Accepted Manuscript

Title: Investigating the relationship between environmentalfactors and respiratory health outcomes in school childrenusing the Forced Oscillation Technique

Authors: Jonathon Boeyen, Anna C. Callan, David Blake,Amanda J. Wheeler, Peter Franklin, Graham L. Hall, ClaireShackleton, Peter D. Sly, Andrea Hinwood

PII: S1438-4639(16)30271-1DOI: http://dx.doi.org/doi:10.1016/j.ijheh.2017.01.014Reference: IJHEH 13045

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Received date: 27-8-2016Revised date: 28-12-2016Accepted date: 3-1-2017

Please cite this article as: Boeyen, Jonathon, Callan, Anna C., Blake, David,Wheeler, Amanda J., Franklin, Peter, Hall, Graham L., Shackleton, Claire, Sly,Peter D., Hinwood, Andrea, Investigating the relationship between environmentalfactors and respiratory health outcomes in school children using the ForcedOscillation Technique.International Journal of Hygiene and Environmental Healthhttp://dx.doi.org/10.1016/j.ijheh.2017.01.014

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1

Investigating the relationship between environmental factors and

respiratory health outcomes in school children using the Forced Oscillation

Technique.

Jonathon Boeyen a, Anna C. Callan

b, David Blake

a, Amanda J. Wheeler

a, Peter Franklin

c,

Graham L. Hall d, Claire Shackleton

e, Peter D. Sly

e. Andrea Hinwood

a*

a Centre for Ecosystem Management, Edith Cowan University, 270 Joondalup Drive,

Joondalup, WA 6027, Australia; e-mail: j.boeyen@ecu.edu.au. a.hinwood@ecu.edu.au.

a.wheeler@ecu.edu.au. d.blake@ecu.edu..au. b School of Medical and Health Sciences, Edith Cowan University, 270 Joondalup Drive,

Joondalup, WA 6027, Australia; e-mail: a.callan@ecu.edu.au. c School of Population Health, The University of Western Australia, 35 Stirling Highway,

Crawley, WA 6009, Australia; e-mail: peter.franklin@uwa.edu.au. d Children’s Lung Health Telethon Kids Institute, School of Physiotherapy and Exercise

Science Curtin University, Centre for Child Health Research, University of Western

Australia, PO Box 855, West Perth WA 6872 Australia e-mail:

graham.hall@telethonkids.org.au e Child Health Research Centre, The University of Queensland, 62 Graham St, South

Brisbane Qld 4101 Australia; e-mail: p.sly@uq.edu.au.

*Corresponding author:

Andrea Hinwood

Centre for Ecosystem Management

Edith Cowan University

270 Joondalup Drive, Joondalup, WA 6027, Australia

Telephone: +61 8 6304 5372

E-mail: a.hinwood@ecu.edu.au

2

Abstract

The environmental factors which may affect children’s respiratory health are complex, and

the influence and significance of factors such as traffic, industry and presence of vegetation is

still being determined. We undertook a cross-sectional study of 360 school children aged 5 to

12 years who lived on the outskirts of a heavy industrial area in Western Australia to

investigate the effect of a range of environmental factors on respiratory health using the

forced oscillation technique (FOT), a non-invasive method that allows for the assessment of

the resistive and reactive properties of the respiratory system. Based on home address,

proximity calculations were used to estimate children’s exposure to air pollution from traffic

and industry and to characterise surrounding green space. Indoor factors were determined

using a housing questionnaire. Of the outdoor measures, the length of major roads within a

50m buffer was associated with increased airway resistance (Rrs8). There were no

associations between distance to industry and FOT measures. For the indoor environment the

presence of wood heating and gas heating in the first year of life was associated with better

lung function. The significance of both indoor and outdoor sources of air pollution and effect

modifiers such as green space and heating require further investigation.

Keywords: children, FOT, green space, industry, traffic, respiratory health

3

Introduction

It is well established that children are more vulnerable to the effects of air pollution as they

spend more time participating in outdoor physical activities, display elevated breathing rates,

inhale and retain more air than adults per unit of body weight, and their narrow bronchioles

are more prone to constriction upon exposure to environmental irritants (Hansen et al., 2003;

Committee on Environmental Health, 2004). Environmental factors have been shown to have

an influence on the respiratory health of children and both indoor and outdoor sources of air

pollution have been implicated (Brauer et al. 2007; Markandya & Wilkinson, 2007; Fuentes-

Leonate et al. 2009; Gillespie-Bennett et al. 2011).

Exposure to traffic-related air pollution has been associated with the prevalence and

exacerbation of asthma and respiratory symptoms in children (Brauer et al. 2007; Gehring et

al. 2010; Gordian et al. 2006; Kim et al. 2008; McConnell et al. 2006; Nordling et al. 2008).

However adverse effects to respiratory health are not observed in all studies (Fuertes et al.

