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Air Pollution and Respiratory Infections during Early Childhood: An Analysis of 10 European Birth Cohorts within the ESCAPE Project

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Few studies have investigated traffic-related air pollution as a risk factor for respiratory infections during early childhood. To investigate the association between air pollution and pneumonia, croup and otitis media in 10 European birth cohorts - BAMSE (Sweden), GASPII (Italy), GINIplus and LISAplus (Germany), MAAS (UK), PIAMA (The Netherlands), and four INMA cohorts (Spain) - and to derive combined effect estimates using meta-analysis. Parent report of physician diagnosed pneumonia, otitis media and croup during early childhood were assessed in relation to annual average pollutant levels [NO2, NOx, PM2.5, PM2.5 absorbance, PM10, PM2.5-10 (coarse PM)] estimated using land use regression models and assigned to children based on their residential address at birth. Identical protocols were used to develop regression models for each study area as part of the ESCAPE project. Logistic regression was used to calculate adjusted effect estimates for each study, and random-effects meta-analysis was used to calculate combined estimates. For pneumonia, combined adjusted odds ratios (ORs) were elevated and statistically significant for all pollutants except PM2.5 (e.g., OR = 1.30; 95% CI: 1.02, 1.65 per 10-µg/m³ increase in NO2 and OR = 1.76; 95% CI: 1.00, 3.09 per 10-µg/m³ PM10). For otitis media and croup, results were generally null across all analyses except for NO2 and otitis media (OR = 1.09; 95% CI: 1.02, 1.16 per 10-µg/m³). Our meta-analysis of 10 European birth cohorts within the ESCAPE project found consistent evidence for an association between air pollution and pneumonia in early childhood, and some evidence for an association with otitis media.
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Environmental Health Perspectives
volume 122 | number 1 | January 2014
107
Research
|
Children’s Health
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Introduction
Respiratory infections are a leading reason for
outpatient physician visits and hospitaliza-
tions among children (Williams et al. 2002).
Most infections resolve with minimal use
of health care resources; however, episodes
of severe or recurrent infection may require
hospitalization or surgery, and the resultant
burden on resources is substantial (Black
et al. 2010).
Young children are particularly suscep-
tible to respiratory pathogens and also to
air pollution (Bateson and Schwartz 2008;
Heinrich and Slama 2007). ere is strong
evidence that indoor air pollution, such as
secondhand smoke and the use of biomass,
is a risk factor for respiratory infections in
children (da Costa et al. 2004). Evidence is
growing to support an association with out-
door air pollution as well (Brauer et al. 2006;
Leonardi et al. 2000; MacIntyre et al. 2011).
e European Study of Cohorts for Air
Pollution Effects (ESCAPE 2009) is a proj-
ect aimed at investigating the impacts of
long-term exposure to air pollution through
the development of harmonized exposure
data assigned to previously established
cohorts that have collected information on
specific health outcomes of interest for air
pollution research. We analyzed data from
Address correspondence to J. Heinrich, Institute of Epidemiology
I, Helmholtz Zentrum München, Ingolstädter Landstraße 1,
85764 Neuherberg Germany. Telephone: 49-(0)89-3187-4150.
E-mail: heinrich@helmholtz-muenchen.de
Supplemental Material is available online (http://dx.doi.
org/10.1289/ehp.1306755).
The research leading to these results was funded by the
European Community’s Seventh Framework Program
(FP7/2007–2011) under grant 211250. The BAMSE study
was supported by the Swedish Research Council FORMAS
(for Environment, Agricultural Sciences and Spatial Planning),
the Stockholm County Council, the Swedish Foundation for
Health Care Sciences and Allergy Research, and the Swedish
Environmental Protection Agency. e GINIplus study was sup-
ported for the first 3 years by the Federal Ministry for Education,
Science, Research and Technology, Germany (interventional
arm) and Helmholtz Zentrum München, Germany (former
GSF; National Research Center for Environment and Health)
(observational arm). e LISAplus study was supported by grants
from the Federal Ministry for Education, Science, Research
and Technology, Germany; Helmholtz Zentrum München,
Germany (former GSF); Helmholtz Centre for Environmental
Research–UFZ, Germany; Marien-Hospital Wesel, Germany;
and Pediatric Practice, Bad Honnef, Germany. The PIAMA
study is supported by e Netherlands Organization for Health
Research and Development; The Netherlands Organization
for Scientific Research; The Netherlands Asthma Fund; The
Netherlands Ministry of Spatial Planning, Housing, and the
Environment; and e Netherlands Ministry of Health, Welfare,
and Sport. MAAS was supported by an Asthma UK Grant
(04/014); the JP Moulton Charitable Foundation, UK; and the
James Trust and Medical Research Council, UK (G0601361).
INMA was funded by grants from the Spanish Ministry of
Health-Instituto de Salud Carlos III (Red INMA G03/176,
CB06/02/0041, FISPI041436, FIS-PI081151, FIS-PI042018,
FIS-PI09/02311, FIS-PI06/0867, FIS-PS09/00090, FIS-FEDER
03/1615, 04/1509, 04/1112, 04/1931, 05/1079, 05/1052, 06/1213,
07/0314, and 09/02647); Generalitat de Catalunya-CIRIT,
Spain (1999SGR 00241); Conselleria de Sanitat Generalitat
Valenciana, Spain; Universidad de Oviedo, Obra social Cajastur,
Spain; Department of Health of the Basque Government, Spain
(2005111093 and 2009111069); Provincial Government of
Gipuzkoa (DFG06/004 and DFG08/001), Spain; and
Fundación Roger Torné, Spain. GASPII was funded by The
Italian Ministry of Health (ex art.12 D.Lgs 502/92, 2001).
The authors declare they have no actual or potential
competing financial interests.
Received: 4 March 2013; Accepted: 30 September 2013;
Advance Publication: 22 October 2013; Final Publication:
1 January 2014.
Air Pollution and Respiratory Infections during Early Childhood: An Analysis
of 10 European Birth Cohorts within the ESCAPE Project
Elaina A. MacIntyre,1 Ulrike Gehring,2 Anna Mölter,3 Elaine Fuertes,1,4 Claudia Klümper,5 Ursula Krämer,5 UlrichQuass,6
Barbara Hoffmann,5,7 Mireia Gascon,8,9 Bert Brunekreef,2,10 Gerard H. Koppelman,11,12 RobBeelen,2 Gerard Hoek,2
Matthias Birk,1 Johan C. de Jongste,13 H.A. Smit,10 Josef Cyrys,14 Olena Gruzieva,15 MichalKorek,15 Anna Bergström,15
Raymond M. Agius,3 Frank de Vocht,3 Angela Simpson,16 Daniela Porta,17 FrancescoForastiere,17 Chiara Badaloni,17
Giulia Cesaroni,17 Ana Esplugues,9,18 Ana Fernández-Somoano,9,19 Aitana Lerxundi,20,21 Jordi Sunyer,8,9,22,23
Marta Cirach,8,9 Mark J. Nieuwenhuijsen,8,9 Göran Pershagen,15 and Joachim Heinrich1
1Institute of Epidemiology I, Helmholtz Zentrum München, German Research Centre for Environmental Health, Munich, Germany; 2Institute for Risk Assessment
Sciences, Utrecht University, Utrecht, the Netherlands; 3Centre for Epidemiology, Institute of Population Health, Manchester Academic Health Sciences Centre,
The University of Manchester, Manchester, United Kingdom; 4School of Population and Public Health, University of British Columbia, Vancouver, British
Columbia, Canada; 5IUF-Leibniz Research Institute for Environmental Medicine, University of Düsseldorf, Düsseldorf, Germany; 6Air Quality & Sustainable
Nanotechnology, IUTA (Institut für Energie-und Umwelttechnik e.V.), Duisburg, Germany; 7Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf,
Germany; 8Centre for Research in Environmental Epidemiology (CREAL), Barcelona, Catalonia, Spain; 9Spanish Consortium for Research on Epidemiology
and Public Health (CIBERESP), Spain; 10Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands;
11Department of Pediatric Pulmonology and Pediatric Allergology, Beatrix Children’s Hospital, and 12GRIAC (Groningen Research Institute of Asthma and COPD),
University of Groningen, University Medical Center Groningen, Groningen, the Netherlands; 13Department of Pediatrics, Division of Respiratory Medicine,
Erasmus University Medical Center/Sophia Children’s Hospital, Rotterdam, the Netherlands; 14Institute of Epidemiology II, Helmholtz Zentrum München, German
Research Centre for Environmental Health, Munich, Germany; 15Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden; 16Institute
of Inflammation and Repair, Manchester Academic Health Sciences Centre, The University of Manchester, Manchester, United Kingdom; 17Department of
Epidemiology, Lazio Regional Health Service ASL RME (Dipartimento di epidemiologia ASL Roma E), Rome, Italy; 18Centre of Public Health Research (CSISP),
Valencia, Spain; 19Department of Preventive Medicine, University of Oviedo, Asturias, Spain; 20BioDonostia Health Research Institute, Donostia, Spain;
21Department of Public Health and Preventive Medicine, University of Basque Country, Bilbão, Spain; 22Hospital del Mar Medical Research Institute (IMIM),
Barcelona, Catalonia, Spain; 23Pompeu Fabra University, Department of Experimental and Health Sciences, Barcelona, Catalonia, Spain
Background: Few studies have investigated traffic-related air pollution as a risk factor for
respiratory infections during early childhood.
oBjectives: We aimed to investigate the association between air pollution and pneumonia, croup,
and otitis media in 10 European birth cohorts—BAMSE (Sweden), GASPII (Italy), GINIplus
and LISAplus (Germany), MAAS (
United Kingdom
), PIAMA (the Netherlands), and four INMA
cohorts (Spain)—and to derive combined effect estimates using meta-analysis.
