The Relationships of Early Use of Marijuana With Substance Use and Violence in Adolescent Gamblers and Non-Gamblers.

Abstract

Objectives

Marijuana use (MU) and gambling are prevalent among adolescents, and marijuana products are becoming increasingly available and normalized globally. This study explored the relationships between early- (age <13 years), later- (age ≥13 years), and no-MU and substance use and violence among adolescents who gambled and did not gamble.

Methods

It analyzed data from the 2019 Youth Risk Behavior Surveillance survey (n=2015) on MU, gambling, demographics, substance use, and violence, using adjusted multivariate logistic regression models.

Results

The odds of current cigarette smoking, alcohol use, and heavy alcohol use; lifetime use of any substance and cocaine; current and lifetime electronic vapor product use; and physical fighting were higher across adolescents with early and later MU than those with no MU. Gambling adolescents with early-MU, compared to later- and no-MU, respectively, had greater odds of any substance use and prescription opiate misuse. Non-gambling adolescents with later-MU had higher odds of having experienced forced sexual intercourse than those with no-MU. MU by gambling status interactions were identified for prescription opiate misuse and any substance use, and having experienced bullying at school and forced sexual intercourse. However, while the simple main effects of MU on the odds of experiencing bullying among gamblers was approximately 3.9 times greater than that among non-gamblers, they were not statistically significant in either gambling group.

Conclusion

Early MU is associated with risky behaviors involving the use of other substances and violence, and its relationships with several factors differ according to gambling status. Reducing early engagement in addictive behaviors may be important for preventions against substance use disorders and interpersonal violence.

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Keywords: Addictive behaviors, Marijuana use, Gambling, Violence, Adolescents

Article notes

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Received 2025 Feb 27; Revised 2025 May 6; Accepted 2025 May 27; Issue date 2025 Jul 1.

Author notes

INTRODUCTION

Marijuana use (MU) is prevalent among adolescents and is associated with adverse health consequences and risky behaviors, including the use of other illicit drugs and non-marijuana substance use disorders (SUDs), neurocognitive concerns, aggression, and violence [1-8]. Despite these negative health consequences, the current proportion of adolescents who perceive MU as harmful is among the lowest recorded. In one study, only 28% of 12th graders attributed greater health risks to regular MU [9]. Estimates of past-year MU in high school students remain considerable at 18%–29%, and daily MU has recently increased [10]. While the prevalence of MU is greatest during later adolescence and early adulthood, reflecting an age-related pattern of substance use across the lifespan, an estimated 1.3% of individuals aged 12–13 years initiated MU in the previous 12 months, and 4.9% of high school adolescents reported MU by the age of 13 [11-13]. Thirteen is a critical age that marks the transition from the end of middle school to starting of high school, during which major biological, socio-emotional, and cognitive changes and the establishment of identity occur [14]. Relatedly, an age threshold of less than 13 years was used previously in the nationwide Youth Risk Behavior Surveillance (YRBS) survey to assess early substance use and other risky behaviors [15].

Initiating substance use at an earlier age has been linked to outcomes of more severe substance use involvement and problems [16,17]. Prospective associations have been found between early MU and subsequent frequent use, and the odds of having a cannabis use disorder were reduced by 11% for each year the initiation was delayed [18]. Among adolescents, early compared with later MU was associated with greater odds of having used other substances (e.g., tobacco, alcohol, cocaine, and depressants) [19,20] and MU problems [21]. Early adolescent substance use has also been linked to concurrent and prospective aggression, temper concerns, and nonconforming behavior [22]. In one study, the largest difference in alcohol use of individuals with minor versus serious violent offenses was observed among early adolescence at age 13, and youths who did not exhibit violent behaviors reported the lowest levels of MU [23]. Early MU was also the strongest predictor of frequent violence perpetration in later adolescence, with a fivefold increase in likelihood [23]. Individuals exhibiting early MU and violence perpetration share similar underlying problems of self-regulation and impulsivity, which may be exacerbated by substance use and have been proposed to mediate the relationship between early MU and aggression [24,25].

In addition to aggression and perpetration of violence (i.e., fighting, purposefully damaging property), early MU has also been linked to experiences of violence and traumatic stress [19,23]. However, little is known about the relationship between early MU and victimization involving violence. In a scoping review of 26 studies, victims of peer violence and perpetrators with victimization had greater risks of MU [26]. A meta-analysis of more than a decade of studies on adolescents showed that those with MU had 54% greater odds of having experienced physical dating violence [27]. While adolescents may use marijuana to cope with distress from experiences of victimization, MU may impair decision-making and decrease recognition of danger cues [27-30]. Hence, relative to later ages during which MU more typically occurs, early adolescence is a critical developmental period during which MU may potentiate risky behaviors and negative health consequences [31]. In light of the increasing legalization of marijuana in multiple jurisdictions and the acceptance of MU [32], further research is needed to determine the relationships between early-, later-, and no-MU and substance use and violent behaviors among adolescents.

Similar to MU, gambling is linked to adverse health consequences in youths [33-35]. Gambling is prevalent among adolescents, with up to 80% of whom having gambled, and 0.2%–12.3% across five continents having met criteria for problem gambling [36-38]. Higher prevalence estimates of gambling disorder are likely, given recent changes in the availability and popularity of different types of gambling (e.g., online sports gambling) and a lower threshold for diagnosis (the DSM-5 decreased the threshold for gambling disorder diagnosis to four out of nine criteria) [39]. Adolescent gambling and problem gambling have been linked to the use of alcohol, cigarettes, and illicit substances, and SUDs [33,35,40-42]. Youths who gambled in the past year were more likely to experience violence, including physical fighting and weaponcarrying [35]. Furthermore, adolescents with at-risk/problem gambling had greater odds of tobacco smoking and alcohol use. Follow-up studies have shown that adolescents with weapon-carrying had more permissive attitudes toward gambling and greater odds of at-risk/problem gambling [43]. However, these studies did not specify whether these violence related behaviors were motivated by victimization, perpetration of bullying, or other factors. In representative data on adolescent victimization, males who gambled had greater odds of being threatened and bullied at school and of having experienced sexual dating violence and forced sexual intercourse [44]. Females who gambled also had greater odds of having experienced physical and sexual dating violence.

Problem-behavior theory proposes that gambling, substance use, and violence commonly co-occur owing to person– environment interactions that contribute to a “multicondition” behavioral syndrome that increases concurrent risk behaviors [37,45,46]. The problem-behavior theory has been described in detail elsewhere [47]. Briefly, three systems of explanatory variables determine the proneness (i.e., risk) of a problem behavior that transgresses norms. Personality proneness, including low self-esteem, tolerance for deviance, and low goal orientation, may drive problem behaviors. Environmental proneness may include lower parental support and peer-supported deviancy. Behavioral proneness may include lower involvement in conventional norms, use of a range of substances, and involvement with other problem behaviors beyond those predicted. This framework may explain multiple behavioral problems across adolescent groups, and it has been proposed that while these behaviors appear phenotypically different, they may share similar underlying genotypes [47,48]. The pathway model proposes three major pathways with associated vulnerabilities that lead to problem gambling [49]. In the first pathway, greater access to gambling and the acquisition of gambling behaviors through conditioning contribute to gambling problems. In addition to behaviorally conditioned gambling, gamblers in the second pathway, are characterized with a history of anxiety and depression and motivation to gamble to relieve depression or uncomfortable affective states lead to gambling problems [49,50]. Gamblers in the third pathway also experience socioemotional vulnerabilities as well as impulsivity and antisociality, which lead to a wide range of behavioral problems such as excessive polysubstance use, low tolerance for boredom, and criminal behavior [49]. Engaging in gambling may also alleviate aversive affective states and help individuals escape stressful life events or problems [51], which is consistent with coping-related motivations for substance use [52]. In line with these frameworks, substance use and violence may be linked to gambling in adolescents through shared vulnerabilities in impulse control, affective states, and social environments, especially during early adolescence, in which neuropsychological regulation has not fully matured [53-55].

In the United States and other countries, policies legalizing marijuana co-occurred with the expansion of gambling [56]. Most American states that allow recreational MU have lotteries, casinos, other gambling venues, online sports betting, and/or other legal gambling options [56,57]. Within this context, converging data have shown links between MU and gambling, and suggested shared vulnerabilities between SUDs and problem gambling [56,58-62]. In a clinical sample of adolescents seeking treatment for MU problems, 22% reported having gambling problems [40]. Additionally, those with problem gambling exhibited greater MU frequency and quantity. In an epidemiological cohort of high school adolescents, those with MU had greater odds of at-risk/problem gambling, more frequent and severe gambling concerns, and stronger motivations to gamble than those with no MU [35,63]. Individuals with both SUDs and problem gambling have demonstrated particularly severe problems, including psychiatric distress, social and emotional impairment, and hostility [64-68]. However, random sampling data have been a standard in these clinical and subclinical studies of risky behavior problems in youths. The use of representative data from the YRBS, which weighs demographic characteristics in each jurisdiction and adjusts for nonresponse and oversampling of groups, helps ensure that conclusions from a sample reflect the characteristics of the larger population being studied [69,70]. Additionally, representative data weights can be trimmed and distributed to reduce extremes [71] such that sampling variances are not inflated and the weighted proportions of students in each grade level match the population projections for the surveyed period. Analyses that used the YRBS data have shown associations between having past-12-months of gambling and MU, synthetic MU, other substance use, and violence [44]. However, the relationship between early MU, gambling, and violence has not been well explored in representative data.

While adolescent gambling and MU have been separately associated with negative health consequences, further research is needed to understand the relationship between MU and risky behaviors in youths who also gamble. Furthermore, while non-representative samples showed relationships between MU, gambling, and problem behaviors, differences in these relationships by type of MU (i.e., potentially riskier early-, later-, and no-MU) in adolescents require further examination, especially in representative data. One of the few studies that has investigated interactions between MU and gambling status demonstrated that lifetime MU moderated the associations between problem-gambling severity and light-to-moderate substance use, academic performance, and social gambling [63]. The limited findings in early adolescents in the YRBS indicated that an earlier MU age-of-onset accounted for significant variance in health-risk behaviors [72]. However, the differences in these effects by MU type (i.e., early-, later-, and no-MU) have not been explored, and their relationships with gambling are not well understood.

This study used representative YRBS data from high school adolescents to systematically examine differences in substance use and violence among those with early- (age <13 years), later- (age ≥13 years), and no-MU, stratified by gambling status. It was hypothesized that: 1) youths with early MU have greater odds of exhibiting lifetime and current substance use and violence than those with later or no MU, and 2) MU by gambling status interactions exist for substance use and violence experiences such that early-MU has greater associations with substance use and violence in youths who gamble relative to those who do not. Understanding these relationships may improve targeted interventions, particularly in younger cohorts with early MU.

