Accepted Manuscript

Title: Tobacco and e-cigarette use amongst illicit drug users inAustralia

Author: Rachel Sutherland Natasha Sindicich GavinEntwistle Elizabeth Whittaker Amy Peacock AllisonMatthews Raimondo Bruno Rosa Alati Lucy Burns

PII: S0376-8716(15)01777-9DOI: http://dx.doi.org/doi:10.1016/j.drugalcdep.2015.10.035Reference: DAD 5828

To appear in: Drug and Alcohol Dependence

Received date: 27-2-2015Revised date: 20-10-2015Accepted date: 23-10-2015

Please cite this article as: Sutherland, R., Sindicich, N., Entwistle, G., Whittaker, E.,Peacock, A., Matthews, A., Bruno, R., Alati, R., Burns, L.,Tobacco and e-cigaretteuse amongst illicit drug users in Australia, Drug and Alcohol Dependence (2015),http://dx.doi.org/10.1016/j.drugalcdep.2015.10.035

This is a PDF file of an unedited manuscript that has been accepted for publication.As a service to our customers we are providing this early version of the manuscript.The manuscript will undergo copyediting, typesetting, and review of the resulting proofbefore it is published in its final form. Please note that during the production processerrors may be discovered which could affect the content, and all legal disclaimers thatapply to the journal pertain.

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Tobacco and e-cigarette use amongst illicit drug users in Australia

Rachel Sutherland*1, Natasha Sindicich1, Gavin Entwistle1, Elizabeth Whittaker1, Amy Peacock2,

Allison Matthews2, Raimondo Bruno2, Rosa Alati3, Lucy Burns1

1National Drug and Alcohol Research Centre, UNSW, Australia

2 School of Medicine (Psychology), University of Tasmania, Australia

3School of Social Science, University of Queensland, Australia

Corresponding Author*: Rachel Sutherland

National Drug and Alcohol Research Centre, University of New South Wales, Sydney, NSW, 2052,

AUSTRALIA; Phone: +61 2 9385 0256; Facsimile: +61 (0)2 9385 0222; E-mail: rachels@unsw.edu.au

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ABSTRACT

Objective: To examine the rates and patterns of tobacco and e-cigarette use amongst two samples of

illicit drug users in Australia. Method: Data were obtained from the 2015 Illicit Drug Reporting

System (IDRS) and the 2015 Ecstasy and Related Drugs Reporting System (EDRS). These studies

comprised cross-sectional samples of 888 people who inject drugs (PWID) and 763 regular

psychostimulant users (RPU). Results: Tobacco was consumed by the majority of both samples,

however, use in the six months preceding interview was significantly higher amongst PWID (92.2%)

than RPU (82.4% [OR 2.53 95% CI 1.86-3.44]). Inversely, PWID were less likely to have a history of e-

cigarette use: 31.5% of PWID reported lifetime use of e-cigarettes (vs. 57.0% of RPU [OR 0.35 95% CI

0.28-0.42]) and 18.1% reported use in the six months preceding interview (vs. 33.7% of RPU [OR 0.44

95% CI 0.35-0.55]). PWID were more than three times as likely than RPU to report using e-cigarettes

as a smoking cessation tool (OR 3.09 95% CI 2.03-4.71), but were less likely to use e-liquids that

contained nicotine (OR 0.52 95% CI 0.32-0.83). Higher levels of poly drug use, daily tobacco use,

recent use of synthetic cannabinoids and employment status were found to be significantly

associated with e-cigarette use. Conclusion: The use of e-cigarettes was relatively common amongst

Australian samples of PWID and RPU. Whilst the majority of PWID reported using e-cigarettes as a

smoking cessation tool, it appears that RPU are using them for experimental or recreational

purposes.

KEYWORDS: E-cigarettes; Electronic cigarettes; ENDS; tobacco; smoking; illicit drugs

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1. INTRODUCTION

Tobacco smoking continues to decline at the population level in Australia. Recent data from

the National Drug Strategy Household Survey (NDSHS; AIHW, 2014) indicates that daily smoking has

decreased significantly amongst people aged 14 years and older, from 15.1% in 2010 to 12.8% in

2013. However, there are particular cohorts in which the rate of tobacco smoking remains

significantly higher than the population average. Tobacco use has been found to be particularly

prominent amongst populations of illicit drug users, with previous studies reporting rates of use that

range from 71% to 96% (Cogger et al., 2008; McKetin et al., 2010; Ritcher et al., 2002; Ross et al.,

2002; Sindicich and Burns, 2015; Stafford and Burns, 2015). Those who continue to smoke despite

increasing pressures to quit may represent a particular subpopulation of smokers who are more

resistant, or ‘hardened’ in their smoking behaviours (Clare et al., 2014) and, as such, it is important

to consider alternatives which may help reduce rates of tobacco smoking amongst these groups.

E-cigarettes offer one possible alternative, providing a new option for individuals who are

unable or unwilling to quit by permitting total or partial replacement of smoked tobacco without

making any commitment to reduce or abstain from recreational nicotine use (Dockreall et al., 2013).

Instead of burning tobacco, e-cigarettes use a heating element which vaporises a mixture of

chemicals, often known as ‘e-liquids’ (Grana et al., 2014). These liquids usually contain flavouring

agents and carrier substances, and may be purchased with or without nicotine; the nicotine content

can vary between 0 and 20mg/ml depending on the brand (Trehy et al., 2011). The first nicotine

based e-cigarette was patented in 2003 and the global e-cigarette market is currently estimated to

be worth US$3 billion (WHO, 2014); in January 2014 there were reported to be 466 different brands

and 7,764 unique flavours available (Zhu et al., 2014).

