Comparing the Central Eight Risk Factors: Do They Differ Across Age Groups of Sex Offenders?

Abstract

Following the risk-need-responsivity (RNR) model, cognitive-behavioral therapy is considered most effective in reducing recidivism when based on dynamic risk factors. As studies have found differences of these factors across age, exploring this seems beneficial. The current study investigates the Central Eight (C8) risk factors across six age groups of outpatient sex offenders (N = 650). Results showed that recidivism rates and age were inversely related from 19 years and up. Half of the C8 did not predict general recidivism at all, substance abuse, antisocial cognition, antisocial associates, and history of antisocial behavior in only one or several age groups. However, factors differed between age groups, with the youngest group demonstrating the most dysfunction in several areas and the oldest group the least. It is concluded that the C8 risk factors seem to lose significance in the older age groups. Results may benefit targeting treatment goals.

Keywords

Central Eight risk factors RNR sex offenders age

Introduction

Sexual offending behavior is a severe type of delinquency, with serious and often long-lasting potential negative consequences for the victim’s health and well-being, such as depression, posttraumatic stress disorder, psychosis, personality disorders, suicidal ideation, substance abuse, revictimization, interpersonal problems, sexual dysfunction, and self-esteem impairment (Cutajar et al., 2010; Hornor, 2010; Maniglio, 2009). Various meta-analyses have shown that cognitive-behavioral therapy for sex offenders is an effective way to combat recidivism (Lösel & Schmucker, 2005; Reitzel & Carbonell, 2006; Walker, McGovern, Poey, & Otis, 2004), emphasizing the importance of appropriate care for these individuals.

According to the risk-need-responsivity (RNR) model (Andrews & Bonta, 2006, 2010), the most beneficial effects are obtained when therapy matches the offender’s risk level, target the criminogenic needs related to their offending behavior, and respond to the offender’s learning style and abilities. Andrews and Bonta composed a list of eight broad risk factors (known as the “Central Eight” [C8]) divided into the “Big Four” (i.e., antisocial cognition, antisocial associates, antisocial personality pattern, and history of antisocial behavior) and the “Moderate Four” (i.e., family/marital, school/work, leisure/recreation, and substance abuse). In their meta-analysis, Bonta, Blais, and Wilson (2014) found evidence for the predictive validity of these C8 factors for general and violent (including sexual) recidivism among adult male offenders, as well as among juvenile, female, and sex offenders.

Although the importance of the C8 risk factors are found relevant in juvenile, young adult, and adult sex offenders, the predictive strength differs across age groups (Caldwell, 2010; Fortune & Lambie, 2006; Hanson & Morton-Bourgon, 2005). In juveniles who sexually offended (JSOs), risk factors for sexual reoffending have been: previous convictions for a sex offense, multiple victims, stranger victims, deviant sexual interest, social isolation, and a lack of treatment success. In addition, childhood abuse, antisocial interpersonal orientation, impulsivity, and negative peer influences are likely to be related to sexual reoffending in JSOs. In adults who sexually offended (ASOs), sexual deviancy and antisocial orientations were found to be the strongest predictors of persistent sexual offending behavior. With regard to nonsexual reoffending behavior, previous studies demonstrated that a dysfunctional family history, delinquent peers, and poor school performance are important risk factors in JSOs, whereas an antisocial orientation was found to be the strongest predictor of general reoffending in ASOs (Efta-Breitbach & Freeman, 2005; Grieger & Hosser, 2013; Hanson & Morton-Bourgon, 2005; Worling & Långström, 2003). Also, there seems to be a difference in sexual and general recidivism rates linked to age. Sexual recidivism rate of JSOs averaged from 7% to 10%, whereas the sexual reoffense rate for ASOs was 14% on average. The general reoffense rates, on the contrary, showed an inversed difference across age groups with 43% on average in JSOs and 36% on average in ASOs (Caldwell, 2010; Fortune & Lambie, 2006; Hanson & Morton-Bourgon, 2005).

Despite the differential impact of criminogenic risk factors on sexual and general recidivism across age groups, little research has been conducted in which several age groups of sex offenders were investigated in a single study. Fazel, Sjöstedt, Långström, and Grann (2006) found that recidivism rates decreased significantly in older age groups, confirming findings from other studies which showed an inverse relationship between age and recidivism in sex offenders and as such indicating age to be a robust predictor of recidivism (Rice & Harris, 2014). Fazel et al. (2006) mentioned lower sexual arousal in older men, increased self-control with age, and less victim access as possible explanations for the decrease of sexual and violent reoffending in older age groups. Craig (2008, 2011), however, pointed out that there are also study results unsupportive of the notion that age is inversely related to the risk of sexual reconviction. He proposes the possibility of a plateau effect in sexual reconviction rates for the middle age band (35-44 years). In doing so, he nuanced the assumed negative linear correlation between age and recidivism and emphasizes the need for more research into the link between these variables. In his 2008 paper, Craig already labeled research on the role of age in the risk of relapse in sexual offenses a relatively new and developing area, likely to be of great interest for forensic practitioners.

Recently, Spruit, van der Put, Gubbels, and Bindels (2017) investigated the severity, impact, and relative importance of dynamic risk factors for recidivism (measured by The Recidivism Risk Assessment Scales [RISc]; Adviesbureau Van Montfoort & Reclassering Nederland, 2004) of 8,665 various types of Dutch offenders in four age groups: 18 to 25 years, 26 to 30 years, 31 to 40 years, and 41+ years. For most risk domains (i.e., accommodation, financial management and income, relationship to partner and family, drug misuse, alcohol misuse, emotional well-being, and attitude and orientation), the severity of risk factors generally increased with age and decreased again after middle adulthood (31-40 years old). In general, the impact of dynamic risk factors on recidivism increased with age, whereas the relative importance also varied across age. However, problems with education/work, relationships with friends and acquaintances, and alcohol misuse provided a unique contribution to the prediction of recidivism for all age groups. In addition, in the age group 18 to 25 years old, problems in attitude and orientation were uniquely associated to recidivism. Spruit et al. (2017) concluded that their results suggest that the potential treatment effects targeting these dynamic risk factors increase during adulthood.