METHODS

Data were drawn from the 2019 YRBS in Connecticut, USA. The YRBS is the largest public health surveillance in the United States and monitors health-risk behaviors in high school adolescents [70]. The data, collected biennially by the Centers for Disease Control and Prevention (CDC), has been used to support health-promoting initiatives, and its items have been adopted by other studies on health risk behaviors [35,73,74]. The data collection procedures are detailed elsewhere (http://www.cdc.gov/yrbss) and are briefly described below.

Participants

High schools in the state of Connecticut, USA, were systematically selected using a random start with a probability proportional to the enrollment size in grades 9–12. Thirtythree public, charter, and vocational schools participated in the survey. Classes were selected using systematic equalprobability sampling with a random start. Permission for survey collection was obtained at different levels as required by each school. Initially, superintendents were notified that a school in their district was selected. Following approval, permission was obtained from the school administrators. Teachers and students in the selected classrooms were permitted to decline participation. Surveys were anonymous and confidential. The survey data underwent quality-control procedures, were weighted, and underwent post-stratification adjustments to be representative of Connecticut students.

All students in the classrooms selected in the sampling frame were eligible to participate in accordance with the CDC’s methodology for the YRBS [70]. Students in alternative and special education schools, vocational schools while also attending another school, schools operated by the U.S. Department of Defense or Bureau of Indian Education, and schools with an enrollment of ≤40 were excluded. Additionally, students with questionnaires containing fewer than 20 responses or the same response to at least 15 consecutive items failed quality control and were excluded from the dataset, following CDC procedures [70]. The overall response rate was 54%, and 2015 questionnaires were usable for analyses.

The study was approved by the Yale Institutional Review Board (ID# 2000023801) and the Connecticut State Health Department (HIC# 55E), and all procedures were performed in accordance with the 1964 Declaration of Helsinki and its amendments. Passive consent procedures were used for data collection by the CDC, in which parents could decline permission for the students. Letters were sent to parents that instructed them to contact the school should they deny permission. Active permission, which asked parents to authorize permission, was obtained when required by the district or school. Permission was obtained following local procedures and policies such that certain schools used active permission, whereas other schools used passive permission. Identifying information was not collected. School and classroom codes were omitted from the final dataset.

Measures

The study variables were based on the 2019 YRBS questionnaire and are listed in Table 1. The reliability and validity of these measures have been described previously [70,75]. Two test–retest reliability studies of the survey were conducted using different samples. The first study, which administered the survey twice to 1679 students, demonstrated substantial reliability (kappa ≥61%) in over 75% of items and no difference in prevalence between both assessments [76]. The second study, which administered the survey twice to 4619 students, showed that only 10 items had kappa values <61% and a different prevalence for each assessment [77]. The validity of the YRBS measures have also been reviewed and tested [78-83].

Table 1Youth Risk Behavior Surveillance questions and corresponding variable included in analyses

Variables	Questions
Independent variables
Marijuana use	How old were you when you tried marijuana for the first time?
Gambling	During the past 12 months, how many times have you gambled on a sports team, gambled when playing cards or a dice game, played one of your state’s lottery games, gambled on the Internet, or bet on a game of personal skill such as pool or a video game?
Dependent variables
Current substance use
Cigarettes	During the past 30 days, on how many days did you smoke cigarettes?
Alcohol	During the past 30 days, on how many days did you have at least one drink of alcohol?
Heavy alcohol	During the past 30 days, on how many days did you have 4 or more drinks of alcohol in a row (female) or 5 or more in a row (male)?
Electronic vapor products	During the past 30 days, on how many days did you use an electronic vapor product?
Frequent use	During the past 30 days, on how many days did you use an electronic vapor product?
Daily use	During the past 30 days, on how many days did you use an electronic vapor product?
Excessive use	During the past 30 days, on the days you used an electronic vapor product, how many times did you vape per day?
Use at school	During the past 30 days, on how many days did you use an electronic vapor product on school property?
Lifetime substance use
Cocaine	During your life, how many times have you used any form of cocaine, including powder, crack, or freebase?
Heroin	During your life, how many times have you used heroin (also called smack, junk, or China White)?
Methamphetamines	During your life, how many times have you used methamphetamines (also called speed, crystal meth, crank, ice, or meth)?
Ecstasy	During your life, how many times have you used ecstasy (also called MDMA)?
Synthetic marijuana	During your life, how many times have you used synthetic marijuana?
Injected drugs	During your life, how many times have you used a needle to inject any illegal drug into your body?
Prescription opiate misuse	During your life, how many times have you taken prescription pain medicine without a doctor’s prescription or differently than how a doctor told you to use it? (count drugs such as codeine, Vicodin, OxyContin, Hydrocodone, and Percocet)
Electronic vapor products	Have you ever used an electronic vapor product?
Violence-related measures
Weapon carrying at school	During the past 30 days, on how many days did you carry a weapon such as a gun, knife, or club on school property?
Felt unsafe at school	During the past 30 days, on how many days did you not go to school because you felt you would be unsafe at school?
Threatened at school	During the past 12 months, how many times has someone threatened or injured you with a weapon such as a gun, knife, or club at school?
Physical fighting	During the past 12 months, how many times were you in a physical fight?
Sexual dating violence	During the past 12 months, how many times did someone you were dating force you to do sexual things that you did not want to do?
Physical dating violence	During the past 12 months, how many times did someone you were dating with physically hurt you on purpose?
Bullying at school	During the past 12 months, have you ever been bullied on school property?
Electronic bullying	During the past 12 months, have you ever been electronically bullied (count texting, Instagram, Facebook, or other social media)?
Forced sexual intercourse	Have you ever been physically forced to have sexual intercourse when you did not want to?

To assess the prevalence of gambling, substance use, and violence, responses were coded dichotomously by the CDC in the final dataset [15]. While the responses were initially on Likert scales, the prevalence was calculated as the proportion of a population with a characteristic or discrete number of incidences over time. Hence, interpretability is limited by modeling variables “continuously,” which requires comparing the prevalence of individual values within each variable (e.g., prevalence of gambling 1–2 times vs. 3–9 times vs. 10–19 times). Consistent with epidemiological methodology and YRBS reports, dichotomous measures were used to estimate the prevalence of each health risk behavior [84-88].

Demographics

Demographic variables included age (≤14, 15, 16, 17, and ≥18 years), sex (female, male), grade (grades 9–12, other grade), and race/ethnicity (Asian, African American, Caucasian, Hispanic, Other). Multivariate analyses were adjusted for demographic variables to account for potential confounding effects.

Marijuana use

Participants were asked, “How old were you when you tried marijuana for the first time?” (never, ≤8, 9–10, 11–12, 13–14, 15–16, and ≥17 years). Consistent with reports from the CDC on early MU in the YRBS data [84], those who initiated MU before the age of 13 years were classified as having early-MU. Those who initiated MU at age ≥13 years were classified as having later-MU, or otherwise classified as having no-MU.

Gambling status

Participants were asked, “During the past 12 months, how many times have you gambled on a sports team, gambled when playing cards or a dice game, played one of your state’s lottery games, gambled on the Internet, or bet on a game of personal skill such as pool or a video game?” (none, 1–2, 3–9, 10–19, 20–39, and ≥40 times). Those who gambled one or more times were classified as having gambled or if otherwise, were classified as not having gambled, as previously defined in YRBS reports [44,89,90].

Substance use

Participants reported on having lifetime use of synthetic marijuana, cocaine, heroin, methamphetamines, ecstasy, injected drugs, and electronic-vapor products; prescription opiate misuse (prescription pain medicine without a doctor’s prescription e.g., codeine, Vicodin, OxyContin, Hydrocodone, and Percocet); and current (past 30 days) cigarette smoking, alcohol use (≥1 drink), heavy alcohol use (≥4 consecutive drinks in females, ≥5 consecutive drinks in males), and use of electronic-vapor products. As electronic-vapor products may include nicotine- and non-nicotine-containing devices, these results are presented separately from those of other substance use behaviors.

Violence

Participants were assessed on their experiences of engaging in violent behavior and victimization involving violence. Engagement in violent behavior included whether participants had carried a weapon at school in the past 30 days or had physically fought in the past 12 months. Victimization included whether participants had felt unsafe at school in the past 30 days; had been threatened or injured with a weapon at school, experienced sexual assault or physical assault by a dating partner, or had been bullied at school and electronically (bullying through texting and social media) in the past 12 months; or had ever experienced forced sexual intercourse.

Statistical analysis

Exploratory analyses were conducted using SPSS Complex Samples software (IBM Corp.) to compute the weighted prevalence of substance use and violence in adolescents with early-, later-, and no-MU stratified by gambling status. The CDCderived sampling strata, primary sampling units, and overall analysis weights within the final dataset were entered as strata, clusters, and sample weights, respectively, in the complex sample analysis plan. Computed weighted prevalences were thus representative of total students in grades 9–12 in the sampled population (see https://www.cdc.gov/yrbs/methods/index.html for the usage of weighted YRBS data). Separately in those with gambling and non-gambling, chi-square analyses were conducted to examine differences in age, sex, grade, and race/ethnicity among early-, later-, and no-MU groups. Adjusted odds ratios (^aORs) and 95% confidence intervals (CIs) were calculated to assess the association between MU and substance use and violence outcomes. Interactions between MU and gambling status were tested using multivariate logistic regression models. The models were adjusted for demographic variables to control for potential confounding effects. Statistical significance was evaluated at the p≤0.05 level.

RESULTS

Demographic characteristics

Demographic characteristics and chi-square results of differences in sex, age, grade, and race/ethnicity among adolescents with no-, later-, and early-MU, stratified by gambling status, are shown in Table 2. Among non-gambling adolescents, 68.1%, 30.5%, and 1.3% reported no, later, and early MU, respectively; and among gambling adolescents, 50.6%, 40.0%, and 9.4% reported no, later, and early MU, respectively, yielding a significant association (χ²=88.13, p<0.001). Relative to non-gambling adolescents, a greater proportion of those who gambled had early and later MU, and a lower proportion had no MU.

