Adverse Childhood Experiences and Adolescent Exposure to Violence

Abstract

Adverse childhood experiences (ACEs) are relatively common and can lead to harmful outcomes in adolescence and adulthood. The current study investigates the relationship between ACEs and exposure to violence in adolescence, an important area of research given the high rates of victimization in adolescence and the need for evidence-based strategies to prevent and reduce the negative consequences of victimization. The study also examines sex differences in the effects of ACEs, given that some research finds that the prevalence and impact of ACEs vary for females and males. Research questions were analyzed using prospective data from 766 to 773 high-risk youth and caregivers participating in the Longitudinal Studies of Child Abuse and Neglect (LONGSCAN). A total of 10 ACEs were assessed, including five types of child maltreatment measured using official data from child protective services agencies and five types of household dysfunction reported primarily by caregivers. Exposure to three types of violence (verbal intimidation, physical victimization, and witnessing violence) were measured using youth self-reports. Logistic regression analyses examined the relationship between the number of ACEs experienced before age 12 and the likelihood of violence exposure from ages 15 to 18. Youth experiencing more ACEs had a significantly greater likelihood of physical victimization (with an adjusted odds ratio of 1.15), but not intimidation (Adjusted Odds Ratio [AOR] = 1.10) or witnessing violence (AOR = 1.11). Sex did not significantly moderate these relationships, but in sex-specific analyses, ACEs significantly increased intimidation and victimization for girls and witnessing violence for boys. Although the findings showed inconsistent evidence of a relationship between ACEs and exposure to violence, they support the need for interventions to reduce ACEs and their impact on exposure to violence during adolescence.

Keywords

gender victimization exposure to violence adverse childhood experiences (ACEs)child maltreatment

Adverse childhood experiences (ACEs) are traumatic and harmful intrafamilial events. They may include child maltreatment (i.e., neglect and abuse) and residence in a dysfunctional home (e.g., having caregivers or other family members with a history of criminality, mental illness, or substance abuse; Felitti et al., 1998). Research indicates that ACEs are relatively common and tend to co-occur, which amplifies their potential to cause harm. In the 2011-2014 Behavioral Risk Factor Surveillance System survey, 62% of adults reported having at least one ACE before age 18, 15% had four or more, and the mean number of ACEs was 1.57 (Merrick et al., 2018). Other studies find that high-risk populations are especially likely to have ACEs (Baglivio & Epps, 2016; Brown & Shillington, 2017; Clements-Nolle & Waddington, 2019; Cronholm et al., 2015). For example, a mean of 4.15 ACEs were reported by a national child welfare sample of 11- to 17-year-olds (Brown & Shillington, 2017). Research also suggests sex differences in exposure to ACEs, with some studies indicating more ACEs among females (Baglivio & Epps, 2016; Duke et al., 2010; Merrick et al., 2018; Petruccelli et al., 2019). In addition, according to official (Sedlak & Basena, 2014) and youth self-reports (Finkelhor et al., 2015), males are somewhat more likely to experience physical abuse, whereas girls are more likely to experience sexual assault. Official data also show that males are somewhat more likely to experience neglect and that the two sexes have similar rates of emotional abuse (Sedlak & Basena, 2014).

Although there is variation across studies in the number, type, and time frame in which ACEs are measured, the majority of research demonstrates a graded relationship between the number of ACEs and the likelihood of physical, mental, and behavioral problems (Felitti et al., 1998; Hughes et al., 2017; Petruccelli et al., 2019). For example, one of the first ACEs studies found that the greater the number of ACEs reported retrospectively by adults, the greater their likelihood of obesity, depression, suicide attempts, alcoholism, illicit drug use, and sexually transmitted diseases (Felitti et al., 1998). The majority of subsequent research has also examined the relationship between ACEs retrospectively reported by adults and the prevalence of adult health problems (Hughes et al., 2017), but a small and growing number of studies have investigated short-term relationships between ACEs and adolescent problem behaviors. Most of this research has shown significant associations between ACEs and adolescent problems including alcohol and/or drug use (Fagan & Novak, 2018; Forster et al., 2017; Schilling et al., 2007), general delinquency (Duke et al., 2010; Schilling et al., 2007), violence (Duke et al., 2010), and arrest (Fagan & Novak, 2018).

Despite the expansion of ACEs research to examine adolescent problems, few studies have investigated the impact of ACEs on adolescent exposure to violence. This is an important gap in the research given the large number of teenagers who experience and/or witness violence (Finkelhor et al., 2015; Kann et al., 2018; Modecki et al., 2014), and because adolescent exposure to violence can increase the likelihood of substance use/abuse and offending over the life course (Buka et al., 2001; Foster & Brooks-Gunn, 2009; Macmillan, 2001). More information about what contributes to adolescent exposure to violence is needed to develop effective violence prevention programs. The goal of the current study is to fill this research gap by investigating the relationship between ACEs experienced in the home and adolescent victimization measured primarily in the peer, school, and neighborhood contexts. Sex differences in these relationships are also examined.

Theoretical Explanations of the Relationship Between ACEs and Adolescent Exposure to Violence

Although little prior research has examined whether ACEs influence adolescent exposure to violence, several theoretical perspectives suggest the possibility of such a relationship. For example, the cycle of violence theory, which posits that child maltreatment increases victims’ perpetration of violence, also suggests that victims will be at risk for additional exposure to violence. Cycle of violence research has indicated that maltreatment can reduce children’s ability to regulate their emotions (Kim & Cicchetti, 2010), increase their impulsivity (Day et al., 2013), and lead to a hostile attribution style that increases the likelihood that children will interpret situations and individuals as threatening (Dodge et al., 1990). These problems may increase children’s aggressive behaviors, which can, in turn, elicit aggressive responses from others (Benedini et al., 2016; Lereya et al., 2013; Yoon et al., 2018). Conversely, some studies have shown that victims of maltreatment have an increased risk of becoming submissive, socially withdrawn, and/or isolated, which can make them targets of intimidation, bullying, and other types of relational violence (Juvonen & Graham, 2014; Lereya et al., 2013; Widom, 2014).

The general theory of crime (Gottfredson & Hirschi, 1990) is also relevant for explaining a relationship between ACEs—particularly dysfunctional caregivers—and victimization. According to this theory, caregivers who have a history of criminal behavior, substance use/abuse, and/or mental illness often fail to adequately monitor children and correct their misbehavior. Children who are not adequately socialized are likely to develop low levels of self-control, which manifests in high levels of impulsivity, an inability to consider the consequences of one’s actions, and a preference for seeking out risky situations and engaging in risky behaviors (Gottfredson & Hirschi, 1990). Empirical tests of this theory have indicated that children with lower levels of self-control are more vulnerable to victimization, including witnessing and experiencing violence (Gibson, 2012; Pratt et al., 2014; Schreck, 1999). Similarly, research has shown that adolescents who spend more time engaging in unsupervised, risky activities have an increased risk of exposure to violence (Jensen & Brownfield, 1986; Maimon & Browning, 2012; Schreck & Fisher, 2004).

