Antisocial personality disorder

INTRODUCTION

The reoccurring association between antisocial personality disorder (APD), substance abuse, and crime has stimulated treatment outcome evaluations for substance abusers with this disorder. Estimates of APD range from 25 to 54% among treatment-seeking opioid abusers (1), from 30 to 40% of therapeutic community (TC) admissions (2), and from 16 to 49% of alcoholism treatment program admissions (3). The essential feature of APD is a consistent pattern of disregard for the rights of others (4). People with APD have no inhibitions about committing antisocial acts and have no fear of impending punishments (5).

There is a general consensus among treatment providers and some social scientists that persons with APD, by nature of their disorder, are less likely to change behaviors and more likely to relapse to both substance abuse and high risk behaviors (6-8). However, recent empirical research has not supported this hypothesis. Findings from treatment evaluations for substance abusers with APD indicate that its diagnosis does not appear strongly related to outcomes (9-13).

Gill et al. (12) compared the treatment outcomes of 55 consecutive methadone maintenance admissions with and without APD. Forty-two percent (n = 23) were diagnosed with APD using the National Institute of Mental Health Diagnostic Interview Schedule (DIS-III). The DIS-III is a fully structured interview focusing on observable behavioral traits to diagnose APD and is highly correlated with the Diagnostic and Statistical Manual of Mental Disorders (DSMIII-R) (14). Although the findings are limited by the small sample, no significant differences were found between those with and without APD on any 12-month treatment outcome variables, including treatment retention.

Cacciola et al. (10) reported the outcomes of APD patients admitted to a 28-day inpatient or day-treatment program for veterans. The authors conducted a short-term (7-month) outcome evaluation of 224 men with alcohol and cocaine dependency. Thirty-four percent were diagnosed with APD (n = 77) using the DIS-III. Those with APD reported more cocaine and alcohol use at baseline than those without APD. However, participants responded similarly and positively to the treatment in a number of areas at follow-up, including reductions in substance use, regardless of APD diagnosis. Moreover, those with APD experienced a greater degree of improvement in the family/social domain compared with non-APD subjects. The authors suggested that a diagnosis of APD does not predict short-term treatment response.

Compton et, al. (11) compared the 18-month outcomes of 333 cocaine users with and without APD participating in an HIV risk-reduction intervention. Thirty-four percent (n = 113) were diagnosed with APD using the DIS-III. The authors found similar drug-related improvements in HIV risk behaviors (e.g., drug use, drug injection, and needle sharing) for the entire group, regardless of a diagnosis of APD. The authors suggest that cocaine users with APD respond to HIV risk-reduction intervention just as much as those without APD even though they may be at a higher risk for development of such behaviors.

Brooner et al. (9) reported preliminary results that also support the viability of reducing both heroin and cocaine use in antisocial opioid abusers in methadone substitution therapy. Clients were administered the Structured Clinical Inventory for the DSM-III-R (SCID-II). The SCID-II is a semistructured interview for making Axis II diagnoses using DSM-III-R classifications. The SCID-II symptom criteria for a diagnosis of APD, like the DIS-III, focuses predominately on behavioral patterns (15). Those diagnosed with APD (n = 40) were randomly assigned to an experimental (i.e., methadone substitution therapy with a structural behavioral intervention using positive and negative contingencies) or control condition (i.e., standard methadone substitution therapy). Both groups showed reductions in drug use during the 17-week preliminary outcome evaluation. The authors contend that these findings are not only contrary to what is commonly thought about APD clients in traditional treatment, but enhanced treatment programs as well. However, the study was limited by its small sample and by the absence of a non-antisocial control group.

