Clinical and Personality Disorders in a Danish Treatment-Seeking Sample of Intimate Partner Violence Perpetrators

Abstract

Intimate partner violence (IPV) is a global public health concern with profound psychological consequences. Perpetrators often have a history of childhood trauma and a range of co-occurring psychiatric problems, which may have implications for treatment. This study examines the prevalence of psychiatric and personality disorders (PD) among perpetrators and the association between a range of demographic, childhood trauma, and adult criminality variables for the most prominent disorders. Data were collected from IPV perpetrators (n = 529) engaging in a treatment program, ‘Dialogue Against Violence’. High rates of childhood trauma were observed. There was significant variation in the prevalence of clinical disorders and PDs, with Antisocial PD and Anxiety Disorder being the most common. A clinical disorder was the strongest predictor of PDs, likewise a PD was the strongest predictor of clinical disorders. Findings demonstrated that IPV perpetrators have a number of personality and clinical disorders and traumatic histories that need to be considered within a treatment perspective.

Keywords

intimate partner violence perpetrator characteristics psychopathology Millon Clinical Multiaxial Inventory personality disorders

Introduction

Intimate partner violence (IPV) is a global public health concern affecting both males and females across every culture, social class, and age (Garcia-Moreno, Jansenm, Ellsberg, Heise, & Watts, 2006; Goldenson, Spidel, Greaves, & Dutton, 2009). Worldwide estimates indicate that between 15% and 71% of women and between 4.6% and 55.4% of men have experienced IPV at least once in their lifetime (Ameh, Shittu, Abdul, Bature, & Oyefabi, 2012; Garcia-Moreno et al., 2006). The sequelae of IPV includes higher lifetime prevalence of poor physical and mental health, increased substance abuse, and delinquency (Campbell, 2002). From a preventive perspective, knowledge on modifiable risk factors and pathways leading to IPV perpetration is crucial.

Gender differences in the prevalence of IPV perpetration are inconsistent with several studies demonstrating gender symmetry (e.g., Archer, 2000; Straus, 2009) while others dispute this claim (Okuda et al., 2015; Tjaden & Thoennes, 2000). Gender differences also emerge in the types of IPV perpetration, for example, Okuda et al. (2015) found women more commonly engaged in less-severe types of violence, while sexually violent acts were more commonly perpetrated by men. Furthermore, a meta-analytical review found that women were more likely than men to use one or more acts of physical aggression and to use such acts more frequently, whereas men were more likely to inflict injury with 62% of those injured by a partner being female (Archer, 2000). Previous studies have also found risk factors for IPV perpetration also vary by gender. For example, Fang and Corso (2007) found the indirect effects of childhood neglect and physical abuse on IPV perpetration through youth violence perpetration were significant for both males and females; however, when controlling for youth violence perpetration, neglect/physical abuse remained a strong, direct predictor of IPV perpetration for females but not for males. Notably, they also found that childhood sexual abuse was not significantly associated with youth violence perpetration for either gender and although childhood sexual abuse was the strongest predictor of IPV perpetration for males, it was not significant for females.

Exposure to childhood trauma is a robust risk factor for the development of psychiatric disorders (Kessler et al., 2010) and has been reported to increase the risk of engagement in violence during later life, particularly gender-based (Crombach & Manasse, 2015; Ehrensaft et al., 2003; Fang & Corso, 2007; Stith et al., 2000; Whitfield, Anda, Dube, & Felitti, 2003). A meta-analytical review demonstrated that childhood maltreatment was associated with a two- to threefold increase in risk of IPV victimization and perpetration (Whitfield et al., 2003). Furthermore, studies have documented a link between witnessing family violence as a child (Ehrensaft et al., 2003) and later perpetration. For example, one study found that male perpetrators were 3 times more likely to report more violence in childhood (Roberts, McLaughlin, Conron, & Koenen, 2011). This association may be explained within the context of Social Learning Theory (SLT; Bandura, 1973) whereby, children who witness interparental conflict may learn that these interactions are normal and incorporate such behaviours in adult romantic relationships. From a treatment perspective, understanding the cycle of violence, from victimization to perpetration across the life span, is critical for designing successful prevention interventions (Fang & Corso, 2007).

