Validity of three measures of antisociality in predicting HIV risk behaviors in methadone-maintenance patients

Drug and Alcohol Dependence 47 (1997) 99 – 107

Validity of three measures of antisociality in predicting HIV risk behaviors in methadone-maintenance patients Karen Tourian a,b,*, Arthur Alterman a,b, David Metzger a,b, Megan Rutherford a,b, John S. Cacciola a,b, James R. McKay a,b a

Uni6ersity of Pennsyl6ania, Treatment Research Center, 3900 Chestnut Street, Philadelphia, PA 19104, USA b Philadelphia Veterans Affairs Medical Center, Philadelphia, PA, USA Received 22 November 1996; accepted 21 May 1997

Abstract Most opiate users are injection drug users (IDUs). A significant percentage of IDUs have antisocial personality disorder (APD). APD has been found by some researchers to be an additional risk factor for human immunodeficiency virus (HIV) infection in IDUs. The present study evaluated the association of sociodemographic characteristics, substance abuse history, and several measures of antisociality including the DSM-III-R diagnosis made by the Personality Disorder Examination, the California Psychological Inventory-Socialization Scale, and Hare’s Revised Psychopathy Checklist, to behaviors associated with HIV risk in 289 opiate-dependent methadone-maintained subjects. The presence of drug- and sex-related risky behaviors measured by the Risk Assessment Battery was predicted more consistently by measures of personality traits associated with antisociality than by a diagnosis of APD. © 1997 Elsevier Science Ireland Ltd. Keywords: Substance abuse; Antisocial personality disorder; Sociopathy; IDU; HIV Risk Factors

1. Introduction The vast majority of heroin users are injection drug users (IDUs). IDUs have been identified as one of the high risk groups for contracting human immunodeficiency virus (HIV) and significant numbers of IDUs are already infected with the virus. The routes of transmission are thought to be through drug-related behaviors such as sharing infected needles, as well as risky sexual practices. Certain subgroups, such as IDUs with psychiatric symptoms, may be at further increased risk. For example, increased psychological distress, as measured by the SCL-90, has been found to be associated with higher rates of needle sharing among opiate-using IDUs (Metzger et al., 1991). Identification of high risk or higher risk groups is important as there are effective interventions for risk behavior reduction (Wingood and * Corresponding author.

DiClemente, 1996). Antisocial personality disorder (APD) is a syndrome including impulsive behaviors and reckless disregard for safety of self or others (American Psychiatric Association, 1994), traits that may place those with APD at higher risk for HIV infection. Previous work has found the rate of APD in opiate users to be between 26 and 54% (Rounsaville et al., 1982, 1983; Khantzian and Treece, 1985; Rounsaville and Kleber, 1985). Several researchers have begun to explore the relationships between APD in substance abusers and HIV infection risk behaviors. Brooner et al. (1990) examined HIV risk behavior in 100 IDUs in and out of methadone treatment. The subjects in treatment had been in for at least a month. HIV drug-related risk behaviors were assessed with a non-standardized questionnaire. Thirty-six percent of the subjects met DSM-III-R criteria for APD, as determined by the Alcohol Research Center Intake Inter-

0376-8716/97/$17.00 © 1997 Elsevier Science Ireland Ltd. All rights reserved. PII S 0 3 7 6 - 8 7 1 6 ( 9 7 ) 0 0 0 7 6 - 8

