Differences Between Juvenile Offenders With and Without AD(H)D in Recidivism Rates and Risk and Protective Factors for Recidivism

Abstract

Objective: This study examined differences between juvenile offenders with AD(H)D (n = 1,348), with both AD(H)D and conduct problems (n = 933), and without AD(H)D or conduct problems (n = 2,180) in recidivism rates, prevalence of risk and protective factors, and strength of associations between risk/protective factors and recidivism. Method: Existing data were used, collected with the Washington State Juvenile Court Assessment. ANOVA, Pearson correlations, and Fisher’s z tests were calculated. Results: Recidivism was highest in the AD(H)D-comorbid group, followed by the AD(H)D-only group and lowest in the comparison group. In offenders with AD(H)D, especially comorbid AD(H)D, the number of risk factors was considerably larger than the number of protective factors, whereas the number of risk and protective factors was the same in the comparison group. Conclusion: Juvenile offenders with AD(H)D may benefit most from interventions that focus on risk and protective factors in multiple domains.

Keywords

juvenile offenders with ADHD recidivism risk and protective factors comorbidity conduct problems

Juvenile delinquency is a burden for society and therefore much attention has been directed toward identifying developmental pathways to serious delinquency to identify children and adolescents who are most at risk to persist in offending. Moffitt (1993) made a distinction between “adolescence-limited” and “life-course persistent” antisocial behavior. Adolescence-limited antisocial behavior appears to be more or less normative in adolescence as a life phase, considering the high prevalence of antisocial behavior during adolescence. Life-course persistent antisocial behavior, on the other hand, starts at an early age and continues through adolescence and into adulthood. Life-course persistent antisocial behavior is thought to be explained by individual factors that are subsequently reinforced by a high-risk environment. The risk of developing antisocial personality (ASP) disorder is high among the life-course persistent type. In the study of Moffitt, Caspi, Harrington, and Milne (2002), only 15% of 87 boys with early onset behavioral problems had no ASP disorder or other serious adjustment problems in adulthood. AD(H)D is considered as one of the individual factors that are predictive for the life-course persistent pathway of criminal behavior (Moffitt, 1990, 1993; Moffitt & Caspi, 2001).

Empirical evidence for the relationship between AD(H)D and delinquency is provided by studies that found an increased AD(H)D prevalence rate among adolescent prisoners (Bulten, Nijman, & Von der Staak, 2009; Vermeiren, 2003). In addition, a meta-analysis by Pratt, Cullen, Blevins, Daigle, and Unnever (2002) identified ADHD as a risk factor for delinquency. However, to understand the complex relationship between ADHD and delinquency, it is important to take into account the high comorbidity of ADHD with other forensically relevant disorders (Grieger & Hosser, 2012). About 30% to 50% of children and adolescents with ADHD are also diagnosed with another externalizing disorder, such as oppositional defiant disorder (ODD) and conduct disorder (CD; for example, Biederman, Newcorn, & Sprich, 1991; Brown et al., 2001; Cantwell, 1996; Elia, Ambrosini, & Berrettini, 2008; Hinshaw, 1992; Jensen, Martin, & Cantwell, 1997; Spencer, 2006). Because of the high rate of comorbidity, it is difficult to identify the particular influence of ADHD on delinquency (Lilienfeld & Waldman, 1990).

Studies that have examined the incremental predictive validity of ADHD for delinquency, controlling the influence of CD, showed mixed results (e.g., Gittelman, Mannuzza, Shenker, & Bonagura, 1985; Mannuzza et al., 1991; Mordre, Groholt, Kjelsberg, Sandstad, & Myhre, 2011; Satterfield et al., 2007; Sibley et al., 2011). Mordre and colleagues (2011) found no direct association between ADHD alone and delinquency. A similar result was found in a study by Satterfield and colleagues (2007), showing that hyperactive boys without conduct problems in childhood had no increased risk to develop criminality in adulthood. However, a recent study among boys showed that although childhood ADHD + CD creates the highest risk for delinquency, boys with ADHD-only and ADHD + ODD also appear at a higher risk of later offending (Sibley et al., 2011). In addition, other studies have found that children with ADHD without CD symptoms were much more likely to be diagnosed with an ASP disorder than children without ADHD (Gittelman et al., 1985; Mannuzza et al, 1991). Because of these inconsistent results, there is an ongoing debate about whether ADHD itself is a risk factor for the development of antisocial behavior and delinquency, or whether the development of these problems is primarily the result of comorbidity with other disruptive disorders (Von Polier, Vloet, & Herpertz-Dahlmann, 2012). In addition, only a few studies have investigated the predictive validity of ADHD for recidivism (Gordon & Moore, 2005; Grieger & Hosser, 2012; Plattner et al., 2009; Vermeiren, Schwab-Stone, Ruchkin, De Clippele, & Deboutte, 2002; Wierson & Forehand, 1995), and these studies also showed mixed results, with some studies reporting a positive relationship between ADHD and recidivism (Gordon & Moore, 2005; Vermeiren et al., 2002), whereas others have not found such a relationship (e.g., Grieger & Hosser, 2012; Plattner et al., 2009; Wierson & Forehand, 1995). Because of the equivocal results, the first aim of our study was to exploratively examine differences in criminal history and recidivism rates between juveniles with AD(H)D only, juveniles with AD(H)D and conduct problems, and juveniles without AD(H)D or conduct problems (comparison group).

Delinquent behavior can be seen as the result of complex interactions between risk factors and protective factors (e.g., Loeber, Farrington, Stouthamer-Loeber, & White, 2008; Prinzie, Hoeve, & Stams, 2008). Risk factors are those factors that increase the likelihood of delinquent behavior, whereas protective factors are associated with a smaller probability of delinquent behavior. Risk and protective factors comprise, on one hand, personal characteristics of the individual and, on the other hand, factors in the social environment, including the family, peers, school, and community (Howell, 2003; Loeber, DeLamatre, Keenan, & Zhang, 1998; Loeber, Slot, & Stouthamer-Loeber, 2008; Stouthamer-Loeber, Loeber, Wei, Farrington, & Wikström, 2002). Examples of risk factors are psychopathic traits, high truancy, low academic achievement, antisocial friends, high parental stress, running away, bad neighborhood, and examples of protective factors are high intelligence, a positive attitude toward school, prosocial friends, good supervision, and high family socioeconomic status (Loeber, Slot, et al., 2008).