2013; Rosenlund et al. 2009).

Exposure to industry-related air pollution may also result in adverse respiratory health effects

in children (Cara et al. 2007; Rusconi et al. 2011), however, the type and volume of

pollutants emitted from industrial facilities varies enormously, and hence the potential for

health impacts is also variable. Industrial activities which may influence children’s

respiratory health include fossil fuel electricity generation (Markandya & Wilkinson, 2007),

metal works (Cara et al. 2007) and the use of heavy vehicles and their subsequent diesel

emissions (Riedl & Diaz-Sanchez, 2005).

Indoor sources of air pollution include gas and wood burning for heating and cooking which

have been associated with respiratory symptoms and effects in children (Triche et al. 2002;

Fuentes-Leonarte et al. 2009; Gillespie-Bennett et al. 2011). Contrary findings have also been

reported (Bennett et al. 2010).

Few studies have investigated the influence of effect modifiers such as urban vegetation on

air quality and human exposure to air pollution. Nowak et al. (2006) found that urban trees

absorb a variety of air pollutants, improving air quality and potentially reducing exposures

and hence reducing the effects of exposure to air pollutants. A study of pregnant women by

Dadvand et al. (2012) associated living in greener areas with reduced levels of personally-

monitored air pollution exposure to fine particulate matter (PM) and nitrogen oxides (NOx).

Studies investigating the relationship between vegetation density and asthma and allergic

4

sensitisation have generated conflicting results with some showing improvements in health

outcomes with increasing greenness and others the reverse (Fuertes et al. 2014; Lovasi et al.

2008; 2013). Given these findings, further work is required to clarify the role of these

potential effect modifiers and sources of pollution on children’s respiratory health. This is

imperative as exposure to pollutants during critical stages of children’s growth may affect

both lung structure and function (Kajekar, 2007).

The forced oscillation technique (FOT) is a non-invasive lung function method which allows

the assessment of respiratory system resistance (Rrs) and reactance (Xrs) related to

respiratory system compliance (Oostveen et al., 2003). The FOT is an attractive lung function

assessment technique for children due to its reduced requirement for participant cooperation

and high success rates when compared with other established lung function tests such as

spirometry (Navajas & Farre, 2001). The FOT has been used to evaluate alterations in

respiratory mechanics in infants, children, young people and adults with common respiratory

diseases, including recurrent wheeze, asthma, bronchial hyperresponsiveness, cystic fibrosis

and broncopulmonary dysplasia (Oostven et al. 2003; Rosenfeld et al. 2013). Additionally,

the FOT has been used to examine the effects of atopy and environmental tobacco exposure

among other variables (Calogero et al. 2013; Gray et al. 2015), suggesting that it may be

useful when investigating the effects of environmental factors on lung function.

This cross-sectional study was conducted to examine the influence of environmental factors

on the lung function (using FOT) of primary school-aged children in the Kwinana region of

Western Australia using questionnaire-based information and spatial analyses. The Kwinana

Industrial Area contains a range of light and heavy industries and is Western Australia’s

primary area for industrial development. Industries include oil and alumina refining, nickel

processing, cement works, chemical and pesticides manufacturing as well as shipping and

metal works-type activities (www.npi.gov.au).

Materials and Methods

Study Population and Recruitment

Children, aged between 5 and 12 years, were recruited from ten primary schools in the

Kwinana region of Western Australia (Figure 1). The number of children recruited for the

study was 591 of a total of 2466 who were invited, a response fraction of 24.0%. Parents

completed a respiratory health questionnaire and a questionnaire on the child’s current and

5

past home environments. Children with parental consent and personal assent completed lung

function testing using the FOT and atopy testing (skin prick test). All testing was conducted

at the schools during normal school hours. Testing was done in June 2009 (winter). All

children who volunteered to participate in the study and were at school on the day of testing

were included in the study. The presence of evidence that children presented with an upper

respiratory tract infection (‘cold’) at the time of testing was noted.

Data Collection

Questionnaire

Information on each child’s demographic, lifestyle and health characteristics was obtained as

reported by parents via questionnaire. The questionnaire included questions found in the

literature to impact upon lung health. These included: family history of asthma, premature

birth, maternal smoking, smoking in the home, use of unflued gas heaters in the home,

presence of an internal garage, exposure to pets and parental education. Duration of residency

in Kwinana was also examined, as children born and raised in locations with different air

pollution levels can have different respiratory health outcomes (McConnell et al. 2006).

Information was also provided on respiratory symptoms/disease in the last year and

medication use, both historical and present.

Lung function

Respiratory system impedance, a measure of respiratory function, was attempted in all

children consented into the study using the FOT according to international guidelines

(Oostveen 2003, Beydon et al. 2007) and as reported previously by Hall et al. (2007) and

Calogero et al. (2013). Measurements were obtained from a commercially available FOT

device (12M, Chess Medical, Belgium) which uses a pseudo-random noise forcing signal

between 4 and 48 Hz. The device was verified daily using a known resistance and measured

impedances corrected for the impedance of the device and mouthpiece.