Feminist theories can also be used to explain the effect of ACEs on victimization and suggest that the relationship may be stronger for females than males. According to feminist theories, girls spend more time in the home than do boys, and they place greater importance on social relationships (Chesney-Lind, 1997; Gilligan, 1982), which means that they will be more vulnerable to the negative effects of maltreatment and household adversity. Furthermore, feminist theories have posited that maltreatment is more likely to lead to internalizing problems (including substance use and mental health problems) for females and to externalizing problems for males, given gendered socialization practices that discourage female but not male aggression (Chesney-Lind, 1997; Kristman-Valente & Wells, 2013; Kruttschnitt, 2016). In turn, substance use and mental health problems have each been shown to mediate the relationship between child maltreatment and exposure to violence (e.g., Day et al., 2013; Smith et al., 2018), perhaps because they can diminish victims’ ability to recognize and avoid potentially harmful situations. Although feminist theory and some prior research may suggest that girls who experience ACEs will have a greater likelihood of exposure to violence in adolescence compared with boys, this hypothesis requires further testing.

Prior Research on ACEs

Some research examining the cycle of violence has found that child maltreatment increases the likelihood of (re)victimization in adulthood (e.g., Widom et al., 2008) and in adolescence (Benedini et al., 2016; Day et al., 2013; Smith et al., 2018; Tyler et al., 2008; Yoon et al., 2018). While the ACEs literature has not focused extensively on victimization at either developmental period, two meta-analyses (Hughes et al., 2017; Petruccelli et al., 2019) found that higher numbers of ACEs are associated with a greater likelihood of victimization. However, these reviews were based on a small number of studies (six studies in the analysis by Hughes et al., 2017 and three studies in the review by Petruccelli et al., 2019), and respondents in all studies were adults.

The current study identified only one prior study examining the impact of ACEs on adolescent victimization. This research involved a cross-sectional survey of more than 36,000 ninth-grade students in Minnesota, who provided self-reported information on six ACEs (verbal abuse, physical abuse, sexual abuse, exposure to intimate partner violence, household substance use, and parental incarceration) and several types of victimization perpetrated by peers in the school setting (Forster et al., 2020). The findings indicated that ACEs increased the likelihood of relational bullying, physical victimization, verbal threats, and theft/destruction of property. Although the study suggests that ACEs may increase exposure to violence, the data were cross-sectional, which limits the ability to draw strong conclusions about the impact of ACEs on victimization, and the sample was largely White and is not generalizable to other youth populations.

The Minnesota study (Forster et al., 2020) also suggested that the relationship between ACEs and some types of violence exposure differed for boys and girls. ACEs increased the likelihood of physical victimization for boys, but not girls, and the relationship between ACEs and being threatened with a weapon was somewhat larger for males than females. Although the results suggested a stronger association between ACEs and victimization for boys, statistical tests that compared these sex differences were not reported. In the broader ACEs literature, there has been mixed evidence regarding sex differences in the impact of ACEs on adolescent problem behaviors. For example, Schilling et al. (2007) reported that the cumulative impact of ACEs on drug use and delinquency in late adolescence was stronger for boys than girls. However, cross-sectional studies of youth in foster care (Garrido et al., 2018) and a different analysis of the Minnesota student survey data (Duke et al., 2010) have shown similar relationships between ACEs and violence, drug use, and delinquency for boys and girls.

To summarize, prior research has demonstrated that ACEs can have many detrimental outcomes. However, much of this research has focused on adults, has not drawn on prospective data to ensure proper temporal ordering between ACEs and consequences, and has not investigated the impact of ACEs on adolescent victimization. The goal of this study is to address these gaps in the literature by analyzing prospective data to assess the relationship between a relatively large number (10) of ACEs and subsequent exposure to three types of victimization. Moreover, the sample is geographically diverse and includes large numbers of youth from minority racial/ethnic groups, which is important as minority youth are disproportionately exposed to adversities in childhood (Cronholm et al., 2015; Fagan & Novak, 2018) and violence in adolescence (Sheats et al., 2018). Given theoretical discussion and empirical evidence that the prevalence of ACEs, as well as their impact on adolescent problem behaviors, may vary for males and females, the current study also seeks to expand prior research by examining sex differences in the relationship between ACEs and victimization.

Method

The study is based on prospective data from child and caregiver respondents in the Longitudinal Studies of Child Abuse and Neglect (LONGSCAN). The study was begun in 1990 with a high-risk sample of 1,354 children aged 4 to 6 years old and their caregivers (primarily female) recruited from five regions of the United States (Baltimore, Chicago, San Diego, Seattle, and Chapel Hill, NC; Runyan et al., 1998). Participants were selected based on having a history of maltreatment according to Child Protective Service (CPS) agencies or because they were considered at risk for maltreatment based on risk factors such as parents’ low socioeconomic status (SES) and maternal substance use. At baseline, 52% of the 1,354 youth participants were female, 53% were Black, 26% were White, 7% were Hispanic, and 14% reported their race/ethnicity as Mixed or Other. About one third (32%) of caregivers were married, and 62% reported receipt of welfare.

Data were collected from children and/or caregivers every 1 or 2 years from baseline (i.e., ages 4 to 6) to age 18. The current study includes data from baseline to age 16, when 831 youth (61%) remained in the study. Although attrition was high, there were no significant differences in participant demographic characteristics or the mean number of ACEs between those present at baseline and those remaining in the study at age 16. Missing data on the variables included in the analysis were relatively low and addressed using listwise deletion, resulting in an analysis sample of 766 to 773 participants (depending on the outcome). As shown in Table 1, at the age 16 interview, about half (53%) the sample was female; the mean age was 16.3 (range: 15-17.6 years); 62% resided in a household with a single, divorced, separated, or widowed parent; and 26% were in very low-income households (i.e., those earning less than $15,000 annually).

Table 1.