Risk and protective factors have been conceptualized as static or dynamic. Static factors are historic and cannot be changed, such as age at first offense, temperament, and IQ. Dynamic risk factors can potentially be changed, such as the youth’s friends or school performance. Investigating the strength of associations between dynamic risk/protective factors and recidivism is considered important not only for the development of evidence-based interventions but also for the purpose of adequate risk assessment of recidivism and successful referral to behavioral interventions targeting desistance from crime (see Andrews & Bonta, 2010). Research that aimed to examine differences between delinquent youth with and without AD(H)D has so far primarily focused on differences in the extent and severity of delinquent behavior (e.g., Babinski, Hartsough, & Lambert, 1999; Bulten et al., 2009; Lilienfeld & Waldman, 1990; Pratt et al., 2002; Satterfield et al., 2007; Sourander et al., 2006). However, to be able to provide adequate treatment, it is important to identify potential differences in dynamic risk and protective factors between juvenile offenders with and without AD(H)D. Therefore, the second aim of our study was to examine differences in the prevalence of dynamic risk and protective factors between juveniles with AD(H)D only, juveniles with both AD(H)D and conduct problems, and juveniles without AD(H)D or conduct problems. Children and adolescents with AD(H)D, especially those with conduct problems, have much more genetic, neurocognitive, and psychosocial problems than do children without AD(H)D (Moffitt & Scott, 2008). Therefore, we expected most risk factors and least protective factors to be present in juvenile offenders with both AD(H)D and conduct problems, followed by juvenile offenders with AD(H)D only, and least risk factors and most protective factors to be present in the comparison group.

To our knowledge, there are no previous studies in which a comparison is made between juvenile offenders with and without AD(H)D in the association between risk/protective factors and recidivism. However, it is important to know to what extent risk and protective factors are related to recidivism for the purpose of being able to refer juveniles with AD(H)D for the appropriate behavioral interventions, because interventions will be most effective if the target dynamic risk/protective factors that are most closely related to recidivism (Andrews & Bonta, 2003, 2010; Andrews, Bonta, & Hoge, 1990; Lösel, 1995). The third aim of our study was therefore to exploratively examine differences in the strengths of associations between dynamic risk/protective factors and recidivism between the various diagnostic groups.

Former studies found differences in background characteristics (gender, age, and race) and in prevalence of mental health problems between juveniles with and without ADHD. ADHD is about 4 to 9 times more frequent in boys than in girls (American Psychiatric Association [APA], 1994) and is more prevalent in early adolescence than in late adolescence (Loeber & Keenan, 1994). Furthermore, ADHD is more prevalent among White youth than among minority children, partly because minority children are often underdiagnosed and undertreated (Gordon & Moore, 2005; Olfson, Gameroff, Marcus, & Jensen, 2003; Zito, Safer, DosReis, & Riddle, 1998). ADHD is well known for its high rate of comorbidities, which occur in more than two thirds of cases (Jensen et al., 2001). Grieger and Hosser (2012) provide an overview of these comorbid disorders, which include externalizing disorders, such as ODD and CD, internalizing disorders, such as anxiety and mood disorders, substance use disorders, learning disorders, and low intellectual functioning. We therefore first examined differences between the various diagnostic groups in background characteristics and mental health problems. This is of importance because the other analyses should be controlled for these differences. Based on the above-mentioned prior research, we expect more boys, more Caucasians, and more comorbidity in the AD(H)D groups than in the comparison group. In summary, we focused on the following research questions:

Research Question 1: Are there differences between juvenile offenders with AD(H)D only, juveniles with both AD(H)D and conduct problems, and juveniles without AD(H)D or conduct problems in background characteristics and mental health problems?

Research Question 2: Are there differences between the various diagnostic groups in criminal history and recidivism rates?

Research Question 3: Are there differences between the various diagnostic groups in the prevalence of dynamic risk and protective factors for delinquency?

Research Question 4: Are there differences between the various diagnostic groups in the strength of the associations between dynamic risk/protective factors and recidivism?

Method

Sample

For this study, secondary data from the Washington State Juvenile Court Assessment (WSJCA) validation study were used (Barnoski, 2004a). This data set consisted of 13.613 American juvenile offenders, aged 12 to 18 years, who were convicted by a juvenile court and for whom the WSJCA was completed (see instrument section). From this data set, the following groups were selected:

AD(H)D-only group: All juvenile offenders in the data set with a formal diagnosis (Diagnostic and Statistical Manual of Mental Disorders [4th ed.; DSM-IV; APA, 1994] or International Classification of Diseases [ICD]) of AD(H)D made by a professional in the social service/health care field (e.g., child psychiatrists or psychologists, developmental/behavioral pediatricians, or behavioral neurologists who are trained in differential diagnosis). The juveniles of this group are special education students or have a formal diagnosis of a special education need because of the AD(H)D diagnosis (n = 1,348).

AD(H)D-comorbid group: All juvenile offenders in the data set with a formal diagnosis (DSM or ICD) of AD(H)D made by a professional in the social service/health care field. The juveniles of this group are special education students or have a formal diagnosis of a special education need because of both AD(H)D and behavioral problems (n = 933).

Comparison group: A random sample of all juvenile offenders in the data set without a formal diagnosis of AD(H)D or a special education need because of behavior problems (n = 2,180).

Instruments and Procedure

WSJCA

The WSJCA is a screening and risk-assessment instrument, which was developed in Washington State (Barnoski, 2004a, 2004b). The WSJCA maps out the most important risk and protective factors for recidivism on a large number of domains. The development of the instrument was based on a review of the following types of research: recidivism prediction literature and instruments, for example, the Wisconsin Risk Scale (Baird, Storrs, & Connelly, 1984) and the Youth Level of Service and Case Management Inventory (Hoge & Andrews, 1994), theoretical models for juvenile delinquency, risk and protective factor research, resiliency research, and research on effective juvenile delinquency programs (see Barnoski, 2004a). The selection of domains and items took place on the basis of this review and then was modified, based on feedback from an international team of experts (Barnoski, 2004a).

Probation officers complete the WSJCA during the intake, on the basis of information from a structured motivational interview with the youth and youth’s family. Probation officers were trained in conducting the assessment by probation staff who were certified trainers. This training includes reviewing videotaped interviews and the resulting assessment to ensure the probation officer has mastered the assessment skills. There is a manual available for the WSJCA and quality assurance is an important part of the assessment structure and organization in Washington State (Barnoski, 2004b).

The WSJCA measures both static (historical) and dynamic (current) risk and protective factors. In this study, we only examined dynamic factors, because these factors are used to guide the rehabilitative effort. The dynamic factors were measured over a period of 6 months prior to the assessment, so the dynamic risk factors were present at the time of the assessment or shortly before (maximum 6 months). All questions were asked to the youth and the family. The items concerning schools (e.g., grades) were checked with the schools the juveniles were attending. If conflicting answers were given by the youth and his family, the probation officer made an estimation of the accuracy of the answers and the most appropriate response.

Items were rated on a 3-point scale (strong promotive side, neutral middle part, and strong risk side), a 4-point scale (strong promotive side, weak promotive side, weak risk side, and strong risk side), or a 5-point scale (strong promotive side, weak promotive side, neutral middle part, weak risk side, and strong risk side). Each item was recoded in two separate dichotomous variables as follows: a promotive factor (1 if the strong promotive side was present and 0 if the strong promotive side was absent) and a risk factor (1 if the strong risk side was present and 0 if the strong risk side was absent). Thus, the meaning of the scale-points of the items was decisive for determining which response categories were designated as promotive or risk factor. For example, the response categories of the item “believes getting education of value” were coded as follows: “believes getting education of value” (promotive), “somewhat believes education of value” (neutral), and “does not believe education of value” (risk).