Table 2Estimated prevalence and chi-square results of sociodemographic characteristics in no-, later-, and early-marijuana-use groups, stratified by gambling

	Non-gambling								Gambling
	No-marijuana-use		Latermarijuana-use		Earlymarijuana-use		χ²	p	No-marijuana-use		Latermarijuana-use		Earlymarijuana-use		χ²	p
	Weighted % (SE)	n	Weighted % (SE)	n	Weighted % (SE)	n	χ²	p	Weighted % (SE)	n	Weighted % (SE)	n	Weighted % (SE)	n	χ²	p
Total	68.1 (2.3)	907	30.5 (2.3)	400	1.3 (0.3)	22	-	-	50.6 (2.7)	217	40.0 (2.7)	168	9.4 (1.4)	45	88.13	<0.001
Sex				17.24	0.001				2.47	0.162
Female	52.9 (2.6)	483	65.0 (2.9)			254	48.3 (9.8)	10			30.9 (3.9)	71	35.7 (2.9)	58	23.7 (5.5)	11
Male	47.1 (2.6)	421	35.0 (2.9)			144	51.7 (9.8)	12			69.1 (3.9)	144	64.3 (2.9)	109	76.3 (5.5)	34
Age				96.38	<0.001				19.15	0.030
≤14 yr	19.9 (1.4)	183	6.5 (1.1)			31	35.0 (10.8)	7			14.9 (2.1)	35	7.3 (1.9)	13	26.0 (7.9)	12
15 yr	28.2 (2.0)	272	14.5 (2.9)			57	28.4 (5.7)	6			25.6 (3.4)	56	18.0 (3.7)	33	16.7 (6.2)	8
16 yr	21.5 (1.7)	188	29.8 (2.6)			119	16.7 (8.3)	4			19.5 (3.8)	39	19.9 (3.1)	32	21.7 (7.8)	8
17 yr	22.9 (1.9)	193	34.8 (2.1)			134	13.2 (7.2)	3			23.0 (3.5)	49	31.5 (3.0)	51	23.1 (6.1)	11
≥18 yr	7.4 (1.2)	70	14.4 (3.3)			56	6.7 (4.3)	2			16.9 (2.9)	36	23.3 (4.8)	39	12.5 (4.2)	6
Grade				99.35	<0.001				38.51	0.004
9th	32.4 (1.8)	315	11.7 (1.4)			53	40.0 (11.1)	8			27.8 (3.2)	63	12.7 (2.7)	25	34.8 (11.3)	17
10th	27.8 (3.3)	248	22.0 (3.5)			90	28.4 (5.7)	6			23.1 (4.3)	48	21.7 (4.4)	35	19.3 (7.8)	8
11th	22.1 (2.3)	191	30.6 (3.6)			120	15.3 (7.9)	4			21.9 (3.0)	47	27.2 (4.5)	45	15.5 (5.1)	6
12th	17.7 (2.1)	151	35.7 (3.9)			135	16.3 (9.4)	4			27.2 (3.3)	56	38.4 (4.8)	63	25.5 (8.2)	12
Other	-	-	-			-	-	-			-	-	-	-	4.9 (3.2)	2
Race/ethnicity				25.75	0.035				21.92	0.016
Asian	5.4 (0.6)	61	2.4 (0.9)			11	-	-			2.6 (1.0)	8	1.1 (0.7)	3	-	-
African American	10.6 (2.0)	74	11.0 (3.3)			32	-	-			15.4 (4.6)	25	9.9 (2.7)	14	16.6 (6.5)	6
Caucasian	57.8 (4.3)	447	60.7 (6.0)			215	32.8 (12.7)	7			54.7 (6.4)	103	65.0 (6.0)	96	32.3 (8.0)	12
Hispanic	5.2 (1.0)	62	5.0 (1.3)			26	3.4 (3.2)	1			5.0 (1.1)	15	5.5 (2.4)	12	5.3 (3.0)	3
Other	21.0 (2.1)	252	20.9 (3.0)			114	63.8 (12.3)	14			22.4 (3.4)	64	18.5 (3.3)	43	46.0 (4.8)	24

MU was associated with sex among non-gambling adolescents (χ²=17.24, p=0.001), but not among those who gambled (χ²=2.47, p=0.162). Among non-gambling adolescents, a greater proportion of those with no- (52.9%) and later-MU (65.0%), relative to a lower proportion of those with early-MU (48.3%), were female. By contrast, a lower proportion of gambling adolescents with no- (30.9%), later- (35.7%), and early-MU (23.7%) were female. Across the sample, between-group differences were found in age, grade, and race/ethnicity. Among nongambling adolescents, greater proportions of those with early-MU had younger ages (χ²=96.38, p<0.001) and lower grade levels (χ²=99.35, p<0.001), and identified as other race/ethnicity (χ²=25.75, p=0.035). Similarly, among adolescents who gambled, greater proportions of those with early-MU had younger ages (χ²=19.15, p=0.030) and lower grade levels (χ²=38.51, p=0.004), and identifying as other race/ethnicity (χ²=21.92, p=0.016).

Substance use

The weighted prevalence and ^aORs for having used each substance among adolescents with no-, later-, and early-MU stratified by gambling status are shown in Table 3. Among adolescents who gambled, the estimated prevalence of current and lifetime use of each substance ranged from 1.7% to 59.4% and 0.5% to 58.6%, respectively, and the prevalence of current and lifetime use of electronic-vapor products ranged from 8.9% to 83.2% and 24.3% to 93.0%, respectively. Among non-gambling adolescents, the estimated prevalence of current and lifetime use of each substance ranged from 0.6% to 63.2% and 0.1% to 23.1%, respectively, whereas the prevalence of current and lifetime use of electronic-vapor products ranged from 7.2% to 64.8% and 19.8% to 83.4%, respectively. In both gambling and non-gambling adolescents, those with early- and later-MU, compared with no-MU, had greater odds of current cigarette smoking, alcohol use, and heavy use of alcohol, lifetime use of any substances and cocaine, and current and lifetime use of electronic-vapor products. Additionally, those with later-MU, compared with no-MU, had greater odds of prescription opiate misuse, and those with early-MU, compared to later-MU, had greater odds of current cigarette smoking and lifetime ecstasy use.

Table 3Estimated prevalence and adjusted odds ratios of substance-use between gambling and non-gambling adolescents with no-, later-, early-marijuana-use

	No-marijuana-use		Latermarijuana-use		Early marijuana-use		Early- vs. No-marijuana-use	Later- vs. No-marijuana-use	Early- vs. Later-marijuana-use
	Weighted % (SE)	n	Weighted % (SE)	n	Weighted % (SE)	n	^aOR (CI)	^aOR (CI)	^aOR (CI)
Gambling
Current substance use
Cigarettes	1.7 (0.9)	4	8.7 (2.4)	14	28.9 (6.5)	12	23.76 (6.69-84.34)^***	4.09 (1.18-14.18)^*	5.38 (2.30-12.55)^**
Alcohol	19.3 (2.1)	40	59.4 (4.2)	97	57.1 (7.1)	25	5.60 (2.85-10.97)^***	5.74 (3.46-9.52)^***	0.93 (0.47-1.81)
Heavy alcohol	6.3 (1.5)	15	41.8 (4.2)	65	45.0 (5.9)	20	12.21 (6.79-21.93)^***	10.05 (6.36-15.89)^***	1.20 (0.62-2.32)
Lifetime substance use
Any use	10.1 (1.3)	21	29.6 (3.2)	46	76.3 (7.1)	34	28.79 (10.52-78.80)^***	3.73 (2.41-5.80)^***	7.01 (3.10-15.87)^***
Synthetic marijuana	-	-	17.1 (1.8)	26	52.2 (8.1)	23	-	-	5.47 (2.45-12.22)^***
Prescription opiate misuse	8.7 (1.3)	18	16.2 (3.2)	25	58.6 (6.7)	27	14.73 (6.71-30.40)^***	1.92 (1.05-3.54)^*	6.07 (2.98-15.06)^***
Cocaine	1.0 (0.7)	2	6.8 (1.8)	9	38.0 (8.4)	17	62.71 (9.02-435.98)^***	7.41 (1.46-37.50)^*	8.40 (3.34-21.13)^***
Heroin	-	-	4.9 (1.3)	7	33.1 (7.7)	15	-	-	8.84 (3.08-25.41)^***
Methamphetamine	0.5 (0.5)	1	6.0 (1.6)	9	33.1 (7.7)	15	94.30 (7.92-112.39)^***	12.73 (1.46-110.65)^*	6.98 (2.46-19.85)^***
Ecstasy	1.0 (0.7)	2	5.2 (1.7)	8	34.6 (8.6)	15	49.33 (7.95-306.08)^***	4.83 (0.96-24.23)	9.42 (4.91-18.07)^***
Injected drugs	1.3 (0.8)	3	4.6 (1.6)	7	32.2 (7.7)	14	37.15 (7.77-177.68)^***	3.08 (0.73-12.98)	9.88 (3.50-27.86)^***
EVPs
Current EVP	8.9 (1.9)	19	69.2 (3.2)	101	83.2 (7.4)	29	51.66 (14.26-187.11)^***	23.95 (12.90-44.45)^***	2.19 (0.71-6.79)
Lifetime EVP	24.3 (3.2)	51	93.0 (1.9)	149	92.3 (4.5)	39	35.76 (9.03-141.55)^***	44.71 (24.76-80.72)^***	0.68 (0.22-2.15)
Non-gambling
Current substance use
Cigarettes	0.6 (0.3)	5	4.7 (1.3)	19	17.8 (7.9)	4	56.18 (12.07-248.40)^***	7.03 (1.91-25.98)^**	7.58 (2.24-25.74)^**
Alcohol	9.0 (1.1)	80	46.2 (3.1)	177	63.2 (11.9)	12	18.83 (5.14-68.95)^***	7.35 (4.89-11.06)^***	2.25 (0.79-6.40)
Heavy alcohol	2.2 (0.4)	19	23.4 (2.6)	93	29.1 (10.9)	6	21.39 (5.22-87.64)^***	11.24 (6.83-18.50)^***	1.80 (0.50-6.44)
Lifetime substance use
Any use	6.5 (1.0)	69	20.5 (1.9)	82	30.8 (8.5)	7	6.36 (2.23-18.17)^**	4.03 (2.68-5.87)^***	2.03 (0.86-4.77)
Synthetic marijuana	0.3 (0.2)	4	11.2 (1.9)	42	23.1 (8.9)	5	139.79 (21.22-920.71)^***	59.83 (17.39-205.88)^***	2.64 (0.82-8.53)
Prescription opiate misuse	6.3 (0.9)	65	10.0 (1.5)	43	8.0 (4.8)	2	1.20 (0.27-5.37)	1.65 (1.11-2.46)^*	1.05 (0.24-4.63)
Cocaine	0.1 (0.1)	1	1.6 (0.6)	8	8.5 (5.6)	2	92.80 (5.10-1690.27)^**	18.60 (2.19-158.12)^**	5.54 (0.86-35.63)
Heroin	-	-	0.5 (0.4)	2	8.5 (6.0)	2	-	-	-
Methamphetamines	-	-	0.4 (0.4)	1	8.8 (6.3)	2	-	-	-
Ecstasy	-	-	1.8 (0.7)	7	11.9 (5.6)	4	-	-	6.51 (1.44-29.53)^*
Injected drugs	-	-	0.3 (0.2)	2	3.4 (3.5)	1	-	-	-
EVPs
Current EVP Use	7.2 (1.1)	62	60.8 (4.5)	228	64.8 (15.5)	11	25.04 (5.41-115.91)^***	19.68 (11.82-33.06)^***	1.18 (0.31-4.41)
Lifetime EVP Use	19.8 (1.7)	177	83.4 (3.0)	334	77.3 (11.1)	15	14.58 (3.99-53.23)^***	18.51 (12.53-27.36)^***	0.66 (0.19-2.27)