IPV perpetration has consistently been associated with several psychiatric and personality disorders (Kessler, Molnar, Feurer, & Appelbaum, 2001; Okuda et al., 2015; Taft, Monson, Hebenstreit, King, & King, 2009) highlighting the importance of psychopathology in understanding violent behaviour. A recent study demonstrated that IPV perpetrators were twice as likely as nonperpetrators to have a clinical disorder and personality disorder (Okuda et al., 2015). Other studies show that IPV perpetrators commonly present with traits characteristic of antisocial personality disorder (ASPD) and borderline personality disorder (BPD) (Dutton, Starzomski, & Ryan, 1996; Stuart et al., 2008). Furthermore, posttraumatic stress disorder (PTSD) and depression have also been consistently associated with IPV (Bell & Orcutt, 2009; Kim & Capaldi, 2004; Riggs, Caulfield, & Street, 2000; Shorey, Febres, Brasfield, & Stuart, 2012; Tolman & Bennett, 1990). Research further highlights high rates of co-occurring alcohol and substance misuse in perpetrators of IPV (Riggs et al., 2000; Thomas, Bennett, & Stoops, 2012; Ting, Jordan-Green, Murphy, & Pitts, 2009; Tolman & Bennett, 1990).

Evidence demonstrates high attrition and recidivism rates in IPV treatment programs (Feder & Wilson, 2005), which may be the result of programs treating perpetrators as a homogeneous group (Chiffriller, Hennessy, & Zappone, 2006). However, more recently, it has been recognized that myriad characteristics and treatment needs are associated with IPV perpetrators (e.g., Fowler & Westen, 2011; Stover, Meadows, & Kaufman, 2009). From a treatment perspective, it is important to acknowledge that perpetrators of IPV commonly have complex trauma histories and high rates of both psychiatric and personality disorders. To promote treatment efficacy and reduce recidivism, these factors should be considered when deciding on which treatment approach is better tailored to the individual’s needs. For example, certain personality disorders (e.g., borderline, paranoid, and schizotypal) do not respond well to group therapy (Hamberger & Hastings, 1989); therefore, assessment of personality pathology is an essential component in designing and implementing interventions. Several other factors have been found to be negatively associated with treatment efficacy such as co-occurring substance abuse (Jewell & Wormith, 2010; Ting et al., 2009; Tollefson & Gross, 2006), psychiatric problems (Tollefson, Gross, & Lundahl, 2008), previous criminal records (Hamberger & Hastings, 1989; Jewell & Wormith, 2010; Tollefson et al., 2008), and referral status (Jewell & Wormith, 2010).

In light of these findings, this study aimed to first examine if gender and referral status were associated with childhood and adult risk factors in a sample of IPV perpetrators. The second aim was to estimate the prevalence of personality and psychiatric disorders. Subsequently, demographic (e.g., gender), childhood maltreatment, and adult criminology variables were then examined to determine if these risk factors had common (or specific) effects across the most common personality and psychiatric diagnoses. Based on the previous studies, it was hypothesized that ASPD, BPD, and dependent personality disorders and anxiety and depressive disorders would be more common in perpetrators of IPV. Based on evidence documenting the links between childhood trauma and IPV perpetration, it was also hypothesized that rates of childhood maltreatment (e.g., physical, sexual, and psychological abuse) would be high.

Method

Participants and Procedure

Data were collected from the Dialogue Against Violence (DMV) database of clients undergoing therapy. Therapy is provided by psychotherapists and psychologists and all treatment is free. Clients can be referred by social services, prison, and probation services or by self-referrals. Individual therapy is provided to all clients that consists of cognitive-behaviour therapy and mindfulness. Individual therapy consists of weekly or fortnightly sessions lasting 14 to 26 weeks. Clients can also attend a couples/group therapy session that use IMAGO couples therapy and Emotional Focused therapy, which are offered for 6 to 10 weeks following individual therapy. As part of DMV’s treatment program, therapists routinely collect treatment-relevant information about the clients, to be used both for the course of therapy and for further research. The data used in this study consisted of information taken at the beginning of the treatment and covered a time period of May 2010 and September 2014. The participants in the treatment program completed measures in collaboration with the therapist. Clients are informed that further use of the data is under anonymous terms. The majority of the sample were men (85.9%) with the remaining (14.1%) females, with a mean age of 35.64 (SD = 10.01) years. In addition, 69.5% reported being in a relationship and 67.2% have children. More than half of the perpetrators were employed (58.2%). Clients entering the program were either voluntarily committed to treatment (66.7%) or court mandated (33.3%) by the Prison Service.

