Research objectives

This chapter uses data from Waves 1, 2 and 3 of TTM to detail the use of illegal substances among males in Australia who were aged 18-57 at Wave 1 (2013/14), and to investigate changes in drug use among this group over time. Specifically, it aimed to:

• detail the prevalence and frequency of use of:
  • a. cannabis, ecstasy [3,4-methylenedioxy-methamphetamine (MDMA)], meth/amphetamine ('speed' powder and crystal methamphetamine/'ice') and cocaine among adult Australian males across three time points (2013/14, 2015/16 and 2020/21), and compare to prevalence estimates from the National Drug Strategy Household Survey (NDSHS; AIHW, 2020) for the same period
  • b. ketamine, steroids and GHB, hallucinogens, inhalants, synthetic cannabis, anabolic steroids and heroin among Australian men in 2020/21 (because items regarding these substances were first included in the Wave 3 survey).
• investigate the effect of cannabis use on the experience of depressive symptoms over time
• identify socio-demographic and psychosocial factors associated with past-year cocaine use among adult males.

Method

This section describes the key measures and data analysis techniques used to address the above objectives. Information regarding the overall methodology of the TTM study is detailed elsewhere (e.g. Bandara, Howell, Silbert, & Daraganova, 2021; Swami et al., 2022).

Measures

Drug use

TTM participants have been surveyed about their use of licit and illicit substances at each data collection wave. Questions varied over the first three waves with regard to the types of drugs participants were surveyed about:

• Wave 1 (2013/14): Cannabis/marijuana, 'amphetamines' (speed, crystal methamphetamine/ice), ecstasy, cocaine, heroin, other opiates (methadone, morphine, pethidine) for non-medical reasons.
• Wave 2 (2015/16): Cannabis/marijuana, amphetamines (speed, crystal methamphetamine/ice), ecstasy, cocaine.
• Wave 3 (2020/21): Cannabis/marijuana, ice/crystal methamphetamine, other meth/amphetamine (speed and other forms), cocaine, ecstasy, GHB, hallucinogens, heroin, inhalants, ketamine, synthetic cannabis, other psychoactive/synthetic drugs, non-medical use of anabolic steroids. Participants were also able to nominate 'other' drugs they had ever and recently used.

At Waves 1 and 2, adult TTM participants were asked how many times (frequency) they had used each drug in the past year and during their lifetimes.

At Waves 1 and 2, participants were asked about their use of 'amphetamines', which included all meth/amphetamine types such as speed and crystal methamphetamine/'ice'. At Wave 3, participants were asked if they had used each of crystal methamphetamine/ice or other meth/amphetamine, which predominantly includes 'speed' powder in Australia (Australian Crime Intelligence Commission [ACIC], 2021; Scott, Caulkins, Ritter, Quinn, & Dietze, 2015). These two groups were combined to generate the 2020/2021 meth/amphetamine figures presented here.

At Wave 3, participants were asked whether they had ever used different drug types, the age they first used each drug, frequency of drug use in the past year, and frequency of drug use in the past four weeks.

Table S3.1 in the supplementary materials lists the drug types asked of adult TTM participants at each data collection wave. The TTM data dictionary and study questionnaires, available at tentomen.org.au, also provide the exact questions and information collected from TTM participants relating to illicit drug use.

Comparisons with national estimates

Australia's NDSHS is a triennial, national, serial cross-sectional survey of alcohol and other substance use (AIHW, 2020). National prevalence estimates for use of cannabis, cocaine, ecstasy and meth/amphetamine among Australian males were calculated from NDSHS datasets from surveys undertaken in 2013, 2016 and 2019, which corresponded somewhat with Waves 1-3 of TTM (2013/14, 2015/16 and 2020/21, respectively). Tables S3.2-S3.5 in the supplementary materials provide estimates with 95% confidence intervals for the following age categories calculated from TTM and NDSHS: 18-24, 35-44, 45-54 and 55-64 years.

