Sage Journals: Discover world-class research

Abstract

Objective:

Numerous reviews have examined risk and protective factors for alcohol-related negative consequences, but no equivalent review of risk and protective factors exists for cannabis-related negative consequences (CRNCs)-a gap filled by the present study. This scoping review examined survey-based research of risk and protective factors for CRNCs such as neglecting responsibilities, blacking out, or needing more cannabis.

Methods:

Three databases (PubMed, PsycINFO, and Google Scholar) were searched for peer-reviewed manuscripts published between January 1, 1990, and December 31, 2021. A qualitative synthesis was performed using the matrix method and the results were organized using the socioecological model as a framework.

Results:

Eighty-three studies were included in the review. There was considerable variation in measures and operationalizations of CRNCs across studies. Risk factors were identified in the intrapersonal (depression, social anxiety, PTSD, impulsivity, sensation seeking, motives, expectancies), interpersonal/community (trauma, victimization, family and peer substance use, social norms), and social/policy (education, employment, community attachment, legalization, availability of substances) domains of influence. Protective behavioral strategies were a robust protective factor for CRNCs. Males consistently reported more CRNCs than females, but there were no differences observed across race.

Conclusions:

Future research should identify person- and product-specific patterns of CRNCs to refine theoretical models of cannabis misuse and addiction. Public health interventions to reduce the risk of negative consequences from cannabis should consider utilizing multilevel interventions to attenuate the cumulative risk from a combination of psychological, contextual, and social influences.

Keywords

Marijuana cannabis consequence problem misuse review

From policy to practice, there have been notable changes in the public perception of cannabis use including public support for state-level changes in the medicalization and legalization of cannabis throughout the United States,^1,2 and changes in modes of consumption including the rise of “vaping” THC.^3,4 Not surprisingly, as attitudes toward cannabis use (or marijuana use) have become more favorable, the prevalence of cannabis use has increased from 10.4% in 2002 to 15.4% in 2017,⁵ and among adults in the United States, cannabis use disorder (CUD; being unable to stop using cannabis despite it causing health and social problems) treatment rates have increased. Cannabis use has been linked to long-term adverse health outcomes including vaping-induced lung injury, cardiovascular problems, and mental illness.^6-8 Therefore, identifying what factors are associated with the proximal consequences of cannabis use is an important step toward developing prevention and intervention programs that can interrupt the progression toward disorder (eg, cannabis use disorder) and potential downstream negative outcomes.

Cannabis-related negative consequences (CRNCs) are defined here as the harmful effects for users in the psychological (needing more cannabis to get high, etc.), interpersonal (getting into fights with friends or family, etc.) and social (being late for school/work, etc.) domains.⁹ While there are merits to examining the associations between substance use and specific consequences—such as driving under the influence—most research tends to aggregate negative consequences to ascertain the global burden of substance use on overall health and well-being. Self-report assessments measuring CRNCs include, among others, the Marijuana Problem Scale (19-items; example “Has marijuana ever caused you to neglect your family”),¹⁰ the Marijuana Problem Index (29-items, example “Passed out or fainted suddenly”),¹¹ the Cannabis Problems Questionnaire (27-items, example “Have you felt more antisocial after smoking?”),¹² and the Marijuana Consequences Questionnaire (50-items, example “I haven’t been as sharp mentally because of my marijuana use”),¹³ Reviews of psychometric properties and the application of these scales in community and clinical practice are available elsewhere.^14,15

Some of the aforementioned scales assessing CRNCs have been adopted from research investigating alcohol-related negative consequences (ARNCs) with “marijuana” being substituted for “alcohol” in some cases. As such, it is pertinent to consider the breadth of evidence explaining experiences of ARNCs as this has likely influenced the existing work done to document and describe experiences of CRNCs. To date, there have been several reviews of ARNCs, or problem alcohol use, across multiple populations (youth, college students, adults, treatment seeking populations, clinical populations, etc.). Zucker et al ¹⁶ reviewed early developmental processes in relation to underage drinking and problem drinking while other researchers have focused on predictors of co-occurring problems involving alcohol¹⁷ or initiation of alcohol in adolescence leading to future problem alcohol use.¹⁸ Grigsby et al¹⁹ documented risk and protective factors for ARNCs among adolescents noting important psychological (eg, impulsivity and sensation seeking), interpersonal (eg, peer use, family history) and societal influences (eg, media exposure) as well as differences by demographic characteristics (such as sex or gender and race or ethnicity). Not surprisingly, most reviews on ARNCs have focused on college student populations^20-23 as these are the peak years for risky alcohol use behavior and diagnosis of alcohol dependence.²⁴

This is not the first review to explore the causes and consequences of cannabis use though many have focused on a narrow set of adverse physical, behavioral, or social outcomes. A review of neuropsychological studies²⁵ found that adolescents who use cannabis heavily tend to have disadvantaged attention, learning, and processing speed; subtle abnormalities in brain structure; increased activation during cognitive tasks despite intact performance; and compromised objective indicators of sleep quality. Gordon et al²⁶ described the extant evidence base indicating that cannabis has physical health effects on humans—particularly diseases of the liver—aside from mental and behavioral health, and societal morbidity. Blavos et al²⁷ reviewed cannabis use among college students and identified 7 studies assessing CRNCs measured as academic problems, legal issues, enrollment disruptions and unsafe sexual practices and a few that focused on substance-related traffic risk, including driving while high or riding with a driver who was high. Two of the studies examined neurobiological consequences and reasons for cannabis use, and one focused on physical health outcomes. One meta-analysis²⁸ identified cannabis use frequency and quantity had a medium-sized association with consequences although there were high levels of heterogeneity and differences across the self-report measure used. Pearson²⁸ concluded that additional factors—psychological, behavioral, and social correlates of substance (ie, risk and protective factors)—are likely important in predicting who does and does not experience CRNCs. As such, this review fills an important gap in the literature by synthesizing existing research focused on risk and protective factors for CRNCs in non-institutionalized populations across multiple domains of functioning using standardized measures (described above). In addition to explaining factors that might contribute to risk for experiencing CRNCs beyond cannabis use behavior itself, this review can assist practitioners with identifying potential intervention targets.

Organization and categorization of results in this review are guided by asocial-ecological framework.²⁹ The social-ecological model considers the complex interplay between individual, relational, community, and societal factors in predicting and explaining health behaviors. Moreover, this model can benefit practitioners by identifying intervention targets for reducing cannabis use and related consequences by summarizing the independent and combined contributions of individual (eg, expectancies, motives, beliefs, and attitudes), relational (eg, peers, family, teachers), community (eg, media messaging, school, and work policies), and societal factors (eg, media, policy, built environment) that influence the likelihood of experiencing CRNCs. This is crucial for the development of community-based intervention programs. While the social ecological model can inform social or policy intervention targets—changes beyond the individual level—it does not provide guidance on specific variables within those levels of influence that should be targeted. As such, identifying mediators (ie, a variable that explains the process through which 2 variables are related) and moderators (ie, variables that influence the strength or direction of a relationship between 2 variables) at various levels of influence can increase the efficacy of interventions and improve our ability to customize them for specific populations or across contexts.

Current study

Experiencing recurring CRNCs is likely indicative of a pattern of misuse, and potentially an early warning sign of addiction and future diagnosis of CUD.³⁰ Describing the methods, measures and correlates used to study the incidence, prevalence, and etiology of CRNCs can guide clinical and public health professionals in developing screening and early intervention protocols for cannabis users experiencing use related negative consequences. The goal of the present review is to examine the literature on risk and protective factors for CRNCs (ie, problematic cannabis use) in observational research with community populations.

Methods

The PRISMA for Scoping Reviews (PRISMA-ScR) statement³¹ has been followed where applicable to ensure accuracy and transparency of this review and its methodology (Supplemental Table 1). No protocol was published in advance. Computer-based searches of Google Scholar, PubMed, and PsycINFO were conducted to search for publications between January 1, 1990, and December 31, 2021 (see Supplemental Table 2 for sample search strategy). We combined search terms for cannabis use (marijuana, cannabis, THC), negative consequences (abuse, misuse, problem, problematic, “negative consequence”), and study design (survey, questionnaire, self-report). Additionally, we searched the “cited by” articles in Google Scholar of validated CRNC scales described in the introduction. The combination of reference and keyword searches increases the likelihood of identifying articles using validated and non-validated measures of CRNCs. A total of 83 manuscripts were included in the final review. Figure 1 provides a flow diagram for the literature search and selection process.

Figure 1.

PRISMA flow chart.

The following inclusion criteria were used to select articles for the review: (1) the outcome was cannabis-related negative consequences, (2) the data were from observational research with any time frame (cross-sectional, prospective, retrospective), (3) the sample was non-clinical, and (4) the work was published in an English language peer-reviewed journal. Articles were excluded if (a) authors reported consequences of alcohol use or other drugs (eg, stimulants, opioids, hallucinogens) as the sole outcome, (b) CRNCs were a predictor of other health or behavior outcomes, (c) the study reported results of an intervention or treatment to reduce CRNCs, or (d) the article was a methodological study, experimental study, or a non-peer reviewed work. Studies that defined the outcome of interest as a risky behavior (eg, escalation of use, transition from one drug to another) were not considered as we were not interested in frequency or quantity of use as the outcome, but rather in the negative consequences that result from cannabis use (eg, accidents, injuries, neglecting responsibilities). Articles were screened at the title and abstract stage by 2 co-authors and the at the full-text stage by the lead author. Differences of views about inclusion were resolved through discussion and consensus with the other authors.

Data extraction and information synthesis

We consulted the Matrix Method guidelines³² for data extraction and information synthesis of the literature. The lead author and 2 co-authors abstracted author names and year of publication, study design (cross sectional, follow-up with data from 2 time points or longitudinal with data from 3 or more time points), sample characteristics (sample size, age, sex or gender, race or ethnicity), hypothesized predictors, mediators, moderators, and covariates, measurement and operationalization of negative consequences, and a summary of the main findings. We structured the results section and associated tables using the social-ecological model as a framework. As such, themes of risk and protective factors for CRNCs are presented within the theoretical domain (eg, level of influence) of the appropriate predictor including intrapersonal factors (those influencing behavior such as knowledge, attitudes, beliefs, mental health, and personality), interpersonal factors (social norms and relationships with family and peers), and social/policy factors (community attachment, drug use policy and availability, social determinants of health). We also investigated the influence of frequency and quantity of use. Finally, we explore gender and racial/ethnic differences in (a) predictors of CRNCs and (b) types and patterns of CRNCs experienced.

