Criminal History and Adverse Childhood Experiences in Relation to Recidivism and Social Functioning in Multi-problem Young Adults

Abstract

This study examines the relationship between criminal history and adverse childhood experiences (ACEs) and how they collectively predict (a) recidivism and (b) positive social functioning among multi-problem young adults. Criminal records and self-report data regarding ACEs and adult education/employment and quality of life (QoL) were collected for 692 multiproblem young adults (18–27 years). Results indicated that an extensive criminal history was related to non-violent and violent recidivism and lack of involvement in education/employment in young adulthood. On the contrary, a higher number of ACEs was related to lower QoL later in life, while this was not associated with recidivism or education/employment. These findings highlight again that past criminal behavior is a strong predictor of future criminality, particularly within this group of young adults with multiple problems. Furthermore, experiencing negative events in childhood shows to have long-term negative effects on QoL even for these individuals who already experience multiple life problems. Implications are discussed.

Keywords

multi-problem young adults social functioning quality of life recidivism criminal history ACEs

The age of onset of delinquent behavior, as well as the severity and frequency of offending, affects the nature of future criminal pathways (Liberman et al., 2008; Loeber & Farrington, 2000; Van Hazebroek et al., 2019). A variety of developmental offending careers from adolescence to young adulthood have been described: non-offenders, one-time offenders, recidivists, and chronic offenders (Blokland & Palmen, 2015). Moffitt (2018) described the pathways of life course persistent and adolescent-limited offending behavior. Adolescent-limited offending is common and generally considered to be a normative response to the social context of an adolescent, whereas life course persistent offending is more rare and often related to experienced family adversities, inadequate parenting, neurocognitive problems, and temperament and behavior problems (Moffitt & Caspi, 2001). According to previous studies, youth who commit frequent and serious crimes in adolescence tend to be less successful later in life than non-offending youth: They are frequently engaged in multiple types of crimes during young adulthood (Piquero et al., 2012) and experience difficulty in finishing school and finding employment (Bullis & Yovanoff, 2002; Van der Geest et al., 2016). In turn, this relates negatively to quality of life (QoL) in young adulthood (Lanctôt et al., 2007). QoL is defined by the World Health Organization as “an individual’s perception of his or her position in life, in the context of the culture and value system in which he or she lives and in relation to personal goals, expectations and concerns” (The World Health Organization Quality of Life assessment [WHOQOL Group], 1995).

On the contrary, positive life events, such as a romantic relationship and parenthood, can influence desistance from offending into young adulthood and may improve social functioning, such as attending education or employment (Blokland et al., 2012; Scales et al., 2016; Van der Geest et al., 2011; Verbruggen et al., 2012). These positive life events may also increase the perceived QoL. Research on the association between criminal history and QoL in young adulthood is scarce and has shown inconclusive results; some studies found a negative relation between delinquency and QoL (Akbarizadeh & Hosseinzadeh Jolgeh, 2016), while other studies found no association (Barendregt et al., 2018). Another study found an indirect negative relation via mental health problems between QoL and offending (Van Damme et al., 2016).

In particular, much is unknown regarding how criminal history during adolescence and adverse childhood experiences (ACEs) affect the road back to positive social functioning of multi-problem young adults. These young adults dysfunction in society and suffer from multiple problems, including financial problems, a low level of education, judicial contacts, psychological problems, and substance abuse (Van Duin et al., 2017). Their wide variety of problems complicates successful development—for example, finding suitable employment—in young adulthood (Scales et al., 2016). According to previous studies among young adults, the frequency of violent and non-violent offending peaks in late adolescence and decreases again during young adulthood (Piquero et al., 2012). However, some studies show a later peak in offending during young adulthood, specifically among young adults with serious mental health problems (Davis et al., 2015). Youth who persist in offending during young adulthood are often behind in their psychosocial development, in terms of future orientation, impulse control, and suppression of aggression, in contrast to youth who desist from offending (Monahan et al., 2009). As young adults with multiple problems are more vulnerable than other young adults, this group appears to be at particular risk of continuation of their offending behavior into young adulthood. Therefore, it is expected that past violent and non-violent offending are associated positively with (non-)violent recidivism. In addition, based on previous studies, it is hypothesized that past (non-)violent offending relates negatively with participation in education or employment in young adulthood.

Besides juvenile delinquency, ACEs likely have a negative impact on social functioning in young adulthood. Following an ecobiodevelopmental framework, in the ACE study by Felitti et al. (1998), an overview was created of the most prominent ACEs, such as maltreatment (abuse, neglect) and household dysfunction. These ACEs have since shown to be related to multiple negative outcomes in childhood, adolescence, and young adulthood (Basto-Pereira et al., 2016; Fang & Corso, 2007; Gilbert et al., 2009; Hughes et al., 2017; Kessler et al., 2010; Thompson et al., 2015), such as internalizing, externalizing, and behavioral problems (Van Duin et al., 2017); delayed brain development (Berens et al., 2017); long-term health problems (Flaherty et al., 2013; Thompson et al., 2015); violent and chronic offending (Baglivio et al., 2015; Basto-Pereira et al., 2016; Fang & Corso, 2007; Fox et al., 2015; Garbarino, 2017; Lansford et al., 2007; Perez et al., 2018; Reavis et al., 2013; Wolff & Baglivio, 2017); and the intergenerational transfer of violent behavior (Duke et al., 2010; Fang & Corso, 2007; Fox et al., 2015; Widom & Maxfield, 2001). ACEs have also been linked to specific aspects of social (dys)functioning, such as unemployment or serious job problems, financial problems, absenteeism (Anda et al., 2004; Currie & Spatz Widom, 2010; Liu et al., 2013; Metzler et al., 2017; Zielinski, 2009), and a lower QoL (Abajobir et al., 2017; Mosley-Johnson et al., 2019).

Not surprisingly, both elaborate criminal history and ACEs are highly prevalent in multi-problem youth (Van Duin et al., 2017, 2019). These vulnerable youth experience more difficulties to succeed in community integration in young adulthood than other youth, regarding participating in employment, attending education, generating a stable income (Metzler et al., 2017; Scales et al., 2016; Van der Geest et al., 2016; Van Hazebroek et al., 2019), or finding independent housing (Osgood et al., 2010). Such failure complicates their development in becoming self-sufficient and well-functioning adults (Collins, 2001; Osgood et al., 2005, 2010; Scales et al., 2016), and negatively relates to their perceived QoL (Nevarez-Flores et al., 2019). In turn, this may increase their likelihood of future criminal behavior and convictions (Van Duin et al., 2017, 2019). In addition, multi-problem youth and young adults often experience psychological problems (Van Duin et al., 2017, 2019), which may have a negative effect on their experienced QoL in young adulthood and subsequently also increase the likelihood of future criminality and unemployment (Davis et al., 2015; Van Damme et al., 2016).