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view, an interview derived from the SADS-L. APD subjects had significantly higher rates of equipment sharing and equipment-sharing partners but no greater number of injections as compared to non-APD subjects. APD diagnosis was a better predictor of these behaviors than gender, treatment status, or current cocaine use. An extension of these results in 1993 (Brooner et al., 1993) with an additional 178 patients found that all risk behaviors studied were elevated in IDUs with APD. In the second analysis, these investigators included HIV testing for all subjects, and found a 12% overall HIV-positive rate. The difference in HIV-positive rates between APD (17.6%) and nonAPD IDUs (7.8%) was significant, which indicated that the higher rates of risky behaviors in APDs were resulting in greater seroconversion rates. Gill et al. (1992) studied 55 methadone-treated patients at clinic admission. Risk factors were determined by the HIV risk behavior interview section of the National Institute of Drug Abuse (NIDA) AIDS Initial Assessment. This instrument surveyed both risky injection practices as well as sexual behaviors, including prostitution, promiscuity and condom use. Psychiatric diagnosis was determined by the Diagnostic Interview Schedule (DIS); 42% were diagnosed with DSM-III-R APD. Patients with APD reported more needle sharing, had a greater number of sharing partners, had a greater number of sexual partners, and were more likely to engage in prostitution than those without APD. There were no differences in rates of equipment cleaning or condom use. Compton et al. (1995) examined similar issues in a population of cocaine addicts recruited from the community. Some began treatment, and others remained out of treatment. They surveyed detailed drug- and sex-related risk behaviors in 351 cocaine users with a non-standardized interview. DSM-III-R APD (35% by DIS) was associated with an increase in some, but not all drug- and sex-related risk behaviors. Compared to non-APD subjects, those with APD were more likely to share syringes and to be promiscuous. In contrast to the findings of increased association, Abbott et al. (1994) did not find subjects with APD to have higher HIV risk scores. They evaluated 144 opiate addicts entering methadone treatment with the Risk Assessment Battery (RAB) (Metzger et al., 1990). The APD diagnosis was made by the SCID-II. No association of DSM-III-R APD (31.3%) to needle sharing was found; no other scores from the RAB were reported. The diagnosis of APD may not be sufficient to capture the features that contribute to risky behavior and is complicated in substance abusers by including antisocial behaviors that may be associated with drug use itself. In disentangling behaviors as consequences of drug use versus the driving forces of such behaviors, it would be helpful also to examine the underlying personality features of antisociality.

The objectives of the current study were to conduct a more comprehensive evaluation of the relationship of antisociality to HIV risk. Toward this end, we examined not only the relationship of the APD diagnosis to risk behaviors, but also that of two other alternative measures of antisociality. These were the Revised Psychopathy Checklist (PCL-R), developed by Hare and his colleagues (Hare, 1991) and the Socialization scale of the California Psychological Inventory (CPI-So) (Gough, 1987; Gough and Bradley, 1992). The PCL-R measures the concept of psychopathy put forward by Cleckley (1941), and has been shown to be a good predictor of recidivism in parolees (Hare, 1991; Harris et al., 1991). It has also been found to be reliable in substance abusers (Alterman et al., 1993). The CPI-So has been shown to be a valid predictor of differential treatment response in a study of alcoholic patients conducted by Kadden et al. (1989). In this study, we evaluated the separate and combined validity for predicting HIV risk behaviors of these three measures of antisociality. In addition to replicating the correlation of APD diagnosis to HIV risk behaviors, the current study attempted to determine the best predictor(s) of HIV risk behaviors among measures of antisociality.

2. Methods

2.1. Subjects The subjects were 289 opiate-dependent methadone patients, recruited as a part of an ongoing study of antisociality in opiate-dependent subjects. Participation was independent of their ongoing treatment. Subjects were recruited between 1989 and 1994 from two clinics, a VA outpatient methadone clinic (212 subjects) and an urban community methadone program (77 subjects), between 3 and 6 weeks into their methadone maintenance treatment, in order to allow time for stabilization on methadone. Although the subjects were stabilized on methadone to reduce confounds of its acute effects or side effects in symptom reporting, this procedure may have introduced a selection bias by not including a group of early treatment drop-outs who may have different characteristics. Most of the studies reviewed above had evaluated the patients in an early phase of their treatment. No out-of-treatment subjects were recruited for this project. The subjects were between 18 and 55 years old. Exclusion criteria included mental retardation, organic mental disorder, psychosis or a diagnosis of schizophrenia. Exclusion diagnoses were determined by an evaluating psychiatrist at intake or by a semi-structured diagnostic interview (SCID) as part of the research evaluation.

