Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2022 Jan 1.
Published in final edited form as: Drug Alcohol Depend. 2020 Oct 12;218:108339. doi: 10.1016/j.drugalcdep.2020.108339

Ordering in alcohol and cannabis co-use: Impact on daily consumption and consequences

Rachel L Gunn a, Alexander Sokolovsky a, Angela K Stevens a, Jane Metrik a,b, Helene White c, Kristina Jackson a
PMCID: PMC7928210  NIHMSID: NIHMS1637906  PMID: 33092910

Abstract

Background:

Co-use of alcohol and cannabis is highly prevalent among young adults and college students. Between-person reports suggest that co-use is associated with the experience of more frequent consumption and related consequences, compared to single substance use. However, recent studies have found conflicting evidence regarding the impact of co-use use on consumption and consequences in daily or event-level investigations. Conflicting evidence may be due to understudied factors, such as the order in which alcohol and cannabis are used. The current study aimed to examine the effect of substance use order on a) alcohol consumption; b) cannabis consumption; and c) negative alcohol and cannabis consequences.

Methods:

Data were collected from U.S. undergraduate college student alcohol and cannabis users (N=258) who completed two 28-day longitudinal online assessment bursts examining alcohol and cannabis co-use patterns. Data were collected five times per day during both bursts (three months apart).

Results:

Controlling for between-person of alcohol and cannabis use, within-person mixed-effects models indicated that using cannabis first within a co-use day was associated with lower daily alcohol consumption, but greater daily cannabis consumption. Substance use ordering was not linked to consequences, whereas between-person levels of alcohol consumption and within-person number of drinks in a day were positive predictors of consequences.

Conclusions:

Overall, results highlighted that order of substance use is a robust predictor of consumption on co-use days. Therefore, future research on co-use use should consider patterns of use in addition to level of use.

Keywords: Simultaneous Use, Alcohol, Marijuana, Cannabis, Consequences

1.0. Introduction

Alcohol and cannabis are among the most commonly used substances among college students and young adults in the U.S. (Center for Behavioral Health Statistics and Quality, 2016; Midanik et al., 2007; Schulenberg et al., 2018). Concomitant increases in legalization of recreational cannabis and rates of cannabis use among young adults and college students (Schulenberg et al., 2018; Substance Abuse and Mental Health Services Administration, 2019) highlight the importance of examining the impact of alcohol and cannabis co-use on consumption and consequences. The need for this research is reflected by recent national and multi-campus surveys suggesting one quarter of college students report past-year simultaneous use of alcohol and cannabis (i.e., use of both substances together, so that the effects overlap) (Terry-McElrath and Patrick, 2018) and three-quarters of alcohol- and cannabis-using college students report past-year simultaneous use (White et al., 2019). College students who use alcohol and cannabis report a number of academic and social consequences from their use (Arria et al., 2008; Caldeira et al., 2008; Merrill and Read, 2010; Schulenberg and Patrick, 2012). Moreover, regarding alcohol specifically, college students consistently show higher rates of use and associated consequences, compared to their age-matched non-college attending peers (Blanco et al., 2008; Linden-Carmichael and Lanza, 2018; Slutske, 2005; Slutske et al., 2004). In studies comparing co-users to single substance users, co-users (in some cases simultaneous users specifically) reported more frequent negative substance-related consequences (Brière et al., 2011; Midanik et al., 2007; Subbaraman and Kerr, 2015; Yurasek et al., 2017). Results from a study using timeline follow-back interviews found that using cannabis (versus no cannabis use) was associated with higher levels of alcohol use in the same day (Metrik et al., 2018). Another study of college students revealed that using cannabis and alcohol on the same day was associated with increased alcohol use, and heavier use of cannabis in a single week was associated with more alcohol-related consequences (Gunn et al., 2018).

Recent event-level studies provide conflicting evidence and suggest that cannabis use may not consistently increase alcohol consumption or negative consequences. For instance, a recent ecological momentary assessment (EMA) study of adolescents found that co-use occasions were associated with higher likelihood of consequences, but these consequences could be largely attributed to the number of drinks consumed within an occasion (Lipperman-Kreda et al., 2017). In another event-level study examining consequences occurring on alcohol, cannabis, and simultaneous use days among college students, results suggested that using cannabis on heavy drinking days did not carry increased risk of consequences compared to heavy alcohol-only days (Mallett et al., 2019). In addition, recent work from our group suggests that number of drinks on a co-use occasion was the strongest predictor of number of daily consequences experienced (Sokolovsky et al., 2020). Conflicting findings may be due to understudied factors, such as the order in which substances are consumed and how between-person differences in alcohol or cannabis use (e.g., individual patterns of use) as well as within-person deviations from that pattern or other event- or day-level factors may impact use-related outcomes. Although no studies thus far have examined the impact of the order in which alcohol and cannabis are used within a co-use day on consequences, there is often informal wisdom among users regarding the impact of order of use. For example, colloquially, co-users tend to associate worse outcomes (e.g., nausea, dizziness, vomiting) when using cannabis after alcohol, whereas they anticipate fewer consequences when cannabis is used first (e.g., “grass before beer, you’re in the clear”).

Controlled laboratory studies often examine combined effects of alcohol and cannabis using a specific order of administration, where fixed alcohol doses are administered prior to fixed doses of cannabis when participants are at peak breath alcohol concentration. Findings from these studies suggest that combined administration reduces peak plasma ethanol levels and delays time to peak, but increases blood Δ−9 tetrahydrocannabinol (THC) levels, positive subjective effects, and subjective impairment (Downey et al., 2013; Hartman et al., 2016, 2015; Lukas et al., 1992; Lukas and Orozco, 2001; Perez-Reyes et al., 1988). These multiplicative intoxicating effects are confirmed with self-report data from young adult co-users and may contribute to increased risk associated with co-use (Lee et al., 2017). In one study where a very low dose of oral THC was administered with alcohol such that peak THC levels would coincide with peak ethanol levels, ratings for “wanting” more alcohol were attenuated relative to THC alone (Ballard and De Wit, 2011), suggesting robust interaction effects from combined administration of alcohol and cannabis. One study found low to moderate doses of alcohol did not influence subsequent cannabis self-administration among regular cannabis users (Chait and Perry, 1992). Preliminary data from another placebo-controlled laboratory study suggested that cannabis acutely decreases subsequent alcohol consumption (Metrik et al., 2019). However, no other studies to our knowledge have examined the effects of order of administration of one substance on self-administration of the other substance. Further, no naturalistic studies have compared the order in which alcohol and cannabis are used and its impact on consumption or consequences within the same day.