Results

Cannabis-related negative consequences (CRNCs) were the main outcome of all studies; however, some reports described the outcome as “cannabis abuse,” “problem use,” “cannabis use problems,” “cannabis misuse,” or “problematic cannabis use.” When interpreting the findings of this review there are 2 important considerations; first, a combination of validated, investigator-modified, and investigator-created self-report measures were used (Table 1), and the conceptualization and operationalization of CRNCs varied as a result—like review findings for alcohol-related negative consequences.¹⁹ Second, negative consequences were observed over different time periods (ie, past week, past month, past year, etc.) and settings (ie, school based, community based). As shown in Table 1, the majority of studies (n = 83) identified in this review were cross-sectional (75.9%). The main findings of studies are presented in Supplemental Table 1.

Table 1.

Authors and year of publication, CRNC scale used, research design/setting, and sample characteristics for studies included in review.

Authors/year	Scale for DV of interest	Country & setting	Sample characteristics	Thematic domain
Longitudinal Studies (presented alphabetically)
Anderson et al⁸¹	MRAPI (Modified Rutgers Alcohol Problem Index)	USA, Community	n = 434; Age: age 12 at baseline and age 25 at time 4; 48% female; Ethnicity: 90% non-Hispanic White	Intrapersonal
Brown et al³³	Cannabis Use Problems Identification Test (CUPIT), MACQ	USA, Community	n = 230; Age: M = 24.6 (SD:3.1); 50% female; Ethnicity: unknown	Frequency/Quantity of use Intrapersonal
D’Amico et al¹⁰¹	Cannabis Use Disorder Identification Test Short-Form (CUDIT-SF), MACQ	USA, Community	n = 758 ; Age: 21.6 (SD = 0.8) ; 49.2% female ; Ethnicity: 44.1% Hispanic	Interpersonal Societal/policy Gender differences
Dyar et al¹⁰⁷	CUDIT-R	USA, Community	n = 321 ; Age: M = 20.76 (SD:3.34) ; 77.9% female; Ethnicity: 33.6% African-American	Societal/policy Gender differences
Goodhines et al³⁵	Adapted Severity of Dependence Scale	USA, High School	n = 217 ; Age: M = 19.33 (SD = 1.11) ; 70% female ; Ethnicity: 72% White	Intrapersonal
Hines et al⁴⁷	CAST	USA, Community	n = 1087 Age: M = 16.7 (SD:3.0); 53.3% female; Ethnicity: 94.7% White	Frequency/Quantity of use
Kogan et al¹¹³	Investigator created measure	USA, Community	n = 505 ; Age: M = 20.3 ; 100% male ; Ethnicity: 100% African American	Societal/policy
Metrik et al³⁴	Diagnostic and Statistical Manual of Mental Disorders (DSM-IV-TR) criteria	USA, Workplace	n = 361; Age: M = 33.56 (SD:9.44); 7% female; Ethnicity: 80.0% White	Intrapersonal Interpersonal
Oesterle et al¹¹²	DSM-IV Diagnostic Interview Schedule	USA, Elementary	n = 808 ; Age at last time point: M = 33 (SD = 0.52) ; 9.0% female ; Ethnicity: 47% White	Societal/policy
Pacheco-Colón et al⁹⁴	Structured Clinical Interview for DSM IV	USA, Community	n = 401; Age: M = 15.4 (SD = 0.72); 55.9% female; Ethnicity: 76.8% White	Intrapersonal
Patterson et al⁷⁴	CUDIT	USA; College	n = 1870 ; Age: M = 20.7 (SD = 1.9) ; 63.6% female ; Ethnicity: 69.1% White	Intrapersonal Gender differences
Ramirez et al⁹⁶	Marijuana-harm IAT, CUDIT-R	USA, Community	n = 187; Age: M = 20.3 (SD = 1.8); 56% female; Ethnicity; 57.8% White	Intrapersonal
Simpson et al⁵²	CAST	USA, Community	n = 1007; Age: M = 19.2 (SD = 0.8); 36.7% female; Ethnicity: 48.2% Hispanic	Frequency/quantity of use
Steers et al⁹⁷— Study 2	RMPI	USA, College	n = 352 ; Age: M = 18.00 (SD = 0.33) ; 54% female ; Ethnicity: 77% White	Intrapersonal
Swann et al⁵⁵	CUDIT-R	USA, Community	n = 552 ; Age: M_baseline = 18.92 (SD = 1.34) ; 100% male ; Ethnicity: 51.5% African-American	Frequency/quantity of use
Tucker et al⁵³	CUDIT-SF, MCQ	USA, Middle School	n = 2258 ; Age: M = 20.67 (SD = 0.70); 54.38% female ; Ethnicity: 45.20% Hispanic	Frequency/quantity of use
Tucker et al¹⁰⁴	MCQ	USA, Middle School	n = 671 ; Age: M = 19 (SD = 0.70); 51.4% female ; Ethnicity: 42.3% Hispanic	Societal/policy
White et al¹¹	RMPI	USA, Community	College n = 326 ; 52.7% female ; Noncollege n = 221 ; 49.3% female ; Ethnicity: Unknown	Frequency/quantity of use Gender differences
Follow-up Studies (presented alphabetically)
Goodhines et al⁹²	MACQ	USA, College	n = 83 ; Age: M = 19.33 (SD = 1.11) ; 70% female ; Ethnicity: 72% White	Intrapersonal
Moitra et al⁷⁹	Marijuana Problem Scale (MPS), Reasons for Marijuana Use (RMU), Timeline Follow-back measure	USA, Community	n = 226; Age: Range = 18-25; 44.7% female; Ethnicity: 66.8% non-Latinx White	Intrapersonal
Cross-sectional Studies (presented alphabetically)
Amiet et al⁸³	Cannabis Use Problems Identification Test (CUPIT)	USA, Community	n = 329; Age: M = 25.95 (SD: 3.29); 40.4% female; Ethnicity: 77% Caucasian	Intrapersonal
Arias-de la Torre et al¹¹⁰	Cannabis Abuse Screening Test (CAST)	Spain, Community	n = 1674; Age: Range = 15-35; 27.5% female; Ethnicity: 100% Spanish	Societal/policy Gender differences
Brackenbury et al⁸²	DAST-10	USA, College	n = 357 ; Age: M = 20.3 (SD = 1.5) 71.1% female ; Ethnicity: 94.5% Caucasian	Intrapersonal Interpersonal
Bravo et al⁷³	Marijuana Consequences Questionnaire (MACQ)	USA, College	n = 2093 ; Age: Med = 19 ; 60.2% female ; Race: 70.4% White Ethnicity 80.2% Not Hispanic or Latino	Intrapersonal
Bravo et al³⁸	MACQ	USA, College	n = 2129 ; Age: M = 19.95 (SD = 3.66) ; 59.2% female ; Ethnicity: 60.4% Non-Hispanic White	Intrapersonal Gender differences
Buckner et al⁶⁶	Marijuana Problems Scale (MPS)	USA, College	n = 1009; Age: M = 20.2 (SD = 2.0); 79.7% female; Ethnicity: 76.1% Non-Hispanic White	Intrapersonal
Buckner et al⁶⁰	MPS	USA, College	n = 265; Age: M = 18.7 (SD = 1.2); 62.3% female; Ethnicity: 78.1% Caucasian	Intrapersonal
Buckner et al⁶¹	Brief Marijuana Consequences Questionnaire (B-MACQ)	USA, College	n = 102; Age: M = 19.6 (SD:1.6); 80.4% female; Ethnicity 91.2% Hispanic/Latinx	Intrapersonal
Bujarski et al⁸⁰	Cannabis Problems Questionnaire (CPQ)	Australia, Community	n = 118; Age: M = 29.84 (SD = 12.41); 33.9% female; Ethnicity: 74.1% Australia as birthplace (White)	Intrapersonal Gender differences
Caldeira et al⁵⁴	Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) criteria	USA, College	n = 1253 ; Age: Range = 17-20 ; 51.4% female ; Ethnicity: 72.4% White	Frequency/quantity of use
Callaghan et al⁵⁰	Alcohol Use Disorder and Associated Disabilities Interview Schedule-5 (AUDADIS-5)	USA, Community	n = 36 309 ; Age: Range = 18-65+ ; 51.8% female ; Ethnicity: Unknown	Frequency/quantity of use
Cavalli and Cservenka⁵⁶	Marijuana Problem Scale (MPS)	USA, Community	n = 852; Age: M = 26.88 (SD: 6.71); 37% female; Ethnicity: 65.5% White	Intrapersonal
Cohen et al¹⁰⁶	Cannabis Use Disorders Identification Test–Revised (CUDIT-R)	USA, Community	n = 163 ; Age: M = 40.4 (SD = 14.3); 23% female ; Ethnicity: 67.1% White	Societal/policy
Davis and Arterberry⁹⁰	Rutgers Marijuana Problem Index (RMPI)	USA, Community	n = 524 ; Age: M = 23.8 (SD = 9.0) 12% female ; Ethnicity: 86% White	Intrapersonal
Destrée et al⁶⁹	Cannabis Use Problems Identification Test (CUPIT)	USA, Community	n = 329; Age: M_Males = 25.83 (SD = 3.27); Age M_Females = 26.12 (SD = 3.31); 40.4% female; Ethnicity: 82.1% White	Intrapersonal
