Bullying Victimization is Associated with a Significantly Greater Risk of Illicit Substance Use among US Female Adolescents: YRBS 2011 to 2017

Abstract

Bullying victimization among adolescents is associated with increased illicit substance use. This research estimates whether the association between bullying victimization and substance use is significantly greater among female adolescents. Using R software and the national Youth Risk Behavior Survey (2011, 2013, 2014, and 2017), interactions were estimated to determine the association between self-reported school or electronic bullying victimization and previous 30-day alcohol use, binge drinking, marijuana use, cigarette smoking, and electronic vaping product use. Bullying victimization was significantly associated with each of the substance use variables, Effects were significantly greater in female students. Efforts should be put in place in schools and communities to reduce bullying, mitigate the harmful effects of this form of victimization, and reduce illicit substance use.

Keywords

bullying victimization substance use adolescents binge drinking marijuana use

Bullying has been defined by the U.S. Centers for Disease Control and Prevention (CDC)

“. . . as any unwanted aggressive behavior(s) by another youth or group of youths who are not siblings or current dating partners that involves an observed or perceived power imbalance and is repeated multiple times or is highly likely to be repeated” (Gladden et al., 2014, p. 7).

Most often, traditional bullying among adolescence occurs in school settings, though it can occur outside the school as well (CDC, 2018). Overall, school bullying victimization has remained relatively unchanged from 2009 to 2015, with a reported 20% prevalence according to the Youth Risk Behavior Survey (YRBS) (Centers for Disease Control and Prevention [CDC], 2018; Musu et al., 2019). However, when trends in school bullying victimization (2009–2015) were measured by gender using YRBS data, male students reported a 16% decrease, but female students reported a 17% increase (Pontes, Ayres, Lewandowski, et al. 2018). By race/ethnicity, Asian female students reported significantly lower rates of school bullying victimization compared to White students (Pontes, Ayres, Lewandowski, et al., 2018).

With increases in technological advances and access, as well as the rise of the social media era, adolescents began taking part in electronic bullying. Also termed cyberbullying, is defined as “willful and repeated harm inflicted through the use of computers, cell phones, and other electronic devices” (Hinduja & Patchin, 2009, p. 5). Recently, surveillance studies with national samples reported that around 15% reported electronic bullying victimization (CDC, 2018; Musu et al., 2019). By race and ethnicity, Asian Female students, and Black and Hispanic students—male and female—report less electronic bullying compared to White and Multi-race students (Pontes, Ayres, Lewandowski, et al., 2018). Moreover, research indicates that adolescents are likely to be victimized by traditional bullying in conjunction with electronic bullying (Mehari & Ferrell, 2018). Currently, no national estimates of adolescents victimized by both bullying types combined have been reported.

Bullying has been examined extensively by researchers yet remains a significant topic due to the vast amounts of negative outcomes that are linked to bullying victimization. While bullying has long been considered as an adolescent “rite of passage,” more recently bullying victimization has been reframed as an adverse childhood experience (ACEs) that has the potential for short-term and life-long consequences. For example, research consistently shows that adolescent bullying victims are more likely to have increased levels of anxiety, depression, and suicidality (Pontes, Ayres, & Pontes, 2018; Reed et al., 2015; Salmivalli et al., 1999; Strohacker et al., 2019), increased violence-related behaviors, such as weapon-carrying and higher levels of aggression (Duggins et al., 2016; Nansel et al., 2003; Pontes & Pontes, 2019; Priesman & Wright, 2018), and are at higher risk of dangerous sexual behaviors, including sexual harassment and violence (Hertz et al., 2015; Holt et al., 2013; Leemis et al., 2019; Pontes & Pontes, 2019, Okafor et al., 2020). Similarly, both types of bullying victimization have been linked with substance use and alcohol consumption (Chan & La Greca, 2016; Goebert et al., 2011; Luk et al., 2012; Mitchell et al., 2007; Tharp-Taylor et al., 2009; Valdebenito et al., 2015), as well as the misuse of prescription drugs and over-the-counter drugs (Baiden & Tadeo,2019; Moore et al, 2017). Fewer studies have examined the negative outcomes that stem from the combination of traditional and electronic bullying victimization (Priesman & Wright, 2018; Priesman et al., 2018; Strohacker et al., 2019).

Illicit Substance Use among Adolescents in the US

Alcohol Use

Alcohol use among adolescents in the US raises significant public health concerns. Data from the National Survey on Drug Use and Health (NSDUH) showed that in 2018, approximately 2.2 million (9.0%) adolescents aged 12 to 17 reported drinking alcohol in the month before the survey, while 1.2 million (4.7%) reported binge drinking in the same period (Substance Abuse and Mental Health Services Administration [SAMHSA], 2018). Underage alcohol consumption has been associated with an increased risk of psychosomatic issues, such as anxiety and depression, inadequate sleep or disrupted sleep patterns, alcohol poisoning, and risky sexual behavior (Marshall, 2014; SAMHSA, 2018). Similarly, underage drinking has also been associated with an increased risk of addiction and other substance use (SAMHSA, 2018).