Measures

The Millon Clinical Multiaxial Inventory (MCMI)-III

The MCMI-III (Millon, Millon, & Davis, 1994; Millon, Millon, Davis, & Grossman, 2006) is a self-report measure of psychopathology. It consists of 175 true–false questions that measure 14 personality disorder scales and 10 clinical syndromes. This study used the 10 personality disorders that are included in the Diagnostic and Statistical Manual of Mental Disorders (5th ed.; DSM-5; American Psychiatric Association, 2013) which means that the Depressive, Aggressive, Negativistic, and Self-Defeating subscales were excluded from analyses. Prevalence of pathology on the MCMI-III was estimated using base rate (BR) scores that range from 0 to 115. A cut-off score of 75 or more for each of the clinical syndrome scales and personality was used to be indicative of a probable diagnosis. Profiles that did not fulfil the MCMI-III validity criteria were excluded. Psychometric research indicates that the MCMI-III is a reliable and valid diagnostic tool in clinical and general population samples and process of translating and validating the Danish version of the MCMI-III has been well documented (Simonsen & Elklit, 2008).

Participants were also asked to complete a questionnaire that assessed demographic variables (age, gender, relationship status, if they had children, and employment status). Childhood trauma was assessed by the following questions: (a) Have you experienced physical abuse during childhood (below 12 years)? (b) Have you experienced psychological abuse (below 12 years)? and (c) Have you experienced sexual abuse during childhood (below 12 years)? Participants also were asked these questions during adolescence (12-18 years). Three dichotomous variables were created to include physical, psychological, and sexual abuse below the age of 18. Criminal behaviour was assessed in adolescence and adulthood and included a question relating to whether they had received a criminal conviction after the age of 18. Participants were also asked about the type of IPV they committed; whether it involved physical violence, psychological violence, or sexual violence. All questions were answered “yes” (1) or “no” (0).

Analysis

Bivariate analyses were conducted to assess gender differences and referral status differences between childhood trauma and adult criminality variables and the subscales of the MCMI clinical syndromes and personality disorders. A series of binary logistic models were then conducted on the four most commonly reported personality disorders and the three most commonly reported clinical disorders to examine the unique and shared risk of the childhood trauma and criminality variables. All profiles were interpreted in accordance with Millon’s system of BR scores. BR75 were assigned for those who met criteria for the particular disorder or condition. Findings are presented in terms of odds ratios (ORs) and 95% confidence intervals. The subsequent ORs indicate the expected increase/decrease in the likelihood of scoring positively on a given variable compared with the reference or control group, in this case, no psychiatric/personality disorder.

Results

A high proportion of the sample reported experiencing physical (84.8%) and psychological (67.8%) violence during childhood and adolescence and 6.5% reported experiencing sexual violence. More than half (52.4%) of the sample engaged juvenile delinquency, with 59.3% engaging in criminal activity in adulthood, and 51.3% had a criminal conviction. Almost all participants reported perpetrating physical violence (91.4%) against their partner, 69.3% exerted psychological violence, and 3.7% reported sexually abusing their partner.

Table 1 shows the bivariate associations between childhood maltreatment, youth and adult criminal behaviours, and self-reported drug and alcohol problems by gender and referral status. Females were significantly more likely to report childhood sexual abuse and engage in psychological violence against their partner than males, while males were significantly more likely to engage in juvenile criminal behaviour than females. In terms of referral status, there were several notable differences. Court-mandated clients were significantly more likely to report childhood physical abuse, engage in juvenile and adult criminal behaviours, and have a criminal conviction. Furthermore, court-mandated clients were also significantly more likely to report sexual violence perpetration against their partner than voluntary clients. Voluntary clients were significantly more likely to report experiencing childhood sexual abuse than court-mandated clients.