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2.2. Subject characterization Of the 289 subjects, 32 (11%) were women; the mean age was 40 (sd 5.3) with a range of 24 – 56; and 118 were white (41%), 158 were African-American (55%), 13 were other or unidentified ethnic groups (4%). The subjects had a mean educational level of 12.3 years (sd 1.6); 96 (36%) were currently employed; and 64 (24%) were currently married. They had been using heroin an average of 15.3 years (sd 7.9), cocaine an average of 3.3 years (sd 4.8) and sedatives an average of 2.1 years (sd 4.8), and 89% had injected drugs within 6 months of study intake. Two and a half percent were court ordered into treatment.

2.3. Procedures The research project was approved by both the Philadelphia Veterans Affairs Medical Center and University of Pennsylvania Institutional Review Boards prior to collection of data. All potentially qualifying subjects were provided full information about the study, and those who agreed to participate provided informed consent prior to the assessments. The research evaluations consisted of several sessions conducted by research staff trained in use of the specific instruments by the investigators (see below). The same staff conducted assessments at both clinics. The VA clinic patients were administered the Addiction Severity Index (ASI) as part of the clinic intake rather than several weeks later at study baseline, but all other measures, and all of the measures for the community clinic were done at the time of the research interviews after stabilization. In the community clinic sample, ASI questions that covered the past 30 days were asked in reference to the date of clinic admission, to make the assessment period comparable to that in the VA clinic population.

2.4. Measures Demographic and substance use information was gathered with the Addiction Severity Index (ASI) (McLellan et al., 1980), a semi-structured interview that assesses seven problem areas affected in substance abuse: medical, employment/financial, drug use, alcohol use, family/social, legal, and psychiatric. There are summary measures in each area: interviewer severity ratings, which help assess the need for additional treatment in these areas; and composite scores, which assess the problem level in each of these areas during the past 30 days. The composite scores are arithmetically derived scores ranging from 0 (no problems) to 1 (severe problems). The ASI has good inter-rater and test-retest reliabilities (McLellan et al., 1985; Alterman et al., 1994).

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The diagnosis of APD was made with the Personality Disorder Examination (PDE) (Loranger et al., 1988), a semi-structured interview that determines the presence or absence of criteria needed to meet the DSM-III-R diagnosis of APD and other Axis II disorders. Only the APD items were used in this study. Information from the interview, clinical chart review and criminal history information was used to make the final APD diagnosis. An attempt to have raters separate APD secondary to substance use and primary APD resulted in poor interrater reliability in the diagnosis, and thus the full criteria were ascertained regardless of whether the antisocial acts were in the context of drug use or acquisition. Antisociality was also determined with the California Psychological Inventory-Socialization Scale (CPI-So), a 46-item self-report scale measuring socialization, social judgment, and normative behaviors which provides a measure of antisociality. The CPI-So has excellent reliability and validity (Gough, 1987). As it is a socialization score it is scored in the opposite direction of the other measures, with a low score of less than 22 considered pathological. The questions do not pertain to drug use. The final measure of antisociality was Hare’s Revised Psychopathy Checklist (PCL-R), a semi-structured interview, which also makes use of collateral information, and measures the construct of psychopathy. The PCL-R asks about childhood and adult behaviors, health status, relationships, upbringing, school and work history, and legal history. It does not differentiate behaviors as a result of drug use in scoring, although that information can be obtained in the interview. A score greater than 24 is considered pathological. HIV risk was determined by the Risk Assessment Battery (RAB) (Metzger et al., 1990), a 41-item, multiple-choice, self-report form which categorizes and quantifies risk behaviors for HIV infection within the previous 6 months, resulting in a total score, and sub-scores of drug risk and sex risk, from items related to behaviors directly (e.g., sharing needles) and indirectly (e.g., using drugs in a ‘shooting gallery,’ where needle sharing may occur) related to possible HIV transmission. Analyses of data from over 1100 IDUs have produced evidence of concurrent, discriminant, and predictive validity. Items from the RAB questionnaire have been found to correlate highly with responses to similar questions asked via personal interview. For example, for needle-sharing, exact agreement between the two approaches was 94% at the 24-month follow-up, and the Kappa for this item was 0.88. Interestingly, slightly higher rates of sharing were reported on the RAB than during the personal interview. RAB items have also been able to discriminate seropositive from seronegative subjects and, most importantly, RAB scores were found to be significantly higher for those who subsequently seroconverted when

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compared with scores of those who remained uninfected (Metzger et al., 1993).

