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Title

Assessment of the Performance of the Australian Asbestos Job Exposure Matrix

(AsbJEM)

Candidate

Hiroyuki Kamiya, MD, PhD, MM

This thesis is presented for the degree of Master of Philosophy of The University of

Western Australia

School of Population and Global Health

Occupational Respiratory Epidemiology

2018

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Thesis declaration

I, Hiroyuki Kamiya, certify that this thesis has been substantially accomplished during

enrolment in the degree.

This thesis does not contain any material which has been accepted for the award of any

other degree or diploma in my name, in any university or other tertiary institution.

No part of this work will, in the future, be used in a submission in my name, for any

other degree or diploma in any university or other tertiary institution without the prior

approval of The University of Western Australia and where applicable, any partner

institution responsible for the joint-award of this degree.

This thesis does not contain any material previously published or written by another

person, except where due reference has been made in the text and, where relevant, in

the Declaration that follows.

The works are not in any way a violation or infringement of any copyright, trademark,

patent, or other rights whatsoever of any person.

The work described in this thesis was not funded by any grant although the Asbestos

Review Program is continually funded by the Western Australia Department of

Health.

This thesis does not contain any work that I have published, nor work under review

for publication.

The research in this thesis was assessed and exempted from ethics review at the

University of Western Australia Human Research Ethics Committee (RA/4/20/4746).

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This thesis contains work prepared for publication, some of which has been co-authored.

Hiroyuki Kamiya Peter Franklin

(Student) (Coordinating supervisor)

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ABSTRACT

Introduction and Aims

Australia used to be one of the largest per capita producers and consumers of asbestos

(Soeberg et al., 2018). Although the use of asbestos-containing products was

completely banned in Australia in 2003 (Soeberg et al., 2018), the incidence of

malignant mesothelioma has just reached its peak in recent years (Musk et al., 2017)

and the prevalence of all asbestos-related diseases (ARDs) remain high (Park et al.,

2008). In Australia, previous reports of assessing risk of ARDs were mostly based on

workers employed in mines and mills (de Klerk et al., 1989) and

general-population-based data regarding occupational asbestos exposures is scarce

although there are large numbers of former workers who are at risk of developing

ARDs (Park et al., 2008).

A job exposure matrix (JEM) is a cross-tabulation of job titles in a variety of industries

and exposure estimates, which enables systematic estimates of occupational asbestos

exposure (Pannett et al., 1985; Hardt et al., 2014; Offermans et al., 2014; Peters et al.,

2016). The Australian Asbestos Job Exposure Matrix (AsbJEM) has been developed to

improve the assessment of dose-response relationships between asbestos exposure and

the diseases as well as to better predict future occurrence of ARDs for Australian

workers (van Oyen et al., 2015). The objective of the research was to assess the

reliability of the AsbJEM and investigate the assumptions that were used to construct

it. We also aimed to clarify dose-response relationships between asbestos exposure and

non-malignant ARDs such as asbestosis and pleural plaques using the AsbJEM.

Methods and Results

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Two studies were undertaken for this thesis. The first project assessed the relationship

between asbestos exposures, estimated using the AsbJEM, and malignant

mesothelioma. This study was also used to check some of the original assumptions of

the AsbJEM to determine if it needed to be refined. The second project used the

amended AsbJEM to investigate dose-response relationships between asbestos and

both asbestosis and pleural plaques. For both projects subjects were the

non-Wittenoom workers who participated in an asbestos health surveillance program,

the Asbestos Review Program (ARP).

Project 1: This study investigated the relationship between estimated asbestos exposure

and malignant mesothelioma. Exposure was estimated for the original AsbJEM

assumptions and variations of these assumptions. Modifications included duration and

frequency of “peak” and intensity of “mode” exposures. The association of asbestos

exposure level with the occurrence of malignant mesothelioma was estimated using

Cox proportional hazards model for each modification of the AsbJEM. The validity of

the tool was confirmed by the significant association and the best estimate of asbestos

exposure was determined by the steepest dose-response relationship calculated from

these models (Lenters et al., 2011). There were 40 cases of malignant mesothelioma

out of 1602 participants eligible for the analysis. Both cumulative asbestos exposure

and the average intensity of asbestos exposure were significantly associated with the

incidence of malignant mesothelioma using all of the variations of the AsbJEM. The

risk of mesothelioma was estimated as HRs of 1.66, 3.00 and 5.71 for cumulative

asbestos exposures of 0.93, 2.23 and 12.3 fibre-years/ml, respectively, compared with

the reference exposure of 0.09 fibre-years/ml. The model that provided the best

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estimate (steepest slope) was the model that doubled the frequency of peak exposure of

the published AsbJEM.

Project 2: The modified AsbJEM was applied to the same cohort of participants to

estimate the risk of both asbestosis and pleural plaques. For this study the association

of asbestos exposure with these non-malignant ARDs was estimated using logistic

regression. Both cumulative asbestos exposure and the average intensity of asbestos

exposure were found to be significantly associated with both asbestosis and pleural

plaques. The risk of asbestosis was estimated as odds ratios (ORs) of 1.27, 1.35 and

1.58 for cumulative asbestos exposures of 0.41, 1.11 and 14.9 fibre-years/ml,

respectively compared with the reference exposure of 0.002 fibre-years/ml. The risk of

pleural plaques was estimated with the ORs of 1.25, 1.33 and 1.54 for cumulative

asbestos exposures of 0.30, 1.00 and 16.4 fibre-years/ml, respectively compared with

the reference exposure of 0.004 fibre-years/ml.

Conclusions

These findings indicated that the AsbJEM provides a reasonable estimate of

occupational asbestos exposure including low dose exposure and is an effective tool to

estimate the risk of developing ARDs.

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TABLE OF CONTENTS

Thesis declaration ........................................................................................................ 3

Abstract ........................................................................................................................ 5

Acknowledgement ..................................................................................................... 12

Authorship declaration ............................................................................................... 13

Significance of research ............................................................................................. 15

List of tables ............................................................................................................... 17

List of figures ............................................................................................................. 18

Chapter One: Introduction ......................................................................................... 19

1.1 Background ........................................................................................................ 20

1.1.1 History of asbestos use in Australia .............................................................. 20

1.1.2 Asbestos burden in Australia ......................................................................... 21

1.1.3 Uncertainty about the relationship between asbestos exposure and

asbestos-related disease .............................................................................................. 22

1.1.4 Asbestos exposure estimates ......................................................................... 24

1.1.5 Asbestos exposure estimates in Australia and the Asbestos Job Exposure

Matrix ......................................................................................................................... 28

1.1.6 Validation of a Job Exposure Matrix ............................................................. 31

1.2 Aim ..................................................................................................................... 33

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1.3 Overview of thesis.............................................................................................. 33

Chapter Two: Project 1 Validation of the Asbestos Job Exposure Matrix (AsbJEM) in

Australia: Exposure-Response Relationships for Malignant Mesothelioma ............. 34

2.1 Abstract .............................................................................................................. 35

2.2 Introduction ........................................................................................................ 37

2.3 Methods .............................................................................................................. 38

2.3.1 Subjects ......................................................................................................... 38

2.3.2 Asbestos exposure estimations ...................................................................... 39

2.3.3 Manipulation of the AsbJEM ........................................................................ 40

2.3.3.1 Intensity of mode exposure ...................................................................... 40

2.3.3.2 Duration of peak exposure ....................................................................... 41

2.3.3.3 Frequency of peak exposure..................................................................... 41

2.3.4 Case ascertainment ........................................................................................ 41

2.3.5 Statistical analysis ......................................................................................... 42

2.4 Results ................................................................................................................ 44

2.4.1 Characteristics of subjects ............................................................................. 44

2.4.2 Univariate analysis ........................................................................................ 48

2.4.3 Multivariate analysis ..................................................................................... 49

2.4.4 Selection of the best estimation of asbestos exposure .................................. 53

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2.4.5 Validity of statistical assumptions ................................................................. 54

2.5 Discussion .......................................................................................................... 55

2.6 Conclusion ......................................................................................................... 58

Chapter Three: Project 2 Relationship between asbestos exposure and asbestosis and

pleural plaque in Australian workers ......................................................................... 59

3.1 Abstract .............................................................................................................. 60

3.2 Introduction ........................................................................................................ 62

3.3 Methods .............................................................................................................. 63

3.3.1 Population ..................................................................................................... 63

3.3.2 Asbestos exposure estimation ....................................................................... 63

3.3.3 Case ascertainment ........................................................................................ 64

3.3.4 Statistical analysis ......................................................................................... 65

3.4 Results ................................................................................................................ 67

3.4.1 Characteristics of subjects ............................................................................. 67

3.4.2 Risk estimates of asbestosis .......................................................................... 69

3.4.3 Risk estimates of pleural plaques .................................................................. 71

3.4.4 The fit of the model ....................................................................................... 74

3.5 Discussion .......................................................................................................... 74

3.6 Conclusion ......................................................................................................... 78

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Chapter Four: Conclusion .......................................................................................... 79

References .................................................................................................................. 87

Appendices ............................................................................................................... 102

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ACKNOWLEDGEMENTS

I appreciate all the advice and support that our research members in Occupational

Respiratory Epidemiology group provided with me. In particular, I would like to thank

my principal and coordinating supervisor, Dr. Peter Franklin, for his constant assistance

and considerate care that he demonstrated in my entire research period so that I could

keep up with my work and proceed to the right direction.

I would also like to express my gratitude to my family. Minako, as my wife, always

stood by my side and encouraged me to overcome any difficulty that I encountered

throughout my research. Morina, my beloved daughter, was always charming and her

sight relieved my fatigue.

I acknowledge that I could not have completed my research and made this achievement

without all of these people.

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AUTHORSHIP DECLARATION: CO-AUTHORED PUBLICATIONS

I, Hiroyuki Kamiya, certify that the projects in this thesis were conceived by Dr.

Peter Franklin, my principal supervisor, and supported by other research members in

the Occupational Respiratory Epidemiology group, the University of Western

Australia.

All data required to conduct the research were collected from patients in the

Asbestos Review Program and provided with me in de-identified form by the data

administrator, Mr Robin Mina.

I undertook all the analyses myself and completed the thesis on my own. I take

full responsibility for the integrity of the data and the accuracy of the analysis.

The two main projects will be prepared for publication, namely:

1. Validation of the Asbestos Job-Exposure Matrix (AsbJEM) in Australia:

Exposure-Response Relationships for Malignant Mesothelioma

2. Relationship between asbestos exposure and asbestosis and pleural plaques in

Australian workers

I am the primary author of all the manuscripts and my contribution to these has

been over 75%. All co-authors have provided their consent to include these

manuscripts in this thesis and agreed with this estimated contribution by me.

Susan Peters Nicholas de Klerk

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Bill Musk Fraser Brims

Alison Reid Nita Sodhi-Berry

Hiroyuki Kamiya

(Student)

8/08/2018

I, Peter Franklin, certify that the student statements regarding his contribution to each

of the works listed above are correct.

Peter Franklin

(Coordinating supervisor)

8/08/2018

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Significance of research

Asbestos is carcinogenic and causes a number of malignant and non-malignant

asbestos-related diseases (ARDs). Although the complete ban of the use, manufacture

and import of all asbestos and asbestos-containing products was enacted in 2003 in

Australia, there are a large number of former workers exposed occupationally to

asbestos who are still at the risk of developing ARDs. Accurate asbestos exposure

estimates are important to evaluate the risk of future occurrence of ARDs for people

with occupational asbestos exposure since low-quality exposure assessments are

reported to underestimate dose-response relationships. However, population-based data

on occupational asbestos exposure is scarce in Australia with the relationship between

asbestos exposure and ARDs mostly reported based on the research of miners and

millers. The tool that we have constructed called the Asbestos Job Exposure Matrix

(AsbJEM) is based on comprehensive review of previous reports on asbestos

concentrations in a workplace and expert assessments by occupational hygienists. It is

specifically focused on working conditions in Australia and the use of asbestos

products across the nation. The AsbJEM is characterized by its coverage of a wide

range of jobs and industries, which to date includes 537 combinations of 224

occupations and 60 industries over four time periods since 1943 (this is increasing).

Asbestos exposures were estimated quantitatively using two exposure indicators, mode

and peak, and a change of exposure over time was also captured. Although a JEM is

subject to misclassification of exposure estimates and some assumptions used to

construct the AsbJEM have been questioned, it was demonstrated in this thesis that the

AsbJEM is a valid tool to estimate occupational asbestos exposure for Australian

workers. Significant relationships between occupational asbestos exposure and

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mesothelioma, asbestosis and pleural plaques were all observed in this cohort. The

AsbJEM could improve the risk assessment of ARDs for low-dose exposure as it

detected mesothelioma cases that developed at low-dose estimates of asbestos

exposure. The instrument could be applied to other cohorts of Australian workers with

occupational asbestos exposure to assess dose-response relationships between asbestos

exposure and future occurrence of ARDs because a quantitative evaluation can be

systematically executed to assess occupational asbestos exposure.