Depression

Experience of depressive symptoms during the two-week period prior to interview was assessed among adult TTM participants at each wave using the Patient Health Questionnaire (PHQ-9; Kroenke, Spitzer, & Williams, 2001). The total continuous PHQ-9 score range is 0-27, with higher scores indicating greater experience of depressive symptoms. Cut points allow for the classification of respondents into five different depression categories: No or minimal depression (0-4), mild (5-9), moderate (10-14), moderately severe (15-19), and severe depression (20-27).

PHQ-9 findings for Waves 1 and 2 of TTM have been published previously (e.g. Swami, Prattley, Terhaag, Rowland, & Quinn, 2021; Terhaag, Quinn, Swami, & Daraganova, 2020; Van Doorn, Teese, & Gill, 2021), and chapter 2 of this report provides further detail on the distribution of PHQ-9 scores/depression categories among the TTM cohort over Waves 1-3.

Analysis

Survey data from the first three waves of TTM (2013/14, 2015/16 and 2020/21; i.e. all available waves at the time of writing) were used in analyses to address the research objectives. Specific analytical approaches to address each objective are outlined below.

Objective 1

Descriptive analyses investigated and compared the estimated prevalence and uptake of cannabis, cocaine, ecstasy and meth/amphetamine use among adult Australian males who were initially aged 18-57 in 2013/14 (i.e. they comprised the 'adult' sub-cohort of the TTM sample at Wave 1/recruitment) at each of the three data collection time points. For these analyses, prevalence statistics provided in relation to these four drugs were derived from a 'balanced' sample to enable comparisons between time-points among the same group of men; that is, adult TTM participants who provided valid self-report data regarding their lifetime and past-year use of these specific drugs at all three waves of data collection between 2013 and 2021. Significant changes in prevalence of use across each wave were determined by non-overlapping 95% confidence intervals (refer to Table 3.1).

Use frequency estimates and age statistics for males who used each of these four drugs in 2020/21 were calculated using survey responses from all participants who provided valid data at Wave 3.

Prevalence estimates for the use of cannabis, cocaine, ecstasy and meth/amphetamine according to both TTM and NDSHS were calculated for each time period by age group (see Tables S3.2-S3.5 in the supplementary materials). These estimates were not from a balanced TTM sample; i.e. all participants with valid data at each wave were included, regardless of their participation status at other waves. This was to enhance comparability with the NDSHS which has a serial cross-sectional study design. All TTM estimates were calculated using raked cross-sectional population weights; 2011 census-based Statistical Areas Level 1 (SA1) and Australian Statistical Geography Standard (ASGS) codes were used for Wave 1, and 2016 census-based SA1 and ASGS codes for Waves 2-3. All estimates are presented for completeness but some have a degree of uncertainty due to relatively small sample sizes (cell sizes of n < 50 are indicated by '#' in these tables). The true difference between estimates obtained from TTM and NDSHS may therefore vary from that reported here.

Descriptive statistics were also calculated to determine the prevalence of use of GHB, hallucinogens, ketamine, inhalants, synthetic cannabis, anabolic steroids and heroin in 2020/21.

Objective 2

Bivariate analyses were first undertaken to investigate cross-sectional associations between cannabis use and the experience of depressive symptoms at each data collection wave. Three sequential multivariable growth curve models¹ were then developed to examine the effect of recent cannabis use frequency on depression over time (using age in years as a proxy for time), with the second and third models controlling for key socio-demographic and psychosocial factors:

• Time-variant control factors/variables (i.e. those measured at multiple time points) included residential location, employment status, highest level of education, level of personal wellbeing, cigarette smoking status and relationship status.
• Time-invariant control factors/variables (i.e. those measured at baseline/Wave 1, as defined above) included Aboriginal and Torres Strait Islander identity, country of birth (Australia vs elsewhere), culturally and linguistically diverse status, sexual identity and level of conformity to masculine norms.²

The first (Model 1) established the relationship between depression (continuous PHQ-9 score) and age (a proxy for time). Model 2 controlled for the socio-demographic and psychosocial variables listed above. The final model (Model 3) incorporated the main variable of interest; namely, frequency of marijuana use, and included an interaction term between marijuana use and age.