2.5. Staff training Training of research staff and establishment of interrater reliability for the PCL-R interview is described elsewhere in detail (Alterman et al., 1993). The average inter-rater correlation (z transformed Pearson’s r) of the four interviewers used in this study with consensus ratings by Hare’s group was 0.65 for Factor 1, 0.85 for Factor 2 and 0.87 for the Total score. The intraclass correlations for the four raters was 0.89 for Factor 1, 0.74 for Factor 2, and 0.85 for the Total score. Training for the PDE consisted of several steps: providing an overview of the concept of antisocial personality emphasizing measurement using the PDE; a review of PDE items; and practice scoring of several video taped and observed interviews followed by a discussion of problem areas. Each interviewer was required to have perfect agreement with an expert consensus diagnosis on three consecutive taped PDE interviews prior to beginning interviewing for the study. Although inter-rater reliability for the ASI was not formally established, training for this interview was intensive and ongoing. Training of staff involved several didactic sessions, review of individual items and scoring techniques, review of taped and observed interview(s), and finally administration of interviews with a senior interviewer observing. Periodic review of interviews and scoring procedures was also done by a senior interviewer throughout the study.

2.6. Independent 6ariables The independent variables included demographic information from the ASI: age, race, gender, education; drug use severity from the ASI as shown by years of heroin use, years of cocaine use and years of sedative use; and measures of antisociality: APD diagnosis, CPISo, PCL-R, and the legal composite score from the ASI. Age was normally distributed, and remained continuous for all but the logistic regression analyses, where it was dichotomized to less than 35 years of age, or 35 and above. Heroin use and the legal composite score remained continuous, but were log transformed as their distributions were non-normal. Race was coded as white versus other. Gender (coded for women) and APD diagnosis were dichotomous variables; education, cocaine use, sedative use, CPI-So, and PCL-R were transformed to dichotomous variables as their distributions were markedly skewed from normal even with log transformation. The sociodemographic and drug use variables were used in all analyses to control for their effects, if any.

2.7. Dependent 6ariables The outcome of interest was reported HIV risk behaviors and this was measured by the RAB total score, the drug risk sub-score, the sex risk sub-score, as well as by two individual items from the RAB, which were chosen on the basis of previous research. The specific items chosen were sharing needles, and multiple sexual partners, defined as more than one partner in the 6-month time frame of the RAB. Shared needles was the dependent variable in a number of the studies reviewed above. The latter measure was found to be related to sexual transmission (Friedland et al., 1985; Watkins et al., 1993; Woody et al., 1994). The RAB total score and drug and sex sub-scores were log transformed because of the marked deviations from normality of these variables.

2.8. Statistical analysis For the first analysis, unpaired t-tests and x 2-tests were computed between the diagnosis of APD and the dependent variables, for comparison with previously reported studies. Secondly, bivariate correlations were calculated between the independent variables and each of the dependent variables to evaluate associations among the variables. Finally, multiple linear and logistic regression models were computed to evaluate the predictive contribution of the independent variables. The regressions were performed using computer-driven stepwise forward selection with 0.05 for the entry probability and 0.10 for the removal probability. The variables were entered in blocks. In the first step demographic and drug use variables were entered. These included age, race, gender, and education. To evaluate drug abuse severity, years of heroin and cocaine and sedative-hypnotic use were also entered in at this step. These two sets of variables were entered first to test that the variance was in fact due to the antisocial measures of interest. In the next step the three measures of antisociality previously described, as well as the legal composite score, were entered. Separate analyses were conducted for each of the dependent measures. Linear regression was used for continuous outcome measures (RAB total and sub-scale scores) and logistic regression when the outcome measure was dichotomous (RAB individual items). The ROC curve was used to test the sensitivity and specificity of the final logistic regression models. The ROC curve is similar to the model R 2 for linear regression; the value can range from 0.5 (no fit) to 1.0 (perfect fit). As a check for the amount of variability contributed by each of the independent variables, the regression models were also computed two additional ways. In the first, all of the independent variables entered at once. The second method allowed each of the antisociality variables to enter in their own

