Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2023 Jun 21.
Published in final edited form as: Addict Behav. 2021 Aug 21;124:107091. doi: 10.1016/j.addbeh.2021.107091

Age-varying time trends in cannabis- and alcohol-related risk perceptions 2002–2019

Jack T Waddell 1,*
PMCID: PMC10284276  NIHMSID: NIHMS1906361  PMID: 34461500

Abstract

Background:

Alcohol- and cannabis-related risk perceptions are strong predictors of use behavior. Studies suggest that attitudes toward cannabis are becoming increasingly permissive, however studies have yet to comprehensively a) compare time trends in cannabis-related attitudes to those of other commonly used substances, such as alcohol, and b) test whether trends significantly differ by age.

Method:

Public access data from the National Study on Drug Use and Health from 2002 to 2019 were used (N = 1,005,421). Structural Equation Models tested whether study year (linear trend), was associated with alcohol- and cannabis-related risk perceptions (correlated outcomes), and whether age (adolescence [12–17], emerging adulthood [18–25], adulthood [26–35], middle adulthood [36–49], and older adulthood [50+]) moderated time trends. Sex, race/ethnicity, and use frequency were covaried.

Results:

The linear trend of study year was associated with decreased cannabis-related risk perceptions (p < .001). There was also a significant interaction of age by study year for cannabis-related risk perceptions, such that adults, emerging adults, and middle adults had the largest decrease in attitudes over time. For alcohol-related risk perceptions, the linear trend of study year was significantly associated with increased risk perceptions (p = .001), but the interaction of time by age was non-significant; alcohol-related effects were extremely small (b < 0.01).

Conclusions:

Findings suggest that cannabis-related risk perceptions are becoming more permissive with time across ages, but particularly in adults, emerging adults, and middle adults. In contrast, alcohol-related risk perceptions have stayed relatively stable over time, with only negligible increases. Findings underscore the importance of targeting permissive cannabis-related attitudes via prevention efforts.

Keywords: Alcohol, Cannabis, Attitudes, Risk Perceptions, Permissive Attitudes

1. Introduction

Alcohol and cannabis are the two most frequently used psychotropic drugs in the United States (U.S.; NIDA, 2020). Epidemiological surveys in the U.S. suggest that the use of alcohol and cannabis, separately, is increasing (Grant et al., 2017; Hasin et al., 2017) and is associated with myriad negative consequences, including impaired driving, poor academic/work performance, cognitive functioning difficulties, and comorbid medical/mental health concerns (Perkins, 2002; Read et al., 2007; Meier et al., 2012; Volkow et al., 2014). Thus, it is of high importance to understand risk factors for alcohol and cannabis use that can be targeted during early preventive interventions.

One important risk factor is perceived risk/harm of using each substance, often assessed by asking participants how much they believe using alcohol or cannabis will harm/cause problems to themselves. More permissive risk perceptions (i.e., perceptions of less harm) are strongly associated with substance use. Specifically, studies find that lower perceived risk is a risk factor (and higher perceived risk is a protective factor) for heavier alcohol use (e.g., Henry et al., 2005; Hustad et al., 2014; Wetherill & Fromme, 2007) and cannabis use (e.g., Kilmer et al., 2007; Parker & Anthony, 2018a; Parker & Anthony, 2018b; Schleimer et al., 2019). In addition, research suggests that lower perceived risk of using cannabis (in addition to alcohol) is associated with being an alcohol and alcohol/cannabis co-user (Merianos et al., 2017). Although some research investigates relative risk/harm perceptions (i.e., how one substance/product compares to another), the current study focuses on absolute risk perceptions.

Furthermore, several recent studies show that cannabis-related attitudes have become more permissive since the early 2000s. Okaneku et al. (2015) found that, across U.S. participants of all ages, the proportion of individuals rating “great risk” of frequent and occasional cannabis use decreased, while the proportion of individuals rating “no risk” increased (Okaneku et al., 2015). Similarly, Salas-Wright et al. (2017) found that U.S. middle and older adults’ disapproval of cannabis decreased from 2002 to 2014 (Salas-Wright et al., 2017), and Schuermeyer et al. (2014) found that reporting “great risk” of cannabis use decreased from 2007 to 2011 in Colorado state. Finally, research suggests that U.S. adolescents’ perceptions of any harm from cannabis significantly decreased from 2002 to 2014 (Sarvet et al., 2018), and that U.S. pregnant and non-pregnant females reporting “no risk” of cannabis use substantially increased from 2005 to 2015 (Jarlenski et al, 2017). While it is important to take into account that medical and recreational cannabis legislation may cause more permissive cannabis-related attitudes in some states versus others, perceived risk of cannabis use shows downward trends across several studies and populations.