Likelihood ratio tests were used to compare models. To account for the inter-correlation and clustering of repeated measures for individual participants, a random intercept for individuals using participants' unique identifier was included in the model. The random intercept also accounts for participants experiencing different levels of depressive symptoms at baseline.

Objective 3

A bivariate model was first developed to determine whether there was an unadjusted association between age (a proxy for time) and use of cocaine in the past year. A multivariable multilevel mixed-effects logistic regression model was then generated to investigate key socio-demographic and psychosocial factors associated with past-year cocaine use among adult TTM participants over this period.

Time-variant factors included in the model were: household income, residential location, highest level of education, personal wellbeing, employment status and relationship status.

Time-invariant factors included Aboriginal and Torres Strait Islander identity, country of birth (Australia vs elsewhere), culturally and linguistically diverse status and sexual identity.

Findings

Drug use prevalence

Drug use prevalence 2013-20: Cannabis, cocaine, ecstasy, meth/amphetamine

The prevalence of past-year use of four key illicit drugs - cocaine, ecstasy, cannabis and meth/amphetamine - at three time points between 2013 and 2021 among Australian males who were initially aged 18-54 years in 2013/14 (Wave 1 of TTM) is detailed in Table 3.1. Findings regarding each of these four drugs are detailed in subsections below. Prevalence statistics provided throughout much of this section were derived from TTM participants who provided valid self-report data regarding their lifetime and past-year drug use at all three waves of data collection between 2013 and 2020; exceptions include estimates relating to drug use frequency and the age of people using drugs in 2020/21.

Note: Participants were excluded if they did not have valid drug use data for all three waves. 
Source: TTM data, Waves 1-3, adult cohort, balanced sample for each individual drug, weighted

Cannabis

Of the illicit substances examined across all three waves of TTM (see Box 3.1), cannabis was the most commonly used among adult Australian men between 2013/14 and 2020/21 (Table 3.1).

In 2013/14, over half (54%) of Australian males aged 18-57 were estimated to have ever used cannabis. A further 6% of males in this age group initiated cannabis use over 2013-20/21.

The prevalence of past-year cannabis use among this group remained relatively stable between 2013/14 and 2020/21; around 17% of Australian males who were aged 18-57 in 2013/14 were estimated to have engaged in past-year use of cannabis at three time points over this period.

Cannabis use in 2020/21

In 2020/21, most Australian men aged 18-63 who had used cannabis in the past year had done so infrequently; overall, around 40% had used the drug once or twice in the past year, and 16% had done so every few months. Figure 3.1 shows that infrequent use (once/twice per year) was the most common cannabis consumption pattern among this group regardless of age. Nevertheless, older Australian men who had used cannabis in the past year reported more frequent consumption patterns than younger men; for example, around one-fifth (20%) of males aged 45-54 who had used cannabis in the past year did so on a daily basis, compared to about 8% of those aged 18-24.

The average age of Australian men aged 18-63 who used cannabis in the past year was 34 (SD = 12.5). In 2020/21, approximately 10% (95% CI [9.2, 11.4]) of Australian males aged 18-63 were estimated to have used cannabis in the past four weeks.

Figure 3.1: Estimated frequency of cannabis use in the past year among Australian men aged 18-63 who had recently used the drug in 2020/21, by age group

Notes: N = 1,265 adult TTM participants who provided valid cannabis use data in 2020/21. 95% confidence intervals. 
Source: TTM data, Wave 3, weighted

Cocaine

In 2013/14, an estimated 12% of adult Australian males aged 18-54 had used cocaine at least once during their lifetimes. A further seven percent initiated cocaine use (i.e. used it for the first time) between 2013/14 and 2020/21.

Of the four illicit drugs TTM participants were surveyed about at the first three waves of TTM, cocaine was the only one to see a significant change in prevalence of past-year use among Australian men between 2013/14 and 2020/21; estimated past-year consumption of cocaine increased from around four percent of men aged 18-54 in 2013/14, to approximately seven percent of this group in 2020/21 (Table 3.1).