Methods: Parent report of physician-diagnosed pneumonia, otitis media, and croup during early
childhood were assessed in relation to annual average pollutant levels [nitrogen dioxide (NO2), nitro-
gen oxide (NOx), particulate matter ≤ 2.5 μm (PM2.5), PM2.5 absorbance, PM10, PM2.5–10 (coarse
PM)], which were estimated using land use regression models and assigned to children based on their
residential address at birth. Identical protocols were used to develop regression models for each study
area as part of the ESCAPE project. Logistic regression was used to calculate adjusted effect estimates
for each study, and random-effects meta-analysis was used to calculate combined estimates.
results: For pneumonia, combined adjusted odds ratios (ORs) were elevated and statistically sig-
nificant for all pollutants except PM2.5 (e.g., OR = 1.30; 95% CI: 1.02, 1.65 per 10-μg/m3 increase
in NO2 and OR = 1.76; 95% CI: 1.00, 3.09 per 10-μg/m3 PM10). For otitis media and croup,
results were generally null across all analyses except for NO2 and otitis media (OR = 1.09; 95% CI:
1.02, 1.16 per 10-μg/m3).
conclusion: Our meta-analysis of 10 European birth cohorts within the ESCAPE project found
consistent evidence for an association between air pollution and pneumonia in early childhood, and
some evidence for an association with otitis media.
citation: MacIntyre EA, Gehring U, Mölter A, Fuertes E, Klümper C, Krämer U, Quass U,
Hoffmann B, Gascon M, Brunekreef B, Koppelman GH, Beelen R, Hoek G, Birk M, de Jongste JC,
Smit HA, Cyrys J, Gruzieva O, Korek M, Bergström A, Agius RM, de Vocht F, Simpson A, Porta D,
Forastiere F, Badaloni C, Cesaroni G, Esplugues A, Fernández-Somoano A, Lerxundi A, Sunyer J,
Cirach M, Nieuwenhuijsen MJ, Pershagen G, Heinrich J. 2014. Air pollution and respiratory infec-
tions during early childhood: an analysis of 10 European birth cohorts within the ESCAPE project.
Environ Health Perspect 122:107–113; http://dx.doi.org/10.1289/ehp.1306755
MacIntyre et al.
108
volume 122 | number 1 | January 2014
Environmental Health Perspectives
10 European birth cohorts and completed
a meta-analysis of air pollution and respira-
tory infection (pneumonia, croup, and otitis
media) during early childhood.
Methods
Study population. We included 10 ESCAPE
birth cohorts. e inclusion criteria for each
birth cohort were that data on at least one
outcome of interest were available during
early childhood, and that the ESCAPE expo-
sure assignment was complete.
BAMSE (Children, Allergy, Milieu,
Stockholm, Epidemiological Survey) is a
population-based prospective birth cohort
of children born during 1994–1996 in
Stockholm County, Sweden (Wickman et al.
2002). GASPII (Gene and Environment:
Prospective Study on Infancy in Italy) is a
prospective birth cohort of children born dur-
ing 2003–2004 in Rome, Italy (Porta et al.
2007). GINIplus (German Infant Nutrition
Intervention Study Plus environmental and
genetic influences on allergy development)
is a population-based prospective birth
cohort, with a nutritional intervention, of
children born during 1995–1998 in Wesel
and Munich, Germany (Zirngibl et al. 2002).
LISAplus (Influence of Life-style Factors on
the Development of the Immune System
and Allergies in Childhood Plus the influ-
ence of traffic emissions and genetics) is a
population-based prospective birth cohort
study of children born during 1997–1999 in
Wesel, Munich, Leipzig, and Bad Honnef,
Germany (Heinrich et al. 2002). INMA
(INfancia y Medio Ambiente; Environment
and Childhood) is a network of Spanish
birth cohorts. The four INMA cohorts in
the present analysis comprise children born
during 2004–2008 in both major cities and
rural towns—Asturias, Gipuzkoa, Sabadell,
and Valencia (Guxens et al. 2012). MAAS
(Manchester Asthma and Allergy Study) is an
unselected, prospective population-based birth
cohort study (with a small nested allergen
control intervention) of children born during
1995–1997 in the Greater Manchester conur-
bation in the United Kingdom (Custovic et al.
2002). Finally, the PIAMA (Prevention and
Incidence of Asthma and Mite Allergy) study
is a population based prospective birth cohort,
with an intervention component, of children
born during 1996–1997 in cities and small
towns across the Netherlands (Brunekreef
et al. 2002). Each cohort obtained parental
consent and protocols were approved by all
relevant ethical review boards.
Exposure to outdoor air pollution was
estimated using land use regression (LUR)
models, and methods were harmonized across
each cohort (Beelen et al. 2013; Cyrys et al.
2012; Eeftens et al. 2012a, 2012b; ESCAPE
2013). Briefly, sampling sites for particulate
matter (n = 20–40) and nitrogen oxides
(n = 40–80) were strategically chosen for each
study area to represent the spatial distribution
of concentrations at the residential address of
each child at birth, with some overrepresen-
tation of locations with heavy traffic where
the largest heterogeneity was expected. ree
2-week sampling campaigns were spread out
over 1 year and used to estimate an annual
average. Measurement data used to develop
the LUR models were collected during
2008–2009 (BAMSE, GINIplus/LISAplus,
PIAMA), 2009 (INMA), 2009–2010
(MAAS), and 2010–2011 (GASPII). The
annual average was temporally adjusted using
continuous measurement data from a fixed
monitor that was used to capture background
levels in each study area. Models based on
GIS (geographic information system) vari-
ables related to traffic, land use, population
density, altitude, and regional background
pollution were developed using common
ESCAPE protocols to predict measured air
pollution concentrations.
Separate models were developed for nitro-
gen dioxide (NO2), nitrogen oxides [NOx
(NO2 + NO)], PM2.5 (particulate matter
2.5 μm in diameter), PM2.5 absorbance,
PM10 ( 10 μm in diameter), and coarse PM
(PM10–PM2.5). In addition, two variables
were created to describe traffic intensity at
the residential address: traffic intensity on the
nearest street, and traffic load on all major
roads within a 100-m buffer. Annual average
air pollution concentrations and traffic inten-
sity variables were assigned to children for
the first year of life based on their residential
address reported at birth. e LISAplus study
centers of Leipzig and Bad Honnef were not
included in the ESCAPE exposure assess-
ment, so children from these cities could not
be included in the meta-analysis.
The air pollution data used to derive the
ESCAPE exposure models were measured
in 2008–2011, whereas children included in
the study cohorts were born as early as 1994.
Therefore, we conducted sensitivity analyses
using routine monitoring data to back-extrap-
olate exposure estimates based on LUR to each
child’s year of birth. We used two approaches
for the back-extrapolation: e first used the
ratio of the average concentration measured
from the date of birth through the second
birthday to the average concentration mea-
sured during the ESCAPE monitoring year;
the second used the absolute difference between
the average concentrations at each time period
(ESCAPE 2013). Both methods altered the
spatial contrast derived from the current LUR
models without affecting the spatial patterns of
air pollutants in the study areas.
