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. Author manuscript; available in PMC: 2022 Jul 1.
Published in final edited form as: Alcohol Clin Exp Res. 2021 Jun 5;45(7):1458–1467. doi: 10.1111/acer.14629

Dual trajectories of cannabis and alcohol use among young adults in a state with legal nonmedical cannabis

Katarina Guttmannova 1, Charles B Fleming 1, Isaac C Rhew 1, Devon Alisa Abdallah 1, Megan E Patrick 2, Jennifer C Duckworth 3, Christine M Lee 1
PMCID: PMC8357031  NIHMSID: NIHMS1703961  PMID: 34089527

Abstract

Background:

Understanding the nature of the association between cannabis and alcohol use within individuals over time in the era of legalized cannabis is of crucial importance for assessing the public health consequences of increasing cannabis use. An important unanswered question is whether cannabis and alcohol use substitute for one another. Specifically, is greater use of one substance associated with less use of the other substance (i.e., a negative association) or are the substances complementary and their association positive?

Methods:

We used 24 consecutive months of data on a young adult sample (n = 774; 56% female, age 18–25 during the study) who drank alcohol in the year prior to enrollment. The sample was recruited in Washington State in 2015/2016 (after legalization of nonmedical cannabis) using media advertisements and community flyers and outreach. Using parallel process latent growth curve models, we assessed three types of association between cannabis and alcohol use across the 24-month period: (1) an association between average levels of cannabis and alcohol use; (2) an association between rates of change in cannabis and alcohol use; and (3) correlations between shorter-term deviations/fluctuations off of longer-term trajectories of level and change in cannabis and alcohol use.

Results:

We found a positive association between the average frequency of cannabis and alcohol use; individuals who used cannabis more frequently on average also drank alcohol more frequently on average. Change over time in cannabis use was positively associated with change in alcohol use. There was also a contemporaneous positive association between fluctuations in cannabis and alcohol use.

Conclusions:

Overall, we found no evidence of substitution. Rather, the results suggest a complementary relationship between cannabis and alcohol use, such that the use of cannabis and alcohol rises and falls together.

Keywords: alcohol use, cannabis use, substitution, young adults

INTRODUCTION

Liberalization of cannabis policy at the state and federal levels has raised concern that changes in availability, cost, and potency of cannabis may lead to increases in misuse of cannabis, with deleterious public health consequences (Cambron et al., 2017; Caulkins et al., 2016; Pacula & Sevigny, 2014a; Volkow et al., 2016). Some have argued, however, that consequences of increased cannabis use will be outweighed by benefits of legalization. These benefits include reductions in the number of individuals experiencing arrest and incarceration and the associated adverse human and societal costs, increases in tax revenue from cannabis sales, and reductions in the source of revenue for criminal organizations (for discussion see, e.g., Hawken et al., 2013; Kilmer, 2017; Pacula & Sevigny, 2014a, 2014b). An additional pro-legalization argument is that cannabis use may substitute for misuse of alcohol and other drugs leading to substantial benefits and savings if cannabis use has less deleterious public health consequences than alcohol or other substance use (e.g., Anderson & Rees, 2014; for critical review see Guttmannova et al., 2016; Pacula & Sevigny, 2014a, 2014b; Smart & Pacula, 2019). However, it is also possible that cannabis and alcohol complement one another, in which case increases in cannabis use in a context where cannabis has been legalized for nonmedical use among adults could be accompanied by increases in alcohol consumption (e.g., Wen et al., 2015) and related public health and safety costs (Guttmannova et al., 2016; Hall, 2015, 2017; Pacula & Sevigny, 2014a, 2014b).

The past decade and a half have been characterized by increases in cannabis use among young adults (Schulenberg et al., 2020; Substance Abuse & Mental Health Services Administration, 2020), but there is no strong and consistent evidence that those increases were greater in states that liberalized their cannabis laws. Cerdá et al. (2020) examined data from the National Survey of Drug Use and Health and found no statistically significant increases in the prevalence of any and frequent cannabis use or cannabis use disorder among young adults (18–25 years of age) before and after enactment of nonmedical or “recreational” cannabis laws. In terms of other substance use and its association with the loosening of state-level restrictions on cannabis, the evidence is also mixed (for review see, e.g., Darnell, 2020; Guttmannova et al., 2016; Risso et al., 2020; Smart & Pacula, 2019). Most recently, Veligati et al. (2020) demonstrated that neither alcohol nor cigarette consumption (as measured by state tax receipt data) has increased or decreased as a result of “recreational” and medical cannabis legalization. Evidence regarding substitution versus complementarity in the association between cannabis and other substances likely remains mixed because these population-level studies do not allow examination of whether intraindividual change over time in use of one substance is associated with intraindividual change in use of another (for a brief review of design and dataset recommendations, see, e.g., Guttmannova et al., 2016, 2019).

Thus, studies that track the associations between substances within individuals are needed to more clearly elucidate the possibility of substitution; that is, whether increases in cannabis use are associated with decreases in alcohol use over time. Cross-sectional data on general population samples of adolescents and adults consistently indicate positive correlations among use of cannabis and alcohol (e.g., Fleming et al., 2016). There is some evidence of substitution between alcohol and cannabis among medical cannabis patients (e.g., Hayat & Piper, 2020; Reiman, 2009; for review, see Subbaraman, 2014) but young adults who use cannabis are also more likely to drink alcohol (for review, see, e.g., Yurasek et al., 2017), and daily cannabis use predicts greater amount of daily alcohol intake (Gunn et al., 2018; Lee et al., 2020). Event-level data have also indicated that, among college students, there is a positive association between overall levels of cannabis use and alcohol consumption, as well as consumption on a given occasion (O’Hara et al., 2016). Additional evidence is needed on whether changes in the two types of substance use, either as trends across time or as shorter-term fluctuations in use, are positively or negatively correlated, particularly in the context where the use of both substances is legal for adults. Even if overall levels are positively correlated, it is possible that an increase in cannabis use will be associated with decreases in alcohol use, either across time or within a shorter time period, especially if the use of both substances has the same legal consequences and cannabis use is perceived as having fewer individual harms. Thus, longitudinally and in the context where both cannabis and alcohol have been legalized for nonmedical use among those 21 or older, there could be a negative association between the two substances, which would point to substitution with respect to these dimensions of within-individual change. Or, consistent with other studies in general populations, the positive association could also be evidenced over time indicating that as cannabis use increases, alcohol use also increases. This would be particularly problematic especially during the young adult years, a vulnerable period marked by continued brain development and acquisition of vital educational, labor-market, and personal roles that could be derailed by increases in alcohol and cannabis use (e.g., Arria et al., 2015; Batalla et al., 2013; Brook et al., 2013; Gorey et al., 2019; Meda et al., 2017; Yurasek et al., 2017).