Furthermore, research estimating time trends in alcohol and cannabis-related attitudes concurrently within the same study are rare, despite implications for public health and theory. Thus, it is possible that as cannabis-related risk perceptions decrease, alcohol-related risk perceptions may either concurrently decrease or increase. Furthermore, risk perceptions may decrease globally across substances, however, it is also possible that trends of perceiving less risk for one substance may be coupled with perceiving risk for other substances. Only one study to our knowledge has tested such an effect, finding that cannabis-related attitudes became less permissive from 2008 to 2018, whereas alcohol-related risk perceptions showed no change (Cunningham & Koski-Jännes, 2019). However, this study used only 2 time points (2008, 2018), and was a Canadian sample. Thus, testing U.S. trends in both alcohol and cannabis use risk perceptions side-by-side across several decades may provide integral information for public health, as decreasing attitudes related to one substance may also be coupled with decreases (or increases) in the other.

In addition, testing whether trends in alcohol- and cannabis-related risk perceptions differ by age has public health implications. As stated for cannabis, studies across several populations and ages find that cannabis-related risk perceptions are becoming more permissive in the U.S. However, it is possible that such decreases in cannabis-related risk perceptions may be coupled with decreases or increases in alcohol-related risk perceptions for specific ages. For instance, several studies suggest that cannabis legalization may have an impact on adolescent and emerging adults’ cannabis use and risk perceptions/attitudes (Hammond et al., 2020; Blevins et al., 2018; Tibbo et al., 2018). Thus, considering alcohol is also legal, adolescent/emerging adult perceptions may be more permissive for both. However, considering middle-age and older adults are more likely to use cannabis for therapeutic purposes (i.e., sleep for middle-aged adults, chronic health conditions for older adults; Haug et al., 2017), decreases in cannabis-related risk perceptions may be coupled with increases in (or stable levels of) alcohol-related risk perceptions, since alcohol may not have the same perceived health benefits. Data from the Monitoring the Future (MTF) and National Study on Drug Use and Health (NSDUH) annual report suggest that cannabis-related risk perceptions have become increasingly permissive across age groups (e.g., Schulenberg et al., 2020; SAMHSA, 2020), whereas binge drinking risk perceptions declined only modestly over the past 5 years for adolescents and adults age 26+ (SAMHSA, 2020). However, more comprehensive research testing whether trends in risk perceptions of alcohol and cannabis differ by age across the past 2 decades may be informative for theory and prevention.

Therefore, the current study used a large, nationally representative dataset of U.S. civilians to test time-varying trends in cannabis- and alcohol-related risk perceptions from 2002 to 2019. In addition, the current study tested whether time trends were moderated by age bands spanning adolescence to older adulthood. It was hypothesized that, across ages, cannabis-related risk perceptions would decrease over time, whereas changes in alcohol-related risk perceptions were considered exploratory. However, there were specific hypotheses for age by time interactions. First, it was hypothesized that decreases in adolescent and emerging adult cannabis-related risk perceptions would be coupled with decreases in alcohol-related risk perceptions. Second, it was hypothesized that decreases in middle-age and older adult cannabis-related risk perceptions would be coupled with increases in alcohol-related risk perceptions. Effects for adulthood (age 26–34) were considered exploratory.

2. Methods

2.1. Participants

The current study used public-access data from the National Study on Drug Use and Health (NSDUH) collected between 2002 and 2019 (N = 1,005,421). The NSDUH is a large, nationally representative sample that enrolls nearly 50,000 non-institutionalized U.S. civilians age 12 or older each year. The current study used deidentified participant data that was made available to the public. The NSDUH methodology team uses a stratified multistage probability sampling procedure to obtain a representative cross-sectional sample for each study year. See Table 1 for participant demographics. Due to deidentified data, the current study was exempt from additional human subject review.

Table 1.

Study Demographics.