The legality of e-cigarettes is complex and varies across countries and jurisdictions. In

Australia, e-cigarettes have not been approved for use by the Therapeutic Goods Administration and

as such it is illegal to sell e-cigarettes that contain nicotine. Evidence regarding the efficacy of e-

cigarettes as a smoking cessation tool is mixed (see Harrell et al., 2014) and public health experts

remain divided as to the health implications of e-cigarette use. Nevertheless, it has been found that

e-cigarettes, particularly older brands, are often marketed on their advantages over conventional

cigarettes and promoted as effective smoking cessation aids (Zhu et al., 2014); subsequently, they

are often viewed as a tool to quit smoking conventional cigarettes (Brown et al., 2014; Choi and

Forster, 2013; Czoli et al., 2014; Goniewicz et al., 2013; Kralikova et al., 2013; Vickerman et al.,

2013). A systematic review of 49 studies looking at the awareness, use, reactions to, and beliefs

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about e-cigarettes found that the most common reasons for using e-cigarettes were quitting

smoking and using a product that is healthier than cigarettes (Pepper and Brewer, 2014).

There is limited information regarding rates of e-cigarette use and the socio-demographic

profile of consumers. International research suggests relatively high rates of use, particularly

amongst people who already smoke. For example, it has been estimated that 20.3% of smokers,

4.7% of ex-smokers and 1.2% of lifetime non-smokers in the European Union (approximately 29.3

million adults) have a lifetime history of e-cigarette use (Vardavas et al., 2014). In addition, the

lifetime use of e-cigarettes has doubled in several countries between 2008 and 2012 (Grana et al.,

2014) and prevalence has increased amongst various age groups (Chapman and Wu, 2014). In the

Australian context, rates of current use increased from 0.6% in 2010 to 6.6% in 2013 (Yong et al.,

2014). However, these studies are limited to current and former smokers and there appear to be no

existing studies which have looked at the use of e-cigarettes amongst illicit drug users. With this in

mind, and in the context of high levels of smoking and low levels of cessation amongst illicit drug

users, this study will investigate the rates, patterns and correlates of tobacco and e-cigarette use

amongst people who inject drugs (PWID) and regular psychostimulant users (RPU) in Australia.

2. METHOD

2.1 Study design

This paper uses data from the 2015 Illicit Drugs Reporting System (IDRS) and the Ecstasy and

Related Drugs Reporting System (EDRS), two Australian national monitoring studies funded by the

Australian Government Department of Health. These studies are aimed at detecting emerging trends

in illicit drug markets and have been conducted in all states and territories of Australia since 2000

and 2003 respectively. Both studies have ethical approval from the University of New South Wales

Human Research Ethics Committee.

To compare rates of e-cigarette use amongst our IDRS and EDRS samples with general

population estimates, this paper draws upon the 2013 National Drug Strategy Household Survey

(NDSHS), which collects data on alcohol, tobacco and illicit drug consumption amongst the general

population in Australia. In 2013, the NDSHS collected data from 23,855 Australians aged 12 and over

and, for the first time, individuals were asked about their use of e-cigarettes in the 12 months

preceding interview. Further in-depth details of the survey are reported elsewhere (AIHW, 2014)

2.2 Participants and procedure

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IDRS participants were recruited through drug treatment services and peer referral, and

were selected on the basis of at least monthly injection of illicit drugs in the six months prior to

interview (hereafter referred to as people who inject drugs (PWID)). EDRS participants were

recruited through street-press advertisements, online forums and peer referral, and were selected

on the basis of at least monthly use of psychostimulants in the six months preceding interview

(hereafter referred to as regular psychostimulant users (RPU)). In both studies, participants had to

be 16 years of age or older, have resided in the city where the interview took place for at least 12

months prior to the interview, and were non-random self-selected samples. Face-to-face one-hour

structured interviews were conducted by trained interviewers with participants at a negotiated time

and location. All information provided was confidential, and participants were reimbursed AUD$40

for their time. EDRS interviews were conducted from March-July 2015, and IDRS interviews were

conducted from May-August, 2015.

Further in-depth details of the methodology are described elsewhere (Stafford and Burns,

2015; Sindicich and Burns, 2015).

2.3 Measures relevant to the current study

In addition to demographic questions, participants were asked about their lifetime and past

six month use of licit and illicit substances, including both tobacco and e-cigarettes. Participants who

had used e-cigarettes in the past six months were then asked if they had used them as a smoking

cessation tool, and whether the e-cigarette contained nicotine, cannabis, neither or both.

Participants in both studies completed the Severity of Dependence Scale (SDS; Gossop et al., 1995)

for stimulants; IDRS participants also completed the SDS for opioids. Cut-off scores of ≥4 and ≥5

were used to measure stimulant dependence (Topp and Mattick, 1997) and opioid dependence

(Castillo et al., 2010) respectively. EDRS participants were asked if they had binged on stimulants in

the past six months (defined as stimulant use for 48 hours or more without sleep), and in both

samples the number of illicit drug classes used in the past six months was used to measure levels of

poly drug use (maximum of 22 drug classes for IDRS participants and 28 drug classes for EDRS

participants).

Participants in both studies were administered the Kessler 10 (K10) Psychological Distress

Scale (Kessler et al., 2003), a 10-item screening measure of psychological distress with a five-point

response scale (1 ‘none of the time’ to 5 ‘all of the time’). A cut-off score of ≥22 (score range 10-50)

was used to measure high to very high psychological distress (Andrews and Slade, 2001). The K10

has been shown to be a reliable and valid screening tool for current affective disorders amongst drug

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users, with high internal consistency (Cronbach’s α=0.84) and high predictive accuracy for the

presence of Diagnostic and Statistical Manual IV affective disorder diagnosis (77%; Hides et al., 2007)

The Alcohol Use Disorders Identification Test (AUDIT), a 10-item scale, was administered to

EDRS participants to identify individuals with potential alcohol-related problems (Saunders et al.,

1993). A cut-off score of ≥8 was used to measure hazardous and harmful alcohol use (Babor et al.,

2002). The AUDIT-C, a 3-item shorter version, was administered to IDRS participants (Bush et al.,

1998), with a score of ≥5 indicating the need for further assessment (Haber et al., 2009). The AUDIT

has been shown to be internally consistent even when used with diverse samples and in a broad

range of settings (Reinert and Allen, 2002).

2.4 Statistical analysis

2.4.1. IDRS/EDRS data. The sample was divided into three groups based on their e-cigarette use in

the six months preceding interview: no use; infrequent use (i.e., <6 days of use); and frequent use

(i.e., ≥6 days of use). Pearson’s χ2 was used to determine statistical significance between groups and

adjusted residuals were used to analyse which cell differences contributed to the overall χ2 results.