As outcomes of previous studies as to how risk factors vary with age are inconclusive, the aim of the current study is to compare the C8 across age groups in a sample of 650 Dutch sex offenders. As sex offenders more often recidivate with nonsexual offenses (Caldwell, 2010; Fortune & Lambie, 2006; Hanson & Morton-Bourgon, 2005) and sexual reoffense base rates are relatively low (Grossi, 2017; Hanson, 2014), the study focuses on general recidivism. In line with the risk factor literature, it was hypothesized that different C8 factors would be found most predictive of general recidivism across age groups with peers and school showing the strongest predictive factors for general recidivism in the youngest age groups, and antisocial cognition and personality pattern in the older age groups. Following Spruit et al. (2017), it was expected that impact of the investigated risk factors would first increase per age group and then decrease after middle adulthood.

As far as we are aware of, this is the first study in which the C8 factors were investigated as predictors of general recidivism in several age groups of sex offenders in a single study. Results will add to the existing knowledge because they provide further insight into the relevance of risk factors per age group, which in turn might shape efficiency of risk assessment and influence treatment approach.

Method

The data used in the present study were collected from file records of Dutch male sex offenders¹ who were accepted for treatment at a center for outpatient forensic mental health in the Netherlands between 2008 and 2012. Client files are computerized in a client registration system, and contain, among others, information from intake reports, risk assessments, and treatment plans. The forensic outpatient center offers various types of (mainly cognitive behavioral) interventions to clients aged 12 years and older. Although, interventions are primarily provided individually, group therapy (composed of same sex offense type offenders, such as child abuse materi downloaders or child molesters) can be offered as well. If considered necessary, treatment is supplemented with psychopharmaceutic medication and the involvement of spouses and/or parents in sessions (the latter is mandatory for interventions with minors). The 45-minute sessions are generally planned weekly or biweekly, following protocolled treatment modules addressing topics regarding, among others, sexual (dys)function, interpersonal, and self-regulation skills. By means of informed consent, all clients (in case of youth, including their parents) had previously given written consent to use their anonymized data for scientific purposes.

Research Sample

Of the original 691 clients whose risk assessments’ data were available, 26 were excluded because the sexual offense was not their index offense (i.e., the offense leading to the client entering treatment) or there was no offense at all (e.g., clients voluntarily entering treatment because of disturbing sexual fantasies or sexual addiction). Also excluded were two clients who were still in treatment at reference date and nine who had not received any treatment at all. Furthermore, as the sample included only four females, women were excluded, leaving a remainder of 650 male sex offenders.

Sexual offenses included hands-on (e.g., child molest, rape, sexual assault; 61.8%) and hands-off (e.g., possession of child abuse material, indecent exposure; 38.2%) offenses. The main part of the group (88.4%) was born in the Netherlands. The mean age of the clients at the time of their admission was 37.21 years (SD = 16.23; range: 10-79 years). There were four clients younger than 12 years old (minimum age of criminal responsibility in Dutch law), admitted by exception at the request of school or youth care because of alarming behavior. The highest level of completed education varied between elementary school (22.9%), middle school (43.5%), high school (6.4%), vocational education (13.8%), and college/university (13.3%). Two thirds of the sample (67.2%) was following some form of education and/or was in employment at the time of treatment. Clients were distributed into six age groups: younger than 18 years (youth; n = 115), 19 to 24 years (emerging adults; n = 74), 25 to 34 years (n = 101), 35 to 44 years (n = 130), 45 to 54 years (n = 128), and 55 years and older (n = 102).

Measures

C8

The C8 factors were measured with items of the Risk Assessment for outpatient Forensic Mental Health (RAF MH youth; for JSOs; van Horn, Wilpert, Bos, & Mulder, 2008) and RAF MH adults (for ASOs; van Horn, Wilpert, Scholing, & Mulder, 2008). The RAF MH is a generic structured professional judgment (SPJ) instrument based on well-known risk factors for recidivism (Andrews & Bonta, 1995; Webster, Douglas, Eaves, & Hart, 1997), with some adjustments to fit the circumstances of clients referred to Dutch forensic outpatient treatment. The risk assessment is carried out by trained psychologists or psychiatrists with at least 2 years of experience in risk assessment. In the current study, the risk assessment performed at the start of treatment was used. The RAF MH consists of 79 risk factors distributed among 12 domains: (a) previous and current offenses, (b) education/employment, (c) finances, (d) accommodation/living environment, (e) family/partner, (f) social network, (g) leisure time, (h) substance abuse, (i) personal/emotional, (j) attitude, (k) risk management, and (l) sexual problems. Items within the domains are scored dichotomously (yes/no) or on a 3-point scale (0 = absent, 1 = in some degree present, 2 = present). To approximate the definitions of the C8, relevant RAF MH items were summed into a mean-score. A brief definition of the employed operationalizations of the C8 risk factors is listed in Table 1.

Table 1.

Operationalization of the Central Eight Factors Based on the RAF MH.

Central Eight factors
Antisocial cognition	Socio-political, religious, (sub)cultural, and personal beliefs/opinions supporting/condoning delinquency.
Antisocial associates	Associates who have a criminal record or are involved in criminal activities as well as a lack of prosocial associates.
Antisocial personality pattern	Impulsivity (mood swings, irritability, inconsistency) and anger management problems (expressions of negative, hostile affect).
History of antisocial behavior	Convictions, charges, misconduct, use of weapons/serious threats, imprisonment, escalation in frequency and severity of offenses, young age at the first manifestation of antisocial behavior, and violation of probation.
Family/marital circumstances	Instability/conflict in relationships with partner, parents/caregivers, and other family members.
School/work	Problems with performance at and interest/involvement in education and employment.
Leisure/recreation	Lack of involvement in prosocial leisure activities.
Substance abuse	Alcohol and drugs abuse and/or dependability in present (last year) or past.