Cocaine use in 2020/21

As indicated below in Figure 3.2, in 2020/21, Australian men aged 18-63 typically used cocaine infrequently, regardless of age. Overall, the majority of men who had used cocaine in the past year had used it only once or twice (67%) or every few months (23%). Around 10% used the drug at least monthly. No TTM participants reported using cocaine on a daily basis in 2020/21.

Among men aged 18-63 in 2020/21, the average age of those who had used cocaine in the past year was 31 years (SD = 9.7). Around 3% (95% CI [2.1, 3.2]) of Australian men aged 18-63 were estimated to have used cocaine in the past four weeks in 2020/21.

Figure 3.2: Estimated frequency of cocaine use in the past year among Australian men aged 18-63 who had recently used the drug in 2020/21, by age group

Notes: N = 506 adult TTM participants who provided valid cocaine use data in 2020/21. No participants reported daily cocaine use at Wave 3. 
Source: TTM data, Wave 3, weighted

Ecstasy

In 2013/14, around 16% of Australian males aged 18-54 were estimated to have ever used ecstasy. A further 5% of males in this age group initiated ecstasy use over 2013-2020/21.

The prevalence of past-year ecstasy use among this group remained relatively low and stable between 2013/14 and 2020/21; as shown in Table 3.1, around 3%-5% of Australian males who were aged 18-54 in 2013/14 were estimated to have engaged in past-year use of ecstasy at three time points over this period.

Ecstasy use in 2020/21

Figure 3.3 shows that, in 2020/21, the majority of Australian men aged 18-63 who had used ecstasy in the past year used it infrequently; overall, 59% (95% CI [50.3, 66.6]) had only used it once or twice, and 27% (95% CI [20.2, 34.8]) every few months. Fourteen per cent (95% CI [9.5, 21.3]) of men who had used ecstasy in the past year had consumed it monthly or more often during that time but not daily.

Among men aged 18-63 in 2020/21, the average age of those who had used ecstasy was 28 years (SD = 8.7). Approximately 2% (95% CI [1.1, 2.1]) of Australian males aged 18-63 were estimated to have used ecstasy in the past four weeks in 2020/21.

Figure 3.3: Estimated frequency of ecstasy use in the past year among Australian men aged 18-63 who had recently used the drug in 2020/21, by age group

Notes: N = 355 adult TTM participants who provided valid ecstasy use data in 2020/21. No participants reported weekly or daily ecstasy use at Wave 3. 
Source: TTM data, Wave 3, weighted

Meth/amphetamine

In 2013/14, around 13% of Australian males aged 18-54 were estimated to have ever used meth/amphetamine. A further 3% of males in this age group initiated meth/amphetamine use over 2013-2020/21.

The prevalence of past-year meth/amphetamine use among Australian men remained relatively low and stable between 2013/14 and 2020/21. As shown in Table 3.1, around 3%-4% of Australian males who were aged 18-54 in 2013/14 were estimated to have engaged in past-year use of meth/amphetamine at three time points over this period. There were no significant differences in estimated prevalence of meth/amphetamine use at each time point.

Meth/amphetamine use in 2020/21

Estimated frequency of use of crystal methamphetamine/ice and other meth/amphetamine³ are reported separately here because, as the TTM sample was asked about recent consumption of each of these meth/amphetamine forms separately at Wave 3, in the event of participants using both forms it was not possible to determine overall frequency of any meth/amphetamine use.

In 2020/21, use of other meth/amphetamine among Australian men aged 18-63 who had used the drug in the past year was mainly infrequent. An estimated 69% (95% CI [55.3, 79.6]) of this group had used other meth/amphetamine only once or twice; a further 12% (95% CI [6.8, 20.6]) used it every few months. In comparison, an estimated 11% (95% CI [5.1, 22.0]) of Australian men used other meth/amphetamine about monthly, 7% (95% CI [2.1, 21.8]) 2-3 times per month, and 1.1% (95%CI [0.3, 4.0]) about once per week.