Descriptive Statistics for All Variables, for the Total Sample and by Sex.^a

Variable	Total Sample (N = 762)	Females (N = 403)	Males (N = 359)
ACEs
Physical abuse	32.2	30.5	34.0
Sexual abuse	15.7	21.1	9.7*
Failure to provide	47.4	50.3	44.3
Lack of supervision	40.2	39.0	41.5
Emotional abuse	31.4	31.5	31.2
Intimate partner violence	10.5	10.4	10.6
Caregiver depression	49.3	48.4	50.4
Caregiver substance use/abuse	36.4	33.0	40.1*
Caregiver criminality	30.8	28.3	33.7
Family trauma	40.2	40.2	40.4
Number of ACEs (range)	3.34 (0–10)	3.33 (0–10)	3.36 (0–9)
Dependent Variables
Intimidation	26.3	29.6	22.5*
Victimization	23.3	22.4	24.4
Witnessing violence	69.6	67.2	72.1
Control Variables
Female	52.9	–	–
White	23.9	21.1	27.0
Black	53.4	55.6	51.0
Hispanic	7.6	7.4	7.8
Other race/ethnicity	15.1	15.9	14.3
Age	16.3 (15–17.6)	16.3 (15–17.6)	16.3 (15.7–17.5)
Single parent	61.7	62.5	60.7
Low-income household	27.4	27.3	27.6
Neighborhood disorder	26.2 (14–55)	26.4 (12–56)	26.2 (14–52)

Note. ACE = Adverse Childhood Experiences.

Percentage of the sample (for each type of ACE, all dependent variables, female, single parent, and low-income household) and mean scores (for the total number of ACEs, age, and neighborhood disorder).

*Statistically significant (at p < .05) differences, based on chi-square analysis.

Measures

The measures are based on data collected from CPS agencies, primary caregivers, and children. Descriptive information on the measures, for the full sample and by sex, are provided in Table 1.

Adverse childhood experiences (ACEs)

In all, 10 types of ACEs were measured, five representing maltreatment and five representing different types of household dysfunction. All ACEs were coded as binary variables and indicate exposure to the ACE at least once during childhood, defined in this study as ages 0 to 12.¹

CPS agency records were used to identify children as victims of maltreatment. Although official reports underestimate the number of youth who experience maltreatment, self-reports are also problematic given that memories of traumatic events can be distorted by the shame and emotional distress caused by the events (Cicchetti & Toth, 2005; Smith et al., 2008). To measure maltreatment, LONGSCAN staff reviewed CPS data every 2 years and identified victims of different types of maltreatment using the Modified Maltreatment Coding System (MMCS; Barnett et al., 1993). Five types of maltreatment are used in the current study: physical abuse, sexual abuse, neglect-failure to provide (i.e., physical neglect), neglect-lack of supervision (e.g., the caregiver’s failure to ensure the child’s safety inside and outside the home), and emotional abuse (e.g., the caregiver’s ridiculing, ignoring, or intimidating the child). Participants were considered victims of each type of maltreatment if they had one or more official allegations² of maltreatment from ages 0 to 12.

Data on household dysfunctions were based primarily on reports from primary caregivers. All five measures were coded as binary variables that indicated whether or not any of the ACEs were reported at any time point. Caregivers reported on intimate partner violence (IPV) using items from the Conflict Tactics Scale (CTS; Straus, 1979) at the age 6 and 8 interviews and the Revised Conflict Tactics Scale (CTS2; Straus et al., 1996) at age 12. Somewhat different items were included on the CTS and CTS2, but both surveys asked respondents to report on the frequency of minor and more serious forms of physical aggression occurring between themselves and their intimate partners. This study relied on six to seven items (depending on the year data were collected) measuring severe/serious IPV (e.g., getting beat up, choked, or threatened with a weapon) reported by the primary caregiver at the age 6, 8, and 12 surveys. If the caregiver reported any perpetration or victimization of any act at any of the three time points, IPV was coded as having occurred.³

The primary caregiver’s depression was assessed using self-reports at the age 4, 6, 8, and 12 surveys. At ages 4, 6, and 12, caregivers reported the frequency of 20 depressive symptoms (e.g., “I feel sad,” “I could not get going,” and “I had crying spells”) drawn from the Center for Epidemiologic Studies Depression Scale (CES-D; Radloff, 1977) and rated on a 4-point Likert-type scale (0 = never, 1 = sometimes, 2 = occasionally, 3 = most of the time). As in prior LONGSCAN studies (Flaherty et al., 2013; Thompson et al., 2015), the items were summed at each data point, and those with scores of greater than 16 were coded as having elevated depression. At age 8, caregivers completed the 53-item Brief Symptoms Inventory (BSI), a more comprehensive survey of mental health that includes depression (Derogatis, 1993). Respondents indicated the frequency of positive and negative feelings experienced in the past week using a 5-point Likert-type scale. These items were summed and those with scores greater than 63 were coded as having elevated mental health problems.

The primary caregiver’s alcohol and substance use/abuse was based on self-reports from the age 4 and 8 surveys and children’s reports at age 12. At age 4, caregivers rated their problematic alcohol use according to four binary (no/yes) items (e.g., “have you ever felt you should cut down on your drinking?”) from the four-question “CAGE” questionnaire (Ewing, 1984). Those who rated any of the items as “yes” were classified as problem drinkers. At age 8, caregivers reported on the frequency of alcohol use and current use of marijuana, cocaine, crack, phencyclidine (PCP)/acid, heroin, speed/uppers, or tranquilizers/downers. Those who reported drinking on a daily basis and/or using any of the illegal drugs were coded as drug users/abusers. At age 12, youth were asked to report if “anyone they lived with” got drunk or high; used marijuana, cocaine/crack, or methamphetamines; or injected drugs. A binary variable was created to differentiate youth who reported “yes” to any of these five items from those who did not.

Caregiver criminality was assessed using annual primary caregiver reports from ages 6 through 12. At each year, caregivers reported whether or not the child’s mother or father was arrested or incarcerated in the year prior to the survey. Affirmative answers to any of the questions indicated the presence of parent criminality.

The last ACE, serious family trauma, was also measured using annual reports from the primary caregiver from ages 6 through 12. Each year, the caregiver reported if the child’s mother, father, or sibling had a serious accident or illness in the prior year, or if one of these family members died. Affirmative answers to any of the questions designated the child as having experienced a serious family trauma at that time point.

The 10 individual ACEs were summed to create a variable measuring the total number of ACEs recorded from ages 0 to 12. Because very few respondents reported experiencing seven or more ACEs, an eight-category variety score (ranging from 0 to 7+ ACEs) was used in the analyses.⁴

Dependent variables

Three binary dependent variables measured exposure to different types of violence reported by adolescents at the age 16 interview. The first type, intimidation, was based on one item:

Since you turned 12, about how often did any kids pick on you by chasing you, trying to scare you, threatening you, grabbing your hair or clothes or, forcing you to go somewhere, or do something, you did not want to do?

