Examining the association between bullying victimization and prescription drug misuse among adolescents in the United States

Journal of Affective Disorders 259 (2019) 317–324

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Journal of Affective Disorders journal homepage: www.elsevier.com/locate/jad

Research paper

Examining the association between bullying victimization and prescription drug misuse among adolescents in the United States

T

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Philip Baiden , Savarra K. Tadeo School of Social Work, The University of Texas at Arlington, 211 S. Cooper St., Box 19129, Arlington, TX 76019, United States

A R T I C LE I N FO

A B S T R A C T

Keywords: Prescription drug misuse School bullying victimization Cyberbullying victimization Substance use

Background: Although studies have examined the association between bullying victimization and adolescent substance behaviors, there is a dearth of research investigating the association between bullying victimization and prescription drug misuse. The objective of this study was to examine the cross-sectional association between bullying victimization and prescription drug misuse among adolescents. Methods: Data for this study came from the 2017 Youth Risk Behavior Survey. A sample of 9974 adolescents aged 14–18 years (50.1% female) were analyzed using binary logistic regression with prescription drug misuse as the outcome variable and bullying victimization as the main explanatory variable. Results: Of the 9,974 adolescents, 13.1% misused prescription drugs. One in ten adolescents were victims of both school bullying and cyberbullying, 5.1% were victims of only cyberbullying, 9% were victims of only school bullying, and 75.8% experienced neither school bullying nor cyberbullying victimization. In the binary logistic regression model, adolescents who experienced both school bullying and cyberbullying victimization had 1.66 times higher odds of misusing prescription drugs (AOR = 1.66, p < .001, 95% CI = 1.34–2.06) and adolescents who experienced only school bullying victimization had 1.30 times higher odds of misusing prescription drugs (AOR = 1.30, p < .05, 95% CI = 1.02–1.64). Being lesbian, gay, or bisexual; feeling sad or hopeless; cigarette smoking; binge drinking; cannabis use; and illicit drug use were statistically significantly associated with prescription drug misuse. Conclusions: Understanding the association between bullying victimization and prescription drug misuse could contribute to early identification of adolescents who may misuse prescription drugs.

1. Introduction Prescription drug misuse (PDM) is generally defined to mean taking prescription drugs in a manner or dose other than prescribed or taking prescription drugs solely for the feelings and euphoria that it produce (i.e., to get high) (National Institute of Drug Abuse, 2018). PDM has been identified as a major public health concern in the United States (US) given the recent unprecedented opioid epidemic (Centers for Disease Control and Prevention, 2017; McCabe et al., 2017; Rudd et al., 2016; Stumbo et al., 2017). This unprecedented opioid epidemic in part has been attributed to the overprescribing of opioid analgesics in pain management (Ballantyne, 2017; Stumbo et al., 2017). This increase in overprescribing partly has also led to the rise in PDM among adolescents (Fortuna et al., 2010; Schepis et al., 2018) and an increase in adverse consequences such as overdose-related deaths and emergency room visitation (McCabe et al., 2017; Miech et al., 2015; Rudd et al., 2016).

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Data from the National Survey on Drug Use and Health (NSDUH) showed that in 2016, about 11.8 million individuals aged 12 and older misused prescription drugs during the past year (Substance Abuse and Mental Health Services Administration, 2017). Between 5–20% of adolescents in the US are known to have ever used prescription drugs for nonmedical purposes (Havens et al., 2011; McCabe et al., 2015; Zullig et al., 2015). The notion that prescription drugs are less harmful than street drugs and the fact that prescription drugs could be obtained from conventional sources have been cited as some of the reasons for the misuse of prescription drugs (Baiden et al., 2019a; Schulenberg et al., 2018; Stewart et al., 2013). Other reasons why adolescents misuse prescription drugs include to self-medicate to relieve physical pain symptoms, the desire to feel good or get high, and combining prescription drugs with other drugs to increase their effects (Johnston et al., 2018; McCabe et al., 2019; Stewart and Baiden, 2013). PDM has been linked to a number of adverse outcomes including poor academic performance (Arria et al., 2017; Garnier-Dykstra et al.,

Corresponding author. E-mail address: [email protected] (P. Baiden).

https://doi.org/10.1016/j.jad.2019.08.063 Received 28 March 2019; Received in revised form 10 July 2019; Accepted 18 August 2019 Available online 19 August 2019 0165-0327/ © 2019 Elsevier B.V. All rights reserved.