Note. RAF MH = Risk Assessment for outpatient Forensic Mental Health.

Several studies (van Horn et al., 2016; van Horn, Wilpert, Bos, Eisenberg, & Mulder, 2009) report on psychometric qualities of the RAF MH, with interrater reliabilities varying from good to excellent (intraclass correlation coefficient [ICC] ≥ .77) and predictive validities that can be qualified as good (area under the curve [AUC] ≥ .77).

Criminal history and recidivism

Judicial history (no, yes) was based on convictions for prior offenses. Recidivism (no, yes) was defined by the first new police contact/charge for any offense (general recidivism), including sexual offenses. The information about judicial history and recidivism of the sample was requested from the judicial authorities (Judicial Documentation System [JDS]). The JDS is managed by the Central Judicial Documentation Service (CJD), listing all (legal) persons registered for violations of the law. In this study, all recorded police contacts were included as offense. The follow-up time (start of treatment until reference date: July 2, 2014) varied between 24.80 and 77.63 months (M = 53.61 months, SD = 13.09).

Statistical Analyses

SPSS Version 23.0 was used to analyze the data. The C8 variables—with 0.8% to 3.5% missing values—were checked for normality, which showed that none of the variables departed from normality according to the critical ratio (CR) criterion of 2 (Newsom, 2015) and with 1.9 the kurtosis was below the threshold of 3 (West, Finch, & Curran, 1995). Variance inflation factors (VIFs) for the independent variables were all below 1.5, far below the criterion of 10, indicating no multicollinearity between the predictor variables (O’Brien, 2007). As items differed in number of scoring categories, variables were standardized. Differences in demographic variables, judicial history, and recidivism between the six age groups were analyzed using chi-square tests.

Recidivism rates were calculated using Kaplan–Meier survival analysis, which takes into account the individual differences in follow-up periods (Wartna, 2000). The survival analyses were performed with the dates of the first recidivism offense; all subsequent reoffenses were excluded. The Log rank test was included to detect significant differences in cumulative recidivism percentages between the age groups (Hosmer & Lemeshow, 1998).

Group differences on the C8 scores were examined using a MANOVA with Bonferroni post hoc test to adjust for Type I errors. As suggested by Field (2005), omega squared (ω²) effect sizes were reported for the MANOVA results, using the following formula: (SS= sum of squares; MS = mean square), the effect size guidelines for omega squared are small = 0.01, medium = 0.06, large = 0.14.

Point-biserial correlations (r_pb) were calculated between the C8 risk factors and the dichotomous recidivism variable. Following the guidelines provided by Cohen (1988), the strength of the correlation is interpreted as follows: r ≥ .10 = weak, r ≥ .30 = moderate, r ≥ .50 = strong.

A series of Cox regression analyses (ENTER method) were performed to identify significant C8 predictors for general recidivism (criterion) in each age group. Cox regression analysis is a statistical model (Cox, 1972) to calculate a survival curve which takes into account the influence of covariables and the varying follow-up periods of each client. Cox regression calculates a probability of reoffending for every day between identification and reoffense and uses these probabilities to calculate a hazard function (cf. Landau, 2002). The hazard ratio (i.e., rate ratio; exp [B]) resulting from Cox regression analysis is an indicator of the strength of the association between predictor and outcome (in this study: general recidivism). Diagnostics were performed to examine possible violations of assumptions of proportional hazards (i.e., the relative risk for the event to occur is assumed to be proportional for all groups).

To rule out the possibility of “overfitting” (Type I error), “underfitting” (Type II error), and paradoxical fitting (i.e., a known risk factor may paradoxically seem to predict increased survival), the number of events per variable (EPV) was checked. The EPV in each variable was ≥ 10. As a rule of thumb, a minimum of 10 EPV is recommended in Cox regression models (cf. Concato, Peduzzi, Holford, & Feinstein, 1995). For all analyses, alpha levels were set at p = .05.

Results

Demographic Variables, Judicial History, and Recidivism Across Age Groups

Table 2 lists various demographic variables, judicial history, and recidivism per age group.

Table 2.

Demographic Variables, Judicial History, and Recidivism by Age Group.

	<18	19-24	25-34	35-44	45-54	55+
	n (%)	n (%)	n (%)	n (%)	n (%)	n (%)
Origin
Dutch	103 (89.6)	68 (93.2)	85 (84.2)	116 (89.2)	114 (89.1)	88 (86.3)
Non-Dutch	12 (10.4)	5 (6.8)	16 (15.8)	14 (10.8)	14 (10.9)	14 (13.7)
Education
Elementary school	51 (44.3)	12 (16.2)	13 (12.9)	21 (16.2)	18 (14.1)	31 (30.4)
Middle school	54 (47)	42 (56.8)	40 (39.6)	57 (43.8)	59 (46.1)	38 (37.3)
High school	9 (7.8)	11 (14.9)	4 (4.0)	11 (8.5)	4 (3.1)	2 (2.0)
Vocational education	1 (0.9)	8 (10.8)	29 (28.7)	18 (13.8)	21 (16.4)	11 (10.8)
College/university	—	1(1.4)	15 (14.9)	23 (17.7)	26 (20.3)	20 (19.6)
Currently in school/employed
No	3 (2.6)	26 (35.1)	28 (27.7)	35 (26.9)	40 (31.3)	46 (45.1)
Yes	94 (81.7)	43 (58.1)	72 (71.3)	91 (70.0)	84 (65.6)	53 (52)
Unknown	18 (15.7)	5 (6.8)	1 (1.0)	4 (3.1)	4 (3.1)	3 (2.9)
Offense type
Hands-off	15 (13.0)	17 (23)	57 (56.4)	68 (52.3)	51 (39.8)	40 (39.2)
Hands-on	100 (87.0)	57 (77)	44 (43.6)	62 (44.7)	77 (60.2)	62 (60.8)
Judicial history
Yes	35 (30.4)	35 (47.3)	36 (35.6)	65 (50.0)	66 (51.6)	59 (57.8)
Recidivism
General	21 (18.3)	24 (32.4)	27 (26.7)	30 (23.1)	22 (17.2)	16 (15.7)
Sexual	0 (0)	3 (4.1)	6 (5.9)	9 (6.9)	2 (1.6)	7 (6.9)

Note. Due to missing values, the numbers in the table do not add up exactly to the total sample.