The present study aims to investigate to what extent criminal history characteristics and ACEs are related, separately and collectively, to recidivism and social (dys)function of multi-problem males in young adulthood. Recidivism, violent and nonviolent, is regarded as a negative outcome, while participation in education/employment and perceived positive QoL are seen as markers for positive social functioning. Based on previous studies, we hypothesize that a more extensive criminal history (frequency of past [violent] offending and earlier age of onset) is positively correlated with the presence of multiple ACEs during childhood (Hypothesis 1). Moreover, we hypothesize that the presence of a more extensive criminal history and more ACEs increases the risk of non-violent and violent recidivism in multi-problem young adults (Hypothesis 2). In addition, we expect that an extensive criminal history and multiple ACEs decrease the likelihood of positive social functioning regarding participation in education/employment (Hypothesis 3) and negatively affect QoL in young adulthood (Hypothesis 4). Finally, we anticipate that a model including both criminal history characteristics and ACEs will provide a better prediction for each of these outcomes than these individual predictors separately (Hypothesis 5).

Method

Participants and Setting

A total of 696 multi-problem young adults were recruited in Rotterdam, The Netherlands, between 2014 and 2016 (Luijks et al., 2017; Van Duin et al., 2017, 2019). Participants were all male, between 18 and 27 years old, and had sufficient knowledge of the Dutch language to understand the study procedure and the questionnaires. The participants were recruited at two sites: at a municipal agency where all young adults aged between 18 and 27 years can request social welfare (Dutch: Jongerenloket) and at multimodal day treatment program New Opportunities (Dutch: De Nieuwe Kans [DNK]) for multi-problem young adult males. At DNK, 173 participants were recruited. Participants signed up for DNK themselves or were referred by the Jongerenloket, youth care, probation services, mental health services, or other social organizations. DNK offers a multimodal intervention for multi-problem young adult males, while social welfare can be requested at the Jongerenloket.

At Jongerenloket, 523 participants were recruited. During the intake, the youth coach rated the Self-Sufficiency Matrix–Dutch version (SSM-D; Fassaert et al., 2014). The SSM-D has scores of in crisis (1), vulnerable (2), stable (3), safe (4), and thriving (5). Young adults were eligible for participation in this study when they met multi-problem criteria based on the SSM-D: (a) a score of 1 or 2 on the domains of Income and Daytime Activities; (b) a maximum score of 3 on at least one of the following domains: Addiction, Mental Health, Social Network, and Justice; and (c) a minimum score of 3 on the domain of Physical Health. A minimum score of 3 on Physical Health was required to be able to engage in interactive components of the intervention, such as offered at DNK. This multi-problem definition was based on the SSM-D profile of DNK participants who had an intake at Jongerenloket in 2012 (M. J. A. Luijks et al., 2017). Of the total study sample (N = 696), the final sample consisted of 692 participants (99.4%), who gave informed consent regarding the record and register research. Baseline measures were assessed at start of treatment at DNK or within 2 weeks after the intake at Jongerenloket. At follow-up, 12 to 14 months after baseline, the response rate was 77.7% (n = 538). The study was approved by the Medical Ethics Review Committee of VU University Medical Center.¹ Participants gave informed consent for their voluntary participation after a member of the research team provided oral and written information. Trained researchers administered the questionnaires by means of an interview.

Measures

Independent Variables

Criminal history

History of delinquency was documented as the number of previous convictions for violent and for non-violent offenses until baseline measurement, as officially registered in the Research and Policy database Judicial Documentation (Dutch: Onderzoek- en Beleidsdatabase Justitiële Documentatie [OBJD]) of the Ministry of Justice and Security in The Netherlands (Wartna et al., 2011). A total number of past violent offenses was computed by means of combining the number of committed violent offenses in the following three offense categories: violence toward others, property crime with violence, and hands-on sexual offenses. A total number of past non-violent offenses was computed by means of adding the number of committed offenses in all other offense categories, that is, property crime without violence, drug offenses, destruction/minor aggression/disturbance public order, traffic offenses, and other offenses. Age of onset was computed by means of calculating the difference between the date of birth and the date when the first officially registered offense was committed.

Adverse childhood experiences

The total ACE score was operationalized as the self-reported exposure to 11 types of adverse experiences during childhood, within three main categories: abuse (3), neglect (2), and household dysfunction (6). Abuse and neglect were assessed by means of the 24-item Dutch Childhood Trauma Questionnaire–Short Form (CTQ-SF), resulting in five ACEs: physical abuse (five items; Cronbach’s α = .91), emotional abuse (five items; Cronbach’s α = .89), sexual abuse (four items; Cronbach’s α = .95), physical neglect (five items; Cronbach’s α = .63), and emotional neglect (five items; Cronbach’s α = .91) (Thombs et al., 2009). The item response categories were Likert-type: never true (1) to very often true (5). For each of the ACEs, a total score was calculated by adding up the item scores. For sexual abuse, this total score was then prorated to arrive at a similar range for all ACEs (range = 5–25). For physical abuse and physical neglect, a score of 8 and above was categorized as the ACE was (partially) present. For sexual abuse, emotional neglect, and emotional abuse, scores upward from 6, 10, and 9, respectively, were categorized as (partially) present (Bernstein et al., 2003; Walker et al., 1999). The ratings were dichotomized, using these cutoff scores to determine whether the specific type of abuse was present or not during childhood, as applied in previous studies by Bernstein et al. (2003), Walker et al. (1999), and Van Duin et al. (2019). Household dysfunction consists of six dichotomous ACEs: alcohol abuse in the family, drug abuse in the family, police contact in the family, psychological problems in the family, domestic violence, and growing up in a single-parent family. It was assessed whether or not participants had these 11 types of abuse, neglect, and household dysfunction ACEs (Yes/No), and the total ACE score was computed for each individual by summing up the number of “Yes” answers (range = 1–11; Van Duin et al., 2019).

Outcome Measures

Recidivism

Information regarding recidivism was collected during follow-up: convictions for new (violent or non-violent) offenses committed between baseline measurement and January 2018 (mean follow-up time = 900.1 days) were retrieved from the OBJD database in The Netherlands (conviction date). Dichotomous measures of violent and non-violent recidivism were computed. In addition, time (in days) from baseline measurement to first violent recidivism and to first non-violent recidivism was calculated.

Education/employment

The first outcome measure reflecting positive social functioning was education/employment. This was measured at follow-up, 14 months after the baseline measurement, by means of self-report questions: (a) Are you currently enrolled in education? (Yes/No) and (b) Do you currently have a job? (Yes/No). Based on the answers, the dichotomous variable education/employment was computed (Yes/No).

Quality of Life

The second outcome measure reflecting positive social functioning was QoL, as measured 14 months after baseline by means of the Manchester Short Assessment of Quality of Life (MANSA). Sixteen questions were assessed, four of these investigating objective QoL and 12 concerning satisfaction on various life domains: overall life satisfaction, unemployment/job, financial situation, friendships, leisure activities, accommodation, personal safety, people in the person’s household, sexual life, family, physical and mental health. The item response categories are Likert-type (1–7): could not be worse (1) to could not be better (7). Self-reported QoL was calculated by means of the total MANSA score quality of life score. The MANSA is a brief and modified version of the Lancashire Quality of Life Profile (LQLP). Its internal consistency is satisfactory (Cronbach’s α = .74); it has adequate concurrent validity with the LQLP (Priebe et al., 1999) and is used both in clinical and nonclinical samples (Priebe et al., 2011).