Estimated frequency of crystal methamphetamine/ice use in 2020/21 among Australian men who used the drug was higher than that for other meth/amphetamine. Around 41% (95% CI [25.2, 59.2]) of men who had used crystal methamphetamine/ice in the past year had used it only once or twice during that time, and a further 17% (95% CI [7.2, 35.7]) used it every few months. In comparison, 11% (95% CI [4.3, 24.1]) used it about monthly, 11% (95% CI [4.0, 25.4]) 2-3 times per month, 11% (95% CI [5.2, 21.5]) about once per week, and a further 10% (95% CI [3.5, 23.7]) every day.

In 2020/21, approximately 1% (95% CI [0.5, 1.0]) of Australian males aged 18-63 were estimated to have used meth/amphetamine in the past four weeks.

Drug use prevalence in 2020/21: GHB, hallucinogens, ketamine, inhalants, synthetic cannabis, anabolic steroids, heroin

Table 3.2 lists the lifetime and recent prevalence of use of other illicit substances among adult Australian males aged 18-63 in 2020/21, including GHB, hallucinogens generally (including LSD and magic mushrooms), inhalants generally (including glue, amyl nitrite and nitrous oxide), ketamine, steroids and synthetic cannabis. It also details age of first use/initiation and frequency of use of these drugs in the past year and past four weeks. TTM participants were asked about these drugs for the first time in 2020/21, and low numbers of participants reporting use of each drug type generally prevented further analyses beyond the descriptive statistics shown in Table 3.2.

Of the different drug types, hallucinogens were estimated to be the one most commonly used among Australian males aged 18-63 in 2020/21; around 16% had ever used hallucinogens, and 5% had done so in the past year.

The next most commonly used drug was ketamine; in 2020/21, approximately seven percent of Australian men had used ketamine at least once during their lives, with around three percent having done so in the past year. Six percent had ever used inhalants, with two percent having done so in the past year. Fewer men had ever used synthetic cannabis (4%), GHB (2%), heroin (1%) and steroids (1%).

Of the substances listed in Table 3.2, the youngest average age of initiation (around 21 years) was recorded for inhalants and hallucinogens. Inhalants were also the most frequently used drug type; around one-fifth of males who had used inhalants in the last year had done so on at least a monthly basis, compared to 12% of those who had used ketamine in the past year and eight percent of those who had used hallucinogens during that time (note: the differences between frequency of use for each drug are not significant due to overlapping confidence intervals, likely a result of relatively small numbers of TTM participants reporting such drug use).

Not shown in Table 3.2 due to low numbers of TTM participants reporting use: among Australian men aged 18-63 in 2020/21, around one percent were estimated to have ever used heroin. The mean age of heroin initiation among this group was about 24 years (SD = 8.3). Only one TTM participant reported any past-year heroin use, and none reported use in the past four weeks. NDSHS estimates indicate that 3.8% of adult Australian males aged 14 and over had used any illicit opioid in the past year (including heroin, non-medical use of pain-killers/pain-relivers, and non-medical use of methadone and buprenorphine), of which 3.4% had used heroin (AIHW, 2020).⁴

Notes: N = 7,675. CI = confidence interval; SD = standard deviation. ^a Among those who had ever used the drug; ^b Among those who had used in the past year; ^c Numbers of missing responses ranged from n = 8 (steroids) to n = 31 (both GHB and hallucinogens). 
Source: TTM data, Wave 3, adult cohort, weighted

The effect of cannabis use on the experience of depressive symptoms over time

Participants also self-reported on their lifetime and past-year use of cannabis at the first three waves of TTM data collection (see Box 3.2).

Cross-sectional associations between cannabis use and experience of depressive symptoms at three time points

With regard to the distribution of PHQ-9 categories among adult TTM participants according to their baseline age (the age at which participants first responded to the TTM survey; i.e. Wave 1 in 2013/14), most of the sample (88%) was classified as having no or minimal depression. Prevalence of no-minimal depression did vary somewhat by age, ranging from 83% of participants aged 18-24 year to 89% among those aged 45-55. Rates of moderate depression reduced from 11% among 18-24 year olds to 7% among those aged 45-55; likewise, 6% of 18-24 year olds were classified as having severe or very severe depression compared to 4% of the oldest age group. Figure S3.6 in the supplementary materials provides a visual representation of these data.

