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. Author manuscript; available in PMC: 2022 Aug 1.
Published in final edited form as: Addict Behav. 2021 Apr 6;119:106946. doi: 10.1016/j.addbeh.2021.106946

Alcohol-related cognitions: Implications for concurrent alcohol and marijuana use and concurrent alcohol and prescription stimulant misuse among young adults

Dana M Litt 1, Ashley Lowery 1, Cassidy LoParco 1, Melissa A Lewis 1
PMCID: PMC8113100  NIHMSID: NIHMS1696865  PMID: 33866222

Abstract

Introduction:

This study examined the associations between alcohol-related cognitions within the social reaction pathway of the Prototype Willingness Model and concurrent (use of two or more substances within a specified time period) use of 1) alcohol and marijuana and 2) alcohol and prescription stimulant misuse.

Methods:

A convenience sample of 1,062 emerging adults in the U.S. (18 – 20 years old; 54.5% female) who reported past 3-month alcohol use completed a baseline survey as part of a larger randomized controlled trial.

Results:

Results indicate that controlling for age, biological sex, race, ethnicity, and college enrollment, perceived descriptive norms and willingness to drink were associated with past 3-month concurrent alcohol and marijuana use and concurrent alcohol and prescription stimulant misuse. However, alcohol prototype similarity and alcohol-related perceived vulnerability were not associated with either concurrent use outcome examined.

Discussion:

These findings suggest that alcohol-related perceived descriptive norms and willingness to drink are associated with concurrent substance use among young adults. Thus, it is possible that existing efficacious alcohol interventions that target descriptive norms and willingness to drink may have the added benefit of also reducing concurrent substance cognitions and ultimately use.

Keywords: Young Adults, Concurrent Substance Use, Alcohol, Cognitions, Prototype Willingness Model

1. Introduction

Substance use among young adults in the United States is a major public health concern (Birnbaum et al., 2011; Florence et al., 2016; Rehm et al., 2009). Despite possible consequences, young adults continue to use and/or misuse substances, including alcohol, marijuana, and prescription stimulants (National Institute of Drug Abuse [NIDA], 2015; Substance Abuse and Mental Health Services Administration [SAMHSA], 2020). Alcohol is often the first substance that adolescents and young adults experiment with prior to initiating use of other substances, including marijuana and prescription stimulants (Aiken et al., 2017; Richmond-Rakard et al., 2017; SAMHSA, 2019a). Moreover, early alcohol initiation is associated with later occurrences of abuse and dependence (DeWit et al., 2000; Grant et al., 2001; King & Chassin, 2007; Warner & White, 2003; York et al., 2004).

Despite alcohol being the most commonly used substance among young adults (SAMHSA, 2020), research indicates that many young adults who use alcohol also report using other substances including marijuana and nonmedical prescription drugs such as stimulants (Barrett et al., 2006; Derefinko et al., 2016; Pape et al., 2009; SAMHSA, 2020; Schulenberg & Maggs, 2002; Wiesner & Windle, 2004). Marijuana is the second most used substance within the U.S. (SAMHSA, 2020). Rates of marijuana use peak in young adulthood (SAMHSA, 2020) and are higher among males, racial and ethnic minorities, and those in college (Miech et al., 2016). The use of marijuana has been linked to acute effects such as impaired cognitive and motor functioning, as well as longer-term effects such as impaired memory and respiratory infections (Meier et al., 2012; Volkow et al., 2014).

Although used less frequently than alcohol or marijuana, the misuse of prescription stimulants is also problematic among young adults. Prescription stimulant misuse is typically defined as taking stimulants without a valid prescription or use of stimulants other than as prescribed, and occurs regularly in young adults (Austic, 2015; Benson et al., 2015; McCabe et al., 2014; Weyandt et al., 2013). In 2019, nearly 2% of all adults in the U.S. reported having ever misused prescription medications, with rates of use being significantly higher (nearly 6%) among young adults (SAMHSA, 2020) and those enrolled in college (between 6 – 17%; Benson et al., 2015), and lower among racial and ethnic minorities (Compton et al., 2018). Risks associated with excessive prescription stimulant use can include cardiovascular failure, irregular heartbeat, high blood pressure, and paranoia (Volkow, 2005).