The next study overcame some of the weaknesses mentioned previously (i.e., small sample sizes or short-term outcome analyses). Messina et al. (13) compared the treatment outcomes of 338 (predominately cocaine dependent) substance-abusers who were randomly assigned to two TC programs differing primarily in length (10months inpatient with 2months outpatient treatment vs. 6months inpatient with 6 months outpatient). Clients were administered the SCID-II after a three week stabilization period. Clients diagnosed with APD (n = 166) were compared to 172 clients with no APD on three treatment outcomes; treatment completion (both inpatient and outpatient treatment), drug use at 12-month follow-up, and post-discharge arrest. Using logistic regression analyses to control for relevant factors, the authors found that clients with APD were as likely to complete TC treatment as other clients and they exhibited the same patterns of reduced drug use and criminal activity as did non-APD clients.

The findings described previously raise interesting questions about the association of APD and treatment outcomes. Is the general consensus of the field incorrect or is there some problem with the predominately behavioral classifications for a DSM diagnosis of APD? The concept of psychopathy offers an alternative view of APD that includes both antisocial behavior and psychopathic personality traits such as total self-centeredness, lack of remorse, empathy, or guilt (16). Fortunately, the Messina et al. (13) study had two very different measures of APD at baseline, the SCID-II and the Millon Clinical Multiaxial Inventory, second edition (MCMI-II), thus affording an opportunity for the present research to pursue this topic further.

The MCMI-II is a self-report inventory consisting of 175 true/false items designed to assess basic personality styles, severe personality disorders, and clinical syndromes (17). The MCMI-II focuses on capturing underlying pathological personality traits to indicate the presence of APD (18). Raw scores are converted into Base Rate (BR) scores prevalence rates of the disorder (19). The MCMI-II has been used previously with substance-abusing populations to identify a substance-abuse problem, to assess the personality of the known abuser, or to predict treatment response (20). Several reviews have reported good to excellent reliability and validity of the MCMI-II's assessment of APD in substance-abusing populations (17,19,21-24).

The current study extends the prior study by Messina and associates (13) in the same TC population using the MCMI-II diagnosis of APD to further explore the relationship of this disorder to TC treatment outcomes. Preliminary findings from the current study showed that the prevalence rates of APD diagnosed by the MCMI-II and the SCID-II for this sample were different. The MCMI-II estimated a higher prevalence rate of APD than the SCID-II counterpart (68 vs. 47% of the 275 clients assessed with both instruments) (15).

In light of the findings of Messina et al. (13) regarding treatment outcomes for substance abusers with APD, SCID-II diagnosis of APD is possible, using primarily behavioral traits as measured in the DSM, has little utility in predicting treatment outcome among substance abusers with substantial criminal histories. The MCMI-II's focus on the underlying pathological personality traits may generate an APD group that is more consistent with prior theorists beliefs about this disorder and response to treatment. Thus, we hypothesize that clients diagnosed with APD via the MCMI-II will be less likely to complete treatment and more likely to test positive for drugs at follow-up and to have at least one post-discharge arrest, compared with clients without APD.

METHODS

The data used in the first study (13) were analyzed again for the second study, with one exception. The SCID-II diagnosis of APD was replaced by the MCMI-II diagnosis of APD. Data came from the District of Columbia Treatment Initiative (DCI), a large drug-abuse treatment outcome experiment (25). Clients (n=412) were randomly assigned to two TC treatment programs, called the "Standard Inpatient" or "Abbreviated Inpatient" programs. The standard inpatient program was designed to reflect TC treatment that was customarily available in the United States, consisting of approximately 10 months of inpatient care followed by two months of outpatient care. The abbreviated inpatient program provided six months of inpatient care, six months of outpatient care and a wide range of extra services. Both programs were managed by Second Genesis, Incorporated.

Subjects

This study analyzes the data from a subsample of 314 DCI clients who were administered both the SCID-II and the MCMI-II at treatment admission. Of these 314 clients, 39 cases were scored as invalid via the computer-generated scoring system for the MCMI-II and were subsequently excluded, leaving a final subsample of 275 clients. Of these, 136 clients had been randomly assigned to the standard inpatient program and 139 were in the abbreviated inpatient program.

