Gender, cocaine and during-treatment HIV risk reduction among injection opioid users in methadone maintenance

ELSEVIER

Drug and Alcohol Dependence41 (1996)l-7

Gender, cocaine and during-treatment HIV risk reduction among injection opioid users in methadone maintenance L. Mabel Camacho*, Norma G. Bartholomew, George W. Joe, Melynda A. Cloud, D. Dwayne Sim.pson Institute of Behavioral Research, Texas Christian University, TCU Box 29874, Fort Worth, TX 76129, USA

Received29 August 1995;accepted24 November1995

Abstract HIV risk behavior was examined in relation to gender and cocaine use among a sample of 327 daily opioid users in methadone maintenance treatment. Womenand cocaineuserstendedto be at higher risk than men and non-usersprior to treatmententry. Significant reductions in both injection and sex-related risks occurred from intake to months 3 and 6 of treatment; HIV risks were also reduced among the subsample of clients who continued to inject drugs during treatment. The rate of HIV risk reduction was similar for both cocaine and non-cocaine users, but women had lower rates of risk reduction than men in terms of injecting with used equipment and number of sex partners. Measures of risk behavior at month 6 of treatment showed that women used ‘dirty works’ more often than men,and cocaineusersinjectedmore frequently and had more sexpartnersthan non-cocaineusers.Study outcomes highlight the need for specialized interventions targeting women and cocaine-using opioid addicts. Keywords:Cocaine;Gender;HIV risk; Injection; Methadonemaintenance

1. Introduction

As we approach the third decade of public health concern over human immunodeficiency virus (HIV) disease and its social and economic consequences, the relationship between injection drug use and HIV transmission grows more troubling. AIDS cases among adults in the United States linked to injection drug use has grown to about 37%; among women, however, injection drug use or having a sex partner who injects drugs may account for 65% of cases (Centers for Disease Control, 1994). Clearly not all IDUs (injecting drug users) are at equal risk - geography, sexual and injection practices, drugs of choice, gender, and access to and engagement in drug treatment have emerged as key research issues in addressing the AIDS epidemic among IDUs and their sex partners (see Auerbach et al., 1994). Opioid injectors seeking treatment through methadone clinics were among the first subjects reported in HIV prevalence and risk factor studies (Marmor et al., * Correspondingauthor.

1987; Novick et al., 1986), and drug treatment was logically viewed as a viable strategy for HIV risk reduction among IDUs. Supporting this, Ball et al. (1988) found that injection drug use and needle sharing dropped dramatically during methadone maintenance (MM) treatment - 71% who remained in treatment 1 year or more ceasedinjection use. There also is growing evidence that MM may actually lower the incidence of HIV infection rates among populations of IDUs. Metzger et al. (1993) found a 6-fold difference in HIV seroconversion rates between in-treatment (MM) and out-of-treatment opioid injectors based on an 18-month follow-up sample. Those who remained in MM showed seroconversion rates of 3.5%, compared to 22% for IDUs not in treatment. Similarly, Williams et al. (1992) found that only 2% of a continuously in-treatment sample seroconverted, compared to 19% of those who interrupted their treatment. Other research (Caplehorn and R.oss, 1995; Watkins et al., 1992) also supports the impact of methadone treatment on reduction of HIV risks. The concomitant epidemic of cocaine abuse among opioid abusers raises further concerns, as cocaine use has been found to increase risky sex as well as injection

0376-8716/96/$15.00 0 1996ElsevierScienceIreland Ltd. All rights reserved PII SO376-8716(96)01235-S