Briefly children performed the tests sitting upright with their head in a neutral position.

Measurements were obtained with the child breathing via a mouthpiece incorporating a

bacterial filter (Suregard, Bird Healthcare, Australia). Children were instructed to breathe

normally while wearing a nose clip with the cheeks and floor of the mouth supported by an

investigator. Measurements were excluded if leak, mouth or tongue movement, swallowing,

glottal closure, talking or audible noise occurred during the test. Three to five acceptable

6

measurements of respiratory system impedance (Zrs) were obtained for each child. Rrs and

Xrs were calculated at each frequency and the mean of the total acceptable measurements

was reported. The frequency dependence of the Rrs between 4 and 24 Hz and the resonant

frequency (the point at which Xrs is zero) were determined from the average of Zrs of all

acceptable measurements. The area under the reactance curve (AX) was calculated between 6

Hz up to the resonant frequency (being the frequency at which the Xrs curve equals zero).

We aimed to obtain a within-test variability of Rrs of less than 10%. However, individual

measurements and the subsequent averaged Zrs data were not excluded if this criterion was

not met.

The primary FOT outcomes reported in this study were Rrs and Xrs at a frequency of 8 Hz

(Rrs8 and Xrs8), Fres and AX, expressed as standardised z-scores controlling for each

individual’s age, sex and height based on reference equations from Calogero et al. (2013) and

Fdep (the frequency dependence). Higher scores for Rrs8, Fres and AX are associated with

poorer respiratory function (Oostveen et al. 2003), whereas higher scores for Xrs8 and Fdep

correspond to better respiratory function (Calogero et al. 2013).

Atopy

Atopy testing was undertaken using skin prick tests for the following locally prevalent

allergens: peanut, cat, dog, grass mix, house dust mite and mould mix, together with negative

(saline) and positive (histamine) controls. A positive response was taken as a weal of 3 mm

or greater in size than the saline control. Children were marked as atopic by the investigators

if a positive response to any of the six allergens was shown.

Ethical approval

This study received approval from the Princess Margaret Hospital Human Research Ethics

Committee in 2009. Subsequently, approval was obtained from both Princess Margaret

Hospital and Edith Cowan University (ECU) Human Research Ethics Committee (Project

Number 11329) specifically for the analysis described in this paper. No personal identifiers

were sent to ECU researchers and all addresses were geocoded.

Exposure assessment

Exposure assessment was conducted using children’s homes as proxies for their location,

with linear and buffer calculations used to estimate children’s exposure to traffic and industry

and to determine their surrounding greenness based on the ESCAPE protocols (Beelen et al.

7

2014). Linear distance from each child’s residence to the geographic centre of the Kwinana

Industrial Area was measured. Nearest distance of a child’s residence to a weighted centre

point for NOx emissions (derived from annual pollution inventory data for heavy industry in

the Kwinana region) was used as an estimate of exposure to industry. NOx has previously

been shown as a strong marker for many hazardous air pollutants including PM and other

oxides of nitrogen (Beckerman et al. 2008). All spatial analyses were conducted using

ArcGIS (ESRI, released 2013, ArcGIS Version 10.2., Redlands CA: ESRI).

Nearest distance to the closest major road and road density within a buffer were used as

surrogates of traffic exposure as applied in previous respiratory health studies (Brauer et al.

2007). For road density calculations, buffers of 50m, 100m, and 200m, 300 and 500m in

radius were applied following the ESCAPE protocols (Beelen et al. 2014), with smaller

buffer sizes excluded due to the lower number of subjects participating in this study, and

larger buffer sizes not considered due to the size of the study area. Road-based traffic

calculations were conducted using Main Roads Western Australia’s road hierarchy data set

for 2012, with major roads classified as such if they were a ‘Primary’, ‘District’ or ‘Regional’

distributor under the related criteria (Main Roads Western Australia, 2011a; 2011b).

Surrounding greenness at each child’s location was determined using the Normalized

Difference Vegetation Index (NDVI) derived from Landsat 7 ETM SCL-off data at a

resolution of 30 m x 30 m. The Landsat 7 ETM imagery obtained was cloud-free, captured

during the daytime and dated within the month of the original KCRHS sampling period.

NDVI approximates green vegetation density based on the visible (red) and near-infrared

light reflected by the land surface (Wade & Sommers, 2006). NDVI values fall within -1 to 1,

where values of ≤0 indicate areas with no green vegetation content and values approaching 1

indicate areas with high green vegetation density (Wade & Sommers, 2006). Buffer

calculations were used to approximate vegetation around children’s locations in a similar

manner to the techniques applied by Lovasi et al. (2013) and Markevych et al. (2014) in

related health studies. As limited information on how proximity to vegetation influences

children’s lung function exists, the mean NDVI values within a 100 m, 200 m, 300 m and 500

m radius of children’s locations were considered. Surface water features were masked out

prior to calculation of the mean to avoid influencing the NDVI results.

