Childhood Adversity Trajectories and Violent Behaviors in Adolescence and Early Adulthood

Abstract

Violence and other antisocial behaviors, including fighting and weapon carrying, are highly prevalent among adolescents but usually decrease in young adulthood. Childhood adversities, including exposure to abuse, intimate partner violence, and household substance use and mental health problems, have been linked to violent behaviors in adolescence and adulthood. However, few studies of childhood adversity as determinants of persistent violent behavior among community-based samples have been conducted. Furthermore, the effects of adversity timing and duration on subsequent violent behaviors are unclear. We examined the association between five childhood adversity trajectories (representing stable-low, stable-mild, decreasing, increasing, and stable-high adversity from birth through age 11.5 years) and physical fighting and weapon carrying at ages 13-20 years among a sample of young adults followed continuously since birth from the Avon Longitudinal Study of Parents and Children (n = 9,665). The prevalence of violent behaviors declined sharply as participants aged (e.g., whereas 42.8% reported engaging in physical fighting in the past year at ages 13-15 years, this dropped to 10.4% at ages 17-20 years). Childhood adversity trajectories exhibited a strong dose-response relation with physical fighting and weapon carrying, with particularly pronounced relations for violent behaviors persisting across both adolescence and early adulthood (e.g., for physical fighting at both ages 13-15 years and 17-20 years compared to no fighting at either period, adjusted odds ratio [aOR] = 1.62, 95% confidence interval [CI] = 1.31-2.00 for stable-mild; aOR = 2.33, 95% CI = 1.64-3.33 for decreasing; aOR = 3.18, 95% CI = 2.20-4.60 for increasing; and aOR = 3.73, 95% CI = 2.13-6.52 for stable-high adversity, compared to stable-low adversity). This work highlights the substantial implications of exposure to childhood adversity for youth violence prevention.

Keywords

ALSPAC childhood adversity adverse childhood experiences trajectories violence violent behaviors physical fighting weapon carrying

Introduction

Childhood adversities, including exposure to abuse, intimate partner violence, and household substance use and mental health problems, often co-occur (Felitti et al., 1998). The cumulative burden of these childhood adversities may increase risks for a variety of negative health consequences and behaviors (Campbell et al., 2016; Dube et al., 2003; Tracy et al., 2019). Exposure to childhood adversity has been linked to violent behaviors in adulthood, including physical attacks and intimate partner violence (Bellis et al., 2014; González et al., 2016), and to aggression and violent behaviors in childhood and adolescence (Duke et al., 2010; Forster et al., 2020; Hunt et al., 2017; Saner & Ellickson, 1996). Childhood adversities have also been implicated in contact with the criminal justice system and persistent trajectories of criminal offending (Basto-Pereira et al., 2016; Craig et al., 2017; Fox et al., 2015; Graf et al., 2021). Violent behaviors in adolescence and young adulthood may thus reflect childhood victimization in a variety of settings and may have serious lifelong implications, including increased risks of mental health and substance use problems (Brady et al., 2008; Green et al., 2014) and continued involvement in violence in later adulthood (Capaldi et al., 2012; Honorato et al., 2016; Reed et al., 2014).

Some prior work on the relation between childhood adversity and violent behavior has relied on the retrospective reporting of childhood adversities by adults or been limited to cross-sectional data (Bellis et al., 2014; Duke et al., 2010; Forster et al., 2020). However, longitudinal studies of these relations have also been conducted. For example, Hunt et al. found strong relations between prospectively assessed childhood adversities through age 5 years and externalizing behavior problems at age 9 years in the Fragile Families and Child Well-being Study, a predominantly lower-income U.S.-based birth cohort study (Hunt et al., 2017). Another prospective study demonstrated increased risk of engagement in multiple types of violent behaviors among adults who had documented histories of child abuse and/or neglect, reinforcing evidence for the “cycle of violence” (Milaniak & Widom, 2015).

These prospective findings help illuminate the many mechanisms that may link experiences of childhood adversity to violent behavior later in life. Studies on the “cycle of violence” often cite Bandura’s social learning theory (Bandura, 1977), in which aggressive and abusive behaviors in adolescents and adults are modeled on behaviors by their family members that they observed as children (Basto-Pereira et al., 2016; Brewer-Smyth et al., 2015; Milaniak & Widom, 2015; Saner & Ellickson, 1996). Childhood abuse may also disrupt cognitive processes around social information, leading to hostile attribution biases and aggressive responses (Basto-Pereira et al., 2016; Keiley et al., 2001; Lansford et al., 2007). More broadly, exposure to adversities in childhood may disrupt the development of the biological stress system and alter brain functioning around stress response and self-control, leading to emotion dysregulation and psychopathology, which increase the risk of violent behaviors (Basto-Pereira et al., 2016; Brewer-Smyth et al., 2015; Loeber & Hay, 1997). Furthermore, chronic exposure to a wide array of adversities throughout childhood may strain individuals’ coping resources and weaken bonds to family and other institutions that positively influence social norms, resulting in anger, aggression, and other deviant behaviors (Karriker-Jaffe et al., 2011; Keiley et al., 2001; Milaniak & Widom, 2015; Saner & Ellickson, 1996).

The relation between childhood adversity and violent behavior may also vary for different types of youth violent behaviors, which can take many different forms, including physical fighting (e.g., hitting, slapping, and kicking), weapon carrying, weapon use, and dating violence (CDC, 2019). Although much previous work only considers determinants of composite measures of violent behaviors in adolescence and young adulthood (Brumley et al., 2017; Karriker-Jaffe et al., 2011; Lansford et al., 2007; Stoddard et al., 2013), some studies suggest that weapon carrying may be more heavily influenced than physical fighting and other forms of violent behavior by past victimization experiences and life stressors (Duke et al., 2010; Saner & Ellickson, 1996; Walsh et al., 2013). Additional studies highlight the excess risk of other forms of violent behaviors including self-harm, bullying, and intimate partner violence among those exposed to high levels of childhood adversity (Björkenstam et al., 2018; McMahon et al., 2015; Vaughn et al., 2011).

Finally, the timing of both adversity exposures and violent behavior may have important implications for long-term outcomes. A small body of work has examined the timing of adversity onset during childhood in relation to subsequent developmental outcomes, including aggressive behaviors. For example, results from one study of 578 children assessed repeatedly from kindergarten through eighth grade indicated that children who were maltreated before age 5 years demonstrated consistently higher levels of internalizing and externalizing behaviors later in childhood and early adolescence than other children; however, children who were maltreated after age 5 years exhibited a more rapid increase in externalizing behaviors than early- or non-maltreated children, according to their teachers (Keiley et al., 2001). Other studies have generated both theoretical and empirical evidence for greater effects of both early- and later-onset adversity (Dunn, Nishimi, et al., 2018; Keiley et al., 2001; Lansford et al., 2007; Schroeder et al., 2020; Thornberry et al., 2001; Yoon, 2020); however, few longitudinal studies have included adverse exposures across multiple developmental periods (Russell et al., 2016) to examine their effects on violent behaviors in adolescence and young adulthood.

Violence and other antisocial behaviors, including fighting and weapon carrying, are highly prevalent among adolescents (Kosterman et al., 2001), but usually decrease in young adulthood (Loeber & Hay, 1997). Moffitt’s developmental taxonomy of antisocial behavior suggests that adolescence-limited violence, undertaken by a high proportion of adolescents as a way of asserting independence and gaining status, should be distinguished from life-course-persistent violence, which comprises only a small subset of individuals who manifest more severe antisocial behaviors starting in childhood and persisting into adulthood (Moffitt, 1993). Individuals who are still engaging in violent and antisocial behaviors in early adulthood and beyond are at the highest risk for escalation to criminal offending and more serious forms of violence (Kosterman et al., 2001; Moffitt, 1993), and risk factors for this type of persistent violence need to be better understood. However, few studies of childhood adversity as determinants of persistent violent behaviors among community-based samples have been conducted.

We aimed to examine the association between trajectories of adversity during childhood and violent behaviors in adolescence and young adulthood, including physical fighting and weapon carrying, among a sample of young adults who have been followed continuously since birth in the UK. Our use of prospectively collected information on a broad array of both chronic and acute adverse experiences allowed us to more fully characterize patterns of childhood adversity, including timing and duration, while accounting for the high levels of co-occurrence among specific adversity domains. In addition, we were able to investigate childhood adversity trajectories as predictors of these specific violent behaviors across multiple time periods in order to gain insight into adolescence-limited violence versus violence persisting into young adulthood in this population-based sample. We hypothesized that young adults who had experienced higher levels of adversity at any time in childhood would have a greater risk of persistent violent behavior in adolescence and young adulthood than those exposed to consistently low levels of childhood adversity.