This item was a modified version of the bullying question from the Juvenile Victimization Questionnaire (Finkelhor et al., 2005). The second dependent variable, physical victimization, was based on one item: “Since you turned 12, about how often did any kids physically hurt you?” The third variable, witnessing violence, was based on six items asking how often in the past year youth had seen someone hit/beaten up, being threatened with a gun, stabbed, shot, killed, and raped. All three variables were rated on a 4-point Likert-type scale (0 = no times, 1 = 1 time, 2 = 2 to 3 times, and 3 = 4 or more times) and were dichotomized to indicate whether or not any victimization was reported. Binary variables were created to be consistent with the majority of ACEs literature and because frequency was skewed, with few respondents reporting multiple exposures to violence.⁵

Although the first two variables were based on only one item each, follow-up questions asked youth to report on their relationship to the perpetrator and (for the physical victimization variable) the type of victimization inflicted and/or injury sustained (e.g., was cut, had broken bones, etc.), which helps ensure the reliability of these items. The witnessing violence measure included multiple items, demonstrated consistency over multiple waves of data collection, and has shown convergent validity (Knight et al., 2014).

Control variables

Five child and family demographic characteristics were included as control variables. Children’s age is a continuous measure based on their date of birth. Children’s sex (female = 1) is based on caregiver reports at baseline. The three family demographic variables were reported by primary caregivers at the age 16 interview (or the age 12 interview if information was missing at age 16). Marital status (i.e., single-parent) was a binary variable differentiating those who report being married (coded as “0”) from those who were single, separated, divorced, or widowed (coded as “1”). Household income was originally assessed using an 11-category ordinal scale. Responses were highly skewed, so they were recoded to differentiate low-income households (less than $15,000, coded as “1”) and higher-income households (greater than $15,000, coded as “0”). The neighborhood disorder variable was included to control for residence in a high-crime neighborhood. Caregivers rated their level of agreement on a 4-point Likert-type scale (1 = strongly disagree, 4 = strongly agree) to 14 items asking about crime and disorder in their neighborhoods (e.g., “there is vandalism … abandoned cars … open drug activity”). These items were summed (α = .94).

Analysis Strategy

All analyses were performed in STATA version 15 (StataCorp, 2017). Logistic regression analyses with robust standard errors were performed given the binary dependent variables and the fact that participants were drawn from five regions of the United States. The relationships between the number of ACEs and the three dependent variables were estimated controlling for the child/family demographic variables. To test sex differences in the impact of ACEs on exposure to violence, interaction terms (sex by ACEs) were included in the main analyses and sex-specific logistic regression models were also estimated.

Results

As shown in Table 1, ACEs were common in this high-risk sample, with a mean of 3.34 adversities and a range of 0 to 10 ACEs (prior to the variable being truncated at 7+ ACEs). The prevalence of individual ACEs ranged from 10.5% (for intimate partner violence) to 47.4% (for neglect-failure to provide). Males and females had similar levels of exposure to most ACEs, but females were significantly more likely to be sexually abused and males to have experienced caregiver substance use/abuse. In terms of the dependent variables, intimidation (26.3%) and victimization (23.3%) were less common than witnessing violence (69.6%), and females were more likely than males to experience intimidation but not the other forms of violence.

The results of the multivariable logistic regression analyses are shown in Table 2. For the full sample, youth with a greater number of ACEs were not significantly more likely to report intimidation compared with those with fewer ACEs (adjusted odds ratio [AOR] = 1.10). Three control variables were related to intimidation. Females (AOR = 1.46) compared with males and older compared with younger (AOR = 1.66) adolescents were significantly more likely to report any intimidation. In addition, Black adolescents were less likely than Whites (AOR = 0.67) to report intimidation.

Table 2.

The Relationship Between the Number of ACEs Reported at Ages 0 to 12 and the Odds of Reporting Adolescent Exposure to Violence.

Variable	Intimidation (N = 766)	Victimization (N = 771)	Witnessing Violence (N = 773)
Number of ACEs	1.10 [0.98, 1.24]	1.15 [1.02, 1.30]*	1.11 [0.98, 1.26]
Female	1.46 [1.16, 1.83]**	0.94 [0.77, 1.17]	0.75 [0.54, 1.05]
Age	1.66 [1.14, 2.43]**	1.94 [1.41, 2.68]**	0.75 [0.32, 1.77]
Blacka	0.67 [0.46, 0.98]*	0.62 [0.34, 1.13]	2.57 [1.39, 4.74]**
Hispanica	1.09 [0.76, 1.56]	0.65 [0.29, 1.46]	1.29 [0.86, 1.95]
Othera	1.03 [0.78, 1.36]	0.67 [0.34, 1.35]	1.27 [0.91, 1.77]
Single parent	0.99 [0.66, 1.50]	1.41 [1.04, 1.89]*	1.05 [0.78, 1.44]
Low income	0.74 [0.41, 1.33]	0.67 [0.27, 1.67]	0.86 [0.45, 1.64]
NB disorder	0.97 [0.93, 1.01]	0.98 [0.95, 1.02]	1.02 [1.00, 1.05]

Note. Models are based on logistic regression analysis with robust standard errors to adjust for clustering of participants by site. Adjusted odds ratios are shown with 95% confidence intervals in parentheses. ACE = Adverse Childhood Experiences; NB = neighborhood.

Compared with Whites.

*p < .05.

**p < .01 (two-tailed).

In the full sample, the number of ACEs was significantly related to physical victimization (see Table 2). The adjusted odds ratio of 1.15 indicated that, for every additional ACE, there was a 15% increase in the odds that youth would experience physical victimization. Older compared with younger adolescents (AOR = 1.94), and those living in single-parent compared with two-parent homes (AOR = 1.41), were more likely to report any physical victimization.

The number of ACEs did not significantly predict the chances that adolescents would report any intimidation (AOR = 1.10). Three of the control variables were related to intimidation. Females (AOR = 1.46) compared with males and older compared with younger (AOR = 1.66) adolescents were significantly more likely to report any intimidation. In addition, Black adolescents were less likely than Whites to report intimidation (AOR = 0.67), but there were no significant differences between Whites and youth from other racial/ethnic groups. The number of ACEs was not significantly related to witnessing violence (AOR = 1.11). Only one variable predicted this outcome: the odds of witnessing violence were greater (AOR = 2.57) for Blacks compared with Whites, but no other racial/ethnic differences were found for this outcome.