Sex
 Female 52.1%
  Male 47.9%
Age
 12–17 29.7%
  18–25 30.3%
  26–34 11.9%
  35–49 16.2%
  50+ 11.9%
Race/Ethnicity
 Non-Hispanic/Latinx White 61.8%
  Non-Hispanic/Latinx Black 12.7%
  Hispanic/Latinx 16.5%
  Other/Multiracial 9.1%
Alcohol-Related Risk Perceptions M = 3.12 (SD = 0.85)
  Adolescents M = 3.13 (0.85)
  Emerging Adults M = 3.11 (0.99)
  Adults M = 3.10 (0.85)
  Middle Adults M = 3.20 (0.82)
  Older Adults M = 3.28 (0.80)
Cannabis-Related Risk Perceptions M = 2.88 (SD = 1.07)
  Adolescents M = 3.12 (0.99)
  Emerging Adults M = 2.59 (1.10)
  Adults M = 2.67 (1.11)
  Middle Adults M = 2.95 (1.04)
  Older Adults M = 3.15 (0.98)
Open in a new tab

Note. Adolescence was defines as age 12–17, emerging adulthood as age age 18–25, adulthood as age 26–34, middle adulthood as age 35–49, and older adulthood as age 50+; Risk perceptions were measured on a scale of (1) no risk, (2) slight risk, (3) moderate risk, and (4) great risk.

2.2. Measures

Demographics.

Participants reported their age, sex, and race across the 18 study years. Age was a five-category variable (12–17 [adolescence], 18–25 [emerging adulthood], 26–34 [adulthood], 35–49 [middle adulthood], 50+ years old [older adulthood]), sex was a two-category variable (male, female), and race was a four-category variable (non-Hispanic/Latinx white, non-Hispanic Black, Hispanic/Latinx, and Other/Multiracial; see Table 1 for study demographics).

Alcohol- and Cannabis-Related Risk Perceptions.

Attitudes related to risk from weekly cannabis and weekly binge alcohol use were assessed via two items asking participants “How much do people risk harming themselves physically and in other ways when they smoke marijuana once or twice a week?” and “How much do people risk harming themselves physically and in other ways when they have five or more drinks of an alcoholic beverage once or twice a week?”. Item responses were (1) no risk, (2) slight risk, (3) moderate risk, and (4) great risk. Across study years, mean rating of cannabis-related risk was 2.88 (SD = 1.07), and alcohol-related risk was 3.12 (SD = 0.85).

Alcohol and Cannabis Use Frequency.

Frequency of use was assessed by asking participants how many days in the past year (1–366 days) they used each substance. Frequency variables were broken down in accordance with other longitudinal studies (e.g., Chassin et al., 1992; Sher et al., 1991) and other NSDUH-related studies (e.g., Waddell, 2021) such that 1 = 1–2 times (over the past year), 2 = 3–5 times, 3 = 5 + times but less than monthly, 4 = 1–2 times a month, 5 = 1–2 times a week, 6 = 3–5 times a week, and 7 = nearly daily.

2.3. Statistical analysis

Before primary analyses, assumptions of normality were tested, and transformations were made if necessary. However, variable skewness and kurtosis ratings were within acceptable range and thus no variable adjustments were needed. All analyses used sample weighting and variance adjustments to account for the complex survey design of the NSDUH data. Models used the Maximum Likelihood Estimation with Robust Standard Errors (MLR) estimator and used Full Information Maximum Likelihood (FIML) to estimate missing data.

Structural Equation Modeling in Mplus Version 8.5 was used to test time trends in alcohol- and cannabis-related risk perceptions. In a first model, both alcohol- and cannabis-related risk perceptions were specified as correlated outcomes (i.e., their residual covariances were allowed to freely covary), which were regressed on study year (2002–2019), participant age, race/ethnicity (with non-Hispanic/Latinx White as the reference group), sex, and substance use frequency. Study year was treated as a continuous predictor (i.e., 1–18) to test linear trends in risk perceptions. In a second model, alcohol- and cannabis-related risk perceptions were specified as correlated outcomes regressed again on study covariates and the interaction between study year and age; all covariate*age interactions were also estimated to allow effects of race/ethnicity, and sex to vary by age group. If a significant interaction between age and study year was detected, simple slopes of study year on risk perceptions were estimated within each age group. All continuous predictors were mean-centered.

