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. Author manuscript; available in PMC: 2024 Aug 1.
Published in final edited form as: Exp Clin Psychopharmacol. 2022 Nov 10;31(4):780–785. doi: 10.1037/pha0000616

Frequency Matters: Relations Among Alcohol and Cannabis Co-Use Frequency and Alcohol Use Disorder Symptoms in Emerging Adults

Jack T Waddell 1
PMCID: PMC10290519  NIHMSID: NIHMS1906362  PMID: 36355682

Abstract

Emerging adult alcohol and cannabis co-use is on the rise and enacts risk for alcohol misuse/alcohol use disorder (AUD). However, few studies have differentiated whether levels of cannabis use (rather than any cannabis use) moderate between-person risk. Considering low-frequency co-users may use both substances via substitution, low-risk/frequency co-use may not enact the same risk for AUD as higher risk co-use. The present study sought to test this assertion. Public access data on emerging adults from the National Study on Drug Use and Health were used (2002–2019; N = 231,681). Participants reported on their past year alcohol use, cannabis use, as well as AUD symptoms. Regression models tested whether levels of cannabis use frequency moderated the association between alcohol use frequency and AUD symptom counts. A significant interaction of cannabis use frequency by alcohol use frequency predicting AUD symptoms was detected. Individuals who co-used alcohol and cannabis reported more AUD symptoms than those who only used alcohol at the same frequency or less. However, co-use was associated with lower AUD symptom counts than alcohol-only use at higher frequency. Findings suggest that all co-users (and alcohol-only users) are not necessarily created equal, and that relations among co-use and risk for AUD symptoms are complex. Rather than a dichotomy of co-users versus alcohol-only users, between-group risk may depend on frequency of both alcohol and cannabis use.

Keywords: alcohol, cannabis, co-use, alcohol use disorder, alcohol dependence

Alcohol and cannabis co-use, defined as using both alcohol and cannabis during a given time period but not necessarily on the same occasion (Gunn et al., 2018, 2022), is a consistent risk factor for alcohol misuse/alcohol use disorder (AUD; Midanik et al., 2007; Waddell, 2021). Alcohol and cannabis are the two most popular psychotropic drugs in the United States (National Institute on Drug Abuse, 2020), and rates of co-using the two together have risen, particularly in emerging adults (Age 18–25; McCabe et al., 2021). Furthermore, being a co-user is associated with heavier drinking (Patrick et al., 2017; Shillington & Clapp, 2006; Waddell et al., 2022; Waddell, Gunn, et al., 2021; Weiss & Dilks, 2015), negative alcohol consequences (Green et al., 2019; Gunn et al., 2018; Jackson et al., 2020; Linden-Carmichael et al., 2019; Waddell, Blake, et al., 2021; Wardell et al., 2020), and alcohol dependance/AUD (Midanik et al., 2007; Waddell, 2021, 2022). Studies find that co-users have more positive alcohol expectancies, willingness to experience negative consequences, and display more risky behavior, all of which serve as mechanisms explaining the link between co-use and negative consequences (Linden-Carmichael et al., 2019; Waddell, Blake, et al., 2021). However, several studies dichotomize alcohol and cannabis co-use versus alcohol-only use, which may ignore substantial variability in frequency of use when making between-group comparisons. In support, studies also find that frequency of cannabis use frequency is associated with increased negative outcomes (e.g., Gunn et al., 2018; Wardell et al., 2020). However, studies have not looked at how levels of cannabis use frequency increases between-group risk at certain levels of alcohol use (i.e., certain levels of alcohol use in alcohol-only users and certain levels of alcohol and cannabis use in co-users).

It is theoretically important to go beyond a dichotomy of alcohol-only versus co-user when making claims about between-person risk for negative outcomes (e.g., Patrick et al., 2018). For example, it is possible that co-users who use both alcohol and cannabis occasionally may be at lower risk compared to moderate alcohol-only drinkers, as low-frequency co-use could be indicative of lower risk use. In addition, it is also possible that heavier drinkers who do not use cannabis may be at higher risk compared to moderate co-users who drink less heavily. Such a theory is supported by (a) Ecological Momentary Assessment (EMA) studies that find that heavier drinking occurrences are associated with more alcohol problems compared to co-use occurrences coupled with lighter drinking (e.g., Mallett et al., 2019) and (b) EMA studies that find that accounting for levels of drinking diminishes the effect of co-use on negative consequences (e.g., Lee et al., 2020).