The present study used 24 consecutive months of data on a young adult sample in Washington State, where cannabis is legal for those 21 or older, to examine longitudinal associations between cannabis and alcohol use. Using parallel process growth models of the two types of substance use, we examined the following three research questions: (1) Do young adults who use cannabis more on average drink more on average?; (2) Are rates of change in cannabis use across two years positively or negatively associated with rates of change in alcohol use across that same time period?; and (3) Are short-term increases or decreases in cannabis use relative to individual trajectories of change in cannabis use correlated with short-term deviations/fluctuations off of trajectories of change in alcohol use?

METHODS

Participants and procedures

Data come from 774 young adults who were part of a longitudinal study on substance use and young adult social role transitions (Lee, Cadigan, & Patrick, 2017; Patrick, Fairlie, & Lee, 2018). At the time participants met eligibility for the project, participants were age 18 to 23 years, had reported consuming alcohol in the prior year, lived within 60 miles of the study office in Seattle, WA, and were willing to come to the study office for consent and completion of a baseline assessment. From January 2015 to January 2016, we used a multimethod recruitment strategy that included online, print, and social media advertisements, posted community flyers, outreach at community colleges, and friend referral to recruit participants. Those interested in being part of the project completed an online eligibility survey followed by an in-person session in the study offices, during which we verified identity and age, explained study procedures, and obtained informed consent. Immediately after enrolling in the project, participants completed an online baseline assessment while still in the study office, for which they received a $40 gift card.

Beginning the first day of the subsequent month, participants completed 24 consecutive months of online surveys. Participants did monthly surveys within seven to 10 days at the beginning of each calendar month. Most survey items asked about experiences from the previous calendar month. We emailed Amazon gift card codes as compensation for each completed survey (up to $680 total). The University of Washington’s Institutional Review Board approved all procedures.

Of the 779 participants enrolled in the project, five were excluded from the current study because they did not complete at least one monthly survey in which alcohol and cannabis use were assessed. The analytic sample’s (n = 774) mean age at baseline was 21.11 years (SD = 1.70), and 56.2% of the sample reported sex at birth as female. In this study, 8.9% participants identified as Hispanic/Latinx. Of those participants who identified as non-Hispanic/Latinx, 55.0% identified as White, 17.7% as Asian, 9.8% as multiracial, 4.5% as Black/African American, 0.7% as Indian/Alaskan Native, 0.5% as Native Hawaiian/Pacific Islander, and 2.8% as “other”. At the beginning of the study, 74.7% of participants were in school and 59.4% were employed at least part-time. Ten participants were married, and nine had at least one child. Thirty-four percent of participants reported scores of eight or higher on the Alcohol Use Disorder Identification Test (AUDIT; Babor et al., 2001) and 26% reported scores of eight or higher on the Cannabis Use Disorder Identification Test-Revised (CUDIT-R; Adamson et al., 2010) denoting hazardous levels of drinking and cannabis use, respectively. Retention rates were high with more than 70% completing 80% or more of their monthly surveys.

Measures

Substance use

For measures of both cannabis and alcohol use in each month, we used ordinal measures with seven categories capturing frequency of use. The monthly alcohol use measure was based on the item, “How often did you usually have any kind of drink containing alcohol?” (NIAAA, 2003). Response options were as follows: 0 = Never, 1 = Once a month, 2 = 2 to 3 days a month, 3 = 1 day a week, 4 = 2 days a week, 5 = 3 to 4 days a week, 6 = 5 to 6 days a week, and 7 = Every day. Due to sparse endorsement, the top two categories were collapsed in a 5 or more-days-per-week category. The monthly cannabis use measure was based on the item, “In the past 30 days, how many days did you use marijuana?” In the monthly surveys, we used the term “marijuana” since this is the term the majority of young adults use. We defined “marijuana” as “any form of the drug cannabis, including marijuana (weed, pot), hashish or kief, and any method of use, including dried buds/flowers/leaves for smoking or in edibles, or hash oil.” Responses were recoded to correspond to the alcohol use variable into the following categories: 0 = Never, 1 = Once a month, 2 = 2 to 3 days a month, 3 = 1 day a week, 4 = 2 days a week, 5 = 3 to 4 days a week, 6 = 5 to 6 days a week, and 7 = Every day.

Covariates

Biological sex (0 = male, 1 = female), age at baseline, and race/ethnicity (mutually exclusive dummy-codes for Hispanic/Latinx, non-Hispanic/Latinx Asian, and other—combining the aforementioned categories with relatively low prevalence; non-Hispanic/Latinx White served as the reference group) were included as covariates.