Interaction terms included in the three models were not statistically significant (results not shown), which indicated that the impact of ACEs on victimization was similar for males and females. However, when the sample was split by sex, small differences in effects were evidenced. As shown in Table 3, for females but not males, the number of ACEs significantly increased the likelihood that youth would experience intimidation (AOR = 1.19) and physical victimization (AOR = 1.19). For males but not females, a greater number of ACEs increased the likelihood of witnessing any violence (AOR = 1.17). Some differences in the impact of control variables on outcomes were also evidenced in the sex-specific models (see Table 3). For example, for females, Black youth and those from other racial/ethnic groups were more likely to report intimidation compared with White females; however, Black males were less likely than White males to report intimidation and victimization, but more likely to witness violence. Similarly, Hispanic females were less likely than White females to report victimization, but Hispanic males were more likely to witness violence than White males.

Table 3.

The Relationship Between the Number of ACEs Reported at Ages 0 to 12 and the Odds of Reporting Adolescent Exposure to Violence, by Sex.

Variable	Intimidation		Victimization		Witnessing Violence
Variable	Female (N = 405)	Male (N = 361)	Female (N = 408)	Male (N = 363)	Female (N = 410)	Male (N = 363)
Number of ACEs	1.19* [1.01, 1.40]	1.00 [0.87, 1.16]	1.19** [1.06, 1.34]	1.12 [0.91, 1.38]	1.08 [0.91, 1.27]	1.17* [1.03, 1.32]
Age	1.85* [1.08, 3.17]	1.55 [0.68, 3.54]	1.74** [1.17, 2.58]	2.55** [1.57, 4.16]	0.96 [0.42, 2.20]	0.57 [0.21, 1.53]
Black^a	1.28* [0.21, 0.96]	0.31** [0.15, 0.64]	1.02 [0.70, 1.47]	0.36* [0.14, 0.93]	2.94** [1.39, 6.22]	2.41** [1.33, 4.35]
Hispanic^a	1.10 [0.91, 1.33]	1.23 [0.72, 2.10]	0.30** [0.12, 0.74]	1.09 [0.41, 2.90]	0.91 [0.47, 1.75]	2.25** [1.28, 3.95]
Other^a	1.45* [1.01, 2.08]	0.93 [0.57, 1.54]	0.71 [0.41, 1.21]	0.66 [0.25, 1.74]	1.50 [0.96, 2.35]	0.99 [0.54, 1.83]
Single parent	0.85 [0.47, 1.54]	1.37 [0.70, 2.68]	1.24 [0.60, 2.57]	1.58 [0.92, 2.71]	0.78 [0.54, 1.13]	1.52* [1.05, 2.20]
Low income	0.53* [0.29, 0.98]	1.07 [0.53, 2.17]	0.72 [0.26, 1.97]	0.57 [0.23, 1.42]	1.03 [0.57, 1.87]	0.71 [0.34, 1.45]
NB disorder	0.99 [0.94, 1.05]	0.93** [0.91, 0.95]	0.98* [0.96, 1.00]	0.99 [0.93, 1.04]	1.03 [0.99, 1.07]	1.01 [0.99, 1.03]

Compared with Whites.

*p < .05.

**p < .01 (two-tailed).

Discussion

The results of this study add to an accumulating body of research demonstrating that ACEs have many negative consequences for youth, and the current study extends this research to suggest that ACEs increase the likelihood of certain types of adolescent victimization. In this study, the number of ACEs experienced in childhood (ages 0-12) increased the likelihood of physical but not verbal violence (i.e., intimidation) or witnessing violence. These findings differ somewhat from those demonstrated in the one prior study that also assessed the relationship between ACEs and adolescent victimization (Forster et al., 2020). That cross-sectional study of 11th graders in Minnesota found more consistent evidence of a relationship between ACEs and victimization, as students reporting more ACEs in that study were significantly more likely than those with fewer ACEs to report all four types of victimization examined (relational bullying, physical bullying, being threatened with a weapon and being a victim of theft or destruction of property; Forster et al., 2020).

It should be emphasized, however, that the variation in findings between the two studies could be due to a variety of methodological differences. For example, the Minnesota study measured fewer ACEs (six, compared with 10 in the current study) and relied on youth to report ACEs and outcomes (which could have inflated the relationship given same-source reporting bias), whereas the current study used independent sources for the independent and dependent variables. In addition, Forster et al. (2020) analyzed cross-sectional data and so could not rule out the possibility of reciprocal effects, whereas the current results were based on prospective data. The current study involved a high-risk sample of mostly minority youth, the majority of whom had had official contact with the child welfare system, whereas Forster and colleagues (2020) analyzed data from a largely White sample of high school students from Minnesota. Although results of the current study cannot be generalized to other types of populations, the high-risk sample is a strength, as it is especially important to understand the causes and consequences associated with the elevated levels of childhood adversities among high-risk populations (e.g., the current sample experienced a mean of 3.3 ACEs compared with 0.54 in the Minnesota study).

The methods used in the current study represented a rigorous test of the relationship between ACEs and exposure to violence, and the findings from the main analyses suggest that ACEs are more likely to affect interpersonal violence (e.g., physical victimization) than intimidation or witnessing violence. However, the factors that may explain this difference were not examined in this study, and additional research is needed to identify if and why ACEs lead to different types of victimization and the mechanisms that may mediate these relationships. More research is also needed to explore the possibility that ACEs may affect males and females in different ways. The fact that the interaction terms included in the main analyses were not statistically significant indicate that sex differences in the relationship between ACEs and exposure to violence were not large. Nonetheless, results from the sex-specific analyses indicated that ACEs significantly increased the likelihood of intimidation and physical victimization for females but not males and witnessing violence for males but not females. These findings differed somewhat from the Forster et al. (2020) study that suggested that ACEs had a stronger effect for males than females on three types of victimization (physical victimization, theft, and being threatened by a weapon), but the results of formal tests of sex differences were not reported in that study.

Feminist research has suggested that child maltreatment places girls at greater risk than boys for substance use and other internalizing problems (Kristman-Valente & Wells, 2013; Kruttschnitt, 2016), and these problems could increase their risk of victimization. Conversely, boys are more likely than girls to spend time engaging in risky activities and to associate with delinquent peers (McCarthy et al., 2004; Mears et al., 1998; Osgood et al., 1996), both of which are likely to increase their potential to witness violence as found in the current study. However, these hypotheses require further testing as the factors that may mediate the relationship between ACEs and exposure to violence for girls and boys were not examined in the current study. Future research should include adequate numbers of males and females to conduct sex-specific analyses and test whether or not sex differences exist in the relationships between ACEs and different types of victimization.

Additional research is also needed to address some of the other limitations of this study. Regarding the dependent variables, the item used to assess physical victimization does not specify the intent of the party causing harm and may, therefore, capture some accidental acts of violence. More generally, all three outcomes were based on binary indicators, and different results may be evidenced when taking frequency and/or multiple victimizations into account. Future research should, therefore, investigate if ACEs affect repeated or frequent exposure to violence as well as poly-victimization (i.e., exposure to multiple types of victimization; see Finkelhor et al., 2007). More research is also needed to examine a wider array of victimization experiences, including sexual assault and harassment, as well as sex differences in these outcomes.