Existing literature shows that bullying victimization has been linked with increased alcohol use, though findings vary depending on the type of victimization (Chan and La Greca 2016; Hertz et al., 2015; Priesman et al., 2018). For instance, Goebert et al. (2011) found that experiencing electronic bullying victimization tripled the likelihood of binge drinking among youths. Chan and La Greca (2016) reported similar findings among electronic bullying victims, though they found a gendered relationship as girls were more likely to report frequent drinking than boys. Priesman et al. (2018) found similar results supporting the potentially gendered relationship between electronic victimization and binge drinking. Further, when exploring electronic bullying victimization in conjunction with traditional bullying victimization, Priesman et al. (2018) found that adolescents who reported only electronic bullying victimization or both types of victimization (traditional and electronic) were more likely to report binge drinking. Various studies have reported finding no significant relationship between traditional victimization and binge drinking (Priesman et al., 2018; Hertz et al., 2015), however, Luk et al. (2012) found that bullying victimization was associated with alcohol use and alcohol-related problems.

Marijuana Use

Marijuana is consistently found to be the most prevalently used illicit substance among adolescents (Parnes et al., 2019). In 2018, NSDUH found that approximately 3.1 million (12.5%) adolescents aged 12 to 17 reported the use of marijuana in the past year, which was similar to the percentages of prior years (SAMHSA, 2018). While adolescents may consider the use of marijuana to be low risk (SAMHSA, 2018), existing literature shows that there is some evidence that heavy marijuana use can be a risk factor for reduced academic performance, increased depressive symptoms, and higher levels of reported anxiety and suicidal ideation (National Academies of Sciences, Engineering, & Medicine, 2017).

Prior studies that examine bullying victimization typically examine marijuana use under the umbrella term of “substance use,” which generally includes the use of alcohol, tobacco, and other illicit drugs (Hertz et al., 2015; Okafor et al., 2020; Tharp-Taylor et al., 2009). For instance, Okafor et al. (2020) reported a significant relationship between substance use and victims of traditional bullying as well as victims of electronic bullying but does not have a standalone measure for marijuana use. Fewer studies, however, examine this relationship with a distinct measure of marijuana use (Goebert et al., 2011; Priesman et al., 2018). Goebert et al. (2011) found that students who reported experiencing electronic bullying victimization were more than twice as likely to use marijuana than those who did not report victimization. Priesman et al. (2018) found that adolescents who reported electronic bullying victimization and those that reported both types of victimization (traditional and electronic) were more likely to engage in marijuana use, whereas traditional bullying victimization was significantly associated with marijuana use only for respondents who were ages 17 and 18. Similarly to binge drinking, the findings of Priesman et al. (2018) suggest a gendered relationship between electronic bullying and substance use.

Cigarette Smoking and Electronic Vapor Product Use

Within the U.S., the CDC (2020a, p. 1) reports that “smoking is the leading cause of preventable death.” After significant public health efforts to reduce smoking, data from NSDUH shows a steady decline in the percentage of adolescents reporting past-month smoking from 2002 to 2018, with findings declining from 13.0% to 2.7% respectively (SAMHSA, 2019). Though there has been a steady decline in smoking among adolescents, if the American youth continue to smoke at the current rate, the CDC (2020a) projects that 5.6 million of those currently under the age of 18 will die prematurely due to smoking-related illnesses.

Like marijuana use, much existing literature examines cigarette smoking under the term of “substance use” (Hertz et al., 2015; Okafor et al., 2020;). Of those studies that specifically examine tobacco use, a significant relationship between bullying victimization and cigarette smoking is found (Case et al., 2016; Tharp-Taylor et al., 2009). Tharp-Taylor et al. (2009) found that adolescents who reported bullying victimization were more likely to report cigarette smoking than those who were not bullied. Case et al. (2016) expanded on this by finding both types of bullying were more likely to report cigarette smoking. Gender differences emerged between types of bullying and smoking, with girls being more likely to smoke when experiencing traditional bullying only whereas boys were more likely to report smoking when experiencing electronic bullying only (Case et al. 2016).

Although cigarette smoking among adolescents has generally declined over the past decade or so (CDC, 2018; SAMHSA, 2019), some of this may be attributed to the increased use of electronic vapor products (e-vape use). Electronic vaporizing devices (i.e., vaping), specifically e-cigarettes and vape pens, allows the user to inhale nicotine and/or cannabinoids. While vaping is not a new phenomenon, its increased popularity among adolescents has moved to the forefront of academic and public health research. Researchers posit that the increased varieties of products targeting the younger population (i.e., youth appealing flavorings) coupled with the ease of obtainment pose a particular risk to adolescents as e-vape use can function as a gateway to traditional smoking of cigarettes and/or marijuana use. The CDC (2018) found that 42.2% of adolescents between the ages of 12 and 17 reported ever vaping, 13.2% reported vaping at least one time in the month before the survey. Similarly, Miech et al. (2019) reported that lifetime e-vape use among 12th graders increased from 25% in 2017 to 40% in 2019.