Two between-person studies to date have sought to differentiate alcohol-only and co-users by using mixture modeling approaches (Haas et al., 2015; Patrick et al., 2018). Patrick et al. (2018) found four profiles of adolescents who reported: (a) past-12-month nonbinge drinking and no cannabis use, (b) past-12-month nonbinge drinking and concurrent cannabis use, (c) past-12-month nonbinge drinking and simultaneous cannabis use, and (d) past-12-month binge drinking and simultaneous cannabis use. Thus, Patrick et al. (2018) found that being in either simultaneous use profile was associated with more negative consequences compared to concurrent co-users and alcohol-only users, and that binge drinking simultaneous users had more negative consequences than nonbinge drinking simultaneous users. Similarly, Haas et al. (2015) found four profiles of college students who reported: (a) moderate drinking (Quantity × Frequency) and past 30-day cannabis use, (b) moderate drinking and no past 30-day cannabis use, (c) light drinking and no past 30-day cannabis use, and (d) heavy, binge drinking and past 30-day cannabis use. Haas et al. (2015) found that moderate drinking co-users reported more alcohol-related problems than moderate alcohol-only drinkers, and that heavy drinking co-users reported more alcohol-related problems than moderate drinking co-users.

Put together, both studies found that classes of co-users were at heightened risk for negative outcomes and alcohol-related problems. However, due to the pattern-centered approach used, data-driven rather than theoretical classes emerged. For instance, neither Patrick et al. (2018) nor Haas et al. (2015) found a class of low-risk co-users (i.e., infrequent alcohol use with cannabis use), and Patrick et al. (2018) did not find a class of high-risk alcohol-only users. Thus, one alternative to using pattern-centered approach is testing an interaction within a variable-centered approach, such that frequency of cannabis use moderates the effect of alcohol use frequency. Furthermore, the use of a cannabis frequency variable (rather than a yes/no cannabis use variable) would allow for an investigation of low-to-high frequency alcohol use confers risk for negative outcomes at levels of cannabis use (no-to-high frequency).

Therefore, the present study used nationally representative data on U.S. civilians to test whether levels of cannabis use (rather than any co-use) moderated the effect of alcohol use frequency on AUD symptoms. The present study focused on emerging adults (Age 18–25), considering that emerging adult co-use is on the rise (McCabe et al., 2021) and emerging adulthood is a peak period for substance use (Arnett, 2005). It was hypothesized that individuals who used alcohol at mean levels would report more AUD symptoms than individuals who use alcohol at −1 SD levels, regardless of whether alcohol-only or co-use. Similarly, it was hypothesized that individuals who used alcohol at +1 SD levels would report more AUD symptoms than individuals who use alcohol at mean levels, regardless of whether alcohol-only or co-use. Finally, it was hypothesized that co-users would report more AUD symptoms than alcohol-only users at equivalent levels of alcohol use; it was also hypothesized that +1 SD cannabis use would be associated with more AUD symptoms than mean cannabis use, regardless of alcohol frequency. Since data were public access, the present study was exempt from local institutional review board approval.

Method

Participants

Public access data from the 2002–2019 National Study of Drug Use and Health (NSDUH) surveys were used for analyses. The NSDUH collects cross-sectional samples each year of noninstitutionalized U.S. civilians Age 12 and older. The NSDUH uses stratified multistage probability sampling to obtain representative U.S. samples yearly. Inclusion criteria were being Age 18–25 and reporting at least 1 day of alcohol use over the past year (N = 231,681). Participants were 46.1% female, 42.4% were Age 18–20 (57.6 Age 21–25), and were 62.2% non-Hispanic/Latinx White/Caucasian, 14.2% non-Hispanic Black/African American, 15.1% Hispanic/Latinx, 2.1% non-Hispanic Asian, and 6.4% Other/Multiracial.

We report how we determined our sample size, all data exclusions (if any), all manipulations, and all measures in the study.

Measures

Demographics

Participants reported on their sex (male, female), race/ethnicity (non-Hispanic/Latinx White/Caucasian, non-Hispanic/Latinx Black/African American, Hispanic/Latinx, and other/multiracial), and Age (18–20 or 21–25).

Alcohol and Cannabis Use Frequency

Frequency of substance use was assessed by asking participants how many days over the past year (1–366 days) they reported using alcohol and marijuana. A frequency of use variable was computed for each in line with past longitudinal and NSDUH studies (e.g., Chassin et al., 1992; Sher et al., 1991; Waddell, 2021, 2022), such that 0 = no use, 1 = 1–2 times over the past year, 2 = 3–5 times over the past year, 3 = 5+ times over the past year but less than monthly, 4 = 1–2 times a month, 5 = 1–2 times a week, 6 = 3–5 times a week, and 7 = nearly daily.