Finally, due to the complexity of modeling a 4-level variable as a continuous outcome, a binary logistic regression model with a logit link was estimated where any risk perceptions (i.e., values of 2–4) versus perceptions of no risk (i.e., value of 1) from cannabis and alcohol were the outcomes. Covariates, predictors, and interactions were identical to previous models. In all models, exogenous variables were allowed to freely covary. All model parameters are presented as unstandardized effects rounded to three decimals for in-text reporting; odds ratios are reported for logistic regression models.

3. Results

3.1. Primary analyses

In a model testing main effects, all covariates were significantly associated with both cannabis- and alcohol-related risk perceptions. Female sex was associated with stronger cannabis-related (b = 0.224, p < .001, 95% CI = [0.218, 0.230]) and alcohol-related (b = 0.231, p < .001, 95% CI = [0.227, 0.237]) perceptions of risk; being Hispanic/Latinx (b = 0.251, p < .001, 95% CI = [0.243, 0.259]), Black/African American (b = 0.029, p < .001, 95% CI = [0.018. 0.039]), and other/multiracial (b = 0.193, p < .001, 95% CI = [0.181, 0.205]) were associated with stronger cannabis-related risk perceptions, and being Hispanic/Latinx (b = 0.203, p < .001, 95% CI = [0.195, 0.211]), Black/African American (b = 0.16, p < .001, 95% CI = [0.151, 0.170]), and other/multiracial (b = 0.054, p < .001, 95% CI = [0.04, 0.067]) were associated with stronger alcohol-related risk perceptions, compared to non-Hispanic/Latinx White participants; older age was associated with stronger cannabis-related (b = 0.068, p < .001, 95% CI = [0.066, 0.071]) and alcohol-related (b = 0.076, p < .001, 95% CI = [0.074, 0.077]) perceptions of risk. Heavier cannabis use frequency was associated with weaker cannabis-related perceptions of risk (b = −0.227, p < .001, 95% CI = [−0.228, −0.225]), and heavier alcohol use frequency was associated with weaker alcohol-related perceptions of risk (b = −0.056, p < .001, 95% CI = [−0.057, −0.055]). The linear trend of time was associated with decreasing risk perceptions for cannabis (b = −0.041, p < .001, 95% CI = [−0.042, −0.04]), and increasing risk perceptions for alcohol (b = 0.001, p = .001, 95% CI = [0.000, 0.001; Fig. 1).

Fig. 1.

Fig. 1.

Open in a new tab

Next, interaction terms were tested above and beyond main effects. The interaction between the linear time trend and age was significantly associated with cannabis-related (b = 0.004, p < .001, 95% CI = [0.004, 0.005]) but not alcohol-related (b < 0.001, p = .135, 95% CI = [−0.000, 0.000]) risk perceptions. The significant interaction was probed by estimating simple slopes of the linear time trend within each age subgroup. The linear time trend was significantly associated with decreased cannabis-related risk perceptions across age groups (adolescents: b = −0.035, p < .001, 95% CI = [−0.036, −0.035]; emerging adults: b = −0.055, p < .001, 95% CI = [−0.056, −0.054]; adults: b = −0.059, p < .001, 95% CI = [−0.06, −0.058]; middle adults: b = −0.040, p < .001, 95% CI = [−0.041, −0.038]; older adults: b = −0.034, p < .001, 95% CI = [−0.035, −0.033]; Figs. 2 and 3), with the largest effects in adults, emerging adults, and middle adults (sequentially).

Fig. 2.

Fig. 2.

Open in a new tab

Fig. 3.

Fig. 3.

Open in a new tab

3.2. Logistic regression sensitivity models

Logistic regression models were estimated where any risk perceptions (i.e., values of 2–4) versus perceptions of no risk (i.e., value of 1) from cannabis and alcohol were the outcomes to ensure consistency across models. For cannabis-related risk perceptions, the time trend (OR = 0. 889, p < .001, 95% CI = [0.887, 0.892]) and the interaction between time and age (OR = 1.01, p < .001, 95% CI = [1.008, 1.011]) remained significant and in the same direction. For alcohol-related risk perceptions, the time trend became non-significant (OR = 0.997, p = .089, 95% CI = [0.993, 1.001) and the interaction between time and age remained non-significant (OR = 1.001, p = .611, 95% CI = [0.998, 1.003].

