Factors Associated With Bullying Victimization and Bullying Perpetration in Children and Adolescents With ADHD: 2016 to 2017 National Survey of Children’s Health

Abstract

Objective:

To identify characteristics associated with bullying involvement in pediatric ADHD.

Methods:

Data from the 2016 to 2017 National Survey of Children’s Health for children aged 6 to 17 years with ADHD were evaluated to assess the association between parent-reported bullying victimization or perpetration and the following potential predictors: demographic characteristics, family factors, school factors, and child conditions/behaviors.

Results:

Among children with ADHD, 46.9% were bullying victims and 16.2% were perpetrators. Factors associated with victimization included having family financial strain, developmental delay or intellectual disability, friendship difficulties, and school reports about problems. Factors linked to perpetration included being male, receiving government assistance, lack of school engagement, school reports about problems, and having difficulties with friendships, staying calm, and arguing.

Conclusions:

Children with ADHD frequently were bullying victims and sometimes bullying perpetrators. Factors related to family financial strain, developmental disabilities, emotional regulation, peer relationships, and school functioning may help to identify risk for bullying and opportunities for anti-bullying interventions.

Keywords

ADHD-associated problems bullying behavioral interventions

Introduction

Bullying (defined as experiencing unwanted, purposeful, repeated aggression over time involving a power imbalance by one or more people (Gladden et al., 2014; Olweus & Limber, 1983)) is a serious public health problem occurring more commonly in children with ADHD than in the general pediatric population. Bullying involvement can include being a bullying victim (BV) or bullying perpetrator (BP) or both BV and BP (Chou et al., 2018); some studies have shown bullying involvement rates as high as 58% in children with ADHD versus 35% in the general population (Chou et al., 2018; Fogler et al., 2022; Wiener & Mak, 2009). ADHD and bullying involvement share heightened risk for a range of negative outcomes—including impaired peer relationships, poor academic performance (Grygiel et al., 2018; Oliveira et al., 2018; Van Cleave & Davis, 2006), mental health disorders, and substance abuse (Craig & Pepler, 2003; Houbre et al., 2006; Kumpulainen et al., 1998)—underscoring the need for bullying prevention programs in this vulnerable population (Taylor et al., 2010).

Identifying and targeting factors that are associated with an increased prevalence of BV or BP in children with ADHD could inform the development of effective interventions. However, prior studies of bullying involvement and ADHD with severity and treatment-related factors have been inconclusive. For example, some studies have found no association between ADHD symptom severity and bullying involvement (Chou et al., 2018; Efron et al., 2021; Fite et al., 2014; Verlinden et al., 2015), while other studies report that children with severe ADHD symptoms are more likely to be perpetrators, as well victims, of bullying (Holmberg & Hjern, 2008; Yang et al., 2013; Yen et al., 2014). The relationship between ADHD medication treatments and BV or BP is similarly unclear (Efron et al., 2021). Some studies indicate that taking medications is associated with a greater risk for bullying (Epstein-Ngo et al., 2016; Unnever & Cornell, 2003). In contrast, other studies showed that stimulants can decrease negative social behaviors (Whalen et al., 1989) and protect against negative functional social outcomes such as criminality (Boland et al., 2020), although these studies did not specifically evaluate the link between bullying involvement and ADHD medications. Little is known whether ADHD behavioral treatment impacts bullying involvement. In samples of typically developing children, a number of demographic as well as child, family, and school-related factors have been linked to increased BV rates. Some BV risk factors in general pediatric samples such as presence of internalizing and other mental disorders, developmental and learning disorders, and lack of supportive friends (Adams et al., 2011; Aguado-Gracia et al., 2021; Bacchini et al., 2008; Cardoos & Hinshaw, 2011; Lebrun-Harris et al., 2019) are also associated with increased risk of BV in children with ADHD (Becker et al., 2016; Cardoos & Hinshaw, 2011; Fogler et al., 2022; Wolke & Lereya, 2015). However, other characteristics appear to portend differential risk for children with and without ADHD. For example, in the general population, boys tend to have higher rates of BV than girls (Melnick & Hinshaw, 2000; Roland & Idsøe, 2001; Steinfeldt et al., 2012), while several studies of children with ADHD found no association with gender (Becker et al., 2016; Chou et al., 2018; Efron et al., 2021; Fogler et al., 2022; Gardner & Gerdes, 2015). In addition, although a meta-analysis of 28 non-ADHD samples found a small increased risk of BV in children with low socioeconomic status, two prior studies of children with ADHD did not link economic disadvantage and BV (Efron et al., 2021; Tippett & Wolke, 2014). Other factors associated with a higher likelihood of being bullied in samples of children with typical development—such as increased family strain (e.g., living in a single parent household (Flouri & Buchanan, 2003; Shetgiri, Lin, Avila, et al., 2012), adverse parent-child relationships (Burkhart et al., 2013; Chou et al., 2018; Lebrun-Harris et al., 2019; Rajendran et al., 2016; Shetgiri, Lin, & Flores, 2012), parental mental disorders (Arseneault et al., 2010; Chou et al., 2018), and low child engagement in school, work, and the community)— have received scant attention in prior studies of children with ADHD.

Investigators have also been interested in identifying factors linked to bullying perpetration. In samples of children with and without ADHD, an increased risk for BP has been noted in individuals with externalizing disorders, higher levels of anger, learning difficulties, and poor school performance (Fogler et al., 2022; Holmberg & Hjern, 2008; Schnoes et al., 2006; Wiener & Mak, 2009). However, other factors linked to increased BP rates in typically developing samples, such as gender, parental educational attainment, and socioeconomic status, have not been linked to an increased BP risk in studies of children with ADHD (Becker et al., 2016; Chou et al., 2018; Unnever & Cornell, 2003). As with BV, the relationship between BP and measures of family risk, parental mental disorders, and strained family relationships have been the focus of limited previous investigation in children with ADHD (Chou et al., 2018) despite being associated with BP in the general pediatric population.

Given the aforementioned knowledge gaps, we sought to investigate the factors that predicted bullying involvement (both BV and BP) among children with ADHD; specifically the association between bullying and demographic factors, child mental health and behavioral characteristics, child learning and developmental disorders, family factors, and treatment/services receipt in a sample of children with ADHD from the National Survey of Children’s Health (NSCH). We hypothesized that (1) BV is associated with co-occurring anxiety or depression, learning/developmental disorders, difficulties with friendship, and high levels of socioeconomic or family strain, and (2) BP is linked to behavioral or conduct problems or other externalizing behaviors, poor school functioning, and high levels of socioeconomic or family strain.

Methods

Sample

To examine the associations of child and family characteristics with BV and BP in children with ADHD, we utilized data from the 2016 to 2017 NSCH (N = 71,811). The NSCH is a cross-sectional, nationally representative sample of households with parent/guardians (from here on referred to as parents) living in the U.S. who reported on one, randomly selected 0- to 17-year-old per household. Weighting procedures were conducted to account for non-response, complex sampling, and survey design (Ghandour et al., 2018). Within the 2016 to 2017 NSCH datasets, we focused on 6- to 17-year-olds whose parents indicated that they had a current ADHD diagnosis (by answering affirmatively to: “Has a doctor or other health care provider ever told you that this child has ADHD? Does this child currently have the condition?” [n = 5,932]), comprising 10.2% (95% CI [9.7–10.8]) of the total weighted sample of 6- to 17-year-olds.

Measures

Primary outcomes/dependent variables

Our primary outcomes/dependent variables of interest were being a (1) BV and (2) BP. BV was assessed by asking parents to rate the phrase “This child is bullied, picked on, or excluded by other children” as being “Deﬁnitely true,” “Somewhat true,” or “Not true.” BP was assessed by asking parents to rate the phrase “This child bullies others, picks on them, or excludes them” as being “Deﬁnitely true,” “Somewhat true,” or “Not true.” We defined responses of “Deﬁnitely true” and “Somewhat true” to indicate the presence of BV or BP (Lebrun-Harris et al., 2019) and created separate “yes/no” dichotomous variables for BV and BP.