Method

Study Population

The Avon Longitudinal Study of Parents and Children (ALSPAC) is a prospective birth cohort study based in the southwest region of England. At the study’s inception, 14,541 pregnant women with expected delivery dates between April 1, 1991 and December 31, 1992 and who remained in the study area through their third trimester of pregnancy were enrolled (Boyd et al., 2013; Fraser et al., 2013; Golding et al., 2001; Northstone et al., 2019). Ethical approval for the study was obtained from the ALSPAC Ethics and Law Committee and the Local Research Ethics Committees, including the University at Albany Institutional Review Board. Information on the child, mother, and mother’s partner has been collected at multiple time points from gestation through young adulthood, with both questionnaires and clinical assessments. The ALSPAC study website contains details of all the data that are available, through a fully searchable data dictionary and variable search tool (http://www.bris.ac.uk/alspac/researchers/our-data).

Measures

Violent behaviors.

Physical fighting was assessed by asking participants how often in the past year they had “hit, kicked, punched or attacked someone with the intention of really hurting them.” Weapon carrying was assessed by asking participants how often in the past year they had “carried a knife or other weapon for protection or in case it was needed in a fight.” These questions were repeated 5 times, when the participants were 13, 15, 17, 18, and 20 years old, on average. At each time point, participants were identified as having engaged in the violent behavior if they reported fighting or weapon carrying one or more times in the past year. We examined any physical fighting and any weapon carrying from ages 13-20 years as the primary outcomes of the analysis, but also examined each behavior separately at ages 13-15 and 17-20 years given past research and empiric evidence regarding the age at which violent behaviors begin to decline (Moffitt, 1993). In particular, we classified participants as having engaged in violence during neither time period, ages 13-15 years only, ages 17-20 years only, or both time periods.

Childhood adversity.

Five trajectories of childhood adversity were identified among the study sample and have been described previously (Tracy et al., 2019). Life events, behaviors, and social circumstances in the family were reported by mothers and mothers’ partners regularly throughout their offspring’s childhood (Slopen et al., 2013). In order to identify trajectories of childhood adversity, we derived summary measures of adversities reported at each of eight time points when children were, on average, aged 8 months, 1.5 years, 2.5 years, 3.5 years, 5 years, 6.5 years, 9 years, and 11.5 years, using data reported for the time since the last assessment (or since the child’s birth, when the child was 8 months old). Ten adversity domains were included, reflecting both acute and chronic adverse exposures: (1) death or illness in the family; (2) child’s experience of violent victimization (e.g., physical or sexual abuse); (3) inter-parental conflict; (4) family disruption; (5) parental employment difficulties; (6) parental legal difficulties; (7) parental psychopathology; (8) parental substance use; (9) financial hardship; and (10) housing inadequacies or instability (refer to **Table A1 in Appendix A for a list of all items included in each adversity domain, by age at assessment). These domains extend beyond the assessment of childhood abuse, neglect, and household dysfunction in the landmark adverse childhood experiences (ACEs) study (Felitti et al., 1998), particularly to include household financial and economic difficulties, as well as potentially traumatic illnesses and deaths in the family; these additional domains have been associated with mental and physical health outcomes and health risk behaviors, commonly co-occur with other types of ACEs, and have been suggested by others as important additions to childhood adversity scales and measures for improved identification of those at risk (Afifi et al., 2020; Finkelhor et al., 2013, 2015; Melchior et al., 2007).

Semi-parametric group-based trajectory modeling (Jones et al., 2001) was used to derive five trajectories of childhood adversity (Tracy et al., 2019). First, we used the Bayesian Information Criterion to identify the optimal number of trajectory groups, accounting for nonrandom attrition (Haviland et al., 2011). We then considered the statistical significance of higher-order parameters to determine the appropriate shape of each trajectory group, in line with prior applications of SAS PROC TRAJ for trajectory modeling (Cerdá et al., 2008; Wiesner et al., 2007). Study participants were assigned to their highest-probability trajectory group, and these posterior probabilities of group membership were very high (.86-.96).

Other covariates.

Potential confounders of the association between childhood adversity and subsequent violent behaviors included race (Bruce, 2004; Fox et al., 2015; Widom et al., 2014), parental education (Bruce, 2004), mother’s exposure to childhood maltreatment (Widom, 1998), mother’s age at delivery (Lansford et al., 2007), and perceived neighborhood violence (Karriker-Jaffe et al., 2011; Stewart & Simons, 2010). Race (White or non-White) and parental education (highest level achieved by either parent, categorized as below O-level, O-level, A-level, and university degree or above) were reported at 32 weeks gestation. Mothers reported on their experiences of childhood maltreatment, including emotional abuse, physical neglect, physical abuse, or sexual abuse before age 17 years, at 32 weeks gestation, and when the study child was approximately 3 years old. Mothers who reported any type of abuse or neglect in childhood at either assessment were identified as exposed to childhood maltreatment. Perceived neighborhood violence was reported by mothers at 8 weeks gestation. Mothers who reported being “very worried” about burglaries, robberies or muggings, sexual assault or harassment, or vandalism in their neighborhoods were identified as living in neighborhoods with high levels of perceived violence. We did not have information on neighborhood violence at multiple time points in childhood, so we included this characteristic as a potential confounder rather than as part of our childhood adversity trajectory measure.

Child sex, recorded at birth, was also assessed as an effect measure modifier of the relation between childhood adversity and violent behaviors, given some evidence of differential responses to adversity by sex (Duke et al., 2010; Hunt et al., 2017; Lansford et al., 2007).

Statistical Analysis

We compared study participants with (n = 4,431) and without (n = 9,188) complete data for our outcomes, exposure, and potential confounders (Figure 1) and found that missingness was related to childhood adversity and other risk factors for violent behaviors. We, therefore, conducted multiple imputations of missing covariate and outcome data using fully conditional specification implemented in SAS version 9.4 (SAS Institute Inc., Cary, NC, USA). The imputed sample were singleton births alive at 1 year of age who had data for at least four of the eight adversity time points from birth through age 11.5 years (n = 9,665; Figure 1) to ensure that the multiple imputations were based on adequate amounts of observed information, consistent with the “missing at random” assumption (Little & Rubin, 2002). Variables included in the imputation model were those known to be associated with violence in adolescence and young adulthood (e.g., externalizing problems in childhood, bullying behaviors, and early initiation of substance use) as well as those associated with missingness (e.g., socio-demographics) and all other variables in the main analyses. We created 75 imputed datasets (Berglund, 2015), each with 25 burn-in iterations, using logistic regression, multinomial logistic regression, linear regression, and predictive mean matching as appropriate, producing a relative efficiency close to one (>.99) for all effects.

Figure 1.

Flow chart of participation in the Avon Longitudinal Study of Parents and Children.

We examined bivariate associations between the outcomes of interest and all covariates using chi-square tests, t-tests, and analysis of variance. Logistic regression was used to model any physical fighting and weapon carrying at ages 13-20 years, and multinomial logistic regression was used to model the four-category measures of fighting and weapon carrying across the age 13-15 and 17-20 years periods, adjusting for childhood adversity trajectory group and other covariates. Estimates for all analyses were combined across imputed datasets and variance estimates were calculated according to Rubin’s rules (Rubin, 1987). An interaction term between childhood adversity trajectory group and sex was also included in the models to assess effect modification on the multiplicative scale. Finally, we conducted sensitivity analyses using alternative versions of the childhood adversity measures, including restricting to adversity items that were assessed consistently across all childhood time points and weighting adversity domains (refer to Appendix A for more information).

Results

Among the 9,665 participants included in this analysis, the vast majority (95.8%) were of White race, about a quarter (23.5%) had at least one parent with a university degree or higher education, and about a quarter (24.8%) had mothers who reported experiencing childhood maltreatment, including emotional, physical, or sexual abuse (Table 1). Overall, 46.5% reported physical fighting and 11.7% reported carrying a weapon in adolescence or young adulthood (at ages 13-20 years; Table 1). The prevalence of these violent behaviors declined sharply as participants aged. Specifically, whereas 42.8% of participants reported physical fighting at ages 13-15 years, this dropped to 10.4% at ages 17-20 years; weapon carrying similarly declined from 13.1% at ages 13-15 years to 3.4% at ages 17-20 years. Only 9.3% and 2.1% of participants reported physical fighting and weapon carrying, respectively, at both time periods.

Table 1.

Bivariate Associations Between Covariates of Interest and Violent Behaviors at Ages 13-20 years (n = 9,665).