Dias et al¹⁰²	Cannabis Abuse Screening Test (CAST)	Portugal, High School	n = 529; Age: Range = 14-21; 46.1% female; Ethnicity: 100% Portuguese	Interpersonal Gender differences
Di Blasi et al⁵⁷	CUPIT	USA, College	n = 671 ; Age: M = 23.23 (SD = 2.55) ; 69.4% female ; Ethnicity: 100% Caucasian	Intrapersonal
Drazdowski et al⁹¹	Reduce Annoyed Guilty Start Scale (RAGS)	USA, College	n = 529 Age: M = 16.90(SD: 1.26); 46.1% female; Ethnicity: 57% White	Gender differences Racial/ethnic differences
Ecker and Buckner⁵⁹	MPS	USA, College	n = 230 ; Age: M = 19.68 (SD = 1.34) ; 63.0% female ; Ethnicity: 92.6% non-Hispanic/Latino	Intrapersonal Interpersonal
Ecker et al¹⁰³	MPS	USA, College	n = 103 ; Age: M = 21.2 (SD = 2.4) ; 78.6% female ; Ethnicity: 86.4% White/Caucasian	Interpersonal Racial/ethnic differences
Estoup et al¹⁰⁹	Alcohol and Drug Use Consequences Questionnaire	USA, High School	n = 262 ; Age: M = 16 (range: 13-19); 36% female ; Ethnicity: 45% Caucasian	Societal/policy
Farris et al⁶⁴	MPS	USA, Community	n = 103 ; Age: M = 21.2 (SD = 4.32) ; 35.9% female ; Ethnicity: 100% non-Hispanic White	Intrapersonal
Feingold et al³⁹	DSM-5 SUD criteria	USA, Community	n = 11 272; Age: 18 years and older; 43.10% female; Ethnicity; 73.50% White	Interpersonal
Feinstein and Newcomb⁹⁸	CUDIT-R, DAST	USA, Community	n = 300 ; Age: M = 26.8 (SD = 8.25); 100% female (lesbian, bisexual, queer women); Ethnicity: 76.67% NH White	Interpersonal
Fond et al⁹⁹	Cannabis Abuse Screening Test (CAST), Alcohol Use Disorder Identification Test (AUDIT)	France, College	n = 10 985 ; Age: M = 21.8 (SD: 3.3) ; 68.4% female; Ethnicity: 100% French	Interpersonal Gender differences
Gunn et al³⁷	MACQ	USA; College	n = 1390 ; Age: M = 19.8 (SD = 1.34) ; 62% female Ethnicity: 69% White	Frequency/quantity of use
Hamonniere et al⁹⁵	CUDIT, MPS	USA, Community	n = 157; Age: M = 26.9 (SD:9.6); 43.3% female; Ethnicity: unknown	Intrapersonal
Brooks Holliday and Pedersen¹¹⁴	CUDIT-R	USA, Community	n = 734 ; Age: M = 28.29 (SD = 3.39) ; 12.40% female Ethnicity: 76.29% White	Societal/policy Gender differences Racial/ethnic differences
Horváth et al⁷⁵	CAST	USA, Community	n = 538; Age: M = 29.24 (SD: 7.55); 29.58% female; Ethnicity: 100% Hungarian	Intrapersonal
Jordan et al¹⁰⁰	Brief Marijuana Consequences Questionnaire (B-MACQ)	USA, College	n = 1107 ; Age: M = 20.26 (SD: 3.32) ; 68.8% female; Ethnicity: 66.5% White	Intrapersonal
Kentopp et al⁷²	MACQ	USA, College	n = 8141 ; Age: M = 20.4 (SD = 4.19) ; 66.9% female ; Ethnicity: 64.7% Caucasian	Intrapersonal
Lawn et al⁴³	Diagnostic and Statistical Manual of Mental Disorders Fifth Edition (DSM-5) CUD criteria	USA, Community	n = 274 ; Age: Range = 16-29; 49.6% female ; Ethnicity: 64.6% White	Frequency/quantity of use
Lee et al⁷⁶	RMPI	USA, College	n = 634 ; Age: M = 18.0 (SD = 0.33) ; 57.9% female ; Ethnicity: 67.5% Caucasian/White	Intrapersonal
Luba et al⁴⁶	Cannabis Associated Problem Scale	USA, Community	n = 195; Age: M = 27.7 (SD unknown); 72.7% male; Ethnicity: 68.7% White	Frequency/quantity of use Gender differences
Meier⁴⁸	MACQ	USA, College	n = 821 ; Age: M = 22.6 (SD = 6.3) 65.1% female ; Ethnicity: 78.7% White	Frequency/quantity of use
Millar et al⁴²	Diagnostic and Statistical Manual of Psychiatric Disorders (DSM 5) criteria	Ireland, Community	n = 2979; Age: 15 and older; 35.6% female; Ethnicity: 100% Irish	Frequency/quantity of use
Montes and Napper⁸⁹	RMPI	USA, College	n = 211; Age: M = 18.92 (SD = 0.96) 47.4% female ; Ethnicity: 72.0% White	Intrapersonal Interpersonal
Montes et al⁸⁸	B-MACQ	USA, College	Study 2: n = 2077; Age: M = 19.99 (SD:2.91); 61.20% female; Ethnicity: unknown	Intrapersonal
Mueller et al⁶³	MPS	USA, Community	n = 279; Age: M = 30.95 (SD:7.37); 44.3% female; Ethnicity 65.6% White	Intrapersonal
Neugebauer et al⁸⁴	MACQ	USA, College	n = 2128 ; Age: M = 19.95 (SD = 3.66) ; 59.2% female ; Ethnicity: 60.4% Non-Hispanic White	Intrapersonal
Parnes et al ⁸⁵	MACQ	USA, College	n = 2091 ; Age: M = 19.92 (SD = 3.50) ; 60.2% female ; Ethnicity: 64.8% White	Intrapersonal Gender differences
Pearson and Bravo⁸⁶	CUDIT-R	USA, College	n = 1123 ; Age: M = 20.32 (SD = 3.54) ; 69.3% female ; Ethnicity: 65.27% Non-Hispanic White	Intrapersonal Gender differences
Pearson et al⁵¹	MACQ	USA, College	n = 8141 ; Age: M = 20.40 (SD = 4.19) ; 66.9% female ; Ethnicity: 64.7% White	Frequency/quantity of use
Pearson et al⁷⁰	MACQ	USA, College	n = 2129 ; Age: M = 19.95 (SD = 3.66) ; 59.2% female ; Ethnicity: 60.4% White	Intrapersonal
Pearson et al⁷⁸	MACQ	USA; College	n = 43 ; Age: Median = 20 ; 72.1% female ; Ethnicity: 83.7% White	Intrapersonal
Petrucci et al⁶⁷	MPS, CUDIT-R	USA, College	n = 1168; Age: M = 20.54 (SD: 3.60); 68.2% female; Ethnicity: 68.8% White	Intrapersonal
Phillips et al⁵⁸	RMPI	USA, College	n = 300 ; Age: M = 20.32 (SD = 0.82) ; 60% female ; Ethnicity: 69% White/Caucasian	Intrapersonal Gender differences
Schuster et al⁴⁰	CUDIT-R	USA, Community	n = 76 ; Age: M = 21.79 (SD = 1.74) ; 44.74% female ; Ethnicity: 63.2% White	Intrapersonal
Simons et al⁴⁴	RMPI	USA, College	n = 831 ; Age: M = 20.20 (SD = 2.81) ; 70% female ; Ethnicity: 89% White	Intrapersonal
Spradlin et al⁶⁵	MPS, CUDIT-R	USA, College	n = 430 ; Age: Unknown ; 66.1% female ; Ethnicity: 69% Caucasian	Intrapersonal
Stautz et al¹¹⁵— Study 3	CUDIT-R (1 item); Cannabis Problem Questionnaire	USA, College	n = 115 ; Age: M = 19.67 (SD = 2.76) ; 73.9% female ; Ethnicity: Unknown	Intrapersonal Gender differences
Steeger et al⁴⁹	Marijuana Dependence Scale (MDS)	USA, Community	n = 300 Age: M = 34.81(SD: 14.90); 42.3% female; Ethnicity: 78% White	Frequency/quantity of use
Struble et al⁴¹	Cannabis Consequences Checklist	USA, Community	n = 184 ; Age: M = 28.97 (SD = 6.31) ; 38.6% female ; Ethnicity: 85.5% African American	Intrapersonal Gender differences
Swan et al³⁶	B-MACQ, CUDIT-R	USA, College	n = 368; Age: M = 20 (SD:2.06); 71% female; Ethnicity: unknown	Frequency/quantity of use Gender differences
Rømer Thomsen et al⁷¹	CUDIT-R, DUDIT	Denmark, Community	n = 1930 ; Age: M = 21.7 (SD = 2.7) ; 31.2% female ; Ethnicity: 100% Danish	Intrapersonal
Trangenstein et al¹¹¹	CUDIT-R	USA, Community	n = 172; Age: R = 15-19; 22% female; Ethnicity: 57.56% White	Societal/policy
Walukevich-Diesnst et al.⁶²	MPS	USA, College	n = 278; Age: M = 17.1 (SD:2.0); 79.9% female; Ethnicity: 75.5% Non-Hispanic Caucasian	Intrapersonal
Wardell et al⁴⁵	MPS	USA, Community	n = 232 ; Age: M = 19.75 (SD = 1.06) ; 52.6% female ; Ethnicity: 59% Caucasian	Intrapersonal
Wisener and Khoury⁷⁷ [Study 2]	B-MACQ	USA; School	n = 165 ; Age: M = 20.2 (SD = 1.21) ; 82.2% female ; Ethnicity: Unknown	Intrapersonal
Wilson et al⁸⁷	MCQ	USA, College	n = 8141 ; Age: Unknown ; % female unknown; Ethnicity unknown	Frequency/quantity of use
Wong et al¹⁰⁸	Severity of Dependence Scale, DSM-5	USA, Community	n = 301 ; Age: R = 18-26; 35% female; 43% Hispanic/Latinx	Societal/policy
Woodruff and Shillington¹⁰⁵	Addiction Severity Index-Lite (ASI-Lite)	USA, Community	n = 292 ; Age: M = 35.3 (SD = 12.8); 24% female ; Ethnicity: 33.7% non-Hispanic White	Societal/policy
Yurasek et al⁹³	Investigator-created items	USA, College	n = 267; Age:M = 19.9 (SD:1.4); 61% female; Ethnicity 67% White	Intrapersonal

Abbreviations: C, cross sectional; F-U, follow-up (2 time points); L, longitudinal (3+ time points).