An adjusted residual score of greater than 2.0 or below -2.0 indicated that the cells differed

significantly. The Benjamini-Hochberg procedure was applied to control the false discovery rate and

was used because it yields much greater power than the widely administered Bonferroni technique

(Thissen et al., 2002).

Variables found to be significant based on univariate comparisons were entered into

separate multivariate logistic regression models for PWID and RPU, along with variables that have

previously been shown to be associated with tobacco smoking: specifically gender (WHO, 2003); age

(Jha et al., 2002); unemployment (De Vogli and Santinello, 2005); and poor mental health (Hirshbein,

2015; Jorm, 2008). To allow comparability across the two samples, the same variables were entered

into the regression models for both PWID and RPU. The referent group comprised participants who

reported no e-cigarette use in the past six months. Associations were set for statistical significance at

p < 0.05. All analyses were conducted using IBM SPSS software, Version 22.

2.4.2. NDSHS data. Analyses were conducted using SAS Enterprise Guide 6.4, taking into account the

effects of complex sampling methods. Data were weighted to correct for differential response rates

and to account for over-sampling in some of the smaller jurisdictions. Strata and cluster variables

were used in the analyses to account for the multilevel stratification of recruitment of the sample.

3. RESULTS

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3.1 Demographics

Eight hundred and eighty-eight IDRS participants were interviewed in 2015. Briefly, 66.8% of

PWID were male with a median age of 42 years (range 17-71), 97.7% were of English speaking

background, 57.5% were tertiary qualified (this includes university and trade qualifications), 1.6%

were students, 83.3% were unemployed and 47.2% were currently in drug treatment. Over half

(55.1%) had a prison history and almost one-third (31.4%) had been arrested in the 12 months

preceding interview.

Seven hundred and sixty-three participants were interviewed for the 2015 EDRS. Briefly,

62.4% of RPU were male with a median age of 21 years (range 16-55), 95.7% were of English

speaking background, 46.4% were tertiary qualified, 33.3% were students, 12.2% were unemployed

and 2.1% were currently in drug treatment. Three percent had a prison history and 10.1% had been

arrested in the 12 months preceding interview.

3.2 Rate and frequency of tobacco use

Tobacco was consumed by the majority of both PWID and RPU in 2015, although PWID did

report significantly higher rates of tobacco use compared to RPU. Specifically, 97.3% of PWID

reported lifetime use of tobacco (vs. 92.1% of RPU [OR 3.08 95% CI 1.90-4.99]) and 92.2% reported

use of tobacco in the six months preceding interview (vs. 82.4% of RPU [OR 2.53 95% CI 1.86-3.44]).

In addition, PWID had ten-fold increased odds of reporting daily use of tobacco compared to RPU

(87.0% of PWID vs. 39.4% of RPU [OR 10.32 95% CI 8.08-13.17]).

3.3 Rate and frequency of e-cigarette use

Almost one-third (31.5%) of PWID reported lifetime use of e-cigarettes and 18.1% reported

use in the six months preceding interview. This was significantly lower than reported by RPU, of

whom 57.0% reported lifetime use (OR 0.35 95% CI 0.28-0.42) and 33.7% reported recent use (OR

0.44 95% CI 0.35-0.55). However, frequency of use was comparable amongst both groups, with

PWID reporting e-cigarette use on a median of four days in the past six months (range: 1-180) and

RPU reporting use on a median of three days in the past six months (range: 1-180; p=0.722). A

substantial minority of PWID and RPU reported using e-cigarettes on only one day in the past six

months (26.2% and 32.5% respectively [OR 1.36 95% CI 0.88-2.10]), whilst daily use was low

amongst both samples (1.5% and 1.3% respectively [OR 1.12 95% CI 0.49-2.57]). Figure 1 compares

the use of e-cigarettes amongst PWID and psychostimulant users in the general population, with e-

cigarette use amongst PWID and RPU in the IDRS and EDRS samples. As can be seen, e-cigarette use

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amongst IDRS participants was comparable to rates of use amongst PWID in the general population

(18.1% vs. 15.9%; p=0.079; 95% CI: -0.049 − -0.002). In contrast, recent e-cigarette use amongst RPU

in the EDRS sample was higher than reported by psychostimulant users in the general population

(33.7% vs. 16.0%; p<0.001; 95% CI -0.23 − -0.15).

3.4 Tobacco and e-cigarettes: Dual use

The majority of participants who reported any recent e-cigarette use also reported a history

of tobacco use. That is, amongst PWID who had recently used e-cigarettes, 98.8% had a lifetime

history of tobacco use and 98.1% had used tobacco in the past six months. Amongst RPU who had

recently used e-cigarettes, 96.1% reported a lifetime history of tobacco use and 90.3% reported use

of tobacco in the past six months.

As shown in

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Table 1: Tobacco and e-cigarette use amongst PWID and RPU, 2015

PWID

N=888

%

RPU

N=763

%

OR (95% CI) p value

Tobacco

Lifetime use 97.3 92.1 3.08 (1.90-4.99) <0.001

Recent use# 92.2 82.4 2.53 (1.86-3.44) <0.001

Daily use 87.0 39.4 10.32 (8.08-13.17) <0.001

E-cigarettes

Lifetime use 31.5 57.0 0.35 (0.28-0.42) <0.001

Recent use# 18.1 33.7 0.44 (0.35-0.55) <0.001

Single day of use 26.2 32.5 1.36 (0.88-2.10) 0.168

Daily use 1.5 1.3 1.12 (0.49-2.57) 0.785 Note: OR = odds ratio; CI=confidence interval; PWID = people who inject drugs; RPU = regular psychostimulant users #Recent

use = use in the past six months

Table 2, PWID were more than three times as likely than RPU to report using e-cigarettes as a

smoking cessation tool (54.3% vs. 27.8%, [OR 3.09 95% CI 2.03-4.71]), although they were less likely

to report using e-cigarettes that contained nicotine (67.1% vs. 79.7%, [OR 0.52 95% CI 0.32-0.83]).