For each cohort, parents reported (yes/
no) physician-diagnosed pneumonia, otitis
media, and croup during early childhood (see
Supplemental Material, p. 2, for the specific
questions used for each cohort). Outcomes
were assessed at 6 months (GASPII,
LISAplus), 1 year (BAMSE, GINIplus, INMA
Valencia, LISAplus, PIAMA), 14 months
(INMA Gipuzkoa, INMA Sabadell),
15 months (GASPII), 18 months (LISAplus,
INMA Asturias), 2 years (BAMSE, GINIplus,
LISAplus, PIAMA), and 3 years (MAAS). It
was not possible to evaluate respiratory infec-
tions restricted to the first 2 years of life for
the MAAS birth cohort because these out-
comes were not assessed in the full cohort
until 3 years of age. Pneumonia data were
available for all cohorts; otitis media data
were available for all except GINIplus and
MAAS; and croup data were available for all
except GASPII, the four INMA cohorts, and
PIAMA. Cumulative incidence was modeled
in each analysis, unless otherwise specified.
We used logistic binomial regression in
all individual cohort analyses, and statistical
significance was defined by p-values < 0.05.
Air pollution was entered as a continuous
variable and was not transformed. Models
were assessed using the Hosmer–Lemeshow
goodness-of-fit test and the Pearson’s chi-
square test. Potential confounders were iden-
tified from previous literature and selected
a priori. Individual cohort models were
adjusted for municipality/city (BAMSE only),
sex, older siblings (any/none), partial or exclu-
sive breastfeeding at 6 months, atopy of either
parent, child-care attendance reported at any
time during follow-up, maternal smoking
during pregnancy, secondhand smoke in the
home reported at any time during follow-
up (not available for INMA), visible mold
or dampness in the home, use of gas stove,
birth season (winter: January–March; spring:
April–June; summer: July–September; fall:
October–December), parental socio economic
status [highest education attained by either
parent (BAMSE, GINIplus, LISAplus,
PIAMA, INMA: low, medium, high); highest
occupational level by either parent (GASPII:
low, medium, high); or household income
(MAAS: < £10,000; £10,000–20,000;
£20,000–30,000; > £30,000)], and interven-
tion (GINIplus, MAAS, and PIAMA only).
Models for traffic intensity and traffic load
were additionally adjusted for background
NO2 concentrations. Children with missing
data for any covariate were excluded from
individual analyses. Based on the ESCAPE
protocol, we calculated estimates for the fol-
lowing increments in exposure: 10 μg/m3 for
NO2, 20 μg/m3 for NOx, 1 unit for PM2.5
absorbance, 5 μg/m3 for PM2.5, 10 μg/m3 for
PM10, 5 μg/m3 for coarse PM, 5,000 vehi-
cles/day for traffic intensity on the nearest
street; and 4,000 vehicle-km/day for traffic
load on major roads within a 100-m buffer.
We assessed heterogeneity of effect estimates
Air pollution and infant respiratory infections
Environmental Health Perspectives
volume 122 | number 1 | January 2014
109
between studies using the I2 statistic (Huedo-
Medina et al. 2006). We used random-effects
meta-analysis models to calculate combined
estimates (DerSimonian and Laird 1986).
We used sensitivity analyses to test the
robustness of effect estimates to the inclusion
of additional potential confounders: birth
weight, maternal age at birth, and area-level
socioeconomic indicators. In addition, we
stratified associations for outcomes that were
diagnosed during the first year of life and out-
comes diagnosed during the second year of
life for cohorts that completed follow-ups at
1 and 2 years of age (BAMSE, GINIplus,
LISAplus, PIAMA). Additional analyses were
stratified by sex, parental socioeconomic sta-
tus (low, middle, or high), and residential
mobility (moved from the birth address at
any time during the follow-up period) to
examine potential effect modification. As
noted above, we also performed sensitivity
analyses using exposure estimates that were
recalculated for selected pollutants using
back-extrapolation techniques to assess the
consistency of associations. In addition, we
performed a sensitivity analysis of the influ-
ence of neighborhood clustering by including
an area-level variable (BAMSE: neighbor-
hood; GINIplus: ZIP code; LISAplus: ZIP
code; INMA: rural indicator; PIAMA: neigh-
borhood) as a random effect in adjusted
models. Area-level data were not available
for GASPII or MAAS. Finally, we used two-
pollutant models to estimate the independent
effects of NO2 and PM.
All individual and combined analyses
were completed using identical protocols.
Individual estimates are presented by
cohort except for the German birth cohorts
(LISAplus and GINIplus), which had
almost identical study designs and parental
questionnaires, and are presented as GINI/
LISA North (Wesel) and GINI/LISA South
(Munich) because separate air pollution mod-
els were developed for each area as part of
ESCAPE. Statistical analyses were completed
using SPSS version 20 (IBM SPSS, Armonk,
NY, USA) and SAS version 9.1 (SAS Institute
Inc., Cary NC, USA).
Results
ere was complete outcome (at least one),
exposure (a minimum of NO2 and NOx)
and potential confounder information for
16,059 children across all 10 cohorts (79.6%
of the total recruited population). Children
excluded due to missing data were more
likely to have parents of lower socioeconomic
status (BAMSE, GINI/LISA South, GINI/
LISA North, MAAS, PIAMA), mothers who
smoked during pregnancy (BAMSE, GASPII,
GINI/LISA South, GINI/LISA North,
PIAMA); and were less likely to be breastfed
for at least 6 months (GASPII, GINI/LISA
South, GINI/LISA North, MAAS, PIAMA)
or to have atopic parents (GINI/LISA South,
INMA Sabadell). Table 1 shows the cumula-
tive incidence of parent-reported physician-
diagnosed respiratory infection, by cohort.
The cumulative incidence of pneumonia
during early childhood ranged from 1.5% in
INMA Sabadell to 7.9% in BAMSE (0.7–
3.6% during the first year only). Otitis media
ranged from 21.8% in GASPII to 50.0%
in BAMSE (6.8–26.6% for the first year),
and croup ranged from 10.6% in MAAS
to 12.9% in GINI/LISA North (4.2–5.6%
for the first year). ere were differences in
breastfeeding, child-care attendance, paren-
tal atopy, and secondhand smoke exposure
among the cohorts (see Supplemental
Material, Table S1). Air pollution concen-
trations were highest in GASPII and lowest
in BAMSE; GINI/LISA South, GINI/LISA
North and PIAMA had similar mean concen-
trations (Table 1). Additional statistics on air
pollutant concentrations by cohort are avail-
able in Supplemental Material, Table S2. Air
pollutant concentrations were moderately to
highly correlated (see Supplemental Material,
Table S3; e.g., correlation between PM2.5 and
NO2 ranged between 0.42 and 0.80, and cor-
relations between PM2.5 absorbance and NO2
ranged between 0.40 and 0.93).
Associations between air pollution and
respiratory infection during early childhood
are presented in Figure 1 for a) individual and
b) combined effect estimates. Table 2 presents
combined effect estimates for crude (adjusted
for sex and municipality) and adjusted
(adjusted for all potential confounders) mod-
els and p-values for heterogeneity. e hetero-
geneity between studies varied and the largest
I2 statistics were for models of pneumonia and
NO2, PM2.5, and PM10. Effect estimates were
robust to adjustment for older siblings, breast-
feeding, parental atopy, child care, maternal
smoking during pregnancy, environmental
tobacco smoke, visible mold or dampness, use
of gas stove, birth season, and parental socio-
economic status. For pneumonia, elevated
odds ratios (ORs) were found in almost all
analyses, and the combined estimates were
statistically significant for all measures of air
pollution except PM2.5 (OR = 2.58; 95% CI:
0.91, 7.27 for a 5-μg/m3 increase). For otitis
media and croup, results were generally null
across all analyses except for NO2 and otitis
media, for which the adjusted OR was 1.09
(95% CI: 1.02, 1.16 for a 10-μg/m3 increase).
Table1. The cumulative incidence of respiratory infections and distribution of air pollution for each ESCAPE birth cohort.