Cannabis use behaviors (Age of onset, frequency & quantity of use, product type)

There was no evidence of a linear, or dose-response, association between frequency of cannabis use (number of times used over a predefined time frame) and experience of CRNCs across 3 longitudinal,^33-35 2 cross-sectional^36,37 studies. Age of initiation was discussed in several cross-sectional studies,^38-43 but the association between age at first use and the risk of experiencing CRNCs remains unclear with no evidence of direct effects. Frequency of use was a significant mediator for substance use motives⁴⁴ and experiencing CRNCs in another cross-sectional study. Frequency of use reduced the significant association between urgency and cannabis problems to non-significance in one cross-sectional study⁴⁵ and Luba et al ’s⁴⁶ cross-sectional analysis showed that as the level of savoring (ie, the ability to recognize and appreciate positive experiences) decreased, the association between frequency of cannabis use and related consequences increased. High potency THC concentration was associated with increased odds of experiencing CRNCs among adolescents in a longitudinal study⁴⁷ and young adult college students in a cross-sectional study,⁴⁸ but potency did not predict dependence scores in another cross-sectional study of adults.⁴⁹ One cross-sectional analysis⁵⁰ identified a frequency-by-quantity interaction where CRNCs decreased as frequency increased, and vice versa while another cross-sectional study found wide variation in perceptions, use, and CRNCs.⁵¹ Two longitudinal studies^52,53 and 2 cross-sectional studies^36,37 suggest severity of CRNCs may be a function of cannabis product use, product types, and patterns. Lastly, one cross-sectional study reported young adults endorsing CUD criteria reported more types of cannabis related problems,⁵⁴ and another longitudinal study showed that problems grow as individuals transition from adolescence to young adulthood.⁵⁵

Intrapersonal influences

Anxiety and depression

Experiencing stressful life events and reporting higher perceived stress is associated with more CRNCs and were moderated by level of emotion dysregulation in one cross-sectional study.⁵⁶ Multiple forms of anxiety were investigated in relation to CRNCs; however, all studies were cross-sectional in nature. There is limited and mixed evidence to support social anxiety as a direct predictor of CRNCs.^57,58 Social anxiety may moderate the association between social norms and cannabis use problems,⁵⁹ and one study reported social anxiety was indirectly associated with cannabis-related problems through solitary use frequency.⁶⁰ Social anxiety was also associated with CRNCs via underutilization of protective behavioral strategies (ie, planned behaviors to decrease the use of cannabis) and serial effects with cannabis use frequency and peak quantity⁶¹ as well as the effect on negative and positive affect and sequential effects on frequency of use and problems.⁶² For men, but not women, the association between social anxiety symptoms and cannabis use problems may be mediated by safety behaviors—cognitive or behavioral strategies used to mitigate social anxiety, such as avoiding eye contact.⁶³ Anxiety sensitivity might also be a correlate of CRNCs⁶⁴ though evidence is scant. One study identified symptom severity of obsessive-compulsive disorder (OCD) as a significant correlate of CRNCs after adjusting for stress, depression, and general anxiety⁶⁵ while another study found features of schizotypy a were associated with CRNCs cross-sectionally and prospectively.³³ Psychological factors were examined in several studies as a predictor and mediator of CRNCs. Depression and depressive symptomology were a significant predictor of CRNCs in one longitudinal study³⁴ and across 3 cross-sectional studies^58,60,66 and was a confounder of associations in a cross-sectional study.⁶⁷

Impulsivity and sensation seeking

Impulsivity is acting, or tending to act suddenly, without careful forethought of the outcome of ones’ actions that has been conceptualized as a multidimensional construct comprising elements of a state (thoughts or behaviors unique to a point in time) and trait (a pattern of characteristics that generalize across time).⁶⁸ Several cross-sectional studies described an association between higher scores on self-reported impulsivity measures and the experiences of CRNCs.^44,57,69-71 Sensation seeking—the trait of seeking novel and intense experiences—was also significantly associated with CRNCs^45,70,72 with one exception, a significant, negative association to CRNCs among persons with relatively low use of protective behavioral strategies (ie, acting as a mediator for the risk and protective factors of cannabis consequences).⁷³ There were no longitudinal studies investigating this relationship identified in our search.

Motives and expectancies

Substance use motives refer to reasons for using drugs, such as coping with distress or enhancing a positive mood, whereas expectancies represent “if-then” contingencies regarding the outcome of a substance use behavior. Motives were generally categorized as positive (including social and enhancement motives) and negative (including conformity and coping motives). One longitudinal study showed CRNCs were associated with positive motives⁷⁴ whereas reporting more negative motives, specifically coping motives, were associated with more CRNCs in cross sectional studies^{38,44,64,73,75-77} though one cross-sectional study found CRNCs were associated with both positive and negative motives.⁷⁸ One longitudinal study of cannabis users⁷⁴ reported expansion motives as having the strongest association to CRNCs with conformity motives having a null association with cannabis problems when all motives were modeled simultaneously while another follow-up (2 time point) study indicated coping-related motives prospectively predicted problems.⁷⁹ Three cross-sectional studies suggested that motives mediate the association between psychological symptoms and CRNCs^65,80 and hostility and CRNCs.⁷⁵ One longitudinal study⁸¹ found that reporting a high number of motives to abstain buffered the influence of negative reinforcement motives on the experience of cannabis-related problems in young adulthood. Only 3 cross-sectional studies examined the role of expectancies with CRNCs. One described how an individuals’ expectancies that cannabis causes cognitive and behavioral impairment was negatively associated with cannabis use disorder criteria.⁴⁰ The second found that higher scores on the cessation expectancy questionnaire and Marijuana Effects Expectancy Questionnaire predicted higher scores on the Drug Abuse Screening Test (DAST—a self-report assessment that screens for drug abuse and drug dependence disorders) among young adult users.⁸² Finally, a latent class analysis of motives and expectancies⁸³ identified the “high motives and high expectancies” class scored significantly higher than the “low motives and low expectancies” class on the total score and both subscales of the Cannabis Use Problems Identification Test (CUPIT—a self-report assessment that identify present and potential harmful cannabis use), indicating worse problematic cannabis use and impaired control.

Protective Behavioral Strategies (PBS)

PBS are behaviors that individuals use before, during, or after initiating substance use to reduce consumption, intoxication, and related adverse outcomes such as blacking out. Examples of PBS include setting limits on how much or how often one uses, not mixing cannabis with other substances, and using only around trusted peers. The measurement used to assess PBS for cannabis misuse across identified studies was the Protective Behavioral Strategies for Marijuana Scale. Across 8 cross-sectional studies there was clear evidence that reporting more PBS is associated with fewer negative consequences from cannabis use.^{38,73,78,84-88} PBS were also found to mediate, cross-sectionally, the association between risk factors and CRNCs, such as risk and experience seeking,⁸⁴ demographic factors (eg, sexual orientation⁸⁵), and cannabis self-identity,⁸⁹ Of note, one study⁷⁸ found that no single PBS was significantly associated with CRNCs using a within-subject statistical approach which may suggest that cumulative PBS, rather than specific PBS, are beneficial to reducing CRNCs. No longitudinal investigations of direct relationships between PBS and CRNCs were identified.

Other potential intrapersonal influences

There is emerging evidence from 2 cross-sectional studies that higher scores on measures of refusal self-efficacy might be associated with reduced frequency of CRNCs.^86,90 One longitudinal study³⁵ and one cross sectional study⁹¹ identified that using cannabis as a sleep aid was associated with past month and past year CRNCs, respectively, but not daily consequences as shown in one follow-up study.⁹² Finally, one cross-sectional study indicated that insomnia severity was a predictor of CRNCs in another study and possibly mediated by mood and moderated by gender.⁹³ Delayed memory was not associated with CUD onset among teens in one longitudinal study,⁹⁴ and findings of a cross-sectional study suggest dimensions of repetitive negative thinking is not associated with cannabis use problem severity although dimensions of metacognitive thinking are.⁹⁵ In a longitudinal study, cannabis-harm implicit association test scores significantly predicted concurrent risk of CUD and use such that stronger cannabis-harm associations were associated with less use and risk of CUD.⁹⁶ Finally, harmonious passion was a stronger predictor of increased consumption than was obsessive passion, whereas obsessive passion was a stronger predictor of CRNCs longitudinally.⁹⁷

Interpersonal influences

Experiences of stigma and trauma

Cross-sectional evidence suggests that unlike internalized stigma (ie, an individual applies negative beliefs of their mental illness to oneself), perceived stigma (ie, an individual’s viewpoints on how others discriminated) was not associated with CRNCs,⁹⁸ but CRNCs were associated with experiences of general victimization.⁸¹ Lastly, cross-sectional studies suggest exposure to traumatic events (eg, adverse childhood experiences or childhood trauma) is associated with CRNCs^39,99 and may be mediated by negative urgency.⁴⁵ In a longitudinal study, posttraumatic stress disorder (PTSD—a psychiatric condition that may affect individuals who see or go through a traumatic event) was prospectively associated with CUD symptoms in a sample of veterans.³⁴ In a cross-sectional study using a double mediation model,¹⁰⁰ PTSD was associated with use of fewer PBS strategies that in turn was associated with higher cannabis use frequency/quantity, which was associated with more CRNCs.