The ^aORs of MU by gambling status interactions on substance use are presented in Table 4. An interaction between early-MU, compared to later-MU, and gambling status was found for any lifetime substance use (^aOR=4.60, p=0.03, CI=1.23–17.23). As seen in Fig. 1A, simple main effects showed that among gamblers, but not non-gamblers, those with early-MU had greater odds of having any substance use (^aOR=7.01, p<0.001, CI=3.10–15.87) than those with later-MU. Interactions between early-MU, compared to no-MU (^aOR=13.71, p=0.003, CI=2.69–69.83) and later-MU (^aOR=9.98, p=0.01, CI=1.81–54.98), and gambling status was observed for prescription opiate misuse. As displayed in Fig. 1B, simple main effects showed that among gamblers, but not nongamblers, those with early-MU, compared with no-MU (^aOR=14.73, p<0.001, CI=6.71–30.40) and later-MU (^aOR=6.07, p<0.001, CI=2.98–15.06), had greater odds of prescription opiate misuse. While MU was significantly associated with the use of other substances in both gambling and non-gambling adolescents, no additional MU by gambling status interactions for use of other substances were identified.

Table 4Adjusted odds ratios of marijuana-use by gambling interactions on substance-use

	Early- vs. No-marijuana-use by gambling interaction	Later- vs. No-marijuana-use by gambling interaction	Early- vs. Later-marijuana-use by gambling interaction
	^aOR (CI)	^aOR (CI)	^aOR (CI)
Current substance use
Cigarettes	0.63 (0.08-4.71)	0.69 (0.12-3.97)	0.95 (0.23-3.86)
Alcohol	0.35 (0.08-1.65)	0.77 (0.37-1.61)	0.48 (0.14-1.71)
Heavy alcohol	0.73 (0.19-2.72)	0.87 (0.41-1.88)	0.84 (0.23-3.14)
Lifetime substance use
Any use	5.06 (0.89-28.92)	1.01 (0.46-2.22)	4.60 (1.23-17.23)^*
Synthetic marijuana	-	2.16 (0.51-9.14)
Prescription opiate misuse	13.71 (2.69-69.83)^**	-	9.98 (1.81-54.98)^**
Cocaine	0.65 (0.01-32.25)	1.21 (0.51-3.04)	1.91 (0.27-13.44)
Heroin	-	0.34 (0.01-12.84)	0.57 (0.04-8.08)
Methamphetamine	-	-	0.29 (0.01-6.52)
Ecstasy	-	-	1.30 (0.28-6.06)
Injected drugs	-	-	0.85 (0.04-20.05)
EVPs			-
Current EVP use	2.23 (0.44-11.41)	1.15 (0.51-2.63)	1.97 (0.48-8.12)
Lifetime EVP use	2.75 (0.62-12.29)	2.11 (0.99-4.47)	1.44 (0.37-5.68)

Fig. 1Simple main effects of marijuana use on substance use outcomes stratified by gambling status. A: Any substance use. B: Prescription opiate misuse. ***p≤0.001. MU, marijuana use; ns, non-significant.

Violence

The weighted prevalence and ^aORs of engaging in violence and victimization involving violence among adolescents with no-, later-, and early-MU, stratified by gambling status, are shown in Table 5. The estimated prevalence of having experienced violence ranged from 2.4% to 66.3% in gambling adolescents and from 1.3% to 71.8% in non-gambling adolescents.

Table 5Estimated prevalence and adjusted odds ratios of violence between gambling and non-gambling adolescents with no-, later-, and early-marijuana-use

	Nomarijuana-use		Latermarijuana-use		Earlymarijuana-use		Early- vs. No-marijuana-use	Later- vs. No-marijuana-use	Early- vs. Later-marijuana-use
	Weighted % (SE)	n	Weighted % (SE)	n	Weighted % (SE)	n	^aOR (CI)	^aOR (CI)	^aOR (CI)
Gambling
Weapon carrying at school	2.4 (1.1)	6	5.2 (1.9)	8	23.5 (7.0)	11	9.94 (3.26-30.34)^***	2.17 (0.50-9.47)	5.03 (1.61-15.69)^**
Safety concern at school	6.9 (1.8)	16	13.4 (2.4)	25	21.2 (4.9)	10	3.74 (1.86-7.51)^***	1.88 (0.86-4.08)	2.05 (0.99-4.22)
Threatened at school	5.6 (1.4)	13	11.7 (2.6)	20	20.9 (6.6)	9	3.63 (1.48-8.89)^**	2.34 (1.04-5.27)^*	1.59 (0.67-3.75)
Physical fighting	23.7 (4.0)	50	37.5 (3.8)	62	66.3 (10.8)	30	6.17 (2.23-17.09)^**	2.20 (1.06-4.59)^*	2.66 (0.73-9.68)
Forced sexual intercourse	6.5 (1.5)	14	12.9 (3.2)	22	19.9 (6.3)	9	4.30 (1.40-13.18)^**	1.80 (0.80-4.02)	2.64 (0.65-10.64)
Sexual dating violence	9.9 (2.9)	12	18.5 (3.2)	25	25.7 (6.0)	10	3.24 (1.15-9.12)^*	1.91 (0.75-4.84)	1.68 (0.74-3.81)
Physical dating violence	3.6 (1.3)	5	13.0 (3.0)	18	25.8 (6.2)	10	8.15 (4.21-15.77)^***	3.73 (1.54-9.04)^**	2.31 (1.11-4.82)^*
Bullying at school	17.5 (2.9)	40	19.1 (3.0)	35	28.0 (5.9)	14	1.77 (0.89-3.50)	1.04 (0.60-1.80)	1.66 (0.78-3.53)
Electronic bullying	16.2 (2.7)	36	21.8 (2.6)	38	27.3 (4.4)	13	1.80 (0.80-4.09)	1.27 (0.83-1.95)	1.44 (0.79-2.64)
Non-gambling
Weapon carrying at school	1.3 (0.4)	11	3.2 (1.3)	14	6.6 (4.8)	2	5.47 (0.82-36.66)	1.99 (0.78-5.03)	2.11 (0.20-22.02)
Safety concern at school	5.3 (1.2)	50	5.6 (0.9)	22	10.3 (5.9)	3	1.86 (0.51-6.83)	1.13 (0.60-2.15)	1.17 (0.26-5.23)
Threatened at school	3.7 (0.8)	37	8.6 (1.8)	36	3.4 (3.5)	1	0.67 (0.06-7.30)	3.03 (1.27-7.20)^*	0.27 (0.03-2.60)
Physical fighting	10.3 (1.5)	96	19.8 (2.0)	84	71.8 (8.9)	15	19.87 (6.55-60.32)^***	2.77 (1.88-4.10)^***	6.63 (2.40-18.33)^**
Forced sexual intercourse	2.0 (0.5)	22	9.4 (1.4)	38	10.1 (4.4)	3	6.69 (2.05-21.88)^**	4.21 (2.30-7.71)^***	1.29 (0.30-5.52)
Sexual dating violence	6.1 (1.2)	26	16.3 (1.9)	53	13.0 (9.8)	2	2.45 (0.30-19.99)	2.81 (1.71-4.65)^***	0.87 (0.15-5.16)
Physical dating violence	3.6 (0.8)	16	9.6 (1.5)	32	8.4 (5.5)	2	2.23 (0.41-12.25)	2.47 (1.30-4.71)^**	1.12 (0.19-6.78)
Bullying at school	17.1 (1.1)	153	19.9 (2.6)	78	12.4 (6.6)	3	0.67 (0.19-2.33)	1.30 (0.94-1.81)	0.43 (0.14-1.34)
Electronic bullying	10.5 (0.8)	95	16.4 (2.7)	68	21.8 (10.3)	5	2.63 (0.66-10.58)	2.04 (1.24-3.34)^**	1.26 (0.31-5.10)

Among both gambling and non-gambling adolescents, those with early-MU, compared with no-MU, had greater odds of physical fighting and victimization involving forced sexual intercourse. Additionally, adolescents with later-MU, compared with no-MU, showed greater odds of having engaged in physical fighting, and experienced victimization involving physical dating violence, and threats or injuries at school. The ^aORs of MU by gambling status interactions for violence are presented in Table 6. An interaction between later-MU, compared to no-MU, and gambling status was found for victimization involving forced sexual intercourse (^aOR=0.48, p=0.05, CI=0.24–0.99). As seen in Fig. 2A, simple main effects showed that those with later-MU, compared with no-MU, had greater odds of having experienced forced sexual intercourse (^aOR=4.21, p<0.001, CI=2.30–7.71), but only among non-gambling adolescents. Furthermore, a significant interaction between early-MU, compared to later-MU, and gambling status was found for victimization involving having been bullied at school (^aOR=3.93, p=0.04, CI=1.05–14.73). Although, simple main effects were not significant within gambling and non-gambling adolescents, separately (Fig. 2B), adolescents who gambled and had early-MU had numerically greater odds of having been bullied at school (^aOR=1.66), relative to non-gambling adolescents with early-MU (^aOR=0.43).

Table 6Adjusted odds ratios of marijuana-use by gambling interactions on violence

	Early- vs. No-marijuana-use by gambling interaction	Later- vs. No-marijuana-use by gambling interaction	Early- vs. Later-marijuana-use by gambling interaction
	^aOR (CI)	^aOR (CI)	^aOR (CI)
Weapon carrying at school	2.45 (0.23-26.63)	0.94 (0.17-5.15)	2.59 (0.24-28.30)
Safety concern at school	1.96 (0.34-11.51)	1.87 (0.74-4.70)	1.06 (0.20-5.76)
Threatened at school	5.68 (0.50-66.87)	0.86 (0.28-2.61)	6.40 (0.68-59.75)
Physical fighting	0.31 (0.06-1.49)	0.83 (0.35-1.98)	0.37 (0.06-2.09)
Forced sexual intercourse	0.78 (0.16-3.77)	0.48 (0.24-0.99)^*	1.80 (0.33-9.98)
Sexual dating violence	1.46 (0.15-14.57)	0.71 (0.23-2.16)	2.08 (0.37-11.66)
Physical dating violence	4.03 (0.59-27.45)	1.53 (0.57-4.12)	2.55 (0.36-18.28)
Bullying at school	3.18 (0.66-15.39)	0.93 (0.45-1.95)	3.93 (1.05-14.73)^*
Electronic bullying	0.89 (0.14-5.59)	0.82 (0.44-1.52)	1.21 (0.22-6.53)

Fig. 2Simple effects of marijuana use on violence measures stratified by gambling status. A: Experienced forced sexual intercourse. B: Experienced bullying at school. ***p≤0.001. MU, marijuana use; ns, non-significant.