BAMSE GASPII
GINI/LISA
South
GINI/LISA
North
INMA
Asturias
INMA
Gipuzkoa
INMA
Sabadell
INMA
Valencia MAAS PIAMA
Respiratory infections [n (%)]
Total included 3,821 (100) 678 (100) 3,321 (100) 2,460 (100) 360 (100) 437 (100) 402 (100) 559 (100) 695 (100) 3,475 (100)
Pneumonia, 0–1 year 137 (3.6) 5 (0.7)a81 (2.4) 80 (3.3) 10 (1.8)b 84 (2.4)
Pneumonia, 0–2 years 301 (7.9) 14 (2.1)c198 (6.0) 144 (5.9) 8 (2.2)d9 (2.1)e6 (1.5)e 13 (1.9)f150 (4.3)
Otitis media, 0–1 year 1,017 (26.6)g46 (6.8)a202(16.4)h49 (18.2)h 139 (24.9)b 603 (18.0)
Otitis media, 0–2 years 1,911 (50.0)g148 (21.8)c422 (34.2)h103 (38.3)h143 (39.7)d161 (36.8)e144 (35.8)e 1,144 (32.9)
Croup, 0–1 year 201 (5.3) 140 (4.2) 135 (5.6)
Croup, 0–2 years 410 (10.7) 362 (10.9) 318 (12.9) 74 (10.6)f
Air pollution (median [IQR])
NO2 (μg/m3) 12.4 (9.3) 43.2 (10.4) 20.8 (8.3) 23.2 (3.2) 22.2 (14.4) 18.4 (5.7) 41.7 (12.4) 27.9 (18.1) 23.0 (2.4) 23.1 (8.4)
NOx (μg/m3) 20.9 (18.2) 65.8 (23.9) 34.6 (12.2) 33.3 (8.4) 44.0 (39.0) 37.2 (11.1) 69.7 (21.2) 44.7 (31.1) 38.7 (5.2) 32.8 (11.0)
PM2.5 (μg/m3) 8.1 (1.9) 18.8 (2.0) 13.3 (1.2) 17.2 (0.9) 14.6 (1.1) 9.4 (0.0) 16.5 (1.2)
PM2.5 absorbance (10–5/m) 0.6 (0.3) 2.5 (0.4) 1.7 (0.2) 1.2 (0.2) 2.2 (0.5) 1.1 (0.2) 1.2 (0.3)
PM10 (μg/m3) 15.6 (3.9) 34.9 (6.1) 20.4 (2.9) 25.2 (1.6) 26.4 (3.4) 17.0 (0.2) 24.6 (1.2)
Coarse PM (μg/m3) 7.7 (3.0) 15.7 (4.3) 6.5 (2.0) 8.4 (0.7) 11.3 (2.5) 6.9 (0.8) 8.1 (0.8)
Traffic intensity on nearest street
(vehicles/day)
500 (1,450) 500 (0) 500 (0) 500 (0) 215 (436)
Traffic load on major streets within
100-m buffer (vehicle×m/day)
0 (1,621,333) 0 (1,318,179) 0 (0) 0 (0) 0 (0)
IQR, interquartile range.
aAssessed at 6 months. bAssessed at 12 months. cAssessed at 15 months. dAssessed at 18 months. eAssessed at 14 months. fAssessed at 3 years. gRequiring antibiotic. hOtitis media
was collected only in the LISAplus study (total South n=1,242; North n=280).
MacIntyre et al.
110
volume 122 | number 1 | January 2014
Environmental Health Perspectives
Effect estimates in two-pollutant
models that included NO2 plus one of the
PM exposures were closer to the null (vs.
estimates from single-pollutant models),
and the only statistically significant finding
was for NO2 and otitis media (OR = 1.13;
95% CI: 1.01, 1.26 for a 10-μg/m3 increase
in NO2) when adjusted for coarse PM
(see Supplemental Material, Table S4).
Confidence intervals increased substantially in
two-pollutant models, reflecting the high cor-
relation between pollutants (see Supplemental
Material, Table S3).
All measures of air pollution were
associated with pneumonia (p < 0.05)
in analyses restricted to the first year of
life (e.g., OR = 4.06; 95% CI: 1.93, 8.57
for a 5-μg/m3 increase in PM2.5) (Table 3).
Further, the combined effect estimate for
all associations (pneumonia, otitis media,
and croup) increased when analyses were
restricted to outcomes in the first year of life.
Stratified meta-analyses suggested slightly
stronger effects in females and in those
from middle socioeconomic groups (see
Supplemental Material, Tables S5 and S6).
In analyses stratified by residential mobility
during follow-up, the associations between
air pollution and respiratory infection were
not consistent by strata: Pneumonia effects
were greater for movers (OR = 1.62; 95% CI:
1.20, 2.18 vs. 1.21; 95% CI: 0.88, 1.67
for NO2), whereas otitis media effects were
greater for nonmovers (OR = 1.08; 95% CI:
1.01, 1.16 vs. 1.03; 95% CI: 0.71, 1.48 for
NO2; see Supplemental Material, Table S7).
Inclusion of additional covariates into the
individual cohort models (birth weight,
maternal age and area level socioeconomic
indicators) did not change air pollution effect
estimates or improve model fit (data not
shown). There was no consistent evidence
for spatial clustering when area-level variables
were included as a random effect in mod-
els (data not shown). Finally, analyses using
back-extrapolated monitoring data were gen-
erally consistent with the main findings (see
Supplemental Material, Table S8).
Discussion
As part of the ESCAPE project we had the
unprecedented opportunity to examine out-
door air pollution as a risk factor for respi-
ratory infection during early childhood in
Figure1. Forest plots of individual cohort and combined effect estimates (ORs) by outcome for (A) NO2, (B) NOx, (C) PM2.5, (D) PM2.5 absorbance, (E) PM10,
(F)coarse PM, (G) traffic intensity on nearest street, and (H) traffic load on all major roads. Weight indicates relative weight (%) assigned using random-effects
meta-analysis. The lifetime cumulative incidence of respiratory infection (pneumonia, otitis media, croup) was assessed at 12 months (INMA Valencia), 14 months
(INMA Gipuzkoa, INMA Sabadell), 15 months (GASPII), 18 months (INMA Asturias), 24 months (BAMSE, GINI/LISA North, GINI/LISA South, PIAMA), and 36
months (MAAS) of age. Individual cohort models were adjusted for municipality (BAMSE), sex, older siblings, breastfeeding at 6 months, atopy of either parent,
any child-care reported during follow-up, maternal smoking during pregnancy, any environmental tobacco smoke in the child’s home reported during follow-up,
visible mold or dampness in the home, use of gas stove, birth season, parental socioeconomic status (low, medium, high), and intervention (GINIplus, MAAS,
PIAMA). Associations are presented for the following increments in exposure: 10μg/m3 for NO2, 20μg/m3 for NOx, 5μg/m3 for PM2.5, 1 unit for PM2.5 absorbance,
10μg/m3 for PM10, 5 μg/m3 for coarse PM, 5,000 vehicles/day for traffic intensity on the nearest street; and 4,000 vehicle-km/day for traffic load on major roads
within a 100-m buffer.