K. Tourian et al. / Drug and Alcohol Dependence 47 (1997) 99–107

step prior to the block with the remaining antisociality variables, to remove the contribution of the other antisocial variables, and then to assess the variance after the entry of the other variables, in order to test each antisocial variable separately. The logistic regression analyses were also confirmed with a backwards stepwise entry, using the blocks as above. For the most part, these approaches yielded similar results to the forward stepwise, block entry, and thus only these results will be fully reported. A few exceptions to this trend will be noted. The consistency of results across multiple methods of analyses attest to the robustness of the outcomes.

3. Results

3.1. Sample comparisons For the analyses the data were pooled from the two clinic populations. Comparisons of the two clinics did not show any significant differences in the dependent variables (RAB total score, t =0.90, df = 266, P = 0.37; RAB drug score, t=1.20, df =274, P =0.23; RAB sex score, t= − 1.16, df =267, P =0.25; shared needles x 2 = 0.42, df=1 P= 0.52; multiple sex partners, x 2 = 2.65, df= 1, P=0.10)1. The failure to find significant differences is particularly notable given the size of the total sample.

3.2. Descripti6e data Twenty-eight percent of the sample had a diagnosis of APD, consistent with previous findings. Forty percent were in the pathological range on the CPI-So, and 21% on the PCL-R. There was only a modest degree of overlap in the three different antisociality measures, indicating that they were not measuring identical constructs. Sixteen percent had an APD diagnosis and pathological CPI-So score, 11% had an APD diagnosis and pathological PCL-R score, 13% had pathological CPI-So and PCL-R scores, and 8% had the APD diagnosis and pathological scores on both the CPI-So and PCL-R (Fig. 1). For the individual RAB items, 25% reported needle sharing, 25% reported multiple sex partners, and 9% were positive for both. Thus these behaviors were common, but not present in the majority of subjects.

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3.3. Antisocial personality disorder and HIV risk beha6iors The diagnosis of APD and the RAB HIV risk measures were examined together as had been done in previous studies. t-Tests with the APD diagnosis and the dependent variables showed that those with APD had significantly higher total RAB scores (t= −3.29, df=266, P= 0.001) and RAB drug scores (t= −3.41, df= 274, P= 0.001). With the x 2-test, the APD diagnosis was significantly correlated with sharing needles (x 2 = 6.79, df= 1, P= 0.009). Neither the RAB sex score nor multiple sex partners showed significant differences between APD and non-APD subjects. Comparisons with the CPI-So showed the same pattern as the APD diagnosis, and the PCL-R showed significantly higher scores on all RAB measures between the groups who had pathological scores and non-pathological scores (data not shown).

3.4. Bi6ariate correlations The only demographic variable significantly correlated (P5 0.01 adopted for all correlations to control for the number of comparisons) was white race with sharing needles. The antisocial variables were more consistently related to the RAB scores: APD diagnosis, pathological CPI-So score and pathological PCL-R score correlated positively with a higher total RAB, higher RAB drug score and shared needles. The ASI Legal composite score correlated with the RAB drug total and shared needles sub-score (Table 1).

1

In these and subsequent analyses, the inconsistent degrees of freedom reflect small amounts of missing data which differed by variable.

Fig. 1. Relationship between diagnosis of APD and pathological scores on the CPI-So and PCL-R in 289 subjects.

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Table 1 Bivariate correlations between independent antisocial variables and dependent behavioral risk variables Variables

Age White race Female gender ]HS education Log heroin years Cocaine use Sedative use APD Dx CPI-So B22 PCL-R \24 Log legal (ASI)

RAB Log total

Log drug total

Log sex total

Shared needles

Multiple sex partners

−0.12 −0.01 −0.06 0.04 0.03 0.11 −0.06 0.20*** −0.22*** 0.24*** 0.14

−0.02 0.02 −0.12 0.00 0.13 0.14 −0.12 0.20*** −0.27*** 0.21*** 0.24***

−0.13 −0.06 0.06 0.01 −0.08 0.04 0.04 0.06 −0.04 0.15 −0.07

−0.10 0.20*** −0.06 −0.01 0.00 0.05 0.06 0.16** −0.25*** 0.27*** 0.17**

−0.08 −0.11 −0.01 −0.01 0.01 0.15 −0.03 0.03 −0.11 0.15 0.09

RAB, Risk Assessment Battery; CPI-So, California Psychological Inventory, Socialization Scale; PCL-R, Psychopathy Checklist-Revised; ASI, Addiction Severity Index. **P50.01; ***P50.001.