Notably, research indicates that a significant portion of young adults engage in concurrent substance use, which is commonly defined as the use of two or more substances within a specified time. Concurrent substance use is particularly important to understand among young adults as this is the time when alcohol, marijuana, and prescription stimulant misuse peak (SAMSHA, 2020; Schulenberg et al., 2002). Estimates indicate that concurrent alcohol and marijuana use among young adults ranges between 34 – 75% (Haas et al., 2015; Moss et al., 2014) and that 48.7% of young adults who use alcohol use also report misusing prescription stimulants (McCabe et al., 2015). Examining concurrent substance use during young adulthood is critical, as individuals who engage in concurrent substance use, including using alcohol with marijuana or stimulants, have an increased risk of experiencing negative health and social consequences, such as substance use disorders and negative alcohol-related consequences compared to those who are single-substance users (Conway et al., 2013; Green et al., 2016; Kelly et al., 2015; Lewis et al., under review; Moss et al., 2014; Yurasek et al., 2017). As such, it is important to identify social cognitive factors that may place one at risk for such use.

1.1. Prototype Willingness Model

The Prototype Willingness Model (PWM) posits that risk behavior among young adults operates through two pathways: a more planful and reasoned pathway and a social reaction pathway wherein risk behaviors are conceptualized as behaviors that are not planned but instead are reactions to risk-conducive environments (Gerrard et al., 2008; Gibbons et al., 2003). Although both the reasoned and social reaction pathways have utility in predicting risk behavior, the social reaction pathway is more predictive of behavior among adolescents and young adults (Gerrard et al., 2008; Pomery et al., 2009) and as such is the primary focus of the present paper. The PWM social reaction pathway is comprised of descriptive norms, prototypes, perceived vulnerability, and behavioral willingness (Gerrard et al., 2008). Descriptive norms, or perceptions of the quantity and frequency of a behavior among others (Cialdini et al., 1991; Reno et al., 1993), are a key part of the PWM social reaction pathway. In addition, prototypes are images of the type of person who engages in specific behaviors (e.g., typical drinker their age) and are often conceptualized as being two constructs, prototype favorability (i.e., perception that the prototype is associated with positive and/or negative attributes) and prototype similarity (i.e., perception of oneself as similar to the prototype)(Gerrard et al., 2008). However, recent research indicates that prototype similarity, but not favorability, is a stronger predictor of adolescent and young adult alcohol use when examining heavy-episodic drinking (Litt et al., 2020; Teunnisen et al., 2017). Perceived vulnerability is the perceived chance an individual has of experiencing negative consequences if they were to engage in a risky behavior (Gerrard et al., 2008). Finally, behavioral willingness is an openness to engage in a risk behavior under certain circumstances (Gerrard et al., 2008).

PWM social reaction pathway risk cognitions such as descriptive norms, prototype similarity, perceived vulnerability, and willingness are often established well before a behavior occurs (Ajzen 1985; Fishbein & Ajzen 1975; Gerrard et al., 2008). Therefore, cognitions related to a specific health behavior may also be described as having a predisposition to engage in that behavior. For example, endorsing riskier alcohol-related cognitions has consistently been found to be significantly associated with greater alcohol use (Andrews et al., 2008; Gerrard et al., 2002; Pomery et al., 2009; Rivis et al., 2006). Although less studied than alcohol use, research indicates that the PWM social reaction pathway is an appropriate model to apply to young adult marijuana use (Hampson et al., 2008; Lewis et al., 2018) and prescription stimulant misuse (Molloy et al., 2019; Stock et al., 2013).

Despite clear associations between PWM social reaction pathway alcohol-related cognitions and alcohol use, no research to date has examined whether these same cognitions are associated with engagement in other substance use, such as marijuana and prescription stimulant misuse. Given that alcohol is often the first substance initiated among adolescents and young adults (Grant et al., 2001; King & Chassin, 2007; York et al., 2004), determining what specific PWM social reaction pathway alcohol-related cognitions may be associated with concurrent use of other substances is important when determining when and on what factors to intervene. For example, alcohol is the most common prevention target for substance use among colleges and medical providers (Blevins & Khanna, 2016; SAMHSA, 2019b). Therefore, knowing whether certain PWM social reaction pathway alcohol-related cognitions are associated with concurrent use of other substances among current drinkers is one way to reduce screening burden as one may be able to determine whether current drinkers may also be at risk for concurrent substance use based on known associations between alcohol-related risk cognitions and the use of other substances. Thus, the present study aimed to determine whether alcohol-related PWM social reaction pathway cognitions are associated with concurrent substance use.