	Total		Physical Fighting^a			Weapon Carrying
	n	%	n fight	% Fight	p-Value	n Weapon	% Weapon	p-Value
Total	9,665		4,495	46.5		1,135	11.7
Adversity trajectory group
1 - Stable low	4,472	46.3	1,819	40.7	<.001	383	8.6	<.001
2 - Stable mild	3,589	37.1	1,766	49.2		456	12.7
3 - Decreasing from moderate to mild	856	8.9	467	54.5		145	16.9
4 - Increasing from mild to high	508	5.3	295	58.1		96	18.9
5 - Stable high	240	2.5	149	61.9		55	23.1
Child sex
Female	4,682	48.4	1,752	37.4	<.001	284	6.1	<.001
Male	4,983	51.6	2,743	55.0		852	17.1
Child race
White	9,260	95.8	4,282	46.2	.037	1,067	11.5	.014
Non-White	405	4.2	213	52.5		69	16.9
Highest level of parental education
Below O-level	1,547	16.0	786	50.8	<.001	247	16.0	<.001
O-level	2,606	27.0	1,236	47.4		332	12.7
A-level	3,237	33.5	1,493	46.1		357	11.0
University degree or higher	2,274	23.5	980	43.1		200	8.8
Maternal age at child’s birth
15-24 years	1,728	17.9	852	49.3	.090	262	15.2	.010
25-29 years	3,859	39.9	1,782	46.2		428	11.1
30-34 years	3,007	31.1	1,368	45.5		315	10.5
35-44 years	1,071	11.1	493	46.1		130	12.2
Maternal childhood maltreatment^b
No	7,272	75.2	3,266	44.9	<.001	775	10.7	<.001
Yes	2,393	24.8	1,229	51.4		360	15.1
Perceived neighborhood violence^c
Low	9,027	93.4	4,142	45.9	<.001	1,030	11.4	.008
High	638	6.6	354	55.4		105	16.5

^aStudy participant reported hitting, kicking, punching, or attacking someone with the intention of really hurting them.

^bMother reported experiencing emotional abuse, physical abuse, physical neglect, or sexual abuse before age 17 years.

^cMother reported being “very worried” about burglaries, robberies or muggings, sexual assault or harassment, or vandalism in her neighborhoods at 8 weeks gestation.

Table 2.

Results of Unadjusted and Adjusted Logistic Regression Models Predicting Violent Behaviors at Ages 13-20 years (n = 9,665).

	Physical Fighting, Age 13-20 Years				Weapon Carrying, Age 13-20 Years
	Unadjusted		Adjusted		Unadjusted		Adjusted
	OR	95% CI	OR	95% CI	OR	95% CI	OR	95% CI
Adversity trajectory group
1 - Stable low	1.00	(Ref)	1.00	(Ref)	1.00	(Ref)	1.00	(Ref)
2 - Stable mild	1.41	1.28-1.57	1.38	1.24-1.53	1.56	1.29-1.87	1.46	1.21-1.76
3 - Decreasing from moderate to mild	1.75	1.46-2.10	1.67	1.38-2.01	2.17	1.63-2.88	1.95	1.46-2.61
4 - Increasing from mild to high	2.02	1.63-2.51	1.98	1.59-2.48	2.48	1.81-3.41	2.35	1.69-3.28
5 - Stable high	2.37	1.71-3.30	2.19	1.54-3.10	3.20	2.09-4.89	2.72	1.69-4.35
Child sex
Female	1.00	(Ref)	1.00	(Ref)	1.00	(Ref)	1.00	(Ref)
Male	2.05	1.85-2.27	2.07	1.86-2.30	3.20	2.69-3.80	3.26	2.74-3.89
Child race
White	1.00	(Ref)	1.00	(Ref)	1.00	(Ref)	1.00	(Ref)
Non-White	1.28	1.02-1.62	1.13	.89-1.45	1.56	1.09-2.21	1.30	.90-1.89
Highest level of parental education
Below O-level	1.36	1.16-1.60	1.31	1.10-1.55	1.97	1.52-2.55	1.86	1.39-2.48
O-level	1.19	1.04-1.36	1.15	1.00-1.33	1.51	1.20-1.91	1.45	1.13-1.86
A-level	1.13	1.00-1.28	1.13	.99-1.28	1.29	1.03-1.60	1.28	1.02-1.61
University degree or higher	1.00	(Ref)	1.00	(Ref)	1.00	(Ref)	1.00	(Ref)
Maternal age at child’s birth
15-24 years	1.13	.99-1.30	1.00	.86-1.15	1.43	1.17-1.75	1.18	.96-1.46
25-29 years	1.00	(Ref)	1.00	(Ref)	1.00	(Ref)	1.00	(Ref)
30-34 years	.97	.87-1.08	1.00	.89-1.12	.94	.78-1.12	.99	.82-1.20
35-44 years	1.00	.85-1.16	.98	.84-1.16	1.11	.87-1.42	1.14	.88-1.48
Maternal childhood maltreatment^a
No	1.00	(Ref)	1.00	(Ref)	1.00	(Ref)	1.00	(Ref)
Yes	1.30	1.15-1.45	1.10	.97-1.25	1.48	1.24-1.78	1.20	.98-1.46
Perceived neighborhood violence^b
Low	1.00	(Ref)	1.00	Rref)	1.00	(Ref)	1.00	(Ref)
High	1.47	1.19-1.80	1.26	1.01-1.56	1.52	1.12-2.08	1.14	.82-1.61

^aMother reported experiencing emotional abuse, physical abuse, physical neglect, or sexual abuse before age 17 years.

^bMother reported being “very worried” about burglaries, robberies or muggings, sexual assault or harassment, or vandalism in her neighborhood at 8 weeks gestation.

Table 3.

Results of Adjusted Multinomial Logistic Regression Models Predicting Violent Behaviors Across Adolescence and Early Adulthood (n = 9,665).^a

	Physical Fighting, Age 13-20 Years						Weapon Carrying, Age 13-20 Years
	13-15 Years Only^b		17-20 Years Only^b		Both^b		13-15 Years Only^b		17-20 Years Only^b		Both^b
	OR	95% CI	OR	95% CI	OR	95% CI	OR	95% CI	OR	95% CI	OR	95% CI
Adversity trajectory group
1 - Stable low	1.00	(Ref)	1.00	(Ref)	1.00	(Ref)	1.00	(Ref)	1.00	(Ref)	1.00	(Ref)
2 - Stable mild	1.34	1.19-1.51	1.23	.90-1.69	1.62	1.31-2.00	1.45	1.18-1.79	1.18	.65-2.15	1.73	1.04-2.86
3 - Decreasing from moderate to mild	1.53	1.24-1.89	1.58	.95-2.62	2.33	1.64-3.33	1.81	1.30-2.53	1.91	.86-4.28	2.59	1.33-5.05
4 - Increasing from mild to high	1.66	1.29-2.13	2.68	1.57-4.56	3.18	2.20-4.60	2.25	1.50-3.39	1.87	.69-5.01	3.15	1.45-6.85
5 - Stable high	1.89	1.30-2.75	1.70	.59-4.87	3.73	2.13-6.52	2.15	1.23-3.77	3.05	.87-10.68	4.67	1.77-12.29
Child sex
Female	1.00	(Ref)	1.00	(Ref)	1.00	(Ref)	1.00	(Ref)	1.00	(Ref)	1.00	(Ref)
Male	1.81	1.62-2.03	1.80	1.34-2.43	3.81	3.10-4.68	3.08	2.51-3.78	2.29	1.39-3.79	5.75	3.44-9.62
Highest level of parental education
Below O-level	1.20	.99-1.46	1.40	.86-2.29	1.73	1.26-2.38	1.58	1.13-2.22	1.88	.81-4.37	3.72	1.74-7.95
O-level	1.11	.94-1.30	1.20	.81-1.79	1.36	1.03-1.79	1.35	1.02-1.79	1.07	.50-2.31	2.58	1.32-5.03
A-level	1.13	.98-1.30	1.00	.68-1.47	1.19	.92-1.53	1.22	.94-1.58	1.11	.59-2.09	1.92	1.01-3.63
University degree or higher	1.00	(Ref)	1.00	(Ref)	1.00	(Ref)	1.00	(Ref)	1.00	(Ref)	1.00	(Ref)

^aAlso adjusted for child race, maternal age at child’s birth, maternal childhood maltreatment, and perceived neighborhood violence.

^bCompared to no physical violence reported in either time period.

The five childhood adversity trajectory groups reflected stable low levels of adversity (46.3%), stable mild levels (37.1%), adversity decreasing from moderate to mild levels (8.9%), adversity increasing from mild to high levels (5.3%), and stable high levels of adversity (2.5%) from birth through age 11.5 years (Tracy et al., 2019; refer to **Figure A1 in Appendix A). Childhood adversity trajectory group membership was strongly associated with both physical fighting and weapon carrying, which increased in a dose-response fashion as adversity levels and duration increased (Table 1). The persistence of violent behaviors also varied across the childhood adversity trajectory groups, as shown in Figures 2 and 3. Reporting physical fighting across both adolescent and early adulthood time periods was more common among participants in the trajectory groups with higher levels of adversity (e.g., 18.8% in the stable high group, 15.7% in the increasing adversity group, 13.4% in the decreasing adversity group, and 10.0% in the stable mild adversity group, compared to 6.8% in the stable low group; Figure 2). Reporting physical fighting at ages 17-20 years only was more common among participants who had experienced increases in adversity from mild levels in early childhood to high levels in late childhood (6.4% compared to 3.4-4.2% in the other groups). Patterns were similar when examining weapon carrying over time across childhood adversity trajectory groups, although the percentage of participants who reported weapon carrying only at ages 17-20 years was highest in the stable high adversity group (3.6% compared to 1.0-2.2% in the other groups; Figure 3).