Independent variables

In line with other studies of bullying, the following parent-reported variables were designated a priori for inclusion in all models: age, sex, race/ethnicity, and parent level of education. To identify additional relevant independent variables, we considered a set of candidate NSCH variables that were associated with BV or BP in at least one prior study. The following parent-reported socioeconomic (SES) measures were examined: household federal poverty level (FPL; reported household income below vs. over 200% of FPL), receipt of government assistance (any of cash assistance, Supplemental Nutrition Program for Women, Infants and Children [WIC], food stamps, and free or reduced-cost school breakfast/lunch), and family “finds it hard to cover basics like food or housing” (never vs. rarely, somewhat, or very often). The following parent-reported ADHD and treatment related measures were examined: ADHD severity (mild, moderate, or severe), current use of ADHD medication, ADHD behavioral treatment in the past 12 months, receipt of treatment by mental health provider, and current use of medications taken because of difficulties with “emotions, concentration, or behavior.” The following parent-reported comorbidities and behavioral characteristics were examined: healthcare provider diagnosis of the child ever having behavioral or conduct problems, anxiety/depression (indicating either the presence of anxiety and/or depression), substance abuse disorder, developmental delay and/or intellectual disability, speech or other language disorder, learning disability, autism spectrum disorder, Tourette syndrome, or other genetic or inherited conditions. Parents also reported on externalizing behaviors such as if children had problems staying calm when faced with a challenge, and problems with arguing too much. The following parent-reported child-centered characteristics were tested: difficulty making or keeping friends, child has a health condition that affects functioning, school engagement (“child cares about doing well in school,” “does all required homework”), involvement in community service, involvement in paid work, participation in extra school activities, amount of physical activity, time spent watching television/videos or playing video games, and time spent using recreational computers/cell phones/handheld video games. The following parent-reported family-related factors were examined: parent marital status, how many times child has moved to a new address, parent attendance at child’s events or activities, and family shared mealtime. Parent-reported school-related factors included: number of times a school contacted the household about school problems, perception of child safety at school, child retention in a grade, and having ever had an early intervention plan such as an Individualized Educational Plan (IEP) or having received special services for developmental needs such as speech, occupational, or behavioral therapy.

Analysis

All analyses were conducted using SAS v.9.4 survey procedures (SAS Institute, Inc.; Cary, NC) and SAS-callable SUDAAN v.11.0.1 (RTI International; Cary, NC) to account for the complex sampling design and sample weights. The variables used to construct the FPL variable had a relatively high percentage of missing data, and thus all analyses with this variable were conducted utilizing six multiply imputed FPL implicates that are included for use in the NSCH public use files and the appropriate analytic procedures to provide accurate estimates and standard errors (Danielson et al., 2018; Lebrun-Harris et al., 2019). All proportions were calculated with Clopper-Pearson confidence intervals. Rao Scott chi-square tests were conducted between each independent variable and the outcomes (BV and BP) to identify bivariate associations. Next, we conducted separate preliminary logistic regressions for the BV and BP outcomes. The four demographic variables of age, sex, race/ethnicity, and parent level of education were selected a priori for inclusion in all adjusted models, with additional independent variables included in these preliminary regression models if they had bivariate associations with either BV or BP that had at least a small effect size based on the Cramer’s V calculated from the Rao-Scott chi-square test and the associated degrees of freedom; for example, a Cramer’s V of 0.10 or higher on a test with one degree of freedom was considered to have at least a small effect size (Cohen, 1988). Finally, variables that were found to be statistically significant at an alpha of .05 in the preliminary logistic regression models to predict the specific outcome (i.e., BV or BP) were retained in the final models for that outcome, along with each of the four demographic variables selected for inclusion a priori.

Bullying victimization and perpetration can occur in the same individual (Adams et al., 2010; Haynie et al., 2001; Perry et al., 1992; Prinstein & Cillessen, 2003; Schwartz, 2000; Schwartz et al., 2001). This in an important subgroup; however, the size of the subgroup of participants who were both victims and perpetrators (N = 644; 11.2% of the weighted analytic sample) was not sufficient for separate analysis. To ensure that an individual’s status as having both BV and BP did not impact the findings, post hoc analyses were conducted for the final logistic regression models in which the bullying category not used as the outcome was added to the model (i.e., BV models were adjusted for BP and BP models were adjusted for BV).

Results

Sample Characteristics

Overall, we found that 46.9% (95% CI [44.0%−49.8%]) of children and adolescents with ADHD were bullying victims and 16.2% (95% CI [14.1%−18.5%]) were bullying perpetrators. Table 1 shows the weighted frequencies for the bullying indicators and independent variables considered for the regression models. More than half of children with current ADHD (56%) were adolescents aged 12 to 17 years; 44% were aged 6 to 11 years. A majority of the children (68%) were male, and 58% were non-Hispanic white. Most were from families with financial strain, with 73% endorsing having some difficulty covering basic necessities, and over half receiving one or more types of government assistance. Nearly half (45%) had moved three or more times. Approximately one quarter (26%) had co-occurring developmental delay or intellectual disabilities, and 54% had behavior or conduct problems. Staying calm when faced with a challenge was not true for about 27% and somewhat true for 57% of the study population; 21% were described as definitely and 39% as somewhat arguing too much; and 19% had a lot of difficulty and 37% a little difficulty making friends. Among the children, 19% had a health condition that affected their ability to do things, 8% had low school engagement, and parents of 42% had been contacted two or more times by the school about school problems. The majority (64%) were taking medications for ADHD, while slightly less than half (45%) had received ADHD behavioral treatment.

Table 1.

Frequencies and Weighted Proportions of Factors Associated With Bullying Involvement Among Children aged 6 to 17 Years With Current ADHD, National Survey of Children’s Health, 2016 to 2017.