The strong promotive and the risk ends (extreme ends) of the variables measured were school: good behavior/severe behavior problems, good/poor academic performance, good attendance/often truancy, good/poor relationship with teachers, participation in school activities (yes/no), believes school is encouraging (yes/no), believes getting education of value (yes/no), very likely to graduate/not likely to graduate; use of free time: involvement/interest in structured recreational activities (yes/no), involvement/interest in unstructured recreational activities (yes/no); relationships: positive adult nonfamily relationships (yes/no), prosocial community ties (strong/none), friends (only prosocial/only antisocial), romantically involved (prosocial person/antisocial person), admiration of antisocial peers (yes/no), resistance to influence of antisocial peers (yes/rarely); family: relationship with parents (close/not close), well-managed conflicts/domestic violence, parental supervision (adequate/inadequate), following family rules (usually/consistently disobeys), parental punishment (adequate/inadequate), family support network (strong/not available); attitude: high aspirations/low aspirations, problems with impulsiveness (yes/no), belief in control over antisocial behavior (yes/no), empathy, remorse, sympathy/feelings for victims (yes/no), respect for others’ property, authority figures and/or rules (yes/no), accepts responsibility for behavior (yes/no), thinks he/she can comply with measures (yes/no); Aggression: frustration tolerance (rarely gets upset/often gets upset), interpretation of other’s behavior/intentions (primarily positive, primarily hostile), belief in verbal and/or physical aggression to solve a conflict (rarely appropriate/often appropriate); skills: consequential thinking (good/lack of skills), goal setting (good/lack of skills), problem-solving (good/lack of skills), situational perception (good/lacks skills), dealing with others and/or feelings and/or difficult situations (good/lacks skills), control of internal and/or external triggers (good/poor), control of impulsive behaviors (good/poor), control of aggression (good/poor); alcohol/drugs: alcohol/drugs causing family conflict, disrupting education, causing health problems, interfering with keeping prosocial friends and/or contribution to criminal behavior. No protective factors were measured in the alcohol and drugs domain.

The predictive validity of the WSJCA has been tested in two studies: a study of Barnoski (2004a) and a study of researchers from Orbis Partners (2007). In the first study, the area under the receiver operating characteristic curve (AUC) of the Washington State Juvenile Court Pre-Screen Assessment (WSJCPA) was .64, and in the second study, the AUC was .63. In a meta-analysis of the predictive validity of risk-assessment instruments for juveniles, it was shown that the AUC varied from .53 to .78, with an average AUC of .64 (Schwalbe, 2007). The AUC of the WSJCPA is therefore comparable with the average AUC of juvenile justice risk-assessment instruments.

Outcome measure

Recidivism was defined as the occurrence of one or multiple new convictions within 18 months after completing the WSJCA. Data on recidivism were based on official records, both juvenile and adult records. To adequately measure 18-months recidivism, a period of 30 months was needed for gathering the information: an 18-month reoffending follow-up period and another 12-month period to allow for any reoffenses to be adjudicated (Barnoski, 1997). Recidivism was treated as a dichotomous variable (whether or not convicted for any new offense).

Analyses

A total risk score was calculated for each domain by adding the number of risk factors, and a total protective score was calculated for each domain by adding the number of protective factors. We used ANOVA to determine whether there were differences between the different groups in the prevalence of protective and risk factors. Pearson correlation coefficients were calculated to determine the strength of the relation between the protective factors and recidivism, and between the risk factors and recidivism. Fisher’s z tests were used to examine whether the strengths of the correlations differed significantly between the offender groups.

Results

Differences in Background Characteristics, Criminal Histories, and Recidivism Rates

The background characteristics are presented in Table 1. Boys had a higher representation in the AD(H)D groups than in the comparison group. European Americans had a higher representation in the AD(H)D-only group than in the AD(H)D-comorbid group and the comparison group. African Americans had a higher representation in the AD(H)D-comorbid group, whereas Hispanic Americans and Americans with “other” ethnicity had a higher representation in the comparison group. The average age was lowest in the AD(H)D-comorbid group, followed by the AD(H)D-only group. Learning disabilities and mental retardation were most common in the AD(H)D-comorbid group (53% and 3%, respectively), followed by the AD(H)D-only group (27% and 1%, respectively) and least common in the comparison group (15% and 0%, respectively). In addition, other mental health problems, such as schizophrenia, mood disorders, personality disorders, and adjustment disorders, were most common in the AD(H)D-comorbid group (67%), followed by the AD(H)D-only group (46%) and least common in the comparison group (13%).

Table 1.

Background Characteristics and Mental Health Problems for Each Diagnostic Group.

	Comparison (n = 2,180)	AD(H)D-only (n = 1,348)	AD(H)D- comorbid (n = 933)	F
Male gender	69.9%	85.1%_a	87.4%_a	115.97*
Ethnicity
European Americans	66.2%	84.5%	78.1%	72.91*
African Americans	10.2%	7.7%	13.6%	8.66*
Hispanic Americans	13.9%	4.4%_a	3.7%_a	55.97*
Other	9.7%	3.3%_a	4.6%_a	33.84*
Average age at the time of the assessment	15.5	15.3	14.8	79.12*
Other diagnoses
Learning disabilities	15%	27%	53%	273.60*
Mental retardation	0%	1%	3%	30.03*
Other mental health problems (e.g., schizophrenia, bipolar, mood, thought, personality and adjustment disorder)	13%	46%	67%	630.58*

Note. Values sharing the same subscript do not differ significantly (p < .05).

p < .001.

Table 2 shows the criminal history scores for the different groups. We used ANOVA to examine differences between the various diagnostic groups in the criminal history scores. We controlled the ANOVA analyses for gender, ethnicity, age, and mental health diagnoses because of the differences found between the diagnostic groups on these background characteristics. The average number of misdemeanor offenses and misdemeanor offenses against persons were highest in the AD(H)D-comorbid group, followed by the AD(H)D-only group and were lowest is the comparison group. Juveniles with comorbid AD(H)D were youngest at the time of their first offense, followed by juveniles with only AD(H)D, whereas juveniles in the comparison group were oldest at the time of their first offense.

Table 2.

Criminal History (Mean Values) for Each Diagnostic Group.

	Comparison (n = 2,180)	AD(H)D-only (n = 1,348)	AD(H)D-comorbid (n = 933)	F ^a
Felony offenses	1.07	1.07	1.06	0.32
Violent felony offenses	0.23	0.28	0.30	1.00
Misdemeanor offenses	1.99	2.26	2.99	50.28*
Violent misdemeanor offenses	0.59	0.79	1.40	77.90*
Average age at time of first offense	13.7	13.5	12.8	21.90*

The ANOVA analyses are corrected for gender, ethnicity, age, and mental health diagnoses.

p < .001.