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practices (Condelli et al., 1991; Kolar et al., 1990). Several studies have documented risk-taking differences between cocaine-using and non-cocaine-using clients in treatment for opioid abuse (Anthony et al., 1991; Chaisson et al., 1989; Iguchi et al., 1992; Joe and Simpson, 1995). Joe and Simpson (1995), for example, found that among opioid addicts entering MM, those who used cocaine engaged in more HIV-risky injection practices than non-users and were also more likely to have unprotected sex in exchange for drugs or money or to have sex with an IDU. While there has been much treatment research that suggests these dual drug users are more difficult to treat (e.g., Condelli et al., 1991), some researchers (Magura et al., 1991) have suggested that MM treatment helps reduce both opioid and cocaine injection frequency which may contribute to lower injection-associated HIV risk. However, risk reduction in terms of needle sharing and sexual risks among cocaine-using opioid addicts in MM treatment still needs attention. In addition to cocaine use, studies show that gender is also associated with HIV risk behavior. When Freeman et al. (1994) compared male and female IDUs, they found 70% of the women had a sex partner who was an IDU (compared to 30% for men), and they were significantly more likely to inject drugs with a sex partner; Booth et al. (1995) found similar results. Brown and Weissman (1993) likewise note that women are more likely to have an IDU sex partner and that power and economic differences may contribute to women having more injection risks in terms of sharing paraphernalia. Involvement in a treatment program appears to help women reduce their HIV risk behaviors (Brown and Weissman, 1993; Kosten et al., 1993). However, because of the association between cocaine use and HIV risk taking, it is necessary to study during-treatment risk reduction among women in conjunction with cocaine use. Research has shown a relationship between MM treatment and reductions in HIV-risky behaviors such as reduced drug use and injection frequency, less sharing of injection equipment, and fewer needle sharing partners (Gottheil et al., 1993; Longshore et al., 1993) in addition to condom use, fewer sexual partners, and decreased exchange of sex for money or drugs. (Magura et al., 1990; Watkins et al., 1992). These findings are encouraging, but it would be helpful to know when and how HIV risk reduction occurs during treatment, and the relationship of these during-treatment changes to gender and cocaine use. Another unresolved issue is whether risk reduction is simply a by-product of less frequent drug use while in treatment, or if there are risk-reduction behavioral changes actually occurring as a result of being in treatment. The present study focuses on during-treatment HIV risks for clients while in MM treatment and the interac-

tions of gender and cocaine use on risk reduction over time. Specifically, changes in injection frequency, injections with used (dirty) paraphernalia, number of sex partners, and frequency of unprotected sex (i.e., without a condom) are examined from intake, to months 3 and 6 after treatment entry, making comparisons between men and women, as well as between cocaine users and non-users. It was expected that women and cocaine users would show the highest levels of risk behaviors at intake, but we also anticipated risk levels for these subgroups would be lowered and become more similar to those reported by men and non-cocaine users. 2. Methods 2.1. Sample

A total of 910 opioid addicts were admitted to methadone treatment programs as part of the Drug Abuse Treatment for AIDS-Risk Reduction (DATAR) project between 1990 and 1993 (see Simpson et al., 1993). Approximately 14% of the clients terminated treatment within 30 days, 37% within 90 days, and 57% within 180 days. A subsample of 326 daily opioid users who remained in treatment at least 6 months (all had injected drugs in the 30 days prior to treatment entry) were selected for this study. Limiting the sample was necessary in order to assessHIV-risky behavior change from intake to months 3 and 6 of treatment. Early treatment dropouts have been examined in more detail by Simpson and Joe (1993); compared with them, the longer-retention clients in the present study sample included a higher percentage of African and Mexican Americans (versus whites) and fewer speedball users. (See Boatler et al., 1994; Simpson et al., 1995; Joe and Simpson, 1995; Carnacho et al., in press, for related studies using other samples from this treatment population). University guidelines for informed consent were followed and clients received free treatment services in exchange for participation. The 326 clients in the present study were drawn from MM programs in Corpus Christi (n = 190), Dallas (n = 49), and Houston (n = 87). Overall, 68% of the sample was male and the mean age was 37 years (median = 37); 16% were African American, 45% Mexican American, 36% White, and 4% other (see Table 1). Forty-two percent were married, 37% were high-school graduates, and 49% reported some employment in the 6 months prior to treatment entry. Substantial percentages used alcohol (71%), cocaine (46%), and speedball (46%) in the 30 days before treatment; however, crack use was less than 7% (data not shown). Methadone dosage at all clinics was lower than currently recommended levels, and averaged about 40 mg

L.M.