Family and friends

One longitudinal study showed that obtaining cannabis from family or friends was associated with reduced odds of experiencing cannabis problems compared to purchasing cannabis for medicinal or recreational purposes.¹⁰¹ In a cross-sectional study, the number of friends reported using cannabis mediated the relationship between cannabis onset age use and CRNCs.¹⁰²

Social norms

Several cross-sectional studies, but no longitudinal studies, investigated the relationship of social norms and CRNCs. In a college student sample, descriptive norms (ie, how people behave based on our observations) and injunctive norms (ie, what we think others approve or disapprove of) had unique negative direct effects on CRNCs.⁷⁰ In another college sample, for students reporting cannabis use as central to their identity, injunctive norms were positively associated with consequences.⁸⁹ Descriptive norms were positively associated with CRNCs for students with a “weak marijuana identity” but were negatively associated with consequences for those with a “strong marijuana identity.”⁷⁰ Ecker and Buckner⁵⁹ identified a significant interaction between social anxiety and peer descriptive norms as well as social anxiety and parental injunctive norms on cannabis use problems while Ecker et al and colleagues found African-American college students reported more permissive perceived parental injunctive norms was related to greater severity of cannabis-related problem severity.¹⁰³

Societal/policy influences

Community membership

Two longitudinal studies,^101,104 one cross-sectional study¹⁰⁵ showed that possessing a medicinal cannabis card (provided by a medical doctor to purchase cannabis for medicinal purposes) was associated with an increased odds of experiencing CRNCs. However, in a cross-sectional study of medicinal users, those reporting use for chronic pain reported fewer problems than those using it for other medicinal purposes.¹⁰⁶

College cannabis use culture, where affinity for use was greater, mediated the effects of sensation seeking and impulsivity on CRNCs in a cross-sectional analysis.⁷⁰ Dyar et al¹⁰⁷ found that among cisgender minority women and minority non-binary individuals, using cannabis with sexual minority women and non-binary individuals, but not with sexual minority men, heterosexual men, or women, was associated with increases in problematic use 6-months later.

Drug use policy and availability

Legalization of cannabis predicted 17% of the variance of CRNCs in a study of young adults, but legalization status did not moderate the association between impulsivity and CRNCs.⁶⁹ Individuals with the strongest belief that legalization impacted their attitudes and beliefs about cannabis use reported the greatest number of CRNCs.¹⁰⁸ Estoup et al¹⁰⁹ reported that cannabis legalization and perceived risk of use explained 22% of the variance in reported CRNCs, and perceived risk mediated the association between legalization and consequences. There were no longitudinal studies investigating the effect of legalization on experiences of CRNCs.

One recent longitudinal study found that increases in the total number of cannabis sources (dispensary, retailer, family, friend, stranger/dealer) was associated with increases in CRNCs and cannabis use disorder symptoms.¹⁰¹ Moreover, the researchers noted that the total number of consequences was significantly greater for persons acquiring cannabis from strangers/dealers or institutions relative to family and friends. A cross-sectional study¹¹⁰ found that availability and exposure to consumption situations was associated with problematic cannabis use among men, but not women. Finally, exposure to cannabis advertisements on social media platforms was associated with an increased odds of CUD among adolescents in a cross-sectional analysis.¹¹¹

Education, employment, and socioeconomic status

In a longitudinal study, men between 18 and 33 years of age with limited postsecondary education had the highest rates of cannabis-related problems.¹¹² In another longitudinal study, cannabis use frequency and related problems were strongly related in disadvantaged communities, whereas in less disadvantaged communities, cannabis use quantity and problems were not significantly associated.¹¹³ A cross-sectional study evidenced that psychological consequences from cannabis use were more common among individuals with lower educational attainment.⁴¹ One cross-sectional study¹¹⁴ reported that income was associated with cannabis use problems, but specific associations were not presented while another cross-sectional study in Spain¹¹⁰ identified education level and work status as important correlates of problem cannabis use with higher proportions of problem users in the lower education and unemployed categories.

Overall differences by sex and gender

Of the 3 longitudinal studies,^11,74,101 8 cross-sectional studies^{38,55,58,86,91,99,110,115} that explored sex and gender differences, males consistently reported more cumulative negative consequences than females. One moderated mediation analysis found that coping motives had a stronger mediational association between distress tolerance scores and cannabis related problems among distress intolerant women compared to men—though a cross-sectional approach was used.⁸⁰ One longitudinal study and one cross-sectional study reported no difference in the experience of CRNCs between men and women.^46,107 Readers should note that the terms sex and gender were used interchangeably across studies and did not necessarily reflect one’s sex assigned at birth compared to their preferred identity.

Overall differences by race

While multiple studies controlled for race or ethnicity as a covariate in analyses, only 3 cross-sectional studies investigated race differences in CRNCs.^91,103,114 Drazdowski et al⁹¹ found no differences in past month or past year CRNCs between White and non-White participants. Race moderated the relationship between injunctive norms regarding parents and cannabis-related problem severity for African-American, but not Caucasian college students.¹⁰³ Specifically, more permissive perceived parental injunctive norms was related to greater cannabis-related problem severity (but not cannabis use frequency) for African-American participants. Finally, Brooks Holliday and Pedersen¹¹⁴ noted significant race differences in cannabis misuse but did not elaborate on specific associations.

Discussion

As there have been no previous literature reviews describing risk and protective factors for negative consequences of drugs other than alcohol, this review builds on extant literature documenting the complexity of interactions that contribute to risk for negative consequences associated with cannabis use behavior. In line with previous reviews,²⁸ there was a modest, yet varied, association between frequency or quantity of cannabis use and experiences of related negative consequences—a finding also observed with alcohol use consequences.¹¹⁶ It is likely that the associations between cannabis use frequency and a sum of predefined number of self-reported negative consequence items represents an artificial upper boundary of associations between cannabis use and adverse outcomes²⁸ or that self-report assessments do not sufficiently capture individual differences in patterns of substance misuse (ie, patterns of consequences may be unique to individuals or there are adverse outcomes that are not being assessed in existing measures leading to underreporting). In examining negative events resulting from cannabis use as independent outcomes, compared with a sum of experiences, researchers may be better positioned to identify person-centered patterns of CRNCs and develop effective secondary prevention programs to reduce immediate risk of harm and chances of escalating to the threshold of CUD. In addition, researchers can characterize cannabis misuse with greater specificity by ascertaining multiple measures of use—including frequency, quantity, and duration of use—that contextualize the patterns of use amongst those experiencing CRNCs.

Similar to reviews on alcohol-related negative consequences,¹⁹ most studies included in the present review investigated intrapersonal (ie, psychological) predictors of CRNCs. Mental health issues, such as anxiety and psychological distress, appear to be important correlates of CRNCs and should be addressed in substance misuse prevention programing. Some individuals undergoing treatment for a substance use disorder might also require services that address concurrent mental illness,¹¹⁷ a finding that underscores the need to address ongoing mental health concerns among individuals reporting substance misuse.¹¹⁸ Personality traits might also play an important role in our understanding of who does and does not experience CRNCs as they do in research on alcohol consequences.¹⁹ A review of controlled laboratory studies¹¹⁹ identified impulsivity as a facilitator and consequence of drug use which suggests that some personality traits, like impulsivity, are exacerbated by drug use and might contribute to more frequent, and perhaps more severe consequences as use escalates. In fact, low levels of impulsivity has been described as a predictor of treatment success.¹²⁰ The next logical step in this line of inquiry is to explore whether interventions to lower impulsivity would predict success in secondary prevention interventions to reduce CRNCs among those who do not have a clinical disorder (ie, CUD).

Such associations may not be limited to impulsivity alone as we found evidence that sensation- and risk-seeking might engender problematic cannabis use behaviors as well. As demonstrated previously, sensation-seeking is a quantitatively distinct phenomenon from impulsivity⁶⁸ and more work is needed to explore how these unique traits influence problematic cannabis use. To accomplish this, longitudinal studies that can establish baseline personality traits and examine trends over time as individuals initiate and escalate their cannabis use are needed. Motives, but not expectancies, were investigated frequently as a predictor of CRNCs with coping motives emerging as a robust risk factor.^38,73,79 As suggested by mediational findings, individuals using substances to cope with underlying mental health issues are likely at risk for problematic substance use. The only protective factor identified in this review that would be more amenable to modification were protective behavioral strategies (PBS)—a trend that aligns with alcohol research. PBS were generally defined as the approaches individuals used to reduce the quantity and frequency of cannabis use in order to avoid adverse outcomes (ie, CRNCs). Of note, it appears no single PBS reduces the odds of experiencing CRNCs, but that the use of multiple PBS can attenuate the risk of experiencing adverse outcomes following cannabis use. As such, interventions should prioritize the teaching of multiple methods or consider developing individualized plans for users based on their perceived helpfulness of a particular strategy, self-efficacy to employ a specific strategy, and patterns of use. Researchers should also consider investigating other factors (eg, religiosity) that have been identified as protective against alcohol-related consequences,^19,121 but have not been examined in relation to cannabis.

While the literature has documented the influence of peers and family members on substance use initiation and frequency/quantity of use,^122-124 there is significantly less research exploring the role of peer/family influence on experiences of CRNCs. Given the clear association between peer/family use and alcohol consequences^19,125,126 more work is needed to understand the importance of peer and family influences to determine whether family-based or social network interventions could facilitate a reduction in CRNCs among cannabis users. Previous research has described the impact of social and cultural norms in initiation of drug use,^127,128 and findings from this review suggest that social norms also contribute to CRNCs. Injunctive norms may be particularly important for youth who have not fully established their personal and social identity and turn to peers for approval of their beliefs and actions. Conversely, norms-focused interventions (eg, normative feedback) have shown effectiveness at reducing alcohol use and related consequences¹²⁹ as well as general substance use¹³⁰ and should continue being used to offset pro-drug use social and cultural norms that might influence experiences of CRNCs.

Education and income were 2 important social determinants that had an inverse association with CRNCs. That is, the odds or frequency of CRNCs decreased as education and income increased. Policies addressing these social determinants can have sweeping, downstream effects on health and continued efforts to address ongoing economic disparities can reduce the economic and social costs associated with problematic cannabis use. At the community level, efforts are needed to reduce pro-drug cultures (eg, alcohol and drug use are commonplace in college culture) that promote heavy or more frequent use that can lead for example, to more college students experiencing CRNCs. Similarly, initiatives to protect vulnerable communities, such as sexual and gender diverse populations and veterans from experiences of trauma and stigma, important risk factors for SRNCs identified in the present review, may help mitigate risk for CRNCs. As policies challenging the legality of cannabis and new state-level policies across the US decriminalize or legalize recreational cannabis use; more comparative work is needed to understand the role of policy in the severity of problematic use.