The recidivism rates for the different groups are presented in Table 3. ANOVAs (controlling for gender, ethnicity, age, and mental health diagnoses) were used to examine differences between the various diagnostic groups in recidivism rates. Recidivism (both total recidivism, felony recidivism and felony violent recidivism) was highest in the AD(H)D-comorbid group, followed by the AD(H)D-only group, and recidivism was lowest in the comparison group.

Table 3.

Recidivism Rates for Each Diagnostic Group.

	Comparison (n = 2,180)	AD(H)D-only (n = 1,348)	AD(H)D-comorbid (n = 933)	F ^a
Total recidivism	38.9%	45.5%	55.6%	14.46**
Felony recidivism	19.4%	23.1%	28.8%	6.00*
Violent felony recidivism	5.2%	7.8%	11.5%	7.77**

The ANOVA analyses are corrected for gender, ethnicity, age, and mental health diagnoses.

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

Differences in the Prevalence of Dynamic Risk and Protective Factors

The total risk and protective scores per domain are presented in Table 4. ANOVAs (controlling for gender, ethnicity, age, and other mental health diagnoses) were used to examine differences between the various diagnostic groups in the prevalence of dynamic risk and protective factors. Risk factors in the domains of school, relationship, family, attitude, aggression, and skills were most common in the AD(H)D-comorbid group, followed by the AD(H)D-only group, whereas risk factors in these domains were least common in the comparison group. On the contrary, risk factors in the alcohol/drugs domain were more common in the comparison group than in the AD(H)D groups. The average number of risk factors in the comparison group (12.71) was about 1.2 times smaller than the average number of risk factors in the AD(H)D-only group (15.20) and about 1.6 times smaller than the average number of risk factors in the AD(H)D-comorbid group (20.46).

Table 4.

Prevalence of Dynamic Risk and Protective Factors for Each Diagnostic Group.

	Dynamic risk factors				Dynamic protective factors
	Comparison (n = 2,180)	AD(H)D-only (n = 1,348)	AD(H)D-comorbid (n = 933)	F ^a	Comparison (n = 2,180)	AD(H)D-only (n = 1,348)	AD(H)D-comorbid (n = 933)	F ^a
School	2.28_a	2.52_a	3.15	17.01*	1.46	1.22	0.88	29.83*
Use of free time	0.85_a	0.82_a	0.93	14.52*	0.32_a	0.34_a	0.29	8.25
Relationships	1.73_a	1.70_a	2.13	18.93*	1.17_a	1.11_a	0.84	21.38*
Family	2.36	2.59	3.08	20.27*	2.83	2.64	2.18	18.40*
Alcohol/drug	1.37	1.36	1.32	0.86	—	—	—	—
Attitude	1.76	2.26	3.57	91.83*	3.15	2.51	1.54	66.03*
Aggression	0.80	1.13	1.79	101.44*	1.45	1.10	0.64	65.86*
Skills	3.08	4.18	5.90	70.89*	2.62	1.62	0.91	66.25*
Total number	12.71	15.20	20.46	86.01*	13.16	10.55	7.30	70.63*

Note. Values sharing the same subscript do not differ significantly (p < .05).

The ANOVA analyses are controlled for gender, ethnicity, age, and mental health diagnoses.

p < .001.

Protective factors in the domains of school, relationship, family, attitude, aggression, and skills were most common in the comparison group, followed by the AD(H)D-only group, whereas protective factors in these domains were least common in the AD(H)D-comorbid group. The average number of protective factors in the comparison group (13.16) was about 1.2 times larger than the average number of protective factors in the AD(H)D-only group (10.55) and about 1.8 times larger than the average number of protective factors in the AD(H)D-comorbid group (7.30).

Differences in the Strength of the Associations Between Dynamic Risk/Protective Factors and Recidivism

The correlations between the total risk scores per domain and recidivism for the different groups are presented in Table 5. Most of the total risk scores were significantly related to recidivism in the three different offender groups. Only in the AD(H)D-comorbid group, the risk domains’ use of free time and alcohol/drugs were not significantly related to recidivism. We also examined whether there were significant differences in the strength of the correlations between the ADHD groups and the comparison groups by calculating Fisher’s z tests. Only a few significant differences were found: The correlation between the aggression risk score and recidivism was stronger in the AD(H)D-comorbid group than in the comparison group, the correlations between the relationship risk score and recidivism was less strong in the AD(H)D-only group than in the comparison group, and the correlation between the use of free time risk score and recidivism was less strong in the AD(H)D-comorbid group than in the comparison group.

Table 5.

Correlations Between the Dynamic Risk Factors and Recidivism for Each Diagnostic Group.

	Comparison (n = 2,180)	AD(H)D-only (n = 1,348)		AD(H)D-comorbid (n = 933)
	R	R	z	R	z
School	.11***	.09**	0.58	.13***	0.52
Relationships	.14***	.06*	2.33*	.16***	0.52
Family	.10***	.10***	0	.07*	0.77
Use of free time	.09***	.08*	0.29	−.01	2.05*
Alcohol/drugs	.07**	.09**	0.58	.06	0.26
Attitudes	.14***	.10***	1.17	.16***	0.52
Aggression	.09*	.12***	0.87	.20***	2.61**
Skills	.12***	.08**	1.17	.11**	0.26

Note. z = Fisher’s z significance test for the difference between the AD(H)D groups and the comparison group in the strength of the correlations between the risk factors and recidivism.

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

The correlations between the total protective scores per domain and recidivism for the different offender groups are presented in Table 6. All total protective scores were significantly related to recidivism in the different offender groups. We calculated Fisher’s z tests to examine the significance of the differences between the AD(H)D groups and the comparison group in the strength of the correlations between the protective factors and recidivism. There were no significant differences between the AD(H)D groups and the comparison groups in the strength of the correlations between the protective factors and recidivism.

Table 6.

Correlations Between the Dynamic Protective Factors and Recidivism for Each Diagnostic Group.

	Comparison (n = 2,180)	AD(H)D-only (n = 1,348)		AD(H)D-comorbid (n = 933)
	R	R	z	R	z
School	−.13***	−.12***	0.29	−.10***	0.78
Relationships	−.12***	−.11***	0.29	−.13***	0.26
Family	−.14***	−.10***	1.17	−.07*	1.81
Use of free time	−.07**	−.03*	1.16	−.07*	0
Alcohol/drugs	—	—	—	—	—
Attitudes	−.14***	−.13***	0.29	−.17***	0.78
Aggression	−.12***	−.10***	0.58	−.11***	0.26
Skills	−.13***	−.10***	0.88	−.14***	0.24

Note. z = Fisher’s z significance test for the difference between the AD(H)D groups and the comparison group in the strength of the correlations between the protective factors and recidivism.