Data analysis

Analyses were conducted with Mplus 8.4 (L. K. Muthén & Muthén, 1998–2019). After examining descriptive data on substance use across months, we used latent growth models (Curran & Hussong, 2003; Duncan et al., 2006; McArdle, 1991) to capture the levels (intercepts), rates of change (slopes), and monthly fluctuations of cannabis and alcohol use frequency across the two-year period and to assess how these elements of substance use trajectories were associated across the two substances over time. For both substances, loadings of slope factors were specified so that the intercept represented the middle of the two-year study period and the slope represented linear change. Although other dimensions of change, such as acceleration or deceleration, that would be captured by additional growth factors are possible, the linear change addressed our second research question (i.e., Is rate of change in cannabis use across two years associated with rate of change in alcohol use across that same time period?) and this parallel process linear growth model specification showed excellent fit to the data. Growth factors were regressed on model covariates (age, sex, race/ethnicity) and covariances of growth factors were estimated, as were covariances among residuals for concurrent indicators of cannabis and alcohol use for each study month. Figure 1 depicts the model tested and the associations of interest. We applied the diagonally weighted least square estimation with mean and variance (WLSMV) correction to accommodate the distributional properties of the outcomes (ordered categorical data) and model complexity as well as to reduce potential bias due to missing data (Hox et al., 2010; Muthén et al., 1997). Two sets of sensitivity analyses were specified. First, to assess whether associations differed for individuals over or under age 21, given that purchase of both alcohol and cannabis in Washington State is legal starting at age 21, we ran multiple-group models, comparing fit of models with parameters of interest constrained and unconstrained across the two age-groups (i.e., those who were below 21 vs. 21 or older at baseline). Second, we assessed whether the patterns of associations were similar when consistent nonusers of cannabis were excluded (n = 171 of participants indicated they did not use cannabis in any of the monthly assessments and had at least 66% of nonmissing data over time—i.e., were assessed at least 16 out of 24 monthly times).

FIGURE 1.

FIGURE 1

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Model of longitudinal and concurrent associations between alcohol and cannabis use

RESULTS

Descriptive information on substance use

Descriptive statistics for alcohol and cannabis use, based on all the monthly data, are shown in Table 1. Approximately 22% did not drink in a given month; approximately 68% did not use cannabis.

TABLE 1.

Descriptive statistics of outcome variables across time

M1 M2 M3 M4 M5 M6 M7 M8 M9 M10 M11 M12 M13 M14 M15 M16 M17 M18 M19 M20 M21 M22 M23 M24
Outcome % % % % % % % % % % % % % % % % % % % % % % % %
Cannabis use
 Never 63.8 62.3 68.0 66.2 68.9 67.0 66.2 66.4 66.7 68.9 68.9 67.3 69.1 71.7 69.7 68.3 64.6 67.5 66.9 67.4 70.8 70.3 68.3 69.2
 Once/month 6.4 8.2 6.5 6.2 5.7 5.3 6.7 8.0 6.2 5.3 6.4 5.4 5.3 5.3 5.0 4.9 6.0 5.8 5.4 5.3 5.3 4.1 5.3 4.2
 2–3 days/ month 7.1 6.4 4.2 5.2 4.1 6.3 5.9 5.5 6.0 5.2 5.5 6.5 5.8 4.2 5.3 5.4 8.5 7.3 6.4 7.5 4.2 5.7 6.5 5.7
 1 day/ week 1.2 1.1 1.9 1.3 1.6 1.2 1.1 2.1 1.9 2.0 1.5 1.2 2.2 1.5 1.5 2.7 1.9 1.7 2.0 1.1 1.8 0.9 1.4 1.5
 2 days/ week 6.1 5.4 4.7 4.1 3.9 3.6 2.9 3.1 4.2 3.7 2.8 4.3 3.0 2.2 3.6 3.2 3.6 3.6 2.5 2.6 2.7 3.0 3.5 3.4
 3–4 days/ week 3.6 5.6 4.5 4.8 5.7 4.8 5.1 3.4 3.7 4.0 3.8 3.1 3.7 3.5 4.3 4.2 3.6 2.4 3.9 2.6 3.4 4.4 3.9 4.9
 5–6 days/week 4.8 5.0 4.2 4.4 4.1 4.4 5.9 6.0 4.9 5.3 4.7 5.7 4.2 5.3 4.5 4.7 5.1 4.2 7.0 5.6 4.6 4.4 3.2 3.7
 Everyday 7.0 6.1 6.0 7.8 6.2 7.4 6.1 5.5 6.3 5.7 6.4 6.3 6.8 6.3 6.2 6.7 6.7 7.6 5.9 7.9 7.2 7.1 7.9 7.3
Alcohol use
 Never 17.8 17.2 21.5 21.8 24.2 22.3 23.9 22.7 24.4 22.5 23.4 21.1 22.5 20.4 25.6 23.2 23.0 22.3 23.0 22.6 23.6 22.7 25.0 22.9
 Once/month 12.9 14.6 12.4 12.0 9.6 9.9 9.8 9.3 11.7 10.7 10.9 12.9 10.8 12.0 9.5 10.0 9.5 10.2 8.6 11.4 11.4 11.1 8.8 11.8
 2–3 days/month 21.9 21.5 21.9 20.5 21.5 21.0 18.4 20.7 19.6 18.9 17.4 19.7 22.5 20.3 18.4 20.8 21.2 18.7 17.5 17.3 21.4 18.0 16.4 17.4
 1 day/week 14.4 15.8 15.2 15.0 15.5 17.0 17.7 18.0 16.8 20.3 17.1 16.0 14.2 15.8 15.9 16.5 13.4 17.7 20.4 17.3 14.5 14.8 18.0 16.7
 2 days/week 18.0 17.9 15.1 16.3 15.0 17.3 15.7 15.5 13.9 13.3 17.9 15.7 15.2 15.2 15.7 14.9 18.4 17.1 15.1 17.5 15.7 18.4 17.2 17.4
 3–4 days/week 12.4 9.7 11.5 11.6 10.1 8.7 10.5 10.7 10.4 10.4 9.5 11.4 11.6 13.2 11.3 10.2 11.2 9.6 11.0 10.4 9.2 10.6 10.7 9.8
 5+ days/week 2.5 3.3 2.3 2.9 4.2 3.7 4.0 3.0 3.1 3.9 3.7 3.3 3.3 3.2 3.5 4.4 3.2 4.5 4.4 3.6 4.3 4.4 3.8 3.9
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Abbreviation: M = Month of assessment.