0.1 110
0.1 110 0.1 110
0.1 110
Pneumonia
BAMSE 3,694 1.82 (1.21, 2.75) 14.2
GASPII 678 0.74 (0.39, 1.39) 9.1
GINI/LISA South 3,321 1.08 (0.85, 1.37) 19.4
GINI/LISA North 2,460 1.87 (1.28, 2.73) 15.2
INMA Asturias 360 0.57 (0.26, 1.26) 6.7
INMA Gipuzkoa 437 0.62 (0.12, 3.36) 1.9
INMA Sabadell 402 2.75 (0.94, 8.02) 4.2
INMA Valencia 559 1.37 (0.74, 2.53) 9.4
MAAS 694 4.71 (0.25, 89) 0.6
PIAMA 3,454 1.34 (1.04, 1.71) 19.2
Combined 16,059 1.3 (1.02, 1.65)
Otitis media
BAMSE 3,694 1.01 (0.81, 1.27) 8.2
GASPII 678 1.02 (0.84, 1.23) 11.7
INMA Asturias 360 1.1 (0.89, 1.36) 10.1
INMA Gipuzkoa 437 1.64 (1.03, 2.63) 0.4
INMA Sabadell 402 0.94 (0.76, 1.15) 9.4
INMA Valencia 559 1.13 (0.95, 1.34) 1.9
LISA South 1,241 1.05 (0.86, 1.29) 9.7
LISA North 269 0.9 (0.33, 2.44) 14.2
PIAMA 3,454 1.15 (1.03, 1.29) 34.3
Combined 11,094 1.09 (1.02, 1.16)
Croup
BAMSE 3,694 1.07 (0.75, 1.53) 18.4
GINI/LISA South 3,321 0.94 (0.77, 1.14) 62.8
GINI/LISA North 2,460 0.94 (0.65, 1.35) 17.3
MAAS 695 1.18 (0.34, 4.13) 1.5
Combined 10,170 0.96 (0.83, 1.12)
Pneumonia
BAMSE 3,694 2.04 (1.02, 4.07) 20.6
GASPII 678 0.61 (0.14, 2.65) 15.7
GINI/LISA South 3,321 0.68 (0.32, 1.43) 20.4
GINI/LISA North 2,460 10.3 (3.41, 30.9) 18
INMA Sabadell 402 7.46 (0.26, 213) 6.7
PIAMA 3,454 8.57 (3.06, 24.1) 18.6
Combined 14,009 2.58 (0.91, 7.27)
Otitis media
BAMSE 3,694 0.95 (0.66, 1.38) 26.2
GASPII 678 0.75 (0.46, 1.22) 21.3
INMA Sabadell 402 0.8 (0.34, 1.87) 11.2
LISA South 1,241 1.06 (0.58, 1.94) 17.2
LISA North 269 0.85 (0.13, 5.42) 3.1
PIAMA 3,454 2.06 (1.25, 3.39) 20.9
Combined 9,738 1.06 (0.75, 1.49)
Croup
BAMSE 3,694 1.01 (0.56, 1.82) 38.6
GINI/LISA South 3,321 0.75 (0.42, 1.32) 41.9
GINI/LISA North 2,460 1.12 (0.49, 2.57) 19.3
MAAS 695 0.03 (0.01, 152) 0.2
Combined 10,170 0.9 (0.63, 1.3)
Pneumonia
BAMSE 3,694 1.39 (1.05, 1.86) 17
GASPII 678 0.81 (0.47, 1.42) 8.2
GINI/LISA South 3,321 1.15 (0.88, 1.5) 17.7
GINI/LISA North 2,460 1.61 (1.23, 2.11) 17.6
INMA Asturias 360 0.68 (0.38, 1.23) 7.5
INMA Gipuzkoa 437 0.62 (0.11, 3.44) 1.2
INMA Sabadell 402 1.89 (0.9, 3.96) 5.3
INMA Valencia 559 1.38 (0.71, 2.65) 6.5
MAAS 694 7.39 (1.21, 45.2) 1.1
PIAMA 3,454 1.25 (0.96, 1.63) 17.9
Combined 16,059 1.26 (1.04, 1.52)
Otitis media
BAMSE 3,694 0.96 (0.81, 1.13) 14.3
GASPII 678 1 (0.83, 1.2) 12
INMA Asturias 360 1.07 (0.91, 1.26) 7.3
INMA Gipuzkoa 437 1.66 (1.03, 2.67) 0.7
INMA Sabadell 402 0.93 (0.77, 1.13) 15.7
INMA Valencia 559 1.13 (0.93, 1.36) 1.7
LISA South 1,241 1.06 (0.84, 1.34) 11.1
LISA North 269 0.83 (0.4, 1.74) 11.6
PIAMA 3,454 1.1 (0.97, 1.24) 25.6
Combined 11,094 1.05 (0.98, 1.12)
Croup
BAMSE 3,694 1.05 (0.81, 1.36) 29.8
GINI/LISA South 3,321 0.94 (0.75, 1.18) 39.4
GINI/LISA North 2,460 1 (0.77, 1.3) 28.9
MAAS 695 1.03 (0.37, 2.91) 1.9
Combined 10,170 0.99 (0.86, 1.14)
Pneumonia
BAMSE 3,694 3.3 (1.04, 10.4) 7.9
GASPII 678 1.06 (0.42, 2.65) 12.3
GINI/LISA South 3,321 1.6 (0.89, 2.87) 30.9
GINI/LISA North 2,460 2.39 (1.1, 5.21) 17.2
INMA Sabadell 402 3.68 (0.55, 24.4) 3
MAAS 694 2.27 (0.09, 57.6) 1.1
PIAMA 3,454 2.43 (1.31, 4.5) 27.6
Combined 14,703 1.99 (1.44, 2.75)
Otitis media
BAMSE 3,694 1.19 (0.63, 2.21) 12.3
GASPII 678 0.79 (0.55, 1.12) 24.7
INMA Sabadell 402 0.87 (0.53, 1.43) 16.9
LISA South 1,241 1.08 (0.63, 1.87) 15.1
LISA North 269 2.47 (0.51, 12) 2.6
PIAMA 3,454 1.42 (1.06, 1.91) 28.3
Combined 9,738 1.08 (0.83, 1.39)
Croup
BAMSE 3,694 1.07 (0.4, 2.89) 13
GINI/LISA South 3,321 0.93 (0.56, 1.54) 50.3
GINI/LISA North 2,460 0.98 (0.51, 1.87) 30.3
MAAS 695 2.84 (0.67, 12) 6.3
Combined 10,170 1.03 (0.72, 1.47)
nOR (95% CI) nOR (95% CI) OR (95% CI) WeightOR (95% CI) Weight
Pneumonia Pneumonia, combined Otitis media Otitis media, combined Croup Croup, combined
Air pollution and infant respiratory infections
Environmental Health Perspectives
volume 122 | number 1 | January 2014
111
an analysis combining 10 European birth
cohorts (NTotal = 16,059) with data on par-
ent-reported physician-diagnosed pneumonia,
otitis media, and croup; we also examined
individual air pollution exposure estimates
based on common ESCAPE protocols. We
found consistent evidence for an association
between air pollution and pneumonia, and
some evidence for otitis media, during the
first two years of life.
Urban air pollution has been associated
with respiratory tract infections (Jedrychowski
et al. 2013; Lin et al. 2005), pneumonia
(Gouveia and Fletcher 2000), croup (Schwartz
et al. 1991), persistent cough (Esplugues et al.
2011), and otitis media (MacIntyre et al.
2011) during childhood. Associations have
also been reported for indoor air pollution and
pneumonia in developing countries (da Costa
et al. 2004; Mahalanabis et al. 2002) where
concentrations are considerably higher than
in our study areas. Our findings are consistent
with previous studies that used similar meth-
ods to examine air pollution and otitis media
in three of our cohorts—PIAMA (Brauer et al.
2006), LISAplus Munich (Brauer et al. 2006),
and INMA (Aguilera et al. 2013)—and a
recent meta-analysis on long-term PM2.5 and
acute lower respiratory infection in children,
which also included the PIAMA study (Mehta
et al. 2013).
Similar to secondhand smoke (U.S.
Department of Health and Human Services
2006), air pollution is thought to increase
susceptibility to respiratory infections pri-
marily via an inflammatory response (Li
et al. 2008). Urban air pollution may impair
defense mechanisms (Clarke et al. 2000;
Leonardi et al. 2000), and oxidant pollutants,
in particular, may exacerbate virus-induced
inflammation of the respiratory system
(Lambert et al. 2003; Spannhake et al. 2002).
Analyses were restricted to the first years
of life to include the period of greatest age-
specific incidence of respiratory infections
(Schnabel et al. 2009; Walker et al. 2013).
Our findings suggested that air pollution
effects may be slightly stronger during the
first year (Table 3). is finding could high-
light a unique period of susceptibility when
children are at increased risk of respiratory
infections due to air pollution (Gehring et al.
2002; Gouveia and Fletcher 2000; Heinrich
and Slama 2007). It is also possible that the
null findings for infections during the second
year of life are due to increased exposure mis-
classification as older children may spend less
time at their home address due to increased
child-care enrollment.
A unique strength of LUR models is their
ability to capture small-scale spatial variabil-
ity in exposure; however, the measurements
used to create the ESCAPE exposure mod-
els were taken after the birth year (Eeftens
et al. 2012a; Cyrys et al. 2012), and this
may have introduced exposure misclassifi-
cation. Although it is possible that overall
levels of air pollution changed during this
period, previous findings suggest that the
spatial distribution of air pollutants within
each area remained consistent (Cesaroni
et al. 2012; Eeftens et al. 2011; Wang et al.
2013). Further, our sensitivity analyses using
monitoring data to back-extrapolate expo-
sure estimates to the actual first year of life
were consistent with our main findings (see
Supplemental Material, Table S8).
The wording of parental questionnaires
was similar across each cohort, and previous
research has shown good agreement between
maternal recall and medical records during
early childhood (D’Souza-Vazirani et al.
2005; Vernacchio et al. 2007). Geographic
differences in the prevalence of outcomes
across the cohorts were most pronounced
for otitis media and may point to potential
diagnostic biases or disease misclassification
between countries. It was not possible to
adjust for epidemics, the impact of vaccina-
tions, or the frequency of infections because
data were not available across all cohorts.
Furthermore, defining upper respiratory tract
infections (otitis media, croup) by physician
Figure1. Continued.