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

Multiple logistic regression analyses were used to examine the unique contribution of the predictors. Separate multivariate prediction models were tested for risk factors (see Table 7) and protective factors (see Table 8) in the various diagnostic groups. The following risk factors were found to be uniquely related to recidivism in the comparison group: school, relationships, family, and attitudes. In the AD(H)D-only group, the risk factors alcohol/drugs, attitudes, and aggression were uniquely related to recidivism, and in the AD(H)D-comorbid group, the risk factors relationships, use of free time, attitudes, and aggression were uniquely related to recidivism. With regard to the protective factors, it was found that school, family, and attitudes were uniquely related to recidivism in the comparison group; school and attitudes were uniquely related to recidivism in the AD(H)D-only group; and attitudes and skills were uniquely related to recidivism in the AD(H)D-comorbid group.

Table 7.

Logistic Regression Coefficients Predicting Recidivism From Risk Factors for Each Diagnostic Group.

	Comparison (n = 2,180)				AD(H)D-only (n = 1,348)				AD(H)D-comorbid (n = 933)
	B	SE	Wald	Exp(B)	B	SE	Wald	Exp(B)	B	SE	Wald	Exp(B)
School	0.05	.02	6.48*	1.05
Relationships	0.08	.03	6.06*	1.08					0.17	.05	11.50**	1.18
Family	0.03	.02	5.55*	1.04
Use of free time									−0.24	.08	8.68**	0.79
Alcohol/drugs					0.11	.04	6.31*	1.11
Attitudes	0.04	.02	3.82*	1.04	0.07	.03	6.64**	1.07	0.08	.04	4.36*	1.08
Aggression					0.08	.04	3.24⁺	1.08	0.15	.05	7.39**	1.16
Skills
Constant	−1.23	.11	124.44***	0.29	−0.81	.12	42.76***	0.45	−1.01	.20	24.35***	0.37
χ(df)	69.18(4)***				36.38(3)***				52.40(4)***

p < .05. **p < .01. ***p < .001. ⁺p < .10.

Table 8.

Logistic Regression Coefficients Predicting Recidivism From Protective Factors for Each Diagnostic Group.

	Comparison (n = 2,180)				AD(H)D-only (n = 1,348)				AD(H)D-comorbid (n = 933)
	B	SE	Wald	Exp(B)	B	SE	Wald	Exp(B)	B	SE	Wald	Exp(B)
School	−0.07	.03	7.11**	0.94	−0.11	.03	12.36***	0.90
Relationships
Family	−0.05	.02	7.23**	0.96
Use of free time
Alcohol/drugs
Attitudes	−0.05	.02	4.78*	0.95	−0.06	.03	4.95*	0.94	−0.12	.04	8.21**	0.89
Aggression
Skills									−0.08	.04	4.87*	0.92
Constant	0.08	.08	0.82	1.08	0.18	.08	4.12*	1.19	0.54	.09	35.64***	1.72
χ(df)	63.63(3)***				34.65(2)***				29.16(2)***

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

Discussion

This study aimed to examine differences between juvenile offenders with AD(H)D-only, juvenile offenders with both AD(H)D and conduct problems, and a comparison group of juvenile offenders without AD(H)D or conduct problems in recidivism rates, prevalence of risk and protective factors, and strength of associations between risk/protective factors and recidivism. First, differences in background characteristics and other mental health problems were examined. In line with our expectations, we found a higher representation of boys and Caucasian in the AD(H)D groups and a lower mean age in the AD(H)D groups, compared with the comparison group. In addition, learning disabilities, mental retardation, and other mental health problems were most common in the AD(H)D-comorbid group, followed by the AD(H)D-only group and least common in the comparison group.

Second, criminal history and recidivism rates were found to be highest in the AD(H)D-comorbid group, followed by the AD(H)D-only group and lowest is the comparison group, even after controlling for differences in background characteristics. Therefore, the condition AD(H)D-only and AD(H)D-comorbid were associated with delinquency and recidivism, whereby AD(H)D-comorbid was a stronger predictor than AD(H)D-only. These findings are consistent with some of the few previous studies on this topic (e.g., Babinski et al., 1999; Gordon & Moore, 2005; Sourander et al., 2006; Vermeiren et al., 2002).

Third, differences in the prevalence of risk and protective factors were examined. Most risk factors and fewest protective factors in the domains of school, relationship, family, attitude, aggression, and skills were present in the AD(H)D-comorbid group, followed by the AD(H)D-only group, whereas fewest risk factors and most protective factors were present in the comparison group. We found no differences between the AD(H)D groups and the comparison group in the number of risk factors in the alcohol and drugs domain. Earlier studies on differences between adolescents and young adults with and without ADHD in substance abuse showed mixed results. Some studies found that adolescents with ADHD may be at elevated risk of problematic patterns of substance use (e.g., Molina & Pelham, 2003; Rooney, Chronis-Tuscano, & Yoon, 2012). However, Grieger and Hosser (2012) found no differences between young adult offenders with and without ADHD in the prevalence of substance dependency. In addition, other studies showed that young adults with ADHD were more likely to abuse alcohol or have alcohol use disorder, but they did not appear to use alcohol at higher rates than their peers without ADHD (Smith, Molina, & Pelham, 2002; Weiss & Hechtman, 1993).

In the comparison group, the number of risk factors was about equal to the number of protective factors, whereas in the AD(H)D-only group, the number of risk factors was about 1.5 times larger than the number of protective factors, and in the AD(H)D-comorbid group, the number of risk factors was about 3 times larger than the number of protective factors. The higher delinquency and recidivism rates in juvenile offenders with ADHD, especially in the AD(H)D-comorbid group, may therefore be partly explained by the higher prevalence of risk factors in various domains in combination with the lower prevalence of protective factors to compensate for risks.

Fourth, differences in the strength of associations between risk/protective factors and recidivism were examined. We found no significant differences between the offender groups in the strength of the correlations between the protective factors and recidivism, and only few differences between the groups in the importance of risk factors for recidivism. These results suggest that the same kinds of behavioral interventions can be used in the various diagnostic groups, given that behavioral interventions should target criminogenic factors to effectively reduce recidivism (Andrews & Bonta, 2010). Treatment of juvenile offenders with AD(H)D may require more complex and comprehensive interventions than treatment of juvenile offender without AD(H)D because the goal of treatment is not only rehabilitation of the offender but also reducing AD(H)D symptoms and associated impairments (Young & Thome, 2011). There is a growing evidence that multimodal treatments (i.e., a combination of behavioral therapy and drug treatments) lead to greater improvement in symptoms of ADHD and to less behavioral problems (for a review, see Murray et al., 2008). Based on the results of this study, it may be suggested that the behavioral component of the treatment of juvenile offenders with AD(H)D, especially juveniles with both ADHD and conduct problems, should be focused on risk and protective factors in multiple domains, because of the high prevalence of risk factors and the low prevalence of protective factors in the various domains in the ADHD groups.