Results show that the age groups did not differ in country of birth, χ²(5, N = 649) = 4.135, p = .53. With regard to education and current schooling/employment, the number of clients in each cell was too small to calculate differences reliably. Offense type differed significantly, χ²(5, N = 650) = 63.503, p ≤ .001, youth, emerging adults, and older offenders committed mostly hands-on offenses, whereas in the middle age groups (25-44 years) hands-off offenses were more prominent. The judicial history significantly varied across age groups, χ²(5, N = 650) = 23.801, p ≤ .001. Criminal antecedents increased per age group (from 30.4% to 57.8%), with the exception of the emerging adults group, which displayed a higher percentage of previous offenses (47.3%) than the 25 to 34 group. The Log rank survival distributions for the general recidivism of the six age groups did not reach the level of significance, χ²(5) = 8.595, p = .126. The same applies to sexual recidivism, χ²(5) = 10.272, p = .068.

To explore possible predictive value of the demographic variables, Cox regression analysis was used (see Table 3). The goodness-of-fit of the overall model was significant, χ²(6) = 21.450, p = .002.

Table 3.

Cox Regression of General Recidivism With Demographic Variables.

Demographic variables	B	SE	Wald	Significance	Exp	95% CI
Origin	.086	.255	0.113	.737	1.090	[0.661, 1.797]
Education	−.294	.079	13.726	.000	0.746	[0.638, 0.871]
Currently in school/employed	−.156	.201	0.602	.438	0.855	[0.577, 1.269]
Offense type	−.394	.217	3.286	.070	0.675	[0.441, 1.032]
Judicial history	.294	.212	1.912	.167	1.341	[0.885, 2.034]
Age group	−.124	.059	4.433	.035	0.883	[0.787, 0.991]

Note. CI = confidence interval.

C8 Across Age Groups

In Table 4, mean scores, standard deviations, and MANOVA results of the C8 variables per age group are presented. At multivariate level, a significant effect of age was found for the C8 risk factors, Pillai’s trace = .231, F(40, 2820) = 3.418, p < .001.

Table 4.

MANOVA of the Central Eight Factors of Sex Offenders Across Age Groups.

Central Eight	<18 years(n = 94)	19-24 years(n = 65)	25-34 years(n = 98)	35-44 years(n = 112)	45-54 years(n = 113)	55 + years(n = 91)	F(567)	p	ω²
Central Eight	M (SD)	M (SD)	M (SD)	M (SD)	M (SD)	M (SD)	F(567)	p	ω²
Antisocial cognition	.21 (0.90)	−.42 (0.72)	.10 (1.08)	−.10 (0.98)	−.10 (1.01)	.17 (1.15)	4.360	.001	0.028
Antisocial associates	−.05 (1.23)	−.08 (1.05)	.05 (0.92)	.00 (0.93)	.13 (0.93)	−.10 (0.88)	.735	.598	−0.002
Antisocial personality pattern	.35 (1.01)	.27 (0.99)	.22 (1.03)	−.09 (1.02)	−.23 (0.91)	−.33 (0.97)	7.768	.000	0.063
History of antisocial behavior	.31 (0.70)	.15 (1.00)	.07 (0.96)	−.02 (1.14)	−.10 (0.98)	−.24 (1.02)	3.668	.003	0.023
Family/marital circumstances	−.24 (1.18)	−.22 (0.99)	.08 (0.93)	.12 (0.90)	.12 (0.87)	−.08 (1.02)	2.637	.023	0.014
School/work	.23 (0.96)	.03 (0.99)	.11 (1.00)	−.03 (0.98)	.02 (0.99)	−.26 (0.97)	2.601	.024	0.014
Leisure/recreation	−.32 (0.95)	−.06 (0.96)	.14 (0.97)	.18 (1.05)	.07 (0.95)	−.03 (1.03)	3.326	.006	0.020
Substance abuse	−.25 (0.89)	.10 (1.02)	.23 (1.16)	−.01 (0.95)	.06 (0.99)	−.22 (0.72)	3.532	.004	0.022

At univariate level, results indicated that all factors, except antisocial associates, showed significant between group differences. Post hoc analyses demonstrated that the emerging adults (19-24 years) displayed significantly less antisocial cognition than all the other age groups. At the high end of scores, the <18 group presented more antisocial cognition than the 35 to 44 and the 45 to 54 group. The history of antisocial behavior of the <18-year-olds was significantly larger than that of the 35- to 44-, 45- to 54-, and 55+ year-olds. The 55+ group, on the contrary, had the lowest history of antisocial behavior compared to the 19- to 24- and 25- to 34-year-olds. Concerning antisocial personality pattern, this decreases per age group, resulting in the lowest three age groups (<18-34) significantly differing from the highest (35-55+). The <18 group had the least problematic family/marital circumstances, significantly differing from the 25- to 54-year-olds (three groups). Also, the 19 to 24 group displayed less family/marital dysfunction than the 35 to 44 and 45 to 54 groups. Regarding school/work, the 55+ group scored significantly better than the <18, 25 to 34, and 45 to 54 groups. The <18-group presented itself significantly better in leisure/recreation than all the other age groups. Substance abuse, finally, was significantly lower in the <18 and 55+ groups than the 19 to 24, 25 to 34, and 45 to 54 groups. Effect sizes were small (ω² = −0.002 to 0.028), except for the antisocial personality pattern (ω² = 0.063).