Cross-sectional associations between PHQ-9 scores and past-year cannabis use among adult Australian males at each of the three waves of TTM data collection are presented in Table 3.3 and Figure 3.4. At each time point, there was a significant association between any use of cannabis in the past year with greater levels of depressive symptoms (Table 3.3). For example, at each wave, significantly more men who had engaged in past-year cannabis use were classified as experiencing moderately severe to severe depressive symptoms compared to those who had not used cannabis during that time. Conversely, significantly fewer men who had used cannabis in the past year were classified as experiencing no or mild depressive symptoms.

Likewise, average PHQ-9 scores were significantly higher among men who had used cannabis at least weekly at each time point compared to those who had used cannabis less than weekly or not at all (Figure 3.4).

Overall, depression levels associated with recent cannabis use increased over the three time points; the proportion of respondents with no depressive symptoms who had recently smoked cannabis decreased, while the proportion of respondents with moderate or moderately severe symptoms increased.

Notes: Participants were excluded if aged less than 18 years in Wave 1 or they did not participate at all three waves. PHQ = Patient Health Questionnaire. 
Source: TTM data, Waves 1-3, adult cohort, balanced sample, weighted

Figure 3.4: Mean PHQ-9 score (range: 0-27) at each TTM data collection wave by frequency of past-year cannabis use among Australian males aged 18-55 in 2013/14

Notes: N > 6,273. Participants were excluded if aged less than 18 years in Wave 1 or they did not participate at all three waves. 
Source: TTM data, Waves 1-3, adult cohort, balanced sample, weighted

Multivariable model: Cannabis use frequency and depression (PHQ-9 score)

Table 3.4 shows results from three sequential multivariable growth curve models developed to examine the effect of recent cannabis use frequency on depression over time, using age in years as a proxy for time.

The significant, consistent, negative association between age and PHQ-9 score (coef. < -0.02) across Models 1-3 indicates that, as TTM participants aged, their experience of depressive symptoms tended to be lower on average; that is, the average adult male TTM participant had a decreasing PHQ-9 score as they aged.

Model 3 shows that, on average, men who used cannabis had higher depression scores, irrespective of age or other confounding variables. Indeed, when compared with men who had no recorded cannabis usage in the past year, those who used cannabis weekly or more had - on average - a 1.52 point higher depression score, after adjustment for related factors. On the total PHQ-9 continuous scale ranging from 0-27, this represents an average difference of around 6%.

Importantly, there was no interaction found between cannabis use frequency and age, suggesting that the association between cannabis use frequency and depression was not influenced by the age of respondent.

Notes: *** p < 0.001; **p < 0.01; *p < 0.05; ^a Remote/very remote and 'other' education excluded (n = 228 total); ^c Measured using the 22-item CMNI (Mahalik et al., 2003). A likelihood ratio test comparing Models 2 and 3 was found to be non-significant (p < 0.001), meaning that adding cannabis use frequency to Model 2 significantly improved the fit of the final model. 
Source: TTM data, Waves 1-3, adult cohort, balanced sample

Figure 3.5 shows estimated or predicted PHQ-9 scores (with 95% confidence intervals) by age/time and cannabis use frequency for Model 3. The Figure shows that, overall, PHQ-9 scores decline with age, regardless of cannabis use; that is, experience of depressive symptoms decreases as men grow older. However, a significant, consistent difference was observed in predicted PHQ-9 scores between men who had not used any cannabis in the past year and those who used at the highest frequency category (approximately weekly or more) during that time. This means that adult Australian males who use cannabis at least weekly have an increased likelihood of experiencing greater depressive symptoms compared to those who do not use cannabis at all.

Also of note was the lack of difference in estimated PHQ-9 scores between men who had only used cannabis once or twice in the past year and those who used cannabis more often than this but less than weekly.