Studies examining the association between e-vape use and bullying victimization are scarce. Ragavan et al. (2020) found positive associations between the two when examining lifetime use, however, the relationship did not remain statistically significant when other substances—alcohol, tobacco, and marijuana—were introduced. Similarly, Azagba et al. (2020) found that bullying victimization was associated with e-vape use, and when stratified by sex, found that, though statistically significant among both sexes, girls who were victimized were at higher odds of e-vape use. Doxbeck (2020) found that school bullying victimization was not associated with e-vape use, whereas electronic bullying victimization was linked to increased e-vape use. With the increasing prevalence and popularity of vaping among the adolescent population, it is pertinent that more research is done in this area to examine the association between e-vape use and traditional bullying and/or electronic bullying victimization.

Interaction Effects between Bullying Victimization and Gender on Illicit Substance Use

There is limited research that estimated gender × bullying victimization interaction effects on alcohol use, binge drinking, marijuana use, cigarette smoking, or E-Vape use. For example, using the 1997 National Longitudinal Survey of Youth, Connolly (2017) found that bullying victimization during adolescence increased the risk of later substance use. Both male and female respondents had greater increases in cigarette use as they aged compared to those who were not bullied. The increased rate of increase in marijuana use by age due to bullying victimization was greater among male students. Connolly (2017) presented the effect sizes separately for males and females and did not test for interactions.

Two cross-sectional studies (Kim, 2019; Turner et al., 2018) published similar findings. Results from the Ontario Student Drug Use and Health Survey (Kim, 2019) showed that female but not male students in grades 7 to 12 (4,940) who reported electronic bullying victimization were more likely to report binge drinking (OR 1.64 vs. 1.54), marijuana use (OR 2.05 vs. 0.96), and tobacco use (OR 1.82 vs. 1.38). Although Kim (2019) reported ORs by gender, no interactions between bullying victimization and gender on illicit substance use were reported. Another Canadian study (Turner et al., 2018) also reported ORs for the relationship between bullying victimization and marijuana use separately by gender and grade level. This research showed that bullying victimization was associated with increased marijuana use; although Turner et al. (2018) reported ORs by gender, no interactions between bullying victimization and gender on illicit substance use were reported. It should also be noted that none of these studies reported risk differences by gender. In summary. separate analyses of effect sizes have been reported by gender but gender × bullying victimization interactions have not been reported.

Due to the vast array of negative outcomes that can occur, examining bullying victimization—both traditional and electronic—remains important to educators, researchers, and public officials alike. Similarly, adolescent alcohol, marijuana, and cigarette use remain prominent public health issues, and concern about adolescent vaping is rising. This study seeks to expand upon existing literature to explore the additive and multiplicative interactions between school and electronic bullying victimization and alcohol use, binge drinking, marijuana use, cigarette smoking, and e-vape use by gender among US high school students using the nationally representative YRBS dataset (2011, 2013, 2015, 2017).

General Strain Theory

Agnew’s (1992) General Strain Theory (GST) is well established in the criminal justice field and postulates that deviance-producing strain can result from the presentation of noxious stimuli, the removal of positively valued stimuli, or the inability to achieve positively valued goals. As it pertains to this study, GST is used as a framework to postulate that the presence of bullying victimization (i.e., negative experience/stimuli) could result in deviant coping patterns that can result in negative behaviors, such as substance use, and other delinquency (Agnew, 1992; Peck et al., 2018). Additionally, as the theory developed, GST posited females and males respond to negative stimuli in different ways. For instance, Broidy and Agnew (1997) discussed that both males and females can experience anger as a result of strain, but females generally also experience more co-occurring emotions, such as depression, anxiety, shame, and guilt. Further, males are more likely to respond to negative stimuli with other-directed deviance, such as violence, compared to females who are more likely to internalize their responses and engage in more self-destructive deviance, like substance use (Broidy & Agnew, 1997; Piquero & Sealock, 2004). Although this research does not test this theory, it is of importance to note that existing literature shows there are differential outcomes for male and female victims when the GST framework is used to examine victimization and substance use (Cullen et al., 2008; DeCamp & Newby, 2015; Glassner & Cho, 2018).