1.1. Current Study

Given the high rates of alcohol and cannabis co-use (and corresponding negative acute public health harms) in college students (Jackson et al., 2020; O’Hara et al., 2016), we targeted a robust sample of undergraduate students for the present study. Specifically, we used daily-level data collected from college students to examine the effect of order (i.e., alcohol or cannabis first) in a co-use day on: a) alcohol consumption, b) cannabis consumption, and c) negative alcohol- and cannabis-related consequences. Given preliminary laboratory work suggesting THC may attenuate alcohol craving (Ballard and De Wit, 2011), we hypothesized that using cannabis first would result in less same-day alcohol use, more cannabis use, and fewer consequences. However, given recent event-level data suggesting that amount of alcohol consumption predicts consequences over and above co-use (Lipperman-Kreda et al., 2017; Mallett et al., 2019; Sokolovsky et al., 2020), we did not anticipate that order would uniquely predict consequences when number of drinks per day was also accounted for.

2.0. Methods

2.1. Procedures

2.1.1. Screening.

Undergraduate students were recruited to participate in a two-wave longitudinal study on alcohol and cannabis use. Recruitment targeted three state universities in states with different recreational cannabis-use laws; medical use of cannabis was legalized in all states. Recreational cannabis use was criminalized in School A’s state; illegal but decriminalized at School B’s state; and legal (for adults age 21+) at School C’s state. We randomly selected 8,000 undergraduate students age 18–24 from each university’s registrar database (stratified by anticipated graduation year; total N=24,000) to e-mail online screening survey invitations. The invitation explained that the study was about alcohol and cannabis use but that the student did not have to use alcohol or cannabis to participate in the screening survey. Eligibility criteria for the baseline survey included: (1) full-time enrollment at one of the universities; (2) age 18–24 years; (3) past-year alcohol and cannabis use, and (4) verified e-mail address (see White et al., 2019 for details on recruitment and representativeness). Of 7,000 screening responses, 2,874 (41.1%) met eligibility criteria. Participants were incentivized with a lottery to win a $100 Amazon.com gift card (10 per campus).

2.1.2. Baseline Survey.

A sample of 2,501 eligible students (approximately evenly divided by school to ensure equal representation) were e-mailed invitations to participate in the baseline survey (before the daily component of the study began). In selecting this 2,501, we oversampled for past-month alcohol and cannabis use to ensure enough students would be eligible for the subsequent daily survey phase. Of invitees who provided consent and were enrolled into the study (N=1,610), we retained 1,390 participants (86.3%) in the baseline sample; participants who provided baseline data inconsistent with eligibility criteria (and thus inconsistent with their own screening information) and those with technical issues were excluded from the study. Inconsistencies included no longer reporting past-year alcohol and/or cannabis use, falling outside the eligible age-range of 18–24, and no longer attending one of the three universities full-time. Compensation for the baseline survey was a $25 Amazon gift card. (For greater detail on the baseline survey, see Stevens et al., 2020 Supplemental Material.) Demographic data (e.g., race/ethnicity, sex, age, and class status) were also collected in the baseline survey.

2.1.3. Daily Surveys.

Students who reported past-month use of alcohol and cannabis “at the same time so their effects overlapped” at baseline were eligible for the daily phase of the study. These potential participants were sampled by frequency of past-month co-use occasions (i.e., 1–2 times versus 3+ times representing infrequent and frequent co-use, respectively) and sex (assigned at birth) to ensure heterogeneity, with a generally equal number of students invited from each school. Of students given access to the daily application (n=379), 343 accepted (90.5%); two students discontinued data collection during the first two days and were excluded from further analyses, resulting in a final sample of 341 who completed the daily surveys. A flowchart depicting screening into the daily surveys is available in Supplemental Materials (Figure S2). For these daily surveys, participants completed a 26-day (the first two days were deleted due to technical issues) burst of intensive longitudinal experience sampling comprising five daily surveys delivered via a custom-developed smartphone application. Three months later, 316 students (92.7% of the first burst) completed a second 28-day burst. Participants were compensated $1 for each completed survey (up to $5 per day/$35 per week) with potential bonuses of $10 each week if they completed 85% of surveys for that week, $20 at the end of 4 weeks if they completed 90% of survey, totaling $200 maximum (Amazon gift cards) per burst.

Daily surveys were predictably scheduled (9:00am; 2:00pm; 5:00pm; 8:00pm; 11:00pm). Surveys remained active for two hours (five hours for 9:00am survey). Each survey asked about behavior from the exact time that the last survey was completed (including up to one missed survey) until the time that the current survey was completed (except in the case of two missed surveys in a row, in which case the current survey would use the scheduled time of the previous survey as the first time anchor). For full details on missed surveys, see supplemental material in Stevens et al., (2020). Survey completion took approximately 1–2 minutes with an additional 2–3 minutes for the morning survey (triggered at 9:00am) because this survey contained two parts: (1) a daily survey assessing prior-day substance use behavior between the time of the last completed survey and bedtime, and (2) a morning survey which included items that retrospectively assessed yesterday’s behavior from wake time through bedtime, including consequences. Completion of at least one daily survey was high (85.8%). All procedures were approved by the Institutional Review Board of Brown University and a Certificate of Confidentiality was obtained from NIDA.

2.1.4. Co-Use.

Recent work from our group with this sample suggests no difference in subjective intoxication or negative consequences when comparing different operationalizations of simultaneous use (i.e., co-use based on timing of use) ranging from 1 to 240 minutes within a day (Sokolovsky et al., 2020). Therefore, and given that consequences were aggregated at the day level and assessed the following morning, we examined co-use at the day level. Across the two measurement bursts, a subset of 284 participants (83.2% of the full sample of 341) reported same-day co-use at least once (meaning reporting alcohol use on at least one survey and cannabis use on at least one survey on the same day). Across all participants and assessment waves, we observed 2,024 total co-use days. Given the need to determine substance use ordering within a day, we removed 443 total days with any period of data missingness, resulting in a final total of 1,581 days and 258 participants. There were no significant differences in demographic characteristics between those students completing all daily assessments and the final sample (Cohen’s h=.01–.02).