DISCUSSION

This study systematically explored differences in substance use and violence among adolescents with early, late, and no MU, stratified by gambling status, using representative YRBS data. Consistent with our first hypothesis, both gambling and non-gambling adolescents with early and later MU showed higher odds of current cigarette smoking and alcohol use and lifetime use of any substance, cocaine, and electronic-vapor products. Later-MU, compared to no-MU, was associated with prescription opiate misuse. Additionally, both gambling and non-gambling adolescents with early-MU, compared to later-MU, had higher odds of engaging in physical fighting and experiencing victimization involving forced sexual intercourse. Those with later-MU compared to no-MU were more likely to engage in physical fighting and encounter victimization involving threats and injuries at school and physical dating violence. Regarding the second hypothesis, an interaction between MU and gambling status, involving early-MU compared to no-MU, was observed for prescription opiate misuse. Other interactions between MU and gambling involving early-MU compared to later-MU, were found for any substance use, prescription opiate misuse, and victimization involving having been bullied at school. Furthermore, an interaction between MU and gambling, involving later-MU compared to no-MU, was identified for victimization involving forced sexual intercourse.

In both gambling and non-gambling adolescents, similar associations were found between MU and the use of other substances. However, interactions between MU and gambling status were found for outcomes of any substance use and prescription opiate misuse, such that those with early-MU, compared to later-MU, had greater odds of having any substance and prescription opiate misuse, but only in gambling adolescents. Previous studies have also found that MU and gambling, separately, were associated with more frequent use of alcohol and tobacco, as well as behavioral problems that portend substance use [40,59]. Early engagement in substance use has been previously associated with higher risks for prescription drug misuse [91,92]. In the National Epidemiological Survey on Alcohol and Related Conditions cohort, the risk of prescription drug misuse increased with each successive younger age at which individuals initiated alcohol use, and reached a 10-fold increase when alcohol use initiation occurred before the age of 14 [91]. Notably, one epidemiological report highlighted that youths with early-MU had a 47-fold greater odds of prescription drug misuse [92].

The predictors and motivations driving MU and prescription drug misuse in early adolescents overlap substantially, including the belief that substance use might be a “good way of dealing with problems” and disadvantageous decisionmaking tendencies [93]. As both early-MU and prescription drug misuse may be motivated by coping with ongoing cognitive and emotional problems, the pathway model similarly showed that a significant proportion of individuals with problem gambling have vulnerabilities, including a history of negative emotions, and inadequate coping and problem-solving skills [49]. Gambling has also been proposed to help regulate negative mood states and as an emotional escape from preexisting conditions and negative life events. Similarly, within problem behavior theory, early-MU was linked to perceptions of substance use as being less harmful and having high tolerance for deviant behavior, which may increase the risk of using other substances and of underage gambling [72,94]. As with the behavioral system of problem behavior theory, researchers have suggested that the use of different substances may cluster together along with violent behaviors [95]. Gambling can normalize participation in maladaptive behaviors aimed at regulating mood, potentially leading to more harmful health consequences such as opiate misuse. Early MU and gambling together may indicate a vulnerable group of adolescents who rely on maladaptive coping strategies, which may increase the risk of prescription drug misuse.

Individuals with gambling and problem gambling may have more positive perceptions of risk and positive expectations of outcomes [96]. Despite the negative consequences of gambling, motivations for gambling may be supported by cognitive biases toward overweighing positive outcomes and underweighting negative outcomes [96]. Chasing that involves continued gambling despite losses has been shown to depend on gambling expectancies, including the belief that it would lead to one feeling better, similar to a “positive illusion,” as well as a focus toward the present relative to consideration for the future [97]. This is consistent with the positive– negative function discrepancy in problem behavior theory, wherein problem behavior occurs when positive reasons (e.g., gambling makes things better) outweigh negative reasons (e.g., gambling leads to loss of control) [47]. Similarly, those who perceived lower negative outcomes and harm from the non-medical use of prescription opiates and sensation-seeking driven by positive reinforcement had a higher risk of prescription opiate misuse [98,99]. According to problem behavior theory, exposure to models of risky behaviors in the environment, including neighborhood opportunities for substance use, reduces the protective effects of perceiving substance use as harmful [100]. Prevention efforts for prescription opiate misuse should focus on youths who were exposed early to addictive behaviors and to whom substance use and gambling have been normalized.

Adolescent gamblers generally had a higher prevalence of forced sexual intercourse regardless of whether they had early-, later-, or no-MU. This is consistent with the finding that over half of the patients in a clinical sample of people seeking treatment for problem gambling had experienced victimization involving family and intimate partner violence, which occurred after their engagement in gambling [101]. Similarly, meta-analyses have shown that individuals with problem gambling had experienced victimization involving intimate partner violence and perpetrated it [102]. An interaction involving later-MU, compared to no-MU, and gambling was observed for forced sexual intercourse. However, simple main effects showed that later-MU was associated with having experienced forced sexual intercourse in non-gambling but not in gambling adolescents. This may appear counterintuitive as gambling has been linked to victimization involving aggression and intimate partner violence [44,102]. It has been proposed that gambling interacts with violence reinforcers to exacerbate intimate partner violence, particularly among women [103]. To better understand the finding presented above, among the current study’s non-gambling adolescents, 2% of those with no-MU reported having experienced forced sexual intercourse. Additionally, forced sexual intercourse was reported at a rate fivefold greater in non-gambling adolescents with later-MU compared to no-MU (9.4% vs. 2.0%) and at a rate twofold greater in gambling adolescents with later-MU compared to no-MU (12.9% vs. 6.5%). This fivefold difference between non-gambling adolescents with later-MU compared to no-MU yielded a MU by gambling status interaction and a greater association level between later-MU and forced sexual intercourse among non-gambling youths, suggesting that some of the variance in the association between forced sexual intercourse and MU is linked to gambling.

An MU by gambling status interaction was observed for having been bullied at school such that the odds of being bullied were numerically greater in gambling adolescents with early-MU compared to later-MU, but lower in nongambling adolescents with early-MU compared to later-MU. Previous reports have linked victimization by bullying at school to gambling, drug use, and underage drinking as well as greater social dysfunction, negative mood, and poor emotional coping [104]. Adolescents who have experienced victimization involving bullying may manifest cognitive and decisionmaking impairments that contribute to risk-taking. Those who have been bullied devoted less time deliberating and had higher sensitivity to rewards during gambling responses, as well as inflated benefits of risky behaviors [105,106]. Additionally, those chosen for bullying often had been rejected by peer groups and receive less social acceptance [105,107]. Together, bullying may exacerbate vulnerabilities in reward sensitivity and socioemotional coping, which may increase the risk of problem gambling, as proposed in the pathway model [49,108]. Bullying prevention that bolsters cognitive control and socioemotional coping may be aimed toward youths with early-MU and gambling. Future studies may also examine the effects of early-MU and gambling on risky behaviors among youths with depression and anxiety as a means of self-medication and coping with persistent negative affect. The interactions of positive and negative reinforcement motivations with MU and gambling on risky behavior may also be considered in the context of the pathway model.

While the current study focused on adolescents in the United States, research on MU and gambling should be considered within historic and cultural contexts in light of the dynamic global landscapes of cannabis and gambling legalization, perceptions, and opportunities [109,110]. MU varied considerably in a study of 11 European countries but remained stable in countries where cannabis legalization was unchanged and decreased in countries where legalization was changed [111]. While less evidence on the effects of cannabis legalization is available in Asian countries, MU and perceptions of MU harmfulness increased from 2019 to 2021 in Thailand, the first Asian country to legalize medicinal cannabis (2019) and recreational use (2021) [112]. Additionally, new gambling opportunities such as sports betting may differ among countries [113]. In the United Kingdom and Australia, marketing for the perceived safety of sports betting has been prominent, and qualitative data from sports bettors indicate that sports betting is perceived as normative [114,115]. In contrast, China, Taiwan, and South Korea have imposed stricter regulations on sports betting, including limiting legal forms of sports betting to only national sports lotteries, which may reduce its popularity and accessibility [113,116-118].

Study limitations should be considered. As the YRBS measures a range of health-risk behaviors, criteria corresponding to problem gambling and SUDs were not assessed. Previous studies that included diagnostic criteria in non-representative adolescent and adult cohorts have been published [35,119]. However, representative YRBS data provide important descriptive and inferential information to assist policymakers and treatment specialists in improving prevention efforts in adolescents before the onset of disorders. The survey did not assess the underlying motivations for violence. As individuals who perpetrate violence are often victims themselves [120], those who acted in self-defense or perpetrated violence were considered together. Other measures of adolescents’ behaviors were not included in the data collection to confirm the self-reports. As adolescents may not readily disclose sensitive information regarding their risky behaviors, reports from other individuals may contain inaccuracies. While crosssectional data may limit interpretations of the relationships among gambling, early substance use, and violence, future longitudinal studies may help elucidate these pathways. Furthermore, the current study used the 2019 YRBS data because the COVID-19 pandemic has raised challenges and lowered participation in school-based assessments in the presently disseminated 2021 YRBS data [121]. Exploratory analyses of representative data did not adjust for multiple comparisons, as in previous epidemiological studies [86,122], to inform the prevention of risky behaviors in vulnerable youth. Further studies may also confirm these early MU and gambling effects and interactions on risky behaviors in representative data from other states and national populations. The current findings can serve as a historical comparison for future studies examining the prevalence of and relationships among gambling, substance use, and violence before and after the pandemic.

CONCLUSIONS

This exploratory study systematically examined representative epidemiological data on the prevalence and odds of substance use and violence in adolescents with early-, later-, and no-MU stratified by gambling status. Overall, early-MU was associated with elevated odds of using other substances, including prescription opiate misuse, and violence and school bullying, especially among adolescents who gambled. Additionally, non-gambling adolescents with later-MU had greater odds of having experienced forced sexual intercourse than those with no-MU, likely because gambling accounted for some of the variance in the relationship between MU and forced sexual intercourse. Taken together, prevention efforts should consider employing multiple-behavior interventions that target gambling, substance use, and violence. Considering the pressing public health and clinical concerns as the number of jurisdictions with policies favoring marijuana legalization continues to grow, these findings may be particularly relevant for interventions in adolescents with early exposure to marijuana. Additionally, adequate psychological and socio-emotional support for adolescents experiencing violence may be a critical component of substance use interventions and treatments.

