Introduction: The shifting landscape in Australia's tobacco and cannabis policies and emerging new products and modes of administration may increase experimentation and the risks of addiction to these drugs. Methods: We analysed cross‐sectional data from the 2019 National Drug Strategy and Household Survey (n = 22,015) of Australians aged 14 and above. Latent class analysis was used to identify distinct groups based on types of tobacco and cannabis products used. The socio‐demographic, health‐rated correlates and past‐year substance use of each latent class was examined. Results: A four‐class solution was identified: co‐use of tobacco and cannabis (2.4%), cannabis‐only (5.5%), tobacco‐only (8.0%) and non‐user (84.0%). Males (odds ratio [OR] range 1.5–2.9), younger age (OR range 2.4–8.4), moderate to high psychological distress (OR range 1.3–3.0), using illicit substances in the last year (OR range 1.41–22.87) and high risk of alcohol use disorder (OR range 2.0–21.7) were more likely to be in the tobacco/cannabis use classes than non‐users. Within the co‐use class, 78.4% mixed tobacco with cannabis and 89.4% had used alcohol with cannabis at least once. Discussion and Conclusions: Approximately 16% of respondents used tobacco or cannabis, or both substances, and no major distinct subgroups were identified by the use of different product types. Mental health issues and the poly‐substance use were more common in the class who were co‐users of cannabis and tobacco. Existing policies need to minimise cannabis and tobacco‐related harms to reduce the societal burden associated with both substances.
Keywords: cannabis; cigarettes; joints; marijuana; tobacco
Tobacco and cannabis are the most commonly used psychoactive substances after alcohol in Australia [[
Co‐use of tobacco and cannabis is prevalent in many countries including Australia [[
In Australia, the regulatory environment for tobacco and cannabis is rapidly evolving. To reduce tobacco smoking, Australia has implemented plain packaging, health warnings on tobacco products, tobacco advertising bans [[
The study aims to use latent class analysis (LCA) to identify distinct groups within the Australian population based on 11 tobacco and cannabis products used. In addition, the socio‐demographic, general health, psychological distress and substance use correlate for each latent class is identified. The main benefit of the LCA approach is that it can group individuals according to the use of a wide range of tobacco and cannabis products, hence, allowing a more nuanced characterisation of how different products might be used in combination [[
Studies examining the patterns of poly‐substance use using LCA have found distinct subpopulations of tobacco and cannabis users that used different products in combination [[
The 2019 National Drug Strategy Household Survey (NDSHS) is a multi‐stage, stratified survey that is conducted triennially to assess the attitudes and behaviours in relation to drug use in Australia. The sampling frame includes respondents aged 14 and above in residential households across Australia. A total of 22,015 people (mean age 45.3 years, 42% male) were surveyed in 2019, with a response rate of 49%. Full details of the survey are described elsewhere [[
The four cannabis product variables analysed were: (i) joint; (ii) bong; (iii) edibles; and (iv) vapes. Respondents were asked if they had used cannabis in the last 12 months. If so, they were asked 'how have you used Marijuana/Cannabis?'. Options to this question were: 'Smoked as Joints', 'Smoked from a bong/pipe', 'Inhaled through a vaporising device' and 'By eating it'. Respondents could report more than one form of use. Four dichotomous variables were created for each method of use (yes vs. no for each variable).