Journal of Affective Disorders 259 (2019) 317–324

P. Baiden and S.K. Tadeo

2015, 2017; Tharp-Taylor et al., 2009).

2012), delinquent and criminal behavior (Vaughn et al., 2012), and risky sexual behaviors such as early sexual debut (Berenson and Rahman, 2011) and unprotected sexual intercourse (Johnson et al., 2013). PDM among adolescents is also known to co-occur with other substances such as cigarette smoking (Cerdá et al., 2018), alcohol use (Teesson et al., 2012), cannabis use (Baiden et al., 2014; McCabe et al., 2015), and illicit drug use (Heck et al., 2014; Simoni-Wastila et al., 2004). The extant literature also shows that lesbian, gay, bisexual, and transgender adolescents are at increased risk of misusing prescription drugs as compared to their heterosexual counterparts (Heck et al., 2014; Li et al., 2018). Ford and McCutcheon (2012), in their study, also found that adolescents who reported having symptoms of depression were more than four times more likely to misuse prescription drugs. Although past studies have focused on understanding the effects of child abuse and neglect on PDM (see e.g., Conroy et al., 2009; Ghertner et al., 2018; Guo et al., 2018; Lei et al., 2018), there is a dearth of research investigating the association between bullying victimization and PDM among adolescents (Hertz et al., 2015). Bullying is a major public health concern in the US (Jochman et al., 2017; Kaynak et al., 2015; Reisner et al., 2015; Williford and Zinn, 2018; Zhang et al., 2019). The term bullying is often defined to mean “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, 2014, p. 7). Estimates show that between one in ten to one in three adolescents are bullied in school at least once during the past month, depending on the sample and instrument used in measuring bullying (Baiden et al., 2017; Datta et al., 2016; Huang and Cornell, 2015; Lessne and Yanez, 2016). The past month prevalence of cyberbullying victimization among adolescents is also estimated to range between 10–20% (Carter and Wilson, 2015; Cénat et al., 2015; Hertz et al., 2015). Various studies have found the negative effects of school bullying on substance use behaviors such as cigarette smoking (Case et al., 2016; Jiang et al., 2016), alcohol use (Lambe and Craig, 2017; Rosario et al., 2014), cannabis use (Mackie et al., 2013), and illicit drugs use (McGee et al., 2005; Zapolski et al., 2018). Other studies have also found a strong association between cyberbullying victimization and substance use outcomes (Cénat et al., 2018; Goebert et al., 2011; Kim et al., 2019). Priesman et al. (2018) recently examined data from the 2013 Youth Risk Behavior Survey (YRBS) and found that adolescent victims of both school bullying and cyberbullying were more likely to engage in binge drinking and use cannabis. Various systematic reviews and meta-analytic studies have also found support for the link between school bullying victimization and substance use among adolescents (e.g., Maniglio, 2015; Valdebenito et al., 2015). This study is informed by the self-medication theory (Khantzian, 1997). Self-medication theory posits that substance use problems may be part of an individual's response to emotional or psychological distress or an individual's decision to use a particular substance is based on the substance's effect on the subjective affect regulation mechanism (Khantzian, 1997). Applying the self-medication theory to the present study, PDM may be viewed as a response to the emotional or psychological distress arising out of the experience of bullying victimization. This view is supported by past research which suggests that adolescent victims of bullying are at increased risk of experiencing higher levels of emotional distress, feelings of loneliness, sadness, and hopelessness (Idsoe et al., 2012; Lardier et al., 2016; Reed et al., 2015; Sulkowski and Simmons, 2018). A strong statistically significant association has also been observed between these mental health symptoms and adolescent substance use behaviors (Baiden et al., 2019b; Feingold et al., 2018; Gower et al., 2018; Stewart et al., 2013). A number of studies have found utility for the self-medication theory in understanding substance use behaviors among adolescents with a history of child abuse and neglect (Gomez et al., 2015; Vilhena-Churchill and Goldstein, 2014) and adolescent victims of bullying (Maniglio,