Some of the ACEs measures included in this study also had limitations. Official data based on allegations were used to measure maltreatment, and these reports likely under-estimated the prevalence of adversities in the sample. It is also possible that different results would have been found if substantiated cases, rather than allegations, were relied upon. In terms of the household adversity measures, the prevalence of intimate partner violence may have been underestimated given that children whose caregivers were not cohabitating were coded as not experiencing this ACE. Finally, although the study aimed to assess parental substance use/abuse, the age 12 measure asked children to report substance use by household members, not caregivers.

One substantive limitation of the current study is that it did not examine if ACEs varied in prevalence or impact by sex and other demographic characteristics such as race/ethnicity. Investigation of the interrelationships between ACEs, sex, and race/ethnicity was beyond the scope of this study, but this is an important area of future research given evidence that minority youth may be disproportionately exposed to and/or affected by ACEs (Cronholm et al., 2015; Fagan & Novak, 2018) and have higher rates of victimization in adolescence (Sheats et al., 2018).

While there is a need for additional research, findings from the current study add to a growing body of the literature indicating that ACEs have detrimental consequences for adolescents. They also emphasize the need for strategies to prevent ACEs and adolescent exposure to violence. For example, interventions should be implemented to reduce child maltreatment and improve child-rearing skills, such as home visitation programs that help new mothers care for their newborn children (Gomby et al., 1999; Olds, 2002) and programs that help parents of older children provide supportive and nurturing caregiving and avoid abusive behaviors (Carr, 2014; Chaffin et al., 2004; Prinz & Sanders, 2007). School-based programs can also be delivered to help youth cope with trauma, improve their social and emotional learning skills, and reduce their potential to experience interpersonal violence (Greenberg et al., 2003; Hahn et al., 2007; U.S. Department of Justice & U.S. Department of Health and Human Services, 2011). Such services include, for example, bullying prevention programs that can be implemented at the school, classroom, and individual levels (Bradshaw, 2015; Evans et al., 2014). Finally, teachers and other school staff and administrators may benefit from training to ensure their interactions take into account the potential that youth have experienced trauma. Greater use of all of these interventions may reduce both ACEs and adolescent revictimization.

Supplemental Material

sj-pdf-1-jiv-10.1177_0886260520926310 - Supplemental material for Adverse Childhood Experiences and Adolescent Exposure to Violence

Supplemental material, sj-pdf-1-jiv-10.1177_0886260520926310 for Adverse Childhood Experiences and Adolescent Exposure to Violence by Abigail A. Fagan PhD in Journal of Interpersonal Violence

Footnotes

Author’s Note

The data used in this study were made available by the National Data Archive on Child Abuse and Neglect (NDACAN). NDACAN does not bear any responsibility for the analyses or conclusions presented here.

Declaration of Conflicting Interests

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

Funding

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

ORCID iD

Abigail A. Fagan

Supplemental Material

Supplemental material for this article is available online.

Notes

Author Biography

Abigail A. Fagan is a professor in the Department of Sociology and Criminology & Law at the University of Florida. Her research focuses on the causes and prevention of adolescent offending, particularly family and community risk factors for delinquency, as well as the role of gender and exposure to violence on offending. She also examines issues related to the identification, implementation, and dissemination of evidence-based preventive interventions.

References

Baglivio

M. T.

Epps

(2016). The interrelatedness of adverse childhood experiences among high-risk juvenile offenders. Youth Violence and Juvenile Justice, 14, 179–198.

Barnett

Manly

J. T.

Cicchetti

(1993). Defining child maltreatment: The interface between policy and research. In Cicchetti

Toth

S. L.

(Eds.), Advances in applied developmental psychology: Child abuse, child development and social policy (pp. 7–73). Ablex.

Benedini

K. M.

Fagan

A. A.

Gibson

C. L.

(2016). The cycle of victimization: The relationship between childhood maltreatment and adolescent peer victimization. Child Abuse and Neglect, 59, 111–121.

Bradshaw

C. P.

(2015). Translating research to practice in bullying prevention. American Psychologist, 70, 322–332.

Brown

S. M.

Shillington

A. M.

(2017). Childhood adversity and the risk of substance use and delinquency: The role of protective adult relationships. Child Abuse and Neglect, 63, 211–221.

Buka

S. L.

Stichick

T. L.

Birdthistle

Earls

(2001). Youth exposure to violence: Prevalence, risk and consequences. American Journal of Orthopsychiatry, 71, 298–310.

Carr

(2014). The evidence base for family therapy and systemic interventions for child-focused problems. Journal of Family Therapy, 36, 107–157.

Chaffin

Silovsky

J. F.

Funderburk

Valle

L. A.

Brestan

E. V.

Balachova

, … Bonner

B. L.

(2004). Parent–child interaction therapy with physically abusive parents: Efficacy for reducing future abuse reports. Journal of Clinical Child and Adolescent Psychology, 72, 500–510.

Chesney-Lind

(1997). The female offender: Girls, women and crime. Sage.

10.

Cicchetti

Toth

S. L.

(2005). Child maltreatment. Annual Review of Clinical Psychology, 1, 409–438.

11.

Clements-Nolle

Waddington

(2019). Adverse childhood experiences and psychological distress in juvenile offenders: The protective influence of resilience and youth assets. Journal of Adolescent Health, 64, 49–55.

12.

Collins

W. A.

(Ed.). (1984). Development during middle childhood: The years from six to twelve. National Academies Press.

13.

Cronholm

P. F.

Forke

C. M.

Wade

Bair-Merritt

M. H.

Davis

Harkins-Schwarz

Pachter

L. M.

(2015). Adverse Childhood Experiences: Expanding the concept of adversity. American Journal of Preventive Medicine, 49, 354–361.

14.

Day

D. M.

Wanklyn

S. G.

McCay

Hart

Macpherson

Burnier

(2013). Potential mediators between child abuse and both violence and victimization in juvenile offenders. Psychological Services, 10, 1–11.

15.

Derogatis

L. R.

(1993). Brief symptom inventory. Pearson Education.

16.

Dodge

K. A.

Bates

J. E.

Pettit

G. S.

(1990). Mechanisms in the cycle of violence. Science, 250, 1678–1683.

17.

Dube

S. R.

Felitti

V. J.

Dong

Chapman

D. P.

Giles

W. H.

Anda

R. F.

(2003). Childhood abuse, neglect, and household dysfunction and the risk of illicit drug use: The Adverse Childhood Experiences study. Pediatrics, 111, 564–572.