The seven tobacco product variables analysed were: (i) manufactured cigarettes; (ii) roll‐your‐own cigarettes; (iii) e‐cigarettes; (iv) cigarillos; (v) cigars; (vi) water pipe tobacco; and (vii) other tobacco products. Respondents were asked 'how often, if at all, do you now smoke manufactured cigarettes, roll‐your‐own cigarettes, cigarillos, cigars, water pipe tobacco, or pipe tobacco?'. Response options to this question were 'Daily', 'At least weekly (but not daily)', 'Less often than weekly' or 'Not at all'. Six dichotomous variables were created for each tobacco product with any endorsement 'daily or at least weekly' or 'less often than weekly' to the frequency question coded as 'yes' to the specific tobacco product. The NDSHS also asked 'Which, if any, of the following products [chewing tobacco, snuff, snus, bidis] have you ever used, and which have you used in the last 12 months?'. Four dichotomous variables were created with endorsement to 'used in last 12 months' coded as 'yes'. Finally, the e‐cigarette question was based on 'How often, do you currently use electronic cigarette?'. Response options to this question were 'Daily', 'At least weekly (but not daily)', 'At least monthly (but not weekly)', 'Less than monthly', 'I used to use them but no longer use', 'I only tried them once or twice' or 'Not at all'. Endorsement to the first three responses was coded 'yes' to e‐cigarette use. Low prevalence (<0.5%) tobacco products (chewing tobacco, snuff, snus, bidis and pipe tobacco) were grouped as 'other tobacco product'.
The following variables were used in the analysis: sex ('male', 'female'), marital status ('never married', 'divorced, separated or widowed', 'currently married including de‐facto'), highest education attainment ('high school or less', 'certificate or diploma', 'bachelor degree or higher'), remoteness of the area of dwelling ('major cities', 'inner regional', 'outer regional, remote or very remote area'), country of birth ('Australia', 'not Australia') and current employment status ('not in labour force', 'unemployed', 'currently employed'). Household annual income were coded by dividing the income distribution into quartiles ('low income', 'low‐average', 'high‐average', 'high income').
Self‐rated general health was measured using the question 'In general, would you say your health is 'excellent', 'very good', 'good', 'fair', 'poor'?'. The Kessler psychological distress scale (K10), a 10‐item questionnaire based on respondents' anxiety and depressive symptoms in the past month, was used to assess the levels of psychological distress—('low', 'moderate', 'high or very high level') [[
Alcohol use status was assessed using the Alcohol Use Disorders Identification Test Consumption short‐form (AUDIT‐C) scale [[
Descriptive statistics were computed to estimate the weighted prevalence of tobacco and cannabis products. LCA is a statistical method for identifying unobservable or latent groups of respondents that are similar based on a set of observed categorical variables [[
We fitted a series of LCA models (2–5 classes). The optimal number of classes was determined using a range of information criteria (Akaike Information Criteria [AIC], Bayesian Information Criteria [BIC], Sample size adjusted BIC) and likelihood ratio test (Vuong‐Lo–Mendell–Rubin adjusted likelihood ratio test) and classification diagnostics (entropy and average posterior probabilities). Lower values in information criterion were preferred as it indicates a better balance between model fit and model parsimony. The Vuong‐Lo–Mendell–Rubin adjusted likelihood ratio test is also used to compare models with k classes versus k‐1 classes. A significant p‐value suggests that the model with k classes fit the data better than models with k‐1 classes [[
The correlates, patterns of substance use and health factors of the class membership were examined using multivariate multinomial logistic regressions in STATA17.0 [[
All analyses were weighted to adjust for differential probabilities of selection within households and to adjust for non‐response rates and to match the samples to the population sociodemographic distribution. Significance level, α, was set at 0.0029 (α = 0.05/17) to adjust for multiple comparisons. The codes for the analysis can be found at https://github.com/clim072/patterns-of-tobacco-and-cannabis-use.
Table 1 shows the prevalence of cannabis and tobacco use by product type. In the total sample, joints (9.4%) and manufactured cigarettes (11.2%) were the most common cannabis and tobacco products, respectively. Among current cannabis users, the most prevalent cannabis products were joints (82.3%) while the most prevalent tobacco products were manufactured cigarettes (32.6%). The most prevalent cannabis and tobacco products respectively among current tobacco users were joints (28.1%) and manufactured cigarettes (78.0%).