1.1. Current study Although studies have examined the effects of bullying victimization on adolescent substance behaviors, there is a dearth of research investigating the association between bullying victimization and PDM (Hertz et al., 2015). Understanding the association between bullying victimization and PDM would allow for early identification of adolescents who might be at risk of misusing prescription drugs and ultimately prevent overdose-related deaths. School bullying and cyberbullying victimization may serve as important factors associated with PDM among adolescents particularly given that adolescent victims of school bullying have been found to be at increased risk of experiencing emotional and psychological distress (Baiden et al., 2017; Lardier et al., 2016; Thomas et al., 2016). Therefore, drawing on a large nationally representative sample of adolescents, this study seeks to examine the cross-sectional association between bullying victimization and PDM among adolescents. In this study, PDM was defined as having misused prescribed pain medications on at least one occasion. We hypothesized that there would be an association between bullying victimization and PDM. Given the co-occurrence of PDM with other substances, we also hypothesized that there would be an association between cigarette smoking, binge drinking, cannabis use, and illicit drug use and PDM. 2. Methods 2.1. Data source and participants Data for this study came from the 2017 YRBS, a cross-sectional national study conducted biennially by the Centers for Disease Control and Prevention (CDC) to examine health-risk behaviors that contribute to the leading causes of death and disability among youth in the US (YRBS, 2017). The YRBS recruited 9th-12th graders from both public and private schools to complete self-administered surveys. The YRBS utilized a three-stage cluster sample design to create a nationally representative sample of high school students. Detailed information about the YRBS including the objectives, methodology, and sampling procedure is available at www.cdc.gov/yrbss and in other publications (Brener et al., 2013; Kann et al., 2018). The study protocol for conducting the YRBS was approved by the CDC's Institutional Review Board (IRB), and the publicly available data has been de-identified (Brener et al., 2013); hence, no additional IRB approval was required. A sample of 9974 respondents was analyzed. 2.2. Variables 2.2.1. Outcome variable The outcome variable examined in this study is PDM and was measured as a binary variable based on response to the question “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)”. Following the recommendation of other scholars (Cerdá et al., 2018; Groenewald et al., 2019; Kann et al., 2016), adolescents who took prescription pain medicine without a doctor's prescription at least once were recoded as 1; whereas those who have never taken prescription pain medicine without a doctor's prescription were coded as 0. 2.2.2. Explanatory variables The main explanatory variable examined in this study is bullying victimization and was measured based on two questions. The 2017 YRBS defined the term bullying to mean, “Bullying is when one or more students tease, threaten, spread rumors about, hit, shove, or hurt another student over and over again. It is not bullying when two students 318

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of about the same strength or power argue or fight or tease each other in a friendly way.” School bullying victimization was measured based on response to the question “During the past 12 months, have you ever been bullied on school property?” whereas cyberbullying victimization was measured based on response to the question “During the past 12 months, have you ever been electronically bullied? (Count being bullied through texting, Instagram, Facebook, or other social media).” Responses to both questions were coded as yes or no. Based on these two questions, a nominal variable with four categories was created: “0 = not bullied”, “1 = victim of only school bullying”, “2 = victim of only cyberbullying”, and “3 = victim of both school bullying and cyberbullying”. Adolescents who answered yes to school bullying and yes to cyberbullying were recoded as 3; adolescents who answered no to school bullying but yes to cyberbullying were recoded as 2; adolescents who answered yes to school bullying but no to cyberbullying were recoded as 1; and adolescents who answered no to both school bullying and cyberbullying were recoded as 0.