Acknowledgments

We thank Ms. Celeste Jorge, MPH, and the State of Connecticut for facilitating access to the YRBS data.

Footnotes

Footnote Group

Conflicts of Interest All authors declare no competing interests with respect to the content of this manuscript. MNP has consulted for Baria-Tek and Boehringer Ingelheim; has been involved in a patent application with Yale University and Novartis; has received research support from Mohegan Sun Casino and the Connecticut Council on Problem Gambling; has participated in surveys, mailings or telephone consultations related to drug addiction, internet use, impulse-control disorders or other health topics; has consulted for and/or advised gambling, non-profit, healthcare and legal entities on issues related to internet use, impulse control and addictive disorders; has performed grant reviews for researchfunding agencies; has edited journals and journal sections; has given academic lectures in grand rounds, CME events and other clinical or scientific venues; and has generated books or book chapters for publishers of mental health texts. The other authors do not report disclosures. The views presented in this manuscript do not necessarily represent those of the funding agencies.

REFERENCES

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References

1.Ames ME, Leadbeater BJ, Merrin GJ, Thompson K. Patterns of marijuana use and physical health indicators among Canadian youth. Int J Psychol. 2020;55:1–12. doi: 10.1002/ijop.12549.
2.Fergusson DM, Horwood LJ, Swain-Campbell N. Cannabis use and psychosocial adjustment in adolescence and young adulthood. Addiction. 2002;97:1123–1135. doi: 10.1046/j.1360-0443.2002.00103.x.
3.Fergusson DM, Boden JM, Horwood LJ. Cannabis use and other illicit drug use: testing the cannabis gateway hypothesis. Addiction. 2006;101:556–569. doi: 10.1111/j.1360-0443.2005.01322.x.
4.Leadbeater BJ, Ames ME, Linden-Carmichael AN. Age-varying effects of cannabis use frequency and disorder on symptoms of psychosis, depression and anxiety in adolescents and adults. Addiction. 2019;114:278–293. doi: 10.1111/add.14459.
5.Nguyen N, Barrington-Trimis JL, Urman R, Cho J, McConnell R, Leventhal AM, et al. Past 30-day co-use of tobacco and marijuana products among adolescents and young adults in California. Addict Behav. 2019;98:106053. doi: 10.1016/j.addbeh.2019.106053.
6.Rostam-Abadi Y, Stefanovics EA, Zhai ZW, Potenza MN. An exploratory study of the prevalence and adverse associations of inschool traditional bullying and cyberbullying among adolescents in Connecticut. J Psychiatr Res. 2024;173:372–380. doi: 10.1016/j.jpsychires.2024.03.033.
7.Satybaldiyeva N, Delker E, Bandoli G. Childhood aggressive behavior and adolescent substance use initiation. Subst Use Addctn J. 2024;45:415–422. doi: 10.1177/29767342231226084.
8.Silins E, Horwood LJ, Patton GC, Fergusson DM, Olsson CA, Hutchinson DM, et al. Young adult sequelae of adolescent cannabis use: an integrative analysis. Lancet Psychiatry. 2014;1:286–293. doi: 10.1016/S2215-0366(14)70307-4.
9.Miech RA, Johnston LD, Patrick ME, O'Malley PM, Bachman JG, Schulenberg JE. Monitoring the future. National survey results on drug use, 1975-2022: secondary school students. Institute for Social Research, University of Michigan; Ann Arbor, MI: 2023.
10.Miech RA, Johnston LD, Patrick ME, O'Malley PM, Bachman JG. Monitoring the future. National survey results on drug use, 1975-2023: secondary school students. Institute for Social Research, University of Michigan; Ann Arbor, MI: 2023.
11.Azofeifa A, Mattson ME, Schauer G, McAfee T, Grant A, Lyerla R. National estimates of marijuana use and related indicators - national survey on drug use and health, United States, 2002-2014. MMWR Surveill Summ. 2016;65:1–25. doi: 10.15585/mmwr.ss6511a1.
12.Griffin KW. In: Public health in the 21st Century. Finkel M, editor. Praeger; Santa Barbara, CA: 2011. Substance use across the lifespan; pp. 351–370.
13.Rotermann M, Langlois K. Prevalence and correlates of marijuana use in Canada, 2012. Health Rep. 2015;26:10–15.
14.National Academies of Sciences, author. The promise of adolescence: Realizing opportunity for all youth. National Academies Press; Washington, DC: 2019. pp. 37–75.
15.Kann L, McManus T, Harris WA, Shanklin SL, Flint KH, Queen B, et al. Youth risk behavior surveillance - United States, 2017. MMWR Surveill Summ. 2018;67:1–114. doi: 10.15585/mmwr.ss6708a1.
16.Moss HB, Chen CM, Yi HY. Early adolescent patterns of alcohol, cigarettes, and marijuana polysubstance use and young adult substance use outcomes in a nationally representative sample. Drug Alcohol Depend. 2014;136:51–62. doi: 10.1016/j.drugalcdep.2013.12.011.
17.Warner LA, White HR. Longitudinal effects of age at onset and first drinking situations on problem drinking. Subst Use Misuse. 2003;38:1983–2016. doi: 10.1081/JA-120025123.
18.Millar SR, Mongan D, Smyth BP, Perry IJ, Galvin B. Relationships between age at first substance use and persistence of cannabis use and cannabis use disorder. BMC Public Health. 2021;21:997. doi: 10.1186/s12889-021-11023-0.3dfc4ca4e5e740ed8fb73c1c38e6ab9c
19.Hawke LD, Wilkins L, Henderson J. Early cannabis initiation: substance use and mental health profiles of service-seeking youth. J Adolesc. 2020;83:112–121. doi: 10.1016/j.adolescence.2020.06.004.
20.Kokkevi A, Nic Gabhainn S, Spyropoulou M Risk Behaviour Focus Group of the HBSC, author. Early initiation of cannabis use: a crossnational European perspective. J Adolesc Health. 2006;39:712–719. doi: 10.1016/j.jadohealth.2006.05.009.
21.Ellickson PL, Tucker JS, Klein DJ, Saner H. Antecedents and outcomes of marijuana use initiation during adolescence. Prev Med. 2004;39:976–984. doi: 10.1016/j.ypmed.2004.04.013.
22.Brook JS, Whiteman MM, Finch S. Childhood aggression, adolescent delinquency, and drug use: a longitudinal study. J Genet Psychol. 1992;153:369–383. doi: 10.1080/00221325.1992.10753733.
23.White HR, Loeber R, Stouthamer-Loeber M, Farrington DP. Developmental associations between substance use and violence. Dev Psychopathol. 1999;11:785–803. doi: 10.1017/S0954579499002321.
24.Loher M, Steinhoff A, Bechtiger L, Ribeaud D, Eisner M, Shanahan L, et al. Disentangling the effects of self-control and the use of tobacco and cannabis on violence perpetration from childhood to early adulthood. Eur Child Adolesc Psychiatry. 2025;34:1063–1074. doi: 10.1007/s00787-024-02536-1.
25.Moulin V, Framorando D, Gasser J, Dan-Glauser E. The link between cannabis use and violent behavior in the early phase of psychosis: the potential role of impulsivity. Front Psychiatry. 2022;13:746287. doi: 10.3389/fpsyt.2022.746287.dfbb7dad89ca462d92586ac154f6c033
26.Maniglio R. Association between peer victimization in adolescence and cannabis use: a systematic review. Aggress Violent Behav. 2015;25:252–258. doi: 10.1016/j.avb.2015.09.002.
27.Johnson RM, LaValley M, Schneider KE, Musci RJ, Pettoruto K, Rothman EF. Marijuana use and physical dating violence among adolescents and emerging adults: a systematic review and metaanalysis. Drug Alcohol Depend. 2017;174:47–57. doi: 10.1016/j.drugalcdep.2017.01.012.
28.Grant JE, Chamberlain SR, Schreiber L, Odlaug BL. Neuropsychological deficits associated with cannabis use in young adults. Drug Alcohol Depend. 2012;121:159–162. doi: 10.1016/j.drugalcdep.2011.08.015.
29.Shorey RC, McNulty JK, Moore TM, Stuart GL. Being the victim of violence during a date predicts next-day cannabis use among female college students. Addiction. 2016;111:492–498. doi: 10.1111/add.13196.
30.Weiss NH, Duke AA, Sullivan TP. Evidence for a curvilinear doseresponse relationship between avoidance coping and drug use problems among women who experience intimate partner violence. Anxiety Stress Coping. 2014;27:722–732. doi: 10.1080/10615806.2014.899586.
31.El-Menshawi M, Castro G, Rodriguez de la Vega P, Ruiz Peláez JG, Barengo NC. First time cannabis use and sexual debut in U.S. high school adolescents. J Adolesc Health. 2019;64:194–200. doi: 10.1016/j.jadohealth.2018.08.018.
32.Dills A, Goffard S, Miron J, Partin E. The effect of state marijuana legalizations: 2021 Update [Internet] Cato Institute; Washington, DC: 2021. [cited 2025 Jan 28]. Available from: https://doi.org/10.36009/PA.908 .
33.Lynch WJ, Maciejewski PK, Potenza MN. Psychiatric correlates of gambling in adolescents and young adults grouped by age at gambling onset. Arch Gen Psychiatry. 2004;61:1116–1122. doi: 10.1001/archpsyc.61.11.1116.
34.Potenza MN, Wareham JD, Steinberg MA, Rugle L, Cavallo DA, Krishnan-Sarin S, et al. Correlates of at-risk/problem internet gambling in adolescents. J Am Acad Child Adolesc Psychiatry. 2011;50:150–159.e3. doi: 10.1016/j.jaac.2010.11.006.
35.Yip SW, Desai RA, Steinberg MA, Rugle L, Cavallo DA, Krishnan-Sarin S, et al. Health/functioning characteristics, gambling behaviors, and gambling-related motivations in adolescents stratified by gambling problem severity: findings from a high school survey. Am J Addict. 2011;20:495–508. doi: 10.1111/j.1521-0391.2011.00180.x.
36.Calado F, Alexandre J, Griffiths MD. Prevalence of adolescent problem gambling: a systematic review of recent research. J Gambl Stud. 2017;33:397–424. doi: 10.1007/s10899-016-9627-5.
37.Kryszajtys DT, Hahmann TE, Schuler A, Hamilton-Wright S, Ziegler CP, Matheson FI. Problem gambling and delinquent behaviours among adolescents: a scoping review. J Gambl Stud. 2018;34:893–914. doi: 10.1007/s10899-018-9754-2.