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List of tables

Table 1 Comparison of participants in a cohort with and without mesothelioma ...... 45

Table 2 Comparison of cumulative exposure estimated with different assumptions for

participants with and without mesothelioma ............................................................. 45

Table 3 Comparison of average intensity of asbestos exposure estimated with different

assumptions for participants with and without mesothelioma ................................... 47

Table 4 Regression coefficients of cumulative asbestos exposure estimated from

multivariate Cox proportional hazards models based on different assumptions of mode

intensity and peak duration and frequency................................................................. 49

Table 5 Regression coefficients of average intensity of asbestos exposure estimated

from multivariate Cox proportional hazards models based on different assumptions of

mode intensity and peak duration and frequency ....................................................... 51

Table 6 Risk of mesothelioma associated with cumulative asbestos exposure estimated

from the multivariate Cox proportional hazards model ............................................. 53

Table 7 Comparison of participants in a cohort with and without asbestosis ............ 68

Table 8 Comparison of participants in a cohort with and without pleural plaques .... 68

Table 9 Risk of asbestosis estimated from the multivariate logistic regression model ..

.................................................................................................................................... 69

Table 10 Risk of pleural plaques estimated from the multivariate logistic regression

model .......................................................................................................................... 72

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List of figures

Figure 1 Risk of asbestosis by cumulative asbestos exposure and the time since first

asbestos exposure ....................................................................................................... 71

Figure 2 Risk of pleural plaques by cumulative asbestos exposure and the time since

first asbestos exposure ............................................................................................... 73

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Chapter One: Introduction

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1.1 Background

1.1.1 History of asbestos use in Australia

Asbestos is a fibrotic mineral that exists in natural environment (Dumortier et al.,

1998). It was used extensively in the building and construction industry because of its

excellence as fire-proofing, heat-insulation and resistance (Gray et al., 2016). Australia

used to be one of the largest producers and consumers of asbestos around the world

(Soeberg et al., 2018). Over 700,000 tons of asbestos were consumed in the 1970s and

asbestos consumption per capita was the highest around the globe at that time (Allen et

al., 2018). Asbestos was mined in various places in Australia, in particular, Wittenoom

in Western Australia (crocidolite) (Musk et al., 2008) and Woodsreef in New South

Wales (chrysotile) (Soeberg et al., 2016). Australia also imported both raw asbestos

such as chrysotile and amphibole (crocidolite and amosite) and asbestos-containing

products such as friction material, gaskets and cement from other countries (Soeberg et

al., 2018). In Australia almost all of the asbestos fibres was used in the asbestos cement

manufacturing industry (Soeberg et al., 2018). Asbestos consumption started declining

from 1960s in the United Kingdom and the United States and its use has been banned

for a few decades in many developed countries due to its carcinogenicity (Allen et al.,

2018). In Australia, after Wittenoom and Woodsreef were closed in 1966 and 1983,

respectively, bans on the use of asbestos in building products began in the 1980s and

the complete ban of the use, manufacture and import of all asbestos and

asbestos-containing products was enacted in 2003 (Soeberg et al., 2018). Nevertheless,

many asbestos-containing products and materials remain in situ in residential and

non-residential buildings in Australia (Gray et al., 2016).

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1.1.2 Asbestos burden in Australia

Asbestos fibre can be released into the air and inhaled into the lungs and causes

malignant diseases such as malignant mesothelioma and lung cancer (Markowitz,

2015). It also causes non-malignant parenchymal and pleural disorders such as

asbestosis, diffuse pleural thickening and pleural plaques. Although non-malignant

diseases are not immediate threats to life in contrast to malignant diseases, the former

diseases impair pulmonary function (Park et al., 2014) and increase the risk of the

malignant diseases (Markowitz et al., 2013). Asbestosis is a progressive disease that

can lead to premature death (Markowitz et al., 1997). Despite the asbestos bans there

has been an increasing number of patients who suffer from these asbestos-related

diseases (ARDs) and it is expected to grow further in the future (Kwak et al., 2017).

This is mainly due to historical exposures and the long period of latency for most

ARDs (Reid et al., 2014). In Australia, the incidence of mesothelioma started rising

progressively since 1970s and has demonstrated an exponential increase thereafter

(Leigh and Driscoll, 2003). Although it seems to have finally plateaued in recent years,

newly diagnosed mesothelioma cases still amounted to 650 in 2015 (Musk et al., 2017)

and the age-standardized incidence rate is the second highest in the world behind the

United Kingdom, at 3.2 per 100,000 (Bianchi and Bianchi, 2014). In addition,

mesothelioma death rate is still one of the highest around the world (Furuya et al.,

2018). Previous mesothelioma incidence and death are mostly attributed to two waves

of asbestos exposure in Australia. The first wave involved the miners and millers of

asbestos and workers involved in manufacturing asbestos-containing products while

the second one included people working with and using the products. A recently

described third wave relates to people exposed to asbestos in situ (occupationally and

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environmentally or domestically) and makes additional contributions to the incidence

and death from mesothelioma (Musk et al., 2017; Armstrong and Driscoll, 2016). As

asbestos remains ubiquitous and widespread in the built environment, some people are

still at the risk of occupational asbestos exposure in certain jobs or industries such as

removal and demolition work (Park et al., 2013) while non-occupational asbestos

exposure such as home renovation and maintenance is also a concern (Olsen et al.,

2011).

1.1.3 Uncertainty about the relationship between asbestos exposure and

asbestos-related disease

Some ARDs, such as mesothelioma and asbestosis, are noted to be caused exclusively

by asbestos exposure, while others such as lung cancer are also influenced by other

carcinogenic agents including cigarettes, air pollutants and other occupational agents

(Hodgson and Darnton, 2000). Although it is easily conceivable that the risk of ARDs

would be enhanced with increasing dose of asbestos exposure, the exact relationship

between asbestos exposure and the development of ARDs remains uncertain, in

particular, at low dose exposures. This is mainly because of a variety of methodologies

for asbestos exposure estimates and different circumstances for asbestos exposure. It is

also related to the fact that most of the previous studies only reported asbestos

exposure in high-risk industries (de Klerk et al., 1989; Gasparrini et al., 2008; Ferrante

et al., 2016; Clin et al., 2011; Lacourt et al., 2014).

Many authors use the term, low level asbestos exposure, to describe non-occupational

exposure such as air pollution from asbestos mines (Hansen et al., 1998), domestic

exposure for the family of an asbestos worker (Magnani et al., 1993) and home

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renovation (Olsen et al., 2011). However, occupational exposure can also occur in

non-classic industries with relatively low dose exposure and some of these examples

include paper industry (Järvholm et al., 1988), sugar refineries (Maltoni et al., 1994)

and jewellery workers (Kern et al., 1992). In addition, exposure to in-situ asbestos in

demolition work under current regulations can also cause low dose exposure (Hillerdal,

1999). Given the extensive use of asbestos in many industries, a number of former

workers may also possibly have been exposed to certain levels of asbestos during their

working life and it is difficult or almost impossible to reveal all of these possible

exposure estimates (Hillerdal, 1999). For these reasons the risk at low dose exposure is

still controversial and extrapolation from high to low risk based on inferential

statistical models is also reported to be fraught with uncertainty (Case et al., 2011).

Therefore, dose-response relationships between asbestos exposure and ARDs should

be further scrutinized, in particular, focusing on low level asbestos exposure and

obtaining detailed and accurate information regarding potential asbestos exposure

which would improve the prediction of future occurrence of the diseases (Lenters et

al., 2011).

Although mesothelioma is a lethal disease regardless of early diagnosis and

intervention, other diseases such as lung cancer may be curable or controlled with

appropriate treatment (Maemondo et al., 2010) and thus it is imperative to closely

follow up people who are at a higher risk of developing the disease and not to miss the

chance for the best therapeutic option available (Musk et al., 2017).

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1.1.4 Asbestos exposure estimates

It is usually difficult to precisely estimate asbestos exposure. The gold standard of

estimating asbestos burden for an individual person is counting fibres in pulmonary

tissues, which are usually procured from diseased lung (Dufresne A et al., 1995;

Warmock, 1989; Dodson et al., 2003). Dufresne et al (1995) found that the mean fibre

concentration were over 1.0 million/g dry lung in all miners with mesothelioma

irrespective of fibre types. Similarly, Warmock (1989) reported the median

concentration of amphibole fibres as 2.7 million/g dry lung for construction workers

with mesothelioma while former workers in the township of Wittenoom in Western

Australia demonstrated higher mean concentration of fibres of 187 million/g dry lung

(de Klerk et al., 1996). These findings indicate substantial variation in fibre counts

between studies, which is likely due to different working conditions, fibre types and

the difference in procedures to count fibres in pulmonary specimens. As this technique

is almost always performed only on patients with the disease, it is inappropriate to

estimate the risk of ARDs for people who have not developed the disease (Case et al.,

2011). Furthermore, asbestos exposure assessment using this methodology may

possibly overestimate asbestos burden for the development of the disease because it is

only based on diseased cases (Lijmer et al., 1999). Alternatively, bronchoalveolar

lavage fluid (BALF) analysis may be implemented to assess asbestos exposure as it is

less invasive (Cruz et al., 2017). It is reported that asbestos body concentration in

BALF corresponds with occupational asbestos exposure and is related to the presence

of asbestosis or pleural plaques (Nuyts et al., 2017). However, an application of this

procedure to healthy people also gives rise to an ethical issue. It is also likely to be

affected by the experience of operators (Pairon et al., 1994). Although fibre counts

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inside the body are considered to directly reflect asbestos burden (Velasco-Garcia et

al., 2011), it is unethical and unrealistic to apply these diagnostic procedures to the

general population and thus inappropriate for epidemiological studies to investigate the

relationship between asbestos exposure and the occurrence of ARDs for cohorts with

and without the disease.

The risk of ARDs for workers in certain jobs or industries used to be evaluated by

atmospheric dust sampling. The asbestos concentration in a workplace was calculated

by counting airborne asbestos fibres collected on a filter with microscopy (Baron et al.,

2001). Using this technique de Klerk et al (1996) demonstrated a positive correlation

between airborne asbestos concentration and asbestos fibre counts in pulmonary tissues

in Wittenoom miners and millers while Clin et al (2011) revealed a dose-response

relationship between occupational asbestos exposure calculated by a membrane filter

method and cancer incidence. However, there is uncertainty about the accuracy of this

historical airborne asbestos measurement due to changes over time in measurement

devices and sampling and counting procedures (Case et al., 2011). In addition,

although this method is useful to estimate asbestos exposure in a specific working

environment, it will be insufficient to estimate the risk of ARDs in a population-based

study as participants usually have had multiple jobs where asbestos exposure data may

not be available (Hansell, 2014). Self-reported exposure assessments may be the most

convenient under these circumstances (Villeneuve et al., 2012). However, the

limitation of this method is recall bias as former workers quite often do not remember

details of their previous occupational exposure (Hardt et al., 2014). As a result, it is

well recognized that this method could inflate risk estimates (Delclos et al., 2009;

Quinlan et al., 2009; Adegoke et al., 2004; de Vocht et al., 2005). In addition,

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self-reported exposure assessments will not usually allow detailed analysis of

dose-response relationship because quantitative exposure data can hardly be obtained

from all exposed individuals. Case-by-case expert assessments will overcome this

limitation of self-reported exposure estimates and were reported to provide accurate

estimates of asbestos exposure (Bourgkard et al., 2013). Using this exposure

assessment method French research groups found dose-response relationship between

cumulative asbestos exposure and mesothelioma where they assigned very high

intensity of exposure to a category of >10 fibres/ml (Iwatsubo et al., 1998; Lacourt et

al., 2014), while an Italian group identified the same trend of dose-response

relationship by assigning the highest level of exposure to a different category of

30-300 fibres/ml (Ferrante et al., 2016). As demonstrated by these reports, there will be

a variation of exposure estimates between studies since working conditions are usually

different depending on regions and exposure estimates will be affected by the quality

of available data and expertise of occupational hygienists. Although expert

assessments are usually used as the reference in a population-based study (Bourgkard

et al., 2013), they are costly and time-consuming as they require experts to evaluate the

level of asbestos exposure on an individual basis (Nam et al., 2005).

A job-exposure matrix (JEM) is a tool that could overcome these problems and provide

a method for estimating occupational asbestos exposure in population-based studies

(Pannett et al., 1985; Coughlin and Chiazze, 1990; Fletcher et al., 1993; Offermans et

al., 2014; Peters et al., 2016). A JEM is depicted as a matrix that contains occupations

and/or industries on one axis and an exposure index such as probability of exposure,

annual mean exposure, intensity and frequency on the other axis (Peters et al., 2016;

Kauppinen et al., 1998; Offermans et al., 2012; Choi et al., 2017). A time axis may

27

also be included and exposure estimates are expressed in each cell of the matrix

semiquantitatively or quantitatively. JEMs are constructed based on expert judgement

of occupational hygienists, published literature and/or databases of direct measures

(Hardt et al., 2014). As JEMs can systematically and cost-effectively evaluate

occupational exposure within a diverse workforce and avoid drawbacks caused by

self-reporting assessments, it has become increasingly common in epidemiological

research involving occupational exposure estimates (Pannett et al., 1985; Coughlin and

Chiazze, 1990; Fletcher et al., 1993; Offermans et al., 2014; Peters et al., 2016). A

number of studies investigated the performance of JEMs compared with other

methodologies to estimate occupational exposure. Hardt et al (2014) demonstrated

more consistent exposure estimates with previous findings if they were based on a

JEM than on self-reported exposure assessments. Similarly, Fletcher et al (1993)

reported that a JEM could be more reliable for estimating asbestos exposure than a

simple question. Pukkala et al (2005) concluded that the JEM-based method gave valid

results. Furthermore, Peters et al (2011) stated that expert assessment approach provids

little, if any, advantage over JEM approach for exposure estimates. The odds ratio for

mesothelioma for low and high asbestos exposure for the NOHS-JEM were 2.0 and

2.5, respectively (Cicioni et al., 1991) while Offermans et al (2014) reported that the

hazard ratio for mesothelioma for ever versus never-exposed people was 3.0 by

FINJEM and 2.6 by DOMJEM. These results derived from JEMs seem to be consistent

between studies as opposed to those obtained from other exposure assessment

methods. This may be partly because a variance of exposure estimates between

individual persons was reduced by categorization based on job titles (Pannett et al.,

1985; Coughlin and Chiazze, 1990; Fletcher et al., 1993).