As part of the original DCI study, 93% of the sample were successfully reinterviewed approximately 19months after treatment discharge. Clients interviewed ranged in age from 20 to 54years, with a mean age of 32.8. Approximately 72% of the sample are male and the majority are black (98%). Clients completed an average of 11 years of education and 67% had never been married. Ninety-two percent of the sample had a history of prior arrest, with an average of 8.6 adult arrests prior to treatment admission. This was a primarily cocaine-abusing sample, 47% of the clients were diagnosed with cocaine/crack as their primary drug disorder and 46% had problems with both cocaine and heroin.

Procedure

People who sought treatment at the Central Intake Division (CID) run by the D.C. alcohol and Drug Abuse Services Administration (ADASA) or who were ordered by the court to obtain treatment were eligible to volunteer to participate in the DCI (25).

Extensive personal interview and psychometric data were collected from each respondent at admission. Clients who had an appropriate reading grade level (at least sixth grade) were administered a battery of psychological tests which included the SCID-I, SCID-II, and the MCMI-II.

Initial testing for the MCMI-II did not begin until two weeks postadmission in order to allow for detoxification and stabilization of mental status. For this study, APD was diagnosed with a BR score of 75 or greater. MCMI-II diagnostic assignments usually begin with a BR score exceeding 70. Scores of 75-84 suggest a chronic and moderately severe level of personality functioning. While BR scores of 85 and above are often indicative of a more decompensated personality pattern (i.e., the most severe levels of APD).

Urine specimens (n = 206) were collected immediately after the follow-up interviews and were used to measure drug use during the three days prior to the follow-up interview. Urine specimens were not collected from 43 clients who were incarcerated at the time of the follow-up interview, from five clients who were interviewed over the phone, or from one client who refused. Criminal arrest record information was obtained from local jurisdictions to gain a measure of recidivism.

RESULTS

The MCMI-II was administered to 275 DCI clients at treatment admission. The MCMI-II diagnosed 68% of the sample with APD, and 32% with no APD. Bivariate analyses revealed that there were no differences between clients with "no disorder" and clients with "disorders other than APD", with regard to treatment completion, drug use at follow-up, and post-discharge arrest. Therefore, the "no APD" group combines 11 clients with "no disorders" and 77 clients with "other disorders excluding APD". Because it is difficult to know the full association between other disorders and the three outcome variables, all subsequent regression models control for the existence of other Axis I and Axis II disorders for the APD and the no APD group.

To assess the construct validity of the MCMI-II diagnosis, APD and no APD clients were compared on measures of deviant behavior from interviews and official records (see Table 1). In addition, APD clients with BR scores of 85 or greater are shown separately to assess whether they exhibit the expected patterns of the most severe deviant behaviors. Clients with the most severe levels of APD were first arrested at a younger age compared with the no APD group (mean age = 18.9 years vs. 22.0, p < 0.01). Clients with the most severe levels of APD were more likely to be under some form of criminal justice supervision than those with moderately severe levels of APD, but not those with no APD (73 vs. 52 vs. 73%, p < 0.05). All groups of clients were likely to have multiple prior adult arrests and multiple drug dependencies. There were no differences between the groups with regard to primary drug disorder, use of needles, or age first used alcohol or marijuana.

With a few exceptions, the above findings do not show substantial differentiation between the groups with regard to deviant behaviors. It is apparent from these findings that the entire sample has had extensive criminal and substance abusing histories throughout their late teen and early adult years. However, it is the existence of childhood antisocial behaviors and pathological personality traits that should truly differentiate the APD clients from the no APD clients. Unfortunately, the data does not allow for these comparisons.

Antisocial Personality Disorder and Treatment Outcomes

Bivariate associations between a diagnosis of APD and the three outcome variables (e.g., treatment completion, recent drug use, and post-discharge arrest) were assessed via Chi-Square analysis, by treatment program. There were no significant differences in treatment completion, drug use at follow-up, or post discharge arrest, between APD and no APD clients in the two treatment programs. Forty-two percent of the total sample completed the treatment, 38% tested positive for drugs at follow-up, and 47% obtained a post-discharge arrest.