Rationale for Estimation of Interactions

The CONSORT 2010 (Item 12b, p. 14) recommends that statistical tests for interactions should always be performed and reported when researchers test for differences in effects across subgroups (Moher et al., 2010). The presence of one significant and one non-significant p value for separate analyses withing subgroups is not sufficient evidence for a significant interaction effect (Altman & Bland, 2003; Moher et al., 2010). Large sample sizes, such as those obtained by pooling several waves of YRBS data are usually necessary to have adequate power to test for interactions; most studies are underpowered to detect interactions (Altman & Bland, 2003). A rule of thumb is that the sample size to test for an interaction should be 16 times larger than the sample size to test for a main effect (Gelman, 2018).

Rationale for Estimation of Additive Interactions

This research uses data from YRBS to estimate additive interactions and thus investigate whether the risk difference in illicit substance associated with bullying victimization varied significantly between males and females. Researchers state that “difference measures provide much more informative evidence regarding the magnitude of public health impact of exposures than ratio measures” (Keyes & Galea, 2017). STROBE guidelines state (pg. 1639), “There is consensus that the additive scale, which uses absolute risks, is more appropriate for public health and clinical decision making” (Vandenbroucke et al., 2007). Additive interactions are estimated when risk differences are used as the outcome measure (Rothman, 2014). Additive interactions between bullying victimization and substance use test whether the difference of the risk difference in Females (RD_F) versus males (RD_M), RD_F − RD_M is significantly greater than zero. This research also reports average marginal predicted percentages (Norton et al., 2019).

Rationale for Estimation of Multiplicative Interactions

Multiplicative interactions are estimated with relative measures of association, such as the odds ratio (Rothman, 2014.) Multiplicative interactions between bullying victimization and substance use test whether the ratio of the odds ratio in Females (OR_F) versus males (OR_M), OR_F/OR_M is significantly >1. CONSORT guidelines recommend both absolute and relative measures (Moher et al., 2010). Therefore, we report additive interactions with absolute measures (risk differences) and multiplicative interactions with relative measures (odds ratios).

Methods

Dataset

Four waves of pooled data (2011, 2013, 2105, 2017) from 59,397 respondents to the Youth Risk Behavior Survey (YRBS) were used for this research (CDC, 2020b). The nationally representative YRBS dataset uses a complex three-stage cluster design to collect self-reported data from US high school students in grades 9 to 12. These paper and pencil surveys, oversample Black, Hispanic, Asian and mixed-race students and use sampling weights to adjust for oversampling and nonresponse. More information is available online about the questionnaire, sampling design, variable measurement and data collection methodology (CDC, 2020b). For the YRBS, student participants’ self-administered survey included more than 80 items including questions about their age, sex, race/ethnicity and a variety of health-related experiences and behaviors (CDC, 2020b). Due to the sampling design, sample weights are needed to estimate population statistics (CDC, 2020b).

Outcome Variables

The dependent variables for this research are alcohol use, binge drinking, cigarette use, e-vape use, and marijuana use within the past 30 days (CDC, 2020b). Respondents were asked, “During the past 30 days, on how many days did you have at least one drink of alcohol? Respondents who reported one or more days were coded as “Yes” for alcohol use, respondents who reported 0 days were coded as “No.” Respondents were asked, “During the past 30 days, on how many days did you have four or more drinks of alcohol in a row (if you are female) or five or more drinks of alcohol in a row (if you are male)? Respondents who reported one or more days were coded as “Yes” for binge drinking; respondents who reported 0 days were coded as “No.” (This question was only asked in YRBS 2017 (N = 14,765). Respondents were also asked, “During the past 30 days, how many times did you use marijuana?” Students who reported one or more times were coded as “Yes” for marijuana use; students who reported zero times were coded as “No”. “During the past 30 days, on how many days did you smoke cigarettes?” Students who reported one or more days were coded as “Yes” for cigarette use, students who reported zero times were coded as “No.” Respondents were also asked, “During the past 30 days, on how many days did you use an electronic vapor product?” Students who reported one or more days were coded as “Yes” for e-vape use; students who reported zero times were coded as “No”. (This question was only asked in YRBS 2015 and 2017 (N = 30,389).

Predictor Variables

The predictor variables were bullying victimization within the past 12 months, survey year, race/ethnicity, grade level and sex (CDC, 2020b). Respondents were asked two binary choice variables, 1) “During the past 12 months, have you ever been bullied on school property? and 2) “During the past 12 months, have you ever been electronically bullied? (Count being bullied through texting, Instagram, Facebook, or other social media)”. Students who replied “Yes” to either of these two questions were coded as “Yes” for Bullying Victimization, students who reported “No” to both questions were coded as “No”.

Race/ethnicity was recoded into four categories, Hispanic, non-Hispanic White, non-Hispanic Black, and non-Hispanic other (this includes all participants not included in the other three categories). Grade level and survey year were used as recorded. Bullying victimization, sex, interaction between bullying victimization and sex, race-ethnicity and grade level, and survey year were coded as categorical variables.