18.

Duke

Pettingell

S. L.

McMorris

B. J.

Borowsky

(2010). Adolescent violence perpetration: Associations with multiple types of adverse childhood experiences. Pediatrics, 125, e778–e786.

19.

Evans

C. B. R.

Fraser

M. W.

Cotter

K. L.

(2014). The effectiveness of school-based bullying prevention programs: A systematic review. Aggression and Violent Behavior, 19, 532–544.

20.

Ewing

J. A.

(1984). Detecting alcoholism: The CAGE questionnaire. Journal of the American Medical Association, 252, 1905–1907.

21.

Fagan

A. A.

Novak

(2018). Adverse childhood experiences and adolescent delinquency in a high-risk sample: A comparison of White and Black youth. Youth Violence and Juvenile Justice, 16, 395–417.

22.

Felitti

V. J.

Anda

R. F.

Nordenberg

Williamson

D. F.

Spitz

A. M.

Edwards

V. J.

, … Marks

J. S.

(1998). Relationship of childhood abuse and household dysfunction to many of the leading causes of death in adults: The Adverse Childhood Experiences (ACE) Study. American Journal of Preventive Medicine, 14, 245–258.

23.

Finkelhor

Ormrod

R. K.

Turner

H. A.

(2007). Poly-victimization: A neglected component in child victimization. Child Abuse and Neglect, 31, 7–26.

24.

Finkelhor

Ormrod

R. K.

Turner

H. A.

Hamby

S. L.

(2005). Measuring poly-victimization using the Juvenile Victimization Questionnaire. Child Abuse and Neglect, 29, 1297–1312.

25.

Finkelhor

Turner

H. A.

Shattuck

Hamby

Kracke

(2015). Children’s exposure to violence, crime, and abuse: An update. Office of Juvenile Justice and Delinquency Prevention, US Department of Justice.

26.

Flaherty

E. G.

Thompson

Dubowitz

Harvey

E. M.

English

Proctor

L. J.

Runyan

D. K.

(2013). Adverse childhood experiences and child health in early adolescence. Journal of the American Medical Association Pediatrics, 167, 622–629.

27.

Flaherty

E. G.

Thompson

Litrownik

A. J.

Zolotor

A. J.

Dubowitz

Runyan

, … Everson

M. D.

(2009). Adverse childhood exposures and reported child health at age 12. Academic Pediatrics, 9, 150–160.

28.

Forster

Gower

A. L.

Borowsky

I. W.

McMorris

B. J.

(2017). Associations between adverse childhood experiences, student-teacher relationships, and non-medical use of prescription medications among adolescents. Addictive Behaviors, 68, 30–34.

29.

Forster

Gower

A. L.

McMorris

B. J.

Borowsky

I. W.

(2020). Adverse childhood experiences and school-based victimization and perpetration. Journal of Interpersonal Violence, 35, 662–881.

30.

Foster

Brooks-Gunn

(2009). Toward a stress process model of children’s exposure to physical family and community violence. Clinical Child and Family Psychology Review, 12, 71–94.

31.

Garrido

E. F.

Weiler

L. M.

Taussig

H. N.

(2018). Adverse childhood experiences and health-risk behaviors in vulnerable early adolescents. Journal of Early Adolescence, 38, 661–680.

32.

Gibson

C. L.

(2012). An investigation of neighborhood disadvantage, low self-control, and violent victimization among youth. Youth Violence and Juvenile Justice, 10, 41–63.

33.

Gilligan, C. (1982). In a different voice: Psychological theory and women's development. Cambridge, MA: Harvard University Press.

34.

Godinet

M. T.

Berg

(2014). Early childhood maltreatment and trajectories of behavioral problems: Exploring gender and racial differences. Child Abuse and Neglect, 38, 544–556.

35.

Gomby

D. S.

Culross

P. L.

Berhman

R. E.

(1999). Home visiting: Recent program evaluations—Analysis and recommendations. The Future of Children, 9, 4–26.

36.

Gottfredson

M. R.

Hirschi

(1990). A general theory of crime. Stanford University Press.

37.

Greenberg

M. T.

Weissberg

R. P.

O’Brien

M. U.

Zins

J. E.

Fredericks

Resnik

Elias

M. J.

(2003). Enhancing school-based prevention and youth development through coordinated social, emotional, and academic learning. American Psychologist, 58, 466–474.

38.

Hahn

Fuqua-Whitley

Wethington

Lowy

Crosby

Fullilove

, … Dahlberg

(2007). Effectiveness of universal school-based programs to prevent violence and aggressive behavior: A systematic review. American Journal of Preventive Medicine, 33, S114–S129.

39.

Hughes

Bellis

M. A.

Hardcastle

K. A.

Sethi

Butchart

Mikton

, … Dunne

M. P.

(2017). The effect of multiple adverse childhood experiences on health: A systematic review and meta-analysis. Lancet Public Health, 2, e356–e366.

40.

Hussey

J. M.

Marshall

J. M.

English

D. J.

Knight

E. D.

Lau

A. S.

Dubowitz

Kotch

J. B.

(2005). Defining maltreatment according to substantiation: Distinction without a difference? Child Abuse and Neglect, 29, 479–492.

41.

Institute of

Medicine

National

Research Council.

(2014). New directions in child abuse and neglect research. The National Academies Press.

42.

Jensen

G. F.

Brownfield

(1986). Gender, lifestyles, and victimization: Beyond routine activity. Violence and Victims, 1, 85–99.

43.

Juvonen

Graham

(2014). Bullying in schools: The power of bullies and the plight of victims. Annual Review of Psychology, 65, 159–185. https://doi.org/10.1146/annurev-psych-010213-115030

44.

Kann

McManus

Harris

W. A.

Shanklin

S. L.

Flint

K. H.

Queen

, … Ethier

K. A.

(2018). Youth Risk Behavior Surveillance–United States, 2017. MMWR Surveillance Summary, 67, 1–114.

45.

Kim

J. E.

Cicchetti

(2010). Longitudinal pathways linking child maltreatment, emotion regulation, peer relations, and psychopathology. Journal of Child Psychology and Psychiatry, 51, 706–716.

46.

Knight

E. D.

Smith

J. B.

Martin

L. M.

Lewis

, & The LONGSCAN Developers. (2014). Measures for assessment of functioning and outcomes in Longitudinal Research on Child Abuse and Neglect Volume 4: Middle adolescence (age 16). LONGSCAN Coordinating Center, University of North Carolina.

47.

Kohl

P. L.

Jonson-Reid

Drake

(2009). Time to leave substantiation behind: Findings from a national probability study. Child Maltreatment, 14, 17–26.