Study measures	Children with current ADHD
Study measures	n	Weighted proportion % (95% CI)
Bullying outcome variables
Child is bullied/excluded
No	3,053	53.1 (50.2–56.0)
Yes	2,774	46.9 (44.0–49.8)
Child bullies/excludes others
No	4,993	83.8 (81.5–85.9)
Yes	843	16.2 (14.1–18.5)
Demographic factors
Age
6–11 years	2,285	44.4 (41.6–47.3)
12–17 years	3,647	55.6 (52.7–58.4)
Sex
Male	4,039	68.4 (65.5–71.2)
Female	1,893	31.6 (28.8–34.5)
Ethnicity and race
Non-Hispanic White	4,364	58.1(55.0–61.1)
Non-Hispanic Black	407	16.5 (14.1–19.3)
Non-Hispanic Other	565	6.9 (5.9–8.1)
Hispanic/Latino	596	18.5 (15.7–21.5)
Highest level of education in the household
No college	1,013	31.2 (28.0–34.5)
Some college or higher	4,806	68.8 (65.5–72.0)
Socioeconomic status characteristics
Family poverty level (FPL)^a
Less than 200% FPL	4,033	53.1 (50.1–56.1)
More than 200% FPL	1,899	46.9 (43.9–49.9)
Hard to cover basics
Never	1,883	27.5 (25.0–30.1)
Any	3,941	72.5 (69.9–75.0)
Receipt of government assistance
None	3,760	47.7 (44.9–50.5)
1–2 types	1,735	42.7 (39.7–45.6)
3–4 types	230	9.7 (7.4–12.3)
Family-Related Factors
Times moved to a new address
0 times	1,375	20.6 (18.1–23.1)
1–2 times	2,064	34.9 (32.1–37.6)
3 or more times	2,259	44.5 (41.7–47.3)
Parent attends child events or activities
Always/Usually	4,862	79.2 (76.7–81.5)
Sometimes	461	10.6 (8.7–12.6)
Rarely/Never	492	10.3 (8.7–12.0)
Family eats meals together
0 day	307	5.3 (4.1–6.7)
1–3 days	1,741	28.2 (25.4–31.1)
More than 4 days	3,815	66.6 (63.6–69.4)
Comorbidities^b and behavioral characteristics
Developmental delay and/or intellectual disability
None	4,421	73.8 (71.4–76.2)
> 1	1,475	26.3 (23.8–28.8)
Behavior or conduct problems
Yes	3,053	54.4 (51.5–57.2)
No	2,850	45.6 (42.8–48.5)
Speech or other language disorder
Yes	1,075	20.3 (17.9–22.9)
No	4,837	79.7 (77.1–82.1)
Learning disabilities
Yes	2,208	39.8 (37.0–42.7)
No	3,693	60.2 (57.3–63.0)
Autism spectrum disorder
Yes	777	14.7 (12.3–17.2)
No	5,130	85.3 (82.8–87.7)
Tourette syndrome
Yes	89	1.4 (0.9–2.0)
No	5,825	98.6 (98.0–99.1.)
Other genetic or inherited condition
Yes	825	15.3 (13.4–17.4)
No	5,076	84.7 (82.6–86.6)
Anxiety or depression
Yes	2,554	41.0 (38.2–43.9)
No	3,343	59.0 (56.1–61.8)
Substance abuse disorder
Yes	82	1.1 (0.7–1.6)
No	5,761	98.9 (98.4–99.3)
Child-Centered Factors
Stays calm when faced with a challenge
Definitely true	972	16.0 (14.1–18.1)
Somewhat true	3,455	57.4 (54.5–60.3)
Not true	1,404	26.6 (23.9–29.3)
Argues too much
Definitely true	1,212	20.9 (18.8–23.1)
Somewhat true	2,244	38.7 (35.5–41.8)
Not true	2,388	40.4 (37.7–43.2)
Difficulty making or keeping friends
No difficulty	2,578	44.1 (41.3–46.9)
A little difficulty	2,111	37.1 (34.1–40.1)
A lot of difficulty	1,140	18.8 (16.7–21.1)
Health condition affects functioning
No condition	388	6.9 (5.7–8.4)
Never/sometimes	4,501	73.9 (71.4–76.3)
Usually/always	1,015	19.1 (17.0–21.4)
School engagement (cares about school, does homework)
Definitely true to both	5,379	91.9 (90.4–93.3)
Not or somewhat true to ≥1	464	8.1 (6.7–9.6)
Involved in community service
Yes	2,444	35.6 (33.1–38.2)
No	3,324	64.4 (61.8–69.8)
Involved in paid work
Yes	1,650	22.5 (20.4–24.7)
No	4,145	77.5 (75.3–79.6)
Participates in extra school activities
Yes	4,437	72.1 (69.2–74.9)
No	1,325	27.9 (25.1–30.8)
Days of physical activity
0 day	775	13.9 (11.9–16.1)
1–3 days	1,245	38.1 (35.3–41.0)
4–6 days	1,554	26.0 (23.5–28.6)
Everyday	1,157	21.9 (19.7–24.3)
Time spent watching TV
Less 1 hour	2,414	39.5(36.8–42.2)
2–3 hours	2,656	45.8 (42.9–48.7)
More than 3 hours	813	14.7 (12.9–16.8)
Time spent with computer, cell phone or electronic device
Less 1 hour	2,270	40.5 (37.7–43.3)
2–3 hours	2,375	39.9 (37.0–42.9)
More than 3 hours	1,240	19.6 (17.6–21.7)
Born in the USA
Yes	5,719	96.7 (95.2–97.9)
No	185	3.3 (2.1–4.8)
School-related factors
Times school contacted household about problems
0 times	2,395	40.6 (37.8–43.6)
1 time	1,036	17.1 (15.1–19.3)
≥ 2 times	2,382	42.2 (39.4–45.1)
Safe at school
Definitely agree	4,020	68.7 (65.8–71.5)
Somewhat agree	1,509	26.8 (24.0–29.6)
Somewhat disagree	161	3.2 (2.3–4.2)
Definitely disagree	39	1.4 (0.4–2.5)
Repeated any grade
Yes	826	16.6 (14.6–18.6)
No	4,987	83.4 (81.4–85.4)
IEP or special developmental services^c
No services	2,217	34.8 (34.9–40.5)
Receives services	3,659	62.4 (59.5–65.2)
Child ADHD characteristics/treatment interventions
ADHD severity
Mild	2,593	40.8 (38.1–43.6)
Moderate	2,619	43.9 (41.0–46.8)
Severe	651	15.3 (12.9–17.9)
Currently taking medication for ADHD
Yes	3,878	64.1 (61.3–66.9)
No	2,018	35.9 (33.1–38.7)
Received ADHD behavioral treatment
Yes	2,672	45.1 (42.3–48.0)
No	3,238	54.9 (52.0–57.7)
Received treatment by mental health provider
Yes	2,891	47.4 (44.6–50.2)
No, but child needed	333	7.3 (5.8–8.7)
No, but child did not need	2,687	45.3 (42.4–48.1)
Received medications for behavior or emotions
Yes	4,021	65.6 (62.7–68.3)
No	173	34.5 (31.7–37.3)

The family poverty level measure is a ratio created using a combination of information about family income and household size. To address missing data for these measures, the family poverty level was multiply imputed and these values are included in the publicly available dataset. All analyses are conducted in a manner to create estimates based on all the multiple imputations.

Parents reported that a healthcare provider ever told them that a child had this disorder, and they reported that the child currently had this disorder.

Parents reported that their child ever had an EIP or early intervention plan, and/or that their child received special services for developmental needs, such as speech, occupational, or behavioral therapy.

Results of Bivariate and Preliminary Regression Analyses

Table 2 shows the findings for the Rao-Scott chi-square analyses testing the associations between BV or BP and each independent variable that was included in the final logistic regression models (see Supplemental Table 1 for additional variables). The effect sizes for the Cramer’s V met Cohen’s criteria for at least a small effect size for 19 variables (10 non-demographic variables in Table 2 and 9 variables in Supplemental Table 1) in their associations with either BV, BP, or both. These 19 variables, along with the four a priori selected demographic variables (sex, race/ethnicity, age, and highest level of parent education) were included in separate preliminary logistic regression analyses for BV and BP. The preliminary logistic regression models identified five significant predictors for bullying victimization (difficulties with basic necessities, developmental disability, difficulty making or keeping friends, health problems affecting the child’s ability to do things, and more school contacts to the household) and six significant predictors of bullying perpetration (receipt of government assistance, having problems staying calm, arguing too much, difficulty making friends, reduced engagement/interest in school, and more school contacts to the household), which were entered into the final logistic regression models along with the four a priori selected demographic measures. A factor of particular interest, diagnosis of behavior or conduct problems, for which bullying behavior can be a symptom, was not a significant predictor in the final model, despite a strong unadjusted association particularly for BP.

Table 2.

Weighted Prevalence for Core Demographics and Factors Associated With Bullying Victimization and Perpetration Among Children with ADHD, National Survey of Children’s Health, 2016 to 2017.