Data Analysis

Multiple statistical analyses were performed using Statistical Package for the Social Sciences (SPSS), Version 22 (Field, 2009). To test Hypothesis 1, the relationship between the predictor variables was investigated, and correlations between the predictors were calculated. In addition, correlations between the outcomes were tested. Subsequently, the independent predictors were investigated for each outcome by means of multiple Receiver Operating Characteristic (ROC) analyses. This analysis results in an area under the curve (AUC) value for each predictor. AUC values present the predictive ability of the independent variables. Values between .556 and .638 are considered small, between .639 and .713 considered moderate, and values of .714 and above considered large (Rice & Harris, 2005).

Furthermore, two hierarchical Cox regression analyses were executed to further explore the predictive value of non-violent recidivism and violent recidivism over time using the predictors criminal history and ACEs (Hypothesis 2). Finally, to test Hypothesis 3, a hierarchical logistic regression analysis and to explore Hypothesis 4, a linear regression analysis were also executed to further investigate the association between the predictors criminal history and ACEs and the outcome measures education/employment and QoL in young adulthood, respectively. For each of the Cox, logistic, and linear regressions, the independent variables were entered in multiple steps to test Hypothesis 5: (a) the total number of past non-violent offenses and of past violent offenses (together criminal history) and (b) the total number of ACEs. In addition, a third step was added to the regression models to control for age. Age of onset was not included in the regression analyses as this could not be calculated for those individuals without criminal history (n = 119).

Results

Table 1 shows the descriptives of self-reported demographic characteristics, ACEs, officially registered prior offending, official recidivism, and self-reported education/employment and QoL at follow-up. The majority of the participants were from non-Dutch descent (87.5%), conform the Statistics Netherlands (Dutch: CBS) definition. A participant was classified as non-Dutch if he or one of his parents was not born in The Netherlands (Statistics Netherlands, 2016). Prior delinquency was highly prevalent; 82.8% of the sample was convicted at least once before baseline (n = 573). Property crime without violence was the most common prior offense. ACEs were also highly prevalent; a mean of 3.6 ACEs per participant was reported. The most common ACE reported was emotional neglect (69.0%).

Table 1:

Percentage or Mean Score of Demographic Characteristics, Criminal History Characteristics, ACEs, and Outcome Measures (N = 692)

Predictors and Outcomes	Yes %/M (SD)
Demographic characteristics
Mean age (N = 692)	22.0 (2.4)
Ethnicity (N = 692)
Dutch	12.5%
Moroccan	20.1%
Antillean	17.5%
Surinamese	17.4%
Cape Verdean	7.9%
Turkish	6.9%
Other Western	4.2%
Other non-Western	13.5%
Criminal history
Age of onset (n = 573)	16.0 (3.0)
Type of offense
Violence (n = 573)	32.4%
Property crime with violence (n = 573)	26.8%
Sex offense (n = 573)	6.2%
Property crime without violence (n = 573)	53.4%
Destruction/minor aggression/disturbance public order (n = 573)	35.7%
Drug offense (n = 573)	15.2%
Traffic offense (n = 573)	12.3%
Other (n = 573)	30.4%
Number of nonviolent offenses (N = 692)	3.3 (4.4)
Number of violent offenses (N = 692)	1.2 (1.8)
ACEs
Mean total ACEs (N = 692)	3.6 (2.0)^a
Maltreatment
Emotional abuse (n = 689)	32.5%
Physical abuse (n = 690)	34.4%
Sexual abuse (n = 690)	10.4%
Emotional neglect (n = 690)	69.0%
Physical neglect (n = 689)	39.0%
Household dysfunction
Single-parent family (N = 692)	40.6%
Family problems—Alcohol abuse (N = 692)	10.7%
Family problems—Drug abuse (N = 692)	9.2%
Family problems—Police contact (N = 692)	15.6%
Family problems—Psychological problems (N = 692)	9.8%
Family problems—Domestic violence (N = 692)	13.6%
Recidivism
Non-violent (N = 692)	33.5%
Days to non-violent recidivism	596.0 (361.4)
Violent (N = 692)	17.3%
Days to violent recidivism	800.6 (326.3)
Employment/education at follow-up (n = 537)	44.6%
QoL at follow-up (n = 537)	60.1 (10.0)

Note. ACEs = adverse childhood experiences; QoL = quality of life.

Minimum = 1, Maximum = 11, Median = 3.

Table 2 shows the correlations between characteristics of criminal history and ACEs. ACEs were not associated with criminal history characteristics. Past non-violent and past violent offenses were positively associated and both were associated with a lower age of onset of first offense. In the follow-up period of 30 months average (range = 412–1,028 days), 37.8% of the participants were convicted for any new offense—for a non-violent offense and/or for a violent offense. Almost half of the participants were involved in employment or education at the time of follow-up, and the mean self-reported total score on QoL at follow-up was 60.1 (range = 20–84). Table 2 also shows the correlations between the different outcome measures. Violent and non-violent recidivism were positively associated. Involvement in education/employment and QoL were also positively associated.

Table 2:

Correlations of all Predictors and Outcome Measures

	ACEs	Past violent offenses	Past non-violent offenses	Age of onset	Days to non-violent recidivism	Days to violent recidivism	QoL
ACEs (N = 692)	—	—	—	—	—	—	—
Past violent offenses (N = 692)	.022	—	—	—	—	—	—
Past non-violent offenses (N = 692)	.020	.607***	—	—	—	—	—
Age of onset (n = 573)	−.006	−.394***	−.415***	—	—	—	—
Days to non-violent recidivism (N = 692)	.060	−.226**	−.292***	.082	—	—	—
Days to violent recidivism (N = 692)	−.041	−.144**	−.116**	.086*	.477***	—	—
QoL (n = 538)	−.119**	.050	−.036	−.051	.036	−.007	—
Education/employment (n = 538)	.050	−.095*	−.144**	.044	.075	−.013	.416***

Note. ACEs = adverse childhood experiences; QoL = quality of life.

p < .05. **p < .01. ***p < .001.

Unique Associations of Criminal History and ACEs with Recidivism, Education/Employment, and QoL

The predictive ability of the four independent variables was investigated: past non-violent offenses, past violent offenses, age of onset, and ACEs. Table 3 shows the results of the AUC analyses for the four different outcome measures: non-violent recidivism, violent recidivism, QoL, and education/employment. Past non-violent offenses had a large predictive ability for non-violent recidivism, while a history of violent offenses had a moderate predictive validity for non-violent recidivism. Past non-violent offenses and past violent offenses both had a large predictive validity for violent recidivism. Overall, these results show that individuals with a more extensive criminal history were generally more prone to recidivate with non-violent as well as violent offenses. Age of onset had a low, yet significant, predictive value for non-violent recidivism and violent recidivism, indicating that a lower age at first offense slightly increased the likelihood of recidivism.