1 TABLE Prevalence of cannabis and tobacco product use.
Products Total sample ( Among current cannabis users Among current tobacco users % 95% CI % 95% CI % 95% CI I. Cannabis 2273 11.5 11.0 12.1 ‐ ‐ ‐ ‐ ‐ ‐ Joint 1825 9.4 8.9 9.9 1825 82.3 80.3 84.3 809 28.1 26.1 30.2 Bong 1623 8.2 7.7 8.7 1623 71.8 69.4 74.1 780 26.9 24.9 28.9 Edible 1105 5.6 5.2 6.0 1105 48.8 46.2 51.4 480 16.3 14.7 18.0 Vape 140 0.8 0.6 1.0 140 7.1 5.7 8.5 58 2.2 1.5 2.8 II. Tobacco/nicotine 3221 14.6 14.0 15.2 ‐ ‐ ‐ ‐ ‐ ‐ Manufactured cigarettes 2420 11.2 10.7 11.7 717 32.6 30.1 35.0 2420 78.0 76.2 79.8 Roll‐your‐own cigarettes 1250 6.0 5.6 6.4 547 25.6 23.3 28.0 1250 41.7 39.5 43.9 E‐cigarettes 393 2.0 1.8 2.3 146 7.8 6.3 9.3 233 7.9 6.7 9.1 Cigarillos 328 1.5 1.3 1.7 101 4.6 3.4 5.7 328 10.2 8.9 11.5 Cigars 155 0.8 0.6 0.9 56 2.5 1.7 3.2 155 5.3 4.2 6.3 Water pipe tobacco 116 0.6 0.4 0.7 79 3.5 2.7 4.4 116 4.1 3.3 5.0 Other tobacco products 210 1.1 0.9 1.2 87 3.9 2.9 4.9 210 4.6 3.7 5.5 Chewing tobacco 107 0.5 0.4 0.7 34 1.6 1.0 2.2 59 2.1 1.5 2.7 Pipe tobacco 68 0.3 0.2 0.4 36 1.3 0.8 1.9 68 2.0 1.4 2.6 Snuff 56 0.3 0.2 0.4 23 1.4 0.8 2.1 31 1.3 0.8 1.8 Snus 54 0.3 0.2 0.4 24 1.2 0.7 1.7 29 1.1 0.6 1.5 Bidis 48 0.3 0.2 0.4 14 0.8 0.35 1.3 29 1.2 0.7 1.7
1 Abbreviation: CI, confidence interval.
- 2 a Weighted prevalence.
- 3 b Assessed among those with current cannabis use (use in the last year).
- 4 c Assessed among current tobacco smokers (excluding e‐cigarette use) ‐ comprised of daily and non‐daily smokers.
- 5 d Chewing tobacco, pipe tobacco, snuff, snus, bidis.
Table S2 shows the model fit statistics for a 2–5 class solution based on the latent class model on patterns of cannabis and tobacco use. The AIC, BIC, Sample Size Adjusted BIC statistics showed the lowest value for the 5‐class solution but the difference between the 4‐ and 5‐class solution is very small, suggesting both solutions fit the data well. The LRM‐LRT indicated that the 5‐class solution did not fit the data better than the 4‐class solution. Entropy and average posterior probabilities from the 4‐class solution were close to 1, indicating clear class separation. Given that the classes from the 4‐class solution were clearly interpretable and meaningful, the 4‐class solution was chosen as the most optimal.
Figure 1 shows the item‐response probabilities for each tobacco and cannabis item. Item response probabilities greater than or equal to 0.5 are considered to be high probabilities [[
Table 2 shows the multinomial logistic regression model using class membership as the outcome and socio‐demographic correlates, general health, psychological distress, past‐year substance use and risk of AUD as the predictors. Non‐user was used as the reference for the outcome while females, persons aged 60+, currently married, high household income, major cities, bachelor or higher education, living outside of Australia, currently employed, excellent general health, low psychological distress, did not endorse past‐year substance use, and no or low risk for AUD were specified as the reference groups for the predictors.