2.2. Measures

2.2.1. Substance Use Quantity.

Participants were asked at each survey if they used alcohol, cannabis, both, or neither for each time interval: “What did you use between [time X] and [time Y]?” The timeframe for this question was anchored between the time the previous survey was submitted and the time the current survey was triggered. If alcohol and/or cannabis were endorsed, participants were presented with a timeline overlaid on a grid with time anchors (in minutes) and were instructed to tap on the screen at points corresponding to times when they had a drink or used cannabis (see Supplemental material, Figure S1). Instructions were: “Tap your finger in the blue box each time you had a drink/used marijuana at the corresponding time”). Day-level alcohol and cannabis use quantity were indexed as the sum of the number of drinks (taps) and cannabis uses (taps), respectively, across all surveys in a given day.

2.2.1. Ordering.

After aggregating the data to the day level, “ordering” was defined as the substance (alcohol or cannabis in any form) that was used first during a given day (reference group [ref]: alcohol). Days on which the first use of alcohol and cannabis reportedly occurred during the same minute (9 days across all respondents; 0.57% of observations) were omitted from analyses.

2.2.3. Consequences.

On the morning survey following co-use days, participants indicated whether the following consequences occurred “because of yesterday’s use of [alcohol, marijuana, or alcohol and marijuana together]”: hangover, nausea/vomiting, hurt self, drove car drunk/high, blackout, rude/aggressive, and unwanted sex. Categories of consequences were adapted from Read and colleagues (2006) consequences subscales, the Brief Young Adult Alcohol Consequences Questionnaire (BYAACQ; (Kahler et al., 2005)), the Marijuana Consequence Scale (MACQ; Simons et al., 2012), the Rutgers Alcohol Problem Index (RAPI; White and LaBouvie, 1989), and the Rutgers Marijuana Problem Index (RMPI; Johnson and White, 1995), with a focus on selecting acute consequences that would be observed at the daily level. Total daily number of consequences experienced was indexed as the number endorsed each day (range: 0–7)1. As consequences were identical across substances, daily consequences were measured here as the sum of consequences endorsed, regardless of substance.

2.2.4. Covariates.

All models controlled for baseline demographic characteristics, including: sex (assigned at birth, ref: female), age (dichotomized as below age 21 [ref] or 21+ years old, ref), school (ref: School A), race (White [ref] versus non-White), and ethnicity (non-Hispanic/Latinx [ref] versus Hispanic/Latinx). Models also controlled for weekday (dichotomized as weekday [ref] or weekend [Friday or Saturday], based on inspection of use patterns), any drug use other than cannabis (dichotomized at the daily level, ref: no), and, due to the delayed and extended intoxication effects of edibles compared to other forms of cannabis (Barrus et al., 2016; Grotenhermen, 2003), whether edible cannabis was used (dichotomized at the daily level, ref: no)2. In alcohol and cannabis use models (see Analysis Plan), we included measures of typical quantity collected at the baseline survey: “How many drinks containing alcohol do you have on a typical day when you are drinking?”; 0=“1 or 2’, 1=“3 or 4”, 2=“5 or 6”, 3=“7, 8, or 9”, 4=“10 or more” and hours high (given there is no quantity item on the CUDIT) “How many hours were you ‘stoned’ on a typical day when you had been using cannabis?”; 0=“Less than 1”, 1=“1 or 2”, 2=“3 or 4”, 3=“5 or 6”, 4=“7 or more”; taken from the Alcohol Use Disorder Identification Test (AUDIT; Saunders et al., 1993) and the Cannabis Use Disorder Identification Test Revised (CUDIT-R; Adamson et al., 2010), respectively. These covariates were selected in order to control for between-person differences in typical quantity used within an occasion to further parse out between- and within-person effects in each model. We also controlled for between-person and within-person alcohol use (average number of drinks per co-use day across the study and daily deviation from that average, respectively) in models examining number of cannabis uses as the dependent variable and between-person and within-person cannabis use (average number of cannabis uses per co-use day across the study and daily deviation from that average, respectively) in models examining number of alcohol drinks as the dependent variable.

2.3. Analysis Plan

To evaluate the influence of day-level substance ordering on alcohol and cannabis consumption and consequences, we structured the data such that each row represented one day for one participant. We conducted a series of generalized linear mixed models (GLMMs; Hedeker, 2005) specifying a negative binomial distribution (Ferrari and Cribari-Neto, 2004) with variance increasing quadratically with the mean due to overdispersion (Hardin and Hilbe, 2007), except for the GLMM examining consequences where a poisson distribution was specified (data were not overdispersed; σ=1.03, p[σ>1]=.181). Models were fit using the glmmTMB (Brooks et al., 2017) package in R 3.6.1 (R Core Team, 2017), using maximum likelihood estimation. Due to clustering of repeated observations (i.e., days) within participants, which violates typical assumptions of statistical independence, we computed intraclass correlations (ICCs) for focal outcomes and used a mixed-modeling analytic approach as noted above. In separate models, we regressed number of daily drinks (model 1), number of cannabis uses (model 2), and total number of consequences (model 3) onto the focal predictor of ordering, between- and within-person alcohol use (models 2 and 3), between- and within-person cannabis use (models 1 and 3), AUDIT consumption item (model 1), CUDIT-R consumption item (model 2), and all other person-level (sex, age, school, race, ethnicity) and day-level (weekday, use of other drugs, use of edibles) covariates (all models)3. Random intercept terms for subject were included in all models. Estimated model coefficients and 95% confidence intervals (CIs) were exponentiated to obtain incidence rate ratios (IRRs) which are presented for all model terms. This analysis was not pre-registered and thus results should be considered exploratory.

3.0. Results

The average day-level delay between a participant’s first drink or first cannabis use and their first use of the other substance was M=243.73 minutes (SD = 210.26). However, this delay was notably positively skewed, with 24.8% of days involving first use of both substances within 1 hour, 35.9% within 2 hours, and 47.7% within 3 hours (range 1–1,258 minutes). A narrow timeframe was often observed between any one drink and any one cannabis use on a given day (20.8% within 1 minute, 54.1% within 10 minutes, 76.5% within 1 hour, 85.8% within 2 hours, 90.3% within 3 hours, and 93.3% within 4 hours). Across all participants and days, cannabis was slightly more commonly endorsed as the first substance used that day (54.1% of days). Across all co-use days, the mean time for first alcohol use was 7:39 pm and first cannabis use was 6:32 pm. Descriptive statistics for the subset of participants utilized for the present analyses are presented in Table 1.