Table 3:

AUC Values per Outcome

	Non-violent recidivism		Violent recidivism		QoL		Education/Employment
	AUC [95% CI]	N	AUC [95% CI]	N	AUC [95% CI]	N	AUC [95% CI]	N
Non-violent offenses	.76** [0.73, 0.80]	N = 692	.72** [0.66, 0.77]	N = 692	.47 [0.42, 0.52]	n = 537	.42* [0.37, 0.47]	n = 537
Violent offenses	.67** [0.62, 0.71]	N = 692	.73** [0.68, 0.79]	N = 692	.52 [0.47, 0.57]	n = 537	.45* [0.40, 0.50]	n = 537
Age of onset	.43* [0.38, 0.47]	n = 573	.43* [0.37, 0.49]	n = 573	.49 [0.43, 0.55]	n = 440	.53 [0.47, 0.58]	n = 440
ACEs	.48 [0.44, 0.53]	N = 692	.49 [0.43, 0.55]	N = 692	.42* [0.37, 0.47]	n = 537	.53 [0.48, 0.58]	n = 537

Note. AUC = area under the curve; QoL = quality of life; CI = confidence interval; ACEs = adverse childhood experiences.

p < .05. **p < .01.

The predictive validity of past non-violent offenses and past violent offenses for the positive outcome education/employment was small, but significant, indicating that people with a more extensive criminal history were slightly less likely to be involved in education or employment at follow-up. The predictive validity of ACEs for the positive outcome QoL was small but significant, indicating that individuals who experienced more ACEs reported slightly lower QoL at follow-up.

In the following, through combined prediction models, the added value of the frequency of past non-violent offending, the frequency of past violent offending, and ACE scores for the prediction of the four outcomes (non-violent recidivism, violent recidivism, education/employment, and QoL) was examined, while controlling for age.

Criminal History and ACEs in Relation to Recidivism Over Time

Hierarchical Cox regression analyses were executed to explore the prediction of non-violent recidivism and violent recidivism over time (see Table 4). In the first step of the non-violent recidivism analysis, past non-violent offenses and past violent offenses were entered, which both significantly predicted non-violent recidivism over time. In the second step, total ACEs were added to the model, which did not contribute significantly to the prediction of nonviolent recidivism over time, Change model χ²(1) = 3.63, p = .06. In the third step, age was added as a control variable, which contributed significantly to the model, Change model χ²(1) = 22.93, p < .001. A hierarchical Cox regression model was also executed to explore the prediction of violent recidivism over time (see Table 4). In the first step of the violent recidivism analysis, past non-violent offenses and past violent offenses were entered, which both significantly predicted violent recidivism over time. In the second step, total ACEs were added to the model, which also did not contribute significantly to the prediction of violent recidivism over time, Change model χ²(1) = 0.04, p = .84. In the third step, age was added as a control variable, which did not contribute significantly to the model, Change model χ²(1) = 1.72, p = .19.

Table 4:

Cox Regression Model: The Incremental Predictive Validity of Criminal History, ACEs, and Age to Non-violent Recidivism and Violent Recidivism over Time (N = 692)

	Nonviolent recidivism				Violent recidivism
	B (SE)	Wald (df = 1)	Hazard ratio [95% CI]	p value	B (SE)	Wald (df = 1)	Hazard ratio [95% CI]	p value
Step 1
Prior non-violent offenses	.38 (.06)	42.32	1.46 [1.30, 1.64]	<.001	.19 (.08)	5.29	1.21 [1.03, 1.43]	.021
Prior violent offenses	.22 (.07)	9.91	1.25 [1.09, 1.44]	.002	.36 (.08)	20.22	1.44 [1.23, 1.69]	<.001
Step 2
Prior non-violent offenses	.40 (.06)	44.21	1.49 [1.33, 1.68]	<.001	.19 (.08)	5.30	1.21 [1.03, 1.43]	.021
Prior violent offenses	.22 (.07)	9.24	1.24 [1.08, 1.43]	.002	.36 (.08)	20.06	1.44 [1.23, 1.69]	<.001
Total ACEs	−.12 (.06)	3.51	0.89 [0.78, 1.01]	.061	−.02 (.09)	0.04	0.98 [0.82, 1.17]	.838
Step 3
Prior nonviolent offenses	.44 (.06)	49.10	1.55 [1.37, 1.76]	<.001	.19 (.08)	5.35	1.21 [1.03, 1.43]	.021
Prior violent offenses	.20 (.08)	7.46	1.23 [1.06, 1.42]	.006	.38 (.08)	20.80	1.46 [1.24, 1.72]	<.001
Total ACEs	−.09 (.06)	2.01	0.91 [0.81, 1.04]	.157	−.01 (.09)	0.00	0.99 [0.83, 1.19]	.956
Age	−.13 (.03)	22.65	0.88 [0.83, 0.92]	.000	−.05 (.04)	1.72	0.95 [0.88, 1.02]	.190

Note. ACEs = adverse childhood experiences; CI = confidence interval.

Criminal History and ACEs in Relation to Social Functioning

A hierarchical logistical regression analysis was conducted to examine the association between past non-violent offending, past violent offending, ACEs, and the outcome education/employment, controlling for age (see Table 5). In the first step of the education/employment prediction model, past non-violent offending and past violent offending were entered in the model. Non-violent offending was related negatively to education/employment. Thus, participants who were convicted less for non-violent offenses prior to baseline had an increased likelihood to be involved in education/employment at the end of the follow-up period. In the second step, total ACEs were added, which did not significantly contribute to the prediction model of education/employment, ΔR² = .01, p = .15. In the third step, age was added as a control variable, which did not significantly contribute to the model, ΔR² = .01, p = .46.

Table 5:

Logistic Regression Analysis: The Incremental Predictive Validity of Criminal History, ACEs, and Age to Education/Employment (n = 537)

Predictors	B (SE)	95% CI for odds ratio
Predictors	B (SE)	Lower	Odds ratio	Upper
Step 1
Constant	.038 (.113)		1.039
Non-violent offenses	−.077 (.029)	0.876	0.926**	0.979
Violent offenses	−.015 (.065)	0.868	0.985	1.118
Step 2
Constant	−.193 (.195)		0.824
Non-violent offenses	−.077 (.028)	0.875	0.925**	0.978
Violent offenses	−.018 (.065)	0.865	0.982	1.115
Total ACEs	.064 (.044)	0.978	0.978	1.163
Step 3
Constant	−.786 (.823)		0.456
Non-violent offenses	−.079 (.029)	0.873	0.924**	0.977
Violent offenses	−.016 (.065)	0.967	0.984	1.117
Total ACEs	.064 (.044)	0.978	1.066	1.163
Age	.027 (.037)	0.956	1.028	1.104

Note. Statistics for model. Step 1: R² = .03 (Nagelkerke). Model χ²(2) = 13.22, p = .001. Step 2: R² = .04, Model χ²(3) = 15.33, p < .01. Step 3: R² = .04 (Nagelkerke). Model χ²(4) = 15.88, p = .003. Hosmer–Lemeshow test: 7.44 (df = 8), p = .490. ACEs = adverse childhood experiences; CI = confidence interval.