Camacho et al. /Drug

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and Alcohol Dependence 41 (1996) 1-7

Table 1 Description of sample Total (N = 326) Gender 68 % Male % Female 32 Age % 18-30 17 % 31-35 24 32 % 36-40 % Over 40 (:) (Mean age) Race-ethnicity 16 % African American % Mexican American 45 % White 36 4 % Other Marital status 42 % Married High school graduate 37 % Yes Employment % Some employment 49 Alcohol use % Never 30 % Less than weekly 23 48 % At least weekly Cocaine use % Never 55 22 % Less than weekly 24 % At least weekly Speedball use 54 % Never 15 % Less than weekly 31 % At least weekly Methadone dose - month 3 (ms) 40.9 (12.8) Mean (SD.) O-90 Range Methadone dose - month 6 b8) 40.1 (14.2) Mean (S.D.) O-85 Range Counseling sessions month 3 Mean (SD.) 7.6 (3.4) 2-19 Range Counseling sessions month 6 Mean (S.D.) 14.8 (6.3) 3-36 Range

Male (N = 223)

Female (N = 103)

100 0

100

12 22 34 (ii)

0

27 28 26 18 (34)

Cocaine (N = 147)

No cocaine (N = 179)

71 29

67 34

19 23 36

15 25 28

(ii)

(if)

15 53 30 3

18 27 50 6

14 42 39 5

17 46 34 3

42

41

38

45

39

31

39

35

49

51

45

53

27 19 54

35 31 34

22 24 54

‘. 36

53 23 24

58 17 24

0

48 52

100 0 0

52 15 33

56 16 28

27 23 50

75 9 16

22 43

42.3 (11.9) 15-90

38.0 (14.2) o-75

41.7 (11.9) lo-80

40.3 (13.5) O-90

40.9 (13.6) O-85

38.4 (15.3) O-85

40.9 (13.5) 5-85

39.5 (14.7) O-85

7.6 (3.4) 2-19

7.8 (3.3) 2-18

8.1 (3.4) 2-19

7.3 (3.4) 2-17

14.8 (6.2) 3-35

14.7 (6.4) 4-36

15.2 (5.9) 3-34

14.5 (6.5) 3-36

throughout treatment, ranging up to 90 mg in some instances. Clients were encouraged to attend multiple counseling sessions per month, although program policy based on state guidelines required only one session per month. On average, the cumulative number of counseling sessionsattended per client by months 3 and 6 of treatment was 7.6 and 14.8, respectively; individual counseling typically averaged 45 min while group counseling averaged 80 min. As indicated in Table 1, background characteristics, methadone dose, and amount of

counseling were very similar between the male and female clients and between the two cocaine subgroups. 2.2. Measures A comprehensive set of intake, during-treatment, and follow-up data collection instruments was used in the DATAR project (Simpson, 1992). The Intake Form was completed by all clients and consisted of a faceto-face hour-long interview that addressed sociodemo-

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L.M. Camacho et al. / Drug and Alcohol Dependence 41 (1996) 1- 7

Table 2 During-treatment effects for Time, Time x Gender, and Time x Cocaine on AIDS risky behavior (n = 326) Time

Male

% % % % %

Injected’ Used dirty works Sex partners z 1 Unprotected sex Risk composite = 1

Time x Cocaine

Time x Gender Female

Cocaine

No cocaine

I

M3

M6

x2

I

M3

M6

I

M3

M6

x2

I

M3

M6

I

M3

M6

x2

100 60 18 74 89

59 15 7 61 67

53 15 7 62 66

184.3’ 28.7’ 22.3’ 62.6’

100 57 14 74 89

58 9 8 60 67

50 9 7 64 67

100 66 25 76 89

65 26 5 63 67

58 26 8 57 65

8.6b 6.2” 3.7 0.8

100 73 26 74 93

65 19 7 62 68

59 18 11 61 67

100 49 11 74 85

55 11 7 60 65

47 12 4 62 65

1.5 2.8 1.2 1.4

‘Analysis not possible because 100% injected at intake. aP < 0.05; bP < 0.01; ‘P < 0.001.