The use of cannabis in e-cigarettes was rare amongst both groups (8.6% vs. 4.2% [OR 2.13 95% CI

0.90-5.06]), and a substantial minority reported using e-cigarettes which contained neither nicotine

nor cannabis (27.1% vs. 19.8%, [OR 1.51 95% 0.92-2.46]).

3.6 Correlates of e-cigarette use

3.6.1 Univariate analyses. At the univariate level, PWID who reported frequent e-cigarette use (i.e. ≥

6 days of use) in the six months preceding interview were more likely to have used more than five

illicit drug classes in the past six months (p<0.001) and were less likely to unemployed (p=0.004).

Infrequent e-cigarette users (i.e. <6 days use) were more likely to have recently used synthetic

cannabinoids (p=0.001), whilst PWID who had not used any e-cigarettes in the past six months were

less likely to have recently used synthetic cannabinoids (p=0.001) and less likely to have used more

than five illicit drug classes in the past six months. Inversely, they were more likely to be

unemployed (p=0.004).

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With respect to RPU, both frequent and infrequent e-cigarette users were more likely to

have recently used a new psychoactive substance (NPS) (p<0.001) and were more likely to have used

more than five drug classes in the past six months (p<0.001). Inversely, non e-cigarette users were

less likely to have used an NPS in the six months preceding interview, and were less likely to have

used more than five drug classes.

3.6.2 Multivariate analyses. When the variables significant in the univariate analyses were entered

into logistic regression models, controlling for sex, age, mental health and tobacco use, the following

factors remained statistically significant: poly drug use, daily tobacco use, recent synthetic

cannabinoid use and employment status. Specifically, PWID who were unemployed were less likely

to report infrequent (AOR 0.44 95% CI 0.25-078) and frequent (AOR 0.50 95% CI 0.27-0.92) e-

cigarette use when compared to non-users. Daily tobacco (AOR 3.85 95% CI 1.18-12.60) and recent

synthetic cannabinoid users (AOR 2.36 95% CI 1.12-4.96) were more likely to report infrequent e-

cigarette use, whilst participants who had used more than five drug classes in the past six months

were more likely to report frequent e-cigarette use (AOR 2.30 95% CI 1.30-4.08).

With respect to RPU, only poly drug use remained significant. That is, when compared to

non e-cigarette users, participants who had used more than five drug classes in the past six months

were more than twice as likely to report infrequent (AOR 2.15 95% CI 1.43-3.23) and frequent (AOR

2.47 95% CI 1.49-4.09) e-cigarette use.

4. DISCUSSION

We found tobacco use to be common practice amongst our samples of PWID and RPU, with

smoking rates remaining high over time. Most recently, in 2015, 92.2% of PWID and 82.4% of RPU

reported tobacco use in the six months preceding interview (compared to 92.40% in 2000 and

74.60% in 2003 respectively; Topp et al., 2001; Breen et al., 2004). These figures are in contrast to

smoking rates amongst the general Australian population, which have been declining over the past

decade (AIHW, 2014); currently, less than one-fifth of Australians aged 14 years and older report

being current smokers. Overall, this suggests that smoking rates continue to be substantially higher

amongst regular drug users; clearly, smoking behaviours amongst these groups are less amenable to

current anti-smoking interventions.

We also found the use of e-cigarettes to be relatively common amongst our samples of

Australian illicit drug users, with use being particularly high amongst RPU. Approximately one-third

of RPU reported use of e-cigarettes in the six months preceding interview, which was significantly

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higher than reported amongst psychostimulant users in the general population. In contrast, almost

one in five PWID reported the use of e-cigarettes in the six months preceding interview, which was

comparable to general population estimates. However, these rates remain substantially higher than

reported by other Australian studies. For example, the International Tobacco Control Four-Country

Survey of current and former smokers found that, although 20% of the Australian cohort were aware

of e-cigarettes, only 2% had tried them in their lifetime and 1% were current users (Adkison et al.,

2013). Greater variability has been evident in an international context: amongst samples of current

and former smokers, lifetime rates range between 37-45% and past month rates range between 14-

41% (Brown et al., 2014; Christensn et al., 2014; Czoli et al., 2014; Dockrell et al., 2013; Giovenco et

al., 2014; Vardavas et al., 2014; Vickerman et al., 2013).

Interestingly, higher levels of poly drug use was the only consistent predictor of e-cigarette

use amongst both samples. It could be argued that poly drug users are more innovative users, willing

to seek out or experiment with new or novel products such as e-cigarettes. Furthermore,

unemployed PWID were less likely to have recently used e-cigarettes; it seems likely that these

participants had a lower disposable income, which may have hindered their use of e-cigarettes. That

is, although e-cigarettes are arguably cheaper than conventional cigarettes over the long-term, they

have a higher start-up cost, which may deter lower-income participants from initiating use of these

products. Surprisingly, age, gender and poor mental health were not associated with e-cigarette use

in either sample, despite their well-established association with tobacco use.

Given the high rates of tobacco use amongst both PWID and RPU, it was of interest to

determine if participants were using e-cigarettes as a smoking cessation tool. PWID were more than

three times as likely than RPU to report using e-cigarettes in an attempt to quit smoking tobacco;

however, they were less likely to use e-cigarettes that contained nicotine. It is unclear what may be

driving these differences; the greater likelihood of using e-cigarettes as a smoking cessation tool

could perhaps be attributed to higher rates of daily tobacco consumption amongst PWID (and

possibly a greater desire to cease their tobacco use), whilst the lower levels of nicotine use is likely

explained by the fact that, in Australia, e-liquids that contain nicotine are largely available for

purchase from online stores (the legality of which depends on whether the importer holds a

prescription from an Australian registered medical practitioner, as well as the quantity that is

imported; see Douglas et al. 2015 for further details). Previous studies have shown that PWID are

generally less engaged with current technologies and online marketplaces (Genz et al., 2015;

Sindicich and Burns, 2012; Stafford and Burns, 2012), and hence they would have limited

accessibility to products sold predominantly on online marketplaces.