38.Volberg RA, Gupta R, Griffiths MD, Olason DT, Delfabbro P. An international perspective on youth gambling prevalence studies. Int J Adolesc Med Health. 2010;22:3–38. doi: 10.1515/9783110255690.21.
39.Potenza MN, Balodis IM, Derevensky J, Grant JE, Petry NM, Verdejo-Garcia A, et al. Gambling disorder. Nat Rev Dis Primers. 2019;5:51. doi: 10.1038/s41572-019-0099-7.
40.Petry NM, Tawfik Z. Comparison of problem-gambling and nonproblem-gambling youths seeking treatment for marijuana abuse. J Am Acad Child Adolesc Psychiatry. 2001;40:1324–1331. doi: 10.1097/00004583-200111000-00013.
41.Richard J, Potenza MN, Ivoska W, Derevensky J. The stimulating nature of gambling behaviors: relationships between stimulant use and gambling among adolescents. J Gambl Stud. 2019;35:47–62. doi: 10.1007/s10899-018-9778-7.
42.Weinberger AH, Franco CA, Hoff RA, Pilver CE, Steinberg MA, Rugle L, et al. Gambling behaviors and attitudes in adolescent high-school students: relationships with problem-gambling severity and smoking status. J Psychiatr Res. 2015;65:131–138. doi: 10.1016/j.jpsychires.2015.04.006.
43.Zhai ZW, Hoff RA, Magruder CF, Steinberg MA, Wampler J, Krishnan-Sarin S, et al. Weapon-carrying is associated with more permissive gambling attitudes and perceptions and at-risk/problem gambling in adolescents. J Behav Addict. 2019;8:508–521. doi: 10.1556/2006.8.2019.42.
44.Zhai ZW, Duenas GL, Wampler J, Potenza MN. Gambling, substance use and violence in male and female adolescents. J Gambl Stud. 2020;36:1301–1324. doi: 10.1007/s10899-020-09931-8.
45.Cook S, Turner NE, Ballon B, Paglia-Boak A, Murray R, Adlaf EM, et al. Problem gambling among Ontario students: associations with substance abuse, mental health problems, suicide attempts, and delinquent behaviours. J Gambl Stud. 2015;31:1121–1134. doi: 10.1007/s10899-014-9483-0.
46.Donovan JE, Jessor R. Structure of problem behavior in adolescence and young adulthood. J Consult Clin Psychol. 1985;53:890–904. doi: 10.1037/0022-006X.53.6.890.
47.Jessor R. Problem-behavior theory, psychosocial development, and adolescent problem drinking. Br J Addict. 1987;82:331–342. doi: 10.1111/j.1360-0443.1987.tb01490.x.
48.Mobley M, Chun H. Testing Jessor's problem behavior theory and syndrome: a nationally representative comparative sample of Latino and African American adolescents. Cultur Divers Ethnic Minor Psychol. 2013;19:190–199. doi: 10.1037/a0031916.
49.Blaszczynski A, Nower L. A pathways model of problem and pathological gambling. Addiction. 2002;97:487–499. doi: 10.1046/j.1360-0443.2002.00015.x.
50.Gupta R, Nower L, Derevensky JL, Blaszczynski A, Faregh N, Temcheff C. Problem gambling in adolescents: an examination of the pathways model. J Gambl Stud. 2013;29:575–588. doi: 10.1007/s10899-012-9322-0.
51.Shead NW, Derevensky JL, Gupta R. Risk and protective factors associated with youth problem gambling. Int J Adolesc Med Health. 2010;22:39–58.
52.Turner S, Mota N, Bolton J, Sareen J. Self-medication with alcohol or drugs for mood and anxiety disorders: a narrative review of the epidemiological literature. Depress Anxiety. 2018;35:851–860. doi: 10.1002/da.22771.
53.Gonzalez R, Schuster RM, Mermelstein RM, Diviak KR. The role of decision-making in cannabis-related problems among young adults. Drug Alcohol Depend. 2015;154:214–221. doi: 10.1016/j.drugalcdep.2015.06.046.
54.Leeman RF, Potenza MN. Similarities and differences between pathological gambling and substance use disorders: a focus on impulsivity and compulsivity. Psychopharmacology (Berl) 2012;219:469–490. doi: 10.1007/s00213-011-2550-7.
55.Vitaro F, Ferland F, Jacques C, Ladouceur R. Gambling, substance use, and impulsivity during adolescence. Psychol Addict Behav. 1998;12:185–194. doi: 10.1037/0893-164X.12.3.185.
56.Winters KC, Whelan JP. Gambling and cannabis use: clinical and policy implications. J Gambl Stud. 2020;36:223–241. doi: 10.1007/s10899-019-09919-z.
57.Sabet KA, Winters KC. Contemporary health issues on marijuana. Oxford University Press; New York: 2018.
58.Barnes GM, Welte JW, Hoffman JH, Tidwell MC. Gambling, alcohol, and other substance use among youth in the United States. J Stud Alcohol Drugs. 2009;70:134–142. doi: 10.15288/jsad.2009.70.134.
59.Barnes GM, Welte JW, Hoffman JH, Tidwell MC. The co-occurrence of gambling with substance use and conduct disorder among youth in the United States. Am J Addict. 2011;20:166–173. doi: 10.1111/j.1521-0391.2010.00116.x.
60.Barnes GM, Welte JW, Tidwell MC, Hoffman JH. Gambling and substance use: co-occurrence among adults in a recent general population study in the United States. Int Gambl Stud. 2015;15:55–71. doi: 10.1080/14459795.2014.990396.
61.Chambers RA, Taylor JR, Potenza MN. Developmental neurocircuitry of motivation in adolescence: a critical period of addiction vulnerability. Am J Psychiatry. 2003;160:1041–1052. doi: 10.1176/appi.ajp.160.6.1041.
62.Kerber CS, Black DW, Buckwalter K. Comorbid psychiatric disorders among older adult recovering pathological gamblers. Issues Ment Health Nurs. 2008;29:1018–1028. doi: 10.1080/01612840802274933.
63.Hammond CJ, Pilver CE, Rugle L, Steinberg MA, Mayes LC, Malison RT, et al. An exploratory examination of marijuana use, problem-gambling severity, and health correlates among adolescents. J Behav Addict. 2014;3:90–101. doi: 10.1556/JBA.3.2014.009.
64.Feigelman W, Kleinman PH, Lesieur HR, Millman RB, Lesser ML. Pathological gambling among methadone patients. Drug Alcohol Depend. 1995;39:75–81. doi: 10.1016/0376-8716(95)01141-K.
65.Ibáñez A, Blanco C, Donahue E, Lesieur HR, Pérez de Castro I, Fernández-Piqueras J, et al. Psychiatric comorbidity in pathological gamblers seeking treatment. Am J Psychiatry. 2001;158:1733–1735. doi: 10.1176/ajp.158.10.1733.
66.Lister JJ, Milosevic A, Ledgerwood DM. Personality traits of problem gamblers with and without alcohol dependence. Addict Behav. 2015;47:48–54. doi: 10.1016/j.addbeh.2015.02.021.
67.Petry NM. Psychiatric symptoms in problem gambling and nonproblem gambling substance abusers. Am J Addict. 2000;9:163–171. doi: 10.1080/10550490050173235.
68.Steinberg MA, Kosten TA, Rounsaville BJ. Cocaine abuse and pathological gambling. Am J Addict. 1992;1:121–132. doi: 10.1111/j.1521-0391.1992.tb00017.x.
69.Grummel JA. In: Encyclopedia of social measurement. Kempf-Leonard K, editor. Elsevier; New York: 2005. Population vs. sample; pp. 135–140.
70.Underwood JM, Brener N, Thornton J, Harris WA, Bryan LN, Shanklin SL, et al. Overview and methods for the youth risk behavior surveillance system - United States, 2019. MMWR Suppl. 2020;69:1–10. doi: 10.15585/mmwr.su6901a1.
71.Potter F. A study of procedures to identify and trim extreme sampling weights. American Statistical Association; 1990. pp. 225–230.
72.DuRant RH, Smith JA, Kreiter SR, Krowchuk DP. The relationship between early age of onset of initial substance use and engaging in multiple health risk behaviors among young adolescents. Arch Pediatr Adolesc Med. 1999;153:286–291. doi: 10.1001/archpedi.153.3.286.
73.Farhat LC, Roberto AJ, Wampler J, Steinberg MA, Krishnan-Sarin S, Hoff RA, et al. Self-injurious behavior and gambling-related attitudes, perceptions and behaviors in adolescents. J Psychiatr Res. 2020;124:77–84. doi: 10.1016/j.jpsychires.2020.02.016.
74.Mottola F, Abbamonte L, Ariemma L, Gnisci A, Marcone R, Millefiorini A, et al. Construct and criterion validity of the HEXACO medium school inventory extended (MSI-E) PLoS One. 2023;18:e0292813. doi: 10.1371/journal.pone.0292813.10a522456f1f411bae22595ebbca616b
75.Brener ND, Kann L, Shanklin S, Kinchen S, Eaton DK, Hawkins J, et al. Methodology of the youth risk behavior surveillance system- 2013. MMWR Recomm Rep. 2013;62:1–20. doi: 10.1037/e548562006-001.
76.Brener ND, Collins JL, Kann L, Warren CW, Williams BI. Reliability of the youth risk behavior survey questionnaire. Am J Epidemiol. 1995;141:575–580. doi: 10.1093/oxfordjournals.aje.a117473.
77.Brener ND, Kann L, McManus T, Kinchen SA, Sundberg EC, Ross JG. Reliability of the 1999 youth risk behavior survey questionnaire. J Adolesc Health. 2002;31:336–342. doi: 10.1016/S1054-139X(02)00339-7.
78.Brener ND, Billy JO, Grady WR. Assessment of factors affecting the validity of self-reported health-risk behavior among adolescents: evidence from the scientific literature. J Adolesc Health. 2003;33:436–457. doi: 10.1016/S1054-139X(03)00052-1.
79.Goodman E, Hinden BR, Khandelwal S. Accuracy of teen and parental reports of obesity and body mass index. Pediatrics. 2000;106:52–58. doi: 10.1542/peds.106.1.52.
80.May A, Klonsky ED. Validity of suicidality items from the youth risk behavior survey in a high school sample. Assessment. 2011;18:379–381. doi: 10.1177/1073191110374285.
81.Mercado-Crespo MC, Mbah AK. Race and ethnicity, substance use, and physical aggression among U.S. high school students. J Interpers Violence. 2013;28:1367–1384. doi: 10.1177/0886260512468234.
82.Paschall MJ, Ringwalt CL, Gitelman AM. The validity of state survey estimates of binge drinking. Am J Prev Med. 2010;39:179–183. doi: 10.1016/j.amepre.2010.03.018.
83.Strauss RS. Comparison of measured and self-reported weight and height in a cross-sectional sample of young adolescents. Int J Obes Relat Metab Disord. 1999;23:904–908. doi: 10.1038/sj.ijo.0800971.