Based on the extant literature, we hypothesized that when controlling for important covariates (i.e., age, biological sex, race, ethnicity, and college enrollment), alcohol-related PWM social reaction pathway cognitions (descriptive norms, prototype similarity, perceived vulnerability, and willingness) would be associated with 1) concurrent use of alcohol and marijuana and 2) concurrent use of alcohol and prescription stimulant misuse over the past 3 months among young adult drinkers age 18 – 20.

2. Method

2.1. Participants and Procedures

Participants for the present study included 1,065 18–20-year-old young adults who were participating in a larger study evaluating an intervention for alcohol-related risky sexual behavior. Data for the present analyses come from the baseline assessment. Demographics for the baseline sample include a mean age of 19.17 years old (SD = 0.79) and 54.5% were female. Ethnic and racial representation of the baseline sample was as follows: 15.1% Hispanic/Latino; 70.5% White, 10.3% Other/More than one race, 9.7% Asian, 7.9% African American, 1.2% American Indian/Alaska Native, and 0.4% Native Hawaiian/Pacific Islander. College enrollment for the sample was 72.70% attending a 4-year university, 11.20% not enrolled in any form of schooling, 7.56% attending a community college, 1.87% attending high school, 0.69% attending a technical/vocational college, and 0.49% attending a graduate/professional school. Participants for this study were recruited nationally through various methods (e.g., social media advertisements, flyering, and newspaper ads) and were asked to complete a five-minute web-based screening survey to determine if they met inclusion criteria for the longitudinal study. Eligibility criteria included: age 18 – 20; not be in a monogamous relationship, or be in a monogamous relationship for less than three months and be open to having a sexual relationship with someone other than a monogamous partner; have had sex in the past three months; and have had an alcoholic drink at least twice a month on average over the past 3 months. Of those participants who completed the eligibility survey (N = 17,899), 1,144 participants were eligible and were invited to complete the baseline survey and longitudinal study.

Upon receiving the baseline survey invitation, participants were presented with a full information statement. Of the participants invited to the baseline survey, 1,065 (93.1%) completed the survey. Three participants were removed from analyses as they did not report any alcohol use at baseline, despite having reported past 3-month alcohol use at screening; thus, the final analytic sample consisted of 1,062 participants. Participants who completed the baseline survey received a $25 gift certificate. A Federal Certificate of Confidentiality was obtained to help ensure privacy of research participants. All study procedures were approved by the University’s Institutional Review Board, and no adverse events were reported.

2.2. Measures

2.2.1. Alcohol Use

Participants were asked to report, “On average, during the past 3 months, how often have you consumed alcohol” and responded on an 11-point scale ranging from 0 (Never) to 11 (Every Day; Collins et al., 1985).

2.2.2. Marijuana Use

In an item parallel to the alcohol use item, participants were asked “Within the past 3 months, how often have you used marijuana or cannabis” and responded on an 11-point scale ranging from 0 (Never) to 11 (Every day).

2.2.3. Prescription Stimulant Misuse

Participants reported how many times they used prescription stimulants (e.g., Ritalin, Adderall, Concerta, methylphenidate) either without a prescription or in a way not prescribed in the past 3 months. Responses were open-ended but capped at 90 times.