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Less than one-third of RPU reported using e-cigarettes as a smoking cessation tool, which

suggests that they are largely being used for experimental or recreational purposes. Indeed, a

number of recent studies have shown that the use of e-cigarettes does not clearly relate to interest

in smoking cessation (Kinnunen et al., 2014; Lippert, 2014; Tucker at al., 2014) and that the most

common reason for e-cigarette use was the capacity for use in smoke-free zones (Adkison et al.,

2013; Tucker et al, 2014). For example, a study of homeless youth reported that not having to go

outside to smoke was the most common reason for using ECs (38%), whilst it was less common to

report using e-cigarettes to quit smoking (17-18%; Tucker et al., 2014). Given that the EDRS sample is

made up of recreational psychostimulant users who largely use ‘party drugs’ in a nightclub setting, it

is possible that e-cigarettes are mainly being used in situations where they are not permitted to

smoke tobacco (e.g., nightclubs) due to Australian legislation. This, however, is speculative given the

limitations of our data described below. Studies with greater capacity to test motivations behind the

use of e-cigarettes are needed to better understand the public health implications of e-cigarette use

in Australia. Given the wealth of public health reform in Australia over the past few decades targeted

at prevention and intervention for tobacco smoking (e.g., Chapman and Wakefield, 2001), and the

apparent resistance of these groups to such efforts, understanding the motivations for tobacco and

e-cigarette use is imperative to reduce the continuing health and economic burden from these

behaviours.

4.1 Limitations

This study has a number of limitations. Firstly, the sentinel nature of the sample means that

it does not represent all PWID or RPU in Australia, which has implications for the generalizability of

our findings. However, both the IDRS and EDRS employ a purposive convenience sampling method,

which has been shown as an appropriate recruitment method when working with populations

engaged in illegal activity. Secondly, our analysis is reliant upon self-report data from participants

which may be subject to bias, although evidence points to sufficient validity and reliability of self-

report in studies assessing illicit drug use (Darke, 1998). Future studies might like to include other

measures of substance use, such as urine tests, hair analysis and collateral interviews, to

substantiate their findings. Thirdly, whilst our survey asked about the use of e-cigarettes as a

smoking cessation tool, it did not explore other motivations for use of these devices; it is critical that

future studies capture this information. Lastly, it is important to note that differences in time-frames

limit the comparability of NDSHS and IDRS/EDRS data. That is, the NDSHS collected information

about e-cigarette use in the past 12 months, whilst the IDRS and EDRS asked about use in the past

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six months. Additionally, the NDSHS data was collected in 2013, whilst the IDRS and EDRS data was

collected in 2015. It is possible that population estimates of e-cigarette use have shifted since 2013.

4.2 Conclusions

This study represents, to the authors’ knowledge, the first in-depth analysis of the rate and

patterns of e-cigarette use amongst samples of illicit drug users. The use of e-cigarettes was

relatively common amongst both PWID and RPU, with higher levels of poly drug use the only

consistent predictor amongst both samples. Interestingly, PWID largely reported using e-cigarettes

in an attempt to cease their tobacco use, whilst the majority of RPU were using e-cigarettes for

purposes other than as a smoking cessation tool.

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FIGURE LEGEND

Figure 1. Recent# e-cigarette use amongst people who inject drugs and psychostimulant users:

Comparing the IDRS and EDRS samples to general population estimates.

#Amongst IDRS and EDRS samples, this refers to participants who had used e-cigarettes in the past six months. Amongst NDSHS

participants, this refers to individuals who had used e-cigarettes in the past 12 months; ##

Relative Standard Error>25%, interpret with

caution; *** p<0.001; 95% CI -0.21 − -0.14.

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REFERENCES

Adkison, S.E., O’Connor, R.J., Bansal-Travers, M., Hyland, A., Borland, R., Yong, H-H., Cummings,

K.M., McNeil, A., Thrasher, J.F., Hammond, D., Fong, G.T., 2013. Electronic nicotine delivery systems.

International tobacco control four-country survey. Am. J. Prev. Med. 44, 207-215.

Andrews, G., Slade, T., 2001. Interpreting scores on the Kessler Psychological Distress Scale (K10).

Aust. N-Z J. Public Health 25, 494−497.

Australian Institute of Health and Welfare. 2014. 2013 National Drug Strategy Household Survey:

Detailed Findings. Australian Institute of Health and Welfare, Canberra.

Breen, C., Degenhardt, L., White, B., Bruno, R., Chanteloup, F., Fischer, J., Johnston, J., Kinner, S.,

Moon, C., Proudfoot, P., Weekley, J., 2004. Australian Party Drug Trends 2003: Findings from the

Party Drugs Initiative (PDI). National Drug & Alcohol Research Centre, UNSW Australia, Sydney.

Brown, J., West, R., Beard, E., Michie, S., Shahab, L., McNeil, A., 2014. Prevalence and characteristics

of e-cigarette in Great Britain: findings from a general population survey of smokers. Addict. Behav.

39, 1120−1125.

Chapman, S., Wakefield, M., 2001. Tobacco control advocacy in Australia: Reflections on 30 years of

progress. Health Educ. Behav. 28, 274−289.

Chapman, S.L.C., Wu, L-T., 2014. E-cigarette prevalence and correlates of use among adolescents

versus adults: a review and comparison. J. Psychiatric Res. 54, 43−54.

Cho, J.H., Shin, E., Moon, S.S., 2011. Electronic-cigarette smoking experience among adolescents. J.

Adolesc. Health 49, 542−546.

Page 16 of 26

Accep

ted

Man

uscr

ipt

16

Choi, K., Forster, J., 2013. Characteristics associated with awareness, perceptions, and use of

electronic nicotine delivery systems among young US Midwestern adults. Am. J. Public Health 103,

556−561.

Christensen, T., Welsh, E., Faseru, B., 2014. Profile of e-cigarette use and its relationship with

cigarette quit attempts and abstinence in Kansas adults. Prev. Med. 69, 90−94.

Clare, P., Bradford, D., Courtney, R., Martire, K., Mattick, R., 2014. The relationship between

socioeconomic status and ‘hardcore’ smoking over time – greater accumulation of hardened

smokers in low-SES than high-SES smokers. Tob. Control 23, e133−e138.