Study measures	Bullying victim		Bullying perpetrator
Study measures	Weighted proportion	Rao ScottChi-squarep-Value	Weighted proportion	Rao ScottChi-squarep-Value
Demographic factors
Age
6–11 years	48.8 (44.3–53.3)	.2542	19.5 (15.7–23.7)	.0067
12–17 years	45.4 (41.6–49.2)		13.6 (11.3–16.6)
Ethnicity and race
Non-Hispanic White	48.6 (45.7–51.5)	.3747	15.2 (13.0–17.6)	.6652
Non-Hispanic Black	48.0 (38.7–57.3)		17.2 (11.9–23.6)
Non-Hispanic Other	41.2 (33.8–48.9)		15.0 (10.2–20.9)
Hispanic/Latino	42.5 (33.5–51.9)		18.8 (11.6–27.9)
Sex
Male	45.8(42.6–49.1)	.3089	17.7 (15.0–20.7)	.0275
Female	49.2 (43.3–55.1)		12.9 (10.0–16.4)
Highest level of education in the household
No college	50.8 (43.5–58.0)	.1594	20.5 (15.3–26.5)	.0212
Some college or higher	45.4 (42.6–48.1)		14.2 (12.2–16.5)
Socioeconomic status characteristics
Family finds it hard to cover basics
Never	36.0 (31.2–41.1)	<.0001	13.4 (10.1–17.2)	.0724
Any	51.4 (47.9–54.8)		17.5 (14.8–20.5)
Receipt of government assistance
None	41.6 (38.5–44.8)	<.0001	10.9 (9.0–13.0)	<.0001
1–2 types	49.6 (44.6–54.6)		20.2 (16.2–24.6)
3–4 types	65.1 (52.9–76.0)		26.4 (16.4–38.4)
Family-related factors
Times moved to a new address
0 times	47.3 (40.1–54.5)	.0388	15.9 (9.7–22.2)	.0003
1–2 times	41.7 (36.9–46.4)		10.4 (7.9–12.8)
3 or more times	50.4 (46.3–54.4)		21.2 (17.6–24.8)
Comorbidities and behavioral characteristics
Developmental delay and/or intellectual disability
Yes	65.1 (60.2–69.9)	<.0001	22.1 (17.1–27.7)	.0022
No	40.7 (37.4–44.1)		14.2 (12.0–16.6)
Stays calm when faced with a challenge
Definitely true	23.7 (19.0–29.1)	<.0001	2.6 (1.2–5.1)	<.0001
Somewhat true	46.0 (42.3–49.8)		12.0 (9.8–14.5)
Not true	62.7 (56.0–69.1)		33.0 (27.2–39.2)
Argues too much
Definitely true	64.9 (59.5–70.1)	<.0001	44.4 (38.8–50.1)	<.0001
Somewhat true	49.9 (44.4–55.4)		15.1 (11.3–19.5)
Not true	34.6 (30.9–38.5)		2.7 (1.7–4.0)
Child-centered characteristics
Difficulty making or keeping friends
No difficulty	21.4 (18.6–24.5)	<.0001	5.7 (4.3–7.5)	<.0001
A little difficulty	58.0 (52.2–63.6)		19.6 (15.9–23.7)
A lot of difficulty	84.5 (80.1–88.2)		33.5 (26.7–40.8)
Health condition affects functioning
No condition	33.1 (24.4–42.8)	<.0001	9.8 (4.9–17.1)	<.0001
Never/sometimes	42.6 (39.2–46.0)		12.3 (10.3–14.6)
Usually/always	67.6 (61.2–73.5)		34.2 (27.5–41.3)
School engagement (cares about school, does homework)
Definitely or somewhat true to one	46.7 (43.7–49.8)	.9078	14.5 (12.3–16.9)	<.0001
Not true to both	47.3 (38.2–56.4)		35.7 (27.0–45.2)
School-related factors
Times school contacted household about problems
0 times	31.2 (27.5–35.2)	<.0001	5.8 (4.1–8.0)	<.0001
1 time	48.2 (41.5–54.9)		13.3 (7.3–21.8)
≥ 2 times	61.3 (57.1–65.5)		27.4 (23.7–31.5)

Note. All non-demographic measures presented in this table had at least a small effect size based on Cramer’s V calculated from the Rao-Scott chi-square test and the associated degrees of freedom using Cohen’s parameters (Cohen, 1988).

Factors Linked to Bullying Victimization

The final logistic regression model for BV (see Table 3) yielded four significant predictors: family having a hard time covering basic needs, having a developmental delay or intellectual disability, having difficulty with making or keeping friends, and number of times contacted by school. Specifically, when families found it hard to cover basic needs or the child had a developmental delay or intellectual disability, the odds of being bullied were 1.4 to 1.5 higher, respectively. Additionally, school contact with the household about problems had an association with being bullied: compared to no contact, with one contact, the odds of BV were 50% higher, while odds of BV were double with two or more contacts. Finally, the largest BV associations were found for having difficulty making or keeping friends. Participants who had a little or a lot of difficulty making or keeping friends had a 4.6-fold and a 16.5-fold higher odds of being bullied, respectively, compared to those with no friendship difficulties. This pattern of results was maintained in the post-hoc analysis that controlled for BP in the BV model.

Table 3.

Multivariable Logistic Regression Results for Bullying Victimization Among Children with ADHD, National Survey of Children’s Health, 2016 to 2017.

	Bullying victim
	Odds ratio	Odds ratio 95% CI
Age
6–11 years (reference (ref))	—
12–17 years	0.86	0.67–1.11
Ethnicity/Race
Non-Hispanic White (ref)	—
Non-Hispanic Black	0.93	0.66–1.39
Non-Hispanic Other	0.67	0.43–1.04
Hispanic/Latino	0.73	0.47–1.13
Sex
Female (ref)	—
Male	0.88	0.66–1.17
Highest level of education in the household
Some college or higher (ref)	—
No college	0.95	0.68–1.32
Hard to cover basics
Never (ref)	—
Any	1.43	1.07–1.92
Developmental delay and/or intellectual disability
None (ref)	—
Present	1.47	1.09–1.99
Difficulty making or keeping friends
No difficulty (ref)	—
A little	4.61	3.49–6.09
A lot	16.50	11.36–23.97
Health condition affects functioning
No condition (ref)	—
Never/sometimes	0.97	0.63–1.50
Usually/always	0.64	0.37–1.10
Times school contacted household about problems
0 times (ref)	—
1 time	1.45	1.04–2.03
≥ 2 times	2.00	1.47–2.71

Note. All predictors included in the model were found to be significant at p < .05 in preliminary models. Bold numbers are significant at p < .05 in final model.

Factors Linked to Bullying Perpetration

The final logistic regression model for BP (see Table 4) identified seven significant predictors: sex, receipt of three or four types of government assistance, difficulty staying calm, arguing too much, difficulty with friends, lack of interest/engagement in school, and number of times family was contacted by school. Males had almost twice the odds of BP than females. When families utilized three or more types of government assistance, children had a 2.2-fold higher odds of BP compared to those receiving no government assistance. Children who had difficulty staying calm when faced with a challenge had also 2.4 times higher odds of BP than those with no such difficulties. Those who had a little or a lot of difficulty making or keeping friends had 2.7- to 3.0-fold higher odds of BP, respectively, compared to no friendship problems. Those who were reported to either “not care about school” or not do homework had almost twice the odds of BP than those who both cared about school and completed their homework. In addition, children whose families had been contacted two or more times by the school about problems have 2.7 times higher odds of BP than those whose families had no school contacts. Finally, the strongest predictors of BP were found when parents endorsed that it was “somewhat true” or “definitely true” that their child “argues too much,” that is, there were 3.8- to 14.1-fold higher odds compared to children for whom this was rated as “definitely not true.” This pattern of results was also maintained in the post hoc analysis that controlled for BV along with the other factors in the final BP model.

Table 4.

Multivariable Logistic Regression Results for Being a Bullying Perpetrator Among Children with ADHD, National Survey of Children’s Health, 2016 to 2017.

	Bullying perpetrator
	Odds ratio	Odds ratio 95% CI
Age
6–11 (reference (ref))	—
12–17	0.68	0.48–0.97
Ethnicity/Race
Non-Hispanic White (ref)	—
Non-Hispanic Black	1.16	0.68–1.96
Non-Hispanic Other	1.29	0.75–2.23
Hispanic/Latino	1.49	0.87–2.57
Sex
Female (ref)	—
Male	1.76	1.19–2.60
Highest level of education in the household
Some college or higher (ref)	—
No college	0.84	0.56–1.27
Government assistance
No assistance (ref)	—
1–2 types	1.25	0.86–1.82
3–4 types	2.22	1.11–4.43
Times moved to a new address
0 times (ref)	—
1–2 times	0.64	0.36–1.15
3+ times	0.94	0.53–1.68
Stays calm when faced with a challenge
Definitely true (ref)	—
Somewhat true	1.42	0.64–3.17
Not true	2.44	1.11–4.43
Argues too much
Not true (ref)	—
Somewhat true	3.75	2.26–6.51
Definitely true	14.06	8.07–25.49
Difficulty making or keeping friends
No difficulty (ref)	—
A little difficulty	2.67	1.76–4.07
A lot of difficulty	2.97	1.77–4.99
School engagement (cares about school, does homework)
Definitely/somewhat true to one item (ref)	—
Not true to both items	2.03	1.23–3.34
Times school contacted household about problems
0 times (ref)	—
1 time	1.60	0.86–2.97
≥ 2 times	2.73	1.77–4.22

Note. All predictors included in the model were found to be significant at p < .05 in preliminary models. Bold numbers are significant at p < .05.