This study showed that the correlations between individual risk/protective factors and recidivism were small. This applies not only to the AD(H)D groups but also to the juvenile delinquents without AD(H)D. Other studies have also shown that correlations between individual risk factors and recidivism are relatively small (e.g., Spanjaard, Van der Knaap, Van der Put, & Stams, 2012; Van der Knaap, Alberda, Oosterveld, & Born, 2011). It is therefore important that interventions focus on multiple domains simultaneously rather than on one individual domain to achieve the greatest effect on reducing recidivism. In addition, associations between risk/protective factors and recidivism are considerably stronger in early adolescence than in late adolescence (Van der Put et al., 2011, 2012). Therefore, it is important to intervene as early as possible with youth who exhibit delinquent behavior. The strength of the association between most of the risk factors and recidivism is comparable with the strength of the association between protective factors and recidivism, so a decrease in recidivism can be achieved by focusing on strengthening protective factors and decreasing the number of risk factors. Because a focus on reinforcing/increasing protective factors is considered to be important for motivating adolescents and promoting therapy compliance (Ward, 2002; Ward & Gannon, 2006; Ward & Stewart, 2003), it deserves to be given at least as much attention as a focus on risk factors.

Some limitations of this study need to be mentioned. First, we included only juveniles in the AD(H)D-only group and in the AD(H)D-comorbid group for whom official diagnosis of AD(H)D and/or conduct problems were present. It is possible, however, that there were juvenile offenders in the comparison group with AD(H)D or conduct problems for whom no formal diagnosis of AD(H)D or conduct problems was present, but who suffered from these problems. However, the information as obtained in this study is the information that is usually present in court. Therefore, it is important to use this information to come to treatment decisions. Second, although the ADHD diagnosis is made by a professional outside the school, this is not always the case for conduct problems. The juveniles in the ADHD-comorbid group are juveniles with a formal diagnosis of ADHD in combination with a special education need of ADHD and behavioral problems. We therefore are not sure whether the juveniles in the AD(H)D-comorbid group do actually meet all criteria of a conduct disorder (ODD or CD). Consequently, we have not used the terms ODD and/or CD in our manuscript to describe the juveniles in our sample, instead we have used the term behavioral problems. Third, we did not have information about other specific disorders (anxiety, depression, and bipolar disorders). The WSJCA only measured whether mental disorders were present (other than AD(H)D, CD/ODD and alcohol and drugs which were measured separately) and not their number or degree. Fourth, the WSJCA was not designed to provide an in-depth examination of risk factors. Instead, it is a risk-assessment tool that is designed to be used by juvenile justice professionals and clinicians to summarize juveniles’ risks and needs, classify their overall risk level, and plan treatment and supervision strategies. Fifth, the training of the probation officers to conduct motivational interviews was not provided by mental health professionals such as psychologists or psychiatrists but by probation staff who were certified trainers. However, the technique of motivational interviewing is not unknown for probation officers, as they are involved in guiding and motivating juvenile offenders and their families in their daily work. In addition, the goal of the motivational interview is not only gathering information but the motivational interview is also the first step in the rehabilitative process in which the probation counselor lets the youth and family know the counselor is interested in their strengths as well as their weaknesses. Sixth, youth with a criminal record and their families may have a tendency to give socially desirable answers. This may have influenced the results of this study by giving a too positive picture of the youth. It would thus be possible that in reality, more risk factors and less protective factors were present. However, answers were checked with schools and this possible bias applies to all diagnostic groups, making it unlikely that this has influenced the conclusions drawn about differences between the various diagnostic groups. Seventh, there are no research results available regarding the interrater reliability of the WSJCA. However, quality assurance is an important part of the assessment structure and organization in Washington State, and probation officers receive intensive training to adequately administer and reliably score the WSJCA (Barnoski, 2004a, 2004b).

Notwithstanding these limitations, this study provides important information for clinical practice on differences between juvenile offenders with and without AD(H)D in the prevalence of risk and protective factors and in the strength of associations between risk/protective factors and recidivism. This study stressed that recidivism risks are largest in the AD(H)D groups, with the largest risk in the comorbid group. Consequently, interventions should be adjusted to this recidivism risk and the needs of the AD(H)D groups. Because of the high prevalence of risk factors and the low prevalence of protective factors in the various domains in the ADHD groups, especially the comorbid group, it seems important to refer these juveniles to interventions that focus on risk and protective factors in multiple domains. Given that the same risk and protective factors seem to be important in the comparison and AD(H)D groups, existing “evidence-based interventions” such as Functional Family Therapy (Alexander & Parsons, 1973; Friedman, 1989; Klein, Alexander, & Parsons, 1977) and Multi Systemic Therapy (Henggeler, Melton, Brondino, Scherer, & Hanley, 1997; Henggeler, Melton, & Smith, 1992) may be successful also with the AD(H)D groups.

Footnotes

Authors’ Note

This study meets the ethical guidelines, including adherence to the legal requirements of the study country. The authors had a formal permission from the Washington State for Public Policy to analyze the data set and publish about the results. The collection of data was approved by official authorities of the Ministry of Justice of Washington State.

Declaration of Conflicting Interests

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

Funding

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

Author Biographies

Claudia E. van der Put is an assistant professor of Forensic Child and Youth Care Sciences at the University of Amsterdam. Her research interests concern risk- and needs assessment and statistical modeling. She is also an advisor of the Dutch Council of Child Care and Protection on developing and implementing risk-assessment instruments for the juvenile justice system.

Jessica J. Asscher, developmental psychologist, is an assistant professor of Forensic Child and Youth Care Sciences at the University of Amsterdam. Her research interests concern the effectiveness of (juvenile justice) interventions and the development of behavior problems.

Geert Jan J. M. Stams is a professor of Forensic Child and Youth Care Sciences at the University of Amsterdam, Netherlands and has conducted meta-analyses, longitudinal research, and intervention studies in the area of socioemotional and moral development. Important research topics are parent–child relationship quality (e.g., attachment), juvenile delinquency, developmental psychopathology, and effectiveness of (forensic) youth care.

References

Alexander

J. F.

Parsons

B. V.

(1973). Short-term behavioral intervention with delinquent families: Impact on family process and recidivism. Journal of Abnormal Psychology, 81, 219-225.

American Psychiatric Association. (1994). Diagnostic and statistical manual of mental disorders (4th ed.). Washington, DC: Author.

Andrews

D. A.

Bonta

(2003). The psychology of criminal conduct (3rd ed.). Cincinnati, OH: Anderson.

Andrews

D. A.

Bonta

(2010). Rehabilitating criminal justice policy and practice. Psychology, Public Policy, and Law, 16, 39-55.

Andrews

D. A.

Bonta

Hoge

(1990). Classification for effective rehabilitation: Rediscovering psychology. Criminal Justice and Behavior, 17, 19-51.

Babinski

L. M.

Hartsough

C. S.

Lambert

N. M.

(1999). Childhood conduct problems, hyperactivity-impulsivity, and inattention as predictors of adult criminal activity. Journal of Child Psychology and Psychiatry, 40, 347-355.

Baird

S. C.

Storrs

G. M.

Connelly

(1984). Classification of juveniles in corrections: A model systems approach. Washington, DC: Arthur D. Little.

Barnoski

(1997) Standards for improving research effectiveness in adult and juvenile justice. Olympia: Washington State Institute for Public Policy.