28

Although the efficiency and reliability of JEMs are emphasized, they also contain

some methodological limitations. These include a lack of coverage of some jobs and/or

industries, insufficient information of a change over time and limited generalizability.

A JEM generated in one country will not necessarily be applicable to other countries

because working conditions will be diverse and types of exposed agents may also be

different. As a result, although a large number of research groups constructed their

own JEMs including FINJEM in Finland (Kauppinen et al., 1998), DOM-JEM

(Offermans et al., 2012) and SYN-JEM in Netherlands (Peters et al., 2016) and

GPJEM in Korea (Choi et al., 2017) to investigate the risk of ARDs, only the FINJEM

has been adopted to other studies conducted in a different region (Offermans et al.,

2014).

1.1.5 Asbestos exposure estimates in Australia and the Asbestos Job Exposure Matrix

In Australia, previous reports of assessing risk of ARDs were mostly based on workers

in mines and mills where exposure estimates were conducted by dust sampling or

post-mortem tissue examination of mesothelioma cases registered in a national

surveillance program that sought to reveal lung asbestos fibre burden. For Wittenoom

miners and millers, de Klerk et al (1989) reported that the odds ratio for mortality from

mesothelioma was 1.26 for one-unit increase in fibre concentration (fibres/ml) while

Rogers et al (1991) stated that the risk of mesothelioma increased by 29.4 with a

10-unit increase in fibre concentration in pulmonary tissues (fibres/microgram) for

mesothelioma cases reported to the Australian Mesothelioma Surveillance Program.

Population-based data on the risk of ARDs with occupational asbestos exposure is

scarce although there are a large number of former workers who are at the risk of

29

developing ARDs. As dose-response relationships between asbestos exposure and the

occurrence of ARDs may be different between miners and millers and other

occupations (Churg and Wiggs, 1986), it is necessary to clarify the risk estimates for

occupational asbestos exposure using exposure assessment tools specific to these

circumstances. In 2010 Hyland et al (2010) reviewed Australian asbestos exposure

measurements for the purpose of providing reference exposure levels that may assist in

determining a worker’s retrospective asbestos exposure and constructed a device called

asbestos task exposure matrix (ASTEM). It was a combination of 12 asbestos-related

tasks and 8 asbestos products over 2 time periods, which was derived from data on

occupational asbestos exposure in New South Wales (Hyland et al., 2010). The

potential advantage of this tool over JEMs was that it could address the variability

between workers within an occupation and day-to-day variability within an individual

worker (Benke et al., 2000). However, the limitation was that the majority of estimates

came from the manufacturing industry and, therefore, might not reflect the conditions

of other industries. In addition, as the ASTEM does not cover the time prior to 1970

due to the lack of reliable data, it cannot be used for occupations before this time

period. There will be a number of former workers who had been exposed to asbestos

occupationally before 1970 that are still at high risk of developing ARDs. Furthermore,

as ASTEM was only derived from data in New South Wales, it may not be applicable

nationwide. To date there have been no published reports investigating the risk of

ARDs with this device for former workers who used to be engaged in asbestos-related

tasks.

30

We recently created a tool called the Asbestos Job Exposure Matrix (AsbJEM), which

is specifically focused on working conditions in Australia and the use of asbestos

products in the whole nation (van Oyen et al., 2015). The AsbJEM is characterized by

its coverage of a wide range of jobs and industries, which included 537 combinations

of 224 occupations and 60 industries over four time periods dating back to 1943. Based

on all available hygiene measurements asbestos exposures were estimated

quantitatively by a panel of qualified and experienced industrial hygienists using two

exposure indicators, mode and intensity and a change of exposure over time was also

captured by time axis. These features are similar to other JEMs such as

INTEROCC-JEM (McElvenny et al., 2018) and NOCCA-JEM (Kauppinen et al.,

2009) where cumulative exposure could be calculated quantitatively. Other established

JEMs such as ECOJEM (Le Moual et al., 2000) and DOMJEM (Olsson et al., 2017)

calculate semiquantitative exposures by classifying exposure into three categories

according to the probability of exposure in an occupation. Therefore, the AsbJEM is

expected to improve the assessment of dose-response relationship between asbestos

exposure and future occurrence of ARDs for Australian workers as a quantitative

evaluation can be systematically executed to assess occupational asbestos exposure

(van Oyen et al., 2015). Furthermore, as the AsbJEM covers the time period after the

complete ban of the use of asbestos-containing products, it could be used to estimate

occupational exposure to in-situ asbestos remaining in the built environment, which

will be associated with a lower dose exposure than other traditional jobs that used

asbestos-containing materials. Although the risks of low dose asbestos exposure are

still uncertain because most of the previous findings were only derived from relatively

high-risk industries (de Klerk et al., 1989; Gasparrini et al., 2008; Ferrante et al., 2016;

31

Clin et al., 2011; Lacourt et al., 2014), the AsbJEM could improve the estimates of

dose-response relationship at lower exposures.

1.1.6 Validation of a Job Exposure Matrix

The AsbJEM has yet to be validated and its performance for estimating exposures also

remains unclear. The accuracy of JEMs can be influenced by the quality of available

data and expertise of the occupational hygienists who developed it. In addition,

misclassification of exposure estimates is also concerning as JEMs disregard potential

differences in exposure among individuals with the same job titles (Fletcher et al.,

1993). Indeed, some questions have been raised against the AsbJEM regarding the

accuracy of its estimates of occupational asbestos exposure (Kottek and Kilpatrick,

2016). Although the reference standard is usually required to confirm the accuracy of a

diagnostic tool, there is no gold standard to confirm asbestos exposure. Counting fibres

in pulmonary tissues can directly estimate asbestos burden. It is, however, not ethical

to people who have not developed a disease. Instead, expert assessments on an

individual basis are usually implemented to evaluate the performance of JEMs

(Solovieva et al., 2012; Sjöström et al., 2013). However, it is also noted that it can

suffer from misclassification and thus the consistency of exposure estimates between

JEMs and expert assessments does not necessarily guarantee the validity of the tool. As

a result it is difficult to achieve a true validation for exposure estimates within a JEM

using traditional methods. On the other hand, Bouyer and Hemon (1993) and Goldberg

et al (1993) suggested that estimates within a JEM could be considered reliable if they

could detect known associations between risk factors and disease occurrence, for

example exclusive association between asbestos exposure and malignant mesothelioma.

32

In addition, Lenters et al (2011) reported that better quality exposure assessment is

associated with steeper slopes of the dose-response relationships. Based on these

previous reports we aimed to conduct a validation study to prove the accuracy of the

AsbJEM by applying the tool to a cohort of former Australian workers with a history

of asbestos exposure to investigate the dose-response relationship between

occupational asbestos exposure and the development of mesothelioma. According to

the previous reports (Bouyer and Hemon, 1993; Goldberg et al, 1993), the AsbJEM

would be validated if a significant relationship between asbestos exposure, which is

estimated by the instrument, and mesothelioma is confirmed. In addition, the published

AsbJEM could be refined by comparing different assumptions of estimating asbestos

exposure and identifying the steepest slope of the dose-response relationship. A

previous report explained that when a JEM is implemented to estimate a dose-response

relationship, approximately three times more subjects are required to reach the same

precision in the estimation of the odds ratio than that achieved with experts because of

a loss of information between workers in the same job (Bouyer et al., 1995). However,

the same article also stated that an unbiased JEM could provide a statistical power

close to that expected in practice with a good expert (Bouyer et al., 1995). Therefore,

the validated and possibly refined AsbJEM could further be applied to a cohort of

former Australian workers to cost-effectively assess and clarify dose-response

relationship for other ARDs, in particular, non-malignant diseases such as asbestosis

and pleural plaques.

33

1.2 Aim

The aim of the research was:

firstly to validate the AsbJEM as a tool to estimate occupational asbestos exposure and

refine it if it can be improved to produce more reliable estimates,

secondly to estimate exposure-response relationships between occupational asbestos

exposure and non-malignant ARDs such as asbestosis and pleural plaques using the

AsbJEM.

1.3 Overview of thesis

This thesis is composed of reports of two research projects. The first is a validation

study for the AsbJEM, which was created to standardize the estimates of occupational

asbestos exposure and is expected to help evaluate precisely the risk of developing

ARDs. The second is an epidemiological study, which aims to estimate

exposure-response relationships for asbestosis and pleural plaques, using the AsbJEM

with some modification, based on the findings obtained from the first project.

34

Chapter Two: Project 1

Validation of the Asbestos Job Exposure Matrix (AsbJEM) in Australia:

Exposure-Response Relationships for Malignant Mesothelioma

35

2.1 Abstract

Introduction

An Asbestos Job Exposure Matrix (AsbJEM) to estimate cumulative asbestos exposure

for Australian jobs has been published. However, certain assumption in its calculations

has been questioned. We aimed to assess the reliability of the AsbJEM by evaluating

exposure-response relationships between exposure estimates and the incidence of

malignant mesothelioma. A further aim is to investigate the assumptions used in the

development of the AsbJEM.

Subjects

Eligible subjects were participants in an annual health surveillance program who had at

least 3-months’ occupational history with likely asbestos exposure.

Methods

Asbestos exposure estimates included cumulative asbestos exposure and the average

intensity of asbestos exposure, which were calculated using intensity and frequency of

exposure in the AsbJEM and duration of employment. Various exposure estimates

were calculated by manipulating assumptions about the intensity of ‘mode’ exposure

and the duration and frequency of ‘peak’ exposure. Cox proportional hazards models

were used to describe the associations between mesothelioma incidence and each of

the calculated cumulative exposure estimates and the average intensity estimates.

Results

Data were collected from 1602 male participants. Of these, 40 developed mesothelioma

during the study period (incidence rate; 45 per 100,000 person-years). Twenty-four

36

exposure estimates were calculated using three exposure intensities (50th

, 75th

and 90th

percentile of the range of mode exposure), 4 peak durations (15, 30, 60 and 120 minutes)

and 2 patterns of peak frequency. There were significant associations between

mesothelioma incidence and both cumulative asbestos exposure and the average intensity

of asbestos exposure for all estimates. The strongest association, based on the

regression-coefficient from the proportional hazards models, was found for the 50th

percentile of mode exposure and duration of 15 minutes and doubled, from the original

model, frequency of peak exposure. Using these assumptions, the Hazard Ratios for

mesothelioma were 1.66, 3.00 and 5.71 for cumulative asbestos exposures of 0.93, 2.23

and 12.3 fibre-years/ml, respectively, compared with the reference exposure of 0.09

fibre-years/ml. It was also estimated as 1.84, 2.31 and 4.40 for the average intensity of

asbestos exposure of 0.03, 0.06 and 0.42 fibres/ml, respectively, compared with the

reference exposure of 0.003 fibres/ml.

Conclusion

We have validated the AsbJEM by detecting a positive exposure-response trend

between mesothelioma incidence and both estimated cumulative asbestos exposure and

average intensity of asbestos exposure. The strongest relationship was found when the

frequency of peak exposure was doubled from the original published version. The

AsbJEM may be a useful and non-invasive tool for identifying exposure-response

relationships between occupational asbestos exposures and other asbestos related

diseases.

37

2.2 Introduction

Asbestos is a known cause of a number of diseases, including malignant mesothelioma,

lung cancer and asbestosis (Jamrozik et al., 2011). Exposure-response relationships

have been established between asbestos exposure and these asbestos-related diseases

(ARDs) (Clin et al., 2011; Paris et al., 2009; Lenters, et al., 2011; Olsson, et al., 2017).

However, there has been a considerable variation in the slope of the exposure-response

relationship across individual studies, which is partly due to the diversity of the quality

of the exposure assessments in different studies (Lenters et al., 2011). A robust

measure of exposure is important as poor quality exposure estimates will lead to

misclassification of exposure and misleading exposure-response relationships.

A Job Exposure Matrix (JEM) is a standardized method to transform job histories into

specific exposure-estimates. It is shown as a cross-tabulation of job titles in various

industries and the estimated exposure of each job (Pannett, 1985). JEMs have become

an increasingly common method to systematically and cost-effectively evaluate

occupational exposures within the wider workforce for a variety of exposure-agents in

various industries and occupations over different time periods and are specific for

different countries (Coughlin and Chiazze, 1990; Peters et al., 2016). The use of a JEM

allows standardized exposure assessment and reduces reporting bias (Hardt et al.,

2014). However, the accuracy of a JEM depends on the quality of the exposure data

and can be subject to intra-occupational misclassification since workers at the same job

are not necessarily exposed to the same level of occupational asbestos (Coughlin and

Chiazze, 1990; Bouyer et al., 1995).

38

While true validation of estimates within a JEM can rarely be achieved, the

performance of estimates can be evaluated. Bouyer and Hemon (1993) and Goldberg et

al (1993) suggested that estimates within a JEM could be considered reliable if they

could detect known associations between risk factors and a disease, for example the

well-established association between asbestos exposure and malignant mesothelioma.

We recently published an Asbestos Job Exposure Matrix (AsbJEM) to estimate

occupational asbestos exposure for Australian workers (van Oyen et al., 2015).