**p < .01.

A hierarchical linear regression analysis was carried out to examine whether past offending (non-violent and violent), ACEs, and age were associated with the outcome measure QoL in the follow-up period (see Table 6). In the first step, past non-violent offending (nonsignificant) and past violent offending were entered in the model. Past violent offending was positively related with QoL, surprisingly indicating that more prior violent offenses resulted in a slightly higher reported QoL. In the second step, total ACEs were added to the model. This model had the best fit, R² = .03, ΔR² = .01, p = .01, in which ACEs were negatively related to QoL, indicating that more ACEs resulted in a lower QoL. In the third step, age was added to the model, which did not contribute significantly, R² = .03, ΔR² = .01, p = .06. Concluding, more past violent offending was marginally related to a slightly increased QoL, although this was not confirmed in the AUC analysis (see Table 3). ACEs significantly improved the prediction model: The experience of more ACEs in the past was related to a lower QoL at follow-up.

Table 6:

Linear Regression Analysis: The Incremental Predictive Validity of Criminal History, ACEs, and Age to QoL (n = 537)

Predictors	B	SE B
Step 1
Constant	60.127	.547
Nonviolent offenses	−.102	.126 (−.240)
Violent offenses	.111*	.303 (.627)
Step 2
Constant	62.252	.939
Nonviolent offenses	−.098	.125 (−.230)
Violent offenses	.114*	.301 (.644)
Total ACEs	−.119**	.214 (−.593)
Step 3
Constant	69.483	3.956
Nonviolent offenses	−.088	.126 (−.208)
Violent offenses	.110*	.300 (.622)
Total ACEs	−.118**	.213 (−.589)
Age	−.081	.176 (−.332)

Note. Statistics for model. R² = .009 for Step 1; R² = .023 for Step 2; R² = .030 for Step 3 (df = 1). ACEs = adverse childhood experiences; QoL = quality of life.

p < .05. **p < .01.

Discussion

This study aimed to increase the knowledge of the predictive value of criminal history and ACEs on the functioning of a young adult group with multiple severe problems in different life domains. The purpose of this study was twofold. The first aim was to explore to what extent criminal history and ACEs predicted non-violent and violent recidivism among multi-problem young adults. Contrary to our hypothesis, findings did not indicate a relation between extensive criminal history and the presence of multiple ACEs. As expected, the much replicated finding that criminal history (past non-violent offenses, past violent offenses, and age of onset) predicts (violent) recidivism was confirmed for this sample of multi-problem young adults. Age only demonstrated to significantly influence these findings for the outcome of non-violent recidivism. However, the current study did not find that within the multi-problem young adult group, ACEs were a sound predictor of violent or non-violent recidivism and did not demonstrate that a model including both criminal history characteristics and ACEs would provide a better prediction of recidivism over time than each of these predictors separately.

A possible explanation for the lack of predictive power for ACEs in the present study might be the high prevalence of ACEs within this sample. ACEs were more prevalent in this sample in contrast with the general population of adult men as reported in the ACE study (Chapman et al., 2007) and in comparison with other high-risk samples (Flaherty et al., 2013; Thompson et al., 2015). Other studies generally include control groups and have a wider distribution of ACE scores in their investigation (Widom & Maxfield, 2001), which might generate more effect. The dispersion of ACE scores in the current study is rather low and a large proportion of the sample had at least two or more ACEs. It is possible that ACE scores discriminate less within such a highly traumatized sample. Another explanation might be that specific ACEs differ in their association with (violent) recidivism. A study by Basto-Pereira et al. (2016) showed sexual abuse as strongest predictor of recidivism, whereas a study by Lansford et al. (2007) demonstrated physical abuse as the strongest predictor of juvenile violent offending. This was not examined in the current study, because our focus was on total ACEs to investigate the cumulative effect of the presence of multiple ACEs. Regarding recidivism, other factors such as poverty or a weak social support network might also influence whether one reoffends or not (Bright & Jonson-Reid, 2015). These factors were not taken into account in the present study.

The second aim was to investigate the prediction of positive social functioning in terms of participation in education/employment and experienced QoL among multi-problem young adults. In the follow-up period, almost half of the sample was involved in education/employment, whereas they were not participating at baseline. Criminal history showed to be a negative predictor of education/employment. A possible explanation might be that a more extensive offending history could have led to more frequent convictions in the past, including imprisonment which may have resulted in lower educational achievement. In turn, this possibly resulted in limited employment opportunities. According to previous research, the experience of imprisonment complicates graduation or finding employment (Bullis & Yovanoff, 2002; Van der Geest et al., 2016). Another explanation might be possible stigmatization during the job application process as a result of past convictions or imprisonment (Decker et al., 2015). ACEs did not improve the prediction model of education/employment. Again, this might be due to the high prevalence of ACEs in the current sample. A study by Lansford et al. (2007) demonstrated that specifically a history of physical abuse appears to complicate attaining education or employment. However, other ACEs might be less relevant for this particular outcome and thus blur the findings.

In contrast to the nonsignificant findings regarding the prediction of recidivism and education/employment, the analysis of the prediction of QoL did show that the presence of multiple ACEs early in life were a good predictor of QoL during young adulthood and that ACEs added significantly to the prediction model over criminal history: less ACEs predicted a better QoL. This confirms the expectations based on previous research (Abajobir et al., 2017; Mosley-Johnson et al., 2019), even despite the high prevalence of ACEs in this sample. Surprisingly, in the regression analysis, past violent offending was marginally related to a higher QoL. However, this was not confirmed in the AUC analysis, which showed a chance-level prediction of past violent offending for QoL.

Overall, the results from this study show that criminal history and ACEs have varied importance for social functioning of this multi-problem young adult group. While, in accordance to the literature, a more extensive criminal history demonstrated to be related to ongoing delinquent behavior even for this multi-problem group of young adults, multiple ACEs were only related to lower subjective well-being later in life. In addition, markers of criminal history were related to less educational and employment involvement.

The present study had several limitations. First, with regard to criminal history and recidivism, the baseline measurement and follow-up moments were based on the time of participation in an intervention, rather than set ages and follow-up times for all participants. Second, education/employment and QoL were self-reported measures at 14 months after baseline measurement. The stability of positive outcome variables over time—for example, regarding attending school or employment or regarding QoL—was not taken into account. Also, objective information regarding the extent of participation in education/employment was not considered. In future studies, it would be valuable to include more sound measures of different types of positive social and community outcomes, such as registered information regarding long term participation in education or employment, financial management, or the social involvement with friends, family, and intimate relationships. Furthermore, a recommendation for further research would be to investigate the contribution of each of the ACEs separately in the prediction of various outcomes, in addition to the predictive validity of the total ACE score. Finally, it is recommended that more studies include the notion of positive life events such as romantic relationships and parenthood when studying success factors for social functioning.