I corresponds to measure at intake, M3 corresponds to month 3, and M6 corresponds to month 6.

graphic background, family background, peer relations, criminal history, health and psychological status, drug use history, and HIV-risky behavior. In the first 3 months after admission, monthly duringtreatment assessments were completed; thereafter, these were administered quarterly. Items measuring HIV-risky behavior (last 30 days) were included in both the Intake Form and the monthly status assessment.Two measures of needlerelated HIV risk were used: self-reported injection frequency and frequency of injections with ‘dirty works’. Use of ‘dirty works’ was computed by subtracting from the injection frequency measure the frequency with which a set of ‘works’ - needle, cooker, cotton - had been used solely by the client. This provided an estimate of injections with previously-used paraphernalia. Sex-related risks were measured by number of sex partners and frequency of unprotected sex. Finally, an HIV risk composite was formed by summing the dichotomized scores for ‘dirty works’, sex partners, and unprotected sex; this measure in turn was also categorized into ‘none’ vs. ‘some’. Injection frequency was not included because all study participants were injectors and this would limit later analyses. 2.3. Data analyses

Due to the highly skewed nature of these data, measures were categorized such that (in most cases) any risk was recoded as ‘1’ and the absence of risk was left at ‘0’. An exception involved the number of sex partners measure where a value of ‘0’ was assigned if there was one or no sex partners and a value of ‘1’ for more than one partner. Multi-way contingency tables (except injection frequency) for gender, cocaine use, and HIV-risky behavior measured across three points in time were analyzed using the repeated measurement option in SAS for performing categorical data modeling (PROC CATMOD;

SAS Institute Inc., 1989). This option uses the GSK weighed least-squares estimation procedure (Grizzle et al., 1969) to test the linear model that the independent variables (gender and cocaine use) predict the repeated-measures dependent variables - HIV risk measured at intake, month 3, and month 6. The analysis allows each source of variance to be tested for significance, similar to analysis of variance procedures. However, the significance of each source of variation is evaluated in terms of a statistic distributed as chi-square rather than Fisher’s F-statistic. This type of analysis is not possible when all of the sample falls into one category, as with injection frequency (everyone injected at intake), and thus repeated-measures analysis of variance (ANOVA) was applied for this measure.

3. Results 3.1. Description of HIV risk at intake

Table 2 presents needle and sex risk behaviors at intake and during treatment. Overall, about 60% used ‘dirty works’ at least once in the 30 days preceding treatment entry, 18% had more than one sex partner, and 74% had unprotected sex. The risk composite categorized 89% of the sample as engaging in risky behavior at intake. Of theoretical importance was whether HIV risk at intake differed across gender and cocaine subgroups. Results showed that even though women were at higher risk than men, only the number of sex partners was significantly different (25% vs. 14%; ~~(1,326) = 5.8, P < 0.02). For cocaine use at intake, users scored significantly higher than nonusers on ‘dirty works’ (73% vs. 49%; ~~(1,326) = 20.8, P < O.OOl), number of sex partners (26% vs. 11%; ~~(1,326) = 11.9, P < O.OOl), and the risk composite (93% vs. 85%; ~~(1,326) = 4.9, P -C 0.03).