Discussion

Among children with ADHD, 47% were bullying victims and 16% perpetrators, respectively, which was more than double the prevalence for the general population using the same National Survey of Children’s Health dataset and definitions of bullying victimization and perpetration (i.e., 23% for BV and 6% for BP) (Lebrun-Harris et al., 2019). Our results are consistent with previous studies showing that children and youth with ADHD are often victims and sometimes perpetrators of bullying (Fogler et al., 2022; Holmberg, 2010; Unnever & Cornell, 2003). Given the adverse sequelae of both BV and BP, these findings underscore the importance of addressing and preventing bullying in children and youth with ADHD. Our identification of factors linked to bullying involvement in children with ADHD may inform and facilitate the development of effective anti-bullying interventions in this population (David-Ferdon et al., 2016). Similar to studies of general pediatric samples (Melnick & Hinshaw, 2000; Roland & Idsøe, 2001), boys were more likely than girls and younger children were more likely than older children to be bullying perpetrators in our ADHD-specific sample. Prior investigators have postulated that this may be because younger peer groups compared to older adolescent peer groups are more likely to find aggression to be acceptable (Troop-Gordon, 2017) and because peer groups in general tend to find aggression perpetrated by males to be more acceptable than aggression perpetrated by females (Underwood, 2003).

We found that difficulty covering basic needs portended BV and receipt of government assistance portended BP among children with ADHD. Previous studies have also linked factors indicating family financial strain, such as household food insecurity with bullying in typically developing children and adolescents (Jackson & Vaughn, 2017; Jackson et al., 2018). A possible mechanism for the link between BV and lack of resources in different samples of children (Jackson et al., 2018; Tippett & Wolke, 2014) may be the association of parental stress with harsh punishment and authoritarian parenting (Rajendran et al., 2016), which predisposes children to bullying behavior.

Regarding child-related characteristics, difficulties with self-control when faced with challenges and a tendency toward arguing were both linked to bullying perpetration in our analysis of children with ADHD. Overall, these findings suggest that these two aspects of emotional regulation, which are associated with disruptive behavior disorders and represent common issues for those with ADHD (Arseneault et al., 2010; Fogler et al., 2022; Kaltiala-Heino et al., 2000; Normand et al., 2020; Unnever & Cornell, 2003), are important to consider when addressing bullying perpetration (Fite et al., 2014; Kokkinos & Panayiotou, 2004). Overall, the findings around emotional regulation issues and both BV and BP fit what is known around the particular type of aggression exhibited by those with ADHD, reactive aggression. Reactive aggression is ineffectual and particularly disliked by peers, so such individuals are more likely to perpetuate bullying using this type of aggression and more likely to be the target of bullying since the overall peer group dislikes them (Adams et al., 2010). It may also be that frequent arguing is a marker for problematic family relationships, which set the stage for a problematic style of negotiation with peers and a bully perpetration dynamic (Chou et al., 2018), although our data does not allow for evaluation of this hypothesis. This is the first study to our knowledge which showed both aspects of emotional regulation to be associated with BP when considered in the same model. Interventions for ADHD can address children’s confrontational behavior in social situations and support their ability to maintain self-control when faced with adversity.

Among child characteristics, difficulties with making or keeping friends was a significant predictor of both BV and BP among children with ADHD. Children with ADHD are predisposed to problematic peer relationships (Baumeister et al., 2008; Grygiel et al., 2018; Mrug et al., 2012), and thus at an increased risk of bullying victimization. Friendship difficulties were also associated with bullying perpetration, which is in line with previous findings. Specifically, prior studies have shown that aggressive children with other executive functioning issues, like those with ADHD often have, are especially prone to problematic peer relationships and BP (Hinshaw & Melnick, 1995; Jia & Mikami, 2015; Poulin & Boivin, 1999). The findings around the associations between friendship difficulties and BP might also be explained by peer group perceptions of reactive aggression. Given that this type of aggression is ineffectual and particularly disliked by peers, those who use this type of aggression in a bullying manner are less likely to have friends (Adams et al., 2010). It is also notable that many of the factors we identified as predictors of being a bully perpetrator, such as difficulties making friends and emotion regulation difficulties (e.g., being argumentative and having difficulties staying calm), are often correlated and part of a general pattern of social dysfunction. However, these cross-sectional data do not allow us to examine the mechanisms by which these factors are related to each other and linked to BP.

We also examined whether co-occurring developmental-behavioral conditions, such as anxiety/depression, substance abuse disorder, developmental delay and/or intellectual disability, speech or other language disorder, learning disability, autism spectrum disorder, or Tourette syndrome, predicted bullying victimization or perpetration. In our final models, none of these conditions were significantly associated with BP, while just one factor—developmental delay or intellectual disability—was independently linked to BV (Baumeister et al., 2008). This association between bullying victimization and having a developmental disability has been shown in several prior studies of ADHD samples as well as in samples of the general population (Baumeister et al., 2008; Lebrun-Harris et al., 2019; Pastor & Reuben, 2015).

Two school-related factors predicted bullying problems in our sample. First, both BV and BP were linked to increased contacts with families about problems at school. Second, children who did not care about doing well in school and/or did not do homework had an increased risk of bullying perpetration, echoing previous studies showing an association of BP with lower grades and school absences (Haynie et al., 2001; Kowalski & Limber, 2013; Nansel et al., 2001). This finding highlights the lack of engagement in school as a potentially important risk for bullying perpetration.

While race/ethnicity did not have a significant main effect on either bullying outcome, research on this topic suggests that the associations between race/ethnicity and peer victimization might be too complex to be explained by just simple main effects. One consideration may be that non-white, and particularly African American, children are frequently labeled by teachers as “problem” students,(Riddle & Sinclair, 2019; Staats, 2014) so that any association between race/ethnicity and BV or BP may be obscured or denoted in our analyses by the association between frequent contact from the school about problems and these outcomes. Upcoming studies on this topic should consider the work of Graham, Juvonen, and colleagues (Graham et al., 2009; Juvonen & Graham, 2014) and explore possible mediated and moderated effects in terms of context (e.g., racial/ethnic make-up of the location) and social attributions by the individuals involved in the bullying to better understand the role of race/ethnicity in BV and BP.

Limitations

This study has several limitations. Due to the cross-sectional nature of the survey, inferences about causal relationships or directions of associations between the measures cannot be determined. Additionally, most factors were measured using a single item, based on one reporter, the parent, for all measures, leading to possible shared method variance based on the single approach of data collection. The survey did not provide a definition of bullying, and parents’ perspectives regarding what constitutes bullying victimization or perpetration may have varied, thus limiting the comparability of this study to studies that assess bullying in more detail. Furthermore, parents may have difficulty accurately reporting child experiences for which they are not present, such as bullying experiences at school or difficulties with friends (Adams et al., 2010; Perry et al., 1992; Prinstein & Cillessen, 2003). They may be unaware or may rely on others to identify if their child is a bullying victim or perpetrator. Future research integrating multiple informants may provide a more complete understanding of children’s peer relationships (Normand et al., 2020). The survey also did not address whether children’s current type of bullying involvement may be different from past involvement. Parent report of ADHD diagnosis by a healthcare professional is subject to recall error and would exclude children who have ADHD but have not been diagnosed. While post hoc analyses did not find that co-occurrence of BP and BV impacted the individual findings for BP and BV, further studies of individuals with ADHD may benefit from taking a more specific and measured approach given that children who are both victimized and engage in aggression are described as ineffectual and reactive in their aggression, disliked by the peer group, and have high rates of internalizing problems (Fogler et al., 2022; Perry et al., 1992; Prinstein & Cillessen, 2003; Schwartz, 2000; Schwartz et al., 2001).

An additional limitation was based on the variable selection method. While we identified factors that had higher relative contributions to the outcome, we also excluded factors of clinical significance but share variance with other variables that were included. Nine such variables were found to have significant bivariate associations with the bullying variables but were not significant in the preliminary regression analyses that accounted for the variance across all other measures (see Supplemental Table), for example, diagnosis of behavior or conduct problems. Future studies on bullying in ADHD populations should consider these constructs given that they most likely are associated with both bullying indicators and some of the other predictors of bullying issues.

Future Directions for Intervention and Conclusions

Previous studies have shown that pediatricians rarely address bullying (Borowsky et al., 2004). Thus, our findings may help clinicians to recognize youth with ADHD who are most at risk for BV or BP (Jones et al., 2020). For example, we identified several predictors (Adams et al., 2010) for bullying involvement that would be apparent to clinicians, such as having a developmental disability (associated with BV) or being male (associated with BP). During a pediatric visit, providers could also assess other identified predictors, such as having problems with friendships or frequent contact by the school about problems, both of which were linked to BV and BP. Similarly, determining whether a child has problems with school engagement (associated with BP) could also be determined.