Table 1.

Bivariate Associations for Study Variables by Gender and Referral Status.

	Males	Females	Total	χ² (df) p	Court mandated	Voluntary	Total	χ² (df) p
Physical violence during childhood	306 (85.2%)	43 (82.7%)	349 (84.9%)	0.23 (1) p = .632	118 (92.9%)	221 (81.0%)	339 (84.8%)	9.60 (1) p = .002
Psychological violence during childhood	238 66.3	40 76.9	278 67.6	2.34 (1) p = .126	88 69.3	183 67.0	271 67.8	0.20 (1) p = .653
Sexual violence during childhood	17 (4.7%)	10 (19.2%)	27 (6.6%)	15.56 (1) p = <.001	3 (2.4%)	23 (8.4%)	26 (6.5%)	5.24 (1) p = <.022
Criminal behaviour (12-18)	199 (55.0%)	21 (40.4%)	220 (53.1%)	3.89 (1) p = .049	104 (67.1%)	140 (44.7%)	244 (52.1%)	20.79 (1) p = <.001
Alcohol abuse	30 (8.4%)	7 (13.5%)	37 (9.1%)	1.39 (1) p = .238	22 (14.6%)	20 (6.4%)	42 (9.1%)	8.15 (1) p = .004
Drug abuse	36 (10.1%)	7 (13.5%)	43 10.5%)	0.54 (1) p = .463	29 (19.2%)	22 (7.1%)	51 11.0%)	15.23 (1) p = <.001
Criminal activity more than 18 years	208 (58.4%)	28 (53.8%)	236 (57.8%)	0.39 (1) p = .532	137 (90.7%)	137 (44.1%)	274 (59.3%)	91.76 (1) p = < .001
Criminal conviction more than 18 years	182 (51.1%)	22 (42.3%)	204 (50.0%)	1.41 (1) p = .235	133 (88.1%)	103 (33.1%)	236 (51.1%)	122.87 (1) p = <.001
Type of IPV
Physical violence	323 (90.7%)	49 (94.2%)	372 (91.2%)	0.69 (1) p = .406	141 (93.4%)	282 (90.7%)	423 (91.6%)	0.96 (1) p = .327
Psychological violence	236 (66.3%)	43 (82.7%)	279 (68.4%)	5.64 (1) p = .018	101 (66.9%)	219 (70.4%)	320 (69.3%)	0.60 (1) p = .440
Sexual violence	14 (3.9%)	1 (1.9%)	15 (3.7%)	0.52 (1) p = .472	13 (8.6%)	4 (1.3%)	17 (3.7%)	15.38 (1) p = <.001

Note. Significant effects in bold. IPV = intimate partner violence.

The prevalence of any psychiatric disorder (using the BR75 criteria) was high at 69.8% (M = 1.60, SD = 1.56, range = 0-8) and personality disorder 76% (M = 1.71, SD = 1.43, range = 0-7). Bivariate analyses indicated that 87.2% of those who met the threshold for any clinical disorder also met the criteria for any personality disorder and this finding was significant, χ²(1) = 77.65, p < .001; OR = 6.73. Table 2 shows the prevalence of probable clinical diagnosis for each of the MCMI clinical scales. For the overall sample, the most common disorder was anxiety (56.1%) followed by dysthymic disorder (28.5%), and alcohol dependence (25.2%). Clinical rates of delusional disorder (3.7%), somatoform disorder (5.1%), and bipolar disorder (5.7%) were relatively low. Bivariate analyses indicated that males were significantly more likely to meet the criteria for dysthymic disorder and alcohol dependence, while females had higher rates of major depression and delusional disorder. There were no further significant gender differences with regard to remaining disorders. In terms of referral status, the most common disorder was anxiety and least common was delusional disorder for both court-mandated and voluntary clients. Table 2 further indicates that court-mandated clients were significantly more likely to report probable anxiety disorders, drug dependence, and delusional disorder.

Table 2.

Prevalence of Personality Disorders by Gender and Referral Status.