2 TABLE Associations between latent class memberships and socio‐demographics, health‐rated correlates and history of past‐year substance use with non‐use as reference group (Class 4).
Co‐use tobacco and cannabis (Class 1) Cannabis (Class 2) Tobacco (Class 3) OR 99.7% CI OR 99.7% CI OR 99.7% CI Sex (ref: female) Male 2.9 1.8 4.8 F = 43.7 1.5 1.2 1.9 F = 30.4 1.7 1.4 2.1 F = 61.9 Age group, years (ref: 60+) 14–29 8.4 2.4 29.6 F = 9.2 4.9 2.8 8.6 F = 27.2 2.4 1.5 3.9 F = 55.2 30–39 7.8 2.3 26.9 4.9 2.8 8.4 4.1 2.8 6.2 40–59 7.0 2.2 22.3 4.0 2.4 6.7 4.0 2.8 5.6 Marital status (ref: currently married) Never married 1.9 1.0 3.5 F = 5.2, p = 0.006 1.9 1.4 2.7 F = 23.0 1.6 1.2 2.2 F = 24.7 Divorced/separated/widowed 1.8 0.9 3.9 1.7 1.2 2.5 1.8 1.4 2.4 Household income (ref: 1st quartile: high) 2nd quartile (high‐average) 2.1 1.0 4.5 F = 4.6 1.2 0.9 1.8 F = 2.0, p = 0.110 1.7 1.2 2.5 F = 12.1 3rd quartile (low average) 2.1 0.9 4.5 1.3 0.9 1.9 1.6 1.2 2.3 4th quartile (low) 2.6 1.1 6.3 1.3 0.8 2.1 2.1 1.4 3.1 Remoteness (ref: major cities) Inner regional 1.1 0.6 2.0 F = 2.7, p = 0.071 1.2 0.9 1.6 F = 3.6, p = 0.027 1.4 1.1 1.8 F = 16.3 Outer regional, remote or very remote 1.6 0.9 3.0 1.3 0.9 1.8 1.6 1.2 2.1 Highest education attainment (ref: bachelor or higher) High school or less 1.6 0.8 3.2 F = 5.1, p = 0.006 1.0 0.7 1.4 F = 2.3, p = 0.098 2.3 1.7 3.3 F = 30.3 Certificate or diploma 2.1 1.0 4.1 1.2 0.9 1.6 2.2 1.6 3.0 Country of birth (ref: outside of Australia) Australia 1.4 0.7 2.9 F = 1.7, p = 0.188 1.1 0.8 1.5 F = 1.6, p = 0.202 0.9 0.7 1.1 F = 2.1, p = 0.144 Employment (ref: currently employed) Not in labour force 0.9 0.4 2.0 F = 3.4, p = 0.034 0.7 0.5 1.1 F = 2.2, p = 0.106 0.8 0.6 1.2 F = 6.4 Unemployed 1.7 0.9 3.3 0.9 0.6 1.5 1.4 1.0 1.9 General health (ref: excellent) Very good 1.1 0.5 2.4 F = 7.6 1.2 0.9 1.7 F = 2.3, p = 0.059 2.1 1.3 3.3 F = 36.7 Good 2.2 1.0 4.8 1.3 0.9 1.8 3.7 2.3 5.8 Fair 3.4 1.4 8.5 1.6 1.0 2.7 5.2 3.1 8.7 Poor 3.3 0.7 15.9 1.6 0.6 4.1 6.7 3.5 12.9 Psychological distress (ref: K10 low) K10—moderate 1.6 0.9 2.8 F = 15.8 1.3 1.0 1.8 F = 11.6 1.0 0.8 1.3 F = 7.6 K10—high to very high 3.0 1.7 5.3 1.6 1.2 2.3 1.4 1.1 1.9 Past‐year substance use (ref: no) Ecstasy 3.5 1.4 8.5 F = 17.1 3.2 1.6 6.4 F = 26.6 1.6 0.7 3.9 F = 2.9, p = 0.089 Meth/amphetamine 21.7 5.3 88.6 F = 42.3 9.5 2.5 36.3 F = 24.9 11.0 3.3 36.8 F = 35.0 Cocaine 8.9 4.4 18.0 F = 86.2 4.9 3.0 8.0 F = 92.9 3.2 1.8 5.6 F = 37.3 Hallucinogens 10.2 3.1 33.7 F = 33.4 8.8 3.2 24.0 F = 41.1 2.2 0.6 8.0 F = 3.0, p = 0.084 Inhalants 1.8 0.5 5.6 F = 2.0, p = 0.152 1.5 0.6 3.7 F = 2.1, p = 0.150 1.2 0.5 3.2 F = 0.4, p = 0.491 Opioids 4.2 2.0 8.8 F = 32.7 1.6 0.8 3.0 F = 4.7, p = 0.030 2.0 1.2 3.3 F = 17.7 AUDIT‐C (Risk of alcohol use disorder) (ref: no or low risk) High risk 5.3 2.9 9.5 F = 71.5 3.8 2.9 4.9 F = 229.3 2.2 1.8 2.7 F = 117.6