In conclusion, this study demonstrated that, in particular, criminal history predicts a greater likelihood of (violent) recidivism and reduced participation in education/employment in this multi-problem young adult sample. On the contrary, ACEs appear more important for the self-perceived QoL. Based on these findings, it seems advisable that treatment programs for (multi-problem) young adults carefully take into account past criminal offending and ACEs to provide the best possible care and support that stimulates community reintegration and QoL improvement. Paying careful attention to these negative early life experiences could provide another piece of the puzzle that breaks the cycle of criminality, trauma, social dysfunctioning, and unsuccessful community participation for these multi-problem young individuals.

Footnotes

Authors’ Note:

We would like to thank the participants, the social welfare agency in Rotterdam (Jongerenloket), and DNK (Dutch: De Nieuwe Kans) for their cooperation with this study. We would like to thank the De Verre Bergen Foundation for funding this study. We have no known conflict of interest to disclose. Laura van Duin (1st author) and Michiel de Vries Robbé (1st author) collaborated on the first draft of the manuscript. This research project is funded by De Verre Bergen Foundation. De Verre Bergen Foundation is a venture philanthropy organization that aims to build a better Rotterdam through substantial investments in innovative, impactful social ventures. The financer was not involved in the design of the study nor the drafting of the manuscript. Furthermore, the financer was not involved in the process of data collection, analysis, and interpretation. Direct financer inquiries to Marvin Pires, Parklaan 22, 3016 BB Rotterdam, The Netherlands; e-mail: mpires@sdvb.com.

ORCID iD

Laura van Duin

Notes

Laura van Duin is a PhD student in the Department of Child and Adolescent Psychiatry of the Amsterdam University Medical Centers. Her research focuses on the care trajectories and judicial histories of multiproblem young adults. She now also works as a social worker with multiproblem young adult men.

Michiel de Vries Robbé, PhD, is senior researcher in the Department of Child and Adolescent Psychiatry of the Amsterdam University Medical Centers, and associate professor at the Department of Psychiatry and Behavioral Neurosciences of McMaster University, Hamilton, Canada. His research focuses primarily on violence risk assessment, in particular risk screening and protective factors for (violence) risk for adults, juveniles, and children.

Reshmi Marhe, PhD, is an assistant professor in the Erasmus University Rotterdam, Department of Psychology, Education & Child Studies. Her research focuses on neurocognitive mechanisms of externalizing behavior, such as antisocial behavior and addiction.

Floor Bevaart, PhD, was, until recently, a postdoctoral researcher in the Amsterdam University Medical Centers, Department of Child and Adolescent Psychiatry. Her research was focused on the clustering of mental health-, judicial-, and social-economic problems. She now works as a freelance science-journalist and writer.

Josjan Zijlmans, PhD, is a postdoctoral researcher in the Department of Child and Adolescent Psychiatry of the Amsterdam University Medical Centers. His research focuses on the neuroscience of antisocial behavior.

Marie-Jolette A. Luijks is a PhD student in the Department of Child and Adolescent Psychiatry of the Amsterdam University Medical Centers. Her research project is focused on the effectiveness of interventions for young adults with severe, multiple problems and the predictors of treatment participation and completion.

Theo A. H. Doreleijers, MD, PhD, was head of the Department of Child and Adolescent Psychiatry of the Amsterdam University Medical Centers, and he was a professor of forensic psychiatry at the Faculty of Law of Leiden University. At the time of the study, he was the director of the Academic Workplace in Rotterdam and the supervisor of the first author.

Arne Popma, MD, PhD, is head of the Department of Child and Adolescent Psychiatry and Psychosocial Care of Amsterdam University Medical Centers. He is a Child and Adolescent Psychiatrist at Levvel Amsterdam and Chair of the Child and Adolescent Psychiatry section of the Dutch Psychiatry Association. At the time of the study, he was the supervisor of the first author.

References

Abajobir

A. A.

Kisely

Williams

Strathearn

Clavarino

Najman

J. M.

(2017). Does substantiated childhood maltreatment lead to poor quality of life in young adulthood? Evidence from an Australian birth cohort study. Quality of Life Research, 26(7), 1697–1702. https://doi.org/10.1007/s11136-017-1517-5

Akbarizadeh

Hosseinzadeh Jolgeh

(2016). Analysis of the relationship between quality of life with youth delinquency (case study of youth in Zahedan). International Journal of Humanities and Social Science Invention, 5(5), 69–73.

Anda

R. F.

Fleisher

V. I.

Felitti

V. J.

Edwards

V. J.

Whitfield

C. L.

Dube

S. R.

Williamson

D. F.

(2004). Childhood abuse, household dysfunction, and indicators of impaired adult worker performance. The Permanente Journal, 8(1), 30–38. https://doi.org/10.7812/tpp/03-089

Baglivio

M. T.

Wolff

K. T.

Piquero

A. R.

Epps

(2015). The relationship between adverse childhood experiences (ACE) and juvenile offending trajectories in a juvenile offender sample. Journal of Criminal Justice, 43(3), 229–241. https://doi.org/10.1016/j.jcrimjus.2015.04.012

Barendregt

C. S.

Van der Laan

A. M.

Bongers

I. L.

Van Nieuwenhuizen

(2018). Quality of life, delinquency and psychosocial functioning of adolescents in secure residential care: Testing two assumptions of the Good Lives Model. Child and Adolescent Psychiatry and Mental Health, 12(4), 1–10. https://doi.org/10.1186/s13034-017-0209-9

Basto-Pereira

Miranda

Ribeiro

Maia

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

Berens

A. E.

Jensen

S. K. G.

Nelson

C. A.

(2017). Biological embedding of childhood adversity: From physiological mechanisms to clinical implications. BMC Medicine, 15(1), 1–12. https://doi.org/10.1186/s12916-017-0895-4

Bernstein

D. P.

Stein

J. A.

Newcomb

M. D.

Walker

Pogge

Ahluvalia

Stokes

Handelsman

Medrano

Desmond

Zule

(2003). Development and validation of a brief screening version of the Childhood Trauma Questionnaire. Child Abuse & Neglect, 27(2), 169–190. https://doi.org/10.1016/S0145-2134(02)00541-0

Blokland

A. A. J.

Palmen

. (2015). Patterns of criminal careers. In Loeber

Hoeve

Slot

Van der Laan

P. H.

(Eds.), Young offenders growing up. What affects their behavior and what function does justice have? (1st ed., pp. 39–76). SWP Uitgeverij.

10.

Blokland

A. A. J.

Palmen

Van San

(2012). Criminal behavior in young adulthood. Tijdschrift Voor Criminologie, 54(2), 85–98.

11.

Bright

C. L.

Jonson-Reid

(2015). Multiple service system involvement and later offending behavior: Implications for prevention and early intervention. American Journal of Public Health, 105(7), 1358–1364. https://doi.org/10.2105/AJPH.2014.302508

12.

Bullis

Yovanoff

(2002). Those who do not return: Correlates of the work and school engagement of formerly incarcerated youth who remain in the community. Journal of Emotional and Behavioral Disorders, 10(2), 66–78. https://doi.org/10.1177/10634266020100020101

13.

Chapman

D. P.

Dube

S. R.

Anda

R. F.

(2007). Adverse childhood events as risk factors for negative mental health outcomes. Psychiatric Annals, 37(5), 359–364. https://doi.org/10.3928/00485713-20070501-07

14.