Statistical Analyses

8

The independent effect of children’s individual characteristics (personal, health, housing and

lifestyle) and environmental variables on their respiratory function was investigated using

SPSS (IBM Corp., released 2013, IBM SPSS Statistics for Windows Version 22.0, Armonk,

NY: IBM Corp.). Fdep and standardised z-scores for Rrs8, Xrs8, Fres, and AX were used as

indicators of the children’s respiratory function. Spearman correlations and Mann Whitney U

tests were performed to identify individually significant relationships (p-value <0.05), with

all variables with a p-value <0.05 included in subsequent multiple regression analyses.

Multiple linear regressions for each standardised FOT measure were performed. Each FOT

variable was considered separately. All variables shown to have an effect on each FOT

variable in descriptive analyses and for which literature indicated it may be significant were

included in the analysis. All included variables added to each model via forced entry, using a

p-value of >0.05 for removal.

Where included variables were thought to overlap (e.g. length of roads within a 50 m buffer

and length of roads within a 100 m buffer) the variable with the smallest beta value was

excluded from the regression model. These models allowed the effects of exposure to traffic,

industry and surrounding green space on children’s respiratory health to be assessed, as well

as other factors known to affect children’s respiratory health such as current cold. Only those

variables that were significant in regression models were included in the final analysis.

Results

Characteristics of the study population

Of the 591 children who participated in the study only 360 had satisfactory FOT measures

(60.9% of participating children and 14.6% of the total invited population). The personal,

health, housing and lifestyle characteristics of these children are presented in Table 1.

Several characteristics known to influence respiratory health were prevalent in the cohort,

including maternal smoking during pregnancy and maternal smoking during the child’s first

year of life (approximately 35%). Approximately 13% of children were reported as currently

having asthma with over 40% reported to have used asthma medication in their lifetime.

Cough and wheeze symptoms in the past year were reported in relatively few participants.

Nearly 40% of the cohort currently had a cat in the home. The highest average level of

9

education of parents of children in this study was secondary high school, with approximately

30% of parents having a university education. Most children were reported as having lived in

the Kwinana region for more than 5 years with a mean (range) residency time of 6.5 years

(0.21 to 12.3 years).

Table 1. Personal characteristics of the study population with valid FOT measures.

Measurement (N = 360) n Mean Range

Age (years) 360 9.1 5.0 - 12.4

Height (cm) 360 134 108 - 164

Weight (kg) 359 34.7 17.6 - 87.6

BMI 359 18.6

12.9 - 33.0

Characteristic n n valid % prevalence

Currently has asthma 47 356 13.2

Ever taken medication for asthma 137 331 41.4

Wheeze in the past year 58 354 16.4

Cough lasting two weeks in the past year 27 353 7.6

Ever had bronchitis and had cough during

the night in the past year

23

350

6.6

Premature birth (gestation <37 weeks) 25 356 7.0

Parental history of ever having asthma,

wheezing, eczema or allergic rhinitis

260 351 74.1

Mother smoked during pregnancy 115 321 35.8

Mother smoked during the child’s first year

of life

110 320 34.4

Mother smokes now

95 318 29.9

Smoking frequency inside the home

Never

Occasionally

Frequently

296

28

7

331

89.4

8.5

2.1

Pet in home at present

Cat in the home at present

279

119

321

304

86.9

39.1

Cat in the home during the first year of life 100 286 35.0

Father’s highest level of education 213

Primary school 11 5.2

Secondary school 126 59.2

Higher than secondary school 76 35.7

Mother’s highest level of education 223

Primary school 2 0.9

Secondary school 120 53.8

Higher than secondary school 101 45.3

10

The housing characteristics of the cohort are shown in Table 2. Most homes were older (>5

years) with a third having a garage with internal access to the home. Use of gas in homes was

common with over three-quarters of participants using gas for cooking, and gas heating

currently being used in nearly 50% of the children’s homes. Approximately 14% of children

lived in a home with no reported heating and there were several types of heating used in the

same home for approximately 25% of participants. Less than 10% of children reported living

within 50 m of a major road.

Table 2. Housing characteristics of the study population with valid FOT measures.

Characteristic (n = 360) n n valid % prevalence

Age of home <5 years old 76 331 23.0

Home has an attached vehicle garage 205 308 66.6

Home has vehicle garage with internal access at

present

98 306 32.0

Types of heating used at present

No heating

One type

Two or more types

51

217

92

360

14.2

60.3

25.5

Types of heating used in the first year of life

No heating

One type

Two or more types

94

177

89

360

26.1

49.2

24.7

Gas used for cooking in home at present 253 321 78.8

Any wood burning for heat at present 54 360 15.0

Any wood burning for heat used during first year

of life

88 360 24.4

If gas heating used vented to outside at present 115 301 38.2

If gas heating used vented to outside during first

year of life.