- 6 Abbreviations: AUDIT‐C, Alcohol Use Disorders Identification Test Consumption short‐form; CI, confidence interval; OR, odds ratio.
- 7 * Significant at the 0.0029 level, two sided test.
Males, respondents aged less than 60, and those with low household income were more likely to be classified in the co‐use class (Class 1) (p < 0.05). Meanwhile, males, respondents aged less than 60, and never having married or divorced were likely to be assigned to the cannabis‐only class (Class 2) (p < 0.001). Males, respondents aged less than 60, those who were never married or divorced, not having a high household income, not living in major cities and obtained less than a bachelor degree were more likely to be in the tobacco‐only class (Class 3) (p < 0.05).
Respondents reporting fair level of general health were more likely to be in the co‐use class (Class 1) (p < 0.001). Respondents reporting all but excellent level of general health were more likely to be in the tobacco‐only class (Class 3) (p < 0.001). Respondents who reported high to very high levels of psychological distress were likely to be in co‐use and tobacco‐only class (p < 0.001 to p = 0.001) while respondents reported moderate to very high levels of psychological distress were likely to be in cannabis‐only class (p < 0.001).
Respondents that reported past‐year use of ecstasy, methamphetamine, cocaine, hallucinogens or opioids were likely to be in the co‐use class (Class 1) (p < 0.001). Meanwhile, respondents reporting past‐year ecstasy, methamphetamine, cocaine or hallucinogen use were more likely to be assigned to the cannabis‐only class (Class 2) (p < 0.001). Individuals reporting past‐year methamphetamine, cocaine or opioid use were more likely to be in the tobacco‐only class (Class 3) (p < 0.001). Finally, respondents with high risk of AUD were likely to be assigned to all three substance classes (p < 0.001). The prevalence of correlates and other factors for each latent class can be found in Table S3.
Sensitivity analyses were performed by running the multinomial logistic regression on complete cases (listwise deletion of cases) (see Table S4). While the results remained largely similar between complete case analysis and multiple imputation analysis, the results from the complete case analysis had larger standard errors and wider confidence intervals, which may result in less statistical power than multiple imputation results.
Table 3 shows the multinomial logistic regression model using class membership as the outcome. The first column of the table used cannabis class as the reference for the outcome while the second column used tobacco‐only class as the reference for the outcome. The reference groups for the predictors remained the same.
3 TABLE Associations between latent class memberships and correlates, health outcomes and history of past‐year substance use with tobacco and cannabis class as reference group.