We also investigated demographic differences in CRNCs. Overall, males appear to be at a greater risk of experiencing CRNCs relative to females which aligns with findings that clinical populations in treatment for substance use disorder are disproportionately male.¹³¹ Males, particularly adolescent or young adult males, should be considered a priority population for cannabis misuse interventions aimed at reducing CRNCs. Far more research focusing on sexual and gender minority populations is needed as the evidence to date suggests that correlates and experiences of SRNCs among sexual and gender minority youth and young adults have not been adequately studied. Similarly, there is a dearth of literature examining racial/ethnic differences in CRNCs. The samples for many studies included in this review were majority non-Hispanic White and female therefore more attention should be given to identifying risk and protective factors for CRNCs in racially and ethnically diverse samples.

Limitations of the literature

First, most of the research identified in this review relied solely on self-reported survey-based responses. No studies reported collecting corroborating evidence such as peer or family reports of CRNCs experienced by the user. Additionally, the majority of included studies (75.9%) were cross-sectional limiting our results to describing associations with little confidence that identified risk and protective factors could be described as casual mechanisms of CRNCs. Second, a burgeoning literature examining correlates of simultaneous and concurrent substance use, with an emphasis on consequences of combinations of alcohol and cannabis, was not captured here. A review of polysubstance use research is needed to compare to extant work examining the consequences of alcohol, cannabis, and other drugs independently.

Third, most studies identified and included in this review were comprised of relatively young samples—primarily adolescents and young adults, and most research with young adults sampled college students who may not represent the broader population of young adults. Future research would benefit from examining differences in CRNCs across the lifespan. As evidenced by Schepis et al¹³² the prevalence, typology, motives, and risk factors of prescription drug misuse can vary considerably across age groups. Given that most individuals mature out of risky alcohol and substance use in early adulthood,^133,134 users who continue to practice unsafe substance use into middle or late adulthood are likely at increased risk for cannabis use disorder but remain an understudied group.

Limitations of this review and conclusions

This review is not without limitations. First, we did not search for specific substance use consequences—such as driving under the influence—and cannot draw conclusions regarding independent consequences of use as a result. Second, we may not have captured the entirety of literature due to the search terms used, repositories searched, and publication bias leading to an underreporting of null findings. Moreover, we limited our search to self-report studies of CRNCs—akin to previous reviews on alcohol-related negative consequences. Therefore, the scope of other methodological approaches, such as qualitative research, that might provide insights into lived experiences of individuals experiencing cannabis misuse was not described. Third, the time frame for cannabis use and associated consequences varied across studies (eg, past week, past month, past year) and caution should be exercised when considering the nature of the associations between various risk and protective factors with patterns of CRNCs. Lastly, the utilized electronic databases utilized in this review may not have captured earlier research not indexed online.

Notwithstanding the limitations of the existing literature and present review, the findings highlight the need for multilevel strategies public health interventions that can reduce the risk of negative cannabis use consequences to attenuate the cumulative risk of psychological, social, and contextual influences. Importantly, future research should aim to identify relevant protective factors for CRNCs and prioritize the investigation of environmental, interpersonal, and policy factors associated with problematic cannabis use, especially in racially and ethnically diverse populations. Addressing the current gaps in this literature will bolster the effectiveness of future primary and secondary multi-level prevention programs.

Supplemental Material

sj-docx-1-sat-10.1177_11782218231166622 – Supplemental material for A Scoping Review of Risk and Protective Factors for Negative Cannabis Use Consequences

Supplemental material, sj-docx-1-sat-10.1177_11782218231166622 for A Scoping Review of Risk and Protective Factors for Negative Cannabis Use Consequences by Timothy J. Grigsby, Andrea Lopez, Larisa Albers, Christopher J. Rogers and Myriam Forster in Substance Abuse: Research and Treatment

Footnotes

Funding:

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: The publication fees for this article were supported by the UNLV University Libraries Open Article Fund.

Declaration of Conflicting Interests:

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Author Contributions

Conceptualization: TG and MF. Literature search: TG, AL, and LA. Data extraction: AL and LA. Data synthesis and Writing – Original Draft: TG. Writing – Review and editing: All authors.

Supplemental Material

Supplemental material for this article is available online.

References

Denham

. Attitudes toward legalization of marijuana in the United States, 1986-2016: Changes in determinants of public opinion. Int J Drug Policy. 2019; 71:78-90.

Sabet

. Lessons learned in several states eight years after states legalized marijuana. Curr Opin Psychol. 2021;38:25-30.

Borodovsky

Crosier

Lee

Sargent

Budney

. Smoking, vaping, eating: is legalization impacting the way people use cannabis? Int J Drug Policy. 2016;36:141-147.

Lim

CCW

Sun

Leung

, et al. Prevalence of adolescent cannabis vaping: a systematic review and meta-analysis of US and Canadian Studies. JAMA Pediatr. 2022;176:42-51.

Compton

Han

Jones

Blanco

. Cannabis use disorders among adults in the United States during a time of increasing use of cannabis. Drug Alcohol Depend. 2019;204:107468.

Christiani

. Vaping-induced acute lung injury. N Engl J Med. 2020;382: 960-962.

Di Forti

Marconi

Carra

, et al. Proportion of patients in south London with first-episode psychosis attributable to use of high potency cannabis: a case-control study. Lancet Psychiatry. 2015;2:233-238.

Page

Allen

Kloner

, et al. Medical marijuana, recreational cannabis, and cardiovascular health: a scientific statement from the American Heart Association. Circulation. 2020;142:e131-e152.

Grigsby

Forster

Tsai

Rohrbach

Sussman

. Negative substance use consequences associated with noncondom use among male, but not female, alternative high school students. J Sch Health. 2018;88:531-537.

10.

Stephens

Roffman

Curtin

. Comparison of extended versus brief treatments for marijuana use. J Consult Clin Psychol. 2000;68:898-908.

11.

White

Labouvie

Papadaratsakis

. Changes in substance use during the transition to adulthood: a comparison of college students and their noncollege age peers. J Drug Issues. 2005;35:281-306.

12.

Copeland

Gilmour

Gates

Swift

. The Cannabis Problems Questionnaire: factor structure, reliability, and validity. Drug Alcohol Depend. 2005;80: 313-319.

13.

Simons

Dvorak

Merrill

Read

. Dimensions and severity of marijuana consequences: development and validation of the Marijuana Consequences Questionnaire (MACQ). Addict Behav. 2012;37:613-621.

14.

Piontek

Kraus

Klempova

. Short scales to assess cannabis-related problems: a review of psychometric properties. Subst Abuse Treat Prev Policy. 2008;3:25.

15.

Grigsby

Sussman

Chou

Ames

. Assessment of substance misuse. In: VanGeest

Johnson

Alemagno

, eds. Research Methods in the Study of Substance Abuse. Springer International Publishing; 2017;197-233.

16.

Zucker

Donovan

Masten

Mattson

Moss

. Early developmental processes and the continuity of risk for underage drinking and problem drinking. Pediatrics. 2008;121(Supplement_4):S252-S272.

17.

Saraceno

Munafó

Heron

Craddock

van den Bree

. Genetic and non-genetic influences on the development of co-occurring alcohol problem use and internalizing symptomatology in adolescence: a review. Addiction. 2009;104: 1100-1121.

18.

Petit

Kornreich

Verbanck

Cimochowska

Campanella

. Why is adolescence a key period of alcohol initiation and who is prone to develop long-term problem use?: A review of current available data. Socioaffect Neurosci Psychol. 2013;3:21890.

19.

Grigsby

Forster

Unger

Sussman

. Predictors of alcohol-related negative consequences in adolescents: A systematic review of the literature and implications for future research. J Adolesc. 2016;48:18-35.

20.

Ham

Hope

. College students and problematic drinking: A review of the literature. Clin Psychol Rev. 2003;23:719-759.

21.

Perkins

. Surveying the damage: a review of research on consequences of alcohol misuse in college populations. J Stud Alcohol Suppl. 2002;14:91-100. doi:10.15288/jsas.2002.s14.91

22.

Mallett

Varvil-Weld

Borsari

Read

Neighbors

White

. An update of research examining college student alcohol-related consequences: new perspectives and implications for interventions. Alcohol Clin Exp Res. 2013;37: 709-716.

23.

Forster

Rogers

Tinoco

Benjamin

Lust

Grigsby

. Adverse childhood experiences and alcohol related negative consequence among college student drinkers. Addict Behav. 2023;136:107484.

24.

Hingson

Heeren

Winter

. Age at drinking onset and alcohol dependence: age at onset, duration, and severity. Arch Pediatr Adolesc Med. 2006;160: 739-746.

25.

Jacobus

Bava

Cohen-Zion

Mahmood

Tapert

. Functional consequences of marijuana use in adolescents. Pharmacol Biochem Behav. 2009;92: 559-565.

26.

Gordon

Conley

Gordon

. Medical consequences of marijuana use: a review of current literature. Curr Psychiatry Rep. 2013;15:419.

27.

Blavos

Glassman

Sheu

Thompson

DeNardo

Diehr

. Marijuana and college students: a critical review of the literature. Am J Health Educ. 2017;48:167-184.

28.

Pearson

. A meta-analytic investigation of the associations between cannabis use and cannabis-related negative consequences. Psychol Addict Behav. 2019;33:190-196.

29.

Bronfenbrenner

. Toward an experimental ecology of human development. Am Psychol. 1977;32:513-531.

30.

Grigsby

. Leaving “drug abuse” behind: a theoretical and methodological heuristic to selecting “problem drug use” or “drug misuse” as alternative terms. Subst Use Misuse. 2021;56:2074-2077.

31.

Tricco

Lillie

Zarin

, et al. PRISMA extension for scoping reviews (PRISMA-ScR): checklist and explanation. Ann Intern Med. 2018;169:467-473.

32.

Garrard

. Health Sciences Literature Review Made Easy: The Matrix Method. 4th ed. Jones & Bartlett Learning; 2013.

33.

Brown

Leonard

Stasiewicz

Testa

. Do schizotypy features predict cannabis problems in partnered community adults? Addict Behav. 2021;116: 106819.

34.