C8 and General Recidivism

Point-biserial correlation coefficients are listed in Table 5. The significant correlations between general recidivism and antisocial associates, antisocial personality pattern, history of antisocial behavior, school/work, and substance abuse of the total group can be assumed below weak or weak with coefficients ranging from .077 to .233. Substance abuse correlated with general recidivism in all the groups except the 55+ group. For this group, none of the C8 factors correlated with recidivism. In the 25 to 34 group, on the contrary, almost all of the C8 (except antisocial associates and leisure/recreation) correlated with relapse. This latter factor of the Moderate Four had no significant correlations with recidivism in any of the groups. Family/marital circumstances, school/work, and antisocial cognition had also no connections to relapse in any age groups except for the 25- to 34-year-olds. Antisocial personality pattern and history of antisocial behavior emerged as relevant for 19- to 44-year-olds (three groups), whereas antisocial associates mattered only in the youngest two groups (<18 and 19-24 years old).

Table 5.

Point Biserial Correlations Between Central Eight and General Recidivism by Age Group.

Central Eight	<18 years(n = 94)	19-24 years(n = 65)	25-34 years(n = 98)	35-44 years(n = 112)	45-54 years(n = 113)	55 + years(n = 91)	Total
Central Eight	r _pb	r _pb	r _pb	r _pb	r _pb	r _pb	r _pb
Antisocial cognition	−.103	.219	.358**	.052	−.006	.043	.075
Antisocial associates	.216*	.248*	.127	−.132	.073	−.032	.086*
Antisocial personality pattern	−.023	.278*	.379**	.220*	.080	.143	.193**
History of antisocial behavior	.171	.283*	.293**	.238**	.086	−.015	.183**
Family/marital circumstances	.136	−.021	.197*	.076	.053	.010	.077
School/work	.116	.138	.224*	.123	.138	.096	.143**
Leisure/recreation	.005	.113	.134	.094	.051	.005	.072
Substance abuse	.259**	.279*	.199*	.194*	.327**	.018	.233**

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

The Cox regression results in Table 6 make evident that, except for the oldest age group in which no significant predictors were found, in each of the other age groups, only one or two C8 risk factors significantly predicted general recidivism. Substance abuse was predictive of recidivism in three of the six age groups: <18, 19 to 24, and 45 to 54 years. For the 25- to 34-year-olds, the most salient C8 factor predictive of recidivism was antisocial cognition, whereas for the 35 to 44 group, these were antisocial associates and a history of antisocial behavior.

Table 6.

Cox Regression of General Recidivism by Age Group.

Central Eight per age group	B	SE	Wald	Significant	Exp	95% CI
<18 years
Antisocial cognition	−.454	.323	1.968	.161	0.635	[0.337, 1.198]
Antisocial associates	.181	.245	0.541	.462	1.198	[0.740, 1.938]
Antisocial personality pattern	−.477	.327	2.125	.145	0.621	[0.327, 1.179]
History of antisocial behavior	.354	.416	0.727	.394	1.425	[0.631, 3.219]
Family/marital circumstances	.312	.266	1.378	.241	1.367	[0.811, 2.302]
School/work	.064	.347	0.034	.854	1.066	[0.540, 2.104]
Leisure/recreation	−.451	.345	1.709	.191	0.637	[0.324, 1.252]
Substance abuse	.584	.237	6.104	.013	1.794	[1.128, 2.852]
19-24 years
Antisocial cognition	.104	.344	0.092	.762	1.110	[0.565, 2.179]
Antisocial associates	.259	.275	0.888	.346	1.295	[0.756, 2.220]
Antisocial personality pattern	.175	.315	0.311	.577	1.192	[0.643, 2.208]
History of antisocial behavior	.659	.393	2.816	.093	1.934	[0.895, 4.176]
Family/marital circumstances	−.529	.286	3.421	.064	0.589	[0.336, 1.032]
School/work	−.089	.316	0.079	.779	0.915	[0.493, 1.700]
Leisure/recreation	.049	.279	0.031	.860	1.051	[0.609, 1.814]
Substance abuse	.457	.231	3.914	.048	1.580	[1.004, 2.485]
25-34 years
Antisocial cognition	.460	.186	6.131	.013	1.584	[1.101, 2.279]
Antisocial associates	−.106	.310	0.118	.731	0.899	[0.490, 1.651]
Antisocial personality pattern	.394	.244	2.603	.107	1.483	[0.919, 2.394]
History of antisocial behavior	.520	.301	2.990	.084	1.682	[0.933, 3.033]
Family/marital circumstances	.012	.315	0.001	.971	1.012	[0.546, 1.876]
School/work	−.151	.250	0.363	.547	0.860	[0.527, 1.404]
Leisure/recreation	.056	.266	0.044	.834	1.057	[0.628, 1.780]
Substance abuse	.144	.184	0.612	.434	1.155	[0.805, 1.658]
35-44 years
Antisocial cognition	−.079	.227	0.120	.729	0.924	[0.592, 1.442]
Antisocial associates	−.556	.252	4.876	.027	0.574	[0.350, 0.939]
Antisocial personality pattern	.104	.213	0.238	.625	1.110	[0.731, 1.685]
History of antisocial behavior	.505	.246	4.216	.040	1.657	[1.023, 2.684]
Family/marital circumstances	.210	.307	0.466	.495	1.233	[0.675, 2.253]
School/work	.094	.228	0.171	.679	1.099	[0.703, 1.716]
Leisure/recreation	.030	.198	0.023	.880	1.030	[0.699, 1.519]
Substance abuse	.223	.209	1.132	.287	1.249	[0.829, 1.882]
45-54 years
Antisocial cognition	−.131	.276	0.226	.635	0.877	[0.510, 1.507]
Antisocial associates	.096	.274	0.123	.726	1.101	[0.643, 2.039]
Antisocial personality pattern	−.053	.322	0.027	.869	0.948	[0.504, 1.783]
History of antisocial behavior	.132	.296	0.198	.657	1.141	[0.638, 2.039]
Family/marital circumstances	.019	.284	0.005	.945	1.020	[0.585, 1.777]
School/work	.137	.274	0.249	.618	1.146	[0.670, 1.961]
Leisure/recreation	−.178	.258	0.479	.489	0.837	[0.505, 1.386]
Substance abuse	.674	.259	6.803	.009	1.963	[1.182, 3.258]
55+ years
Antisocial cognition	.081	.288	0.078	.780	1.084	[0.616, 1.907]
Antisocial associates	.329	.391	0.708	.400	1.389	[0.646, 2.987]
Antisocial personality pattern	.523	.308	2.873	.090	1.687	[0.922, 3.088]
History of antisocial behavior	−.013	.362	0.001	.972	0.987	[0.486, 2.007]
Family/marital circumstances	−.390	.382	1.045	.307	0.677	[0.320, 1.430]
School/work	.515	.322	2.560	.110	1.673	[0.891, 3.144]
Leisure/recreation	−.221	.381	0.337	.562	0.802	[0.380, 1.692]
Substance abuse	.158	.441	0.128	.720	1.171	[0.493, 2.780]