Table 1.

Sample characteristics

Variable M (SD) or %
Sex (female) 51.9%
Age 19.9 (1.35)
Race/Ethnicity
  Non-Hispanic White 70.2%
  Non-Hispanic Black 2.7%
  Hispanic/Latinx 9.3%
  Asian 12.0%
  Other 1.9%
  More than 1 8.1%
School
  A (illegal) 29.5%
  B (decriminalized) 37.9%
  C (legal) 32.6%
Class
  Freshmen 19.8%
  Sophomore 25.6%
  Junior 24.8%
  Senior 24.8%
  ≥ 5-year senior 5.0%
AUDIT 9.78 (5.27)
CUDIT-R 10.26 (6.11)
Drinks per co-use day 5.70 (3.46)
Cannabis uses per co-use day 4.54 (5.19)
Proportion of edible use days per co-use day 7.9%
Proportion other drug use days per co-use day 6.8%
Open in a new tab

Note. n=258 Proportion of leaf-only use days, edible days and other drug use days presented in Table 1 were aggregated to the subject level (i.e., mean of between-person proportions of co-use use days). AUDIT= Alcohol Use Disorders Identification Test; CUDIT-R=Cannabis Use Disorder Identification Test Revised.

3.1. Alcohol Quantity

Results from LMEMs predicting daily number of drinks from substance use order (i.e., cannabis first vs. alcohol first) and disaggregated between- and within-person daily cannabis use are presented in Table 2. Results indicate that using cannabis first was associated with a lower daily alcohol quantity after adjusting for all covariates. By contrast, between-person average levels of cannabis use and within-person escalations in daily cannabis use (deviation relative to each participant’s average) were both linked to increased daily alcohol quantity after adjusting for covariates. Covariate effects are reported in Table 2.

Table 2.

Effect of daily ordering of alcohol and cannabis use on daily number of drinks

Predictors B (SE) 95% CI Low IRR 95% CI High p
Intercept 1.232 (0.090) 2.872 3.428 4.091 -
Ordering (C >A) −0.249 (0.038) 0.723 0.780 0.841 <.001
Cannabis use (B) 0.031 (0.007) 1.017 1.031 1.046 <.001
Cannabis use (W) 0.024 (0.003) 1.018 1.025 1.031 <.001
School (A) −0.026 (0.073) 0.844 0.974 1.125 0.723
School (B) −0.017 (0.071) 0.854 0.981 1.128 0.793
Male −0.007 (0.059) 0.890 0.993 1.114 0.904
Age 21 −0.122 (0.059) 0.788 0.885 0.994 0.040
Non-white 0.006 (0.072) 0.874 1.006 1.159 0.933
Hispanic/Latinx 0.180 (0.104) 0.977 1.197 1.467 0.083
Weekend 0.295 (0.034) 1.257 1.344 1.437 <.001
Other drug use 0.247 (0.059) 1.141 1.280 1.436 <.001
Edible cannabis use −0.143 (0.088) 0.730 0.867 1.030 0.105
AUDIT Item 2 0.160 (0.028) 1.111 1.174 1.241 <.001
Open in a new tab

Note. n=258. IRR = incidence rate ratio. Ordering=first substance cannabis (vs. first substance alcohol). B = between-person effect; W = within-person effect. AUDIT=Alcohol Use Disorders Identification Test; C >A = cannabis first. Statistically significant effects (p < .05) are in bold typeface. ICC for between-person clustering of number of drinks is .26.

3.2. Cannabis Quantity

Results from LMEMs predicting daily cannabis use from substance use order (i.e., cannabis first vs. alcohol first) and disaggregated between- and within-person daily alcohol use are presented in Table 3. Results indicate that using cannabis first was associated with an increased number of daily cannabis uses after adjusting for covariates. Likewise, between-person average levels of alcohol use and within-person elevations in daily alcohol consumption (deviation relative to each participant’s average) were both related to an increased cannabis consumption over and above covariates. Covariate effects are reported in Table 3.

Table 3.

Effect of daily ordering of alcohol and cannabis use on daily cannabis use

Predictors B (SE) 95% CI Low IRR 95% CI High p
Intercept 0.305 (0.121) 1.068 1.357 1.723 -
Ordering (C >A) 0.480 (0.032) 1.517 1.615 1.720 <.001
Alcohol use (B) 0.084 (0.013) 1.060 1.087 1.115 <.001
Alcohol use (W) 0.031 (0.003) 1.025 1.032 1.038 <.001
School (A) 0.133 (0.084) 0.969 1.142 1.345 0.112
School (B) 0.24 (0.080) 1.088 1.272 1.488 0.003
Male −0.029 (0.067) 0.853 0.972 1.106 0.695
Age 21 −0.094 (0.067) 0.800 0.910 1.104 0.605
Non-white −0.078 (0.083) 0.786 0.924 1.087 0.160
Hispanic/Latinx −0.017 (0.121) 0.775 0.983 1.247 0.596
Weekend −0.015 (0.029) 0.931 0.985 1.042 0.942
Other drug use 0.067 (0.048) 0.972 1.069 1.175 0.215
Edible cannabis use −0.042 (0.073) 0.831 0.959 1.106 0.483
CUDIT-R Item 2 0.160 (0.041) 1.082 1.173 1.271 <.001
Open in a new tab

Note. n=258. IRR = incidence rate ratio. Ordering=first substance cannabis (vs. first substance alcohol). B = between-person effect; W = within-person effect. CUDIT=Cannabis Use Disorders Identification Test-Revised; C >A = marijuana first. Statistically significant effects (p < .05) are in bold typeface. ICC for between-person clustering of cannabis use is .44.

3.3. Alcohol and Cannabis Consequences

Results from LMEMs predicting daily consequences from substance use order (i.e., cannabis first vs. alcohol first), between- and within-person daily alcohol use, and between- and within-person daily cannabis use are presented in Table 4. Between- and within-person alcohol use was associated with an increased number of consequences after adjusting for covariates. However, neither substance use ordering nor between- and within-person cannabis use were linked to consequences. Covariate effects are reported in Table 4.