Table 1 Sample characteristics (n = 9974). Variables Outcome variable Misused prescription drugs No Yes Explanatory variables Victim of bullying Not bullied School bullying only Cyberbullying only Both school bullying and cyberbullying Age 14 years 15 years 16 years 17 years 18 years or older Sex Male Female Lesbian, gay, or bisexual No Yes Grade level 9th grade 10th grade 11th grade 12th grade Race/ethnicity Non-Hispanic White Black/African-American Hispanic/Multiple Hispanic Other Felt sad or hopeless No Yes Cigarette use No Yes Binge drinking No Yes Cannabis use No Yes Illicit drug use No Yes

2.2.3. Covariates Covariates examined included feeling sad or hopeless, cigarette smoking, binge drinking, cannabis use, and illicit drug use. Adolescents who answered “yes” to the question “During the past 12 months, did you ever feel so sad or hopeless almost every day for two weeks or more in a row that you stopped doing some usual activities?” were coded as 1; whereas those who answered “no” were coded as 0. Adolescents who smoked cigarette at least once during the past 30 days were considered smokers and were coded as 1; otherwise, they were considered as nonsmokers and were coded as 0. Adolescents who had 4 or more alcoholic drinks (for a female) or 5 or more alcoholic drinks (for a male) in a single seating within a couple of hours at least once during the past 30 days were considered to have engaged in binge drinking and were coded as 1; otherwise they were coded as 0. Adolescents who reported ever using cannabis at least once were coded as 1; whereas adolescents who have never used cannabis were coded as 0. A measure of illicit drug use was included as a binary variable (0 = no illicit drug use versus 1 = illicit drug use) based on a positive response to ever having used any of the following illicit drugs: cocaine (powder, crack, or freebase); inhalants (glue, aerosol spray cans, paints); heroin (smack, junk, or China White); methamphetamines (speed, crystal, crank, or ice); ecstasy (MDMA); hallucinogenic drugs (LSD, acid, PCP, angel dust, mescaline, or mushrooms); synthetic marijuana (K2, Spice, fake weed, King Kong, Yucatan Fire, Skunk, or Moon Rocks); and steroid pills. 2.2.4. Demographic variables The study controlled for the following demographic variables. Age was measured in years whereas sex was coded as “0 = male” and “1 = female”. Adolescents who self-identified as lesbian, gay, or bisexual were coded as 1; otherwise, they were coded as 0. Grade level was coded into “9th grade”, “10th grade”, “11th grade”, and “12th grade”. Race/ethnicity was coded as a nominal variable into the following categories “0 = non-Hispanic White”, “1 = Black/African American”, “2 = Hispanic/Multiple Hispanic”, “3 = Other”.

N (%)

8671 (86.9) 1302 (13.1)

7557 (75.8) 901 (9.0) 513 (5.1) 1003 (10.1) 1118 2507 2537 2458 1353

(11.2) (25.1) (25.4) (24.6) (13.7)

4980 (49.9) 4994 (50.1) 8607 (86.3) 1366 (13.7) 2699 2552 2419 2304

(27.1) (25.6) (24.2) (23.1)

5441 1168 2312 1053

(54.5) (11.7) (23.2) (10.6)

6879 (69.0) 3094 (31.0) 9189 (92.1) 785 (7.9) 8635 (86.6) 1339 (13.4) 6569 (65.9) 3404 (34.1) 8682 (87.0) 1291 (13.0)

operating characteristic (ROC) curve for binary outcomes (Cook, 2008). Generally, the area under the ROC curve ranges from 0.5 to 1.0, with a value of 1 indicating a perfect fit model, whereas values closer to 0.5 indicate that the model is no better than that which could have been obtained by chance (Cook, 2008). Adjusted odds ratios (AOR) are reported together with their 95% Confidence Intervals (C.I.). Variables were considered statistically significant if the p-value was less than 0.05. Stata's “svy” command was used to account for the weighting and complexity of the sampling design employed by the YRBS. All analyses were performed using Stata version 15 (Stata Corp., College Station, Texas, USA).

2.3. Data analyses Data were analyzed using descriptive, bivariate, and multivariate analytic techniques. The general distribution of all the variables included in the analysis was first examined using percentage. Next, we examined the bivariate association between PDM and the study variables using Pearson chi-square test of association. The main analysis involves the use of binary logistic regression to examine the association between bullying victimization and PDM while controlling for the effects of demographic factors, feeling sad or hopeless, and substance use factors. The proportion of variance in PDM explained by the set of factors was assessed using the pseudo R square. The predictive performance of the model was estimated using the area under the receiver

3. Results 3.1. Sample characteristics Table 1 shows the general distribution of the study variables. Of the 9974 adolescents, 13.1% misused prescribed pain medications on at least one occasion. One in ten adolescents (10.1%) were victims of both school bullying and cyberbullying, 5.1% were victims of only cyberbullying, 9% were victims of only school bullying, and 75.8% 319