0.1 1100.1 110
0.1 1 1.5 2 2.5 342161286
Pneumonia
BAMSE 3,694 1.32 (0.93, 1.89) 29.3
GASPII 678 0.87 (0.33, 2.29) 22.5
GINI/LISA South 3,321 1.28 (0.69, 2.36) 23.5
GINI/LISA North 2,460 6.65 (1.81, 24.5) 11.5
INMA Sabadell 402 6.4 (2.6, 12) 1
MAAS 694 4.49 (0.02, 926) 1
PIAMA 3,454 2.76 (0.73, 10.4) 11.2
Combined 14,703 1.76 (1, 3.09)
Otitis media
BAMSE 3,694 0.94 (0.78, 1.14) 61.6
GASPII 678 0.84 (0.59, 1.2) 18.2
INMA Sabadell 402 1.06 (0.56, 1.98) 5.7
LISA South 1,241 1.23 (0.73, 2.07) 8.3
LISA North 269 1.21 (0.11, 14) 0.4
PIAMA 3,454 1.61 (0.86, 3.01) 5.8
Combined 9,738 0.98 (0.84, 1.14)
Croup
BAMSE 3,694 0.97 (0.72, 1.32) 65.8
GINI/LISA South 3,321 0.73 (0.46, 1.18) 27.3
GINI/LISA North 2,460 1.48 (0.54, 4.01) 6.1
MAAS 695 0.92 (0.06, 13.5) 1.8
Combined 10,170 0.92 (0.72, 1.18)
Pneumonia
BAMSE 3,694 1.18 (0.9, 1.54) 14.9
GINI/LISA South 3,321 1.18 (1.04, 1.35) 62.7
GINI/LISA North 2,460 1.04 (0.44, 2.44) 1.5
MAAS 694 1.41 (1.06, 1.88) 13.3
PIAMA 3,454 1.2 (0.83, 1.75) 7.6
Combined 13,623 1.21 (1.09, 1.34)
Otitis media
BAMSE 3,694 0.95 (0.81, 1.11) 33.3
LISA South 1,241 0.96 (0.84, 1.1) 41.7
LISA North 269 3.55 (0.78, 16.2) 0.5
PIAMA 3,454 1.06 (0.87, 1.29) 24.5
Combined 8,658 0.99 (0.89, 1.1)
Croup
BAMSE 3,694 1.02 (0.8, 1.3) 24.9
GINI/LISA South 3,321 0.99 (0.85, 1.15) 64.5
GINI/LISA North 2,460 0.81 (0.42, 1.55) 3.4
MAAS 695 0.9 (0.58, 1.42) 7.2
Combined 10,170 0.98 (0.87, 1.11)
Pneumonia
BAMSE 3,694 1.19 (0.95, 1.5) 69.4
GASPII 678 1.06 (0.54, 2.05) 8.1
GINI/LISA South 3,321 1.29 (0.8, 2.08) 15.5
GINI/LISA North 2,460 2.75 (0.85, 8.87) 2.6
INMA Sabadell 402 9.99 (0.76, 131) 0.5
MAAS 694 1.1 (0.04, 31.5) 0.3
PIAMA 3,454 1.42 (0.53, 3.83) 3.6
Combined 14,703 1.24 (1.03, 1.5)
Otitis media
BAMSE 3,694 0.97 (0.85, 1.09) 67.9
GASPII 678 0.89 (0.69, 1.15) 15.9
INMA Sabadell 402 0.98 (0.62, 1.57) 4.7
LISA South 1,241 1.07 (0.71, 1.62) 6
LISA North 269 0.55 (0.06, 5.14) 0.2
PIAMA 3,454 1.27 (0.82, 1.97) 5.3
Combined 9,738 0.97 (0.88, 1.08)
Croup
BAMSE 3,694 0.99 (0.81, 1.2) 75
GINI/LISA South 3,321 0.86 (0.59, 1.26) 19.5
GINI/LISA North 2,460 1.12 (0.49, 2.59) 4.1
MAAS 695 1.65 (0.4, 6.8) 1.4
Combined 10,170 0.97 (0.82, 1.15)
Pneumonia
BAMSE 3,694 1.09 (0.97, 1.23) 20.9
GINI/LISA South 3,321 1.08 (1.01, 1.15) 67.8
GINI/LISA North 2,460 1.09 (0.8, 1.48) 3.2
PIAMA 3,454 1.16 (0.96, 1.4) 8.2
Combined 13,623 1.09 (1.03, 1.15)
Otitis media
BAMSE 3,694 0.96 (0.89, 1.04) 35.7
LISA South 1,241 1 (0.94, 1.06) 50
LISA North 269 1.28 (0.73, 2.25) 0.6
PIAMA 3,454 0.92 (0.82, 1.04) 13.7
Combined 8,658 0.98 (0.93, 1.02)
Croup
BAMSE 3,694 0.95 (0.84, 1.07) 22.6
GINI/LISA South 3,321 1 (0.94, 1.08) 70.6
GINI/LISA North 2,460 1 (0.79, 1.26) 6.1
MAAS 695 0.85 (0.42, 1.73) 0.7
Combined 10,170 0.99 (0.93, 1.05)
nOR (95% CI) nOR (95% CI) OR (95% CI) WeightOR (95% CI) Weight
Pneumonia Pneumonia, combined Otitis media Otitis media, combined Croup Croup, combined
MacIntyre et al.
112
volume 122 | number 1 | January 2014
Environmental Health Perspectives
diagnosis is complicated by the fact that not
all infections present with acute symptoms
severe enough to warrant a physician visit,
in contrast with pneumonia, which routinely
presents with a high fever and/or difficulty
breathing (Edmond et al. 2012).
Conclusion
Our meta-analysis of 10 European birth
cohorts found consistent evidence for an
association between traffic-related air pol-
lution and pneumonia, and some evidence
to suggest an association with otitis media.
Policies aimed at reducing air pollution may
be successful in reducing the overall burden of
pneumonia in early childhood.
correction
e value for “Traffic load on major streets
within 100-m buffer” for GINI/LISA
South in Table 1 was incorrect in the man-
uscript originally published online. It has
been corrected here.
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Table2. Combined estimates from random-effects meta-analyses for residential air pollution and respira-
tory infections during early life (up to 36 months).a
CrudebAdjustedc
OR (95% CI) I2p-Value OR (95% CI) I2p-Value
Pneumonia
NO21.25 (1.04, 1.50)* 37.1 0.112 1.30 (1.02, 1.65)* 52.9 0.024
NOx1.23 (1.06, 1.41)* 22.2 0.239 1.26 (1.04, 1.52)* 44.0 0.066
PM2.5 2.13 (0.82, 5.49) 79.7 0.000 2.58 (0.91, 7.27) 81.7 0.000
PM2.5 absorbance 1.78 (1.30, 2.43)* 0 0.734 1.99 (1.44, 2.75)* 0 0.663
PM10 1.55 (1.03, 2.34)* 29.2 0.205 1.76 (1.00, 3.09)* 51.2 0.051
Coarse PM 1.23 (1.02, 1.47)* 0 0.626 1.24 (1.03, 1.50)* 0 0.579
Traffic, nearest street 1.08 (1.03, 1.14)* 0 0.997 1.09 (1.03, 1.15)* 0 0.969
Traffic, major streets 1.19 (1.08, 1.31)* 0 0.979 1.21 (1.09, 1.34)* 0 0.843
Otitis media
NO21.08* (1.01, 1.15) 4.8 0.395 1.09 (1.02, 1.16)* 0 0.515
NOx1.04 (0.98, 1.10) 0.5 0.430 1.05 (0.98, 1.12) 0 0.458
PM2.5 1.02 (0.71, 1.45) 55.5 0.047 1.06 (0.75, 1.49) 47.9 0.088
PM2.5 absorbance 1.05 (0.80, 1.37) 46.7 0.095 1.08 (0.83, 1.39) 39.9 0.139
PM10 0.98 (0.83, 1.17) 11.6 0.341 0.98 (0.84, 1.14) 0 0.539
Coarse PM 0.96 (0.87, 1.06) 0 0.608 0.97 (0.88, 1.08) 0 0.805
Traffic, nearest street 0.98 (0.94, 1.03) 1.4 0.385 0.98 (0.93, 1.02) 0 0.497
Traffic, major streets 1.00 (0.91, 1.09) 0 0.462 0.99 (0.89, 1.10) 18.2 0.300
Croup
NO20.92 (0.80, 1.07) 0 0.884 0.96 (0.83, 1.12) 0 0.909
NOx0.96 (0.83, 1.10) 0 0.895 0.99 (0.86, 1.14) 0 0.936
PM2.5 0.83 (0.58, 1.19) 0 0.760 0.90 (0.63, 1.30) 0 0.703
PM2.5 absorbance 0.95 (0.66, 1.37) 5.0 0.368 1.03 (0.72, 1.47) 0 0.554
PM10 0.89 (0.70, 1.13) 0 0.586 0.92 (0.72, 1.18) 0 0.595
Coarse PM 0.95 (0.80, 1.12) 0 0.551 0.97 (0.82, 1.15) 0 0.787
Traffic, nearest street 0.98 (0.93, 1.04) 0 0.926 0.99 (0.93, 1.05) 0 0.853
Traffic, major streets 0.97 (0.86, 1.09) 0 0.734 0.98 (0.87, 1.11) 0 0.901
Associations are presented for the following increments in exposure: 10μg/m3 for NO2, 20 μg/m3 for NOx, 5μg/m3 for
PM2.5, 1 unit for PM2.5 absorbance, 10μg/m3 for PM10, 5μg/m3 for coarse PM, 5,000 vehicles/day for traffic intensity on
the nearest street; and 4,000 vehicle-km/day for traffic load on major roads within a 100-m buffer; associations with traf-
fic intensity and traffic load were additionally adjusted for background NO2 concentrations.