Personality disorders	Females	Males	Total	χ² (df) p	Court mandated	Voluntary	Total	χ² (df) p
Schizoid	8 11.1%	88 21%	96 19.5%	3.79 (1) p = .052	28 (21.4%)	48 (19.1%)	76 (19.9%)	0.27 (1) p = .601
Avoidant	8 11.1%	111 26.4%	119 24.2%	7.86 (1) p = .005	30 (22.9%)	66 (26.3%)	96 (25.1%)	0.57 (1) p = .468
Dependent	25 34.7	131 31.2%	156 31.7	0.354 (1) p = .552	32 (24.4%)	88 (35.1%)	120 (31.4%)	4.52 (1) p = .034
Histrionic	22 30.6%	9 2.1%	31 6.3%	84.04 (1) p = <.001	4 (3.1%)	17 (6.8%)	21 (5.5%)	2.29(1) p = .130
Narcissistic	12 16.7%	73 17.4%	85 17.3%	0.02 (1) p = .882	28 (21.4%)	36 (14.3%)	64 (16.8%)	3.05 (1) p = .081
Antisocial	9 12.5%	150 35.7%	159 32.3%	15.14 (1) p = < .001	55 (42.0%)	64 (25.5%)	119 (31.2%)	10.91 (1) p = .001
Obsessive-compulsive	5 6.9%	0 -	5 1%	29.46 (1) p = <.001	0 -	2 (0.8%)	2 (0.5%)	1.05 (1) p = .306
Schizotypal	4 5.6%	26 6.2%	30 6.1%	0.043 (1) p = 835	11 (8.4%)	9 (3.6%)	20 (5.2%)	4.02 (1) p = .045
Borderline	16 22.2%	91 21.7%	107 21.7%	0.011 (1) p = .916	31 (23.7%)	49 (19.5%)	80 (20.9%)	0.89 (1) p = .345
Paranoid	10 13.9%	47 11.2%	57 11.6%	0.437 (1) p = .509	20 (15.3%)	17 (6.8%)	37 (9.7%)	7.10 (1) p = .008

Note. Significant effects in bold.

Table 3 shows the prevalence of the MCMI personality domains. For the overall sample, the most common disorder was ASPD (32.3%) followed by dependent (31.7%) and avoidant (24.2%). Clinical rates of obsessive compulsive (OC; 1%), schizotypal (6.1%), and histrionic (6.3%) were relatively low. The chi-square statistics indicated that males were significantly more likely to meet the criteria for avoidant and ASPD, while females had higher rates of OC and histrionic. No further gender differences emerged with regard to remaining personality disorders. In terms of referral status, the most commonly endorsed personality disorder for court-mandated clients was ASPD and the least commonly reported was OC with none of this group meeting a probable diagnosis for this disorder, followed by schizotypal disorder. For voluntary clients, the most common disorders were ASPD and dependent and the least common were OC and schizotypal. Table 3 further shows that referral status did differentially predict a probable personality disorder with voluntary clients more likely to report dependent personality types and court mandated were more likely to report ASPD and paranoid subtypes.

Table 3.

Prevalence of Clinical Syndromes by Gender and Referral Status.