Note. CI = confidence interval.

Discussion

In an effort to add to the knowledge about dynamic risk factors and age, this study focused on the C8 factors in 650 male sex offenders divided into six age groups (<18, 18-24, 25-34, 35-44, 45-54, and 55+) and their general recidivism rates. Results showed that the C8 varied between age groups, however, just slightly in line with the hypotheses. As expected, school/work demonstrated to be problematic for JSOs, yet not significantly distinctive from the other groups or predictive of recidivism. Also, the factor antisocial associates did not emerge as risk for JSOs. Inconsistent with assumptions, antisocial personality pattern was not mainly represented in the ASO group. On the contrary, it declined in the older age groups, being highest in the JSO group. Antisocial cognitions and history of antisocial behavior were also highest in the <18 group, revealing the JSOs as most dysfunctional of the groups, centering their difficulties mainly within the Big Four. Strikingly, in the oldest age group (55+), none of the C8 added to recidivism risk. Compared with the other groups, these individuals distinguished themselves as least maladapted on the factors antisocial associates, antisocial personality pattern, history of antisocial behavior, and school/work. Only one factor of the C8 predicted relapse for multiple age groups (the <18, 19-24, and 45-54-year-olds), which was substance abuse. Several other C8 factors were uniquely predictive for specific age groups. Antisocial cognition, for example, predicted general recidivism in the 25- to 34-year-olds, and antisocial associates and history of antisocial behavior were predictive in the 35- to 44-year-olds. These findings were supported by significant, but mostly weak, correlations between the C8 risk factors and general recidivism. In the 25- to 34-year-olds group, almost all the C8 correlated with recidivism, except for antisocial associates and leisure. Similar to Spruit et al.’s (2017) outcome, the impact of the investigated risk factors first increased and then decreased again after middle adulthood.

Despite the varying quantity of presence of the C8 across age groups, no significant differences were found in general recidivism rates. Recidivism rate mimicked the age-crime curve, showing a peak in the 19 to 24 group followed by a decline in the subsequent age groups, corresponding with results of other studies (Fazel et al., 2006; Rice & Harris, 2014). Thus, the emerging adults differentiated themselves with the highest recidivism rate although they scored less problematic on the C8 compared to most other age groups and, for example, displayed significantly the least antisocial cognitions. This notable result raises questions, for instance, if the emerging adults should be risk assessed with the youth instrument instead of the adult. However, as the scores of the 19- to 24-year-olds were closer to the adults than to the minors, the use of the adult instrument could be justified. As to the high recidivism rate, over one third (35.1%) of the emerging adults appeared to lack advanced education or employment, while their judicial history was relatively extensive (47.3%). Perhaps this adds to the explanation of why this age group recidivated most of all of the groups in this study, as they seem to have so much time at hand and prior offenses have been shown to predict recidivism (Hanson & Bussière, 1998; Worling & Långström, 2003).

Surprisingly, the factor history of antisocial behavior declined per age group, despite the fact that this factor included the component prior offenses, which increased per age group. This implies that judicial history was not the leading component in assessing this Big Four factor in the current sample, but that other information was considered more influential concerning the risk of history of antisocial behavior (e.g., use of weapons/serious threats, imprisonment, escalation in frequency and severity of offenses, young age at the first manifestation of antisocial behavior, and violation of probation). Antisocial personality pattern also showed a negative linear relationship with age, corresponding with earlier study and review outcomes (Blonigen, 2010; Huchzermeier, Friedemann, Köhler, Bruß, & Hinrichs, 2008). This decline in antisocial orientation per age group possibly explains the compatible decrease of recidivism.

Limitations and Future Directions

As this study only covers part of the Dutch outpatient forensic population, the generalizability of results is limited. Also, base rates of specific recidivism (i.e., recidivism with the same offense as the index offense, in this study, sexual reoffenses) were too low for further analyses. Longer follow-up time and a larger sample size could increase statistical opportunities, such as enabling a longitudinal design in which the developmental pathways of sex offenders are studied in terms of risk factors. The cross-sectional design of the current study calls for caution in drawing conclusions from outcomes concerning (the course of) age. Also, the age groups themselves were based on arbitrary cut offs, in the absence of a theoretically or statistically grounded distinction. In future studies, cluster analyses could offer more insight as to an appropriate age group distribution. Another complication of the current study is the operationalization of the C8 factors, based on the RAF MH. With items from other risk assessment instruments, the results could have been more in line with other studies (Grieger & Hosser, 2013). For the purpose of comparing results between studies, it would be favorable if there was consensus in used measurements. Unfortunately, advantages and disadvantages of different measures create discrepancies in selection preferences. Selecting a measure for recidivism, for example, is equally complicated. As not all charges lead to convictions, this outcome type could result in overrepresentation. On the contrary, relying on conviction as a measure for recidivism might underestimate the real proportion of reoffending sex offenders, considering the so-called “dark number.” Overall, more information from different sources would refine data, possibly increasing substantiation of results.