However, it has been suggested that exposures may have been underestimated for

some job titles because a higher level of asbestos exposure was confirmed for many

reported cases of ARDs for these jobs (Kottek and Kilpatrick, 2016). Particular

concern was raised about the duration and frequency of peak exposure for some of the

job titles (Kottek and Kilpatrick, 2016). Therefore, the aim of this study was, firstly, to

validate the AsbJEM by assessing the exposure-response relationship with

mesothelioma based on the fact that it is almost exclusively caused by asbestos

exposure and secondly, to investigate different assumptions of duration, intensity and

frequency of exposure so as to refine the AsbJEM if required.

2.3 Methods

2.3.1 Subjects

Subjects were participants of an annual health surveillance program for people with

significant occupational or environmental exposure to asbestos (the Asbestos Review

Program (ARP)) (Hansen et al., 1997; Hansen et al., 1998; Musk et al., 1998). People

eligible to join the ARP include former workers of the crocidolite mine and mill at

Wittenoom, Western Australia, ex-residents of the mining township of Wittenoom,

39

people who have had at least 3-months full-time jobs that involved exposure to

asbestos, and people who had evidence of pleural plaques. Asbestos exposure

estimates for the former Wittenoom workers (de Klerk et al., 1989) and ex-residents of

the Wittenoom township (Hansen et al., 1997) have previously been published and

validated (de Klerk et al., 1996). Only asbestos-exposed workers, who were not former

workers or residents of Wittenoom, were included in this study. This study was

exempted from ethics review by the University of Western Australia Human Research

Ethics Committee (RA/4/20/4746).

2.3.2 Asbestos exposure estimations

Cumulative asbestos exposure (fibre-years/ml) for each participant in the cohort was

estimated using the AsbJEM (van Oyen et al., 2015). Exposure estimates for

occupation-industry combinations over four time periods (1943-1966, 1967-1986,

1987-2003 and ≥2004) were evaluated by an expert panel of industrial hygienists

familiar with likely job exposures. ‘Mode’ was the most common exposure level and

‘peak’ was the short-term intense exposure in a particular job. ‘Background’ was the

same as the exposure from the general environment. The frequency and intensity of

mode, peak and background exposures were pre-defined for each time period, except

for the frequency of background exposure, which was calculated by subtracting the

mode frequency from the total number of working days in the working year (240 days

assuming two days off a week and four weeks of holidays). The background, peak and

mode exposures were each calculated as the multiplicative product of intensity and

frequency. The average exposure was calculated as the sum of all these exposures

divided by 240. This exposure index is expressed as fibre-year/ml and 1 fibre-year/ml

40

indicated that a daily exposure of 1 fibre/ml continued for one year. Accordingly,

cumulative asbestos exposure for an individual in a specific occupation was calculated

as the annual average exposure for that job multiplied by the length of employment

(van Oyen et al., 2015).

2.3.3 Manipulation of the AsbJEM

The AsbJEM was manipulated regarding the intensity of mode exposure, and the

duration and frequency of peak exposure. The exposure-response relationship was

iteratively examined in order to determine the best estimation of exposure, which was

determined by the slope of the relationship. A steeper slope was assumed to indicate a

better estimation of asbestos exposure as it suggests less exposure misclassification

and less underestimation of exposure-response relationships (Lenters et al., 2011).

2.3.3.1 Intensity of mode exposure

The intensity of exposure was classified into background (0.0001 fibres/ml), low

(0.01-0.1 fibres/ml), medium (0.1-1 fibres/ml), high (1-25 fibres/ml) and very high

(25-50 fibres/ml) (van Oyen et al., 2015). The AsbJEM adopted the mid-point of the

range of exposure in each category to represent the intensity of mode exposure.

However, it is possible that for ARP participants, that value may have underestimated

exposure because the AsbJEM is estimating the average occupational asbestos

exposure for the industry/occupation pairing while the subjects in this study joined the

ARP due to their awareness that they may have undertaken tasks which were at the

higher end of the occupation-exposure combination. Therefore, we assessed whether

the 75th and 90th percentile of the range of exposure better represented the level of

exposure for this cohort than the 50th percentile.

41

2.3.3.2 Duration of peak exposure

During the development of the AsbJEM, the duration of peak exposure was arbitrarily

set at 15 minutes. This had been discussed extensively at that time and was finalized

based on the expertise of occupational hygienists. However, as pointed out by Kottek

and Kilpatrick (2016), “many of the historic reports of peak exposure are short-term

measurements made during a task that was carried out for a much longer period”.

Therefore, we further assessed other peak exposure durations, specifically 30, 60 and

120 minutes.

2.3.3.3 Frequency of peak exposure

Frequency of exposure peaks was distributed at 1, 2, 11 or 48 days per year across

various industry-occupation combinations in the AsbJEM, which was built on the

experts’ assessment of exposure in a particular job or industry. However, Kottek and

Kilpatrick (2016) mentioned the possibility of the expert panel underestimating the

frequency. Therefore, we also investigated whether twice the original frequency of

peak exposure, in particular for the first two time periods (<1943 and 1943-1966),

would result in a stronger exposure-response relationship for malignant mesothelioma.

2.3.4 Case ascertainment

All incident cases of mesothelioma and related deaths in Western Australian are

reported to the Western Australian Mesothelioma Registry and the Australian

Mesothelioma Registry. Cases were determined by data linkage of ARP participants

with the Mesothelioma Registry. The Registry records both the date of diagnosis and

42

the date of death, if the patient had died. Data were available until 31st Dec 2014,

which served as our censoring date for this study.

2.3.5 Statistical analysis

When the association of asbestos exposure with mesothelioma incidence is estimated,

cumulative asbestos exposure will often be utilized as the exposure index. However,

cumulative exposure is calculated as the multiplication of the average asbestos

exposure estimated using the AsbJEM and the length of employment, which can be

obtained without the tool. Accordingly, exposure-response relationship estimated using

cumulative exposure will be affected by the information not related to the AsbJEM and

as a result the meaning of the tool will be undermined. Therefore, exposure-response

relationship between asbestos exposure and the occurrence of malignant mesothelioma

was evaluated in two multivariate statistical models using Cox Proportional Hazards

regression. The rate of mesothelioma incidence with a particular cumulative asbestos

exposures and the average intensity of asbestos exposure was calculated in each model,

respectively. Time to mesothelioma diagnosis following entry into the ARP was

defined as the outcome variable while explanatory variables included cumulative

asbestos exposure in the first model and the average intensity of asbestos exposure and

duration of exposure in the second model. Duration of exposure was not included in

the first model due to its high collinearity with cumulative asbestos exposure.

Cumulative asbestos exposure was re-calculated based on new assumptions of the

intensity of mode exposure and the duration and frequency of peak exposure as

described above. The average intensity of asbestos exposure was calculated as the

division of cumulative asbestos exposure estimated using the AsbJEM by duration of

43

exposure, which was defined as the total number of years of occupational asbestos

exposure above the background level.

For all calculations the distribution of cumulative asbestos exposure and the average

intensity of asbestos exposure were skewed and logarithmic transformation was

performed and used for the analyses. The incidence rate of mesothelioma was

calculated as the number of mesothelioma cases divided by person-years since the

entry to the ARP. Age at first exposure was included in all models regardless of its

statistical significance as this is known to influence mesothelioma incidence (Reid et

al., 2007).

The linearity of continuous variables such as cumulative asbestos exposure was

examined in two different ways. Firstly, the log rank test for trend was performed to

see the linear trend for each variable by categorizing it into four groups using quartiles.

Secondly, it was checked graphically by plotting the β coefficient for each group,

which was estimated from the model, against the mid-point of each quartile. If the

linear assumption had been violated, the variables were handled as categorical.

The presence of the effect modification of cumulative asbestos exposure and the

average intensity of asbestos exposure by other potential confounders was evaluated

using interaction terms. A variable with the least significance showing p value over

0.01 was eliminated one by one from the model until it reached the least variables.

The proportional hazards assumption was evaluated in two different ways. Firstly, the

Schoenfeld residuals of all explanatory variables were examined graphically.

Secondly, a time-dependent covariate for each explanatory variable was created and its

significance was checked in a multivariate Cox proportional hazards model.

44

Continuous data were expressed as either mean (standard deviation) or median

(interquartile range). Student’s t-test or Wilcoxon rank sum test were performed for a

comparison of the mean or median of two independent groups, respectively. For

categorical data, a chi-square or Fisher’s exact tests were conducted. Statistical

significance was set at p-value ≤0.05. The risk of developing the outcome was

estimated by the Hazard Ratio (HR). The strongest association of mesothelioma

incidence with cumulative asbestos exposure and the average intensity of asbestos

exposure was identified by the largest value of the coefficient estimated from the Cox

regression model. Accordingly, the AsbJEM model that provided the steepest slope of

the relationship, was considered as the best estimate of occupational asbestos exposure

based on the fact that mesothelioma is caused exclusively by asbestos exposure. The

result estimated from the model with the average intensity as the asbestos exposure

index was prioritized over cumulative exposure to discover the best estimates of

asbestos exposure if inconsistent results were obtained for the same reason as

described above. Participants lost to follow-up were assumed to have lived up to the

end of study or 90 years of age. All statistical analyses were conducted using SAS 9.4

(SAS Institute Inc., Cary, NC, USA).

2.4 Results

2.4.1 Characteristics of subjects

A total of 2084 people, who were not former workers or ex-residents of Wittenoom,

attended the ARP as of December, 2015. Of these 1602 men were included in the

analyses after excluding possible non-occupational asbestos exposure (n=388),

participants with an uncertain occupational history (n=81) and women (n=13). At data

45

linkage of ARP participants with the Mesothelioma Registry in 2015, 40 cases of

mesothelioma were identified out of all the participants included in the analysis (45

cases per 100,000 person-years) (Table 1). Compared to participants who did not

develop mesothelioma, the mean age at first exposure was significantly lower for

mesothelioma cases (15.7±2.2 vs. 17.0±4.6, p=0.0008) and the mean duration of

exposure was significantly longer (37.5±11.2 vs. 31.9±13.9 years, p=0.01) (Table 1).

Table 1 Comparison of participants in a cohort with and without mesothelioma

Mesothelioma

(n=40)

No mesothelioma

(n=1562)

p-value

Duration of exposure (years) 37.5±11.2 31.9±13.9 0.01

Age at first exposure (years) 15.7±2.2 17.0±4.6 0.0008

Time since first exposure (years) 55.1±8.2 56.8±10.5 0.33

Time since entry to the ARP (years) 8.7±6.1 12.1±7.6 0.001

Data expressed as mean±SD; ARP, Asbestos Review Program;

The median of cumulative asbestos exposure was significantly higher for

mesothelioma cases compared with non-cases for all calculations of the AsbJEM

(Table 2).

Table 2 Comparison of cumulative exposure estimated with different assumptions for

participants with and without mesothelioma

Mesothelioma

(n=40)

No mesothelioma

(n=1562)

p-value

Median cumulative exposure (IQR) (fibre-years/ml)

Original frequency of peak exposure

46

Mode 50%_Peak duration_15 min 1.63 (3.72) 0.49 (1.22) <0.0001

30 min 2.11 (4.33) 0.72 (1.77) <0.0001

60 min 2.53 (8.01) 1.13 (2.66) <0.0001

120 min 4.35 (13.40) 1.76 (4.82) 0.0001


30 min 2.78 (6.12) 0.85 (2.15) <0.0001

60 min 3.36 (8.21) 1.31 (3.04) <0.0001

120 min 4.53 (15.49) 2.01 (5.10) 0.0001


30 min 3.09 (6.94) 0.92 (2.32) <0.0001

60 min 3.86 (8.41) 1.39 (3.35) <0.0001

120 min 4.78 (15.96) 2.16 (5.24) <0.0001

Doubled frequency of peak exposure


30 min 2.32 (6.18) 0.85 (2.13) <0.0001

60 min 3.36 (11.35) 1.32 (3.76) <0.0001

120 min 5.99 (17.21) 2.11 (6.71) <0.0001


30 min 3.05 (6.87) 0.98 (2.52) <0.0001

60 min 3.95 (12.17) 1.56 (3.93) <0.0001

120 min 6.25 (20.12) 2.37 (7.25) <0.0001


30 min 3.53 (7.82) 1.09 (2.73) <0.0001

60 min 4.41 (12.25) 1.66 (4.13) <0.0001

47

120 min 6.50 (12.79) 2.51 (7.38) <0.0001

Mode 50%, 75% and 90% indicate the 50th, 75

th and 90

th percentile of the range of mode

intensity, respectively. Mode 50%_Peak duration_15 min indicates mode intensity set at the

50th percentile of the range of mode intensity and peak duration of 15 minutes. Other labels are

defined likewise. IQR, interquartile range;

Mesothelioma cases also demonstrated higher average intensity of asbestos exposure

than participants without mesothelioma for all calculations of the AsbJEM (Table 3).