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Table 2 Bivariate association between PDM and sample characteristics (n = 9974). Variables

Victim of bullying Not bullied School bullying only Cyberbullying only Both school and cyberbullying Age 14 years 15 years 16 years 17 years 18 years or older Sex Male Female Lesbian, gay, or bisexual No Yes Grade level 9th grade 10th grade 11th grade 12th grade Race/ethnicity Non-Hispanic White Black/African-American Hispanic/Multiple Hispanic Other Felt sad or hopeless No Yes Cigarette use No Yes Binge drinking No Yes Cannabis use No Yes Illicit drug use No Yes

χ2 value

PDM No (%)

Table 3 Multivariate logistic regression results predicting the likelihood of PDM (n = 9974). Variables

AOR (95% C.I.)

p-value

Age in years Sex (Male) Female Lesbian, gay, or bisexual (No) Yes Grade level (9th grade) 10th grade 11th grade 12th grade Race/ethnicity (non-Hispanic White) Black/African-American Hispanic/Multiple Hispanic Other Felt sad or hopeless (No) Yes Cigarette smoking (No) Yes Binge drinking (No) Yes Cannabis use (No) Yes Illicit drug use (No) Yes Victim of bullying (No) School bullying only Cyberbullying only Both school and cyberbullying Pseudo R square Area under the ROC curve

0.93 (0.82–1.05)

.235

1.01 (0.87–1.16)

.930

1.23 (1.02–1.48)

.025

1.09 (0.85–1.39) 1.14 (0.83–1.56) 1.43 (0.95–2.16)

.491 .430 .085

1.03 (0.84–1.27) 0.85 (0.72–1.01) 1.08 (0.86–1.36)

.770 .068 .502

1.92 (1.66–2.23)

.001

2.23 (1.81–2.76)

.001

2.18 (1.82–2.60)

.001

2.69 (2.28–3.16)

.001

4.34 (3.69–5.09)

.001

1.30 (1.02–1.64) 0.84 (0.62–1.15) 1.66 (1.34–2.06) 23.74 82.79%

.033 .283 .001

Yes (%) 204.43 (p < .0001)

88.2 84.3 85.2 73.3

10.8 15.7 14.8 26.7

90.6 88.6 87.8 84.6 83.5

9.4 11.4 12.2 15.4 16.5

88.2 85.7

11.8 14.3

45.63 (p = .0081)

13.40 (p = .0011)

71.67 (p < .0001) 88.1 79.8

11.9 20.2

89.8 87.8 85.5 84.2

10.2 12.2 14.5 15.8

39.73 (p = .0033)

9.74 (p = .1784) 86.5 89.8 86.9 86.1

13.5 10.2 13.1 13.9

91.2 77.5

8.8 22.5

353.80 (p < .0001)

Notes: Reference category is identified in bracket; AOR = Adjusted Odds Ratios; CI = Confidence Intervals.

1050.86 (p < .0001) 90.1 49.5

9.9 50.5

90.9 61.4

9.1 38.6

94.7 71.9

5.3 28.1

92.2 51.4

7.8 48.6

identified as lesbian, gay or bisexual (20.2%) compared to 11.9% of adolescents who self-identified as heterosexual misused prescription drugs (χ2(1) = 71.67, p < .0001). About 23% of adolescents who felt sad or hopeless compared to 8.8% of adolescents who did not feel sad or hopeless misused prescription drugs (χ2(1) = 353.80, p < .0001). Adolescents were also more likely to misuse prescription drugs if they smoke cigarettes, binge drink, had ever used cannabis, or had ever used illicit drugs.

890.66 (p < .0001)

1022.99 (p < .0001)

1653.11 (p < 0.0001)

experienced neither school bullying nor cyberbullying. The sample was evenly distributed by sex with 50.1% being female, and 13.7% of the adolescents self-identified as lesbian, gay, or bisexual. Almost a third (31%) of the adolescents felt sad or hopeless almost every day for two weeks or more in a row. About 8% of the adolescents currently smoke cigarettes, 13.4% binge drink, 34.1% had ever used cannabis, and 13% had ever used illicit drugs.