Barnoski

(2004a). Assessing risk for re-offense: Validating the Washington state juvenile court assessment. Olympia: Washington State Institute for Public Policy.

10.

Barnoski

(2004b). Washington State Juvenile Court Assessment Manual, Version 2.1. Olympia: Washington State Institute for Public Policy.

11.

Biederman

Newcorn

Sprich

(1991). Comorbidity of attention deficit hyperactivity disorder with conduct, depressive, anxiety, and other disorders. American Journal of Psychiatry, 148, 564-577.

12.

Brown

R. T.

Freeman

W. S.

Perrin

J. M.

Stein

M. T.

Amler

R. W.

Feldman

H. M.

Wolraich

M. L.

(2001). Prevalence and assessment of attention-deficit/hyperactivity disorder in primary care settings. Pediatrics, 107, E43.

13.

Bulten

Nijman

Von der Staak

(2009). Psychiatric disorders and personality characteristics of prisoners at regular prison wards. International Journal of Law and Psychiatry, 32, 115-119.

14.

Cantwell

D. P.

(1996). Attention deficit disorder: A review of the past 10 years. Journal of American Academy of Child & Adolescent Psychiatry, 35, 978-987.

15.

Elia

Ambrosini

P. J.

Berrettini

(2008). ADHD characteristics: I. Concurrent co-morbidity patterns in children & adolescents. Child and Adolescent Psychiatry and Mental Health, 2, 15.

16.

Friedman

A. S.

(1989). Family therapy vs. parent groups: Effects on adolescent drug abusers. American Journal of Family Therapy, 17, 335-347.

17.

Gittelman

Mannuzza

Shenker

Bonagura

(1985). Hyperactive boys almost grown up, I: Psychiatric status. Archives of General Psychiatry, 42, 937-947.

18.

Gordon

J. A.

Moore

P. M.

(2005). ADHD among incarcerated youth: An investigation on the congruency with ADHD prevalence and correlates among the general population. American Journal of Criminal Justice, 30, 87-97.

19.

Grieger

Hosser

(2012). Attention deficit hyperactivity disorder does not predict criminal recidivism in young adult offenders: Results from a prospective study. International Journal of Law and Psychiatry, 35, 27-34.

20.

Henggeler

S. W.

Melton

G. B.

Brondino

M. J.

Scherer

D. G.

Hanley

J. H.

(1997). Multisystemic therapy with violent chronic juvenile offenders and their families: The role of treatment fidelity in successful dissemination. Journal of Consulting and Clinical Psychology, 65, 821-833.

21.

Henggeler

S. W.

Melton

G. B.

Smith

L. A.

(1992). Family preservation using multisystemic therapy: An effective alternative to incarcerating serious juvenile offenders. Journal of Consulting and Clinical Psychology, 60, 953-961.

22.

Hinshaw

S. P.

(1992). Academic underachievement, attention deficits, and aggression: Comorbidity and implications for intervention. Journal of Consulting and Clinical Psychology, 60, 893-903.

23.

Hoge

R. D.

Andrews

D. A.

(1994). The Youth Level of Service/Case Management, Inventory and Manual. Ottawa, Ontario, Canada: Department of Psychology, Carleton University.

24.

Howell

J. C.

(2003). Diffusing research into practice using the comprehensive strategy for serious, violent, and chronic juvenile offenders. Youth Violence and Juvenile Justice, 1, 219-245.

25.

Jensen

P. S.

Hinshaw

S. P.

Kraemer

H. C.

Lenora

Newcorn

J. H.

Abikoff

H. B.

Vitiello

(2001). ADHD comorbidity findings from the MTA study: Comparing comorbid subgroups. Journal of American Academy of Child & Adolescent Psychiatry, 40, 147-158.

26.

Jensen

P. S.

Martin

Cantwell

D. P.

(1997). Comorbidity in ADHD: Implications for research, practice and DSM-V. Journal of American Academy of Child & Adolescent Psychiatry, 36, 1065-1079.

27.

Klein

N. C.

Alexander

J. F.

Parsons

B. V.

(1977). Impact of family systems intervention on recidivism and sibling delinquency: A model of primary prevention and program evaluation. Journal of Consulting and Clinical Psychology, 45, 469-474.

28.

Lilienfeld

S. O.

Waldman

I. D.

(1990). The relation between childhood attention- deficit/hyperactivity disorder and adult antisocial behavior reexamined: The problem of heterogeneity. Clinical Psychology Review, 10, 699-725.

29.

Loeber

DeLamatre

Keenan

Zhang

(1998). A prospective replication of developmental pathways in disruptive and delinquent behavior. In Cairns

Bergman

Kagan

(Eds.), Methods and models for studying the individual (pp. 185-215). Thousand Oaks, CA: Sage.

30.

Loeber

Farrington

D. P.

Stouthamer-Loeber

White

H. R.

(2008). Violence and serious theft: Development and prediction from childhood to adulthood. New York, NY: Routledge.

31.

Loeber

Keenan

(1994). Interaction between conduct disorder and its comorbid conditions: Effects of age and gender. Clinical Psychology Review, 14, 497-523.

32.

Loeber

Slot

Stouthamer-Loeber

(2008). A cumulative developmental model of risk and promotive factors. In Loeber

Koot

H. M.

Slot

N. W.

van der Laan

P. H.

Hoeve

(Eds.), Tomorrow’s criminals: The development of child delinquency and effective interventions (pp. 133-161). Hampshire, UK: Ashgate Publishing.

33.

Lösel

(1995). The efficacy of correctional treatment. A review and syndissertation of meta-evaluations. In McGuire

(Ed.), What works: Reducing reoffending, guidelines from research and practice (pp. 79-111). Chichester, UK: Wiley.

34.

Mannuzza

Klein

R. G.

Bonagura

Malloy

Giampino

T. L.

Addalli

K. A.

(1991). Hyperactive boys almost grown up, V: Replication of psychiatric status. Archives of General Psychiatry, 48, 77-83.

35.

Moffitt

T. E.

(1990). Juvenile delinquency and attention deficit disorder: Boys’ developmental trajectories from age 3 to age 15. Child Development, 61, 893-910.

36.

Moffitt

T. E.

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

37.

Moffitt

T. E.

Caspi

(2001). Childhood predictors differentiate life-course persistent and adolescence limited antisocial pathways, among males and females. Development & Psychopathology, 13, 355-375.

38.

Moffitt

T. E.

Caspi

Harrington

Milne

B. J.

(2002). Males on the life-course- persistent and adolescence-limited antisocial pathways: Follow-up at age 26 years. Development and Psychopathology, 14, 179-207.

39.

Moffitt

T. E.

Scott

(2008). Conduct disorders. In Rutter

Bishop

Pine

Schott

Stevenson

Taylor

Thapar

(Eds.), Rutter’s child and adolescent psychiatry (5th ed., pp. 543-564). Oxford, UK: Blackwell.

40.

Molina

B. S. G.

Pelham

W. E.

Jr. (2003). Childhood predictors of adolescent substance use in a longitudinal study of children with ADHD. Journal of Abnormal Psychology, 112, 497-507.