Table 3 Comparison of average intensity of asbestos exposure estimated with different

assumptions for participants with and without mesothelioma

Mesothelioma

(n=40)

No mesothelioma

(n=1562)

p-value

Median of average intensity of asbestos exposure

(IQR) (fibres/ml)


Mode 50%_Peak duration_15 min 0.04 (0.09) 0.02 (0.04) 0.0007

30 min 0.06 (0.12) 0.03 (0.05) 0.0011

60 min 0.08 (0.21) 0.04 (0.09) 0.0018

120 min 0.12 (0.40) 0.06 (0.15) 0.0028


30 min 0.07 (0.14) 0.03 (0.07) 0.0008

60 min 0.09 (0.22) 0.05 (0.10) 0.0014

120 min 0.14 (0.41) 0.07 (0.16) 0.0024


48

30 min 0.07 (0.16) 0.03 (0.08) 0.0007

60 min 0.10 (0.24) 0.05 (0.10) 0.0011

120 min 0.15 (0.41) 0.08 (0.18) 0.0021



30 min 0.06 (0.16) 0.03 (0.07) 0.0009

60 min 0.11 (0.27) 0.05 (0.12) 0.0011

120 min 0.18 (0.54) 0.07 (0.22) 0.0013


30 min 0.07 (0.18) 0.04 (0.08) 0.0008

60 min 0.12 (0.28) 0.06 (0.13) 0.0010

120 min 0.20 (0.53) 0.08 (0.23) 0.0011


30 min 0.08 (0.19) 0.04 (0.09) 0.0007

60 min 0.13 (0.30) 0.06 (0.13) 0.0010

120 min 0.21 (0.54) 0.09 (0.24) 0.0011

Mode 50%, 75% and 90% indicate the 50th, 75

th and 90

th percentile of the range of mode

intensity, respectively. Mode 50%_Peak duration_15 min indicates mode intensity set at the

50th percentile of the range of mode intensity and peak duration of 15 minutes. Other labels are

defined likewise. IQR, interquartile range;

2.4.2 Univariate analysis

The age at first exposure was handled as a categorical variable as the linear assumption

failed to hold by both the log rank test for trend and a graphical examination while

49

cumulative asbestos exposure, average intensity of asbestos exposure and duration of

exposure were all included as continuous variables in Cox proportional hazards model.

Univariate analyses with Cox proportional hazards model revealed that mesothelioma

incidence was significantly associated with cumulative asbestos exposure and average

intensity of exposure for all calculations of the AsbJEM. There was also significant

association between mesothelioma incidence and duration of exposure but the age at

first exposure demonstrated no significant association with the disease.

2.4.3 Multivariate analysis

All explanatory variables of interest were included to build multivariate Cox

proportional hazards model regardless of their statistical significance in univariate

analysis. As a result, the first model was composed of cumulative exposure and the age

at first exposure while the second model consisted of average intensity of exposure,

duration of exposure and the age at first exposure. As there was also no effect

modification of cumulative asbestos exposure and the average intensity of asbestos

exposure by other variables in each model, the final models for the analysis were set as

described above.

After adjusting for the age at first exposure, there was a significant association of

mesothelioma incidence with cumulative asbestos exposure for all calculations of the

AsbJEM (Table 4).

Table 4 Regression coefficients of cumulative asbestos exposure estimated from multivariate

Cox proportional hazards models based on different assumptions of mode intensity and peak

duration and frequencya

50

Median cumulative exposure (IQR)

(fibre-years/ml)

β-coefficient (SE) p-value


Mode 50%_Peak duration_15 min 0.2683 (0.0777) 0.0006

30 min 0.2668 (0.0809) 0.0010

60 min 0.2595 (0.0832) 0.0018

120 min 0.2472 (0.0837) 0.0032


30 min 0.2606 (0.0781) 0.0008

60 min 0.2579 (0.0810) 0.0015

120 min 0.2497 (0.0827) 0.0025


30 min 0.2576 (0.0769) 0.0008

60 min 0.2566 (0.0801) 0.0014

120 min 0.2503 (0.0822) 0.0023



30 min 0.2712 (0.0826) 0.0010

60 min 0.2648 (0.0845) 0.0017

120 min 0.2550 (0.0848) 0.0026


30 min 0.2653 (0.0798) 0.0009

60 min 0.2631 (0.0826) 0.0015

120 min 0.2565 (0.0840) 0.0023


51

30 min 0.2623 (0.0787) 0.0009

60 min 0.2618 (0.0818) 0.0014

120 min 0.2568 (0.0836) 0.0021

a: Models including age at first asbestos exposure as a covariate aside from cumulative

asbestos exposure; Mode 50%, 75% and 90% indicate the 50th, 75

th and the 90

th percentile of

the range of mode intensity, respectively; IQR: interquartile range; SE: standard error;

After adjusting for duration of exposure and the age at first exposure, there was also a

significant association of mesothelioma incidence with the average intensity of

exposure for all calculations of the AsbJEM (Table 5).

Table 5 Regression coefficients of average intensity of asbestos exposure estimated from

multivariate Cox proportional hazards models based on different assumptions of mode

intensity and peak duration and frequencya

Median of average intensity of asbestos

exposure (IQR) (fibres/ml)

β-coefficient (SE) p-value



30 min 0.2291 (0.0824) 0.0054

60 min 0.2181 (0.0841) 0.0095

120 min 0.2033 (0.0841) 0.0157


30 min 0.2259 (0.0796) 0.0046

60 min 0.2191 (0.0822) 0.0076

120 min 0.2077 (0.0833) 0.0127

52


30 min 0.2239 (0.0784) 0.0043

60 min 0.2190 (0.0813) 0.0070

120 min 0.2091 (0.0829) 0.0116



30 min 0.2317 (0.0839) 0.0057

60 min 0.2220 (0.0851) 0.0091

120 min 0.2101 (0.0849) 0.0134


30 min 0.2286 (0.0812) 0.0049

60 min 0.2226 (0.0835) 0.0077

120 min 0.2132 (0.0843) 0.0115


30 min 0.2268 (0.0801) 0.0046

60 min 0.2224 (0.0827) 0.0072

120 min 0.2142 (0.0840) 0.0108

a: Models including duration of exposure and age at first asbestos exposure as a covariate aside

from the average intensity of asbestos exposure; Mode 50%, 75% and 90% indicate the 50th,

75th and the 90

th percentile of the range of mode intensity, respectively; IQR: interquartile

range; SE: standard error;

However, no significant association was identified between mesothelioma incidence

and the age at first exposure in both models (p>0.05).

53

2.4.4 Selection of the best estimation of asbestos exposure

The results demonstrated that a revised AsbJEM with doubled peak frequency and no

change for other indexes (the 50th

percentile of the range of each exposure category as

mode intensity and peak duration of 15 minutes) produced the strongest dose-response

relationship between mesothelioma incidence and cumulative asbestos exposure (Table

4). The same finding was obtained for the relationship between mesothelioma

incidence and the average intensity of exposure (Table 5). Although the differences

between all assumptions were small, the β-coefficient showed the largest value of

0.2723 and 0.2367 with this revised AsbJEM for both cumulative asbestos exposure

and the average intensity of asbestos exposure, respectively (Table 4 and Table 5). The

risk of mesothelioma was estimated as HR of 1.31 (95%CI: 1.12-1.53) for every 1-log

fibre-year/ml increase of cumulative asbestos exposure. Mesothelioma risk was also

estimated as HRs of 1.66, 3.00 and 5.71 for cumulative asbestos exposures of 0.93,

2.23 and 12.3 fibre-years/ml, respectively, compared with the reference exposure of

0.09 fibre-years/ml (Table 6). The risk of mesothelioma was also estimated as HR of

1.27 (95%CI: 1.08-1.49) for every 1-log fibre/ml increase of the average intensity of

asbestos exposure. Mesothelioma risk was estimated as HRs of 1.84, 2.31 and 4.40 for

the average intensity of asbestos exposure of 0.03, 0.06 and 0.42 fibres/ml, respectively,

compared with the reference exposure of 0.003 fibres/ml (Table 6).

Table 6 Risk of mesothelioma associated with cumulative asbestos exposure and average

intensity of asbestos exposure estimated from the multivariate Cox proportional hazards model

Cumulative exposure (fibre-years/ml)a Hazard ratio (95% confidence interval)

54

Continuous 1.31 (1.12-1.53) (per log fibre-years/ml)

Categorized

0.09 1 (reference)

0.93 1.66 (0.69-3.99)

2.23 3.00 (1.22-7.38 )

12.3 5.71 (2.41-13.55)

Median of average intensity of asbestos

exposure (fibres/ml)b

Hazard ratio (95% confidence interval)

Continuous 1.27 (1.08-1.49) (per log fibres/ml)

Categorized

0.003 1 (reference)

0.03 1.84 (0.75-4.53)

0.06 2.31 (0.96-5.60 )

0.42 4.40 (1.78-10.91)

a, Cumulative asbestos exposure was calculated using the AsbJEM with a modification to peak

frequency (peak duration of 15 minutes, mode intensity at the 50th percentile of the range and

doubled peak frequency during the first two periods); b, Average intensity of asbestos exposure

was calculated using the AsbJEM with a modification to peak frequency (peak duration of 15

minutes, mode intensity at the 50th percentile of the range and doubled peak frequency during

the first two periods);

2.4.5 Validity of statistical assumptions

The proportional hazards assumption was confirmed to hold for all explanatory

variables by checking the Schoenfeld residuals, which were randomly scattered around

zero and the time-dependent covariate method, which showed non-significant results.

55

2.5 Discussion

The AsbJEM provided estimates of occupational asbestos exposure, enabling the

detection of an exposure-response relationship with mesothelioma incidence in

Western Australian workers. This was consistent with previously published literature

(Hansen et al., 1998; Iwatsubo et al., 1998; Gasparrini et al., 2008; Clin B et al., 2011;

Lacourt et al., 2014; Offermans et al., 2014; Ferrante et al., 2016). As the association of

mesothelioma incidence with asbestos exposure has been established, the significant

result of this study using the AsbJEM indicates that the tool serves to provide realistic

estimations for occupational asbestos exposure in a wide range of jobs throughout

Australia as it incorporates all available exposure data relevant to Australian conditions.

The significant result of exposure-response relationship between malignant

mesothelioma and asbestos exposure was demonstrated in two different models

including cumulative asbestos exposure and the average intensity of asbestos exposure,

respectively. This finding reinforces the effectiveness of the AsbJEM because the latter

exposure index is only calculated based on the AsbJEM while the former exposure

index also relies on information obtained irrespective of the tool, i.e. duration of

occupation/exposure. We explored various assumptions of the AsbJEM and found that

although differences between models were small, the published formula could be better

refined with some modification to the frequency of peak intensity since the strongest

dose-response relationship was demonstrated when it was doubled in the first two

periods of time in the AsbJEM.

Mesothelioma incidence was 45 per 100,000 person-years in our study. This figure is

lower than 135-203 per 100,000 patient-years among former asbestos miners and

56

millers in Western Australia (Berry et al., 2012) while it is higher than 5.8 per 100,000

patient-years from a population-based study in Italy (Mensi et al., 2016). Previous

studies of railway rolling stock workers in Italy and shipyard workers in Sweden

reported mesothelioma incidence as 22 and 54 per 100,000 patient-years, respectively

(Gasparrini et al., 2008; Sanden et al., 1992), which were comparable with the rate

observed in this study. These findings indicate that the ARP cohort is representative of

people at the risk of developing mesothelioma from occupational asbestos exposures

and the AsbJEM can adequately estimate the level of such exposures for this group.

Mesothelioma was also observed in workers with relatively low-dose occupational

asbestos exposure, again consistent with previous studies. The lowest quartile of

cumulative asbestos exposure for mesothelioma cases in this study was 0.54

fibre-years/ml (data only shown as the median and interquartile range in Table 1).

Offermans et al. (2014) described HR of 2.69 for cumulative asbestos exposure of 0.20

fibre-years/ml compared with never-exposed subjects. Lacourt et al. (2014)

demonstrated that when cumulative asbestos exposure ranged from 0 to 0.1

fibre-years/ml, the risk of mesothelioma became 4 times higher than never-exposed

subjects. They also stated that 15% of men with mesothelioma had cumulative asbestos

exposure below 0.1 fibre-years/ml. In addition, Ferrante et al. (2016) reported that the

odds ratio of mesothelioma rose to 4.4 when cumulative asbestos exposure increased

from the background level below 0.1 to a range of 0.1 to 1 fibre-years/ml. Furthermore,

our study confirmed four cases of mesothelioma at an even lower cumulative asbestos

exposure ranging from 0.001 to 0.1 fibre-years/ml. The occurrence of mesothelioma

with such a low dose of exposure is not anecdotal as Iwatsubo et al. (1998) reported

the risk of mesothelioma as OR of 1.2 with cumulative exposure from 0.001 to 0.49

57

fibre-years/ml. Therefore, the AsbJEM will be helpful to estimate the risk of

mesothelioma for workers with a low dose of occupational asbestos exposure. There

were some differences between our study and others. Most previous studies were

population-based and thus subjects never-exposed to asbestos were included in the

analysis (Ferrante et al., 2016; Lacourt et al., 2014; Offermans et al., 2014). This was

also true for a hospital-based study (Iwatsubo et al., 1998). However, in our study we

used a cohort with known occupational asbestos exposure and participants with only

background level of asbestos exposure were not included. If never-exposed subjects

had been included in our analyses, the results would have been overestimated because

those people were theoretically at no risk of developing mesothelioma. This would

then be analogous to a previous study where a significant exposure-response

relationship was obtained only when never-exposed subjects were included in the

analysis (Offermans et al., 2014).