Table 4.

Effect of daily ordering of alcohol and cannabis use on total consequences

Predictors B (SE) 95% CI Low IRR 95% CI High p
Intercept −1.979 (0.235) 0.087 0.138 0.219 -
Ordering (C>A) 0.044 (0.106) 0.849 1.045 1.285 0.681
Alcohol use (B) 0.083 (0.009) 1.027 1.086 1.149 0.004
Alcohol use (W) 0.080 (0.009) 1.064 1.084 1.104 <.001
Cannabis use (B) 0.003 (0.016) 0.973 1.003 1.035 0.832
Cannabis use (W) −0.012 (0.008) 0.973 0.988 1.004 0.145
School (A) −0.252 (0.178) 0.548 0.778 1.103 0.159
School (B) 0.237 (0.163) 0.922 1.267 1.743 0.145
Male −0.169 (0.133) 0.651 0.844 1.096 0.203
Age 21 0.115 (0.138) 0.856 1.122 1.469 0.405
Non-white 0.056 (0.172) 0.754 1.057 1.481 0.749
Hispanic/Latinx −0.144 (0.258) 0.522 0.866 1.437 0.577
Weekend 0.358 (0.095) 1.188 1.430 1.723 <.001
Other drug use 0.190 (0.145) 0.911 1.210 1.610 0.188
Edible cannabis use 0.254 (0.213) 0.849 1.289 1.956 0.234
Open in a new tab

Note. n=258. IRR = incidence rate ratio. Ordering=first substance cannabis (vs. first substance alcohol). B = between-person effect; W = within-person effect; C >A = cannabis first. Statistically significant effects (p < .05) are in bold typeface. ICC for between-person clustering of daily consequences is .17.

4.0. Discussion

This study took a detailed look at co-use days to examine the effect of order (i.e., alcohol or cannabis first) on daily alcohol consumption, cannabis consumption, and negative consequences, while controlling for between-and within-person levels of use. Descriptively, we found that, across all sample days and individuals, cannabis was only slightly more likely to be used first (54% cannabis first) and cannabis use tended to start earlier in the day. This is not surprising given the frequency of morning use (colloquially known as “wake and bake”) among daily cannabis users (Earleywine et al., 2016). Results suggested that using cannabis first predicted more cannabis use, but less alcohol use (fewer drinks), within a co-use day. Substance use order did not predict number of consequences experienced, after controlling for consumption, whereas number of drinks within a co-use day positively predicted more consequences.

This is the first naturalistic study to examine the effect of ordering of alcohol and cannabis use on consumption and provides an important context for understanding the conflicting literature in this area. Given recent findings suggesting use of alcohol and cannabis concurrently (compared to alcohol alone) predicts increased alcohol consumption at the between person-level in adults (Subbaraman and Kerr, 2015) and the daily level in veterans (Metrik et al., 2018) and college students (Gunn et al., 2018), it is important to examine understudied factors that may contribute to this increased use. Our findings indicate that order has an important role in consumption rates and that using alcohol first on co-use days significantly contributes to more drinking, even though alcohol use tends to start later in the day. Together with studies suggesting earlier cannabis use in the day (i.e., morning use) predicts more cannabis-related problems (Earleywine et al., 2016), our findings support the idea that cannabis users should consider delaying their use later in the day to reduce overall consumption levels.

To our knowledge, this is also the first study to examine ordering and consider both within- and between- person predictors of use in the context of co-use days. Results suggested that both being a heavier cannabis user and using more cannabis within the day (deviation from typical use) predicted more drinking within a co-use day. Additionally, being a heavier drinker and drinking more alcohol within the day (deviation from typical use) predicted more frequent cannabis uses within the day. Results are consistent with between-person examinations, suggesting that more cannabis use predicts heavier drinking (Gunn et al., 2018; Metrik et al., 2018; Midanik et al., 2007; Stinson et al., 2006) and that heavier alcohol use is associated with more frequent cannabis use (Jackson et al., 2008; Terry-McElrath et al., 2017). This distinction of between- and within-person use is essential to consider when examining the impact of co-use, particularly given recent within-person investigations showing cannabis use can lead to decreased drinking and related consequences both acutely, in the lab, and in the natural environment (Mallett et al., 2019; Metrik et al., 2019). These findings have important implications for prevention and intervention messages. In particular, results suggest that although using cannabis first is associated with less drinking within a day, heavier users of cannabis and more use of cannabis within the day is associated with increased drinking. In turn, heavier drinking (both within- and between-person) is also associated with greater consequences, suggesting that interventions for co-use should focus on both cannabis and alcohol use.

This is the first study to examine the impact of co-use ordering with between-person indicators of substance use and daily predictors of consequences. Between-person examinations (co-users versus single substance users) have found that co-use is associated with more consequences (Brière et al., 2011; Jackson et al., 2020; Patrick et al., 2016), even controlling for frequency of heavy drinking episodes (Shillington and Clapp, 2006, 2001). In contrast, more recent studies focusing on event-level associations have suggested that daily alcohol consumption may be the primary driver of consequences in co-use occasions (Lipperman-Kreda et al., 2017; Mallett et al., 2019, 2017; Metrik et al., 2016; Sokolovsky et al., 2020), and that using cannabis may not be a primary driver of increased risk. Rather, individual frequency of use of both substances and alcohol consumption (at the event or daily level) remain more proximal predictors of consequences experienced. Our findings contribute to this mounting evidence, suggesting that alcohol consumption, but not cannabis consumption, is a significant predictor of consequences within a co-use day, whereas order of substance use is not. However, our selection of some consequences may have been biased toward consequences that are more acutely experienced due to alcohol use (e.g. hangover, blackout), to the exclusion of consequences that are more pertinent to cannabis. Nonetheless, prior work from our group shows that college students are more likely to attribute most consequences to alcohol alone than either to cannabis or co-use (Jackson et al., 2020). More research examining specific cannabis-related consequences on co-use days is needed. Further, with the rapidly evolving legislation surrounding recreational cannabis use, the impact of co-use should be considered when regulating marketing and sales of alcohol and cannabis together. Finally, as cannabis is increasingly being considered for medicinal purposes, treatment providers and patients should make careful considerations regarding the use of cannabis among those in treatment for alcohol misuse.