Clinical syndromes	Female	Male	Total	χ² (df) p	Court mandated	Voluntary	Total	χ² (df) p
Anxiety	38 (52.8%)	238 (56.75)	276 (56.1%)	0.377 (1) p = .539	82 (62.6%)	127 (50.6%)	209 (54.7%)	5.00 (1) p = .025
Somatoform	6 (8.3%)	19 (4.5%)	25 (5.1%)	1.85 (1) p = .174	7 (5.3%)	13 (5.2%)	20 (5.2%)	0.01 (1) p = .945
Bipolar	5 (6.9%)	23 (5.5%)	28 (5.7%)	0.247 (1) p = .619	9 (6.9)	13 (5.2%)	24 (6.3%)	0.12 (1) p = .732
Dysthymia	11 (15.3%)	129 (30.7%)	140 (28.5%)	7.19 (1) p = .007	35 (26.7%)	74 (29.5%)	109 (28.5%)	0.32 (1) p = .570
Alcohol dependence	4 (5.6%)	120 (28.6%)	124 (25.2%)	17.27 (1) p = <.001	40 (30.5%)	61 (24.3%)	101 (26.4%)	1.72 (1) p = .190
Drug dependence	9 (12.5%)	57 (13.6%)	66 (13.4%)	0.061 (1) p = .805	24 (18.3%)	19 (7.6%)	43 (11.3%)	9.96 (1) p = .002
PTSD	8 (11.1%)	37 (8.8%)	45 (9.1%)	0.39 (1) p = .531	15 (11.5%)	15 (6.0%)	30 (7.9%)	3.57 (1) p = .059
Thought disorder	5 (16.9%)	25 (6.0%)	30 (6.1%)	0.10 (1) p = .745	10 (7.6%)	17 (6.8%)	27 (7.1%)	0.10 (1) p = .755
Major depression	12 (16.7%)	23 (5.5%)	35 (7.1%)	11.65 (1) p = .001	13 (9.9%)	13 (5.2%)	26 (6.8%)	3.06 (1) p = .081
Delusional disorder	6 (8.3%)	12 (2.9%)	18 (3.7%)	5.23 (1) p = .022	9 (6.9%)	3 (1.2%)	12 (3.1%)	9.11 (1) p = .003

Note. Significant effects in bold. PTSD = posttraumatic stress disorder.

The results from the binary logistic regression analyses for the four most common personality disorders are presented as ORs in Table 4. Findings indicated that the presence of any MCMI clinical disorder was strongly associated with ASPD, dependent, avoidant, and BPD. The strongest effect was for BPD (OR = 10.04) and weakest effect was for avoidant (OR = 3.25). The only other risk factor associated with a personality disorder was being of male gender, which conferred a threefold increase in risk of ASPD. Table 5 outlines the results for the three most common clinical disorders. The presence of at least one personality disorder was associated with an increased odds of having an anxiety disorder, dysthymia, and alcohol dependence with the strongest effect for dysthymia (OR = 6.48) and weakest for alcohol dependence (OR = 2.92). Being male was a shared risk factor for both dysthymia and alcohol dependence (OR = 3.51 and 6.64), respectively. Finally, involvement in adult criminal behaviour was a unique risk factor for alcohol dependence.

Table 4.

Binary Logistic Regression Results for Childhood and Adult Predictors of Personality Disorder.

	Antisocial		Dependent		Avoidant		Borderline
	OR	95% CI	OR	95% CI	OR	95% CI	OR	95% CI
Gender	3.26*	[1.26, 8.43]	0.72	[0.36, 1.44]	1.76	[0.76, 4.05]	0.56	[0.26, 1.23]
Physical abuse	1.19	[10.39, 3.66]	1.15	[0.43, 3.07]	1.07	[0.38, 3.01]	1.95	[0.70, 5.41]
Psychological abuse	0.80	[0.33, 1.15]	1.02	[0.46, 2.28]	1.55	[0.67, 3.56]	1.75	[0.58, 5.21]
Sexual abuse	0.77	[0.40, 1.49]	1.01	[0.54, 1.89]	0.62	[0.34, 1.15]	0.87	[0.42, 1.81]
Juvenile crime	1.53	[0.84, 2.80]	0.63	[0.35, 1.11]	1.26	[0.69, 2.28]	0.96	[0.50, 1.86]
Adult crime	2.18	[0.77, 6.22]	0.68	[0.27, 1.77]	0.97	[0.36, 2.59]	1.69	[0.56, 5.10]
Adult conviction	1.98	[0.76, 5.16]	0.81	[0.32, 2.26]	0.58	[0.33, 2.21]	1.60	[0.58, 4.42]
Court mandated	1.02	[0.57, 1.82]	1.23	[0.68, 2.26]	1.26	[0.69, 2.32]	1.37	[0.72, 2.60]
Clinical disorder	4.35***	[2.21, 8.59]	7.63***	[3.90, 14.93]	3.52***	[1.85, 6.69]	10.04***	[3.47, 29.03]

Note. OR = odds ratio; CI = confidence interval.

p < .05. **p < .005. ***p < .001.

Table 5.