There are some caveats to interpret the findings of this study. First, we aimed to

validate the AsbJEM based on the assumption that the strongest dose-response

relationship, identified through the steepest slope, for the well-established association

between asbestos and mesothelioma incidence would indicate the best estimation of

asbestos exposure. Some may argue that this is not necessarily correct and the gold

standard of counting the number of asbestos fibres inside the lung is necessary to

confirm the exposure level (Tuomi et al., 1991). However, it is unrealistic and

unethical to apply such an invasive procedure to the whole population included in this

study and adopting a substitute analytical method is more practical to estimate the level

of exposure (Hardt et al., 2014). In fact, the above-mentioned assumption seems

58

reasonable as dose-response relationships are expected to improve if the

misclassification of the estimation can be reduced (Lenters et al., 2011). Second, some

participants were excluded from the analysis due to an unclear occupational history

regardless of potential asbestos exposure. This may have caused under- or

overestimation of the relationship between the incidence of mesothelioma and asbestos

exposure. However, as the number of these patients was relatively small compared

with the entire sample size (5%), the influence of this issue was likely to be trivial.

Furthermore, there was some uncertainty regarding asbestos exposure before 1943. We

assumed that the exposure level before 1943 was the same as the subsequent period

from 1943 to 1966 and applied the same level of exposure. This decision was

supported by an occupational hygienist from the expert panel that was involved in the

development of the AsbJEM (personal communication with L.G.) although there were

uncertainties for some industries. However, this extrapolation was only necessary for a

small number of jobs (n=88) and thus would not have affected the findings.

2.6 Conclusion

The recently published AsbJEM was validated using a cohort with a diverse

occupational asbestos exposure. The original published calculation can be better

refined with some modification to the frequency of peak intensity as it demonstrated

the strongest dose-response relationship and therefore was considered the best estimate

of occupational asbestos exposure. We expect the results of this study to facilitate more

widespread use of the AsbJEM in future research including risk assessment of other

ARDs.

59

Chapter Three: Project 2

Relationship between asbestos exposure and asbestosis and pleural plaques in

Australian workers

60

3.1 Abstract

Introduction

Dose-response relationships between asbestos exposure and non-malignant

asbestos-related diseases (ARDs) have been well described. However, there has been

substantial variation in the estimates of the relationship, which may, in part, be due to

misclassification of occupational asbestos exposure. An Asbestos Job Exposure Matrix

(AsbJEM) has been validated as a tool to estimate occupational asbestos exposure for

Australian workers. The aim of this study was to investigate dose-response

relationships between asbestos and both asbestosis and pleural plaques using the

AsbJEM.

Methods

Subjects were participants in an asbestos health surveillance program who had at least

3 months’ occupational exposure to asbestos. The level of asbestos exposure for a

participant was estimated using the AsbJEM. Asbestosis and pleural plaques were

identified from plain chest x-ray films. Asbestosis was diagnosed as the International

Labour Office (ILO) profusion classification of 1/0 or higher while pleural plaques

were defined as localized pleural thickening. The association of asbestosis and pleural

plaques with cumulative asbestos exposure and the average intensity of asbestos

exposure was analysed using multivariate logistic regression models.

Results

Of the 1602 men included in the analysis, there were 477 cases of asbestosis (30.0%)

and 901 cases of pleural plaques (66.2%). After adjusting for the age at first asbestos

61

exposure and smoking history, there were significant associations of asbestosis with

both cumulative asbestos exposure and the average intensity of asbestos exposure

(odds ratio (OR) (95% confidence interval (CI)): 1.06 (1.01-1.10) and 1.06 (1.02-1.11),

respectively). There was a monotonic increase in risk across exposure quartiles with

ORs of 1.27, 1.35 and 1.58 for cumulative asbestos exposures of 0.41, 1.11 and 14.9

fibre-years/ml, respectively compared with the reference exposure of 0.002

fibre-years/ml. The risk of asbestosis also steadily increased to ORs of 1.29, 1.38 and

1.50 as the average intensity of asbestos exposure increased from the reference

exposure of 0.0002 to 0.01, 0.04 and 0.17 fibres/ml, respectively. Results for pleural

plaques were similar and there was a significant association of pleural plaques with

cumulative asbestos exposure (OR (95%CI): 1.05 (1.00-1.11)). As cumulative asbestos

exposures elevated to 0.30, 1.00 and 16.4 fibre-years/ml from the reference exposure

of 0.004 fibre-years/ml, the risk of pleural plaques increased to ORs of 1.25, 1.33 and

1.54, respectively. However, there was no significant association of pleural plaques

with the average intensity of asbestos exposure (OR (95%CI): 1.04 (0.99-1.09)).

Conclusion

The risk of asbestosis and pleural plaques was closely related to cumulative asbestos

exposure. These diseases developed at a low level of asbestos exposure.

62

3.2 Introduction

Asbestos can cause non-malignant diseases such as asbestosis and pleural plaques

(PPs) (Hillerdal, 1994). Asbestosis is a fibrotic change of pulmonary parenchyma

mixed with inflammation while PPs are focal thickness of thoracic pleura, which often

involves calcification (American Thoracic Society, 2004). Although these

non-malignant asbestos related diseases (ARDs) are not usually an immediate threat to

life (American Thoracic Society, 2004), which is different from malignant

mesothelioma, asbestosis can damage pulmonary function and respiratory failure may

ensue whereas PPs have negligible effect on lung function (Algranti et al., 2001;

Markowitz et al., 1997; Berry et al., 1979). Positive dose-response relationships

between asbestos and both asbestosis and PPs have consistently been reported

(Jakobsson et al., 1995; Järvholm, 1992; Boffetta, 1998). However, there has been

substantial variation in the estimates of dose-response relationships for both of these

conditions (Matrat et al., 2004; Huang, 1990; Courtice et al., 2016; Paris et al., 2009;

Ehrlich et al., 1992). Some of this variation may be due to misclassification of

occupational asbestos exposure (Lenters et al., 2011) and other possible reasons

include different types of fibres and follow-up periods of time (Berman and Crump,

2008). To better understand these relationships, particularly at the lower end of

exposure where data is sparce, robust exposure measures are required (Fletcher et al.,

1993; van der Bij et al., 2013). We have recently published an Asbestos Job Exposure

Matrix (AsbJEM) for Australian workers (van Oyen et al., 2015) and the reliability of

the AsbJEM has been demonstrated using malignant mesothelioma (refer to the second

chapter of this thesis). It has also been demonstrated that the tool can accurately

63

estimate lower levels of occupational asbestos exposure (refer to the second chapter of

this thesis). Therefore, the aim of this study was to estimate the risk of both asbestosis

and PPs using the AsbJEM for a cohort of Australian workers with occupational

asbestos exposure.

3.3 Methods

3.3.1 Population

Eligible subjects in this study were participants of an annual health surveillance

program for people with significant occupational or environmental exposure to

asbestos (the Asbestos Review Program (ARP)) (Hansen et al., 1997; Hansen et al.,

1998; Musk et al., 1998). People recruited to join the ARP included former workers of

the crocidolite mine and mill at Wittenoom, Western Australia, ex-residents of the

township that serviced the mine and people who had had at least 3-months’ working

history with possible occupational asbestos exposure, and people who had evidence of

PPs. Only asbestos-exposed workers, who were not former workers or residents of

Wittenoom, were included in this study.

3.3.2 Asbestos exposure estimation

The level of asbestos exposure for an individual person in the cohort was estimated

using the AsbJEM based on their work histories (van Oyen et al., 2015). Exposure

estimates for occupation-industry combinations over four time periods (1943-1966;

1967-1986; 1987-2003; ≥2004) were developed by an expert panel. ‘Mode’ was the

most common exposure level and ‘peak’ was the short-term intense exposure in a

particular job. ‘Background’ was the same as the exposure from the general

64

environment. The frequency and intensity of mode, peak and background exposures

were pre-defined for each time period aside from the frequency of background

exposure, which was calculated by subtracting the mode frequency from the total

number of working days in the working year (240 days assuming two days off a week,

four weeks of holidays and Christmas). The background, peak and mode exposures

were each calculated as the multiplicative product of intensity and frequency and an

annual average exposure was calculated as the sum of all these exposures.

Accordingly, a cumulative asbestos exposure for an individual person in a specific

occupation was calculated as the annual average exposure multiplied by the length of

employment (van Oyen et al., 2015). As the previous research confirmed that the

published AsbJEM could be better improved if the frequency of peak exposure was

doubled for the first two periods of time, the AsbJEM with this modification was

implemented in this analysis.

3.3.3 Case ascertainment

All participants in the ARP had annual chest screening using plain chest x-ray (CXR)

film before 2013 and it was subsequently replaced by low dose computed tomography

(LDCT) scan. In this study, CT was utilized for 241 people who participated in the

program after this year and only the result of asbestosis was available for these people.

Asbestosis and PPs were ascertained by experienced readers. Each CXR was read by at

least two and mostly three readers. Blinding for radiological evaluation was not

attempted because the readers were aware that participants had occupational asbestos

exposure. Asbestosis was diagnosed using the International Labour Office (ILO)

profusion classification 1/0 or higher on CXR or reticular opacities, interlobular septal

65

thickening, traction bronchiectasis or honeycombing on LDCT (ILO, 2003; ILO, 2011;

Akira and Morinaga, 2016). PPs were defined as localized pleural thickening

regardless of the site and extent and the presence of calcification (ILO, 2003; ILO,

2011). For the analyses both asbestosis and PPs were recorded as binary variables

(present/absent). The date of the diagnosis of each disease was set at the time when

corresponding radiological findings were identified for the first time during the

follow-up periods of time or at the first visit if they were present at that time. For the

latter case, the profusion level was higher than 1/0 for some cases if they attended the

program at an advanced stage. Non-cases were those without any corresponding

radiological findings during the follow-up periods of time and ascertained at the last

visit.

3.3.4 Statistical analysis

The association of asbestosis and PPs with asbestos exposure was estimated by two

differet models using the multivariate logistic regression analysis. As an index of

asbestos exposure, cumulative asbestos exposure was included in the first model while

the second model included the average intensity of asbestos exposure alongside

duration of employment. Cumulative asbestos exposure was calculated for an

individual person using the AsbJEM and the average intensity of asbestos exposure

was calculated as the division of cumulative asbestos exposure by duration of

employment. Asbestosis and PPs were analyzed separately although some people had

both conditions (n=326).

The first occupational asbestos exposure was defined as the exposure beyond the

background level and the time since first occupational asbestos exposure was

66

calculated as the period up to the ascertainment of the disease for cases and the last

visit for non-cases.

As the distribution of cumulative asbestos exposure and the average intensity of

asbestos exposure was skewed, logarithmic transformation was performed and used for

the analyses.

Other covariates included in the model were the age at first occupational asbestos

exposure and the time since first occupational asbestos exposure (Paris et al., 2008).

Age was excluded from the model due to concerns of collinearity with the time since

first asbestos exposure. Smoking history was only included for the analysis of

asbestosis as it has no relationship with PPs (Hillerdal, 1994). As all these variables

were likely to influence the prevalence of the diseases, they were included in the

multivariate analysis regardless of statistical significance in the univariate analysis.

The presence of the effect modification of cumulative asbestos exposure and the

average intensity of asbestos exposure by other potential confounders was evaluated

using interaction terms. A variable with the least significant p-value over 0.01 was

eliminated one by one from the model until it reached the least variables.

The fit of the model was assessed by Hosmer and Lemeshow goodness of fit test.

P-value ≤0.05 was considered as the model not fitting the data well.

Through all the process of building logistic regression models, fractional polynomials

were used to fit the best numerical form of continuous variables into the models.

Continuous data was expressed as either mean (standard deviation (SD)) or median

(interquartile range (IQR)). Student’s t-test or Wilcoxon rank sum test was performed

for a comparison of the mean or median of two independent groups, respectively. For

67

categorical data, chi-square test was conducted. Statistical significance was set at

p-value ≤0.05. All statistical analyses were conducted using SAS 9.4 (SAS Institute

Inc., Cary, NC, USA) or Stata 14 (STATA Corp LLC., College Station, TX, USA).

This study was exempted from ethics review by the University of Western Australia

Human Research Ethics Committee (RA/4/20/4746).

3.4 Results

3.4.1 Characteristics of subjects

A total of 2084 people, who were not former workers or ex-residents of Wittenoom,

attended the ARP. Of these, 1602 men were included in the analyses after excluding

those with additional non-occupational asbestos exposure (n=373), participants with an

uncertain occupational history (n=96) and women (n=13). There were 477 cases of

asbestosis (30.0%) and 901 cases of PPs (66.2%) (Table 7 and Table 8). The profusion

level for asbestosis included 1/0 in 450, 1/1 in 8, 1/2 in 6, 2/1 in 7, 2/3 in 3 and 3/2 in 3.

Compared with participants who did not develop asbestosis or PPs, the mean age at

first exposure was significantly higher for asbestosis cases (17.5±5.5 vs. 16.7±4.2,

p=0.0006) while it was lower for PP cases (16.6±4.5 vs. 17.9±5.4, p<0.0001). The

calculated median cumulative asbestos exposure was significantly higher for both

asbestosis and PP cases compared with non-cases (0.64 vs. 0.45 fibre-years/ml,

p=0.0013 and 0.63 vs. 0.44 fibre-years/ml, p=0.0002, respectively) (Table 7 and Table

8). The median of the calculated average intensity of asbestos exposure was also

significantly higher for asbestosis cases compared with non-cases (0.027 vs. 0.018

fibres/ml, p=0.0003) (Table 7) while it was not significantly different between PP cases

and non-cases (0.023 vs. 0.026 fibres/ml, p=0.14) (Table 8).