3.3. Logistic regression examining the association between bullying victimization and PDM Model fitness indices indicated that the multivariate model was fit, and the variables included made significant contributions to the model. Based on the pseudo R square, all the variables together explained 23.74% of the variance in PDM. Based on the area under the ROC curve, 82.79% of adolescents were correctly classified into PDM versus no PDM. Table 3 shows the multivariate binary logistic regression results examining the association between bullying victimization and PDM. Age, sex, grade level, and race/ethnicity were not statistically significantly associated with PDM. However, adolescents who self-identified as lesbian, gay, or bisexual had 1.23 times higher odds of misusing prescription drugs when compared to their heterosexual peers (AOR = 1.23, p < .05, 95% CI = 1.02–1.48). Odds were almost doubled for adolescents who felt sad or hopeless to misuse prescription drugs when compared to their counterparts who did not feel sad or hopeless (AOR = 1.92, p < .001, 95% CI = 1.66–2.23). Adolescents were more likely to misuse prescription drugs if they smoke cigarettes, binge drink, or use cannabis. Adolescents who had ever used illicit drugs were 4.34 times more likely to misuse prescription drugs when compared to their peers who have never used illicit drugs (AOR = 4.34, p < .001, 95% CI = 3.69–5.09). Compared to adolescents who

3.2. Bivariate association between prescription drug misuse and sample characteristics Table 2 shows the bivariate association between PDM and the study variables. More than one in four adolescent victims of both school bullying and cyberbullying (26.7%) misused prescription drugs compared to 14.8% of adolescents who experienced only cyberbullying victimization, 15.7% of adolescents who experienced only school bullying victimization, and 10.8% of adolescents who experienced neither school bullying nor cyberbullying (χ2(3) = 204.43, p < .0001). Age and grade level were both positively associated with PDM with older adolescents and adolescents in upper grades more likely to misuse prescription drugs. The proportion of adolescent females that misused prescription drugs (14.3%) was statistically significantly greater than the proportion of adolescent males that misused prescription drugs (11.8%; χ2(1) = 13.40, p = .0011). One in five adolescents who self320

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medication to manage pains and psychological problems more often than their counterparts who were not bullied. They also found that the increased in the odds of medication use was independent of symptom severity among those bullied (Due et al., 2007). Also, Garmy et al. (2019) recently examined the association between school bullying and use of analgesics among students aged 11–15 years from Iceland and found that being bullied was associated with double the odds of use of analgesics, net of the effects of age, gender, socioeconomic status, and pain severity. The positive association between school bullying and PDM uncovered in our study is also consistent with past studies that suggest that adolescent victims of school bullying may engage in substance use in order to self-regulate trauma-related maladaptive thoughts (Hertz et al., 2015; Mereish et al., 2017). In support of the self-medication theory, Müller et al. (2015) also found that individuals with a history of sexual abuse manifest more severe responses to symptoms of posttraumatic stress which in turn, make them more vulnerable to increased use of alcohol and other illicit drugs to selfmedicate. Confounding factors such as genetics, historical factors, or family history of substance use may also explain the association between bullying victimization and PDM. It is possible that adolescents who have been bullied may resort to the use of prescription drugs in dealing with the pain from being bullied. Researchers have long conceptualized bullying victimization as a stressful life event with enduring pain that sometimes lasts for decades (Sulkowski and Simmons, 2018; Vaillancourt et al., 2011). For instance, one victim of bullying described the enduring pain of his experience by saying “I feel like, emotionally, they [his bullies] have been beating me with a stick for 42 years” (Vaillancourt et al., 2013, p. 242). The bullying experienced by this participant was as a result of school officials allowing the publication of his picture in the high school yearbook with the caption “Fag” (Vaillancourt et al., 2013). Vaillancourt et al. (2011) referred to the pain that follows the feelings and experiences associated with bullying as social pain, a humiliating experience that is not easy to forget. In the YRBS, there are no measures of pain or pain-related factors; hence, we were unable to control for the effects of pain or painrelated variables to understand their effects on PDM. This is an important avenue of research for future studies to explore. The observed association between feeling sad or hopeless and PDM is consistent with the self-medication theory and studies that have found support for the association between feelings of sadness or hopelessness and substance use behaviors (Reed et al., 2015; Stewart et al., 2011). Feelings of hopelessness is a precursor for depression (Joiner Jr, 2000), and for individuals feeling hopeless or depressed, engaging in substance use may serve as an escape from their current mental state (Zullig and Divin, 2012). Indeed, the co-occurrence of depressive symptomatology and substance use disorder has been well established, with prevalence estimates ranging between 11–27% in community samples (Hasin et al., 2005; Nesvåg et al., 2015). Lord et al. (2009), in their study, found that the quest to manage symptoms of depression was one of the motives cited for the misuse of prescription drugs among college students. It is possible that adolescents who reported feeling sad or hopeless may be using prescription drugs as a maladaptive coping strategy in dealing with their feelings of sadness or hopelessness. Various studies have also observed associations between depressive symptoms and the use of maladaptive coping strategies such as substance use (Adan et al., 2017; Bettis et al., 2016). Another important finding of this study is that PDM co-occurs with other substances such as cigarette smoking, binge drinking, cannabis use, and illicit drug use. This finding corroborates past studies that have found PDM co-occurs with other substances (Housman and Williams, 2018; Subramaniam et al., 2010; Veliz et al., 2016). Analyzing data from the Monitoring the Future (MTF), McCabe et al. (2015) found that among past-year prescription drug abusers, 51% co-ingested prescription drugs with cannabis and 48% co-ingested prescription drugs with alcohol. Schepis et al. (2016) also found that almost three out of four adolescents nonmedical tranquilizer (e.g., Xanax, Valium, Ativan) users