67 301 22.3

Questionnaire-reported nearest major road within 50m

100m

150m

200m

20

30

66

96

346

346

346

346

5.8

8.7

19.1

27.7

Environmental variables such as proximity to traffic and surrounding greenness were

estimated for children using their home address. For 19 children, environmental variables

11

were not calculated due to concerns about the positional accuracy of their geocodes. The

environmental variables calculated for children with valid FOT measures are summarised in

Table 3. The mean NDVI values around children’s homes were low (means <0.16) indicative

of low vegetation content being present in Kwinana’s residential area. No children were

located within 3.7 km of the weighted centre point for NOx emissions for heavy industry in

the Kwinana region (Table 3).

Table 3. Descriptive analysis of GIS-calculated environmental variables for traffic,

greenness and industry based on the location of children’s homes.

Environmental variable (n = 360) Mean Range SD

Length of roads (m) within a buffer size of

50 m 103 0.00 - 241 49.8

100 m 418 0.00 - 945 154

200 m 1,550 0.00 - 2890 517

300 m 3,290 59.9 - 5,620 1,070

500 m 8,210 588 - 15,300 2,560

Length of major roads (m) within a buffer size of

50 m 5.44 0.00 - 161 24.9

100 m 29.3 0.00 - 397 91.8

200 m 143 0.00 - 1,330 283

300 m 348 0.00 - 2,080 490

500 m 997 0.00 - 3,520 970

Length of Primary Distributor roads (m) within a

buffer size of

50 m 2.4 0.00 - 138 12.5

100 m 15.4 0.00 - 375 60.5

200 m 82.8 0.00 - 1030 206

300 m 211 0.00 - 1594 388

500 m 613 0.00 - 2715 810

NDVI within a buffer size of

100 m 0.12 -0.04 - 0.35 0.07

200 m 0.13 -0.01 - 0.32 0.07

300 m 0.14 0.03 - 0.31 0.06

500 m 0.15 0.05 - 0.32 0.05

Linear distance to industrial area centre point

weighted by annual NOx emissions (m)

7,930

3,790 - 25,200

2,920

Surface water area (m2) within a buffer size of

500 m 9,112 0 - 292,000 28,464

1000 m 84,500 0 - 1,530,000 160,000

Baseline FOT z-score outcomes and Fdep for the children are shown in Table 4. Children

also completed Skin Prick Tests (SPT) and of the 343 children who successfully completed

them, 40.8% were atopic.

12

The effect of questionnaire-reported and geographical information system (GIS) generated

variables on FOT outcomes was assessed and shown in Supplementary Tables S1-3. Atopy,

characterised by skin prick tests had no influence on any of the FOT measures. Increased

resistance (Rrs8) was weakly correlated with increased length of roads within 200m (rs =

0.112, p = 0.038) and 500m (rs = 0.120, p = 0.026) and the length of primary roads within

100m (rs = 0.123, p = 0.023); reactance was weakly correlated with length of roads within

50m (rs = -0.113, p = 0.035) (Spearman correlation coefficients for all environmental

variables shown in Supplementary Table S1).

Table 4. FOT values of the study population.

Measure* n Mean SD Range

Resistance (z-score) Rrs8 360 0.33 0.93 -2.04 - 3.03

Reactance (z-score) Xrs8 360 -0.23 0.98 -3.94 - 1.63

Area under reactance curve (z-score) AX 360 0.20 1.03 -2.16 - 3.38

Resonant frequency (z-score) Fres 357 0.19 1.01 -2.51 - 4.30

Frequency dependence of Rrs Fdep 360 -0.10 0.07 -0.33 - 0.11

*Frequency 8 Hz; Rrs8, Xrs8, AX and Fres presented as z-scores standardised using reference

equations from Calogero et al. (2013).

Green space variables were not associated with FOT outcomes (Supplementary Table S1).

For linear distance to industry using a cut point of 5km, living within 5km was shown to have

a negative effect on Fres (z = 2.243, p = 0.025) and Fdep (z = -2.598, p = 0.009). Children

who lived in homes <5 years had poorer lung function than those who lived in homes >5

years: Xrs8 (z = -2.525, p = 0.012); AX (z = 2.419, p = 0.016) and Fdep (z = -2.125, p =

0.034) (Supplementary Table S2).