Parallel process growth model

Figure 1 depicts the tested model and highlights the parameters of interest in terms of the associations among growth factors and concurrent associations among residuals. Model fit was excellent as indicated by CFI and TLI greater than 0.95 and RMSEA less than 0.05 (Hu & Bentler, 1999). Table 2 shows the fit statistics and the estimates representing the parameters of interest. Table 3 shows the associations between the demographic covariates and the latent factors. To answer our first question, there was a positive overall association between average levels of cannabis and alcohol use captured by the positive correlation between intercepts (Path A, see Figure 1 and Table 2). Addressing our second question, there was also a positive association between rates of change in substance use across the two-year time span indicating that an increase in cannabis use was associated with an increase in alcohol use; this is captured by the positive correlation between the linear growth factors (Path B, see Figure 1 and Table 2). This association was small and statistically significant only for the standardized (but not the unstandardized) estimate. Finally, there was also a positive association between month-to-month fluctuations in cannabis and alcohol use, captured by the positive within-time-point correlation between residuals for cannabis and alcohol use (Path C, see Figure 1 and Table 2). In other words, individuals used alcohol more often in a month (relative to their expected frequency of use given their average frequency and rate of change in frequency of alcohol use across 24 months) when they also used cannabis more often (relative to their trajectories of cannabis use).

TABLE 2.

Fit statistics and estimates representing the parameters of interest in the parallel process growth model of cannabis and alcohol use

Tested associations coeff S.E. stand coeff p-Value
Association between average levels of cannabis and alcohol use (Path A in Figure 1) 1.156 0.231 0.323 <0.001
Association between change in cannabis and change in alcohol use (Path B in Figure 1) 0.114 0.064 0.110 0.046
Concurrent association between cannabis and alcohol use (Path C in Figure 1) 0.160 0.034 0.160 <0.001
Association between average level and change in alcohol use 0.247 0.116 0.260 0.004
Association between average level and change in cannabis use 1.924 0.956 0.491 <0.001
Association between average level of alcohol use and change in cannabis use 0.257 0.149 0.141 0.027
Association between average level of cannabis use and change in alcohol use 0.128 0.102 0.063 0.180
Fit Statistics
 CFI 0.990
 TLI 0.991
 RMSEA 0.032
 90% CI for RMSEA (0.030; 0.034)
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Abbreviations: Coeff = unstandardized coefficient; SE = standard error; stand coeff = standardized coefficient; p-value = p-value associated with the standardized coefficient; coefficients in bold are statistically significant at p-value < 0.05; CFI = Comparative Fit Index; TLI = Tucker–Lewis Index; RMSEA = Root Mean Square Error of Approximation; CI = confidence interval.

TABLE 3.

Associations between the demographic covariates and the latent factors in the final parallel process growth model of cannabis and alcohol use

Cannabis Intercept Cannabis Slope Alcohol Intercept Alcohol Slope
Predictors Coeff SE Stand coeff p-value Coeff SE Stand coeff p-value Coeff SE Stand coeff p-value Coeff SE Stand coeff p-value
Age Age −0.050 0.071 −0.017 0.481 −0.078 0.047 −0.055 0.086 0.146 0.031 0.107 <0.001 −0.059 0.020 −0.079 0.005
Sex Female −0.477 0.246 −0.167 0.045 0.021 0.163 0.015 0.897 −0.166 0.100 −0.122 0.092 0.057 0.066 0.077 0.391
Race/ethnicity Non-Hispanic Asian −1.854 0.409 −0.647 <0.001 −0.265 0.277 −0.186 0.301 −0.876 0.144 −0.642 <0.001 −0.054 0.091 −0.073 0.545
Non-Hispanic Other −0.611 0.322 −0.213 0.052 −0.213 0.213 −0.149 0.304 −0.545 0.131 −0.399 <0.001 −0.079 0.107 −0.107 0.452
Hispanic −0.551 0.431 −0.192 0.194 0.068 0.303 0.048 0.824 −0.381 0.190 −0.279 0.043 0.001 0.135 0.001 0.996
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Abbreviations: Coeff = unstandardized coefficient; SE = standard error; stand coeff = standardized coefficient; p-value = p-value associated with the standardized coefficient; coefficients in bold are statistically significant at p-value < 0.05.

The associations between covariates (age at baseline, sex, and race/ethnicity) and the growth factors indicate that, controlling for the other covariates in the model, older participants had higher average frequency and smaller change in alcohol use. Non-Hispanic/Latinx White participants had higher average frequency of alcohol use than participants who identified as non-Hispanic/Latinx Asian, non-Hispanic/Latinx Other, and Hispanic/Latinx participants, although their change in alcohol use was not statistically different from the other racial/ethnic groups. Non-Hispanic/Latinx White participants had also higher average frequency of cannabis use than non-Hispanic/Latinx Asian participants. Overall, females reported significantly lower average frequency of cannabis use than males. No other sex differences were statistically significant.

Sensitivity analyses

The first set of sensitivity analyses examined whether these associations were similar for those below 21 and those 21 or older, given that purchase of both alcohol and cannabis in Washington State is legal starting at age 21. We analyzed these associations in a multiple-group model with the parameters of interest constrained to be equal across age-groups. The test of equality of constraints supported the conclusion that the associations between latent factors were not statistically different for the two age-groups (chi-square(11) = 7.68; p = 0.742).

The second set of sensitivity analyses tested whether the patterns of associations were similar when consistent nonusers of cannabis were excluded and these results are shown in Table 4. The patterns of associations were similar to the full sample model, with estimates of all three associations of interest positive and statistically significant.

TABLE 4.