Collins

M. E.

(2001). Transition to adulthood for vulnerable youths: A review of research and implications for policy. Social Service Review, 72(2), 271–291. https://doi.org/10.1086/322209

15.

Currie

Spatz Widom

(2010). Long-term consequences of child abuse and neglect on adult economic well-being. Child Maltreatment, 15(2), 111–120. https://doi.org/10.1038/mp.2011.182.doi

16.

Davis

Sheidow

A. J.

McCart

M. R.

(2015). Reducing recidivism and symptoms in emerging adults with serious mental health conditions and justice system involvement. Journal of Behavioral Health Services & Research, 42(2), 172–190. https://doi.org/10.1016/j.physbeh.2017.03.040

17.

Decker

S. H.

Ortiz

Spohn

Hedberg

(2015). Criminal stigma, race, and ethnicity: The consequences of imprisonment for employment. Journal of Criminal Justice, 43(2), 108–121. https://doi.org/10.1016/j.jcrimjus.2015.02.002

18.

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–e786. https://doi.org/10.1542/peds.2009-0597

19.

Fang

Corso

P. S.

(2007). Child maltreatment, youth violence, and intimate partner violence. American Journal of Preventive Medicine, 33(4), 281–290. https://doi.org/10.1016/j.amepre.2007.06.003

20.

Fassaert

Lauriks

Van de Weerd

Theunissen

Kikkert

Dekker

Buster

De Wit

(2014). Psychometric properties of the Dutch version of the Self-Sufficiency Matrix (SSM-D). Community Mental Health Journal, 50(5), 583–590. https://doi.org/10.1007/s10597-013-9683-6

21.

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 adverse childhood experiences (ACE) study. American Journal of Preventive Medicine, 14(4), 245–258. https://doi.org/10.1016/S0749-3797(98)00017-8

22.

Field

(2009). Discovering statistics using SPSS (3rd ed.). SAGE.

23.

Flaherty

E. G.

Thompson

Dubowitz

Harvey

E. M.

English

D. J.

Proctor

L. J.

Runyan

D. K.

(2013). Adverse childhood experiences and child health in early adolescence. JAMA Pediatrics, 167(7), 622–629. https://doi.org/10.1001/jamapediatrics.2013.22

24.

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

25.

Garbarino

(2017). ACEs in the criminal justice system. Academic Pediatrics, 17(7), S32–S33. https://doi.org/10.1016/j.acap.2016.09.003

26.

Gilbert

Widom

C. S.

Browne

Fergusson

Webb

Janson

(2009). Burden and consequences of child maltreatment in high-income countries. The Lancet, 373(9657), 68–81. https://doi.org/10.1016/S0140-6736(08)61706-7

27.

Hughes

Bellis

M. A.

Hardcastle

K. A.

Sethi

Butchart

Mikton

Jones

Dunne

M. P.

(2017). The effect of multiple adverse childhood experiences on health: A systematic review and meta-analysis. Lancet Public Health, 2(8), e356–e366. https://doi.org/10.1016/S2468-2667(17)30118-4

28.

Kessler

R. C.

McLaughlin

K. A.

Green

J. G.

Gruber

M. J.

Sampson

N. A.

Zaslavsky

A. M.

Aguilar-Gaxiola

Alhamzawi

A. O.

Alonso

Angermeyer

Benjet

Bromet

Chatterji

De Girolamo

Demyttenaere

Fayyad

Florescu

Gal

Gureje

… Williams

D. R.

(2010). Childhood adversities and adult psychopathology in the WHO world mental health surveys. British Journal of Psychiatry, 197(5), 378–385. https://doi.org/10.1192/bjp.bp.110.080499

29.

Lanctôt

Cernkovich

S. A.

Giordano

P. C.

(2007). Delinquent behavior, official delinquency, and gender: Consequences for adulthood functioning and well-being. Criminology, 45(1), 131–157. https://doi.org/10.1111/j.1745-9125.2007.00074.x

30.

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

31.

Liberman

A. M.

Piquero

A. R.

Shaw

D. S.

Gross

H. E.

Krohn

M. D.

Thornberry

T. P.

Siennick

S. E.

Osgood

D. W.

Uggen

Wakefield

Huizinga

Henry

K. L.

Loeber

Farrington

D. P.

(2008). The long view of crime: A synthesis of longitudinal research ( Liberman

A. M.

, Ed.). Springer Science & Business Media.

32.

Liu

Croft

J. B.

Chapman

D. P.

Perry

G. S.

Greenlund

K. J.

Zhao

Edwards

V. J.

(2013). Relationship between adverse childhood experiences and unemployment among adults from five US states. Social Psychiatry and Psychiatric Epidemiology, 48(3), 357–369. https://doi.org/10.1007/s00127-012-0554-1

33.

Loeber

Farrington

D. P.

(2000). Young children who commit crime: Epidemiology, developmental origins, risk factors, early interventions, and policy implications. Development and Psychopathology, 12(4), 737–762. https://doi.org/10.1017/S0954579400004107

34.

Luijks

M.-J. A.

Bevaart

Zijlmans

van Duin

Marhe

Doreleijers

T. A. H.

Tiemeier

Asscher

J. J.

Popma

(2017). A multimodal day treatment program for multi-problem young adults: Study protocol for a randomized controlled trial. Trials, 18(1), 1–15. https://doi.org/10.1186/s13063-017-1950-3

35.

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

36.

Moffitt

T. E.

(2018). Male antisocial behaviour in adolescence and beyond. Nature Human Behaviour, 2(3), 177–186. https://doi.org/10.1038/s41562-018-0309-4

37.

Moffitt

T. E.

Caspi

(2001). Childhood predictors differentiate life-course persistent and adolescence-limited antisocial pathways among males and females. Development and Psychopathology, 13(2), 355–375. https://doi.org/10.1017/S0954579401002097

38.

Monahan

K. C.

Steinberg

Cauffman

Mulvey

E. P.

(2009). Trajectories of antisocial behavior and psychosocial maturity from adolescence to young adulthood. Developmental Psychobiology, 45(6), 1654–1668. https://doi.org/10.1037/a0015862

39.

Mosley-Johnson

Garacci

Wagner

Mendez

Williams

J. S.

Egede

L. E.

(2019). Assessing the relationship between adverse childhood experiences and life satisfaction, psychological well-being, and social well-being: United States longitudinal cohort 1995–2014. Quality of Life Research, 28(4), 907–914. https://doi.org/10.1007/s11136-018-2054-6

40.

Nevarez-Flores

A. G.

Sanderson

Breslin

Carr

V. J.

Morgan

V. A.

Neil

A. L.

(2019). Systematic review of global functioning and quality of life in people with psychotic disorders. Epidemiology and Psychiatric Sciences, 28(1), 31–44. https://doi.org/10.1017/S2045796018000549

41.

Osgood

D. W.

Foster

E. M.

Courtney

M. E

. (2010). Vulnerable populations and the transition to adulthood. The Future of Children, 20(1), 209–229. https://doi.org/10.1353/foc.0.0047

42.