84.Byregowda H, Alinsky R, Wang X, Johnson RM. Non-medical prescription opioid use among high school students in 38 U.S. States. Addict Behav Rep. 2023;17:100498. doi: 10.1016/j.abrep.2023.100498.
85.Hasin DS, Stinson FS, Ogburn E, Grant BF. Prevalence, correlates, disability, and comorbidity of DSM-IV alcohol abuse and dependence in the United States: results from the national epidemiologic survey on alcohol and related conditions. Arch Gen Psychiatry. 2007;64:830–842. doi: 10.1001/archpsyc.64.7.830.
86.Kahn NF, Sequeira GM, Asante PG, Kidd KM, Coker TR, Christakis DA, et al. Estimating transgender and gender-diverse youth populations in health systems and survey data. Pediatrics. 2024;153:e2023065197. doi: 10.1542/peds.2023-065197.
87.Liu K, Benedetti M, Evans A, Zhu M. Prescription drug monitoring programs and prescription pain medication misuse among U.S. high school students-2019. BMC Public Health. 2024;24:1276. doi: 10.1186/s12889-024-18698-1.7e8dbdbbaea048f78c8d461c29eb7d35
88.Weerakoon SM, Henson-Garcia M, Abraham A, Vidot DC, Messiah SE, Opara I. Adolescent polysubstance use and co-occurring weapon carrying, bullying victimization, and depressive symptomology: patterns and differences in the United States. Child Psychiatry Hum Dev. 2025;56:456–467. doi: 10.1007/s10578-023-01573-2.
89.Proimos J, DuRant RH, Pierce JD, Goodman E. Gambling and other risk behaviors among 8th- to 12th-grade students. Pediatrics. 1998;102:e23. doi: 10.1542/peds.102.2.e23.
90.Stefanovics EA, Gueorguieva R, Zhai ZW, Potenza MN. Gambling participation among Connecticut adolescents from 2007 to 2019: potential risk and protective factors. J Behav Addict. 2023;12:490–499. doi: 10.1556/2006.2023.00027.
91.Hermos JA, Winter MR, Heeren TC, Hingson RW. Early age-ofonset drinking predicts prescription drug misuse among teenagers and young adults: results from a national survey. J Addict Med. 2008;2:22–30. doi: 10.1097/ADM.0b013e3181565e14.
92.Stanley LR, Swaim RC, Smith JK, Conner BT. Early onset of cannabis use and alcohol intoxication predicts prescription drug misuse in American Indian and non-American Indian adolescents living on or near reservations. Am J Drug Alcohol Abuse. 2020;46:447–453. doi: 10.1080/00952990.2020.1767639.
93.Khan S, Griffin KW, Botvin GJ. Onset of the non-medical use of prescription and over-the-counter medications during early adolescence: comparison with alcohol, tobacco, and marijuana. Children (Basel) 2023;10:1298. doi: 10.3390/children10081298.6e9f59d129774edb96ac0f0fe8a34d66
94.Brook JS, Brook DW, Rosen Z, Rabbitt CR. Earlier marijuana use and later problem behavior in Colombian youths. J Am Acad Child Adolesc Psychiatry. 2003;42:485–492. doi: 10.1097/01.CHI.0000037050.04952.49.
95.Basen-Engquist K, Edmundson EW, Parcel GS. Structure of health risk behavior among high school students. J Consult Clin Psychol. 1996;64:764–775. doi: 10.1037/0022-006X.64.4.764.
96.Spurrier M, Blaszczynski A. Risk perception in gambling: a systematic review. J Gambl Stud. 2014;30:253–276. doi: 10.1007/s10899-013-9371-z.
97.Nigro G, Matarazzo O, Ciccarelli M, Pizzini B, Sacco M, Cosenza M. Positive illusions: the role of cognitive distortions related to gambling and temporal perspective in chasing behavior. J Gambl Stud. 2022;38:889–904. doi: 10.1007/s10899-021-10068-5.
98.Arria AM, Caldeira KM, Vincent KB, O'Grady KE, Wish ED. Perceived harmfulness predicts nonmedical use of prescription drugs among college students: interactions with sensation-seeking. Prev Sci. 2008;9:191–201. doi: 10.1007/s11121-008-0095-8.
99.Kenne DR, Hamilton K, Birmingham L, Oglesby WH, Fischbein RL, Delahanty DL. Perceptions of harm and reasons for misuse of prescription opioid drugs and reasons for not seeking treatment for physical or emotional pain among a sample of college students. Subst Use Misuse. 2017;52:92–99. doi: 10.1080/10826084.2016.1222619.
100.Zimmerman GM, Farrell C. Parents, peers, perceived risk of harm, and the neighborhood: contextualizing key influences on adolescent substance use. J Youth Adolesc. 2017;46:228–247. doi: 10.1007/s10964-016-0475-5.
101.Suomi A, Dowling NA, Thomas S, Abbott M, Bellringer M, Battersby M, et al. Patterns of family and intimate partner violence in problem gamblers. J Gambl Stud. 2019;35:465–484. doi: 10.1007/s10899-018-9768-9.
102.Dowling N, Suomi A, Jackson A, Lavis T, Patford J, Cockman S, et al. Problem gambling and intimate partner violence: a systematic review and meta-analysis. Trauma Violence Abuse. 2016;17:43–61. doi: 10.1177/1524838014561269.
103.O'Mullan C, Hing N, Mainey L, Nuske E, Breen H. Understanding the determinants of gambling-related intimate partner violence: perspectives from women who gamble. Violence Against Women. 2022;28:3037–3059. doi: 10.1177/10778012211051399.
104.Delfabbro P, Winefield T, Trainor S, Dollard M, Anderson S, Metzer J, et al. Peer and teacher bullying/victimization of South Australian secondary school students: prevalence and psychosocial profiles. Br J Educ Psychol. 2006;76:71–90. doi: 10.1348/000709904X24645.
105.Flouri E, Papachristou E. Peer problems, bullying involvement, and affective decision-making in adolescence. Br J Dev Psychol. 2019;37:466–485. doi: 10.1111/bjdp.12287.
106.Poon K. Understanding risk-taking behavior in bullies, victims, and bully victims using cognitive- and emotion-focused approaches. Front Psychol. 2016;7:1838. doi: 10.3389/fpsyg.2016.01838.b4ba4480fd3846ae9bd81b5ef43d5bc5
107.Veenstra R, Lindenberg S, Munniksma A, Dijkstra JK. The complex relation between bullying, victimization, acceptance, and rejection: giving special attention to status, affection, and sex differences. Child Dev. 2010;81:480–486. doi: 10.1111/j.1467-8624.2009.01411.x.
108.Nower L, Blaszczynski A, Anthony WL. Clarifying gambling subtypes: the revised pathways model of problem gambling. Addiction. 2022;117:2000–2008. doi: 10.1111/add.15745.
109.Oei TPS, Raylu N, Loo JMY. In: Gambling disorder. Heinz A, Romanczuk-Seiferth N, Potenza MN, editors. Springer; Cham: 2019. Roles of culture in gambling and gambling disorder; pp. 271–295.
110.Rafei P, Englund A, Lorenzetti V, Elkholy H, Potenza MN, Baldacchino AM. Transcultural aspects of cannabis use: a descriptive overview of cannabis use across cultures. Curr Addict Rep. 2023;10:458–471. doi: 10.1007/s40429-023-00500-8.
111.Gabri AC, Galanti MR, Orsini N, Magnusson C. Changes in cannabis policy and prevalence of recreational cannabis use among adolescents and young adults in Europe-an interrupted time-series analysis. PLoS One. 2022;17:e0261885. doi: 10.1371/journal.pone.0261885.117d0af5142d4e8e9353a47319d61198
112.Kalayasiri R, Boonthae S. Trends of cannabis use and related harms before and after legalization for recreational purpose in a developing country in Asia. BMC Public Health. 2023;23:911. doi: 10.1186/s12889-023-15883-6.5ceb672f032d4e1e9bbbf524fd47eb1c
113.Etuk R, Xu T, Abarbanel B, Potenza MN, Kraus SW. Sports betting around the world: a systematic review. J Behav Addict. 2022;11:689–715. doi: 10.1556/2006.2022.00064.
114.Lopez-Gonzalez H, Estévez A, Griffiths MD. Controlling the illusion of control: a grounded theory of sports betting advertising in the UK. Int Gambl Stud. 2018;18:39–55. doi: 10.1080/14459795.2017.1377747.
115.McGee D. On the normalisation of online sports gambling among young adult men in the UK: a public health perspective. Public Health. 2020;184:89–94. doi: 10.1016/j.puhe.2020.04.018.
116.Han S. Match-fixing under the state monopoly sports betting system: a case study of the 2011 K-League scandal. Crime Law Soc Change. 2020;74:97–113. doi: 10.1007/s10611-020-09888-0.
117.Lee PC, Huang HC, Jiang RS, Lee KW. The establishment of a sports lottery in Taiwan. Int J Hist Sport. 2012;29:601–618. doi: 10.1080/09523367.2012.658192.
118.Wu AM, Lau JT. Gambling in China: socio-historical evolution and current challenges. Addiction. 2015;110:210–216. doi: 10.1111/add.12710.
119.Loo JMY, Kraus SW, Potenza MN. A systematic review of gambling-related findings from the National Epidemiologic Survey on Alcohol and Related Conditions. J Behav Addict. 2019;8:625–648. doi: 10.1556/2006.8.2019.64.
120.Leary MR, Kowalski RM, Smith L, Phillips S. Teasing, rejection, and violence: case studies of the school shootings. Aggress Behav. 2003;29:202–214. doi: 10.1002/ab.10061.
121.Mpofu JJ, Underwood JM, Thornton JE, Brener ND, Rico A, Kilmer G, et al. Overview and methods for the youth risk behavior surveillance system - United States, 2021. MMWR Suppl. 2023;72:1–12. doi: 10.15585/mmwr.su7201a1.
122.Grant BF, Goldstein RB, Saha TD, Chou SP, Jung J, Zhang H, et al. Epidemiology of DSM-5 alcohol use disorder: results from the national epidemiologic survey on alcohol and related conditions III. JAMA Psychiatry. 2015;72:757–766. doi: 10.1001/jamapsychiatry.2015.0584.

Studies

Conditions

Cannabinoids and active compounds

Related research

Abstract

Objectives

Methods

Results

Conclusion

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Article notes

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Author notes

INTRODUCTION

METHODS

Participants

Measures

Demographics

Marijuana use

Gambling status

Substance use

Violence

Statistical analysis

RESULTS

Demographic characteristics

Substance use

Violence

DISCUSSION

CONCLUSIONS

Acknowledgments

Footnotes

Footnote Group

REFERENCES

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References

Additional materials