Cannabis (Class 2) as reference group Tobacco (Class 3) as reference group OR 99.7% CI OR 99.7% CI Sex (ref: female) Male 1.9 1.2 3.2 F = 15.3 1.7 1.0 2.8 F = 9.8 Age group, years (ref: 60+) 14–29 1.7 0.4 6.6 F = 0.6, p = 0.601 3.5 0.9 12.8 F = 3.9, p = 0.009 30–39 1.6 0.4 6.0 1.9 0.5 6.7 40–59 1.7 0.5 6.0 1.8 0.5 5.7 Marital status (ref: currently married) Never married 1.0 0.5 1.8 F = 0.1, p = 0.947 1.1 0.6 2.2 F = 0.2, p = 0.791 Divorced/separated/widowed 1.1 0.5 2.4 1.0 0.5 2.2 Household income (ref: 1st quartile: high) 2nd quartile (high‐average) 1.7 0.8 3.6 F = 2.2, p = 0.083 1.2 0.6 2.7 F = 0.3, p = 0.819 3rd quartile (low average) 1.6 0.7 3.5 1.3 0.5 2.9 4th quartile (low) 2.0 0.9 4.8 1.2 0.5 3.1 Remoteness (ref: major cities) Inner regional 0.9 0.5 1.7 F = 0.9, p = 0.391 0.8 0.4 1.5 F = 0.6, p = 0.539 Outer regional, remote or very remote 1.3 0.7 2.5 1.0 0.5 1.9 Highest education attainment (ref: bachelor or higher) High school or less 1.6 0.8 3.2 F = 2.8, p = 0.063 0.7 0.3 1.5 F = 2.0, p = 0.136 Certificate or diploma 1.7 0.9 3.4 1.0 0.5 2.0 Country of birth (ref: outside of Australia) Australia 1.2 0.6 2.6 F = 0.6, p = 0.430 1.6 0.7 3.4 F = 3.2, p = 0.073 Employment (ref: currently employed) Not in labour force 1.2 0.4 3.0 F = 2.6, p = 0.076 1.0 0.4 2.5 F = 0.4, p = 0.662 Unemployed 1.8 0.8 3.8 1.2 0.6 2.5 General health (ref: excellent) Very good 0.9 0.4 2.0 F = 4.2, p = 0.020 0.5 0.2 1.3 F = 1.2, p = 0.291 Good 1.7 0.8 3.8 0.6 0.2 1.4 Fair 2.1 0.8 5.5 0.6 0.2 1.8 Poor 2.1 0.4 11.6 0.5 0.1 2.5 Psychological distress (ref: K10—low) K10—moderate 1.2 0.7 2.2 F = 4.4, p = 0.013 1.6 0.9 2.9 F = 6.9 K10—high to very high 1.8 1.0 3.3 2.1 1.2 3.9 Past‐year substance use (ref: no) Ecstasy 1.1 0.5 2.3 F = 0.1, p = 0.783 2.1 0.8 5.4 F = 5.7, p = 0.017 Meth/amphetamine 2.3 1.0 5.0 F = 9.9 2.0 0.8 4.7 F = 5.4, p = 0.020 Cocaine 1.8 0.9 3.6 F = 7.2, p = 0.007 2.8 1.3 5.9 F = 17.4 Hallucinogens 1.2 0.5 2.6 F = 0.3, p = 0.568 4.7 1.3 17.7 F = 12.3 Inhalants 1.1 0.4 3.1 F = 0.1, p = 0.696 1.4 0.4 5.3 F = 0.6, p = 0.444 Opioids 2.6 1.2 5.5 F = 14.4 2.1 1.0 4.4 F = 8.0, p = 0.005 AUDIT‐C (Risk of alcohol use disorder) High risk 1.4 0.8 2.6 F = 2.6, p = 0.106 2.4 1.3 4.4 F = 19.0
- 8 Abbreviations: AUDIT‐C, Alcohol Use Disorders Identification Test Consumption short‐form; CI, confidence interval; OR, odds ratio.
- 9 * Significant at the 0.0029 level, two sided test.