Metrik

Stevens

Gunn

Borsari

Jackson

. Cannabis use and posttraumatic stress disorder: prospective evidence from a longitudinal study of veterans. Psychol Med. 2022;52:446-456.

35.

Goodhines

Wedel

Dobani

Zaso

Gellis

Park

. Cannabis use for sleep aid among high school students: concurrent and prospective associations with substance use and sleep problems. Addict Behav. 2022;134:107427.

36.

Swan

Ferro

Thompson

. Does how you use matter? The link between mode of use and cannabis-related risk. Addict Behav. 2021;112:106620.

37.

Gunn

Aston

Sokolovsky

White

Jackson

. Complex cannabis use patterns: associations with cannabis consequences and cannabis use disorder symptomatology. Addict Behav. 2020;105:106329.

38.

Bravo

Prince

Pearson

; Marijuana Outcomes Study Team. Can I use marijuana safely? An examination of distal antecedents, marijuana protective behavioral strategies, and Marijuana Outcomes. J Stud Alcohol Drugs. 2017;78(2): 203-212.

39.

Feingold

Livne

Rehm

Lev-Ran

. Probability and correlates of transition from cannabis use to DSM-5 cannabis use disorder: results from a large-scale nationally representative study. Drug Alcohol Rev. 2020;39:142-151.

40.

Schuster

Hareli

Moser

, et al. Cross-domain correlates of cannabis use disorder severity among young adults. Addict Behav. 2019;93:212-218.

41.

Struble

Ellis

Cairncross

Lister

Lundahl

. Demographic, cannabis use, and depressive correlates of cannabis use consequences in regular cannabis users. Am J Addict. 2019;28:295-302.

42.

Millar

Mongan

Smyth

Perry

Galvin

. Relationships between age at first substance use and persistence of cannabis use and cannabis use disorder. BMC Public Health. 2021;21:997.

43.

Lawn

Mokrysz

Lees

, et al. The CannTeen study: Cannabis use disorder, depression, anxiety, and psychotic-like symptoms in adolescent and adult cannabis users and age-matched controls. J Psychopharmacol. 2022;36(12):1350-1361.

44.

Simons

Gaher

Correia

Hansen

Christopher

. An affective-motivational model of marijuana and alcohol problems among college students. Psychol Addict Behav. 2005;19:326-334.

45.

Wardell

Strang

Hendershot

. Negative urgency mediates the relationship between childhood maltreatment and problems with alcohol and cannabis in late adolescence. Addict Behav. 2016;56:1-7.

46.

Luba

Earleywine

Melse

Gordis

. Savoring moderates the link between Marijuana Use and marijuana problems. Subst Use Misuse. 2020;55: 291-295.

47.

Hines

Freeman

Gage

, et al. Association of high-potency cannabis use with mental health and substance use in adolescence. JAMA Psychiatr. 2020; 77:1044-1051.

48.

Meier

. Associations between butane hash oil use and cannabis-related problems. Drug Alcohol Depend. 2017;179:25-31.

49.

Steeger

Hitchcock

Bryan

Hutchison

Hill

Bidwell

. Associations between self-reported cannabis use frequency, potency, and cannabis/health metrics. Int J Drug Policy. 2021;97:103278.

50.

Callaghan

Sanches

Kish

. Quantity and frequency of cannabis use in relation to cannabis-use disorder and cannabis-related problems. Drug Alcohol Depend. 2020;217:108271.

51.

Pearson

Liese

Dvorak

. College student marijuana involvement: Perceptions, use, and consequences across 11 college campuses. Addict Behav. 2017;66:83-89.

52.

Simpson

Cho

Barrington-Trimis

. The association of type of cannabis product used and frequency of use with problematic cannabis use in a sample of young adult cannabis users. Drug Alcohol Depend. 2021;226:108865.

53.

Tucker

Pedersen

Seelam

Dunbar

Shih

D’Amico

. Types of cannabis and tobacco/nicotine co-use and associated outcomes in young adulthood. Psychol Addict Behav. 2019;33:401-411.

54.

Caldeira

Arria

O’Grady

Vincent

Wish

. The occurrence of cannabis use disorders and other cannabis-related problems among first-year college students. Addict Behav. 2008;33:397-411.

55.

Swann

Bettin

Clifford

Newcomb

Mustanski

. Trajectories of alcohol, marijuana, and illicit drug use in a diverse sample of young men who have sex with men. Drug Alcohol Depend. 2017;178:231-242.

56.

Cavalli

Cservenka

. Emotion dysregulation moderates the association between stress and problematic cannabis use. Front Psychiatry. 2021;11:597789.

57.

Di Blasi

Cavani

Pavia

, et al. Mediating Effects of global negative effect expectancies on the association between Problematic Cannabis Use and social anxiety. Front Psychiatry. 2017;8:249.

58.

Phillips

Duck

. Factors associated with marijuana use and problems among college students in Colorado. Subst Use Misuse. 2018;53: 477-483.

59.

Ecker

Buckner

. Cannabis use behaviors and social anxiety: the roles of perceived descriptive and injunctive social norms. J Stud Alcohol Drugs. 2014;75: 74-82.

60.

Buckner

Ecker

Dean

. Solitary cannabis use frequency mediates the relationship between social anxiety and cannabis use and related problems. Am J Addict. 2016;25(2):99-104.

61.

Buckner

Morris

Zvolensky

. Social anxiety and risky marijuana use: the role of underutilization of protective behavioral strategies. Addict Behav. 2021;123:107078.

62.

Walukevich-Dienst

Lewis

Buckner

. Cannabis-related impairment and social anxiety: the role of use to manage negative and positive affect in social situations. Subst Use Misuse. 2020;55:271-280.

63.

Mueller

McDermott

Cougle

. The role of safety behaviors in the relationship between social anxiety and marijuana use problems. Subst Use Misuse. 2021;56:1305-1311.

64.

Farris

Metrik

Bonn-Miller

Kahler

Zvolensky

. Anxiety sensitivity and distress intolerance as predictors of cannabis dependence symptoms, problems, and craving: the mediating role of Coping Motives. J Stud Alcohol Drugs. 2016;77:889-897.

65.

Spradlin

Mauzay

Cuttler

. Symptoms of obsessive-compulsive disorder predict cannabis misuse. Addict Behav. 2017;72:159-164.

66.

Buckner

Keough

Schmidt

. Problematic alcohol and cannabis use among young adults: the roles of depression and discomfort and distress tolerance. Addict Behav. 2007;32:1957-1963.

67.

Petrucci

LaFrance

Cuttler

. A comprehensive examination of the links between cannabis use and motivation. Subst Use Misuse. 2020;55:1155-1164.

68.

MacKillop

Weafer

C. Gray

Oshri

Palmer

de Wit

. The latent structure of impulsivity: impulsive choice, impulsive action, and impulsive personality traits. Psychopharmacology. 2016;233(18):3361-3370.

69.

Destrée

Amiet

Carter

, et al. Exploring the association of legalisation status of cannabis with problematic cannabis use and impulsivity in the USA. Drugs Context. 2018;7:1212541-1212545.

70.

Pearson

Hustad

JTP

Neighbors

Conner

Bravo

. Personality, marijuana norms, and marijuana outcomes among college students. Addict Behav. 2018;76:291-297.

71.

Rømer Thomsen

Callesen

Hesse

, et al. Impulsivity traits and addiction-related behaviors in youth. J Behav Addict. 2018;7(2):317-330.

72.

Kentopp

Johnson

Fresquez

Prince

Conner

; Marijuana Outcomes Study Team. Risk seeking moderates the association between emotion dysregulation and cannabis-related consequences. J Drug Issues. 2019;49:559-569.

73.

Bravo

Anthenien

Prince

Pearson

. Marijuana protective behavioral strategies as a moderator of the effects of risk/protective factors on marijuana-related outcomes. Addict Behav. 2017;69:14-21.

74.

Patterson

Haardörfer

Windle

Berg

. Motives for alcohol and marijuana use as predictors of use and problem use among young adult college students. J Drug Issues. 2020;50:359-377.

75.

Horváth

Kökönyei

Sárosi

Koós

Demetrovics

Urbán

. The mediating effect of anger rumination, coping and conformity motives on the association between hostility and problematic cannabis use. Addict Behav Rep. 2022;16:100447.

76.

Lee

Neighbors

Woods

. Marijuana motives: Young adults’ reasons for using marijuana. Addict Behav. 2007;32:1384-1394.

77.

Wisener

Khoury

. Specific emotion-regulation processes explain the relationship between mindfulness and self-compassion with coping-motivated alcohol and marijuana use. Addict Behav. 2021;112:106590.

78.

Pearson

Bravo

Conner

Parnes

. A day in the life: a daily diary examination of marijuana motives and protective behavioral strategies among college student marijuana users. J Drug Issues. 2020;50:142-156.

79.

Moitra

Anderson

Herman

Stein

. Longitudinal examination of coping-motivated marijuana use and problematic outcomes among emerging adults. Addict Behav. 2021;113:106691.

80.

Bujarski

Norberg

Copeland

. The association between distress tolerance and cannabis use-related problems: the mediating and moderating roles of coping motives and gender. Addict Behav. 2012;37:1181-1184.

81.

Anderson

Sitney

White

. Marijuana motivations across adolescence: Impacts on use and consequences. Subst Use Misuse. 2015;50:292-301.

82.

Brackenbury

Ladd

Anderson

. Marijuana use/cessation expectancies and marijuana use in college students. Am J Drug Alcohol Abuse. 2016;42:25-31.

83.

Amiet

Youssef

Hagg

, et al. Young Adults with higher motives and expectancies of regular cannabis use show poorer psychosocial functioning. Front Psychiatry. 2020;11:599365.

84.

Neugebauer

Parnes

Prince

Conner

; Marijuana Outcomes Study Team. Protective behavioral strategies mediate the relation between sensation seeking and marijuana-related consequences. Subst Use Misuse. 2019;54: 973-979.

85.

Parnes

Prince

Conner

. A mediated multigroup model examining marijuana use consequences by sexual orientation in us college students. Addict Behav. 2018;87:86-91.