Binary Logistic Regression Results for Childhood and Adult Predictors of Psychological Disorders.

	Anxiety		Dysthymia		Alcohol abuse
	OR	95% CI	OR	95% CI	OR	95% CI
Gender	0.78	[0.39, 1.53]	3.51*	[1.32, 7.54]	6.64**	[1.95, 22.64]
Physical abuse	1.42	[0.53, 3.77]	0.94	[0.32, 2.72]	0.48	[0.13, 1.84]
Psychological abuse	1.04	[0.51, 3.77]	1.88	[0.81, 4.33]	1.06	[0.45, 2.52]
Sexual abuse	1.66	[0.94, 2.92]	1.10	[0.59, 2.04]	1.27	[0.67, 2.43]
Juvenile crime	1.37	[0.81, 2.34]	0.89	[0.49, 1.62]	0.76	[0.41, 1.41]
Adult crime	1.87	[0.72, 4.89]	0.88	[0.31, 2.50]	4.55**	[1.62, 12.77]
Adult conviction	0.62	[0.24, 1.60]	1.22	[0.45, 3.35]	0.71	[0.28, 1.83]
Court mandated	0.64	[0.36, 1.12]	1.51	[0.63, 2.10]	1.12	[0.62, 2.05]
Personality disorder	4.18***	[2.46, 7.11]	6.48***	[2.98, 14.11]	2.92***	[1.46, 5.81]

Note. OR = odds ratio; CI = confidence interval.

p < .05. **p < .005. ***p < .001.

Discussion

The overarching aim of the current study was to assess the prevalence of psychiatric and personality disorders and to examine the associations between childhood trauma, gender, and adult criminal behaviour in a large sample of IPV perpetrators engaged in a treatment program. First, consistent with our hypothesis, exposure to child maltreatment was high in the current sample for both males and females. This finding supports a large evidence base documenting the links between adverse childhood experiences and IPV perpetration and victimization in adulthood (Crombach & Manasse, 2015; Ehrensaft et al., 2003; Fang & Corso, 2007; Stith et al., 2000; Whitfield et al., 2003). In terms of gender differences, males were more likely to have a probable diagnosis of schizoid, avoidant, and ASPD while females were more likely to have OC and histrionic personality types. For psychiatric disorders, males were more likely to report dysthymia and alcohol dependence and females were more likely to report major depressive disorder (MDD) and delusional disorder. Furthermore, being of male gender remained a significant predictor of ASPD, dysthymia, and alcohol dependence compared with those without these personality disorders. This is consistent with studies documenting significantly higher rates of depression (Shorey et al., 2012; Stuart, Moore, Kahler, & Ramsey, 2003), ASPD (Fazel & Danesh, 2002), and alcohol abuse (Stuart et al., 2003; Stuart, O’Farrell, & Temple, 2009; Taft et al., 2009) in male perpetrators.

Findings further revealed several differences between court-mandated and voluntary clients with court-mandated clients demonstrating higher rates of childhood trauma, drug, and alcohol misuse and involvement in juvenile and adult criminal behaviour. Bivariate associations indicated that court-mandated clients were more likely to have a probable ASPD and paranoid PD whereas voluntary clients were more likely to have a dependant PD. This finding was not unexpected as it is plausible to speculate that those individuals who are court mandated to attend treatment have committed more serious offenses. Furthermore, this finding compliments a study that found ASPD and paranoid PD (50.4% and 22.6%, respectively) were the most common personality disorders in a prisoner population (Coid et al., 2009). However, in the multivariate logistic regression models, referral status was not significantly associated with a probable personality or clinical diagnosis relative to those without the disorders.