Analyses

Data were analyzed using R software and the R survey package to incorporate sampling design variables and sampling weights and generate nationally representative weighted estimates (Lumley, 2020; R Core Team, 2020). The R survey package function, “svypredmeans,” estimated the average predicted marginal percentages (adjusted for race/ethnicity, survey year, and participant grade level) of the dependent variables by bullying victimization and sex (Lumley, 2018). The R survey package function, “svycontrast,” estimated adjusted risk differences, odds ratios, and their respective confidence intervals as described (Lumley, 2018).

Results

There is a large gender disparity in the prevalence of bullying victimization (either school or electronic or both) among US high school students. The prevalence of bullying victimization within the past 12 months is significantly greater among female students (31.0%) than among male students (20.1%), RD = 10.9% [9.8, 12.0], t^RD = 19.40, p < .001, OR = 1.78 [1.69, 1.89], t^OR = 19.99, p < .001 (Table 1A). Among 8,278 female students in the sample who experience bullying victimization (2011–2017), 3,206 used alcohol, 1,333 used cigarettes, and 2,092 used marijuana during the 30 days preceding the survey (Table 1B). Among 5,448 male students in the sample who experience bullying victimization (2011–2017), 1,807 used alcohol, 955 used cigarettes, and 1,373 used marijuana during the 30 days preceding the survey. (Table 1B Among 4,306 female students in the sample who experience bullying victimization (2015–2017), 984 reported e-vape use during the 30 days preceding the survey (Table 1B). Among 1,370 male students in the sample who experience bullying victimization (2015–2017), 758 reported e-vape use during the 30 days preceding the survey (Table 1B). Finally, among 2,044 female students in the sample who experience bullying victimization (2017), 370 binge drank alcohol during the 30 days preceding the survey (Table 1B). Among 1,315 male students in the sample who experience bullying victimization (2017), 154 binge drank alcohol during the 30 days preceding the survey (Table 1B).

Table 1.

(A) Unweighted Number of Students and Weighted Percentage of Students who Experienced Bullying Victimization During Previous 12 Months (2011–2017) ^a.

Gender	N	Bullying Victimization		% (SE)	RD [95% CI]	t ^RD	p	OR [95% CI]	t ^OR	p
Gender	N	Yes (N)	No (N)	% (SE)	RD [95% CI]	t ^RD	p	OR [95% CI]	t ^OR	p
Female	28,663	8,278	20,385	31.0 (0.59)	10.9 [9.8, 12.0]	19.40	<.001	1.78 [1.69, 10.89]	19.99	<.001
Male	28,351	5,448	22,903	20.1 (0.43)

N = Unweighted number of sample respondents; % = weighted percentage of students who were bullied; SE = standard error of estimate; RD = risk difference; 95% CI = 95% confidence interval; t^RD = t statistic (risk difference); p = probability (significance level); t^OR = t statistic (odds ratio).

Does not include sample respondents with missing data on either sex of respondent or bullying victimization.

(B) Unweighted Number of High School Students Who Were Bullied and Had Alcohol, Cigarette, E Vape, or Marijuana Use.

Gender	2011–17				2015–17		2017
Gender	All	Alcohol use	Cigarette smoking	Marijuana use	All	E-Vape use	All	Binge drinking
Female	8,278	3,206	1,333	2,092	4,306	984	2,044	370
Male	5,448	1,807	956	1,370	2,776	758	1,315	154

Note. The unweighted number of sample respondents are reported to indicate sample size. E vape use was first measured in 2015, and binge drinking was first measured in 2017.

Results show that the relationship between bullying victimization and either alcohol use, binge drinking, cigarette use, e-vape use, or marijuana use, each within the 30 days preceding the survey administration, is each significantly greater among female students. There were significant positive additive interactions between female gender and bullying victimization on alcohol use, AI_RD (RD_F – RD_M) = 5.6% [2.7, 8.5], t ^AI_RD = 3.79, p < .001, on binge drinking, AI_RD = 4.5% [0.9, 8.2], t ^AI_RD = 2.47, p = .014, on cigarette use AI_RD = 2.2% [0.9, 7.2], t ^AI_RD = 2.24, p = .025, and on marijuana use, AI_RD = 4.4% [2.1, 6.7], t ^AI_RD = 3.74, p < .001, (t ^AI_RD = t statistic for additive interaction) (Table 2 and Figure 1). There was a positive but nonsignificant additive interaction between female gender and bullying victimization on e-vape use, AI_RD = 2.6% [-0.3, 5.5], t ^AI_RD = 1.77, p = .077 (Table 2 and Figure 1). There were significant multiplicative interactions between female gender and bullying victimization on alcohol use, MI_OR = 1.27 [1.12, 1.44], t ^MI_OR = 3.72, p < .001, on binge drinking, MI_OR = 1.39 [1.03, 1.88], t ^MI_OR = 2.18, p = .029, on cigarette use, MI_OR = 1.31 [1.13, 1.50], t ^MI_OR = 3.73, p < .001, on e-vape use, MI_OR = 1.27 [1.08, 1.50], t ^MI_OR = 2.90, p = .004, and on marijuana use, MI_OR = 1.32 [1.17, 1.49], t ^MI_OR = 4.54, p < .001; in all instances, odds ratios were significantly greater among females than males (Table 2 and Figure 2).