4.1. Limitations

The current study should be understood in the context of its limitations. First, given the widely varying formulations and patterns of cannabis use, it was at the discretion of the participant to define a cannabis use event. Importantly, the potential impact of this limitation was mitigated somewhat by including edible use as a day-level covariate, as edible use differs most significantly from concentrate and flower product use (eating a substance once versus smoking or vaping in multiple “hits”) and by conducting within-person analyses. Second, although we sampled a diverse set of college students from three states differing in recreational cannabis laws, these findings may not generalize to other populations. Third, as discussed above, the consequences included in the daily surveys were derived primarily from subscales of alcohol consequences (Read et al., 2006), and, therefore, may not reflect consequences that are specific to co-use. Future studies should examine consequences that may be specific to co-use occasions or consequences that may be predicted by ordering. For instance, using alcohol first may predict blackout via increased alcohol use, whereas using cannabis first may predict driving under the influence, which we have found is more common than driving under the influence of alcohol alone (Jackson et al., 2020). Finally, this study focused specifically on day-level outcomes of co-use order, and therefore does not generalize to long-term outcomes that may result from co-use. Related to this, we measured order within the day, rather than within a specific event, resulting in some large time gaps between use occasions.

4.2. Conclusions

In summary, results indicated that using cannabis before alcohol within a co-use day predicted fewer drinks consumed but more cannabis uses. Order of substance use did not predict consequences experienced when controlling for consumption. Results further suggested that heavier use of alcohol and cannabis, both at the between- and within-person level, predicted increased use of the other substance within a co-use day. Finally, results suggested that heavier alcohol consumption at the between-person and within-person level predicted increased consequences on co-use days, whereas heavier cannabis use did not. Overall, results highlighted that order of substance use is a robust predictor of consumption within co-use days.

Supplementary Material

1

Highlights.

  • Alcohol and cannabis were relatively equally used first on co-use days.

  • When cannabis was used first, less alcohol and more cannabis were used.

  • Order of use did not predict number of consequences experienced.

  • More drinks per day predicted more consequences.

  • Between-person levels of alcohol use positively predicted consequences.

Role of Funding Sources:

This study was supported by R01 DA040880 (MPIs Jackson, White); T32 AA007459 (Gunn), K08 AA027551 (Gunn); T32 DA016184 (Sokolovsky, Stevens).

Footnotes

Publisher's Disclaimer: 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 proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Disclosures

The authors have no conflicts of interest to declare.

Conflict of Interest: All of the listed authors declare that they have no conflicts of interests.

1

Preliminary analyses examining the three most commonly endorsed consequences were also examined as binary outcomes with the same set of covariates. Order was not associated with any individual consequence.

2

Given that edible consumption is associated with delayed effects, we also conducted a set of analyses in which all days including edible use were removed from the model, and the patterns of results were consistent (i.e., all effects were in the same direction and at the same level of statistical significance).

3

We also conducted an additional analysis for model 3 in which we tested the interaction between order of use and within-person use frequency (cannabis and alcohol) and found no significant effects of either interaction on consequences.