The bivariate relationships above do not take into account the effects of other factors predictive of treatment outcomes that might mask the effect of APD. Several other variables were previously examined for an association with the three outcome variables (13). Pre-admission factors that were related to one or more of the three outcome variables included gender, marital status, age, criminal justice status at admission, total number of prior adult arrests, primary drug disorder, number of drugs dependent, and treatment program attended. Education, past use of needles, and treatment referral sources were not associated significantly with any of the three outcome variables and were excluded from the regression analyses. Race/ethnicity was not examined because 98% of the sample are black. The next section tests each of the hypotheses regarding the relationship of APD to treatment outcomes, while controlling for other factors related to these outcomes through logistic regression analyses.

Hypothesis A: Antisocial Personality Disorder and Treatment Completion

Contrary to our hypothesis, after controlling for related variables, an MCMIII diagnosis of APD was not significantly related to treatment completion (see Table 2). Clients with APD were as likely to complete treatment as those without APD. In fact, the only variable in the model that was related to completing treatment was criminal justice status at admission. Being under some form of criminal justice supervision increased the odds of completing treatment by 88% (p = 0.05).

Time in treatment has been shown to be a strong predictor of treatment outcomes (26). We therefore first looked at the relationship of APD to a positive urinalysis and post-discharge arrest, while controlling for treatment completion. We found no differences between APD and non-APD clients with regard to drug use and recidivism. However, as expected, persons who completed treatment were less likely to test positive for drugs (28 vs. 55%,p < 0.01) and to recidivate at follow-up (29 vs. 62%, p < 0.01). The effect of treatment completion was so great that we tested our remaining hypotheses (below) by including in our models a variable that coded the four possible combinations of treatment completion and APD diagnosis. This variable allowed us to test, for example, whether APD clients who completed treatment had similar outcomes as no APD clients who completed treatment.

Hypothesis B: Antisocial Personality Disorder and Positive Urinalysis at Follow-Up (for Any Drug)

The only significant predictor of a positive urinalysis at follow-up was treatment completion for APD clients. Clients with an MCMI-II diagnosis of APD who completed treatment reduced their odds of testing positive for drugs at follow-up by 81%, compared with clients with no APD who dropped out of treatment (p < 0.01). Non-APD clients who completed treatment and APD clients who dropped out of treatment had similar patterns of drug use at follow-up as non-APD clients who dropped out (see Table 3).

Hypothesis C: Antisocial Personality Disorder and Post Discharge Arrest

Because the time between treatment discharge and the cutoff date for obtaining criminal justice record information varied considerably among the sample members, time at risk for arrest was controlled by adding a variable to the model that reflected the number of months between client discharge and the last date for which arrest information was obtained from criminal records (average = 25.3 months; ranging between 8 and 43 months). Table 4 shows that clients with or without APD who completed treatment both had marked reductions in the odds of a post discharge arrest. In fact, clients with no APD who completed treatment reduced the odds of an arrest by 82% and clients with APD who completed treatment reduced the odds of an arrest by 78%, compared with clients with no APD who dropped out of treatment (p < 0.01). Clients with APD who dropped out had similar arrest patterns as those with no APD who dropped out. Moreover, for each one year increase in age and each additional drug dependency, the odds of arrest were reduced by 10 and 23% (p < 0.05). Being under some form of criminal justice supervision at admission and attending the abbreviated inpatient program increased the odds of a post-discharge arrest by 198 and 97% (p < 0.05).

DISCUSSION

We hypothesized that clients diagnosed with APD by the MCMI-II would have poorer treatment outcomes than those with no APD. This hypothesis was not supported. A MCMI-II diagnosis of the APD was generally unrelated treatment outcomes. This finding suggests that persons who have been diagnosed with extreme personality disorder did as well in TC treatment as those without this diagnosis. Moreover, findings are consistent with the previous DCI study using the SCID-II diagnostic instrument to assess APD (13).