Figure 2.

Persistence of physical fighting across adolescence and early adulthood, by childhood adversity trajectory group (n = 9,665).

Figure 3.

Persistence of weapon carrying across adolescence and early adulthood, by childhood adversity trajectory group (n = 9,665).

In logistic regression models adjusted for child sex, race, parent education, mother’s age at delivery, mother’s exposure to childhood maltreatment, and perceived neighborhood violence (Table 2), childhood adversity trajectory group remained associated with physical fighting in adolescence or early adulthood (adjusted odds ratio [aOR] = 1.38, 95% confidence interval [CI] = 1.24-1.53 for stable mild; OR = 1.67, 95% CI = 1.38-2.01 for decreasing; OR = 1.98, 95% CI = 1.59-2.48 for increasing; and OR = 2.19, 95% CI = 1.54-3.10 for stable high, all compared to the stable low adversity group). Similar patterns were observed for weapon carrying at ages 13-20 years (aOR = 1.46, 95% CI = 1.21-1.76 for stable mild; OR = 1.95, 95% CI = 1.46-2.61 for decreasing; OR = 2.35, 95% CI = 1.69-3.28 for increasing; and OR = 2.72, 95% CI = 1.69-4.35 for stable high, all compared to the stable low adversity group). Lower parent education (OR = 1.31, 95% CI = 1.10-1.55 for below O-level compared to university degree) and high perceived neighborhood violence (OR = 1.26, 95% CI = 1.01-1.56) were also associated with physical fighting in the adjusted model, and lower parent education was associated with weapon carrying in the adjusted model (OR = 1.86, 95% CI = 1.39-2.48 for below O-level; OR = 1.45, 95% CI = 1.13-1.86 for O-level; and OR = 1.28, 95% CI = 1.02-1.61 for A-level, all compared to university degree). The odds of physical fighting and weapon carrying were more than doubled for boys compared to girls (OR = 2.07, 95% CI = 1.86-2.30 for physical fighting; OR = 3.26, 95% CI = 2.74-3.89 for weapon carrying). However, the associations between childhood adversity trajectory group and violent behaviors did not vary by child sex (p-value for interaction terms in adjusted models were .370 and .486 for physical fighting and weapon carrying outcomes, respectively).

In adjusted multinomial logistic regression models (Table 3), stable mild, decreasing, increasing, and stable high adversity trajectories were associated in a dose-response fashion with adolescent-limited violence (i.e., at ages 13-15 years only) and persistent violence across both age periods for both physical fighting and weapon carrying. The odds of persistent physical fighting and weapon carrying were very strongly patterned by childhood adversity trajectory, including extremely elevated risks for those with stable high levels of adversity (compared to stable-low levels) throughout childhood (OR = 3.73, 95% CI = 2.13-6.52 for physical fighting; OR = 4.67, 95% CI = 1.77-12.29 for weapon carrying). However, only the increasing adversity trajectory group was significantly associated with reporting physical fighting at ages 17-20 years only (OR = 2.68, 95% CI = 1.57-4.56, compared to the stable low group). None of the adversity trajectory groups exhibited significantly increased risk of initiating weapon carrying at ages 17-20 years, which was an uncommon outcome in this sample. Results of all analyses were similar when using two alternate versions of the adversity trajectory measures (refer to Appendix A).

Discussion

In this large sample of young adults who reported on their violent behaviors throughout adolescence and young adulthood, childhood adversity trajectory exhibited a strong dose-response relation with physical fighting and weapon carrying, with particularly pronounced relations for violent behaviors persisting across both adolescence and early adulthood. Furthermore, experiencing increasing levels of adversity throughout late childhood was strongly associated with physical fighting in early adulthood, including among young adults who had not previously reported physical fighting in adolescence.

The prevalence of past-year physical fighting during adolescence in our sample was broadly similar to that observed in other studies of adolescent populations worldwide, with physical fighting commonly reported by about one-quarter to one-third of samples (Djerboua et al., 2016; Han et al., 2019; Kann et al., 2018; Perlus et al., 2014). However, weapon carrying was somewhat less common in this UK-based sample than among adolescent and young adult populations elsewhere, particularly those in the United States, where past-month weapon carrying is reported by about 15% of adolescents nationwide (Kann et al., 2018; Perlus et al., 2014), with an even higher prevalence among urban youth (Finigan-Carr et al., 2015; Walton et al., 2009). The declines in violent behaviors we observed with increasing age have been confirmed in other populations (Cauffman et al., 2017; Harris et al., 2006). As expected, only a small fraction of our study sample persisted in violent behaviors across both adolescent and early adulthood time periods (Moffitt, 1993).

Consistent with previous work and our hypothesis, we observed a higher risk of violent behaviors among youth who had experienced higher levels of adversity in childhood (Bellis et al., 2014; Duke et al., 2010; Fox et al., 2015; Saner & Ellickson, 1996; Stoddard et al., 2013). Cumulative adversities, often defined as the number of distinct types of ACEs, have been found to increase risk of violent behaviors in a dose-response fashion in previous work among youth (Duke et al., 2010; Hunt et al., 2017; Schroeder et al., 2020; Stoddard et al., 2013). In our sample, youth who had experienced multiple adversities at one or more time points (classified in the decreasing, increasing, or stable high adversity groups) exhibited markedly elevated risk for both physical fighting and weapon carrying compared to those with stable low levels of adversity throughout childhood. This dose-response patterning was especially apparent for the risk of persistent violence across both adolescence and early adulthood periods. This suggests that the effect of cumulative childhood adversity on persistent violence that has been observed in juvenile and adult offending populations (Basto-Pereira et al., 2016; Craig et al., 2017; Fox et al., 2015) may also apply to less severe violence among community-based populations. Children experiencing high levels of adversity at any time in childhood need to be identified through clinical practice screening (Purewal et al., 2016) or at the point of any household contact with the child services or justice systems (Logan-Greene et al., 2020; O’Dea et al., 2020) and reached with effective trauma-informed services and violence prevention interventions, without stigmatizing children or their families (Dube, 2018; Finkelhor, 2018; Murphey & Dym Bartlett, 2019). Violence prevention programs that involve families or mentors may be particularly effective for violence prevention among high-risk children and adolescents (Reese et al., 2000; World Health Organization, 2004). These may include training parents in pro-social coping skills and parenting practices (Ikeda et al., 2001) and pairing youth with community mentors who serve as role models and empower youth to develop leadership skills and work for self and community improvement (Heinze et al., 2016; Sheehan et al., 1999).

The timing of childhood adversity was also associated with risk for violent behaviors in our sample. Children who experienced increasing levels of adversity exhibited the greatest risk of initiating physical fighting at ages 17-20 years. Adversity that begins in late childhood or early adolescence, when cognitive and social awareness has increased, may lead to greater disruptions in internalizing and externalizing behaviors than earlier-onset adversity (Keiley et al., 2001; Yoon, 2020). However, in our sample, elevated risk of violent behaviors in adolescence and adulthood was also present among those whose earlier-onset adversity had decreased by late childhood. Exposures to adversity during early developmental periods may disrupt key cognitive and attachment processes among vulnerable children who have not yet developed mechanisms for coping with adversity (Keiley et al., 2001; Lansford et al., 2007). Finally, the small subset of youth in our sample with chronically high levels of adversity throughout childhood had extremely elevated risks of physical fighting and weapon carrying, highlighting the negative effects of both accumulation and duration of childhood adversity. This finding is consistent with work that has found the highest risk of health and behavior problems among those with chronic childhood maltreatment or other adversities (Dunn, Soare, et al., 2018; Jaffee & Maikovich-Fong, 2011; Slopen et al., 2014), though conflicts with some past work finding similar or even greater risks associated with intermittent adversity (Schroeder et al., 2020). Overall, these findings illustrate the negative consequences of cumulative childhood adversity, which may influence social learning, cognitive processing, emotion regulation, psychopathology, and psychosocial strain, and increase risk for aggressive reactions and violent behaviors (Basto-Pereira et al., 2016; Brewer-Smyth et al., 2015; Karriker-Jaffe et al., 2011; Keiley et al., 2001; Lansford et al., 2007; Loeber & Hay, 1997; Milaniak & Widom, 2015; Saner & Ellickson, 1996).