Our findings may also inform anti-bullying interventions. To address BV, it is important to both end the victimization and to mitigate its negative sequelae (such as depression and anxiety). Across many studies, friendships are important for protecting children and youth from BV and from negative outcomes (Adams & Cantin, 2013; Adams et al., 2011; Hodges et al., 1997, 1999; Prinstein et al., 2001). Behavioral peer interventions are an evidence-based approach to ADHD treatment that may assist both with managing ADHD symptoms and improving peer relationships (Evans et al., 2014). Further, strategies to improve self-regulation skills so that children can use positive interaction skills and better handle challenging situations have documented evidence for preventing bullying perpetration (Adamski & Adams, 2008; Bryn, 2011; Espelage et al., 2004; Mersky et al., 2017; Newman-Carlson & Horne, 2004; Saracho, 2017; Stevens et al., 2001). Broader strategies to decrease bullying and prevent youth violence can also be implemented (David-Ferdon et al., 2016; HHS, 2017). For example, professionals can coach parents to work with school officials to prevent bullying using current anti-bullying tools (see https://www.stopbullying.gov/ for resources) (Bryn, 2011).

Given that ours and prior studies have linked family financial strain to bullying perpetration, and economic support has demonstrated positive impacts on children’s mental health (Robinson et al., 2020), future research could investigate the impact of family economic supports on decreasing bullying involvement. Since additional ACEs other than those around financial strain (e.g., various forms of abuse) have also been shown to be associated with bullying (HHS, 2017; Mersky et al., 2017), it may be important to examine their roles as well when addressing bullying and pinpointing groups who may benefit from targeted resources, interventions, and support. Overall, future research is needed to investigate the extent to which population-based approaches are effective for, or could be modified to be more effective for, addressing bullying involvement among children with ADHD.

Supplemental Material

sj-docx-1-jad-10.1177_10870547221085502 – Supplemental material for Factors Associated With Bullying Victimization and Bullying Perpetration in Children and Adolescents With ADHD: 2016 to 2017 National Survey of Children’s Health

Supplemental material, sj-docx-1-jad-10.1177_10870547221085502 for Factors Associated With Bullying Victimization and Bullying Perpetration in Children and Adolescents With ADHD: 2016 to 2017 National Survey of Children’s Health by Carolina Cuba Bustinza, Ryan E. Adams, Angelika H. Claussen, Daniel Vitucci, Melissa L. Danielson, Joseph R. Holbrook, Sana N. Charania, Kaila Yamamoto, Nichole Nidey and Tanya E. Froehlich in Journal of Attention Disorders

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.

Disclaimer

The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention.

ORCID iDs

Carolina Cuba Bustinza

Angelika H. Claussen

Tanya E. Froehlich

Supplemental Material

Supplemental material for this article is available online.

Author Biographies

Carolina Cuba Bustinza, MD is a developmental-behavioral pediatrician at Dayton Children’s Hospital in Dayton, Ohio. Her work includes child development, high risk infants, early childhood developmental concerns with focus in ADHD and Autism.

Ryan E. Adams, PhD is a developmental social psychologist and associate professor of pediatrics at Cincinnati Children’s Hospital Medical Center. His research focuses on the impact of the adolescent social world on mental health.

Angelika H. Claussen, PhD is a research psychologist with the Child Development Studies Team (CDS) in the National Center on Birth Defects and Developmental Disabilities at the CDC in Atlanta, Georgia, focusing on child development, parenting, children’s mental health, ADHD, and Tourette syndrome.

Daniel Vitucci, BS is a Clinical Psychology Doctoral Student at Ohio University. His research focuses on the development of novel treatments for children with ADHD.

Melissa L. Danielson, MSPH is a statistician with the CDS at NCBDDD/CDC. Her work focuses on the epidemiology of ADHD and other mental, emotional, and behavioral disorders among children, including disorder prevalence, clinical presentation, service utilization, and outcomes.

Joseph R. Holbrook, PhD is a Lead Health Scientist with the Disability Science and Program Team at NCBDDD. His work focuses on the epidemiology of disabilities and supporting organizations that reduce health disparities and improve the health of people with disabilities.

Sana N. Charania, MPH is a Health Scientist at NCBDDD. Her work focuses on the epidemiology of developmental disabilities such as newborn hearing screening and MBDDDs.

Kaila Yamamoto, BS is a clinical research coordinator in the Division of Developmental and Behavioral Pediatrics. She works on ADHD and Down Syndrome studies at Cincinnati Children’s Hospital.

Nichole Nidey, PhD is an epidemiologist and assistant professor at Cincinnati Children’s Hospital Medical Center, focused on maternal mental health, substance use, adverse childhood events, and child development.

Tanya E. Froehlich, MD is a developmental-behavioral pediatrician and professor of pediatrics at Cincinnati Children’s Hospital Medical Center. Her research focuses on ADHD and has approaches the disorder from the vantage points of epidemiology, etiology, and treatment.

References

Adamski

A. L.

Adams

R. E.

(2008). Minimizing female bullying in middle school students through anti-bullying programs. Saint Xavier University.

Adams

R. E.

Bartlett

N. H.

Bukowski

W. M.

(2010). Peer victimization and social dominance as intervening variables of the link between peer liking and relational aggression. The Journal of Early Adolescence, 30(1), 102–121. https://doi.org/10.1177/0272431609342985

Adams

R. E.

Cantin

(2013). Self-disclosure in friendships as the moderator of the association between peer victimization and depressive symptoms in overweight adolescents. The Journal of Early Adolescence, 33(3), 341–362. https://doi.org/10.1177/0272431612441068

Adams

R. E.

Santo

J. B.

Bukowski

W. M.

(2011). The presence of a best friend buffers the effects of negative experiences. Developmental Psychology, 47(6), 1786–1791. https://doi.org/10.1037/a0025401

Aguado-Gracia

Mundo-Cid

Lopez-Seco

Acosta-García

Cortes-Ruiz

M. J.

Vilella

Masana-Marín

(2021). Lifetime victimization in children and adolescents with ADHD. Journal of Interpersonal Violence, 36, n3241–n3262. https://doi.org/10.1177/0886260518771680

Arseneault

Bowes

Shakoor

(2010). Bullying victimization in youths and mental health problems: ‘Much ado about nothing’? Psychological Medicine, 40(5), 717–729. https://doi.org/10.1017/S0033291709991383

Bacchini

Affuso

Trotta

(2008). Temperament, ADHD and peer relations among schoolchildren: The mediating role of school bullying. Aggressive Behavior, 34(5), 447–459.

Baumeister

A. L.

Storch

E. A.

Geffken

G. R.

(2008). Peer victimization in children with learning disabilities. Child and Adolescent Social Work Journal, 25(1), 11–23.

Becker

S. P.

Mehari

K. R.

Langberg

J. M.

Evans

S. W.

(2016). Rates of peer victimization in young adolescents with ADHD and associations with internalizing symptoms and self-esteem. European Child & Adolescent Psychiatry, 26(2), 201–214.

10.

Boland

DiSalvo

Fried

Woodworth

K. Y.

Wilens

Faraone

S. V.

Biederman

(2020). A literature review and meta-analysis on the effects of ADHD medications on functional outcomes. Journal of Psychiatric Research, 123, 21–30.

11.

Borowsky

I. W.

Mozayeny

Stuenkel

Ireland

(2004). Effects of a primary care-based intervention on violent behavior and injury in children. Pediatrics, 114(4), e392–e399.

12.

Bryn

(2011). Stop bullying now! A federal campaign for bullying prevention and intervention. Journal of School Violence, 10(2), 213–219.

13.

Burkhart

K. M.

Knox

Brockmyer

(2013). Pilot evaluation of the ACT raising safe kids program on children’s bullying behavior. Journal of Child and Family Studies, 22(7), 942–951.

14.

Cardoos

S. L.

Hinshaw

S. P.

(2011). Friendship as protection from peer victimization for girls with and without ADHD. Journal of Abnormal Child Psychology, 39(7), 1035–1045.