Aside from problems with the operationalization of the current study variables, it can be argued that the C8 are not representative (enough) of the construct dynamic risk factors and that the construct itself lacks validity altogether, as Ward and Fortune (2016) pointed out in their article about conceptual difficulties concerning dynamic risk factors. They offer a methodological framework to make better use of dynamic risk factors in our field of research, helping avoid common pitfalls in future studies.

Implications

Careful interpretation of the results of the current study might put forth several suggestions for outpatient forensic psychiatric treatment of sex offenders. In line with the results of Spruit et al. (2017), substance abuse emerged as a prominent risk factor for general reoffending in different age groups. Therefore, it should be appropriately assessed and addressed to benefit treatment effectivity. Looman and Abracen (2011), for example, found that the Michigan Alcohol Screening Test (MAST; Selzer, 1971) added to the prediction of serious recidivism among sexual offenders, supporting its value in the diagnostic process.

Furthermore, as the youngest group in this study demonstrated to be the most dysfunctional and multiproblematic, and it seemed that overall maladaptation declined in the older age groups, frequency of treatment could be adjusted accordingly, following the risk principle of the RNR-model. Subsequently, the question raised by Olver, Nicholaichuk, Gu, and Wong (2013), if older sex offenders derive fewer benefits from sex offender treatment than younger, was added relevance by the results of this study, warranting more research. Also, the alarming relatively high recidivism rate of the emerging adults demands further exploring as to possible causes.

Footnotes

Declaration of Conflicting Interests

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

Funding

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

Notes

ORCID iD

Julia Wilpert

J. E. van Horn

References

Adviesbureau Van Montfoort, & Reclassering Nederland. (2004). RISc versie 1.0: Recidive Inschattings Schalen: Handleiding [RISc version 1.0: Recidivism Risk Assessment Scales: Manual]. Harderwijk, The Netherlands: Flevodruk.

Andrews

D. A.

Bonta

J. L.

(1995). Level of Service Inventory–Revised. Toronto, Ontario, Canada: Multi-Health Systems.

Andrews

D. A.

Bonta

J. L.

(2006). The psychology of criminal conduct (4th ed.). Newark, NJ: LexisNexis.

Andrews

D. A.

Bonta

J. L.

(2010). The psychology of criminal conduct (5th ed.). New Providence, NJ: Matthew Bender.

Blonigen

D. M.

(2010). Explaining the relationship between age and crime: Contributions from the developmental literature on personality. Clinical Psychology Review, 30, 89-100.

Bonta

J. L.

Blais

Wilson

H. A.

(2014). A theoretically informed meta-analysis of the risk for general and violent recidivism for mentally disordered offenders. Aggression and Violent Behavior, 19, 278-287.

Caldwell

M. F.

(2010). Study characteristics and recidivism base rates in juvenile sex offender recidivism. International Journal of Offender Therapy and Comparative Criminology, 54, 197-212.

Cohen

(1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum.

Concato

Peduzzi

Holford

T. R.

Feinstein

A. R.

(1995). Importance of events per independent variable in proportional hazards regression analysis I: Background, goals, and general strategy. Journal of Clinical Epidemiology, 48, 1495-1501.

10.

Cox

D. R.

(1972). Regression models and life tables (with discussion). Journal of Royal Statistical Society, 34, 187-220.

11.

Craig

L. A.

(2008). How should we understand the effect of age on sexual recidivism? Journal of Sexual Aggression, 14, 185-198.

12.

Craig

L. A.

(2011). The effect of age on sexual and violent reconviction. International Journal of Offender Therapy and Comparative Criminology, 55, 75-97.

13.

Cutajar

M. C.

Mullen

P. E.

Ogloff

J. R. P.

Thomas

S. D.

Wells

D. L.

Spataro

(2010). Psychopathology in a large cohort of sexually abused children followed up to 43 years. Child Abuse & Neglect, 34, 813-822.

14.

Efta-Breitbach

Freeman

K. A.

(2005). Recidivism and resilience in juvenile sexual offenders: An analysis of the literature. Journal of Child Sexual Abuse, 13, 257-279.

15.

Fazel

Sjöstedt

Långström

Grann

(2006). Risk factors for criminal recidivism in older sexual offenders. Sexual Abuse: A Journal of Research and Treatment, 18, 159-167.

16.

Field

(2005). Discovering statistics using IBM SPSS statistics (2nd ed.). London, England: SAGE.

17.

Fortune

C.-A.

Lambie

(2006). Sexually abusive youth: A review of recidivism studies and methodological issues for future research. Clinical Psychology Review, 26, 1078-1095.

18.

Grieger

Hosser

(2013). Which risk factors are really predictive: An Analysis of Andrews and Bonta’s “central eight” risk factors for recidivism in German youth correctional facility inmates. Criminal Justice and Behavior, 41, 613-634.

19.

Grossi

L. M.

(2017). Sexual offenders, violent offenders, and community reentry: Challenges and treatment considerations. Aggression and Violent Behavior, 34, 59-67.

20.

Hanson

R. K.

(2014). Treating sexual offenders: How did we get here and where are we headed? Journal of Sexual Aggression, 20, 3-8. doi:10.1080/13552600.2013.861524

21.

Hanson

R.K.

Bussière

M.T.

(1998). Predicting relapse: a meta-analysis of sexual offender recidivism studies. Journal of Consulting and Clinical Psychology, 66(2), 348-362.

22.

Hanson

R. K.

Morton-Bourgon

K. E.

(2005). The characteristics of persistent sexual offenders: A meta-analysis of recidivism studies. Journal of Consulting and Clinical Psychology, 73, 1154-1163.