L.M. Camacho et al. / Drug and Alcohol Dependence 41 (1996) l-7

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Table 3 During-treatment effects for Time x Gender x Cocaine on AIDS risky behavior (n = 326) Male

Female

Cocaine

% % % % %

Injected’ Used dirty works Sex partners > 1 Unprotected sex Risk composite = I

No cocaine

Cocaine

No cocaine

1

M3

M6

I

M3

M6

I

M3

M6

I

M3

M6

x2

100 74 20 72 95

64 13 8 51 64

51 II IO 61 65

100 42 9 15 83

50 7 8 63 68

43 8 5 66 61

100 72 40 79 88

67 35 I 14 77

63 35 14 63 70

100 62 15 73 90

63 20 3 55 60

55 20 3 53 61

3.1 0.3 2.0 10.4”

‘Analysis not possible because 100% injected at intake. “P < 0.01.

I corresponds to measure at intake, M3 corresponds to month 3, and M6 corresponds to month 6.

3.2. Trend analyses

ANOVA results showed that injection frequency dropped significantly from a mean of 111 (S.D. = 59.5) injections at intake to 7 injections at both month 3 (SD. = 18.3) and month 6 (SD. = 19.3; F(2,321) = 464.4, P < 0.001). From the GSK repeated-measuresanalysis, all risk measures showed significant reductions over time. As shown in Table 2, for example, users of ‘dirty works’ dropped from 60% at intake to 15% by month 6 (~~(2,326) = 8.6, P < O.Ol), and persons having unprotected sex declined from 74% to 62% (~~(2,326) = 22.3, P < 0.001). In terms of main effects, females and cocaine users had significantly higher risk taking. There was a gender (x2( 1,326) = 19.9, P < 0.001) and a cocaine use effect (~‘(1,326) = 10.9, P < 0.001) for ‘dirty works,’ and a cocaine effect (~~(1,326) = 6.4, P < 0.01) for number of sex partners. Also shown in Table 2 are significant interactions between time and gender for use of ‘dirty works’ (~~(1,326) = 8.6, P < 0.01) and number of sex partners (~‘(1,326) = 6.2, P < 0.05). In general, there was a greater rate of change for males regarding used paraphernalia and a larger reduction for women with regard to number of sex partners. The interactions between time and cocaine use (see Table 2 for percentages) and between gender and cocaine use (see Table 3 for percentages) were not significant. Between-group differences for gender and cocaine use were also tested using month 6 data only (see Table 2 for percentages). These results showed that women were significantly more likely to use ‘dirty works’ than men (26% vs. 9%; ~~(1,326) = 5.8, P < 0.02); cocaine users injected significantly more than non-users (59% vs. 47%; x2( 1,326) = 4.1, P < 0.04) and had more sex partners than non-cocaine users (11% vs. 4%; ~~(1,326) = 4.8, P < 0.03). Three-way interactions

also were tested for all measures, but results were significant only for the risk composite measure (x*(1,326) = 10.4, P < 0.01; see Table 3). In general, risk reductions for female cocaine users tended to be less 1:hanthe other groups on the risk composite. In order to test whether the lowered levels of HIV risk Itaking during treatment were due to a reduction in drug use or changes in injection practices, further analyses were performed based only on clients who had injected drugs during all 3 time periods (n = 136). Results showed that use of ‘dirty works’ dropped from 63% at intake to 26% and 29% at month 3 and month 6, respectively (x*(2,326) = 52.8, P < 0.001). Similarly, the percentage of clients who had multiple sex partners changed from 15% at intake to 7% at month 3 and lO% at month 6 (~~(2,326) = 7.4, P < 0.02). Distributions for unprotected sex were 78% at intake, and 62% and 60% at months 3 and 6, respectively (~~(2,326) = 19.8, P < 0.001). Finally, the risk composite at intake was 89% but fell to 67% by month 3 and 66% at month 6 (~~(2,326) = 29.3, P < 0.001). Thus, there were HIV risk reductions that occurred beyond those accounted for by reduced drug use alone. There was a concern that differences in terms of demographics, methadone dose, and amount of counseling, across the gender and cocaine groups might compromise the interpretation of results but further examination showed such differences were minimal acros:sgroups and correlational and chi-square analyses did not support this contention. Furthermore, risk taking behavior was not significantly related with methadone dose levels or frequency of counseling sessions.