As expected, boys reported higher levels of violent behaviors than girls of all ages. However, we did not find evidence of differential associations between childhood adversity trajectories and physical fighting or weapon carrying by sex. This stands in contrast to some prior work that has reported stronger effects of childhood adversity on violence risk or other externalizing behaviors among girls compared to boys (Hunt et al., 2017; Lansford et al., 2007; Saner & Ellickson, 1996), or among boys compared to girls (Duke et al., 2010). These inconsistencies across studies may stem from differential relations between specific adverse experiences and violence risk across sex groups. For example, in a large study of middle and high school students, reported childhood sexual abuse was more strongly associated with dating violence, weapon carrying, and self-harm among boys than girls (Duke et al., 2010). By contrast, in another study of middle and high school students, girls were at higher risk than boys for violence after exposure to family disruption (Saner & Ellickson, 1996). Future work should investigate relations between specific childhood adversities and violent behaviors by sex.

Our findings should be considered in light of several limitations. Some childhood adversities (e.g., child abuse and parental substance use) may have been under-reported by parents throughout childhood and reporting may have been influenced by parental mental health. We also did not include information on some types of childhood violent victimization (e.g., sexual violence, bullying, sibling violence, and exposure to neighborhood violence) that may be associated with subsequent violent behaviors. Potential misclassification of childhood adversity as a result of these omissions may have led to an underestimation of effects on subsequent violence. Misclassification of the outcomes may have occurred if study children were reluctant to disclose their participation in violent behaviors or, conversely, overstated their experience with physical fighting and/or weapon carrying. However, children answered all questions regarding their violent behaviors privately, either on a computer or through a written questionnaire, promoting confidentiality. We did not have information on violent behaviors prior to age 13 years or after age 20 years available for this analysis; incorporating this information may have allowed better identification of individuals engaged in life-course-persistent versus adolescence-limited violence (Moffitt, 1993). Due to the longitudinal design of the study, potential selection bias was present in the form of differential attrition. Although we used multiple imputation to address missing data, we restricted our imputed analysis to the sample with adequate adversity information; this may have resulted in an underestimation of violent behaviors and a failure to fully characterize the childhood adversity trajectories experienced by the full ALSPAC cohort. Finally, the generalizability of these findings may be limited given the relatively homogeneous nature of the sample. These findings may not apply to more diverse, urban settings or settings where certain types of violent behaviors (e.g., firearm carrying and use) are more common than in the UK.

Notwithstanding these limitations, our study was able to explore the temporal relationship between patterns of childhood adversity and subsequent violent behaviors, including considering adolescent-limited violence versus violence that started or persisted in young adulthood. Future research should explore biological, behavioral, and social factors as potential mediators and moderators of the association between childhood adversity trajectories and violent behaviors to identify additional targets for intervention. For example, beliefs and attitudes toward violence may be an important mediator related to social learning theory, as children exposed to violence and weapon carrying in their households and communities may accept such behaviors as a “norm” (Reed et al., 2011; Simon et al., 1997; Stewart & Simons, 2010) and go on to participate in violence themselves. Lack of parental support and involvement has been identified as a strong predictor of life-course-persistent antisocial behavior (Moore et al., 2017) and may serve to exacerbate the effects of childhood adversities on subsequent outcomes, whereas positive parental support may promote resilience in the face of adversity (Shek & Tang, 2003; Tracy et al., 2018). Investigations into the independent and comparative effects of specific domains of childhood adversity have largely led to conflicting results (Yoon, 2020) because of the difficulties inherent in isolating the effects of co-occurring experiences. Using more sophisticated analytic tools for model and variable selection (Lenters et al., 2018; Mishra et al., 2009; Smith et al., 2015) to examine relations between specific childhood adversities and the severity and frequency of a broader array of violent behaviors, including dating violence and weapon use, across multiple developmental stages will create new insights into modifiable risk factors for persistent violent behavior and opportunities for intervention.

Integrating screening for childhood adversities into clinical practice may hold promise for identifying high-risk youth, without increasing stigma (Dube, 2018; Finkelhor, 2018; Purewal et al., 2016). However, screening should be tied to enhanced access to trauma-informed care for youth in families experiencing adversity (Murphey & Dym Bartlett, 2019). Finally, efforts to address the systemic factors that allow childhood adversities to flourish, like structural racism and discrimination against communities of color and indigenous communities, inequalities in our educational system, and disparities in our criminal justice system (Metzler et al., 2017; Wade et al., 2014; Williams et al., 2019), are critically important to disrupt the “cycle of violence” in families and communities and to protect adolescents from engaging in harmful violent behaviors with serious long-term consequences.

Supplemental Material

Supplemental material for this article is available online.

Supplemental Material for Childhood Adversity Trajectories and Violent Behaviors in Adolescence and Early Adulthood by Madeleine Salo, Allison A. Appleton, and Melissa Tracy, in Journal of Interpersonal Violence

Footnotes

Acknowledgments

We are extremely grateful to all the families who took part in this study, the midwives for their help in recruiting them, and the whole ALSPAC team, which includes interviewers, computer and laboratory technicians, clerical workers, research scientists, volunteers, managers, receptionists, and nurses.

Declaration of Conflicting Interests

Funding

The author(s) disclosed receipt of the following financial support for the research and/or authorship of this article: The UK Medical Research Council and the Wellcome Trust (Grant ref: 102215/2/13/2) and the University of Bristol provide core support for ALSPAC. A comprehensive list of grant funding is available on the ALSPAC website (http://www.bristol.ac.uk/alspac/external/documents/grant-acknowledgements.pdf); collection of outcome information was specifically funded by the Medical Research Council (Grant ref: G0800612/86812) and the National Institutes of Health (NIH; Grant ref: 5R01AA018333-05). This publication is the work of the authors, who will serve as guarantors for the contents of this paper. This work was partially supported by grants from the National Institute of Child Health and Human Development of the NIH (R24-HD044943, R15-HD094162). Allison Appleton is supported by a JPB Environmental Health Fellowship award granted by the JPB Foundation and managed by the Harvard T.H. Chan School of Public Health.

ORCID iD

Melissa Tracy

Author Biographies

Madeleine Salo, MS, is currently an Institutional Research Analyst at the State University of New York College of Agriculture and Technology at Cobleskill. She completed this work for her Master’s thesis while enrolled in the MS program in Epidemiology at the University at Albany School of Public Health. She is interested in the role childhood adversity plays as a determinant of later-life social and behavioral outcomes.

Allison A. Appleton, ScD, MPH, is an Associate Professor of Epidemiology at the University at Albany School of Public Health, State University of New York. Her research focuses on the associations between adversities experienced during sensitive periods of development and health risk over the life course. She also considers the role of positive factors in promoting good health and mitigating risk from deleterious exposures.

Melissa Tracy, PhD, MPHis an Associate Professor of Epidemiology at the University at Albany School of Public Health, State University of New York. Her research uses novel analytic approaches to explore the transmission of violence within families and social networks. She is also interested in the pathways through which childhood adversities affect mental health, substance use, and violent behaviors later in life.

References

Afifi

T. O.

, Salmon

, Garcés

, Struck

, Fortier

, Taillieu

, Stewart-Tufescu

, Asmundson

G. J. G.

, Sareen

, & MacMillan

H. L.

(2020). Confirmatory factor analysis of adverse childhood experiences (ACEs) among a communi-ty-based sample of parents and adolescents. BMC Pediatrics , 20(1), 178. https://doi.org/10.1186/s12887-020-02063-3

Bandura

(1977). Social learning theory . Prentice Hall.

Basto-Pereira

, Miranda

, Ribeiro

, & Maia

Â.

(2016). Growing up with adversity: From juvenile justice involve-ment to criminal persistence and psychosocial problems in young adulthood. Child Abuse & Neglect , 62, 63–75. https://doi.org/10.1016/j.chiabu.2016.10.011

Bellis

M. A.

, Lowey

, Leckenby

, Hughes

, & Harrison

(2014). Adverse childhood experiences: Retrospective study to determine their impact on adult health behaviours and health outcomes in a UK population. Journal of Public Health , 36(1), 81–91. https://doi.org/10.1093/pubmed/fdt038

Berglund

P. A.

(2015). Multiple imputation using the fully conditional specification method: A comparison of SAS, Stata, IVEware, and R. Paper 2081–2015 . SAS Global Forum Proceedings. http://support.sas.com/resources/papers/proceedings15/2081-2015.pdf

Björkenstam

, Hjern

, Björkenstam

, & Kosidou

(2018). Association of cumulative childhood adversity and adolescent violent offending with suicide in early adulthood. JAMA Psychiatry , 75(2), 185–193. https://doi.org/10.1001/jamapsychiatry.2017.3788

Boyd

, Golding

, Macleod

, Lawlor

D. A.

, Fraser

, Henderson

, Molloy

, Ness

, Ring

, & Davey Smith

(2013). Cohort profile: The ‘children of the 90s’–the index offspring of the Avon longitudinal study of parents and children. International Journal of Epidemiology , 42(1), 111–127. https://doi.org/10.1093/ije/dys064

Brady

S. S.

, Tschann

J. M.

, Pasch

L. A.

, Flores

, & Ozer

E. J.

(2008). Violence involvement, substance use, and sexual activity among Mexican-American and European-American adolescents. The Journal of Adolescent Health , 43(3), 285–295. https://doi.org/10.1016/j.jadohealth.2008.02.007

Brewer-Smyth

, Cornelius

M. E.

, & Pickelsimer

E. E.

(2015). Childhood adversity, mental health, and violent crime. Journal of Forensic Nursing , 11(1), 4–14. https://doi.org/10.1097/JFN.0000000000000062

10.