2.2.4. Concurrent Use

Because all participants in the sample reported alcohol use within the past 3 months, any use of either marijuana or prescription stimulant misuse in the same time frame (i.e., past 3 months) was considered to be concurrent use in the present study. To create the concurrent alcohol and marijuana use outcome variable, descriptive statistics were conducted for those participants who reported any marijuana use. Among those with at least 1 day of marijuana use, the median days used was 10 days, which was used as the cut-off to delineate lower frequency marijuana use from higher frequency marijuana use. Thus, concurrent alcohol and marijuana use was coded into three categories which represented alcohol only (past 3 months any alcohol use and no use of marijuana), alcohol and lower frequency of marijuana use (past 3 months any alcohol use and 10 or fewer days of marijuana use), and alcohol and higher frequency marijuana use (past 3 months of any alcohol and 11 or more days of marijuana use). Because of lower base rates of prescription stimulant misuse, concurrent alcohol and prescription stimulant misuse was dichotomized into two categories which represented alcohol only (past 3 months any alcohol use and no prescription stimulant misuse) and concurrent alcohol and prescription stimulant misuse (past 3 months any alcohol use and past 3 months any prescription stimulant misuse).

2.2.5. Perceived Descriptive Drinking Norms

A single item from the Drinking Norms Rating Form (DNRF; Baer et al., 1991) was used to assess perceived peer drinking over the past 3 months with the question, “How often do you think a typical male/female your age consumes alcohol? Responses were on a scale ranging from 0 (Never) to 11 (Every Day).

2.2.6. Prototype Similarity

Based on previous research indicating that prototype similarity, but not favorability, is associated with heavy drinking (Litt et al., 2020; Teunnisen et al., 2017), only prototype similarity was utilized in the present study. Prototype similarity was assessed with the item “How similar are you to the typical male/female your age who drinks 4/5 or more drinks in about 2 hours?” (Litt et al., 2020). Responses ranged from 1 (Not at All Similar) to 7 (Very Similar).

2.2.7. Perceived Vulnerability

Perceived vulnerability was assessed with four items (Litt & Lewis, 2016) in response to a stem that read “How much do you think drinking alcohol at the varying levels (i.e., having 1 – 2 drinks nearly every day, having 1 – 2 drinks each weekend, having 3 – 4 drinks each weekend, and having 5 – 6 drinks each weekend) might cause you risk?” Response options ranged from 0 (Very Unlikely) to 5 (Very Likely). Responses were mean scored (α = .78).

2.2.8. Willingness to Drink

Participants were presented with the following hypothetical scenario (Gerrard et al., 2008; Litt & Lewis, 2016): “Suppose that you are at a party. After two drinks you are beginning to feel that you may have had enough, and you are getting ready to leave. A friend you haven’t seen for a while starts talking to you and offers to get you another drink. How willing would you be to do each of the following?”: “choose a non-alcoholic drink instead” (reverse scored), “stay and have one more drink”, “stay and continue to drink 2/3 (based on birth sex) or more drinks”, “say no to the drink offer” (reverse scored), and “leave the party to reduce pressure to drink from others” (reverse scored). Responses ranged from 0 (Not at all Willing) to 4 (Completely Willing). The mean of the five questions (α = .85) was used to represent a participant’s willingness to drink.

2.2.9. Covariates

Age, biological sex (Female = 0, Male = 1), race (0 = Non-White, 1 = White), ethnicity (0 = Non-Hispanic, 1 = Hispanic) and college enrollment (0 = Not enrolled in 4-year college, 1 = Enrolled in 4-year college) were included as covariates based on associations with substance use (Schulenberg et al., 2020).

2.3. Data Analytic Plan

Because of the ordinal nature of the marijuana use outcome variable (0 = alcohol only, 1 = alcohol and lower frequency of marijuana use, and 2 = alcohol and higher frequency of marijuana use), an ordered logistic regression model evaluated concurrent alcohol and marijuana use. For the model evaluating past 3-month concurrent alcohol and prescription stimulant misuse (0 = alcohol only, 1 = alcohol and prescription stimulant misuse), a logistic regression model was used. All models controlled for age, birth sex, race, ethnicity, and college enrollment.

3. Results

3.1. Descriptive Statistics and Correlations

Participants reported drinking, on average, nearly twice per week (M = 5.82, SD = 1.72). On average, participants reported using marijuana a little more than twice per month (M = 4.34, SD = 3.76) and prescription stimulants were misused on an average of 1.19 (SD = 5.38) occasions in the past 90 days. Over the past 3 months, 14.5% of participants only used alcohol, 85% of the sample reported concurrent alcohol and marijuana use, 24% reported concurrent alcohol and prescription stimulant misuse, while 25.3% used all three substances. Correlation analyses (Table 1) indicate that both concurrent alcohol and marijuana use and concurrent alcohol and prescription stimulant misuse were positively associated with being male, White, enrolled in 4 year college, and having higher perceived descriptive norms and willingness to drink. All correlation results should be interpreted cautiously due to the large sample size.