Sensitivity analyses: Fit statistics and estimates representing the parameters of interest in the final parallel process growth model of cannabis and alcohol use for the sample that excludes consistent nonusers of cannabis

Tested Associations Coeff SE Stand coeff p-Value
Association between average levels of cannabis and alcohol use (Path A in Figure 1) 0.492 0.145 0.183 <0.001
Association between change in cannabis and change in alcohol use (Path B in Figure 1) 0.131 0.063 0.136 0.016
Concurrent association between cannabis and alcohol use (Path C in Figure 1) 0.172 0.035 0.172 <0.001
Association between average level and change in alcohol use 0.192 0.113 0.224 0.031
Association between average level and change in cannabis use 1.363 0.669 0.450 <0.001
Association between average level of alcohol use and change in cannabis use 0.105 0.104 0.065 0.268
Association between average level of cannabis use and change in alcohol use 0.116 0.083 0.072 0.134
Fit Statistics
 CFI 0.985
 TLI 0.987
 RMSEA 0.035
 90% CI for RMSEA (0.032; 0.037)
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Abbreviations: Coeff = unstandardized coefficient; SE = standard error; stand coeff = standardized coefficient; p-value = p value associated with the standardized coefficient; coefficients in bold are statistically significant at p-value < 0.05; CFI = Comparative Fit Index; TLI = Tucker–Lewis Index; RMSEA = Root Mean Square Error of Approximation; CI = confidence interval.

DISCUSSION

Understanding the nature of associations between cannabis and alcohol use in young adulthood is of critical public health and policy importance. Past population- as well as event-level studies have shown positive associations between cannabis and alcohol use among young adults (e.g., Gunn et al., 2018; Wen et al., 2015; Yurasek et al., 2017) but there is also some evidence of substitution relationship between cannabis and alcohol (for review, see, e.g., Guttmannova et al., 2016; Risso et al., 2020; Subbaraman, 2014, 2016) and longitudinal studies, particularly in legalized policy context, are needed to enhance this understanding (Guttmannova et al., 2019). Results from our unique longitudinal study of young adults residing in a state where both cannabis and alcohol are legal to use for those 21 or older show a positive association between the average frequencies of cannabis and alcohol use, with individuals who use cannabis more frequently on average also drinking more frequently on average. Our findings also indicate that the average rate of change in cannabis use over a two-year period was positively associated with average rate of change in alcohol use, although this association was small. Finally, controlling for the level and rate of change, we found a positive association between concurrent monthly deviations in cannabis and alcohol use off of two-year trajectories. In other words, months with unusually frequent cannabis use were associated with unusually frequent alcohol use in that same month. Taken together, these results do not support the substitution hypothesis that young adults who increase their cannabis use, either in terms of rate of change in cannabis use across two years or in terms of concurrent increases, would decrease their alcohol use with respect to either of those dimensions of within-person change. Instead, our findings point to a modest complementary relationship between cannabis and alcohol use and are in line with past research that demonstrated the positive association between these two substances (e.g., Gunn et al., 2018; Lee et al., 2020; O’Hara et al., 2016).

Similar to prior research with national samples (e.g., Schulenburg et al., 2020), females reported lower average frequency of cannabis use than males and non-Hispanic/Latinx White young adults reported higher average frequency of cannabis and alcohol use than some other racial/ethnic groups. No differences by sex or racial/ethnic group were observed for changes in cannabis or alcohol use and the tested associations between cannabis and alcohol use did not differ significantly by whether individuals were below 21 and 21 or older. Future research should explore additional potential moderators of associations between cannabis and alcohol use.

It is noteworthy that months with higher frequency of cannabis use were also linked with higher frequency of alcohol use, suggesting that there may be time periods when young adults are more likely to engage in both high-risk cannabis and alcohol use. Cannabis and alcohol use may vary by calendar month (Fleming et al., 2021), and special events or holidays may increase risk for heavy use (Bravo et al., 2017; Lee et al., 2006; Lewis et al., 2009; Patrick & Lee, 2012). In tailoring preventive interventions that address time periods of heightened risk, our results suggest the need for addressing both cannabis and alcohol use since short-term increases in one substance are likely to be accompanied by short-term increases in the other. Future studies could examine whether young adults are more likely to engage in simultaneous use, that is, using cannabis and alcohol at the same time so that their effects overlap, during these times of heightened risk. Some studies have found that simultaneous use was associated with greater negative outcomes than using cannabis or alcohol alone (Duckworth & Lee, 2019; Egan et al., 2019; Lee et al., 2017; Lee et al., 2020; Lipperman-Kreda et al., 2017). Future studies should also focus on associated health risk behaviors such as driving under the influence of both substances and on malleable risk factors that predict such behaviors to inform prevention messaging and preventive intervention efforts. Finally, future research could examine whether particularly heavy cannabis or alcohol use at one time period is associated with increased simultaneous use during the same time period.

Limitations and additional directions for future research

This intensive, rich longitudinal study is not without limitations. Although we had 24 consecutive assessments of substance use, the cannabis and alcohol use data were based on self-report and retrospectively asked about the prior month, which may be subject to bias. However, the recall period for monthly assessments was relatively short, which should improve accuracy. Second, this study examined frequency of alcohol and cannabis use, rather than quantity consumed. It is possible that substitution still occurs at the event or day-level and future studies, particularly those that involve repeated reports in real time using ecological momentary assessment would be useful to address this issue. Third, ours is a community sample of young adults who reported drinking alcohol in the past year and may not be representative of a general population of young adults in Washington State. Fourth, although age was included as a covariate in our analyses and associations between cannabis and alcohol use were similar for individuals under and over age 21, we modeled trajectories of substance use with reference to month of the study rather than age. This modeling approach matched our research questions concerning the nature of associations in young adults in general but did not assess trajectories of substance use by age. Future studies could explicitly incorporate age as time in their analyses and test more nuanced developmental hypotheses. An interesting extension of this research in future studies could involve examination of motivations to use substances. It may be that substitution of cannabis for alcohol is specific to young adults who use for coping reasons. For example, O’Hara et al. (2016) found positive association between cannabis and alcohol use among college students whose primary reason for drinking and cannabis use was social. In contrast, for those who reported using these substances to cope with stressful events, there was an evidence of negative association (or substitution) between alcohol and cannabis so that the more alcohol they consumed on an event, the less likely they were to use cannabis (O’Hara et al., 2016). Understanding the association between different types of motives and patterns of use as they relate to substitution vs. complementarity would be particularly informative for interventions aimed at reducing use and cessation of misuse of these substances.