3.5. Regression analyses 3.5.1. Total RAB score In the linear regression for the total RAB score, none of the demographic variables entered into the model in the first step. In the second step, when the antisociality variables were entered, total PCL-R and CPI-So score contributed significantly to the model with the final model F=11.66 (PB0.001, R 2 =0.08). These two variables accounted for 8% of the variance in the model (Table 2). The analyses were re-run three times with each of the antisociality variables allowed to enter first, and the others entering as the final block. When APD diagnosis was the second block, it entered at a significance level of 0.002, with the model F= 10.04 (P= 0.017, R 2 =0.04). When the CPI-So and PCL-R variables entered in the next block, as they had above, APD dropped in significance to 0.073, and the model F = 8.92 (PB0.001, R 2 =0.10). Thus the CPI-So and PCL-R contributed to the model above and beyond the APD diagnosis no matter which order the variables went in. Conversely, when the other variables entered in first, the APD diagnosis did not enter. 3.5.2. RAB drug risk score For the drug risk sub-score, when the demographic variables were entered at the first step of the linear regression, cocaine use and years of heroin entered into the model (F =4.64, P = 0.010, R 2 =0.03). In the second step the legal composite score, APD diagnosis and CPI-So were significant and entered in, with all of the model variables accounting for 16% of the variance in the total RAB score (F= 9.96, P B0.001, R 2 =0.16). Cocaine use remained in the final model as it was entered in the first step, although it was no longer

significant (P=0.130). In the alternative analysis with each antisociality variable entered alone in the second step, PCL-R entered at a significance level of 0.002 (model F= 6.48, P= 0.003, R 2 = 0.07), but when the other antisociality variables entered, the PCL-R dropped in significance to 0.08. The PCL-R also kept APD from entering into the final model (P= 0.07), and thus did exert some influence on the variance. Nonetheless, the CPI-So maintained a significance level of B 0.001 no matter what order the variables entered, and was clearly the strongest predictor of the Total drug risk score.

3.5.3. RAB sex risk score For the sex risk sub-score, when the demographic variables were entered in a linear regression model, none were significant. When the antisociality variables entered, the PCL-R score was significantly related to RAB sex risk score, but only accounted for 2% of the total variance (F=5.15, P= 0.024, R 2 = 0.02). No other antisociality measures entered into the model with alternate entry techniques. 3.5.4. RAB shared needles Using logistic regression to predict whether or not the subjects had shared needles, when the demographic variables were added in the first step, the only significant variable was race, with whites more likely to share. The model x 2 was 11.15 (df= 1, P=0.001). The measures of antisociality then entered sequentially. When PCL-R was added, the model improvement x 2 was 16.86 (df= 1, PB 0.001); and when CPI-So was added the model improvement x 2 was 5.07 (df= 1, P=0.024). APD diagnosis was not significant and thus did not enter into the model. The final model x 2 was 33.16

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Table 2 Multiple linear regression models for RAB total and sub-scores

Log total RAB scorea CPI-So B22 PCL-R \24 Log drug risk sub-scoreb Log heroin years Cocaine use APD diagnosis CPI-So B22 Log legal composite Log sex risk sub-scorec PCL-R \24

P

Parameter estimate

Parameter estimate 95% CI

0.006 0.002

−0.226 0.307

(−0.385, −0.066) (0.118, 0.497)

0.014 0.132 0.033 0.000 0.006

0.015 0.154 0.233 −0.397 0.726

(0.003, 0.027) (−0.046, 0.354) (0.019, 0.447) (−0.595, −0.199) (0.211, 1.240)

0.024

0.250

(0.033, 0.467)

CI, confidence interval. a Final model F= 11.66, PB0.001, R 2 = 0.08. b Final model F =9.96, PB0.001, R 2 = 0.16. c Final model F=5.15, p= 0.024, R 2 = 0.02.