Premature birth was associated with poorer lung function as measured by Fres (z = 2.910, p =

0.004) and children who had ever taken medication for asthma had poorer lung function: Rrs8

(z = 2.347, p = 0.019). There was a significant difference between those who presented with a

current cold (n = 37) and those without for AX (z = 2.318, p = 0.020); Fres (z = 2.341, p =

0.019) and Fdep (z = -2.011, p = 0.044) with children presenting with a cold having poorer

lung function. Of the information on symptoms, only the questionnaire-reported diagnosis of

bronchitis (n = 33) was associated with poorer lung function as measured by Rrs8 (z = 2.630,

p = 0.009).

13

When the presence of a heater at the present time was investigated there were significant

differences in Xrs8 (z = -2.603, p = 0.009); AX (z = 2.422, p = 0.015) and Fdep (z = -4.414, p

= <0.001) showing better lung function associated with heating use. The same pattern was

shown for any heating and no heating at the first year of life: Xrs8 (z = -3.501; p = <0.001);

AX (z = 3.051, p = 0.002); Fdep (z = -3.641, p = <0.001) (Supplementary Table S3).

When home heating type was analysed, both the nomination of gas and wood heating types

were associated with better lung function. Wood burning for heat at present compared with

no wood burning heater at the present time resulted in no significant differences in lung

function except for Fdep (z = -1.981, p = 0.048). When wood heating use in the first year of

life was examined, lung function measures were better: Xrs8 (z = -2.090, p = 0.037); AX (z =

2.057, p=0.040) in those using wood heaters. Similar results were observed for gas heating at

present and in the first year of life as well as central heating for the first year of life

(Supplementary Table S3). The combined effect of emission heaters versus non-emission

heater use was assessed with those using emission-based heating having better lung function

(Supplementary Table S3). The effect of venting gas from a gas heater to the outside was also

assessed and for participants reporting venting their gas heater to the outside at the present

time, a higher Rrs8 was observed indicating poorer lung function. The same result was seen

when data for the first year of life was examined (Supplementary Table S3).

Few variables, considered significant in univariate analyses, remained significant predictors

of lung function in subsequent multivariate regression analyses. Very small percentages of

the variation in FOT variables were explained by environmental factors as shown by the very

small R2 values (between 1.8% and 6.4% of the variation in the respective FOT variables

(Table 5). Lung function as measured by FOT was influenced by asthma medication use

(Rrs8), being born preterm (Fres) and had recorded a current cold at the time of testing (AX

and Fres). After adjusting for these factors, FOT outcomes demonstrated small but significant

changes associated with environmental factors. Home heating in the first year of life was

associated with a small but significant improvement in Rrs8, Xrs8, AX and Fdep. Increased

length of roads within a 50m buffer was associated with poorer lung function as measured by

Rrs8 but did not influence other FOT outcomes (Table 5). Age of home <5 years was linked

to worse AX and Fdep. Resonant frequency (Fres) was not associated with environmental

factors.

14

Table 5. Linear regression analyses of children’s environmental variables and their

influence on standardised FOT measures (z-scores).

Outcome Variables in Model Standardised

coefficients*

95% Confidence

Interval for Beta

Beta p-value Lower

bound

Upper

bound

R2 Adjusted

R2**

Rrs8 Constant 0.002 0.174 0.534 0.073 0.064

n = 318 Any heating during the first

year of life

Length of major roads within

50m

Ever taken medication for

asthma

-0.174

0.159

0.132

0.001

0.004

0.016

-0.254

0.002

0.046

-0.061

0.009

0.438

Xrs8 Constant <0.001 -0.540 -0.260 0.030 0.028

n = 358 Any heating during the first

year of life

0.174

0.001

0.070

0.271

AX

n = 329

Constant

Any heating during the first

year of life

Age of home <5 years

-0.115

0.335

0.101

0.051

0.012

-0.029

-0.231

0.073

0.327

0.001

0.596

0.048 0.039

Current cold 0.452 0.051 0.093 0.811

Fres Constant 0.077 -0.011 0.210 0.050 0.044

n = 351 Born prematurely

Current cold

0.680

0.393

0.001

0.022

0.272

0.058

1.088

0.728

Fdep

n = 330 Constant

Any heating during the first

year of life

Age of home <5 years

0.103

-0.113

<0.001

0.059

0.039

-0.109

<0.001

-0.037

-0.085

0.015

-0.001

0.024 0.018

* Standardised Coefficient Beta: estimate from the linear regression modelling if model were fitted to

standardised data.

** Adjusted R2 is the explanatory ability of the model to predict the variability in the responses where there

are a variety of predictor variables.

Discussion

This study suggests the impact of environmental factors on children’s lung function is

complex with subtle variations in effect. Using the forced oscillation technique to quantify

respiratory function, we found no effect of green space and a small and non-significant effect

15

from proximity to industry. In contrast proximity to traffic and newer homes were associated

with poorer respiratory function outcomes and home heating in the first year of life was

associated with improved lung function.

Increased length of road within a 50 m buffer was a predictor of poorer lung health (as

measured by Rrs8), while several other traffic variables were significant in univariate

analyses. This finding was consistent with the results of studies reported by Nordling et al.