15.

Chou

W.-J.

Liu

T.-L.

Yang

Yen

C.-F.

H.-F.

(2018). Bullying victimization and perpetration and their correlates in adolescents clinically diagnosed with ADHD. Journal of Attention Disorders, 22(1), 25–34.

16.

Cohen

(1988). Statistical power analysis for the behavioral sciences (2nd ed.). Erbaum Press.

17.

Craig

W. M.

Pepler

D. J.

(2003). Identifying and targeting risk for involvement in bullying and victimization. The Canadian Journal of Psychiatry, 48(9), 577–582.

18.

Danielson

M. L.

Bitsko

R. H.

Ghandour

R. M.

Holbrook

J. R.

Kogan

M. D.

Blumberg

S. J.

(2018). Prevalence of parent-reported ADHD diagnosis and associated treatment among US children and adolescents, 2016. Journal of Clinical Child & Adolescent Psychology, 47(2), 199–212.

19.

David-Ferdon

Vivolo-Kantor

A. M.

Dahlberg

L. L.

Marshall

K. J.

Rainford

Hall

J. E.

(2016). A comprehensive technical package for the prevention of youth violence and associated risk behaviors. National Center for Injury Prevention and Control, Centers for Disease Control and Prevention.

20.

Efron

Wijaya

Hazell

Sciberras

(2021). Peer victimization in children with ADHD: A community-based longitudinal study. Journal of Attention Disorders, 25(3), 291–299.

21.

Epstein-Ngo

Q. M.

McCabe

S. E.

Veliz

P. T.

Stoddard

S. A.

Austic

E. A.

Boyd

C. J.

(2016). Diversion of ADHD stimulants and victimization among adolescents. Journal of Pediatric Psychology, 41(7), 786–798.

22.

Espelage

D. L.

Mebane

S. E.

Adams

R. S.

(2004). Empathy, caring, and bullying: Toward an understanding of complex associations. In Espelage

D. L.

Swearer

S. M.

(Eds.), Bullying in American Schools (pp. 59–84). Routledge.

23.

Evans

S. W.

Owens

J. S.

Bunford

(2014). Evidence-based psychosocial treatments for children and adolescents with attention-deficit/hyperactivity disorder. Journal of Clinical Child & Adolescent Psychology, 43(4), 527–551.

24.

Fite

P. J.

Evans

S. C.

Cooley

J. L.

Rubens

S. L.

(2014). Further evaluation of associations between attention-deficit/hyperactivity and oppositional defiant disorder symptoms and bullying-victimization in adolescence. Child Psychiatry & Human Development, 45(1), 32–41.

25.

Flouri

Buchanan

(2003). The role of mother involvement and father involvement in adolescent bullying behavior. Journal of Interpersonal Violence, 18(6), 634–644.

26.

Fogler

J. M.

Weaver

A. L.

Katusic

Voigt

R. G.

Barbaresi

W. J.

(2022). Recalled experiences of bullying and victimization in a longitudinal, population-based birth cohort: The influence of ADHD and co-occurring psychiatric disorder. Journal of Attention Disorders, 26(1), 15–24. https://doi.org/10.1177/1087054720969981

27.

Gardner

D. M.

Gerdes

A. C.

(2015). A review of peer relationships and friendships in youth with ADHD. Journal of Attention Disorders, 19(10), 844–855.

28.

Ghandour

R. M.

Jones

J. R.

Lebrun-Harris

L. A.

Minnaert

Blumberg

S. J.

Fields

Bethell

Kogan

M. D.

(2018). The design and implementation of the 2016 National Survey of Children’s Health. Maternal and Child Health Journal, 22(8), 1093–1102.

29.

Gladden

R. M.

Vivolo-Kantor

A. M.

Hamburger

M. E.

Lumpkin

C. D.

(2014). Bullying surveillance among youths: Uniform definitions for public health and recommended data elements. Version 1.0. National Center for Injury Prevention and Control, (U.S.). Division of Violence Prevention.

30.

Graham

Bellmore

Nishina

Juvonen

(2009). “It must be me”: Ethnic diversity and attributions for peer victimization in middle school. Journal of Youth and Adolescence, 38(4), 487–499.

31.

Grygiel

Humenny

Rębisz

Bajcar

Świtaj

(2018). Peer rejection and perceived quality of relations with schoolmates among children with ADHD. Journal of Attention Disorders, 22(8), 738–751.

32.

Haynie

D. L.

Nansel

Eitel

Crump

A. D.

Saylor

Simons-Morton

(2001). Bullies, victims, and bully/victims: Distinct groups of at-risk youth. The Journal of Early Adolescence, 21(1), 29–49.

33.

HHS. (2017). Bullying as an adverse childhood experience. https://www.stopbullying.gov/sites/default/files/2017-10/bullying-as-an-ace-fact-sheet.pdf

34.

Hinshaw

S. P.

Melnick

S. M.

(1995). Peer relationships in boys with attention-deficit hyperactivity disorder with and without comorbid aggression. Development and Psychopathology, 7(4), 627–647.

35.

Hodges

E. V.

Boivin

Vitaro

Bukowski

W. M.

(1999). The power of friendship: Protection against an escalating cycle of peer victimization. Developmental Psychology, 35(1), 94–101.

36.

Hodges

E. V.

Malone

M. J.

Perry

D. G.

(1997). Individual risk and social risk as interacting determinants of victimization in the peer group. Developmental Psychology, 33(6), 1032–1039.

37.

Holmberg

(2010). The association of bullying and health complaints in children with attention-deficit/hyperactivity disorder. Postgraduate Medicine, 122(5), 62–68.

38.

Holmberg

Hjern

(2008). Bullying and attention-deficit- hyperactivity disorder in 10-year-olds in a Swedish community. Developmental Medicine and Child Neurology, 50(2), 134–138.

39.

Houbre

Tarquinio

Thuillier

Hergott

(2006). Bullying among students and its consequences on health. European Journal of Psychology of Education, 21(2), 183–208.

40.

Jackson

D. B.

Newsome

Vaughn

M. G.

Johnson

K. R.

(2018). Considering the role of food insecurity in low self-control and early delinquency. Criminal Justice Journal, 56, 127–139.

41.

Jackson

D. B.

Vaughn

M. G.

(2017). Household food insecurity during childhood and adolescent misconduct. Preventive Medicine, 96, 113–117.

42.

Jia

Mikami

A. Y.

(2015). Peer preference and friendship quantity in children with externalizing behavior: Distinct influences on bully status and victim status. Journal of Abnormal Child Psychology, 43(5), 957–969.

43.

Jones

C. M.

Merrick

M. T.

Houry

D. E.

(2020). Identifying and preventing adverse childhood experiences: Implications for clinical practice. The Journal of the American Medical Association, 323(1), 25–26. https://doi.org/10.1001/jama.2019.18499

44.

Juvonen

Graham

(2014). Bullying in schools: The power of bullies and the plight of victims. Annual Review of Psychology, 65, 159–185.

45.

Kaltiala-Heino

Rimpelä

Rantanen

Rimpelä

(2000). Bullying at school: An indicator of adolescents at risk for mental disorders. Journal of Adolescence, 23(6), 661–674.

46.

Kokkinos

C. M.

Panayiotou

(2004). Predicting bullying and victimization among early adolescents: Associations with disruptive behavior disorders. Aggressive Behavior, 30(6), 520–533.

47.

Kowalski

R. M.

Limber

S. P.

(2013). Psychological, physical, and academic correlates of cyberbullying and traditional bullying. Journal of Adolescent Health, 53(1 Suppl), S13–S20.

48.

Kumpulainen

Räsänen

Henttonen

Almqvist

Kresanov

Linna

S.-L.

Moilanen

Piha

Puura

Tamminen

(1998). Bullying and psychiatric symptoms among elementary school-age children. Child Abuse & Neglect, 22(7), 705–717.

49.

Lebrun-Harris

L. A.

Sherman

L. J.

Limber

S. P.

Miller

B. D.

Edgerton

E. A.

(2019). Bullying victimization and perpetration among US children and adolescents: 2016 National Survey of Children’s Health. Journal of Child and Family Studies, 28(9), 2543–2557.

50.