Table 2.

Adjusted Additive and Multiplicative Interactions between Female Gender × Bullying Victimization.

Outcome Variable: Illicit Substance Use.

Illicit substance use	AI_RD [95% CI]	t ^AI_RD	p	MI_OR [95% CI]	t ^MI_OR	p
Alcohol use	5.6 [2.7, 8.5]	3.79	<.001	1.27 [1.12, 1.44]	3.72	<.001
Binge drinking	4.5 [0.9, 8.2]	2.47	.014	1.39 [1.03, 1.88]	2.18	.029
Cigarette smoking	2.2 [0.9, 7.2]	2.24	.025	1.31 [1.13, 1.50]	3.73	<.001
E-vape use	2.6 [-0.3, 5.5]	1.77	.077	1.27 [1.08, 1.50]	2.90	.004
Marijuana use	4.4 [2.1, 6.7]	3.74	<.001	1.32 [1.17, 1.49]	4.54	<.001

Note. Substance use – substance use during the 30 days before survey completion. Alcohol use, cigarette smoking, and Marijuana use – data from YRBS 2011, 2013, 2015, 2017. E-vape use – data from YRBS 2015, 2017 pooled. Binge drinking – data from YRBS 2017. AI_RD = additive interaction risk difference (risk difference females - risk difference males); 95% CI = 95% confidence interval; t ^AI_RD = t statistic for AI_RD: female gender × bullying victimization (school or electronic); p = probability (significance level); MI_OR = multiplicative interaction ratio (odds ratio females/odds ratio males); t ^MI_OR = t statistic for MI_OR: female gender × bullying victimization (school or electronic).

Figure 1.

Relationship between bullying victimization and illict substance use within past 30 days by gender: adjusted risk difference (RD).

Figure 2.

Relationships between bullying victimization and illicit substance use within past 30 days by gender: adjusted odds ratio (OR).

Results show a significant relationship between bullying victimization and alcohol use, binge drinking, cigarette use, e-vape use, and marijuana use among all students as well as separately among male and female students; relationship effect sizes are significantly greater among female students (Table 3). Risk differences with bullying victimization within the past 12 months as the predictor variable (and no bullying victimization within the past 12 months as the reference group) were significantly greater than zero among male students, female students, and all students and significantly greater among female students (significant female gender × bullying victimization additive interactions); alcohol use (female = 12.4%, male = 6.8%, all students = 10.0%), binge drinking (female = 6.2%, male = 1.6%, all students = 4.5%), cigarette use (female = 7.7%, male = 6.8%, all students = 6.3%), marijuana use (female = 9.0%, male = 4.6%, all students = 6.6%). Risk differences (e-vape use) were significantly greater than zero for male students, female students, and all students, and non-significantly greater among female students than male; (female = 9.5%, male = 6.9%, all students = 7.6%) (Table 3 and Figure 1). Odds ratios with bullying victimization within the past 12 months as the predictor variable (and no bullying victimization within the past 12 months as the reference group) were significantly >1 among male students, female students, and all students and significantly greater among female students than male students (significant female gender × bullying victimization multiplicative interactions); alcohol use (female = 1.71, male = 1.34, all = 1.54), binge drinking (female = 1.61, male = 1.15, all = 1.43), cigarette use (female = 1.98, male = 1.52, all = 1.67), e-vape use (female = 1.88, male = 1.48, all = 1.59), marijuana use (female = 1.70, male = 1.28, all = 1.44 (Table 3 and Figure 2). (Confidence Intervals and t-statistics are displayed in Table only for brevity).

Table 3.

Multivariate Relationships between Bullying Victimization and Illicit Substance Use Within Past 30-Days by Gender.