Among men younger than their mid-forties, those who used cannabis relatively infrequently were estimated to have significantly higher PHQ-9 scores than those who did not use the drug at all. Among men between their mid-twenties to around 50, those who used cannabis at least weekly were estimated to have significantly higher scores than those who used the drug less frequently. Taking PHQ-9 categories into consideration (Kroenke et al., 2001), this could be a difference between experiencing no/minimal depression (PHQ-9 score: 0-4) and mild depression (PHQ-9 score: 5-9).

Figure 3.5: Predictive margins of PHQ-9 score (with 95% CIs) by cannabis use frequency and age among Australian males aged 18-55 in 2013/14, N = 10,791

Notes: Figure is a graphical representation of statistics calculated from predictions of Model 3 in Table 3.4. It depicts the adjusted mean of PHQ-9 score for each level of cannabis use frequency by average age. 
Source: TTM data, Waves 1-3, adult cohort, balanced sample

Socio-demographic and psychosocial factors associated with past-year cocaine use among adult males

TTM data presented in Table 3.1 of this chapter and NDSHS data presented in Table S3.3 in the supplementary materials indicate that the prevalence of recent cocaine use increased significantly among adult Australian males over the last decade.

In this section, some demographic and psychosocial factors associated with cocaine use over the first three waves of TTM data collection (2013/14 to 2020/21) among the study sample are identified.

The findings presented in Table 3.5 indicate that, overall, older age was associated with reduced odds of using cocaine (aOR = 0.93; 95% CI [0.91, 0.94]); specifically, on average, for each additional year of age, the odds of using cocaine decreased by 7%.

Further findings in Table 3.5 point to numerous characteristics associated with past-year cocaine use among the TTM sample. Higher income brackets were associated with cocaine use. Specifically, relative to a weekly household income of $959 per week or less (<$49,999/year), men who resided in households with a total weekly income of $2,880-$3,839 or >$3,840 had almost double (aOR = 1.93; 95% CI [1.26, 2.96]) and triple (aOR = 3.06; 95% CI [1.98, 4.72]) the odds of engaging in past-year cocaine use. Men who identified as non-heterosexual (i.e. homosexual or bisexual; aOR = 1.91; 95% CI [1.11, 3.28]) and men with greater conformity to masculine norms (aOR = 2.44; 95% CI [1.85, 3.22]) also had significantly greater odds of using cocaine in the past year.

Alcohol use was associated with cocaine use. Men who had drunk alcohol in the past year had over five times greater odds of having also consumed cocaine in the past year compared to those who had not drunk alcohol in the past year.

Factors associated with reduced odds of having recently used cocaine included being born outside of Australia (aOR = 0.69; 95% CI [0.48, 0.99]) and having a culturally and linguistically diverse background (aOR = 0.20; 95% CI [0.09, 0.43]). Residing in non-metropolitan areas of Australia was associated with around 75%-80% lower odds of recent cocaine use compared to living in a major city. Men in married or de facto relationships had about half the odds of men who had never married of recently engaging in cocaine use.

Notes: *** p < 0.001; **p < 0.01; *p < 0.05; ^a Remote/very remote and 'other' education excluded (n = 197 total); ^b Measured using the 22-item CMNI (Mahalik et al., 2003); ^c To account for the inter-correlation and clustering of repeated measures for individual participants, a random intercept for individuals using participants' unique identifier was included in the model. aOR = adjusted Odds Ratio; CI = Confidence Interval; p/a = per annum; p/w = per week. Prior to this final model, a bivariate model (not shown here) showed an unadjusted association between age (a proxy for time) and use of cocaine in the past year (OR = 0.92; 95% CI: 0.91, 0.93). 
Source: TTM data, Waves 1-3, adult cohort

Summary

The research presented in this Insights chapter is substantially unique in that it comprises analyses using data from a large, prospective cohort of adult Australian males that was representative of men aged 18-55 at the time of recruitment in 2013/14. There has been a paucity of such research examining illicit drug use among Australian men with an established, longitudinal, community-based sample of this scope; the data presented in this chapter therefore address a considerable gap in the Australian substance-use literature. Findings supplement existing data sources, including the NDSHS (AIHW, 2020), in providing a more comprehensive picture of drug use among men across the country over 2013/14-2020/21.