Bruce

M. A.

(2004). Inequality and adolescent violence: An exploration of community, family, and individual factors. Journal of the National Medical Association , 96(4), 486–495.

11.

Brumley

L. D.

, Jaffee

S. R.

, & Brumley

B. P.

(2017). Pathways from childhood adversity to problem behaviors in young adulthood: The mediating role of adolescents’ future expectations. Journal of Youth and Adolescence , 46(1), 1–14. https://doi.org/10.1007/s10964-016-0597-9

12.

Campbell

J. A.

, Walker

R. J.

, & Egede

L. E.

(2016). Associations between adverse childhood experiences, high-risk be-haviors, and morbidity in adulthood. American Journal of Preventive Medicine , 50(3), 344–352. https://doi.org/10.1016/j.amepre.2015.07.022

13.

Capaldi

D. M.

, Knoble

N. B.

, Shortt

J. W.

, & Kim

H. K.

(2012). A systematic review of risk factors for intimate partner violence. Partner Abuse , 3(2), 231–280. https://doi.org/10.1891/1946-6560.3.2.231

14.

Cauffman

, Fine

, Thomas

A. G.

, & Monahan

K. C.

(2017). Trajectories of violent behavior among females and males. Child Development , 88(1), 41–54. https://doi.org/10.1111/cdev.12678

15.

CDC. (2019). Youth violence . CDC. https://www.cdc.gov/violenceprevention/youthviolence/index.html

16.

Cerda

, Vlahov

, Tracy

, & Galea

(2008). Alcohol use trajectories among adults in an urban area after a disaster: Evidence from a population-based cohort study. Addiction , 103(8), 1296–1307. https://doi.org/10.1111/j.1360-0443.2008.02247.x

17.

Craig

J. M.

, Piquero

A. R.

, Farrington

D. P.

, & Ttofi

M. M.

(2017). A little early risk goes a long bad way: Adverse child-hood experiences and life-course offending in the Cambridge study. Journal of Criminal Justice , 53, 34–45. https://doi.org/10.1016/j.jcrimjus.2017.09.005

18.

Djerboua

, Chen

B. E.

, & Davison

(2016). Trends and demographic characteristics of physical fighting and fighting-related injuries among Canadian youth, 1993–2010. Canadian Journal of Public Health , 107(3), e231–e238. https://doi.org/10.17269/cjph.107.5274

19.

Dube

S. R.

(2018). Continuing conversations about adverse childhood experiences (ACEs) screening: A public health perspective. Child Abuse & Neglect , 85, 180–184. https://doi.org/10.1016/j.chiabu.2018.03.007

20.

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(3), 564–572. https://doi.org/10.1542/peds.111.3.564

21.

Duke

N. N.

, Pettingell

S. L.

, McMorris

B. J.

, & Borowsky

I. W.

(2010). Adolescent violence perpetration: Associations with multiple types of adverse childhood experiences. Pediatrics , 125(4), e778–786. https://doi.org/10.1542/peds.2009-0597

22.

Dunn

E. C.

, Nishimi

, Gomez

S. H.

, Powers

, & Bradley

(2018). Developmental timing of trauma exposure and emotion dysregulation in adulthood: Are there sensitive periods when trauma is most harmful? Journal of Affective Disorders , 227, 869–877. https://doi.org/10.1016/j.jad.2017.10.045

23.

Dunn

E. C.

, Soare

T. W.

, Raffeld

M. R.

, Busso

D. S.

, Crawford

K. M.

, Davis

K. A.

, Fisher

V. A.

, Slopen

, Smith

A. D. A. C.

, Tiemeier

, & Susser

E. S.

(2018). What life course theoretical models best explain the relationship between exposure to childhood adversity and psychopathology symptoms: Recency, accumulation, or sensitive periods? Psychological Medicine , 48(15), 2562–2572. https://doi.org/10.1017/S0033291718000181

24.

Felitti

V. J.

, Anda

R. F.

, Nordenberg

, Williamson

D. F.

, Spitz

A. M.

, Edwards

, Koss

M. P.

, & Marks

J. S.

(1998). Relationship of childhood abuse and household dysfunction to many of the leading causes of death in adults. The Ad-verse Childhood Experiences (ACE) study. American Journal of Preventive Medicine , 14(4), 245–258. https://doi.org/10.1016/s0749-3797(98)00017-8

25.

Finigan-Carr

N. M.

, Cheng

T. L.

, Gielen

, Haynie

D. L.

, & Simons-Morton

(2015). Using the theory of planned behavior to predict aggression and weapons carrying in urban African American early adolescent youth. Health Education & Behavior , 42(2), 220–230. https://doi.org/10.1177/1090198114548479

26.

Finkelhor

(2018). Screening for adverse childhood experiences (ACEs): Cautions and suggestions. Child Abuse & Neglect , 85, 174–179. https://doi.org/10.1016/j.chiabu.2017.07.016

27.

Finkelhor

, Shattuck

, Turner

, & Hamby

(2013). Improving the adverse childhood experiences study scale. JAMA Pediatrics , 167(1), 70–75. https://doi.org/10.1001/jamapediatrics.2013.420

28.

Finkelhor

, Shattuck

, Turner

, & Hamby

(2015). A revised inventory of adverse childhood experiences. Child Abuse & Neglect , 48, 13–21. https://doi.org/10.1016/j.chiabu.2015.07.011

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(3–4), 662–681. https://doi.org/10.1177/0886260517689885

30.

Fox

B. H.

, Perez

, Cass

, Baglivio

M. T.

, & Epps

(2015). Trauma changes everything: Examining the relationship between adverse childhood experiences and serious, violent and chronic juvenile offenders. Child Abuse & Neglect , 46, 163–173. https://doi.org/10.1016/j.chiabu.2015.01.011

31.

Fraser

, Macdonald-Wallis

, Tilling

, Boyd

, Golding

, Davey Smith

, Henderson

, Macleod

, Molloy

, Ness

, Ring

, Nelson

S. M.

, & Lawlor

D. A.

(2013). Cohort profile: The Avon longitudinal study of parents and children: ALSPAC mothers cohort. International Journal of Epidemiology , 42(1), 97–110. https://doi.org/10.1093/ije/dys066

32.

Golding

, Pembrey

, Jones

, & Study Team.

ALSPAC

(2001). ALSPAC–the Avon longitudinal study of parents and children. I. Study methodology. Paediatric and Perinatal Epidemiology , 15(1), 74–87. https://doi.org/10.1046/j.1365-3016.2001.00325.x

33.

González

R. A.

, Kallis

, Ullrich

, Barnicot

, Keers

, & Coid

J. W.

(2016). Childhood maltreatment and violence: Mediation through psychiatric morbidity. Child Abuse & Neglect , 52, 70–84. https://doi.org/10.1016/j.chiabu.2016.01.002

34.

Graf

G. H. J.

, Chihuri

, Blow

, & Li

(2021). Adverse childhood experiences and justice system contact: A systematic review. Pediatrics , 147(1), e2020021030. https://doi.org/10.1542/peds.2020-021030

35.

Green

J. G.

, Johnson

R. M.

, Dunn

E. C.

, Lindsey

, Xuan

, & Zaslavsky

A. M.

(2014). Mental health service use among high school students exposed to interpersonal violence. The Journal of School Health , 84(2), 141–149. https://doi.org/10.1111/josh.12125

36.

Han

, You

, Gao

, Duan

, Hu

, Wang

, Liu

, & Zeng

(2019). Unintentional injuries and violence among adolescents aged 12–15 years in 68 low-income and middle-income countries: A secondary analysis of data from the Global School-Based Student Health Survey. The Lancet, Child & Adolescent Health , 3(9), 616–626. https://doi.org/10.1016/S2352-4642(19)30195-6

37.

Harris

K. M.

, Gordon-Larsen

, Chantala

, & Udry

J. R.

(2006). Longitudinal trends in race/ethnic disparities in lead-ing health indicators from adolescence to young adulthood. Archives of Pediatrics & Adolescent Medi-cine , 160(1), 74–81. https://doi.org/10.1001/archpedi.160.1.74

38.