(df = 3, PB 0.001). The ability of the final model to predict shared needles was 76.49%, with a sensitivity of 16.67% and specificity of 96.04%. The odds ratios were race 2.32 (95% Confidence Interval (CI) (1.24, 4.34)), PCL-R 3.27 (1.67, 6.41), and CPI-So 0.49 (0.26, 0.91) (Table 3). Thus, subjects who were white were more than twice as likely to share needles; those who scored high on the PCL-R were more than three times more likely to share needles; and those who scored low on the CPI-So were twice as likely to share. This model had low sensitivity, although high specificity. The area under the ROC curve for this model was 0.72. As the number of subjects who endorsed sharing needles was small (25%) resulting in unbalanced groups, the analysis was repeated with a randomly selected group of non-needle sharers to produce balanced samples. The final model in this analysis contained the same significant variables: race, PCL-R and CPI-So, with a total prediction of 73.08%, a sensitivity of 65.15% and specificity of 81.25%. Table 3 Logistic regression models for RAB individual items shared needles and multiple sex partners

Shared needles positivea White Race CPI-So B22 PCL-R \24 Multiple sex partnersb Age Cocaine use PCL-R \24

P

Odds ratio

Odds ratio 95% CI

0.008 0.024 0.000

2.32 0.49 3.27

(1.24, 4.34) (0.26, 0.91) (1.67, 6.41)

0.006 0.046 0.023

0.36 1.94 2.15

(0.17, 0.75) (1.01, 3.72) (1.11, 4.14)

3.5.5. RAB multiple sex partners For the question of number of partners, separating multiple partners in the previous 6 months from one or none, the demographic variables were again entered first. The first logistic model included age, with a model x 2 of 5.75 (df= 1, P= 0.016). Cocaine use entered in next, with an improvement x 2 of 5.77 (df = 1, P= 0.016). When the antisociality measures were added in, only PCL-R was significant, and the model improvement x 2 was 5.05 (df= 1, P= 0.025). The final model x 2 was 18.75 (df= 3, P B0.001). The model was able to predict multiple sexual partners 76.05% of the time, with a sensitivity of 4.69% and specificity of 98.99%. The odds ratios were age 0.36 (CI 0.17, 0.75), cocaine use 1.94 (1.01, 3.72), and PCL-R 2.15 (1.11, 4.14) (Table 3). The area under the ROC curve for this model was 0.66. As with needle sharing the groups were unbalanced with only 25% of the total sample endorsing having multiple sexual partners. When the analysis was repeated with balanced groups the significant variables were age and cocaine use, with PCL-R failing to enter into the model. The total model prediction changed to 61.07% with a model sensitivity of 84.38% and specificity of 38.81%.

4. Discussion

a Final model x 2 = 33.16 (df= 3, PB0.001); area under the ROC curve=0.72. b Final model x 2 =18.75 (df= 3, PB0.001); area under the ROC curve=0.66.

Predicting complex behaviors, including behaviors which would put someone at risk for contracting or spreading the HIV virus, is not an easy task. Hence it is not surprising that a narrow focus on specific predictor variables is not sufficient to account for large variations in these behaviors. Nevertheless, in this study two measures of antisociality, the CPI-So and the PCL-R, were more consistently related to risky behaviors than a diagnosis of Antisocial Personality Disorder. These