Cogger, S., McKetin, R., Ross, J., Najman, J., 2008. Methamphetamine Treatment Evaluation Study

(MATES): Findings from the Brisbane site. NDARC Technical Report No. 295. UNSW Australia, Sydney

Czoli, C.D., Hammond, D., White, C.M., 2014. Electronic cigarettes in Canada: prevalence of use and

perceptions among youth and young adults. Can. J. Public Health 105, e97−e102.

De Vogli, R., Santinello, M., 2005. Unemployment and smoking: does psychosocial stress matter?

Tob. Control 14, 389−395.

Dockrell, M., Morrison, R., Bauld, L., McNeill, A., 2013. E-cigarettes: prevalence and attitudes in

Great Britain. Nicotine Tob. Res. 15, 1737−1744.

Douglas, H., Hall, W., Gartner, C., 2015. E-cigarettes and the law in Australia, Aust. Fam. Physician 44,

415-418.

Genz, A., Kirk, G., Piggott, D., Mehta, S.H., Linas, B.S., Westergaard, R.P., 2015. Uptake and

acceptability of information and communication technology in a community-based cohort of people

who inject drugs: implications for mobile health interventions, JMIR MHealth UHealth 3, e70.

Page 17 of 26

Accep

ted

Man

uscr

ipt

17

Giovenco, D.P., Lewis, M.J., Delnevo, C.D., 2014. Factors associated with e-cigarette use: a national

population survey of current and former smokers. Am. J. Prev. Med. 47, 476−80.

Goniewicz, M.L., Lingas, E.O., Hajek, P., 2013. Patterns of electronic cigarette use and user beliefs

about their safety and benefits: an internet survey. Drug Alcohol Rev. 32, 133−140.

Goniewicz, M.L., Zielinska-Danch, W., 2012. Electronic cigarette use among teenagers and young

adults in Poland. Pediatrics 130, e879−885

Grana, R., Benowitz, N., Glantz, S.A., 2014. E-cigarettes: s scientific review. Circulation 129,

1972−1986.

Harrell, P.T., Simmons, V.N., Correa, J.B., Padhya, T.A., Brandon, T.H., 2014. Electronic nicotine

delivery systems (“e-cigarettes”): review of safety and smoking cessation efficacy. Otolaryngol. Head

Neck Surg. 151, 381−393.

Hirshbein, L.D., 2015. Smoking Privileges: Psychiatry, The Mentally Ill, And The Tobacco Industry In

America, Critical Issues In Health And Medicine. Rutgers University Press, Rutgers.

Jha, P., Ranson, M.K., Nguyen, S.N., Yach, D., 2002. Estimates of global and regional smoking

prevalence in 1995, by age and sex. Am. J. Public Health 92, 1002−1006.

Jorm, A.F., 2008. Association between smoking and mental disorders: results from an Australian

national prevalence survey. Aust. N.Z. J. Public Health 23, 245−248.

Kinnunen, J.M., Ollilia, H., El-Tayeb El-Amin, S., Pere, L.A., Lindfors, P.L., Rimpelä, A.H., 2014.

Awareness and determinants of electronic cigarette use among Finnish adolescents in 2013: a

population-based study. Tob. Control doi: 10.1136/tobaccocontrol-2013-051512.

Page 18 of 26

Accep

ted

Man

uscr

ipt

18

Kralikova, E., Novak, J., West, O., Kmetova, A., Hajek, P., 2013. Do e-cigarettes have the potential to

compete with conventional cigarettes? A survey of conventional cigarette smokers’ experiences with

e-cigarettes. Chest 144, 1609−1614.

Lippert, A.M., 2014. Do adolescent smokers use e-cigarettes to help them quit? The

sociodemographic correlates and cessation motivations of U.S. adolescent e-cigarette use. Am. J.

Health Promot. Epub 2014 Jun 26.

Mcketin, R., Sutherland, R., Ross, J., Najman, J., Mamun, A., Baker, A., Mattick, R., Rosenfeld, J.,

2010. Methamphetamine Treatment Evaluation Study (MATES): Three-Year Outcomes From The

Sydney Site, NDARC Technical Report No. 312, Sydney, UNSW Australia

Pepper, J.K., Brewer, N.T., 2014. Electronic nicotine delivery system (electronic cigarette) awareness,

use, reactions and beliefs: a systematic review. Tob. Control 23, 375−384.

Reinert, D.F., Allen, J.P., 2002. The Alcohol Use Disorders Identification Test (AUDIT): a review of

recent research. Alcohol. Clin. Exp. Res. 26, 272−279.

Regan, A.K., Promoff, G., Dube, S.R., Arrazola, R., 2013. Electronic nicotine delivery systems: adult

use and awareness of the ‘e-cigarette’ in the USA. Tob. Control 22, 19−23.

Richter, K.P., Ahluwalia, H.K., Mosier, M.C., Nazir, N., Ahluwalia J.S., 2002. A population-based study

of cigarette smoking among illicit drug users in the United States. Addiction 97, 861−869.

Ross, J., Teeson, M., Darke, S., Lynskey, M., Hetherington, K., Mills, K., Williamson, A., Fairbairn, S.,

2002. Characteristics of Heroin Users Entering Three Treatment Modalities in New South Wales:

Baseline Findings from the Australian Treatment Outcome Study (ATOS), NDARC Technical Report

No.139, UNSW Australia, Sydney.

Page 19 of 26

Accep

ted

Man

uscr

ipt

19

Saunders, J.B., Aasland, O.G., Babor, T.F., de la Fuente, J.R., Grant, M., 1993. Development of the

Alcohol Use Disorders Identification Test (AUDIT): WHO collaborative project on early detections of

persons with harmful alcohol consumption. Addiction 88, 793−804.

Sindicich, N., Burns, L. 2012. Australian Trends in Ecstasy & Related Drug Markets 2011: Findings

from the Ecstasy & Related Drugs Reporting System (EDRS). Australian Drug Trends Series No. 82.

National Drug and Alcohol Research Centre, UNSW Australia, Sydney.

Sindicich, N., Burns, L. 2015. Australian Trends in Ecstasy & Related Drug Markets 2014: Findings

from the Ecstasy & Related Drugs Reporting System (EDRS). Australian Drug Trends Series No. 136.