68

Table 7 Comparison of participants in a cohort with and without asbestosis

Asbestosis

(n=477)

No asbestosis

(n=1125)

p-value

Age (years) 66.9±8.7 67.5±11.2 0.20

Duration of exposure (years) 31.6±14.0 32.3±13.9 0.43

Age at first exposure (years) 17.5±5.5 16.7±4.2 0.004

Time since first exposure (years) 49.4±10.5 50.9±12.2 0.02

Cumulative exposure (fibre-years/ml) 0.64 (1.67) 0.45 (1.17) 0.001

Average intensity of exposure (fibres/ml) 0.027 (0.061) 0.018 (0.043) 0.0003

Presence of smoking history (current or former) 353 (74.0) 834 (74.2) 0.94

Continuous data expressed as mean±SD or median (IQR) while categorical data expressed as

number (%); Student’s t-test and Wilcoxon rank sum test was performed for a comparison of

the mean and median between the two groups, respectively. For categorical data, chi-square

test was conducted.

Table 8 Comparison of participants in a cohort with and without pleural plaquea

Plural plaques

(n=901)

No pleural

plaques (n=460)

p-value

Aage (years) 70.5±8.5 62.7±12.5 <0.0001

Duration of exposure (years) 34.0±13.4 27.3±13.9 <0.0001

Age at first exposure (years) 16.6±4.5 17.9±5.4 <0.0001

Time since first exposure (years) 53.9±9.5 44.7±13.5 <0.0001

Cumulative exposure (fibre-years/ml) 0.63 (1.52) 0.44 (1.20) 0.0002

Average intensity of exposure (fibres/ml) 0.023 (0.051) 0.026 (0.050) 0.14

Presence of smoking history (current or former) 674 (74.8) 346 (75.4) 0.82

Continuous data expressed as mean±SD or median (IQR) while categorical data expressed as

number (%); Student’s t-test and Wilcoxon rank sum test was performed for a comparison of

69

the mean and median between the two groups, respectively. For categorical data, chi-square

test was conducted. a, a total number of participants for this analysis was 1361 out of 1602

participants due to 241 missing data for the outcome

3.4.2 Risk estimates of asbestosis

After adjusting for the age at first asbestos exposure and smoking history, there was a

significant association of asbestosis with calculated cumulative asbestos exposure (OR

(95%CI): 1.06 (1.01-1.10)). The risk of asbestosis was estimated as the ORs of 1.27,

1.35 and 1.58 for calculated cumulative asbestos exposures of 0.41, 1.11 and 14.9

fibre-years/ml, respectively compared with the reference exposure of 0.002

fibre-years/ml (Table 9).

There was also a significant association of asbestosis with the average estimated

intensity of asbestos exposure (OR (95%CI): 1.06 (1.02-1.11)). The risk of asbestosis

was estimated as the ORs of 1.29, 1.38 and 1.50 for the average estimated intensity of

asbestos exposure of 0.01, 0.04 and 0.17 fibres/ml, respectively compared with the

reference exposure of 0.0002 fibres/ml (Table 9).

Table 9 Risk of asbestosis estimated from the multivariate logistic regression model

Cumulative exposure (fibre-years/ml) Odds ratio (95% confidence interval)

Continuous 1.06 (1.01-1.10) (/Iloga+1.31 fibre-years/ml)

Categorized

0.002 1 (reference)

0.41 1.27 (1.05-1.52)

1.11 1.35 (1.06-1.72 )

70

14.9 1.58 (1.10-2.26)

Average intensity of exposure (fibres/ml) Odds ratio (95% confidence interval)

Continuous 1.06 (1.01-1.10) (/Ilogb+4.47 fibres/ml)

Categorized

0.0002 1 (reference)

0.01 1.29 (1.07-1.55)

0.04 1.38 (1.09-1.74 )

0.17 1.50 (1.12-2.02)

Time since first exposure (years) Odds ratio (95% confidence interval)

10 1 (reference)

20 2.05 (1.39-3.03)

30 9.04 (12.92-27.94)

40 11.68 (3.15-43.30)

50 11.02 (12.84-42.73)

The multivariate logistic regression model included the age at first asbestos exposure and

smoking history as covariates. a, logarithmic form of cumulative exposure; b, logarithmic form

of average intensity of exposure;

The time since first asbestos exposure was also significantly related to asbestosis with

the incremental risk over the follow-up periods of time. The ORs elevated to 2.05, 9.04

and 11.7 for 20, 30 and 40 years, respectively with the reference of 10 years (Figure 1).

71

Figure 1 Risk of asbestosis by cumulative asbestos exposure and the time since first asbestos

exposure

After adjusting for the age at first exposure and smoking history by multivariate logistic

regression model, there was a trend that the risk of asbestosis elevated constantly with an

increase of cumulative asbestos exposure from 0.002 to 14.9 fibre-years/ml. There was also a

consistent increase of the risk of asbestosis with the progress of time since first asbestos

exposure from 10 to 40 years. The risk was estimated with cumulative exposure of 0.002

fibre-years/ml and the time since first exposure of 10 years as the reference.

3.4.3 Risk estimates of pleural plaque

After adjusting for the age at first asbestos exposure, there was a significant association

of PPs with calculated cumulative asbestos exposure (OR (95%CI): 1.05 (1.00-1.11)).

The risk of PPs was estimated as the ORs of 1.25, 1.33 and 1.54 for calculated

05

10

15

20

Odd

s R

atio

10 20 30 40 50Time since the first exposure (years)

0.002 fibres-years/ml 0.41 fibres-years/ml


72

cumulative asbestos exposures of 0.30, 1.00 and 16.4 fibre-years/ml, respectively

compared with the reference exposure of 0.004 fibre-years/ml (Table 10).

However, there was no significant association of PPs with the average calculated

intensity of asbestos exposure (OR (95%CI): 1.04 (0.99-1.09)). The risk of PPs was

estimated as the ORs of 1.18, 1.23 and 1.29 for the average calculated intensity of

asbestos exposure of 0.01, 0.04 and 0.17 fibres/ml, respectively compared with the

reference exposure of 0.0002 fibres/ml (Table 10).

Table 10 Risk of pleural plaques estimated from the multivariate logistic regression model

Cumulative exposure (fibre-years/ml) Odds ratio (95% confidence interval)

Continuous 1.05 (1.00-1.11) (/Iloga+1.15 fibre-years/ml)

Categorized

0.004 1 (reference)

0.30 1.25 (1.01-1.54)

1.00 1.33 (1.02-1.74 )

16.4 1.54 (1.03-2.30)

Average intensity of exposure (fibres/ml) Odds ratio (95% confidence interval)

Continuous 1.06 (1.01-1.10) (/Ilogb+4.47 fibres/ml)

Categorized

0.0002 1 (reference)

0.01 1.18 (0.96-1.46)

0.04 1.23 (0.95-1.59 )

0.17 1.29 (0.94-1.77)

73

Time since first exposure (years) Odds ratio (95% confidence interval)

20 1 (reference)

30 5.68 (4.24-7.61)

40 16.10 (10.08-25.72)

50 32.89 (18.25-59.26)

60 55.79 (28.32-109.90)

The multivariate logistic regression model included the age at first asbestos exposure. a,

logarithmic form of cumulative exposure; b, logarithmic form of average intensity of exposure

The time since first asbestos exposure was also significantly related to PPs with the

incremental risk over the follow-up periods of time. The ORs elevated to 5.68, 16.1,

32.9 and 55.8 for 30, 40, 50 and 60 years, respectively with the reference of 20 years

(Figure 2).

02

04

06

08

0

Odd

s R

atio

20 30 40 50 60Time since the first exposure (years)



74

Figure 2 Risk of pleural plaques by cumulative asbestos exposure and the time since first

asbestos exposure

After adjusting for the age at first exposure by multivariate logistic regression model, there was

a trend that the risk of PPs elevated constantly with an increase of cumulative asbestos

exposure from 0.004 to 16.4 fibre-years/ml. There was also a consistent increase of the risk of

PPs with the progress of time since first asbestos exposure from 20 to 60 years. The risk was

estimated with cumulative exposure of 0.004 fibre-years/ml and the time since first exposure

of 20 years as the reference.

3.4.4 The fit of the model

As Hosmer and Lemeshow goodness of fit test demonstrated p-value >0.05 in all

models for both asbestosis and PPs, they were considered as fitting the data well.

3.5 Discussion

This study demonstrated that the risk of asbestosis and PPs was significantly associated

with calculated cumulative asbestos exposure and the time since first asbestos

exposure for asbestos-exposed workers after adjusting for the effect of other potential

confounders such as the age at first asbestos exposure. The average calculated intensity

of asbestos exposure was also significantly associated with asbestosis although it was

not significant for PPs. These findings are compatible with previous reports that

cumulative exposure is most closely associated with the incidence of asbestosis

(Boffetta, 1998) although short-time exposure to high level of asbestos could also

cause the disease as Lacourt et al (2017) described the importance of time dose

relationship over the average concentration. On the other hand, PPs were reported to be

observed for low levels of calculated cumulative asbestos exposure although it would

take a longer latency period. This may be explained by the assumption that asbestos

75

fibres would reach the pleura either directly passing through the lung or transported by

the lymphatic system, both of which would take some time in addition to the

subsequent long-standing fibrotic process (Järvholm, 1992). The importance of time

dose relationship over the simple average concentration may also be related to the

finding that the average calculated intensity of asbestos exposure was not significant

for PPs as it was calculated as the product of cumulative exposure divided by duration

of exposure (Lacourt et al., 2017).

A number of articles described the dose-response relationship between occupational

asbestos exposure and non-malignant ARDs and there was substantial variation in their

estimates. Berry et al (1979) and Huang (1990) reported that the prevalence of

asbestosis for British workers in a textile factory and Chinese workers in chrysotile

product factory was 1% with estimated cumulative asbestos exposure of 45 and 22

fibre-years/ml, respectively. The finding in the latter report was commensurate with

asbestos concentration of 0.63 fibres/ml while in the former report asbestosis occurred

with the concentration between 0.3 and 1.1 fibres/ml. According to the study by Matrat

et al (2004), the majority of French workers in buildings with asbestos-containing

materials demonstrated the profusion classification of 1/0 or higher with cumulative

asbestos exposure of less than 1.5 fibre-years/ml while out of 772 male Italian asbestos

workers, 2% developed asbestosis with the mean cumulative exposure of 370

fibre-years/ml (Mastrangelo et al., 2009). Courtice et al (2016) reported the median

cumulative asbestos exposure of 132 fibre-years/ml for workers in an asbestos product

factory and 22% developed asbestosis. Such a variation of previous findings is also

true for PPs. Paris et al (2009) demonstrated that over 80% of PPs developed with

cumulative asbestos exposure of over 30 fibre-years/ml and another study by the same

76

research group reported the mean of cumulative asbestos exposure as 135

fibre-years/ml for workers with the disease (Paris et al., 2004). Whereas Matrat et al

(2004) reported that 30% of pleural thickening was identified with cumulative asbestos

exposure of less than 0.25 fibre-years/ml.

As compared with these prior reports, calculated cumulative asbestos exposure in our

study was even lower with the median value of 0.64 and 0.63 fibre-years/ml for

workers with asbestosis and PPs, respectively. Similarly, the average intensity of

asbestos exposure was as low as 0.027 and 0.023 fibres/ml, respectively. It was also

discovered that the prevalence of the diseases was 30% for asbestosis and 65% for PPs,

which were higher than previous studies (Koskinen et al., 1998; Stayner et al., 1997;

Algranti et al., 2001) regardless of such a low level of occupational asbestos exposure.

There are several factors that may have caused the diversity of previous reports and

differences between these studies and our research. First, misclassification of the level

of occupational asbestos exposure is a major cause of varied results (Lenters et al.,

2011), in particular, when it is estimated based on an individual worker’s self-report.

As the AsbJEM can improve the estimates of asbestos exposure, in particular, at a

lower level of exposure, the dose-response relationship between occupational asbestos

exposure and non-malignant ARDs demonstrated in this study may be indicating more

reliable estimates. However, the same trend needs to be ascertained in another cohort

of Australian people using the AsbJEM to further investigate the performance of this

tool for identifying dose-response relationships between occupational asbestos

exposure and non-malignant ARDs. In addition, the results in this study are not

necessarily comparable to previous studies that were conducted in other countries

where working conditions related to occupational asbestos exposure will be different

77

from the setting in this study and the results based on a JEM are often not generalizable

to other situations (Lavoue et al., 2012). Second, the difference of fibre types used may

be related to diverse results. Participants in this study were former workers in

asbestos-related industry in Western Australia where crocidolite was mined from 1943

until 1966 and many asbestos products used were mixed types containing crocidolite

(van Oyen et al., 2015). In addition, the percentage of crocidolite in the products may

have been higher in Western Australia than in other areas in Australia. As crocidolite is

more potent than chrysotile for mesothelioma (Berman and Crump, 2008), the use of

products composed of mixed asbestos fibres may explain higher prevalence of ARDs

for a lower level of asbestos exposure in this study. Third, there is also an issue of

sensitivity and specificity for the diagnosis of the disease by conventional x-ray films

(Algranti et al., 2001). It is often difficult to detect early changes of asbestosis and PPs

on plain films and misdiagnoses are not uncommon due to artefacts inherently

accompanied by the modality. Variability between readers can also be involved

(Hopstaken et al., 2004). Some research groups which have implemented CT scan to

screen and more precisely evaluate pulmonary changes have stated that over 85% of

asbestosis developed with cumulative asbestos exposure of over 30 fibre-years/ml

(Paris et al., 2009) and only 2% of the persons with that of less than 25 fibre-years/ml

had CT asbestosis (Paris et al., 2004). However, CT is likely to reveal irrelevant and

incidental pulmonary changes in addition to an early phase of asbestosis and PPs

(Vierikko et al., 2007). This potential overdiagnosis may lead to underestimation of

exposure required for the development of the disease, particularly at lower level

exposure. Furthermore, smoking is noted to cause a mixture of fibrosis and

inflammation in pulmonary parenchyma and develop interstitial lung disease (Kumar

78

et al., 2017), which will be manifested as similar radiological changes as early

asbestosis (Copley et al., 2003). Accordingly, it is possible that smoking history might

have confounded the diagnosis of asbestosis since smoking history was prevalent in

this study although theoretically it cannot cause the disease as they are

pathophysiologically different (Agusti et al., 1988). Finally, although the AsbJEM with

modification was implemented in this study based on the result of the first project, it is

possible that the tool is still underestimating occupational asbestos exposure for the

subjects in the study because they joined the health surveillance program due to their

awareness of asbestos exposure.