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findings refine and extend previous work which has looked only at the APD diagnosis itself. We were able to replicate the analyses of most of the prior studies in looking at the direct relationship of the APD diagnosis to the measure of HIV risk behaviors. Correlations of the risk behaviors were then made with a number of other possible factors, and finally the variables were combined in regression models which could account for the influences of the variables upon one another in predicting behaviors. In these final models, APD was not as strong a predictor as the alternative measures, especially the PCL-R, which was able to predict sexual as well as drug-related behaviors. It is not clear why the results did not agree with the study of Abbott et al. (1994) as both used the RAB, although they used a different interview for making the APD diagnosis (SCID-II instead of PDE). However, the prevalence of APD (31%) in that study was similar to that found in this study (28%). The subjects in the Abbott study were primarily Hispanic (84%), and this study did not have enough Hispanic subjects to be able to compare this group separately. The diagnosis of APD in substance abusers is confounded by the antisocial acts which are often a part of drug use and acquisition. The RDC criteria for Antisocial Personality Disorder require non-drug-related antisocial behaviors to meet criteria. Other workers have attempted to use the DSM diagnostic criteria, but to not count antisocial acts secondary to drug use. It was not possible, in this study, to have raters reliably sort out the antisocial acts related to drug use, and the DSM-III-R instructs that concomitant substance use disorders and APD should both be diagnosed if enough criteria for each are met. The PCL-R also does not make a distinction for drug-use related items. The CPI-So is a self-report form, and most of the questions do not pertain to drug use in any way. The advantage of these alternative measures of antisociality, then, may be their utility in capturing the antisociality features which have clinical relevance, using the instruments as given, without alterations that reduce their reliability. One limitation in a study such as this one is reliance on the self-report data of the RAB. The reporting of risky behaviors by a pen-and-paper or computer entry method is felt to be important in reducing the possibility of shame in reporting these behaviors, as might occur during a direct interview. Although it is not possible in many cases to validate reported methods of drug use or sexual behaviors, other measures such as HIV serology, as used in the study by Brooner et al. (1993), would have strengthened the relationship of self-reported risky behavior to the exposure to and contraction of HIV infection. The CPI-So is also a self-report measure, but the PCL-R and APD diagnosis by the PDE used additional corroborating data.

The PCL-R uses a semi-structured interview covering areas of past and current functioning and attitudes, as well as criminal records and clinical charts in arriving at a final score, which may provide an objective picture of antisociality, and may help explain the success of the PCL-R in the prediction of all types of risky behaviors. An additional consideration in this type of study is that behaviors do not remain static over time. Whereas the ability to change behavior makes intervention possible and meaningful, the baseline behavior of the subjects under study is not constant. There is good evidence that those at higher risk for HIV have made changes in behavior as a result of education, such as decreasing the sharing of needles (Wingood and DiClemente, 1996). Some behaviors have been more resistant to change, for example, the consistent use of condoms during sexual activity. These changes make it more difficult to interpret the results of studies like this one in light of current behaviors and needs of the populations at risk. It is possible that a future replication of this study would not produce the same results, as these data were gathered in the early 1990s. With these caveats in mind, the current study has shown that antisocial opiate-dependent subjects engage in more HIV-risk behaviors than other IDUs. Antisocial behaviors are common in this group as a result of drug use and acquisition, but what consistently distinguished the high and low risk subjects was the presence of antisocial personality traits whether or not they had a diagnosis of APD. These results indicate a need for careful psychiatric evaluation of methadone patients, including collateral data, as an ongoing process during treatment to identify maladaptive personality traits and disorders for the purposes of targeting additional specific interventions for harm reduction and for interventions beyond drug treatment. It is also important to look beyond the dichotomous restrictions of the diagnosis of Antisocial Personality Disorder to the complex features of patients in order to better understand and serve them. In the evaluation of methadone maintenance patients, a self-report measure such as the CPI-So may be a useful addition to the intake materials. It only takes about 10 minutes to complete, and appears to have high sensitivity to antisocial traits, providing an initial screening method to identify such patients. Such identification may also help with determining the needs of patients early in treatment, as many clinicians are reluctant to give a personality disorder diagnosis after only one evaluation. In cases where more specific information is required about antisocial traits as they pertain to high risk behaviors for HIV transmission, the PCL-R interview can be used. In this way, treatments can be better tailored to the specific needs of the patients.

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Acknowledgements This study was funded in part by training grant (K12) cDA00172 from the National Institute of Drug Abuse (NIDA), Center Grant cDA05186 from NIDA, NIDA grant c DA05858 (Dr. Alterman), and the Department of Veterans Affairs. Thanks to Chris Boardman, Mark Belding and Terry Cook for their assistance with the statistical analyses.

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