Typical Drinking Quantity

Typical drinking quantity was assessed by asking, “on days you drank during the past 30 days, how many drinks did you usually have?” A standard drink was defined to participants. Outliers (+3 SD; 1.2% of cases) were winsorized to the next highest value.

AUD Symptoms

Past-year AUD symptoms were assessed according to Diagnostic and Statistical Manual of Mental Disorders, fourth edition (DSM-IV) criteria (American Psychiatric Association [APA], 2000), encompassing four symptoms of alcohol abuse and seven symptoms of alcohol dependance. However, in line with the Diagnostic and Statistical Manual of Mental Disorders, fifth edition (DSM-5) approach to AUD diagnostic criteria (APA, 2013), the symptom of being arrested/having legal problems was removed, and a count of remaining symptoms (i.e., AUD symptoms) was used.

Data Analytic Plan

All analyses were run in Mplus Version 8.5 and used Robust Maximum Likelihood (MLR) estimation. Analyses were estimated in two steps. Regression models were estimated where levels of alcohol use frequency, cannabis use frequency, and their interaction predicted AUD symptoms; main effects were entered first to get nonconditional effect sizes, followed by the inclusion of the interaction term. Covariate effects included age, sex, and race/ethnicity, and were entered in all models. In the presence of a significant interaction, simple slopes were estimated to plot regression lines of the effect of alcohol use frequency on AUD symptoms at different levels of cannabis use. Levels of cannabis use were defined as no cannabis use, mean cannabis use, and +1 SD in cannabis use, allowing the present study to compare alcohol-only use to mean frequency cannabis co-use and high-frequency cannabis co-use at each level of alcohol use frequency.

In addition, in the presence of a significant interaction, marginal means (MM) were estimated for mean as well as ±1 SD levels of alcohol use at each level of cannabis use. Marginal means for alcohol-only use (i.e., −1 SD, mean, and +1 SD frequency alcohol-only use) were pairwise compared with all other marginal means using the Model Constraint function in Mplus Version 8.5. Thus, this analytic approach allowed the present study to test, (a) whether the effect of alcohol use on AUD symptoms varied by levels of cannabis use, and (b) whether specific marginal means of alcohol use (−1 SD, mean, +1 SD alcohol frequency) at specific levels of cannabis use (no, mean, +1 SD cannabis frequency) differed from one another. Considering 36 pairwise comparisons were tested, we set the effective p value at .001. Since AUD symptoms was a count variable, models used a negative binomial distribution; all effect were exponentiated into adjusted rate ratios (RRs).

±1 SD was chosen as alcohol levels of interest based on Aiken et al. (1991), who suggest using these values to indicate mean, low, and high sample-specific means. The present study was not preregistered. All data are publicly available.

Results

Descriptive Statistics and Bivariate Correlations

Q-Q plotting suggested that the residuals of both alcohol and cannabis use frequency fit assumptions of normality. Participants reported an average of monthly to weekly alcohol use (M = 4.49, SD = 1.47), yearly cannabis use (M = 1.69, SD = 2.51), and just under 1 AUD symptom (M = .96, SD = 1.58 Range = 0–10; see Supplemental Material for descriptive statistics). Alcohol use frequency was correlated with cannabis use frequency (r = .25, p < .001) and AUD symptoms (r = .39, p < .001), and cannabis use frequency was correlated with AUD symptoms (r = .23, p < .001).

Primary Analyses

More frequent alcohol use (RR = 1.917, SE = .003, p < .001) and cannabis use (RR = 1.061, SE = .001, p < .001) were associated with higher AUD symptom counts, and there was also a significant interaction between the two predicting AUD symptom counts (RR = .984, SE = .001, p < .001). Simple slopes suggested that more frequent alcohol use was associated with higher AUD symptom counts at +1 SD (b = .276, SE = .002, p < .001), mean (b = .320, SE = .002, p < .001), and no (b = .388, SE = .001, p < .001) cannabis use frequency (see Figure 1).

Figure 1. Marginal Mean Differences in Alcohol Use Disorder Symptoms.

Figure 1

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Note. AUD = alcohol use disorder.

Marginal mean comparisons (see Table 1) were estimated next. At equivalent levels of alcohol frequency, co-users reported more AUD symptoms. In addition, mean frequency alcohol-only users reported higher AUD symptom counts than all alcohol-only as well as co-users (mean, +1 SD cannabis use) who used alcohol at −1 SD levels of alcohol frequency. Similarly, +1 SD frequency alcohol-only users reported higher AUD symptom counts than all alcohol-only as well as co-users (mean, +1 SD cannabis use) who used alcohol at −1 SD and mean levels of alcohol frequency. Alternatively stated, co-users had lower AUD symptom counts than alcohol-only users who used alcohol more frequently (see Figure 1 for estimated marginal means).