aOutcomes assessed up to 12 months (INMA Valencia), 14 months (INMA Gipuzkoa, INMA Sabadell), 15 months
(GASPII), 18 months (INMA Asturias), 24 months (BAMSE, GINIplus, LISAplus, PIAMA), and 36 months (MAAS). bCrude
models were adjusted for sex and municipality (BAMSE). cAdjusted models included municipality (BAMSE), sex, older
siblings, breastfeeding at 6 months, atopy of either parent, any child-care reported during follow-up, maternal smok-
ing during pregnancy, any environmental tobacco smoke in the child’s home reported during follow-up, visible mold or
dampness in the home, use of gas stove, birth season, parental socioeconomic status (low, medium, high), and interven-
tion (GINIplus, MAAS, PIAMA). *p <0.05.
Table3. Adjusted combined estimates for air pollution exposure at the birth address and respiratory
infection by year of life [OR (95% CI)].
Pneumoniaa
(n=12,891)
Otitis mediab
(n=8,722)
Croupc
(n=9,101)
Respiratory infections during the first yeard of life
NO21.47* (1.15, 1.89) 1.19* (1.07, 1.33) 1.05 (0.83, 1.32)
NOx1.45* (1.21, 1.75) 1.09 (0.98, 1.22) 1.10 (0.90, 1.36)
PM2.5 4.06* (1.93, 8.57) 1.21 (0.64, 2.28) 1.15 (0.67, 1.97)
PM2.5 absorbance 2.71* (1.68, 4.37) 1.32 (0.99, 1.75) 1.04 (0.59, 1.83)
PM10 1.77* (1.18, 2.67) 1.24 (0.76, 2.02) 1.07 (0.75, 1.53)
Coarse PM 1.46* (1.11, 1.92) 1.16 (0.80, 1.70) 1.02 (0.80, 1.30)
Traffic, nearest street 1.14* (1.07, 1.22) 0.99 (0.94, 1.04) 1.03 (0.94, 1.13)
Traffic, major streets 1.31* (1.15, 1.50) 1.03 (0.93, 1.14) 1.00 (0.81, 1.24)
Respiratory infections during the second yeare of life
NO21.40* (1.04, 1.88) 1.07 (0.96, 1.20) 0.92 (0.78, 1.09)
NOx1.29* (1.07, 1.55) 1.02 (0.89, 1.17) 0.92 (0.78, 1.08)
PM2.5 2.65 (0.63, 11.2) 1.06 (0.64, 1.74) 0.76 (0.51, 1.15)
PM2.5 absorbance 1.90 (0.93, 3.87) 1.20 (0.80, 1.79) 0.89 (0.59, 1.35)
PM10 1.42 (0.99, 2.03) 1.00 (0.84, 1.19) 0.83 (0.63, 1.09)
Coarse PM 1.24 (0.98, 1.56) 1.00 (0.89, 1.13) 0.89 (0.73, 1.08)
Traffic, nearest street 1.05 (0.98, 1.13) 0.96 (0.90, 1.03) 0.93 (0.81, 1.07)
Traffic, major streets 1.10 (0.90, 1.34) 0.96 (0.83, 1.10) 1.00 (0.88, 1.14)
Associations are presented for the following increments in exposure: 10μg/m3 for NO2, 20μg/m3 for NOx, 5μg/m3 for PM2.5,
1 unit for PM2.5 absorbance, 10μg/m3 for PM10, 5μg/m3 for coarse PM, 5,000 vehicles/day for traffic intensity on the nearest
street; and 4,000 vehicle-km/day for traffic load on major roads within a 100-m buffer; associations with traffic intensity and
traffic load were additionally adjusted for background NO2 concentrations.
aBased on four studies: BAMSE, GINI/LISA North, GINI/LISA South, PIAMA. bBased on 3 studies: BAMSE, LISAplus North,
LISAplus South, PIAMA. cBased on three studies: BAMSE, GINI/LISA North, GINI/LISA South. dDefined as 0–12 months.
eDefined as 13–24 months. Models were adjusted for municipality (BAMSE), sex, older siblings, breastfeeding at 6 months,
atopy of either parent, any child-care reported during follow-up, maternal smoking during pregnancy, any environmental
tobacco smoke in the child’s home reported during follow-up, visible mold or dampness in the home, use of gas stove, birth
season, parental socioeconomic status (low, medium, high), and intervention (GINIplus, PIAMA). *p <0.05.
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The purposes of this study were to quantify the association of the combination of air pollution and genetic risk factors with hypertension and explore the interactions between air pollution and genetic risk. This study included 391,366 participants of European ancestry initially free from pre-existing hypertension in the UK Biobank. Exposure to ambient air pollutants, including particulate matter (PM2.5 p.m.2.5-10, and PM10), nitrogen dioxide (NO2) and nitrogen oxides (NOX), was estimated through land use regression modelling, and the associations between air pollutants and the incidence of hypertension were investigated using a Cox proportional hazards model adjusted for covariates. Furthermore, we established a polygenic risk score for hypertension and assessed the combined effect of genetic susceptibility and air pollution on incident hypertension. The results showed significant associations between the risk of hypertension and exposure to PM2.5 (hazard ratio [HR]: 1.41, 95% confidence interval [CI]: 1.29–1.53; per 10 μg/m³), PM10 (1.05, 1.00–1.09; per 10 μg/m³), and NOX (1.01, 1.01–1.02 per 10 μg/m³). Additive effects of PM2.5 and NOX exposure and genetic risk were observed. Compared to individuals with a low genetic risk and low air pollution exposure, participants with high air pollution exposure and a high genetic risk had a significantly increased risk of hypertension (PM2.5: 71% (66%–76%), PM10: 59% (55%–64%), NOX: 65% (60%–70%)). Our results indicate that long-term exposure to air pollution is associated with an increased risk of hypertension, especially in individuals with a high genetic risk.
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The current authors examined whether mite and cat allergen and bacterial endotoxin levels in dust of the mothers' mattresses were associated with cord blood immunoglobulin (Ig)E (CB-IgE) levels in newborns. Data from 1,332 term and normal weight neonates, from an ongoing birth cohort study, Influences of life-style related factors on the immune system and the development of allergies in childhood (LISA), with complete information on exposure to biocontaminants in mattress dust and CB-IgE were analysed. Two thirds of CB-IgE were undetectable (<0.35 kU·L-1). Thus, 0.35 and 0.45 kU·L-1 (4th quartile) were chosen as cut-offs. Nonparametric smoothing (generalised additive models) showed statistically significant confounder-adjusted associations between elevated CB-IgE levels (?0.45 kU·L-1) and log-transformed exposures to cat (linear), mite (inverse u-shaped), and endotoxin (u-shaped). After adjustment for covariables, elevated CB-IgE levels (logistic regression using the 1st-4th quartiles of exposure) were positively associated with high cat-allergen exposure and medium exposure to mite allergen, but were inversely associated with exposure to endotoxin. The associations were similar, but somewhat weaker, when 0.35 kU·L-1 was used as cut-off. These results, showing an association between prenatal allergen and endotoxin exposures and immunoglobulin E production, suggest that the development of foetal immune responses may be affected
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Acute lower respiratory infections (ALRI) account for nearly one fifth of mortality in young children worldwide and have been associated with exposures to indoor and outdoor sources of combustion-derived air pollution. A systematic review was conducted to identify relevant articles on air pollution and ALRI in children. Using a Bayesian approach to meta-analysis, a summary estimate of 1.12 (1.03, 1.30) increased risk in ALRI occurrence per 10 μg/m3 increase in annual average PM2.5 concentration was derived from the longer-term (subchronic and chronic) effects studies. This analysis strengthens the evidence for a causal relationship between exposure to PM2.5 and the occurrence of ALRI and provides a basis for estimating the global attributable burden of mortality due to ALRI that is not influenced by the wide variation in regional case fatality rates. Most studies, however, have been conducted in settings with relatively low levels of PM2.5. Extrapolating their results to other, more polluted, regions will require a model that is informed by evidence from studies of the effects on ALRI of exposure to PM2.5 from other combustion sources, such as secondhand smoke and household solid fuel use.