Osgood

D. W.

Foster

E. M.

Flanagan

Ruth

G. R.

(Eds.). (2005). The John D. and Catherine T. Macarthur foundation research network on transition to adulthood. On your own without a net: The transition to adulthood for vulnerable populations. The University of Chicago Press.

43.

Perez

N. M.

Jennings

W. G.

Baglivio

M. T.

(2018). A path to serious, violent, chronic delinquency: The harmful aftermath of adverse childhood experiences. Crime & Delinquency, 64(1), 3–25. https://doi.org/10.1177/0011128716684806

44.

Piquero

A. R.

Hawkins

J. D.

Kazemian

(2012). Criminal career patterns. In Loeber

Farrington

D. P.

(Eds.), From juvenile delinquency to adult crime (pp. 14–46). Oxford University Press.

45.

Priebe

Huxley

Knight

Evans

(1999). Application and results of the Manchester Short Assessment of Quality of Life (MANSA). International Journal of Social Psychiatry, 45(1), 7–12. https://doi.org/10.1177/002076409904500102

46.

Priebe

Marchi

Bini

Flego

Costa

Galeazzi

(2011). Mental disorders, psychological symptoms and quality of life 8 years after an earthquake: Findings from a community sample in Italy. Social Psychiatry and Psychiatric Epidemiology, 46(7), 615–621. https://doi.org/10.1007/s00127-010-0227-x

47.

Reavis

J. A.

Looman

Franco

K. A.

Rojas

(2013). Adverse childhood experiences and adult criminality: How long must we live before we possess our own lives? The Permanente Journal, 17(2), 44–48. https://doi.org/10.7812/TPP/12-072

48.

Rice

Harris

(2005). Comparing effect sizes in follow-up studies: ROC area, Cohen’s d, and r. Law and Human Behavior, 29, 615–620. https://doi.org/10.1007/s10979-005-6832-7

49.

Scales

P. C.

Benson

P. L.

Oesterle

Hill

K. G.

Hawkins

J. D.

Pashak

T. J.

(2016). The dimensions of successful young adult development: A conceptual and measurement framework. Applied Developmental Science, 20(3), 150–174. https://doi.org/10.1080/10888691.2015.1082429

50.

Statistics Netherlands. (2016, November 21). Demarcation of generations with a migration background. https://www.cbs.nl/nl-nl/achtergrond/2016/47/afbakening-generaties-met-migratieachtergrond

51.

Thombs

B. D.

Bernstein

D. P.

Lobbestael

Arntz

(2009). A validation study of the Dutch Childhood Trauma Questionnaire–Short Form: Factor structure, reliability, and known-groups validity. Child Abuse & Neglect, 33(8), 518–523. https://doi.org/10.1016/j.chiabu.2009.03.001

52.

Thompson

Flaherty

E. G.

English

D. J.

Litrownik

A. J.

Dubowitz

Kotch

J. B.

Runyan

D. K.

(2015). Trajectories of adverse childhood experiences and self-reported health at age 18. Academic Pediatrics, 15(5), 503–509. https://doi.org/10.1016/j.acap.2014.09.010

53.

Van Damme

Hoeve

Vermeiren

Vanderplasschen

Colins

O. F

. (2016). Quality of life in relation to future mental health problems and offending: Testing the Good Lives Model among detained girls. Law and Human Behavior, 40(3), 285–294. https://doi.org/10.1037/lhb0000177

54.

Van der Geest

Bijleveld

C. C. J. H.

Blokland

A. A. J

. (2011). The effects of employment on longitudinal trajectories of offending: A follow-up of high-risk youth from 18 to 32 years of age. Criminology, 49(4), 1195–1234. https://doi.org/10.1111/j.1745-9125.2011.00247.x

55.

Van der Geest

Bijleveld

C. C. J. H.

Blokland

A. A. J.

Nagin

D. S

. (2016). The effects of incarceration on longitudinal trajectories of employment: A follow-up in high-risk youth from ages 23 to 32. Crime & Delinquency, 62(1), 107–140. https://doi.org/10.1177/0011128713519196

56.

Van Duin

Bevaart

Paalman

C. H.

Luijks

M. J. A.

Zijlmans

Marhe

Blokland

A. A. J.

Doreleijers

T. A. H.

Popma

. (2017). Child Protection Service interference in childhood and the relation with mental health problems and delinquency in young adulthood: A latent class analysis study. Child and Adolescent Psychiatry and Mental Health, 11(66), 1–15. https://doi.org/10.1186/s13034-017-0205-0

57.

Van Duin

Bevaart

Zijlmans

Luijks

M. J. A.

Doreleijers

T. A. H.

Wierdsma

A. I.

Oldehinkel

A. J.

Marhe

Popma

. (2019). The role of adverse childhood experiences and mental health care use in psychological dysfunction of male multi-problem young adults. European Child & Adolescent Psychiatry, 28, 1065–1078. https://doi.org/10.1007/s00787-018-1263-4

58.

Van Hazebroek

B. C. M.

Blokland

A. A. J.

Wermink

H. T.

De Keijser

J. W.

Popma

Van Domburgh

. (2019). Delinquent development among early-onset offenders: Identifying and characterizing trajectories based on frequency across types of offending. Criminal Justice and Behavior, 46(11), 1542–1565. https://doi.org/10.1177/0093854819876306

59.

Verbruggen

Blokland

A. A. J.

Van der Geest

(2012). Effects of employment and unemployment on serious offending in a high-risk sample of men and women from ages 18 to 32 in the Netherlands. British Journal of Criminology, 52(5), 845–869. https://doi.org/10.1093/bjc/azs023

60.

Walker

E. A.

Unutzer

Rutter

Gelfand

Saunders

VonKorff

Koss

M. P.

Katon

(1999). Costs of health care use by women HMO members with a history of childhood abuse and neglect. Archives of General Psychiatry, 56(7), 609–613. https://doi.org/10.1001/archpsyc.56.7.609

61.

Wartna

Blom

Tollenaar

(2011). The Dutch recidivism monitor. https://www.wodc.nl/binaries/the-dutch-recidivism-monitor-2011-20110803_tcm28-78143.pdf

62.

WHOQOL Group. (1995). The World Health Organization Quality of Life assessment (WHOQOL): Position paper from the World Health Organization. Social Science & Medicine, 41(10), 1403–1409. https://doi.org/10.1016/0277-9536(95)00112-K

63.

Widom

C. S.

Maxfield

M. G.

(2001). An update on the “cycle of violence.” US Department of Justice Office of Justice Programs, National Institute of Justice.

64.

Wolff

K. T.

Baglivio

M. T.

(2017). Adverse childhood experiences, negative emotionality, and pathways to juvenile recidivism. Crime & Delinquency, 63(12), 1495–1521. https://doi.org/10.1177/0011128715627469

65.

Zielinski

D. S.

(2009). Child maltreatment and adult socioeconomic well-being. Child Abuse and Neglect, 33(10), 666–678. https://doi.org/10.1016/j.chiabu.2009.09.001