4. Discussion

In accordance with previous studies (Gottheil et al., 1993; Longshore et al., 1993; Magura et al., 1990;

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et al. / Drug and Alcohol Dependence 41 (1996) 1-7

Watkins et al., 1992) our results showed that MM treatment plays an important role in HIV prevention for IDUs addicted to opioid drugs. Specifically, there were significant reductions in risk behavior across the first 6 months of treatment as defined by injection frequency, use of ‘dirty works’, number of sex partners, and unprotected sex. Furthermore, our results generally supported findings by Longshore et al. (1993) and not those of Caplehorn and Ross (1995); that is, we found evidence that risk behavior reduction among treatment clients cannot be explained solely by reductions in drug use occurrences. Those who continued to inject while in treatment appear to reduce risks associated both with injection and sex behaviors. The findings also lend support to previous studies that show cocaine-using opioid addicts and women are at higher risk for HIV because of sex and injection practices (e.g., Booth et al., 1995; Brown and Weissman, 1993; Chaisson et al., 1989; Freeman et al., 1994; Joe and Simpson, 1995). The rate of risk reduction for men and women was similar in terms of unprotected sex and the risk composite, but women had smaller reductions on measures of ‘dirty works’ and greater reductions in number of sex partners. There were similar HIV risk reductions for cocaine and non-cocaine users for all variables examined. At month 6 of treatment, though, women were more likely to use ‘dirty works’ than men, and cocaine users more likely to inject and have more sex partners than non-cocaine users. However, our analyses of the sexual risk measures are limited by our assumption that a single sexual partner represents no risk. We did not assessthe sexual and drug practices of sex partners and thus some of those clients with single partners could be at risk for HIV contraction. It is troubling that women and cocaine users were still at higher risk than men and non-cocaine users at month 6. This reinforces the need for further treatment enhancements, possibly specialized interventions for women and cocaine users in MM treatment. Amaro (1995) notes that much research to date has ignored how gender, women’s social status and economic realities, and women’s roles affect HIV risk behaviors in or out of treatment. Approaches that emphasize selfesteem enhancement, assertivenesstraining, and negotiation skills for women have shown positive results (Bartholomew et al., 1994; El-Bassel and Schilling, 1992). Cognitive enhancements to counseling (Dansereau et al., 1993; Joe et al., 1994) and contingency management approaches (Rowan-Szal et al., 1994; Rowan-Szal et al., in press) have been found with the DATAR project to be useful in reducing cocaine use in MM treatment (see Simpson et al., in press). Further development and testing of these interventions appear warranted. Even though MM seems to be an integral part of

HIV prevention efforts, little is understood about specific treatment components associated with low HIVrisky behaviors. Because in the present study, methadone dose and amount of counseling were at low and very similar levels for almost all clients, one cannot draw definite conclusions about the efficacy of these treatment process components in reducing HIV risk. Additionally, in the DATAR project, state regulations required treatment counselors to discuss HIV/AIDS with clients on a monthly basis, possibly accounting in part for the positive outcomes of this study. More controlled studies with random assignments to treatment conditions which focus on treatment process and how it relates to risk reduction are needed (Simpson et al., 1995). In general, findings from this study support the use of methadone in treating opioid addicts for the containment of the spread of HIV. However, more conclusive results could have been reached if 3 and 6 month post-admission data had been available for the dropout clients. Furthermore, current findings show that reductions can occur early in treatment, but future studies should determine if the adoption of risk-reduction remains constant for longer periods of time for clients who remain in treatment as well as for those who leave.

Acknowledgements This work was supported by the National Institute on Drug Abuse (Grant No. DA06162). The interpretations and conclusions, however, do not necessarily represent the position of NIDA or the Department of Health and Human Services.

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