experienced neither school bullying nor cyberbullying, adolescents who experienced both school bullying and cyberbullying victimization had 1.66 times higher odds of misusing prescription drugs (AOR = 1.66, p < .001, 95% CI = 1.34–2.06) and adolescents who experienced only school bullying victimization had 1.30 times higher odds of misusing prescription drugs (AOR = 1.30, p < .05, 95% CI = 1.02–1.64). Adolescents who experienced only cyberbullying victimization were not statistically significantly different from their peers who experienced neither school bullying nor cyberbullying. 4. Discussion Drawing on the self-medication theory (Khantzian, 1997), the objective of this paper was to examine the cross-sectional association between bullying victimization and PDM after adjusting for the effects of other key factors associated with substance use. We found that 13.1% of the adolescents misused prescription drugs. Controlling for the effects of other factors, we found statistically significant association between experiences of both school bullying and cyberbullying victimization and experiences of only school bullying victimization and PDM. Being lesbian, gay, or bisexual; feeling sad or hopeless, cigarette smoking, binge drinking, cannabis use, and illicit drug use were also statistically significantly associated with PDM. The finding that 13.1% of adolescents misused prescription drugs is fairly consistent with some prior research (Havens et al., 2011; McCabe et al., 2015) and at the same time lower than what has been found by other scholars. For instance, a recent study by Groenewald et al. (2019) that used data from the AD Health Study found that 28% of adolescents reported ever misusing prescription drugs. In the current study, prescription drug misuse was defined as having misused prescribed pain medications on at least one occasion. Differences in prevalence estimates of PDM could be attributed in part to the fact that currently, there is no universally accepted definition of what constitutes PDM (Barrett et al., 2008; McHugh et al., 2015). Other factors such as time frame being assessed (e.g., past month, past year, lifetime) and unclear boundaries between what constitutes “appropriate” use versus “inappropriate” use or misuse, and motives for use may also contributes to variations in prevalence rates (Barrett et al., 2008; McHugh et al., 2015). The lack of a universally accepted definition of PDM thus makes it difficult to compare results across studies. The proportion of adolescent victims of school bullying or cyberbullying is also fairly consistent with past studies that have found prevalence estimates of school bullying and cyberbullying to range between 10 and 30% (Baiden et al., 2017; Gini and Espelage, 2014; Maniglio, 2015). For instance, Hertz et al. (2015) in their study found that 9.4% of adolescents reported experiencing school bullying and cyberbullying victimization, 10.8% experienced only school bullying victimization, and 6.8% experienced only cyberbullying victimization. Numerous studies have shown that school bullying and cyberbullying victimization have deleterious effects on physical health and mental health well-being (Cohen and Kendall, 2015; Cole et al., 2014; Finkelhor et al., 2005; Hamilton et al., 2016; Murphy et al., 2015; Nansel et al., 2001). Prior research also suggests that school bullying victimization increases substance use behaviors (Baiden et al., 2019c), which is employed as a coping mechanism in response to managing the stress and trauma that follows bullying. Nevertheless, few studies have examined the association between bullying victimization and PDM among adolescents (Hertz et al., 2015). The finding regarding the association between bullying and PDM extends previous studies that have found a positive association between school bullying and cyberbullying victimization and risky health behaviors, such as binge drinking (Priesman et al., 2018) and cannabis use (Mackie et al., 2013). Regarding the association between bullying victimization and PDM, Due et al. (2007) in their study found that controlling for demographic, social class, and other mental health factors, adolescent victims of school bullying had higher odds of using 321

