic studies are often susceptible
children, the height growth is often stunned. As a result, the
to a selection bias arising from a loss of information and
prediction pulmonary function values can be less accurate than
misclassiﬁcation of exposure (Elliott and Watenberg, 2000;
for children whose growth track is normal. Since asthmatic
Greenland and Morgenstern, 1989). Differences in personal
children had lower baseline of PF and lower growth rates, the
characteristics (physiological, genetic, diseases, lifestyle, smoking,
reduction of the PFT values may be less affected. Therefore, the
alcohol, drugs, sexual orientation, etc.), and differences in
effect of air pollution on this children subgroup may be less
pollution sources (industries and power plants, motor vehicles,
pronounced. However, further studies are needed to understand
naturally occurring dust storms), as well as differences in
better this phenomenon. Nevertheless, the data already available
topography, and meteorology could lead to these biases (Elliott
provide evidence that children, particularly those with increased
et al., 1992; Morgenstern and Thomas, 1993; Lash et al., 2009).
susceptibility, such as untreated children with chest symptoms,
Since the same cohort of children was tested twice (in 1996 and
may beneﬁt substantially from a reduction in the current levels of
again in 1999), and their health status and residential conditions
air pollution, especially in densely populated urban areas.
were estimated by individual questionnaires, these sources of bias
are less likely to occur in the present study.
As the analysis revealed, the number of children with PFT
deﬁciency was highest in air polluted areas which may be Appendix 1
inﬂuenced by the concentration of low income families in air
polluted neighborhoods. To rule out such a possibility, we See (Table A1).
analyzed the income levels of families by small census areas,
into which the entire study area is divided, and did not ﬁnd
signiﬁcant differences in the average monthly incomes (P40.3). Table A1
Therefore, social–economic status differences are unlikely to Overall number of ‘‘air pollution events’’ in the study area and average integrated
provide explanation for the observed PF discrepancies in the concentration values (ICV) of SO2 and NOx in 1996–1999.
Source: After Dubnov et al. (2007).
To minimize a potential selection bias, we used in our analysis Year Overall number of ‘‘air Integrated concentration values
the ‘‘intent-to-treat’’ approach, commonly used in clinical trials pollution events’’ (ICV) [ppm]n
and based on the ﬁxed health status deﬁnition of the research
subjects, as recorded at the beginning of the study period
(Rothman et al., 2008). 1996 159 5.0 5.6
1997 150 6.1 7.6
1998 157 7.3 8.0
Medical surveillance and medicine intake were analyzed in our study as 1999 162 8.0 9.3
potential confounders, but were not found signiﬁcant. These results are not Total 628 26.4 30.5
reported here for brevit sake and can be obtained from the authors upon request.
ARTICLE IN PRESS
T. Yogev-Baggio et al. / Health & Place 16 (2010) 399–408 407
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