Regardless of these limitations that might have caused over- or underdiagnosis of the

disease, this study remains meaningful as it has demonstrated a relationship between

occupational asbestos exposure and non-malignant ARDs using the AsbJEM, which

has proved to be a valid and reliable tool to estimate the level of occupational asbestos

exposure, in particular, at a lower level. Further research needs to be conducted in

another cohort of Australian people to confirm the result of this study and the risk

assessment for ARDs would be improved with implementation of CT in addition to the

introduction of the AsbJEM.

3.6 Conclusion

This study estimated the risk of asbestosis and PPs for ordinary Australian workers

with occupational asbestos exposure using the AsbJEM. Both calculated cumulative

asbestos exposure and the time since first asbestos exposure were significantly related

to the diseases. Both diseases were noted to develop after a long latency since the

exposure to a low level of asbestos.

79

Chapter Four: Conclusion

80

Asbestos can cause serious health problems and the risk of ARDs has been

investigated by a myriad of research groups around the world (Gasparrini et al., 2008;

Ferrante et al., 2016; Clin et al., 2011; Lacourt et al., 2014). In Australia, previous

reports of assessing risk of ARDs were mostly based on miners and millers (de Klerk

et al., 1989) while population-based data regarding occupational asbestos exposure has

been lacking. As dose-response relationship between asbestos exposure and the

occurrence of ARDs for this high exposure industry may be different from other

ordinary jobs (Churg and Wiggs, 1986), it is difficult and possibly inappropriate to

extrapolate the result of mining workers to estimate the risk of ARDs for general

population. A job exposure matrix (JEM) is a combination of job titles and exposure

estimates, which enables systematic calculations of occupational asbestos exposure

(Pannett et al., 1985; Hardt et al., 2014; Offermans et al., 2014; Peters et al., 2016).

The Asbestos Job Exposure Matrix (AsbJEM) has been developed to estimate

dose-response relationship between occupational asbestos exposure and ARDs for

ordinary Australian workers (van Oyen et al., 2015).

The objective of this thesis was to assess the performance of the AsbJEM, as well as

refine it if required. The thesis was composed of two studies. In the first study, the

AsbJEM was demonstrated to be a reliable and useful tool to systematically and

cost-effectively estimate occupational asbestos exposure. This project found that the

AsbJEM could be refined with some modification, specifically increasing the

frequency of peak exposures. The validity of the tool was demonstrated by a

significant dose-response relationship between asbestos exposure and mesothelioma

incidence. This study also found that some cases developed at very low dose exposure

of less than 0.01fibres/ml. In the second study, we demonstrated a significant

81

dose-response relationship between occupational asbestos exposure and benign ARDs

such as asbestosis and pleural plaques by applying the modified AsbJEM based on the

results of the first project. These findings suggest that the AsbJEM could be utilized as

a reliable instrument to calculate asbestos exposure in various types of job and

industries and improve the estimates of dose-response relationship for Australian

workers.

Epidemiological studies of asbestos-related health problems in Australia have mostly

been conducted for mesothelioma cases (Leigh and Driscoll, 2003). This was

facilitated by a national health surveillance program called Australian Mesothelioma

Surveillance Program and Australian Mesothelioma Registry (Yeung et al., 1999).

Otherwise, it was based on asbestos mining and milling where detailed data for

exposure was available (de Klerk et al., 1989). Leigh and Driscoll (2003) investigated

mesothelioma risks in different occupational groups and reported that the most

common occupational exposures were building industry followed by shipbuilding,

asbestos cement production, railways and Wittenoom crocidolite mining and milling.

Yeung et al (1999) also examined distribution of mesothelioma cases in various

occupations and industries and reported a similar trend. The latter research group also

pointed out that mesothelioma cases from the traditional asbestos industry such as

crocidolite mining and milling and asbestos cement production had been declining or

plateaued and more cases had been observed from occupations such as plumbers,

carpenters and machinists. A recent report from Musk et al (2015) revealed a shift of

major occupational groups for mesothelioma cases over the last five decades in

Western Australia. According to their report the percentage of Wittenoom workers for

82

mesothelioma cases declined from 54.4% in the years between 1960 and 1979 to

13.8% in the years between 2000 and 2009 while building and construction workers

increased from 3.5% to 29.4% in the same time span. Although these trends may be

currently changing over time and the risk at low dose exposure to in-situ asbestos have

recently been attracting much attention after the complete ban of using

asbestos-containing materials, it should still be kept in mind that a large number of

former workers exposed to occupational asbestos are still at the risk of developing the

lethal disease. However, most of the previous reports of the quantitative risk estimates

of asbestos exposure in Australia were based on miners and millers (de Klerk et al.,

1989) and residents of the township that served the industry (Hansen et al., 1998). On

the other hand, there has been scarce data for Australian workers in other occupations

and industries. It is conceivable that it may cause incorrect prediction if the findings of

miners and millers are extrapolated to non-mining and milling occupations and

industries as the intensity and duration of exposure are likely different and these index

of exposure are closely related to the development of ARDs (Gasparrini et al., 2008;

Ferrante et al., 2016; Clin et al., 2011; Lacourt et al., 2014).

Although a large number of studies investigated dose-response relationship between

occupational asbestos exposure and ARDs, results are diverse between studies

(Gasparrini et al., 2008; Ferrante et al., 2016; Clin et al., 2011; Lacourt et al., 2014).

This variability is attributed to the difference of working conditions and asbestos fibres

used, and varied tools of estimating asbestos exposure. de Klerk et al (1996) and Rees

et al (2001) reported asbestos fibre concentrations in lung tissues of crocidolite and

chrysotile mining workers in Australia and South Africa, respectively using the same

83

fibre counting measure called transmission electron microscope fitted with an energy

dispersive X-ray analysis system. The former study group found crocidolite fibres with

the mean concentration of 183 million fibres/g dry tissue while the mean chrysotile

fibre concentration was 0.69 million fibres/g dry tissue in the latter study. Although

both of these two studies investigated mining workers, lung burden of asbestos fibres

was remarkably different and this disparity can be mostly explained by the difference

of the property of fibres regarding turnover in human lungs. Chrysotile is reported to

be more rapidly cleared from the lungs while crocidolite is more likely to be retained

inside the lung (Albin et al., 1994). In addition to the difference of carcinogenicity

(Hodgson and Darnton, 2000), this disparity of physical characteristics of asbestos

fibres may explain the findings of this thesis that both malignant and non-malignant

ARDs developed at relatively low dose asbestos exposure with some cases at very low

level exposure (<0.01fibres/ml) and the prevalence of asbestosis and pleural plaques

was larger in the cohort of this thesis than in previous reports since most of the

asbestos products used in Australia were mixed types containing crocidolite (van Oyen

et al., 2015).

It is important to establish a reliable methodology to calculate occupational asbestos

exposure because it will help more accurately estimate the risk of ARDs (Lenters et al.,

2011). In this thesis the AsbJEM was demonstrated to be a reliable exposure

assessment tool to estimate dose-response relationship for ordinary Western Australian

workers. JEMs are reported to improve the drawbacks of self-reported exposure

estimates (Hardt et al., 2014) and reduce the cost and labour of expert evaluation on an

individual basis (Nam et al., 2005). In addition, the AsbJEM covers a wide range of

84

jobs and industries over a long period of time and could quantitatively estimate

asbestos exposure. This gives it an advantage over the only other published tool for

asbestos task exposure in Australia, asbestos task exposure matrix (ASTEM). The

ASTEM was constructed based on data in New South Wales and only describes 19

task-product combinations over two periods of time (Hyland et al., 2010). Therefore,

the AsbJEM would improve the risk estimates of ARDs and could be accepted for

widespread use across the country to clarify the occupational asbestos burden for the

entire population. As the AsbJEM covers the period after 2003 when the use of

asbestos was completely banned, it could estimate the risk at low dose exposure to

in-situ asbestos such as demolition work under current regulations (Park et al., 2013),

which will be different from previous occupational exposure patterns (Musk et al.,

2017) although the risk of non-occupational exposure to in-situ asbestos might need to

be assessed with a different approach aside from the AsbJEM.

The AsbJEM could also give an impact on clinical practice by improving the estimates

for the risk of ARDs. For example, accurate assessment of asbestosis and pleural

plaques could improve the estimates for the risk of lung cancer as the presence of the

former conditions could enhance the risk of the latter disease (Weiss, 1999). In contrast

to mesothelioma, lung cancer is a curable disease and can be controlled with currently

available pharmacological agents if implemented at an appropriate timeline

(Maemondo et al., 2010). In addition, a new therapeutic option has recently become

available to suppress the progression of parenchymal fibrosis in the lung (Noble et al.,

2011) and may possibly be effective for asbestosis. Therefore, the AsbJEM could make

85

contributions to better estimate the risk of ARDs and help improve clinical outcome for

individual patient with appropriate treatment.

Despite their benefits, JEMs are noted to suffer from misclassification due to

incapability of discriminating individuals in the same occupation as exposure estimates

are classified based on job and industry combinations although the level of exposure

may be varied between workers for some reasons such as different tasks and the

presence of preventive measures. The AsbJEM was validated and refined using a

cohort of former workers who participated in the health surveillance program. They

were involved in the program because they were referred by a doctor, had evidence of

pleural plaques or because of their awareness of probable asbestos exposure. As a

result, the calculation in the AsbJEM may still be underestimated for the cohort and the

modification to the AsbJEM proposed in this thesis may possibly overestimate the risk

of ARDs in another population. Therefore, it is preferable to apply the AsbJEM to a

different cohort in Australia, which may have a different exposure background from

the cohort in this thesis, in order to confirm the same trend of dose-response

relationship between occupational asbestos exposure and ARDs. If a similar result is

obtained, the reliability of the AsbJEM will be reinforced and the applicability will be

facilitated. A direct comparison of estimates of the risk of ARDs will be achieved

between different populations. Conversely, if the result is divergent from the findings

obtained in this thesis, the AsbJEM may need to be further refined using that cohort or

another in Australia. Although there will be a limitation regarding the applicability of

JEMs, FINJEM, one of the most well-recognized JEMs, is reported to be applicable to

other regions (Kauppinen et al., 2014). Therefore, it is a subject of interest to compare

86

the performance of this internationally renowned JEM with the AsbJEM for Australian

workers in the future research. In addition, it is also interesting to see if the AsbJEM is

applicable to people in other countries. This may be possible as international exposure

data was referred in the development of the AsbJEM for the early time periods (van

Oyen et al., 2015). If it is proved to be useful, it would help estimate the risk of

developing ARDs in that region, in particular in a country where a JEM or other

reliable exposure assessment tools are not available yet to facilitate standardized

exposure estimates. Although there are some methodological limitations for the

research projects comprising this thesis, which were described in each section, they are

not serious enough to undermine their significance. The result of dose-response

relationship between occupational asbestos exposure and non-malignant diseases

presented in this thesis will be a reference value for further research of estimating the

asbestos burden for the entire population across the nation as the number of

non-malignant ARDs is far beyond that of mesothelioma cases and it would only be

reliable if it is calculated based on accurate exposure estimates (Lenters et al., 2011).

Additional findings need to be accumulated through further research to acquire

widespread recognition of the AsbJEM and better refine the instrument to

appropriately address the situation in Australia.

87

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Appendices

T +61 8 6488 3703 / 4703F +61 8 6488 8775E [email protected]

Human Ethics

Office of Research Enterprise

The University of Western AustraliaM459, 35 Stirling HighwayCrawley WA 6009 Australia

CRICOS Provider Code: 00126G

Our Ref: RA/4/20/4746

02 August 2018

Dr Peter FranklinSchool of Population and Global HealthMBDP: M431

Dear Doctor Franklin

HUMAN RESEARCH ETHICS OFFICE – EXEMPTION FROM ETHICS REVIEW

Assessment of the performance of the Asbestos Job Exposure Matrix in Australia.

Based on the information you have provided to the the Human Ethics office in relation to the above project, the described activity has beenassessed as exempt from ethics review at the University of Western Australia.

However, should there be any significant changes to the protocol, you must contact the HREO to determine whether your exempt status remainsvalid or whether you will be required to submit an application for ethics approval.

If you have any queries please contact the Human Ethics office at [email protected].

Please ensure that you quote the file reference – RA/4/20/4746 – and the associated project title in all future correspondence.

Yours sincerely

Mark DaviesManager, Human Ethics

Name Faculty / School RoleDr Peter Franklin School of Population and Global Health Chief Investigator

The University of Western AustraliaCrawley WA 6009 Australia

T +61 8 6488 3703 / 4703F +61 8 6488 8775

E [email protected] Provider Code: 00126G

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