4. Discussion

The current study tested linear time trends in alcohol and cannabis-related risk perceptions, and whether time trends differed across age groups. It was hypothesized that, across ages, cannabis-related risk perceptions would decline with time. In addition, it was hypothesized that decreases in cannabis-related risk perceptions would be coupled with decreases in alcohol-related risk perceptions for adolescents (12–17)/emerging adults (18–25) and increases in alcohol-related risk perceptions for middle-age (36–49) and older adults (50+). Findings suggested that cannabis-related risk perceptions decreased over the span of 2002–2019 in the U.S. across all ages, but decreases in cannabis-related risk perceptions were the largest in adults, emerging adults, and middle adults (sequentially). Alcohol-related risk perceptions statistically increased over time but did not differ by age groups; however, the time trend for alcohol-related risk perceptions was extremely small (b < 0.01). Findings are discussed in turn.

Findings showed diverging trends for cannabis and alcohol-related risk perceptions, such that cannabis-related risk perceptions decreased, and alcohol-related risk perceptions increased over time. However, considering that the effect size of time on alcohol-related risk perceptions was extremely small, and effects did not replicate when estimated as a binary variable, changes in alcohol-related risk perceptions are cautiously interpreted. It is worth noting, though, that alcohol-related risk perceptions did not follow a similar trend (i.e., decreases) to cannabis-related risk perceptions. Thus, although alcohol- and cannabis-related risk perceptions were moderately correlated (r = 0.32, p < .001), the current study suggests substantial decreases over time in cannabis-related risk perceptions, coupled with negligible changes in alcohol-related risk perceptions over time. These findings mirror those of Cunningham and Koski-Jännes (2019), suggesting that cannabis-related attitudes are becoming increasingly permissive over time across ages, whereas alcohol-related attitudes have stayed relatively stable (with negligible increases). In addition, findings across age related to cannabis replicate past studies that have found decreasing risk perceptions over time in a multitude of different samples (Okaneku et al., 2015; Salas-Wright et al., 2017; Schermeyer et al., 2014; Sarvet et al., 2018; Jarlenski et al., 2017).

Decreases in cannabis-related risk perceptions over time have direct public health implications. Considering risk perceptions are important associates of substance use (Henry et al., 2005; Kilmer et al., 2007; Schleimer et al., 2019), targeting permissive cannabis-related attitudes may thus be an effective prevention tool in reducing risk for heavier cannabis use and the development of Cannabis Use Disorder (CUD). Further, considering alcohol-related risk perceptions have remained relatively stable, and there was a statistically significant (yet negligible) increase over time, psychoeducational interventions that discuss how alcohol and cannabis use both target similar brain structures (e.g., reward; Lupica et al., 2004; Zehra et al., 2018), and how Alcohol Use Disorder (AUD)/CUD both have similar diagnostic criteria thresholds for disorder (APA, 2013) may be effective. In addition, several studies find that normative perceptions of peers’ cannabis use are largely inaccurate (e.g., Blevins et al., 2018; Dempsey et al., 2016), and thus personalized normative interventions (i.e., normative comparisons about use and attitudes/cognitions), which are effective at reducing cannabis use (e.g., Walukevich-Dienst et al., 2020), may also be effective at reducing permissive cannabis-related attitudes.

Findings were qualified by age interactions. Simple slopes indicated that decreases in cannabis-related risk perceptions were strongest for adults (26–34) and emerging adults (18–25), followed by middle adults (35–49). Considering the intercepts for cannabis-related risk perceptions in adolescents and older adults were substantially higher than the other three age categories (see Figs. 2 and 3), one possibility may be that adolescents and older adults have less permissive (albeit still decreasing) attitudes toward cannabis. Thus, although findings support reducing cannabis-related risk perceptions for all ages, targeting risk perceptions in emerging adults and adults may be particularly important. Considering these ages have developed more permissive attitudes over time, and have substantially more permissive cannabis-related attitudes in the most recent study year (i.e., 2019), targeting cannabis-related risk perceptions may be an effective way to prevent continued use during the maturing out phase (Dawson et al., 2006). If cannabis-related risk perceptions become increasingly permissive during the transition from emerging adulthood to adulthood and adulthood to middle adulthood, permissive attitudes could lead to new cannabis use patterns that could have significant personal and societal impact. Thus, interventions focused on risks related to cannabis use may be particularly important for these age groups.

Furthermore, since the current study found differential decreasing trends by age group, it may be helpful to gear interventions discussed above by one’s age. For instance, psychoeducational interventions be more effective for adolescents who are in their nascent stages of use and are still forming stable cognitions related to substances. On the contrary, age-matched, normative feedback about “normative” declines in substance use and permissive attitudes (i.e., maturing out) may be more effective for emerging and middle adults who already have established, well-informed attitudes about substances.