23.

Hornor

(2010). Child sexual abuse: Consequences and implications. Journal of Pediatric Health Care, 24, 358-364.

24.

Hosmer

D. W.

Lemeshow

(1998). Applied survival analysis: Regression modeling of time to event data. New York, NY: John Wiley.

25.

Huchzermeier

Friedemann

Köhler

Bruß

Hinrichs

(2008). Are there age-related effects in antisocial personality disorders and psychopathy? Journal of Forensic and Legal Medicine, 15, 213-218.

26.

Landau

(2002). Using survival analysis in psychology. Understanding Statistics, 1, 233-270.

27.

Looman

Abracen

(2011). Substance abuse among high-risk sexual offenders: Do measures of lifetime history of substance abuse add to the prediction of recidivism over actuarial risk assessment instruments? Journal of Interpersonal Violence, 26, 683-700.

28.

Lösel

Schmucker

(2005). The effectiveness of treatment for sexual offenders: A comprehensive meta-analysis. Journal of Experimental Criminology, 1, 117-146.

29.

Maniglio

(2009). The impact of child sexual abuse on health: A systematic review of reviews. Clinical Psychology Review, 29, 647-657.

30.

Newsom. (2015). Practical approaches to dealing with nonnormal and categorical variables. Retrieved from http://web.pdx.edu/~newsomj/semclass/ho_estimate2.pdf

31.

O’Brien

R. M.

(2007). A caution regarding rules of thumb for variance inflation factors. Quality & Quantity, 41, 673-690.

32.

Olver

M. E.

Nicholaichuk

T. P.

Wong

S. C. P.

(2013). Sex offender treatment outcome, actuarial risk, and the aging sex offender in Canadian corrections: A long-term follow-up. Sexual Abuse: A Journal of Research and Treatment, 25, 396-422.

33.

Reitzel

L. R.

Carbonell

J. L.

(2006). The effectiveness of sexual offender treatment for juveniles as measured by recidivism: A meta-analysis. Sexual Abuse: A Journal of Research and Treatment, 18, 401-421.

34.

Rice

M. E.

Harris

G. T.

(2014). What does it mean when age is related to recidivism among sex offenders? Law and Human Behavior, 38, 151-161.

35.

Selzer

M. L.

(1971). The Michigan Alcoholism Screening Test: The quest for a new diagnostic instrument. American Journal of Psychiatry, 127, 1653-1658.

36.

Spruit

van der Put

Gubbels

Bindels

(2017). Age differences in the severity, impact and relative importance of dynamic risk factors for recidivism. Journal of Criminal Justice, 50, 69-77.

37.

van Horn

J. E.

Bogaerts

Eisenberg

M. J.

van der Put

C. E.

Dekker

J. M.

van den Hanenberg

F. J. A. C.

Bouman

Y. H. A

. (2016). Kernset K-factoren voor het ambulante forensische veld: Een multi-method onderzoek naar risico-en beschermende factoren in relatie tot algemene recidive, geweldsrecidive en seksuele recidive [Clinical factors for outpatient facilities: A multi-method study on risk-en protective factors in relation to general, violent, and sexual recidivism]. Utrecht, The Netherlands: Kwaliteit Forensische Zorg.

38.

van Horn

J. E.

Wilpert

Bos

M. G. N.

Eisenberg

Mulder

. (2009). WaagSchaal jeugd: de psychometrische kwaliteit van een gestructureerd klinisch risicotaxatie-instrument voor de ambulante forensische psychiatrie [RAF MH youth: The psychometric qualities of a structured clinical risk assessment instrument in outpatient forensic psychiatry]. Panopticon: Tijdschrift Voor Strafrecht, Criminologie En Forensisch Welzijnswerk, 30(2), 23-34.

39.

van Horn

J. E.

Wilpert

Bos

M. G. N.

Mulder

. (2008). Handleiding Waagschaal jeugd: risicotaxatie-instrument voor de ambulante forensische psychiatrie [RAF MH youth manual: Risk assessment instrument for outpatient forensic psychiatry]. Utrecht, The Netherlands: De Waag.

40.

van Horn

J. E.

Wilpert

Scholing

Mulder

. (2008). Handleiding Waagschaal volwassenen: risicotaxatie-instrument voor de ambulante forensische psychiatrie [RAF MH adults manual: Risk assessment instrument for outpatient forensic psychiatry]. Utrecht, The Netherlands: De Waag.

41.

Walker

D. F.

McGovern

S. K.

Poey

E. L.

Otis

K. E.

(2004). Treatment effectiveness for male adolescent sexual offenders: A meta-analysis and review. Journal of Child Sexual Abuse, 13, 281-293.

42.

Ward

Fortune

C.-A.

(2016). From dynamic risk factors to causal processes: A methodological framework. Psychology, Crime & Law, 22, 190-202.

43.

Wartna

(2000). Recidive-onderzoek en survival-analyse: Over het meten van de duur van de misdrijfvrije periode [Recidivsm research and survival analysis: About measuring the crime free period]. Tijdschrift Voor Criminologie, 42, 2-20.

44.

Webster

C. D.

Douglas

K. S.

Eaves

Hart

S. D.

(1997). HCR-20: Assessment risk for violence (Version 2). Burnaby, British Columbia, Canada: Mental health, Law and Policy Institute, Simon Fraser University.

45.

West

S. G.

Finch

J. F.

Curran

P. J.

(1995). Structural equation models with nonnormal variables: Problems and remedies. In Hoyle

R. H.

(Ed.), Structural equation modeling: Concepts, issues, and applications (pp. 56-75). Thousand Oaks, CA: SAGE.

46.

Worling

J. R.

Långström

(2003). Assessment of criminal recidivism risk with adolescents who have offended sexually: A Review. Trauma, Violence, & Abuse, 4, 341-362.