Table 1.

Differences Among Marginal Means

Variable MM 1 2 3 4 5 6 7 8 9
1. −1 SD Alc-only freq .322 — −.113** −.356** −.663** −.956** −1.554** −2.691** −3.433** −4.869**
2. −1 SD Alc freq, M Can freq .435 — −.243** −.550** −.843** −1.441** −2.578** −3.320** −4.756**
3. −1 SD Alc freq, +1 SD Can freq .678 — −.307** −.600** −1.198** −2.335** −3.077** −4.513**
4. M Alc-only freq .984 — −.293** −.891** −2.028** −2.770** −4.207**
5. M Alc freq, M Can freq 1.278 — −.598** −1.735** −2.477** −3.913**
6. M Alc freq, +1 SD Can freq 1.875 — −1.137** −1.879** −3.315**
7. +1 SD Alc-only freq 3.013 — −.742** −2.178**
8. +1 SD Alc freq, M can freq 3.755 — −1.436**
9. +1 SD Alc freq, +1 SD Can freq 5.191 —
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Note. MM = marginal mean; Alc = alcohol; Can = cannabis.

**

p < .01.

Covariate effects suggested that earlier study years (RR = .980, SE = .002, p < .001), and younger age (RR = .812, SE = .001, p < .001) were associated with lesser AUD symptom counts, whereas being Black/African American (RR = 1.023, SE = .01, p = .023), Hispanic/Latinx (RR = 1.201, SE = .01, p < .001), Asian (b = 1.274, SE = .02, p < .001), and other race (RR = 1.263, SE = .03, p < .001) compared to White/Caucasian were associated with higher AUD symptom counts.1

Sensitivity Analyses

Sensitivity analyses were ran with the nontransformed alcohol and cannabis use frequency (1–366 days) variables. There continued to be a significant interaction (b = −.001, p < .001) and all simple slopes and marginal means remained in the same direction.

Sensitivity analyses were also ran with only 2010–2019 and 2015–2019 data included. However, there continued to be a significant interaction in the 2010–2019 (b = −.011, p < .001) and 2015–2019 (b = −.009, p < .001) analyses, and all simple slopes and marginal means remained in the same direction.

Discussion

The present study sought to test whether differing levels of alcohol and cannabis frequency in co-users and alcohol-only users conferred heightened risk for AUD symptoms in a nationally representative sample of emerging adults. The present study sought to build upon other studies using between-person pattern-centered approaches by investigating cannabis use frequency (rather than any cannabis use) as a moderator of the association between alcohol use frequency and AUD symptoms. The present study’s analytic approach allowed for a theoretical test of levels of alcohol and cannabis use frequency, rather than creating data-driven profiles of users that may or may not be at theoretically important levels of alcohol/cannabis use frequency.

Findings suggested that co-users were at higher risk for AUD symptoms compared to alcohol-only users who used alcohol as frequently or less frequently than such co-users. Alternatively stated, a co-user who used cannabis at mean frequency reported more AUD symptoms compared to all other users who used alcohol at mean or −1 SD levels. However, the present study also found that co-users reported lesser difference in AUD symptoms than alcohol-only users who used alcohol more frequently (e.g., higher frequency alcohol-only users vs. mean frequency alcohol and cannabis co-users). Thus, alcohol and cannabis co-use was a risk factor for AUD symptoms compared to alcohol-only users who used alcohol less frequently (or the same frequency) than such co-users, but alcohol and cannabis co-use was not a risk factor for more AUD symptoms when compared to alcohol-only users who used alcohol more frequently than such co-users.

The finding that co-users had higher risk for AUD symptoms compared to alcohol-only users of equal or lesser alcohol frequency is not surprising. This finding mirrors the large literature suggesting that co-use is a risk factor for negative alcohol outcomes (e.g., Gunn et al., 2018; Waddell, Gunn, et al., 2021; Yurasek et al., 2017). However, what is more noteworthy, is that the risk conferred from co-use was lesser when compared to higher frequency alcohol-only users. Thus, frequency of alcohol use, regardless of levels of concurrent cannabis use, appeared to be the stronger predictor of AUD symptoms. There appeared to be a group of lower risk co-users, in that −1 SD (and mean) frequency co-use was associated with lesser difference in AUD symptoms compared to mean (and higher) frequency alcohol-only users.