References

  1. Adamson SJ, Kay-Lambkin FJ, Baker AL, Lewin TJ, Thornton L, Kelly BJ, Sellman JD, 2010. An improved brief measure of cannabis misuse: The Cannabis Use Disorders Identification Test-Revised (CUDIT-R). Drug Alcohol Depend 10.1016/j.drugalcdep.2010.02.017 [DOI] [PubMed]
  2. Arria AM, Caldeira KM, O’Grady KE, Vincent KB, Fitzelle DB, Johnson EP, Wish ED, 2008. Drug exposure opportunities and use patterns among college students: Results of a longitudinal prospective cohort study. Subst. Abus 29, 19–38. 10.1080/08897070802418451 [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Ballard ME, De Wit H, 2011. Combined effects of acute, very-low-dose ethanol and delta(9)-tetrahydrocannabinol in healthy human volunteers. Pharmacol. Biochem. Behav 97, 627–631. 10.1016/j.pbb.2010.11.013 [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Barrus D, Capogrossi K, Cates S, Gourdet C, Peiper N, Novak S, Lefever T, Wiley J, 2016. Tasty THC: Promises and challenges of cannabis edibles. Methods Rep (RTI Press. 2016. 10.3768/rtipress.2016.op.0035.1611 [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Blanco C, Okuda M, Wright C, Hasin DS, Grant BF, Liu SM, Olfson M, 2008. Mental health of college students and their non-college-attending peers: Results from the national epidemiologic study on alcohol and related conditions. Arch. Gen. Psychiatry 65, 1429–1437. 10.1001/archpsyc.65.12.1429 [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Brière FN, Fallu JS, Descheneaux A, Janosz M, 2011. Predictors and consequences of simultaneous alcohol and cannabis use in adolescents. Addict. Behav 36, 785–788. 10.1016/j.addbeh.2011.02.012 [DOI] [PubMed] [Google Scholar]
  7. Brooks ME, Kristensen K, van Benthem KJ, Magnusson A, Berg CW, Nielsen A, Skaug HJ, Mächler M, Bolker BM, 2017. glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. R J 9, 378–400. 10.32614/rj-2017-066 [DOI] [Google Scholar]
  8. Caldeira KM, Arria AM, O’Grady KE, Vincent KB, Wish ED, 2008. The occurrence of cannabis use disorders and other cannabis-related problems among first-year college students. Addict. Behav 29, 397–411. 10.1016/j.addbeh.2007.10.001 [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Center for Behavioral Health Statistics and Quality, 2016. Key substance use and mental health indicators in the United States: Results from the 2015 National Survey on Drug Use and Health (HHS Publication No. SMA 16–4984, NSDUH Series H-51)
  10. Chait LD, Perry JL, 1992. Factors influencing self-administration of, and subjective response to, placebo marijuana. Behav. Pharmacol 3, 545–552. [PubMed] [Google Scholar]
  11. Downey LA, King R, Papafotiou K, Swann P, Ogden E, Boorman M, Stough C, 2013. The effects of cannabis and alcohol on simulated driving: Influences of dose and experience. Accid. Anal. Prev 50, 879–886. 10.1016/j.aap.2012.07.016 [DOI] [PubMed] [Google Scholar]
  12. Earleywine M, Luba R, Slavin MN, Farmer S, Loflin M, 2016. Don’t wake and bake: morning use predicts cannabis problems. Addict. Res. Theory 24, 426–430. 10.1080/16066359.2016.1177027 [DOI] [Google Scholar]
  13. Ferrari SLP, Cribari-Neto F, 2004. Beta regression for modelling rates and proportions. J. Appl. Stat 31, 799–815. 10.1080/0266476042000214501 [DOI] [Google Scholar]
  14. Grotenhermen F, 2003. Pharmacokinetics and Pharmacodynamics of Cannabinoids. Clin. Pharmacokinet 42, 327–360. [DOI] [PubMed] [Google Scholar]
  15. Gunn RL, Norris AL, Sokolovsky A, Micalizzi L, Jennifer E, Barnett NP, 2018. Marijuana use is associated with alcohol use and consequences across the first 2 years of college. Psychol. Addict. Behav 32, 885–894. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Hardin JW, Hilbe JM, 2007. Generalized linear models and extensions Stata Press. [Google Scholar]
  17. Hartman RL, Brown TL, Milavetz G, Spurgin A, Gorelick DA, Gaffney G, Huestis MA, 2016. Controlled vaporized cannabis, with and without alcohol: Subjective effects and oral fluid-blood cannabinoid relationships. Drug Test. Anal 8, 690–701. 10.1002/dta.1839 [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Hartman RL, Brown TL, Milavetz G, Spurgin A, Gorelick DA, Gaffney G, Huestis MA, 2015. Controlled cannabis vaporizer administration: Blood and plasma cannabinoids with and without alcohol. Clin. Chem 61, 850–869. 10.1373/clinchem.2015.238287 [DOI] [PubMed] [Google Scholar]
  19. Hedeker D, 2005. Generalized Linear Mixed Models, in: Encyclopedia of Statistics in Behavioral Science John Wiley & Sons, Ltd. 10.1002/0470013192.bsa251 [DOI] [Google Scholar]
  20. Jackson KM, Sher KJ, Schulenberg JE, 2008. Conjoint developmental trajectories of young adult substance use. Alcohol. Clin. Exp. Res 32, 723–737. 10.1111/j.1530-0277.2008.00643.x [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Jackson KM, Sokolovsky AW, Gunn RL, White HR, 2020. Consequences of alcohol and marijuana use among college students: Prevalence rates and attributions to substance-specific versus simultaneous use. Psychol. Addict. Behav 34, 370–381. 10.1037/adb0000545 [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Johnson V, White HR, 1995. The relationship between work-specific and generalized stress and alcohol and marijuana use among recent entrants to the labor force. J. Drug Issues 10.1177/002204269502500203 [DOI]
  23. Kahler CW, Strong DR, Read JP, 2005. Toward efficient and comprehensive measurement of the alcohol problems continuum in college students: The brief Young Adult Alcohol Consequences Questionnaire. Alcohol. Clin. Exp. Res 29, 1180–1189. 10.1097/01.ALC.0000171940.95813.A5 [DOI] [PubMed] [Google Scholar]
  24. Lee CM, Cadigan JM, Patrick ME, 2017. Differences in reporting of perceived acute effects of alcohol use, marijuana use, and simultaneous alcohol and marijuana use. Drug Alcohol Depend 180, 391–394. 10.1016/j.drugalcdep.2017.08.029 [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Linden-Carmichael AN, Lanza ST, 2018. Drinking Patterns of College- and Non-College-Attending Young Adults: Is High-Intensity Drinking Only a College Phenomenon? Subst. Use Misuse 53, 2157–2164. 10.1080/10826084.2018.1461224 [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Lipperman-Kreda S, Gruenewald PJ, Grube JW, Bersamin M, 2017. Adolescents, alcohol, and marijuana: Context characteristics and problems associated with simultaneous use. Drug Alcohol Depend 179, 55–60. 10.1016/j.drugalcdep.2017.06.023 [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Lukas S, Benedikt R, Mendelson JH, Kouri E, Sholar M, Amass L, 1992. Marihuana attenuates the rise in plasma ethanol levels in human subjects. Neuropsychopharmacology 7, 77–81. [PubMed] [Google Scholar]
  28. Lukas SE, Orozco S, 2001. Ethanol increases plasma D 9 -tetrahydrocannabinol (THC) levels and subjective effects after marihuana smoking in human volunteers. Drug Alcohol Depend 64, 143–149. 10.1016/S0376-8716(01)00118-1 [DOI] [PubMed] [Google Scholar]