Table 1.

Correlations and Descriptive Information Among Key Study Variables

Statistic/Measure 1 2 3 4 5 6 7 8 9 10 11
Correlations
 1. Age -
 2. Biological Sex 0.10 -
 3. Race 0.09** 0.05 -
 4. Ethnicity 0.01 0.01 −0.09** -
 5. College Enrollment 0.03 0.04 0.02 0.08** -
 6. Alcohol Descriptive Norms 0.09** 0.25** −0.02 −0.05 −0.01 -
 7. Alcohol Prototypes 0.06* −0.13** 0.14** −0.04 −0.12* 0.03 -
 8. Alcohol Perceived Vulnerability −0.09** −0.12** −0.09** 0.08** −0.01 −0.20** −0.12* -
 9. Alcohol Willingness −0.02 0.13** 0.07* 0.04 −0.03 0.17** 0.24** −0.22** -
 10. Concurrent Alcohol and Marijuana Use −0.05 0.10** 0.10** 0.01 0.07* 0.09* 0.05 −0.04 0.12** -
 11. Concurrent Alcohol and Prescription Stimulant Misuse 0.02 0.07* 0.89* 0.02 0.07* 0.14** 0.09** −0.09** 0.17** 0.25** -
Mean (SD) 19.17 (0.79) 0.45 (0.50) 0.71 (0.45) 0.15 (0.36) 72.73 (0.44) 5.86 (1.78) 4.01 (1.59) 2.44 (0.66) 2.09 (0.89) 1.19 (0.72) 0.24 (0.43)
Range 18–20 0–1 0–1 0–1 0–1 0–11 1–7 1–4 1–4 1–3 0–1
Open in a new tab

Note. N = 1,062;

*

p < .01

**

p < .001;

Biological sex coded 0 (female) and 1 (male). Race coded 0 (non-White) and 1 (White). Ethnicity coded (0 = Non-Hispanic, 1 = Hispanic). College Enrollment coded 0 (not enrolled in 4-year college) and 1 (enrolled in 4-year college). Point-biserial correlations are presented for correlations among the dichotomous outcomes (age, race, ethnicity, concurrent alcohol and prescription stimulant misuse) and all other key variables; all other correlations are Pearson correlations.

3.2. Concurrent Alcohol and Marijuana Use

Results of the ordered logistic regression indicated that while neither age nor ethnicity were associated concurrent alcohol and marijuana use, being male, White, and a current 4-year college student were associated with increases in the ordered log odds of concurrent alcohol and marijuana use. Higher descriptive norms and willingness to drink were also associated with increases in the ordered log odds of concurrent alcohol and marijuana use, whereas there were not significant associations for prototype similarity or perceived vulnerability. See Table 2.1

Table 2.

Ordered Logistic Regression Results of Alcohol-Related Cognition Predictors of Concurrent Alcohol and Marijuana Use

Predictor B SE Wald χ2 Odds Ratio (95% CI)
Biological Sex 0.34 0.14 6.14* 1.41 (1.07, 1.85)
Age −0.14 0.08 2.94 0.86 (0.74, 1.02)
Race 0.42 0.15 8.56** 1.53 (1.15, 2.04)
Ethnicity −0.02 0.18 0.01 0.98 (0.70, 1.41)
College Enrollment 0.38 0.15 6.04* 1.46 (1.08, 1.98)
Alcohol Descriptive Norms 0.02 0.01 7.21** 1.02 (1.00, 1.03)
Alcohol Prototype Similarity 0.01 0.05 0.01 1.00 (0.92, 1.10)
Alcohol Perceived Vulnerability 0.03 0.11 0.08 1.03 (0.83, 1.28)
Alcohol Willingness 0.22 0.08 8.29** 1.25 (1.07, 1.45)
Open in a new tab

Note. N = 1,062;

*

p < .05;

**

p < .01.