Finally, the current study differed from many of the previous studies mentioned in its use of a continuous measure of perceptions rather than a binary/dichotomous measure. However, it is believed that this adds novelty to the current study, as estimating variance depending on severity/level of risk perceptions provides additional information above and beyond any vs. no risk perceptions. Although not directly comparable, the current study replicated findings related to cannabis risk perceptions via logistic regression models, suggesting that the trends for decreasing cannabis-related attitudes is found for both binary/dichotomous and Likert-based (0–4) measures.

Findings must be interpreted in light of limitations. First, the current study was focused broadly on cannabis and alcohol use, and there could be several aspects of cannabis use that affect findings. One recent study found that trends in cannabis-related attitudes differ for cannabis edibles versus flower (Reboussin et al., 2019), and another study suggested that there may be differences based upon whether individuals use medically or recreationally (Schmidt et al., 2016). There may also be differences in attitudes based upon one’s geographic location within the U.S., and whether cannabis is legal for medical purposes, recreational purposes, or both. Future research should test whether trends in the current study differ as a result of these variables. Second, the items for alcohol and cannabis were not directly comparable, as one measured risk of using cannabis 1–2 times a week, whereas the other measured risk of using a binge alcohol dose (5+ drinks) 1–2 times a week. While it is unfortunate that directly comparable questions were not available for analyses, these variables were selected to operationalize weekly use, but not necessarily daily (very high risk) use. Future research using directly comparable risk perceptions is needed. Third, the current study used a cross-sectional rather than prospective design, and thus future research should test whether there are within-person, longitudinal changes rather than across-year change. Fourth, the current study focused on age moderation, but future studies are needed to test moderation by other demographics such as race/ethnicity. The current study found that ethnic/racial minorities had stronger risk perceptions compared to non-Hispanic/Latinx white participants, and future research should test if there are race-varying time trends in perceptions. Finally, the current study assessed risk perceptions in the U.S. and future research in other countries is needed.

Despite these limitations, the current study provides integral information about changes in alcohol-and cannabis-related risk perceptions over time. Findings suggest that cannabis-related risk perceptions have decreased since the early 2000 s in the U.S., whereas alcohol-related risk perceptions have stayed relatively stable, with significant (but negligible) increases since the early 2000s. Particularly for cannabis-related risk perceptions, effects differed by age, where adult, emerging adult, and middle adult risk perceptions, sequentially, had the largest decreases over time. Prevention and intervention efforts should consider addressing cannabis-related risk perceptions during an increasing time of cannabis legislation, particularly in adult age groups who have developed more permissive attitudes related to cannabis over time. Future research is needed to test the efficacy of interventions attempting to reduce permissive substance use, across different ages.