Clinical implications

As both alcohol and cannabis use peak in young adulthood (e.g., Schulenberg et al., 2020; Terry-McElrath et al., 2017) and cannabis use is increasing nationally (SAMHSA, 2020), understanding how potential trajectories of young adult cannabis and alcohol use are associated and linked over time is critical for informing content and timing of prevention and intervention programs. As public health and individual harms are documented from high-risk alcohol use (e.g., Hingson et al., 2009), programs that support prevention and reductions in alcohol use are important. Due to the positive association between alcohol and cannabis use, current efficacious alcohol interventions (e.g., Dimeff, 1999) could incorporate components on cannabis use and focus on the hazardous simultaneous use of alcohol and cannabis since most of those who use both substances use report such use (Patrick et al., 2019; Subbaraman & Kerr, 2015). Findings from this study conducted in a state with legal non-medical or “recreational” cannabis use suggest that efforts would be worthwhile to prevent initiation and escalations of cannabis use during young adulthood, which in turn may be associated with reduced negative harms of alcohol use, both individually and at the population-level.

CONCLUSIONS

Misuse of substances such as cannabis and alcohol can interfere with the transition to adulthood. Today’s generation of young adults is coming of age and transitioning to young adulthood in the era of legalized cannabis. We found little evidence at the individual level of substitution between cannabis and alcohol such that increases in cannabis use would result in decreases in alcohol use in a sample of young adults.

Acknowledgements:

The project described was supported by National Institute on Alcohol Abuse and Alcoholism grants R01AA022087 (PI: Lee) and manuscript preparation was supported by R01AA027496 (PI: Lee). Partial support for this research in terms of analysis infrastructure came from a Eunice Kennedy Shriver National Institute of Child Health and Human Development research infrastructure, Grant/Award Number: P2CHD042828; to the Center for Studies in Demography & Ecology at the University of Washington. The content of this manuscript is solely the responsibility of the author(s) and does not necessarily represent the official views of the National Institute on Alcohol Abuse and Alcoholism or the National Institutes of Health.

Funding information: National Institute on Alcohol Abuse and Alcoholism, Grant/Award Number: R01AA022087 and R01AA027496; and a Eunice Kennedy Shriver National Institute of Child Health and Human Development research infrastructure, Grant/Award Number: P2C HD042828

Footnotes

CONFLICTS OF INTEREST

The authors report no conflicts of interest.