Haviland

A. M.

, Jones

B. L.

, & Nagin

D. S.

(2011). Group-based trajectory modeling extended to account for nonrandom participant attrition. Sociological Methods & Research , 40(2), 367–390. https://doi.org/10.1177/0049124111400041

39.

Heinze

, Reischl

, Bai

, Roche

, Morrel-Samuels

, Cunningham

, Zimmerman

, Heinze

J. E.

, Reischl

T. M.

, Roche

J. S.

, Cunningham

R. M.

, & Zimmerman

M. A.

(2016). A comprehensive prevention approach to reducing assault offenses and assault injuries among youth. Prevention Science , 17(2), 167–176. https://doi.org/10.1007/s11121-015-0616-1

40.

Honorato

, Caltabiano

, & Clough

A. R.

(2016). From trauma to incarceration: Exploring the trajectory in a qualita-tive study in male prison inmates from north Queensland, Australia. Health & Justice , 4, 3. https://doi.org/10.1186/s40352-016-0034-x

41.

Hunt

T. K. A.

, Slack

K. S.

, & Berger

L. M.

(2017). Adverse childhood experiences and behavioral problems in middle childhood. Child Abuse & Neglect , 67, 391–402. https://doi.org/10.1016/j.chiabu.2016.11.005

42.

Ikeda

R. M.

, Simon

T. R.

, & Swahn

(2001). The prevention of youth violence: The rationale for and characteristics of four evaluation projects. American Journal of Preventive Medicine , 20(1), 15–21. https://doi.org/10.1016/S0749-3797(00)00269-5

43.

Jaffee

S. R.

, & Maikovich-Fong

A. K.

(2011). Effects of chronic maltreatment and maltreatment timing on children’s behavior and cognitive abilities. Journal of Child Psychology and Psychiatry, and Allied Disciplines , 52(2), 184–194. https://doi.org/10.1111/j.1469-7610.2010.02304.x

44.

Jones

, Nagin

, & Roeder

(2001). A SAS procedure based on mixture models for estimating developmental tra-jectories. Sociological Methods & Research , 29, 374–393. https://doi.org/10.1177/0049124101029003005

45.

Kann

, McManus

, Harris

W. A.

, Shanklin

S. L.

, Flint

K. H.

, Queen

, Lowry

, Chyen

, Whittle

, Thornton

, Lim

, Bradford

, Yamakawa

, Leon

, Brener

, & Ethier

K. A.

(2018). Youth risk behavior surveillance—United States, 2017. MMWR Surveillance Summaries , 67(8), 1–114. https://doi.org/10.15585/mmwr.ss6708a1

46.

Karriker-Jaffe

K. J.

, Foshee

V. A.

, & Ennett

S. T.

(2011). Examining how neighborhood disadvantage influences trajectories of adolescent violence: A look at social bonding and psychological distress. The Journal of School Health , 81(12), 764–773. https://doi.org/10.1111/j.1746-1561.2011.00656.x

47.

Keiley

M. K.

, Howe

T. R.

, Dodge

K. A.

, Bates

J. E.

, & Petti

G. S.

(2001). The timing of child physical maltreatment: A cross-domain growth analysis of impact on adolescent externalizing and internalizing problems. Development and Psychopathology , 13(4), 891–912. https://doi.org/10.1017/S0954579401004084

48.

Kosterman

, Graham

J. W.

, Hawkins

J. D.

, Catalano

R. F.

, & Herrenkohl

T. I.

(2001). Childhood risk factors for persistence of violence in the transition to adulthood: A social development perspective. Violence and Victims , 16(4), 355–369. https://doi.org/10.1891/0886-6708.16.4.355

49.

Lansford

J. E.

, Miller-Johnson

, Berlin

L. J.

, Dodge

K. A.

, Bates

J. E.

, & Pettit

G. S.

(2007). Early physical abuse and later violent delinquency: A prospective longitudinal study. Child Maltreatment , 12(3), 233–245. https://doi.org/10.1177/1077559507301841

50.

Lenters

, Vermeulen

, & Portengen

(2018). Performance of variable selection methods for assessing the health effects of correlated exposures in case–control studies. Occupational and Environmental Medicine , 75(7), 522–529. https://doi.org/10.1136/oemed-2016-104231

51.

Little

R. J. A.

, & Rubin

D. B.

(2002). Statistical analysis with missing data (2nd ed.). John Wiley & Sons.

52.

Loeber

, & Hay

(1997). Key issues in the development of aggression and violence from childhood to early adult-hood. Annual Review of Psychology , 48, 371–410. https://doi.org/10.1146/annurev.psych.48.1.371

53.

Logan-Greene

, Kim

B. K. E.

, & Nurius

P. S.

(2020). Adversity profiles among court-involved youth: Translating system data into trauma-responsive programming. Child Abuse & Neglect , 104, 104465. https://doi.org/10.1016/j.chiabu.2020.104465

54.

McMahon

, Hoertel

, Wall

M. M.

, Okuda

, Limosin

, & Blanco

(2015). Childhood maltreatment and risk of intimate partner violence: A national study. Journal of Psychiatric Research , 69, 42–49. https://doi.org/10.1016/j.jpsychires.2015.07.026

55.

Melchior

, Moffitt

T. E.

, Milne

B. J.

, Poulton

, & Caspi

(2007). Why do children from socioeconomically disad-vantaged families suffer from poor health when they reach adulthood? A life-course study. American Journal of Epidemiology , 166(8), 966–974. https://doi.org/10.1093/aje/kwm155

56.

Metzler

, Merrick

M. T.

, Klevens

, Ports

K. A.

, & Ford

D. C.

(2017). Adverse childhood experiences and life opportunities: Shifting the narrative. Children and Youth Services Review , 72, 141–149. https://doi.org/10.1016/j.childyouth.2016.10.021

57.

Milaniak

, & Widom

C. S.

(2015). Does child abuse and neglect increase risk for perpetration of violence inside and outside the home? Psychology of Violence , 5(3), 246–255. https://doi.org/10.1037/a0037956

58.

Mishra

, Nitsch

, Black

, De Stavola

, Kuh

, & Hardy

(2009). A structured approach to modelling the ef-fects of binary exposure variables over the life course. International Journal of Epidemiology , 38(2), 528–537. https://doi.org/10.1093/ije/dyn229

59.

Moffitt

T. E.

(1993). Adolescence-limited and life-course-persistent antisocial behavior: A developmental taxonomy. Psychological Review , 100(4), 674–701.

60.

Moore

A. A.

, Silberg

J. L.

, Roberson-Nay

, & Mezuk

(2017). Life course persistent and adolescence limited conduct disorder in a nationally representative US sample: Prevalence, predictors, and outcomes. Social Psychiatry and Psychiatric Epidemiology , 52(4), 435–443. https://doi.org/10.1007/s00127-017-1337-5

61.

Murphey

, & Dym Bartlett

(2019). Childhood adversity screenings are just one part of an effective policy response to childhood trauma (No. 2019–19). Child Trends, Inc.

62.

Northstone

, Lewcock

, Groom

, Boyd

, Macleod

, Timpson

, & Wells

(2019). The Avon Longitudinal Study of Parents and Children (ALSPAC): An update on the enrolled sample of index children in 2019. Wellcome Open Research , 4, 51. https://doi.org/10.12688/wellcomeopenres.15132.1

63.

O’Dea

, Clough

, Beebe

, DiVietro

, Lapidus

, & Grasso

D. J.

(2020). Using child protective services case record data to quantify family-level severity of adversity types, poly-victimization, and poly-deprivation. Child Abuse & Neglect , 108, 104688. https://doi.org/10.1016/j.chiabu.2020.104688

64.

Perlus

J. G.

, Brooks-Russell

, Wang

, & Iannotti

R. J.

(2014). Trends in bullying, physical fighting, and weapon carrying among 6th- through 10th-grade students from 1998 to 2010: Findings from a national study. American Journal of Public Health , 104(6), 1100–1106. https://doi.org/10.2105/AJPH.2013.301761

65.

Purewal

S. K.

, Marques

S. S.

, Koita

, & Bucci

(2016). Assessing the integration of the center for youth wellness adverse childhood experiences questionnaire (CYW ACE-Q) in a pediatric primary care setting. Journal of Adolescent Health , 58(2), S47. https://doi.org/10.1016/j.jadohealth.2015.10.106

66.

Reed

, Lawrence

D. A.

, Santana

M. C.

, Welles

C. S. L.

, Horsburgh

C. R.

, Silverman

J. G.

, Rich

J. A.

, & Raj

(2014). Adolescent experiences of violence and relation to violence perpetration beyond young adulthood among an urban sample of Black and African American males. Journal of Urban Health , 91(1), 96–106. https://doi.org/10.1007/s11524-013-9805-z

67.