Footnotes

Declaration of Competing Interest

The author declares no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

References

  1. American Psychiatric Association. (2013) DSM 5, American Psychiatric Association. [Google Scholar]
  2. Blevins CE, Marsh E, Banes KE, Stephens RS, Walker DD, & Roffman RA (2018). The Implications of Cannabis Policy Changes in Washington on Adolescent Perception of Risk, Norms, Attitudes, and Substance Use. Substance Abuse, 12, 1–6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. Chassin L, Barrera M Jr, Bech K, & Kossak-Fuller J (1992). Recruiting a community sample of adolescent children of alcoholics: A comparison of three subject sources. Journal of Studies on Alcohol, 53, 316–319. [DOI] [PubMed] [Google Scholar]
  4. Cunningham JA, & Koski-Jännes A (2019). The last 10 years: Any changes in perceptions of the seriousness of alcohol, cannabis, and substance use in Canada? Substance Abuse Treatment, Prevention, and Policy, 14, 1–6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Dawson DA, Grant BF, Stinson FS, & Chou PS (2006). Maturing out of alcohol dependence: The impact of transitional life events. Journal of Studies on Alcohol, 67, 195–203. [DOI] [PubMed] [Google Scholar]
  6. Dempsey RC, McAlaney J, Helmer SM, Pischke CR, Akvardar Y, Bewick BM, … Van Hal G (2016). Normative perceptions of cannabis use among European university students: Associations of perceived peer use and peer attitudes with personal use and attitudes. Journal of Studies on Alcohol and Drugs, 77, 740–748. [DOI] [PubMed] [Google Scholar]
  7. Grant BF, Chou SP, Saha TD, Pickering RP, Kerridge BT, Ruan WJ, … Hasin DS. (2017). Prevalence of 12-Month Alcohol Use, High-Risk Drinking, and DSM-IV Alcohol Use Disorder in the United States, 2001–2002 to 2012–2013: Results From the National Epidemiologic Survey on Alcohol and Related Conditions. JAMA Psychiatry. 74, 911–923. [DOI] [PMC free article] [PubMed] [Google Scholar]
  8. Hammond CJ, Chaney A, Hendrickson B, & Sharma P (2020). Cannabis use among U.S. adolescents in the era of marijuana legalization: A review of changing use patterns, comorbidity, and health correlates. International Review of Psychiatry, 32, 221–234. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Hasin DS, Sarvet AL, Cerdá M, Keyes KM, Stohl M, Galea S, & Wall MM (2017). US Adult Illicit Cannabis Use, Cannabis Use Disorder, and Medical Marijuana Laws: 1991–1992 to 2012–2013. JAMA Psychiatry, 74, 579–588. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Haug NA, Padula CB, Sottile JE, Vandrey R, Heinz AJ, & Bonn-Miller MO (2017). Cannabis use patterns and motives: A comparison of younger, middle-aged, and older medical cannabis dispensary patients. Addictive Behaviors, 72, 14–20. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Henry KL, Slater MD, & Oetting ER (2005). Alcohol use in early adolescence: The effect of changes in risk taking, perceived harm and friends’ alcohol use. Journal of Studies on Alcohol, 66, 275–283. [DOI] [PubMed] [Google Scholar]
  12. Hustad JT, Pearson MR, Neighbors C, & Borsari B (2014). The role of alcohol perceptions as mediators between personality and alcohol-related outcomes among incoming college-student drinkers. Psychology of Addictive Behaviors, 28, 336–347. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Jarlenski M, Koma JW, Zank J, Bodnar LM, Bogen DL, & Chang JC (2017). Trends in perception of risk of regular marijuana use among US pregnant and nonpregnant reproductive-aged women. Am Journal of Obstetrics and Gynecology, 217, 705–707. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Kilmer JR, Hunt SB, Lee CM, & Neighbors C (2007). Marijuana use, risk perception, and consequences: Is perceived risk congruent with reality? Addictive Behaviors, 32, 3026–3033. [DOI] [PubMed] [Google Scholar]
  15. Lupica CR, Riegel AC, & Hoffman AF (2004). Marijuana and cannabinoid regulation of brain reward circuits. British Journal of Pharmacology, 143, 227–234. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Meier MH, Caspi A, Ambler A, Harrington H, Houts R, Keefe RS, … Moffitt TE (2012). Persistent cannabis users show neuropsychological decline from childhood to midlife. Proceedings of the National Academy of Sciences, 109, E2657–E2664. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Merianos AL, Rosen BL, Montgomery L, Barry AE, & Smith ML (2017). Impact of Perceived Risk and Friend Influence on Alcohol and Marijuana Use Among Students. Journal of School Nursing, 33, 446–455. [DOI] [PubMed] [Google Scholar]
  18. National Institute on Drug Abuse (NIDA). What is the scope of marijuana use in the J. Okaneku D. Vearrier R.G. McKeever G.S. LaSala M.I. Greenberg Change in perceived risk associated with marijuana use in the United States from 2002 to 2012 Clinical Toxicology 53 2012. 151 155. [DOI] [PubMed] [Google Scholar]