41.

Mordre

Groholt

Kjelsberg

Sandstad

Myhre

A. M.

(2011). The impact of ADHD and conduct disorder in childhood on adult delinquency: A 30 years follow-up study using official crime records. BMC Psychiatry, 11, 57.

42.

Murray

D. W.

Arnold

L. E.

Swanson

Wells

Burns

Jensen

Strauss

(2008). A clinical review of outcomes of the multimodal treatment study of children with attention-deficit/hyperactivity disorder (MTA). Current Psychiatry Reports, 10, 424-431.

43.

Olfson

Gameroff

M. J.

Marcus

S. C.

Jensen

P. S.

(2003). National trends in the treatment of attention deficit hyperactivity disorder. American Journal of Psychiatry, 160, 1071-1077.

44.

Orbis Partners. (2007). Long-term validation of the Youth Assessment and Screening Instrument (YASI) in New York State juvenile probation. Ottawa, Ontario, Canada: Author.

45.

Plattner

Steiner

The

S. S. L.

Kraemer

H. C.

Bauer

S. M.

Kindler

. . . Feucht

(2009). Sex-specific predictors of criminal recidivism in a representative sample of incarcerated youth. Comprehensive Psychiatry, 50, 400-407.

46.

Pratt

T. C.

Cullen

F. T.

Blevins

K. R.

Daigle

Unnever

J. D.

(2002). The relationship of attention deficit hyperactivity disorder to crime and delinquency: A meta-analysis. International Journal of Police Science and Management, 4, 344-360.

47.

Prinzie

Hoeve

Stams

G. J. J. M.

(2008). Family processes, parent and child personality characteristics. In Loeber

Koot

H. M.

Slot

N. W.

van der Laan

P. H.

Hoeve

(Eds.), Tomorrow’s criminals: The development of child delinquency and effective interventions (pp. 91-102). Hampshire, UK: Ashgate Publishing.

48.

Rooney

Chronis-Tuscano

A. M.

Yoon

(2012). Substance use in college students with ADHD. Journal of Attention Disorders, 16, 221-234.

49.

Satterfield

J. H.

Faller

K. J.

Crinella

F. M.

Schell

A. M.

Swanson

J. M.

Homer

L. D.

(2007). A 30-year prospective follow-up study of hyperactive boys with conduct problems: Adult criminality. Journal of the American Academy of Child & Adolescent Psychiatry, 45, 601-610.

50.

Schwalbe

C. S.

(2007). Risk assessment for juvenile justice: A meta-analysis. Law and Human Behavior, 31, 449-462.

51.

Sibley

M. H.

Pelham

W. E.

Molina

B. S. G.

Gnagy

E. M.

Waschbusch

D. A.

Biswas

. . . Karch

K. M.

(2011). The delinquent outcomes of boys with ADHD with and without comorbidity. Journal of Abnormal Child Psychology, 39, 21-32.

52.

Smith

B. H.

Molina

B. S. G.

Pelham

W. E.

Jr. (2002). The clinically meaningful link between alcohol use and attention deficit hyperactivity disorder. Alcohol Research & Health, 26, 122-129.

53.

Sourander

Elonheimo

Niemelä

Nuutila

A. M.

Helenius

Sillanmäki

Almqvist

(2006). Childhood predictors of male criminality: A prospective population-based follow-up study from age 8 to late adolescence. Journal of the American Academy of Child & Adolescence Psychiatry, 15, 118-125.

54.

Spanjaard

Van der Knaap

Van der Put

C. E.

Stams

G. J. J. M.

(2012). Risk assessment and the impact of risk and protective factors in adolescence and early adulthood. In Loeber

Slot

van der Laan

P. H.

Hoeve

(Eds.), Persisters and desisters in crime from adolescence into adulthood: Explanation, prevention and punishment (pp. 127-157). Aldershot: Ashgate.

55.

Spencer

T. J.

(2006). ADHD and comorbidity in childhood. Journal of Clinical Psychiatry, 67, 27-31.

56.

Stouthamer-Loeber

Loeber

Wei

Farrington

D. P.

Wikström

P.-O. H.

(2002). Risk and promotive effects in the explanation of persistent serious delinquency in boys. Journal of Consulting and Clinical Psychology, 70, 11-23.

57.

Van der Knaap

L. M.

Alberda

D. L.

Oosterveld

Born

M. P.

(2012). The predictive validity of criminogenic needs for male and female offenders: Comparing the relative impact of needs in predicting recidivism. Law and Human Behavior. doi:10.1037/h0093932

58.

Van der Put

C. E.

Deković

Stams

G. J. J. M.

Van der Laan

P. H.

Hoeve

Van Amelsfort

(2011). Changes in risk factors during adolescence: Implications for risk assessment. Criminal Justice and Behavior, 38, 248-262.

59.

Van der Put

C. E.

Stams

G. J. J. M.

Deković

Hoeve

Van der Laan

P. H.

Spanjaard

Barnoski

(2012). Changes in the relative importance of dynamic risk factors for recidivism during adolescence. International Journal of Offender Therapy and Comparative Criminology, 56, 296-316.

60.

Vermeiren

(2003). Psychopathology and delinquency in adolescents: A descriptive and developmental perspective. Clinical Psychology Review, 23, 277-318.

61.

Vermeiren

Schwab-Stone

Ruchkin

De Clippele

Deboutte

(2002). Predicting recidivism in delinquent adolescents from psychological and psychiatric assessment. Comprehensive Psychiatry, 43, 142-149.

62.

Von Polier

G. G.

Vloet

M. D.

Herpertz-Dahlmann

M. D.

(2012). ADHD and delinquency: A developmental perspective. Behavioral Sciences & the Law. doi:10.1002/bsl.2005

63.

Ward

(2002). Good lives and the rehabilitation of offenders: Promises and problems. Aggression and Violent Behavior, 7, 513-528.

64.

Ward

Gannon

(2006). Rehabilitation, etiology, and self-regulation: The good lives model of sexual offender treatment. Aggression and Violent Behavior, 11, 77-94.

65.

Ward

Stewart

(2003). Criminogenic needs and human needs: A Theoretical model. Psychology, Crime & Law, 9, 125-143.

66.

Weiss

Hechtman

(1993). Hyperactive children grown up: ADHD in children, adolescents, and adults (2nd ed.). New York, NY: Guilford.

67.

Wierson

Forehand

(1995). Predicting recidivism in juvenile delinquents: The role of mental health diagnoses and the qualification of conclusions by race. Behavior Research Therapy, 33, 63-67.

68.

Young

Thome

(2011). ADHD and offenders. Word Journal of Biological Psychiatry, 12, 124-128.

69.

Zito

J. M.

Safer

D. J.

DosReis

Riddle

M. A.

(1998). Racial disparity in psychotropic medications prescribed for youths with Medicaid insurance in Maryland. Journal of the American Academy of Child & Adolescent Psychiatry, 37, 179-184.