National Drug and Alcohol Research Centre, UNSW Australia, Sydney.

Stafford, J., Burns, L. 2012. Australian Drug Trends 2011. Findings from the Illicit Drug Reporting

System (IDRS). Australian Drug Trend Series No. 73. National Drug and Alcohol Research Centre,

UNSW Australia, Sydney.

Stafford, J., Burns, L. 2015. Australian Drug Trends 2014. Findings from the Illicit Drug Reporting

System (IDRS). Australian Drug Trend Series No. 127. Sydney: National Drug and Alcohol Research

Centre, UNSW Australia, Sydney.

Topp, L., Darke, S., Bruno, R., Fry, C., Hargreaves, K., Humeniuk, R., McAllister, R., O’Reilly, B.,

Williams, P., 2001. Australian Drug Trends 2000: Findings of the Illicit Drug Reporting System.

National Drug & Alcohol Research Centre, UNSW Australia, Sydney.

Trehy, M.L., Ye, W., Hadwiger, M.E., Moore, T.W., Allgire, J.F., Woodruff, J.T., Ahadi, S.S., Black, J.C.,

Westenberger, B.J., 2011. Analysis of electronic cigarette cartridges, refill solutions, and smoke for

nicotine and nicotine related impurities. J. Liq. Chromatogr. Relat. Technol. 34, 1442–1458.

Tucker, J.S., Shadel, W.G., Golinelli, D., Ewing, B., 2014. Alternative tobacco product use and smoking

cessation among homeless youth in Los Angeles county. Nicotine Tob. Res. 16, 1522−1526.

Page 20 of 26

Accep

ted

Man

uscr

ipt

20

Vardavas, C.I., Filippidis, F.T., Agaku, I.T., 2015. Determinants and prevalence of e-cigarette use

throughout the European Union: a secondary analysis of 26 566 youth and adults from 27 countries.

Tob. Control 24, 442-448. doi: 10.1136/tobaccocontrol-2013-051394.

Vickerman, K.A., Carpenter, K.M., Altman, T., Nash, C.M., Zbikowski, S.M., 2013. Use of electronic

cigarettes among state tobacco cessation quitline callers. Nicotine Tob. Res. 15, 1787−1791.

World Health Organization (WHO), 2003. Gender, Health and Tobacco. Geneva, Department of

Gender and Women’s Health, WHO, Geneva.

Yong, H-H., Borland, R., Balmford, J., McNeill, A., Hitchman, S., Driezen, P., Thompson, M.E., Fong, G

Cummings, K.M. 2014. Trends in e-cigarette awareness, trial, and use under the different regulatory

environments of Australia and the UK. Nicotine Tob. Res. doi: 10.1093/ntr/ntu231.

Zhu, S.H., Sun, J.Y., Bonnevie, E., Cummins, S.E. Gamst, A. Yin, L., Lee, M., 2014. Four hundred and

sixty brands of e-cigarettes and counting: Implications for product regulation. Tob. Control 23, iii3-9.

doi: 10.1136/tobaccocontrol-2014-051670.

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Figure 1

##

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Table 1: Tobacco and e-cigarette use amongst PWID and RPU, 2015

PWID

N=888

%

RPU

N=763

%

OR (95% CI) p value

Tobacco

Lifetime use 97.3 92.1 3.08 (1.90-4.99) <0.001

Recent use# 92.2 82.4 2.53 (1.86-3.44) <0.001

Daily use 87.0 39.4 10.32 (8.08-13.17) <0.001

E-cigarettes

Lifetime use 31.5 57.0 0.35 (0.28-0.42) <0.001

Recent use# 18.1 33.7 0.44 (0.35-0.55) <0.001

Single day of use 26.2 32.5 1.36 (0.88-2.10) 0.168

Daily use 1.5 1.3 1.12 (0.49-2.57) 0.785 Note: OR = odds ratio; CI=confidence interval; PWID = people who inject drugs; RPU = regular psychostimulant users #Recent use = use in the past six months

Table 2: Use of e-cigarettes as a smoking cessation tool amongst PWID and RPU#, 2015

PWID

(n=151)

RPU

(n=252)

OR (95% CI) p value

Used as a smoking cessation tool % 54.3 27.8 3.09 (2.03-4.71) <0.001

E-cigarette contained: (n=140) (n=237)

Nicotine % 67.1 79.7 0.52 (0.32-0.83) 0.006

Cannabis % 8.6 4.2 2.13 (0.90-5.06) 0.082

Neither % 27.1 19.8 1.51 (0.92-2.46) 0.101 Note: OR = odds ratio; CI=confidence interval; PWID = people who inject drugs; RPU = regular psychostimulant users

#Amongst participants who had used e-cigarettes in the past six months

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Table 3: Correlates of recent# e-cigarette use amongst PWID, 2015

Note: AOR=adjusted odds ratio; CI=confidence interval; NPS=new psychoactive substances; K10=Kessler 10 (K10) Psychological Distress Scale; AUDIT= Alcohol Use Disorders Identification Test. *denotes significance using the Benjamini-Hochberg procedure;

#in the six months preceding interview; ~excludes synthetic cannabinoids.

a Univariate analysis were conducted, with adjusted residuals presented in brackets for significant variables;

b Multivariate analyses were conducted using significant variables from the RPU univariate comparisons (NPS use and polydrug use), along with significant variables from the PWID univariate comparisons (synthetic

cannabis use, employment status and polydrug use). Age, sex and mental health status were also included in the model due to their established association with tobacco use. Tobacco use was included in the model

due to its established connection with e-cigarette use; no e-cigarette use was the reference category.