Biological sex coded 0 (female) and 1 (male). Race coded 0 (non-White) and 1 (White). Ethnicity coded (0 = Non-Hispanic, 1 = Hispanic). College Enrollment coded 0 (not enrolled in 4-year college) and 1 (enrolled in 4-year college).

3.3. Concurrent Alcohol and Prescription Stimulant Misuse

Biological sex, age, ethnicity, and college enrollment were not associated with concurrent alcohol and prescription stimulant misuse. Being White was associated with greater odds of concurrent alcohol and prescription stimulant misuse. Similar to marijuana use, while prototypes and perceived vulnerability were not associated with concurrent alcohol and prescription stimulant misuse, results indicated that having greater descriptive normative perceptions and being more willing to use alcohol were associated with having a greater odds of concurrent alcohol and prescription stimulant misuse in the past 3 months. See Table 3.

Table 3.

Logistic Regression Results of Alcohol-Related Cognition Predictors of Concurrent Alcohol and Prescription Stimulant Misuse

Predictor B SE Wald χ2 Odds Ratio (95% CI)
Biological Sex 0.25 0.10 2.32 1.29 (0.93, 1.78)
Age −0.02 0.10 0.04 0.98 (0.81, 1.20)
Race 0.50 0.19 6.79* 1.65 (1.13, 2.41)
Ethnicity 0.05 0.23 0.04 1.05 (0.67, 1.63)
College Enrollment 0.14 0.18 0.59 0.87 (0.61, 1.24)
Alcohol Descriptive Norms 0.02 0.01 8.91** 1.02 (1.01, 1.04)
Alcohol Prototype Similarity 0.09 0.05 2.73 1.09 (0.98, 1.21)
Alcohol Perceived Vulnerability −0.12 0.13 0.87 0.89 (0.70, 1.14)
Alcohol Willingness 0.35 0.10 13.26** 1.41 (1.17, 1.72)
Open in a new tab

Note. N = 1,062;

*

p < .05;

**

p < .01.

Biological sex coded 0 (female) and 1 (male). Race coded 0 (non-White) and 1 (White). Ethnicity coded (0 = Non-Hispanic, 1 = Hispanic). College Enrollment coded 0 (not enrolled in 4-year college) and 1 (enrolled in 4-year college).

4. Discussion

The current findings suggest that, beyond being associated with cognitions about other substances (Andrews et al., 2008; Blanton et al., 1997; Gibbons et al., 2004), several alcohol-related PWM social reaction pathway cognitions are also associated with the actual concurrent use of alcohol with marijuana and prescription stimulant misuse. Results of the study indicate that in particular, perceived descriptive norms and willingness to drink are associated with both concurrent alcohol and marijuana use and concurrent alcohol and prescription stimulant misuse among current drinkers. Given that for drinking, perceived descriptive norms and willingness to drink have been shown to be some of the strongest predictors of alcohol use in comparison to other drinking cognitions (Litt & Lewis, 2016; Neighbors et al., 2007), it is not surprising that in this sample they were associated with the likelihood of engaging in concurrent alcohol and marijuana use and concurrent alcohol and prescription stimulant misuse.

Contrary to hypotheses, neither prototype similarity nor perceived vulnerability were associated with either concurrent use outcome. Although it is unclear why we did not find associations between prototype similarity and concurrent substance use, one possibility is that there are unique images about someone who concurrently uses substances that cannot be captured by alcohol prototypes alone, and thus young adults may not consider alcohol prototype similarity when deciding to engage in concurrent substance use. Further, it is possible that the heavy episodic drinker prototype was not salient for the participants in the present study. Related to the lack of significant findings for perceived vulnerability, it is unclear why perceived vulnerability for alcohol was not associated with concurrent substance use outcomes. This surprising finding should be further explored.

4.1. Clinical Implications

Results of the present study suggest that interventions aiming to reduce specific alcohol-related risk cognitions may also lead to reductions in concurrent use of alcohol with both marijuana and prescription stimulant misuse. Although the current data cannot speak to directionality, based on prior literature (Lewis et al., 2014) it is possible that providing personalized feedback related to risk cognitions for one behavior may also reduce risky cognitions for a related health behavior. These findings indicate that there are likely social cognitive similarities in the underlying mechanism behind substance use in young adults, regardless of the type of substance. Given that alcohol use, marijuana use, and prescription stimulant misuse may be comorbid (Moss et al., 2014; Schuckit, 2009; Whiteford et al., 2013), it is important for researchers to further investigate the associations between the use of various substances and how cognitions about one substance may impact the concurrent use of other substances. Thus, interventions targeting cognitions associated with concurrent use prior to concurrent use starting would be an important area for future research to explore, especially among adolescents.