Reflecting trends seen in other recent studies focused on the general population (AIHW, 2020) and certain subgroups of people who use drugs (e.g. people who use ecstasy and related drugs; Sutherland et al., 2021), TTM data indicated that past-year use of cocaine increased significantly among adult males in Australia during this time period.

Corresponding with this change, law enforcement data have pointed to an increase in the weight of cocaine detections at the Australian border, in addition to record numbers of cocaine seizures and arrests nationwide in 2019/20 (ACIC, 2021). Importantly, a growth in cocaine use among Australia's general community has also resulted in a greater incidence of associated harms. For example, cocaine-related hospitalisations increased from 5.1 to 15.6 per 100,000 people between 2011/12 and 2017/18, and treatment episodes increased from 3.2 to 5.9 per 100,000 people between 2016/17 and 2017/18 (Man et al., 2021). In consideration of this, especially in the context of limited effective and available treatment options for people who use cocaine, Australian researchers have called for the evaluation of relatively uncommon treatment modalities (e.g. contingency management) in clinical settings (Farrell et al., 2019). Furthermore, these data collectively highlight a persistent need for promoting appropriate harm reduction options (e.g. reducing amount of drug used, identifying signs of psychosis) to reduce risks associated with heavy use and acute intoxication, in particular (Man et al., 2021).

One component of addressing cocaine use and related harms is identifying people who use the drug. Our findings indicated that factors associated with past-year cocaine use among Australian men included younger age, residing in households with greater combined incomes, living in major cities rather than inner or outer regional areas of the country, identifying as non-heterosexual, being single/never married, and past-year alcohol consumption. Such information could be beneficial to service providers seeking to identify Australian males who use cocaine, in addition to informing targeted education, referral and harm reduction initiatives.

Additional analyses presented in this chapter demonstrated that cannabis is the most commonly used illicit drug among Australian males. They also showed that more frequent (weekly or more) cannabis use was independently associated with experience of greater depressive symptoms among adult Australian males. This is consistent with some previous research; for example, a systematic review and meta-analysis by Lev-Ran and colleagues (2013) estimated that the odds of developing depression for people who used cannabis 'heavily' was higher than that for both people who engaged in 'light' cannabis use and those who did not use cannabis at all.

In addition to such mental health sequelae, research has associated regular/frequent cannabis use, early initiation and prolonged use with other adverse outcomes, including an increased likelihood of motor vehicle accidents and the development of dependence and cannabis use disorder (Feingold, Hoch, Weinstein, & Hall, 2021; Lopez-Pelayo et al., 2021). As jurisdictions worldwide move towards legalising both medicinal and recreational cannabis (UNODC, 2021), it is crucial that people who use cannabis, health professionals and service providers are aware of the possible adverse outcomes - mental health and other - of using cannabis, especially in relation to heavy or chronic use patterns and prolonged use over time. Intervening before people evolve to heavier (chronic, more frequent) patterns of use could be crucial to preventing associated harms.

It is important to note that although use of cannabis may lead to the onset or exacerbation of depressive symptoms, as indicated by findings presented in this chapter in addition to those of previous research (Asselin et al., 2022; Brodbeck et al., 2007; Feingold & Weinstein, 2021; Kosiba et al., 2019; Wallis et al., 2022), some men will use cannabis (and other drugs) to self-treat or medicate depression and other mental health disorders. The different temporal aspects of primary exposure (past-year cannabis use) vs outcome (depression/PHQ-9 score over the past two weeks) provides evidence that the former does lead to the latter. Regardless, clinical research is needed to better understand the potential therapeutic benefits of cannabis for treating depressive symptoms. Qualitative research could also provide insight into current contexts in which Australian men effectively use illicit cannabis and other substances to address symptoms of depression and other mental health disorders.

Future waves of TTM will afford further opportunities to examine longer-term patterns and outcomes of cannabis and other drug use, including maintenance of harmful use patterns and transitions to abstinence. Linkage with Medicare datasets will enable investigation of service utilisation patterns among different subgroups of people who use drugs.

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