E-cigarette use past six monthsa Multivariate

b

None

(n=727)

<6 days

(n=89)

≥6 days

(n=68)

p-value <6 days use

AOR (95% CI; p-value)

≥6 days use

AOR (95% CI; p-value)

Age (<25) % 1.5 1.1 2.9 0.623 0.84 (0.10-6.82; 0.867) 1.39 (0.28-6.90; 0.684)

Sex (male) % 67.0 69.7 63.2 0.697 1.28 (0.74-2.20; 0.385) 0.96 (0.54-1.71; 0.899)

Age first injected (<18) % 45.0 53.9 45.6 0.283

Completed high school (≥year 10) % 69.6 75.3 75.0 0.377

Tertiary qualifications % 55.5 (-2.3) 60.7 (0.7) 72.1 (2.6) 0.024

In a relationship % 35.1 28.1 45.6 0.075

Unemployed % 85.3 (3.3) 76.4 (-1.9) 72.1 (-2.6) 0.004* 0.44 (0.25-078; 0.005) 0.50 (0.27-0.92; 0.027)

K10 score ≥22 % 54.5 54.4 52.3 0.942

Mental health problem % 46.8 45.2 52.5 0.661 0.96 (0.58-1.58; 0.861) 1.18 (0.69-2.03; 0.542)

Daily tobacco use#

% 86.2 92.1 88.2 0.280 3.85 (1.18-12.60; 0.026) 1.22 (0.53-2.81; 0.643)

Daily cannabis use#

% 31.2 32.6 29.4 0.913

Any NPS use#~

% 2.5 4.5 5.9 0.186 1.92 (0.60-6.17; 0.276) 2.08 (0.65-6.67; 0.217)

Synthetic cannabis use#

% 6.5 (-3.4) 18.0 (3.7) 10.3 (0.8) 0.001* 2.36 (1.12-4.96; 0.024) 1.18 (0.49-2.86; 0.708)

Poly drug use (>5 drug classes#) % 39.3 (-3.9) 51.7 (1.9) 61.8 (3.4) <0.001* 1.09 (0.64-1.86; 0.750) 2.30 (1.30-4.08; 0.004)

AUDIT score ≥5 % 50.2 44.8 60.8 0.217

Opioid dependence % 61.4 62.1 68.2 0.551

Stimulant dependence % 30.7 39.8 34.3 0.206

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Table 4: Correlates of recent# e-cigarette use amongst RPU, 2015

Note: AOR=adjusted odds ratio; CI=confidence interval; NPS=new psychoactive substances; K10= Kessler 10 (K10) Psychological Distress Scale; AUDIT= Alcohol Use Disorders Identification Test. *denotes significance using the Benjamini-Hochberg procedure; adjusted residuals are in brackets for significant variables;

#in the six months preceding interview; ~excludes synthetic cannabinoids.

a Univariate analysis were conducted, with adjusted residuals presented in brackets for significant variables;

E-cigarette use past six monthsa Multivariate

b

None

(n=506)

<6 days use

(n=160)

≥6 days use

(n=96)

p-value <6 days use

AOR (95% CI; p-value)

≥6 days use

AOR (95% CI; p-value)

Age (<25) % 74.9 79.4 76.0 0.513 1.15 (0.73-1.81; 0.541) 1.03 (0.60-1.77; 0.907)

Sex (male) % 61.7 63.7 63.5 0.863 0.98 (0.67-1.45; 0.930) 0.95 (0.59-1.54; 0.846)

Age first tried ecstasy (<18) % 48.5 48.1 53.1 0.687

Completed high school (≥year 10) % 98.0 98.8 99.0 0.711

Tertiary qualifications % 46.9 42.1 50.0 0.425

In a relationship % 35.6 34.4 47.9 0.056

Unemployed % 11.9 11.3 18.8 0.150 0.90 (0.50-1.63; 0.729) 1.43 (0.77-2.66; 0.258)

K10 score ≥22 % 29.5 37.3 35.4 0.131

Mental health problem % 34.1 36.5 42.7 0.267 1.01 (0.68-1.49; 0.980) 1.24 (0.77-1.98; 0.374)

Daily tobacco use#

% 36.6 (-2.2) 40.0 (0.2) 52.6 (2.8) 0.013 0.98 (0.67-1.44; 0.926) 1.58 (0.99-2.51; 0.053)

Daily cannabis use#

% 17.6 (-2.3) 23.3 (1.1) 27.4 (1.9) 0.048

Any NPS use# ~

% 31.3 (-4.6) 48.1 (3.3) 49.0 (2.6) <0.001* 1.43 (0.95-2.15; 0.085) 1.36 (0.82-2.24; 0.233)

Synthetic cannabis use#

% 4.7 (-2.7) 10.6 (2.4) 8.3 (0.8) 0.022 1.94 (0.99-3.83; 0.055) 1.24 (0.52-2.96; 0.626)

Poly drug use (>5 drug classes#) % 32.0 (-6.50) 55.0 (4.30) 59.4 (4.10) <0.001* 2.15 (1.43-3.23; <0.001) 2.47 (1.49-4.09; <0.001)

AUDIT score ≥8 % 78.1 80.6 81.3 0.665

Ecstasy dependence % 18.7 20.4 20.2 0.860

Binged on stimulant drug % 29.6 37.5 37.5 0.091

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b Multivariate analyses were conducted using significant variables from the RPU univariate comparisons (NPS use and polydrug use), along with significant variables from the PWID univariate comparisons (synthetic

cannabis use, employment status and polydrug use). Age, sex and mental health status were also included in the model due to their established association with tobacco use. Tobacco use was included in the model

due to its established connection with e-cigarette use; no e-cigarette use was the reference category.

Highlights � Tobacco was consumed by 92% of people who inject drugs (PWID) and 82% of regular psychostimulant users (RPU) in the preceding six months.

� One-third (34%) of RPU and 18% of PWID had used e-cigarettes in the past six months.

� Higher levels of polydrug use was associated with e-cigarette use among both samples.

� The majority (54%) of PWID reported using e-cigarettes as a smoking cessation tool.

� RPU appear to being using e-cigarettes for experimental or recreational purposes.

Conflict of interest

No conflicts declared.

Role of funding sources

The Illicit Drugs Reporting System and the Ecstasy and Related Drugs Reporting System are funded by the Australian Government Department of Health

under the Substance Misuse Prevention and Service Improvement Grants Fund (RG123583). The funding source played no role in the study design; in the

collection, analysis and interpretation of data; in the writing of the report; or in the decision to submit the paper for publication.

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Contributors

This work was undertaken by the National Drug and Alcohol Research Centre in Australia. All authors contributed to the writing and review of the

manuscript.