This finding also has important implications for theory and future research on alcohol and cannabis co-use. In terms of theory, co-users are thought to be at higher risk for negative outcomes compared to alcohol-only users (e.g., Yurasek et al., 2017), but the present study suggested that relations are more complicated than a dichotomy of co-users versus alcohol-only users. One study found that profiles of use frequency differentiated risk within co-users (Waddell, 2021), and the present study supports this notion at the between-group level as well. Thus, studies that dichotomize co-use and alcohol-only use may mask findings for the heaviest alcohol-only drinkers, who may be, in some cases, at higher risk for negative outcomes (e.g., AUD symptoms) as compared to several co-users. Although dichotomizing co-use may be easier and may provide substantive between-group cell sizes, the current findings point to the importance of differentiating high- versus low-risk co-users, as there were marked differences in their relations with AUD symptoms as compared to high- versus low-risk co-users.

Study findings may also have prevention implications, particularly during emerging adulthood when risk may be at its highest. First, it is important to take into account one’s alcohol and cannabis use frequency when deciding on subgroups of individuals to target for preventive interventions. A comprehensive picture of alcohol and cannabis use frequency may lead to more effective prevention targeting, rather than population-level targeting of co-users who may or may not be at heightened risk compared to mean-level/heavier drinking alcohol-only users. Second, considering both alcohol and cannabis use frequency contributed to risk for AUD symptoms, prevention efforts should seek to reduce alcohol and cannabis use frequency. Particularly for higher and mean levels of alcohol frequency, higher frequency cannabis co-use increased risk. Thus, advocating for the cessation of cannabis, or reductions in cannabis use frequency within a harm reduction standpoint, could have substantial impact on negative alcohol outcomes.

The present study has limitations. First, the present study was unable to establish temporal precedence between AUD symptoms and substance use frequency. Second, the present study focused on concurrent co-use, since the NSDUH does not have measures of simultaneous alcohol and cannabis use. However, it is believed that the present study’s use of a cannabis use frequency variable largely outweighs this limitation, as previous studies reviewed typically consider someone a cannabis user (vs. not), rather than their frequency of cannabis use. Third, the present study focused on alcohol and cannabis use frequency rather than quantity, as there was no measure of cannabis use quantity and quantity of cannabis is very difficult to measure (e.g., Hindocha et al., 2018). Additionally, the NSDUH analyses used symptom counts from the DSM-IV AUD criteria, and thus did not include craving, which was added in the DSM-5 (APA, 2013).

The present study had low levels of cannabis use, as mean levels were yearly (M = 1.69). Similarly, the present study used ±1 SD as levels of interest for alcohol and cannabis use; however, +1 SD was indicative of monthly-to-weekly cannabis use. Thus, future research in more frequent cannabis users is needed. Rates of AUD symptoms were also low (M = .96, SD = 1.58), and replication in higher risk samples is needed. In addition, the present study used AUD symptoms as the outcome of interest; however, future research should consider cannabis use disorder symptoms as well as other nonsubstance use problems/harms that may be heightened for co-users. Finally, public access data for the present study did not have data on state of residence, and future research is needed to test whether geographic area and cannabis availability affect results.

Despite these limitations, the present study is important for theory and prevention as it relates to alcohol and cannabis co-use. Findings suggest that co-use was a risk factor for AUD symptoms when compared to alcohol-only users who used alcohol the same (or lesser) frequency, but was not a risk factor for more AUD symptoms compared to alcohol-only users who used alcohol more frequently. Thus, results highlight that substance users differed in risk for AUD symptoms largely based upon their alcohol use frequency, and secondarily based upon their co-use of cannabis. Findings provide important information for theories of co-use as well as preventive interventions seeking to reduce the impact and development of AUD.

Supplementary Material

supp material

Public Health Significance.

The present study found that lower frequency co-use was associated with lesser risk for AUD symptoms compared to higher frequency alcohol-only use. Findings suggest that blanket-level statements suggesting that all co-users are at heightened risk for negative outcomes may not be accurate.

Footnotes

The author has declared no conflicts of interest to disclose. The present study was not funded by any funding agencies.

The present study was not preregistered. All data are publicly available.

1

Sensitivity analyses were run with the nontransformed alcohol and cannabis use frequency (1–366 days) variables to ensure that the adjusted frequency variables did not drive group differences. There continued to be a significant interaction (b = −.001, p < .001) and all simple slopes and marginal means remained in the same direction. Sensitivity analyses were also run with only 2010–2019 and 2015–2019 data included, to ensure that findings did not differ based upon which years were included in analysis. However, there continued to be a significant interaction in the 2010–2019 (b = −.011, p < .001) and 2015–2019 (b = −.009, p < .001) analyses, and all simple slopes and marginal means remained in the same direction.

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