Illicit substance use	Sex	BV % (SE)	No BV % (SE)	RD [95% CI]	t ^RD	p	OR [95%CI]	t ^OR	p
Alcohol use	All	41.6 (0.89)	31.6 (0.64)	10.0 [8.4, 11.6]	12.24	<.001	1.54 [1.44, 1.65]	12.50	<.001
	Female	43.3 (1.08)	31.0 (0.87)	12.4 [10.4, 14.4]	10.39	<.001	1.71 [1.57, 1.86]	12.24	<.001
	Male	39.0 (1.08)	32.3 (0.87)	6.8 [4.5, 9.1]	5.76	<.001	1.34 [1.22, 1.48]	5.86	<.001
Binge drinking	All	16.9 (0.82)	12.4 (0.87)	4.5 [2.5, 6.4]	4.52	<.001	1.43 [1.22, 1.69]	4.35	<.001
	Female	18.7 (1.10)	12.5 (1.06)	6.2 [3.7, 8.7]	4.85	<.001	1.61 [1.32, 1.96]	4.66	<.001
	Male	14.0 (1.08)	12.3 (0.99)	1.6 [-1.1, 4.4]	1.16	.248	1.15 [0.91, 1.47]	1.16	.247
Cigarette smoking	All	17.9 (0.69)	11.6 (0.54)	6.3 [5.3, 7.3]	12.57	<.001	1.67 [1.54, 1.80]	12.96	<.001
	Female	17.3 (0.83)	9.6 (0.56)	7.7 [6.4, 9.1]	11.19	<.001	1.98 [1.77, 2.21]	11.83	<.001
	Male	18.8 (1.88)	13.3 (0.61)	5.5 [4.1, 7.0]	7.66	<.001	1.52 [1.37, 1.68]	8.05	<.001
E-vape use	All	24.8 (1.34)	17.2 (0.88)	7.6 [5.9, 9.4]	8.52	<.001	1.59 [1.45, 1.75]	9.61	<.001
	Female	23.7 (1.46)	14.2 (0.80)	9.5 [7.3, 11.7]	8.35	<.001	1.88 [1.66, 2.13]	9.82	<.001
	Male	26.6 (1.61)	19.7 (1.12)	6.9 [4.5, 9.2]	5.73	<.001	1.48 [1.30, 1.67]	6.14	<.001
Marijuana use	All	26.8 (0.76)	20.2 (0.51)	6.6 [5.3, 7.9]	9.98	<.001	1.44 [1.35, 1.55]	10.51	<.001
	Female	26.7 (0.95)	17.6 (0.55)	9.0 [7.4, 10.6]	10.94	<.001	1.70 [1.56, 1.85]	12.00	<.001
	Male	27.1 (0.94)	22.4 (0.66)	4.6 [2.8, 6.5]	4.90	<.001	1.28 [1.16, 1.41]	5.03	<.001

Note. Alcohol use, cigarette smoking, and Marijuana use – pooled data from YRBS 2011, 2013, 2015, 2017. E-vape use – pooled data from YRBS 2015, 2017. Binge drinking – data from YRBS 2017. All = all students; BV = bullying victimization; No BV = no bullying victimization; % = average marginal predictions (percentages) of male (female) students who used substance during the past 30 days before survey administration (adjusted for grade level and race/ethnicity); SE = standard error of estimate; RD = risk difference; t ^RD = t statistic: effect size RD; p = probability (significance level); OR = odds ratio; 95% CI = 95% confidence interval; t ^OR = t statistic: effect size OR.

Discussion

This study strengthens previous research reporting the association of bullying victimization (either school or electronic bullying) with the increased risk of alcohol use, marijuana use, cigarettes use, and e-vape use within the previous 30 days among all students, and male and female students separately. Bullying victimization was significantly associated with binge drinking among female but not male students.

Apart from alcohol use (Chan & La Greca, 2016), this study is one of the first to examine whether gender moderates the relationship between bullying victimization and alcohol use. Chan & La Greca (2016) found that gender did not moderate the relationship between electronic bullying victimization and subsequent alcohol use or binge drinking (Chan & La Greca, 2016). The present research reports new findings showing that the effect of bullying victimization on substance use variables was significantly greater among female compared to male students for all but one substance use variable (exception e-vape use). (Note: Respondents were asked to report bullying victimization anytime during the past 12 months and substance use anytime during the past 30 days.)

Based on the general strain theory (Agnew, 1992), and a previous YRBS study (Pontes & Pontes, 2019), these results align with the hypothesis that females experiencing bullying may respond in more internalized, less violent reactions compared to males who have more externalized, more violent reactions (Broidy & Agnew, 1997; Piquero & Sealock, 2004). In this study, substance use may be considered a more internalized response (Cullen et al., 2008).

Importance of Reporting Additive Interactions

This research follows the STROBE guidelines to also report additive interactions and risk differences instead of only multiplicative interactions and odds ratios. Results show that bullying victimization is associated with an increase in the risk of alcohol use by 12.4% among female students compared to 6.8% among male students (Table 3 and Figure 1). Thus, bullying victimization (relative to no bullying victimization) was associated with alcohol use the previous 30 days among 124 additional female students, compared to 68 additional male students (per 1,000 male or female students, respectively). The additive interaction risk difference is RD^F − RD^M = 5.6%; the bullying victimization associated increase in the rate of alcohol use during the last 30 days per 1,000 females (124) was greater than the corresponding increase in the rate of alcohol use during the last 30 days per 1,000 males (68) by 56. For comparison, the bullying victimization associated increase in the rate of alcohol use during the last 30 days per 1,000 students of either sex was 100. Thus, additive interactions and risk differences provide measures that more relevant for public health than ratio measures and demonstrate the value of reporting risk differences by gender (Keyes & Galea, 2017).