REFERENCES

  1. Adamson SJ, Kay-Lambkin FJ, Baker AL, Lewin TJ, Thornton L, Kelly BJ et al. (2010) An improved brief measure of cannabis misuse: the cannabis use disorders identification test – revised (CUDIT-R). Drug and Alcohol Dependence, 110, 137–143. [DOI] [PubMed] [Google Scholar]
  2. Anderson DM & Rees DI (2014) The legalization of recreational marijuana: How likely is the worst-case scenario? Journal of Policy Analysis and Management, 33, 221–232. [DOI] [PubMed] [Google Scholar]
  3. Arria AM, Caldeira KM, Bugbee BA, Vincent KB & O’Grady KE (2015) The academic consequences of marijuana use during college. Psychology of Addictive Behaviors, 29, 564–575. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Babor TF, Higgins-Biddle JC, Saunders JB & Monteiro MG (2001) AUDIT The alcohol use disorders identification test: Guidelines for use in primary health care. 2nd Edition. Geneva, Switzerland: World Health Organization. [Google Scholar]
  5. Batalla A, Bhattacharyya S, Yücel M, Fusar-Poli P, Crippa JA, Nogué S. et al. (2013) Structural and functional imaging studies in chronic cannabis users: A systematic review of adolescent and adult findings. PLoS One, 8(2), e55821. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Bravo AJ, Pearson MR, Conner BT & Parnes JE (2017) Is 4/20 an event-specific marijuana holiday? A daily diary investigation of marijuana use and consequences among college students. Journal of Studies on Alcohol and Drugs, 78(1), 134–139. 10.15288/jsad.2017.78.134 [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Brook JS, Lee JY, Finch SJ, Seltzer N & Brook DW (2013) Adult work commitment, financial stability, and social environment as related to trajectories of marijuana use beginning in adolescence. Substance Abuse, 34(3), 298–305. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Cambron C, Guttmannova K & Fleming CB (2017) State and national contexts in evaluating cannabis laws: A case study of Washington State. Journal of drug issues, 47(1), 74–90. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Caulkins JP, Kilmer B & Kleiman MA (2016) Marijuana legalization: What everyone needs to know. Oxford, UK: Oxford University Press. [Google Scholar]
  10. Cerdá M, Mauro C, Hamilton A, Levy NS, Santaella-Tenorio J, Hasin D. et al. (2020) Association between recreational marijuana legalization in the United States and changes in marijuana use and cannabis use disorder from 2008 to 2016. JAMA Psychiatry, 77(2), 165–171. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Curran PJ & Hussong AM (2003) The use of latent trajectory models in psychopathology research. Journal of Abnormal Psychology, 112(4), 526–544. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Darnell A. (2020) Review of scientific evidence on effects of medical and non-medical marijuana legalization on public health in the United States. Retrieved from: https://en.ofdt.fr/BDD/publications/docs/ASTRACAN-Darnell-EN.pdf.
  13. Dimeff LA (Ed.). (1999) Brief alcohol screening and intervention for college students (BASICS): A harm reduction approach. New York: Guilford Press. [Google Scholar]
  14. Duckworth JC & Lee CM (2019) Associations among simultaneous and co-occurring use of alcohol and marijuana, risky driving, and perceived risk. Addictive Behaviors, 96, 39–42. 10.1016/j.addbeh.2019.03.019 [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Duncan TE, Duncan SC & Strycker LA (2006) Quantitative methodology series. An introduction to latent variable growth curve modeling: Concepts, issues, and applications., 2nd edition. Mahwah, NJ: Lawrence Erlbaum Associates Publishers. [Google Scholar]
  16. Egan KL, Cox MJ, Suerken CK, Reboussin BA, Song EY, Wagoner KG et al. (2019) More drugs, more problems? Simultaneous use of alcohol and marijuana at parties among youth and young adults. Drug and Alcohol Dependence, 202, 69–75. 10.1016/j.drugalcdep.2019.07.003 [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Fleming CB, Duckworth JC, Patrick ME, Fairlie AM, Abdallah DA & Lee CM (2021). Calendar month variation in alcohol and marijuana use in a community sample of young adults. Journal of Studies on Drugs and Alcohol, 82(2), 169–177. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Fleming CB, Guttmannova K, Cambron C, Rhew IC & Oesterle S. (2016) Examination of the divergence in trends for adolescent marijuana use and marijuana-specific risk factors in Washington State. Journal of Adolescent Health, 59(3), 269–275. 10.1016/j.jadohealth.2016.05.008 [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Gorey C, Kuhns L, Smaragdi E, Kroon E & Cousijn J. (2019) Age-related differences in the impact of cannabis use on the brain and cognition: a systematic review. European Archives of Psychiatry and Clinical Neuroscience, 269(1), 37–58. 10.1007/s00406-019-00981-7 [DOI] [PMC free article] [PubMed] [Google Scholar]
  20. Gunn RL, Norris AL, Sokolovsky A, Micalizzi L, Merrill JE & Barnett NP (2018) Marijuana use is associated with alcohol use and consequences across the first 2 years of college. Psychology of Addictive Behaviors, 32(8), 885–894. 10.1037/adb0000416 [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Guttmannova K, Jones AA, Johnson JK, Oesterle S, Johnson RM & Martins SS (2019) Using existing data to advance knowledge about adolescent and emerging adult marijuana use in the context of changes in marijuana policies. Prevention Science, 20(2), 291–299. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Guttmannova K, Lee CM, Kilmer JR, Fleming CB, Rhew IC, Kosterman R. et al. (2016) Impacts of changing marijuana policies on alcohol use in the United States. Alcoholism: Clinical and Experimental Research, 40(1), 33–46. [DOI] [PMC free article] [PubMed] [Google Scholar]
  23. Hall W. (2015) What has research over the past two decades revealed about the adverse health effects of recreational cannabis use? Addiction, 110(1), 19–35. 10.1111/add.12703 [DOI] [PubMed] [Google Scholar]
  24. Hall W. (2017) Alcohol and cannabis: Comparing their adverse health effects and regulatory regimes. International Journal of Drug Policy, 42, 57–62. [DOI] [PubMed] [Google Scholar]
  25. Hawken A, Caulkins J, Kilmer B & Kleiman M. (2013) Quasi-legal cannabis in Colorado and Washington: Local and national implications. Addiction, 5(108), 837–838. [DOI] [PubMed] [Google Scholar]
  26. Hayat A & Piper BJ (2020) Characteristics of dispensary patients that limit alcohol after initiating cannabis. Journal of Psychoactive Drugs, 52(2), 145–152. 10.1101/19003798 [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Hingson RW, Zha W & Weitzman ER (2009) Magnitude of and trends in alcohol-related mortality and morbidity among U.S. college students ages 18–24, 1998–2005. Journal of Studies on Alcohol and Drugs. Supplement, 16, 12–20. 10.15288/jsads.2009.s16.12 [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Hox JJ, Moerbeek M & van de Schoot R. (2010) Multilevel analysis: Techniques and applications. New York: Routledge. [Google Scholar]
  29. Hu LT & Bentler PM (1999) Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55. 10.1080/10705519909540118 [DOI] [Google Scholar]
  30. Kilmer B. (2017) Recreational cannabis—minimizing the health risks from legalization. New England Journal of Medicine, 376, 705–707. 10.1056/NEJMp1614783 [DOI] [PubMed] [Google Scholar]