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P. Baiden and S.K. Tadeo

CRediT authorship contribution statement

simultaneously co-ingested these medications with at least one other substance, mainly alcohol, cannabis, and amphetamine. Combining other substances with PDM could pose a major challenge in identifying drug-specific problems resulting from PDM (Schepis et al., 2016). It is important, therefore, to understand the concurrent effects of other substances on PDM. Such an understanding is important given that the use of multiple substances could lead to an increased risk for adverse drug events (Hilt et al., 2014).

Philip Baiden: Conceptualization, Data curation, Formal analysis, Writing - original draft, Writing - review & editing. Savarra K. Tadeo: Writing - original draft, Writing - review & editing. Declaration of Competing Interest The authors declare that they have no conflicts of interests with respect to the authorship and/or the publication of this paper.

5. Limitations

Acknowledgements

There are some limitations with the current study that are worth noting. First, the use of secondary data limits our ability to examine other theoretically relevant factors that are known to influence PDM, such as psychiatric/neuropsychiatric symptoms, conduct problems, and pain-related factors. As noted earlier, we uncovered a statistically significant association between feeling sad or hopeless, substance use, and PDM. Though important, it is possible that adolescents who experienced bullying victimization also experience feelings of sadness or hopelessness and consequently resort to PDM. However, given the cross-sectional nature of the data, we were unable to ascertain if this indeed is the case. Additional studies that follow adolescents over time is needed to establish the temporal order between bullying victimization, feelings of sadness or hopelessness, chronic pain, and PDM. The cross-sectional nature of the data also limits our ability from making any causal claims between PDM and the study variables. Thus, only an association can be concluded. It is possible that some adolescents may have misused prescription drugs prior to them experiencing bullying victimization or engaging in the use of other substances. A study utilizing longitudinal design is needed to establish the temporal order between bullying victimization, feeling sad or hopeless, substance use, and the onset and maintenance of PDM. Also, longitudinal studies might help to empirically test how all these health risk behaviors are interrelated and what interventions might be helpful in breaking the link between bullying victimization and PDM. Such studies over time can also help us understand other plausible factors related to the onset of PDM. Lastly, although nationally representative, data for this study is based on self-reports and may be subject to recall bias. However, the possibility of recall bias or false reporting was addressed in the YRBS by screening the data for responses that conflict in logical terms (Brener et al., 2013). For instance, if a student responds to one question that he or she has never smoked but then responds to a subsequent question that he or she has smoked two cigarettes during the previous 30 days, the processing system sets both responses to missing, and data are not imputed. For instance, in the 2011 YRBS, 179 logical edits were performed on each standard questionnaire, and a total of 78 representing less than 1% of questionnaires in the national survey failed quality-control checks and therefore were excluded from the dataset (Brener et al., 2013). In conclusion, drawing on a large nationally representative sample of adolescent high school students, the findings of the present study demonstrate an association between bullying victimization and PDM, and this relationship is independent of feeling sad or hopeless, other substance use behaviors, and other factors known to be associated with PDM. Understanding the association between bullying victimization and PDM could contribute to the early identification of adolescents who are likely to engage in PDM. Such an understanding could also help in prevention and intervention efforts to reduce PDM and its overdoserelated deaths.

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