The results indicated a high prevalence of MCMI-III personality diagnoses which is consistent with evidence linking IPV perpetration to a wide range of personality disorders (Dutton, Saunders, Starzomski, & Bartholomew, 1996; Stuart et al., 2008). Notably, there was considerable variation in personality pathology, which supports previous research highlighting the diversity in personality profiles of perpetrators of IPV (Craig, 2003). The prevalence of personality disorders varies substantially with higher rates observed in health care settings and particularly within the criminal justice system (Tyrer, Reed, & Crawford, 2015). Indeed, a systematic review indicated that 65% of male and 42% of female prisoners were diagnosed with a personality disorder (Fazel & Danesh, 2002). Variation in prevalence estimates may also be the result of different personality instruments and poor diagnostic reliability (Tyrer, 2015). For example, some studies that have used the MCMI have applied different analytical approaches such as using mean scores (Dutton et al., 1994) rather than the intended BR threshold with the former likely inflating prevalence estimates. However, one study that used the BR75 to examine personality pathology in a treatment sample of male perpetrators found relatively similar prevalence rates to the current findings (Gibbons, Collins, & Reid, 2011). Furthermore, other studies have also demonstrated that Cluster B symptoms, particularly ASPD and BPD traits, are more prevalent in IPV perpetrators (Dutton et al., 1996; Gibbons et al., 2011; González, Igoumenou, Kallis, & Coid, 2016; Holtzworth-Munroe & Stuart, 1994; Stuart et al., 2008).

In examining a range of preestablished risk factors associated with personality disorders, the strongest and consistent effect was the presence of a psychiatric disorder. The magnitude of this effect was particularly strong for BPD, which was associated with a tenfold increase in risk of meeting the threshold of probable disorder. Likewise, the presence of a personality disorder was the strongest shared factor for the three most prominent clinical conditions—anxiety, dysthymia, and alcohol dependence. This is consistent with evidence demonstrating personality disorders, especially when severe, are often associated with co-occurring mental health disorders (McGlashan et al., 2000; Newton-Howes et al., 2010). For example, Newton-Howes and colleagues (2010) found that 40% of patients accessing community mental health services had a co-occurring personality disorder. Furthermore, patients with a personality disorder compared with patients without a personality disorder, had an increased likelihood of co-occurring depression (OR = 6.15), anxiety (OR = 5.20) and alcohol dependence (OR = 2.62). Despite wide recognition of the comorbidity between clinical and personality disorders, clinical symptoms tend to dominate the treatment plan and evidence suggests that personality disturbance may be a contributing factor to recurrence of common mental health disorders (Tyrer et al., 2015). These findings suggest personality assessment is vital in treatment efficacy as it greatly affects the interaction between health professionals and the individual and has been associated with poorer outcomes, treatment response, and premature mortality (Tyrer et al., 2015).

The present study should be considered in light of the following methodological limitations. First, findings are based on retrospective self-report questions, which may lead to under- or overreporting when assessing sensitive topics, for example, substance abuse and childhood maltreatment as they may be disrupted by recall biases. Second, the questionnaire is filled out in cooperation with a therapist, which could lead to social desirability biases. Third, there was an overrepresentation of males in the current study and a higher proportion of voluntary clients than court-mandated clients, therefore, interpretation of these findings should be considered with this in mind. Finally, the interviews required clients to identify childhood maltreatment using single item questions with yes/no response, therefore, replication using standardized measures of childhood maltreatment would be beneficial.

In conclusion, the present study assessed the prevalence of clinical psychological and personality disorders in a large sample of IPV perpetrators. Overall, the prevalence of MCMI disorders was relatively high in this sample, particularly for Anxiety disorder and Antisocial PD. This study also found that experiencing maltreatment in childhood was high for all IPV perpetrators. The presence of any psychiatric disorder was strongly associated with ASPD, BPD, Dependent, and Avoidant personality disorders (relative to perpetrators without the disorder). Likewise, the presence of at least one personality disorder had the largest specific effects on a probable diagnosis of Anxiety, Dysthymia, and Alcohol dependence disorders. The current findings therefore support the present literature, which indicates that the one-size-fits-all model of IPV perpetrators seems to be insufficient (e.g., Cantos & O’Leary, 2014). Results suggest that practitioners are dealing with a prominently vulnerable group of people with diverse and complex trauma histories and co-morbid psychological and personality disorders. From a treatment perspective, these findings highlight the need to acknowledge individual profile assessment needs of IPV perpetrators and tailor treatments that will more effectively target differing personality pathologies and trauma histories to ensure optimal outcomes.

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) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was partially funded by Dialogue Against Violence, Copenhagen, Denmark.

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