(2008) and Rosenlund et al. (2009). Dales et al. (2009), also reported a modest increase in the

odds of children’s asthma using road density within a 200 m buffer around homes as a

surrogate for exposure to traffic-related air pollution, and Kim et al. (2004) and McConnell et

al. (2006) identified higher risks of asthma living within 25 m of a major road and within 50-

75 m of a major road. This finding also supports the suggestion by Hoek et al. (2008) that

variability in traffic-related air pollution is limited at distances greater than approximately

100 m away from a major road.

While this study did not find surrounding greenness to be a factor that influenced children’s

lung function, there are mixed results in the literature. A negative association between street

tree density and the prevalence of asthma was reported by Lovasi et al. (2008) and later,

Lovasi et al. (2013) identified a positive association between tree canopy coverage and the

prevalence of asthma and allergic sensitisation. Conflicting results were also reported by

Fuertes et al. (2014) in a study of two populations of children residing in different areas

where vegetation density was positively associated with allergic rhinitis and eyes and nose

symptoms in one population, and in the second, vegetation density was negatively associated

with allergic rhinitis, eyes and nose symptoms and aeroallergen sensitisation (Fuertes et al.,

2014).

Univariate analyses indicated an effect of distance from the centre of the Kwinana Industrial

Area using a cut point of 5km, with better lung function found for children living greater than

5km away, however this was not significant in regression analyses. Two stationary air

monitors were active within the study area as part of the Department of Environment and

Conservation’s Background Air Quality Study (BAQS) during the sampling period and air

pollution levels in the Kwinana airshed were well below ambient standards for both NO2 and

PM2.5 and comparable to those found elsewhere in the Perth metropolitan region,

16

(Department of Environment and Conservation, 2011). It is possible that the amount of air

pollution emitted from industry in the Kwinana Industrial Area is low, or that the residential

area is located too far from the source for emitted air pollutants to have any influence on

children’s respiratory health, with most children living >5km away from the centre of the

industrial zone. The locational methods used in this study may also have been too simplistic

to address the complexity of the pollutants emitted to the airshed and subsequent dispersion

patterns of emissions.

Indoor sources of air pollution have previously been shown to have adverse effects on lung

function and the housing questionnaire sought to identify whether housing was an important

predictor. The finding that unflued gas heating and wood heating when being used at the

present time and in the first year of life resulted in better lung function was not expected

although the effect sizes were very small.

Most of the studies that have investigated indoor emitting heaters (those producing pollutants

such as nitrogen dioxide (NO2), PM and volatile organic compounds (VOCs) such as unflued

gas heaters and wood heaters report increased respiratory symptoms (Phoa et al., 2002;

Nitschke et al., 2006; Marks et al., 2010; Bennett et al., 2010) and reduced lung function

(Nitschke et al., 2006). In this study better lung function was observed for any heating

compared with no heating, and therefore the unexpected findings may be due to temperature

rather than emissions. Pierse et al. (2013) showed an improvement in lung function in

asthmatic children with a 1°C increase in temperature in homes and Howard-Chapman et al.

(2008) reported no change in lung function in children in homes where an improved heating

system (less emissions) was installed, however they did observe a significant reduction in

respiratory symptoms in children. Marks et al. (2010) did not observe any effect of unflued

gas heaters on lung function in their study of school children using a double blind cluster

randomised cross over study in New South Wales, although they did see effects on

respiratory symptoms not observed in this study. Further, a wood heater study of children and

adults in Australia did not find an increase in adverse respiratory symptoms in homes with

higher wood heater use and smoke (Bennett et al. 2010).

Li et al. (2016) examined the effect of cold temperatures on lung function found a statistically

significant decrease in forced vital capacity (FVC) with decreasing ambient temperature. We

did not measure temperature or indoor air pollution in this study so cannot determine if either

of these were associated with lung function. It is possible that colder temperatures during

17

infancy and childhood may increase the risk of adverse respiratory outcomes and hence any

heating may improve respiratory health as the data suggests in this study.

There are two other possible explanations for these findings. Firstly the lack of heating may

be related to socioeconomic factors (Hegewald & Crapo, 2007) not assessed in this study but

which may influence lung function development. Of the asthmatic children in this study, 14%

reported no heating and 29% of non-asthmatics reported no heating used at present or in the

first year of life. It is possible that parents with asthmatic children may be more attentive to

their comfort and that the results simply reflect this via the provision of heating. Secondly, it

may be a chance finding. Larger longitudinal studies would be required to investigate these

issues.