Males, those reporting methamphetamine and opioid use in the past year were likely to be assigned to the co‐use class compared to the cannabis‐only class (Class 2) (p < 0.001 to p = 0.002). Those experiencing high to very high psychological distress, reporting cocaine, hallucinogen use or endorse high‐risk AUD were more likely to be in the co‐use class compared to tobacco‐only class (Class 3) (p < 0.001 to p = 0.002).
Table 4 shows among those in Class 1 (co‐use tobacco and cannabis), 78.4% of respondents mixed cannabis and tobacco. They were also likely to use other substances simultaneously in the past year. These included alcohol (89.4%), ecstasy (36.6%), cocaine (30.7%), hallucinogens (25.5%), methamphetamine (22.3%) and opioids (20.6%).
4 TABLE Simultaneous use of other substances among co‐use tobacco and cannabis class in the past year.
Co‐use tobacco and cannabis (Class 1) % 95% CI I. Marijuana/cannabis and tobacco mixed 78.4 75.8 81.1 II. Simultaneous use of other substances Alcohol 89.4 87.5 91.3 Ecstasy 36.6 33.5 39.8 Cocaine, crack 30.7 27.6 33.7 Hallucinogens, LSD, magic mushrooms 25.5 22.5 28.5 Methamphetamine 22.3 19.6 24.9 Pain killers, pain relievers and opioids 20.6 18.1 23.2 Ketamine 13.2 10.9 15.5 Tranquillisers, sleeping pills 8.9 7.1 10.6 Heroin 3.2 2.1 4.3 GHB 2.1 1.2 3.0 Sniffing petrol, glue, aerosols, solvents 2.0 1.1 2.8 Methadone, buprenorphine 1.8 1.1 2.5 Kava 1.6 1.0 2.3 Steroids 1.4 0.6 2.2
- 10 Abbreviation: CI, confidence interval.
- 11 a For non‐medical use.
This study of patterns of tobacco and cannabis use in Australia identified four distinct groups of individuals: co‐use of tobacco and cannabis class (2.4% from the sample), cannabis‐only class (5.5%), tobacco‐only class (8.0%) and a non‐user class (84.0%). We used an LCA approach to explore potential subgroups of individuals who may differ according to the tobacco and cannabis product types used, however, these major distinct subgroups were not identified. Our analysis revealed predominate product types used within each of the latent classes. Our co‐use class was characterised by high probabilities of using joints, bongs, edibles, manufactured cigarettes and roll‐your‐own cigarettes. The cannabis‐only class was characterised by high probabilities of joints, bongs and edible use while the tobacco‐only class was characterised by high probabilities of using manufactured cigarettes and moderate probability of using roll your own. Combustible forms of tobacco and cannabis were common but other routes of administration via inhalation (e.g., vaping) were less common in our study, potentially reflecting the strict regulations imposed on the sales of nicotine and vaping devices [[
The identified classes in this study are consistent with prior literature in showing an overlap between tobacco and cannabis use [[
In terms of socio‐demographics correlates, our study found males, respondents aged less than 60, and having high to very high psychological distress were associated with all tobacco and cannabis classes. The proportion of respondents in the co‐use class was small (2.4%); however, young people (aged 14–29) had an 8‐fold increased odds of being assigned to this class compared to non‐users. The odds of past year use of methamphetamine, opioids, cocaine and hallucinogens were also higher in the co‐use class than tobacco or cannabis‐only classes. Young people are susceptible to risk taking [[
In addition, the present study also showed high rates of combining alcohol (89.4%) with cannabis use among those co‐using both tobacco and cannabis. The high rates of simultaneous use of other substances among co‐users may compromise the success of quit attempts [[
Our study found that 78% of the respondents in the co‐use class had mixed cannabis with tobacco. The 2018 International Tobacco Control Policy Evaluation Study also showed that co‐users in Australia were more likely to mix tobacco with cannabis [[