86.

Pearson

Bravo

; Protective Strategies Study Team. Marijuana protective behavioral strategies and marijuana refusal self-efficacy: Independent and interactive effects on marijuana-related outcomes. Psychol Addict Behav. 2019;33: 412-419.

87.

Wilson

Montes

Bravo

Conner

Pearson

; Marijuana Outcomes Study Team. Making decisions with trees: examining marijuana outcomes among college students using recursive partitioning. Clin Psychol Sci. 2018;6: 744-754.

88.

Montes

Dela Cruz

Weinstein

Pearson

Lindgren

Neighbors

. Alcohol and marijuana protective behavioral strategies mediate the relationship between substance use identity and use-related outcomes: a multi-sample examination. Addict Behav. 2021;112:106613.

89.

Montes

Napper

. Is marijuana identity associated with marijuana use and consequences? An examination of direct and interactive associations. Subst Use Misuse. 2019;54:1286-1296.

90.

Davis

Arterberry

. Passion for marijuana use mediates the relations between refusal self-efficacy and marijuana use and associated consequences. J Psychoactive Drugs. 2019;51:343-350.

91.

Drazdowski

Kliewer

Marzell

. College students’ using marijuana to sleep relates to frequency, problematic use, and sleep problems. J Am Coll Health. 2021;69:103-112.

92.

Goodhines

Gellis

Ansell

Park

. Cannabis and alcohol use for sleep aid: a daily diary investigation. Health Psychol. 2019;38:1036-1047.

93.

Yurasek

Miller

Pritschmann

Curtis

McCrae

. Negative mood as a mediator of the association between insomnia severity and marijuana problems in college students. J Sleep Res. 2020;29:e12985.

94.

Pacheco-Colón

Lopez-Quintero

Coxe

, et al. Risky decision-making as an antecedent or consequence of adolescent cannabis use: findings from a 2-year longitudinal study. Addiction. 2022;117:392-410.

95.

Hamonniere

Milan

Varescon

. Repetitive negative thinking, metacognitive beliefs, and their interaction as possible predictors for problematic cannabis use. Clin Psychol Psychother. 2022;29:706-717.

96.

Ramirez

Cadigan

Lee

. Behavioral economic demand for alcohol among young adults who engage in simultaneous alcohol and marijuana use. Subst Abuse. 2020;41(2):203-207.

97.

Steers

Neighbors

Hove

Olson

Lee

. How harmonious and obsessive passion for alcohol and marijuana relate to consumption and negative consequences. J Stud Alcohol Drugs. 2015;76(5):749-757.

98.

Feinstein

Newcomb

. The role of substance use motives in the associations between minority stressors and substance use problems among young men who have sex with men. Psychol Sex Orient Gender Divers. 2016;3:357-366.

99.

Fond

Picot

Bourbon

, et al. Prevalence and associated factors of cannabis consumption in medical students: the BOURBON nationwide study. Eur Arch Psychiatry Clin Neurosci. 2021;271:857-864.

100.

Jordan

Madson

Bravo

Pearson

; Protective Strategies Study Team. Post-traumatic stress and marijuana outcomes: the mediating role of marijuana protective behavioral strategies. Subst Abuse. 2020;41(3):375-381.

101.

D’Amico

Rodriguez

Dunbar

, et al. Sources of cannabis among young adults and associations with cannabis-related outcomes. Int J Drug Policy. 2020; 86:102971.

102.

Dias

Lopes

Garcia Del Castillo

. Tell me who your friends are?! the mediating role of friends’ use in cannabis abuse. Trends Psychiatry Psychother. 2022;44:44e20210269.

103.

Ecker

Dean

Buckner

Foster

. Perceived injunctive norms and cannabis-related problems: the interactive influence of parental injunctive norms and race. J Ethn Subst Abuse. 2019;18:211-223.

104.

Tucker

Rodriguez

Pedersen

Seelam

Shih

D’Amico

. Greater risk for frequent marijuana use and problems among young adult marijuana users with a medical marijuana card. Drug Alcohol Depend. 2019;194: 178-183.

105.

Woodruff

Shillington

. Sociodemographic and drug use severity differences between medical marijuana users and non-medical users visiting the emergency department. Am J Addict. 2016;25:385-391.

106.

Cohen

Heinz

Ilgen

Bonn-Miller

. Pain, cannabis species, and cannabis use disorders. J Stud Alcohol Drugs. 2016;77:515-520.

107.

Dyar

Feinstein

Newcomb

Whitton

. Cannabis use companions’ gender and sexual orientation: associations with problematic cannabis use in a sample of sexual minorities assigned female at birth. Addict Behav. 2021;118: 106878.

108.

Wong

Mendez

SEA

Conn

Iverson

Lankenau

. Attitudes and beliefs about recreational cannabis legalization among cannabis-using young adults in Los Angeles: Impact on concurrent cannabis practices and problematic cannabis use. Drug Alcohol Depend. 2021;228:109053.

109.

Estoup

Moise-Campbell

Varma

Stewart

. The impact of marijuana legalization on adolescent use, consequences, and perceived risk. Subst Use Misuse. 2016;51:1881-1887.

110.

Arias-de

Torre

Eiroa-Orosa

Molina

, et al. Relación del consumo problemático de cannabis en la población joven de España con el riesgo percibido, los factores ambientales y los factores sociodemográficos. Adicciones. 2021;33:63.

111.

Trangenstein

Whitehill

Jenkins

Jernigan

Moreno

. Cannabis marketing and problematic cannabis use among adolescents. J Stud Alcohol Drugs. 2021;82:288-296.

112.

Oesterle

Hawkins

Hill

. Men’s and women’s Pathways to adulthood and associated substance misuse. J Stud Alcohol Drugs. 2011;72(5):763-773.

113.

Kogan

Cho

Brody

Beach

SRH

. Pathways linking marijuana use to substance use problems among emerging adults: A prospective analysis of young black men. Addict Behav. 2017;72:86-92.

114.

Brooks Holliday

Pedersen

. The association between discharge status, mental health, and substance misuse among young adult veterans. Psychiatry Res. 2017;256:428-434.

115.

Stautz

Dinc

Cooper

. Combining trait models of impulsivity to improve explanation of substance use behaviour. Eur J Pers. 2017;31:118-132.

116.

Prince

Pearson

Bravo

Montes

. A quantification of the alcohol use-consequences association in college student and clinical populations: A large, multi-sample study: alcohol use and consequences. Am J Addict. 2018;27(2): 116-123.

117.

Mericle

Ta Park

Holck

Arria

. Prevalence, patterns, and correlates of co-occurring substance use and mental disorders in the United States: variations by race/ethnicity. Compr Psychiatry. 2012;53:657-665.

118.

Grigsby

Howard

. Prescription opioid misuse and comorbid substance use: Past 30-day prevalence, correlates and co-occurring behavioral indicators in the 2016 National Survey on Drug Use and Health: prescription opioid and comorbid substance use. Am J Addict. 2019;28(2):111-118.

119.

de Wit

. Impulsivity as a determinant and consequence of drug use: a review of underlying processes: impulsivity and drug use. Addict Biol. 2009;14(1):22-31.

120.

Loree

Lundahl

Ledgerwood

. Impulsivity as a predictor of treatment outcome in substance use disorders: Review and synthesis: Impulsivity in substance use treatment. Drug Alcohol Rev. 2015;34(2):119-134.

121.

Russell

Thompson

Sussman

Barry

. Assessing the relationship between youth religiosity and their alcohol use: A meta-analysis from 2008 to 2018. Addict Behav. 2020;106:106361.

122.

Henneberger

Mushonga

Preston

. Peer influence and adolescent substance use: A systematic review of Dynamic Social Network Research. Adolesc Res Rev. 2021;6:57-73.

123.

Rusby

Light

Crowley

Westling

. Influence of parent-youth relationship, parental monitoring, and parent substance use on adolescent substance use onset. J Fam Psychol. 2018;32:310-320.

124.

Stone

Becker

Huber

Catalano

. Review of risk and protective factors of substance use and problem use in emerging adulthood. Addict Behav. 2012;37:747-775.

125.

Elliott

Carey

Bonafide

. Does family history of alcohol problems influence college and university drinking or substance use? A meta-analytical review: family history and college substance use. Addiction. 2012;107(10): 1774-1785.

126.

Song

Smiler

Wagoner

Wolfson

. Everyone says It’s OK: Adolescents’ perceptions of peer, parent, and community alcohol norms, alcohol consumption, and alcohol-related consequences. Subst Use Misuse. 2012;47:86-98.

127.

Eisenberg

Toumbourou

Catalano

Hemphill

. Social norms in the development of adolescent substance use: A longitudinal analysis of the International Youth Development Study. J Youth Adolesc. 2014;43:1486-1497.

128.

Guise

Horyniak

Melo

McNeil

Werb

. The experience of initiating injection drug use and its social context: a qualitative systematic review and thematic synthesis: Initiating injection drug use. Addiction. 2017;112(12):2098-2111.

129.

Foxcroft

Moreira

Almeida Santimano

Smith

. Social norms information for alcohol misuse in university and college students. Cochrane Database Syst Rev. 2015;2015:CD006748.

130.

McAlaney

Bewick

Hughes

. The international development of the ‘Social Norms’ approach to drug education and prevention. Drugs Educ Prev Policy. 2011;18:81-89.

131.

Substance Abuse and Mental Health Services Administration, Center for Behavioral Health Statisticsand Quality. TEDS 2004-2014 National Admissions to Substance Abuse Treatment Services. Substance Abuse and Mental Health Services Administration; 2016:165.

132.

Schepis

Klare

Ford

McCabe

. Prescription drug misuse: taking a lifespan perspective. Subst Abuse Res Treat. 2020;14:117822182090935.

133.

Jochman

Fromme

. Maturing out of substance use: the other side of etiology. In: Scheier L, ed. Handbook of Drug Use Etiology: Theory, Methods, and Empirical Findings. American Psychological Association; 2010;565-578.

134.

Lee

Sher

. Maturing out” of binge and problem drinking. Alcohol Res Curr Rev. 2018;39:31-42.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.04 MB