Age of home, use of asthma medication and being born prematurely were also identified as

significant predictors of children’s respiratory health in the linear regression analyses,

although again the models explained little of the variance in the FOT variables. Being born

prematurely and neonatal chronic lung disease have been consistently demonstrated to lead to

decrements in lung function and increased respiratory symptoms (Vergheggen et al. 2016)

and the findings of this study confirm earlier studies (Harju et al. 2014). Age of home <5

years was a significant predictor, however the results must be treated with caution due to the

small effect size, nevertheless it is possible that there are pollutants associated with newer

homes which may contribute to poorer lung function (Jaakola et al. 2004; Mendell, 2007).

There were a number of limitations with this study, most importantly the cross-sectional

nature of the design of the study and hence temporal variations could not be taken into

account when measuring children’s lung function. Other temporal variations that were not

accounted for included wind speed and direction, atmospheric stability, mixing height and

topography, all factors which may influence air pollution exposure (Hagler et al. 2009;

Hagler et al. 2010; Hitchins et al. 2000; Hu et al. 2009).

Traffic intensity is often a significant indicator of exposure to traffic-related air pollution

(Hoek et al., 2008) and relies on count data which were limited to a few available counters in

this study. Given the small area, discernment of different types of traffic and in particular

heavy traffic was challenging and these factors may have been important as they have been

shown to be important for discerning exposures in other studies (Kanaroglou & Buliung,

2008). Similarly, it was only possible to obtain annual emissions data for a limited proportion

18

of industry within the Kwinana region. This was not ideal and led to the use of singular points

(the weighted centre point for annual NOx emissions or geographic centre point of the

industrial area) in estimating children’s exposure to industry-related air pollution via distance

calculations, which lacked specificity.

In this investigation, NDVI was used to characterise the vegetation content of the Kwinana

region. NDVI is not able to discern between different types of vegetation and is sensitive to

certain soil types, clouds and atmospheric effects (Weier & Herring, 2000). Also, as Landsat

7 imagery has a resolution of 30 m, the influence of vegetation in smaller scales on air

pollution exposure may not have been captured. However, NDVI has been used successfully

in exposure studies using a resolution of 30 m (Markevych et al. 2014; Villeneuve et al.

2012).

Only 14.6% of the invited school population participated in this study. When the results of

the prevalence of asthma and other factors which influence respiratory disease were

compared with other Australian data (University of Queensland, 2012), fewer (-7.1%) of the

Kwinana children were found to be atopic following skin prick testing. However many more

children had mothers who smoked during pregnancy (+13.1%) or during their first year of

life (+13.5%) compared with those from the Australian Child Health and Air Pollution Study

(ACHAPS) (University of Queensland, 2012). The prevalence of other individual

characteristics between this group of children and ACHAPS cohorts were similar, and

therefore the recruited children can be considered largely representative of the wider

population of Australian primary school children (University of Queensland, 2012).

The authors acknowledge that parents with children with asthma or other adverse respiratory

symptoms may have been more inclined to accept the invitation to participate in this study

while they were not targeted specifically and this may have influenced the findings. It is also

possible that acute exposure to air pollutants leading up to children’s lung function

measurements impacted their measurement on the day of testing. This could not be accounted

for in calculations as no information was collected on children’s activities immediately prior

their testing (e.g. method of transport to school).

19

Had extensive air quality monitoring been conducted during the sampling period at various

locations within the Kwinana region (e.g. primary schools) interpolation methods could have

increased the ability of the GIS data model to estimate children’s exposures (Briggs, 2005).

Additionally, as participation in the study was not mandatory, the study population may have

been biased (e.g. skewed towards children with parents who are more educated or have a

greater awareness of health risks, who could have introduced measures to reduce exposures)

and hence not representative of the wider population. Associations have been found between

socioeconomic status (SES) and reduced lung function (Hegewald & Crapo, 2007), and given

the Kwinana region is below-average in SES versus other regions in Australia (Australian

Bureau of Statistics, 2011) it is possible that SES factors not investigated in this study may

have explained some of the variation in children’s lung function.

Despite these limitations, this study has enabled the assessment of a number of air pollution

sources and effect mediators at the individual level to be examined using the forced

oscillation technique.

Conclusions

This study aimed to investigate the relationship between environmental factors and children’s

respiratory health using the forced oscillation technique. The results provide some evidence

for the effect of traffic proximity to adversely affect lung function as measured by respiratory

resistance. The results also suggest home heating may be a more important factor in

children’s respiratory health in their early years than other environmental exposures, however

larger studies with indoor and outdoor measures of exposure, including temperature, would

be required to confirm this. There are few studies of effect modifiers such as temperature and

green space and further effort is required to establish the relative importance of sources of

pollution, housing location and design and green space to better understand how to manage

and prevent impacts on the developing respiratory system.

Acknowledgements

The authors would like to thank the Kwinana parents and children for their participation, the

KCRHS investigators for the use of the data, and the Centre for Ecosystem Management and

Department of Health for funding the study.

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26

Figure 1. Map of the study area including the location of the 10 primary schools from which

participants were recruited.