The strengths of the current study were its large sample from a population‐based survey that comprehensively measured a range of substances and relevant socio‐demographic variables and the use of LCA to simultaneously consider how tobacco and cannabis products were used in combination. There were several limitations inherent in the present work. Although the estimates can be broadly generalised to the Australian population, the NDSHS has limitations pertaining to cross‐sectional surveys. The NDSHS do not sample persons in institutional settings such as hospitals, nursing homes, and those lacking permanent addresses. Interviews were not conducted in languages other than English. The surveys also relied on the retrospective recall of respondents' experience with cannabis and tobacco. Our findings are unlikely to be generalisable to high‐risk or specific sub‐populations. The current analysis has only examined the latest NDSHS dataset, future research could consider examining changes in the demographics associated with class membership over time. Although multiple imputation were used, there are limitations because some variables have up to 31% of missing values, and there could be other variables that were not measured by the survey and so we were not able to include a comprehensive list of all possible variables that may help predict the missing values. Nevertheless, multiple imputations can reduce biases from missing data and previous comparative research has shown that it can be robust even in the presence of large percentages of missing values [[
Approximately 16% of the Australian population used either tobacco, cannabis or both substances in 2019. A small proportion co‐used both substances and these were young people with higher levels of psychological distress who are more likely to simultaneously use other illicit substances. Existing policies need to minimise cannabis and tobacco related harms to reduce societal burden associated with both substances.
Carmen C. W. Lim: Conceptualisation, methodology, formal analysis, writing—original draft preparation. Gary C. K. Chan: Conceptualisation, methodology, supervision, writing—review and editing. Janni K. Y. Leung: Conceptualisation, methodology, supervision, writing—review and editing. Shannon Gravely, Coral Gartner, Daniel Stjepanović, Wayne Hall, Jack Y. C. Chung: Supervision, writing—review and editing. Tianze Sun, Vivian Chiu, Jack Y. C. Chung, Roman W. Scheurer: Writing—review and editing. Each author certifies that their contribution to this work meets the standards of the International Committee of Medical Journal Editors.
Open access publishing facilitated by The University of Queensland, as part of the Wiley ‐ The University of Queensland agreement via the Council of Australian University Librarians.
Carmen C. W. Lim is supported by a National Health Medical Research Council (NHMRC) of Australia Postgraduate Scholarship (APP2005317), The University of Queensland Living Stipend and Tuition Scholarship and a National Centre for Youth Substance Use Research top‐up scholarship. Janni K. Y. Leung is supported by an NHMRC Investigator Fellowship (APP2010008) and The University of Queensland development fellowship. Gary C. K. Chan is supported by an NHMRC Investigator Fellowship (APP1176137). The National Centre for Youth Substance Use Research is supported by Commonwealth funding from the Australian Government provided under the Drug and Alcohol Program. The funding bodies had no role in the study design, collection, analysis or interpretation of the data, writing the manuscript, or the decision to submit the paper for publication. The authors would like to acknowledge the Australian Institute of Health and Welfare for providing access to the 2019 National Drug Strategy Household Survey through the Australian Data Archive (ADA Dataverse) and the NDSHS respondents.
The authors declare no known competing financial interest or personal relationships that could influence the work reported in this paper.
This project has been reviewed by the University of Queensland Human Research Ethics Committee and deemed to be exempt from ethics review (ref: 2021/HE001006).
GRAPH: Table S1. A comparison between AUDIT‐C and 2019 NDSHS questions, and the scoring systems used to derive the risk levels of alcohol use status.Table S2. Fit statistics from latent class models on cannabis and tobacco use in the total sample (N = 22,015).Table S3. Sample characteristics by latent class membership.Table S4. Associations between latent class memberships and socio‐demographics, health rated correlates and history of past‐year substance use with non‐user as reference group (Class 4).Table S5. Number and proportion of missing values.
By Carmen C. W. Lim; Janni K. Y. Leung; Shannon Gravely; Coral Gartner; Tianze Sun; Vivian Chiu; Jack Y. C. Chung; Daniel Stjepanović; Jason Connor; Roman W. Scheurer; Wayne Hall and Gary C. K. Chan
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