4.2. Limitations and Future Directions

Although the current study consisted of a large national sample of young adults aged 18 – 20 years old, there are a few limitations to consider. First, given the select inclusion criteria of the original study to include young adults age 18 – 20 based on both drinking and sex criteria, the authors caution generalizing these results to those outside of these criteria. Compared to recent estimates by the U.S. Census Bureau (2020) for those age 18–24, our sample had fewer males (51% versus 45.5%) as well as fewer White (73% versus 70.5%), African-American (15% versus 7.9%), American Indian/Alaska Native (1.5% versus 1.2%) and Hispanic/Latino (23% versus 15.1%) participants. However, our sample contained greater percentages of Asian (6% versus 9.7%) participants as well as those who identified as other/more than race (4% versus 10.3%). Thus, our results may not generalize to the U.S population as a whole. Future studies should also be conducted among different age groups to test if these associations still hold at younger and older ages, due to cognitions becoming less reactive and more rational as an individual ages (Gerrard et al., 2008; Pomery et al., 2009). In addition, because of the cross-sectional nature of this study, directionality between study variables cannot be tested nor can it provide a thorough understanding of the mechanisms related to how past behavior may influence subsequent social reaction pathway PWM cognitions and future behavior. Thus, future longitudinal and/or daily-level research is needed to understand associations between alcohol-related PWM social reaction pathway cognitions and concurrent substance use. Further, it is possible that PWM social reaction pathway cognitions specific to marijuana use and/or prescription stimulant misuse may be associated with concurrent substance use above and beyond alcohol cognitions, and thus research should determine which substance-related cognitions are the most predictive of concurrent use. Finally, research should examine the utility of alcohol cognitions in predicting simultaneous use (i.e., use with overlapping effect) as well as engagement in alternative categories of concurrent substance use, including concurrent alcohol and tobacco use, as many young adults engage in this behavior (Gubner et al., 2018; McKee & Weinberger, 2013).

4.3. Conclusion

The current study extends the literature related to the associations between alcohol cognitions and health-risk behavior by providing evidence that two alcohol-related PWM social reaction pathway cognitions, perceived descriptive norms and willingness, are not only associated with alcohol use as shown in a variety of other studies (e.g., Blanton et al., 1997; Litt & Stock, 2011; Pomery et al., 2009), but also the concurrent use of alcohol and other substances including marijuana and prescription stimulant misuse.

Highlights.

  • A large number of young adults age 18–20 engage in concurrent substance use.

  • Certain alcohol-related cognitions are associated with concurrent substance use.

  • Reducing alcohol-related cognitions may help reduce concurrent substance use.

Acknowledgments

Data collection and manuscript preparation were supported by a grant from the National Institute on Alcohol Abuse and Alcoholism (R01AA021379) awarded to Dr. Melissa A. Lewis. The content of this manuscript is solely the responsibility of the authors and does not necessarily represent the official views of the National Institute on Alcohol Abuse and Alcoholism or the National Institutes of Health.

Footnotes

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1

A linear regression analysis was conducted examining associations between PWM social reaction pathway risk cognitions and past 3-month alcohol use frequency with the same covariates included in the primary statistical models. Results indicated that all PWM social reaction pathway risk cognitions (descriptive norms: β = 0.12, t = 3.86, p < .001; prototype similarity: β = 0.15, t = 5.15, p < .001; perceived vulnerability: β = −0.18, t = −6.06, p < .001; willingness: β = 0.16, t = 5.40, p < .001) were associated with alcohol use frequency in the expected directions.

Conflict of Interest

No conflict declared.

All authors have seen and approved the final version of the manuscript being submitted and verify that this is the authors’ original work, hasn’t received prior publication and isn’t under consideration for publication elsewhere.

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