Article
Land Use Regression (LUR) models have been used increasingly for modeling small-scale spatial variation in air pollution concentrations and estimating individual exposure for participants of cohort studies. Within the ESCAPE project, concentrations of PM2.5, PM2.5 absorbance, PM10, and PMcoarse were measured in 20 European study areas at 20 sites per area. GIS-derived predictor variables (e.g., traffic intensity, population, and land-use) were evaluated to model spatial variation of annual average concentrations for each study area. The median model explained variance (R-2) was 71% for PM2.5 (range across study areas 35-94%). Model R-2 was higher for PM2.5 absorbance (median 89%, range 56-97%) and lower for PMcoarse (median 68%, range 32-81%). Models included between two and five predictor variables, with various traffic indicators as the most common predictors. Lower R-2 was related to small concentration variability or limited availability of predictor variables, especially traffic intensity. Cross validation R-2 results were on average 8-11% lower than model R-2. Careful selection of monitoring sites, examination of influential observations and skewed variable distributions were essential for developing stable LUR models. The final LUR models are used to estimate air pollution concentrations at the home addresses of participants in the health studies involved in ESCAPE.
Article
Background Land-use regression (LUR) is a cost-effective approach for predicting spatial variability in ambient air pollutant concentrations with high resolution. Models have been widely used in epidemiological studies and are often applied to time periods before or after the period of air quality monitoring used in model development. However, it is unclear how well such models perform when extrapolated over time.Objective The objective of this study was to assess the temporal stability of LUR models over a period of 7 years in Metro Vancouver, Canada.MethodsA set of NO and NO2 LUR models based on 116 measurements were developed in 2003. In 2010, we made 116 measurements again, of which 73 were made at the exact same location as in 2003. We then developed 2010 models using updated data for the same predictor variables used in 2003, and also explored additional variables. Four methods were used to derive model predictions over 7 years, and predictions were compared with measurements to assess the temporal stability of LUR models.ResultsThe correlation between 2003 NO and 2010 NO measurements was 0.87 with a mean (sd) decrease of 11.3 (9.9) ppb. For NO2, the correlation was 0.74, with a mean (sd) decrease of 2.4 (3.2) ppb. 2003 and 2010 LUR models explained similar amounts of spatial variation (R2 = 0.59 and R2 = 0.58 for NO; R2 = 0.52 and R2 = 0.63 for NO2, in 2003 and in 2010 respectively). The 2003 models explained more variability in the 2010 measurements (R2 = 0.58–0.60 for NO; R2 = 0.52–0.61 for NO2) than the 2010 models explained in the 2003 measurements (R2 = 0.50–0.55 for NO; R2 = 0.44–0.49 for NO2), and the 2003 models explained as much variability in the 2010 measurements as they did in the 2003 measurements.ConclusionLUR models are able to provide reliable estimates over a period of 7 years in Metro Vancouver. When concentrations and their variability are decreasing over time, the predictive power of LUR models is likely to remain the same or to improve in forecasting scenarios, but to decrease in hind-casting scenarios.
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Ambient air pollution is suspected to cause lung cancer. We aimed to assess the association between long-term exposure to ambient air pollution and lung cancer incidence in European populations. This prospective analysis of data obtained by the European Study of Cohorts for Air Pollution Effects used data from 17 cohort studies based in nine European countries. Baseline addresses were geocoded and we assessed air pollution by land-use regression models for particulate matter (PM) with diameter of less than 10 μm (PM10), less than 2·5 μm (PM2·5), and between 2·5 and 10 μm (PMcoarse), soot (PM2·5absorbance), nitrogen oxides, and two traffic indicators. We used Cox regression models with adjustment for potential confounders for cohort-specific analyses and random effects models for meta-analyses. The 312 944 cohort members contributed 4 013 131 person-years at risk. During follow-up (mean 12·8 years), 2095 incident lung cancer cases were diagnosed. The meta-analyses showed a statistically significant association between risk for lung cancer and PM10 (hazard ratio [HR] 1·22 [95% CI 1·03-1·45] per 10 μg/m(3)). For PM2·5 the HR was 1·18 (0·96-1·46) per 5 μg/m(3). The same increments of PM10 and PM2·5 were associated with HRs for adenocarcinomas of the lung of 1·51 (1·10-2·08) and 1·55 (1·05-2·29), respectively. An increase in road traffic of 4000 vehicle-km per day within 100 m of the residence was associated with an HR for lung cancer of 1·09 (0·99-1·21). The results showed no association between lung cancer and nitrogen oxides concentration (HR 1·01 [0·95-1·07] per 20 μg/m(3)) or traffic intensity on the nearest street (HR 1·00 [0·97-1·04] per 5000 vehicles per day). Particulate matter air pollution contributes to lung cancer incidence in Europe. European Community's Seventh Framework Programme.
Article
RationaleSpecific airway resistance (sRaw), is a sensitive and reproducible measure of lung function in young children – higher sRaw indicating poorer lung function. We investigated the relationship between single nucleotide polymorphisms (SNPs) in ADAM33 (a disintegrin and metalloprotease 33; Nature 2002;418:426-30) and sRaw at age 5 years in the setting of a prospective birth cohort study.
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Diarrhoea and pneumonia are the leading infectious causes of childhood morbidity and mortality. We comprehensively reviewed the epidemiology of childhood diarrhoea and pneumonia in 2010-11 to inform the planning of integrated control programmes for both illnesses. We estimated that, in 2010, there were 1·731 billion episodes of diarrhoea (36 million of which progressed to severe episodes) and 120 million episodes of pneumonia (14 million of which progressed to severe episodes) in children younger than 5 years. We estimated that, in 2011, 700 000 episodes of diarrhoea and 1·3 million of pneumonia led to death. A high proportion of deaths occurs in the first 2 years of life in both diseases-72% for diarrhoea and 81% for pneumonia. The epidemiology of childhood diarrhoea and that of pneumonia overlap, which might be partly because of shared risk factors, such as undernutrition, suboptimum breastfeeding, and zinc deficiency. Rotavirus is the most common cause of vaccine-preventable severe diarrhoea (associated with 28% of cases), and Streptococcus pneumoniae (18·3%) of vaccine-preventable severe pneumonia. Morbidity and mortality from childhood pneumonia and diarrhoea are falling, but action is needed globally and at country level to accelerate the reduction.
Article
The ESCAPE study (European Study of Cohorts for Air Pollution Effects) investigates relationships between long-term exposure to outdoor air pollution and health using cohort studies across Europe. This paper analyses the spatial variation of PM2.5, PM2.5 absorbance, PM10 and PMcoarse concentrations between and within 20 study areas across Europe. We measured NO2, NOx, PM2.5, PM2.5 absorbance and PM10 between October 2008 and April 2011 using standardized methods. PMcoarse was determined as the difference between PM10 and PM2.5. In each of the twenty study areas, we selected twenty PM monitoring sites to represent the variability in important air quality predictors, including population density, traffic intensity and altitude. Each site was monitored over three 14-day periods spread over a year, using Harvard impactors. Results for each site were averaged after correcting for temporal variation using data obtained from a reference site, which was operated year-round. Substantial concentration differences were observed between and within study areas. Concentrations for all components were higher in Southern Europe than in Western and Northern Europe, but the pattern differed per component with the highest average PM2.5 concentrations found in Turin and the highest PMcoarse in Heraklion. Street/urban background concentration ratios for PMcoarse (mean ratio 1.42) were as large as for PM2.5 absorbance (mean ratio 1.38) and higher than those for PM2.5 (1.14) and PM10 (1.23), documenting the importance of non-tailpipe emissions. Correlations between components varied between areas, but were generally high between NO2 and PM2.5 absorbance (average R2 ¼ 0.80). Correlations between PM2.5 and PMcoarse were lower (average R2 ¼ 0.39). Despite high correlations, concentration ratios between components varied, e.g. the NO2/PM2.5 ratio varied between 0.67 and 3.06. In conclusion, substantial variability was found in spatial patterns of PM2.5, PM2.5 absorbance, PM10 and PMcoarse. The highly standardized measurement of particle concentrations across Europe will contribute to a consistent assessment of health effects across Europe.