Reed

, Silverman

J. G.

, Raj

, Decker

M. R.

, & Miller

(2011). Male perpetration of teen dating violence: Associa-tions with neighborhood violence involvement, gender attitudes, and perceived peer and neighborhood norms. Journal of Urban Health , 88(2), 226–239. https://doi.org/10.1007/s11524-011-9545-x

68.

Reese

L. E.

, Vera

E. M.

, Simon

T. R.

, & Ikeda

R. M.

(2000). The role of families and care givers as risk and protective factors in preventing youth violence. Clinical Child and Family Psychology Review , 3(1), 61–77. https://doi.org/10.1023/a:1009519503260

69.

Rubin

D. B.

(1987). Multiple imputation for nonresponse in surveys . Wiley.

70.

Russell

M. A.

, Vasilenko

S. A.

, & Lanza

S. T.

(2016). Age-varying links between violence exposure and behavioral, mental, and physical health. The Journal of Adolescent Health , 59(2), 189–196. https://doi.org/10.1016/j.jadohealth.2016.03.038

71.

Saner

, & Ellickson

(1996). Concurrent risk factors for adolescent violence. The Journal of Adolescent Health , 19(2), 94–103. https://doi.org/10.1016/1054-139X(96)00131-0

72.

Schroeder

, Slopen

, & Mittal

(2020). Accumulation, timing, and duration of early childhood adversity and be-havior problems at age 9. Journal of Clinical Child and Adolescent Psychology , 49(1), 36–49. https://doi.org/10.1080/15374416.2018.1496440

73.

Sheehan

, DiCara

J. A.

, LeBailly

, & Christoffel

K. K.

(1999). Adapting the gang model: Peer mentoring for violence prevention. Pediatrics , 104(1), 50–54. https://doi.org/10.1542/peds.104.1.50

74.

Shek

D. T.

, & Tang

(2003). Violent behavior in Chinese adolescents with an economic disadvantage. Psychological, family and interpersonal correlates. International Journal of Adolescent Medicine and Health , 15(3), 219–233. https://doi.org/10.1515/ijamh.2003.15.3.219

75.

Simon

T. R.

, Dent

C. W.

, & Sussman

(1997). Vulnerability to victimization, concurrent problem behaviors, and peer influence as predictors of in-school weapon carrying among high school students. Violence and Victims , 12(3), 277–289.

76.

Slopen

, Koenen

K. C.

, & Kubzansky

L. D.

(2014). Cumulative adversity in childhood and emergent risk factors for long-term health. The Journal of Pediatrics , 164(3), 631–638.e1–2. https://doi.org/10.1016/j.jpeds.2013.11.003

77.

Slopen

, Kubzansky

L. D.

, McLaughlin

K. A.

, & Koenen

K. C.

(2013). Childhood adversity and inflammatory processes in youth: A prospective study. Psychoneuroendocrinology , 38(2), 188–200. https://doi.org/10.1016/j.psyneuen.2012.05.013

78.

Smith

A. D. A. C.

, Heron

, Mishra

, Gilthorpe

M. S.

, Ben-Shlomo

, & Tilling

(2015). Model selection of the effect of binary exposures over the life course. Epidemiology , 26(5), 719–726. https://doi.org/10.1097/EDE.0000000000000348

79.

Stewart

E. A.

, & Simons

R. L.

(2010). Race, code of the street, and violent delinquency: A multilevel investigation of neighborhood street culture and individual norms of violence. Criminology , 48(2), 569–605. https://doi.org/10.1111/j.1745-9125.2010.00196.x

80.

Stoddard

S. A.

, Whiteside

, Zimmerman

M. A.

, Cunningham

R. M.

, Chermack

S. T.

, & Walton

M. A.

(2013). The relationship between cumulative risk and promotive factors and violent behavior among urban adolescents. American Journal of Community Psychology , 51(0), 57–65. https://doi.org/10.1007/s10464-012-9541-7

81.

Thornberry

T. P.

, Ireland

T. O.

, & Smith

C. A.

(2001). The importance of timing: The varying impact of childhood and adolescent maltreatment on multiple problem outcomes. Development and Psychopathology , 13(4), 957–979. https://doi.org/10.1017/S0954579401004114

82.

Tracy

, Salo

, & Appleton

A. A.

(2018). The mitigating effects of maternal social support and paternal involvement on the intergenerational transmission of violence. Child Abuse & Neglect , 78, 46–59. https://doi.org/10.1016/j.chiabu.2017.09.023

83.

Tracy

, Salo

, Slopen

, Udo

, & Appleton

A. A.

(2019). Trajectories of childhood adversity and the risk of depression in young adulthood: Results from the Avon longitudinal study of parents and children. Depression and Anxiety , 36(7), 596–606. https://doi.org/10.1002/da.22887

84.

Vaughn

M. G.

, Fu

, Beaver

K. M.

, DeLisi

, Perron

B. E.

, & Howard

M. O.

(2011). Effects of childhood adversity on bullying and cruelty to animals in the United States: Findings from a national sample. Journal of Interpersonal Violence , 26(17), 3509–3525. https://doi.org/10.1177/0886260511403763

85.

Wade

, Shea

J. A.

, Rubin

, & Wood

(2014). Adverse childhood experiences of low-income urban youth. Pediatrics , 134(1), e13–e20. https://doi.org/10.1542/peds.2013-2475

86.

Walsh

S. D.

, Molcho

, Craig

, Harel-Fisch

, Huynh

, Kukaswadia

, Aasvee

, Várnai

, Ottova

, Ra-vens-Sieberer

, & Pickett

(2013). Physical and emotional health problems experienced by youth engaged in physical fighting and weapon carrying. PLoS One , 8(2), e56403. https://doi.org/10.1371/journal.pone.0056403

87.

Walton

M. A.

, Cunningham

R. M.

, Goldstein

A. L.

, Chermack

S. T.

, Zimmerman

M. A.

, Bingham

C. R.

, Shope

J. T.

, Stanley

, & Blow

F. C.

(2009). Rates and correlates of violent behaviors among adolescents treated in an urban emergency department. The Journal of Adolescent Health: Official Publication of the Society for Adolescent Medicine , 45(1), 77–83. https://doi.org/10.1016/j.jadohealth.2008.12.005

88.

Widom

Cathy Spatz

, Czaja

, & Dutton

M. A.

(2014). Child abuse and neglect and intimate partner violence victimiza-tion and perpetration: A prospective investigation. Child Abuse & Neglect , 38(4), 650–663. https://doi.org/10.1016/j.chiabu.2013.11.004

89.

Widom

C.S.

(1998). Child victims: Searching for opportunities to break the cycle of violence. Applied and Preventive Psychology , 7(4), 225–234. https://doi.org/10.1016/S0962-1849(98)80026-7

90.

Wiesner

, Weichold

, & Silbereisen

R. K.

(2007). Trajectories of alcohol use among adolescent boys and girls: Identification, validation, and sociodemographic characteristics. Psychology of Addictive Behaviors , 21(1), 62–75.

91.

Williams

D. R.

, Lawrence

J. A.

, & Davis

B. A.

(2019). Racism and health: Evidence and needed research. Annual Review of Public Health , 40(1), 105–125. https://doi.org/10.1037/0893-164X.21.1.62

92.

World Health Organization. (2004). Preventing violence: A guide to implementing the recommendations of the World report on violence and health . World Health Organization. http://apps.who.int/iris/bitstream/10665/43014/1/9241592079.pdf

93.

Yoon

(2020). Time dimension of childhood adversities and externalizing behavior among children of teen mothers: Sensitive period hypothesis vs. accumulation hypothesis. Child Maltreatment . Advance online publication. https://doi.org/10.1177/1077559520984249

Childhood Adversity Trajectories and Violent Behaviors in Adolescence and Early Adulthood

Abstract

Keywords

Introduction

Method

Study Population

Measures

Violent behaviors.

Childhood adversity.

Other covariates.

Statistical Analysis

Flow chart of participation in the Avon Longitudinal Study of Parents and Children.

Results

Bivariate Associations Between Covariates of Interest and Violent Behaviors at Ages 13-20 years (n = 9,665).

Results of Unadjusted and Adjusted Logistic Regression Models Predicting Violent Behaviors at Ages 13-20 years (n = 9,665).

Results of Adjusted Multinomial Logistic Regression Models Predicting Violent Behaviors Across Adolescence and Early Adulthood (n = 9,665).a

Persistence of physical fighting across adolescence and early adulthood, by childhood adversity trajectory group (n = 9,665).

Persistence of weapon carrying across adolescence and early adulthood, by childhood adversity trajectory group (n = 9,665).

Discussion

Supplemental Material

Supplemental material for this article is available online.

Footnotes

Acknowledgments

Declaration of Conflicting Interests

Funding

ORCID iD

Author Biographies

References

Results of Adjusted Multinomial Logistic Regression Models Predicting Violent Behaviors Across Adolescence and Early Adulthood (n = 9,665).^a