  29. Mallett KA, Turrisi R, Hultgren BA, Sell N, Reavy R, Cleveland M, 2017. When alcohol is only part of the problem: An event-level analysis of negative consequences related to alcohol and other substance use. Psychol. Addict. Behav 31, 307–314. 10.1037/adb0000260 [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Mallett KA, Turrisi R, Trager BM, Sell N, Linden-Carmichael AN, 2019. An examination of consequences among college student drinkers on occasions involving alcohol-only, marijuana-only, or combined alcohol and marijuana use. Psychol. Addict. Behav 33, 331–336. 10.1037/adb0000458 [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Merrill JE, Read JP, 2010. Motivational Pathways to Unique Types of Alcohol Consequences. Psychol. Addict. Behav 10.1037/a0020135 [DOI] [PMC free article] [PubMed]
  32. Metrik J, Aston ER, Gunn RL, MacKillop J, Swift R, Kahler CW, 2019. Marijuana’s effects on alcohol craving and consumption in a laboratory study, in: Alcoholism: Clinical and Experimental Research p. Suppl-S1, P286A.
  33. Metrik J, Caswell AJ, Magill M, Monti PM, Kahler CW, 2016. Sexual risk behavior and heavy drinking among weekly marijuana users. Stud. Alcohol Drugs 77, 104–112. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Metrik J, Gunn RL, Jackson KM, Sokolovsky AW, Borsari B, 2018. Daily patterns of marijuana and alcohol co-use among individuals with alcohol and cannabis use disorders. Alcohol. Clin. Exp. Res 42. 10.1111/acer.13639 [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Midanik LT, Tam TW, Weisner C, 2007. Concurrent and simultaneous drug and alcohol use: Results of the 2000 National Alcohol Survey. Drug Alcohol Depend 90, 72–80. 10.1016/j.drugalcdep.2007.02.024 [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. O’Hara RE, Armeli S, Tennen H, 2016. Alcohol and Cannabis Use among College Students: Substitutes or Complements? Addict. Behav 58, 1–6. 10.1016/j.addbeh.2016.02.004 [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Patrick ME, Terry-Mcelrath YM, Kloska DD, Schulenberg JE, 2016. High-intensity drinking among young adults in the United States: Prevalence, frequency, and developmental change. Alcohol. Clin. Exp. Res 40, 1905–1912. 10.1111/acer.13164 [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Perez-Reyes M, Hicks RE, Bumberry J, Jeffcoat ARJ, Cook CE, 1988. Interaction between marihuana and ethanol: Effects on psychomotor performance. Alcohol. Clin. Exp. Res 12, 268–276. [DOI] [PubMed] [Google Scholar]
  39. R Core Team, 2017. R: A Language and Environment for Statistical Computing
  40. Read JP, Kahler CW, Strong DR, Colder CR, 2006. Development and preliminary validation of the young adult alcohol consequences questionnaire. J. Stud. Alcohol 67, 169–177. 10.15288/jsa.2006.67.169 [DOI] [PubMed] [Google Scholar]
  41. Schulenberg JE, Johnston LD, O’Malley PM, Bachman JG, Miech RA, Patrick ME, 2018. Monitoring the Future national survey results on drug use, 1975–2017: Volume II, college students and adults ages 19–55. Ann Arbor
  42. Schulenberg JE, Patrick ME, 2012. Historical and developmental patterns of alcohol and drug use among college students: Framing the problem., in: College Drinking and Drug Use., Duke Series in Child Development and Public Policy Guilford Press, New York, NY, US, pp. 13–35. [Google Scholar]
  43. Shillington AM, Clapp JD, 2006. Heavy alcohol use compared to alcohol and marijuana use: Do college students experience a difference in substance use problems? J. Drug Educ 36, 91–103. [DOI] [PubMed] [Google Scholar]
  44. Shillington AM, Clapp JD, 2001. Substance use problems reported by college students: Combined marijuana and alcohol use versus alcohol-only use. Subst. Use Misuse 36, 663–672. 10.1081/JA-100103566 [DOI] [PubMed] [Google Scholar]
  45. Simons JS, Dvorak RD, Merrill JE, Read JP, 2012. Dimensions and severity of marijuana consequences: Development and validation of the Marijuana Consequences Questionnaire (MACQ). Addict. Behav 37, 613–621. 10.1016/j.addbeh.2012.01.008 [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Slutske WS, 2005. Alcohol use disorders among US college students and their non-college-attending peers. Arch. Gen. Psychiatry 62, 321–327. 10.1001/archpsyc.62.3.321 [DOI] [PubMed] [Google Scholar]
  47. Slutske WS, Hunt-Carter EE, Nabors-Oberg RE, Sher KJ, Bucholz KK, Madden PAF, Anokhin A, Heath AC, 2004. Do college students drink more than their non-college-attending peers? Evidence from a population-based longitudinal female twin study. J. Abnorm. Psychol 113, 530–540. 10.1037/0021-843X.113.4.530 [DOI] [PubMed] [Google Scholar]
  48. Sokolovsky AW, Gunn RL, Micalizzi L, White HR, Jackson KM, 2020. Alcohol and marijuana co-use: Consequences, subjective intoxication, and the operationalization of simultaneous use. Drug Alcohol Depend 212, 107986. 10.1016/j.drugalcdep.2020.107986 [DOI] [PMC free article] [PubMed] [Google Scholar]
  49. Stevens AK, Sokolovsky AW, Treloar Padovano H, White HR, Jackson KM, 2020. Heaviness of Alcohol Use, Alcohol Problems, and Subjective Intoxication Predict Discrepant Drinking Reports in Daily Life. Alcohol. Clin. Exp. Res 44, 1468–1478. 10.1111/acer.14362 [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. Stinson FS, Ruan WJ, Pickering R, Grant BF, 2006. Cannabis use disorders in the USA: Prevalence, correlates and co-morbidity. Psychol. Med 36, 1447. 10.1017/S0033291706008361 [DOI] [PubMed] [Google Scholar]
  51. Subbaraman MS, Kerr WC, 2015. Simultaneous vs. concurrent use of alcohol and cannabis in the National Alcohol Survey. Alcohol. Clin. Exp. Res 39, 872–879. 10.1111/nyas.12666.Saccadic [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Substance Abuse and Mental Health Services Administration, 2019. Key Substance Use and Mental Health Indicators Results from the 2018 National in the United States: Survey on Drug Use and Health (HHS Publication No. PEP19–5068, NSDUH Series H-54) Rockville, MD. [Google Scholar]
  53. Terry-McElrath YM, O’Malley PM, Johnston LD, Bray BC, Patrick ME, Schulenberg JE, 2017. Longitudinal patterns of marijuana use across ages 18–50 in a US national sample: A descriptive examination of predictors and health correlates of repeated measures latent class membership. Drug Alcohol Depend 171, 70–83. 10.1016/j.drugalcdep.2016.11.021 [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. Terry-McElrath YM, Patrick ME, 2018. Simultaneous alcohol and marijuana use among young adult drinkers: Age-specific changes in prevalence from 1977 to 2016. Alcohol. Clin. Exp. Res 42, 2224–2233. [DOI] [PMC free article] [PubMed] [Google Scholar]
  55. White HR, Kilmer JR, Fossos-Wong N, Hayes K, Sokolovsky AW, Jackson KM, 2019. Simultaneous alcohol and marijuana use among college students: Patterns, correlates, consequences, and norms. Alcohol. Clin. Exp. Res 43, 1545–1555. [DOI] [PMC free article] [PubMed] [Google Scholar]
  56. White HR, LaBouvie EW, 1989. Towards the Assessment of Adolescent Problem Drinking. J. Stud. Alcohol 50, 30–37. [DOI] [PubMed] [Google Scholar]
  57. Yurasek AM, Aston ER, Metrik J, 2017. Co-use of alcohol and cannabis: a review. Curr. Addict. Reports 4, 184–193. 10.1007/s40429-017-0149-8 [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

1
NIHMS1637906-supplement-1.docx (248.5KB, docx)

RESOURCES