Treatment completion again appeared to be the most important correlate of reduced drug use and post-discharge arrests, regardless of a diagnosis of APD. Forty-two percent of the entire sample completed the treatment. This completion rate exceeds those found by previous studies which have reported completion rates generally ranging from 7 to 15% (27). The association between treatment completion and reductions in drug use and criminal involvement are consistent with findings from DeLeon (26) and other research which has shown that posttreatment success is closely related to a client's completion of the program. Yet, the effectiveness of TC treatment and its relationship to outcomes is a complicated issue. For example, it is not known whether it is the duration of treatment, patient compliance, or the dose of specific services received that is the essential operating factor here. Post-treatment outcome measures such as drug use and criminal activity can be influenced by many factors over time, including participation in additional treatment programs or simply individual maturation.

The high client completion rates within these particular TC treatment programs may have been due to the black majority of this sample. It has been reported that blacks tend to stay in TC treatment programs longer compared to other treatment modalities (28). The cultural norms of the TC program are often defined by the racial majority in the program (29). This sample consisted of D.C. residents in D.C. community-based treatment programs. The cultural similarities between the clients, the staff, and the surrounding area may have contributed to the overall completion rates within this study. Perhaps the cultural and social factors were more important in determining outcomes than a diagnosis of APD in this TC sample.

Some limitations of this study should be kept in mind when interpreting the results. First, because 98% of the sample are black clients, examining the racial or ethnic correlates of the MCMI-II diagnosis was not possible. Thus, the generalizability of the findings regarding diagnosis and prevalence rates of APD to more ethnically diverse substance-abusing populations is limited. Second, this study population was a highly homogeneous sample. Both clients with and without APD had substantial histories of drug abuse and crime, making distinctions regarding diagnosis and treatment outcomes between the two groups very difficult. In addition, clients with no APD may have been diagnosed with various other Axis I and Axis II disorders. Although the various combinations of other disorders were not related to treatment outcomes, it is difficult to know the degree to which they confounded the distinction between those with and without APD. Third, the extent to which these treatment outcome findings apply to other TC programs or non-TC populations is not known. The level of success and its correlates may be different for TCs run by other program specialists or for samples of clients receiving treatment in other modalities.

Findings from the current study are in contrast to the previous beliefs about this disorder and its association to treatment outcomes, but are consistent with the existing empirical evidence surrounding this relationship. Perhaps TC treatment programs are becoming better equipped to deal with the disordered and criminal populations entering treatment today. It can be concluded that persons diagnosed with APD, with histories of substantial drug abuse and criminality, can benefit from TC treatment and aftercare or at the very least, do as well as those with no APD. In light of the high prevalence rates of APD in substance-abusing populations, future research should continue to explore the many issues surrounding the diagnosis of APD, as well as its relationship to treatment outcomes. The finding that clients with APD can benefit from the TC experience needs to be replicated in other TC programs.

ACKNOWLEDGMENTS

The District of Columbia Treatment Initiative (DCI) was a cooperative agreement among the D.C. Alcohol and ADASA, the national institute on drug abuse (NIDA), through a contract from Caliber Associates, the Center for Substance Abuse Treatment (CSAT), Koba Associates, Inc., in collaboration with the Research Triangle Institute (RTI), the Institute for Behavior Resources (IBR), and Second Genesis, Inc.

We are grateful to the staff and clients at Second Genesis for their participation. We also appreciate the assistance of Jerome Jaffee, Barry Brown, Herman Diesenhaus, Gary Palsgrove, John Carver, Samuel Karson, Robert Gesumaria, and the Addiction Prevention and Recovery Administration (APRA). Finally, special thanks to the interviewers who conducted the follow-up interviews, who worked day and night to locate the clients. Without the cooperation of all of these parties, this study would not have been possible.

* Corresponding author. E-mail: nmessina@ucla.edu

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Nena P. Messina, (1), * Eric D. Wish, (2) Jeffrey A. Hoffman, (3) and Susanna Nemes (3)

(1) UCLA Drug Abuse Research Center, 11050 Santa Monica Blvd., Suite 150, Los Angeles, CA 90025

(2) Center for Substance Abuse Research (CESAR), University of Maryland, College Park, Maryland

(3) Danya International Incorporated, Silver Spring, Maryland

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