  31. 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 and Alcohol Dependence, 180, 391–394. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Lee CM, Maggs JL & Rankin LA (2006) Spring Break trips as a risk factor for heavy alcohol use among first-year college students. Journal on Studies of Alcohol, 67(6), 911–916. 10.15288/jsa.2006.67.911 [DOI] [PubMed] [Google Scholar]
  33. Lee CM, Patrick ME, Fleming CB, Cadigan JM, Abdallah DA, Fairlie AM et al. (2020) A daily study comparing alcohol-related positive and negative consequences for days with only alcohol use versus days with simultaneous alcohol and marijuana use in a community sample of young adults. Alcoholism: Clinical and Experimental Research, 44(3), 689–696. 10.1111/acer.14279 [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Lewis MA, Lindgren KP, Fossos N, Neighbors C & Oster-Aaland L. (2009) Examining the relationship between typical drinking behavior and 21st birthday drinking behavior among college students: Implications for event-specific prevention. Addiction, 104(5), 760–767. 10.1111/j.1360-0443.2009.02518.x [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Lipperman-Kreda S, Gruenewald PJ, Grube JW & Bersamin M. (2017) Adolescents, alcohol, and marijuana: Context characteristics and problems associated with simultaneous use. Drug and Alcohol Dependence, 179, 55–60. 10.1016/j.drugalcdep.2017.06.023 [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. McArdle JJ (1991) Structural models of developmental theory in psychology. In: Van Geert P & Mos LP (Eds.) Annals of theoretical psychology, Vol 7, New York: Plenum Press. [Google Scholar]
  37. Meda SA, Gueorguieva RV, Pittman B, Rosen RR, Aslanzadeh F, Tennen H. et al. (2017) Longitudinal influence of alcohol and marijuana use on academic performance in college students. PLoS One, 12(3), e0172213. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Muthén BO, du Toit SHC & Spisic D. (1997). Robust inference using weighted least squares and quadratic estimating equations in latent variable modeling with categorical and continuous outcomes. Retrieved July 14, 2010 from http://www.gseis.ucla.edu/faculty/muthen/articles/Article_075.pdf
  39. Muthén LK & Muthén BO (1998–2019) Mplus user’s guide. Los Angeles: Muthén & Muthén. [Google Scholar]
  40. NIAAA (2003, October). Recommended alcohol questions. National Institute on Alcohol and Alcoholics. Retrieved from: https://www.niaaa.nih.gov/research/guidelines-and-resources/recommended-alcohol-questions [Google Scholar]
  41. O’Hara RE, Armeli S & Tennen H. (2016) Alcohol and cannabis use among college students: Substitutes or complements? Addictive Behaviors, 58, 1–6. 10.1016/j.addbeh.2016.02.004 [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Pacula RL & Sevigny EL (2014a) Marijuana liberalization policies: Why we can’t learn much from policy still in motion. Journal of Policy Analysis and Management: [The Journal of the Association for Public Policy Analysis and Management], 33(1), 212–221. 10.1002/pam.21726 [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Pacula RL & Sevigny EL (2014b) Natural experiments in a complex and dynamic environment: the need for a measured assessment of the evidence. Journal of Policy Analysis and Management: [The Journal of the Association for Public Policy Analysis and Management], 33(1), 232–235. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Patrick ME, Fairlie AM & Lee CM (2018) Motives for simultaneous alcohol and marijuana use among young adults. Addictive Behaviors, 76, 363–369. 10.1016/j.addbeh.2017.08.027 [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Patrick ME & Lee CM (2012) Daily variations in Spring Break alcohol and sexual behaviors based on intentions, perceived norms, and daily trip context. Journal of Studies on Alcohol and Drugs, 73, 591–596. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Patrick ME, Terry-McElrath YM, Lee CM & Schulenberg JE (2019) Simultaneous alcohol and marijuana use among underage young adults in the United States. Addictive Behaviors, 88, 77–81. [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Reiman A. (2009) Cannabis as a substitute for alcohol and other drugs. Harm Reduction Journal, 6, 35. 10.1186/1477-7517-6-35 [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Risso C, Boniface S, Subbaraman MS & Englund A. (2020) Does cannabis complement or substitute alcohol consumption? A systematic review of human and animal studies. Journal of Psychopharmacology, 34(9), 938–954. [DOI] [PubMed] [Google Scholar]
  49. Schulenberg JE, Johnston LD, O’Malley PM, Bachman JG, Miech RA & Patrick ME (2020). Monitoring the Future national survey results on drug use, 1975–2019: Volume II, College students and adults ages 19–60. Ann Arbor: Institute for Social Research, The University of Michigan. Retrieved from: http://monitoringthefuture.org/pubs.html#monographs [Google Scholar]
  50. Smart R & Pacula RL (2019) Early evidence of the impact of cannabis legalization on cannabis use, cannabis use disorder, and the use of other substances: Findings from state policy evaluations. The American Journal of Drug and Alcohol Abuse, 45(6), 644–663. 10.1080/00952990.2019.1669626 [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Subbaraman MS (2014) Can cannabis be considered a substitute medication for alcohol? Alcohol, 49(3), 292–298. 10.1093/alcalc/agt182 [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Subbaraman MS & Kerr WC (2015) Simultaneous versus concurrent use of alcohol and cannabis in the National Alcohol Survey. Alcoholism: Clinical and Experimental Research, 39(5), 872–879. [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Subbaraman SM (2016) Substitution and complementarity of alcohol and cannabis: A review of the literature. Substance Use & Misuse, 51(11), 1399–1414. 10.3109/10826084.2016.1170145 [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. Substance Abuse and Mental Health Services Administration (SAMHSA). (2020). Reports and detailed tables from the 2019 National Survey of Drug Use and Health; Tables 7.11B and 7.12B. Retrieved from: https://www.samhsa.gov/data/release/2019-national-survey-drug-use-and-health-nsduh-releases.Accessed December 11, 2020.
  55. 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 and Alcohol Dependence, 171, 70–83. 10.1016/j.drugalcdep.2016.11.021 [DOI] [PMC free article] [PubMed] [Google Scholar]
  56. Veligati S, Howdeshell S, Beeler-Stinn S, Lingam D, Allen PC, Chen LS et al. (2020) Changes in alcohol and cigarette consumption in response to medical and recreational cannabis legalization: Evidence from U.S. state tax receipt data. International Journal of Drug Policy, 75, 102585. 10.1016/j.drugpo.2019.10.011 [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. Volkow ND, Swanson JM, Evins AE, DeLisi LE, Meier MH, Gonzalez R. et al. (2016) Effects of cannabis use on human behavior, including cognition, motivation, and psychosis: a review. JAMA Psychiatry, 73(3), 292–297. [DOI] [PubMed] [Google Scholar]
  58. Wen H, Hockenberry JM & Cummings JR (2015) The effect of medical marijuana laws on adolescent and adult use of marijuana, alcohol, and other substances. Journal of Health Economics, 42, 64–80. 10.1016/j.jhealeco.2015.03.007 [DOI] [PubMed] [Google Scholar]
  59. Yurasek AM, Aston ER & Metrik J. (2017) Co-use of alcohol and cannabis: A review. Current Addiction Reports, 4, 184–193. 10.1007/s40429-017-0149-8 [DOI] [PMC free article] [PubMed] [Google Scholar]

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