For this study, with odds ratios, the effect of bullying victimization on e-vape use was significantly greater (significant multiplicative interaction) in females (OR = 1.88) than in males (OR = 1.48); with risk differences, the effect of bullying victimization on e-vape use was not significantly greater (nonsignificant additive interaction) in females (RD = 9.5%) than in males (RD = 6.9%). This is an example of how a relative measure (odds ratio), relative to absolute measure (risk difference) inflated the relative effect size among females who had lower prevalence of e-vape use in the no bullying victimization group (females, 14.2%, males, 19.7%).

Implications for Practice

As an adverse childhood experience, previous research has linked bullying victimization with many negative outcomes (Hertz et al., 2015, Leemis et al., 2019; Strohacker et al., 2019) that continues into early adulthood and beyond (Leadbeater et al., 2014). The reframing of bullying victimization from a “rite of passage” to an ACE has paralleled with the focused implementation of school-based bullying prevention programs. A recent review and meta-analysis (Gaffney et al., 2019) estimated the positive outcomes of 88 school bullying prevention programs, and found these programs significantly reduced both bullying perpetration and victimization. Many of the programs used a “whole school” approach, and some focused on both physical and relational bullying, but no gender specific focus was mentioned about any of these programs (Gaffney et al., 2019).

Although Healthy People 2020 set the goal to reduce bullying by 10%, overall, there has been no change to date (CDC, 2018). However, upon estimation by gender, school bullying victimization among males significantly decreased (16%) since 2009, but female bullying significantly increased (17%), which highlights the importance of prevention programs that address how male and female students experience bullying victimization differently (Pontes, Ayres, & Lewandowski, 2018). For example, school bullying prevention programs often focus upon more overt physical bullying (more common among male students) compared to relational bullying common among females (Nickerson, 2019). Therefore, further work is needed to address the more nuanced relational bullying victimization that is common among females, but is more difficult to address, and often overlooked.

Likewise, the research linking adolescent substance use and ACEs, including bullying victimization is limited. Too often the identification of substance use in children and adolescents is a result of a school or criminal justice intervention and is considered only a matter of delinquency. However, research exposes the relationship between bullying victimization (Goebert et al., 2011; Okafor et al., 2020; Priesman et al., 2018; Tharp-Taylor et al., 2009) and other ACEs to substance use (Carliner et al., 2016), and a trauma-informed approach is best practice (Ko et al., 2008; Listenbee & Torre, 2012). The Report of the Attorney General’s National Task Force on Children Exposed to Violence recommends a broad-based culturally congruent, trauma-informed approach for all agencies, organizations and individuals who encounter children exposed to violence. Specifically, they advocate,

Coordinated and adaptive approaches to improve the quality of trauma-specific treatments and trauma-focused services and their delivery by organizations and professionals across setting and disciplines to children exposed to violence. (Listenbee & Torre, 2012, p. 13).

Future research is needed to identify the best practice for trauma-informed interventions for children and adolescents who have been victimized by school or electronic bullying, or who have used illicit substances.

Limitations

This research study has limitations. The retrospective design relies upon self-reported answers by high school students about illicit substance use, which may be under-reported due to a social desirability bias. The cross-sectional design limits the results to be only correlational and not causal. The questions about school and electronic bullying refer to the previous 12 months, but the substance use questions refer to the previous 30 days, and as a secondary analysis, these questions cannot be altered. This study only differentiates gender as male or female and is not able to identify transgendered or other self-identification.

Footnotes

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.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Nancy M. H. Pontes

Author Biographies

Nancy M. H. Pontes, PhD, FNP-BC, FNAP, FAAN, is an Assistant Professor in the School of Nursing—Camden at Rutgers, The State University of New Jersey. She is a Family Nurse Practitioner, with experience working among vulnerable populations within diverse communities. Her research focuses on social determinants of health, and her current work explores victimization among adolescents and associated health behaviors and wellbeing.

Emily R. Strohacker, PhD, is a tenure-track Assistant Professor of Criminal Justice in the School of Public Affairs at Penn State Harrisburg where she has been a faculty member since 2017. She completed her Ph.D. in sociology at the University of Central Florida with concentrations in crime and deviance. Her areas of research include criminology, deviance, and victimology, specifically in the areas of cyber and sexual victimization.

Manuel C. F. Pontes, PhD, is a Professor in the College of Business, at Rowan University. His research focuses on the use of R for data analyses and visualization of large data sets using complex survey designs, including YRBS, MEPS, NHIS and BRFSS.

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