  19. Okaneku J, Vearrier D, McKeever RG, LaSala GS, & Greenberg MI (2015). Change in perceived risk associated with marijuana use in the United States from 2002 to 2012. Clinical Toxicology, 53(3), 151–155. [DOI] [PubMed] [Google Scholar]
  20. Parker MA, & Anthony JC (2018a). Population-level predictions from cannabis risk perceptions to active cannabis use prevalence in the United States, 1991–2014. Addictive Behaviors, 82, 101–104. [DOI] [PMC free article] [PubMed] [Google Scholar]
  21. Parker MA, & Anthony JC (2018b). A prospective study of newly incident cannabis use and cannabis risk perceptions: Results from the United States Monitoring the Future study, 1976–2013. Drug and Alcohol Dependence, 187, 351–357. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Perkins HW (2002). Surveying the damage: A review of research on consequences of alcohol misuse in college populations. Journal of Studies on Alcohol Supplement, 14, 91–100. [DOI] [PubMed] [Google Scholar]
  23. Read JP, Merrill JE, Kahler CW, & Strong DR (2007). Predicting functional outcomes among college drinkers: Reliability and predictive validity of the Young Adult Alcohol Consequences Questionnaire. Addictive Behaviors, 32, 2597–2610. [DOI] [PubMed] [Google Scholar]
  24. Reboussin BA, Wagoner KG, Sutfin EL, Suerken C, Ross JC, Egan KL, … Johnson RM (2019). Trends in marijuana edible consumption and perceptions of harm in a cohort of young adults. Drug and Alcohol Dependence, 205, Article 107660. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Salas-Wright CP, Oh S, Goings TC, & Vaughn MG (2017). Trends in Perceived Access to Marijuana Among Adolescents in the United States: 2002–2015. Journal of Studies on Alcohol and Drugs, 78, 771–780. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. SAMHSA. (2020). Key substance use and mental health indicators in the United States. Results from the 2019 National Survey on Drug Use and Health. [Google Scholar]
  27. Sarvet AL, Wall MM, Keyes KM, Cerdá M, Schulenberg JE, O’Malley PM, … Hasin DS (2018). Recent rapid decrease in adolescents’ perception that marijuana is harmful, but no concurrent increase in use. Drug and Alcohol Dependence, 186, 68–74. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Schleimer JP, Rivera-Aguirre AE, Castillo-Carniglia A, Laqueur HS, Rudolph KE, Suárez H, … Cerdá M (2019). Investigating how perceived risk and availability of marijuana relate to marijuana use among adolescents in Argentina, Chile, and Uruguay over time. Drug and Alcohol Dependence, 201, 115–126. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Schmidt LA, Jacobs LM, & Spetz J (2016). Young people’s more permissive views about marijuana: Local impact of state laws or national trend? American Journal of Public Health, 106, 1498–1503. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Schuermeyer J, Salomonsen-Sautel S, Price RK, Balan S, Thurstone C, Min SJ, & Sakai JT (2014). Temporal trends in marijuana attitudes, availability and use in Colorado compared to non-medical marijuana states: 2003–11. Drug and Alcohol Dependence, 140, 145–155. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Schulenberg J, Johnston L, O’Malley P, Bachman J, Miech R, & Patrick M (2020). Monitoring the Future national survey results on drug use, 1975-2019: Volume II, college students and adults ages 19–60. [Google Scholar]
  32. Sher KJ, Walitzer KS, Wood PK, & Brent EE (1991). Characteristics of children of alcoholics: Putative risk factors, substance use and abuse, and psychopathology. Journal of Abnormal Psychology, 100, 427–448. [DOI] [PubMed] [Google Scholar]
  33. Tibbo P, Crocker CE, Lam RW, Meyer J, Sareen J, & Aitchison KJ (2018). Implications of cannabis legalization on youth and young adults. The Canadian Journal of Psychiatry, 63(1), 65–71. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Volkow ND, Baler RD, Compton WM, & Weiss SR (2014). Adverse health effects of marijuana use. New England Journal of Medicine, 370, 2219–2227. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Waddell JT (2021). Between-and within-group effects of alcohol and cannabis co-use on AUD/CUD in the NSDUH 2002–2019. Drug and Alcohol Dependence, 225, Article 108768. [DOI] [PubMed] [Google Scholar]
  36. Walukevich-Dienst K, Lewis EM, Neighbors C, Green JC, & Buckner JD (2020). Online personalized feedback intervention reduces cannabis-related problems among college students with high problem distress. Experimental and Clinical Psychopharmacology: Advanced Online Publication. [DOI] [PubMed] [Google Scholar]
  37. Wetherill RR, & Fromme K (2007). Alcohol use, sexual activity, and perceived risk in high school athletes and non-athletes. Journal of Adolescent Health, 41, 294–301. [DOI] [PMC free article] [PubMed] [Google Scholar]
  38. Zehra A, Burns J, Liu CK, Manza P, Wiers CE, Volkow ND, & Wang GJ (2018). Cannabis Addiction and the Brain: A Review. Journal of Neuroimmune Pharmacology, 13, 438–452. [DOI] [PMC free article] [PubMed] [Google Scholar]

RESOURCES