Melnick

S. M.

Hinshaw

S. P.

(2000). Emotion regulation and parenting in AD/HD and comparison boys: Linkages with social behaviors and peer preference. Journal of Abnormal Child Psychology, 28(1), 73–86.

51.

Mersky

J. P.

Janczewski

C. E.

Topitzes

(2017). Rethinking the measurement of adversity: Moving toward second-generation research on adverse childhood experiences. Child Maltreatment, 22(1), 58–68. https://doi.org/10.1177/1077559516679513

52.

Mrug

Molina

B. S.

Hoza

Gerdes

A. C.

Hinshaw

S. P.

Hechtman

Arnold

L. E.

(2012). Peer rejection and friendships in children with attention-deficit/hyperactivity disorder: Contributions to long-term outcomes. Journal of Abnormal Child Psychology, 40(6), 1013–1026.

53.

Nansel

T. R.

Overpeck

Pilla

R. S.

Ruan

W. J.

Simons-Morton

Scheidt

(2001). Bullying behaviors among US youth: Prevalence and association with psychosocial adjustment. The Journal of the American Medical Association, 285(16), 2094–2100.

54.

Newman-Carlson

Horne

A. M.

(2004). Bully busters: A psychoeducational intervention for reducing bullying behavior in middle school students. Journal of Counseling & Development, 82(3), 259–267.

55.

Normand

Mikami

A. Y.

Savalei

Guiet

(2020). A multiple indicators multiple causes (MIMIC) model of friendship quality and comorbidities in children with attention-deficit/hyperactivity disorder. Psychological Assessment, 32(7), 698–704.

56.

Oliveira

F. R.

de Menezes

T. A.

Irffi

Oliveira

G. R.

(2018). Bullying effect on student’s performance. Economia, 19(1), 57–73.

57.

Olweus

Limber

S. P.

(1983). Olweus bullying prevention program.

58.

Pastor

P. N.

Reuben

C. A.

(2015). Trends in parent-reported emotional and behavioral problems among children using special education services. Psychiatric Services, 66(6), 656–659.

59.

Perry

D. G.

Perry

L. C.

Kennedy

(1992). Conflict and the development of antisocial behavior. In Shantz

C. U.

Hartup

W. W.

(Eds.), Conflict in child and adolescent development (pp. 301–329). Cambridge University Press.

60.

Poulin

Boivin

(1999). Proactive and reactive aggression and boys' friendship quality in mainstream classrooms. Journal of Emotional and Behavioral Disorders, 7(3), 168–177.

61.

Prinstein

M. J.

Boergers

Vernberg

E. M.

(2001). Overt and relational aggression in adolescents: Social-psychological adjustment of aggressors and victims. Journal of Clinical Child Psychology, 30(4), 479–491.

62.

Prinstein

M. J.

Cillessen

A. H.

(2003). Forms and functions of adolescent peer aggression associated with high levels of peer status. Merrill-Palmer Quarterly, 49(3), 310–342.

63.

Rajendran

Kruszewski

Halperin

J. M.

(2016). Parenting style influences bullying: A longitudinal study comparing children with and without behavioral problems. Journal of Child Psychology and Psychiatry, 57(2), 188–195.

64.

Riddle

Sinclair

(2019). Racial disparities in school-based disciplinary actions are associated with county-level rates of racial bias. Proceedings of the National Academy of Sciences of the United States of America, 116(17), 8255–8260.

65.

Robinson

L. R.

McCord

R. F.

Cloud

L. K.

Kaminski

J. W.

Cook

Amoroso

Watts

M. H.

Kotzky

Barry

C. M.

Johnson

Kelleher

K. J.

(2020). Trends over time and jurisdiction variability in supplemental security income and state supplementary payment programs for children with disabilities. Journal of Public Health Management and Practice, 26, S45–S53. https://doi.org/10.1097/PHH.0000000000001122

66.

Roland

Idsøe

(2001). Aggression and bullying. Aggressive Behavior, 27(6), 446–462.

67.

Saracho

O. N.

(2017). Bullying prevention strategies in early childhood education. Early Childhood Education Journal, 45(4), 453–460.

68.

Schnoes

Reid

Wagner

Marder

(2006). ADHD among students receiving special education services: A national survey. Exceptional Children, 72(4), 483–496.

69.

Schwartz

(2000). Subtypes of victims and aggressors in children's peer groups. Journal of Abnormal Child Psychology, 28(2), 181–192.

70.

Schwartz

Proctor

L. J.

Chien

D. H.

(2001). The aggressive victim of bullying: Emotional and behavioral dysregulation as a pathway to victimization by peers. In Juvonen

Graham

(Eds.), Peer harassment in school: The plight of the vulnerable and victimized (pp. 147–174). Guilford.

71.

Shetgiri

Lin

Avila

R. M.

Flores

(2012). Parental characteristics associated with bullying perpetration in US children aged 10 to 17 years. American Journal of Public Health, 102(12), 2280–2286.

72.

Shetgiri

Lin

Flores

(2012). Identifying children at risk for being bullies in the United States. Academic Pediatrics, 12(6), 509–522.

73.

Staats

(2014). Implicit racial bias and school discipline disparities. Exploring the connection.

74.

Steinfeldt

J. A.

Vaughan

E. L.

LaFollette

J. R.

Steinfeldt

M. C.

(2012). Bullying among adolescent football players: Role of masculinity and moral atmosphere. Psychology of Men & Masculinity, 13(4), 340–353.

75.

Stevens

De Bourdeaudhuij

Van Oost

(2001). Anti-bullying interventions at school: Aspects of programme adaptation and critical issues for further programme development. Health Promotion International, 16(2), 155–167.

76.

Taylor

L. A.

Saylor

Twyman

Macias

(2010). Adding insult to injury: Bullying experiences of youth with attention deficit hyperactivity disorder. Children's Health Care, 39(1), 59–72.

77.

Tippett

Wolke

(2014). Socioeconomic status and bullying: A meta-analysis. American Journal of Public Health, 104(6), e48–e59.

78.

Troop-Gordon

(2017). Peer victimization in adolescence: The nature, progression, and consequences of being bullied within a developmental context. Journal of Adolescence, 55, 116–128.

79.

Underwood

M. K.

(2003). Social aggression among girls. Guilford Press.

80.

Unnever

J. D.

Cornell

D. G.

(2003). Bullying, self-control, and ADHD. Journal of Interpersonal Violence, 18(2), 129–147.

81.

Van Cleave

Davis

M. M

. (2006). Bullying and peer victimization among children with special health care needs. Pediatrics, 118(4), e1212–e1219.

82.

Verlinden

Jansen

P. W.

Veenstra

Jaddoe

V. W. V.

Hofman

Verhulst

F. C.

Shaw

Tiemeier

(2015). Preschool attention-deficit/hyperactivity and oppositional defiant problems as antecedents of school bullying. Journal of the American Academy of Child and Adolescent Psychiatry, 54(7), 571–579.

83.

Whalen

C. K.

Henker

Buhrmester

Hinshaw

S. P.

Huber

Laski

(1989). Does stimulant medication improve the peer status of hyperactive children? Journal of Consulting and Clinical Psychology, 57(4), 545–549.

84.

Wiener

Mak

(2009). Peer victimization in children with attention-Deficit/hyperactivity disorder. Psychology in the Schools, 46(2), 116–131.

85.

Wolke

Lereya

S. T.

(2015). Long-term effects of bullying. Archives of Disease in Childhood, 100(9), 879–885.

86.

Yang

S.-J.

Stewart

Kim

J.-M.

Kim

S.-W.

Shin

I.-S.

Dewey

M. E.

Maskey

Yoon

J. S.

(2013). Differences in predictors of traditional and cyber-bullying: A 2-year longitudinal study in Korean school children. European Child & Adolescent Psychiatry, 22(5), 309–318.

87.

Yen

C.-F.

Yang

Wang

P.-W.

Lin

H.-C.

Liu

T.-L.

Y.-Y.

Tang

T.-C.

(2014). Association between school bullying levels/types and mental health problems among Taiwanese adolescents. Comprehensive Psychiatry, 55(3), 405–413.

Supplementary Material

Please find the following supplemental material available below.

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

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

0.00 MB

0.06 MB