48.

Kristman-Valente

Wells

E. A.

(2013). The role of gender in the association between child maltreatment and substance use behavior: A systematic review of longitudinal research from 1995 to 2011. Substance Use and Misuse, 48, 645–660.

49.

Kruttschnitt

(2016). 2015 Presidential address to the American Society of Criminology: The politics, and place, of gender in research on crime. Criminology, 54, 8–29.

50.

Lereya

S. T.

Samara

Wolke

(2013). Parenting behavior and the risk of becoming a victim and a bully/victim: A meta-analysis study. Child Abuse and Neglect, 37, 1091–1108.

51.

Macmillan

(2001). Violence and the life course: The consequences of victimization for personal and social development. American Review of Sociology, 27, 1–22.

52.

Maimon

Browning

C. R.

(2012). Adolescents’ violent victimization in the neighbourhood: Situational and contextual determinants. British Journal of Criminology, 52, 808–833.

53.

McCarthy

Felmlee

Hagan

(2004). Girl friends are better: Gender, friends, and crime among school and street youth. Criminology, 42, 805–835.

54.

Mears

D. P.

Ploeger

Warr

(1998). Explaining the gender gap in delinquency: Peer influence and moral evaluations of behavior. Journal of Research in Crime and Delinquency, 35, 251–266.

55.

Merrick

M. T.

Ford

D. C.

Ports

K. A.

Guinn

A. S.

(2018). Prevalence of adverse childhood experiences from the 2011-2014 Behavioral Risk Factor Surveillance System in 23 states. JAMA Pediatrics, 172, 1038–1044.

56.

Modecki

K. L.

Minchin

Harbaugh

A. G.

Guerra

N. G.

Runions

K. C.

(2014). Bullying prevalence across contexts: A meta-analysis measuring cyber and traditional bullying. Journal of Adolescent Health, 55, 602–611.

57.

Olds

(2002). Prenatal and infancy home visiting by nurses: From randomized trials to community replication. Prevention Science, 3, 153–172.

58.

Osgood

D. W.

Wilson

J. K.

O’Malley

P. M.

Bachman

J. G.

Johnston

L. D.

(1996). Routine activities and individual deviant behavior. American Sociological Review, 61, 635–655.

59.

Petruccelli

Davis

Berman

(2019). Adverse childhood experiences and associated health outcomes: A systematic review and meta-analysis. Child Abuse and Neglect, 9, Article 104127.

60.

Pratt

T. C.

Turanovic

J. J.

Fox

K. A.

Wright

K. A.

(2014). Self-control and victimization: A meta-analysis. Criminology, 52, 87–116.

61.

Prinz

R. J.

Sanders

M. R.

(2007). Adopting a population-level approach to parenting and family support interventions. Clinical Psychology Review, 27, 739–749.

62.

Radloff

L. S.

(1977). The CES-D scale: A self-report depression scale for research in the general population. Applied Psychological Measurement, 1, 385–401.

63.

Runyan

D. K.

Curtis

P. A.

Hunter

W. M.

Black

M. M.

Kotch

J. B.

Bangdiwala

S. I.

, … Landsverk

(1998). LONGSCAN: A consortium for longitudinal studies of maltreatment and the life course of children. Aggression and Violent Behavior, 3, 275–285.

64.

Schilling

E. A.

Aseltine

J. R. H.

Gore

(2007). Adverse childhood experiences and mental health in young adults: A longitudinal survey. BMC Public Health, 7, Article 30.

65.

Schreck

C. J.

(1999). Criminal victimization and low self-control: An extension and test of a general theory of crime. Justice Quarterly, 19, 159–180.

66.

Schreck

C. J.

Fisher

B. S.

(2004). Specifying the influence of family and peers on violence victimization: Extending routine activities and lifestyles theories. Journal of Interpersonal Violence, 19, 1021–1041.

67.

Sedlak

A. J.

Basena

(2014). Online access to the Fourth National Incidence Study of Child Abuse and Neglect. Westat. http://www.nis4.org

68.

Sheats

K. J.

Irving

S. M.

Mercy

J. A.

Simon

T. R.

Crosby

A. E.

Ford

D. C.

, et al, . (2018). Violence-related disparities experienced by Black youth and young adults: Opportunities for prevention. American Journal of Preventive Medicine, 55, 462–469.

69.

Smith

C. A.

Ireland

T. O.

Thornberry

T. P.

Elwyn

(2008). Childhood maltreatment and antisocial behavior: Comparison of self-reported and substantiated maltreatment. American Journal of Orthopsychiatry, 78, 173–186.

70.

Smith

K. Z.

Smith

P. H.

Oberleitner

L. M.

Grekin

E. R.

McKee

S. A.

(2018). Child maltreatment and physical victimization: Does heavy drinking mediate the relationship? Child Maltreatment, 23, 234–243.

71.

StataCorp. (2017). Stata statistical software: Release 15.

72.

Straus

M. A.

(1979). Measuring intra family conflict and violence: The Conflict Tactics Scale. Journal of Marriage and the Family, 41, 75–88.

73.

Straus

M. A.

Hamby

S. L.

Boney-McCoy

Sugarman

D. B.

(1996). The Revised Conflict Tactics Scales (CTS2): Development and preliminary psychometric data. Journal of Family Issues, 17, 283–316.

74.

Thompson

Flaherty

E. G.

English

D. J.

Litrownik

A. J.

Dubowitz

Kotch

J. B.

Runyan

D. K.

(2015). Trajectories of adverse childhood experience and self reported health at age 18. Academic Pediatrics, 15, 503–509.

75.

Tyler

K. A.

Johnson

K. A.

Brownridge

D. A.

(2008). A longitudinal study of the effects of child maltreatment on later outcomes among high-risk adolescents. Journal of Youth and Adolescence, 37, 506–521.

76.

U.S. Department of Justice & U.S. Department of Health and Human Services. (2011). Evidence-based practices for children exposed to violence: A selection from federal databases.

77.

Widom

C. S.

(2014). Longterm consequences of child maltreatment. In Korbin

J. E.

Krugman

R. D.

(Eds.), Handbook of child maltreatment (pp. 225–247). Springer.

78.

Widom

C. S.

Czaja

S. J.

Dutton

M. A.

(2008). Childhood victimization and lifetime revictimization. Child Abuse and Neglect, 32, 785–796.

79.

Yoon

Park

Yoon

(2018). A pernicious cycle: Finding the pathways from child maltreatment to adolescent peer victimization. Child Abuse and Neglect, 81, 139–148.

Supplementary Material

Please find the following supplemental material available below.

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

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

0.00 MB

0.04 MB